Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data: Immunology, Microbiology, Biostatistics, and Big Data (Current Issues in Medicine, 2) [1 ed.] 9814877840, 9789814877848

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ADVANCES IN CLINICAL IMMUNOLOGY, MEDICAL MICROBIOLOGY, COVID-19, AND BIG DATA

Current Issues in Medicine Series Editor

Raj Bawa

Titles in the Series Vol. 1 Advances in Medical Biochemistry, Genomics, Physiology, and Pathology Raj Bawa, Esther H. Chang, Gerald F. Audette, Anil Diwan, and Saadia A. Faiz, eds. 2022

978-981-4877-44-2 (Hardcover)

978-1-003-18044-9 (eBook)

Vol. 2 Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Raj Bawa, ed.

2022

978-981-4877-84-8 (Hardcover)

978-1-003-18043-2 (eBook)

Vol. 3 Advances in Surgical and Medical Specialties Raj Bawa, Paul J. Davis, Jay K. Doshi, and Mario Ganau, eds. 2022

Vol. 4 Advances in Diagnosis and Imaging Raj Bawa, Gerald F. Audette, S. R. Bawa, and Mandira N. Mehra, eds. 2022 Vol. 5 Advances in Drug Delivery Raj Bawa, ed. 2022 Vol. 6 Advances in Therapies and Clinical Applications Raj Bawa, Shaker A. Mousa, Gerald F. Audette, Babak Kateb, and Bela Patel, eds. 2022 Vol. 7 Current Issues in Medicine: Editorials and Perspectives Raj Bawa, ed. 2022

Current Issues in Medicine Vol. 2 Jenny Stanford Series on Christian Relics and Phenomena — Volume 3

Advances in Clinical Byzantine Coins Immunology, Medical Microbiology, COVID-19, Inf luenced by the Shroud of Christ and Big Data

edited by Raj Bawa, PhD, MD'22

Giulio Fanti

Patent Agent, Bawa Biotech LLC, Ashburn, Virginia, USA

Vice President, Guanine Inc., Rensselaer, New York, USA

Scientific Advisor, Teva Pharmaceutical Industries Ltd., Israel

Published by Jenny Stanford Publishing Pte. Ltd. Level 34, Centennial Tower 3 Temasek Avenue Singapore 039190 Email: [email protected] Web: www.jennystanford.com Note from the Series Editor and the Publisher Extensive efforts have been made to make the information provided herein as accurate and as up-to-date as possible. It is important to note that knowledge and best practices in the various fields represented in this book (medicine, clinical immunology, medical microbiology, COVID19, big data, pathology, biochemistry, nanomedicine, precision medicine, genomics, regulatory science, pharmaceutical sciences, etc.) are constantly evolving. As new research and experience broaden our knowledge base, changes in medical care, diagnostics, therapy, research methods, assays, tools and techniques, medical formulations, and/or treatments may become necessary. Therefore, it is imperative that the reader does not solely rely on the information presented herein and always consults drug and device product labels, physician advice, warnings, data, and directions before using or consuming any drug product or using any device. For additional information about a product, please contact the appropriate medical professional, manufacturer, FDA, physician, pharmacist, or other licensed healthcare professional, as appropriate. Similarly, careful evaluation of any procedures, manufacturing steps, medical protocols, regulatory guidances, or assays described herein is warranted. To the fullest extent of the law, neither the publisher nor the editors or authors make any representations or warranties, express or implied, with respect to the information presented in this book, for its use or misuse, or interpretation thereof. In this regard, they assume no liability for any injury and/or damage to persons or property as a matter of product liability, negligence, or otherwise. A catalogue record for this book is available from the Library of Congress and the British Library. Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Copyright © 2022 Jenny Stanford Publishing Pte. Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system now known or to be invented, without written permission from the publisher. For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case, permission to photocopy is not required from the publisher. ISBN 978-981-4877-84-8 (Hardcover) ISBN 978-1-003-18043-2 (eBook)

Dedication

It is an honor and privilege to dedicate this volume to my friend, colleague, mentor, and collaborator Prof. Dr. Shaker A. Mousa

For demonstrating, throughout his distinguished career, the ability to bring novel concepts from the bench to the bedside

For his collaborative spirit, being a global ambassador of science, fostering research passion in students of all ages, dedication to education and support of young investigators For his innovative research in nanomedicines, dyslipidemia, tumor targeted radio- and chemo-therapy, cardiovascular and tumor functional imaging, heparin research, thrombosis, angiogenesis, and bacterial/viral-host interactions For discovery and development of Cardiolite, Marluma, Esmolol, and advancing other key concepts into the clinic

And for serving as an inspirational role model to students and colleagues in academia, industry, and government

Executive Vice President and Chairman

The Pharmaceutical Research Institute, Rensselaer, New York, USA

Professor of Pharmacology and Endowed Chair

Albany College of Pharmacy and Health Sciences, Albany, New York, USA

Also Read Handbook of Clinical Nanomedicine.

Vol. 1. Nanoparticles, Imaging, Therapy, and Clinical

Applications

Raj Bawa, MS, PhD, MD ‘22, Gerald F. Audette, PhD, and Israel Rubinstein, MD (Editors) 978-981-4669-20-7 (Hardcover), 978-981-4669-21-4 (eBook) 1662 pages This handbook (55 chapters) provides a comprehensive roadmap of basic research in nanomedicine as well as clinical applications. However, unlike other texts in nanomedicine, it not only highlights current advances in diagnostics and therapeutics but also explores related issues like nomenclature, historical developments, regulatory aspects, nanosimilars and 3D nanofabrication. While bridging the gap between basic biomedical research, engineering, medicine and law, the handbook provides a thorough understanding of nano’s potential to address (i) medical problems from both the patient and health provider’s perspective, and (ii) current applications and their potential in a healthcare setting. “Dr. Bawa and his team have meticulously gathered the distilled experience of world-class researchers, clinicians and business leaders addressing the most salient issues confronted in product concept development and translation.” Gregory Lanza, MD, PhD Professor of Medicine and Oliver M. Langenberg Distinguished Professor Washington University Medical School, USA “This is an outstanding, comprehensive volume that crosscuts disciplines and topics fitting individuals from a variety of fields looking to become knowledgeable in medical nanotech research and its translation from the bench to the bedside.” Shaker A. Mousa, PhD, MBA Vice Provost and Professor of Pharmacology Albany College of Pharmacy and Health Sciences, USA “Masterful! This handbook will have a welcome place in the hands of students, educators, clinicians and experienced scientists alike. In a rapidly evolving arena, the authors have harnessed the field and its future by highlighting both current and future needs in diagnosis and therapies. Bravo!” Howard E. Gendelman, MD Margaret R. Larson Professor and Chair University of Nebraska Medical Center, USA “It is refreshing to see a handbook that does not merely focus on preclinical aspects or exaggerated projections of nanomedicine. Unlike other books, this handbook not only highlights current advances in diagnostics and therapies but also addresses critical issues like terminology, regulatory aspects and personalized medicine.” Gert Storm, PhD Professor of Pharmaceutics Utrecht University, The Netherlands

Handbook of Clinical Nanomedicine.

Vol. 2. Law, Business, Regulation,

Safety, and Risk

Raj Bawa, MS, PhD, MD ‘22, (Editor), Gerald F. Audette, PhD, and Brian E. Reese, PhD, MBA, JD (Assistant Editors) 978-981-4669-22-1 (Hardcover), 978-981-4669-23-8 (eBook) 1448 pages This unique handbook (60 chapters) examines the entire “product life cycle,” from the creation of nanomedical products to their final market introduction. While focusing on critical issues relevant to nanoproduct development and translational activities, it tackles topics such as regulatory science, patent law, FDA law, ethics, personalized medicine, risk analysis, toxicology, nano-characterization and commercialization activities. A separate section provides fascinating perspectives and editorials from leading experts in this complex interdisciplinary field.

“The distinguished editors have secured contributions from the leading experts in nanomedicine law, business, regulation and policy. This handbook represents possibly the most comprehensive and advanced collections of materials on these critical topics. An invaluable standard resource.” Gregory N. Mandel, JD Peter J. Liacouras Professor of Law and Associate Dean Temple University Beasley School of Law, USA “This is an outstanding volume for those looking to become familiar with nanotechnology research and its translation from the bench to market. Way ahead of the competition, a standard reference on any shelf.” Shaker A. Mousa, PhD, MBA Vice Provost and Professor of Pharmacology Albany College of Pharmacy, USA “The editors have gathered the distilled experience of leaders addressing the most salient issues confronted in R&D and translation. Knowledge is power, particularly in nanotechnology translation, and this handbook is an essential guide that illustrates and clarifies our way to commercial success.” Gregory Lanza, MD, PhD Professor of Medicine and Oliver M. Langenberg Distinguished Professor Washington University Medical School, USA “The title of the handbook reflects its broad-ranging contents. The intellectual property chapters alone are worthy of their own handbook. Dr. Bawa and his coeditors should be congratulated for gathering the important writings on nanotech law, business and commercialization.” Richard J. Apley, JD Chief Patent Officer Litman Law Offices/Becker & Poliakoff, USA “It is clear that this handbook will serve the interdisciplinary community involved in nanomedicine, pharma and biotech in a highly comprehensive way. It not only covers basic and clinical aspects but the often missing, yet critically important, topics of safety, risk, regulation, IP and licensing. The section titled ‘Perspectives and Editorials’ is superb.” Yechezkel (Chezy) Barenholz, PhD Professor Emeritus of Biochemistry and Daniel Miller Professor of Cancer Research Hebrew University-Hadassah Medical School, Israel

Immune Aspects of Biopharmaceuticals

and Nanomedicines

Raj Bawa, MS, PhD, MD ‘22, János Szebeni, MD, PhD, DSc, Thomas J. Webster, MS, PhD, and Gerald F. Audette, PhD (Editors) 978-981-4774-52-9 (Hardback), 978-0-203-73153-6 (eBook) 1038 pages The enormous advances in the immunologic aspects of biotherapeutics and nanomedicines in the past two decades has necessitated an authoritative and comprehensive reference source that can be relied upon by immunologists, biomedical researchers, clinicians, pharmaceutical companies, regulators, venture capitalists, and policy makers alike. This text provides a thorough understanding of immunology, therapeutic potential, clinical applications, adverse reactions, and approaches to overcoming immunotoxicity of biotherapeutics and nanomedicines. It also tackles critical, yet often overlooked topics such as immune aspects of nanobio interactions, current FDA regulatory guidances, complement activation-related pseudoallergy (CARPA), advances in nanovaccines, and immunogenicity testing of protein therapeutics. “This outstanding volume represents a review of the various effects of biopharmaceuticals and nanomedicines on the immune system: immunotherapy, vaccines, and drug delivery; challenges and overcoming translational barriers stemming from immunotoxicity; strategies to designing more immunologically friendly formulations.” África González-Fernández, PhD, MD Professor of Immunology and President of the Spanish Society of Immunology, University of Vigo, Spain “For those who are specialists, and for those interested in a broader understanding of biologics and nanomedicines, this is a superb book, with internationally accomplished contributors. It serves both as a reference and as a practical guide to the newest advances in these important fields. Highly recommended!” Carl R. Alving, MD Emeritus Senior Scientist, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA “A skillfully produced book that addresses an often-missed topic: immune aspects of biologicals and nanoscale therapeutics, with an emphasis on clinical relevance and applications.” Rajiv R. Mohan, PhD Professor and Ruth M. Kraeuchi Missouri Endowed Chair Professor, University of Missouri, Columbia, USA “An indispensable masterpiece! It represents a rich source of information on interactions of biologics and nanodrugs with the immune system—all critical for medical applications. Volume 3, once again, achieves the series’ high standards.” László Rosivall, MD, PhD, DSc Med, Med habil. Széchenyi Prize Laureate and Professor, Faculty of Medicine, Semmelweis University, Budapest, Hungary “Hats off to Dr. Bawa for producing yet another impressive volume in terms of scope, timeliness, and relevance. With expert contributions from around the globe, this book addresses topics germane to researchers, clinicians, drug and biotherapeutic companies, regulators, policymakers, and patients.” Sara Brenner, MD, MPH Associate Professor and Assistant Vice President, SUNY Polytechnic Institute, Albany, New York, USA

The Road from Nanomedicine to Precision Medicine

Shaker A. Mousa, PhD, MBA, Raj Bawa, MS, PhD, MD ‘22, and Gerald F. Audette, PhD (Editors) 978-981-4800-59-4 (Hardback), 978-0-429-29501-0 (eBook) 1208 pages The enormous advances in nanomedicine and precision medicine in the past two decades has necessitated a growing need for an authoritative and comprehensive reference source that can be relied upon by biomedical researchers, clinicians, pharmaceutical scientists, regulators, and lawyers alike. This stand-alone, full-color book provides a broad survey of various interconnected topics, all accomplished in a user-friendly format. Each chapter contains key words, tables, and figures in color, future projections, and an extensive list of references. It is intended to be a standalone reference volume that broadly surveys and highlights innovative technologies and advances pertaining to nanomedicine and precision medicine. In addition, it also addresses often-neglected yet key issues such as translational medicine, intellectual property law, FDA regulatory issues, nanomedicine nomenclature, and artificial nanomachines—all accomplished in a user-friendly, broad yet interconnected format. The book is essential reading for the novice and expert alike in diverse fields such as medicine, law, genomics, pharmaceutical sciences, biomedical sciences, ethics, and regulatory science. The book’s multidisciplinary approach will attract a global audience. It will serve as a valuable reference resource for the industry, academia, and government. “The carefully selected range of topics in this masterpiece is perfect for academia, physicians, drug industry, healthcare systems, policymakers, regulatory bodies, and governments. In the coming decade, efforts in nanomedicine and precision medicine will be translated from the bench to the bedside, paving the way for more accurate diagnosis and more precise therapeutics. This volume is a standard reference for anyone involved in the coming healthcare revolution.” Tatiana K. Bronich, PhD Parke-Davis Professor, University of Nebraska Medical Center, USA Editor, Nanomedicine (Elsevier) “The first 3 volumes in this wonderful series have been inspirational. They form the most definitive and useful references about the clinical, technical, legal, and business aspects of nano. This fourth volume was awaited with great interest.” Peter J. Dobson, PhD, OBE Academic Director, Begbroke Science Park, and Professor (retd), University of Oxford, UK “Ehrlich’s vision of ‘magic bullets’ postulated in 1908 will be realized along the road from nanomedicine to precision medicine. The power unleashed by elucidation of the genome coupled with the elegance of sitespecific drug delivery will revolutionize healthcare in the next century. In my 70-year career as a researcher and university professor, nothing has held greater potential to diagnose and treat diseases in a more customizable, targeted manner. This book reflects innovations, potential applications, and possible bottlenecks in these two interrelated fields.” S. R. Bawa, MSc, PhD Founding Head and Professor of Biophysics (retd), Panjab University, India “Precision medicine and targeted nanomedicines are the ‘Holy Grail’ of medicine and drug delivery; this comprehensive volume highlights their salient features and interconnectivity. A team of distinguished editors and authors have done a superb job focusing on the critical and current issues, masterfully dissecting hype from reality.” János Szebeni, MD, PhD, DSc Director, Nanomedicine Research & Education Center, Semmelweis University CEO, SeroScience, Hungary “The growth, opportunity, and promise of nanomedicine have become breathtaking, which is why this book is my ‘go to’ reference. It puts cutting-edge nano-developments in context of precision medicine, and the lessons learned from applications in one clinical challenge may serve as a template for other challenges. Use this volume as a reference, but be sure to read it for inspiration.” Nicholas Borys, MD Senior Vice President and Chief Medical Officer, Celsion Corporation, USA

Endorsed by

The American Society for Nanomedicine (ASNM) (https://www.nanomedus.org) is a nonprofit, professional medical organization based in Ashburn, Virginia, USA. It was founded in 2008 by Dr. Raj Bawa of Bawa Biotech LLC and Dr. Esther Chang of Georgetown Medical Center. The ASNM comprises members drawn from diverse fields, including medicine, law, nanotechnology, pharma, biotech, engineering, and biomedical sciences with the common goal of advancing nanomedicine research to benefit global health. These goals are achieved through an open forum of ideas and collaborative efforts as well as close cooperation with our partner organizations. Since its inception, the ASNM has organized and sponsored major international conferences. Specifically, the vision of the ASNM includes •

•

• •

promoting research related to all aspects of nanomedicine and providing a forum through scientific meetings for the presentation of basic, clinical, and population-based research; promoting and facilitating the formal training of physicians, basic medical scientists, engineers, molecular biologists, statisticians, and allied healthcare providers in nano-related medical research and education; encouraging preventive measures and nano-based technologies to reduce the incidences of various diseases; facilitating the establishment of programs and policies that can better serve early diagnosis.

The European Foundation for Clinical Nanomedicine (CLINAM) (https://www. clinam.org), founded in 2007 by Beat Löffler, MA, MD (Hon), and Patrick Hunziker, MD, is an organization based in Basel, Switzerland. Its primary mission is to advance medicine to the benefit of individuals and society through the application of nanoscience and targeted medicine. Aiming at prevention, diagnosis, and therapy, it supports clinically focused research and the interaction and information flow between clinicians, researchers, and the public. The major goal is to support the development and application of nanomedicine and targeted medicine and having in scope all nanomedicine-related fields. The foundation runs a lab, creates an annual summit for clinical nanomedicine, and established the European Journal of Nanomedicine. The CLINAM Summits held annually in Basel bring together over 500 participants from around the globe. CLINAM founded the European Society for Nanomedicine (ESNAM), which has more than 1,000 members today. ESNAM was the driving force for the formation of the International Society for Nanomedicine, (ISNM) which brings together members from Japan, Korea, USA, Canada, Europe, South America, Australia, Africa, and India. CLINAM organizes worldwide summer schools.

 The Society for Brain Mapping and Therapeutics (SBMT) is a nonprofit society  organized for the purpose of encouraging basic and clinical scientists who are interested in areas of brain mapping, engineering, stem cell, nanotechnology, imaging, and medical device to improve the diagnosis, treatment, and rehabilitation of patients afflicted with neurological disorders. This society promotes the public welfare and improves patient care through the translation of new technologies/ therapies into life-saving diagnostic and therapeutic procedures. The society is committed to excellence in education, and scientific discovery. The society achieves its mission through multidisciplinary collaborations with government  agencies, patient advocacy groups, educational institutes, and industry, as well as  philanthropic organization.





 

Brain Mapping Foundation (BMF) is  a nonprofit, charitable organization, established for the purpose of facilitating multi-disciplinary brain and spinal cord research and expediting integration and translation of cutting-edge technologies into the field of neuroscience. BMF is focused on translating state-of-the-art technologies from space and defense industries into neuroscience in order to bring the most advanced medicine to wounded warriors as well as civilians. 





The NCNBE mission is to establish collaborating research laboratories and network throughout the state of California and beyond in order to rapidly develop solutions for neurological disorders employing advances in nanotechnology, stem cell research, and medical devices (nanobioelectronics) while fostering biotech spinoffs for the purpose of job creation. NCNBE promotes the public welfare and improves patient care through the translation of new technologies into life-saving diagnostic and therapeutic procedures. The center is committed to excellence in education, and scientific discovery. The NCNBE achieves its mission through multidisciplinary collaborations/consortium with government agencies, patient advocacy groups, educational institutions, private sector, industry, and philanthropic organizations.

The Editor

Raj Bawa, MS, PhD, MD  ‘22, is president of Bawa Biotech LLC (founded in 2002), a biotech/pharma consultancy and patent law firm based in Ashburn, Virginia, USA. Trained as a microbiologist and biochemist, he is an inventor, entrepreneur, professor, and registered patent agent (since 2002) licensed to practice before the US Patent & Trademark Office. He is currently a scientific advisor to Teva Pharmaceutical Industries Ltd. (Israel), a visiting research scholar at the Pharmaceutical Research Institute of Albany College of Pharmacy (Albany, NY), and vice president/chief IP officer at Guanine, Inc. (Rensselaer, NY). He is also a medical student and will receive the MD degree in 2022. He has served as a principal investigator of various research grants, most recently as a principal investigator of a CDC grant to develop an assay for carbapenemase-resistant bacteria. He was an adjunct professor at Rensselaer Polytechnic Institute (Troy, NY) from 1998 to 2018, where he received his doctoral degree in three years (biophysics/biochemistry). In the 1990s, Dr. Bawa held various positions at the US Patent & Trademark Office (Washington, DC), including primary examiner from 1996–2002. Presently, he is a life member of Sigma Xi, co-chair of the nanotech and precision medicine committees of the American Bar Association, and founding director of the American Society for Nanomedicine (founded in 2008). He has authored over 100 publications, co-edited 8 texts, and serves on the editorial boards of numerous peer-reviewed journals, including serving as an associate editor of Nanomedicine (Elsevier). Some of Dr. Bawa’s awards include the Innovations Prize from the Institution of Mechanical Engineers, London, UK; Appreciation Award from the Undersecretary of Commerce, Washington, DC; Key Award from Rensselaer’s Office of Alumni Relations; and Lifetime Achievement Award from the American Society for Nanomedicine.

Contents Corresponding Authors Note from the Series Editor Dr. Robert Koch addressing a conference at St James’s Hall, Piccadilly, London Dr. Ignaz Philipp Semmelweis Lord Lister with his house surgeons and dressers

1. SARS-CoV-2 and COVID-19: A Perspective

xxvii xxxi

1 3 5

7

Raj Bawa, MS, PhD, MD  ‘22

1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10

Pandemics: A Clear and Present Danger The Invader and the Host: A Delicate Dance Did SARS-CoV-2 Leak from a Chinese Lab? COVID-19 Vaccines: Facts and Fiction Will We Ever Achieve Herd Immunity? Patents and COVID-19 Vaccine Passports: Another Bad Government Idea COVID-19 Testing COVID-19 Convalescent Plasma: Is There a Benefit? Looking Back and Moving Forward: Will We Win?

8 10 15 17 25 28 28 30 33 34

Section 1 Clinical Immunology 2. Specific Reactivity of Anti-Citrullinated Protein Antibodies to Citrullinated Peptides Associated with Rheumatoid Arthritis

41

Nicole H. Trier, PhD, Bettina E. Holm, Paul R. Hansen, PhD, Ole Slot, MD,

Henning Locht, MD, and Gunnar Houen, PhD

2.1 2.2 2.3 2.4

Introduction Materials and Methods Results Discussion

3. Complement Activation, Immunogenicity, and Immune Suppression as Potential Side Effects of Liposomes

41 43 45 49

55

Janos Szebeni, MD, PhD, DSc, and Yechezkel (Chezy) Barenholz, PhD

3.1 3.2

Introduction Types and Features of Immune Responses to Liposomes

55 57

xvi

Contents

3.3 3.4 3.5 3.6 3.7

General Causes behind Immune Recognition of Liposomes Consequences of Immune Recognition of Liposomes Immunogenicity of Liposomes Immune Suppression by Liposomes Conclusions and Outlook

4. Human Clinical Relevance of the Porcine Model of Pseudoallergic

Infusion Reactions

58

61

65

66

67

77

János Szebeni, MD, PhD, DSc, and Raj Bawa, MS, PhD, MD  22 ‘

4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9

Introduction Challenge to the Pig Model’s Human Relevance and Utility in Preclinical Safety Assessment Scrutiny of the Challenge to the Pig Model: Facts and Questionable Conclusions The Paradox of Healthy Disease Model Concordant Symptoms of Pseudoallergy in Pigs and Humans The Predictive Power of the Pig Test Research Needed to Further Validate the Pig Model Problems in the Criticism of the Pig Model Conclusions and Future Perspectives

5. Myelin Antigens and Antimyelin Antibodies

77

78

82

92

93

94

95

95

97

109

Fredrick J. Seil, MD

5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8

Introduction Myelin EAE and Anti-MBP Antibodies Other CNS Myelin Antigens PNS Myelin Antigens and EAN Significance of Antimyelin Antibodies Human Demyelinating Disorders Conclusions

6. Advances in the Understanding of the Inflammatory Milieu and Its

Correlations with Neurological Disorders

109

110

112

113

114

115

117

118

127

Mario Ganau, MD, PhD, MBA, Sibel E. Huet, MD, Nikolaos Syrmos, MD, PhD,

Mohammad Iqbal, MBBS, and Marco Meloni, MD

6.1 6.2 6.3 6.4

Inflammation and Mechanism in the Body Immune Molecules and Proteins of the Inflammatory Milieu Neurotrauma and Secondary Neurodegeneration Role of Inflammation in Oncogenesis

127

128

129

130

Contents

6.5 Emerging Methods for Diagnosis and Follow-Up 6.6 Future Outlook and Conclusion

7. Role of Ligustrum in Allergic Disease

131

133

137

Priyadharshini Vellore Suresh, PhD, Tania Robledo Retana, PhD,

Blessy M. Mani, PhD, Ana Paulina Barba de la Rosa, PhD, and

Luis M. Teran, MD, PhD

7.1 7.2 7.3 7.4

Introduction Ligustrum and Allergic Disease Ligustrum Allergens Conclusions

8. Protective or Detrimental? The Role of Host Immunity in Leishmaniasis

137

139

140

145

151

Camila dos Santos Meira and Lashitew Gedamu, PhD

8.1 8.2 8.3 8.4

Introduction Clinical Aspects of Leishmaniasis The Immunobiology of Leishmaniasis Promising Approaches for Drug Development: A Special Focus on

the Host 8.5 Vaccines for Leishmaniasis 8.6 Concluding Remarks

9. Personalized Nanomedicines for Treatment of Autoimmune Disease

151

152

154

167

169

171

187

Cheng Lin, Huihua Ding, MD, Congcong Li, PhD, MD, Nan Shen, PhD, MD,

Aimin Zhao, PhD, MD, and Matthias Bartneck, PD, PhD

9.1 Introduction 9.2 Systemic Lupus Erythematosus as Representative Autoimmune

Disease 9.3 Immune-Mediated Recurrent Pregnancy Loss: Features of

Autoimmune Disease 9.4 Therapeutic Modulation of Immune Cells and Their Cytokine Secretion 9.5 Conclusions

10. Intracellular Antibody Immunity and Its Applications

187

190

193

195

198

207

Jingwei Zeng, PhD, and Leo C. James, PhD

10.1 What Is Intracellular Antibody Immunity? 10.2 What Is Tripartite Motif-Containing Protein 21 (TRIM21)? 10.3 What Does TRIM21 Do? 10.4 How Does TRIM21 Work? 10.5 How Can We Exploit TRIM21? 10.6 What Next?

207

208

208

210

211

212

xvii

xviii

Contents

11. Maternal Antibody Interference Contributes to Reduced Rotavirus

Vaccine Efficacy in Developing Countries

217

Claire E. Otero, Stephanie N. Langel, PhD, Maria Blasi, PhD,

and Sallie R. Permar, MD, PhD

11.1 Rotavirus Vaccine Efficacy Is Reduced in Lower- and Middle-Income Countries

217

11.3 Establishing a Causal Link between matAb Interference and Low

RV Vaccine Efficacy in LMICs and Defining Mechanisms

219

11.2 Evidence Supports matAb Interference as a Mechanism of Reduced RV Vaccine Efficacy 11.4 Potential Solutions for matAb Interference to RV Vaccines

11.5 Prospects for Overcoming matAb Interference to Infant RV Vaccination

218

221

223

Section 2 Medical Microbiology 12. Reflections on 40 Years of AIDS

231

Kevin M. De Cock, MD, Harold W. Jaffe, MD, MA, and James W. Curran, MD, MPH

12.1 Evolving Epidemiology

12.2 Evolving Science and Program

12.3 Evolving Global Health

12.4 Preparing for the Fifth Decade of AIDS

12.5 Lessons from HIV/AIDS and Other Epidemics 12.6 Conclusions

12.7 Addendum

13. Formation and Maturation of the Oral Microbiota

232

233

236

238

239

240

240

247

Luca Fiorillo, DDS, Gabriele Cervino, DDS, PhD, and Marco Cicciù, DDS, MSc, PhD

14. Could the Environment Affect the Mutation of H1N1 Influenza Virus?

255

Dong Jiang, PhD, Qian Wang, PhD, Zhihua Bai, PhD, Heyuan Qi, PhD,

Juncai Ma, PhD, Wenjun Liu, PhD, Fangyu Ding, PhD, and Jing Li, PhD

14.1 Introduction

14.2 Methods 14.3 Results

14.4 Discussion

14.5 Conclusions

255

256

260

263

265

Contents

15. Current Perspec ves in Medical Microbiology

269

15.1 Continued Poxvirus Research: From Foe to Friend

270

15.4 Clostridioides

in the Gut?

290

15.2 Staphylococcus epidermidis—Skin Friend or Foe? 15.3

15.5 Cesarean Section and Childhood Infections: Causality for

Concern? 15.6 Infectious Hypothesis of Alzheimer Disease

15.7 Insights into Malaria Pathogenesis Gained from Host Metabolomics

274

281

298

301

308

15.8 Japanese Encephalitis Virus and Its Mechanisms of Neuroinvasion

317

15.11 Coinfections in Wildlife: Focus on a Neglected Aspect of

Infectious Disease Epidemiology

341

15.9 Bringing the Path toward an HIV-1 Vaccine into Focus

15.10 Quorum Sensing across Bacterial and Viral Domains

15.12 Isoniazid-Resistant Tuberculosis: A Problem We Can No Longer Ignore

16. Bacterial Virulence Plays a Crucial Role in Methicillin-Resistant S. aureus Sepsis

324

332

347

353

Gordon Y. C. Cheung, PhD, Justin S. Bae, Ryan Liu, MSc, Rachelle L. Hunt, Yue Zheng,

and Michael Otto, PhD

16.1 Introduction

353

16.4 Materials and Methods

365

16.2 Results

16.3 Discussion

16.5 Supporting Information

17. Where Cancer and Bacteria Meet

355

362

368

377

Alexandra Merlos, PhD, Ricardo Perez-Tomás, PhD, José López-López, PhD,

and Miguel Viñas, PhD

17.1 Introduction

377

17.4 Bacteria and Bacterial Products in Cancer Treatment

385

17.2 Infection and Neoplasia

17.3 Head and Neck Cancers and Bacterial Oral Microbiota

378

382

xix

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Contents

18. Fungal Diseases as Neglected Pathogens: A Wake-Up Call to Public

Health Officials

399

Marcio L. Rodrigues, PhD, and Joshua D. Nosanchuk, MD, PhD

18.1 Fungal Diseases: A Real Threat to Public Health 18.2 AIDS and Opportunistic Fungal Diseases: Problem Solved or

Current Threat? 18.3 Systemic Mycoses Are Neglected Diseases 18.4 Present and Future Problems: The Unknown 18.5 The Need for Improved Diagnosis of Fungal Infections 18.6 Funding for Research and Innovation in Fungal Diseases 18.7 Perspectives

19. Catch the Wave: Metabolomic Analyses in Human Pathogenic Fungi

399

401

401

404

405

406

406

413

Philipp Brandt, Enrico Garbe, and Slavena Vylkova, PhD

19.1 Introduction

413

19.4 Integrated OMICs Approaches

416

19.2 What Methods Are Available to Study Metabolomics?

19.3 How to Get the Most Out of Your Metabolomics Data?

19.5 What Have We Learned from Metabolomics of Human Pathogenic

Fungi to This Date? 19.6 Conclusions and Perspectives

20. The Unmet Medical Need for Trypanosoma cruzi-Infected Patients:

Monitoring the Disease Status

414

414

416

419

425

Maan Zrein, PhD, and Eric Chatelain, PhD

20.1 20.2 20.3 20.4

Introduction Direct Biomarkers Indirect Biomarkers Conclusion

21. Present and Future of Carbapenem-Resistant Enterobacteriaceae

Infections

425

428

428

432

435

Beatriz Suay-García, PhD, and María Teresa Pérez-Gracia, PhD

21.1 Introduction 21.2 Mechanisms of Drug Resistance 21.3 Current Resistance Status 21.4 Treatment Options 21.5 Conclusions

435

436

440

442

448

Contents

22. Human Plague: An Old Scourge That Needs New Answers

457

Xavier Vallès, PhD, Nils Chr. Stenseth, PhD, Christian Demeure, PhD, Peter Horby, MD, PhD, Paul S. Mead, MD, Oswaldo Cabanillas, PhD, Mahery Ratsitorahina, PhD, Minoarisoa Rajerison, PhD, Voahangy Andrianaivoarimanana, PhD, Beza Ramasindrazana, PhD, Javier Pizarro-Cerda, PhD, Holger C. Scholz, PhD, Romain Girod, PhD, B. Joseph Hinnebusch, PhD, Ines Vigan-Womas, PhD, Arnaud Fontanet, PhD, David M. Wagner, PhD, Sandra Telfer, PhD, Yazdan Yazdanpanah, MD, PhD, Pablo Tortosa, PhD, Guia Carrara, PhD, Jane Deuve, PhD, Steven R. Belmain, PhD, Eric D’Ortenzio, MD, and Laurence Baril, MD, PhD

22.1 Introduction 22.2 Which Hosts and Vectors Should Be Targeted for Human Plague Control? 22.3 What Are the Drivers of Human Plague? 22.4 Which New Diagnostic Tools for Plague Are Needed? 22.5 How Can Plague Surveillance and Case Management Be Improved? 22.6 What Are the Gaps in Knowledge about Y. pestis Biology? 22.7 Discussion

Section 3 Big Data and Artificial Intelligence

23. Human Brain/Cloud Interface

458

460 464 465 467 470 471

485

Nuno R. B. Martins, PhD, Amara Angelica, BGE, Krishnan Chakravarthy, PhD, MD, Yuriy Svidinenko, MS, Frank J. Boehm, Ioan Opris, PhD, Mikhail A. Lebedev, PhD, Melanie Swan, MS, Steven A. Garan, PhD, Jeffrey V. Rosenfeld, PhD, MD, Tad Hogg, PhD, and Robert A. Freitas Jr., JD

23.1 23.2 23.3 23.4 23.5 23.6 23.7

Introduction The Human Brain The Cloud Potential of Current Technologies toward a Brain/Cloud Interface Neuralnanorobotic Brain/Cloud Interface Human Brain/Cloud Interface Applications Conclusion

24. Artificial Intelligence in Drug Discovery: What Is New, and What Is Next?

486 490 494 495 502 515 520

539

Francesca Lake, PhD

24.1 24.2 24.3 24.4

Designing a Computational Computer Chemist The Computer Brain versus the Human Brain for Drug Design Harnessing AI for Hit Identification Who Holds the IP in AI Drug Discovery?

539 540 541 541

xxi

xxii

Contents

24.5 The Inevitable Question of Ethics 24.6 Open Source 24.7 So What Is Next for AI in Drug Discovery?

25. Now the Future, We See Our Dreams: Artificial Intelligence in Drug

Discovery

542

543

543

547

Ray Lawrence, PhD

26. Big Data and Artificial Intelligence Meet the COVID-19 Pandemic:

Potential Applications and Promises

553

Nicola Luigi Bragazzi, MD, PhD, Haijiang Dai, PhD, Giovanni Damiani, PhD, Masoud Behzadifar, PhD, Mariano Martini, PhD, and Jianhong Wu, PhD

26.1 The Ongoing COVID-19 Outbreak 26.2 Artificial Intelligence and Big Data 26.3 Short-Term Applications of Artificial Intelligence and Big Data:

A Quick and Effective Pandemic Alert 26.4 Short-Term Applications of Artificial Intelligence and Big Data:

Tracking and Diagnosing COVID-19 Cases 26.5 Medium-Term Applications of Artificial Intelligence and Big Data: Identifying a Potential Pharmacological Treatment 26.6 Medium-Term Applications of Artificial Intelligence and Big Data:

Facilitating the Implementation of Public Health Interventions 26.7 Long-Term Applications of Artificial Intelligence and Big Data:

Building Smart, Health, Resilient Cities 26.8 Artificial Intelligence and Big Data for COVID-19: Conclusions and Future Prospects

27. Paradigm Shift in Medicinal Chemistry towards Data-Driven Approaches

553

555

555

556

557

560

561

562

567

Jürgen Bajorath, PhD

27.1 Introduction 27.2 Data-Driven Medicinal Chemistry 27.3 Historical Data 27.4 Data Integration 27.5 Data Science 27.6 Machine Learning and Data Mining 27.7 Large-Scale Modeling 27.8 Perspective

28. The Importance of Proper Statistical Methods in Developing Robust

Predictive Models Using Chemodescriptors and Biodescriptors in the

Twenty First Century

567

568

568

569

569

570

570

571

573

Subhash C. Basak, PhD

28.1 Introduction

573

Contents

28.2 Chemodescriptors: How Much Can Structural Chemistry Alone Help? 28.3 The Advent of Rank Deficiency and Need for Robust Statistical Methodology 28.4 Biodescriptors: Descriptors Derived from Proteomics and DNA/RNA Sequence Data 28.5 Quo Vadimus?

29. Fit-for-Purpose?—Challenges and Opportunities for Applications of Blockchain Technology in the Future of Healthcare

574 575 576 578

583

Tim K. Mackey, PhD, Tsung-Ting Kuo, PhD, Basker Gummadi, MS, Kevin A. Clauson, PhD, George Church, PhD, Dennis Grishin, PhD, Kamal Obbad, Robert Barkovich, PhD, and Maria Palombini, MBA

29.1 Background 29.2 Using Privacy-Preserving Predictive Models and Blockchain Technology for Electronic Health Records 29.3 Blockchain-Enabled Medical Professional Credentialing and Licensing 29.4 Can We Use Blockchain to Improve Clinical Trial Management? 29.5 Blockchain Technology to Advance Biomedical Research? 29.6 Blockchain Technology Set to Modernize the Pharmaceutical Supply Chain? 29.7 Entering the Genomics Age with the Help of Blockchain Technology 29.8 The Future of the Health Blockchain: Promising Use Cases and the Importance of Technical Standards Setting

30. mHealth Approach to Clinics in Rural Settings in Nutrition Counseling

583 589 590 592 594 596 598 600

611

Melissa D. Olfert, PhD, Makenzie L. Barr, PhD, Rebecca L. Hagedorn, PhD, Dustin M. Long, PhD, Treah S. Haggerty, MD, Mathew Weimer, MD, Joseph Golden, MD, Mary Ann Maurer, DO, Jill D. Cochran, PhD, Tracy Hendershot, MD, Stacey L. Whanger, MD, Jay D. Mason, MD, and Sally L. Hodder, MD

30.1 30.2 30.3 30.4 30.5

Introduction Materials and Methods Results Discussion Conclusions

31. Ten Simple Rules for Engaging with Artificial Intelligence in Biomedicine

611 613 617 621 624

631

Avni Malik, Paranjay Patel, MD, Lubaina Ehsan, MD, Shan Guleria, MD, Thomas Hartka, MD, Sodiq Adewole, and Sana Syed, MD

31.1 Introduction 31.2 Conclusion

631 642

xxiii

xxiv

Contents

32. Five Key Aspects of Metaproteomics as a Tool to Understand

Functional Interactions in Host Associated Microbiomes

647

Fernanda Salvato, PhD, Robert L. Hettich, PhD, and Manuel Kleiner, PhD

32.1 What Information Can Be Gained Using Metaproteomics?

32.2 What Are the Prerequisites for Starting a Metaproteomics Study? 32.3 What Does a General Metaproteomics Workflow Look Like?

32.4 How Accessible Is Metaproteomics to the General Scientific

Community, and How Much Does It Cost as Compared to Other

Meta-Omics Technologies? 32.5 What Do the Data Look Like, and How Can They Be Analyzed?

Section 4 Sars-Cov-2 and Covid-19

33. COVID-19: Hundred Questions and Answers for Healthcare Providers

and the Public 33.1 FDA, EUA, and COVID-19 Vaccines

33.2 General Information on Safety and Prevention

648

650

651

653

654

663

670

686

33.3 Biologics, Human Tissues, and Blood Products

697

33.6 Pregnancy and COVID-19

720

33.4 Development and Use of FDA-approved Drugs for COVID-19 33.5 Diagnostic Testing for SARS-CoV-2 33.7 Personal Protective Equipment

33.8 Food Products

33.9 Animals, Pets, and Animal Drug Products

34. SARS-CoV-2 Tropism, Entry, Replication, and Propagation:

Considerations for Drug Discovery and Development

703

713

721

724

730

753

Nicholas Murgolo, PhD, Alex G. Therien, PhD, Bonnie Howell, PhD,

Daniel Klein, PhD, Kenneth Koeplinger, PhD, Linda A. Lieberman, PhD,

Gregory C. Adam, PhD, Jessica Flynn, PhD, Philip McKenna, PhD,

Gokul Swaminathan, PhD, Daria J. Hazuda, PhD, and David B. Olsen, PhD

34.1 Introduction

34.2 Scope/Prior Reviews

753

755

34.3 Entry Mechanisms and Proteases

755

34.6 Cell Line Tropism/Expression

759

34.4 TMPRSS2 and Furin in Cell Surface Entry 34.5 Lysosomal Cathepsins and Endocytosis

34.7 Nucleotide/Side Import and Conversion

758

759

762

Contents

34.8 Primary Cells/Model Systems 34.9 Innate Immune Cells 34.10 Concluding Remarks

35. Presence of Antibody-Dependent Cellular Cytotoxicity against SARS-CoV-2 in COVID-19 Plasma

763

765

766

777

For Yue Tso, Salum J. Lidenge, Lisa K. Poppe, Phoebe B. Peña, Sara R. Privatt,

Sydney J. Bennett, John R. Ngowi, Julius Mwaiselage, Michael Belshan,

Jacob A. Siedlik, Morgan A. Raine, Juan B. Ochoa, Julia Garcia-Diaz,

Bobby Nossaman, Lyndsey Buckner, W. Mark Roberts, Matthew J. Dean,

Augusto C. Ochoa, John T. West, and Charles Wood

35.1 Introduction

777

35.4 Discussion

787

35.2 Materials and Methods 35.3 Results

36. Performance of SARS-CoV-2 Serology Tests: Are They Good Enough?

778

782

791

Isabelle Piec, PhD, Emma English, Mary Annette Thomas, MPhil,

Samir Dervisevic, MD, William D. Fraser, MD, and William Garry John

36.1 Introduction

791

36.4 Discussion

799

36.2 Materials and Methods 36.3 Results

36.5 Supporting Information

37. Forecasting the Novel Coronavirus COVID-19

792

794

803

809

Fotios Petropoulos, DEng, and Spyros Makridakis, PhD

37.1 Introduction 37.2 Analysis and Forecasting 37.3 Discussion and Conclusion

38. Pandemic Responses: Planning to Neutralize SARS-CoV-2 and Prepare

for Future Outbreaks

809

810

815

821

The PLOS Medicine Editors

39. Pandemic Preparedness and Responses: WHO to Turn to in a Crisis?

825

The PLOS Medicine Editors

40. Links between Integrin αvβ3 and COVID-19: Impact on Vascular and

Thrombotic Risk Marwa S. Hamza, PhD, and Shaker A. Mousa, PhD, MBA

829

xxv

xxvi

Contents

41. The Ocular Surface and the Coronavirus Disease 2019: Does a Dual

‘Ocular Route’ Exist?

839

Pietro Emanuele Napoli, MD, Matteo Nioi, MD, Ernesto d’Aloja, MD, and

Maurizio Fossarelo, MD

41.1 Introduction 41.2 Ocular Surface Findings in Case of COVID-19 and Controversial Issues 41.3 Ocular Transmission and the ACE2 Receptors in the Ocular Surface 41.4 Discussion

42. Exploring Links between Vitamin D Deficiency and COVID-19

839

840

840

842

847

Mradul Mohan, MS, PhD, Jerin Jose Cherian, MD, MBA, and Amit Sharma, PhD

42.1 42.2 42.3 42.4

SARS-CoV-2 Infection and the Cytokine Storm Vitamin D and the Host Immune System Vitamin D Deficiency and COVID-19 Conclusions

43. Convalescent Serum Therapy for COVID-19: A 19th Century Remedy

for a 21st Century Disease

847

849

850

851

857

Daniel Montelongo-Jauregui, PhD, Taissa Vila, PhD, Ahmed S. Sultan, BDS, PhD,

and Mary Ann Jabra-Rizk, PhD

43.1 43.2 43.3 43.4 43.5

Bridging the Gap between Now and Then Historical Precedent for the Use of Antibody Therapy Buying Time with the Help of the Convalescent Limitations and Potential Risks Future Perspectives

44. Preexisting and Inducible Endotoxemia as Crucial Contributors to the

Severity of COVID-19 Outcomes

857

858

860

862

863

869

Ilja L. Kruglikov, PhD, Dr Sci, and Philipp E. Scherer, PhD

Index

877

Corresponding Authors

Jürgen Bajorath, PhD Department of Life Science Informatics B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry Rheinische Friedrich-Wilhelms-Universität Bonn, Germany [email protected] Yechezkel (Chezy) Barenholz, PhD Institute of Medical Research Israel-Canada The Hebrew University-Hadassah Medical School Jerusalem, Israel [email protected] Matthias Bartneck, PD, PhD Department of Internal Medicine III University Hospital RWTH Aachen Aachen, Germany [email protected] Subhash C. Basak, PhD Department of Chemistry and Biochemistry University of Minnesota Duluth Duluth, Minnesota, USA [email protected]

Bonnie L. Bassler, PhD Department of Molecular Biology Princeton University Princeton, New Jersey, USA [email protected]

Raj Bawa, MS, PhD, MD  ‘22 Bawa Biotech LLC, Ashburn, Virginia, USA Guanine, Inc., Rensselaer, New York, USA Albany College of Pharmacy Albany, New York, USA Teva Pharmaceutical Industries, Israel [email protected] Susanna R. Bidgood, MSci, PhD MRC Laboratory for Molecular Cell Biology University College London London, UK [email protected]

Nicola Luigi Bragazzi, MD, PhD Department of Mathematics & Statistics York University Toronto, Ontario, Canada [email protected] Regina Joice Cordy, PhD Department of Biology Wake Forest University Winston-Salem, North Carolina, USA [email protected] Kevin M. De Cock, MD Centers for Disease Control and Prevention Atlanta, Georgia, USA [email protected]

Fangyu Ding, PhD Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing, China [email protected] Luca Fiorillo, DDS Department of Biomedical and Dental Sciences University of Messina Messina, Italy [email protected] Mario Ganau, MD, PhD, MBA Department of Neurosciences John Radcliffe Hospital University of Oxford Oxford, UK [email protected]

Axel O. G. Hoarau Université de La Réunion Processus Infectieux en Milieu Insulaire Tropical INSERM Réunion Island, France [email protected]

xxviii

Corresponding Authors

Alexander R. Horswill, PhD Department of Immunology and Microbiology University of Colorado School of Medicine Aurora, Colorado, USA [email protected] Gunnar Houen, PhD Department of Biomarkers and Autoimmunity

Statens Serum Institut

Copenhagen, Denmark [email protected]

Leo C. James, PhD The Medical Research Council Laboratory of Molecular Biology Cambridge, UK [email protected] Ashley L. St. John, PhD Program in Emerging Infectious Diseases Duke-National University of Singapore Medical School Singapore [email protected] Manuel Kleiner, PhD Department of Plant and Microbial Biology North Carolina State University Raleigh, North Carolina, USA [email protected] Francesca Lake, PhD Newlands Press Unitec House, London, UK [email protected]

Ray Lawrence, PhD Drug Discovery Unit Cancer Research UK Manchester Institute The University of Manchester Manchester, UK [email protected]

Jing Li, PhD Savaid Medical School University of Chinese Academy of Sciences Beijing, China [email protected]

Tim K. Mackey, PhD Department of Anesthesiology and Division of Infectious Disease and Global Public Health University of California San Diego School of Medicine San Diego, California, USA [email protected]

Nuno R. B. Martins, PhD Lawrence Berkeley National Laboratory Berkeley, California, USA [email protected] Nicholas Murgolo, PhD Department of Genetics and Pharmacogenomics Merck & Co., Inc. Kenilworth, New Jersey, USA [email protected]

Camila dos Santos Meira Department of Biological Sciences University of Calgary Calgary, Canada [email protected]

Daniel Montelongo-Jauregui, PhD Department of Oncology and Diagnostic Sciences School of Dentistry University of Maryland Baltimore, Maryland, USA [email protected] Shaker A. Mousa, PhD, MBA The Pharmaceutical Research Institute Albany College of Pharmacy and Health Sciences Rensselaer, New York, USA [email protected]

Corresponding Authors

Pietro Emanuele Napoli, MD Department of Surgical Science University of Cagliari, Eye Clinic Cagliari, Italy [email protected] Martha I. Nelson, PhD Fogarty International Center National Institutes of Health Bethesda, Maryland, USA [email protected]

Joshua D. Nosanchuk, MD, PhD Department of Medicine (Infectious Diseases) Department of Microbiology & Immunology Albert Einstein College of Medicine Bronx, New York, USA [email protected]

Melissa D. Olfert, PhD Department of Animal and Food Science West Virginia University Morgantown, West Virginia, USA [email protected]

Michael Otto, PhD Pathogen Molecular Genetics Section Laboratory of Bacteriology National Institute of Allergy and Infectious Diseases National Institutes of Health Bethesda, Maryland, USA [email protected] Madhukar Pai, MD, PhD Department of Epidemiology, Biostatistics and Occupational Health McGill University Montreal, Quebec, Canada [email protected] María Teresa Pérez-Gracia, PhD Área de Microbiología Departamento de Farmacia Instituto de Ciencias Biomédicas Facultad de Ciencias de la Salud Universidad Cardenal Herrera-CEU Valencia, Spain [email protected]

Sallie R. Permar, MD, PhD Duke Human Vaccine Institute Duke University Medical Center Durham, North Carolina, USA [email protected] Fotios Petropoulos School of Management University of Bath Bath, UK [email protected] Isabelle Piec, PhD BioAnalytical Facility Faculty of Medicine University of East Anglia Norwich, UK [email protected]

Marcio L. Rodrigues, PhD Instituto de Microbiologia Paulo de Góes Universidade Federal do Rio de Janeiro Rio de Janeiro, Brazil [email protected] Amit Sharma, PhD Parasite-Host Biology Group National Institute of Malaria Research New Delhi, India [email protected] Fernanda Salvato, PhD Department of Plant and Microbial Biology North Carolina State University Raleigh, North Carolina, USA [email protected]

Philipp E. Scherer, PhD Touchstone Diabetes Center Department of Internal Medicine Dallas, Texas, USA [email protected] Fredrick J. Seil, MD Department of Neurology Oregon Health & Science University Portland, Oregon, USA [email protected]

xxix

xxx

Corresponding Authors

Gordon C. S. Smith, MD, PhD, DSc Department of Obstetrics & Gynaecology University of Cambridge Cambridge, UK [email protected]

Sana Syed, MD Department of Pediatrics Division of Gastroenterology, Hepatology and Nutrition School of Medicine University of Virginia

Charlottesville, Virginia, USA

[email protected] Janos Szebeni, MD, PhD, DSc Nanomedicine Research and Education Center Semmelweis University and Bay Zoltan Foundation for Applied Research Budapest, Hungary

Faculty of Health Sciences

Miskolc University

Miskolc, Hungary

[email protected]

Luis M. Teran, MD, PhD Instituto Nacional de Enfermedades Respiratorias Mexico City, Mexico [email protected] Georgia D. Tomaras, PhD Duke Center for Human Systems Immunology Duke University

School of Medicine Durham, North Carolina, USA [email protected] Nicole H. Trier, PhD Department of Biomarkers and Autoimmunity

Statens Serum Institut

Copenhagen, Denmark [email protected]

Meera Unnikrishnan, PhD Division of Biomedical Sciences Warwick Medical School University of Warwick Coventry, UK [email protected]

Xavier Vallès, PhD Epidemiology and Clinical Research Unit Institut Pasteur de Madagascar Antananarivo, Madagascar [email protected] Miguel Viñas, PhD Department of Pathology and Experimental Therapeutics Medical School University of Barcelona Hospitalet, Barcelona, Spain [email protected]

Slavena Vylkova, PhD Septomics Research Center Friedrich Schiller University and Leibniz Institute for Natural Product Research and Infection Biology Hans Knöll Institute Jena, Germany [email protected] Donna M. Wilcock, PhD Sanders-Brown Center on Aging University of Kentucky Lexington, Kentucky, USA [email protected] Charles Wood Nebraska Center for Virology University of Nebraska-Lincoln Lincoln, Nebraska, USA [email protected]

Maan Zrein, PhD InfYnity Biomarkers Lyon, France [email protected]

Note from the Series Editor

The incredible pace at which technology and medicine have advanced during the past two decades has revolutionized how we approach public health and deliver medical care. This has necessitated a growing need for a comprehensive reference series that highlights the current issues in medicine. This series does exactly that, all in a readily accessible, user-friendly format. Volume 2 in the Current Issues in Medicine series highlights current thinking, critical concepts, best practices, and perspectives in clinical immunology, medical microbiology, COVID-19, and big data. These subjects are the underpinnings of medicine and part of most first-year medical school curricula. They are the basic and applied sciences that make the art of medicine possible. Specific chapters cover antibodies associated with rheumatoid arthritis and myelin, complement activation and liposomes, porcine model of pseudoallergic infusion reactions, role of host immunity in Leishmaniasis, Rotavirus vaccine efficacy, HIV/AIDS, H1N1 Influenza, poxvirus research, Staphylococcus epidermidis, Clostridioides difficile biofilms, malaria pathogenesis, Japanese encephalitis, quorum sensing, Isoniazid-resistant Tuberculosis, MRSA sepsis, fungal diseases, metabolomic analyses, Trypanosoma cruzi, Carbapenem-resistant Enterobacteriaceae infections, human plague, human brain/cloud interface, artificial intelligence in drug discovery, applications of blockchain technology in healthcare, metaproteomics, convalescent serum therapy, SARS-CoV-2 and COVID-19. This book is essential reading for physicians, medical students, nurses, fellows, residents, undergraduate and graduate students, educators, policymakers, and biomedical researchers. Undoubtedly, it will be a valuable reference for health facilities, medical institutions, industry, academia, and governments. The range of topics covered in the Current Issues in Medicine series and the expertise of the contributing authors accurately reflect the rapidly evolving areas within medicine—from basic medical sciences to clinical specialties. Volumes 1 and 2 in this series are focused on the current issues in basic medical science, subjects that are fundamental to the practice of medicine. These subjects, traditionally taught in the first two years of medical school that precede clinical instruction, provide a core of basic knowledge critical to the success in clinical medicine during rotations, training, and medical practice. Obviously, knowledge gleaned from these subjects leads to better ways to predict, prevent, diagnose, and treat disease. Specifically, Volume 1 covers medical biochemistry, genomics, physiology, and pathology. Volume 2 discusses clinical immunology, medical microbiology, COVID-19, and big data. Surgical and clinical specialties are covered in Volume 3. Volume 4 is directed towards diagnosis and imaging techniques, Volume 5 focuses on drug delivery, and Volume 6 highlights novel therapeutics and clinical applications. Volume 7 is directed to critical editorials and perspectives pertaining to medicine and ancillary fields.

xxxii

terms of occurrence, prediction, prevention, and mechanism. Furthermore, critical, yet often overlooked topics such as immune aspects of nano-bio interactions, current FDA regulatory guidance, immunogenicity testing of therapeutic protein products, and engineering bio/nanotherapeutics to overcome barriers to Note from the Series Editor immunotherapy are also covered. I express my sincere gratitude to the authors, coeditors, and reviewers co-editors, for their excellent in undertaking this project I am grateful to the authors, and effort reviewers for meticulously with great enthusiasm. I thank my father, Dr. S. R. Bawa, for ensuring the accuracy and completeness of information presented herein. I also meticulously reviewing various chapters of this book. Finally, thank Mr. Stanford Chong and Ms.thank Jenny Jenny I also Mr. Rompas Stanford of Chong and Stanford Ms. Jenny Publishing Rompas of Pan Stanford Publishing for commissioning me to editPublishing this volume. for commissioning this outstanding series. The staff at Jenny Stanford Mr. Arvind Kanswal of Pan Stanford Publishing and my staff at and at Bawa Biotech LLC are acknowledged for their valuable assistance with Bawa Biotech LLC are acknowledged for their valuable assistance research, graphics, secretarialwith assistance, and publication coordination. publication coordination.

RajDr. Bawa, PhD RajMS, Bawa Series Editor Series Editor Ashburn, Virginia, USA June 7, 2018*

*The day my beloved Washington Capitals ice hockey team won the Stanley Cup for the irst time in franchise history!

MEDICAL EDUCATION: A HISTORY IN 100 IMAGES Robert Koch reading his address to a conference at St James’s Hall, Piccadilly. Dr. Robert Koch addressing a conference at St James’s Hall, Piccadilly, London. Gouache by F.C. Dickinson, 1901. (Credit: Wellcome Library [1].) Gouache by F.C. Dickinson, 1901.

Robert Koch (1843–1910) was a German physician and medical microbiologist. He discovered the anthrax disease cycle (1876), and the bacteria responsible for tuberculosis (1882) and cholera (1883). He also worked extensively on tropical diseases, including African trypanosomiasis and malaria. He is considered as one of the founders of medical bacteriology and the father of microbiology (with Louis Pasteur). His discovery of Bacillus anthracis in 1876 is considered to mark the birth of modern bacteriology. He astounded his parents at the age of five by telling them that he had taught himself to read, a feat which foreshadowed the intelligence and methodical persistence that were to be his hallmarks in later life. He was the first to grow bacteria in laboratory using agar and glass plates (later developed as Petri dishes by his assistant Julius Petri). In appreciation of his brilliant research, in 1885 Koch was appointed Professor at the University of Berlin as well as the Director of the newly established Institute of Hygiene there. In 1891, he became an Honorary Professor of the Medical Faculty of Berlin and Director of the new Institute for Infectious Diseases, where his stellar colleagues were Ehrlich, von Behring, and Kitasato. In 1905, he was awarded the Nobel Prize in Physiology or Medicine for his work on tuberculosis. Koch also claimed to have found a cure for tuberculosis. Unfortunately this was not the case as the drug, Tuberculine, turned out to be useful for diagnosing tuberculosis rather than

treating it. In fact, Tuberculine, which is a purified protein derivative (PPD) from cultures of tubercle bacillus, is still used today in a skin test by hypodermic injection for infection with or immunity to tuberculosis. Koch established guidelines for determining whether a particular microorganism is the causative agent of a specific infectious disease. These four basic criteria are known as Koch’s Postulates and remain the gold standard for determining the cause of any infectious microbial disease. Koch’s Postulates are still considered relevant today, though subsequent developments, such as the discovery of microorganisms that cannot grow in cellfree culture (viruses and obligate intracellular bacterial pathogens), have caused these guidelines to be reinterpreted for the molecular era. Although Koch worked out the principles, three of the Postulates were introduced by his assistant Loeffler and the fourth added by plant pathologist Smith. Koch’s relationship with Louis Pasteur deteriorated following their first meeting. Although the Koch–Pasteur rivalry erupted into scientific disputes throughout his life, it led to indisputable accomplishments that would outlive the rancor. He was generous with his knowledge and nurtured the careers of many researchers who went on to become prominent figures in their own right. He was keen to share credit with his co-researchers, something that I strongly believe should be a bedrock principle of modern biomedical research. He remained a modest man till the end of his life: “If my efforts have led to greater success than usual, this is due, I believe, to the fact that during my wanderings in the field of medicine, I have strayed onto paths where the gold was still lying by the wayside. It takes a little luck to be able to distinguish gold from dross, but that is all.” —Dr. Raj Bawa

Dr. Ignaz Philipp Semmelweis. Photograph.

Ignaz Philipp Semmelweis (1818–1865) (German: [ˈɪɡnaːts ˈzɛml̩vaɪs]; Hungarian: Semmelweis Ignác Fülöp [ˈsɛmmɛlvɛjs ˈiɡnaːts ˈfyløp]) was a Hungarian physician who discovered that simple antiseptic procedures such as handwashing could markedly reduce the occurrence of puerperal (or childbed) fever and save mothers’ lives in maternity wards. He was popularly known as “the saviour of mothers.” He established the link that medical students were infecting pregnant women in maternity wards from autopsies (midwives did not attend autopsies). Once he ordered the students to wash their hands in a solution of chlorinated lime before each examination, the mortality rates dramatically dropped. However, his observations conflicted with the established scientific and medical opinions of the time. The medical establishment rejected his outstanding work and he complained that “most medical lecture halls continue to resound with lectures on epidemic childbed fever and with discourses against my theories.” To avoid spreading microbes like SARS-CoV-2, the least controversial and most effective

tactic today is to "properly" wash your hands with soap and water. However, in the 19th century, this simple practice was considered scandalous. Although surgeons began regularly scrubbing up in the 1870s, the importance of routine handwashing became universally accepted only a century later. In fact, hand hygiene was officially incorporated into US healthcare in the 1980s when the first national hand hygiene guidelines were issued. In 1865, Semmelweis suffered a breakdown and was committed to an asylum by his colleagues, where he died. His illness and death were caused by the infection of a wound on his hand due to an operation he had performed before he was taken ill. His doctrine was eventually accepted by medical science and control of infection was hailed by Joseph Lister: “I think with the greatest admiration of him and his achievement and it fills me with joy that at last he is given the respect due to him.” In my humble opinion, Dr. Semmelweis deserves to be portrayed as one of the greatest physicians and public health advocates of all time. Semmelweis University is a research-led medical school in the stunningly beautiful city of Budapest in Hungary. Founded in 1769, it was renamed in 1969 in honor of Dr. Semmelweis. I have regularly visited this outstanding institution for conferences and research activities for the past 15 years. —Dr. Raj Bawa

Lord Lister with his house surgeons and dressers. Photograph by Barrauds, between 1890 and 1899.

Joseph Lister (1827–1912) was an English surgeon who introduced antiseptics during surgery. Based on the concepts of Pasteur, he employed carbolic acid to clean surgical wounds and insisted that his surgical team use aseptic technique during surgery. Before Lister’s studies of surgery, it was believed that chemical damage from exposure to “bad air” (miasma) was responsible for infections in wounds. Lister’s studies showed that using antiseptics, washing hands, and wearing gloves drastically reduced the incidence of surgical site infections—basic hygiene principles that are critical in the era of COVID-19. He is remembered and honored for his remarkable accomplishments that earned him the title “father of modern surgery.” Lister summarized the conclusions of his research: “But since the antiseptic treatment has been brought into full operation, and wounds and abscesses no longer poison the atmosphere with putrid exhalations, my wards, though in other respects under precisely the same circumstances as before, have completely changed their character; so that during the last nine months not a single instance of pyemia, hospital gangrene, or erysipelas has occurred in them.” Although asepsis and sterile technique have replaced antisepsis as the primary principle in combating infection, Lister’s application of germ theory laid the foundation for surgery. —Dr. Raj Bawa

Chapter 1

SARS-CoV-2 and COVID-19: A Perspective Raj Bawa, MS, PhD, MD ‘22 Patent Law Department, Bawa Biotech LLC, Ashburn, Virginia, USA Albany College of Pharmacy and Health Sciences, Pharmaceutical Research Institute, Albany, New York, USA Teva Pharmaceutical Industries Ltd., Israel Guanine Inc., Rensselaer, New York, USA [email protected]

Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa Copyright © 2022 Raj Bawa ISBN 978-981-4877-84-8 (Hardcover), 978-1-003-18043-2 (eBook) www.jennystanford.com

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Copyright © 2022 Raj Bawa. All rights reserved. This work is free and may be used by anyone for any purpose. As a service to authors and researchers, as copyright holder, I permit unrestricted use, distribution, online posting, and reproduction of this article or unaltered excerpts therefrom, in any medium, provided the original source is clearly identified and properly credited.

I dedicate this chapter to my late mother, Mrs. Sudesh Bawa (1935– 2020), in whose memory I have established the Sudesh Bawa Medical Foundation. This section is based, in part, on discussions I have had with my 92-year-old father, Dr. S. R. Bawa, an anatomist and a retired university professor/chair. My parents were married in 1954 and exemplified how a lifelong relationship of love, dedication and perseverance gives meaning to life.

Keywords: antibody test, antigen presentation, B cells, biosafety level-4, cellular immunity, Centers for Disease Control and Prevention, contact-tracing, convalescent plasma, coronavirus, Coronavirus Disease 2019, CoV-2 virus, COVID-19, COVID-19 Vaccines Global Access, emergency use authorization, exocytosis, genetically engineered, herd immunity, heterologous prime-boost, immunosurveillance, major histocompatibility complex, Middle East Respiratory Syndrome, neutralization capacity, Operation Warp Speed, over-the­ counter, pandemic, passive immunotherapy, patents, point-of-care, SARS-CoV, SARS­ CoV-2, sensitivity, serology tests, Severe Acute Respiratory Syndrome, single-stranded RNA genome, Spanish Flu, specificity, transmissibility, transmission electron micrograph, US Food and Drug Administration, vaccination, vaccine passports, variants of concern, viral surveillance, virulence, World Health Organization, Wuhan Institute of Virology, zoonotic, zoonotic reservoirs, zoonotic spillover

1.1 Pandemics: A Clear and Present Danger Messieurs, c’est les microbes qui auront le dernier mot. (Gentlemen, it is the microbes who will have the last word.) —Louis Pasteur

Epidemics on the other side of the world are a threat to us all. No epidemic is just local.

—Peter Piot

As a microbiologist, I am fully aware that our world is a playground for microbes. Emerging pathogens pose a clear and present danger, and pandemic preparedness is critical. Pandemics can be triggered by unavoidable or uncontrollable natural processes like genetic variations and climate change. They can also arise from risks generated by human activities or practices. Examples include antibiotic

Pandemics

overuse or misuse, destruction of forest habitats of microbe-carrying animals, and an increase in the ease and speed of global transportation that spreads diseasecausing pathogens. By some estimates, of the 1,461 diseases now recognized in humans, approximately 60% are due to multi-host pathogens characterized by their movement across species lines.1 Other reports conclude that over the past three decades about 75% of new emerging human infectious diseases have a zoonotic origin.2 Clearly, human-animal interactions and interdependence are likely the most critical risk factor to our health regarding infectious diseases. To gauge a “spillover event” enormous scientific resources are directed towards predicting where the deadliest viruses reside, their life cycles, susceptibilities, and ability to cross species barriers. Throughout history, viruses, bacteria, and parasites have killed more humans than wars and natural disasters. Viral diseases were recorded ever since humans began living together in communities, with smallpox being the first reported around 10,000 BC. Smallpox was the deadliest human disease to ever exist with a devastating 20–60% mortality rate, killing an estimated 300 million people in the 20th century alone. In 1918, the H1N1 Spanish Flu infected one-third of the world’s population and killed an estimated 50–100 million people. Other recent influenza pandemics include the 1957 H2N2 (Asian Flu) that originated in China and killed around 4 million people worldwide, the 1968 H3N2 (Hong Kong Flu) that killed 1 million people worldwide, the 2005 H5N1 (Bird Flu) which caused few deaths and the 2009 H1N1 (Swine Flu) which caused 18,000 human deaths. In addition to influenza pandemics, coronaviruses have also caused regional epidemics prior to the current COVID-19 pandemic (Figs. 1.1–1.4). Coronaviruses of zoonotic origin have caused large-scale cluster outbreaks of severe respiratory disease. These include the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) epidemic in 2003 in mainland China and the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) epidemic in 2012 in Saudi Arabia and in 2015 in South Korea. SARS-CoV (the first coronavirus) spread to 26 countries before the outbreak was contained with over 8,000 people infected and a case fatality rate of approximately 10%. Regarding MERS-CoV, infections are still occurring and have been reported in almost 30 countries. While human-to-human transmission for MERS-CoV is rare, some studies show the case fatality rate to be greater than 30%. The year 2020 will forever be marked by the presence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)3 and the associated COVID-19 pandemic. COVID-19 has had a catastrophic effect on the world’s F. Torrey and R. H. Yolken. (2005). Beasts of the Earth. Rutgers University Press, New Brunswick, NJ. H. Taylor, S. M. Latham, and M. E. Woolhouse (2001). Risk factors for human disease emergence. Philos. Trans. R. Soc. Lond. B Biol. Sci. 356:983–989. 3It is important to distinguish the SARS-CoV-2 virus from the disease it causes, namely, COVID-19. In this chapter, I will use the terms SARS-CoV-2 virus, CoV-2 virus, and coronavirus 2 interchangeably to refer to the virus that causes COVID-19. 1E. 2L.

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demographics resulting in ~3.76 million deaths so far. After the first cases of this predominantly respiratory viral illness were “officially” reported by the Chinese government in late December 2019, SARS-CoV-2 rapidly circumvented the globe in a matter of weeks, compelling the World Health Organization (WHO) to declare it as a global pandemic on March 11, 2020. As of June 8, 2021, globally, there have been 174,591,505 coronavirus cases and 3,757,419 deaths, and 157,941,391 patients have recovered from COVID-19.4 Virtually overnight, this pandemic profoundly altered the world as it struggled to contain the coronavirus while mitigating its health, economic and social impact. For a global pandemic to occur, the following requirements are needed: emergence of a new human microbe; reduced or minimal population immunity to that microbe; and a relatively simple mode of transmission. SARS-CoV-2 fulfills all three of these criteria.

1.2 The Invader and the Host: A Delicate Dance The viruses, instead of being single-minded agents of disease and death, now begin to look more like mobile genes. We live in a dancing matrix of viruses; they dart, rather like bees, from organism to organism, from plant to insect to mammal to me and back again, and into the sea, tugging along pieces of this genome, strings of genes from that, transplanting grafts of DNA, passing around heredity as though at a great party. They may be a mechanism for keeping new, mutant kinds of DNA in the widest circulation among us. If this is true, the odd virus disease, on which we must focus so much of our attention in medicine, may be looked on as an accident, something dropped. —Lewis Thomas, Lives of a Cell: Notes of a Biology Watcher, 1974

A delicate dance between a virus (the invading pathogen) (Figs. 1.1 and 1.2) and the immune system (host defense mechanisms) unfolds each time the virus infects the host (Figs. 1.3 and 1.4). Host immune cells and antibodies are in constant battle with virus invaders, with one just barely keeping the other in check. One of the most confusing and fundamental questions around viruses is why they are severely pathogenic to some hosts and asymptomatic in others. After all, it is in the virus’s best interest, from an evolutionary perspective, to co-exist in the host in a chronic asymptomatic state without causing major damage. Smart viruses maintain a carrier state, a sort of equilibrium with the host’s immune cells in an asymptomatic fashion. 4Worldometer. COVID-19 outbreak live update. Available at: https://www.worldometers.info/coronavirus

(accessed on June 8, 2021). Another authoritative source is the Johns Hopkins Coronavirus Resource Center: https://coronavirus.jhu.edu/. Please note that limited testing and challenges in the attribution of the cause of death means that the number of confirmed deaths may not be an accurate count of the actual number of deaths from COVID-19.

The Invader and the Host

Figure 1.1a Structural view of a coronavirus. Source: https://commons.wikimedia.org/ wiki/File:3D_medical_animation_coronavirus_structure.jpg.

Figure 1.1b The peplomers of a SARS-CoV-2. This illustration reveals the surface morphology/topography of the virus nanoparticle. Note the spikes that adorn the outer surface of the virus, which impart the look of a corona surrounding it, when viewed electron microscopically. A peplomer (Greek: peplos, ‘robe’, ‘[woman’s] dress’ + meros, ‘part’) is one of the knoblike spike structures (red, orange), generally composed of glycoproteins (spike protein) and projecting from the lipid bilayer of the surface envelope. Peplomers play important roles in the infection process. Figure courtesy of the CDC.

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Figure 1.1c 3D print of one of the peplomers of SARS-CoV-2. Courtesy of NIH.

Figure 1.2 Digitally colorized transmission electron micrograph of SARS-CoV-2. Virus particles isolated from a patient in the US are shown emerging from the surface of cells cultured in the lab. Courtesy of NIH.

Figure 1.3 Transmission and life cycle of SARS-CoV-2 causing COVID-19. SARS-CoV-2 is transmitted via respiratory droplets of infected cases to oral and respiratory mucosal cells. The virus, possessing a single-stranded RNA genome wrapped in nucleocapsid (N) protein and three major surface proteins: membrane (M), envelope (E), and spike, replicates and passes to the lower airways potentially leading to severe pneumonia. The gateway to host cell entry (magnified view) is via Spike-converting enzyme 2 (ACE2) interaction with cleavage of spike in the prefusion state by proteases TMPRSS-2/furin. A simplified depiction of the life cycle of the virus is shown along with potential immune responses elicited. Source: C. D. Funk, C. et al. (2020). A snapshot of the global race for vaccines targeting SARS-CoV-2 and the COVID-19 pandemic. Front. Pharmacol. 11:937.

The Invader and the Host 13

Figure 1.4 COVID-19 pathogenesis. 1. A. SARS-CoV-2 enters the epithelial cell either via endocytosis or by membrane fusion through binding to ACE2 receptor and releasingits RNA into the cytoplasm. B. Viral RNA uses the cell’s machinery to translate its viral non-structural and structural proteins and replicate its RNA. C. Viral structural proteins S, E, and M assemble in the rough endoplasmic reticulum (RER). D. Viral structures and nucleocapsid subsequently assemble in the endoplasmic reticulum Golgi intermediate (ERGIC). E. New virion packed in Golgi vesicles fuse with the plasma membrane and gets released via exocytosis. 2. SARS-CoV-2 infection induces inflammatory factors that lead to activation of macrophages and dendritic cells. 3. Antigen presentation of SARS-CoV-2 via major histocompatibility complexes I and II (MHC I and II) stimulates humoral and cellular immunity resulting in cytokine and antibody production. 4. In severe COVID-19 cases, the virus reaches the lower respiratory tract and infects type II pneumocytes leading to apoptosis and loss of surfactant. The influx of macrophages and neutrophils induces a cytokine storm. Leaky capillaries lead to alveolar edema. Hyaline membrane is formed. All of these pathological changes result in alveolar damage and collapse, impairing gas exchange. Source: N. Chams, et al. (2020). COVID-19: a multidisciplinary review. Front. Public Health 8:383.

14 SARS-CoV-2 and COVID-19

Did SARS-CoV-2 Leak from a Chinese Lab?

1.3 Did SARS-CoV-2 Leak from a Chinese Lab? Life on Earth is at the ever-increasing risk of being wiped out by a disaster, such as sudden global nuclear war, a genetically engineered virus or other dangers we have not yet thought of. —Stephen Hawking

Is it possible that SARS-CoV-2 began when an animal virus found its way unaided into humans, i.e., a zoonotic spillover? Is it more likely that the virus began in a Chinese government laboratory in Wuhan? Was it genetically engineered at the lab to enhance its virulence and infectivity? Is it a man-made bioweapon? Was SARS-CoV engineered into SARS-CoV-2? Did it accidentally leak from the lab? We do not know the precise answer to these critical questions at the moment. But, strong circumstantial evidence is building that points towards a lab leak and Chinese cover-up. China’s response to the COVID-19 outbreak has been scrutinized since the virus was first detected in its Wuhan province. In fact, China’s lack of transparency and questionable tactics, especially in the first few weeks, have contributed to the spread of SARS-CoV-2. This has led to calls for an open investigation into the possibility that the coronavirus leaked from a lab. Even if SARS-CoV-2 originated naturally, from animal-human contact, it does not preclude the possibility that the virus was the result of an accidental leak from China’s Wuhan Institute of Virology, where coronavirus research was being conducted on bats. This institute is only a few kilometers from the Huanan Seafood Market where SARS-CoV-2 was first detected.5 To deflect blame for a potential leak, the Chinese government has promoted unsubstantiated theories that the virus may have entered China via frozen food. The WHO does not have the regulatory authority to force governments to disclose information. It has been particularly weak dealing with China on the COVID-19 pandemic. China did recently invite a small WHO team of disease experts to “investigate” the outbreak, but its findings were of limited value since the team’s constraints reveal how little power it had to conduct a fair probe. To me, this visit was akin to a student field trip where the final conclusions were predetermined and scientifically fraudulent. No wonder, the WHO was broadly criticized by many governments over its limited access to “complete, original data and samples” and overly deferential treatment of China throughout the course of this study. Moreover, this study was co-authored by 17 Chinese scientists, several of them from Chinese government-run institutions. As an editor of peerreviewed journals for almost two decades, this represents a clear conflict of interest and scientific misconduct on the part of the authors of this “invalid” WHO report. A US State Department fact sheet from January 2021 highlights reports of sick lab researchers at the Wuhan Institute of Virology in the fall of 2019. It also points 5On

December 30, 2019, the Wuhan Municipal Health Commission issued two urgent notices to local hospitals about cases of pneumonia of unknown origin linked to the Huanan Seafood Market. The Wuhan Institute of Virology sequenced almost the entire genome of the virus on January 2, 2020. This sequence and further sequences were made public later in January 2020. Chinese scientists successfully isolated the virus by January 7, 2020, and developed a PCR testing reagent for the virus by January 10, 2020.

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to research with virulent coronavirus strains and indicates secret Chinese military activity at the lab. In May 2021, strong evidence from a previously undisclosed US intelligence report was made available that details three researchers from this institute becoming ill enough to seek hospital care in the autumn of 2019.6 Their illness occurred a few months prior to the official Chinese government disclosure of SARS-CoV-2 to the world. Clearly, more rigorous investigations are required to establish the original source of this pandemic, with or without China’s assistance. I predict that the Chinese government will continue to stonewall any efforts to determine the true origins of SARS-CoV-2. As a side note, the question has also arisen if plaintiffs from other countries can sue China for COVID-19 and hold it legally accountable in their respective courts. In fact, first lawsuits against the government of China and the Chinese Communist Party were filed in 2020 with a heavy emphasis on mass torts and class actions.7 However, a major obstacle to these lawsuits is the bedrock doctrine of sovereign immunity which protects a nation from being sued in another nation. As an adjunct professor at Rensselaer Polytechnic Institute in Troy, NY, from 1998 to 2018, I designed and taught a course for over a decade titled “Biodefense: A clear and present danger.” It included lectures on emerging and re-emerging infectious diseases and noted the potential of coronaviruses to cause pandemics. A few lectures covered microbial bioweapons and biodefenses against them. The inspiration for the course was based on a half-day meeting at the Center for Biodefense at George Mason University in Virginia with its director, Dr. Kenneth “Ken” Alibek (Col. Kanatzhan “Kanat” Alibekov). He was the First Deputy Director of Biopreparat from 1988–1992, the offensive biological weapons program of the Soviet Union. This gigantic clandestine biowarfare project, at its height, had 50,000+ employees. There, he oversaw projects that included weaponizing microbes that cause glanders, smallpox, plague, tularemia, Ebola, and Marburg, and the creation of a new “battle strain” of anthrax. The size and scope of the Soviet Union’s bioweapon’s efforts were truly staggering. They stockpiled tons of anthrax bacilli and smallpox virus, some for use in intercontinental ballistic missiles. Dr. Alibek gifted me his superb book, titled Biohazard,8 excerpts of which I have continued to use in the classroom for the past 15 years. The subtitle for the book, “The chilling true story of the largest covert biological weapons program in the world—Told from the inside by the man who ran it,” is an appropriate summary of the book’s contents. It is a must-read for any medical student, microbiologist, policy-maker, or health-care professional. The book highlights that (i) despite international conventions banning bioweapons development, secrecy regarding genetic manipulation of microbes to enhance their pathogenicity and virulence is a reality at state-run labs of numerous countries; and 6M.

R. Gordon, W. P. Strobel, and D. Hinshaw (2021). Intelligence on sick staff at Wuhan lab fuels debate on Covid-19 origin. Wall Street J., May 23 issue. Among the first 27 documented hospitalized patients, most cases were epidemiologically linked to Huanan Seafood Wholesale Market, a wet market that sold live animals, including wildlife. On December 31, 2019, the Wuhan Municipal Health Commission notified the public of a pneumonia outbreak of unidentified cause and also informed the WHO. 7D. Ricker. (2020). Suing China for COVID-19. ABA J., August/September issue, page 17.

8K. Alibek and S. Handelman. (1999). Biohazard. Random House, New York, NY.

COVID-19 Vaccines

(ii) the potential that the world’s most dangerous pathogens can escape from biosafety labs, including the controversial biosafety level-4 (BSL-4) labs, is typically maintained as a state secret. Given this backdrop, it is possible that the Wuhan Institute of Virology created the SARS-CoV-2 virus via genetic engineering and it accidently leaked out into the nearby wet market, resulting in the current COVID-19 pandemic. If this did happen, it also points to the wider concern many experts have had that microbial leaks, even at BSL-4 labs like the one in Wuhan, present serious public health concerns. In fact, concerns were raised during the certification of the Wuhan Institute of Virology as meeting the standards of BSL-4 back in 2017: “Some scientists outside China worry about pathogens escaping, and the addition of a biological dimension to geopolitical tensions between China and other nations ... The SARS virus has escaped from high-level containment facilities in Beijing multiple times.”9 Such worries are real with documented leaks provided as evidence. Elaborate coverups, which possibly are also underway at Wuhan, are typical of dictatorships like China and Russia.10 Tighter security is needed at BSL-4 labs to prevent theft, accidents, or terrorism.

1.4 COVID-19 Vaccines: Facts and Fiction

I hope that someday the practice of producing cowpox in human beings will spread over the world—when that day comes, there will be no more smallpox. —Edward Jenner

Without equity, pandemic battles will fail. Viruses will simply recirculate, and perhaps undergo mutations or changes that render vaccines useless, passing through the unprotected populations of the planet. —Laurie Garrett

Vaccines and immunizations are among the most effective public health interventions of the last century. They represent great strides in taming or conquering microbial diseases. By some estimates, 2.5 million child deaths around the world are prevented each year by immunization. According to recent Centers for Disease Control and Prevention (CDC) data, routine childhood vaccinations prevented 732,000 early deaths from 1994 to 2013.

1.4.1 The First COVID-19 Vaccines

Thankfully, there was positive news in early 2021 in the battle against SARS-CoV-2, though the war is far from won. Here in the US, the massive $10 billion investment of the Trump administration in Operation Warp Speed to fast-track

Cyranoski. (2017). Inside China’s pathogen lab. Nature 554:339–340. F. Frischknecht. (2003). The history of biological warfare. EMBO Reports 4:S47–S52: “In 1979, the Soviet secret police orchestrated a large cover-up to explain an outbreak of anthrax in Sverdlovsk, now Ekaterinburg, Russia, with poisoned meat from anthrax-contaminated animals sold on the black market. It was eventually revealed to have been due to an accident in a bioweapons factory, where a clogged air filter was removed but not replaced between shifts.”

9D.

10See,

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the development of SARS-CoV-2 vaccines within one year has paid dividends and resulted in a dozen or more potential vaccine candidates. Importantly, a few vaccines have been approved under an emergency use authorization (EUA)11 and made available in the US, albeit the rollout has been disastrous. Currently, there are six approved vaccines authorized for use in select countries (Fig. 1.5a–c and Table 1.1): mRNA-1273 (Moderna/NIAID), BNT162b2 (Pfizer-BioNTech), Ad26.COV2.S (Johnson & Johnson/Janssen), ChAdOx1 nCoV-19 (University of Oxford/AstraZeneca), Gam-COVID-Vac/Sputnik V (Gamaleya Research Institute of Epidemiology and Microbiology, Russia), and BBIBP-CorV (Sinopharm/Beijing Institute of Biological Products, China). Based on published clinical trial data,12 their efficacies range from 65.5% to 94.6% in preventing symptomatic COVID-19. The overall efficacies of the three FDA-authorized vaccines currently marketed in the US13 (e.g., Pfizer-BioNTech, Moderna, Johnson & Johnson/Janssen)14 tested prior to the emergence of deadlier variant varieties are in the 90%+ range. I am not sure how effective any of these first generation vaccines will be once they encounter multiple virulent variants of SARS-CoV-2. Preliminary data reported in May 2021 from a trial of more than 600 people are the first to show the benefits of combining different vaccines (heterologous prime-boost). Such mix-and-match COVID-19 vaccination strategies may trigger stronger, more robust immune responses than will two doses of a single vaccine while simplifying immunization efforts where vaccine supplies are less reliable. I wonder what the long-term safety data of such an approach will be given that RNA vaccines (in contrast to traditional vaccines) tend to trigger stronger side effects with added doses? It is important to note that for other coronaviruses, such as the common cold virus (SARS-CoV) and the MERS virus, immunity declines over time. But, at this stage, it is uncertain as to how long antibodies and immunity lasts for those 11In

certain emergencies, the FDA can issue an EUA to provide more timely access to critical medical products (including medicines and tests) when there are no adequate, approved, and available alternative options. 12There were 549 clinical trials on COVID-19 recorded in early April 2020. By January 2021, there were already 4,000 studies registered. Available at: https://www.globaldata.com/covid-19-clinical-trials­ increased-639-us-leading-way/ (accessed on June 8, 2021). 13Traditional vaccines contain ingredients to generate an immune response, usually protein fragments (active agents) of the microbe that causes the disease along with preservations and excipients (inactive agents). In the case of the COVID-19 vaccine, instead of using the whole virus to generate an immune response, vaccines formulations comprise RNA sequences correspond to the coronavirus’ outer spike proteins, which are what antibodies use to recognize the virus. In other words, the genetic code used by the virus to synthesize the spike proteins is the active agent in the vaccine formulation. The RNA is protected by a lipid coating, forming a nanoparticle (technically it is a nanomedicine). When injected into a patient, the RNA enters healthy cells where it helps orchestrate the production of coronavirus spike proteins, kickstarting the immune system and producing antibodies. 14Two vaccines not (yet) available in the US are Oxford-AstraZeneca and Novavax. For an excellent comparison of all vaccines, see: Comparing the COVID-19 vaccines: How are they different? Available at: https://www.yalemedicine.org/news/covid-19-vaccine-comparison (accessed on June 8, 2021). Also see: COVID-19 vaccine & therapeutics tracker. Available at: https://biorender.com/covid-vaccine­ tracker (accessed on June 8, 2021).

COVID-19 Vaccines

vaccinated, or even those exposed to the virus. In my assessment, we will require regular booster shots for the virus as novel variants continue to emerge and as COVID-19 morphs into a chronic multisystemic viral disease. More effective vaccines of broader scope, preferably single-shot, are urgently needed.15 As countries roll out vaccines against the SARS-CoV-2 virus, studies are under way to determine whether shots can also stop viral transmission as this could be critical to bringing the pandemic under control but only if enough people are vaccinated. Some studies suggest that some vaccines are likely to have a transmission-blocking effect. However, this is not easy to establish because a drop in infections can be due to other factors, such as lockdowns and personal behavior.

15In

Figure 1.5a How viral vector COVID-19 vaccines work. Courtesy of the CDC.

May 2021, the UK’s Medicines and Healthcare products Regulatory Agency (MHRA) announced that it had approved a single-shot coronavirus vaccine developed by Johnson & Johnson/Janssen. However, it is 67% effective overall at preventing moderate to severe COVID-19, with studies suggesting that it also offers complete protection from admission to hospital and death. According to Johnson & Johnson, the vaccine works across multiple variants of coronavirus.

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Figure 1.5b Three vaccine types for forming SARS-CoV-2 proteins to prompt an immune response: (1) RNA vaccine, (2) subunit vaccine, (3) viral vector vaccine. Courtesy of the Government Accountability Office.

Figure 1.5c Vaccine platforms being employed for SARS-CoV-2. Whole virus vaccines include both attenuated and inactivated forms of the virus. Protein and peptide subunit vaccines are usually combined with an adjuvant to enhance immunogenicity. The main emphasis in SARS-CoV-2 vaccine development has been on using the whole spike protein in its trimeric form, or components of it, such as the RBD region. Multiple non-replicating viral vector vaccines have been developed, particularly focused on adenovirus, while there has been less emphasis on the replicating viral vector constructs. Source: K. L. Flanagan, et al. (2020). Progress and pitfalls in the quest for effective SARS-CoV-2 (COVID-19) vaccines. Front. Immunol. 11:579250.

COVID-19 Vaccines

Figure 1.5d Timeline of vaccine production and approval. Abbreviations: BP, BioNTech/ Pfizer; M, Moderna; AZ, Oxford/Astra Zeneca; J&J, Janssen/Johnson and Johnson; NV, Novavax; SPV, Sputnik V; CoV, CoronaVac; Ad5, Ad5-nCOV; BCG, Mycobacterium bovis. Blue arrows, phase I/II trials; green arrows, phase III trials; yellow arrows, roll out; purple star, (anticipated) approval date. Trial start dates were taken from http://www.clinicaltrials. gov. Approximate end dates of phase I/II trials are the publication dates. End points for unpublished trials are guesses by the author. For the last three vaccines, a guess for an approval date was not possible. Note that all start and end dates of trials, as well as approval dates are approximations. Source: B. M. Prüβ (2021). Current state of the first COVID-19 vaccines. Vaccines 9(1):30. Table 1.1 Major CoVID-19 candidate vaccine platforms in clinical evaluation Vaccine name

Vaccine platform

Developer

Clinical trial phase Clinical trial registrations

BNT162b1/ BNT162b2

RNA-based vaccine

Pfizer-BioNTech, Fosun Pharma

Phases I–III in USA, Germany, and China

INO-4800

DNA plasmid vaccine

Inovio Phases I–III in USA Pharmaceuticals, International Vaccine Institute

mRNA-1273

GX-19

RNA-based vaccine

DNA plasmid vaccine

Moderna, NIAID

Genexine Consortium

Phases I–III in USA

Phases I and II in South Korea

NCT04368728, NCT04380701, NCT04523571 NCT04470427, NCT04405076, NCT04283461

NCT04447781, NCT04336410 NCT04445389

(Continued)

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Table 1.1 (Continued) Vaccine name

Vaccine platform

ChAdOx1 nCov-19 (AZD1222)

Adenovirus University vector, non- of Oxford, replicating AstraZeneca

Developer

Clinical trial phase Clinical trial registrations Phases I–III in UK, South Africa, USA and Brazil

NCT04324606, ISRCTN89951424, EudraCT2020-001228-32, PACTR202006922165132, EudraCT2020-001072-15

Ad26.CoV2-S Adenovirus Johnson & vector, non- Johnson replicating

Phases I–III in USA and Belgium

Gam-COVIDVac

Phases I–III in Russia NCT04530396 NCT04436471 NCT04437875

Ad5-nCoV

PiCoVacc

COVID-19 vaccine BBIBP-CorV SCB-2019 NVXCoV2373

Adenovirus CanSino Biologics Phases I and II; vector, non- Inc., Beijing phase II studies in replicating Institute of China and Canada Biotechnology Adenovirus Health Ministry vector, non- of the Russian replicating Federation

NCT04436276 NCT04505722 NCT04535453 NCT04509947

ChiCTR2000031781, ChiCTR2000030906, NCT04341389 NCT04313127

Inactivated Sinovac Biotech SARS-CoV-2

Phases I–III; phase III in China and Brazil

Inactivated Sinopharm, SARS-CoV-2 Beijing Institute of Biological Products Co. Ltd

Phases I–III in China ChiCTR2000034780, and United Arab ChiCTR2000032459 Emirates

Protein subunit

Phases I–III in Australia, USA and UK

Inactivated Sinopharm, SARS-CoV-2 Wuhan Institute of Biological Products Co. Ltd

Protein subunit

Phases I–III in China ChiCTR2000034780, ChiCTR2000031809

Clover Phase I in Australia Pharmaceuticals, GlaxoSmithKline, Dynavax Novavax

NCT04456595, NCT04383574, NCT04352608

NCT04405908

NCT04368988 NCT04583995 NCT04533399

Source: C. Wang, Z. Wang, G. Wang, et al. (2021). COVID-19 in early 2021: current status and looking forward. Sig. Transduct. Target. Ther. 6:114.

Another important point is whether asymptomatic individuals can serve as viral carriers. Early data indicate that vaccines will likely help prevent asymptomatic transmission, although most of it is not peer-reviewed. Still, it is worth mentioning. For example, data from the Israeli Health Ministry and Pfizer demonstrated an 89% reduction in both symptomatic and asymptomatic infections

COVID-19 Vaccines

following vaccination while a vaccine trial by Johnson & Johnson found that its vaccines prevented asymptomatic infection in 74% of recipients. Even based on this incomplete picture regarding asymptomatic viral spread, I cannot underscore enough the need for universal vaccination.

1.4.2 Emergence of SARS-CoV-2 Variants

Over time, viruses are prone to mutations of their genomes which arise from random genomic changes as they replicate in an infected person. This results in variants that may have different characteristics than their ancestral strains. Variants pose different concerns of differing degrees. These relate to their: (i) transmissibility (propensity to spread); (ii) virulence (severity of illness); (iii) neutralization capacity (likelihood they will infect people who have recovered from a previous bout of COVID-19), and (iv) potential impact on vaccination through their ability to evade immunosurveillance. The SARS-Cov-2 genomic RNA is unusually large, the RNA polymerase is error-prone, and mutations accumulate with increasing frequency during infections. With continued uncontrolled viral replication and infection, mutations that give the virus a fitness advantage will emerge. Obviously, SARS-CoV-2 variants16 that are more virulent or infectious—or both—are of particular concern. According to a few studies, SARS-CoV-2 shares approximately 50–79% of its genetic sequence with MERS-CoV and the first coronavirus, SARS-CoV. Also, interestingly, SARS-CoV-2 shares the receptor-binding domain structure with SARS-CoV. Monitoring the coronavirus for key mutation(s) in important genomic regions is critical. Most mutations may not affect the virus’s virulence or transmissibility because they do not alter the major proteins involved in infection. These are eventually outcompeted by variants with mutations that are more beneficial to the coronavirus. Since the genome sequence of SARS-CoV-2 was first reported in January 2020, thousands of variants have been reported. Most of the genetic and antigenic variations are innocuous and do not contribute to enhanced virulence or infectivity. However, the emergence of a few, referred to as variants of concern (VOCs), have caused considerable consternation. Generally, VOCs have one or more mutations that confer worrisome epidemiologic, immunologic, or pathogenic properties. The B.1.1.7 lineage (or VOC 202012) variant was the first VOC described in the UK in late December 2020 (Fig. 1.6). This variant is considerably more contagious than the original virus and recent evidence indicates that infection with this B.1.1.7 variant also comes with an increased risk of severe illness and death. A second variant, the B.1.351 lineage (or 501Y.V2) was reported in South Africa in late 2020. A third VOC, B.1.1.248/B1.1.28/P1 (or 501Y.V3), was reported in Brazil in early January 2021. As of May 2021, all three variants have been found in the US. A fourth variant, the 20A.EU1 variant, 16During

replication, a virus often undergoes genetic mutations that may create what are called variants (sometimes referred to as strains). Some mutations weaken the virus while others may yield some advantage that enables the variants to proliferate. A variant that deviates significantly from its

viral ancestors may be identified as a new lineage, or branch on the evolutionary tree.

23

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SARS-CoV-2 and COVID-19

first identified in Spain, contains a mutation called A222V on the viral spike protein. According to the WHO, another VOC, labeled the B.1.617 variant, has become the dominant strain across India. Evidence is growing that this variant might be more transmissible and slightly better at evading immunity than the existing variants.

Figure 1.6 False-color transmission electron micrograph of a SARS-Cov-2 B.1.1.7 variant. A single virus particle is shown (yellow). The variant’s increased transmissibility is believed to be due to changes in the structure of the spike proteins (green). Image captured at the NIAID Integrated Research Facility in Fort Detrick, Maryland. Courtesy of NIH.

I am skeptical if vaccine companies can develop new vaccine versions promptly prior to the emergence of deadlier second and third generation variant waves. In my view, genomic surveillance is key here as it serves as an early warning system that can detect threatening mutations before they become more widespread. Furthermore, genetic variations in a virus can render diagnostic tests ineffective.17 What makes the future of this virus so hard to predict is that it is not just the individual mutations that matter, but also the order and combinations in which 17For

molecular tests, their sensitivity and specificity depend on the number and location of genes that the test targets. Most antigen-based tests should continue to work as most are targeting the N antigen of the virus, a region that has so far remained conserved in the variants. But this can change in the near future.

Will We Ever Achieve Herd immunity?

they occur. Will SARS-CoV-2 retain its ability to cause enhanced infection and virulence as it mutates further and more people gain immunity through infections or vaccines?

1.5 Will We Ever Achieve Herd immunity? Achieving herd immunity (Box 1.1; Fig. 1.7) requires vaccination (or natural infection). According to few estimates and my own calculations, given the various variants of SARS-CoV-2, we may need a vaccination rate of 80–90% if some degree of normalcy is desired. A tremendous effort will be required to achieve such high vaccination rates. This would mean that all adults and adolescents in the US will have to be fully vaccinated to approach 80% vaccination—a high bar indeed. Is it possible? I am not sure if this herd immunity threshold will ever be attainable. Here in the US, daily vaccination rates are slipping, viral variants are emerging, poor infectious disease management policies are reappearing, and many pandemic restrictions are being relaxed prematurely. Global distribution of the COVID-19 vaccine has been lopsided.18 So far, in July 2021, major populations like India have only fully vaccinated 4.1% of their populations while here in the US the vaccination rate currently stands ~48%. BBC reports that as of March 2021, 80% of the vaccines have been administered to the developed nations while only 20% have gone to the developing nations. According to data collected by Bloomberg, as of May 29, 2021, countries/regions with the highest incomes are getting vaccinated more than 30 times faster than those with the lowest. In fact, data shows that more than 1.84 billion doses have been administered around the world—enough to only fully vaccinate 12% of the global population. As discussed above, this is still a far cry from what will be needed for herd immunity and to minimize emergence of virulent novel variants. A failure to vaccinate much of the developing world will leave a large reservoir of circulating virus, giving it the chance to mutate and spill over to developed countries. Another important point to remember is that it is hard to achieve our goal of herd immunity in the absence of vaccinations to infants and young people. By focusing solely on adult vaccination R&D, we have left out the vulnerable, immunologically naïve ~25% of the population that still have no available shots: kids. In my view, a pediatric vaccine for the disease is an urgent global health priority and the time for that to happen is now. This is especially true since we have substantial safety data from adult vaccine R&D, clinical trials, and field use. Vaccines given to kids will not only help curb the spread of SARS-CoV-2 but also protect young people who are at high risk. Big pharma is finally turning its attention towards this important demographic and clinical trials in adolescents or young children are underway. 18The

COVID-19 Vaccines Global Access (COVAX) Facility, a nonprofit, is purchasing shots in bulk at a discount and distributing them to the world’s most resource-strapped countries.

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Box 1.1 What is herd immunity? Herd immunity, sometimes called community immunity, is the indirect protection from an infectious disease that occurs when a high percentage of population is immune either through vaccination and/or immunity developed through previous infection. Theoretically, this makes the spread of the infectious disease from person to person unlikely. Herd immunity protects the most vulnerable members of the population (babies who have not received vaccinations, pregnant women, the immunocompromised or those on immunosuppressive drugs). Unlike the unethical and rash approach of Sweden, herd immunity against COVID-19 should be achieved through vaccination, not by exposing the population to the virus. Achieving herd immunity via vaccines makes diseases rarer and saves lives. On the contrary, letting COVID-19 spread through populations, of any age or health status will lead to unnecessary infections, suffering, and death. To safely achieve herd immunity a substantial proportion of a population would need to be vaccinated, lowering the overall amount of virus able to spread in the whole population. Although the proportion of the population that must be vaccinated against SARS-CoV-2 to begin inducing herd immunity is unknown, I believe that it could be as high as 85–90%. This means that about 85–90% of a population will need to be vaccinated while the remaining 10–15% will be protected by the fact that COVID-19 will not spread among those who are not vaccinated. There is also enormous confusion, disagreement, lack of scientific knowledge, and conflicting information pertaining to the delivery, use, and safety of COVID-19 vaccines. For instance, according to the latest official guidance from the CDC, pregnant women who are health-care personnel or essential workers “may choose to be vaccinated.” The major problem is that there is hardly any data available on COVID-19 vaccine safety with respect to pregnant women, given that they were excluded from clinical trials as has historically been the case. According to the CDC and the FDA, preliminary data from their safety monitoring systems did not identify any safety concerns for pregnant women who were vaccinated or for their babies. Two recent research studies19 (not clinical trials) show that the two COVID-19 mRNA vaccines currently available in the US appear to be safe and effective in pregnancy, with the potential to benefit both mother and baby. In my view, more data derived from robust clinical trials is warranted. 19A.

Y. Collier, et al. (2021). Immunogenicity of COVID-19 mRNA vaccines in pregnant and lactating women. JAMA e217563, doi: 10.1001/jama.2021.7563; E. D. Shanes, et al. (2021). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination in pregnancy: Measures of immunity and placental histopathology. Obstet. Gynecol., doi: 10.1097/AOG.0000000000004457.

Will We Ever Achieve Herd immunity?

Figure 1.7 Building the herd: The concept of herd immunity. When a critical portion of a community is immunized against a contagious disease, most members of the community are protected against that disease. The principle of community immunity applies to control of a variety of contagious diseases, including influenza, measles, mumps, rotavirus, and pneumococcal disease. The top box depicts a community in which no one is immunized, and an outbreak occurs. In the middle box, some of the population is immunized but not enough to confer community immunity. In the bottom box, a critical portion of the population is immunized, protecting most community members. Legend: not immunized, healthy immunized, healthy not immunized, sick, and contagious. Courtesy of Tkarcher, CC BY-SA 4.0 via Wikimedia Commons.

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1.6 Patents and COVID-19 Waiving vaccine patents to evenly manufacture and distribute COVID-19 vaccines (and other therapeutics) around the world is a hotly debated topic in June 2021. This gained increased traction following the explosion of cases and deaths in India in May 2021. The easing of patent protections is essential in a pandemic. The campaign was initiated by India and South Africa, and is being backed by more than 100 countries, the WHO, UNAIDS, etc. The US has joined in and supports a waiver on intellectual property for COVID vaccines, even though big pharma and most developed countries do not support it. I consider this a historic move. As a patent agent for the past two decades, I prefer compulsory licensing over outright patent waivers. However, the scope of the current pandemic makes waivers appropriate. In addition to waivers, patent pooling is also an excellent mechanism to pool our global intellectual property resources and is suggested by many20: “We call on pharmaceutical companies to contribute to a pool of patents set up by the World Health Organization (WHO). That will speed up the manufacture of generic, affordable COVID-19 vaccines and treatments while protecting firms’ incentives to invest in future research … The practice of pooling patented technologies to produce medicines already occurs for HIV, hepatitis C and tuberculosis treatments. Fees are typically lower when licenses are negotiated as a bundle with generics producers, implying increased volume. Yet firms can anticipate extra revenue from participation in a voluntary pool, and thus be more willing to maintain innovation and share know-how than with compulsory licensing.”

1.7 Vaccine Passports: Another Bad Government Idea

They that can give up essential liberty to obtain a little temporary safety deserve neither liberty nor safety. —Benjamin Franklin, Historical Review of Pennsylvania, 1759 No culture can live if it attempts to be exclusive.

—Mahatma Gandhi

Jurisdictions in the US and around the world are handling the current pandemic in variety of ways. Some are enforcing mask mandates while others are passing laws where masks cannot be forced upon a person. Some are using contact-tracing applications and systems to conduct “viral surveillance” while others are passing laws that grant citizens complete freedom to decide if they should participate in such programs. Some issue stringent requirements for social distancing while others forbid such actions. Some are passing regulations about self-isolation and quarantines while others are not. The list goes on and on. In essence, we have a maze of confusing policies, along with protests, dissent, 20E.

B. de Villemeur, et al. (2021). Pool patents to get COVID-19 vaccines and drugs to all. Nature 591:529.

Vaccine Passports

shutdowns, riots, pandemic fatigue, and fear. In this crisis, we have to balance competing legal, ethical, medical, privacy, and moral principles. Easier said than done. In this backdrop, I discuss below the concept of “vaccine or immunity passports,” and why they are a bad idea. Immune response to SARS-CoV-2 is a complex topic. For instance, an immune response to the live virus is different from the response to a single viral protein introduced via a vaccine. And then there are those who have been vaccinated following COVID-19. Our knowledge regarding the humoral immune response to SARS-CoV-2 has been rapid, though areas of uncertainty persist. We are a long way from understanding the characteristics of the antibody response, its dynamics over time, its determinants, and the immunity it confers to different age groups and disease. On the other hand, relatively less is known about cell-mediated immunity to SARS-CoV-2. We are slowly learning more. For example, a recent report21 demonstrated that blood levels of antibodies fall sharply following acute infection, while memory B cells remain quiescent in the bone marrow, ready to act as needed. In any case, clinical, policy, and economic implications will be greatly driven by our knowledge of the immunology of SARS-CoV-2. These include the proposed use of an “immunity passport” or a “risk-free certificate,”22 a form of certification for individuals with positive detection of antibodies that can enable them to avoid quarantine, and allow them to travel or to return to work. The assumption is that they are protected against reinfection. But, what about reinfection from variants that are undetectable via current tests? What about reinfection from an asymptomatic carrier state following infection? The idea is being floated in Germany, the United Kingdom, and other nations. Australia, Denmark, and Sweden have committed to implementation; and Israel is already issuing “green passes” to vaccinated residents. Hungary has introduced a policy allowing people to enter the country if they can provide evidence that they have already recovered from COVID-19. Iceland is planning on introducing a similar policy that will allow people who have already had COVID-19 to be exempt from the nationwide mask mandate. The European Union plans a “Digital Green Certificate” enabling free travel within the bloc. Although travel eligibility has been the primary focus here, some use these certificates to regulate access to social events, recreational activities, sporting arenas, theatrical performances, and more. New York’s “Excelsior Pass” permits attendance at theaters, arenas, event venues, and large weddings. Airlines could soon introduce “vaccine passports” to facilitate international travel. Currently, in this evolving pandemic, the issue of immunity passports is a poor proposal given the uncertainties relating to COVID-19 immunity. There is simply not enough evidence about the effectiveness of antibody-mediated immunity 21J.

S. Turner, et al. (2021). SARS-CoV-2 infection induces long-lived bone marrow plasma cells in humans. Nature, https://doi.org/10.1038/s41586-021-03647-4. 22Technically, any documentation that proves a full dose of COVID-19 vaccination can be considered a vaccine passport. Presently, yellow fever is the only disease indicated in the International Health Regulations for which nations may require vaccination proof as a condition for entry. Obviously, WHO can recommend (via advisories) that countries require vaccination proofs.

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to guarantee such certification.23 As discussed earlier, the data is incomplete on asymptomatic spread in vaccinated individuals. Such certificates falsely assume that a second infection will not occur if a person has recovered from COVID-19, has had a positive COVID-19 test, or has been vaccinated. Immunity passports also raise ethical, legal, and practicality issues, doubtful economic benefits, privacy concerns, and the risk of discrimination. In fact, they may lull individuals into a false sense of security, leading them to ignore public health advice and increasing the risks of continued viral spread. Even if immunity passports were limited to health-care personnel, the number of tests required would be unfeasible. Many respectable health organizations, medical societies, religious leaders, and medical editors have opposed vaccine passports.

1.8 COVID-19 Testing

You’re paying billions of dollars in this very inequitable way to get the most worthless test results of any country in the world. No other country has this testing insanity. —Bill Gates

In February 2020, the FDA began authorizing tests to diagnose active COVID­ 19 infections. During a crisis, the FDA can grant an emergency use authorization (EUA) for medical products using a lowered approval standard rather than the full approval based upon more extensive evidence. According to the FDA: “The EUA process is different than FDA approval, clearance, or licensing because the EUA standard requires less evidence than the full approval, clearance, or licensing standard. Under an EUA, the data must show that a product may be effective and that the known and potential benefits outweigh the known and potential risks. This enables the FDA to authorize the emergency use of medical products that meet the criteria within days or weeks rather than months to years. The FDA has prioritized review of EUA requests for tests where authorization would increase testing accessibility (such as point-of-care (POC) tests, home collection tests, and at-home tests) or would significantly increase testing capacity (such as tests that reduce reliance on test supplies and high-throughput, widely distributed tests).” Serology tests (i.e., antibody testing) are critically important for virus outbreaks. Also, it is important that national and international regulatory agencies like the FDA and EMA not approve substandard serology tests to be marketed or unauthorized products to appear in the marketplace. Unfortunately, this did happen and made viral testing confusing and unreliable. This reflected the broader problem with governments who were ill equipped to handle a pandemic. Clearly, they lacked a coordinated preparedness plan and there was over reliance on their antiquated regulatory system, or they approved COVID-19 tests

23As

of the June 2021, there is no conclusive data that establishes with certainty that the presence of antibodies to SARS-CoV-2 confers immunity to subsequent infections. I strongly believe that we will require booster shots periodically.

COVID-19 Testing

under extreme political and public pressure. Maybe, it would be helpful to be proactive and evaluate test performance prior to global microbial outbreaks? A common approach to validating test design and performance is urgently needed and federal governmental agencies need to step up their game in this regard. Independent assessment of molecular diagnostic, antigen, and serology test accuracy is needed. Along with this, test developers and biomedical researchers should receive robust and coordinated assistance from national and international mechanisms in obtaining patient specimens or other clinical samples to validate their tests. Table 1.2 Coronavirus disease 2019 testing basics: comparing diagnostic and antibody tests. Courtesy of the FDA.

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No test can ever be 100% accurate. Any COVID-19 test’s performance will vary and is based on disease prevalence in the tested population. In fact, diagnostic tests may be less accurate in populations with a low prevalence of disease and in asymptomatic individuals, individuals who shed little virus, or individuals who are early or late in the course of illness. We all know that tests are rated on their sensitivity and specificity. In simple terms, sensitivity of a test is defined as the fraction of positive cases that the test correctly identifies as positive, and specificity of a test is defined as the fraction of negative cases that the test correctly identifies as negative. A highly sensitive test will generally have a low false negative rate but will run a risk of false positives if the test’s specificity is low. A highly specific test will generally have a low false positive rate but will run a risk of false negatives if the test’s sensitivity is low. To reduce the risk of false negative results, it is important to perform the test in accordance with its authorization and as described in the authorized labeling. To mitigate the false results, most COVID-19 tests are ordered by clinicians and are prescription-only so that the results can be interpreted for patients. Any tests authorized for non-prescription use (i.e., direct-to-consumer (DTC) or over-the-counter” (OTC) use), directs patients to consult their health-care provider for result interpretation. There are two different types of tests—diagnostic tests and antibody tests, as discussed in Table 1.2 and Fig. 1.8. The latest guidance (dated May 13, 2021) for antigen testing for SARS-CoV-2 is available on the CDC’s website (https://www. cdc.gov/coronavirus/2019-ncov/lab/resources/antigen-tests-guidelines.html).

Figure 1.8 Diagnostic tests with alternative options. Courtesy of the FDA.

COVID-19 Convalescent Plasma

One of the most important aspects of COVID-19 testing centers around interpretation of results. In fact, there are problems with interpretation of serology test results to inform patient care. These problems continue to this day. Again, the role of federal agencies is paramount here as well. The misuse of serology tests for diagnosis; the potential for false positive results when a single test is used in populations with a low rate of infection; and the perception of immunity can all result in a skewed picture of the pandemic. This leads not only to misdiagnosis but also imposition of improper quarantine and other restrictive measures.

1.9 COVID-19 Convalescent Plasma: Is There a Benefit? I need hardly add that the fight against cattle tuberculosis only marks a stage on the road which leads finally to the effective protection of human beings against the disease. —Emil Adolf von Behring, Nobel Lecture, 1901

Intravenous human immunoglobulin delivery for prophylaxis and treatment is well known for numerous microbial diseases. Passive immunotherapy24 has been used since the late 19th century. In fact, the first Nobel Prize to von Behring in 1901 was awarded for passive serum therapy (immune serum containing neutralizing antibodies) for patients with diphtheria. During the Spanish Flu of 1918, serum from convalescent (recovered) patients was used. Similarly, its use is advocated for the treatment of patients with COVID-19.25 The idea is to give convalescent plasma to an infected patient (i.e., that person is getting antiviral antibodies) because it may take weeks to produce his/her own antibodies while the virus can continue replicating unchecked. Convalescent plasma (“survivor’s plasma”) can be used for prophylaxis of high-risk people before they get infected or for treatment of patients who are already infected but are not fighting the virus well. Plasma harvested from convalescent COVID-19 patients, containing antibodies against SARS-CoV-2, can be used in two ways (Fig. 1.9). The primary proposed protective mechanism here is neutralization, via the delivery of antibodies, although antibody-dependent cellular cytotoxicity and phagocytosis may also play a role. I wish to point out that despite what the FDA says, use of convalescent plasma therapy against SARS-CoV-2 is not a simple or straightforward issue26: “At this time, convalescent plasma should be reserved for patients in whom the duration, severity, and risk of progression of illness are similar to those in the patients in this trial. Younger high-risk patients (and certain immunodeficient patients) with these disease characteristics should be considered as well. Uncontrolled compassionate use of convalescent plasma in patients other than those with an early infection that is likely to progress to more severe illness should be 24On

the other hand, vaccination therapy is a form of active immunotherapy.

than 100,000 people in the United States and many more worldwide have already been treated

with it since the pandemic began. 26L. M. Katz. (2021). (A little) clarity on convalescent plasma for COVID-19. N. Engl. J. Med. 384(7): 666–668. 25More

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discouraged, even though clinicians recognize how difficult it can be to “just stand there” at the bedside of a patient in the ICU.” In my view, antibody cocktails in test tubes can never be equated to vaccines for a variety of reasons, including the effectiveness of the latter in priming humoral and cellular arms of the immune system. Also, there is data that antibody cocktails, such as those currently being tested by Regeneron and Eli Lilly, may be less effective against mutations present in the B.1.351 variant of SARS-CoV-2. Several published randomized controlled trials were halted early due to concern regarding a lack of benefit, low enrollment, or the finding that most recipients had baseline neutralizing antibodies with similar titers to the donors. In March 2021, the NIH halted a clinical trial evaluating the safety and effectiveness of COVID-19 convalescent plasma in treating emergency department patients who developed mild to moderate symptoms of COVID-19.

Figure 1.9 Overview of the use and applications of convalescent plasma therapy. Source: D. Montelongo-Jauregui, et al. (2020). Convalescent serum therapy for COVID-19: a 19th century remedy for a 21st century disease. PLoS Pathog. 16(8):e1008735.

1.10 Looking Back and Moving Forward: Will We Win?

… have the infectious diseases that we observe today always existed? Or have some of them appeared in the course of history? Can we assume that new ones will appear? Can we assume that some of these diseases will disappear? Have some of them already disappeared? Finally, what will become of humanity and domestic animals if, as a

Looking Back and Moving Forward

result of more and more frequent contacts between people, the number of infectious diseases continues to increase? —Charles Nicolle, Destin des maladies infectieuses, 1933

Alone we can do so little; together we can do so much.

—Helen Keller

The final trajectory of the pandemic is impossible to predict given that our track record for eliminating viruses has been a poor one. Wuhan, China, was ground zero for SARS-CoV-2 but now we are in this together and to survive we will need a concerted effort. Disease spread is inevitable in our interconnected world. In fact, for decades experts have warned us of impending danger, recommended setting surveillance programs to recognize emerging/reemerging microbes, and proposed methods of intervention.27 Ending, or at least stabilizing, the current pandemic and addressing future public health emergencies should be the focus. How people and health systems respond to the current pandemic will be key, not only to planning for and protecting from emerging microbes of the future, but for maintaining economic and political stability for the 2020s and beyond. Public health infrastructures, big pharma, a wealth of drugs, hospitals, health-care providers, and scientists, all have fared poorly to contain this pandemic. The volatile mix of politics (Box 1.2), globalization, national rivalries, inept health organizations, misinformation, arrogance, and ignorance all fueled the development and spread of COVID-19. In the critical early phases, nations failed to implement basic infectious disease control management measures such as data gathering, testing, contact tracing, quarantines, and distribution of critical medical supplies to health-care providers. Box 1.2 COVID-19 and politics: disarray, blame, and mismanagement

The highly politicized response to the pandemic did not help. Politicians

certainly share a major portion of blame for the pandemic and its perpetuation.

Political leaders either failed to follow established basic pandemic-response

plans or never fully and reliably funded existing pandemic plans. The

haphazard and disjointed approach of political leaders and health-care

organizations shows how miserably they have failed in addressing the

seriousness that this microbe poses with respect to its impact on society,

political stability, and the economy. We have so much talent and brilliance on

this planet, yet it is stifled by politicians, religious leaders, and fanatics. Societal divisiveness and political upheaval continue to cause us to stumble (Continued)

Lederberg, R. E. Shope, and S. C. Oaks, Jr. (1992). Emerging Infections: Microbial Threats to Health in the United States. National Academies Press, Washington, DC. M. S. Smolinski, M. A. Hamburg, and J. Lederberg. (2003). Microbial Threats to Health: Emergence, Detection, and Response. National Academies

Press, Washington, DC.

27J.

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Box 1.2 (Continued)

as we struggle to convince citizens to get vaccinated or follow the latest guidelines. This dangerous trajectory is likely to continue for years. Coordinated preparation and action was critical. Instead, government leaders, healthcare organizations, and society failed to provide an effective response. Demands for freedom pushed aside commonsense approaches crucial to tackling a serious infectious microbe. The misinformation pandemic has created confusion. Objectivity and fairness in journalism has been supplanted by “opinion news,” championed by cablenews networks like CNN, Fox News, and others. Inaccurate reporting of the pandemic has been costly with lives lost here at home and throughout the world. It is dangerous to have self-annotated experts provide viewpoints rather than medical facts. Many political leaders from Britain to Brazil to India ignored advice of their own health advisors. They found political gain more expedient. They continued to hold vast rallies at super spreader events. They even belittled those who were seriously ill with COVID-19. Some of them spun the pandemic to shine a spotlight on themselves and highlight their own perceived achievements. Authoritarians and politicians always seize the megaphone for themselves. A classic example is that of “Dr.” Andrew M. Cuomo, the now disgraced governor of New York state, who gave daily briefings and PowerPoint presentations on TV. He authoritatively ticked through the latest statistics on infections, hospitalizations, nursing homes patients, and deaths—all sprinkled with medical errors and politics. Many called it “The Andrew Cuomo Show.” It was especially painful for me to watch this arrogance on display while my helpless 85-year-old mother languished in a private nursing home in New York state, whose health department had lost control over the pandemic along with nursing home data. It was clear that this corrupt politician only cared about his ratings and image. Health-care agencies tasked with springing into action during a pandemic have also been in disarray and not fared much better than the politicians that controlled them. The WHO correctly received poor marks and harsh criticism for its passivity in the face of the pandemic. Since the Centers for Disease Control and Prevention failed miserably in the early phase of the pandemic and played a side role, some labeled it the “Centers for Disease Observation.” The FDA was a mess as well with the rollout of its COVID-19 testing. It became clear that a large proportion of COVID-19 negative results were inaccurate (“false negatives”) because of an issue inherent in the tests’ design resulting from inadequate regulatory reviews. Handling a pandemic in a decentralized manner where local politicians dictate events is not an ideal approach. However, this is the flaw that we must endure in a democracy where government power is not concentrated and there is a patchwork of decentralized mechanisms to address pandemics. Microbes do not recognize boundaries, timelines, politics, or policies, but rather feed on chaos, arrogance, global conflicts, confusion, and divisiveness.

Looking Back and Moving Forward

The disastrous start with inaccurate and chaotic testing has continued with vaccination rates far below what would be considered as potentially rendering herd immunity. Due to globalization, the risk of pandemics is shared by the entire planet, but vaccines, testing, and therapeutics remain prioritized to exclusive, usually wealthy nations. Obviously, while it may make us feel safer here in the US, variants from the developed world will evolve further and infect the globe. This is likely to reduce efficacy or render useless the current generation of vaccines here. So, have we learnt any lessons? One is the need for integration between science and policy. Another is that accurate dissemination of information to the public is essential. Trust is the primary currency of good crisis communication and political leaders quickly lost trust with inaccurate, untimely, inconsistent policies, and information. In a foreshadowing of COVID-19 outbreaks all over the world, health-care systems and facilities were completely overwhelmed. Unfortunately, this vicious cycle continues to play out as the pandemic roars on from one epicenter to another. A flattening of the curve in one region will lead to a peak elsewhere. Today we may rejoice lowered infections here in the US only to repent a spike in the months to follow, only this time the attack may be with virulent variants. An epidemic in one spot may morph into a pandemic in another area. Hopefully, we will not become complacent as we feel secure and consider this someone else’s problem. Our track record is poor as after each disease threat faded, so did urgency and governmental funding. Flexible adaptation is key to managing pandemics. What is a great approach today may need to be modified or discarded tomorrow in favor of policies that are more sensible. A reasonable leeway for balancing protection of public health with a return to pre-pandemic life is essential, though I am doubtful that we will ever return to pre-pandemic normalcy. In my view, here and abroad, we will continue to face a patchwork of ineffective policies, poor contact tracing efforts, and inequitable vaccination rates, as novel variants evolve. Let us not lose sight of the fact that while we may be far from others in distance and perspective, we are brought nearer in our common conflict with this deadly nanoparticle, the SARS-CoV-2. This is a story of humanity, of fear and resignation, of compassion and dilemma, of persistence and hope. As global citizens, the pandemic has made painfully real our interconnectedness while also strengthening us with the many acts of kindness and compassion that serve as a uniting thread. The solutions for pandemic control are well-known, based on lessons from the past. They include adequately investing in public health systems to detect early signs of a microbial outbreak; ensuring that public and private labs collaborate on testing, tracing, and quarantining people exposed to an infection; ensuring an adequate supply of facilities (hospital beds, personal protective equipment, drugs, health-care staff, and medical supplies); and investing in an efficient R&D infrastructure to develop, scale up, and distribute vaccines and therapeutics. Declaring hollow victories, issuing vaccine passports, ignoring contact tracing, or hoarding vaccines and therapeutics will not ensure global safety, slow viral

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transmission, or prevent virulent mutants from evolving. In fact, all these poor approaches will have the exact opposite effect. A viral wave elsewhere today will arrive as a spike here tomorrow. I am certain that virulent microbes will continue to evolve from their zoonotic reservoirs and jump to humans. This reality must spur everyone—lawmakers, big pharma, citizens, regulatory agencies, global health organizations, biomedical researchers, physicians—to examine what went wrong again and create a more workable plan for future outbreaks. After all, alone we can do so little, together we can do so much. After all, we are all in this together.

Disclosures and Conflict of Interest The views expressed in this chapter are those of the author and do not necessarily reflect the official policy or position of the companies or educational institutions he is employed at or affiliated with. This work was supported by a grant from Bawa Biotech LLC, Ashburn, VA, USA. The author is a scientific advisor to Teva Pharmaceutical Industries Ltd., Israel. No writing assistance was utilized in the production of this chapter and no payment was received for its preparation.

SECTION 1

CLINICAL IMMUNOLOGY

Chapter 2

Specific Reactivity of Anti-Citrullinated Protein Antibodies to Citrullinated Peptides Associated with Rheumatoid Arthritis Nicole H. Trier, PhD,a Bettina E. Holm,b Paul R. Hansen, PhD,c Ole Slot, MD,d Henning Locht, MD,e and Gunnar Houen, PhDa aDepartment

of Neurology, Rigshospitalet Glostrup, Denmark of Clinical Immunology, Rigshospitalet, Copenhagen N, Denmark cDepartment of Drug Design and Pharmacology, Copenhagen Ø, Denmark dDepartment of Rheumatology, Rigshospitalet Glostrup, 2600 Glostrup, Denmark eDepartment of Rheumatology, Frederiksberg Hospital, Frederiksberg, Denmark bDepartment

[email protected], [email protected]

Keywords: antibodies, anti-citrullinated protein antibodies, citrulline, cyclic citrullinated peptide, enzyme-linked immunosorbent assay, epitopes, Epstein-Barr virus, Epstein-Barr nuclear antigen, filaggrin, homo-citrulline, modified amino acids, peptides, peptidyl aginine deiminase, rheumatoid arthritis, specificity

2.1 Introduction Rheumatoid arthritis (RA) is an autoimmune disease of unknown etiology, which causes inflammation in the joints and in severe cases erosion of the underlying bone [1]. The disease primarily affects women with a 2:1 female/male ratio and onset of disease symptoms typically peak in the forties to sixties [2]. The disease affects approximately 1% of the population worldwide [1, 3]. Several autoantibodies are associated with RA. One of the most known is the rheumatoid factor (RF), which recognizes the fragment crystallizable (Fc) domain of IgGs, that is the second and third constant regions of IgG. Although present in 60–70% of individuals with RA, this group of antibodies is not specific for RA as RF also occasionally is detected in other connective tissue diseases [4, 5]. Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

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Specific Reactivity of Anti-Citrullinated Protein Antibodies to Citrullinated Peptides Associated with RA

Another group of antibodies that is associated with RA is the anti-citrullinated protein antibodies, also referred to as ACPAs. ACPAs recognize the post-translationally modified amino acid citrulline, originating from the amino acid arginine [6, 7]. The peptidylarginine deiminase (PAD) catalyzes the conversion of arginine to citrulline, where the nitrogen atom is replaced with an oxygen atom and the positive charge of the guanidino group is eliminated [8]. ACPAs are detected in up to 80% of serum from RA individuals and have been reported to be associated with a more severe disease course and disease outcome compared to ACPA-negative RA, and may even precede disease symptoms [9, 10, 11, 12]. These antibodies are specific for RA, although they also have been sporadically reported to be associated with other diseases [13]. Several antibody-based assays exist for detection of ACPAs. The majority of the assays employ cyclic citrullinated peptides (CCP) [12, 14–16]. Dependent on the origin of the assays and the number of peptides, the assays are divided into three generations, CCP1–CCP3 [9, 12, 14–16]. The assays employ different peptides, indicating that the presence of citrulline, rather than a specific citrullinated epitope itself, is essential for ACPA detection, which is in accordance with their cross-reactive nature. ACPAs recognize several citrullinated protein targets, preferably with a Cit–Gly motif [7, 17–21]. Although no true citrullinated autoantigen has been identified, several citrullinated targets have been reported, e.g., collagen, fibrinogen, Epstein–Barr nuclear antigen (EBNA)-1, EBNA-2, α-enolase, and vimentin [18, 22–27]. As a consequence, ACPAs have been described as a group of antibodies with overlapping reactivity, characterized by significant cross-reactivity to citrullinated targets [24, 25, 28, 29]. Several studies have analyzed the importance of the amino acids surrounding citrulline for a stable antibody–antigen interaction. Studies find that the Cit–Gly motif is essential for antibody reactivity, although other motifs occasionally are tolerated as well [17, 20, 21]. Substitution studies illustrate that substitutions in positions –x–x–Cit–G–x– have no influence on antibody reactivity, clearly illustrating the importance of the central Cit–Gly motif rather than a specific epitope, which the majority of antigens demonstrate [17, 19–21, 23]. However, only limited information is available in relation to modification of the specific amino acid citrulline. It has been reported that ACPAs occasionally may interact with homocitrullinated peptides as well [30]. Homo-citrulline is similar to citrulline, a posttranslational modification of arginine. The functional group of homo-citrulline and citrulline is identical, but the carbon backbone of homo-citrulline contains and additional -CH2- compared to citrulline. Antibodies recognizing citrulline- and homo-citrulline-containing peptides contain partly overlapping binding sites in RA sera, suggesting some kind of relationship between these two groups of antibodies [31–33]. The relationship between homo-citrulline and RA remains unclear. In the present study, we examined the importance of the specific citrulline unit in order to obtain further knowledge about the nature of citrulline-dependent

Materials and Methods

antibody–antigen interactions and ACPA response in RA patients. This was done by substituting citrulline with amino acids of similar functionality, e.g., D-citrulline, homo-citrulline, methyl-arginine, and arginine, and analyzing antibody reactivity in traditional immunoassays. We found that antibody reactivity was significantly reduced when citrulline was substituted with D-citrulline, homo-citrulline, or methyl arginine. These findings illustrate that the presence of L-citrulline is crucial for specific antibody reactivity, and even the smallest alterations in the amino acid side chain or side chain presentation interferes with the specific antibody– antigen interaction.

2.2 Materials and Methods 2.2.1 Materials

Synthetic citrulline-, arginine- and homo-citrulline-containing peptides were from Schafer-N (Lyngby, Denmark). Streptavidin, alkaline phosphatase (AP)-conjugated goat anti-human IgG and para-nitrophenylphosphate (pNPP) were from Sigma Aldrich (St. Louis, Mo, USA). Tris–Tween–NaCl (TTN) buffer (0.05 M Tris, 0.3 M NaCl, 1% Tween 20, pH 7.4) and AP-substrate buffer (1 M diethanolamine, 0.5 mM MgCl2, pH 9.8) were from SSI Diagnostica (Hillerød, Denmark).

2.2.2 Patient Sera

Twenty-five RA-positive sera, diagnosed according to the American College of Rheumatology (ACR) classification criteria [34] were enrolled in this study. The sera were obtained from Rigshospitalet Glostrup. The project was approved by the scientific ethics committees in Denmark (Project ID: 19980024 PMC and H-15009640). Healthy control (HC) sera were obtained from volunteers at Rigshospitalet and at Statens Serum Institut (Copenhagen, Denmark). All participating subjects gave their written informed consent for inclusion before they participated in the study. Twenty-five RA sera were enrolled of which 22 were from females. The average age for RA patients was 55 years. All RA sera were CCP2 positive, with CCP titers ranging from 25 to 3200 U/mL. Eighteen RA sera were RF IgA positive (>15 U/mL) and 21 were RF IgM positive (15 IU/mL). Three sera were RF negative.

2.2.3 Synthetic Peptides

Synthetic peptides containing methylarginie were synthesized by traditional fluorenylmethyloxycarbony peptide synthesis, as previously described [35]. The peptides were approximately 20 amino acids long with a central Cit/hCit/Arg (Nω−Me)/Arg residue. The modified amino acids introduced are presented in Fig. 2.1.

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Specific Reactivity of Anti-Citrullinated Protein Antibodies to Citrullinated Peptides Associated with RA

Figure 2.1 Schematic illustration of the central amino acid substitutions found in the peptides used in this study. The point of origin is citrulline; the circles represent atoms different from citrulline.

As presented in Table 2.1, the peptides selected for analysis in this study were a combination of human proteins and viral proteins, which have previously been identified as potential ACPA targets. In particular, the citrullinated viral EBNA-2 peptide and the human pro-filaggrin peptide have been reported to be good ACPA substrates [7, 12, 24, 25]. Thus, ACPA reactivity was analyzed based on reactivity to human peptides and cross-reactivity to viral peptides. Table 2.1 Peptides used in the current study Peptide

aa

Origin

Substitution

SHQEST–R–GRSRGRS

306–319

Profilaggrin

Cit

GSGGRGRGGSGGRRG–R–GRER

341–360

EBNA1

Cit/hCit

HQSHQEST–R–GRSRGRSGRSGS ARGGSRERARGRGRG–R–GEKR

304–324 361–380

Profilaggrin EBNA1

Cit/hCit Cit/hCit/Arg(Me)

GNGLGE–R–GDTSGPEGSGGSG

11–30

EBNA1

Cit/hCit

FAEVLKDAI–R–DLVMTKPAPT

571–590

EBNA1

Cit/hCit

PQASVPLRLT–R–GSRAPISRAQ

1835–1855

Proteoglycan

Arg(Me)

GRGRGRGE–R–RPRSPSSQSSS GGSKTSLYNLR–R–GTALAIPQ

GQGRGRWRG–R–GRSKGRGRMH

371–390

511–530

313–332

EBNA1

EBNA1

EBNA-2

Note: The underscored amino acids represent the substituted amino acid.

Cit/hCit

Cit/hCit/Arg(Me) Arg(Me)

Results

2.2.4 Streptavidin Capture Enzyme-Linked Immunosorbent Assay

MaxiSorp 96-well microtiter plates (Nunc, Roskilde, Denmark) were precoated with streptavidin (1 µg/mL) for 2 h at room temperature (RT) followed by coating with biotinylated peptides (1 µg/mL) for 2 h at RT. Sera diluted (1:200) in TTN were added to the microtiter plates and incubated for 1 h at RT. Patient sera were analyzed in duplicates. After careful washing with TTN buffer, AP-conjugated goat anti-human IgG diluted in TTN (1 µg/mL) was added to the wells and the plates incubated for 1 h at RT. For antibody quantification, AP activity was determined with pNPP (1 mg/mL) diluted in AP substrate buffer. The absorbance was measured at 405 nm, with background subtraction at 650 nm, using a ThermoMax microtiter plate reader (Molecular Devices, Menlo Park, CA, USA).

2.2.5 Statistical Analyses

Statistical calculations were performed using two measurements of 25 RA sera. The values obtained in this study were compared further by using the two-tailed Student’s t-test for single column analysis and Mann–Whitney U-test, which compared all columns to control columns.

2.3 Results

2.3.1 Reactivity of Rheumatoid Arthritis Sera to D/L-CitrullineContaining Peptides To determine whether the orientation of citrulline in the epitope is essential for antibody reactivity, the reactivity of ACPA-positive RA sera was analyzed to two peptides (SHQEST-Cit-GRSRGRS), originating from pro-filaggrin by streptavidin capture enzyme-linked immunosorbent assay (ELISA). The pro-filaggrin peptide, SHQEST-R-GRSRGRS, was selected for preliminary analysis, as a peptide containing this central region originally was used for detection of ACPAs. One peptide version contained a traditional L-citrulline, whereas the other contained D-citrulline. In total, 25 RA and 20 HC sera were analyzed for reactivity. As presented in Fig. 2.2, the ACPA-positive RA sera reacted with the citrullinated pro-filaggrin peptides (Fig. 2.2a). The RA sera reacted with the D-citrulline-containing peptide as well; however, antibody reactivity was significantly reduced when replacing L-citrulline with D-citrulline (p < 0.0001). None of the HC sera reacted significantly with the pro-filaggrin peptides (Fig. 2.2b). These findings indicate that the orientation of the citrulline side chain is crucial for antibody reactivity.

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Specific Reactivity of Anti-Citrullinated Protein Antibodies to Citrullinated Peptides Associated with RA

Figure 2.2 Antibody reactivity to a citrullinated pro-filaggrin peptide containing D- or L-citrulline analyzed by streptavidin capture ELISA. The Arg-containing pro-filaggrin, with a natural L-Arg was used as negative control. (a) Reactivity of RA sera (n = 25); (b) Reactivity of healthy control (HC) sera (n = 20).

2.3.2 Reactivity of Rheumatoid Arthritis Sera to Citrullinated and Homo-Citrullinated Peptides

Next, the reactivity of RA sera to various citrullinated and homo-citrullinated epitopes was analyzed by streptavidin capture ELISA. In total, 10 RA sera and 10 HC sera were analyzed for reactivity. Peptides originating from pro-filaggrin and EBNA1 were selected as templates, as Epstein–Barr virus (EBV) has been proposed to be involved in the onset of RA, and because ACPAs are very cross-reactive. Some of the EBV peptides have been described as good ACPA candidates, whereas others have been described as poor candidates. This was done in order to determine whether addition of homo-citrulline would increase antibody reactivity, and ultimately, to determine whether antibody reactivity to citrullinated peptides and homo-citrullinated peptides differ from each other. Figure 2.3 illustrates the reactivity of RA and HC sera to the substituted peptides. As presented in Fig. 2.3, significant antibody reactivity occurred with the citrullinated peptides, compared to the homo-citrullinated peptides (Fig. 2.3a,b,e). The RA sera reacted primarily with peptide 3 and 6 of the homo-citrullinated analogues (Fig. 2.3b) and peptides 1, 2, 3, 4 and 6 of the citrullinated analogues (Fig. 2.3a). No reactivity was found to peptide 7, which most likely is due to the absence of a Gly residue on the C-terminal side of citrulline. The RA sera reacted weakly with the homo-citrullinated version of peptide 6, and not with the citrullinated version, which most likely is due to the presence of negatively charged amino acids, which previously have been found to influence antibody reactivity negatively, when located close to the citrulline residue [17, 20]. None of the HC

Results

sera reacted with the substituted peptides (Fig. 2.3c,d). These findings indicate that antibodies to homo-citrullinated peptides have the same restrictions as citrullinated peptides.

Peptide 1 2 3 4 5 6 7

Sequence HQSHQEST-Xxx-GRSRGRSGRSGS GSGGRGRGGSGGRRG-Xxx-GRER ARGGSRERARGRGRG-Xxx-GEKR GGSKTSLYNLR-Xxx-GTALAIPQ GNGLGE-Xxx-GDTSGPEGSGGSG GRGRGRGE-Xxx-RPRSPSSQSSS FAEVLKDAI-Xxx-DLVMTKPAPT

PepCit vs PepHcit *** P sheep > rat> mouse.

• The minimum effective reactogenic dose of liposomes in rats is 10–100 times higher than that in pigs or dogs.

• The individual variation of the cardiopulmonary changes associated with porcine liposome-induced CARPA is lower than that of dogs. • In pigs, pulmonary hypertension, while in dogs, systemic hypotension are the dominating cardiopulmonary symptoms of CARPA.

• Both in pigs and dogs the cardiopulmonary changes can decrease or entirely disappear after the second or third dosing, a reflection of tachyphylaxis (tolerance induction). • The latter phenomenon allows the development of desensitization protocols using empty (placebo) liposomes.

• Both in pigs and dogs leukopenia followed by leukocytosis and thrombocytopenia are varying hematological abnormalities associated with CARPA. • The rise of plasma thromboxane A2 (measured as TXB2) closely parallels CARPA in pigs, indicating that it is a rate limiting mediator. TXB2 also rises in other species during CARPA.

• CARPA can be inhibited in pigs with C inhibitors (e.g., sCR1, anti-porcine C5 antibody and indomethacin).

• Based on the minimal effective reactogenic dose, porcine and canine CARPA may represent a model of human CARPA in hypersensitive individuals.

Mechanisms involved in C activation and CARPA. In commenting on the mechanisms of some of the effects listed in Table 3.4, surface charge is perhaps the best known enhancer of C activation induced by nanoparticles and liposomes. A closer scrutiny of the phenomenon at atomic level revealed that the oxygen moiety of the mPEG-phospholipid is responsible for enhanced C activation, at least for the case of mPEGylated dipalmitoylphosphatidylcholine (DPPC) liposomes [68]. The evidence included the lack of activation by non-PEGylated vesicles and the inhibition of C activation by methylation of the oxygen moiety on mPEGDPPE phosphate [68]. In other studies, it was further shown that the C activating negative charge needs to be part of conventional anionic phospholipids, as carboxylic acid-modified vesicles having the same surface charge and electrophoretic mobility failed to induce significant C activation [78]. These observations suggest that C activation is specific to the structure of acidic groups, and negative surface or zeta potential per se, may not necessarily imply C activating potency. In keeping with the above conclusions, liposomes in which the negatively charged PEG-DSPE was replaced with near-neutral PEG-DS showed lack of (or minimal) cardiopulmonary reactivity in pigs [87].

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Regarding the impact of doxorubicin or similar drugs that can bind to and aggregate liposomes or lipids, it seems important to consider that the number of liposomes introduced in the circulation upon each treatment (in the order of 1013 Doxil liposomes) represents substantial phospholipid bilayer surface (in the order of ~10–30 cm2). Thus, small changes in liposome surface can translate to large changes in overall surface, which may have a major biological impact. In the case of Doxil, the presence of low curvature oval, elongated (coffee-bean-like), or irregular liposomes and aggregates in commercial vials [87], may have a significant impact on the surface area of vesicles exposed to blood. This suggestion, taken together with a recent report on the significant influence of minor differences in liposome surface curvature on C activation via the classical pathway (via IgM binding) [73] raises the possibility that doxorubicin could indirectly contribute to C activation via modifying the surface of liposomes.

3.4.2 Mechanism of Liposome Reactions and Its Individual Variation

Among the symptoms of HSRs (Table 3.3), there is no single, or unique, symptom that would be liposome-specific. Like many micellar and particulate drugs, radiocontrast agents, monoclonal antibodies, and enzyme therapeutics, liposomes are “only” triggers of a complex chain of events that involves (1) C activation, (2) binding of anaphylatoxins to their specific receptors on mast cells, basophils, and other anaphylatoxin-receptor-positive cells (macrophages), (3) activation of the latter cells to release a host of vasoactive mediators, including histamine, tryptase, PAF, LTB2, LTB4, LTC4, LTD4, LTE4, TXA2, PGD2 and TXD4 [57] (Fig. 3.4).

Figure 3.4 Mechanism of complement activation-related pseudoallergy (CARPA) caused by liposomes and micelles.

Immunogenicity of Liposomes

Some of these mediators (e.g., PAF, histamine, tryptase and TXA2) are preformed and liberated from the cells immediately upon activation, while others are de novo synthesized and, hence, are liberated slower [57]. This differential, multistep release of allergomedins from anaphylatoxin-responsive cells may explain the individual variation in the start of clinical symptoms. Specifically, the activation of H1 receptors leads to vasoconstriction and vascular leakage and is responsible for the cardiovascular and cutaneous symptoms of anaphylaxis. H2 receptors, in turn, increase cellular cAMP levels and cause vasodilation, increased heart rate and pulse pressure. Another potentially important factor in individual variation of HSR symptoms is the relative abundance of reactive cells in different organs of response, i.e., in the skin, lung, heart, bowel, etc. [57, 60, 61, 84].

3.5 Immunogenicity of Liposomes

Being built from natural, or natural-like (regarding stereochemistry and composition), synthetic or semisynthetic phospholipids, liposomes are generally not immunogenic. This statement is in apparent conflict with the intense ongoing R&D of liposomal vaccines; however, it should not be forgotten that these vaccines include protein, carbohydrate, or lipid antigens and adjuvants as well as other mediators, such as lipid A, muramyl dipeptide and its derivatives, interleukin-1 and interleukin-2 in addition to the phospholipid bilayer, which also acts as an adjuvant in its own right [3]. When adjuvants are used, specific antibodies are induced against all liposome components, including structural (phospho)lipids, cholesterol and even squalene, a cholesterol precursor triterpene [5–7, 65]. As for the underlying cause of the intrinsic adjuvant capability of phospholipid bilayers, promoting specific immune response to liposomal antigens and nonantigenic lipid components (without additional adjuvant), the “array theory” [91] provides a likely explanation. Adapting this theory to the special case of immunogenic non-vaccine liposomes, it can be proposed that because of their similarity to viruses, liposomes may present their surface conjugates or protruding repetitive surface elements to APC and other immune cells (monocyte/macrophages, dendritic cells, B lymphocytes and mast cells) in the form of an array, which resembles the regular and symmetric spatial arrangement of viral capsid glycolipids and glycoproteins, for which the so-called “pattern recognition receptors” (e.g., LPS and Toll-like receptors (TLRs) on the above cells readily react, generating innate and subsequent specific immune responses. Originally TLRs recognize molecule arrays that are broadly shared by pathogens (called pathogen-associated molecular patterns, PAMPs, such as LPS, lipoproteins, lipopeptides, flagellin, double-stranded RNA or the unmethylated CpG islands of bacterial and viral DNA). However, “liposomal arrays” may also trigger “danger” signaling by pattern recognition receptors on the above immune cells despite the absence of PAMPs, which ultimately leads to antibody production against the “pseudo-PAMPs” on liposomes and their phospholipid support. The resultant immune response may or may not differ from a standard immune response to vaccines, depending on the pathway of immune activation.

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Complement Activation, Immunogenicity, and Immune Suppression as Potential Side Effects of Liposomes

An example of non-standard, partial immunogenicity is the so-called “ABC phenomenon,” i.e., accelerated blood clearance of PEGylated liposomes, a phenomenon that has great clinical relevance. As shown by Ishida and colleagues [49–55, 58, 59], repeated injection of PEGylated liposomes in mice and rats causes rapid clearance of liposomes from the bloodstream, due to the formation of antiPEG IgM in the spleen. Importantly, the phenomenon is absent with PEGylated liposomes encapsulating doxorubicin, which is consistent with the lack of ABC in cancer patients treated with Doxil. On the other hand, free doxorubicin given in doses that correspond to the amount given in Doxil, restores ABC. These data indicate that immune cells responsible for the ABC phenomenon might be selectively affected by doxorubicin encapsulated in PEGylated liposomes, as detailed in the section on liposome-induced immune suppression below. Since the ABC phenomenon was also observed in BALB/c nu/nu mice, but not in BALB/c SCID mice, it was suggested that antibody production represents a T cellindependent, B cell response, and that PEGylated liposomes might be recognized by B cells as a thymus-independent type 2 antigen [58]. Based on the array theory, as delineated above, it can be hypothesized that PEGylated liposomes trigger B cells via some of their TLRs, for PEG looks to B cells like viral spikes. The ABC phenomenon is illustrated in Fig. 3.5.

Figure 3.5 Blood clearance rate of PEGylated liposomes in rats (A) and tissue accumulation 24 hours after administration. The figure, reproduced from [51] (with permission), illustrates the acceleration of liposome clearance after repeated injection (on days 2 to 14), of liposomes with paralleling increase in liver and spleen deposition.

3.6 Immune Suppression by Liposomes

It is well known that liposomes are taken up mainly by cells of the reticuloendothelial system (RES) in the liver, spleen, bone marrow and elsewhere, which cells are also part of the nonspecific, innate immune system. Therefore, it has been

Conclusions and Outlook

asked for a long time whether macrophage saturation by liposomes, leading to immune suppression, could be an issue, a potential risk for infection. There is ample evidence that clinically applied doses of non-cytotoxic liposomes generally do not cause immune suppression, at least not major, clinically important blockage of macrophage function. However, the situation is different with anticancer liposomes loaded with cytotoxic drugs, which may cause different levels of immunosuppression. Administration of Doxil in mice, for example, was shown to interfere with the clearance of bacteria from blood, which was explained by macrophage suppression [79]. Further evidence, in mice, of partial RES blockage by liposomal doxorubicin was the dose-dependent pharmacokinetics of Doxil, resulting in slower clearance and disproportional increase of tumor uptake at higher doses (in the 2.5 to 20 mg/ kg range) [37]. The clearance slow-down effect was not seen with free doxorubicin administration at a similar dose or after doxorubicin-free liposomes were co-administered with free doxorubicin [37]. Although interference with innate antibacterial defense was not found in Doxil-treated patients to date (by now more humans have been injected with Doxil than mice), clinical signs of partial macrophage suppression do seem to be present in cancer patients treated with Doxil. Evidence for this includes the rise of circulation T1/2 of Doxil after repeated administrations [35], and the inhibition of HSRs to carboplatin by co-administered Doxil [1]. The implications of partial immune suppression by Doxil remains to be evaluated, including two potential clinical benefits. One is that tumor uptake of Doxil might increase upon repeated doses (as seen with dose escalation in mice [37]), resulting in increased therapeutic efficacy by the same dose, or allowing dose reduction without loss of efficacy. The second is that Doxil may provide therapeutic advantage over paclitaxel or gemcitabine in combination chemotherapies with carboplatin, as HSRs to carboplatin have become dose-limiting during treatment of platinum sensitive recurrent cancer [1].

3.7 Conclusions and Outlook

Immunosafety may become a key issue in current and future R&D of liposomes and other nanomedicines. Its significance is emphasized by examples of postmarketing drug recalls, industry guidelines on immunotoxicity testing and recent calls for collaborative research in the fields of nanotoxicology and immunogenicity [29, 31, 46]. Clearly, the currently used immunotoxicology endpoints (e.g. lymphoid organ weight and histology, lymphocyte proliferation, cytotoxicity and antibody production tests, cytokine assays, skin irritation test) have little, or no predictive value in assessing the risk of HSRs, nor do they predict the immunogenicity of complex liposomes or other nano-bio-hybrid drugs. An almost insurmountable difficulty in this regard is the high complexity and species and individual variability of the immune processes involved in the latter phenomena. As for assessing the risk of HSRs mediated by C, to our knowledge, the only direct approaches are the in vitro C activation measurements in normal

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Complement Activation, Immunogenicity, and Immune Suppression as Potential Side Effects of Liposomes

human sera (NHS), the in vivo porcine and canine CARPA tests and the basophil leukocyte activation-based allergenicity assays, provided mostly as services by a few contract research organizations (CROs). A combination of these tests can predict in a rough, semiquantitative way the likely presence, severity and frequency of CARPA caused by a certain drug in most people, and also in individual patients. The unique benefit of the large animal (pig and dog) model of CARPA is that the endpoints, i.e., cardiopulmonary and hematological changes, when they occur, not only reveal the potential reactogenicity of the drug, but also model the physiological changes occurring in hypersensitive individuals. As mentioned in the text above (Table 3.5), liposome-induced CARPA in pigs and dogs provides a model of the infusion reactions seen in hypersensitive individuals. Focusing on liposomes, the C and CARPA tests are useful in assessing their risk of causing HSRs, enabling early abandonment of highly reactogenic formulations. For lack of a valid animal model for assessing the immunogenicity of human or humanized protein containing liposomes, securing of immune safety of these products seems to be an even greater challenge than solving the HSR problem. What gives hope in this challenging “terra nova” is that interest will never cease in improving the therapeutic efficacy of available drugs, or creating new drugs whose molecular buildup gets increasingly complex. Hopefully it is not too far in the future that we can equip liposomes and other drug carrier nano-systems with immune evasive capabilities, and/or “teach” the immune system to distinguish these nanotech marvels from harmful viruses or other nano-organisms.

Abbreviations ABC: C: CARPA: CCPs: CR1: CROs: DAF: DPPC: fI: HSRs: MAC: MCP: MLV: NHS: RES: sCR1: SUV: TLRs:

accelerated blood clearance complement

complement activation-related pseudoallergy

complement control proteins

complement receptor type 1

contract research organizations

decay accelerating factor dipalmitoylphosphatidylcholine

plasma serine protease, factor I

hypersensitivity reactions

membrane attack complex

membrane cofactor protein

multilamellar vesicles

normal human sera

reticuloendothelial system

soluble C receptor type I

small unilammelar vesicles

toll-like receptors

References

Disclosures and Conflict of Interest This chapter was originally published as: Szebeni, J., Barenholz, Y. (2021). Complement activation, immunogenicity, and immune suppression as potential side effects of liposomes. In: Peer, D., ed. Handbook of Harnessing Biomaterials in Nanomedicine: Preparation, Toxicity, and Applications, 2nd ed., Jenny Stanford Publishing Pte. Ltd., Singapore, chapter 11, pp. 335–361, and appears here, with edits and updates, by kind permission of the publisher.

Acknowledgments: The authors gratefully acknowledge the financial support by the Barenholz Fund and the National Office for Research and Technology (NKTH), Budapest (CARPA777, NANOMEDI and TÁMOP-4.2.1.B-09/1/KMR-2010-0001 and 4.1.2.B-10/2/KONV-2010-0001, with support by the European Union, co-financed by the European Social Fund).

References

1. Alberts, D. S., Liu, P. Y., Wilczynski, S. P., Clouser, M. C., Lopez, M. A., Michelin, D. P., Lanzotti, V. J., and Markman, M. (2008). Randomized trial of pegylated liposomal doxorubicin (PLD) plus carboplatin versus carboplatin in platinum-sensitive (PS) patients with recurrent ovarian or peritoneal carcinoma after failure of initial platinumbased chemotherapy (South West Oncology group protocol SO200). Gynecol. Oncol. 108, pp. 90–94. 2. Altina, J. G., and Parish, C. R. (2006). Liposomal vaccines—targeting the delivery of antigen. Methods 40, pp. 39–52.

3. Alving, C. R. (1991). Liposomes as carriers of antigens and adjuvants. J. Immunol. Methods 140, pp. 1–13. 4. Alving, C. R., Kinsky, S. C., Haxby, J. A., and Kinsky, C. B. (1969). Antibody binding and complement fixation by a liposomal model membrane. Biochemistry 8, pp. 1582–1587.

5. Alving, C. R., Koulchin, V., Glenn, G. M., and Rao, M. (1995). Liposomes as carriers of peptide antigens: induction of antibodies and cytotoxic T lymphocytes to conjugated and unconjugated peptides. Immunol. Rev. 145, pp. 5–31.

6. Alving, C. R., and Swartz, G. M. (1991). Antibodies to cholesterol, cholesterol conjugates, and liposomes: implications for atherosclerosis and autoimmunity. Crit. Rev. Immunol. 10, pp. 441–453.

7. Alving, C. R., Swartz, G. M. J., Wassef, N. M., Ribas, J. L., Herderick, E. E., Virmani, R., Kolodgie, F. D., Matyas, G. R., and Cornhill, J. F. (1996). Immunization with cholesterol-rich liposomes induces anti-cholesterol antibodies and reduces diet-induced hypercholesterolemia and plaque formation. J. Lab. Clin. Med. 127, pp. 40–49.

8. Alving, C. R., Urban, K. A., and Richards, R. L. (1980). Influence of temperature on complement-dependent immune damage to liposomes. Biochim. Biophys. Acta 600, pp. 117–125.

9. Bangham, A. D. (1993). Liposomes: the Babraham connection. Chem. Phys. Lipids 64, pp. 275–285.

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Complement Activation, Immunogenicity, and Immune Suppression as Potential Side Effects of Liposomes

10. Bangham, A. D., and Horne, R. W. (1964). Negative staining of phospholipids and their structural modification by surface active agents as observed in the electron microscope. J. Mol. Biol. 8, pp. 660–668.

11. Barenholz, Y. (2006). Amphipathic weak base loading into preformed liposomes having a transmembrane ammonium ion gradient: from the bench to approved DOXIL, In Liposome Technology Vol 2 (G. Gregoriadis, ed.), pp. 1–26. Informa Healthcare, New York, NY.

12. Belchetz, P. E., Crawley, J. C., Braidman, I. P., and Gregoriadis, G. (1977). Treatment of Gaucher’s disease with liposome-entrapped glucocerebroside: beta-glucosidase. Lancet 8029, pp. 116–117. 13. Bergers, J. J., Otter, W. D., and Crommelin, J. A. (1996). Liposome-based cancer vaccines. J. Liposome Res. 6, pp. 339–355.

14. Bonte, F., and Juliano, R. L. (1986). Interactions of liposomes with serum proteins. Chem. Phys. Lipids 40, pp. 359–372.

15. Cabriales, S., Bresnahan, J., Testa, D., Espina, B. M., Scadden, D. T., Ross, M., and Gill, P. S. (1998). Extravasation of liposomal daunorubicin in patients with AIDS-associated Kaposi’s sarcoma: a report of four cases Oncol. Nurs. Forum 25, pp. 67–70.

16. Chanan-Khan, A., Szebeni, J., Savay, S., Liebes, L., Rafique, N. M., Alving, C. R., and Muggia, F. M. (2003). Complement activation following first exposure to pegylated liposomal doxorubicin (Doxil): possible role in hypersensitivity reactions. Ann. Oncol. 14, pp. 1430–1437.

17. Chonn, A., Cullis, P. R., and Devine, D. V. (1991). The role of surface charge in the activation of the classical and alternative pathways of complement by liposomes. J. Immunol. 146, pp. 4234–4241.

18. Chopra, R., Blair, S., Strang, J., Cervi, P., Patterson, K. G., and Goldstone, A. H. (1991). Liposomal amphotericin B (AmBisome) in the treatment of fungal infections in neutropenic patients. J. Antimicrob. Chemother. 28 Suppl B, pp. 93–104. 19. Ciftcioglu, N., McKay, D. S., Mathew, G., and Kajander, E. O. (2006). Nanobacteria: fact or fiction? Characteristics, detection, and medical importance of novel self-replicating, calcifying nanoparticles. J. Investig. Med. 54, pp. 385–394.

20. Cunningham, C. M., Kingzette, M., Richards, R. L., Alving, C. R., Lint, T. F., and Gewurz, H. (1979). Activation of human complement by liposomes: a model for membrane activation of the alternative pathway. J. Immunol. 122, pp. 1237–1242.

21. Danenberg, H. D., Golomb, G., Groothuis, A., Gao, J., Epstein, H., Swaminathan, R. V. P., Seifert, P., and Edelman, E. R. (2003). Liposomal alendronate inhibits systemic innate immunity and reduces in-stent neointimal hyperplasia in rabbits. Circulation 108, pp. 2798–2804.

22. de Marie, S. (1996). Liposomal and lipid-based formulations of amphotericin B. Leukemia 10, Suppl. 2., pp. S93–S96.

23. Deamer, D. (2009). On the origin of systems. Systems biology, synthetic biology and the origin of life. EMBO Rep. 10, Suppl 1, pp. S1–4.

24. Deamer, D. W. (2008). Origins of life: how leaky were primitive cells? Nature 454 (7200), pp. 37–38. 25. Deamer, D. W. (2010). From “banghasomes” to liposomes: a memoir of Alec Bangham, 1921–2010. FASEB J. 24, pp. 1308–1310.

References

26. Diminsky, D., Moav, N., Gorecki, M., and Barenholz, Y. (1999). Physical, chemical and immunological stability of CHO derived hepatitis B surface antigen (HBsAg) particles. Vaccine 18, pp. 3–17.

27. Diminsky, D., Schirmbeck, R., Reimann, J., and Barenholz, Y. (1997). Comparison between hepatitis B surface antigen (HBsAg) particles derived from mammalian cells (CHO) and yeast cells (Hansenula polymorpha): composition, structure and immunogenicity. Vaccine 15, pp. 637–647. 28. Doxil. (2010). Package insert. Available at: https://www.baxterpi.com/pi-pdf/Doxil_ Baxter_PI.pdf (accessed on May 10, 2021).

29. Dujmovic, I. H. (2005). Comparison of two guidelines on immunotoxicology testing of medicinal products. Arh. Hig. Rada. Toksikol. 56, pp. 265–268. 30. Eckardt, J. R., Campbell, E., Burris, H. A., Weiss, G. R., Rodriguez, G. I., Fields, S. M., Thurman, A. M., Peacock, N. W., Cobb, P., Rothenberg, M. L., et al. (1994). A phase II trial of DaunoXome, liposome-encapsulated daunorubicin, in patients with metastatic adenocarcinoma of the colon. Am. J. Clin. Oncol. 17, pp. 498–501.

31. EMEA. (2000). Note for guidance on repeated dose toxicity. Available at: https:// www.ema.e uropa.e u/e n/d ocume nts /s cientific-gui deli ne/note -guidance -re peateddose-toxicity_en.pdf (accessed on May 10, 2021).

32. Fossa, S. D., Aass, N., and Paro, G. (1998). A phase II study of DaunoXome in advanced urothelial transitional cell carcinoma. Eur. J. Cancer 34, pp. 1131–1132.

33. Funato, K., Yoda, R., and Kiwada, H. (1992). Contribution of complement system on destabilization of liposomes composed of hydrogenated egg phosphatidylcholine in rat fresh plasma. Biochim. Biophys. Acta 1103, pp. 198–204.

34. Gabizon, A., Catane, R., Uziely, B., Kaufman, B., Safra, T., Cohen, R., Martin, F., Huang, A., and Barenholz, Y. (1994). Prolonged circulation time and enhanced accumulation in malignant exudates of doxorubicin encapsulated in polyethylene-glycol coated liposomes. Cancer Res. 54, pp. 987–992.

35. Gabizon, A., Isacson, R., Rosengarten, O., Tzemach, D., Shmeeda, H., and Sapir, R. (2007). An open-label study to evaluate dose and cycle dependence of the pharmacokinetics of pegylated liposomal doxorubicin. Cancer Chemother. Pharmacol. 61, pp. 695–702.

36. Gabizon, A., Shmeeda, H., and Barenholz, Y. (2003). Pharmacokinetics of pegylated liposomal Doxorubicin: review of animal and human studies. Clin. Pharmacokinet. 42, pp. 419–436.

37. Gabizon, A., Tzemach, D., Mak, L., Bronstein, M., and Horowitz, A. T. (2002). Dose dependency of pharmacokinetics and therapeutic efficacy of pegylated liposomal Doxorubicin (DOXIL) in murine models. J. Drug Targeting 10, pp. 539–548. 38. Gill, P. S., Espina, B. M., Muggia, F., Cabriales, S., Tulpule, A., Esplin, J. A., Liebman, H. A., Forssen, E., Ross, M. E., and Levine, A. M. (1995). Phase I/II clinical and pharmacokinetic evaluation of liposomal daunorubicin. J. Clin. Oncol. 13, pp. 996–1003. 39. Gill, P. S., Wernz, J., Scadden, D. T., Cohen, P., Mukwaya, G. M., von Roenn, J. H., Jacobs, M., Kempin, S., Silverberg, I., Gonzales, G., Rarick, M. U., Myers, A. M., Shepherd, F., Sawka, C., Pike, M. C., and Ross, M. E. (1996). Randomized phase III trial of liposomal daunorubicin versus doxorubicin, bleomycin, and vincristine in AIDS-related Kaposi’s sarcoma. J. Clin. Oncol. 14, pp. 2353–2364.

71

72

Complement Activation, Immunogenicity, and Immune Suppression as Potential Side Effects of Liposomes

40. Girard, P. M., Bouchaud, O., Goetschel, A., Mukwaya, G., Eestermans, G., Ross, M., Rozenbaum, W., and Saimot, A. G. (1996). Phase II study of liposomal encapsulated daunorubicin in the treatment of AIDS-associated mucocutaneous Kaposi’s sarcoma. AIDS 10, pp. 753–757.

41. Guaglianone, P., Chan, K., DelaFlor-Weiss, E., Hanisch, R., Jeffers, S., Sharma, D., and Muggia, F. (1994). Phase I and pharmacologic study of liposomal daunorubicin (DaunoXome). Invest. New Drugs 12, pp. 103–110.

42. Hamad, I., Al-Hanbali, O., Hunter, A. C., Rutt, K. J., Andresen, T. L., and Moghimi, S. M. (2010). Distinct polymer architecture mediates switching of complement activation pathways at the nanosphere-serum interface: implications for stealth nanoparticle engineering. ACS Nano 4, pp. 6629–6638.

43. Harashima, H., Sakata, K., Funato, K., and Kiwada, H. (1994). Enhanced hepatic uptake of liposomes through complement activation depending on the size of liposomes. Pharm. Res. 11, pp. 402–406.

44. Harris, C. L., and Morgan, B. P. (2004). The many faces of the membrane regulators of complement, In The Complement System: Novel Roles in Health and Disease (J. Szebeni, ed.). Kluwer, Boston. 45. Hashimoto, Y., Sugawara, M., and Endoh, H. (1983). Coating of liposomes with subunits of monoclonal IgM antibody and targeting of the liposomes. J. Immunol. Methods 62, pp. 155–162.

46. Hastings, K., L,. (2002). Implications of the new FDA/CDER immunotoxicology guidance for drugs. Int. Immunopharmacol. 2(11), pp. 1613–1618.

47. Haxby, J. A., Gotze, O., Muller-Eberhard, H. J., and Kinsky, S. C. (1969). Release of trapped marker from liposomes by the action of purified complement components. Proc. Natl. Acad. Sci. U.S.A. 64, pp. 290–295.

48. Haxby, J. A., Kinsky, C. B., and Kinsky, S. C. (1968). Immune response of a liposomal model membrane. Proc. Natl. Acad. Sci. U.S.A. 61, pp. 300–307.

49. Ishida, T., Harada, M., Wang, X. Y., Ichihara, M., Irimura, K., and Kiwada, H. (2005). Accelerated blood clearance of PEGylated liposomes following preceding liposome injection: effects of lipid dose and PEG surface-density and chain length of the firstdose liposomes. J. Control. Release 105, pp. 305–317. 50. Ishida, T., Ichihara, M., Wang, X., and Kiwada, H. (2006). Spleen plays an important role in the induction of accelerated blood clearance of PEGylated liposomes. J. Control. Release 115, pp. 243–250.

51. Ishida, T., Ichihara, M., Wang, X., Yamamoto, K., Kimura, J., Majima, E., and Kiwada, H. (2006). Injection of PEGylated liposomes in rats elicits PEG-specific IgM, which is responsible for rapid elimination of a second dose of PEGylated liposomes. J. Control. Release 112, pp. 15–25.

52. Ishida, T., Ichikawa, T., Ichihara, M., Sadzuka, Y., and Kiwada, H. (2004). Effect of the physicochemical properties of initially injected liposomes on the clearance of subsequently injected PEGylated liposomes in mice. J. Control. Release 95, pp. 403–412.

53. Ishida, T., Maeda, R., Ichihara, M., Irimura, K., and Kiwada, H. (2003). Accelerated clearance of PEGylated liposomes in rats after repeated injections. J. Control. Release 88, pp. 35–42.

References

54. Ishida, T., Masuda, K., Ichikawa, T., Ichihara, M., Irimura, K., and Kiwada, H. (2003). Accelerated clearance of a second injection of PEGylated liposomes in mice. Int. J. Pharm. 255, pp. 167–174.

55. Ishihara, T., Takeda, M., Sakamoto, H., Kimoto, A., Kobayashi, C., Takasaki, N., Yuki, K., Tanaka, K., Takenaga, M., Igarashi, R., Maeda, T., Yamakawa, N., Okamoto, Y., Otsuka, M., Ishida, T., Kiwada, H., Mizushima, Y., and Mizushima, T. (2009). Accelerated blood clearance phenomenon upon repeated injection of PEG-modified PLA-nanoparticles. Pharm. Res. 10, pp. 2270–2279.

56. Kinsky, S. C., Haxby, J. A., Zopf, D. A., Alving, C. R., and Kinsky, C. B. (1969). Complementdependent damage to liposomes prepared from pure lipids and Forssman hapten. Biochemistry 8, pp. 4149–4158. 57. Knol, E. F., Mul, F. P., Lie, W. J., Verhoeven, A. J., and Roos, D. (1996). The role of basophils in allergic disease. Eur. Respir. J. Suppl. 22, pp. 126s.

58. Koide, H., Asai, T., Hatanaka, K., Akai S, Ishii T, Kenjo, E., Ishida, T., Kiwada, H., Tsukada, H., and Oku, N. (2010). T cell-independent B cell response is responsible for ABC phenomenon induced by repeated injection of PEGylated liposomes. Int. J. Pharm. 392, pp. 218–223.

59. Koide, H., Asai, T., Hatanaka, K., Urakami, T., Ishii, T., Kenjo, E., Nishihara, M., Yokoyama, M., Ishida, T., Kiwada, H., and Oku, N. (2008). Particle size-dependent triggering of accelerated blood clearance phenomenon. Int. J. Pharm. 362, pp. 197–200. 60. Lieberman, P. (1989). The use of antihistamines in the prevention and treatment of anaphylaxis and anaphylactoid reactions. Singapore Med. J. 30, pp. 290–293.

61. Lieberman, P. (1991). Anaphylactoid reactions to radiocontrast material. Clin. Rev. Allergy 9, pp. 319–338.

62. Lipinski, C. A., Lombardo, F., Dominy, B. W., and Feeney, P. J. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23, pp. 3–25.

63. Marcato, P. D., and Duran, N. (2008). New aspects of nanopharmaceutical delivery systems. J. Nanoscience Nanotechnol. 8, pp. 1–14.

64. Masaki, T., Okada, N., Yasuda, R., and Okada, H. (1989). Assay of complement activity in human serum using large unilamellar liposomes. J. Immunol. Methods 123, pp. 19–24.

65. Matyas, G. R., Wassef, N. M., Rao, M., and Alving, C. R. (2000). Induction and detection of antibodies to squalene. J. Immunol. Methods 245, pp. 1–14. 66. Meunier, F., Prentice, H. G., and Ringdén, O. (1991). Liposomal amphotericin B (AmBisome): safety data from a phase II/III clinical trial. J. Antimicrob. Chemother. 28 Suppl B, pp. 83–91.

67. Moghimi, S. M., Andersen, A. J., Hashem, S. H., Lettiero, B., Ahmadvand, D., Hunter, A. C., Andresen, T. L., Hamad, I., and Szebeni, J. (2010). Complement activation cascade triggered by PEG-PL engineered nanomedicines and carbon nanotubes: the challenges ahead. J. Control. Release 146, pp. 175–181. 68. Moghimi, S. M., Hamad, I., Andresen, T. L., Jörgensen, K., and Szebeni, J. (2006). Methylation of the phosphate oxygen moiety of phospholipid-methoxy(polyethylene glycol) conjugate prevents PEGylated liposome-mediated complement activation and anaphylatoxin production. FASEB J. 20, pp. 2591–2593.

73

74

Complement Activation, Immunogenicity, and Immune Suppression as Potential Side Effects of Liposomes

69. Mold, C., and Gewurz, H. (1980). Activation of human complement by liposomes: serum factor requirement for alternative pathway activation. J. Immunol. 125, pp. 696–700.

70. Money-Kyrle, J. F., Bates, F., Ready, J., Gazzard, B. G., Phillips, R. H., and Boag, F. C. (1993). Liposomal daunorubicin in advanced Kaposi’s sarcoma: a phase II study. Clin. Oncol. (R. Coll. Radiol.) 5, pp. 367–371.

71. Okada, N., Yasuda, T., Tsumita, T., and Okada, H. (1983). Membrane sialoglycolipids regulate the activation of alternative complement pathway by liposomes containing trinitrophenylaminocaproyldipalmitoylphosphatidylethaolamine. Immunology 48, pp. 129–140.

72. Ozato, K., Ziegler, H. K., and Henney, C. S. (1978). Liposomes as model membrane systems for immune attack. II. The interaction of complement and K cell populations with immobilized liposomes. J. Immunol. 121, pp. 1383–1388.

73. Pedersen, M. B., Zhou, X., Larsen, E. K. U., Sorensen, U. S., Kjems, J., Nygaard, J. V., Nyengaard, J. R., Meyer, R. L., Boesen, T., and Vorup-Jensen, T. (2010). Curvature of synthetic and natural surfaces is an important target feature in classical pathway complement activation. J. Immunol. 184, pp. 1931–1945.

74. Richards, R. L., Habbersett, R. C., Scher, I., Janoff, A. S., Schieren, H. P., Mayer, L. D., Cullis, P. R., and Alving, C. R. (1986). Influence of vesicle size on complement-dependent immune damage to liposomes. Biochim. Biophys. Acta 855, pp. 223–230.

75. Richardson, D. S., Kelsey, S. M., Johnson, S. A., Tighe, M., Cavenagh, J. D., and Newland, A. C. (1997). Early evaluation of liposomal daunorubicin (DaunoXome, Nexstar) in the treatment of relapsed and refractory lymphoma. Invest. New Drugs 15, pp. 247–253.

76. Sculier, J. P., Coune, A., Brassinne, C., Laduron, C., Atassi, G., Ruysschaert, J. M., and Fruhling, J. (1986). Intravenous infusion of high doses of liposomes containing NSC 251635, a water-insoluble cytostatic agent. A pilot study with pharmacokinetic data. J. Clin. Oncol. 4, pp. 789–797. 77. Solomon, R., and Gabizon, A. (2008). Clinical pharmacology of liposomal anthracyclines: Focus on pegylated liposomal doxorubicin. Clin. Lymphoma Myeloma 8, pp. 21–32.

78. Sou, K., and Tsuchida, E. (2008). Electrostatic interactions and complement activation on the surface of phospholipid vesicle containing acidic lipids: effect of the structure of acidic groups. Biochim. Biophys. Acta 1778, pp. 1035–1041.

79. Storm, G., ten Kate, M. T., Working, P. K., and Bakker-Woudenberg, A. (1998). Doxorubicin entrapped in sterically stabilized liposomes: effects on bacterial blood clearance capacity of the mononuclear phagocyte system. Clin. Cancer Res. 4, pp. 111–115.

80. Szebeni, J. (1998). The interaction of liposomes with the complement system. Crit. Rev. Ther. Drug Carrier Syst. 15, pp. 57–88.

81. Szebeni, J. (2001). Complement activation-related pseudoallergy caused by liposomes, micellar carriers of intravenous drugs and radiocontrast agents. Crit. Rev. Ther. Drug Carr. Syst. 18, pp. 567–606. 82. Szebeni, J. (2004). Complement activation-related pseudoallergy: mechanism of anaphylactoid reactions to drug carriers and radiocontrast agents, In The Complement System: Novel Roles in Health and Disease (J. Szebeni, ed.), pp. 399–440. Kluwer, Boston.

References

83. Szebeni, J. Baranyi, L. Savay, S. Milosevits, J. Bodo, M. Bunger, R. Alving, C. R. The interaction of liposomes with the complement system: in vitro and in vivo assays. Methods Enzymol. 373, pp. 136–154. 84. Szebeni, J. (2005). Complement activation-related pseudoallergy: a new class of drug-induced immune toxicity. Toxicology 216, pp. 106–121.

85. Szebeni, J., Baranyi, B., Savay, S., Bodo, M., Morse, D. S., Basta, M., Stahl, G. L., Bunger, R., and Alving, C. R. (2000). Liposome-induced pulmonary hypertension: properties and mechanism of a complement-mediated pseudoallergic reaction. Am. J. Physiol. 279, pp. H1319–H1328.

86. Szebeni, J., Baranyi, L., Sávay, S., Bodó, M., Milosevits, J., Alving, C. R., and Bünger, R. (2006). Complement activation-related cardiac anaphylaxis in pigs: role of C5a anaphylatoxin and adenosine in liposome-induced abnormalities in ECG and heart function. Am. J. Physiol. 290, pp. H1050–H1058.

87. Szebeni, J., Bedőcs, P., Rozsnyay, Z., Weiszhár, Z., Rosivall, L., Cohen, R., Garbuzenko, O., Báthori, G., Tóth, M., Bünger, R., and Barenholz, Y. (2011). Liposome-induced complement activation and related cardiopulmonary distress in pigs: factors promoting reactogenicity of Doxil and Ambisome. Nanomed. Nanotechnol. Biol. Med. In press. 88. Szebeni, J., Bunger, R., Baranyi, L., Bedocs, P., Toth, M., Rosivall, L., Barenholz, Y., and Alving, C. R. (2007). Animal models of complement-mediated hypersensitivity reactions to liposomes and other lipid-based nanoparticles. J. Liposome Res. 17, pp. 107–117.

89. Szebeni, J., Fontana, J. L., Wassef, N. M., Mongan, P. D., Morse, D. S., Dobbins, D. E., Stahl, G. L., Bünger, R., and Alving, C. R. (1999). Hemodynamic changes induced by liposomes and liposome-encapsulated hemoglobin in pigs: a model for pseudo-allergic cardiopulmonary reactions to liposomes. Role of complement and inhibition by soluble CR1 and anti-C5a antibody. Circulation 99, pp. 2302–2309.

90. Szebeni, J., Wassef, N. M., Hartman, K. R., Rudolph, A. S., and Alving, C. R. (1997). Complement activation in vitro by the red blood cell substitute, liposome-encapsulated hemoglobin: Mechanism of activation and inhibition by soluble complement receptor type 1. Transfusion 37, pp. 150–159. 91. van de Wert, M., and Moller, E. H. (2008). Immunogenicity of biopharmaceuticals: causes, methods to reduce immunogenicity, and biosimilars, In Immunogenicity of Biopharmaceuticals” (M. van de Wert and E. H. Moller, eds.), pp. 97–111. Springer, AAPS Press.

92. Venkatraman, S. S., Ma, L. L., Natarajan, J. V., and Chattopadhyay, S. (2010). Polymerand liposome-based nanoparticles in targeted drug delivery. Front. Biosci. (Schol. Ed.) 2, pp. 801–814. 93. Wagner, V., Dullaart, A., Bock, A.-K., and Zweck, A. (2006). The emerging nanomedicine landscape. Nature Biotechnol. 24, pp. 1211–1217.

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Chapter 4

Human Clinical Relevance of the Porcine Model of Pseudoallergic Infusion Reactions János Szebeni, MD, PhD, DSc,a,b,c,d and Raj Bawa, MS, PhD, MD ‘22e,f,g aNanomedicine Research and Education Center, Institute of Translational Medicine, Semmelweis University, Budapest, Hungary bSeroScience Ltd., Budapest, Hungary cSeroScience International LLC., Cambridge, Massachusetts, USA dDepartment of Nanobiotechnology and Regenerative Medicine, Faculty of Health, Miskolc University, Miskolc, Hungary dThe Pharmaceutical Research Institute, Albany College of Pharmacy and Health Sciences, Albany, New York, USA fPatent Law Department, Bawa Biotech LLC, Ashburn, Virginia, USA gGuanine Inc., Rensselaer, New York, USA

[email protected], [email protected]

Keywords: adverse drug reactions, anaphylaxis, anaphylactoid reactions, shock, nanomedicine, nanoparticle, pigs, complement, pulmonary intravascular macrophages, hypersensitivity, pseudoallergy, C activation, phagocytosis, pattern recognition receptors, non-biologic complex drugs, lipoprotein, liposome, pulmonary vascular reactions, polymorphonuclear neutrophils, white blood cell, platelet aggregation, toxicity, regulatory oversight, risk/benefit ratio, biocompatibility, disease model, scientific debate

4.1 Introduction Infusion reactions, i.e., acute hypersensitivity reactions (HSRs) induced by intravenously (i.v.) administered drugs and certain other compounds represent an old, yet unsolved immune barrier to the clinical use of numerous nanomedicines, radiologic contrast agents, biologicals, enzymes, muscle relaxants, and a variety of other pharmaceutical products [1–7]. Although the standard, empiric preventive measures effectively attenuate these adverse drug reactions (ADRs) in most cases [3, 4], there has been no breakthrough in the prediction and prevention of occasional Grade IV–V severe adverse reactions (SARs), also known as severe adverse events (SAEs), culminating in anaphylactic (or anaphylactoid) shock or Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

Copyright © 2022 Jenny Stanford Publishing Pte. Ltd.

ISBN 978-981-4877-84-8 (Hardcover), 978-1-003-18043-2 (eBook)

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Porcine Model of Pseudoallergic Infusion Reactions

death [3, 8–13]. Such SARs may not only preclude the patient from treatment with a potentially life-saving drug, but their clustering may entail the suspension or withdrawal of the drug from clinical use, thereby having negative implications for drug manufacturers as well. These facts lend substantial importance to better understanding the mechanism of drug-induced HSRs, which can be perceived as “stress reactions” in blood along the innate immune-circulatory system axis [14]. Since HSRs cannot be reproduced in vitro, research and development in this field necessitates the use of animal models. One such model is the so-called porcine complement (C) activation-related pseudoallergy (CARPA) model [15–18] which involves i.v. injection of the test drug(s) into pigs. In the case of immune reactivity, the drug administration then triggers more or less severe cardiopulmonary, hemodynamic, hematological, skin, and laboratory changes similar to those observed in patients displaying HSRs to a variety of drugs and agents [15–18]. It is this concordance of symptoms that provides a rationale for the use of pigs to model human HSRs. The name “CARPA” derives from a large body of experimental evidence for C activation playing a causal or contributing role in the reactions (Table 4.1). However, it is critical to emphasize that C activation is not the only mechanism of these reactions. The mechanism of HSRs is complex and varies in different species under different conditions, also involving C-independent pathways, referred to as C-independent pseudoallergy (CIPA) [19, 20]. Table 4.1 lists 30 experimental studies [15, 21–49] which utilized the pig model to analyze the cardiopulmonary adverse effects of different nanoparticles (NPs) or other agents. Some of these studies highlighted the concordance of HSR symptoms in pigs and hypersensitive patients [25–29, 32, 33, 46–48], others addressed the mechanism of HSRs [15, 21–33, 36, 41–47], and yet others focused on the prevention of HSRs by pharmacological intervention [15], or by optimizing the structure [24, 44] or administration protocol [24, 37, 48] of NPs. Importantly, many of these studies were initiated mainly for preclinical safety evaluation of nanomedicines [15, 22, 23, 30, 31, 35, 37, 39–43, 46, 49], a term interchangeably applied for “nanoparticulate drugs” or “nanopharmaceuticals,” or “drug carrier nanosystems.”

4.2 Challenge to the Pig Model’s Human Relevance and Utility in Preclinical Safety Assessment

Despite the established use of pigs to study NP-induced cardiopulmonary distress for three decades (Table 4.1), a recent review questioned the suitability of the model for nanomedicine safety assessment, vociferously arguing against its use [50]. This review claimed that nanomedicine safety assessment in the porcine model might be “inappropriate, misleading, scientifically questionable,” and it warned against “advertent promotion and exaggeration” of the model. This evaluation was repeated and extended further in another recent review [51] with the baseless

Table 4.1 Chronological list of pig studies which included the analysis of hemodynamic changes and other endpoints of hypersensitivity reactions to i.v. drugs Tested drugs/agents*

Major findings on HSR**

Refs.

1989

Cholesterol-containing liposomes used for immunizing pigs Liposomes caused major cardiopulmonary distress and TXA2 release [21] against hypercholesterolemia-induced arteriosclerosis via anti-cholesterol antibody-mediated C activation in pig blood.

1992

Albunex microspheres used as ultrasound contrast agents

1999

Liposome-encapsulated hemoglobin used for blood substitution and control liposomes

Hemodynamic changes are due to C activation with subsequent secretion of TXA2. PAP is dose-dependent, highly reproducible endpoint of HSRs.

PEGylated liposomal doxorubicin (Doxil/Caelyx), a reactogenic anticancer drug

Doxil activates C in vitro and its dose-dependent hemodynamic effects [25]

in pigs mimic the human HSRs to this drug.

1994

HSR involves TXA2-mediated pulmonary hypertension in pigs.

[22]

NPs are cleared mainly by pulmonary intravascular macrophages (PIMs) in the lung of pigs.

[23]

[15]

2000

Different liposomes applied to dissect the structural factors Vesicle size, lamellarity, charge and infusion speed are all critical contributing to pulmonary hypertension in pigs determinants of the rise of PAP.

[24]

2005

Negatively charged multilamellar vesicles applied as a model for reactogenic liposomes

[26, 27]

2006

2008 2010

Oversulfated chondroitin sulfate (OCS), a contaminant of heparin that caused a US nationwide outbreak of severe adverse reactions during 2007–2008

Liposomal bisphosphonates (LBPs) developed for the prevention of myocardial infarction via macrophage inhibition

The hemodynamic disturbance during HSRs is also manifested in cerebrovascular changes, explaining the psychic symptoms of HSRs.

The symptoms of HSRs reproduce those of cardiac anaphylaxis. The reaction can be reproduced only partially with injection of C5a.

OCS induced contact and complement system activation and cardiopulmonary distress only in pigs but not in other species, mimicking the human symptoms of severe heparin reactions.

[28]

[29]

LBPs triggered no or minor HSRs in pigs, which correlated with their C activating capability in vitro.

[30]

79

(Continued)

Challenge to the Pig Model’s Human Relevance and Utility in Preclinical Safety Assessment

Year

80

Year

Tested drugs/agents*

Major findings on HSR**

Refs.

2011

PEI-PEG block-copolymers used as models for polymeric drug carrier nanosystems

25K-PEI activated C in vitro and caused HSRs in pigs; its PEGylation decreased, but did not eliminate these effects.

[31]

Hemoglobin vesicles (HbVs) used as an oxygen carrier blood substitute

By optimizing the lipid composition of HbVs both C activation and the [34] HSR of pigs could be attenuated. The reaction was tachyphylactic.

2012

2014

PEGylated liposomal doxorubicin (Doxil, Caelyx) and liposomal amphotericin-B (AmBisome), both are reactogenic in patients

Doxil and AmBisome activated C in vitro and caused proportional HSRs in pigs. The effect of Doxil, but not of AmBisome, was tachyphylactic.

[32, 33]

TRO40303, a cardioprotective sterane compound, inhibitor Consistent with the safety and tolerance in a phase I trial, TRO40303 of transitional permeability pores in mitochondria did not activate C and caused HSR in pigs.

[35]

Inclisiran, a siRNA-containing LNP formulation inhibiting PCSK9 protein to reduce plasma LDL

[37]

Intralipid, used for reversing the symptoms of local anesthetic overdose

2016– Known reactogenic nanomedicines (Doxil, AmBisome, 2018 Cremophor EL)

Nano-systems intended for cardiovascular applications (liposomes, LNPs, polymeric and iron oxide NPs)

Nitroglycerin encapsulated in 1,3-diamidophospholipidcontaining, shear-responsive liposomes developed to alleviate coronary vasoconstriction

A new type of superparamagnetic iron oxide NPs (SPIONdex) used as MRI contrast agents

Intralipid caused major HSR in pigs, although C activation could not be [36]

detected in pig blood in vitro.

A stepwise micro-dosing protocol is reaction-free in pigs, suggesting safety in patients.

The hemodynamic derangement and other changes caused by reactogenic nanomedicines were similar in pigs and Göttingen miniature pigs.

[38]

Despite irregular size, these NPs did not activate C and were not reactogenic in pigs.

[40, 41]

The non-reactive NPs were suggested to have the least risk for HSRs in man.

[39]

C activation and HSR in pigs can be eliminated by reducing the size of [42, 43]

SPIONdex NPs.

Porcine Model of Pseudoallergic Infusion Reactions

Table 4.1 (Continued)

Year

Major findings on HSR**

Refs.

Polystyrene NPs (PS-NPs) used as a model for reactogenic drug delivery nano-systems

PS-NP-induced cardiopulmonary distress depends on the shape of particles, spheres being more reactogenic than rods or disks. C activation was not measurable in pig whole blood.

[44]

Hemostatic NPs based on PEG-PLGA-PLL-PEG-cRGD copolymers, developed to control traumatic blood loss

In a porcine liver injury model, these NPs led to massive vasodilation and exsanguination due to CARPA. This adverse effect could be attenuated by tailoring the zeta potential of NPs.

[46]

Doxil and placebo Doxil (Doxebo) used to clarify the mechanism of HSRs to Doxil and other PEGylated NPs

Liposomal cortisol phosphate developed against chronic inflammatory diseases TC99m-Fucoidan, a sulfated fucose-rich polysaccharide developed for the detection of P-selectin expression in cardiovascular diseases

[45]

Spherical PS-NP-induced cardiopulmonary distress in pigs showed significant correlation with C activation in human serum. PS-NPs were

opsonized in pig serum by C3 derivatives, indicating C activation.

C activation and the HSR caused by Doxil was greatly amplified in Doxebo-immunized animals in which the anti-PEG IgM levels were increased. This provides evidence for the causal role of classical pathway C activation in Doxil reactions.

[47]

The drug did not cause C activation or HSR in pigs, suggesting safety for human use for the imaging of activated endothelium.

[49]

Consistent with the human practice, slow, stepwise infusion with micro-dosing minimizes the risk for HSRs.

[48]

*Trade names and abbreviations: AmBisome, liposomal amphotericin-B; C, complement; Doxil, PEGylated liposomal doxorubicin; HSR, hypersensitivity reaction; HbVs, hemoglobin vesicles; LEH, liposome-encapsulated hemoglobin; LNPs, lipid nanoparticles; LDL, low density lipoprotein; NPs, nanoparticles; OCS, oversulfated chondroitin sulfate; PEI, polyethylene-imine; PEG-PLGA-PLL-PEG-cRGD, cyclic peptide (arginine-glycine-aspartic-glutamic-valine acid, cRGD)-modified monomethoxy (polyethylene glycol)-poly (d,l-lactide-co-glycolide)-poly (l-lysine) nanoparticles; siRNA, small inhibitory ribonucleic acid; PS-NPs, polystyrene NPs; SPIONs, superparamagnetic iron oxide nanoparticles; TRO40303, 3,5-seco-4-nor-cholestan-5-one oxime-3-ol-containing liposomes; PCSK9, proprotein convertase subtilisin/kexin type 9 (LDL uptake blocker). **Conclusions on hemodynamic, TXA2, and other physiological changes observed in response to i.v. administration of test agents.

Challenge to the Pig Model’s Human Relevance and Utility in Preclinical Safety Assessment

2019

Tested drugs/agents*

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statement that “compulsory nanomedicine response tests in pigs should not be advertently promoted, and imposed on pharmaceutical industry.” The reason provided for these broad assertions in the two reviews is that the pulmonary response to NPs is a “global” phenomenon wherein a population of pulmonary intravascular macrophages (PIMs) indiscriminately respond to NPs with the secretion of thromboxane A2 (TXA2), the classic mediator of cardiopulmonary distress. Thus, -the authors argued- the porcine test “excludes otherwise promising nanopharmaceuticals from clinical development on safety grounds that are not relevant to wider human populations” [50, 51]. Given the public focus on the safety of nanomedicines and the fundamental need for an animal model to study infusion-related HSRs, consideration of all information on the different models is important. As for the pig model, in fact, the discordance of HSR frequency to certain nanomedicines between humans and pigs (i.e., roughly 2–10% in man while near 100 % in pigs) has always been a contentious issue, dividing the judgment on the model’s human relevance. Accordingly, the aim of this review is to provide an update regarding the pros and cons of the pig model [18] while addressing the issues raised in the referred critical reviews [50, 51]. Moreover, this review will highlight the CARPA model’s increasing recognition and deployment.

4.3 Scrutiny of the Challenge to the Pig Model: Facts and Questionable Conclusions

The referenced critical reviews [50, 51] contain experimentally established facts as well as conclusions that argue against the utility of the porcine CARPA model. For a systematic analysis and clarity, Table 4.2 separates the facts and claims against the model that we find arguable, along with giving some annotations (italicized text) where necessary for better understanding.

4.3.1 Gaps in the Theory Attributing HSRs to Robust Phagocytosis of NPs by PIM Cells

The first arguable point (Claim 4) in the critique of the pig model is that the HSRs to NPs in this species is a “global” phenomenon due to the robust, non-specific phagocytosis of NPs by PIM cells in the pulmonary circulation of pigs and other cloven-hoof species [44, 50, 51, 59]. Specifically, the mechanism was suggested to involve a C-independent “transient link” between phagocytosis and TXA2 secretion by PIMs which cannot differentiate between reactive and non-reactive NPs [44]. However, in lack of dedicated studies on the role of phagocytosis in TXA2 secretion, the experimental foundation of this proposal is unclear. In fact, such a hypothesis is inconsistent with the known enhancement of phagocytosis by surface-bound C3b and its derivatives, and also with a long list of observations on significant C-dependence of HSRs to NPs in both humans and pigs [19, 45]

Scrutiny of the Challenge to the Pig Model

(Table 4.1). Even the reaction to PS-NPs in pigs, which was claimed as being C-independent [44], turned out to involve C activation-related opsonization [45]. Hence, contrary to the dismissal of CARPA [44, 59], the PIM response to PS-NPs also represents CARPA, at least in part. On the other hand, the really C-independent IgG Fcγ-receptor-mediated anaphylactic pathway [59] would also have specificity to the reactogenic drug or agent, dictated by the Fab of IgG. Table 4.2 Facts and arguable conclusions regarding the pig model of infusion reactions* Experimental facts

Refs.

1. PIM cells are abundantly present in the lung of cloven-hoofed members [50–56] of the mammalian order Artiodactyla, including pigs, sheep, goats, cattle, horse, etc.

2. PIM cells are highly phagocytotic and can secret, among others, vasoactive eicosanoids, including thromboxane A2 (TXA2).

3. The vasoactivity of TXA2 is a key contributor to the massive hemodynamic [15, 20, 48, changes following NP injection of pigs and other animals. 50–58] Arguable claims

4. The NP-induced hemodynamic changes in pigs are due to robust phagocytosis of NPs by PIM cells, the source of thromboxane. That is, PIM phagocytosis is causally involved in HSR, rather than C activation with stimulation of a variety of cells for proinflammatory response via other pathways.

[44, 50, 51, 59]

5. The hemodynamic response to i.v. nanoparticles is a “global outcome,” [50, 51]. implying omnipresent, uniform, non-specific, non-quantitative cardiovascular changes. 6. The discordant prevalence of HSRs in pigs and healthy man makes [50, 51, 60] the model irrelevant to humans, excluding otherwise promising nanopharmaceuticals from the development pipeline on safety grounds that are not relevant to wider human populations. That is, because the HSRs are rare in humans but always observed in the pig model, the pig model overestimates the risk of human HSRs. 7. The pig assay is being advertently promoted and their applications [50, 51] exaggerated or imposed on the pharmaceutical industry as a compulsory nanomedicine response test. This is a baseless presumption.

*Italicized text represents a further clarification to help understanding.

Another problem with the C-independent phagocytosis-TXA2-link hypothesis is the time course discrepancy between phagocytosis, TXA2 release, and pulmonary reactions in pigs. The recent review [51] argues that the time course of phagocytosis coincides with the peak of TXB2 release, and, hence, pulmonary response of pigs, while such coincidence with C activation is not present in a pig whole blood assay in vitro [44]. Specifically, the HSR in pigs starts already at 40–50 s after the injection of PS-NPs and reaches plateau at 1–3 min [15, 61],

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which is paralleled by the time course of PS-NP clearance from pig blood in vivo [44]. On the other hand, C activation by the same NPs in the whole blood assay is absent, or seen only after 5–10 min incubation [44]. However, we would point out several shortcomings of these arguments. First, the evidence of phagocytosis is the visualization of NPs inside macrophages, and the earliest examination performed to establish phagocytosis was performed at 20 min post-treatment [53], which has no relevance to events within 2 min. Second, phagocytosis cannot be equalized with NP capture, as the observed rapid clearance of NPs from blood, which strongly correlates with HSRs, may be a consequence of the binding of NPs to PIMs and other cells without ongoing phagocytosis. Third, there is flow cytometric and Western blot evidence for C3 cleavage and C5b-9 deposition on PS-NPs, i.e., C activation, already at 1–2 min [45], while the validity of the whole blood assay showing no C activation was questioned on technical grounds [19]. Finally, the critical authors themselves judged it inappropriate to extrapolate from in vitro C activation data to HSRs in vivo [51]. Further scrutiny of the time course argument, stating that “robust” phagocytosis of NPs coincides with the HSRs, brings up yet another time-related discrepancy. Namely, the detection of a rise of TXB2 must be preceded by its conversion from TXA2; thus, if TXA2 release is indeed “transiently linked” to robust phagocytosis [44], massive amounts of NPs have to be taken up by PIM cells within 40–50 s. In addition, the plasma levels of TXB2 in pigs displayed an array of peaks closely paralleling the peaks of PAP following repetitive injection of the same liposomes within 30 min or over 7 h (Fig. 4.1A,B) [15]. Thus, if the explanation for the pulsatile release of TXA2 boluses in blood is phagocytosis or any endocytosisinvolving process, it implies not only an instant maximal engulfment of NPs at the first time, but capability for identical “bites” many times on the minute time scale, over hours (Fig. 4.1A,B, respectively). These experimental observations are difficult to reconcile with textbook information on phagocytosis that describes it as a gradual, unidirectional, saturable process requiring receptor binding, and phagosome internalization with rearrangement of the cytoskeleton. It seems to be hardly linkable with pulsating release of TXA2 [15]. Taking these facts and considerations in toto, we suggest that the rapid clearance of PS-NPs and other reactogenic NPs from blood reflects rapid binding to PIM and other cell surfaces, and the instant C-independent liberation of TXA2 may be due to increased arachidonate metabolism at the cell membrane level. Details of this “second hit” on allergy-mediating secretory cells and the exact molecular mechanism of TXA2 release need to be clarified in the future. Another inaccuracy as listed in Claim 4 of Table 4.2 is the reference to PIM cells as sole source of TXA2. Macrophages are not the only possible source of TXA2 in pigs and other cloven-hoof animals undergoing HSRs. In addition to mast cells, that are key players in allergy, platelets, polymorphonuclear neutrophils (PMNs), and endothelial cells have all been shown to spill TXA2 in response to NP exposure in blood [53, 62–64]. Complement activation as a trigger mechanism for these secretory responses by these cells was shown in sheep in the late 1980s [53], providing the earliest proof to the long list of evidence for the validity of the CARPA concept (Table 4.1).

Scrutiny of the Challenge to the Pig Model

Figure 4.1 Time course of liposome-induced changes in plasma TXB2 and PAP in pigs. Two animals were repetitively injected with liposome boluses, and changes in PAP (circles) and plasma TXB2 (bars) were plotted as a function of time for the first two injections in one pig (A) or over 7 h in another pig (B). Other details are in Ref. [15], from where this figure was reproduced with permission. Arrows here indicate the timing of liposome injection.

It should be noted that regarding the source of TXA2 in HSRs the experiment in Fig. 4.1 allows for calculating the total amount of TXB2 released in blood at each liposome exposure. The experiment in Fig. 4.1, using 20–25 kg pigs, showed tens of micrograms of TXB2 released at each liposome injection, altogether >100 microgram over hours. Assuming that the total number of PIM cells in the lung of an adolescent pig is in the order of 108–109 [56], it would be important to find out whether it is possible that most, if not all, TXA2 could derive from PIM cells.

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As a final challenge to Claim 4 in Table 4.2, focusing solely on PIM phagocytosis/ TXA2 release, represents an over-simplification of the mechanism of HSRs. Vasoconstriction by TXA2 is only one pathway in the complex molecular and cellular changes that underlie HSRs (Fig. 4.2A). Specifically, C activation-related activation of anaphylatoxin-receptor positive blood cells entail white blood cell (WBC)–platelet aggregation with subsequent sequestration of micro-emboli in the pulmonary capillary bed [65]. Together with locally formed micro-thrombi and consequent oxidative endothelial damage, these changes act in parallel or in synergy with the vasoconstrictive effect of TXA2 in causing pulmonary blockage of blood flow [15] (Fig. 4.2A,B). Thrombocytopenia and leukopenia with or without secondary leukocytosis are common symptoms of HSRs, reflecting these cells’ direct activation by anaphylatoxins or other stimuli. Thus, whenever these symptoms are present in pigs or other models, a role of TXA2-independent platelet, WBC and endothelial cells activation is likely to be involved. Moghimi et al. [51] referred to the significant inhibition of HSRs by macrophage depletion by pretreatment of pigs with clodronate-liposomes [44] as a further evidence for the key role of PIMs in HSR reactions. However, the study [44] gives no information on HSRs to these liposomes, although other bisphosphonate liposomes were reported to cause mild HSRs in pigs [30]. If the repeated treatment of pigs with clodronate liposomes [44] also caused mild, or even subclinical HSRs, desensitization may also explain the reduction of HSR, just as empty PEGylated liposomes (Doxebo) desensitizes pigs against Doxil reactions [33]. In addition, clodronate has other effects that also explain the inhibition of TXA2 and pulmonary response. Upon reviewing the literature for such a possible effect we found that clodronate liposomes can reduce the clustering and accumulation of PMN in inflammatory lung and kidney diseases [67, 68]. Since an inflammatory cell reaction is likely to contribute to the cardiopulmonary distress in porcine HSRs (Fig. 4.2) [15, 66], PMN-inhibition could also contribute to the HSR-reducing effect of clodronate liposomes [44]. Another open question relates to the observation that, despite the total absence of TXA2 response, the pulmonary hypertensive response was not completely abolished by clodronate liposomes [44]. The remaining 50% rise of PAP is not negligible, for example the pulmonary hypertensive effect of Doxebo is imilar [33]. Therefore, partial inhibition of PAP at a time of total inhibition of TXA2 response may reflect the involvement of a TXA2-independent reaction pathway, another experimental evidence against the linking of HSRs solely to TXA2 release as a reason for disqualifying the pig model [44, 59]. In summary, the key role of PIM cells in nanomedicine-induced HSRs in pigs is undisputed, but referring to these cells’ capability for robust phagocytosis with transiently linked TXA2 secretion as a cause for the sweeping disqualification of the pig model for safety testing is unjustified based on experimentally-derived evidence. The issue should remain open for further scientific analysis and discussion.

Scrutiny of the Challenge to the Pig Model

(a)

(b)

(b)

Figure 4.2 Complex mechanism of liposome-induced CARPA in pigs; schematic (A) and visual (B) illustration of causally related events, reproduced from Refs. [15] and [66], respectively. (A) The arrows indicate causal relationships among the physiological changes; solid and dashed lines indicate experimentally established and hypothetical changes. (B) Imaginary snapshot of a pulmonary capillary during CARPA in pigs; the PIM’s TXA2 response to C5a and liposome binding is combined with microthrombus formation on the capillary wall, amplifying the vasoconstrictive effect of TXA2. Abbreviations: (A) C, complement; HR, heart rate; Mf, macrophage; Indo, indomethacin; CVR, coronary vascular resistance; ST-depr, ST-segment depression on the ECG; sCR1, soluble C receptor type 1, a C inhibitor; GS1, anti-porcine C5a antibody; PVR, pulmonary vascular resistance, CVR, central vascular resistance, CO, cardiac output, SVR, systemic vascular resistance, HR, heart rate, SAP, systemic arterial pressure, PAP, pulmonary arterial pressure; (B) Lip, liposome; aPL, activated platelet; Mo, monocyte; L-P aggr, leukocyte-platelet aggregate; PRR, pattern recognition receptors; En, endothelial cells; SMC, smooth muscle cells.

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4.3.2 The Cardiopulmonary Response of Pigs to NPs Is Not Global If the phrase “global response” implies that the cardiopulmonary reaction of pigs to nanoparticles is common, general, universal, ubiquitous, omnipresent, uniform or indiscriminate, which are synonyms of “global,” then this claim goes against a large body of scientific evidence showing exactly the opposite. Namely, all previous studies using the model (Table 4.1) presented quantitative differences among the reactivities of different nanoparticles and controls. In addition, many studies in Table 4.1 attest to the dose dependence and reproducibility of the response, although different endpoints (i.e., the SAP, HR, blood cell changes, plasma TXB2, and SC5b-9) show more or less individual variation. It is also important to note that there is a phase in porcine HSRs when the animal’s cardiopulmonary response is insensitive to dose escalation, namely, during the state of tachyphylaxis or self-induced tolerance [33]. This phenomenon has been seen in the case of PEGylated liposomes, whereupon the first eactogenic drug dose desensitized the animals for the next and subsequent challenges [33]. As for the specificity of the pig model, Fig. 4.3 shows that the timing of the upand-down deflections and wave forms of PAP, SAP, and HR curves substantially

Figure 4.3 Variation of PAP and SAP waveforms. Panels (A–J) represent reactions to identical or different NPs, selected from different experiments, wherein the CARPAgenic potential of nanoparticulate drugs or drug carriers were tested in pigs. Minutes indicate the timespan of reactions. Blue, red, and green are PAP, SAP, and heart rate curves, respectively. Changes are shown in percent of baseline. Abbreviations (only here): com, commercial; prep, self-prepared; lpd, lipophilic prodrug-containing liposomes; PEI25, 25 kD pegylated poly(ethylene imine); G4 dendrimer, 4th generation dendrimer; MW-CNT, multiwall carbon nanotube. Reproduced from Ref. [17], with permission.

Scrutiny of the Challenge to the Pig Model

differ among NPs under different experimental conditions. On the other hand, the wave peaks and forms are very consistent among different animals for the same nanoparticle trigger under similar experimental conditions. Finally, regarding Claim 5, it should be pointed out that the mentioned study using PS-NPs [44] used three animals in each treatment group to conclude that the cardiopulmonary distress can differentiate among the reactogenicities of 500 nm PS-NPs based on their physical shape (Fig. 4.4).

Figure 4.4 Changes of hemodynamic parameters in pigs after i.v. injection of polystyrene nanoparticles of different shape: spheres (circles), rods (triangles), and disks (squares). Time-dependent changes in pulmonary arterial pressure (PAP) (A), systemic arterial pressure (SAP) (B), and thromboxane B2 (TxB2) (C) following particle injection compared with background (resting phase, before 0 min). injection compared with background (resting phase, before 0 min). Particles (on an equivalent surface area of ~114,300 mm2 per 20 kg body weight) were injected at 0 min. Inset: integrated area under the curve (AUC) of the changes in PAP during the first 10 min of injection. d, the results from pig experiments are expressed as mean ± SEM (n = 3). Reproduced from Ref. [44], with permission.

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This means astonishingly reproducible spatial resolution of nanoparticle surface curvature, an unsurpassable evidence against nonspecific, nonquantitative global response. Taken together, referring to the hemodynamic response of pigs to NPs as global contradicts all experimental evidence, including those published by the main critical author in a top-tier journal [44].

4.3.3 The Issue of Discordant Prevalence of HSRs in Pigs and Humans

Regarding claim 6 in Table 4.2, namely that the discordant prevalence of HSRs in humans and pigs makes the pig model irrelevant to most humans, and, hence, it wrongly excludes otherwise promising nanopharmaceuticals from clinical development, the first question to ask is: Does this difference in HSRs rates really render the pig model irrelevant to humans? The answer to this question lies in the use and goal of the pig CARPA assay. In this context, it is important to consider that the model has many features that distinguish it from the standard toxicity tests. Notably, the CARPA test protocol applies the test drugs in bolus form at 2–3 orders of magnitude lower dose than the drug’s planned or established therapeutic dose, thus mimicking the rise of HSRs in man shortly after starting the drug’s infusion, when only a small portion of the drug has reached the blood. Another major difference relative to standard toxicity protocols is that the spectrum of monitored endpoints in the pig model is limited to cardiopulmonary, hemodynamic, blood cell, skin, and some plasma immune mediator changes, all reflecting allergy-related adverse phenomena. In contrast, standard toxicity models explore a great number of organ and body parameters in search for unforeseen abnormalities. Hence, such studies are performed in healthy animals, using a rodent and a large animal species, and the drugs are tested at their therapeutic level and above, in keeping with the human administration protocol for therapeutic or diagnostic application. These differences in methodology reflect the repeatedly emphasized fact that the porcine CARPA model is a disease model, i.e., that of hypersensitivity to nanomedicines [6, 16–19, 45, 47, 48]. Its use in this context is hazard identification and risk assessment for this kind of HSR, and not as a standard toxicology model. To illustrate that the reproducible hypersensitivity of pigs to certain NPs is an advantage rather than a problem, a good example is the discussed study on PS-NP-induced HSRs in pigs, that led the authors to propose a new approach to prevent HSRs, obviously not only in pigs [44]. If the model would truly reflect the prevalence of human HSRs to nanoparticles (2–10%), a minimum of 90–450 pigs should have been used for the study (instead of nine) to allow for the conclusions made, but preferably three-times these numbers to provide statistical power. Yet another note regarding the prevalence issue, a short editorial by Skotland [60] has been referred to by the critical authors as additional evidence for misusing the pig model. It warns against “trouble” upon performing safety studies by intravenous injection of microparticles in cloven-hoof animals, such as pigs, on the basis of anaphylactic reactions to the ultrasound contrast agent, Albunex, observed in the 1980s. The vivid memory of deadly reactions confirms

Scrutiny of the Challenge to the Pig Model

the timelessness of the problem, and the author added to his “good advice” that the warning against the pig model did not apply if there was “specific reason” for using it. Indeed, there could have been good reason for using the model, to forecast those severe HSRs that have been observed with Albunex, beside the thousands of trouble-free administrations. Albunex was discontinued after the introduction of more effective microbubble-based contrast agents, but the public information still available on the drug’s side effects [69] warns against severe acute allergic reactions requiring emergency measures, and lists dyspnea, arrhythmia, chest pain, swelling of the face, lips, tongue, fever, light-headedness, anxiety, confusion, and sweating among the symptoms, which are also characteristic symptoms of infusion reactions [1–13]. As more evidence of Albunex’s cardiopulmonary reactivity, it was reported to trigger a biphasic pulmonary response in a subgroup of cardiac patients withdrawn from anti-inflammatory medication [70]. The nextgeneration ultrasound contrast agents (SonoVue, Optison, and deFinity) continued to cause severe HSRs that led to their temporary or final suspension [71–79]. This reactogenicity can be modeled in pigs just like the reactogenicity of the drugs listed in Table 4.1 (unpublished data). In summary, taking the discordant prevalence of HSRs in pigs and healthy man as argument against the pig model implies its perception as a standard toxicity, rather than a disease model. It shows misunderstanding of the model’s purpose and utility, despite many previous, strongly emphasized clarifications [6, 16–19, 45, 47, 48]. To reiterate the message in simple words, the pig model is recommended to explore if a hypersensitive individual would become symptomatic to a subtherapeutic dose of the tested drug. The question therefore is not the prevalence of HSR to that drug in the general population but the risk of HSRs to a subtherapeutic dose in the rare cases of hypersensitive patients. Because SAEs even in a small fraction of patents represents a major health and economic problem, contraindicating the porcine assay excludes the identification of nanomedicines that can potentially cause such SAEs.

4.3.4 The Pig Test Can Be Useful for the Pharmaceutical Industry: Regulatory Attention

There is no need to “advertently promote,” “exaggerate,” or “impose” the pig CARPA test on the pharmaceutical industry or regulatory agencies (Claim 7 in Table 4.2), as the model has already been noticed and utilized in these spheres. Most notably, it was used in the development of safe administration protocol for nucleic acidcontaining solid lipid nanoparticles [37], such as Patisiran (Onpattro), the first FDA approved targeted therapy of a genetic disease based on mRNA interference [80, 81]. Numerous other examples are parts of new drug application dossiers (unpublished data). In general, the question that the pharmaceutical industry needs to balance is the risk/benefit ratio of conducting the pig test. Its potential benefit is the identification of the hazard of a few Grade 4 and 5 SAEs (i.e., anaphylaxis and death) [10], which can halt or stop the commercial development of promising

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drug candidates in which millions have already been invested. Apart from human tragedies, the regulatory measures entail major press attention with prestige and financial losses for the companies. Recent examples of such events in the nanomedicine field include the PEGylated drugs Peginesatide (Omontys®) [82, 83], Pegloticase, (Krystexxa®) [84–87], and Pegnivacogin (Revolixys®) [88–90]. It seems logical that avoiding such calamities by conducting the pig test may far outweigh the risk that a promising drug candidate gets triaged in the preclinical stage based on false positivity in the pig test. In fact, no promising drug candidate needs to be abandoned because the pig assay also enables the testing of the efficacy of preventive and/or therapeutic measures. Previous pig studies have already identified some new approaches to prevent or attenuate CARPA, the PS-NP study [44] being one example. Pretreatment with indomethacin and an anti-C5a antibody [15], desensitization with Doxebo [33], and the design of slow, stepwise infusion protocols [48] represent further options. Regarding the alarm on “imposing of the pig test on the pharmaceutical industry as a compulsory nanomedicine response test” (Claim 7, Table 4.2), regulatory agencies have adopted “harmonized standards” (ICH S8 and ICH S6) [91, 92] worldwide, which recommend the extension of standard toxicology studies with immune function tests when “the weight-of-evidence” suggests their need. Obviously, a hazard for SAEs does represent such a need, but regulatory agencies generally do not mandate drug developers to follow certain assays over others, nor do they promote or demote any test protocol specifically. Currently, C activation-related toxicity assays, including CARPA, are recommended for consideration in various guidances issued by the US Food and Drug Administration (FDA), European Medicines Agency (EMA), and the World Health Organization (WHO). These guidelines relate to biocompatibility, immune toxicity, and/or bioequivalence [92–97], and specifically recommend C and/or CARPA assays in the case of need, such as a risk for infusion reactions to liposome products [96]. The use of pigs for that purpose is in keeping with the increasing use of these animals for toxicity testing as non-rodent alternatives to dogs or non-human primates [98, 99], including immune toxicity testing [100]. The porcine CARPA test has been validated in minipigs as well [38], whose benefits in immune toxicology testing is increasingly being recognized [101, 102]. It might be an underestimation of the wisdom and vigilance of experts involved in making regulatory recommendations to assume that a misleading model would be made compulsory, or a useful model would be disallowed because of ex cathedra judgments on it without sufficient experimental support [50, 51].

4.4 The Paradox of Healthy Disease Model

The ambiguities surrounding the human relevance of the pig CARPA test must have a reason, most likely the use of healthy pigs as a disease model. While association of guinea pigs with hypersensitivity tests has a long tradition [103–107],

Concordant Symptoms of Pseudoallergy in Pigs and Humans

the idea that healthy pigs provide a genetically determined natural model for nanomedicine-induced HSRs may not be the easiest concept to grasp in the multidisciplinary field of nanomedicine. However, there are some unmistakable, critical facts that should distinguish the pig CARPA model from the standard immune toxicity tests run in pigs or minipigs. In the latter cases, the tests are done at the therapeutic and higher doses of the drug, while the doses tested in pigs are 2–3 orders of magnitude lower than their therapeutic dose (studies in Table 4.1), and even much lower than their toxic dose in men or other toxicity models.

4.5 Concordant Symptoms of Pseudoallergy in Pigs and Humans

The above concept on the disease model nature of the porcine CARPA tests was based on the presumption that pigs provide a true model of human nanomedicine­ induced HSRs, shown by the similarity of diseases symptoms, technically called “concordance” of symptoms. However, because pigs cannot complain about dyspnea, pain, or anxiety, and man cannot be cannulated for extensive hemodynamic analysis including the measurement of pulmonary arterial pressure, the definition of concordance needs to be extended here to mechanistic concordance, i.e., clinical symptoms taken concordant with experimentally detected physiological changes that explain the clinical symptoms. With this definition, the human symptoms of HSRs, namely dyspnea, chest pain, back pain, tachy- or bradycardia, arrhythmia, light headedness, confusion, fear of death, and panic, developing within minutes after starting the infusion of reactogenic drugs, can be considered as concordant with the circulatory derangement of pigs and minipigs that develop within 2–3 min after injection of reactogenic drugs. The latter derangement, referred to as cardiopulmonary distress, entails transient cardiac, cerebral, and other organ ischemia, which explain the human symptoms. The cutaneous flushing and rash appear identical in man and pigs, as is the pseudo-anaphylactic (cardiac) shock, wherein the tachycardia turns into bradyarrhythmia before death, a known premortal sign in lethal shock in man [24]. As for the concordance of blood cells changes in pigs and man, leukopenia followed by leukocytosis and/or thrombocytopenia were described during drug-induced HSRs in man as C-activation-related [108–111], just as in pigs [15], rats [58, 112], mice [20], and monkeys [113]. Among the non-cellular biomarkers of HSRs, the rise of soluble C terminal complex (sC5b-9) has been shown during HSRs to liposomal doxorubicin (Doxil) in both pigs [47] and cancer patients [114]. Furthermore, the HSR to Doxil follows the same time course and has similar trigger dose in pigs as in humans [25] and the reaction to other reactogenic drugs can be attenuated in pigs by slow infusion [24, 48], just as in man [115]. Importantly, not only NPs can cause HSRs that are concordant with physiological changes in the pig model. Kishimoto et al. showed that pigs, unlike rats and other species, provided a good model to recapitulate the heparininduced HSRs of dialysis patients in the US and Germany during 2007–2008

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[29]. The reactions in hundreds of patients, causing the death of near a hundred patients, were characterized by hypotension, shortness of breath, and other typical symptoms of CARPA occurring within 30 min after heparin administration [29, 116]. The culprit in these cases was not anti-heparin antibodies, but a contaminant of heparin, namely, oversulfated chondroitin sulfate (OCS). In parallel with the pseudoallergy symptoms, this linear hetero-polysaccharide caused rises of plasma C5a, C3a, kallikrein, and bradykinin [29, 116], indicating the coupling of CARPA with contact system activation. In the study by Kishimoto et al. [29] the hypotension and tachycardia could be mimicked in pigs—and only in pigs—by i.v. injection of OCS. Moreover, the reaction proceeded with identical kinetics as seen in pigs injected with C activating NPs (Figs. 4.1, 4.3, and 4.4). A further example for the concordance of immune mechanism and symptoms of NP-induced HSRs in man and pigs can be identified in the “Radar” and “Regulate-PCI” (PCI: percutaneous coronary intervention) trials that tested the efficacy and safety of the PEGylated aptamer anticoagulant, Pegnivacogin (Revolixys kit) [88–90]. These trials were stopped because of HSR-related anaphylactoid reactions in a few patients who had high levels of preformed anti-PEG antibodies in their blood [88–90]. This mechanism, namely anti-PEG antibody-induced C activation leading to pseudo-anaphylaxis, has been recently reproduced in pigs using PEGylated liposomes [47]. Likewise, in other clinical studies on Pegloticase (Krystexxa), a PEGylated recombinant uricase used for the treatment of refractory gout but later withdrawn from the market because of HSRs, the reactions were shown to be correlated with preexisting and induced anti-PEG Abs and rapid loss of efficacy [84–87, 117]. In these studies, too, the HSRs, as well as the loss of clinical efficacy of the drug, are consistent with CARPA, whereupon the loss of drug efficacy can be explained with the mechanism described in pigs [47], i.e., accelerated blood clearance of C-opsonized, anti-PEG antibody-bound drug. Thus, pigs may provide a model not only for HSRs but also for loss of therapeutic efficacy in the case of certain (PEGylated) drugs. In addition to the above clinical data attesting to concordance between NP-induced HSRs in pigs and humans, we reported the coincidence of HSRs in pigs with historic data on HSRs in man in the case of low-molecular weight dextran-coated superparamagnetic iron oxide nanoparticles, Sinerem and Resovist [118].

4.6 The Predictive Power of the Pig Test

It needs to be re-emphasized that the reference population to which the prevalence of pig reactions to certain drugs needs to be compared is not the normal human population but the population of patients who are hypersensitive to the same drug or agent. Depending on the drug, this population varies between a broad range of 0.01% and 80%, median values for different drugs roughly being in the 2–10% range. As for the predictive power of the pig test in terms of

Problems in the Criticism of the Pig Model

sensitivity, specificity, positive and negative predictive values, such statistical calculations using, for example 2 × 2 tables [119], can only be performed when sufficient experimental and clinical data are available, which is not the case at present. Statistical calculations of the pig assay’s predictive power are hampered not only by the low occurrence rate of HSRs, but also by the lack of standard protocols of drug administration and anti-allergic premedication in different patients. Thus, even if we had substantially more patient information on HSRs to a drug, their extensive premedication and immediate stopping of the infusion in reacting patients prevent a truly quantitative correlation of symptoms in man and pigs. Thus, the pig assay’s false positivity would be due to medical intervention rather than inappropriateness of the model. Nevertheless, despite these uncertainties, in absence of alternative approaches of HSR prediction, the discussed concordances give rationale for the use of the pig test to qualitatively assess the reactogenicity of different drugs with the understanding that positivity in the test predicts a general danger for HSRs in hypersensitive patients without quantifying the risk for actual patients or treatment protocols.

4.7 Research Needed to Further Validate the Pig Model

It follows from the above difficulties of correlation analysis between porcine and human HSRs that future studies aimed to further validate the pig model will have to reproduce the human treatment protocol as much as possible, using speciesadjusted therapeutic and initial-exposure bolus doses. In addition, the reactions will have to be conducted under identical or similar experimental conditions regarding the pig source and age, and the HSRs will have to be quantified via standardizable methods. Regarding the latter, the studies to date point to PAP as the most reproducible and quantitative measure of HSR. However, it is also shown in Fig. 4.3 that the SAP and heart rate also change as well as the individual blood cell counts, most importantly those of granulocytes and platelets whose changes are not necessarily paralleling. Furthermore, the plasma levels of vasoactive inflammatory mediators (TXB2, PAF, and leukotrienes) also change to different degrees during CARPA. Our attempts in the past to give a combined index for the quantification of porcine CARPA, called cardiopulmonary abnormality score (CAS) [28], embraced all physiological changes that we could measure. However, other scoring methods are also advisable, one being the principal component analysis [120].

4.8 Problems in the Criticism of the Pig Model

This publication was initiated by the vociferous disapproval of the use of pigs as a model for drug-induced HSRs in recent review articles [50, 51], conceived after >30 years use of the model in research and preclinical drug development

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(Table 4.1). Obviously, shifts of scientific paradigms are essential for progress, for which one should be open, but the attempts in Refs. [50, 51] to change the professional recognition and public image of the pig model did not hold up to closer scrutiny. Our analysis points to many inaccuracies and gaps in the critic’s rationale, including linking the HSRs only to PIM-cell derived TXA2; qualifying the dose-dependent, quantitative, and specific physiological changes as “global”; confusing the purpose of the pig assay by mixing up standard toxicity and disease models; misunderstanding regulatory and industrial procedures; and implying commercial ends in the motivation of basic research efforts in the subject. In addition, the authors do not worry about major self-contradictions, most prominently the acknowledgement that human HSRs are “outwardly reproducible in pigs” [44, 50, 51, 59] (which is the ultimate goal of using animals to study human diseases) and the promotion of a new strategy for the prevention of NP-induced HSRs using the same model, which is now being taunted as “inappropriate,” “misleading,” and “scientifically questionable.” The latter highprofile study [44] provided strong experimental evidence for the capability of the porcine CARPA model to distinguish reactogenic from non-reactogenic NPs based on particle geometry, suggesting that rod- and disk-shape PS-NPs are less reactogenic than spherical ones [44]. The question is, therefore, whether this approach of preventing HSRs can be “advertently” promoted further, or the story perhaps needs revisiting as was done [19, 45] because of premature postulation of the absence of C activation in the same study [44]. Moghimi et al. stated that “Since, a population of PIMs are believed to be the likely source of thromboxane, and the fact that pulmonary hemodynamic and lymph dynamic changes occur in a dose-dependent fashion to particle injection, testing of nanomedicine safety in porcine (and other ruminants) will most likely induces cardiopulmonary distress.” [50] This sentence appears to be a distorted reproduction from the following sentence in Ref. [53]; “Our observations suggest that a population of pulmonary intravascular macrophages is likely to be the source of the thromboxane and the pulmonary hemodynamic and lymph dynamic changes that occur in a dose-dependent fashion, although interactions between liposomes, leukocytes, or endothelial cells, in addition to the macrophages, have not been completely ruled out.” Thus, the second (italicized) part of the “copy-pasted” sentence was replaced by a logically incoherent self-supporting conclusion (also italicized) leaving out an essential portion in the original paper that offered alternatives to the phagocytosis-related TXA2 hypothesis. Likewise, the suggestion in Ref. [53] that “liposomes could conceivably activate production of arachidonic acid metabolites by endothelial cells or the large population of neutrophils in the sheep lung before being phagocytosed by the intravascular macrophages“ has also been neglected. The latter effect, proposed 32 years ago, still represents a likely, yet unexplored explanation for the C-independent “second hit” on PIM and other cells involved in NP-induced HSRs, that may act in synergism with the anaphylatoxin “hit” [16, 19]. In light of these deviations from balanced data presentation and judgment, the alarming language “inappropriate,” “misleading,” “scientifically questionable,”

Conclusions and Future Perspectives

and “should not be advertently promoted,” more appropriately characterize the critical authors’ approach and their over-generalization without proper scientific evidence.

4.9 Conclusions and Future Perspectives

With the advance of complex, targetable nanomedicines (as well as many other biologics and non-biologic complex drugs [NBCDs]) that are recognized by the immune system as foreign, the prediction of potential SAEs will have increasing importance in the future to meet the safety mandates of regulatory agencies. The porcine CARPA test may find utility for SAE hazard assessment and mitigation as an extension of standard toxicology protocols on a case by case basis, wherein “the weight-of-evidence” points to a need for HSR risk analysis. The test satisfies the “3R” precondition of a good animal model, namely robustness, reproducibility, and human relevance [121]. Furthermore, it offers a new tool in allergy, circulatory, and toxicology research at their cross-section with nanomedicine. Obviously, in this context, it is essential to ensure that the experimental conditions are set in a clinically relevant manner, the results are correctly interpreted after consideration of additional validation parameters, and that they are integrated into other experimental and clinical data. Beside advantages, all animal models have certain limitations, and to decide which animal model is ideal to predict human responses to drugs has always been a contentious issue [119, 121]. Note that we are not claiming that the porcine CARPA model is the only one, or the best model, to predict HSRs. However, at least the critical issues discussed in this review were clarified as much as our current knowledge enabled. From our perspective, the pig model’s real challenge for routine safety evaluations lies in the complex logistics, sophisticated instrumentation, and labor intensity involving surgical procedures, the possibility of tachyphylaxis (self-induced tolerance), and the variation of physiological responses to different test drugs and agents. In fact, some or all of these may contribute to making it difficult to standardize the test in terms of drug dose, drug administration protocol, sample collection, and analyte panel in the case of different drugs. These procedures and analyzed variables need to be selected and optimized on a caseby-case basis. However, once this preparative phase is done, the responses are usually highly reproducible in the case of unchanged experimental conditions. Scientific debates such as the present one on the pig model lead to a better understanding of unclear issues. In the present case, the debate has led to compilation of the experimental use (Table 4.1) and concordance of the model with human HSR (Section 4.5) for the first time, as well as to better clarification of the purpose of the model (hazard identification) in preclinical immunotoxicology testing. We believe there is now better justification for recommending the model for pharmaceutical safety testing with or without regulatory mandate. Thus, the rebutted critical reviews [50, 51] can be acknowledged as indirectly advancing the effort to make nanomedicines safer.

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Abbreviations ADRs: aPL: AUC: CARPA: CAS: CIPA: CO: CVR: EMA: En: FDA: GS1: HbVs: HR: HSRs: Indo: L-P aggr: LDL: LEH: LNPs: Mf: Mo: MW-CNT: NBCDs: NPs: OCS: PAP: PCI: PCSK9: PEI: PIMs: PMNs: PRR: PS-NPs: PVR: SARs: SAEs: SAP: sCR1: SMC: siRNA:

adverse drug reactions activated platelet area under the curve complement (C) activation-related pseudoallergy cardiopulmonary abnormality score C-independent pseudoallergy cardiac output coronary vascular resistance European Medicines Agency endothelial cells US Food and Drug Administration anti-porcine C5a antibody hemoglobin vesicles heart rate hypersensitivity reactions indomethacin leukocyte-platelet aggregate low density lipoprotein liposome-encapsulated hemoglobin lipid nanoparticles macrophage monocyte multiwall carbon nanotube non-biologic complex drugs nanoparticles oversulfated chondroitin sulfate pulmonary arterial pressure percutaneous coronary intervention proprotein convertase subtilisin/kexin type 9 polyethylene-imine pulmonary intravascular macrophages polymorphonuclear neutrofils pattern recognition receptors polystyrene nanoparticles pulmonary vascular resistance severe adverse reactions severe adverse events systemic arterial pressure soluble C receptor type 1, a C inhibitor smooth muscle cells small inhibitory ribonucleic acid

References

SPIONs: ST-depr: SVR: TRO40303: TXA2: WBC: WHO:

superparamagnetic iron oxide nanoparticles ST-segment depression on the ECG systemic vascular resistance 3,5-seco-4-nor-cholestan-5-one oxime-3-ol-containing liposomes thromboxane A2 White Blood Cell World Health Organization

Disclosures and Conflict of Interest

This chapter was originally published as: Szebeni, J., Bawa, R. (2020). Human clinical relevance of the porcine model of pseudoallergic infusion reactions. Biomedicines, 8, 82, https://doi.org/10.3390/biomedicines8040082, under the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates, by kind permission of the copyright holders. Funding: This research received no external funding.

Acknowledgments: The authors thank Dr. Marina Dobrovolskaia and Dr. Gabor Szenasi for their critical review of the chapter and for providing valuable comments. The support of the Applied Materials and Nanotechnology Center of Excellence at Miskolc University, Hungary, and Bawa Biotech LLC, a biotech/pharma consultancy and patent law firm based in Ashburn, Virginia, USA, is gratefully acknowledged. Conflicts of Interest: Dr. Szebeni is employed by SeroScience LLC, an immune toxicological CRO providing, among others, the pig tests discussed in the review. Dr. Bawa is a patent agent at Bawa Biotech LLC and VP/Chief IP Counsel at Guanine Inc. in Rensselaer, New York, USA. He is also a scientific advisor to Teva Pharmaceutical Industries Ltd., Israel, and currently a medical student for the MD degree.

References

1. Sear, J., Prys-Roberts, C. Hypersensitivity reactions to infusions of Althesin. Anaesthesia 1983, 38, 1223.

2. Pichler, W.J. Adverse side-effects to biological agents. Allergy 2006, 61, 912–920.

3. Lenz, H.-J. Management and preparedness for infusion and hypersensitivity reactions. Oncologist 2007, 12, 601–609.

4. Vogel, W.H. Infusion reactions: Diagnosing, asessment and management. Clin. J. Oncol. Nurs. 2010, 14, E10–E21.

5. Maggi, E., Vultaggio, A., Matucci, A. Acute infusion reactions induced by monoclonal antibody therapy. Expert Rev. Clin. Immunol. 2011, 7, 55–63. 6. Szebeni, J., Simberg, D., Gonzalez-Fernandez, A., Barenholz, Y., Dobrovolskaia, M.A. Roadmap and strategy for overcoming infusion reactions to nanomedicines. Nat. Nanotechnol. 2018, 13, 1100–1108.

99

100

Porcine Model of Pseudoallergic Infusion Reactions

7. Szebeni, J., Bawa, R. Immunological issues with medicines of nano size: The price of dimension paradox. In Immune Aspects of Biopharmaceuticals and Nanomedicines, Bawa, R., Szebeni, J., Webster, T.J., Audette, G.F., Eds., Pan Stanford Publishing Pte. Ltd.: Singapore, 2019, pp. 83–114.

8. Trotti, A., Colevas, A., Setser, A., Rusch, V., Jaques, D., Budach, V., Langer, C., Murphy, B., Cumberlin, R., Coleman, C., et al. CTCAE v3.0: Development of a comprehensive grading system for the adverse effects of cancer treatment. Semin. Radiat. Oncol. 2003, 13, 176–181.

9. Tang, A.W. A practical guide to anaphylaxis. Am. Fam. Physician 2003, 68, 1325–1332.

10. National Cancer Institute. Common Terminology Criteria for Adverse Events (CTCAE). Available at: https://ctep.cancer.gov/protocoldevelopment/electronic_applications/ docs/ctcae_v5_quick_reference_5x7.pdf (accessed on December 26, 2020).

11. Sahiner, U.M., Yavuz, S.T., Gokce, M., Buyuktiryaki, B., Altan, I., Aytac, S., Tuncer, M., Tuncer, A., Sackesen, C. Anaphylactic reaction to polyethylene-glycol conjugated­ asparaginase: Premedication and desensitization may not be sufficient. Pediatr. Int. 2013, 55, 531–533.

12. Fumery, M., Tilmant, M., Yzet, C., Brazier, F., Loreau, J., Turpin, J., Le Mouel, J.P., Goeb, V., Nguyen-Khac, E., Singh, S., et al. Premedication as primary prophylaxis does not influence the risk of acute infliximab infusion reactions in immune-mediated inflammatory diseases: A systematic review and meta-analysis. Dig. Liver Dis. 2019, 51, 484–488.

13. Madrigal-Burgaleta, R., Bernal-Rubio, L., Berges-Gimeno, M.P., Carpio-Escalona, L.V., Gehlhaar, P., Alvarez-Cuesta, E. A large single-hospital experience using drug provocation testing and rapid drug desensitization in hypersensitivity to antineoplastic and biological agents. J. Allergy Clin. Immunol. Pract. 2019, 7, 618–632.

14. Szebeni, J. Complement activation-related pseudoallergy: A stress reaction in blood triggered by nanomedicines and biologicals. Mol. Immunol. 2014, 61, 163–173. 15. Szebeni, J., Fontana, J.L., Wassef, N.M., Mongan, P.D., Morse, D.S., Dobbins, D.E., Stahl, G.L., Bünger, R., Alving, C.R. Hemodynamic changes induced by liposomes and liposome­ encapsulated hemoglobin in pigs: A model for pseudoallergic cardiopulmonary reactions to liposomes. Role of complement and inhibition by soluble CR1 and anti­ C5a antibody. Circulation 1999, 99, 2302–2309.

16. Szebeni, J., Bedocs, P., Csukás, D., Rosivall, L., Bünger, R., Urbanics, R. A porcine model of complement-mediated infusion reactions to drug carrier nanosystems and other medicines. Adv. Drug Deliv. Rev. 2012, 64, 1706–1716.

17. Urbanics, R., Bedocs, P., Szebeni, J. Lessons learned from the porcine CARPA model: Constant and variable responses to different nanomedicines and administration protocols. Eur. J. Nanomed. 2015, 7, 219–231.

18. Szebeni, J., Bedocs, P., Dézsi, L., Urbanics, R. A porcine model of complement activationrelated pseudoallergy to nano-pharmaceuticals: Pros and cons of translation to a preclinical safety test. Precis. Nanomed. 2018, 1, 63–73.

19. Szebeni, J. Mechanism of nanoparticle-induced hypersensitivity in pigs: Complement or not complement? Drug Discov. Today 2018, 23, 487–492. 20. Őrfi, E., Mészáros, T., Hennies, M., Fülöp, T., Dézsi, L., Nardocci, A., Rosivall, L., Hamar, P., Neun, B.W., Dobrovolskaia, M.A., et al. Acute physiological changes caused by

References

complement activators and amphotericin B-containing liposomes in mice. Int. J. Nanomed. 2019, 14, 1563–1573.

21. Wassef, N.M., Johnson, S.H., Graeber, G.M., Swartz, G.M., Schultz, C.L., Hailey, J.R., Johnson, A.J., Taylor, D.G., Ridgway, R.L., Alving, C.R. Anaphylactoid reactions mediated by autoantibodies to cholesterol in miniature pigs. J. Immunol. 1989, 143, 2990–2995. 22. Ostensen, J., Hede, R., Myreng, Y., Ege, T., Holtz, E. Intravenous injection of Albunex microspheres causes thromboxane mediated pulmonary hypertension in pigs, but not in monkeys or rabbits. Acta Physiol. Scand. 1992, 144, 307–315.

23. Walday, P., Tolleshaug, H., Gjøen, T., Kindberg, G.M., Berg, T., Skotland, T., Holtz, E. Biodistributions of air-filled albumin microspheres in rats and pigs. Biochem. J. 1994, 299, 437–443.

24. Szebeni, J., Baranyi, L., Savay, S., Bodo, M., Morse, D.S., Basta, M., Stahl, G.L., Bünger, R., Alving, C.R. Liposome-induced pulmonary hypertension: Properties and mechanism of a complement-mediated pseudoallergic reaction. Am. J. Physiol. Circ. Physiol. 2000, 279, H1319–H1328.

25. Szebeni, J., Baranyi, L., Savay, S., Lutz, H.U., Jelezarova, E., Bünger, R., Alving, C.R. The role of complement activation in hypersensitivity to pegylated liposomal doxorubicin (Doxil®). J. Liposome Res. 2000, 10, 467–481.

26. Bodo, J.S.M. Rheoencephalographic evidence of complement activation-related cerebrovascular changes in pigs. J. Cereb. Blood Flow Metab. 2005, 25, S550. 27. Bodo, M., Szebeni, J., Baranyi, L., Savay, S., Pearce, F.J., Alving, C.R., Bünger, R. Cerebro­ vascular involvement in liposome-induced cardiopulmonary distress in pigs. J. Liposome Res. 2005, 15, 3–14.

28. Szebeni, J., Baranyi, L., Savay, S., Bodo, M., Milosevits, J., Alving, C.R., Bünger, R. Complement activation-related cardiac anaphylaxis in pigs: Role of C5a anaphylatoxin and adenosine in liposome-induced abnormalities in ECG and heart function. Am. J. Physiol. Circ. Physiol. 2006, 290, H1050–H1058.

29. Kishimoto, T.K., Viswanathan, K., Ganguly, T., Elankumaran, S., Smith, S., Pelzer, K., Lansing, J., Sriranganathan, N., Zhao, G., Galcheva-Gargova, Z., et al. Contaminated heparin associated with adverse clinical events and activation of the contact system. N. Engl. J. Med. 2008, 358, 2457–2467.

30. Epstein-Barash, H., Gutman, D., Markovsky, E., Mishan-Eisenberg, G., Koroukhov, N., Szebeni, J., Golomb, G. Physicochemical parameters affecting liposomal bisphosphonates bioactivity for restenosis therapy: Internalization, cell inhibition, activation of cytokines and complement, and mechanism of cell death. J. Control. Release 2010, 146, 182–195.

31. Merkel, O.M., Urbanics, R., Bedocs, P., Rozsnyay, Z., Rosivall, L., Tóth, M., Kissel, T., Szebeni, J. In vitro and in vivo complement activation and related anaphylactic effects associated with polyethylenimine and polyethylenimine-graft-poly(ethylene glycol) block copolymers. Biomaterials 2011, 32, 4936–4942.

32. Szebeni, J., Bedocs, P., Rozsnyay, Z., Weiszhár, Z., Urbanics, R., Rosivall, L., Cohen, R., Garbuzenko, O., Bathori, G., Tóth, M., et al. Liposome-induced complement activation and related cardiopulmonary distress in pigs: Factors promoting reactogenicity of Doxil and AmBisome. Nanomedicine 2012, 8, 176–184.

101

102

Porcine Model of Pseudoallergic Infusion Reactions

33. Szebeni, J., Bedocs, P., Urbanics, R., Bünger, R., Rosivall, L., Tóth, M., Barenholz, Y. Prevention of infusion reactions to PEGylated liposomal doxorubicin via tachyphylaxis induction by placebo vesicles: A porcine model. J. Control. Release 2012, 160, 382–387. 34. Sakai, H., Suzuki, Y., Sou, K., Kano, M. Cardiopulmonary hemodynamic responses to the small injection of hemoglobin vesicles (artificial oxygen carriers) in miniature pigs. J. Biomed. Mater. Res. Part A 2012, 100, 2668–2677.

35. Le Lamer, S., Paradis, S., Rahmouni, H., Chaimbault, C., Michaud, M., Culcasi, M., Afxantidis, J., Latreille, M., Berna, P., Berdeaux, A., et al. Translation of TRO40303 from myocardial infarction models to demonstration of safety and tolerance in a randomized Phase I trial. J. Transl. Med. 2014, 12, 38.

36. Bedocs, P., Capacchione, J., Potts, L., Chugani, R., Weiszhár, Z., Szebeni, J., Buckenmaier, C.C. Hypersensitivity reactions to intravenous lipid emulsion in swine. Anesthesia Analg. 2014, 119, 1094–1101.

37. Kasperovic, P., Gollob, J. Dosages and methods for delivering lipid formulated nucleic acid molecules, WO2014182661A2, 13 November 2014.

38. Jackman, J.A., Mészáros, T., Fülöp, T., Urbanics, R., Szebeni, J., Cho, N.-J. Comparison of complement activation-related pseudoallergy in miniature and domestic pigs: Foundation of a validatable immune toxicity model. Nanomedicine 2016, 12, 933–943.

39. Matuszak, J., Baumgartner, J., Zaloga, J., Juenet, M., Da Silva, A.E., Franke, D., Almer, G., Texier, I., Faivre, D., Metselaar, J.M., et al. Nanoparticles for intravascular applications: Physicochemical characterization and cytotoxicity testing. Nanomedicine 2016, 11, 597–616. 40. Bugna, S., Buscema, M., Matviykiv, S., Urbanics, R., Weinberger, A., Meszaros, T., Szebeni, J., Zumbuehl, A., Saxer, T., Müller, B. Surprising lack of liposome-induced complement activation by artificial 1,3-diamidophospholipids in vitro. Nanomedicine 2016, 12, 845–849. 41. Buscema, M., Matviykiv, S., Meszaros, T., Gerganova, G., Weinberger, A., Mettal, U., Mueller, D., Neuhaus, F., Stalder, E., Ishikawa, T., et al. Immunological response to nitroglycerinloaded shear-responsive liposomes in vitro and in vivo. J. Control. Release 2017, 264, 14–23.

42. Unterweger, H., Janko, C., Schwarz, M., Dézsi, L., Urbanics, R., Matuszak, J., Őrfi, E., Fülöp, T., Bäuerle, T., Szebeni, J., et al. Non-immunogenic dextran-coated superparamagnetic iron oxide nanoparticles: A biocompatible, size-tunable contrast agent for magnetic resonance imaging. Int. J. Nanomed. 2017, 12, 5223–5238. 43. Unterweger, H., Dezsi, L., Matuszak, J., Janko, C., Pöttler, M., Jordan, J., Bäuerle, T., Szebeni, J., Fey, T., Boccaccini, A.R., et al. Dextran-coated superparamagnetic iron oxide nanoparticles for magnetic resonance imaging: Evaluation of size-dependent imaging properties, storage stability and safety. Int. J. Nanomed. 2018, 13, 1899–1915.

44. Wibroe, P.P., Anselmo, A.C., Nilsson, P., Sarode, A., Gupta, V., Urbanics, R., Szebeni, J., Hunter, A., Mitragotri, S., Mollnes, T.E., et al. Bypassing adverse injection reactions to nanoparticles through shape modification and attachment to erythrocytes. Nat. Nanotechnol. 2017, 12, 589–594.

45. Mészáros, T., Kozma, G.T., Shimizu, T., Miyahara, K., Turjeman, K., Ishida, T., Barenholz, Y., Urbanics, R., Szebeni, J. Involvement of complement activation in the pulmonary vasoactivity of polystyrene nanoparticles in pigs: Unique surface properties underlying

References

alternative pathway activation and instant opsonization. Int. J. Nanomed. 2018, 13, 6345–6357.

46. Onwukwe, C., Maisha, N., Holland, M., Varley, M., Groynom, R., Hickman, D., Uppal, N., Shoffstall, A., Ustin, J., Lavik, E.B. Engineering intravenously administered nanoparticles to reduce infusion reaction and stop bleeding in a large animal model of trauma. Bioconjugate Chem. 2018, 29, 2436–2447.

47. Kozma, G.T., Mészáros, T., Vashegyi, I., Fülöp, T., Örfi, E., Dézsi, L., Rosivall, L., Bavli, Y., Urbanics, R., Mollnes, T.E., et al. Pseudo-anaphylaxis to polyethylene glycol (PEG)coated liposomes: Roles of anti-PEG IgM and complement activation in a porcine model of human infusion reactions. ACS Nano 2019, 13, 9315–9324.

48. Fülöp, T., Kozma, G.T., Vashegyi, I., Mészáros, T., Rosivall, L., Urbanics, R., Storm, G., Metselaar, J.M., Szebeni, J. Liposome-induced hypersensitivity reactions: Risk reduction by design of safe infusion protocols in pigs. J. Control. Release 2019, 309, 333–338. 49. Chauvierre, C., Aid-Launais, R., Aerts, J., Chaubet, F., Maire, M., Chollet, L., Rolland, L., Bonafé, R., Rossi, S., Bussi, S., et al. Pharmaceutical development and safety evaluation of a GMP-grade fucoidan for molecular diagnosis of cardiovascular diseases. Mar. Drugs 2019, 17, 699. 50. Moghimi, S.M., Simberg, D. Translational gaps in animal models of human infusion reactions to nanomedicines. Nanomedicine 2018, 13, 973–975.

51. Moghimi, S.M., Simberg, D., Skotland, T., Yaghmur, A., Hunter, A.C., Hunter, C. The interplay between blood proteins, complement, and macrophages on nanomedicine performance and responses. J. Pharmacol. Exp. Ther. 2019, 370, 581–592.

52. Warner, A.E., Brain, J.D. Intravascular pulmonary macrophages: A novel cell removes particles from blood. Am. J. Physiol. Integr. Comp. Physiol. 1986, 250, R728–R732. 53. Miyamoto, K., Schultz, E., Heath, T., Mitchell, P.M.D., Albertine, K.H., Staub, N.C. Pulmonary intravascular macrophages and hemodynamic effects of liposomes in sheep. J. Appl. Physiol. 1988, 64, 1143–1152.

54. Schneberger, D., Aharonson-Raz, K., Singh, B. Pulmonary intravascular macrophages and lung health: What are we missing? Am. J. Physiol. Cell. Mol. Physiol. 2012, 302, L498–L503.

55. Nakano, T., Miyamoto, K., Nishimura, M., Aida, A., Aoi, K., Kawakami, Y. Role of pulmonary intravascular macrophages in anti-platelet serum-induced pulmonary hypertension in sheep. Respir. Physiol. 1994, 98, 83–99.

56. Csukás, D., Urbanics, R., Wéber, G., Rosivall, L., Szebeni, J. Pulmonary intravascular macrophages: Prime suspects as cellular mediators of porcine CARPA. Eur. J. Nanomed. 2015, 7, 27–36.

57. Szebeni, J., Spielberg, H., Cliff, R.O., Wassef, N.M., Rudolph, A.S., Alving, C.R. Complement activation and thromboxane A2 secretion in rats following administration of liposome­ encapsulated hemoglobin: Inhibition by soluble complement receptor type 1. Art Cells Blood Subs. Immob. Biotechnol. 1997, 25, 347–355.

58. Dézsi, L., Fülöp, T., Mészáros, T., Szénási, G., Urbanics, R., Vázsonyi, C., Őrfi, E., Rosivall, L., Nemes, R., Kok, R.J., et al. Features of complement activation-related pseudoallergy to liposomes with different surface charge and PEGylation: Comparison of the porcine and rat responses. J. Control. Release 2014, 195, 2–10.

103

104

Porcine Model of Pseudoallergic Infusion Reactions

59. Moghimi, S.M. Nanomedicine safety in preclinical and clinical development: Focus on idiosyncratic injection/infusion reactions. Drug Discov. Today 2018, 23, 1034–1042. 60. Skotland, T. Injection of nanoparticles into clover-hoof animals: Asking for trouble. Theranostics 2017, 7, 4877–4878.

61. Hänsch, G.M., Seitz, M., Martinotti, G., Betz, M., Rauterberg, E.W., Gemsa, D. Macrophages release arachidonic acid, prostaglandin E2, and thromboxane in response to late complement components. J. Immunol. 1984, 133, 2145–2150.

62. Lefer, A.M., Smith, J.B., Nicolaou, K.C. Cardiovascular actions of two thromboxane A2 analogs. In Cardiovascular Physiology Microcirculation and Capillary Exchange, Proceedings of the 28th Congress of Physiological Sciences, Budapest, 1980, Kovach, A.G.B., Hamar, J., Szabo, L., Eds., Pergamon Press UK and Akadémia Kiado: Budapest, Hungary, 1981, pp. 91–98.

63. Nakahata, N. Thromboxane A2: Physiology/pathophysiology, cellular signal transduction and pharmacology. Pharmacol. Ther. 2008, 118, 18–35. 64. Rucker, D., Dhamoon, A.S. Physiology, Thromboxane A2. Available at: https://www.ncbi. nlm.nih.gov/books/NBK539817/ (accessed on January 2, 2021).

65. Stahl, G.L., Morse, D.S., Martin, S.L. Eicosanoid production from porcine neutrophils and platelets: Differential production with various agonists. Am. J. Physiol. Content 1997, 272, C1821–C1828.

66. Patko, Z., Szebeni, J. Blood cell changes in complement activation-related pseudoallergy. Eur. J. Nanomed. 2015, 7, 233–244. 67. Kreisel, D., Nava, R.G., Li, W., Zinselmeyer, B., Wang, B., Lai, J., Pless, R., Gelman, A.E., Krupnick, A.S., Miller, M.J. In vivo two-photon imaging reveals monocyte-dependent neutrophil extravasation during pulmonary inflammation. Proc. Natl. Acad. Sci. USA 2010, 107, 18073–18078.

68. Feith, G.W., Bogman, M.J., Assmann, K.J., Van Gompel, A.P., Schalkwijk, J., Van Rooijen, N., Koene, R.A. Decreased PMN accumulation and glomerular damage by clodronate liposome treatment in PMN-dependent anti-GBM nephritis in mice. Exp. Nephrol. 1997, 5, 301–304.

69. Albunex Side Effects. Available at: https://www.drugs.com/sfx/albunex-side-effects. html (accessed on January 2, 2021).

70. Geny, B., Mettauer, B., Muan, B., Bischoff, P., Epailly, E., Piquard, F., Eisenmann, B., Haberey, P. Safety and efficacy of a new transpulmonary echo contrast agent in echocardiographic studies in patients. J. Am. Coll. Cardiol. 1993, 22, 1193–1198.

71. De Groot, M., Van Zwieten-Boot, B.J., Van Grootheest, A.C. [Severe adverse reactions after the use of sulphur hexafluoride (SonoVue) as an ultrasonographic contrast agent]. Ned. Tijdschr. Geneeskd. 2004, 148, 1887–1888.

72. Yamaya, Y., Niizeki, K., Kim, J., Entin, P., Wagner, H., Wagner, P.D. Anaphylactoid response to Optison(R) and its effects on pulmonary function in two dogs. J. Vet. Med Sci. 2004, 66, 1429–1432.

73. Aggeli, C., Giannopoulos, G., Roussakis, G., Christoforatou, E., Marinos, G., Toli, C., Pitsavos, C., Stefanadis, C. Safety of myocardial flash-contrast echocardiography in combination with dobutamine stress testing for the detection of ischaemia in 5250 studies. Heart 2008, 94, 1571–1577.

References

74. Wei, K., Mulvagh, S.L., Carson, L., Davidoff, R., Gabriel, R., Grimm, R.A., Wilson, S., Fane, L., Herzog, C.A., Zoghbi, W.A., et al. The safety of deFinity and Optison for ultrasound image enhancement: A retrospective analysis of 78,383 administered contrast doses. J. Am. Soc. Echocardiogr. 2008, 21, 1202–1206.

75. Geleijnse, M.L., Nemes, A., Vletter, W.B., Michels, M., Soliman, O., Caliskan, K., Galema, T.W., Cate, F.T. Adverse reactions after the use of sulphur hexafluoride (SonoVue) echo contrast agent. J. Cardiovasc. Med. 2009, 10, 75–77. 76. Ionescu, A. Bubble trouble: Anaphylactic shock, threatened myocardial infarction, and transient renal failure after intravenous echo contrast for left ventricular cavity opacification preceding dobutamine stress echo. Eur. J. Echocardiogr. 2009, 10, 707– 710.

77. Solivetti, F., Elia, F., Musicco, F., Bonagura, A., Di Leo, N., Iera, J., Drudi, F.M. Anaphylactic shock induced by sulphur hexafluoride in an individual with no history of heart disease: Case report and literature review. Ultraschall Med. 2012, 33, 597–598.

78. Levano, J.A., Jimenez, M.A., Laiseca, A., Vives, R. Anaphylactic shock due to SonoVue. Ann. Allergy Asthma Immunol. 2012, 108, 208–209.

79. Coudray, S., Fabre, C., Aichoun, I., Perez-Martin, A. Anaphylactic shock with an ultrasound contrast agent. J. Med. Vasc. 2017, 42, 384–387.

80. Hoy, S.M. Patisiran: First Global Approval. Drugs 2018, 78, 1625–1631.

81. Kristen, A.V., Ajroud-Driss, S., Conceição, I., Gorevic, P., Kyriakides, T., Obici, L. Patisiran, an RNAi therapeutic for the treatment of hereditary transthyretin-mediated amyloidosis. Neurodegener. Dis. Manag. 2019, 9, 5–23. 82. Bennett, C.L., Jacob, S., Hymes, J., Usvyat, L.A., Maddux, F.W. Anaphylaxis and hypotension after administration of peginesatide. N. Engl. J. Med. 2014, 370, 2055–2056.

83. Withdrawal Assessment Report for Omontys. 2013. Available at: https:// wwwemaeuropaeu/en/documents/withdrawal -report/withdrawal -assessment­ report-omontys_enpdf (accessed on January 3, 2021). 84. Calabrese, L.H., Kavanaugh, A., Yeo, A.E., Lipsky, P.E. Frequency, distribution and immunologic nature of infusion reactions in subjects receiving pegloticase for chronic refractory gout. Arthritis Res. Ther. 2017, 19, 191.

85. Hershfield, M.S., Ganson, N.J., Kelly, S.J., Scarlett, E.L., Jaggers, D.A., Sundy, J.S. Induced and pre-existing anti-polyethylene glycol antibody in a trial of every 3-week dosing of pegloticase for refractory gout, including in organ transplant recipients. Arthritis Res. Ther. 2014, 16, R63.

86. KRYSTEXXA® (Pegloticase Injection), for Intravenous Infusion: Prescribing Information Revised 2018. Available at: https://www.accessdata.fda.gov/drugsatfda_ docs/label/2012/125293s040lbl.pdf (accessed on January 3, 2021). 87. Pegloticase: Withdrawal of its European marketing authorisation is welcome. Prescrire Int. 2017, 26, 71.

88. Ganson, N.J., Povsic, T.J., Sullenger, B.A., Alexander, J.H., Zelenkofske, S.L., Sailstad, J.M., Rusconi, C.P., Hershfield, M.S. Pre-existing anti-polyethylene glycol antibody linked to first-exposure allergic reactions to pegnivacogin, a PEGylated RNA aptamer. J. Allergy Clin. Immunol. 2015, 137, 1610–1613.e7.

89. Povsic, T.J., Lawrence, M.G., Lincoff, A.M., Mehran, R., Rusconi, C.P., Zelenkofske, S.L., Huang, Z., Sailstad, J., Armstrong, P.W., Steg, P.G., et al. Pre-existing anti-PEG antibodies

105

106

Porcine Model of Pseudoallergic Infusion Reactions

are associated with severe immediate allergic reactions to pegnivacogin, a PEGylated aptamer. J. Allergy Clin. Immunol. 2016, 138, 1712–1715.

90. Povsic, T.J., Vavalle, J.P., Aberle, L.H., Kasprzak, J.D., Cohen, M.G., Mehran, R., Bode, C., Buller, C.E., Montalescot, G., Cornel, J.H., et al. A Phase 2, randomized, partially blinded, active-controlled study assessing the efficacy and safety of variable anticoagulation reversal using the REG1 system in patients with acute coronary syndromes: Results of the RADAR trial. Eur. Heart J. 2012, 34, 2481–2489.

91. Food and Drug Administration (FDA). International Conference on Harmonisation, Guidance on S8 Immunotoxicity Studies for Human Pharmaceuticals, availability. Notice. Fed. Regist. 2006, 71, 19193–19194.

92. Food and Drug Administration (FDA). International Conference on Harmonisation, addendum to International Conference on Harmonisation Guidance on S6 Preclinical Safety Evaluation of Biotechnology-Derived Pharmaceuticals, availability. Notice. Fed. Regist. 2012, 77, 29665–29666. 93. Guidance for Industry: Immunotoxicology Evaluation of Investigational New Drugs. Available at: https://www.fda.gov/regulatory-information/search-fda-guidance­ documents/immunotoxicology-evaluation-investigational-new-drugs (accessed on January 3, 2021).

94. Hastings, K. Implications of the new FDA/CDER immunotoxicology guidance for drugs. Int. Immunopharmacol. 2002, 2, 1613–1618.

95. Association for the Advancement of Medical Instrumentation, International Organization for Standardization. Biological Evaluation of Medical Devices—Part 4: Selection of Tests for Interaction with Blood. ANSI/AAMI/ISO 10993-4:2002/(R), Association for the Advancement of Medical Instrumentation: Arlington, VA, USA, 10 March 2009. 96. Reflection Paper on the Data Requirements for Intravenous Liposomal Products Developed with Reference to an Innovator Liposomal Product. 2013. Available at: https://www.ema.europa.eu/en/documents/scientific-guideline/reflection-paper-datarequirements-intravenous-liposomal-products-developed-reference-innovator_en-0.pdf (accessed on January 3, 2021).

97. Guidelines on the Quality, Safety, and Efficacy of Biotherapeutic Protein Products Prepared by Recombinant DNA Technology. 2013. Available at: https://www.who.int/biologicals/ biotherapeutics/rDNA_DB_final_19_Nov_2013.pdf (accessed on January 3, 2021). 98. Swindle, M.M. The development of swine models in drug discovery and development. Future Med. Chem. 2012, 4, 1771–1772.

99. Swindle, M.M., Makin, A., Herron, A.J., Clubb, F.J., Frazier, K.S. Swine as models in biomedical research and toxicology testing. Vet. Pathol. 2011, 49, 344–356.

100. Gerner, W., Saalmuller, A. The immune system of swine. In Encyclopedia of Immunobiology, Elsevier: Amsterdam, The Netherlands, 2016, pp. 538–548.

101. Van Mierlo, G.J.D., Kuper, C.F., de Zeeuw-Brouwer, M.L., Schijf, M.A., Bruijntjes, J.P., Otto, M., Ganderup, N.C., Penninks, A.H. A sub acute immunotoxicity study in Göttingen minipigs with the immunosuppressive compounds cyclosporin A and dexamethasone. J. Clin. Exp. Pharm. 2013, S4, 1–11.

102. Peachee, V.L., Smith, M.J., Beck, M.J., Stump, D.G., White, K.L. Characterization of the T-dependent antibody response (TDAR) to keyhole limpet hemocyanin (KLH) in the Göttingen minipig. J. Immunotoxicol. 2013, 11, 376–382.

References

103. Hicks, R., Skeldon, N. The influence of adjuvants on antibody production and anaphylactic hypersensitivity in the guinea pig. Int. Arch. Allergy Immunol. 1970, 39, 234–246. 104. Kostiala, A.A. Delayed hypersensitivity in the guinea pig immunized with killed tubercle bacilli in adjuvant. 1. Development of peritoneal cell migration inhibition, skin reactions and antibodies to tuberculin purified protein derivative. Acta Pathol. Microbiol. Scand. B Microbiol. Immunol. 1971, 79, 275–280.

105. Verdier, F., Chazal, I., Descotes, J. Anaphylaxis models in the guinea-pig. Toxicology 1994, 93, 55–61.

106. Weaver, J.L., Staten, D., Swann, J., Armstrong, G., Bates, M., Hastings, K.L. Detection of systemic hypersensitivity to drugs using standard guinea pig assays. Toxicology 2003, 193, 203–217.

107. Ricciardolo, F.L., Nijkamp, F., Rose, V., Folkerts, G. The guinea pig as an animal model for asthma. Curr. Drug Targets 2008, 9, 452–465.

108. Chan, C.K., Jarrett, F., Moylan, J.A. Acute leukopenia as an allergic reaction to silver sulfadiazine in burn patients. J. Trauma 1976, 16, 395–396. 109. Frangi, D., Gardinali, M., Conciato, L., Cafaro, C., Pozzoni, L., Agostoni, A. Abrupt complement activation and transient neutropenia in patients with acute myocardial infarction treated with streptokinase. Circulation 1994, 89, 76–80.

110. Yeh, Y.-W., Wang, T.-Y., Huang, C.-C., Chen, Y.-C. Late-onset hypersensitivity reaction with leukopenia and thrombocytopenia induced by oxcarbazepine treatment in a patient with schizoaffective disorder. J. Clin. Psychiatry 2008, 69, 676–678.

111. Michelmann, I., Bockmann, D., Nurnberger, W., Eckhof-Donovan, S., Burdach, S., Gobel, U. Thrombocytopenia and complement activation under recombinant TNF alpha/IFN gamma therapy in man. Ann Hematol. 1997, 74, 179–184.

112. Dézsi, L., Mészáros, T., Őrfi, E., Fülöp, T., Hennies, M., Rosivall, L., Hamar, P., Szebeni, J., Szénási, G. Complement activation-related pathophysiological changes in anesthetized rats: Activator-dependent variations of symptoms and mediators of pseudoallergy. Molecules 2019, 24, 3283. 113. Galbraith, W.M., Hobson, W.C., Giclas, P.C., Schechter, P.J., Agrawal, S. Complement activation and hemodynamic changes following intravenous administration of phosphorothioate oligonucleotides in the monkey. Antisense Res. Dev. 1994, 4, 201–206.

114. Chanan-Khan, A., Szebeni, J., Savay, S., Liebes, L., Rafique, N.M., Alving, C.R., Muggia, F.M. Complement activation following first exposure to pegylated liposomal doxorubicin (Doxil®): Possible role in hypersensitivity reactions. Ann. Oncol. 2003, 14, 1430–1437.

115. Szebeni, J., Muggia, F., Gabizon, A., Barenholz, Y. Activation of complement by therapeutic liposomes and other lipid excipient-based therapeutic products: Prediction and prevention. Adv. Drug Deliv. Rev. 2011, 63, 1020–1030.

116. Blossom, D.B., Kallen, A.J., Patel, P.R., Elward, A., Robinson, L., Gao, G., Langer, R., Perkins, K.M., Jaeger, J.L., Kurkjian, K.M., et al. Outbreak of adverse reactions associated with contaminated heparin. N. Engl. J. Med. 2008, 359, 2674–2684. 117. Sundy, J., Becker, M.A., Baraf, H.S.B., Barkhuizen, A., Moreland, L.W., Huang, W., Waltrip, R.W., Maroli, A.N., Horowitz, Z., Pegloticase Phase 2 Study Investigators, Reduction of plasma urate levels following treatment with multiple doses of pegloticase

107

108

Porcine Model of Pseudoallergic Infusion Reactions

(polyethylene glycol-conjugated uricase) in patients with treatment-failure gout: Results of a phase II randomized study. Arthritis Rheum. 2008, 58, 2882–2891.

118. Fülöp, T., Nemes, R., Mészáros, T., Urbanics, R., Kok, R.J., Jackman, J.A., Cho, N.-J., Storm, G., Szebeni, J. Complement activation in vitro and reactogenicity of low-molecular weight dextran-coated SPIONs in the pig CARPA model: Correlation with physicochemical features and clinical information. J. Control. Release 2017, 270, 268–274. 119. Shanks, N., Greek, R., Greek, J. Are animal models predictive for humans? Philos. Ethic Humanit. Med. 2009, 4, 2.

120. Giuliani, A. The application of principal component analysis to drug discovery and biomedical data. Drug Discov Today. 2017, 22, 1069–1976.

121. Everitt, J.I. The future of preclinical animal models in pharmaceutical discovery and development: A need to bring in cerebro to the in vivo discussions. Toxicol. Pathol. 2015, 43, 70–77.

Chapter 5

Myelin Antigens and Antimyelin Antibodies Fredrick J. Seil, MD Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA [email protected]

Keywords: myelin, experimental allergic encephalomyelitis, myelin basic protein, immunoglobulin, myelin oligodendrocyte glycoprotein, Freund’s complete adjuvant, demyelination, complement, demyelinating, myelination, myelin-associated glycoprotein, oligodendrocyte, allergic neuritis, Schwann cells, T-cell mediated immune response, B-cell secreted antibodies, multiple sclerosis, demyelinating antibodies, myelination inhibiting antibodies, central nervous system

5.1

Introduction

Over half a century ago, Bornstein and Appel [1] found that sera from rabbits with experimental allergic encephalomyelitis (EAE) induced by inoculation with whole central nervous system (CNS) tissue and Freund’s complete adjuvant (FCA) demyelinated rat organotypic cerebellar cultures. The sera were applied directly as a component of the culture nutrient medium. Demyelination was complement dependent because heating the sera to 56°C abolished the demyelinating activity, which was restored by the addition of fresh guinea pig serum. The demyelination was specific for CNS myelin as rat dorsal root ganglia cultures, which contained peripheral nervous system (PNS) myelin, were unaffected. Remyelination of the cerebellar cultures followed replacement of the sera from animals with EAE induced by inoculation with whole CNS (anti-CNS sera) with normal nutrient medium. In a subsequent study, Appel and Bornstein [2] reported that demyelinating activity was found in the immunoglobulin G2 (IgG2) fraction of anti-CNS sera and was abolished by absorption with homologous or heterologous brain tissue, but not with other tissues such as lung, liver, kidney, or red blood cells. With the use of immunofluorescent techniques, globulins in demyelinating sera were localized on myelin sheaths of cerebellar cultures, which was confirmed in a later Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

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study with immunoperoxidase methods [3]. The presence of demyelinating activity in the IgG2 fraction of anti-CNS sera was also noted by Lebar et al. [4], who did not find similar activity in IgG1 or IgM serum fractions. When Bornstein and Raine [5] applied anti-CNS sera to CNS cultures at explantation, prior to myelin formation, the maturation of oligodendrocytes, the CNS myelin forming cells, and CNS myelination were inhibited. The inhibition of myelination was complement dependent and reversible, was specific for oligodendrocytes and CNS myelin, and was obtained at far lower concentrations than required for demyelination. Because of the last attribute, myelination inhibition appeared to the authors to be a more sensitive and reliable index of antimyelin activity than demyelination. Given that the demyelinating and myelination inhibiting factors appeared to be antibodies, they became collectively known as “antimyelin antibodies.” As EAE could also be induced by sensitization with CNS myelin or myelin components, a series of studies was initiated to investigate which myelin components were responsible for the induction of antimyelin antibodies, and whether or not the induction of these antibodies correlated with the induction of EAE. Soon after the initial description of demyelinating activity in sera from animals with EAE induced by inoculation with whole CNS and FCA, Bornstein [6] reported finding similar demyelinating activity in sera from 68% of human subjects with active multiple sclerosis (MS), the most common of the human demyelinating diseases. The demyelination was reversible upon removal of the patient sera. Most normal human sera did not demyelinate CNS cultures. These results suggested that humoral factors, possibly antibodies, might have a pathogenetic role in MS and led to further investigation of these factors.

5.2 Myelin

Prior to further discussion of demyelination, it would be of value to briefly review some of the salient morphological and biochemical features of normal myelin. More detailed descriptions can be found in other sources [7, 8]. As already noted, the CNS myelin forming cell is the oligodendrocyte, while PNS myelin is formed by Schwann cells. An oligodendrocyte contributes cytoplasmic processes to the formation of multiple internodes of central myelin (Fig. 5.1), while a Schwann cell forms a single internode of peripheral myelin. Injury of an oligodendrocyte results in the loss of a plurality of myelinated internodes, while the destruction of a Schwann cell results in the loss of one internode. The nodes of Ranvier are unmyelinated gaps between internodes of myelinated axonal segments. Both CNS and PNS myelin are formed by the spiral wrapping of cytoplasmic processes of the respective myelin forming cells around central or peripheral axons, followed by extrusion of the cytoplasm to allow apposition of the inner cytoplasmic membranes. These apposed membranes constitute the osmiophilic interperiod or major dense lines evident on ultrastructural

Myelin

examination. The less densely staining intraperiod or minor dense lines are formed by apposition of the outer cytoplasmic membranes of the myelin forming cells. The ultrastructural appearance of a myelin sheath is thus one of alternating dark major and minor dense lines with light intervals between. It is thought that the dense lines contain the protein components of the myelin sheath, while the lucent intervals represent bimolecular leaflets of lipid. The external component of the myelin sheath is an outer cytoplasmic membrane. In the PNS, there is also a basement membrane, which is a component of the Schwann cell.

Figure 5.1 Oligodendrocyte from a mouse cerebellar culture after 15 days in vitro, with slender cytoplasmic processes projecting to contiguous myelin sheaths. The culture was reacted with antibody to human myelin basic protein and processed by the peroxidase­ antiperoxidase method.

CNS and PNS myelin differ in chemical composition. While both varieties have a high lipid:protein ratio and a similar proportion of cholesterol, CNS myelin has more galactolipid (cerebroside and sulfatide) and less phospholipid [8]. The major CNS myelin protein is the proteolipid protein, which constitutes approximately one-half of the protein in central sheaths [9]. One-third of the CNS myelin proteins consists of myelin basic protein, a cathode-migrating protein. A lesser fraction (20%) of CNS myelin protein is an acidic proteolipid protein (Wolfgram protein), whereas glycoprotein is a very small constituent of CNS myelin [9, 10]. The major component of PNS myelin is a glycoprotein, P0 [11]. Two basic protein fractions in PNS myelin are P1, which is identical to CNS myelin basic protein, and P2, which is unique to peripheral myelin. The three proteins, P0, P1, and P2, constitute about 70% of the PNS myelin proteins, while proteolipid and Wolfgram proteins are absent in PNS myelin [12].

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5.3 EAE and Anti-MBP Antibodies The first attempt to correlate EAE induction with the presence of antibodies to specific CNS myelin components was made by Lumsden [13], who applied sera from animals inoculated with a diffusible encephalitogenic myelin peptide to organotypic CNS cultures. The failure of the sera to demyelinate the cultures was attributed by Lumsden to a lack of “antigenicity” of his peptide. The first established CNS myelin protein capable of inducing EAE was myelin basic protein (MBP) [14, 15]. We subsequently tested the ability of sera from guinea pigs with EAE inoculated with the whole MBP molecule plus FCA to demyelinate mouse cerebellar cultures [16]. As a positive control, we also applied sera from whole CNS sensitized guinea pigs to myelinated cultures. In order to ensure that adequate levels of antibody were attained by MBP sensitization, we initially inoculated guinea pigs with MBP plus incomplete Freund’s adjuvant followed by a challenge dose of MBP plus FCA, which produced high titers of antibody to MBP in three gamma globulin classes. None of the sera from animals with EAE induced by sensitization with MBP or from the hyperimmunized animals demyelinated CNS cultures. The cultures were demyelinated by sera from whole CNS sensitized animals, as initially reported by Bornstein and Appel [1], but none of these sera had detectable levels of antibody to MBP. We followed this study with a series of investigations of various aspects of the EAE to anti-MBP antibody relationship. Such studies were always controlled by a comparison with the effects of sera from whole CNS sensitized animals. Initially, we evaluated myelination inhibition by sera from guinea pigs inoculated with heterologous (bovine) MBP plus FCA compared to sera from whole CNS plus FCA sensitized guinea pigs [17]. Most sera from the former, containing high levels of antibody to MBP, failed to inhibit the myelination of cerebellar cultures, while most of the latter sera, with low levels of anti-MBP antibody, inhibited myelin formation. In a following study [18], EAE was induced in subhuman primates by inoculation with either whole CNS plus FCA or MBP plus FCA. None of the sera from MBP sensitized animals inhibited the myelination of cerebellar cultures, while all of the sera from animals with EAE induced by whole CNS were positive for myelination inhibition. Similar results were obtained with sera from Lewis rats sensitized with FCA plus either guinea pig whole CNS tissue or guinea pig MBP [19], as the former prevented myelination in vitro, while the latter did not inhibit the myelination of cerebellar cultures. As a further extension of these studies, myelination inhibiting properties of sera from rabbits sensitized with bovine CNS tissue and sensitized or hyperimmunized with MBP from five different species, including bovine, monkey, human, guinea pig, and rabbit, were compared [20]. All of the rabbits inoculated with whole CNS and FCA developed EAE and all or their sera inhibited myelination, in the absence of detectable levels of the precipitating antibody to MBP. The rabbits sensitized or hyperimmunized with MBP developed a spectrum of possible combinations of EAE and the precipitating antibody. Serum from one of nine such animals was positive for myelination inhibition and this animal had neither EAE nor the anti-MBP

Other CNS Myelin Antigens

antibody, whereas sera from the remaining rabbits, including those with EAE and high levels of the anti-MBP antibody, did not inhibit myelination. In a final study with MBP [21], the majority of sera from guinea pigs inoculated at intervals with MBP plus incomplete Freund’s adjuvant and followed by inoculation with whole CNS plus FCA inhibited myelination in cerebellar cultures, although the animals did not develop EAE. This indicates that protection of guinea pigs with MBP prevented EAE induction by whole CNS, but did not prevent the induction of myelination inhibiting antibodies. Collectively, these studies demonstrated a complete dissociation of serum demyelinating and myelination inhibiting activity, the induction of EAE, and the formation of the anti-MBP antibody.

5.4

Other CNS Myelin Antigens

As it was evident that MBP, the major encephalitogenic myelin protein, did not evoke antimyelin antibodies, other myelin antigens were investigated as possible agents. Dubois-Dalcq et al. [22] reported that sera from rabbits inoculated with FCA plus cerebroside, a lipid component of CNS myelin that did not induce EAE, demyelinated CNS cultures. The demyelination was not restricted to CNS myelin, as some peripherally myelinated fibers in spinal cord plus dorsal root ganglia cultures were also affected. The CNS demyelinating activity of sera from cerebroside sensitized rabbits was confirmed by Fry et al. [23], who additionally showed that these sera inhibited the myelination of CNS cultures. Antimyelin activity was abolished by absorption with cerebroside. The peripherally demyelinating properties of rabbit anticerebroside antisera were duplicated by Saida et al. [24], providing a contrast to the reported specificity of antisera to whole CNS tissue for activity against CNS myelin [1, 5, 25]. Antimyelin activity appeared to be restricted to sera from rabbits sensitized with cerebroside, for sera from Lewis rats inoculated with cerebroside did not inhibit the myelination of cerebellar cultures [19] and sera from guinea pigs inoculated with cerebroside did not demyelinate CNS cultures [4]. Hruby et al. [26] showed that the myelination of cerebellar cultures was inhibited by sera from rabbits sensitized with synthetic galactocerebroside (GC), ruling out any possibility of contamination with other myelin components. Similar results were not obtained with rabbit antisera directed against glucocerebroside, indicating that the antimyelin activity was specific to anti-GC sera. Demyelinating activity was found by Lebar et al. [27] in sera from guinea pigs inoculated with an antigen designated as “M2” found in a “myelin-like” fraction isolated from CNS myelin. The myelin-like fraction did not contain cerebroside or GM1 ganglioside and did not crossreact with MBP or the myelin proteolipid protein. M2 was not present in peripheral myelin and was later determined to be a glycoprotein component of the oligodendrocyte membrane [28]. Its ability to induce EAE was not determined. Since MBP appeared to be localized in the major dense (interperiod) line of CNS myelin [29] and thus not available at the surface of the myelin sheaths, it was of interest to determine the capability of antimyelin antibody induction by

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myelin-associated glycoprotein (MAG), a minor component of CNS myelin that did not induce EAE, but was localized at the myelin membrane surface [30, 31]. Sera from rabbits inoculated with purified MAG neither demyelinated nor inhibited myelination in CNS cultures [32]. The major CNS myelin protein, proteolipid protein (PLP), was reported to induce a chronic form of EAE in rabbits [33, 34]. Rabbit antisera to PLP also did not demyelinate or inhibit the myelination of CNS cultures, as determined in two separate laboratories [35, 36]. An autoimmune demyelinating disease was described in rabbits by inoculation with gangliosides, glycolipid components of myelin and neuronal membranes [37–39]. We found no demyelinating or myelination inhibiting activity in spinal cord plus dorsal root ganglia cultures exposed to four rabbit high titer antisera directed to GM1, a major ganglioside [40]. When CNS myelin is extracted with chloroform-methanol, PLP, almost all of the MBP and most of the lipids are removed [41]. The remaining chloroformmethanol insoluble protein (CMIP) fraction contains a host of low and high molecular weight proteins, including the glycoproteins, among which are MAG, M2, and the myelin oligodendrocyte glycoprotein (MOG), which is a minor myelin protein localized at the external surfaces of myelin sheaths and oligodendrocyte membranes [42–45]. Rabbits inoculated with the CMIP fraction and FCA did not develop clinically manifested EAE, but did have occasional focal mononuclear infiltrates in perivascular spaces, sometimes with spread into the surrounding CNS parenchyma, characteristic of histological features of EAE [46]. Antisera to the CMIP fraction inhibited the myelination of cerebellar cultures and demyelinated centrally myelinated fibers in spinal cord-dorsal root ganglia cultures, while sparing peripherally myelinated fibers. The demyelination and myelination inhibition were rapidly reversed upon the removal of anti-CMIP sera from the cultures. On electron microscopic examination [47], oligodendrocyte maturation was not inhibited, contrary to the effect of anti-CNS antiserum [5]. It appeared that anti-CMIP sera interfered with glial-axonal interactions to inhibit myelin formation rather than with oligodendrocyte maturation. With regard to MOG, which induces both acute and chronic EAE [48–50], the application of a monoclonal anti-MOG antibody to reaggregating myelinated brain cell cultures caused a reversible complement dependent demyelination [51]. Myelination inhibition was not tested and the antibody was not applied to cultures with PNS myelin. As MOG is present in the CMIP fraction of myelin, it is likely that the anti-MOG antibody would inhibit myelination and its antimyelin activity would be specific for CNS myelin. It is also probable that MOG and M2 [28] are the same glycoproteins.

5.5 PNS Myelin Antigens and EAN

A PNS equivalent of EAE is experimental allergic neuritis (EAN), which can be induced with the inoculation of FCA plus whole PNS tissue [52] or with the P2 fraction of PNS myelin [53, 54]. Yonezawa et al. [55] reported the demyelination of PNS cultures by rabbit and guinea pig antisera directed against whole PNS tissue,

Significance of Antimyelin Antibodies

a finding confirmed in later studies [56–58]. We additionally reported the inhibition of peripheral myelination by anti-PNS sera [58]. In these studies, CNS myelinated fibers were also demyelinated and CNS myelination was inhibited by antisera to PNS myelin. Uyemura et al. [59] found demyelination only in PNS cultures exposed to sera from rabbits inoculated with FCA and a myelin fraction derived from bovine spinal roots. Mithen et al. [60] applied serum from a goat sensitized with P2 derived from rabbit sciatic nerve to myelinated dorsal root ganglia cultures and found no demyelination and also no binding to the surfaces of Schwann cells. We [58] found no demyelination or myelination inhibition of either PNS or CNS myelin in mouse spinal cord-dorsal root ganglia cultures by rabbit anti-P2 sera with detectable levels of antibody to P2. The neuritogenic protein, like its encephalitogenic counterpart, MBP, did not induce demyelinating or myelination inhibiting factors. Presumably, these antibodies are directed against some other antigen in peripheral myelin.

5.6 Significance of Antimyelin Antibodies

A summary of the various antigens evaluated for their ability to induce antimyelin antibodies is presented in Table 5.1. Also indicated in the table is whether or not the antigens induced experimental demyelinating disease. It is evident from this table that the induction of EAE and EAN can be completely dissociated from the induction of antimyelin antibodies, raising the question of the relevance of these antibodies to the pathogenesis of disease. Although such antibodies may not be essential to disease induction, they may augment demyelination. Raine et al. [61] inoculated guinea pigs with FCA plus either (a) whole CNS white matter or (b) MBP or (c) MBP and GC or (d) MBP and total myelin lipids. Sensitization to whole white matter or MBP provoked clinically similar EAE, but histologically, both inflammatory and demyelinative lesions were seen in EAE induced by whole white matter, whereas only inflammatory lesions were seen in MBP induced EAE. When MBP was injected in combination with either GC or total myelin lipids, EAE with both inflammatory and demyelinative lesions was evident. Injection of FCA plus either GC or total myelin lipids alone induced neither EAE nor histopathological changes. The lipid haptens appeared to have an augmenting effect on MBP, and the speculation was advanced that MBP triggered the T-cell component of the immune response in EAE and that B-cell secreted factors evoked by GC or other lipid haptens are necessary for demyelination. Bourdette et al. [62] initially gave guinea pigs a suboptimal transfer (insufficient to transfer EAE) of lymphocytes sensitized to MBP, which was then followed by the inoculation of FCA and (a) MBP or (b) chicken brain alone or (c) MBP plus chicken brain or myelin. Chicken MPB is not encephalitogenic in guinea pigs [63], so the chicken brain or myelin served as a source of all of the non-MBP components of CNS myelin, including lipids and glycoproteins. Myelination inhibiting activity in CNS cultures by sera from these guinea pigs was determined and correlated with the degree of histologically graded demyelination of spinal cord and brain sections [62]. Myelination inhibiting antibodies and histologically moderate to

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severe demyelination were found only in sera from animals receiving both MBP and chicken brain or myelin, and not with MBP or chicken brain alone. These findings were consistent with the concept of augmentation of demyelination by antibodies directed against nonencephalitogenic components of CNS myelin. Table 5.1 Induction of EAE or EAN and antimyelin antibodies by myelin antigens. Antigens

EAE/EAN

Antibodies

Demyelinating CNS Whole CNS MBP GC

M2

MAG PLP

GM1

CMIP MOG

Whole PNS P2 protein

+

+

CNS Antigens + –

–

+

+

–

ND –

±

±

a

b

+

+

±

Myelination inhibiting

PNS

CNS

PNS

–

+

–

–

+

–

+

+

ND

ND

–

–

–

–

+

–

–

–

–

ND

+

ND

–

–

–

+

ND

ND

ND

ND

±

+

–

+

PNS Antigens

ND

+

–

ND

+ –

Abbreviations: ND, not determined; EAE, experimental allergic encephalomyelitis; EAN, experimental allergic neuritis; CNS, central nervous system; PNS, peripheral nervous system; MBP, myelin basic protein; GC, galactocerebroside; MAG, myelin-associated glycoprotein; PLP, proteolipid protein; CMIP, chloroform-methanol insoluble protein; MOG, myelin oligodendrocyte glycoprotein. aSeveral reports of induction of an experimental demyelinating disease in rabbits. bNo clinical EAE but occasional inflammatory infiltrates histologically.

The notion of a combined action of cellular and antibody mechanisms to produce inflammatory demyelinating lesions in EAE was further supported by the finding that MOG, which induced EAE with both inflammatory and demyelinative components, elicited a T-cell mediated immune response and a B-cell secreted demyelinating antibody response [48–51]. A correlation was found between in vivo demyelinating activity in guinea pigs with chronic EAE and titers of antibody to MOG [64]. In another study [65], demyelination was greatly augmented by the intravenous injection of a monoclonal anti-MOG antibody during the induction of EAE in rats by the transfer of MBP sensitized T-cells. The combined action of cellular and antibody mechanisms is not an invariable requirement for demyelination in EAE, however, as extensive demyelination can occur in some species or strains and in some circumstances without augmentation by antibodies. In one such study [66] in which chronic relapsing EAE was induced in SJL/J mice by passive transfer with MBP sensitized T-cells, a considerable degree of demyelination

Human Demyelinating Disorders

occurred, even during initial episodes, in the absence of antibodies that bind to myelin. Antimyelin antibodies have also been reported to opsonize myelin for phagocytosis by macrophages [67].

5.7 Human Demyelinating Disorders

Only limited studies have been done on antimyelin antibodies in human PNS demyelinating diseases. Cook et al. [68] found that sera from 84% of patients with idiopathic polyneuritis (Guillain-Barré syndrome), in which peripheral nerves and nerve roots are segmentally demyelinated, showed demyelinating activity when applied to mouse dorsal root ganglia cultures. Demyelination was complement dependent and was blocked by prior exposure of sera to the guinea pig sciatic nerve. The demyelinating activity was associated with serum IgG and IgM fractions. Sera from some patients with idiopathic polyneuritis also demyelinated CNS cultures. Similar results were obtained by Dubois-Dalcq et al. [69], who additionally noted that patterns of myelin breakdown were similar to those seen in the demyelination of tissue cultures by anti-PNS antisera. After Bornstein’s [6] initial study in which CNS cultures were demyelinated by 68% of sera from patients with active MS, Bornstein and Hummelgard [70] reported on serum demyelinating activity in an expanded series of human subjects with MS. They found demyelinating activity in 64% of sera from patients with definitely active disease, 41% of sera from patients without clearly evident disease activity at the time of serum collection, 11% of sera from patients without disease activity, and 7% of sera from normal subjects. When the course of disease was considered, 71% of sera from patients with active exacerbating and remitting MS demyelinated CNS cultures compared to 48% with a chronic progressive course. In more recently reported studies, Ulrich and Lardi [71] found demyelinating activity in 36% of patients with active disease and in 6% with inactive disease. We [72] described the demyelination of CNS cultures by 40% of subjects with active MS compared with 5% of sera collected during stationary periods. Sera from only 14% of patients with progressive MS were positive for demyelination. Dau et al. [73] reported no sera positive for in vitro demyelinating activity in any of seven patients with chronic progressive MS before or after plasmapheresis. Bradbury et al. [74] found demyelinating activity in 74% of sera from patients with MS, in 68% of sera with various neurological diseases other than MS, and in 22% of sera from normal subjects. The occurrence of demyelinating activity in a significant number of sera from patients with other neurological diseases, especially amyotrophic lateral sclerosis (ALS), had been previously reported [6, 75]. Sera from patients with MS specifically demyelinated CNS cultures and did not affect peripherally myelinated fibers [25]. Unexpectedly, demyelinating MS sera did not inhibit myelination of spinal cord and cerebellar cultures [71, 72]. Demyelinating activity was removed by absorption with brain tissue [6] and a nonmyelin tissue pellet that included oligodendrocytes, but not with purified myelin [76]. No immunostained myelin was evident when demyelinating MS sera

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were applied to CNS cultures and fixed and treated with peroxidase conjugated anti-human globulin [3]. Depletion of MS sera of all γ-globulin did not decrease their demyelinating capability and isolated Ig fractions from most MS sera did not demyelinate CNS cultures [77, 78]. These findings indicate that demyelinating activity in most MS sera is associated with some serum factor other than γ-globulin. This, plus the lack of specificity of serum demyelinating factors for MS, led to some questions as to the significance of these factors for the pathogenesis of MS. Of interest, therefore, was a report [79] of the detection of anti-MOG antibodies bound to disintegrating myelin sheaths in acute lesions from three patients with MS. The pattern of demyelination and the anti-MOG antibody binding were identical to those seen in marmosets with EAE induced by the inoculation of FCA and MOG [79, 80]. Thus, MOG could be a target for antibody mediated demyelination in some cases of MS [81]. However, serum demyelinating factors in most cases of MS do not appear to be antibodies [3, 77, 78], so that B-cell demyelination may be restricted to a small subset of MS patients, lending support to the notion that MS is an autoimmune inflammatory demyelinating disease with heterogeneous pathology and pathogenesis [81–85]. More recently, Elliott et al. [86] found complement dependent demyelinating activity when purified IgG from about 30% of MS patients was applied to myelinated CNS cultures and evaluated with a sensitive bioassay for myelin reduction. Demyelinating activity was not observed in cultures exposed to IgG from patients with other neurological diseases or normal controls. IgG preparations with demyelinating activity contained antibodies that bound to fully differentiated oligodendrocytes and to their contiguous myelin sheaths, and not to oligodendrocyte precursors. Demyelinating antibodies were detected more frequently in patients with relapsing-remitting MS than in those with a chronic progressive course. Plasma exchange significantly reduced demyelinating IgG activity in three MS patients. Adsorption of MS patient derived IgG with MOG did not reduce the demyelinative capability of the IgG preparations, indicating that MOG is not the dominant target for demyelinating antibodies in the patients studied. These results reinforced the concept of a heterogeneous pathogenesis for MS. Anti-MOG antibodies have been reported in pediatric inflammatory demyelinative disorders [87, 88] and in a subset of patients with neuromyelitis optica [89]. The primary antigenic target in the latter disease is aquaporin-4, which is present in astrocytes [90]. Anti-MOG antibodies were present in cases of neuromyelitis optica negative for anti-aquaporin-4 antibodies.

5.8 Conclusions

Although demyelination can occur under some circumstances in the experimental animal disease, EAE, in the absence of antimyelin antibodies, it is clear that antimyelin antibodies augment demyelination. The inflammatory component of the disease represents a T-cell mediated immune response, but the demyelinative

Disclosures and Conflict of Interest

component is enhanced by B-cell secreted antibodies. The role of antimyelin antibodies in human demyelinating disorders is less clear. That antibodies contribute to the pathogenesis of some cases of MS is indicated by clinical improvement after plasma exchange in some individuals. The target of most of these antibodies is unknown. Antimyelin antibodies are present in only about 30–40% of cases of MS, and only a small subset of this fraction appear to be directed to an identified myelin antigen, MOG. It is evident that further work is necessary to define both the role and additional targets of antimyelin antibodies in MS, which is not an easy task in a disorder with apparent heterogeneous pathology and pathogenesis.

Abbreviations

ALS: CMIP: CNS: EAE: EAN: FCA: GC: IgG2: MAG: MBP: MOG: MS: ND: PLP: PNS:

amyotrophic lateral sclerosis chloroform-methanol insoluble protein central nervous system experimental allergic encephalomyelitis experimental allergic neuritis Freund’s complete adjuvant galactocerebroside immunoglobulin G2 myelin-associated glycoprotein myelin basic protein myelin oligodendrocyte glycoprotein multiple sclerosis not determined proteolipid protein peripheral nervous system

Disclosures and Conflict of Interest

This chapter was originally published as: Seil, F. J. (2018). Myelin Antigens and Antimyelin Antibodies. Antibodies 7(1), 2, https://doi.org/10.3390/antib7010002, under the Creative Commons Attribution license (http://creativecommons.org/ licenses/by/4.0/). It appears here, with edits and updates, by kind permission of the authors and the publisher, MDPI, Basel.

Acknowledgments: Studies from the author’s laboratory were supported by the Medical Research Service of the U.S. Department of Veterans Affairs. The technical assistance of Marilyn L. Johnson in processing the culture illustrated in the figure is gratefully acknowledged. Conflicts of Interest: The author declares no conflict of interest.

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References 1. Bornstein, M. B., Appel, S. H. (1961). The application of tissue culture to the study of experimental “allergic” encephalomyelitis. J. Neuropathol. Exp. Neurol., 20, 141–157.

2. Appel, S. H., Bornstein, M. B. (1964). The application of tissue culture to the study of experimental allergic encephalomyelitis. II. Serum factors responsible for demyelination. J. Exp. Med., 119, 303–312.

3. Johnson, A. B., Bornstein, M. B. (1978). Myelin binding antibodies in vitro. Immunoperoxidase studies with experimental allergic encephalomyelitis, anti-galactocerebroside and multiple sclerosis sera. Brain Res., 159, 173–182.

4. Lebar, R., Boutry, J.-M., Vincent, C., Robineaux, R., Voisin, A. (1976). Studies on autoimmune encephalomyelitis in the guinea pig. II. An in vitro investigation on the nature, properties and specificity of the serum demyelinating factor. J. Immunol., 116, 1439–1446.

5. Bornstein, M. B., Raine, C. S. (1970). Experimental allergic encephalomyelitis: Antiserum inhibition of myelination in vitro. Lab. Investig., 23, 536–542.

6. Bornstein, M. B. (1963). A tissue culture approach to the demyelinative disorders. NCI Monogr., 11, 197–214. 7. Peters, A., Palay, S. L., Webster, H. F. (1991). The Fine Structure of the Nervous System: The Neurons and Supporting Cells, 3rd ed. Oxford Press, New York, NY, USA, pp. 222–265.

8. Morell, P., Quarles, R. H., Norton, W. T. (1994). Myelin formation, structure and biochemistry. In: Siegel, G. J., Agranoff, B. W., Albers, R. W., Molinoff, P. B., eds. Basic Neurochemistry, 5th ed. Raven Press, New York, NY, USA, pp. 117–143. 9. Eng, L. F., Chao, F.-C., Gerstl, B., Pratt, D., Tavaststjerna, M. G. (1968). The maturation of human white matter myelin. Fractionation of the myelin membrane proteins. Biochemistry, 7, 4455–4465.

10. Quarles, R. H. (1979). Glycoproteins in myelin and myelin related membranes. In: Margolis, R. U., Margolis, R. K., eds. Complex Carbohydrates of Nervous Tissue, Plenum, New York, NY, USA, pp. 209–233. 11. Eylar, E. H., Uyemura, K., Brostoff, S., Kitamura, K., Ishaque, A., Greenfield, S. (1979). Proposed nomenclature for PNS myelin proteins. Neurochem. Res., 4, 289–293.

12. Braun, P. E., Brostoff, S. W. (1977). Proteins of myelin. In: Morell, P., ed. Myelin, Plenum, New York, NY, USA, pp. 201–231. 13. Lumsden, C. E. (1966). Immunopathological events in multiple sclerosis. Int. Congr. Ser. Excerpta Med., 100, 231–239.

14. Kies, M. W., Alvord, E. C., Jr. (1959). Encephalitogenic activity in guinea pigs of water-soluble protein fractions of nervous tissue. In: Kies, M. W., Alvord, E. C., Jr., eds. “Allergic” Encephalomyelitis, Thomas, Springfield, IL, USA, pp. 293–299.

15. Roboz, E., Henderson, N. (1959). Preparation and properties of water-soluble proteins from bovine cord with “allergic” encephalomyelitic activity. In: Kies, M. W., Alvord, E. C., Jr., eds. “Allergic” Encephalomyelitis, Thomas, Springfield, IL, USA, pp. 281– 292.

16. Seil, F. J., Falk, G. A., Kies, M. W., Alvord, E. C., Jr. (1968). The in vitro demyelinating activity of sera from guinea pigs sensitized with whole CNS and with purified encephalitogen. Exp. Neurol., 22, 545–555.

References

17. Kies, M. W., Driscoll, B. F., Seil, F. J., Alvord, E. C., Jr. (1973). Myelination inhibition factor: Dissociation from induction of experimental allergic encephalomyelitis. Science, 179, 689–690.

18. Seil, F. J., Rauch, H. C., Einstein, E. R., Hamilton, A. E. (1973). Myelination inhibition factor: Its absence in sera from subhuman primates sensitized with myelin basic protein. J. Immunol., 111, 96–100.

19. Seil, F. J., Smith, M. E., Leiman, A. L., Kelly, J. M. (1975). Myelination inhibiting and neuroelectric blocking factors in experimental allergic encephalomyelitis. Science, 187, 951–953.

20. Seil, F. J., Kies, M. W., Bacon, M. L. (1975). Neural antigens and induction of myelination inhibition factor. J. Immunol., 114, 630–634.

21. Seil, F. J., Kies, M. W., Agrawal, H. C., Quarles, R. H., Brady, R. O. (1980). Myelin proteins dissociated from induction of antimyelin antibodies. In: Giacobini, E., Vernadakis, A., Shahar, A., eds. Tissue Culture in Neurobiology, Raven Press, New York, NY, USA, pp. 477–488. 22. Dubois-Dalcq, M., Niedieck, B., Buyse, M. (1970). Action of anti-cerebroside sera on myelinated tissue cultures. Pathol. Eur., 5, 331–347.

23. Fry, J. M., Weissbarth, S., Lehrer, G. M., Bornstein, M. B. (1974). Cerebroside antibody inhibits sulfatide synthesis and myelination and demyelinates in cord tissue cultures. Science, 183, 540–542.

24. Saida, T., Silberberg, D. H., Fry, J. M., Manning, M. C. (1977). Demyelinating antigalactocerebroside antibodies in EAN and EAE. J. Neuropathol. Exp. Neurol., 36, 627.

25. Bornstein, M. B., Raine, C. S. (1977). Multiple sclerosis and experimental allergic encephalomyelitis: Specific demyelination of CNS in culture. Neuropathol. Appl. Neurobiol., 3, 359–367. 26. Hruby, S., Alvord, E. C., Jr., Seil, F. J. (1977). Synthetic galactocerebrosides evoke myelination-inhibiting antibodies. Science, 195, 173–175.

27. Lebar, R., Vincent, C., Fischerle Boubennec, E. (1979). Studies on autoimmune encephalomyelitis in the guinea pig. III. A comparative study of two autoantigens of central nervous system myelin. J. Neurochem., 33, 1451–1460.

28. Lebar, R., Lubetzki, C., Vincent, C., Lombrail, P., Boutry, J.-M. (1986). The M2 autoantigen of central nervous sysrem myelin, a glycoprotein present in oligodendrocyte membrane. Clin. Exp. Immunol., 66, 423–443.

29. Herndon, R. M., Rauch, H. C., Einstein, E. R. (1973). Immune-electron microscopic localization of the encephalitogenic basic protein in myelin. Immunol. Commun., 2, 163–172.

30. Quarles, R. H., Everly, J. L., Brady, R. O. (1972). Demonstration of a glycoprotein which is associated with a purified myelin fraction from rat brain. Biochem. Biophys. Res. Commun., 47, 491–497.

31. Poduslo, J. F., Quarles, R. H., Brady, R. O. (1976). External labeling of galactose in surface membrane glycoproteins of the intact myelin sheath. J. Biol. Chem., 251, 153–158.

32. Seil, F. J., Quarles, R. H., Johnson, D., Brady, R. O. (1981). Immunization with purified myelin-associated glycoprotein does not evoke myelination-inhibiting or demyelinating antibodies. Brain Res., 209, 470–475.

121

122

Myelin Antigens and Antimyelin Antibodies

33. Williams, R. M., Lees, M. B., Cambi, F., Macklin, W. B. (1982). Chronic experimental allergic encephalomyelitis induced in rabbits with bovine white matter proteolipid apoprotein. J. Neuropathol. Exp. Neurol., 41, 508–521.

34. Cambi, F., Lees, M. B., Williams, R. M., Macklin, W. B. (1983). Chronic experimental allergic encephalomyelitis produced by bovine proteolipid apoprotein: Immunological studies in rabbits. Ann. Neurol., 13, 303–308.

35. Seil, F. J., Agrawal, H. C. (1980). Myelin proteolipid protein does not induce demyelinating or myelination-inhibiting antibodies. Brain Res., 194, 273–277.

36. Mithen, F., Bunge, R., Agrawal, H. (1980). Proteolipid protein antiserum does not affect CNS myelin in rat spinal cord culture. Brain Res., 197, 477–483.

37. Nagai, Y., Momoi, T., Saito, M., Mitsuzawa, E., Ohtani, S. (1976). Ganglioside syndrome, a new autoimmune neurologic disorder, experimentally induced with brain gangliosides. Neurosci. Lett., 2, 107–111.

38. Cohen, O., Schwartz, M., Cohen, I. R., Sela, B.-A., Eshhar, N. (1981).Multiple sclerosis-like disease induced in rabbits by immunization with brain gangliosides. Isr. J. Med. Sci., 17, 711–714.

39. Konat, G., Offner, H., Lev-Ram, V., Cohen, O., Schwartz, M., Cohen, I. R., Sela, B. A. (1982). Abnormalities of brain myelin in rabbits with experimental autoimmune multiple sclerosis-like disease induced by immunization to gangliosides. Acta Neurol. Scand., 66, 568–574.

40. Bourdette, D. N., Zalc, B., Baumann, N., Seil, F. J. (1989). Antisera to the ganglioside GM1 do not have anti-myelin or anti-axon activities in vitro. Brain Res., 478, 175–180.

41. Gonzales-Sastre, F. (1970). The protein composition of isolated myelin. J. Neurochem., 17, 1049–1056.

42. Wolfgram, F., Kotorii, K. (1968). The composition of the myelin proteins of the central nervous system. J. Neurochem., 15, 1281–1290.

43. Quarles, R. H., Pasnak, C. F. (1977). A rapid procedure for rapidly isolating the major glycoprotein from purified rat brain myelin. Biochem. J., 163, 635–637.

44. Reig, J. A., Rames, J. M., Cozar, M., Aguilar, J. S., Criado, M., Monreal, J. (1982). Purification and chemical characterization of a W2 protein from rat brain myelin. J. Neurochem., 39, 507–511.

45. Brunner, C., Lassman, H., Waehneldt, T. V., Matthieu, J.-M., Linington, C. (1989). Differential ultra-structural localization of myelin basic protein, myelin/oligodendrocyte glycoprotein, and 2, 3-cyclic nucleotide 3-phosphodiesterase in the CNS of adult rats. J. Neurochem., 52, 296–304. 46. Seil, F. J., Garwood, M. M., Clark, H. B., Agrawal, H. C. (1983). Demyelinating and myelination-inhibiting factors induced by chloroform-methanol insoluble proteins of myelin. Brain Res., 288, 384–388.

47. Seil, F. J. (1994). Effects of humoral factors on myelin in organotypic cultures. In: Herndon, R. M., Seil, F. J., eds. Multiple Sclerosis: Current Status of Research and Treatment, Demos, New York, NY, USA, pp. 33–50.

48. Armor, S., Groome, N., Linington, C., Morris, M. M., Dornmair, K., Gardinier, M. V., Matthieu, J. M., Baker, D. (1994). Identification of epitopes of myelin oligodendrocyte glycoprotein for the induction of experimental allergic encephalomyelitis in SJL and AB/H mice. J. Immunol., 153, 4349–4356.

References

49. Adelmann, M., Wood, J., Benzel, I., Fiori, P., Lassmann, H., Matthieu, J. M., Gardinier, M. V., Dornmair, K., Linington, C. (1995). The N-terminal domain of the myelin oligodendrocyte glycoprotein (MOG) induces acute demyelinating experimental autoimmune encephalomyelitis in the Lewis rat. J. Immunol., 63, 17–27.

50. Johns, T. J., Kerloro de Rosbo, N., Menon, K. K., Abo, S., Gonzales, M. F., Bernard, C. C. (1995). Myelin oligodendrocyte glycoprotein induces a demyelinating encephalomyelitis resembling multiple sclerosis. J. Immunol., 154, 5536–5541.

51. Kerloro de Rosbo, N., Honneger, P., Lassmann, H., Matthieu, J.-M. (1990). Demyelination induced in aggregating brain cell cultures by a monoclonal antibody against myelin/ oligodendrocyte glycoprotein. J. Neurochem., 55, 583–587.

52. Waksman, B. H., Adams, R. D. (1955). Allergic neuritis: An experimental disease of rabbits induced by injection of peripheral nervous tissue and adjuvants. J. Exp. Med., 102, 213–235.

53. Carlo, D. J., Karkhanis, Y. D., Bailey, P. J., Wisniewski, H. M., Brostoff, S. W. (1975). Experimental allergic neuritis: Evidence for the involvement of the P0 and P2 proteins. Brain Res., 88, 580–584. 54. Kadlubowski, M., Hughes, R. A. C. (1979). Identification of the neuritogen for experimental allergic neuritis. Nature, 227, 140–141.

55. Yonezawa, T., Ishihara, Y., Matsuyama, H. (1968). Studies on experimental allergic peripheral neuritis. I. Demyelinating patterns studied in vitro. J. Neuropathol. Exp. Neurol., 27, 453–463. 56. Raine, C. S., Bornstein, M. B. (1979). Experimental allergic neuritis. Ultrastructure of serum-induced abberations in peripheral nervous system cultures. Lab. Investig., 40, 423–432. 57. Saida, T., Saida, K., Silberberg, D. H. (1979). Demyelination produced by experimental allergic neuritis serum and anti-galactocerebroside antiserum in CNS cultures. Acta Neuropathol., 48, 19–25.

58. Seil, F. J., Kies, M. W., Bacon, M. L. (1981). A comparison of demyelinating and myelination-inhibiting factor induction by whole peripheral nerve tissue and P2 protein. Brain Res., 210, 441–448. 59. Uyemura, K., Kitamura, K., Ogawa, Y., Matsuyama, H. (1976). Studies on the antigenic protein to induce experimental allergic neuritis (EAN). In: Shiraki, H., Yonezawa, T., Kuroiwa, Y., eds. The Aetiology and Pathogenesis of the Demyelinating Diseases, Japan Science Press, Tokyo, Japan, pp. 181–192.

60. Mithen, F., Agrawal, H., Bunge, R., Fishman, M., Eylar, E. (1980). Effects of antiserum against PNS myelin proteins P1 and P2 on myelinated dorsal root ganglion (DRG) cultures. J. Neuropathol. Exp. Neurol., 39, 376.

61. Raine, C. S., Traugott, U., Farooq, M., Bornstein, M. B., Norton, W. T. (1981). Augmentation of immune-mediated demyelination by lipid haptens. Lab. Investig., 45, 174–182.

62. Bourdette, D. N., Driscoll, B. F., Seil, F. J., Kies, M. W., Alvord, E. C., Jr. (1986). Severity of demyelination in vivo correlates with serum myelination inhibition activity in guinea pigs having a new form of experimental allergic encephalomyelitis. Neurochem. Pathol., 4, 1–9.

63. Martenson, R. E., Deibler, G. E., Kies, M. W., Levine, S., Alvord, E. C., Jr. (1972). Myelin basic proteins of mammalian and submammalian vertebrates: Encephalitogenic activities in guinea pigs and rats. J. Immunol., 109, 262–270.

123

124

Myelin Antigens and Antimyelin Antibodies

64. Linington, C., Lassmann, H. (1987). Antibody responses in chronic relapsing experimental allergic encephalomyelitis: Correlation of serum demyelinating activity with antibody titre to the myelin/oligodendrocyte glycoprotein (MOG). J. Neuroimmunol., 17, 61–69.

65. Linington, C., Bradl, M., Lassmann, H., Brunner, C., Vass, K. (1988). Augmentation of demyelination in rat acute encephalomyelitis by circulating mouse monoclonal antibodies directed against a myelin/oligodendrocyte glycoprotein. Am. J. Pathol., 130, 443–454.

66. Whitham, R. H., Nilaver, G., Bourdette, D. N., Seil, F. J. (1988). Serum antimyelin antibodies in chronic relapsing experimental allergic encephalomyelitis. J. Neuroimmunol., 18, 155–170.

67. Smith, M. E., Sadler, R. H., Sommer, M. A. (1994). The macrophage as the demyelinative agent: A role for antimyelin antibodies. In: Herndon, R. M., Seil, F. J., eds. Multiple Sclerosis: Current Status of Research and Treatment, Demos, New York, NY, USA, pp. 51–66.

68. Cook, S. D., Dowling, P. C., Murray, M. R., Whitaker, J. N. (1971). Circulating demyelinating factors in acute idiopathic polyneuropathy. Arch. Neurol., 24, 136–144.

69. Dubois-Dalcq, M., Buyse, M., Buyse, G., Gorce, F. (1971). The action of Guillain-Barré syndrome serum on myelin. A tissue culture and electron microscope analysis. J. Neurol. Sci., 13, 67–83.

70. Bornstein, M. B., Hummelgard, A. (1976). Multiple sclerosis: Serum induced demyelination in tissue culture. In: Shiraki, H., Yonezawa, T., Kuroiwa, Y., eds. The Aetiology and Pathogenesis of the Demyelinating Diseases, Japan Science Press, Tokyo, Japan, pp. 341–350.

71. Ulrich, J., Lardi, H. (1978). Multiple sclerosis: Demyelination and myelin inhibition of organotypic tissue cultures of spinal cord by sera of patients with multiple sclerosis and other neurological diseases. J. Neurol., 218, 7–16.

72. Seil, F. J., Westall, F. C., Romine, J. S., Salk, J. (1983). Serum demyelinating factors in multiple sclerosis. Ann. Neurol., 13, 664–667.

73. Dau, P. C., Petajan, J. H., Johnson, K. P., Panitch, H. S., Bornstein, M. B. (1980). Plasmapheresis in multiple sclerosis: Preliminary findings. Neurology, 30, 1023–1028.

74. Bradbury, K., Aparicio, S. R., Sumner, D. W., Macfie, A., Sagar, P., Griffin, N. R., Bird, C. C. (1985). Comparison of in vitro demyelination and cytotoxicity of humoral factors in multiple sclerosis and other neurological diseases. J. Neurol. Sci., 70, 167–181. 75. Hughes, D., Field, E. J. (1967). Myelinotoxicity of serum and spinal fluid in multiple sclerosis: A critical assessment. Clin. Exp. Immunol., 2, 295–309.

76. Wolfgram, F., Duquette, P. (1976). Demyelinating antibodies in multiple sclerosis. Neurology, 26 Pt 2, 68–69.

77. Grundke-Iqbal, I., Bornstein, M. B. (1979). Multiple sclerosis: Immunochemical studies on the demyelinating serum factor. Brain Res., 160, 489–503.

78. Grundke-Iqbal, I., Bornstein, M. B. (1980). Multiple sclerosis: Serum gamma globulin and demyelination in organ culture. Neurology, 30, 749–754.

79. Genain, C. P., Cannella, B., Hauser, S. L., Raine, C. S. (1999). Identification of autoantibodies associated with myelin damage in multiple sclerosis. Nat. Med., 5, 170–175.

References

80. Raine, C. S., Cannella, B., Hauser, S. L., Genain, C. P. (1999). Demyelination in primate autoimmune encephalomyelitis and acute multiple sclerosis: A case for antigenspecific antibody mediation. Ann. Neurol., 46, 144–160. 81. Storch, M., Lassmann, H. (1997). Pathology and pathogenesis of demyelinating diseases. Curr. Opin. Neurol., 10, 186–192.

82. Luchinetti, C. F., Brück, W., Rodriguez, M., Lassmann, H. (1996). Distinct patterns of multiple sclerosis pathology indicates heterogeneity in pathogenesis. Brain Pathol., 6, 259–274.

83. Luchinetti, C. F., Brück, W., Parisi, J., Scheithauer, B., Rodriguez, M., Lassmann, H. (2000). Heterogeneity of multiple sclerosis lesions: Implications for the pathogenesis of demyelination. Ann. Neurol., 47, 707–717.

84. Archelos, J. J., Storch, M. K., Hartnung, H.-P. (2000). The role of B cells and autoantibodies in multiple sclerosis. Ann. Neurol., 47, 465–471.

85. Seil, F. J. (2000). Demyelinating antibodies in multiple sclerosis. Ann. Neurol., 48, 948.

86. Elliott, C., Lindner, M., Arthur, A., Brennan, K., Jarius, S., Hussey, J., Chan, A., Stroet, A., Olsson, T., Willison, H., et al. (2012). Functional identification of pathogenic autoantibody responses in patients with multiple sclerosis. Brain, 135, 1819–1833.

87. McLaughlin, K. A., Chitnis, T., Newcombe, J., Franz, B., Kennedy, J., McArdel, S., Kuhle, J., Kappos, L., Rostasy, K., Pohl, D., et al. (2009). Age-dependent B cell autoimmunity to a myelin surface antigen in pediatric multiple sclerosis. J. Immunol., 183, 4067–4076.

88. Probstatel, A.-K., Dornmair, K., Bittner, R., Sperl, P., Jenne, D., Magalhaes, S., Villalobos, A., Breithaupt, C., Weissert, R., Jacob, U., et al. (2011). Antibodies to MOG are transient in childhood acute disseminated encephalomyelitis. Neurology, 77, 580–588. 89. Kitley, J., Woodhall, M., Waters, P. J., Leite, M. I., Devenney, E., Craig, J., Palace, J., Vincent, A. (2012). Myelin oligodendrocyte glycoprotein antibodies in adults with a neuromyelitis optica phenotype. Neurology, 79, 1273–1277.

90. Lennon, V. A., Wingerchuck, D. M., Kryzer, T. J., Pittock, S. J., Lucchinetti, C. F., Fujihara, K., Nakashima, I., Weinshenker, B. G. (2004). A serum autoantibody marker of neuromyelitis optica: Distinction from multiple sclerosis. Lancet, 364, 2106–2112.

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Chapter 6

Advances in the Understanding of the Inflammatory Milieu and Its Correlations with Neurological Disorders Mario Ganau, MD, PhD, MBA,a Sibel E. Huet, MD,a Nikolaos Syrmos, MD, PhD,b Mohammad Iqbal, MBBS,a and Marco Meloni, MDc aDepartment

of Neurosciences, John Radcliffe Hospital, Oxford University, UK of Health Sciences, Aristotle University of Thessaloniki, Greece cDepartment of Neurosurgery, ‘Moriggia-Pelascini’ Hospital, Gravedona ed Uniti, Italy bFaculty

[email protected]

Keywords: central nervous system, inflammation, traumatic brain injury, spinal cord injury, chronic traumatic encephalopathy, Parkinson’s disease, Alzheimer’s disease, gliomas, neuro-oncology, biomarkers, autoimmune disorders, theranostics, astrocytes, microglia, blood–brain barrier, nanotechnology, biomedical engineering, proteomics, epigenomics

6.1 Inflammation and Mechanism in the Body Inflammation is a complex physiological response organized by our body to protect us from harmful stimuli, such as an injury or contact with an infective pathogen, and to eventually coordinate the regeneration process when tissue or cell damage has occurred. For this, inflammation can be thought as a back-andforth dance controlled by many pro-inflammatory and anti-inflammatory cells, constantly releasing various mediators; such scenario constitutes what we call the inflammatory milieu. It is through this intricate and precisely balanced movement that body removes the inflammatory mediators released upon exposure to harmful stimuli and starts a healthy recovery process.

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The dysregulation of such fine-tuned process can lead to a variety of chronic diseases, including autoimmune disorders and malignancies [1]. Furthermore, an aberrant response to a given injury, can also lead to degenerative pathologies; for instance, traumatic brain injury has been well correlated with the onset of chronic traumatic encephalopathy (CTE), Parkinson’s disease (PD), and Alzheimer’s disease (AD). The inflammatory milieu accounts for several epigenetic and proteomic mechanisms responsible for the release and interaction of several inflammatory intermediates regulating the intercellular microenvironment [2]. In this chapter, we will explore the role of the various “players” that characterize the inflammatory milieu with specific focus on neurological disorders pertaining to the sphere of neuro-oncology and neurotraumatology.

6.2 Immune Molecules and Proteins of the Inflammatory Milieu

The most important and primary immune group of cells of the central nervous system (CNS) is represented by the microglia, constituting about 80% of the brain’s immune cells. These cells are responsible for the regulation and secretion of the main pro- and anti-inflammatory substrates listed below: • Tumor Necrosis Factor-α (TNF-α)

TNF is a known potent inflammatory mediator, playing a key role in the inflammatory milieu [3]. Specifically, TNF-α is a major player in coordinating recruitment of other inflammatory cells and mediators. As an example of its multifaceted roles, TNF-α can exert a protective effect by limiting in the acute phase the release of inflammatory mediators to the local area of the CNS affected by an insult. However, in the long term, its pro-inflammatory role can act as a tumor promoter, as nicely shown by experimental evidence of autocrine/ paracrine growth effect when secreted by cancerous cells from primary brain tumors, such as gliomas. These different actions are justified by the different roles of TNF in the regulation of the blood–brain barrier (BBB) and activation of microglia and astrocytes [2]; more importantly, they explain its involvement in many CNS pathologies, such as neurodegenerative diseases, neurotrauma and cancer [2–4].

• Interleukins (ILs)

Interleukins are another family of inflammatory mediators, constituted of both pro- and anti-inflammatory subtypes. A few of ILs have been linked to different key steps in CNS oncogenesis but also in the response to neurotrauma. These include IL-1, IL-6, IL-8, IL-12, and IL-18 [4, 5]. On the opposite side of the spectrum, IL-10 works as an anti-inflammatory mediator and works via inhibition of the pro-inflammatory IL-1 and TNF-α and also inhibits leukocyte adhesion and glial activation, therefore facilitating neurological recovery [5].

Neurotrauma and Secondary Neurodegeneration

• Nuclear Factor Kappa B (NF-kB)

NF-kB is a transcription factor that regulates inflammation and immune response. It is expressed in high levels within the CNS and is therefore believed to hold a regulatory role in the physiological activity of the brain, such as spatial memory formation, synaptic transmission, and neuroplasticity [6]. Its functions, however, remain controversial, as NF-kB was found to lead to different (favorable and unfavorable) outcomes when activated by different stimuli [6]. For example, in neuronal cells, its activation was found to induce apoptosis, while other studies found that its inactivation might also lead to cell death [7].

• Other inflammatory factors

Various molecules and proteins are known to be linked to inflammation and have been widely studied in CNS setting. Those include cyclo-oxygenase 2 (COX-2), chemokines, and 5-lipoxygenase [4, 8], and details about their activities will be provided later on in this chapter.

6.3 Neurotrauma and Secondary Neurodegeneration

Neurotrauma, including traumatic brain injury (TBI) and spinal cord injury (SCI), is one of leading causes of death in developed countries for people under 45 years old [5]. When the CNS is subjected to external forces it displays a staged response: dealing with the primary acute injury in the first instance, and with the cascade of secondary chronic injury process later on [5]. The primary response is initiated as soon as the traumatic event occurs, which leads to an alteration of the CNS structure and therefore alerts the immune system to gather inflammatory molecules to the site in order to start the fighting and regeneration process. This leads to a complex succession of events that is not still entirely understood and constitutes the secondary injury. Such cascade of events can last for several days and even months, paving the way to the development of neurodegenerative diseases, such as post-traumatic epilepsy and less evidently, CTE, PD, and AD [15–17]. In response to the primary injury, the BBB and microvasculature gets disrupted, glial cells are progressively activated, amplifying an even stronger inflammatory response via the recruitment of proinflammatory cytokines and chemokines [15, 18, 19]. The overall outcome is a diffuse interstitial swelling, commonly described as cerebral edema. Such overwhelming response induces the release of excitatory glutamate, leading to apoptosis via dysregulation of ion homeostasis [5, 18]. Activation of the innate complement system is another part of the secondary responses, and contributes to both the immediate neurotoxic, as well as the long-term neurodegenerative process [20]. As mentioned above, microglia activation will result in the secretion of not only pro-inflammatory and cytotoxic molecules (IL-1B, IFN-gamma, TNF-α, oxidative species) [15, 17] but also chemo-attractant molecules that will recruit neutrophils as fast as a few hours after the injury [11, 19, 21].

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The link between the occurrence of TBI and neurodegenerative disease can be further strengthened by the presence of common biomarkers: Studies on CTE have shown that the inflammation of microglia contributed to the accumulation of tau proteins, putting the basis for high incidence of early dementia in professional contact sports [22, 23].

6.4 Role of Inflammation in Oncogenesis

It is a known fact that chronic ongoing inflammation sets a favorable environment for dysplasia, which over time can lead to cancer. Those stimuli do not always directly cause cancer by modification of the genetic information within the cells; rather they promote a noxious environment via the recruitment of leukocytes and other inflammatory cells, which generate harmful reactive oxidative species, such as oxygen and nitrogen. The continuous exposure of genetic material to those agents eventually results in mutations, therefore leading to the development of chronic disease or tumors [9, 10]. Gliomas, the most common primary malignant brain tumors in adults, are the perfect example of how this constant interaction might transform the inflammatory mileu into an oncogenesis-propense microenvironment [11]. As the tumor develops and progresses, the inflammatory milieu might amplify the release of molecules that will directly or indirectly help its growth [12]. Understanding the close two-way relationship that gliomas hold with their local microenvironment is essential in highlighting which signaling mechanisms could become the objective for novel diagnostic and therapeutic strategies. This microenvironment is mostly constituted of reactive microglia concentrating around the tumor bed and cooperating in cancer cell proliferation and invasion [8]. In vitro evidence that whenever microglia regulation is altered pre-cancerous conditions may develop supports the theories behind a correlation between inflammatory milieu and gliomas as well as other CNS malignancies [8, 13]. Specifically, we will outline here the wide range of cells, mediators and cytokines investigated so far to assess their role in the development, progression rate, and final histological grade of the tumor:

• Astrocytes have a role in the progression of any CNS pathology, including tumors, trauma, and ischemia [11]. With regard to gliomas, the subtype A1 has been linked to the activation of the complement system and antigen presentation, and on the contrary the subtype A2 has been shown to have neuroprotective functions and to contribute to the formation of scars [8, 11]. The interaction between microglia and astrocytes favors an antiinflammatory and immunosuppressive environment via secretion of the anti-inflammatory cytokines TGFβ and IL-10, which in turn shields the tumor bed by providing a suitable environment for the continuation the tumor’s development [11]. • Among the biological modulators found in the inflammatory milieu, the upregulation of TNF-α is often linked to the expression of angiogenic growth

Emerging Methods for Diagnosis and Follow-Up

factors in gliomas [4, 14]. A study conducted by Nabors et al. [14] on gliomas concluded that the hyper-vascularization of the tumor bed by VEGF, IL-8 and COX-2 not only supports the early stage of oncogenesis but favors also cells dissemination beyond its boundaries. • Expression of COX-2, which has been found in all stages of cancer development, is nowadays believed to primary support oncogenesis and neo-angiogenesis. Studies show that COX-2 expression level was associated with higher tumoral grade, early recurrence and diminished survival in brain cancers, especially gliomas [8]. • Many pro-inflammatory ILs are frequently associated with CNS tumors. However, IL-8 is particularly important for its role in the promotion of tumors growth and metastasis. High levels of IL-8 have been found in many different types of primary brain tumors such as glioblastomas and astrocytomas, but also in metastatic lesions reaching the CNS from other primary sources elsewhere in the body [4]. Some studies also mention a similar role for IL-6 and IL-1β; both have, in fact, been found in microsatellite spreading outside the boundaries of the gliomas core [8]. • Finally, as mentioned above, NF-kB could also help in providing the resistance of tumoral cells to TNF-a, cytokines, and chemotherapeutic agents. Some studies suggest that NF-kB could actually be fundamental to confer gliomas with their primary line of resistance to treatment [4, 7].

6.5 Emerging Methods for Diagnosis and Follow-Up

Inflammation may be a key factor in the development and progression of several neurodegenerative and neuro-oncological conditions. However, the underlying pathways and mechanisms are so complex that an in-depth monitoring is extremely difficult at present [17]. As such, better and more precise diagnostic tools are needed to enable a timely diagnosis and prognostication. The use of biomarkers has been increasingly sought after in both neurotrauma and neuro-oncology as an avenue to quantitatively measure the level of severity and define the progress of those conditions. One example is how the gold standard for the assessment of BBB disruption changed over time, with the invasive determination of CSF serum albumin quotient being overtaken by non-CSF biomarkers [24]. Currently, many serum and blood biomarkers have been proposed as surrogate for the prediction of severity, prognosis, and progression of neurotrauma (Table 6.1). Those include S-100β, tau proteins, glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), neuron specific enolase (NSE), and micro-RNAs (miRNAs) [15, 21, 25, 26]. A novel approach to the study of the neuroinflammatory milieu is the analysis is the determination of the epigenomic profile, which is proving particularly helpful in neuro-oncology. Genetic and epigenomic profiles determine the response to inflammation and can obviously predict prognosis, more importantly when

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it comes to cancer, they can guide the response to treatment, hence providing scientists and clinicians with a new edge in the management of patients with aggressive tumors. As such, the medical community is shifting away from just a pathology description and classification of tumors, and leveraging on their genomic, proteomic and metabolomic patterns. Of note, Gonda et al. [38] tried to create a genomic atlas for different types of CNS tumors by identifying different key genes and their levels of expression. Another study conducted by Park et al. [39] on metabolic profiles of pediatric medulloblastoma allowed classifying them into four potential groups according to the expression of proteomic pathways, which could represent potential targets for tailored chemotherapy. Overall, these examples show that understanding the genetic response to inflammation could also help us in making more accurate and less invasive diagnosis of other pathological conditions, more importantly those studies could unlock new futuristic theranostic opportunities. Table 6.1 Biomarkers and the neuroinflammatory milieu S-100 β

Gives information not only on the severity and prognosis of TBI but also on the level of disruption of the BBB and its evolution over time. High levels of S-100 protein are routinely associated with a worse outcome and higher mortality [21, 24–29].

Tau Proteins These are relevant biomarkers investigated in the context of repetitive TBI that could lead to the development of those neurodegenerative diseases known as “tautopathies,” such as CTE and AD [22, 30]. Studies show that neuroinflammation in both severe and mild TBI is linked to a significantly greater plasma phosphorylated tau (p-tau) and total tau (t-tau) levels [22, 31]. Moreover, the analysis of tau levels might predict the development of raised intracranial pressure (ICP) and therefore helps determining the outcome of patients following severe TBI [32]. NSE

NSE is a useful biomarker of axonal injury in TBI and SCI patients [21, 26] and has been suggested for its potential role also as a marker of disease progression in PD. The main limitation of NSE is the lack of specificity to the CNS: In fact, erythrocytes from the bloodstream can also abundantly produce NSE, therefore obliging scientists to correct for the hemolytic factor before making statement about the extent of the CNS injury [25, 26].

UCH-L1 and These have a higher CNS specificity and are mainly found inside the cell body of GFAP neurons [24, 25]. They have been proposed as a barometer of neuronal cell body injury [25, 33]. However, both are also well known to have a role in the diagnosis of brain tumors, hence contributing to the thesis that the monitoring of events within the neuroinflammatory milieu matters in both acute and chronic settings. miRNA

Can be easily detected in both CSF and blood post-TBI and SCI, leading to the investigation of their role as potential predictive biomarkers in neurotrauma [34]. Among the large panel of miRNA known to increase following CNS injury, a few deserve mention: miR16, miR-128 miR-307, miR-765, miR-311, and miR-92a [34, 35]. An advantage of these miRNA is their potential to distinguish mild from severe TBI [35]. Furthermore, they seem quite promising also in the neuro-oncology field [36], especially the miR-128 subtype that was shown to be good predictor for various types of cancer, including gliomas [37].

Future Outlook and Conclusion

6.6 Future Outlook and Conclusion Whereas the relation between inflammation and several neurological disorders is still poorly understood, evidence of the close ties between acute response to noxious stimuli and the cascade of events that could unfold years later has started to emerge. The attention to the role of the inflammatory milieu in CNS disorders is leading us to reconsider the current diagnostic and treatment options available for patients with CTE, PD, AD, and various forms of primary brain tumors. In this regard, an important source of information is brought to us via the study of genomic and proteomic profiling, enabled by advances in nanotechnology, biomedical engineering, and neuroinformatics [40–51]. This progress will be of great value in the coming years. In conclusion, being able to understand signaling patterns of neurological disorders following a traumatic event is proving essential in an era where immunotherapy is rapidly evolving. Finding the precise link between immunological response to inflammation and the continuous amplification of the pathological circle leading to chronic diseases will certainly provide us with tremendous opportunities to design immunological agents able to freeze their progression and potentially start a discussion about regenerative therapies.

Abbreviations AD: BBB: CNS: COX-2: CTE: GFAP: ICP: IFN-gamma: ILs: miRNAs: NF-kB: NSE: p-tau: PD: SCI: t-tau: TBI: TNF-α: UCH-L1:

Alzheimer’s disease blood–brain barrier central nervous system cyclo-oxygenase 2 chronic traumatic encephalopathy glial fibrillary acidic protein intracranial pressure interferon gamma interleukins micro-RNAs nuclear factor kappa B neuron specific enolase phosphorylated tau Parkinson’s disease spinal cord injury total tau traumatic brain injury tumor necrosis factor-α ubiquitin C-terminal hydrolase-L1

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Disclosures and Conflict of Interest The opinions and perspectives here reflect the current views of the authors. The authors declare that they have no conflict of interest and have no affiliations or financial involvement with any organization or entity discussed in this chapter. No writing assistance was utilized in the production of this chapter and the authors have received no payment for its preparation.

References

1. Elenkov, I.J., et al., (2005). Cytokine dysregulation, inflammation and well-being. Neuroimmunomodulation. 12(5), 255–269.

2. Mccoy, M.K., et al., (2008). TNF signaling inhibition in the CNS: Implications for normal brain function and neurodegenerative disease. Journal of Neuroinflammation. 5(1), 45.

3. Probert, L., (2015). TNF and its receptors in the CNS: The essential, the desirable and the deleterious effects. Neuroscience. 302, 2–22. 4. Aggarwal, B.B., et al., (2006). Inflammation and cancer: How hot is the link? Biochem. Pharmacol. 72(11), 1605–1621. 5. Schmidt, O., et al., (2004). The role of neuroinflammation in traumatic brain injury. Eur. J. Trauma. 30 (3), 135–149.

6. Guire, C.M., et al., (2013). Nuclear factor kappa B (NF-κB) in multiple sclerosis pathology. Trends Mol. Med. 19 (10), 604–613. 7. Otsuka, G., et al., (1999). Inhibition of NF-kB activation confers sensitivity to TNFa by impairment of cell-cycle progression in human glioma cells. Cancer Research. 59(17), 4446–4452.

8. Mostofa, A., et al., (2017). The process and regulatory components of inflammation in brain oncogenesis. Biomolecules. 7 (4), 34. 9. Coussens, L.M., et al., (2002). Inflammation and cancer. Nature. 420(6917), 860–867.

10. Mantovani, A., (2010). Molecular pathways linking inflammation and cancer. Curr. Mol. Med. 10 (4), 369–373. 11. Heiland, D.H., et al., (2019). Tumor-associated reactive astrocytes aid the evolution of immunosuppressive environment in glioblastoma. Nat. Commun. 10 (1), 2541.

12. Whiteside, T.L., (2008). The tumor microenvironment and its role in promoting tumor growth. Oncogene. 27(45), 5904–5912. 13. Sevenich, L., (2018). Brain-resident microglia and blood-borne macrophages orchestrate central nervous system inflammation in neurodegenerative disorders and brain cancer. Front. Immunol. 9, 697.

14. Nabors, L.B., et al., (2003). Tumor necrosis factor alpha induces angiogenic factor up-regulation in malignant glioma cells: A role for RNA stabilization and HuR. Cancer Res. 63, 4181–4187. 15. Kumar, A., et al., (2012). Neuroinflammation after traumatic brain injury: Opportunities for therapeutic intervention. Brain Behav. and Immun. 26 (8), 1191–1201.

References

16. Shi, K., et al., (2019). Dissemination of brain inflammation in traumatic brain injury. Cel. Mol. Immunol. 16 (6), 523–530.

17. Xiong, Y., et al., (2018). Current understanding of neuroinflammation after traumatic brain injury and cell-based therapeutic opportunities. Chin. J. Traumatol. 21(3), 137–151.

18. Park, E., et al., (2004). The role of excitotoxicity in secondary mechanisms of spinal cord injury: A review with an emphasis on the implications for white matter degeneration. J. Neurotrauma. 21 (6), 754–774.

19. Mietto, B.S., et al., (2015). Neurotrauma and inflammation: CNS and PNS responses. Mediators Inflamm. 2015, 1–14. 20. Dinet, V., et al., (2019). Brain–immune interactions and neuroinflammation after traumatic brain injury. Front. Neurosci. 13, 1178.

21. Albayar, A.A., et al., (2019). Biomarkers in spinal cord injury: Prognostic insights and future potentials. Front. Neurol. 10, 27. 22. Cherry, J.D., et al., (2016). Microglial neuroinflammation contributes to tau accumulation in chronic traumatic encephalopathy. Acta Neuropathol. Commun. 4(1), 112.

23. Maphis, N., et al., (2015). Reactive microglia drive tau pathology and contribute to the spreading of pathological tau in the brain. Brain. 138 (6), 1738–1755.

24. Blyth, B.J., et al., (2009). Validation of serum markers for blood–brain barrier disruption in traumatic brain injury. J. Neurotrauma. 26(9), 1497–1507. 25. Wang, K.K., et al., (2018). An update on diagnostic and prognostic biomarkers for traumatic brain injury. Expert Rev. Mol. Diagn. 18(2), 165–180.

26. Agoston, D.V., et al., (2017). Biofluid biomarkers of traumatic brain injury. Brain Injury. 31(9), 1195–1203.

27. Pleines, U.E., et al., (2001). S-100β reflects the extent of injury and outcome, whereas neuronal specific enolase is a better indicator of neuroinflammation in patients with severe traumatic brain injury. J. Neurotrauma. 18 (5), 491–498.

28. Goyal, A., et al., (2013). S100b as a prognostic biomarker in outcome prediction for patients with severe traumatic brain injury. J. Neurotrauma. 30(11), 946–957.

29. Dadas, A., et al., (2016). Improving the clinical management of traumatic brain injury through the pharmacokinetic modeling of peripheral blood biomarkers. Fluids Barriers CNS. 13(1), 21.

30. Bolton-Hall, A.N., et al., (2019). Experimental designs for repeated mild traumatic brain injury: Challenges and considerations. J. Neurotrauma. 36(8), 1203–1221.

31. Rubenstein, R., et al., (2017). Comparing plasma phospho tau, total tau, and phospho tau–total tau ratio as acute and chronic traumatic brain injury biomarkers. JAMA Neurol. 74(9), 1063–1072.

32. Zemlan, F.P., et al., C-tau biomarker of neuronal damage in severe brain injured patients: Association with elevated intracranial pressure and clinical outcome. Brain Res. 947(1), 131–139. 33. Gan, Z.S., et al., (2019). Blood biomarkers for traumatic brain injury: A quantitative assessment of diagnostic and prognostic accuracy. Front. Neurol. 10, 446.

135

136

The Inflammatory Milieu and Its Correlations with Neurological Disorders

34. Najem, D., et al., (2018). Traumatic brain injury: Classification, models, and markers. Biochem. Cell Biol. 96(4), 391–406.

35. Pietro, V.D., et al., (2017). MicroRNAs as novel biomarkers for the diagnosis and prognosis of mild and severe traumatic brain injury. J. Neurotrauma. 34(11), 1948–1956. 36. Jeffries, J., et al., (2019). miRNA-223 at the crossroads of inflammation and cancer. Cancer Lett. 451, 136–141.

37. Liang, R.F., et al., (2017). Circulating miR-128 as a potential diagnostic biomarker for glioma. Clin. Neurol. Neurosurg. 160, 88–91. 38. Gonda, D.D. ,et al., (2014). The Cancer Genome Atlas expression profiles of low-grade gliomas. Neurosurg. Focus. 36(4), E23. 39. Park, A.K., et al., (2019). Subgroup-specific prognostic signaling and metabolic pathways in pediatric medulloblastoma. BMC Cancer. 19(1), 571. 40. Ganau, M., et al., (2015). A DNA-based nano-immunoassay for the label-free detection of glial fibrillary acidic protein in multicell lysates. Nanomedicine. 11(2), 293–300.

41. Ganau, L., et al. (2018). Understanding the pathological basis of neurological diseases through diagnostic platforms based on innovations in biomedical engineering: New concepts and theranostics perspectives. Medicines (Basel). 5(1), 22. 42. Prisco, L., et al., (2012). Early predictive factors on mortality in head injured patients: A retrospective analysis of 112 traumatic brain injured patients. J. Neurosurg Sci. 56(2), 131–136.

43. Ganau, M., Fehlings, M.G., (2017). Clinical and health policy-related challenges of pediatric spinal cord injuries. Neurol. India. 65(3), 475–476. 44. Ganau, M., Prisco, L. (2013). Comment on “neuromonitoring in traumatic brain injury”. Minerva Anestesiol. 79(3), 310–311.

45. Ganau, M., et al., (2018). Delirium and agitation in traumatic brain injury patients: An update on pathological hypotheses and treatment options. Minerva Anestesiol. 84(5), 632–640. 46. Ganau, M., et al. (2018). Current and future applications of biomedical engineering for proteomic profiling: Predictive biomarkers in neuro-traumatology. Medicines (Basel). 5(1), 19.

47. Ganau, L., (2018). Predicting complexity of tumor removal and postoperative outcome in patients with high-grade gliomas. Neurosurg. Rev. 41(1), 371–373. 48. Ganau M., (2014). Tackling gliomas with nanoformulated antineoplastic drugs: Suitability of hyaluronic acid nanoparticles. Clin. Transl. Oncol. 16(2), 220–223. 49. Ganau, M., et al. (2018). How nanotechnology and biomedical engineering are supporting the identification of predictive biomarkers in neuro-oncology. Medicines (Basel). 5(1), 23.

50. Ganau, L., et al., (2015). Management of gliomas: Overview of the latest technological advancements and related behavioral drawbacks. Behav. Neurol. 2015, 862634.

51. Ganau, M., et al., (2017). Enhancing contrast agents and radiotracers performance through hyaluronic acid-coating in neuroradiology and nuclear medicine. Hell. J. Nucl. Med. 20(2), 166–168.

Chapter 7

Role of Ligustrum in Allergic Disease Priyadharshini Vellore Suresh, PhD,a Tania Robledo Retana, PhD,b Blessy M. Mani, PhD,a Ana Paulina Barba de la Rosa, PhD,c and Luis M. Teran, MD, PhDa aInstituto

Nacional de Enfermedades Respiratorias, Mexico City, Mexico Potosino de Investigación Científica y Tecnológica, San Luis Potosí, México cBiochemistry Department, Queen Mary University of London, London, UK bInstituto

[email protected]

Keywords: Ligustrum, allergens, epitopes, proteomics, allergy, inflammation, asthma, pollen, cross-reactivity, sensitization, T cell, immunoglobulin E, profilin, enolase, enzymelinked immunosorbent assay, polygalacturonase, aminotransferase, adenosine triphosphate synthase, immunotherapy

7.1 Introduction The prevalence of allergy respiratory disease is increasing worldwide and represents a significant economic health burden. The World Allergy Organization (WAO) estimates that 30–40% of the population presents at least one allergic condition, with higher presence in developed and industrialized cities [1]. These allergy reactions have been largely studied [2–3], and they are part of a dysregulation of the immune system characterized by increased synthesis of specific immunoglobulin E (IgE) by B cells for allergen recognition. The cross-linking between allergens and their specific will promote the release of a variety of mediators, including histamine, leukotrienes, prostaglandins, and proteases. Furthermore, cell activation involving IgE leads to a prolonged inflammatory response that lasts for several hours after allergen exposure and is mediated predominantly by the activation of inflammatory cells such as neutrophils, eosinophils, and T cells. Th2 cells cytokines Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

Copyright © 2022 Jenny Stanford Publishing Pte. Ltd.

ISBN 978-981-4877-84-8 (Hardcover), 978-1-003-18043-2 (eBook)

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such as IL-4, IL-5, IL-9, and IL-13 have been proposed to regulate the allergic reaction [4–6]. The most common allergic diseases include allergic rhinitis (AR), conjunctivitis, asthma, skin reactions, and severe systemic reactions represented by anaphylactic shock [7]. Asthma and AR affect 300 and 400 million people worldwide, respectively [8]. The allergy triggered by pollen grains or its components is commonly known as pollinosis or hay fever, and it has been studied since Bostock (1819) [9]. Nowadays it is well established that allergy symptoms could be minimized by avoiding contact with allergens. However, airborne pollen grains and their proteins make it difficult to avoid them completely. Pollen size plays an important role in reaching different levels of the respiratory tract, when they are inhaled: Sizes 5–50 μm reach the upper airways (nose, nasopharynx) by impaction, and smaller particles ( 90% Disseminated nodular and non-ulcerating MCL cases reported in lesions Brazil, Peru and Bolivia) [5, 19] Small nodules or papules at the vector’s bite sites that may progress to ulcerated lesions

Metastatic secondary lesions in nasooral and pharyngeal cavities and tissue destruction

Splenomegaly, hepatomegaly, weight loss, persistent fever and anemia. Post-Kalaazar Dermal Leishmaniasis (PKDL): skin rashes or non-ulcerating cutaneous lesions after apparent resolution of VL disease

usually occurring in immunocompromised patients [5, 7].

Most VL cases reported in Brazil, Ethiopia, India, Nepal, Bangladesh, Kenya, Somalia, South Sudan and Sudan [19]

Clinical Aspects of Leishmaniasis

Cutaneous leishmaniasis predominantly affects the skin of infected individuals and can manifest as localized CL (LCL), diffuse CL (DCL), and mucocutaneous leishmaniasis (MCL) [7]. LCL is the most common manifestation, responsible for up to 95% of all CL cases. It results in a single or small number of lesions at the vector’s biting sites that may gradually progress within weeks or months, from papules/nodules to ulcerated lesions [9]. In sporadic cases, multiple lesions may occur on the body, which is considered a type of disseminated cutaneous disease [10]. Typically, cutaneous lesions resolve spontaneously upon the efficacious establishment of the cell-mediated immune response [11]. Two LCL etiological agents are L. amazonensis, in New World, and L. major, in the Old World, with particular clinical features being attributed to each of them [2]. DCL is an atypical CL form and occurs when a defective cell-mediated immune response against Leishmania parasites gives rise to disseminated, nodular, non-ulcerating, and non-healing cutaneous lesions that affect the entire body with intense parasite proliferation [11]. DCL patients are often refractory to treatment, and DCL cases have been reported in South and Central America, Kenya, and Ethiopia, mainly caused by L. amazonensis, L. mexicana, and L. aethiopica [10]. MCL accounts for 1–10% of CL cases in endemic areas [7]. This form of leishmaniasis is characterized by an exacerbated cell-mediated immunity, which, despite controlling parasite proliferation, also promote intense inflammation and tissue destruction [12]. Patients treated for LCL can manifest MCL later, after apparent resolution of primary lesions. In MCL, parasites metastasize to mucosal tissues of the upper respiratory tract (e.g., naso-oral and pharyngeal cavities), causing an erosive disease that leads to disfiguring lesions and facial mutilations [5]. More than 90% of MCL cases have been reported in three South American countries (Brazil, Peru, and Bolivia) and, although other Leishmania spp. are associated with this manifestation, such as L. major, L. panamensis, L. tropica, and L. infantum, MCL has been commonly observed in L. braziliensis infections [2, 5]. Visceral leishmaniasis (also known as “Kala-azar”) is the most serious form of leishmaniasis and predominantly fatal when patients do not receive proper treatment [10]. VL targets internal organs, such as liver and spleen, after parasite dissemination, compromising the reticuloendothelial system [13]. VL patients may initially develop an asymptomatic infection, which escalates to a systemic condition involving splenomegaly, hepatomegaly, weight loss, persistent fever, anemia, among other syndromes [14]. Infected individuals display high levels of antibodies and intense parasite expansion in the targeted organs, including bone marrow [14]. In 5–15% of VL cases, treated patients develop a chronic form of CL, called post-kala-azar dermal leishmaniasis (PKDL), with the appearance of non-ulcerating cutaneous lesions [15]. VL is typically caused by L. donovani and L. infantum strains in the Old World and New World, respectively, and it is frequently reported in countries such as India, Nepal, Bangladesh, Sudan, and Brazil [15, 16].

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8.3 The Immunobiology of Leishmaniasis The cellular and immunological mechanisms associated with Leishmania infection are not completely clear, and most of the current knowledge on this subject is primarily based on experimental models of leishmaniasis. The wellestablished animal models for leishmaniasis have been shown to reproduce some immunopathological aspects of the human disease, though with limitations, allowing the investigation and characterization of regulatory factors linked to resistance or susceptibility to Leishmania and their implications to the infected host [17]. Although substantial differences are observed between experimental and human leishmaniasis, the crosstalk between innate and adaptive immune components and the type of the cell-mediated responses elicited are critical factors determining the fate of the disease in both cases [14, 17, 18].

8.3.1 Early Events

Once inoculated into the host dermis by an infected sandfly, infective metacyclic promastigotes of Leishmania are engulfed by a variety of immune cells, such as resident dermal dendritic cells, macrophages and infiltrating neutrophils that act as the first line of defense [20–24]. The rapid and massive recruitment of neutrophils to the site of parasite inoculation has been well documented and associated with the enhancement of the disease [20–22]. Neutrophil-driven inflammation seems to be mostly triggered by the insertion of the sandfly’s proboscis into the skin, provoking tissue damage [24, 25]. Other vector-derived components that appear to participate in the recruitment of neutrophils is its saliva, which contains anti-hemostatic mediators (vasodilators and anticoagulants) and immunomodulatory elements [26], such as adenosine, AMP and nucleotidases, delivered to the host along with parasite-derived components, such as exosomes [27] and proteophosphoglycans, particularly the promastigote secretory gel (PSG) [28]. PSG is a mucin-like gel produced by promastigotes in the sandflies that accumulates in and blocks the vector mouthparts, forcing the infected sandflies to regurgitate several times during blood feeding, a behavior hypothesized to enhance the chances of parasite transmission and inflammation [6, 28]. In addition to their contribution to preventing the formation of blood clots, the hydrolysis of ATP by vector-derived nucleotidases followed by increased levels of extracellular AMP and adenosine have been suggested to exacerbate lesions in experimental models of leishmaniasis, most likely through activation of the purinergic receptor A2A on immune cells, leading to inhibition of inflammatory functions, such as monocyte maturation, phagocytosis, and nitric oxide (NO) production [29]. An additional explanation for the large and sustained neutrophil infiltration at the bite sites was recently proposed and involves the immunomodulatory properties of gut microbes from infected sandflies that are co-egested with Leishmania parasites into the skin (Fig. 8.1). Using a VL BALB/c mouse model intradermally infected in the ears by L. donovani, Dey et al. demonstrated

The Immunobiology of Leishmaniasis

that the microbiota of the sandfly midgut enhances the early recruitment of neutrophils and the activation of the inflammasome in these cells, with the production of interleukin (IL)-1β, a potent proinflammatory cytokine [30]. These effects were abrogated with the pre-treatment of sandflies with antibiotic cocktails to decrease the microbial population prior to infection, or when mice were treated with an IL-1 receptor (IL1R) antagonist. The authors concluded that the microbe-mediated IL-1β production serves as an autocrine signal that amplifies neutrophil infiltration at the infection sites and may also aid in parasite dissemination to the spleen [30].

Figure 8.1 Recruitment of innate immune cells in Leishmania infection. Sandfly-derived components, such as anti-hemostatic mediators, adenosine, AMP, and gut microbes, are co-inoculated into the dermis with metacyclic promastigotes and other parasite-derived elements, such as exosomes and PSG. Rapid and sustained recruitment of neutrophils to the site of inoculation is partially driven by immunomodulatory components of the vector’s saliva. Sandfly’s gut microbiota has been demonstrated to induce the inflammasome activation and release of IL-1β, which promotes inflammation and acts as an autocrine signal, amplifying neutrophil infiltration. Neutrophil extracellular traps (NETs) can capture and kill some parasites, while infected neutrophils degranulate and release several inflammatory mediators, including the chemokine Macrophage Inflammatory Protein (MIP)-1β, stimulating the recruitment of monocytes and macrophages. Apoptotic neutrophils with viable parasites act as “Trojan Horses”, silently transferring amastigotes to macrophages. Free promastigotes escaping from apoptotic cells can also be internalized by macrophages, where they differentiate into amastigotes. Image created with BioRender.com.

Indeed, other studies have already attested the chemotactic activity of the sandfly saliva for neutrophils and macrophages employing other experimental

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models, with multiple combinations of Leishmania-sandfly species, and this activity is most likely important for early delivery of parasites to neutrophils [25, 26]. The uptake of Leishmania by neutrophils has been observed as early as 30 min after inoculation, and the majority of parasites were still detected in these cells 18 h after infection [5, 21]. However, an in vivo study tracking L. major parasites in the dermis using intra-vital two-photon imaging detected parasite proliferation only four days after the sandfly bite and a single parasite was enough to initiate the infectious process [24]. Peters et al. noticed that the antibody-induced depletion of neutrophils in C57BL/6 mice, which develop self-healing cutaneous lesions when infected by L. major, greatly reduced the amount of viable L. major parasites at the infection site [21]. Interestingly, they also observed an increased release of IL-1α and IL-1β pro-inflammatory cytokines from cells at the infection sites in neutrophil-depleted mice, one week post-infection [21]. Similarly, early uptake of L. mexicana by neutrophils was associated with poor parasite clearance in vivo, chronic lesions, and impaired recruitment of inflammatory cells to infection sites, while infected neutropenic or antibody-mediated neutrophil-depleted mice had better control of the disease [31]. The infiltration of neutrophils is also observed in chronic lesions of CL patients, but their contribution to the chronic status of leishmaniasis remains elusive [32, 33]. It has been proposed that neutrophils facilitate Leishmania infection by having better access to parasites than other phagocytic cells in extracellular spaces and by promoting their safe transition to mononuclear phagocytes [21, 22]. In this context, apoptotic neutrophils harboring viable parasites could act as “Trojan Horses”, silently transferring parasites to macrophages upon uptake of these dying neutrophils, avoiding cell activation [22]. Later, a mechanism in which parasites escape apoptotic neutrophils to infect macrophages was also admitted [21]. Conversely, neutrophils have also been shown to play protective roles after recognition and phagocytosis of some Leishmania spp. [34]. These cells are able to eliminate microbes in mature lysosomes with granule-associated cytotoxic components and production of reactive oxygen species (ROS), or by induction of NETosis, a cell death mechanism that involves the extracellular release of decondensed chromatin, histones, and multiple microbicidal proteins, forming NETs that capture and kill several pathogens [35, 36]. While NETs from human neutrophils can kill L. amazonensis parasites and are also reported in cutaneous lesions of patients with leishmaniasis, they were unable to exert a similar leishmanicidal effect on L. mexicana, L. donovani, or L. infantum parasites [37]. Therefore, the impact of neutrophils on the disease outcome may differ according to the experimental model and the infecting species being investigated, revealing that the contribution of neutrophils to the pathogenesis of Leishmania infection is far more complex than initially predicted and requires further investigation.

8.3.2 Later Moments after Infection

Though parasite replication within infected neutrophils was already observed for L. mexicana in vitro [38], tissue macrophages and monocytes are the major cellular

The Immunobiology of Leishmaniasis

populations harboring Leishmania parasites several days after infection [39, 40]. The recruitment of monocytes is partially driven by degranulation of infected neutrophils, which also release the chemokine MIP-1β, CCL3, and other inflammatory mediators in response to various stimuli, such as IL-8 produced by tissue-resident macrophages, complement factors (e.g., C5a) and tumor necrosis factor (TNF)-α [41–43] (Fig. 8.1). Macrophages are the main host cells of Leishmania parasites, where internalized promastigotes differentiate into non-motile amastigotes, which undergo robust replication and can persist in phagosomes, promoting latent-infections that can be reactivated [40, 44]. However, immature inflammatory monocytes rather than tissue-resident macrophages or dendritic cells (DCs) were shown to act as major facilitators of L. major expansion and persistence in vivo during primary infections and parasite internalization seems to delay the maturation of these cells [45]. The engulfment of Leishmania promastigotes is mediated by classical membrane-bound phagocytic receptors such as the complement (CR1 and CR3), mannose/fucose (MR) and fibronectin receptors [40, 46, 47], and caveolin-dependent endocytosis was recently demonstrated to mediate the internalization of L. donovani parasites by host cells [48]. A number of promastigote surface proteins are implicated in the initiation of phagocytosis, including the abundant lipophosphoglycan (LPG), the metalloprotease GP63, and proteophosphoglycans (PPGs), which seem to be targeted by host opsonins, such as complement components (C3b/iC3b), galectins, and mannose-binding protein [49–52]. Moreover, amastigotes released after cell rupture can be coated by host IgG and captured by other phagocytes through Fc receptors (FcyR), a strategy that seems to greatly influence downstream signaling that benefits the parasites [53].

8.3.3 The Adaptive Immune Responses in Leishmaniasis

The interaction between Leishmania spp. and their host cells will ultimately determine the infection course and the disease outcome of both experimental and human leishmaniasis. The infection resolution is attributed to the establishment of cell-mediated immunity, specifically the activation and differentiation of T lymphocytes that stimulate the production of cytokines, which induce the activation of infected mononuclear phagocytes and culminate with parasite elimination [54]. The differentiation of CD4+ T cells in T helper type (Th)1 upon antigen recognition on major histocompatibility complex (MHC) II is predominantly associated with the development of a proinflammatory response, characterized by secretion of TNF-α, IL-1β, IL-6, IL-12, IL-18, and IL-23 cytokines; increased production of highly microbicidal ROS and reactive nitrogen species (RNS) (e.g., hydrogen peroxide, superoxide, hydroxyl radicals, and NO) via activation of the NADPH oxidase complex and inducible nitric oxide synthase (iNOS), respectively; and enhanced phagocytosis, leading to infection control especially in experimental models [54, 55]. Meantime, an anti-inflammatory phenotype is correlated with a predominant Th2 response characterized by the production of IL-4, IL-13, IL-10,

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and transforming growth factor (TGF)-β cytokines; enhanced arginase activity; polyamine biosynthesis; and IL-21-mediated down-regulation of iNOS, TNF-α, and toll-like receptor (TLR)-4, favoring intracellular proliferation of Leishmania parasites and disease progression [56, 57] (Fig. 8.2). Additionally, the role of other T cell subtypes in Leishmania pathogenesis, such as T regulatory cells (Treg), CD8+ T cytotoxic, and Th17 effector cells, is coming to light [20, 58, 59]. Th17 cells are suggested to participate in the balance of inflammatory cytokines, modulating adaptive immunity, and also secret the IL-17 cytokine that contributes to neutrophil recruitment [20].

Figure 8.2 Macrophage polarization. Distinct stimuli induce the M1 or M2-like phenotypes in infected macrophages, which depend on CD4+ T cell differentiation in Th1, Th2, Tregs, among other subsets not shown here. Infected macrophages and DCs produce proinflammatory cytokines, such as IL-12, which induce Th1 differentiation. Th1 and natural killer (NK) cells, in turn, release interferon (IFN)-γ, stimulating iNOS expression and activity in infected cells, which promote parasite killing. On the other hand, Th2 differentiation leads to production of anti-inflammatory cytokines, which downregulate iNOS activity and stimulates arginase, culminating in parasite survival and proliferation. Tregs promote the balance of pro- and anti-inflammatory responses, avoiding tissue destruction, and controlling pathology (adapted from Maspi et al. [60]). Image created with BioRender.com.

The Immunobiology of Leishmaniasis

However, the interplay between innate and adaptive immune systems is far more complex in human leishmaniasis and many other factors influencing the natural infection (e.g., vector and parasite-derived components, concomitant infections with viruses and other microorganisms, host background, etc.), which are not reproduced in experimental models, indeed contribute to the fate of the disease [5, 10, 25, 27, 30, 61].

8.3.3.1 Cutaneous manifestations

The clinical manifestations of leishmaniasis are largely influenced by the amplitude of the host immune responses against the infecting Leishmania spp., and these responses can be either protective or pathological. On the one side, patients may develop a strong cellular immunity, controlling parasite proliferation, whereas, on the other side, a response predominantly based on humoral immunity (high levels of anti-leishmanial antibodies) can be mounted, promoting intense parasite proliferation (Fig. 8.3). The balance between these two extremes has been correlated with a moderate disease, where self-healing or chronic lesions are noticed [11, 13, 14, 59].

Figure 8.3 Main features of cutaneous manifestations. This spectrum shows the characteristics of immune responses in MCL (severe disease), CL (moderate disease), and DCL (severe disease). MCL patients develop an exacerbated Th1 response and present high numbers of CD8+ T cells, which promote disease severity. On the other side, DCL is characterized by high parasite loads in lesions, diminished levels of Th1 cytokines, and increased production of IL-10 (adapted from Scott and Novais [11]). Image created with BioRender.com.

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The immunopathological mechanisms of leishmaniasis have been mainly investigated in mice that reproduce some aspects of the human disease, especially LCL [13]. The infection of C57BL/6 mice with L. major is considered a relevant model, since, as in human disease, they develop localized self-healing cutaneous lesions, with low parasite loads, which is associated with activation of DCs and production of IL-12 [21, 23]. This cytokine induces differentiation of CD4+ T cells in Th1 effector lymphocytes, which stimulates microbicidal functions of macrophages [62]. However, substantial differences have been observed among CL diseases caused by distinct species, such as L. mexicana and L. amazonensis. Contrasting with L. major infection, L. mexicana and L. amazonensis, for example, have been shown to survive with a limited Th1 response and the exacerbation of cutaneous lesions caused by L. amazonensis infection has been associated with Th1-induced recruitment of cells that enable parasite persistence [63]. Those differences could be partially attributed to the variations in virulence factors observed for each species, such as the surface component LPG, which seems to be crucial in L. major infection, but it is not a virulence factor required in L. mexicana infection [64] (Box 1). Yet, Th1-related responses seem to be important in controlling L. amazonensis proliferation, since IFN-γ-deficient C57BL/6 mice are more susceptible to L. amazonensis infection than wild-type mice [65]. In addition, the secretion of IL-1β may also be essential in controlling L. amazonensis infection in mice, whereas this cytokine most likely exacerbates disease in L. major-infected mice [11]. Actually, the role of IL-1β during Leishmania infection is considered controversial [66]. IL-1β is produced as a propeptide that is processed upon assembly and activation of the inflammasome, a molecular complex composed of proteins, such as the NOD-, LRR- and pyrin domain-containing 3 (NLRP3) complex and caspase-1, which mediates IL-1β cleavage [67]. It has been hypothesized that Leishmania may activate the inflammasome in the skin indirectly by inducing the production of ROS when parasites are phagocytosed by innate immune cells through C-type lectin receptors [11, 67] or by activating a non-canonical pathway, which involves the participation of LPG [66]. Other vector-derived components, including the midgut microbiota co-egested with parasites, may also participate in IL-1β activation [30]. It has been shown that IL-1β can promote IL-12-mediated expansion of Th1 cells, and stimulates NO and TNF production, which contribute to eliminating parasites [60, 67]. However, a study with patients infected by L. mexicana has associated elevated IL-1β expression with disease severity [68]. Therefore, the mechanisms responsible for resistance or susceptibility also depend on the infecting species that causes CL. L. amazonensis, L. mexicana, and L. aethiopica strains are reported as the main etiological agents of DCL, a severe form of CL characterized by the absence of specific cell-mediated response for Leishmania antigens, high parasite proliferation and dissemination in humans [8, 9] (Fig. 8.3). DCL patients often present with low levels of Th1 cytokines, high antibody titers, and high parasite loads in their lesions [5, 94]. The infection of human monocytes with L. aethiopica isolated from DCL patients was demonstrated to induce IL-10 expression, which appears

The Immunobiology of Leishmaniasis

to be critical for preventing a protective immunity to Leishmania. In contrast, infection with the same species isolated from LCL patients induced higher levels of IFN-γ, IL-6, and IL-4, indicating that distinct immune mechanisms drive the disease outcome of CL subtypes caused by isolates of the same species [95]. Similar observations for IL-10 expression were also made in a recent study with L. mexicana strains isolated from LCL and DCL patients [96]. However, the infection of murine bone marrow-derived DCs with DCL-derived L. mexicana amastigotes not only promoted higher expression of IL-10 but also of IL-12 and TNF-α [96]. IL-10 seems to play an important role in DCL progression since increased production of this regulatory cytokine is also observed in DCL patients [11]. In fact, it was demonstrated that IL-10 could suppress the IFN-γ-mediated killing of L. amazonensis [97]. Other factors that can contribute to parasite proliferation is the higher activation of arginase I and the enhanced production of suppressive TGF-β and prostaglandin E2, which are detected in the plasma and skin biopsies of DCL patients [98]. Box 8.1

Virulence factors involved in the host immune evasion

(i) Lypophosphoglycan (LPG) and the zinc-metalloprotease GP63: major surface components of Leishmania implicated in the impairment of various macrophage functions, including inhibition of phagolysosomal maturation [69, 70], cytokine cleavage [71], and activation of negative regulatory factors [72].

(ii) Cathepsin-like cysteine proteases: papain-like cysteine proteases of Leishmania

were shown to inhibit antigen presentation via MHC class II and modulate IL-12

production in macrophages [73, 74] and DCs [75]. Cathepsin B-like protease was

also implicated in the activation of the latent TGF-β1 in L. infantum, and L. donovani

infected macrophages, inhibiting IFN-γ-induced microbicidal activities [76, 77].

(iii) Nucleotidases: both parasite and vector-derived nucleotidases can modulate purinergic signaling mechanisms through increased generation of adenosine, which stimulates the production of IL-10 and inhibits inflammatory functions in neutrophils, DCs, and macrophages [29, 78]. Enhanced activity of nucleotidases has been correlated with higher virulence of several Leishmania spp. and clinical isolates [79, 80].

(vi) Peroxiredoxins (Prxs): components of the unique antioxidant system of trypanosomatids. Prxs work in association with trypanothione, a glutathione analog, to reduce hydrogen peroxide, hydroperoxide, and hydroxyl radicals [81–84]. Cytosolic Prxs from Leishmania were demonstrated to confer protection against peroxides and increased virulence [85–87]. Prxs have also been linked to resistance against anti-leishmanial drugs [88]. (v) Superoxide dismutases (SODs): antioxidant metalloenzymes that convert superoxide to oxygen and hydrogen peroxide. The iron-dependent superoxide dismutase B1 (SODB1) of L. chagasi and L. major was correlated with parasite proliferation in human macrophages and mice models [89, 90], while superoxide dismutase A (SODA) was associated with differentiation and virulence of L. amazonensis parasites [91]. The up-regulation of SODA has also been linked to anti-leishmanial drug-resistance (miltefosine) in L. donovani infections [92, 93].

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While the full development of a Th1 phenotype, with the involvement of both CD4+ and CD8+ T cells, is critical for the resolution of all forms of CL, the exacerbation of Th1 immune responses leads to extremely severe CL disease [12, 59] (Fig. 8.3). The exaggerated cell-mediated immune response is the striking feature of MCL, whereby patients develop secondary metastatic lesions with intense tissue destruction, high levels of proinflammatory cytokines (e.g., IFN-γ) and increased unresponsiveness to anti-inflammatory cytokines, such as TGF-β and IL-10 [12, 99]. The attenuated expression of IL-10 receptors (IL-10R), increased levels of TNF-α and the activity of NK cells, cytolytic CD8+ T cells and neutrophils also seem to contribute to the clinical outcome of MCL [94, 100]. Indeed, patients infected with L. braziliensis and presenting with ulcerated lesions have been reported to display higher levels of CD8+ T cells than patients with non-ulcerated lesions [101]. Moreover, IL-17, an inflammatory cytokine involved in neutrophil recruitment, is particularly upregulated in MCL patients [102]. The production of IL-17, which is mostly mediated by Th17 cells, is induced by IL-23 and IL-1β [20]. In turn, IL-1β apparently promotes disease progression in C57BL/6 mice by mediating the expansion of Th17 cells [60]. Curiously, in a study evaluating the transcriptional profiles in cutaneous lesions of L. braziliensis-infected patients, higher expression of genes associated with inflammasome pathways was noticed [103]. Thus, considering that L. braziliensis infection is often associated with increased chances of clinical complications, including the development of MCL, it is tempting to speculate that IL-1β exerts an influence on the self-feeding inflammation observed in MCL disease by mediating an exaggerated production of inflammatory cytokines and adhesion molecules, increasing recruitment of other cells and amplifying inflammation [11, 12].

8.3.3.2 Leishmania RNA viruses and their implications in disease severity

The presence of an RNA virus in Leishmania parasites have been correlated with disease severity and MCL cases both in humans and experimental models. The Leishmania double-stranded RNA (dsRNA) viruses (LRV) are cytoplasmic viruses of the family Totiviridae, which include other several viral groups that infect fungi and other parasites, such as Giardia lamblia (GLV) and Trichomonas vaginalis (TVV) viruses [104]. LRVs have been found in several Leishmania spp., including L. braziliensis, L. guyanensis, L. major, and L. aethiopica clinical isolates, and phylogenetic analysis attested the existence of at least two divergent LRV groups, LRV1, and LRV2 [100, 105]. LRV1 has been predominantly observed in Leishmania spp. exclusively found in South America and associated with CL and/or MCL cases, whereas LRV2 has been detected in L. major and L. infantum isolates [106]. However, a potential new LRV strain (LRV-Lae) was more recently characterized from L. aethiopica clinical isolates [107]. It was suggested that one of the evolutionary forces possibly driving LRV1 maintenance in these parasites is related to its ability to increase parasite virulence and survival in the human host [108]. LRV1 is considered a potent innate immunogen that can exacerbate disease by stimulating the production of type I IFN-β via activation of the TLR-3 pathway [11, 100]. The up-regulation of IFN-β

The Immunobiology of Leishmaniasis

is associated with inhibition of IL-12-mediated DC maturation, diminished T cell-mediated IFN-γ production and down-regulation of IFN-γ receptor (IFNγR), which renders infected cells insensitive to stimuli inducing microbicidal activity, such as NO production [109]. Ives et al. noticed a correlation between infection caused by L. guyanensis harboring high levels of LRV1 and disease severity in mice model [110]. Similar to what has been observed for some infections with T. vaginalis harboring TVVs [111], infected mammalian cells were able to sense LRV1 dsRNA via endosomal TLR-3, a pathogen recognition receptor, stimulating an increased production of proinflammatory mediators, such as TNF-α and IL-6, known to contribute to the hyper-inflammatory MCL, and anti-viral type I interferons [110]. There is also a chance that viral dsRNA stimulates other receptors, including NOD-like-receptors (NLRs), which could possibly culminate with the activation of the inflammasome and enhance tissue damage [100]. In addition, LRV1-dependent activation of TLR-3 appears to promote the survival of the infected cells through stimulation of the phosphatidylinositol 3-kinase (PI3K)/Akt signaling pathway, involved in cell growth and proliferation [112]. The inoculation of LRV1-infected L. guyanensis promastigotes in the footpad of TLR3-deficient mice resulted in reduced swelling and lower parasite loads at the infection site compared to wild-type mice, confirming the involvement of this receptor in disease aggravation. Interestingly, high levels of LRV1 were observed in highly metastasizing L. guyanensis strains, which are known to cause MCL [110]. Some studies have indicated a strong correlation between LRV1 in infecting parasites and the development of MCL [113–117]. Ito et al. investigated the presence of LRV1 in MCL patients in the northern part of Brazil and detected the virus in a total of 26 out of 37 cases mainly caused by L. braziliensis [113]. Another study evaluating 147 patients from the western part of the Amazon region in Brazil detected a significantly higher incidence of LRV1 in MCL patients (71.1%) than in those with LCL (36.7%), suggesting a higher chance of developing MCL in the presence of LRV1 [114]. A high frequency of LRV1 was also observed in clinical isolates from French Guiana, where LRV1 was detected in, respectively, 55% and 80% of L. braziliensis and L. guyanensis isolates investigated [115]. Furthermore, treatment failure and disease relapses have been partially attributed to LRV1 in cutaneous infections caused by L. braziliensis and L. guyanensis [116, 117]. However, a cohort study performed with patients from Rio de Janeiro-Brazil revealed that severe manifestations caused by L. braziliensis in endemic regions of this state were not correlated with LRV1, implying that additional factors certainly play major roles in disease progression and in the development of MCL, and they probably vary according to geographical location [118]. The mechanisms leading to LRV dsRNA exposure to receptors in mammalian cells are not well understood. Initially, it was speculated that the viral genome could escape from dying parasites in phagolysosomes and then elicit the TLR-3-dependent hyper-inflammation [100]. Recently, a mechanism involving the hijack of the exosomal pathway of Leishmania parasites by LRV1 was proposed [119]. LRV1 particles from infected L. guyanensis was observed in transmission

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electron micrographs associated with multivesicular bodies (MVBs) and also reaching the extracellular milieu through exosomes released by its host-parasite [119]. The detection of viral proteins and genome in these membrane-enclosed vesicles by western-blot and RT-PCR indicated that LRV1 uses exosomes as envelopes, which confer protection from viral genome degradation [119]. In addition, LRV1-containing exosomes derived from L. guyanensis were able to infect virus-free L. panamenis parasites in a transwell migration assay, though the infection only last a few weeks. Importantly, co-inoculation of non-infected L. panamensis and L. mexicana with LRV1-containing exosomes, but not with naked LRV1, induced disease exacerbation in the mouse model [119]. Hence, the exploitation of exosomes as viral envelopes by LRV1 could possibly explain how the viral genome is exposed to mammalian cells during infection with Leishmania and promote hyper-inflammatory reactions. However, the contribution of LRV1-containing exosomes to the immunopathogenesis in the human host remains to be elucidated.

8.3.3.3 Visceral manifestations

Compared to CL diseases, the immunopathological mechanisms driving VL manifestations are less understood. However, as for CL, the pathogenesis of VL has been extensively studied in rodent models, especially with L. donovani [13]. Yet, contrasting with mice, which mainly develop features of self-healing or subclinical infections, hamsters exhibit disseminated infection, with parasite replication in the spleen, bone marrow, and liver, thus better mimicking, although with limitations, the active human disease [5, 13, 14]. In experimental models, unique features have been associated with L. donovani or L. infantum infections [5]. After inoculation into the skin, L. donovani rapidly multiplies in macrophages in this area during the first week, with the establishment of granulomas within 4–6 weeks [120]. The initial cutaneous infection seems to be controlled around the 8th week, according to histological observations, but parasites can still disseminate to internal organs, triggering the acute VL disease [120]. In L. infantum infections, rapid proliferation of parasites is already observed in the liver during the first four weeks postinfection, nonetheless, as for L. donovani infection, parasite growth in spleen seems to be slower, and they can persist in this organ [121]. Hence, it has been hypothesized that the liver is mostly a place for initial parasite replication, while spleen functions as a reservoir of parasites, promoting their persistence [13]. Indeed, it has been demonstrated that splenectomy (spleen removal) appears to minimize the effects of severe VL in humans and, sometimes, allow the successful treatment of refractory cases [122]. The factors and determinant mechanisms for parasite visceralization are not clear, but some virulence genes identified in L. donovani and L. infantum, such as the A2 gene family, seem to contribute to disease aggravation [123]. A2 is predominantly expressed during the amastigote stage of L. donovani and L. infantum and confers resistance to oxidative stress and heat shock [124, 125]. In some CL-causing species, such as L. major and L. tropica, A2 is a non-expressed

The Immunobiology of Leishmaniasis

pseudogene [123, 126]. The expression of A2 genes in transfected L. major parasites induced higher migration of infected cells and increased survival of these parasites in internal organs of BALB/c mice [127]. Interestingly, lower levels of A2 is observed in L. donovani causing human PKDL, when parasites move to the skin of the patient after VL treatment [128]. Perhaps, this might explain why viscerotropic parasites are able to better tolerate higher temperatures than cutaneous species [123]. The migration of infected macrophages and DCs to distant sites likely contribute to parasite spread to internal organs, since Zhang et al. observed that higher numbers of DCs and macrophages infected by L. donovani leave the intradermal site of parasite inoculation than during L. major infection [127] (Fig. 8.4).

Figure 8.4 Overview of mechanisms leading to parasite dissemination and proliferation in visceral leishmaniasis. Parasites inoculated into the dermis are captured by innate immune cells. It has been proposed that infected macrophages and DCs may leave the infection site and migrate to other areas, disseminating parasites to internal organs, such as liver and spleen. In the liver, early parasite proliferation in Kupffer cells is associated with decreased levels of IL-12 and IFN-γ and up-regulation of anti-inflammatory cytokines. Later, with infiltration of monocytes, neutrophils, CD4+, and CD8+ T cells in liver granulomas, increased levels of proinflammatory cytokines are observed, and infected cells are able to eliminate parasites. However, a heterogeneous pro- and anti-inflammatory responses in the spleen seems to affect the interaction of antigen-presenting cells with T cells and induce intense proliferation of parasites in this lymphoid organ. Image created with BioRender.com.

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Despite the fact that the resolution of VL disease requires both CD8+ and CD4+ T cell participation with IFN-γ and NO production (Th1 response), other immunological factors are involved in this process [5]. It was reported that the disease outcome is primarily associated with specific immune responses generated in the internal organs [13]. In mice liver, Leishmania parasites are initially detected in Kupffer cells, specialized resident macrophages, and the main phagocytic population in the liver. Parasites survive in these cells, and their early proliferation seems to be associated with low levels of IFN-γ and IL-12 [14, 129]. Later control of parasite growth is observed following the formation of granulomas around infected Kupffer cells in the liver and requires infiltration of blood monocytes, neutrophils, CD8+ and CD4+ T cells, and the production of TNF-α, IFN-γ, and IL-12 that activate infected macrophages to generate ROS and NO that eliminate intracellular amastigotes [5, 130] (Fig. 8.4). Using two-photon microscopy, Beattie et al. observed infected Kupffer cells as the only mononuclear population in situ engaging with effector CD8+ T cells for antigen-recognition within L. donovaniinduced granulomas, suggesting that this interaction could be strategically explored for the development of antigen-based vaccines and immunotherapies [131]. However, mature granulomas are not observed in progressive VL in humans [130]. TGF-β seems to contribute to the inhibition of IFN-γ production in liver granulomas during L. chagasi infection, which may lead to parasite survival [132]. Interestingly, increased levels of active TGF-β have been detected in the bone marrow and serum of patients with acute VL, implicating this regulatory cytokine in disease progression [5, 133]. Furthermore, it has been noticed that the production of IFN-γ by VL patient-derived peripheral blood mononuclear cells (PBMCs) as well as their proliferation are impaired when they are stimulated with Leishmania antigen, contrasting with the responses of PBMCs derived from asymptomatic, cured or subclinical cases [134–136]. IL-10 expression has also shown to exert a negative impact on IFN-γ production [13]. This cytokine is considered one of the major suppressors of anti-Leishmania responses and high levels of IL-10 is often detected in the serum of VL patients [137]. The high levels of antibodies observed in VL patients are believed to stimulate macrophages to produce IL-10 through the generation of immune complexes that bind to Fc receptors [138]. However, more studies are required to better elucidate the mechanisms inducing IL-10 and TGF-β production and their activity in acute VL disease. The immune response in the spleen is heterogeneous, and it is possible to observe the production of both Th1 and Th2-related cytokines in spleens of BALB/c mice infected with L. donovani parasites, such as IL-10, IFN-γ, TGF-β and IL-12 [13, 129] (Fig. 8.4). The progressive infection has been attributed to the redistribution of DCs in this lymphoid organ, which affects the interaction of these cells with T cells and the development of an antigen-specific response. The exacerbated induction of TNF-α and IL-10 are pointed as the major factors contributing to the mislocalization of DCs since they induce downregulation of both CCR7 (also known as CD197) chemotactic receptor on these cells and its ligands, the CCL19 and CCL21 chemokines that are produced by stromal cells in the T-cell rich area [13, 14, 23]. The impairment of CCL19/CCL21 chemokines in C57BL/6 mice

Promising Approaches for Drug Development: A Special Focus on the Host

infected with L. donovani results in reduced activation and mobility of DCs in the spleen, and elevated levels of IL-10 mRNA that correlates with higher susceptibility to infection [139]. Moreover, the activation of the hypoxia-inducible factor-1α (HIF-1α), typically occurring in poorly oxygenated microenvironments such as inflamed tissues, led to downregulation of IL-12 and upregulation of IL-10 in splenic DCs from an experimental model of chronic VL [140]. In line with these observations, L. donovani infection was shown to induce the IRF-5-mediated upregulation of HIF-1α in DCs also during acute disease, contributing to parasite survival [141]. The expression of HIF-1α in these splenic cells can potentially affect their mobility during Leishmania infection since the activation of HIF-1α was demonstrated to alter the expression of the CCR7 chemokine receptor and stimulate the production of IL-1β and TNF-α [139, 142]. The cellular relocation in the spleen, with increasing loss of specialized infected macrophages and other cells, leads ultimately to progressive destruction and disruption of the splenic architecture, similar to what is observed in severe VL in humans [14, 16]. Therefore, contrasting with its regulated role in resolving the liver infection, the excessive production of TNF-α in spleen along with IL-10 appear to mediate parasite proliferation and chronic infection [130]. PKDL is usually a VL complication observed in some patients after a treatment that generates apparent clinical cure. Thus, PKDL is considered an intermediate state that is preceding the fully VL resolution [15]. This manifestation can appear months or decades after the initial treatment, and it is possibly related to a suppressed immunity towards parasites that persist in the skin, producing skin rashes or non-ulcerating cutaneous lesions with high parasite loads [138]. It has been shown that monocytes and macrophages from these lesions exhibit increased expression of arginase-1, downregulation of TLR-2/4, and decreased production of ROS and RNS, associated with disease chronicity [55]. Interestingly, this type of response in the skin diverges from the predominant Th1 response induced systemically after VL treatment [13]. Keratinocytes producing TGF-β, TNF-α, IL-10, and IL-12 have also been implicated in the development of PKDL [138]. Indeed, elevated IL-10 levels in the plasma and in the keratinocytes of patients have already been proposed to predict PKDL cases in a study in Sudan [143]. Another source of IL-10 is a subset of Treg cells (CD4+CD25+Foxp3+) that were identified in tissue samples of PKDL patients and can also be correlated with TGF-β production [138]. However, other not well-understood responses seem to be involved in PKDL features, which vary according to geographic regions, parasite strains, and immune status of the individuals [15].

8.4 Promising Approaches for Drug Development: A Special Focus on the Host

Conventional treatment of leishmaniasis is based on few chemotherapies associated with toxic side effects, variable efficacy, and drug resistance [5]. Although the main mechanisms underlying the action of antileishmanial drugs are mostly parasitotoxic, these drugs have been shown to negatively affect the host immune

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system as well [10, 144]. In addition, it has been demonstrated that Leishmania parasites can evade drug action by remodeling their genetic content, enabling rapid development of resistance to antiparasitic drugs [145]. Thus, therapeutic approaches primarily focusing on “boosting” host immunity against Leishmania have been proposed as a better alternative to aggressive treatments currently in use for leishmaniasis that can target host determinants for disease progression [146, 147]. De Muylder et al. observed that incubation of L. donovani-infected THP-1 macrophages with naloxonazine, a potent antagonist of μ-opioid receptors (MOR), decreased the survival of intracellular parasites. They noticed this effect was associated with increased expression of host vacuolar ATPase (vATPase) transporter, a proton pump recruited to phagolysosomes that mediate the acidification of these compartments. When simultaneously treating infected cells with naloxonazine and a vATPase inhibitor (concanamycin A), the proliferation of parasites was restored [148]. Thus, the remodeling of the host vacuolar system may constitute a potential strategy to control Leishmania intracellular growth in acidic compartments. Using BALB/c mice as experimental VL model for infection with antimonyresistant L. donovani, Mukherjee et al. demonstrated the indirectly anti-leishmanial effect of imipramine, an antipsychotic drug used to treat patients with depression [149]. The initial treatment of infected macrophages from BALB/c mice with imipramine was shown to down-regulate IL-10 production and decreased the expression of IL-10-dependent multidrug resistance protein (MDR)-1 pump involved in L. donovani resistance to antimonials, the first-line drugs in the treatment of leishmaniasis. The downregulation of IL-10 was associated with imipramineinduced higher expression of host histone deacetylase (HDAC) 11, which impairs the binding of NF-kB p50/Rel-c complex to IL-10 promoter. Interestingly, a shift towards IL-12 production is observed in these cells, which involves the preferential recruitment of NF-kB p65/RelB complex to IL-12 promoters. When treating infected BALB/c mice with the pentavalent antimonial sodium stibogluconate in combination with oral imipramine, they observed a greater reduction of parasite loads in the spleen and the liver of these animals compared to treatment with sodium stibogluconate alone. This effect could be attributed to the IL-12-stimulated restoration of T cell-mediated responses in these organs [149]. Indeed, a study has already noticed that the addition of IL-12 to PBMCs from VL patients restores IFN-γ production, implicating this cytokine in the resolution of VL disease [136]. Another interesting study investigated the antileishmanial activity of imiquimod, a drug classified as a modifier of the immune response, especially acting on monocytes and macrophages, and used, for example, as a topical treatment for genital warts caused by papillomaviruses [150]. The treatment of L. donovani-infected bone marrow-derived macrophages (BMM) with imiquimod was shown to control parasite proliferation in these cells. Similarly, when used as a topical treatment on cutaneous lesions of L. major-infected BALB/c mice, 5% imiquimod cream induced a significant reduction in the lesions and local parasite loads. Imiquimod did not trigger toxicity directly against parasites, but, instead, it activated infected

Vaccines for Leishmaniasis

cells to produce NO. Surprisingly, the authors had evidence for the activation of pathways related to AP-1 and NF-kB in these cells but not for Jak/STAT1 signaling pathway classically associated with IFN-γ induction of iNOS expression [151]. Later, El Hajj et al. demonstrated that the activity of imiquimod and its analog EAPB0503 was mediated by binding and/or up-regulation of TLR-7, activating the NF-kB pathway. Increased expression of iNOS and production of proinflammatory cytokines, such as IL-12, IL-1β, TNF-α, and IL-6, were also observed after treatment of macrophages infected with distinct CL-causing Leishmania spp. in this study [152]. TLRs are an important class of innate pattern recognition receptors, and they have already been implicated in recognition of Leishmania parasites (e.g., TLR2, TLR4, and TLR9), mediating the establishment of protective immunity [5]. Therefore, the development of drugs that promote the modulation of TLRs to control intracellular growth and survival of these parasites could be a promising approach.

8.5 Vaccines for Leishmaniasis

Traditional strategies for vaccine development to control leishmaniasis have failed to reproduce the lifelong immunity to reinfection observed in patients clinically recovering from the disease but maintaining chronic infections (concomitant immunity) considered subclinical. This inability to design prophylactic vaccines that trigger long-lasting protection against Leishmania antigens in humans reflects the gap in the current understanding of the relationship between disease pathogenesis and host immune responses generated against Leishmania parasites, and the challenge of translating experimental evidence from animal models to human cases [18, 153]. The inoculation of live and virulent Leishmania parasites termed leishmanization was a common vaccination strategy in endemic areas that conferred protection to natural and exacerbated Leishmania infections induced by sandfly transmission, but this practice was largely discontinued due to safety and reproducibility issues [154]. Attempts to emulate the anti-Leishmania responses acquired by “leishmanized” individuals with whole-killed or attenuated parasitebased formulations have raised concerns regarding the low immunogenicity and the potential reversion to a more virulent phenotype, respectively, in addition to variations in vaccine efficacy observed in clinical trials in different areas [153–155]. Yet, the use of adjuvants to tailor immune responses through activation of specific innate pathogen-recognition receptors (PRRs), such as TLRs, has been shown to enhance the generation of Th1 memory cells and parasite-specific responses to secondary challenges [156]. Highly conserved immunogenic proteins or epitopes between Leishmania spp. have the potential to confer cross-protection as demonstrated for L. donovani nucleoside hydrolase (NH36) and/or its recombinant fragments F1 (N-terminal domain) and F3 (C-terminal domain) formulated with saponin, which induced antigen-specific protection in BALB/c mice against L. amazonensis and L. braziliensis [157–159]. Other Leishmania proteins evaluated as vaccine candidates are reviewed in [160].

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Similarly, recombinant proteins composed of fused peptides or epitopes have been developed to improve the efficacy of vaccine candidates in heterogeneous populations [160]. To illustrate, the polyprotein Leish-111f (a combination of L. major thiol-specific antioxidant [TSA] and stress-inducible protein-1 [LmSTI1], and L. braziliensis elongation initiation factor [LeIF]), when adjuvanted with the TLR-4 agonist monophosphoryl lipid A (MPL) + squalene, elicited promising results in trials with healthy and CL patients, and with the recent advances in bioinformatics tools, the identification of more immunogenic combinations of peptides will likely happen [160, 161]. An alternative and attractive vaccine approach is the delivery of recombinant nucleic acids (DNA and RNA) encoding Leishmania antigenic proteins that can be expressed in vivo and loaded into MHC molecules on transfected cells [162, 163]. Some of the advantages of nucleic acid-based vaccines are their stability, the number of antigens and adjuvants that can be encoded by a single vector, the expression of structurally unaltered immunogenic proteins in recipient cells and the antigen-specific stimulation of CD4+ and CD8+ T cell responses [153, 164]. Several tests with DNA-based vaccines have been conducted in experimental models of leishmaniasis using different routes of administration. Overall, they seem to confer or enhance protection against a number of Leishmania spp. in animal models, but their safety and efficacy in humans remain controversial [162, 165– 167]. However, some candidates were shown to be immunogenic in clinical trials, including an adenoviral-based platform (ChAd63-KH) encoding a polyprotein (a combination of kinetoplastid membrane protein-11 [KMP-11] and hydrophilic acylated surface protein B [HASPB] genes from L. donovani), which induced parasite-specific CD8+ T cell responses when tested in Phase I trials, suggesting its potential use as prophylactic and therapeutic vaccine for VL or PKDL [168]. Contrasting with DNA vaccines, mRNAs associated or not with viral vectors have the advantage of accumulating in the cytoplasm and being degraded once proteins are translated, thus minimizing the risks of integration of foreign genetic material into the host genome. Nonetheless, RNA vaccines can be quickly recognized and deteriorated after injection, restricting their availability in the organism [164]. Hence, efforts have been made to improve RNA delivery systems, including the development of more stable formulations with liposomes or nanoparticles [169, 170]. A novel and more organic strategy involve the potential use of exosomes to deliver not only nuclei acids but other parasite-derived components, such as lipids and proteins, that can elicit robust immune responses [27, 171–174]. These small extracellular vesicles are naturally employed in cargo transportation among cells, and exosomes loaded with exogenous cargo molecules, such as miRNA and siRNA, have been tested for therapeutic purposes in the cancer field as a less toxic and more efficient alternative for systemic delivery [175]. Recent studies have shown that, in addition to the generation of long-lived memory T cells, effective vaccination strategies should consider other critical factors contributing to the establishment of Th1 concomitant immunity during persistent infection, such as the effect of vector-related components and the subsets

Concluding Remarks

of CD4+ T cells mediating this process [176, 177]. Using the resistant C57BL/6 mice model chronically infected with L. major, Peters et al. demonstrated that a short-lived subset of IFN-γ-producing effector T cells (CD44+CD62L-T-bet+Ly6C+), which require continuous exposure to antigens and are not derived from reactivated memory cells, mediate Th1 concomitant immune response to a secondary infection [178]. Thus, the development of vaccines promoting the maintenance and recruitment of these effector cells to the infection sites could be a successful approach. Remarkable discrepancies among studies evaluating vaccine efficacy may be caused by differences in the inoculum doses of Leishmania parasites, routes of administration, and experimental models of parasite transmission [18, 179, 180]. Infected sandflies inoculate low doses of parasites into the skin and induce higher inflammatory responses when compared to needle inoculation of parasites in experimental leishmaniasis [26, 179]. While vaccination with autoclaved Leishmania antigen combined with CpG-ODN adjuvant protected C57BL/6 mice against needle challenge with L. major, it failed to protect mice against infected sandfly challenge, implicating the crucial participation of vectorderived factors in the establishment of natural infections [177]. Indeed, sandfly transmission promotes a prolonged influx of neutrophils at the bite sites, which is associated with early modulation of immunological factors that create a favorable microenvironment for parasite growth [21]. Hence, a vaccine that elicits a rapid protective T-cell mediated response and maintains the recruitment of parasitespecific effector cells is likely to prevent the very early establishment of parasites in permissive phagocytes.

8.6 Concluding Remarks

Leishmaniasis is a disease with highly variable and complex manifestations. Although the use of rodent models has allowed the characterization of immunopathological aspects of the disease, they show substantial limitations in reproducing features of human leishmaniasis, especially the immune mechanisms promoting disease severity [5]. Thus, understanding the factors driving the distinct clinical manifestations of leishmaniasis remains a challenge and highlights the complexity and uniqueness involving the relationship of different Leishmania spp., and strains with their hosts. Although a clinical cure can be observed in certain cases, the sterile immunity (complete elimination of parasites) is difficult to be achieved and one of the main goals is to develop vaccines that confer long-term protection [17, 18]. In addition, the combination of schemes directly targeting parasites and modulating the host immune system is definitely a promising approach and, perhaps, an improved alternative to toxic chemotherapies currently in use [146]. Hence, better knowledge on T cell populations participating in Leishmania infection and their interplay with macrophages, monocytes, and DCs, which are also critical in this process [18], will likely lead to novel strategies for the development of efficacious treatments and vaccines.

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Abbreviations BMM: CL: DCs: DCL: dsRNA: GLV: HASPB: HDAC: HIF-1α: IFN: IFNγR: IL: IL1R: iNOS: KMP-11: LCL: LPG: LRV: MCL: MDR: MHC: MIP: MOR: MPL: MVBs: NETs: NK: NO: PBMCs: PI3K: PKDL: PPGs: PRRs: PSG: RNS: ROS: SOD: SODA: SODB1: TGF: TLR:

bone marrow-derived macrophages cutaneous leishmaniasis dendritic cells diffuse CL double-stranded RNA Giardia lamblia viruses hydrophilic acylated surface protein B histone deacetylase hypoxia-inducible factor-1α interferon IFN-γ receptor interleukin IL-1 receptor inducible nitric oxide synthase kinetoplastid membrane protein-11 localized CL lipophosphoglycan Leishmania double-stranded RNA viruses mucocutaneous leishmaniasis multidrug resistance protein major histocompatibility complex Macrophage Inflammatory Protein μ-opioid receptors monophosphoryl lipid A multivesicular bodies neutrophil extracellular traps natural killer nitric oxide peripheral blood mononuclear cells phosphatidylinositol 3-kinase post-kala-azar dermal leishmaniasis proteophosphoglycans pathogen-recognition receptors promastigote secretory gel reactive nitrogen species reactive oxygen species superoxide dismutase superoxide dismutase A superoxide dismutase B1 transforming growth factor toll-like receptor

References

TNF: TSA: TVV: vATPase: VL: WHO:

tumor necrosis factor thiol-specific antioxidant Trichomonas vaginalis viruses vacuolar ATPase visceral leishmaniasis World Health Organization

Disclosures and Conflict of Interest

This chapter was originally published as: dos Santos Meira, C., Gedamu, L. (2019). Protective or detrimental? Understanding the role of host immunity in leishmaniasis. Microorganisms, 7, 695, https://doi.org/10.3390/ microorganisms7120695, under the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates, by kind permission of the copyright holders. Author Contributions: C.d.S.M. wrote the chapter, and L.G. critically revised all versions of the chapter. Funding: This work was supported by a grant provided to LG (University of Calgary, Canada) by the Natural Sciences and Engineering Council of Canada (NSERC; Grant No. 12034). CSM was supported by the Alberta Innovates— Technology Futures (AITF) Graduate Scholarship. The funder had no role in the literature search, decision to publish, or preparation of the chapter. Acknowledgments: The authors would like to thank Matheus Batista Heitor Carneiro (Snyder Institute for Chronic Diseases, Department of Microbiology, Immunology and Infectious Diseases, Cumming School of Medicine, University of Calgary) for his support and input during the preparation of the chapter. Conflicts of Interest: The authors declare no conflict of interest.

References

1. W.H.O. Leishmaniasis. Available at: https://www.who.int/news-room/fact-sheets/ detail/leishmaniasis (accessed on May 15, 2021).

2. Desjeux, P. Leishmaniasis: Current situation and new perspectives. Comp. Immunol. Microbiol. Infect. Dis. 2004, 27, 305–318.

3. Santos, D.O., Coutinho, C.E.R., Madeira, M.F., Bottino, C.G., Vieira, R.T., Nascimento, S.B., Bernardino, A., Bourguignon, S.C., Corte-Real, S., Pinho, R.T., et al. Leishmaniasis treatment—A challenge that remains: A review. Parasitol. Res. 2008, 103, 1–10.

4. Oryan, A., Akbari, M. Worldwide risk factors in leishmaniasis. Asian Pac. J. Trop. Med. 2016, 9, 925–932.

5. Bruschi, F., Gradoni, L. The Leishmaniases: Old Neglected Tropical Diseases, Springer: Berlin, Germany, 2018.

173

174

The Role of Host Immunity in Leishmaniasis

6. Bates, P.A., Rogers, M.E. New insights into the developmental biology and transmission mechanisms of Leishmania. Curr. Mol. Med. 2004, 4, 601–609. 7. Torres-Guerrero, E., Quintanilla-Cedillo, M.R., Ruiz-Esmenjaud, Leishmaniasis: A review. F1000Research 2017, 6, 750.

J.,

Arenas,

R.

8. Natarajan, G., Oghumu, S., Varikuti, S., Thomas, A., Satoskar, A. Mechanisms of immunopathology of leishmaniasis. In Pathogenesis of Leishmaniasis: New Developments in Research, Satoskar, A., Durvasula, R., Eds., Springer, New York, NY, USA, 2014, pp. 1–13.

9. Lainson, R., Shaw, J.J. New World leishmaniasis. In Topley & Wilson’s Microbiology and Microbial Infections, John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2010.

10. Burza, S., Croft, S.L., Boelaert, M. Leishmaniasis. Lancet 2018, 392, 951–970.

11. Scott, P., Novais, F.O. Cutaneous leishmaniasis: Immune responses in protection and pathogenesis. Nat. Rev. Immunol. 2016, 16, 581. 12. Strazzulla, A., Cocuzza, S., Pinzone, M.R., Postorino, M.C., Cosentino, S., Serra, A., Cacopardo, B., Nunnari, G. Mucosal leishmaniasis: An underestimated presentation of a neglected disease. Biomed. Res. Int. 2013, 2013, 7. 13. Wilson, M.E., Jeronimo, S.M.B., Pearson, R.D. Immunopathogenesis of infection with the visceralizing Leishmania species. Microb. Pathog. 2005, 38, 147–160.

14. Kaye, P.M. The immunology of visceral leishmaniasis: Current status. In Leishmania, Farrell, J.P., Ed., Springer US: Boston, MA, USA, 2002, pp. 137–150. 15. Zijlstra, E.E. The immunology of post-kala-azar dermal leishmaniasis (PKDL). Parasites Vectors 2016, 9, 464.

16. Zijlstra, E.E., El-Hassan, A.M. Leishmaniasis in Sudan. 3. Visceral leishmaniasis. Trans. R. Soc. Trop. Med. Hyg. 2001, 95, S27–S58.

17. Kaye, P., Scott, P. Leishmaniasis: Complexity at the host–pathogen interface. Nat. Rev. Microbiol. 2011, 9, 604.

18. Hohman, L.S., Peters, N.C. CD4+ T cell-mediated immunity against the phagosomal pathogen Leishmania: Implications for vaccination. Trends Parasitol. 2019, 35, 423–435. 19. W.H.O. Leishmaniasis: Situation and trends. Available at: https://www.who.int/gho/ neglected_diseases/leishmaniasis/en/ (accessed on May 15, 2021).

20. Gonçalves-de-Albuquerque, S.D.C., Pessoa-e-Silva, R., Trajano-Silva, L.A.M., de Goes, T.C., de Morais, R.C.S., da Oliveira, C.N., de Lorena, V.M.B., de Paiva-Cavalcanti, M. The equivocal role of Th17 cells and neutrophils on immunopathogenesis of leishmaniasis. Front. Immunol. 2017, 8, 1437.

21. Peters, N.C., Egen, J.G., Secundino, N., Debrabant, A., Kimblin, N., Kamhawi, S., Lawyer, P., Fay, M.P., Germain, R.N., Sacks, D. In vivo imaging reveals an essential role for neutrophils in leishmaniasis transmitted by sand flies. Science 2008, 321, 970.

22. Ritter, U., Frischknecht, F., van Zandbergen, G. Are neutrophils important host cells for Leishmania parasites? Trends Parasitol. 2009, 25, 505–510.

23. Gorak, P.M.A., Engwerda, C.R., Kaye, P.M. Dendritic cells, but not macrophages, produce IL-12 immediately following Leishmania donovani infection. Eur. J. Immunol. 1998, 28, 687–695.

24. Carneiro, M.B., Hohman, L.S., Egen, J.G., Peters, N.C. Use of two-photon microscopy to study Leishmania major infection of the skin. Methods 2017, 127, 45–52.

References

25. Lestinova, T., Rohousova, I., Sima, M., de Oliveira, C.I., Volf, P. Insights into the sand fly saliva: Blood-feeding and immune interactions between sand flies, hosts, and Leishmania. PLoS Negl. Trop. Dis. 2017, 11, e0005600. 26. Abdeladhim, M., Kamhawi, S., Valenzuela, J.G. What’s behind a sand fly bite? The profound effect of sand fly saliva on host hemostasis, inflammation and immunity. Infect. Genet. Evol. 2014, 28, 691–703.

27. Atayde, V.D., Suau, H.A., Townsend, S., Hassani, K., Kamhawi, S., Olivier, M. Exosome secretion by the parasitic protozoan Leishmania within the sand fly midgut. Cell Rep. 2015, 13, 957–967. 28. Rogers, M. The role of Leishmania proteophosphoglycans in sand fly transmission and infection of the mammalian host. Front. Microbiol. 2012, 3, 223. 29. Figueiredo, A.B.d., Souza-Testasicca, M.C., Afonso, L.C.C. Purinergic signaling and infection by Leishmania: A new approach to evasion of the immune response. Biomed. J. 2016, 39, 244–250.

30. Dey, R., Joshi, A.B., Oliveira, F., Pereira, L., Guimarães-Costa, A.B., Serafim, T.D., de Castro, W., Coutinho-Abreu, I.V., Bhattacharya, P., Townsend, S., et al. Gut microbes egested during bites of infected sand flies augment severity of leishmaniasis via inflammasomederived IL-1β. Cell Host Microbe 2018, 23, 134–143.e136.

31. Hurrell, B.P., Schuster, S., Grün, E., Coutaz, M., Williams, R.A., Held, W., Malissen, B., Malissen, M., Yousefi, S., Simon, H.-U., et al. Rapid sequestration of Leishmania mexicana by neutrophils contributes to the development of chronic lesion. PLoS Pathog. 2015, 11, e1004929.

32. Daboul, M. Role of neutrophils in cutaneous leishmaniasis. East. Mediterr. Health J. 2010, 16, 1055–1058.

33. Morgado, F.N., Schubach, A., Rosalino, C.M.V., Quintella, L.P., Santos, G., Salgueiro, M., Conceição-Silva, F. Is the in situ inflammatory reaction an important tool to understand the cellular immune response in American tegumentary leishmaniasis? Br. J. Dermatol. 2008, 158, 50–58.

34. Regli, I.B., Passelli, K., Hurrell, B.P., Tacchini-Cottier, F. Survival mechanisms used by some Leishmania species to escape neutrophil killing. Front. Immunol. 2017, 8, 1558.

35. Ribeiro-Gomes, F.L., Sacks, D. The influence of early neutrophil-Leishmania interactions on the host immune response to infection. Front. Cell. Infect. Microbiol. 2012, 2, 59.

36. Guimarães-Costa, A.B., Nascimento, M.T.C., Froment, G.S., Soares, R.P.P., Morgado, F.N., Conceição-Silva, F., Saraiva, E.M. Leishmania amazonensis promastigotes induce and are killed by neutrophil extracellular traps. Proc. Natl. Acad. Sci. USA 2009, 106, 6748.

37. Hurrell, B.P., Regli, I.B., Tacchini-Cottier, F. Different Leishmania species drive distinct neutrophil functions. Trends Parasitol. 2016, 32, 392–401. 38. Hurrell, B.P., Beaumann, M., Heyde, S., Regli, I.B., Müller, A.J., Tacchini-Cottier, F. Frontline Science: Leishmania mexicana amastigotes can replicate within neutrophils. J. Leukoc. Biol. 2017, 102, 1187–1198.

39. Heyde, S., Philipsen, L., Formaglio, P., Fu, Y., Baars, I., Höbbel, G., Kleinholz, C.L., Seiß, E.A., Stettin, J., Gintschel, P., et al. CD11c-expressing Ly6C+CCR2+ monocytes constitute a reservoir for efficient Leishmania proliferation and cell-to-cell transmission. PLoS Pathog. 2018, 14, e1007374.

175

176

The Role of Host Immunity in Leishmaniasis

40. Liu, D., Uzonna, J.E. The early interaction of Leishmania with macrophages and dendritic cells and its influence on the host immune response. Front. Cell. Infect. Microbiol. 2012, 2, 83.

41. Mantovani, A., Cassatella, M.A., Costantini, C., Jaillon, S. Neutrophils in the activation and regulation of innate and adaptive immunity. Nat. Rev. Immunol. 2011, 11, 1474–1741.

42. Brinkmann, V., Zychlinsky, A. Beneficial suicide: Why neutrophils die to make NETs. Nat. Rev. Microbiol. 2007, 5, 577–582. 43. Charmoy, M., Brunner-Agten, S., Aebischer, D., Auderset, F., Launois, P., Milon, G., Proudfoot, A.E.I., Tacchini-Cottier, F. Neutrophil-derived CCL3 is essential for the rapid recruitment of dendritic cells to the site of Leishmania major inoculation in resistant mice. PLoS Pathog. 2010, 6, e1000755.

44. Olivier, M., Gregory, D.J., Forget, G. Subversion mechanisms by which Leishmania parasites can escape the host immune response: A signaling point of view. Clin. Microbiol. Rev. 2005, 18, 293–305.

45. Romano, A., Carneiro, M.B.H., Doria, N.A., Roma, E.H., Ribeiro-Gomes, F.L., Inbar, E., Lee, S.H., Mendez, J., Paun, A., Sacks, D.L., et al. Divergent roles for Ly6C+CCR2+CX3CR1+ inflammatory monocytes during primary or secondary infection of the skin with the intra-phagosomal pathogen Leishmania major. PLoS Pathog. 2017, 13, e1006479. 46. Guy, R.A., Belosevic, M. Comparison of receptors required for entry of Leishmania major amastigotes into macrophages. Infect. Immun. 1993, 61, 1553–1558.

47. Kima, P.E., Constant, S.L., Hannum, L., Colmenares, M., Lee, K.S., Haberman, A.M., Shlomchik, M.J., McMahon-Pratt, D. Internalization of Leishmania mexicana complex amastigotes via the fc receptor is required to sustain infection in murine cutaneous leishmaniasis. J. Exp. Med. 2000, 191, 1063.

48. Kumar, G.A., Karmakar, J., Mandal, C., Chattopadhyay, A. Leishmania donovani internalizes into host cells via caveolin-mediated endocytosis. Sci. Rep. 2019, 9, 12636.

49. Yao, C., Donelson, J.E., Wilson, M.E. The major surface protease (MSP or GP63) of Leishmania sp. Biosynthesis, regulation of expression, and function. Mol. Biochem. Parasitol. 2003, 132, 1–16. 50. Naderer, T., McConville, M.J. The Leishmania–macrophage interaction: A metabolic perspective. Cell. Microbiol. 2008, 10, 301–308.

51. Pelletier, I., Hashidate, T., Urashima, T., Nishi, N., Nakamura, T., Futai, M., Arata, Y., Kasai, K.-i., Hirashima, M., Hirabayashi, J., et al. Specific recognition of Leishmania major poly-β-galactosyl epitopes by galectin-9: Possible implication of galectin-9 in interaction between L. major and host cells. J. Biol. Chem. 2003, 278, 22223–22230.

52. Green, P.J., Feizi, T., Stoll, M.S., Thiel, S., Prescott, A., McConville, M.J. Recognition of the major cell surface glycoconjugates of Leishmania parasites by the human serum mannan-binding protein. Mol. Biochem. Parasitol. 1994, 66, 319–328. 53. Miles, S.A., Conrad, S.M., Alves, R.G., Jeronimo, S.M.B., Mosser, D.M. A role for IgG immune complexes during infection with the intracellular pathogen Leishmania. J. Exp. Med. 2005, 201, 747. 54. Atri, C., Guerfali, F.Z., Laouini, D. Role of human macrophage polarization in inflammation during infectious diseases. Int. J. Mol. Sci. 2018, 19, 1801.

References

55. Tomiotto-Pellissier, F., Bortoleti, B.T.d.S., Assolini, J.P., Gonçalves, M.D., Carloto, A.C.M., Miranda-Sapla, M.M., Conchon-Costa, I., Bordignon, J., Pavanelli, W.R. Macrophage polarization in leishmaniasis: Broadening horizons. Front. Immunol. 2018, 9. 56. Muxel, S.M., Aoki, J.I., Fernandes, J.C.R., Laranjeira-Silva, M.F., Zampieri, R.A., Acuña, S.M., Müller, K.E., Vanderlinde, R.H., Floeter-Winter, L.M. Arginine and polyamines fate in Leishmania infection. Front. Microbiol. 2018, 8, 2682.

57. Li, S.-N., Wang, W., Fu, S.-P., Wang, J.-F., Liu, H.-M., Xie, S.-S., Liu, B.-R., Li, Y., Lv, Q.-K., Li, Z.-Q., et al. IL-21 modulates release of proinflammatory cytokines in LPSstimulated macrophages through distinct signaling pathways. Mediat. Inflamm. 2013, 2013, 12.

58. Alexander, J., Brombacher, F. T helper1/T helper2 cells and resistance/susceptibility to Leishmania infection: Is this paradigm still relevant? Front. Immunol. 2012, 3.

59. Oghumu, S., Natarajan, G. and Satoskar, A.R. Pathogenesis of Leishmaniasis in Humans. In: Singh, S. K., ed. Human Emerging and Re‐emerging Infections, Wiley, 2015, chapter 18, pp. 337–348. 60. Alexander, J., Bryson, K. T helper (h) 1/Th2 and Leishmania: Paradox rather than paradigm. Immunol. Lett. 2005, 99, 17–23.

61. Maspi, N., Abdoli, A., Ghaffarifar, F. Pro-and anti-inflammatory cytokines in cutaneous leishmaniasis: A review. Pathog. Glob. Health 2016, 110, 247–260.

62. Sharma, U., Singh, S. Immunobiology of leishmaniasis. Indian J. Exp. Biol. 2009, 47, 412–423.

63. Qi, H., Ji, J., Wanasen, N., Soong, L. Enhanced replication of Leishmania amazonensis amastigotes in gamma interferon-stimulated murine macrophages: Implications for the pathogenesis of cutaneous leishmaniasis. Infect. Immun. 2004, 72, 988–995. 64. McMahon-Pratt, D., Alexander, J. Does the Leishmania major paradigm of pathogenesis and protection hold for New World cutaneous leishmaniases or the visceral disease? Immunol. Rev. 2004, 201, 206–224.

65. Pinheiro, R.O., Rossi-Bergmann, B. Interferon-gamma is required for the late but not early control of Leishmania amazonensis infection in C57Bl/6 mice. Memórias Do Inst. Oswaldo Cruz 2007, 102, 79–82.

66. Zamboni, D.S., Sacks, D.L. Inflammasomes and Leishmania: In good times or bad, in sickness or in health. Curr. Opin. Microbiol. 2019, 52, 70–76.

67. Lima-Junior, D.S., Costa, D.L., Carregaro, V., Cunha, L.D., Silva, A.L.N., Mineo, T.W.P., Gutierrez, F.R.S., Bellio, M., Bortoluci, K.R., Flavell, R.A., et al. Inflammasome-derived IL-1β production induces nitric oxide–mediated resistance to Leishmania. Nat. Med. 2013, 19, 909. 68. Fernández-Figueroa, E.A., Rangel-Escareño, C., Espinosa-Mateos, V., Carrillo-Sánchez, K., Salaiza-Suazo, N., Carrada-Figueroa, G., March-Mifsut, S., Becker, I. Disease severity in patients infected with Leishmania mexicana relates to IL-1β. PLoS Negl. Trop. Dis. 2012, 6, e1533.

69. Desjardins, M., Descoteaux, A. Inhibition of phagolysosomal biogenesis by the Leishmania lipophosphoglycan. J. Exp. Med. 1997, 185, 2061. 70. Vinet, A.F., Fukuda, M., Turco, S.J., Descoteaux, A. The Leishmania donovani lipophosphoglycan excludes the vesicular proton-ATPase from phagosomes by impairing the recruitment of synaptotagmin V. PLoS Pathog. 2009, 5, e1000628.

177

178

The Role of Host Immunity in Leishmaniasis

71. Antonia, A.L., Gibbs, K.D., Trahair, E.D., Pittman, K.J., Martin, A.T., Schott, B.H., Smith, J.S., Rajagopal, S., Thompson, J.W., Reinhardt, R.L., et al. Pathogen evasion of chemokine response through suppression of CXCL10. Front. Cell. Infect. Microbiol. 2019, 9, 280. 72. Atayde, V.D., Hassani, K., da Silva Lira Filho, A., Borges, A.R., Adhikari, A., Martel, C., Olivier, M. Leishmania exosomes and other virulence factors: Impact on innate immune response and macrophage functions. Cell. Immunol. 2016, 309, 7–18.

73. Silva-Almeida, M., Pereira, B.A.S., Ribeiro-Guimarães, M.L., Alves, C.R. Proteinases as virulence factors in Leishmania spp. infection in mammals. Parasites Vectors 2012, 5, 160.

74. Weinheber, N., Wolfram, M., Harbecke, D., Aebischer, T. Phagocytosis of Leishmania mexicana amastigotes by macrophages leads to a sustained suppression of IL-12 production. Eur. J. Immunol. 1998, 28, 2467–2477.

75. Cameron, P., McGachy, A., Anderson, M., Paul, A., Coombs, G.H., Mottram, J.C., Alexander, J., Plevin, R. Inhibition of lipopolysaccharide-induced macrophage IL-12 production by Leishmania mexicana amastigotes: The role of cysteine peptidases and the NF-κB signaling pathway. J. Immunol. 2004, 173, 3297.

76. Somanna, A., Mundodi, V., Gedamu, L. Functional analysis of cathepsin B-like cysteine proteases from Leishmania donovani complex: Evidence for the activation of latent transforming growth factor β. J. Biol. Chem. 2002, 277, 25305–25312. 77. Gerbaba, T.K., Gedamu, L. Cathepsin B gene disruption induced Leishmania donovani proteome remodeling implies cathepsin B role in secretome regulation. PLoS ONE 2013, 8, e79951.

78. Paletta-Silva, R., Meyer-Fernandes, J.R. Adenosine and immune imbalance in visceral leishmaniasis: The possible role of ectonucleotidases. J. Trop. Med. 2012, 2012.

79. Rai, A.K., Thakur, C.P., Velpandian, T., Sharma, S.K., Ghosh, B., Mitra, D.K. High concentration of adenosine in human visceral leishmaniasis despite increased ADA and decreased CD73. Parasite Immunol. 2011, 33, 632–636.

80. Amit, A., Kumar, S., Dikhit, M.R., Jha, P.K., Singh, A.K., Sinha, K.K., Pandey, K., Das, V.N.R., Das, P., Bimal, S. Up regulation of A2B adenosine receptor on monocytes are crucially required for immune pathogenicity in Indian patients exposed to Leishmania donovani. Cytokine 2016, 79, 38–44. 81. Castro, H., Sousa, C., Novais, M., Santos, M., Budde, H., Cordeiro-da-Silva, A., Flohé, L., Tomás, A.M. Two linked genes of Leishmania infantum encode tryparedoxins localised to cytosol and mitochondrion. Mol. Biochem. Parasitol. 2004, 136, 137–147.

82. Dumas, C., Ouellette, M., Tovar, J., Cunningham, M.L., Fairlamb, A.H., Tamar, S., Olivier, M., Papadopoulou, B. Disruption of the trypanothione reductase gene of Leishmania decreases its ability to survive oxidative stress in macrophages. EMBO J. 1997, 16, 2590. 83. Krauth-Siegel, R.L., Meiering, S.K., Schmidt, H. The parasite-specific trypanothione metabolism of Trypanosoma and Leishmania. Biol. Chem. 2003, 384, 539.

84. Tovar, J., Cunningham, M.L., Smith, A.C., Croft, S.L., Fairlamb, A.H. Down-regulation of Leishmania donovani trypanothione reductase by heterologous expression of a trans-dominant mutant homologue: Effect on parasite intracellular survival. Proc. Natl. Acad. Sci. USA 1998, 95, 5311.

References

85. Barr, S.D., Gedamu, L. Role of peroxidoxins in Leishmania chagasi survival: Evidence of an enzymatic defense against nitrosative stress. J. Biol. Chem. 2003, 278, 10816–10823.

86. Barr, S.D., Gedamu, L. Cloning and characterization of three differentially expressed peroxidoxin genes from Leishmania chagasi: Evidence for an enzymatic detoxification of hydroxyl radicals. J. Biol. Chem. 2001, 276, 34279–34287.

87. Acestor, N., Masina, S., Ives, A., Walker, J., Saravia, N.G., Fasel, N. Resistance to oxidative stress is associated with metastasis in mucocutaneous leishmaniasis. J. Infect. Dis. 2006, 194, 1160–1167.

88. Iyer, J.P., Kaprakkaden, A., Choudhary, M.L., Shaha, C. Crucial role of cytosolic tryparedoxin peroxidase in Leishmania donovani survival, drug response and virulence. Mol. Microbiol. 2008, 68, 372–391. 89. Plewes, K.A., Barr, S.D., Gedamu, L. Iron superoxide dismutases targeted to the glycosomes of Leishmania chagasi are important for survival. Infect. Immun. 2003, 71, 5910. 90. Davenport, B.J., Martin, C.G., Beverley, S.M., Orlicky, D.J., Vazquez-Torres, A., Morrison, T.E. SODB1 is essential for Leishmania major infection of macrophages and pathogenesis in mice. PLoS Negl. Trop. Dis. 2018, 12, e0006921. 91. Mittra, B., Laranjeira-Silva, M.F., Miguel, D.C., Perrone Bezerra de Menezes, J., Andrews, N.W. The iron-dependent mitochondrial superoxide dismutase SODA promotes Leishmania virulence. J. Biol. Chem. 2017, 292, 12324–12338.

92. Getachew, F., Gedamu, L. Leishmania donovani mitochondrial iron superoxide dismutase A is released into the cytosol during miltefosine induced programmed cell death. Mol. Biochem. Parasitol. 2012, 183, 42–51. 93. Veronica, J., Chandrasekaran, S., Dayakar, A., Devender, M., Prajapati, V.K., Sundar, S., Maurya, R. Iron superoxide dismutase contributes to miltefosine resistance in Leishmania donovani. FEBS J. 2019, 286, 3488–3503.

94. Nylén, S., Eidsmo, L. Tissue damage and immunity in cutaneous leishmaniasis. Parasite Immunol. 2012, 34, 551–561.

95. Akuffo, H., Maasho, K., Blostedt, M., Jeberg, B., Britton, S., Bakhiet, M. Leishmania aethiopica derived from diffuse leishmaniasis patients preferentially induce mRNA for interleukin-10 while those from localized leishmaniasis patients induce interferon-gamma. J. Infect. Dis. 1997, 175, 737–741. 96. Rivera-Fernández, I., Argueta-Donohué, J., Wilkins-Rodríguez, A.A., Gutiérrez-Kobeh, L. Effect of two different isolates of Leishmania mexicana in the production of cytokines and phagocytosis by murine dendritic cells. J. Parasitol. 2019, 105, 359–370.

97. Barroso, P.A., Marco, J.D., Calvopina, M., Kato, H., Korenaga, M., Hashiguchi, Y. A trial of immunotherapy against Leishmania amazonensis infection in vitro and in vivo with Z-100, a polysaccharide obtained from Mycobacterium tuberculosis, alone or combined with meglumine antimoniate. J. Antimicrob. Chemother. 2007, 59, 1123–1129.

98. Costa, J.M., França-Costa, J., Van Weyenbergh, J., dos-Santos, W.L.C., Santos de Campos, D.C., Malta-Santos, H., Oliveira, M.C.S., Luz, N.F., Boaventura, V.S., Barral, A., et al. Arginase I, polyamine, and prostaglandin E2 pathways suppress the inflammatory response and contribute to diffuse cutaneous leishmaniasis. J. Infect. Dis. 2014, 211, 426–435.

179

180

The Role of Host Immunity in Leishmaniasis

99. Bacellar, O., Lessa, H., Schriefer, A., Machado, P., Ribeiro de Jesus, A., Dutra, W.O., Gollob, K.J., Carvalho, E.M. Up-regulation of Th1-type responses in mucosal leishmaniasis patients. Infect. Immun. 2002, 70, 6734.

100. Hartley, M.A., Ronet, C., Zangger, H., Beverley, S.M., Fasel, N. Leishmania RNA virus: When the host pays the toll. Front. Cell. Infect. Microbiol. 2012, 2, 99.

101. Faria, D.R., Souza, P.E.A., Durães, F.V., Carvalho, E.M., Gollob, K.J., Machado, P.R., Dutra, W.O. Recruitment of CD8+ T cells expressing granzyme A is associated with lesion progression in human cutaneous leishmaniasis. Parasite Immunol. 2009, 31, 432–439.

102. Oliveira, W.N., Ribeiro, L.E., Schrieffer, A., Machado, P., Carvalho, E.M., Bacellar, O. The role of inflammatory and anti-inflammatory cytokines in the pathogenesis of human tegumentary leishmaniasis. Cytokine 2014, 66, 127–132. 103. Novais, F.O., Carvalho, L.P., Passos, S., Roos, D.S., Carvalho, E.M., Scott, P., Beiting, D.P. Genomic profiling of human Leishmania braziliensis lesions identifies transcriptional modules associated with cutaneous immunopathology. J. Investig. Dermatol. 2015, 135, 94–101.

104. Gómez-Arreaza, A., Haenni, A.L., Dunia, I., Avilán, L. Viruses of parasites as actors in the parasite-host relationship: A “ménage à trois”. Acta Trop 2017, 166, 126–132.

105. Saberi, R., Fakhar, M., Mohebali, M., Anvari, D., Gholami, S. Global status of synchronizing Leishmania RNA virus in Leishmania parasites: A systematic review with meta-analysis. Transbound. Emerg. Dis. 2019, 0.

106. Grybchuk, D., Kostygov, A.Y., Macedo, D.H., Avila-Levy, C.M., Yurchenko, V. RNA viruses in trypanosomatid parasites: A historical overview. Memórias Inst. Oswaldo Cruz 2018, 113, e170487.

107. Zangger, H., Hailu, A., Desponds, C., Lye, L.F., Akopyants, N.S., Dobson, D.E., Ronet, C., Ghalib, H., Beverley, S.M., Fasel, N. Leishmania aethiopica field isolates bearing an endosymbiontic dsRNA virus induce pro-inflammatory cytokine response. PLoS Negl. Trop. Dis. 2014, 8, e2836.

108. Lye, L.F., Owens, K., Shi, H., Murta, S.M.F., Vieira, A.C., Turco, S.J., Tschudi, C., Ullu, E., Beverley, S.M. Retention and loss of RNA interference pathways in trypanosomatid protozoans. PLoS Pathog. 2010, 6, e1001161. 109. Rayamajhi, M., Humann, J., Penheiter, K., Andreasen, K., Lenz, L.L. Induction of IFN-αβ enables Listeria monocytogenes to suppress macrophage activation by IFN-γ. J. Exp. Med. 2010, 207, 327.

110. Ives, A., Ronet, C., Prevel, F., Ruzzante, G., Fuertes-Marraco, S., Schutz, F., Zangger, H., Revaz-Breton, M., Lye, L.F., Hickerson, S.M., et al. Leishmania RNA virus controls the severity of mucocutaneous leishmaniasis. Science 2011, 331, 775.

111. Fichorova, R.N., Lee, Y., Yamamoto, H.S., Takagi, Y., Hayes, G.R., Goodman, R.P., ChepaLotrea, X., Buck, O.R., Murray, R., Kula, T., et al. Endobiont viruses sensed by the human host – beyond conventional antiparasitic therapy. PLoS ONE 2012, 7, e48418.

112. Eren, R.O., Reverte, M., Rossi, M., Hartley, M.-A., Castiglioni, P., Prevel, F., Martin, R., Desponds, C., Lye, L.-F., Drexler, S.K., et al. Mammalian innate immune response to a Leishmania-resident RNA virus increases macrophage survival to promote parasite persistence. Cell Host Microbe 2016, 20, 318–328.

References

113. Ito, M.M., Catanhêde, L.M., Katsuragawa, T.H., Silva Junior, C.F.d., Camargo, L.M.A., Mattos, R.d.G., Vilallobos-Salcedo, J.M. Correlation between presence of Leishmania RNA virus 1 and clinical characteristics of nasal mucosal leishmaniosis. Braz. J. Otorhinolaryngol. 2015, 81, 533–540.

114. Cantanhêde, L.M., da Silva Júnior, C.F., Ito, M.M., Felipin, K.P., Nicolete, R., Salcedo, J.M.V., Porrozzi, R., Cupolillo, E., Ferreira, R.d.G.M. Further evidence of an association between the presence of Leishmania RNA virus 1 and the mucosal manifestations in tegumentary leishmaniasis patients. PLoS Negl. Trop. Dis. 2015, 9, e0004079.

115. Ginouvès, M., Simon, S., Bourreau, E., Lacoste, V., Ronet, C., Couppié, P., Nacher, M., Demar, M., Prévot, G. Prevalence and distribution of Leishmania RNA virus 1 in Leishmania parasites from French Guiana. Am. J. Trop. Med. Hyg. 2016, 94, 102–106.

116. Llanos-Cuentas, A., Arevalo, J., Adaui, V., Zimic, M., Garcia, L., Maes, I., De Doncker, S., Dujardin, J.-C., Dobson, D.E., Lye, L.-F., et al. Association of the endobiont double-stranded RNA virus LRV1 with treatment failure for human leishmaniasis caused by Leishmania braziliensis in Peru and Bolivia. J. Infect. Dis. 2015, 213, 112–121.

117. Bourreau, E., Prévot, G., Ginouves, M., Couppié, P., Bertolotti, A., Sainte-Marie, D., Dufour, J., Ronet, C., Hartley, M.-A., Fasel, N., et al. Presence of Leishmania RNA virus 1 in Leishmania guyanensis increases the risk of first-line treatment failure and symptomatic relapse. J. Infect. Dis. 2015, 213, 105–111.

118. Pereira, L.D.O.R., Maretti-Mira, A.C., Rodrigues, K.M., Lima, R.B., Oliveira-Neto, M.P.D., Cupolillo, E., Pirmez, C., Oliveira, M.P.D. Severity of tegumentary leishmaniasis is not exclusively associated with Leishmania RNA virus 1 infection in Brazil. Memórias Inst. Oswaldo Cruz 2013, 108, 665–667.

119. Atayde, V.D., da Silva Lira Filho, A., Chaparro, V., Zimmermann, A., Martel, C., Jaramillo, M., Olivier, M. Exploitation of the Leishmania exosomal pathway by Leishmania RNA virus 1. Nat. Microbiol. 2019, 4, 714–723.

120. Wilson, M.E., Innes, D.J., de Anastacio, Q.S., Pearson, R.D. Early histopathology of experimental infection with Leishmania donovani in hamsters. J. Parasitol. 1987, 73, 55–63. 121. Wilson, M.E., Sandor, M., Blum, A.M., Young, B.M., Metwali, A., Elliott, D., Lynch, R.G., Weinstock, J.V. Local suppression of IFN-gamma in hepatic granulomas correlates with tissue-specific replication of Leishmania chagasi. J. Immunol. 1996, 156, 2231–2239.

122. Dutra, R.A., Dutra, L.F., de Reis, O., Lambert, R.C. Splenectomy in a patient with treatment-resistant visceral leishmaniasis: A case report. Rev. Soc. Bras. Med. Trop 2012, 45, 130–131.

123. McCall, L.I., Zhang, W.W., Matlashewski, G. Determinants for the development of visceral leishmaniasis disease. PLoS Pathog. 2013, 9, e1003053.

124. Farahmand, M., Atashi Shirazi, H., Nahrevanian, H., Hajjaran, H. Molecular analysis of A2-genes encoding stage-specific S antigen-like proteins among isolates from Iranian cutaneous and visceral leishmaniasis. Iran. J. Basic Med. Sci. 2011, 14, 407–413.

125. McCall, L.I., Matlashewski, G. Localization and induction of the A2 virulence factor in Leishmania: Evidence that A2 is a stress response protein. Mol. Microbiol. 2010, 77, 518–530.

181

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The Role of Host Immunity in Leishmaniasis

126. Garin, Y.J.F., Meneceur, P., Pratlong, F., Dedet, J.-P., Derouin, F., Lorenzo, F. A2 gene of Old World cutaneous Leishmania is a single highly conserved functional gene. BMC Infect. Dis. 2005, 5, 18. 127. Zhang, W.W., Mendez, S., Ghosh, A., Myler, P., Ivens, A., Clos, J., Sacks, D.L., Matlashewski, G. Comparison of the A2 gene locus in Leishmania donovani and Leishmania major and its control over cutaneous infection. J. Biol. Chem. 2003, 278, 35508–35515.

128. Sharma, P., Gurumurthy, S., Duncan, R., Nakhasi, H.L., Salotra, P. Comparative in vivo expression of amastigote up regulated Leishmania genes in three different forms of leishmaniasis. Parasitol. Int. 2010, 59, 262–264.

129. Melby, P.C., Tabares, A., Restrepo, B.I., Cardona, A.E., McGuff, H.S., Teale, J.M. Leishmania donovani: Evolution and architecture of the splenic cellular immune response related to control of infection. Exp. Parasitol. 2001, 99, 17–25.

130. Murray, H.W. Tissue granuloma structure-function leishmaniasis. Int. J. Exp. Pathol. 2001, 82, 249–267.

in

experimental

visceral

131. Beattie, L., Peltan, A., Maroof, A., Kirby, A., Brown, N., Coles, M., Smith, D.F., Kaye, P.M. Dynamic imaging of experimental Leishmania donovani-induced hepatic granulomas detects kupffer cell-restricted antigen presentation to antigen-specific CD8+ T cells. PLoS Pathog. 2010, 6, e1000805.

132. Wilson, M.E., Young, B.M., Davidson, B.L., Mente, K.A., McGowan, S.E. The importance of TGF-β in murine visceral leishmaniasis. J. Immunol. 1998, 161, 6148.

133. Gantt, K.R., Goldman, T.L., McCormick, M.L., Miller, M.A., Jeronimo, S.M.B., Nascimento, E.T., Britigan, B.E., Wilson, M.E. Oxidative responses of human and murine macrophages during phagocytosis of Leishmania chagasi. J. Immunol. 2001, 167, 893–901.

134. Carvalho, E.M., Barral, A., Pedral-Sampaio, D., Barral-Netto, M., Badaro, R., Rocha, H., Johnson, W.D., Jr. Immunologic markers of clinical evolution in children recently infected with Leishmania donovani chagasi. J. Infect. Dis. 1992, 165, 535–540.

135. Ghalib, H.W., Whittle, J.A., Kubin, M., Hashim, F.A., El-Hassan, A.M., Grabstein, K.H., Trinchieri, G., Reed, S.G. IL-12 enhances Th1-type responses in human Leishmania donovani infections. J. Immunol. 1995, 154, 4623.

136. Bacellar, O., Brodskyn, C., Guerreiro, J., Barral-Netto, M., Costa, C.H., Coffman, R.L., Johnson, W.D., Carvalho, E.M. Interleukin-12 restores interferon-γ production and cytotoxic responses in visceral leishmaniasis. J. Infect. Dis. 1996, 173, 1515–1518. 137. Singh, O.P., Gidwani, K., Kumar, R., Nylén, S., Jones, S.L., Boelaert, M., Sacks, D., Sundar, S. Reassessment of immune correlates in human visceral leishmaniasis as defined by cytokine release in whole blood. Clin. Vaccine Immunol. 2012, 19, 961–966.

138. Faleiro, R.J., Kumar, R., Hafner, L.M., Engwerda, C.R. Immune regulation during chronic visceral leishmaniasis. PLoS Negl. Trop. Dis. 2014, 8, e2914.

139. Ato, M., Maroof, A., Zubairi, S., Nakano, H., Kakiuchi, T., Kaye, P.M. Loss of dendritic cell migration and impaired resistance to Leishmania donovani infection in mice deficient in CCL19 and CCL21. J. Immunol. 2006, 176, 5486.

140. Hammami, A., Abidin, B.M., Heinonen, K.M., Stäger, S. HIF-1α hampers dendritic cell function and Th1 generation during chronic visceral leishmaniasis. Sci. Rep. 2018, 8, 3500.

References

141. Hammami, A., Charpentier, T., Smans, M., Stäger, S. IRF-5-mediated inflammation limits CD8+ T cell expansion by inducing HIF-1α and impairing dendritic cell functions during Leishmania Infection. PLoS Pathog. 2015, 11, e1004938.

142. Ato, M., Stäger, S., Engwerda, C.R., Kaye, P.M. Defective CCR7 expression on dendritic cells contributes to the development of visceral leishmaniasis. Nat. Immunol. 2002, 3, 1185–1191.

143. Gasim, S., Elhassan, A.M., Khalil, E.A.G., Ismail, A., Kadaru, A.M.Y., Kharazmi, A., Theander, T.G. High levels of plasma IL-10 and expression of IL-10 by keratinocytes during visceral leishmaniasis predict subsequent development of post-kala-azar dermal leishmaniasis. Clin. Exp. Immunol. 1998, 111, 64–69.

144. Hartley, M.A., Kohl, K., Ronet, C., Fasel, N. The therapeutic potential of immune cross-talk in leishmaniasis. Clin. Microbiol. Infect. 2013, 19, 119–130.

145. Berg, M., Mannaert, A.N., Vanaerschot, M., Van Der Auwera, G., Dujardin, J.C. (Post-) Genomic approaches to tackle drug resistance in Leishmania. Parasitology 2013, 140, 1492–1505.

146. Lamotte, S., Späth, G.F., Rachidi, N., Prina, E. The enemy within: Targeting host–parasite interaction for antileishmanial drug discovery. PLoS Negl. Trop. Dis. 2017, 11, e0005480.

147. Hendrickx, S., Caljon, G., Maes, L. Need for sustainable approaches in antileishmanial drug discovery. Parasitol. Res. 2019, 118, 2743–2752.

148. De Muylder, G., Vanhollebeke, B., Caljon, G., Wolfe, A.R., McKerrow, J., Dujardin, J.C. Naloxonazine, an amastigote-specific compound, affects Leishmania parasites through modulation of host-encoded functions. PLoS Negl. Trop. Dis. 2016, 10, e0005234. 149. Mukherjee, S., Mukherjee, B., Mukhopadhyay, R., Naskar, K., Sundar, S., Dujardin, J.C., Roy, S. Imipramine exploits histone deacetylase 11 to increase the IL-12/IL-10 ratio in macrophages infected with antimony-resistant Leishmania donovani and clears organ parasites in experimental infection. J. Immunol. 2014, 193, 4083.

150. Carmen, C., Tamara, L., Antonio, G.R., Rita, C., Carlo, M., Stefano, C. Imiquimod 5% cream use in dermatology, side effects and recent patents. Recent Pat. Inflamm. Allergy Drug Discov. 2012, 6, 65–69. 151. Buates, S., Matlashewski, G. Treatment of experimental leishmaniasis with the immunomodulators imiquimod and S-28463: Efficacy and mode of action. J. Infect. Dis. 1999, 179, 1485–1494. 152. El Hajj, R., Bou Youness, H., Lachaud, L., Bastien, P., Masquefa, C., Bonnet, P.-A., El Hajj, H., Khalifeh, I. EAPB0503: An imiquimod analog with potent in vitro activity against cutaneous leishmaniasis caused by Leishmania major and Leishmania tropica. PLoS Negl. Trop. Dis. 2018, 12, e0006854.

153. Coutinho De Oliveira, B., Duthie, M.S., Alves Pereira, V.R. Vaccines for leishmaniasis and the implications of their development for American tegumentary leishmaniasis. Hum. Vaccines Immunother. 2019, 1–12. 154. Kumar, R., Engwerda, C. Vaccines to prevent leishmaniasis. Clin. Transl. Immunol. 2014, 3, e13. 155. Moafi, M., Rezvan, H., Sherkat, R., Taleban, R. Leishmania vaccines entered in clinical trials: A review of literature. Int. J. Prev. Med. 2019, 10, 95.

156. Raman, V., Reed, S., Duthie, M., Fox, C., Matlashewski, G. Adjuvants for Leishmania vaccines: From models to clinical application. Front. Immunol. 2012, 3, 144.

183

184

The Role of Host Immunity in Leishmaniasis

157. Alves-Silva, M.V., Nico, D., de Luca, P.M., Palatnik de-Sousa, C.B. The F1F3 recombinant chimera of Leishmania donovani-nucleoside hydrolase (NH36) and its epitopes induce cross-protection against Leishmania (V.) braziliensis infection in mice. Front. Immunol. 2019, 10, 724.

158. Nico, D., Gomes, D.C., Palatnik-de-Sousa, I., Morrot, A., Palatnik, M., Palatnik-deSousa, C.B. Leishmania donovani nucleoside hydrolase terminal domains in crossprotective immunotherapy against Leishmania amazonensis murine infection. Front. Immunol. 2014, 5, 273.

159. Alves-Silva, M.V., Nico, D., Morrot, A., Palatnik, M., Palatnik-de-Sousa, C.B. A chimera containing CD4+ and CD8+ T-cell epitopes of the Leishmania donovani nucleoside hydrolase (NH36) optimizes cross-protection against Leishmania amazonesis infection. Front. Immunol. 2017, 8, 100.

160. De Brito, R.C.F., Cardoso, J.M.D.O., Reis, L.E.S., Vieira, J.F., Mathias, F.A.S., Roatt, B.M., Aguiar-Soares, R.D.D.O., Ruiz, J.C., Resende, D.d.M., Reis, A.B. Peptide vaccines for leishmaniasis. Front. Immunol. 2018, 9, 1043.

161. Duthie, M.S., Raman, V.S., Piazza, F.M., Reed, S.G. The development and clinical evaluation of second-generation leishmaniasis vaccines. Vaccine 2012, 30, 134–141.

162. Cecílio, P., Oliveira, F., Cordeiro-da-Silva, A. Vaccines for human leishmaniasis: Where do we stand and what is still missing. Leishmaniases Reemerging Dis. Rij. Intechopen 2018, 59–93.

163. Duthie, M.S., Van Hoeven, N., MacMillen, Z., Picone, A., Mohamath, R., Erasmus, J., Hsu, F.-C., Stinchcomb, D.T., Reed, S.G. Heterologous immunization with defined RNA and subunit vaccines enhances T cell responses that protect against Leishmania donovani. Front. Immunol. 2018, 9, 2420. 164. Valilou, S.F., Keshavarz-Fathi, M. Chapter 10-Genetic vaccine for cancer. In Vaccines for Cancer Immunotherapy, Rezaei, N., Keshavarz-Fathi, M., Eds., Academic Press: London, UK, 2018, pp. 129–143. 165. Kumar, A., Samant, M. DNA vaccine against visceral leishmaniasis: A promising approach for prevention and control. Parasite Immunol. 2016, 38, 273–281. 166. Kaur, S., Kaur, T., Joshi, J. Immunogenicity and protective efficacy of DNA vaccine against visceral leishmaniasis in BALB/c mice. J. Biomed. Res. 2016, 30, 304–313.

167. Louis, L., Clark, M., Wise, M.C., Glennie, N., Wong, A., Broderick, K., Uzonna, J., Weiner, D.B., Scott, P. Intradermal synthetic DNA vaccination generates Leishmania-specific T cells in the skin and protection against Leishmania major. Infect. Immun. 2019, 87, IAI-00227.

168. Osman, M., Mistry, A., Keding, A., Gabe, R., Cook, E., Forrester, S., Wiggins, R., Di Marco, S., Colloca, S., Siani, L., et al. A third generation vaccine for human visceral leishmaniasis and post kala azar dermal leishmaniasis: First-in-human trial of ChAd63-KH. PLoS Negl. Trop. Dis. 2017, 11, e0005527. 169. Midoux, P., Pichon, C. Lipid-based mRNA vaccine delivery systems. Expert Rev. Vaccines 2015, 14, 221–234. 170. Zhang, C., Maruggi, G., Shan, H., Li, J. Advances in mRNA vaccines for infectious diseases. Front. Immunol. 2019, 10, 594.

References

171. Aline, F., Bout, D., Amigorena, S., Roingeard, P., Dimier-Poisson, I. Toxoplasma gondii antigen-pulsed-dendritic cell-derived exosomes induce a protective immune response against T. gondii infection. Infect. Immun. 2004, 72, 4127–4137. 172. Schnitzer, J.K., Berzel, S., Fajardo-Moser, M., Remer, K.A., Moll, H. Fragments of antigenloaded dendritic cells (DC) and DC-derived exosomes induce protective immunity against Leishmania major. Vaccine 2010, 28, 5785–5793.

173. Wahlund, C.J.E., Güclüler, G., Hiltbrunner, S., Veerman, R.E., Näslund, T.I., Gabrielsson, S. Exosomes from antigen-pulsed dendritic cells induce stronger antigen-specific immune responses than microvesicles in vivo. Sci. Rep. 2017, 7, 17095. 174. Olivier, M., Fernandez-Prada, C. Leishmania and its exosomal pathway: A novel direction for vaccine development. Future Microbiol. 2019, 14, 559–561.

175. Setten, R.L., Rossi, J.J., Han, S.-P. The current state and future directions of RNAi-based therapeutics. Nat. Rev. Drug Discov. 2019, 18, 421–446.

176. Seyed, N., Peters, N.C., Rafati, S. Translating observations from leishmanization into non-living vaccines: The potential of dendritic cell-based vaccination strategies against Leishmania. Front. Immunol. 2018, 9, 1227. 177. Peters, N.C., Kimblin, N., Secundino, N., Kamhawi, S., Lawyer, P., Sacks, D.L. Vector transmission of Leishmania abrogates vaccine-induced protective immunity. PLoS Pathog. 2009, 5, e1000484. 178. Peters, N.C., Pagán, A.J., Lawyer, P.G., Hand, T.W., Henrique Roma, E., Stamper, L.W., Romano, A., Sacks, D.L. Chronic parasitic infection maintains high frequencies of short-lived Ly6C+CD4+ effector T cells that are required for protection against re-infection. PLoS Pathog. 2014, 10, e1004538.

179. Kimblin, N., Peters, N., Debrabant, A., Secundino, N., Egen, J., Lawyer, P., Fay, M.P., Kamhawi, S., Sacks, D. Quantification of the infectious dose of Leishmania major transmitted to the skin by single sand flies. Proc. Natl. Acad. Sci. USA 2008, 105, 10125–10130.

180. Ribeiro-Gomes, F.L., Roma, E.H., Carneiro, M.B.H., Doria, N.A., Sacks, D.L., Peters, N.C. Site-dependent recruitment of inflammatory cells determines the effective dose of Leishmania major. Infect. Immun. 2014, 82, 2713.

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Chapter 9

Personalized Nanomedicines for Treatment of Autoimmune Disease Cheng Lin,a,b Huihua Ding, MD,b Congcong Li, PhD, MD,c Nan Shen, PhD, MD,b Aimin Zhao, PhD, MD,c and Matthias Bartneck, PD, PhDa aDepartment

of Internal Medicine III, University Hospital RWTH Aachen, Aachen, Germany of Rheumatology and Shanghai Institute of Rheumatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, China cDepartment of Obstetrics and Gynecology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, China bDepartment

[email protected]

Keywords: autoimmune disease, antigen-presenting cells, B cells, cytokine, drug delivery, immune cells, immune complexes, immune system, macrophages, nanotechnology, T cells, nanomedicines, nucleic acid-based therapy, silencing RNA, systemic lupus erythematosus, micro-RNA, mRNA, type 1 diabetes, liver inflammation, rheumatoid arthritis, psoriasis, tumor necrosis factor-α, interleukin-6 receptor, recurrent pregnancy loss, anti-cyclic citrullinated peptide antibody, anti-neutrophil cell antibody, Janus kinase inhibitors, regulatory T cells, siRNA, micro-RNA mimics, anti-micro-RNA, snc-RNA, antisense-oligonucleotides, long noncoding RNA, Crispr/Cas technique

9.1 Introduction 9.1.1 Basics and Origin of Autoimmune Disease Autoimmune diseases (AID) are represented by a major dysfunction of the body’s natural defense system, the immune system. In AID, there is a lack of differentiation of our own cells and foreign cells. The result of this is that our own cells mistakenly attack normal cells in the body. In the healthy individual, the immune system can discriminate between foreign and own cells. In AID, the immune system confuses parts of the body, for example skin, as foreign. It generates autoantibodies that bind to healthy cells of the body. AID can be organ specific or systemic. For instance, in type 1 diabetes (T1D) only the pancreas is injured. Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

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Currently, more than 80 types of autoimmune diseases are known which can affect many different parts of the body. Nearly 4% of the world’s population is affected by one of more than 80 different autoimmune diseases, the most common of which include T1D, multiple sclerosis, rheumatoid arthritis, Crohn’s disease, lupus, psoriasis, and scleroderma. It is not clearly known why the prevalence of autoimmune diseases is rising. There are different hypotheses for the strong increase in AID in the industrialized countries. One major hypothesis is the so-called hygiene hypothesis. It says that the high standards in hygiene lead to a below-capacity employment of immune cells. The development of autoimmune diseases depends on genetic, epigenetic, and environmental factors (Fig. 9.1A). In addition to these factors, the severity of AID is impacted by three different levels: (1) the overall reactivity of the immune system, (2) the specific antigen and its presentation, and (3) the target issue [1].

Figure 9.1 Mechanisms of autoimmune diseases. The individual risk for autoimmune disease depends on different factors such as genetic predisposition, environmental factors, and epigenetic factors.

9.1.2 Cellular Mechanisms That Underlie Autoimmune Diseases

All autoimmune diseases are based on a certain dysregulation of the immune system. Therefore, understanding the basic functionality of the immune system might be helpful to understand each specific type of AID. Principally, the immune system is of utmost importance for our well-being. The immune system consists

Introduction

of cells and soluble compounds which protect the body from injury. The distinction between “self” and “non-self” is essential in this regard to decipher foreign harmful substances and pathogens from the body’s own structures. Its cells are also referred to as cellular and the sum of all molecules has been defined as humoral (soluble) immunity. Cytokines are distant acting mediators which play important roles in different types of disease. One of the most prominent mediators generated by macrophages (MΦ), long-lived myeloid immune cells, is the tumor necrosis factor (TNF) which covers a huge spectrum of functions, and is known to be able to cause cell and organ injury [2]. In many types of AID, TNF can trigger necroptotic parenchymal cell death, including, i.e., liver inflammation [3], rheumatoid arthritis [4], autoimmune disease [5], and psoriasis [6]. Genetic factors largely affect the properties of B and T lymphocytes, which fulfill their functions based on clonal expansion. Hence, genetic variations of somatic genes can have a direct influence on autoimmune diseases. Myeloid cells exhibit a high level of plasticity, which is reflected, i.e., by mRNA profiles. Macrophages can thus indirectly contribute to autoimmune disease by means of the expression of their inflammatory mediators [7]. Importantly, there is the major distinction of inflammatory macrophages, M1-MΦ, or anti-inflammatory M2-MΦ, based on their expression of specific markers [8].

Figure 9.2 Immune cell interactions in autoimmune diseases. On the cellular level, dysregulated immune cell interactions are the source of disease mechanisms. T cells get instructed by antigen presenting cells (APC). They are selected in the Thymus and may further instruct B cells with the Autoantigen.

Due to the large numbers of etiologies, we here focus on key mechanisms of AID which can be led back to immune system dysregulation. It is a matter of fact that many of the most well-known cytokines such as interleukin-2, TNF, and interferons can, under specific circumstances, play “bad” as well as “good” roles for disease development [9]. However, the process of antigen presentation from the antigen presenting cells (APC) represents a critical onset of AID. The APC instruct the T cells to be reactive against a certain antigen, and the T cells subsequently

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transmit this information to the B cells. T cells can then kill body own cells directly by cellular lysis, and B cells produce antibodies which bind to an autoantigen in the body (Fig. 9.2).

9.2 Systemic Lupus Erythematosus as Representative Autoimmune Disease

In this section, we outline the currently available clinical information, pathogenesis, and treatment options for the autoimmune disease Systemic lupus erythematosus (SLE).

9.2.1 Clinical Impact of Systemic Lupus Erythematosus

SLE is a quintessential systemic autoimmune disease. The involvement of different organs is one of the most common features of SLE; the skin, joints, and kidneys are most likely to be affected. Pathological damage is mainly manifested in the production of various autoantibodies in the body, such as antinuclear antibodies, anti-dsDNA antibody, anti-single stranded DNA antibody, and anti-ribosome antibody. The resulting immune complexes can precipitate in the vascular wall and in the glomerular basement membrane, hence provoking local immune reactions leading to organ damage. There are five major different types of lupus: systemic lupus erythematosus, lupus of skin, chronic cutaneous lupus erythematosus, drug-induced lupus erythematosus, and neonatal lupus erythematosus [10]. Of course, this disease goes far beyond that. Our current understanding of SLE, which is limited, has been hampered by disease heterogeneity which determines the complexity of the disease itself, as well as the diverse comorbidities of SLE patients. But the good news is that mortality from SLE improved in the second half of the twentieth century, with 10-year survival at 60% in the 1950s to >90% in the 1980s [11].

9.2.2 Pathogenesis of SLE

The exact etiology and pathogenesis of SLE is still unclear, but it is certain that it is the result of the complex interaction among genetic, hormonal and environmental factors, which eventually lead to the loss of self-tolerance [12]. The key nuclear autoantigens recognized by the immune system of SLE patients are related to changes of cell death pathways, release of neutrophil extracellular traps (NETosis), and owed to hampered clearance of necrotic cell-derived substances. In SLE patients, defects in the clearance of apoptotic cells have been described, which may lead to abnormal uptake by macrophages, which then present previous intracellular antigens to T and B cells, thus driving the autoimmune process [13]. These autoantigens are presented to autoreactive B cells in germinal centers of secondary lymphoid organs through restricted human leukocyte antigen (HLA)

Systemic Lupus Erythematosus as Representative Autoimmune Disease

haplotypes by follicular dendritic cells, and activate the differentiation and expansion of CD4+ autoreactive T cells [14, 15]. Multiple factors such as genetic susceptibility and environment factors are involved in the progress of SLE. Generally speaking, single factors do not lead to disease development. In terms of genetic factors, >100 genetic loci associated with SLE have been detected. Further, there are some candidate loci like interferon regulatory factor 5 (IRF5), and mutations thereof are associated with increases in the levels of the type 1 IFN family of molecules in patients with SLE as verified through genome-wide association studies [15]. Those mutations that produce deficiencies in complement pathway gene products (including C2, C4 and C1q) are usually high-risk, and thus might contribute to SLE pathogenesis by impairing the clearance of cellular debris [16]. Scientists have also found another set of SLE related gene mutations that can help to change the threshold of lymphocyte activation or the efficiency of immune cell signaling. For example, signal transducer and activator of transcription 4 (STAT4) contributes to encode proteins for involved in cytokine signaling, tyrosine-protein phosphatase nonreceptor type 22 (PTPN22), tyrosine-protein kinase LYN, B cell scaffold protein with ankyrin repeats (BANK), B lymphocyte tyrosine kinase (BLK) and tumor necrosis factor-α-induced protein 3 (TNFAIP3) influence the efficiency of signal transduction downstream of T cell and B cell surface antigen receptor [17]. It is worth notable, women account for 90% of the SLE cohort. Estrogen and prolactin might enhance the immune response through a variety of mechanisms [11, 18]. Environmental factors can also trigger the disease. Ultraviolet rays are believed to drive cell apoptosis and provide immune stimulation, it can induce DNA breaks that might alter gene expression, generate nucleic acid fragments, or lead to apoptotic or necrotic cell death. Those may explain the possible connection between the sunlight exposure and drug-induced lupus [19]. Viral infection has also been verified to trigger the disease. For example, in most SLE patients, the response of T cells to EBV infection may be weakened, which may be one of the reasons for the increase in the number of EBV infected monocytes and the increase in EBV DNA copy number in the blood (Fig. 9.1) [20].

9.2.3 Diagnostic Criteria for SLE

The diagnosis of systemic lupus erythematosus is based on the combination of typical clinical manifestations and serum. In view of the wide heterogeneity of clinical manifestations, classification criteria have been developed over time in several groups for epidemiological and research purposes. In clinical practice, doctors tend to use the revised American College of Rheumatology (ACR) classification criteria for SLE [21, 22]. Usually, the diagnosis of SLE is based on clinical manifestations (skin rash, chest pain, arthralgias, joint deformities, etc.) and laboratory tests, including autoantibodies (C3, C4, anti-DNA, anti-Sm, etc.), functional tests and imaging examinations (electrocardiographic or echocardiographic, etc.) [23]. SLE Disease Activity Index 2000 (SLEDAI-2k)

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standard are usually used to assess the disease activity, which could facilitate the management of the disease in patients [24]. In fact, the diagnosis of SLE is very challenging because there is no recognized diagnostic standard. The ACR classification and response criteria subcommittee of the Quality Committee recognized the difficulties in developing diagnostic criteria for rheumatic diseases.

9.2.4 Current Management of SLE

SLE is a systemic autoimmune disease with extensive clinical manifestations. Only a small number of patients can get sustained remission [25, 26]. Currently, most treatments are mainly dependent on corticosteroids and immunosuppressive agents, which are associated with severe adverse reactions including various infections [27, 28]. The goal of lupus therapy is (1) to use immunosuppressive agents to maintain minimal activity, appropriate immunosuppression and avoid known triggers, (2) to prevent organ damage caused by active lupus, (3) to reduce comorbidities secondary to lupus and its treatment, especially to accelerate arteriosclerosis, which is the main cause of death, and (4) to address fatigue and pain, which are usually associated with active wolves. This translates into avoiding known flash triggers, the need for sun protection, maximizing the use of immunosuppressive agents (hydroxychloroquine and vitamin D, including compliance monitoring), avoiding the maintenance of prednisone more than 6 mg per day, and using immunosuppressive agents or biological agents when needed to control active diseases. In the traditional treatment, SLE patients were treated with conventional hormone (methylprednisolone) at the initial dose of 20 mg/d. After 1 week of treatment, the dose of methylprednisolone was adjusted or combined with cyclophosphamide according to the results of ANA, anti-dsDNA, IgG and complement C3. When the ANA, IgG and C3 indexes of the patients tend to be normal, continue to maintain the dosage until the disease is stable. Cyclophosphamide pulse therapy combined with intravenous high-dose immunoglobulin can reduce lupus activity index and proteinuria and reduce the incidence of infectious diseases.

9.2.5 Progress in the treatment of SLE

With the deepening understanding of the pathogenesis of autoimmune diseases and the development of technology, the treatment of autoimmune diseases is not limited to traditional hormones and broad-spectrum immunosuppressants. A variety of macromolecular targeted drugs and biological agents targeting the pathogenic cells and cytokines of autoimmune diseases have gradually been widely used in clinic. For example, monoclonal antibodies targeting tumor necrosis factor-α (TNF-α), CD19 and interleukin-6 receptor (IL-6R) can selectively inhibit immunity by binding with their target antigens. Compared with hormones and broad-spectrum immunosuppressive agents, macromolecular targeted drugs

Immune-Mediated Recurrent Pregnancy Loss

have fewer side effects, but they still cannot promote the permanent recovery of immune balance, and patients often need to use drugs for life. SLE is a systemic autoimmune disease, which is characterized by the production of autoantibodies targeting at multiple nuclear antigens, depositing in tissues and activating complement. Plasma cells and their precursor B cells are the basis for the production of these antibodies, so they are the main therapeutic targets for disease intervention. Rituximab is a chimeric human-murine monoclonal antibody directed against CD20 on B cells and their precursors but not against plasma cells, which do not have this antigen. Rituximab has been widely used in the management of lymphoma and is fairly safe and well tolerated. There is increasing evidence that patients with lupus have sustained remission, while patients with lupus have previously been unresponsive to traditional and novel immunosuppressants such as mycophenolate mofetil [12, 29]. Anifrolumab is a monoclonal antibody that blocks the interferon-α/β receptor (IFNAR), which is on the process of the clinical trial [30]. Interferon type I and type II have become key cytokines in the pathogenesis of SLE (and other autoimmune diseases), and their levels rise before the formation of autoantibodies [31, 32]. For instance, interferon type I signal transduction is mediated by IFNAR. Although many studies have presented compelling data that interferon plays an important role in the pathogenesis of SLE, clinical trials of interferon inhibitors have been disappointing. Despite the unexpected results in some trials, an impressive number of novel agents are currently in clinical development for SLE, including the B cell-modulating agent epratuzumab (anti-CD22), IFN antagonists and IL-6 and IL-10 blockers [33], and a previous study provided supportive evidence that low-dose IL-2 treatment might be effective and well tolerated in patients with SLE [34], and led to improved clinical manifestations such as fever, rash, alopecia, edema, nephritis, immune indexes (24 h urinary protein, albumin, anti-dsDNA antibody, complement level, etc.), and disease activity index. Although these technologies are still in the research stage, they are believed to bring benefits to more SLE patients in the future.

9.3 Immune-Mediated Recurrent Pregnancy Loss: Features of Autoimmune Disease 9.3.1 Overview of Reproductive Immunology

Pregnancy is a special allogeneic transplantation process in which the embryo closely contacts with maternal-derived cells, forming the maternal-fetal interface. During normal pregnancy, the embryo will not be attacked or cleared by the maternal immune system. Instead, it can anchor in the uterine cavity through unique mechanisms such as immune escape, and gradually form the placenta, through which the fetus continuously obtains nutrition maintaining its growth

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and development. This process is mediated by maternal-fetal immune tolerance [35–38]. There have been many evidences that the development of immune tolerance depends on the balance of maternal-fetal immune system. The delicate crosstalk among various cells at maternal-fetal interface, including trophoblasts, decidual stromal cells, vascular endothelial cells, natural killer (NK) cells, T cells, dendritic cells, macrophages and other immune cells enables the development of the immune microenvironment favoring the fetal development [37, 39]. The maternal-fetal immune tolerance depends on normal crosstalk, while the disturbance of the crosstalk results in failure of immune tolerance, which leads to pregnancy complications such as RPL, preeclampsia, fetal growth restriction and premature birth [40–42]. In-depth study of the mechanisms of maternal-fetal immune tolerance is of great importance for the understanding of the pathogenesis of pregnancy complications and finding potential novel therapeutic immune targets. In recent years new technologies such as single-cell RNA sequencing facilitates the description of landscape at single cell level, providing novel information on regulations of maternal-fetal immune tolerance [43, 44].

9.3.2 Immune-Mediated Recurrent Pregnancy Loss

Recurrent pregnancy loss (RPL) accounts for 5% of total pregnancies and is a significant problem in the field of reproductive health [45, 46]. There are slight differences in the definitions of RPL. The Royal College of Obstetricians and Gynecologists defines RPL as three or more consecutive pregnancy loss before 24 weeks of gestation [47]. In 2017, the European Society of Human Reproduction and Embryology described RPL as the loss of two or more pregnancies before 24 weeks of gestation [48]. While the RPL is defined as two or more instances of clinical pregnancy loss by the American Society for Reproductive Medicine [50]. A significant proportion of RPL are related to immune dysfunction. The immune-mediated RPL is categorized into either autoimmune RPL or alloimmune RPL [45]. After excluding the possibilities of chromosomal/genetic abnormalities, the endocrinology disorders, the malformation of female genital tracts, the infection or deficiency in coagulation factors, the clinical practitioners should carefully inquest the history of autoimmune diseases and screen for the autoimmune markers to evaluate the relationship between RPL and autoimmune diseases. The markers include antinuclear antibody, extractable nuclear antigens, antiphospholipid antibodies, including lupus anticoagulant, immunoglobulin (Ig)G/IgM/IgA types of anti-cardiolipin antibody, IgG/IgM/IgA types of anti-β2-glycoprotein 1 antibody, anti-double strand DNA antibody, rheumatoid factor, anti-cyclic citrullinated peptide antibody (anti-CCP), anti-neutrophil cell antibody (ANCA), complement C3, C4, CH50, IgG, IgM, IgA, peripheral blood lymphocyte subsets count, antithrombin III, protein S, and protein C. So far, no large-scale multicenter randomized controlled trials have verified that the cytokines, peripheral NK cells or other subsets of lymphocytes are associated with RPL [48, 50]. There is no definite relationship between HLA polymorphism and pregnancy outcomes, thus the detection of HLA molecules has not been recommended within the RPL patients [48].

Therapeutic Modulation of Immune Cells and Their Cytokine Secretion

9.3.3 Autoimmune Aspects of Recurrent Pregnancy Loss The incidence of RPL among patients with autoimmune disorders is much higher than in healthy individuals. During pregnancy, the autoimmune antibodies, autoreactive lymphocytes or some cytokines can attack the trophoblasts, vascular endothelial cells and the fetus, further inducing thrombosis and interfering with implantation and fetal growth [51]. Meanwhile, elevated estrogen during pregnancy can activate the humoral immune pathway, deteriorate the original autoimmune disease [49], and lead to severe consequences. The common autoimmune diseases complicated with RPL include antiphospholipid syndrome, systemic lupus erythematosus, Sjögren’s syndrome, rheumatoid arthritis, systemic sclerosis, undifferentiated connective tissue disease [52]. Patients with autoimmune diseases of reproduction ages should choose the best time of pregnancy and treatment under the guide of experts majoring in obstetrics and AID. There is a strong relationship between antiphospholipid syndrome (APS) and RPL. However, the exact mechanisms underlying RPL in APS remain unclear. Thrombosis at maternal-fetal interface is one mechanisms of RPL, while the inflammation mediated by aPL can also lead to RPL among patients with APS [53]; 20–30% APS patients underwent pregnancy loss although the use of combined anticoagulant heparin and antiplatelet drug aspirin have been used as a combination therapy to prevent placental thrombosis. In this case, besides the anticoagulant and antiplatelet combined therapy, immunosuppressants such as hydroxychloroquine and corticosteroids are also recommended [54–57]. Patients with SLE or other autoimmune diseases should plan pregnancy under guide of both obstetricians and rheumatologists. During pregnancy, the activity of autoimmune diseases and fetal development should be intensively monitored. Also, the immunosuppressants should be applied with caution [58, 59]. The immunosuppressants which are relatively safe during pregnancy include corticosteroids without fluoride, HCQ, sulfasalazine, azathioprine, tacrolimus, and cyclosporin A. Cyclophosphamide could cause fetal malformation and pregnancy loss when applied in early pregnancy and should be used cautiously, and termination of pregnancy should be considered. The limited observative studies indicated the anti-tumor necrosis factor inhibitors can pass through the placental barrier; however, no obvious events have yet been detected once applied. Infliximab and adalimumab can accumulate in fetus, while certolizumab and etanercept barely cross the placental barrier. Methotrexate, leflunomide, mycophenolate mofetil, or thalidomide should be avoided.

9.4 Therapeutic Modulation of Immune Cells and Their Cytokine Secretion

In this chapter, we focus on the cellular mechanisms which can be targeted by specific rugs. These drugs can modulate immune cell activation and their secretion profile of cytokines. These treatment schemes can highly specifically target the genes which are drivers of autoimmune-related dysfunctions.

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9.4.1 Therapeutic Modulation of Different Immune Cells in Autoimmune Disease Treatment of AID can occur at virtually any point of the disease. Blocking the release of cytokines such as the TNF might be a universal strategy for different types of disease, including hepatitis, rheumatoid arthritis, inflammatory bowel disease, or graft versus host disease [60]. Anti-inflammatory cytokines such as IL10 suppress, among others, TNF production [61]. It is known that TNF is for large amounts secreted by activated M1-MΦ. It can be deactivated by pharmaceuticals such as etanercept. Many other pharmaceuticals also inhibit TNF in order to reduce the negative side effects of the cytokine on healthy tissue that occurs in dysregulated inflammatory disease such as arthritis [62]. The novel class of Janus kinase inhibitors aims to target TNF and associated factors. As an example, Tofacitinib, a pan-JAK inhibitor, is approved for inflammatory bowel diseases [63], psoriasis [64], and arthritis [65]. Dendritic cells (DC) have a huge overlap with macrophages, i.e., considering their marker expression [66]. DC, in contrast to MΦ, are specialized on presenting antigen to T cells and are capable to migrate faster to the lymph node than macrophages. The DC thus play a central role for vaccination. Cytotoxic T cells are important effector cells in AID. They have important functions in killing cancer cells but can also kill the body’s own cells in autoimmune dysregulation. It was reported to be of therapeutic benefit if CD8 T cells are depleted using anti-CD8 antibodies [67]. Engaging CD8 T cells is already approved for therapy of several cancer types. In detail, inhibitors of programmed cell death 1 (PD1) such as pembrolizumab are in use. These therapies amplify the killing capabilities of T cells for killing tumor cells [68]. The chimeric antigen receptor (CAR)-T cells represent another T cell therapy that has been approved by the FDA in 2017 for relapsed or refractory B-cell acute lymphoblastic leukemia [69]. The CAR T cells are generated from patients’ own blood immune cells, are genetically engineered using Lentivruses, and are reinjected into the patient where they seek and destroy cancer cells [70]. Regulatory T cells (TReg) are another type of T cell with potential therapeutic value. In the inflammatory ear disease, the inflammation is longer when TReg are depleted by an anti-CD25 antibody [71]. This demonstrates that they play important roles in down-regulating inflammation. It is thus a matter of fact that TReg hold great potential as therapeutic targets in treating autoimmunity. Clemente-Casares et al. demonstrated that nanoparticles decorated with AID-relevant peptides coupled to MHCII complex molecules can induce antigen-specific type I CD4+ T cells (TR1). This strategy turns the disease-primed autoreactive T cells into TR1like cells, which then suppress autoantigen-loaded antigen-presenting cells, and further differentiate cognate into disease-suppressing regulatory B cells (Fig. 9.2) [72]. Interestingly, albumin-based nanoparticles loaded with the tyrosine kinase inhibitor piceatannol led to a potent inactivation of the inflammatory activities of these cells function of activated neutrophils (Table 9.1) [73].

Therapeutic Modulation of Immune Cells and Their Cytokine Secretion

Table 9.1 Selected nanomedicine-based immunotherapies at preclinical stage Cell type

Disease

Drug, outcome

Macrophages

Liver inflammation

Liposomal Dex, reduced hepatitis and fibrosis

Regulatory T cells

AID

Nanoparticles with AID-peptides, TReg reprogramming

[72]

Inflammation

Albumin-encapsulated piceatannol

[73]

Acute hepatitis

MTC anti-TNF siRNA, macrophage reprogramming

T cells

Systemic Lupus miR-125a-loaded mPEG-PLGA-PLL Erythematosus

Platelets

Thrombus prevention

Neutrophils

Reference [87, 88]

Antibody

[89]

[10] [75]

Blood platelets represent the second most frequent and highly functional type of immune cells. It is therefore clear that platelet targeting is very promising due to their high numbers in the body. The platelet expressed collagen receptor glycoprotein VI was demonstrated by Boilard and colleagues to function as a key trigger for the generation of platelet microparticles generation in arthritis disease development [74]. Their glycoprotein (GP) Ib-V-IX receptor regulated their attachment, activation, and procoagulant functionality and has been used as a novel preventive treatment of thrombus formation based on the application of therapeutic antibodies (Table 9.1) [75].

9.4.2 Delivery of Therapeutic Nucleic Acids for Treatment of AID

Lipid-based nanomedicines are somewhat inspired by “natural” nanocarriers such as microvesicles and exosomes [76]. There have been significant improvement in lipid encapsulation of nucleic acids, in particular, rapid mixing technologies that enable formulations for in vivo administration [77]. The lipid-based nanomedicines exhibit the big advantage that they are biodegradable since also cell membranes are among others made of phospholipids. Nucleic acids preserve and transmit the genetic information. Until the beginning of this century, the classical dogma of molecular biology was that, genetic information encoded by DNA is transcribed into mRNA which is subsequently translated into a protein. Yet, 20 years ago, small non-coding RNAs were discovered. Specifically micro-RNAs (miR), whose size is as small as 20 nucleotides, critically regulate mRNA [78]. Bioinformatics science has demonstrated that miR regulates approximately 60% of protein coding genes [79]. The mechanism of action of miR is owed to the binding to the 3′ untranslated region of the target mRNA. Based on the fact that the complementarities of miR to mRNA sequences are only partly, most miR can bind to multiple different mRNAs [78]. MiR plays a critical role in inflammatory liver disease, as demonstrated

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by Roy and colleagues [80]. Tools to direct miR activity are thus useful to modulate biological processes in disease development. While we focus on nanomedicines in this chapter, we also note that viral delivery systems based on modifying the genetic information of T cells, the so-called chimeric antigen receptor T cells (CAR T cells), have entered clinics [70]. The non-viral delivery systems exhibit the big advantage that they are safer; viruses could infect other cells in the body and disturb their function. However, their effects are only transient and usually their effects can last for about 1 week, i.e., when using a lipid-based nanocarrier for Jnk2, we observed a knockdown after 1 week [81]. Besides the well-known siRNA, mRNA has been discovered as a cargo; quite recently, Curevac developed a vaccine based on mRNA such as BNT162b1 [82]. Another alternative from the small non-coding RNA section might be micro-RNAmodulating RNA sequences called micro-RNA mimics or anti-micro-RNA (anti-miR) [83]. Inhibition of mRNA is not limited to snc-RNA, also specific DNA sequences, antisense-oligonucleotides (ASO) can efficiently inhibit mRNA [84]. Of course, the inhibitory effects are not limited to mRNA, but can also target long non-coding RNA (lncRNA) [84], and many other types of nucleic acids. Permanent genetic modifications might in future be based on the Crispr/Cas technique, which is capable of modifying genomic DNA [85].

9.5 Conclusions

Nanomedicines for targeting autoimmune disease are under intensive development. Targeting of natural and endogenous small non-coding RNA such as micro-RNA bears a huge potential. Many different types of therapeutic nucleic acids may aim for a multitude of natural and functional nucleic acids. The knowledge on the “omics” technologies is still accumulating, and interconnections between different omics, i.e., genomics and transcriptomics that were unknown, are now targeted. Nanomedicine should be devoted to deciphering key information from the different “omics” analytics such as genomics, transcriptomics, proteomics, and metabolomics which enabled the discovery of biomarkers for early-stage disease detection of disease [86]. Nanomedical interventions in AID holds great promise for improved treatment based on improved drug delivery, reduced side effects, and the option in being theranostically usable, meaning the option to use carriers for combined therapeutic and diagnostic processes. It is therefore important to develop stable production methods for nanomedicines which aim at the ideal therapeutic target at the site of interest.

Abbreviations ACR: AID:

American College of Rheumatology autoimmune disease

Abbreviations

ANCA: anti-CCP: anti-miR: APC: APS: ASO: BANK: BLK: CAR T cells: CD: DC: Dex: G-CSF: GLF: GP: HLA: IFNAR: Ig: IL-6R: IRF5: LNC: lncRNA: mAB: miR: MTC: NK: PD1: PTPN22: RPL: RGD: RNAi: siRNA: SLE: SLEDAI-2k: STAT4: TH1 cells: T1D: TLR: TNF: TNF-α: TNFAIP3: TR1: TReg:

anti-neutrophil antibodies anti-cyclic citrullinated peptide antibody anti-micro-RNA antigen-presenting cells antiphospholipid syndrome antisense-oligonucleotides B cell scaffold protein with ankyrin repeats B lymphocyte tyrosine kinase chimeric antigen receptor T cells cluster of differentiation dendritic cells dexamethasone granulocyte colony-stimulating factor peptide sequence GLF glycoprotein human leukocyte antigen interferon-α/β receptor immunoglobulin interleukin-6 receptor interferon regulatory factor 5 lipid-based nanocarriers long non-coding RNA monoclonal antibody micro-RNAs mannose-modified trimethyl chitosan-cysteine conjugate nanoparticles natural killer programmed cell death 1 tyrosine-protein phosphatase non-receptor type 22 recurrent pregnancy loss peptide sequence RGD RNA interference silencing RNA systemic lupus erythematosus SLE Disease Activity Index 2000 signal transducer and activator of transcription 4 type I T helper cell type 1 diabetes toll-like receptor tumor necrosis factor tumor necrosis factor-α tumor necrosis factor-α-induced protein 3 type I CD4+ T cells regulatory T cells

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Disclosures and Conflict of Interest L.C. is supported by a CSC stipend from China (grant ID 202008320329). M. B. gratefully acknowledges financial support from the German Research Foundation (DFG), BA 6226/2-1, the Wilhelm Sander Foundation (2018.129.1), and of the COST Action BM1404 Mye-EUNITER (http://www.mye-euniter.eu).

References

1. Marrack P, Kappler J, Kotzin BL. Autoimmune disease: why and where it occurs. Nat Med. 2001;7(8):899–905.

2. Duprez L, Takahashi N, Van Hauwermeiren F, Vandendriessche B, Goossens V, Vanden Berghe T, et al. RIP kinase-dependent necrosis drives lethal systemic inflammatory response syndrome. Immunity. 2011;35(6):908–918.

3. Bartneck M, Fech V, Ehling J, Govaere O, Warzecha KT, Hittatiya K, et al. Histidinerich glycoprotein promotes macrophage activation and inflammation in chronic liver disease. Hepatology. 2016;63(4):1310–1324. 4. Seymour HE, Worsley A, Smith JM, Thomas SHL. Anti-TNF agents for rheumatoid arthritis. Br J Clin Pharmacol. 2001;51(3):201–308. 5. Chatzantoni K, Mouzaki A. Anti-TNF-alpha antibody therapies in autoimmune diseases. Curr Top Med Chem. 2006;6(16):1707–1714.

6. Kircik LH, Del Rosso JQ. Anti-TNF agents for the treatment of psoriasis. J Drugs Dermatol. 2009;8(6):546–559.

7. Murray PJ, Allen JE, Biswas SK, Fisher EA, Gilroy DW, Goerdt S, et al. Macrophage activation and polarization: nomenclature and experimental guidelines. Immunity. 2014 Jul 17;41(1):14–20.

8. Bartneck M, Keul HA, Singh S, Czaja K, Bornemann J, Bockstaller M, et al. Rapid uptake of gold nanorods by primary human blood phagocytes and immunomodulatory effects of surface chemistry. ACS Nano. 2010;4(6):3073–3086.

9. Shea JJ, Ma A, Lipsky PE. Cytokines and autoimmunity. Nat Rev Immunolog. 2002;2: 37–45.

10. Hong SM, Liu C, Yin Z, Wu L, Qu B, Shen N. MicroRNAs in systemic lupus erythematosus: a perspective on the path from biological discoveries to clinical practice. Curr Rheumatol Rep. 2020;22(6):17.

11. Mak A, Cheung WL, Hui JC, Liu Y, Ho CM. Global trend of survival and damage of systemic lupus erythematosus: meta-analysis and meta-regression of observational studies from the 1950s to 2000s. Semin Arthritis Rheum. 2012;41(6):830–839. 12. Al-Maini M, Jeyalingam T, Brown P, Lee JJY, Li L, Su J, et al. A hot spot for systemic lupus erythematosus, but not for psoriatic arthritis, identified by spatial analysis suggests an interaction between ethnicity and place of residence. Arthritis Rheumatol. 2013;65(6):1579–1585.

13. Munoz LE, Gaipl US, Franz S, Sheriff A, Voll RE, Kalden JR, et al. SLE: a disease of clearance deficiency? Rheumatology. 2005 (9):1101–1107.

References

14. Blanco, P. Induction of dendritic cell differentiation by IFN-α in systemic lupus erythematosus. Science. 2001;294(5546):1540–1543.

15. Deng Y, Tsao BP. Genetic susceptibility to systemic lupus erythematosus in the genomic era. Nat Rev Rheumatol. 2010;6(12):683–692. 16. Truedsson L, Bengtsson AA, Sturfelt G. Complement deficiencies and systemic lupus erythematosus. Autoimmunity. 2007;40(8):560–566.

17. Taylor KE, Chung SA, Graham RR, Ortmann WA, Lee AT, Langefeld CD, et al. Risk alleles for systemic lupus erythematosus in a large case-control collection and associations with clinical subphenotypes. PloS Genet. 2011;7(2):e1001311.

18. Chi CM, Tang SSK, Chi HT, Petri M. Incidence and risk factors of thromboembolism in systemic lupus erythematosus: a comparison of three ethnic groups. Arthritis Rheum. 2010;52(9):2774–2782.

19. Dorgham K, Amoura Z, Parizot C, Arnaud L, Frances C, Pionneau C, et al. Ultraviolet light converts propranolol, a nonselective β-blocker and potential lupus-inducing drug, into a proinflammatory AhR ligand. Eur J Immunol. 2016;45(11):3174–3187.

20. Kang I, Quan T, Nolasco H, Park SH, Craft J. Defective control of latent Epstein–Barr virus infection in systemic lupus erythematosus. J Immunol. 2004;172(2):1287–1294.

21. Hochberg MC. Updating the American College of Rheumatology revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheumatol. 2010;40(9):1725.

22. Tan EM. The 1982 revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheumatol. 1982;25(11):1271–1277.

23. Hess EV. A uniform database for rheumatic diseases. Prepared by the computer committee of the American Rheumatism Association. Arthritis Rheumatol. 2014;19(3): 645–648.

24. Abu-Shakra M, Urowitz MB, Gladman DD, Gough J, . Mortality studies in systemic lupus erythematosus. Results from a single center. II. Predictor variables for mortality. J Rheumatol. 1995;22(7):1265–1270.

25. Franklyn K, Hoi A, Nikpour M, Morand EF. The need to define treatment goals for systemic lupus erythematosus. Nat Rev Rheumatol. 2014;10: 567–571.

26. Morand EF, Mosca M. Treat to target, remission and low disease activity in SLE. Best Pract Res Clin Rheumatol. 2017;31(3):342–350.

27. Goldblatt F, Chambers S, Rahman A, Isenberg D. Serious infections in British patients with systemic lupus erythematosus: hospitalisations and mortality. Lupus. 2009;18(8): 682–689. 28. N I, Bruce, Urowitz, Vollenhoven V, Aranow, Fettiplace, et al. Long-term organ damage accrual and safety in patients with SLE treated with belimumab plus standard of care. Lupus. 2016; 25(7):699–709.

29. Anolik JH, Campbell D, Felgar RE, Young F, Sanz I, Rosenblatt J, et al. The relationship of FcgammaRIIIa genotype to degree of B cell depletion by rituximab in the treatment of systemic lupus erythematosus. Arthritis Rheumatol. 2003;48(2):455–459.

30. Furie R, Khamashta M, Merrill JT, Werth VP, Kalunian K, Brohawn P, et al. Anifrolumab, an anti-interferon-α receptor monoclonal antibody, in moderate-to-severe systemic lupus erythematosus. Arthritis Rheumatol. 2017;69(2):376–386.

201

202

Personalized Nanomedicines for Treatment of Autoimmune Disease

31. Lu R, Munroe ME, Guthridge JM, Bean KM, Fife DA, Chen H, et al. Dysregulation of innate and adaptive serum mediators precedes systemic lupus erythematosus classification and improves prognostic accuracy of autoantibodies. J Autoimmun. 2016;74:182–193.

32. Munroe ME, Lu R, Zhao YD, Fife DA, Robertson JM, Guthridge JM, et al. Altered type II interferon precedes autoantibody accrual and elevated type I interferon activity prior to systemic lupus erythematosus classification. Ann Rheum Dis. 2016: 75(11): 2014–2021.

33. Wallace DJ, Kalunian K, Petri MA, Strand V, Houssiau FA, Pike M, et al. Efficacy and safety of epratuzumab in patients with moderate/severe active systemic lupus erythematosus: results from EMBLEM, a phase IIb, randomised, double-blind, placebocontrolled, multicentre study. Ann Rheum Dis. 2014;73(1):183–190.

34. Zhang R, Shao M, Zhao X, Miao M, Chen J, Liu J, et al. Efficacy and safety of low-dose IL-2 in the treatment of systemic lupus erythematosus: a randomised, double-blind, placebo-controlled trial. Ann Rheum Dis. 2020;79(1):141–149. 35. Trowsdale J, Betz AG. Mother’s little helpers: mechanisms of maternal-fetal tolerance. Nat Immunol. 2006;7(3):241–246.

36. Makrigiannakis A, Karamouti M, Drakakis P, Loutradis D, Antsaklis A. fetomaternal immunotolerance. Am J Reprod Immunol. 2008;60(6):482–496.

37. Arck PC, Hecher K. Fetomaternal immune cross-talk and its consequences for maternal and offspring’s health. Nat Med. 2013;19(5):548–556.

38. Mor G, Cardenas I. The immune system in pregnancy: a unique complexity. Am J Reprod Immunol. 2010;63(6):425–433. 39. Yang F, Zheng Q, Jin L. Dynamic function and composition changes of immune cells during normal and pathological pregnancy at the maternal-fetal interface. Front Immunol. 2019;10:2317.

40. Warning JC, McCracken SA, Morris JM. A balancing act: mechanisms by which the fetus avoids rejection by the maternal immune system. Reproduction. 2011;141(6): 715–724. 41. Bansal AS. Joining the immunological dots in recurrent miscarriage. Am J Reprod Immunol. 2010;64(5):307–315. 42. Ander SE, Diamond MS, Coyne CB. Immune responses at the maternal-fetal interface. Sci Immunol. 2019;4(31):eaat6114. 43. Suryawanshi H, Morozov P, Straus A, Sahasrabudhe N, Max KEA, Garzia A, et al. A single-cell survey of the human first-trimester placenta and decidua. Sci Adv. 2018;4(10):eaau4788. 44. Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. 2018;563(7731):347–353.

45. Evaluation and treatment of recurrent pregnancy loss: a committee opinion. Fertil Steril. 2012;98(5):1103–1111. 46. El Hachem H, Crepaux V, May-Panloup P, Descamps P, Legendre G, Bouet PE. Recurrent pregnancy loss: current perspectives. Int J Women Health. 2017;9:331–345.

47. Gynaecologists RCoOa. Green-top Guideline: The investigation and treatment of couples with recurrent first-trimester and second-trimester miscarriage. 2011 (No. 17):1–18.

References

48. Bender Atik R, Christiansen OB, Elson J, Kolte AM, Lewis S, Middeldorp S, et al. ESHRE guideline: recurrent pregnancy loss. Hum Reprod Open. 2018;2018(2):hoy004.

49. Ostensen M, Villiger PM, Forger F. Interaction of pregnancy and autoimmune rheumatic disease. Autoimmun Rev. 2012;11(6–7):A437–446.

50. Practice Committee of the American Society for Reproductive Medicine. Electronic address aao. Definitions of infertility and recurrent pregnancy loss: a committee opinion. Fertil Steril. 2020;113(3):533–535. 51. Gleicher N. Some thoughts on the reproductive autoimmune failure syndrome (RAFS) and Th-1 versus Th-2 immune responses. Am J Reprod Immunol. 2015;48(4):252–254.

52. Carvalheiras G, Faria R, Braga J, Vasconcelos C. Fetal outcome in autoimmune diseases. Autoimmun Rev. 2012;11(6–7):A520–A530.

53. Meroni, Pier, Luigi, Borghi, Maria, Orietta, et al. Obstetric and vascular antiphospholipid syndrome: same antibodies but different diseases? Nat Rev Rheumatol. 2018;14(7): 433–440.

54. Bramham K, Thomas M, Nelson-Piercy C, Khamashta M, Hunt BJ. First-trimester low-dose prednisolone in refractory antiphospholipid antibody-related pregnancy loss. Blood. 2011;117(25):6948–6951.

55. Mekinian A, Alijotas-Reig J, Carrat F, Costedoat-Chalumeau N, Fain O. Refractory obstetrical antiphospholipid syndrome: Features, treatment and outcome in a European multicenter retrospective study. Autoimmun Rev. 2017;16(7):730–734.

56. Sciascia S, Branch DW, Levy RA, Middeldorp S, Hunt BJ. The efficacy of hydroxychloroquine in altering pregnancy outcome in women with antiphospholipid antibodies. Evidence and clinical judgment. Thromb Haemost. 2016;115(2):285–290.

57. Sciascia S, Hunt BJ, Talaveragarcia E, Lliso G, Khamashta MA, Cuadrado MJ. The impact of hydroxychloroquine treatment on pregnancy outcome in women with antiphospholipid antibodies. Am J Obstet Gynecol. 2016;214(2):273.e1–e8.

58. Bundhun PK, Soogund MZS, Huang F. Impact of systemic lupus erythematosus on maternal and fetal outcomes following pregnancy: A meta-analysis of studies published between years 2001–2016. J Autoimm. 2017;79(Complete):17–27. 59. Smeele HTW, Dolhain RJEM. Current perspectives on fertility, pregnancy and childbirth in patients with rheumatoid arthritis. Semin Arthritis Rheum. 2019;49(3S):S32–S35. 60. Dinarello CA. Proinflammatory cytokines. Chest. 2000;118(2):503–508.

61. Trindade MC, Lind M, Nakashima Y, Sun D, Goodman SB, Schurman DJ, et al. Interleukin10 inhibits polymethylmethacrylate particle induced interleukin-6 and tumor necrosis factor-alpha release by human monocyte/macrophages in vitro. Biomaterials. 2001;22(15):2067–2073.

62. Pope RM. Apoptosis as a therapeutic tool in rheumatoid arthritis. Nat Rev Immunol. 2002;2(7):527–535. 63. Danese S, Vuitton L, Peyrin-Biroulet L. Biologic agents for IBD: practical insights. Nat Rev Gastroenterol Hepatol. 2015;12(9):537–545.

64. Kwatra SG, Dabade TS, Gustafson CJ, Feldman SR. JAK inhibitors in psoriasis: a promising new treatment modality. J Drugs Dermatol. 2012;11(8):913–918.

65. Yamaoka K. Janus kinase inhibitors for rheumatoid arthritis. Curr Opin Chem Biol. 2016;32:29–33.

203

204

Personalized Nanomedicines for Treatment of Autoimmune Disease

66. Guilliams M, Dutertre CA, Scott CL, McGovern N, Sichien D, Chakarov S, et al. Unsupervised high-dimensional analysis aligns dendritic cells across tissues and species. Immunity. 2016;45(3):669–684. 67. Clement M, Pearson JA, Gras S, van den Berg HA, Lissina A, Llewellyn-Lacey S, et al. Targeted suppression of autoreactive CD8+ T-cell activation using blocking anti-CD8 antibodies. Sci Rep. 2016 17;6:35332. 68. Hoos A. Development of immuno-oncology drugs: from CTLA4 to PD1 to the next generations. Nat Rev Drug Discov. 2016;15(4):235–247.

69. Panel OKs CAR T Therapy for leukemia. Cancer Discov. 2017;7(9):924.

70. Jackson HJ, Rafiq S, Brentjens RJ. Driving CAR T-cells forward. Nat Rev Clin Oncol. 2016;13(6):370–383.

71. Christensen AD, Skov S, Kvist PH, Haase C. Depletion of regulatory T cells in a hapteninduced inflammation model results in prolonged and increased inflammation driven by T cells. Clin Exp Immunol. 2015;179(3):485–499.

72. Clemente-Casares X, Blanco J, Ambalavanan P, Yamanouchi J, Singha S, Fandos C, et al. Expanding antigen-specific regulatory networks to treat autoimmunity. Nature. 2016;530(7591):434–440.

73. Wang Z, Li J, Cho J, Malik AB. Prevention of vascular inflammation by nanoparticle targeting of adherent neutrophils. Nat Nano. 2014;9(3):204–210.

74. Boilard E, Nigrovic PA, Larabee K, Watts GF, Coblyn JS, Weinblatt ME, et al. Platelets amplify inflammation in arthritis via collagen-dependent microparticle production. Science. 2010;327(5965):580–583.

75. Maurer E, Tang C, Schaff M, Bourdon C, Receveur N, Ravanat C, et al. Targeting platelet GPIbbeta reduces platelet adhesion, GPIb signaling and thrombin generation and prevents arterial thrombosis. Arterioscler Thromb Vasc Biol. 2013 Jun;33(6): 1221–1229.

76. van Dommelen SM, Vader P, Lakhal S, Kooijmans SA, van Solinge WW, Wood MJ, et al. Microvesicles and exosomes: opportunities for cell-derived membrane vesicles in drug delivery. J Control Release. 2012;161(2):635–644.

77. Kulkarni JA, Witzigmann D, Chen S, Cullis PR, van der Meel R. Lipid nanoparticle technology for clinical translation of siRNA therapeutics. Acc Chem Res. 2019 17;52(9):2435–2444. 78. Lam JK, Chow MY, Zhang Y, Leung SW. siRNA versus miRNA as therapeutics for gene silencing. Mol Ther Nucleic Acids. 2015;4:e252.

79. Friedman RC, Farh KK, Burge CB, Bartel DP. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2009;19(1):92–105. 80. Roy S, Benz F, Luedde T, Roderburg C. The role of miRNAs in the regulation of inflammatory processes during hepatofibrogenesis. Hepatobiliary Surg Nutr. 2015;4(1):24–33.

81. Woitok MM, Zoubek ME, Doleschel D, Bartneck M, Mohamed MR, Kiessling F, et al. Lipid-encapsulated siRNA for hepatocyte-directed treatment of advanced liver disease. Cell Death Dis. 2020;11(5):343.

82. Mulligan MJ, Lyke KE, Kitchin N, et al., Phase I/II study of COVID-19 RNA vaccine BNT162b1 in adults. Nature 2020; 586:589–593.

References

83. Rupaimoole R, Slack FJ. MicroRNA therapeutics: towards a new era for the management of cancer and other diseases. Nat Rev Drug Discov. 2017;16(3):203–222. 84. Li M, Ding X, Zhang Y, Li X, Zhou H, Yang L, et al. Antisense oligonucleotides targeting lncRNA AC104041.1 induces antitumor activity through Wnt2B/beta-catenin pathway in head and neck squamous cell carcinomas. Cell Death Dis. 2020;11(8):672.

85. Wilbie D, Walther J, Mastrobattista E. Delivery aspects of CRISPR/Cas for in vivo genome editing. Acc Chem Res. 2019;52(6):1555–1564. 86. Mostafa A, Bartneck, M. Nanotechnology in cancer theranostics. In: Tutar, Y, ed. Advances in Cancer Nanotheranostics for Experimental and Personalized Medicine, Bentham Science Publishers, 2020, pp. 47–81. 87. Bartneck M, Peters FM, Warzecha KT, Bienert M, van Bloois L, Trautwein C, et al. Liposomal encapsulation of dexamethasone modulates cytotoxicity, inflammatory cytokine response, and migratory properties of primary human macrophages. Nanomedicine. 2014;10(6):1209–1220.

88. Bartneck M, Scheyda KM, Warzecha KT, Rizzo LY, Hittatiya K, Luedde T, et al. Fluorescent cell-traceable dexamethasone-loaded liposomes for the treatment of inflammatory liver diseases. Biomaterials. 2015;37:367–382.

89. He C, Yin L, Tang C, Yin C. Multifunctional polymeric nanoparticles for oral delivery of TNF-alpha siRNA to macrophages. Biomaterials. 2013;34(11):2843–2854.

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Chapter 10

Intracellular Antibody Immunity and Its Applications Jingwei Zeng, PhD,a and Leo C. James, PhDb aUniversity bThe

of Cambridge School of Clinical Medicine, Cambridge, UK Medical Research Council Laboratory of Molecular Biology, Cambridge, UK

[email protected]

Keywords: immunoglobulins, type I interferon–inducible cytosolic protein, ubiquitin, tripartite motif-containing protein 21, proteasomes, nuclear factor-κB, major histocompatibility complex (MHC) class I molecules, receptor-mediated endocytosis, Trim-Away, small interfering RNA, proteasomal degradation, ubiquitin-proteasome system

10.1 What Is Intracellular Antibody Immunity? Antibodies or immunoglobulins (Ig) are proteins secreted into the extracellular space by B cells to bind to pathogens and antigens. In doing so, they can prevent infection, neutralize toxins, and stimulate the immune response. Antibodies achieve these effector functions by activating other serum proteins, such as complement, or by engaging antibody receptors expressed on the surface of professional immune cells. Importantly, these processes all take place outside cells. Antibodies are also internalized by cells and engage with receptors expressed in endosomal compartments, such as the neonatal fragment crystallizable region (Fc) receptor and the polymeric Ig receptor. These receptors have roles in recycling and transcytosis—the redistribution of antibodies back into serum or transport through the cell to specific tissue compartments such as the gut epithelium or from mother to fetus. In the last decade, however, it has been discovered that antibodies have

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a second life inside the cytosol, where they engage with a specialized antibody receptor called tripartite motif-containing protein (TRIM) 21 and activate a second line of immune defense [1].

10.2 What Is Tripartite Motif-Containing Protein 21

(TRIM21)?

TRIM21 is a ubiquitously expressed, type I interferon–inducible cytosolic protein that binds to antibodies with high affinity [2,3]; indeed, TRIM21 is the highest affinity IgG receptor in humans [1]. Like other members of the TRIM family, TRIM21 contains a RING-type E3 ubiquitin ligase domain followed by a B-box domain and a coiled-coil domain that is thought to form an antiparallel homodimer [4]. TRIM21 also contains a C-terminal PRYSPRY domain, the 2 copies of which allow simultaneous binding of the 2 heavy-chains found in an antibody [3]. TRIM21 binds to all 4 subclasses of IgG (IgG1, IgG2, IgG3, and IgG4) with comparable affinities, and this binding is remarkably highly conserved, meaning that human and mouse TRIM21 will bind to antibodies from other mammals [2]. In addition, TRIM21 has also been shown to bind to the heavy-chains of IgA and IgM, albeit weaker than IgG [5]. This is in contrast to classical cell surface antibody receptors, which are completely unrelated to TRIM21 and display strong selectivity for specific antibody isotype and subclass.

10.3 What Does TRIM21 Do?

Antibodies don’t normally access the cytosol because they can’t pass through plasma or endosomal membranes. However, they are good at opsonizing (binding to) viruses in the extracellular space. Viruses are obligate intracellular pathogens that have evolved specific mechanisms to trigger endocytosis and disrupt endosomal membranes in order to gain access to cellular machinery. An antibody-bound virus that escapes the endosomal compartment and enters the cytosol during infection will be met by TRIM21, which detects the virus by binding to the antibody Fc region. Importantly, as well as being an antibody receptor, TRIM21 is capable of catalyzing ubiquitination using its RING domain [1, 6]. Once TRIM21 detects an antibody-bound virus, it becomes activated and begins synthesizing ubiquitin chains. These chains have 2 functions: They cause proteasomal degradation of the virus, and they stimulate immune signaling (Fig. 10.1). This combination of sensor and effector responses provides both an immediate countermeasure against the virus and activates an ongoing antiviral state throughout the host. Therefore, TRIM21 provides a crucial mechanism by which non–entry blocking antibodies deposited on the surface of viral particles can mediate a post-entry inhibition to viral replication. For instance, the humoral response to human adenovirus 5 (AdV5) predominantly generates non–entry blocking antibodies directed

What Does TRIM21 Do?

against the viral hexon protein [7], meaning that AdV5 bound by this antibody can still engage cellular receptors and enter cells by endocytosis [8]. Nevertheless, this non–entry blocking anti-hexon antibody has been shown to mediate TRIM21dependent post-entry neutralization of AdV5 [8].

Figure 10.1 Schematic overview of TRIM21-mediated degradation of pathogens and proteins. Abbreviation: TRIM21, tripartite motif-containing protein 21. Image credit: Visual Aids Department, MRC Laboratory of Molecular Biology.

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10.4 How Does TRIM21 Work? When the RING domain of TRIM21 is activated, it begins catalyzing the assembly of self-anchored, K63-linked ubiquitin chains in conjunction with the E2 enzymes Ube2W and Ube2N/2V2 [6]. These self-anchored K63-linked ubiquitin chains are subsequently modified by additional ubiquitin chains with a K48-linkage [6]. TRIM21-mediated ubiquitination results in the recruitment of proteasomes and degradation of the antibody-bound viral particles [1]. This proteasome-mediated degradation of virus-antibody complexes is a remarkably fast process, and in the case of AdV5, it is facilitated by the cofactor AAA-ATPase valosin-containing protein (VCP)/p97 and allows TRIM21 to destroy an incoming virus before it begins replicating [1]. However, VCP is not always required for TRIM21-mediated degradation of simpler substrates, such as IgG Fc protein expressed inside cells [9]. TRIM21-mediated antibody-dependent intracellular neutralization (ADIN) is an incredibly efficient process; just 2 antibody molecules per adenovirus particle can be sufficient for post-entry neutralization of adenovirus in cultured cells [10]. ADIN of nonenveloped viruses has been demonstrated in a diverse range of cell lines from a variety of mammalian species as well as in in vivo mouse models [8, 11]. Most recently, swine TRIM21 has been shown to mediate antiserumdependent neutralization of the foot-and-mouth disease virus [12]. In addition to virus neutralization, TRIM21 mediates antibody-dependent inhibition of intercellular seeding of tau aggregates [13]. Similar to virus neutralization, the inhibition of tau seeding is both VCP and proteasome dependent [13] (Fig. 10.1). When TRIM21 detects antibody-coated pathogens inside the cell, it also triggers innate immune signaling pathways, including NF-κB, AP-1, IRF3, IRF5, and IRF7 [14]. TRIM21 can do this because the K63-linked ubiquitin chains it synthesizes are potent immune activators. Nuclear factor-κB (NF-κB) is a master regulator of innate immune signaling, and K63-linked ubiquitin chains are sufficient for NF-κB pathway activation [15]. TRIM21 has been shown to activate NF-κB upon infection with antibody-bound human adenovirus and rhinovirus, feline calicivirus, and Salmonella enterica [14, 16]. Importantly, TRIM21 synergizes with other pattern-recognition receptors to potentiate immune sensing. When TRIM21 causes the proteasomal degradation of an incoming virus, it exposes the viral genome to cytosolic nucleic acid sensors. TRIM21 has been shown to reveal the genome of adenovirus to cGAS/STING and the genome of rhinovirus to RIG-I/ MAVS [16]. In primary human macrophages, TRIM21-mediated viral genome exposure stimulates a cascade of sensors ultimately leading to activation of the inflammasome, pyroptosis, and the release of IL-1β [17]. Unlike nonimmune cells, macrophages express a variety of Fcγ receptors in addition to TRIM21, and in these cells, the Fcγ receptors were shown to contribute to viral neutralization by targeting antibody-virus complexes for destruction in the phagolysosome compartment [18]. However, even in these Fcγ-expressing professional immune cells, TRIM21 acts as an important safety mechanism to destroy any antibody-coated

How Can We Exploit TRIM21?

viruses that escape into the cytosol, and virus neutralization is only impaired when both of these pathways are suppressed [17]. By targeting antibody-coated virus particles for proteasomal degradation, TRIM21-mediated ADIN can, in theory, generate peptide antigens for presentation on major histocompatibility complex (MHC) class I molecules via the classical antigen presentation pathway. In professional antigen-presenting cells, the viral antigens can also be presented on MHC class II molecules through the crosspresentation pathway. Recently, a mutated anti-adenovirus IgG with increased affinity for TRIM21 was shown to enhance dendritic cell (DC) activation and cytokine secretion upon infection of DCs with mutant IgG complexed adenovirus [19]. In addition, DCs primed with mutant IgG-adenovirus complex were shown to stimulate CD8+ T-cell proliferation and cytokine release in a co-culture system [19]. One major limitation of this TRIM21-mediated ADIN mechanism is the requirement for the virus to carry antibodies with it into the cytosol. Although this is frequently the case for nonenveloped viruses that enter the cell by triggering receptor-mediated endocytosis, it can be circumvented by enveloped viruses that shed their outer lipid envelope during host cell entry.

10.5 How Can We Exploit TRIM21?

The key feature of TRIM21-mediated intracellular antibody immunity is that TRIM21 does not directly engage with its target but with target-bound antibodies. This explains why it works against such a diverse range of targets including viruses, bacteria, and proteopathic agents. Moreover, it means that TRIM21 will target for degradation any antibody-bound protein in the cytosol. This property can be exploited to carry out protein depletion in living cells (Fig. 10.1). In the technology Trim-Away, antibodies against endogenous cellular proteins are delivered into cells by methods such as electroporation or microinjection [9]. The resulting antibody-bound target is recognized by TRIM21, and the entire protein complex is degraded by the ubiquitin-proteasome system [9]. The crossspecies activity of TRIM21 is particularly helpful here as it enables off-the-shelf antibodies raised in different mammals to be used in degradation experiments without the need for modification [9]. Trim-Away provides an alternative to small interfering RNA (siRNA) and can be used in the same kind of experiments to study protein function. However, because Trim-Away works at the protein level, it is much faster and will remove a target protein within hours [9], rather than days as with siRNA. Trim-Away also has 3 further benefits. First, it enables the depletion of proteins in cells that are not amenable to standard genetic-based techniques, such as in primary cells in which active nucleotide-sensing pathways generate unwanted inflammatory responses to DNA or RNA transfection [20]. Indeed, Trim-Away has been used to demonstrate that NLRP3 is vital for inflammasome formation and interleukin-1β secretion by ex vivo human monocyte–derived

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macrophages [9]. Second, Trim-Away can be used in cells that are transcriptionally quiescent, such as oocytes, in which the target protein has a long cytosolic halflife, or to acutely remove proteins that are essential for long-term cell survival. For example, Trim-Away was used to rapidly degrade target proteins at specific stages of cell division during oocyte meiosis [21]. Third, Trim-Away allows transcriptional pathways to be rapidly activated. For instance, Trim-Away has been used to degrade IκB and induce NF-κB activation [9]. Although transient selective protein degradation is a distinguishing feature of Trim-Away, it can also be used to achieve long-lasting persistent protein knockdown by expressing nanobody-Fc fusion proteins [9]. Expression of a nanobody-Fc fusion by mRNA transfection has been shown to allow targeting of nuclear-localized proteins such as histone H2B for degradation, whereas full-length antibodies are too large to access the nuclear compartment [9].

10.6 What Next?

Understanding intracellular antibody immunity and the role of TRIM21 has potential implications for future vaccine and gene therapy design. TRIM21 was shown to be a major contributing factor for inhibiting adenoviral gene delivery in vivo in the presence of preexisting antibody [8]. Preventing this, for instance, by using a small molecule inhibitor of TRIM21, could make repeated viral-based gene therapy treatments viable. Conversely, activating TRIM21 during vaccination could help induce a more robust protective response. This is because TRIM21-mediated proteasomal degradation of viral proteins has the potential to generate peptides for antigen presentation [19], and TRIM21 activation of innate signaling could provide costimulatory signals for professional immune cells. Additionally, there may be ways to expand Trim-Away from a laboratory tool to a therapeutic modality. Trim-Away has been shown to function in living organisms and has been used to study embryogenesis in zebra fish [22]. This was possible despite the fact that TRIM21 is naturally only found in mammals because the ubiquitin-proteasome system that Trim-Away relies on is highly conserved across eukaryotes. In this example, both the TRIM21 and antibody proteins were introduced by microinjection. Therapeutic delivery of antibodies into the cells of complex living organisms is still far off, as the same barriers that ordinarily keep antibodies outside the cell also prevent easy delivery of protein-based drugs into the cytosol. One possible solution may be to replace antibodies with a bispecific small molecule, such as a bispecific proteolysis-targeting chimera (PROTAC). PROTACs recruit E3 ubiquitin ligases to degrade intracellular proteins [23], and TRIM21 may be ideally suited for such an approach, given it already uses an intermediary molecule for target recruitment. However, such technologies require comprehensive understanding of TRIM21 structure, mechanism of regulation, activation and enzymatic catalysis, which are the subject of current and future studies.

References

Abbreviations ADIN: AdV5: DC: Fc: Ig: MHC: NF-κB: PROTAC: siRNA: TRIM: VCP:

antibody-dependent intracellular neutralization adenovirus 5 dendritic cell fragment crystallizable region immunoglobulins major histocompatibility complex nuclear factor-κB proteolysis-targeting chimera small interfering RNA tripartite motif-containing protein valosin-containing protein

Disclosures and Conflict of Interest

This chapter was originally published as: Zeng, J., James, L. C. (2020). Intracellular antibody immunity and its applications. PLoS Pathog., 16(8), e1008657, https://doi.org/10.1371/journal.ppat.1008657, under the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates.

Funding: LCJ is supported by the MRC (UK, U105181010) and a Wellcome Trust Investigator Award to LCJ. JZ received a PhD Fellowship from the Frank Edward Elmore Fund (University of Cambridge) and funding from the Rosetree Trust. The funders had no role in study design, data collection and analysis,

decision to publish, or preparation of the chapter.

Competing interests: The authors have declared that no competing interests exist.

References

1. Mallery DL, McEwan WA, Bidgood SR, Towers GJ, Johnson CM, James LC. Antibodies mediate intracellular immunity through tripartite motif-containing 21 (TRIM21). Proc Natl Acad Sci U S A. 2010;107:19985–90.

2. Keeble AH, Khan Z, Forster A, James LC. TRIM21 is an IgG receptor that is structurally, thermodynamically, and kinetically conserved. Proc Natl Acad Sci USA. 2008;105:6045–50.

3. James LC, Keeble AH, Khan Z, Rhodes DA, Trowsdale J. Structural basis for PRYSPRY-mediated tripartite motif (TRIM) protein function. Proc Natl Acad Sci USA. 2007;104:6200–5. 4. Li Y, Wu H, Wu W, Zhuo W, Liu W, Zhang Y, et al. Structural insights into the TRIM family of ubiquitin E3 ligases. Cell Res. 2014;24:762–5.

5. Bidgood SR, Tam JCH, McEwan WA, Mallery DL, James LC. Translocalized IgA mediates neutralization and stimulates innate immunity inside infected cells. Proc Natl Acad Sci USA. 2014;111:13463–8.

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6. Fletcher AJ, Mallery DL, Watkinson RE, Dickson CF, James LC. Sequential ubiquitination and deubiquitination enzymes synchronize the dual sensor and effector functions of TRIM21. Proc Natl Acad Sci U S A. 2015;112:10014–9.

7. Bradley RR, Lynch DM, Iampietro MJ, Borducchi EN, Barouch DH. Adenovirus serotype 5 neutralizing antibodies target both hexon and fiber following vaccination and natural infection. J Virol. 2012;86:625.

8. Bottermann M, Foss S, van Tienen LM, Vaysburd M, Cruickshank J, O’Connell K, et al. TRIM21 mediates antibody inhibition of adenovirus-based gene delivery and vaccination. Proc Natl Acad Sci. U S A. 2018;115:10440–5.

9. Clift D, McEwan WA, Labzin LI, Konieczny V, Mogessie B, James LC, et al. A method for the acute and rapid degradation of endogenous proteins. Cell. 2017;171:1692–1706.e18.

10. McEwan WA, Hauler F, Williams CR, Bidgood SR, Mallery DL, Crowther RA, et al. Regulation of virus neutralization and the persistent fraction by TRIM21. J Virol. 2012;86:8482–91.

11. Vaysburd M, Watkinson RE, Cooper H, Reed M, O’Connell K, Smith J, et al. Intracellular antibody receptor TRIM21 prevents fatal viral infection. Proc Natl Acad Sci USA. 2013;110:12397–401.

12. Fan W, Zhang D, Qian P, Qian S, Wu M, Chen H, et al. Swine TRIM21 restricts FMDV infection via an intracellular neutralization mechanism. Antiviral Res. 2016;127:32–40.

13. McEwan WA, Falcon B, Vaysburd M, Clift D, Oblak AL, Ghetti B, et al. Cytosolic Fc receptor TRIM21 inhibits seeded tau aggregation. Proc Natl Acad Sci. USA. 2017;114:574–9.

14. McEwan WA, Tam JCH, Watkinson RE, Bidgood SR, Mallery DL, James LC. Intracellular antibody-bound pathogens stimulate immune signaling via the Fc receptor TRIM21 Suplementary. Nat Immunol. 2013;14:327–36.

15. Xia ZP, Sun L, Chen X, Pineda G, Jiang X, Adhikari A, et al. Direct activation of protein kinases by unanchored polyubiquitin chains. Nature. 2009;461:114–9. 16. Watkinson RE, McEwan WA, Tam JCH, Vaysburd M, James LC. TRIM21 promotes cGAS and RIG-I sensing of viral genomes during infection by antibody-opsonized virus. PLoS Pathog. 2015;11:1–20.

17. Labzin LI, Bottermann M, Rodriguez-Silvestre P, Foss S, Andersen JT, Vaysburd M, et al. Antibody and DNA sensing pathways converge to activate the inflammasome during primary human macrophage infection. EMBO J. 2019;38:e101365.

18. Zaiss AK, Vilaysane A, Cotter MJ, Clark SA, Meijndert HC, Colarusso P, et al. Antiviral antibodies target adenovirus to phagolysosomes and amplify the innate immune response. J Immunol. 2009;182:7058–68.

19. Ng PML, Kaliaperumal N, Lee CY, Chin WJ, Tan HC, Au VB, et al. Enhancing antigen cross-presentation in human monocyte-derived dendritic cells by recruiting the intracellular Fc receptor TRIM21. J Immunol. 2019;202:2307–19.

20. Hornung V, Latz E. Intracellular DNA recognition. Nat Rev Immunol. 2010;10:123–30.

21. Zielinska AP, Bellou E, Sharma N, Frombach AS, Seres KB, Gruhn JR, et al. Meiotic kinetochores fragment into multiple lobes upon cohesin loss in aging eggs. Curr Biol. 2019;29:3749–3765.e7.

References

22. Chen X, Liu M, Lou H, Lu Y, Zhou MT, Ou R, et al. Degradation of endogenous proteins and generation of a null-like phenotype in zebrafish using Trim-Away technology. Genome Biol. 2019;20:19. 23. Schapira M, Calabrese MF, Bullock AN, Crews CM. Targeted protein degradation: expanding the toolbox. Nat Rev Drug Discov. 2019;18:949–63.

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Chapter 11

Maternal Antibody Interference Contributes to Reduced Rotavirus Vaccine Efficacy in Developing Countries Claire E. Otero,a,b Stephanie N. Langel, PhD,a,c Maria Blasi, PhD,a,d and Sallie R. Permar, MD, PhDa,b,c aDuke

Human Vaccine Institute, Duke University Medical Center, Durham, North Carolina, USA of Pathology, Duke University School of Medicine, Durham, North Carolina, USA cDepartment of Pediatrics, Duke University Medical Center, Durham, North Carolina, USA dDepartment of Medicine, Duke University Medical Center, Durham, North Carolina, USA bDepartment

[email protected]

Keywords: rotavirus, rotavirus vaccine, Rotarix, Rotateq, Rotavac, RotaSiil, T helper 1/T helper 2 balance, maternal antibody, seroconversion, B cell receptors, memory B cells, Peyer’s patches, Fcγ receptor, intussusception, live-attenuated virus

11.1 Rotavirus Vaccine Efficacy Is Reduced in Lower- and Middle-Income Countries Despite the development of effective vaccines, which have reduced rotavirus (RV)related morbidity and mortality by 67% [1], RV is still one of the most common causes of diarrheal disease in childhood [1, 2]. There are currently 4 vaccines endorsed by the World Health Organization (WHO) to prevent RV-induced gastroenteritis: Rotarix, Rotateq, Rotavac, and RotaSiil, but only Rotarix and Rotateq are widely used globally [3]. These vaccines are orally administered, liveattenuated formulations, each containing different human and/or bovine serotypes of RV (Table 11.1). In first-world countries, RV vaccines are highly efficacious

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(80% to 90%), but in lower- and middle-income countries (LMICs), efficacy plummets to 40% to 60% [4, 5]. Due to this disparity in vaccine efficacy, RV infections still cause significant morbidity and mortality in LMICs [2]. Table 11.1 Current RV vaccines

WHO prequalified Composition

matAb interference reported?

RotaTeq Merck (United States)

2008

Yes [8]

Rotavac Bharat Biotech (India)

2018

Vaccine Developer

Rotarix

GlaxoSmithKline 2009 (Belgium)

Rotasiil Serum Institute 2018 of India (India)

aNo

Pentavalent human–bovine reassortant G1–G4 and P[6] [7]

Monovalent human G1P[6] [6]

Monovalent human–bovine reassortant G9P[10] [3, 11]

Thermostable pentavalent human-bovine reassortant G1, G2, G3, G4, and G9 [12–14]

Yes [9]

Yes [10]

Noa

references indicating matAb does or does not interfere.

Abbreviations: RV, rotavirus; WHO, World Health Organization.

Several reasons for low RV vaccine efficacy in LMICs have been proposed, including higher RV exposure, greater diversity of RV G and P serotypes, malnutrition, microbiome composition, maturation stage of the immune system, reduced vaccine replication due to other enteric pathogens, coadministration of the oral polio virus vaccine, different expression of histo-blood group antigens, skewed T helper 1 (Th1)/T helper 2 (Th2) balance and antibody response to vaccination, and higher incidence of maternal antibody (matAb) interference [15–18]. While it is likely that multiple factors contribute to the reduced RV vaccine efficacy observed in LMICs, matAb interference is likely a major contributor due to greater RV exposure, leading to greater maternal immunity, and higher rates and longer duration of breastfeeding in LMICs [19–21]. This chapter focuses on current evidence supporting matAb interference as a contributor, remaining questions, and proposed modifications to increase the efficacy of current vaccine regimens.

11.2 Evidence Supports matAb Interference as a Mechanism of Reduced RV Vaccine Efficacy MatAbs are transferred to the infant via 2 distinct routes: (1) placental transfer of immunoglobulin G (IgG) into infant circulation; and (2) breast milk transfer of primarily IgA into the infant gastrointestinal tract [22, 23]. Most studies investigating the role of matAb interference focus on placentally transferred IgG [24]. However, evidence from both population-level observational and animal modeling studies suggest that breast milk–derived matAb also interferes with RV vaccine efficacy [10, 25, 26].

Establishing a Causal Link between matAb Interference and Low RV Vaccine Efficacy in LMICs

Rotavac is a recently developed RV vaccine derived from a naturally attenuated and reassorted neonatal RV strain (116E). A clinical trial of this vaccine in Indian infants identified a significant inverse relationship between RV-specific maternal IgG and infant Rotavac vaccine responses. However, matAb inhibition was overcome by increasing the vaccine dose [10]. While the Rotavac trial did not investigate breast milk Abs as a contributor to matAb interference, modeling of RV infection using the murine RV strain Epizoonotic Diarrhea of Infant Mice (EDIM) showed that seropositive BALB/c dams conferred Abs to their pups, primarily through breastfeeding, which impaired pups’ immune responses to live RV inoculation [26]. These findings concur with studies of human cohorts in developing countries, such as Zambia and Vietnam, where babies of mothers with higher titers of anti-RV Abs in breast milk tend to have reduced responses to RV vaccines [9, 20]. These observational studies demonstrate a consistent association between maternal humoral immunity, including both serum IgG and mucosal IgA, and infant responses to RV vaccines.

11.3 Establishing a Causal Link between matAb Interference and Low RV Vaccine Efficacy in LMICs and Defining Mechanisms

While observational studies in animal and human populations have established a link between maternal immunity and infant vaccine efficacy, mechanistic studies demonstrating that matAb interference causes a reduction in RV vaccine efficacy are still needed. Previous studies have demonstrated an inverse correlation between serum and breast milk matAb titers and infant responses [9, 27], but these studies do not isolate this effect to matAb alone. Further, there are several RV G/P serotypes in circulation [28], and maternal exposure to certain serotypes may impact the degree of matAb interference observed, depending on the level of cross-reactivity of matAbs between wild-type RV strains and attenuated vaccine viruses. There are many other potential immune factors conferred from mother to child that may inhibit infant vaccine responses. One study of Zambian children found that lactadherin, an antiviral glycoprotein present in breast milk, negatively associated with infant seroconversion after vaccination with Rotarix [29]. Additionally, genetic host factors, such as expression of histo-blood group antigens, a cellular receptor for RV, may also influence RV vaccine efficacy and susceptibility to disease [30–33]. Thus, studies in which matAb can be isolated as a variable are needed to establish a causal link to reduced RV vaccine efficacy. A major impediment to such studies is the difficulty in modeling human RV infection in animal models due to the limited host range of RVs [34]. Several mechanisms have been proposed for IgG-mediated matAb interference against different viruses, including neutralization of live-attenuated vaccines, epitope masking, cross-linking of B cell receptors (BCRs) and inhibitory Fcγ receptor IIB (FcγRIIB), vaccine antigen removal via antibody-mediated phagocytosis, and

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Figure 11.1 matAb interference to infant RV vaccines [41]. (A) Placentally transferred IgG (red curve) begins reaching the infant as early as 8 weeks of gestation and peaks at term, approximately 40 weeks [22, 42]. Maternally derived IgG wanes in the infant over 12 months after birth [24]. Breast-fed infants receive Abs, primarily IgA, through breast milk, which peaks in colostrum at a concentration of approximately 12 mg/mL and maintains approximately 1 mg/mL in mature milk (light blue curve) [23]. However, due to the volume of milk consumed by the infant, the absolute amount of matAb transferred via breast milk increases over time until the child can start getting energy from other kinds of food (dark blue curve) [43]. RV vaccination typically occurs in 2 to 3 doses when the infant is 2 to 6 months old, as indicated by the black arrows [44]. In breast-fed infants, both types of matAb are present at the time of RV vaccination. (B) RV vaccines are orally administered live-attenuated viruses, which rely on replication in infant enterocytes to elicit a robust immune response. Microfold (M) cells sample antigens from the gut lumen and present them to antigen-presenting cells, which stimulate the adaptive immune response in Peyer’s patches [45]. In the presence of matAbs, several mechanisms have been proposed for reduction of infant immune responses to RV vaccination, including (1) inhibition of vaccine virus replication in enterocytes by matAb neutralization; (2) removal of vaccine antigen by antibody-mediated phagocytosis; (3) inhibition of infant B cell activation by cross-linking BCRs with inhibitory FcγRIIB; (4) epitope masking, which inhibits infant Ab responses by hiding recognizable antigens from infant B cells, which may also shift B cell responses toward nonimmunodominant epitopes; and (5) impacting downstream differentiation of B cells into plasma cells or memory B cells [24, 35]. Abbreviations: Ab, antibody; BCR, B cell receptor; FcγRIIB, Fcγ receptor IIB; IgA, immunoglobulin A; IgG, immunoglobulin G; matAb, maternal antibody; RV, rotavirus.

Potential Solutions for matAb Interference to RV Vaccines

downstream inhibition of B cell differentiation into plasma or memory B cells (Fig. 11.1) [24, 35]. A study of maternal IgG-mediated interference to measles liveattenuated vaccination in the cotton rat model demonstrated that nonneutralizing monoclonal Abs mediated interference, while neutralizing monoclonal Abs did not [36]. This study also indicated that the fragment crystallizable (Fc) region is necessary to inhibit Ab responses to vaccination and that this inhibition is due to interaction with FcγRIIB [36]. However, studies in mice utilizing sheep red blood cells as a model antigen have supported epitope masking as a mechanism mediating this inhibition of B cell responses [37–39]. Notably, one study demonstrated that interference occurs in FcγR-deficient mice, demonstrating that BCR–FcγRIIB is not the sole mechanism of B cell inhibition in the presence of preexisting Ab [39]. Interestingly, RV Abs targeting the middle capsid layer (VP6), which have traditionally been considered nonneutralizing, are capable of intracellular neutralization, suggesting that the impact of such maternal Abs on neonatal vaccine efficacy may not be limited to Fc-mediated “nonneutralizing” effector functions [40]. In another recent study using influenza hemagglutinin as a model antigen, researchers found that matAbs do not impact germinal center formation but modulate which antigens are targeted by infant B cells and, in a dose-dependent manner, inhibit B cell differentiation of plasma and memory B cells through an undefined mechanism [35]. Together, these results suggest that multiple mechanisms may contribute to matAb-mediated inhibition of infant vaccine responses, possibly in an antigen-dependent manner. RV vaccines are orally administered, so they may also be affected by Abs at the intestinal mucosa, primarily IgA delivered to the infant via breast milk. Notably, IgA-mediated interference may not follow the same mechanism(s) as IgGmediated interference due to differences in Fc characteristics. Additionally, it is noteworthy that while breast milk contains mostly IgA Abs, breast milk IgG Abs are present and can be transported to the lamina propria and into circulation [46, 47]. However, studies in multiple LMIC populations have shown that abstaining from breastfeeding for a period before and after RV vaccination does not change seroconversion rates [48–50]. The ineffectiveness of breastfeeding timing on RV vaccination may indicate that circulating, rather than breast milk, maternal IgG is the primary mediator of the interference. Further and more in-depth evaluation of Ab characteristics and the relative contribution of serum IgG and breast milk IgA would be informative for design and evaluation of strategies to overcome matAb interference.

11.4 Potential Solutions for matAb Interference to RV Vaccines

Several strategies can help circumvent matAb interference, but each comes with its own risks. RV vaccination is associated with a slightly increased risk of intussusception, which is generally outweighed by the immense benefit of reduction

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in morbidity and mortality, but must be considered when evaluating alternative vaccination strategies [51]. For example, while increasing the vaccine antigen dose may overcome matAb interference [10], a matAb-exceeding dose could also lead to pathology due to excessive replication of live vaccine virus or an improper immune response. This approach was previously tested using a live-attenuated measles vaccine, which induced some protection in the presence of maternal IgG but also resulted in increased infant mortality, especially in girls, who tended to have less maternal IgG compared to boys [24, 52–54]. Serology testing to quantitate preexisting Ab before vaccination is not feasible in LMICs, so it would not be possible to adjust the antigen dose based on matAb level, which would be an ideal compromise to improve the safety of this approach. Another alternative strategy is changing the timing of vaccination to wait until matAb levels in the infant wane. The measles vaccine follows this strategy as administration after 9 months of age demonstrated reduction of matAb interference [55]. However, later vaccination can also pose a significant risk because it leaves the infant more vulnerable during the period before vaccination when matAb levels are low, which is an important consideration in LMICs with greater RV exposure [19]. However, passive immunotherapy of a breast milk–targeted antibody delivered to the mother may keep mucosal matAb at a protective level until vaccination at a later age. Thus, additional studies are needed to determine the age when RV-specific matAbs have waned enough to achieve successful vaccination without increasing mortality due to RV exposure prior to vaccination. However, the risk of intussusception after RV vaccination increases with infant age, so vaccinating later may not be a viable strategy [56, 57]. Notably, a trial in Indonesia of the RV3-BB vaccine formulation (G3P[7], not currently endorsed by WHO) demonstrated better efficacy when the 3-dose series was administered earlier in life, starting at birth (75%) rather than at 8 weeks old (51%) [58]. This suggests that better efficacy can be achieved by vaccinating earlier in life and may circumvent the additional intussusception risk associated with RV vaccination in older infants. Vaccine formulation other than oral exposure to live-attenuated virus is another potential alternative. For example, a recombinant, truncated VP4 protein was more immunogenic than live-attenuated formulations and was not inhibited in the presence of matAb in a mouse model [26]. However, the efficacy of nonreplicating RV vaccines needs to be further validated with challenge studies using human RV strains. Additionally, a nonreplicating vaccine formulation does not guarantee better infant vaccine response. For example, in a gnotobiotic piglet model of human RV disease, boosting an oral live-attenuated vaccine with RV-like particles resulted in suppression of effector and memory B cell responses [59]. Furthermore, there are several protein vaccines whose efficacies are affected by matAb interference, including tetanus and hepatitis B vaccines [24]. Another potential alternative to oral live-attenuated RV vaccines is a viralvectored vaccine designed for long-term antigen expression. Continuous expression

Abbreviations

of antigen through vectored expression, administered early to release antigen for a longer period, could stimulate the infant immune system after matAbs drop to a noninterfering level [24, 60]. However, gene therapy approaches are held to a higher safety standard due to the potential of vector integration into the genome [61], and further investigation is needed to determine if such an approach would be effective in the context of RV vaccination. While there are several possible approaches, further investigation is needed to determine if their ability to overcome matAb interference outweighs the risks to the infant.

11.5 Prospects for Overcoming matAb Interference to Infant RV Vaccination

Effective RV vaccines currently exist, but efficacy of these vaccines is significantly reduced in LMICs. While many factors likely contribute to this reduction in efficacy, matAb interference is clearly associated with reduced vaccine efficacy, but further study is needed to isolate matAb interference as a contributing factor and fully establish a causal link. Mechanisms of matAb interference to orally administered RV vaccines may differ from those observed in other vaccines due to the importance of mucosal immunity and the potential for breast milk Abs to contribute to interference. Defining the mechanisms of matAb interference in this context will greatly inform alternative vaccination strategies to avoid or overcome matAb interference. Several alternative vaccination strategies have been proposed to reduce matAb interference, but these require further testing to determine the relative safety. Thus, more research into mechanisms of RV vaccine matAb interference and the safety and efficacy of alternative vaccination strategies is needed to ultimately achieve improved RV vaccine efficacy in LMICs and further reduce mortality from the leading diarrheal disease worldwide.

Abbreviations

Ab: BCRs: EDIM: Fc: FcγRIIB: IgA: IgG: LMICs: matAb: RV: Th: WHO:

antibody B cell receptors epizoonotic diarrhea of infant mice fragment crystallizable Fcγ receptor IIB immunoglobulin A immunoglobulin G lower- and middle-income countries maternal antibody reduced rotavirus T helper 1 World Health Organization

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Disclosures and Conflict of Interest This chapter was originally published as: Otero, C. E., Langel, S. N., Blasi, M., and Permar, S. R. (2020). Maternal antibody interference contributes to reduced rotavirus vaccine efficacy in developing countries. PLoS Pathog., 16(11), e1009010, https://doi.org/10.1371/journal.ppat.1009010, under the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates. Funding: CEO is supported by an NIH National Cancer Institute (NCI: https:// www.cancer.gov/) Ruth L. Kirschstein National Research Service Award T32 CA009111. SNL is supported by an NIH National Institute of Allergy and Infectious Diseases (NIAID: https://www.niaid.nih.gov) Ruth L. Kirschstein National Research Service Award T32 AI007392 and a Bill and Melinda Gates Foundation (https:// www.gatesfoundation.org/) award OPP 1189362. MB and SRP are supported by a Bill and Melinda Gates Foundation award OPP 1189362. The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the chapter. The content is solely the view of the authors and does not necessarily represent the official views of the National Institutes of Health or the Bill and Melinda Gates Foundation. Competing interests: Sallie R. Permar serves as a consultant for Pfizer, Sanofi, Moderna, and Merck vaccines and has a sponsored program on preclinical cytomegalovirus vaccine development with Merck and Moderna. All other authors declare no competing interests.

References

1. Burnett E, Jonesteller CL, Tate JE, Yen C, Parashar UD. Global impact of rotavirus vaccination on childhood hospitalizations and mortality from diarrhea. J Infect Dis J Infect Dis. 2017;215:1666–1672. 2. Tate JE, Burton AH, Boschi-Pinto C, Parashar UD, Agocs M, Serhan F, et al. Global, regional, and national estimates of rotavirus mortality in children 0.75, two were between 0.55 and 0.60, and all of the remaining were < 0.22. At the country level, cases with high mutation value occurred only in two countries, the United States (44/46) and the United Kingdom (2/46). Cases with low values, < 0.01, were mostly distributed in Europe and Russia, and 26 of the 37 were located in the Netherlands. For H1N1’s HA mutation from 2000–2019, a simple GLM shows that, of all the seven independent variables, most variables displayed a strong significance with HA mutation, including maximum temperature, minimum temperature, nighttime light, population density, and years fixed factor. Precipitation and urban accessibility were not statistically significant in the basic GLM model (Table 14.2, column a). To capture any possible coefficient variations over the variable range, estimates in a GAM were allowed to vary (Table 14.2, column b). The results of the GAM model show that the relationship of the five above variables and HA mutation was still significant, and the connection between nighttime light and mutation became very significant. At the same time, two variables (precipitation and urban accessibility) displayed significant association with mutation in the GAM but not GLM model. Here, Akaike Information Criterion (AIC) and explained deviance were used to consider the simulation effect of the model. The AIC of GAM is lower than that of GLM, −68,857.5 vs. −67,655.2. The deviance explained of GAM is 90.5%, while that of GLM is 89.4%. These results indicate that the simulation capability of the GAM model is better for this experimental process. Figure 14.1 shows the impact of four variables on mutation, with precipitation (A) and minimum temperature (B) being selected as representatives of environmental factors, and nighttime light (C) and population density (D) as representative social factors. In terms of the relationship between precipitation and mutation (Fig. 14.1A), the mutation rate does not change much with an increase of precipitation within about 2000 mm. Above 2000 mm, the rate of mutation first increases and then decreases with the increase in precipitation, but overall the effect is not significant. Minimum temperature displayed a nonlinear, rising association with HA mutation, with a maximum around 15°C (Fig. 14.1B). The curve as a whole is first stable, then increases, and after maintaining a stable period suddenly rises and then falls. The broad effect of nighttime light on HA mutation forms an undulating wave with three troughs and two peaks (Fig. 14.1C) and reached a minimum around 35. The impacts of the rising section are 15–25, 35–45, and > 50, with a maximum around 25. The association of population density with mutation is simpler than the above three factors, displaying a linear, positive association with mutation (Fig. 14.1D).

Discussion

14.4 Discussion The dependence of influenza virus transmission on environmental factors, including temperature, humidity, and atmospheric pressure, has been documented by many previous studies [15–18]. Therefore, the question arises as to whether these climatic factors are related to influenza virus mutations. In this study, the correlation between climatic factors and HA protein mutation of H1N1 was examined. As for the relationship between precipitation and mutation, the result was shown that the effect on mutations only occurred when precipitation was > 2000 mm, though the relationship was not linear. However, the average annual precipitation of many countries with high rates of H1N1 subtype influenza A virus mutation, especially the United States and United Kingdom, did not exceed 2000 mm [19, 20]. Thus, precipitation is not associated with HA protein mutations in a practical sense. Another climatic factor was the minimum temperature, which is slowly rising due to the influence of human activities. In recent years, multiple studies have proposed that global warming will likely influence the life of all living species, including the evolution of influenza A virus [21, 22]. Furthermore, Yan et al. found that global warming affects many different levels of biological evolution; even intracellular proteins are subject to global warming [23]. It is understandable that all biological functions are interconnected from the macro to micro level. In contrast, from the perspective of cross-species transmission of avian influenza, our previous studies on the correlation between climate factors and avian influenza infection found that there were significant relationships between climate factors and H5/H7 influenza infection, especially temperature variables [24]. Herein, the minimum temperature showed a nonlinear, rising association with HA mutation, with a maximum around 15°C. Interestingly, Diana et al. found that the maximum values of the weekly influenza proxy coincided with the minimum temperature (10–15°C) in the 2010–2015 influenza seasons in Spain [25]. This suggests that the highest mutation rate of HA protein at 15°C could be related to the high incidence and transmission rate of influenza virus at ~15°C. Aside from meteorological factors, many other factors also affect influenza mutation rates. In terms of socioeconomic factors, nighttime light and population density were selected as the test criteria. As is known, there is less research providing information on the correlation between flu mutation rates and nighttime light. Nighttime light data can reflect comprehensive information, and depends not only on population density but also per capita energy consumption and, hence, economic activity [26]. Surprisingly, nighttime light and influenza mutations did not display a positive correlation as predicted but, rather, an unstable fluctuation. Considering the influence of a variety of social factors, the one potential explanation for this result is that the effect of nighttime light on HA mutation is not direct but indirect or combined with other auxiliary factors. However, these trends cannot be ignored. A better understanding of the complex effects of nighttime light will enable

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better prediction and manipulation of the course of influenza evolution in social contexts, so more detailed classification and analysis of various social factors are needed. The association of population density with mutation is simpler than the above three factors, displaying a linear, positive association with mutation. This is to be expected as an excessive population can contribute to the spread of the epidemic. Notably, the continuous infection and propagation of the virus could yield genetic mutation and also affect pathogenicity and virulence [27, 28]. These results indicate that interventions with a focus on municipalities with greater flows and densities of people, especially those with a higher Human Development Index and the presence of municipal air and road transport, could play an important role in mitigating the impact of future influenza pandemics on public health. It is well known that influenza viruses display a remarkable genetic flexibility based on their high mutation rate under different selective pressures [29, 30]. Our study provides an environmental perspective for understanding mutation in the evolution of the H1N1 subtype of influenza viruses. Due to its crucial role in receptor recognition and attachment, the HA protein is considered to be a principal determinant of influenza virus invasion [31, 32]. Consequently, the association between HA mutations and environmental factors has been sought. In this study, a complex non-linear relationship had been found between minimum temperature, nighttime light, and HA protein mutations. Simultaneously, population density was positively correlated with HA protein mutations. These results suggest the possibility of using temperature and population density to approximate the effects that environmental factors have on H1N1 HA mutation. However, we have not analyzed and predicted the influence of these factors on the direction of HA protein mutation. Notably, much statistical data demonstrates that correlation does not mean a cause-consequence relationship. Therefore, even if correlations between two trends have not been found, whether there is any direct or indirect causation remains to be determined. Thus, wider and deeper research needs to be done. In addition, our findings showed that mutation rates of H1N1 subtype viruses were higher in the United States and the United Kingdom than in other countries, suggesting that there may be a greater risk for the emergence of novel pathogenic H1N1 strains in the US and UK. The climate and social economic risk delineated in this study should be considered as important monitoring references for the guidance of H1N1 epidemics caused by mutations. However, it should be noted that there are several limitations in this study. For instance, the environmental variables are annual average data, which does not exactly match the virus dataset on the time scale. In the future research, we will collect macro and micro data on the same time scale as far as possible. In addition, the environmental covariates adopted in this study may not be comprehensive enough due to the availability of data. In the future research, several variables (i.e., the distance to migratory bird migration routes and the distance to water body) will be added to the statistical analysis.

Conclusions

14.5 Conclusions Our findings show that environmental factors systematically affect the mutation of A/H1N1 viruses. Minimum temperature displayed a nonlinear, rising association with mutation, with a maximum ~15°C. The effects of precipitation and social development index (nighttime light) were more complex, while population density was linearly and positively correlated with mutation of A/H1N1 viruses. This study provides a novel insight into understanding the complex relationships between mutation of A/H1N1 viruses and environmental factors, which enhances our capacity to target the potential risk areas, to develop disease control strategies and to allocate medical supplies.

Abbreviations AIC: GAM: GIP: GLM: NASA: WHO:

Akaike information criterion generalized additive model Global Influenza Program generalized linear model National Aeronautics and Space Administration World Health Organization

Disclosures and Conflict of Interest

This chapter was originally published as: Jiang, D., Wang, Q., Bai, Z., Qi, H., Ma, J., Liu, W., Ding, F., Li, J. (2020) Could environment affect the mutation of H1N1 influenza virus? Int. J. Environ. Res. Public Health, 17, 3092, https://doi.org/10.3390/ ijerph17093092, under the Creative Commons Attribution license (http:// creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates, by kind permission of the copyright holders. Author Contributions: J.L. and F.D. conceived and designed the experiments. D.J., Q.W., Z.B. and H.Q. analyzed raw data and constructed tables and figures. D.J., J.L., F.D., Q.W. and Z.B. prepared the chapter and completed its revision. W.L. and J.M. suggested many of the experiments in this study. D.J. and Z.B. contributed equally to this work. All authors have read and agreed to the published version of the chapter. Funding: This work was supported by grants from the Mega-Project of Guangxi Natural Science Foundation (2015GXNSFEA139002), the National Natural Science Foundation of China (Grant No. 31970153, 31630079), the National Key R&D Program of China (2016YFD0500206) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB29010000, XDA19040305). J.L. is supported by the Youth Innovation Promotion Association of CAS (2019091). Conflicts of Interest: The authors declare no competing financial interests.

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References 1. Hutchinson, E.C. Influenza Virus. Trends Microbiol. 2018, 26, 809–810.

2. Webster, R.G., Bean, W.J., Gorman, O.T., Chambers, T.M., Kawaoka, Y. Evolution and ecology of influenza A viruses. Microbiol. Mol. Biol. Rev. 1992, 56, 152–179.

3. Czako, R., Subbarao, K. Refining the approach to vaccines against influenza A viruses with pandemic potential. Future Virol. 2015, 10, 1033–1047.

4. Krammer, F. Novel universal influenza virus vaccine approaches. Curr. Opin. Virol. 2016, 17, 95–103. 5. Kilbourne, E.D. Influenza pandemics of the 20th centur. Emerg. Infect. Dis. 2006, 12(1), 9–14.

6. McMichael, C. Climate change-related migration and infectious disease. Virulence 2015, 6, 548–553.

7. Ogden, N.H. Climate change and vector-borne diseases of public health significance. FEMS Microbiol. Lett. 2017, 364, fnx186. 8. Sharp, C.E., Brady, A.L., Sharp, G.H., Grasby, S.E., Stott, M.B., Dunfield, P.F. Humboldt’s spa: Microbial diversity is controlled by temperature in geothermal environments. ISME J. 2014, 8, 1166–1174. 9. Bleuven, C., Landry, C.R. Molecular and cellular bases of adaptation to a changing environment in microorganisms. Proc. R. Soc. B Biol. Sci. 2016, 283, 20161458.

10. Reiner, R.C., Jr., Smith, D.L., Gething, P.W. Climate change, urbanization and disease: Summer in the city …. Trans. R. Soc. Trop. Med. Hyg. 2015, 109, 171–172.

11. Jnawali, K., Morsky, B., Poore, K., Bauch, C.T. Emergence and spread of drug resistant influenza: A two-population game theoretical model. Infect. Dis. Model. 2016, 1, 40–51.

12. Bauch, C.T., Earn, D.J. Vaccination and the theory of games. Proc. Natl. Acad. Sci. USA 2004, 101, 13391–13394.

13. Leach, J.E., Vera Cruz, C.M., Bai, J., Leung, H. Pathogen fitness penalty as a predictor of durability of disease resistance genes. Annu. Rev. Phytopathol. 2001, 39, 187–224.

14. Basic information (reported province, reported date, latitude, and longitude) for 12401 H1N1 influenza case from 1931 to 2019. Available at: https://www.mdpi. com/1660-4601/17/9/3092/s1 (accessed on January 2, 2021).

15. Fang, L.Q., Wang, L.P., de Vlas, S.J., Liang, S., Tong, S.L., Li, Y.L., Li, Y.P., Qian, Q., Yang, H., Zhou, M.G., et al. Distribution and risk factors of 2009 pandemic influenza A (H1N1) in mainland China. Am. J. Epidemiol. 2012, 175, 890–897.

16. Firestone, S.M., Cogger, N., Ward, M.P., Toribio, J.A., Moloney, B.J., Dhand, N.K. The influence of meteorology on the spread of influenza: Survival analysis of an equine influenza (A/H3N8) outbreak. PLoS ONE 2012, 7, e35284.

17. Yuan, J., Yun, H., Lan, W., Wang, W., Sullivan, S.G., Jia, S., Bittles, A.H. A climatologic investigation of the SARS-CoV outbreak in Beijing, China. Am. J. Infect. Control 2006, 34, 234–236.

18. Fang, L.Q., Li, X.L., Liu, K., Li, Y.J., Yao, H.W., Liang, S., Yang, Y., Feng, Z.J., Gray, G.C., Cao, W.C. Mapping spread and risk of avian influenza A (H7N9) in China. Sci. Rep. 2013, 3, 2722.

References

19. Panagos, P., Ballabio, C., Borrelli, P., Meusburger, K., Klik, A., Rousseva, S., Tadić, M.P., Michaelides, S., Hrabalíková, M., Olsen, P., et al. Rainfall Erosivity in Europe. Sci. Total Environ. 2015, 511, 801–814.

20. Vines, R. European rainfall patterns. J. Climatol. 1985, 5, 607–616.

21. Yan, S., Wu, G. Trends in global warming and evolution of nucleoproteins from influenza A viruses since 1918. Transbound. Emerg. Dis. 2010, 57, 404–413.

22. Galam, S. Public debates driven by incomplete scientific data: The cases of evolution theory, global warming and H1N1 pandemic influenza. Phys. A Stat. Mech. Appl. 2010, 389, 3619–3631.

23. Yan, S.-M., Wu, G. Trends in global warming and evolution of matrix protein 2 family from influenza A virus. Interdiscip. Sci. Comput. Life Sci. 2009, 1, 272–279.

24. Li, J., Rao, Y., Sun, Q., Wu, X., Jin, J., Bi, Y., Chen, J., Lei, F., Liu, Q., Duan, Z., et al. Identification of climate factors related to human infection with avian influenza A H7N9 and H5N1 viruses in China. Sci. Rep. 2015, 5, 18094.

25. Gomez-Barroso, D., León-Gómez, I., Delgado-Sanz, C., Larrauri, A. Climatic factors and influenza transmission, Spain, 2010–2015. Int. J. Environ. Res. Public Health 2017, 14, 1469.

26. Raupach, M.R., Rayner, P.J., Paget, M. Regional variations in spatial structure of nightlights, population density and fossil-fuel CO2 emissions. Energy Policy 2010, 38, 4756–4764.

27. Shao, W., Li, X., Goraya, M.U., Wang, S., Chen, J.L. Evolution of influenza a virus by mutation and re-assortment. Int. J. Mol. Sci. 2017, 18, 1650.

28. Chen, H., Yuan, H., Gao, R., Zhang, J., Wang, D., Xiong, Y., Fan, G., Yang, F., Li, X., Zhou, J., et al. Clinical and epidemiological characteristics of a fatal case of avian influenza A H10N8 virus infection: A descriptive study. Lancet 2014, 383, 714–721.

29. Ge, Y., Chi, Y., Min, X., Zhao, K., Wu, B., Wu, T., Zhu, X., Shi, Z., Zhu, F., Cui, L. The evolution and characterization of influenza A (H7N9) virus under the selective pressure of peramivir. Virology 2019, 536, 58–67.

30. Rejmanek, D., Hosseini, P.R., Mazet, J.A., Daszak, P., Goldstein, T. Evolutionary dynamics and global diversity of influenza A virus. J. Virol. 2015, 89, 10993–11001.

31. Russell, C.J., Hu, M., Okda, F.A. Influenza hemagglutinin protein stability, activation, and pandemic risk. Trends Microbiol. 2018, 26, 841–853.

32. Mohebbi, A., Fotouhi, F., Jamali, A., Yaghobi, R., Farahmand, B., Mohebbi, R. Molecular epidemiology of the hemagglutinin gene of prevalent influenza virus A/H1N1/pdm09 among patient in Iran. Virus Res. 2019, 259, 38–45.

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Chapter 15

Current Perspectives in Medical Microbiology Raj Bawa, MS, PhD, MD ‘22* Bawa Biotech LLC, Ashburn, Virginia, USA Albany College of Pharmacy and Health Sciences, Albany, New York, USA Teva Pharmaceutical Industries, Ltd., Israel [email protected]

Keywords: acyl-homoserine lactones, African swine fever, aggregated microtubule associated protein tau, Alzheimer disease, amyloid precursor protein, antibody antigen-binding fragment, antibody dependent cellular phagocytosis, antimicrobial peptides, antiretroviral therapy, apolipoprotein-E, arbitrium system, atopic dermatitis, autoinducers, bacterial communities, bacteria–phage interactions, beta amyloid, biofilms, blood–brain barrier, bystander effect, CD4 binding, cerebrospinal fluid, cesarean delivery, claudin-5, Clostridioides difficile, coagulase negative staphylococci, coinfection, complement activation, CRISPR antiphage systems, Culex mosquitos, dementia, drug-susceptibility testing, encephalitis, ethambutol, foot-and-mouth disease, fragment crystallizable, ganglia, gp41 HIV-1 envelope glycoproteins, Gram-negative bacteria, Gram-positive bacteria, hemagglutinin, herpesviruses, HIV-1, HIV-1 vaccine, Human Immunodeficiency Virus, influenza A virus, isoniazid-resistant tuberculosis, Japanese encephalitis virus, lysis–lysogeny lifestyle switch, lysophosphatidylcholine, MAIT cells, malaria, matrix protein, maximum clade credibility, metabolomics, metalloproteinase, methicillin-resistant Staphylococcus aureus, microbiome, microglia, multidrug-resistant tuberculosis, mucosal-associated invariant T cells, Mycobacterium tuberculosis, Netherton syndrome, neuraminidase, neuroinflammation, nucleoprotein, occludin, oxidative stresses, pandemic, peripheral nervous system, phenol soluble modulins, phospholipase A2, Plasmodium falciparum, polymerase acidic protein, polymerase basic protein 1, polymerase basic protein 2, preexposure prophylaxis, pyrazinamide, quorum sensing, reactive oxygen species, reverse zoonosis, rifampicin, rifampicin-susceptible tuberculosis, super-resolution microscopy, tight junctions, transcellular infection, trigeminal nerve, trimeric gp120, tuberculosis, vaccinia, vacciniarelated kinases, variola virus, World Health Organization, zonula occludens, zoonoses

*Note: This chapter has been organized and compiled by the Series Editor, Dr. Raj Bawa. The content reflects the most critical and current information in medical microbiology as of the publication date. Given continuous, rapid advances in medicine and health information, this chapter is no substitute for individual patient assessment based on health care professionals’ examination of each patient. As new research and experience broaden our knowledge base, changes in medical care, diagnostics, therapy, research methods, assays, tools and techniques, medical formulations, and treatments may become necessary. Therefore, it is imperative that the reader does not solely rely on the information presented herein. Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

Copyright © 2022 Jenny Stanford Publishing Pte. Ltd.

ISBN 978-981-4877-84-8 (Hardcover), 978-1-003-18043-2 (eBook)

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15.1 Continued Poxvirus Research: From Foe to Friend Susanna R. Bidgood, MSci, PhD MRC Laboratory for Molecular Cell Biology, University College London, London, UK [email protected]

In 1980, the World Health Organization announced an extraordinary scientific achievement that would change the course of human history: their 14-year international vaccination campaign had finally and completely eradicated smallpox. This disease, caused by the poxvirus variola, first appeared around 10,000 B.C. and was the deadliest human disease to ever exist. With a devastating 20% to 60% mortality rate, smallpox killed an estimated 300 million people in the 20th century alone and left its few survivors severely disfigured, with a third of them blind [1]. The practice of vaccination had begun much earlier, in the 18th century. It was commonly recognized at the time that milkmaids, who would catch cowpox from their cattle, never died of smallpox. The English physician Edward Jenner then systematically infected individuals with this nonlethal yet close relative of variola virus and showed that cowpox infection protected a person from smallpox. However, it took the World Health Organization vaccination campaign, now using a vaccine containing the poxvirus vaccinia, to finally rid the world of the devastating killer [1, 2]. Health officials subsequently stopped routine smallpox vaccinations because the risk of using a live virus vaccine against a disease that no longer posed a threat far outweighed the benefits. Due to its bioterrorism potential and likely lethality upon accidental exposure, scientific research on variola virus is now restricted exclusively to government secure facilities where the few remaining stocks of variola virus are kept safely under lock and key. Smallpox has been defeated. This extraordinary victory was achieved before I was born, so I never received a shot of vaccinia to protect me from smallpox. In fact, for most people alive today, vaccinia virus is simply the vaccine once used to protect people from a disease that no longer exists. Despite this, just four years ago, I established a research project to study the fundamental biology of vaccinia virus. I was excited to study vaccinia because, independent of its disease-eradicating heroism, this virus is a powerful tool for cell biology research. Vaccinia virus lends itself to experimental manipulation by being both relatively safe, and quick and easy to perform experiments with. It can be handled at room temperature, maintains infectivity after multiple rounds of freeze-thawing, and is able to infect a broad range of species and cell types. The virus naturally undergoes homologous recombination during viral replication, which enables the easy addition or deletion of genes from its genome, and its complete replication cycle only takes 8 to 12 hours. Moreover, as it has been evolving alongside animals for thousands of years and is highly adapted to control the host cell, establish its

Continued Poxvirus Research: From Foe to Friend

replicative niche inside the cell, and down-regulate the host’s immune surveillance systems, I can learn a lot of cell biology by watching how it does this. In recent years, three specific vaccinia features have proved hugely valuable both for the development of therapeutics to treat major public health threats as well as aiding the development of cutting-edge imaging technologies that will be applicable to all areas of cell biology. Firstly, vaccinia encodes almost all of its own replication machinery despite being an obligate intracellular parasite. Many of these viral proteins are homologues of cellular proteins, giving scientists like me a simpler system in which to begin to understand the function of the closely related cellular proteins. For example, human cells encode over 600 kinase enzymes. As a result, it is sometimes hard to define the role of an individual human kinase within the cell. Vaccinia virus by contrast encodes just two kinases. One of these kinases, called B1, was first identified in the 1980s. The kinase activity of B1 was extensively studied and shown to be essential for viral DNA replication. A decade later, a screen for novel human proteins identified two proteins whose sequences were more than 40% identical to vaccinia B1, suggesting that these proteins would also be active kinases [3]. Today, we know that there is a whole family of human vaccinia-related kinases (VRKs) playing key roles in regulating cellular DNA replication, cell cycle progression, and cell proliferation. Given that cancers can establish themselves when these processes go wrong, VRKs are implicated in several cancers including liver, lung, and breast cancer [4–6]. As a result, this kinase family serves as potential targets for developing cancer therapeutics, with comparative studies of vaccinia B1 still guiding understanding [4, 7] The second key vaccinia feature is its size. Boasting dimensions of 350 nm × 250 nm × 250 nm, vaccinia virus is large (for a virus!) and can be easily tracked by live cell microscopy. In 2008, the power of this feature was exemplified when a novel virus entry pathway into cells was discovered using vaccinia [8]. Jason Mercer, a scientist in Zürich, imaged vaccinia virions as they interacted with the surface of human cells. Upon contact with the plasma membrane, the virus caused the whole cell to produce blebs followed by the internalization of the virion into a large cellular intrusion. This process was highly reminiscent of the uptake of debris released from dying cells, during a process called ‘apoptotic clearance’. Jason’s further experiments showed that vaccinia displays lipids on its surface, making it look just like a piece of debris shed from a dying cell, and thus vaccinia tricks cells into actively taking it up. This ‘apoptotic mimicry’ entry pathway is now known to be employed by 20 clinically relevant viruses, including chikungunya virus, dengue virus, hepatitis A virus, Lassa virus, Marburg virus, and Ebola virus [9]. During the 2014–2016 outbreak, Ebola killed over 11,000 people [10]. Since then, the development of anti-Ebola therapeutics to combat future outbreaks has garnered massive investment. Blocking the Ebola apoptotic mimicry entry pathway is a promising approach. These life-saving studies are only possible because 10 years ago Jason spent some time watching how a virus that does not even cause disease

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in humans, vaccinia, gets into cells. This story illustrates the value of fundamental research aimed at better understanding biology rather than specifically developing therapeutics. Such studies consistently provide new understanding, which later enable the development of clinical therapies. No one in 2008 would have predicted that Jason’s work with vaccinia would prove valuable in combatting Ebola epidemics. Yet performing the experiments with vaccinia was much safer and quicker than performing them with Ebola. The final key feature of vaccinia is its unusual subviral shape, which can clearly be seen by electron microscopy (Fig. 15.1). Poxviruses house their genome inside a peanut-shaped shell, known as the virus core. Two additional protein structures, known as lateral bodies, sit on the outer concave surface of this shell, and the whole lot is wrapped in a membrane. Although unresolvable by standard light microscopy techniques, when fluorescently tagged, these three subviral structures are resolvable using cutting-edge super-resolution microscopes. Large amounts of stable vaccinia virions can easily be purified and bound directly to coverslips, allowing the simultaneous imaging of hundreds of isolated virions. In these images, the elongated core and flanking lateral bodies provide threedimensional orientation information, whereas the very small distance between the two lateral bodies challenges the resolution limit of the microscope.

Figure 15.1 An electron micrograph of vaccinia virus. The three subviral structures, core (C), lateral bodies (L), and membrane (M), are labelled. Reproduced with permission of Jason Mercer.

Two years ago, Rob Gray, a PhD student in London, combined all these features to develop a piece of software that enhances the precision of the super-resolution images. His software automatically detects individual viruses within the image,

References

aligns them, and then averages them together to produce high-confidence models with a 2-fold improvement in resolution. This is a general software that can be applied to a wide array of other data sets independent of the microscope used to collect the images [11]. Vaccinia has also proved to be a powerful tool to enhance the accuracy of super-resolution microscopes. In her paper last year, Siân Culley developed software to automatically detect artifacts sometimes generated during the postacquisition processing of super-resolution microscopy images. Siân used vaccinia images as test data sets during the development of the software [12]. Their critical size and unusual subviral shape, combined with the plentiful electron microscope images available, made them ideal candidates for testing how well the software was detecting problems in the super-resolution images. The super-resolution applications to date have employed isolated virions, but it seems likely that soon, similar approaches will be applied to the context of the infected cells. Therefore, vaccinia is proving an excellent tool to aid the improvement of light microscopy approaches. It follows that the better our microscopes, the newer biology we will be able to see and understand. The eradication of smallpox was undeniably a world-changing accomplishment, but vaccinia virus is more than simply the vaccine that enabled this feat. Vaccinia has a lot more biology to teach us. With vaccinia virus as a tool, pox virologists all over the world are pushing the boundaries of technology, discovering fundamental biology, and developing therapeutics to combat some of the major causes of death and disease in the modern world. Disclosures and Conflict of Interest

This section was originally published as: Bidgood, S. R. (2019). Continued poxvirus research: From foe to friend. PLoS Biol. 17(1), e3000124, https://doi.org/10.1371/ journal.pbio.3000124, under the Creative Commons Attribution license (http:// creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates. Funding: SRB is funded by The Wellcome Trust (WT106080/Z/14/Z).

Competing interests: The author has declared that no competing interests exist.

Acknowledgments: The author thanks Jason Mercer for the figure and Nicholas

Gough, Robert Gray, Miranda Wilson, Moona Huttunen, and Jason Mercer for comments on the text.

References

1. Riedel S. Edward Jenner and the history of smallpox and vaccination. Proc Bayl Univ Med Cent 2005, 18, 21–25.

2. Fenner F., Henerson D.A., Arita I., Jezek Z., Ladnyi I.D., et al. Smallpox and its eradication.

WHO. 1988. Available at: https://apps.who.int/iris/handle/10665/39485 (accessed on

May 17, 2021).

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3. Nezu J., Oku A., Jones M.H., Shimane M. Identification of two novel human putative serine/threonine kinases, vrk1 and vrk2, with structural similarity to vaccinia virus b1r kinase. Genomics 1997, 45, 327–331. 4. Kim S.H., Ryu H.G., Lee J., Shin J., Harikishore A., Jung H.Y., Kim Y.S., Lyu H.N., Oh E., Baek N.I., et al. Ursolic acid exerts anti-cancer activity by suppressing vaccinia-related kinase 1-mediated damage repair in lung cancer cells. Sci Rep 2015, 5, 14570.

5. Salzano M., Vazquez-Cedeira M., Sanz-Garcia M., Valbuena A., Blanco S., Fernandez I.F., Lazo P.A. Vaccinia-related kinase 1 (vrk1) confers resistance to DNA-damaging agents in human breast cancer by affecting DNA damage response. Oncotarget 2014, 5, 1770–1778.

6. Huang W., Cui X., Chen Y., Shao M., Shao X., Shen Y., Liu Q., Wu M., Liu J., Ni W., et al. High vrk1 expression contributes to cell proliferation and survival in hepatocellular carcinoma. Pathol Res Pract 2016, 212, 171–178.

7. Olson A.T., Rico A.B., Wang Z., Delhon G., Wiebe M.S. Deletion of the vaccinia virus b1 kinase reveals essential functions of this enzyme complemented partly by the homologous cellular kinase vrk2. J Virol 2017, 91(15), e00635-17.

8. Mercer J., Helenius A. Vaccinia virus uses macropinocytosis and apoptotic mimicry to enter host cells. Science 2008, 320, 531–535.

9. Amara A., Mercer J. Viral apoptotic mimicry. Nat Rev Microbiol 2015, 13, 461–469.

10. CDC. 2014–2016 Ebola outbreak in West Africa. Available at: https://www.cdc.gov/vhf/ ebola/history/2014-2016-outbreak/index.html (accessed on May 12, 2021).

11. Gray R.D., Beerli C., Pereira P.M., Scherer K.M., Samolej J., Bleck C.K., Mercer J., Henriques R. Virusmapper: Open-source nanoscale mapping of viral architecture through super-resolution microscopy. Sci Rep 2016, 6, 29132. 12. Culley S., Albrecht D., Jacobs C., Pereira P.M., Leterrier C., Mercer J., Henriques R. Quantitative mapping and minimization of super-resolution optical imaging artifacts. Nat Methods 2018, 15, 263–266.

15.2 Staphylococcus epidermidis—Skin Friend or Foe? Morgan M. Browna and Alexander R. Horswill, PhDa,b aDepartment of Immunology and Microbiology,

University of Colorado School of Medicine, Aurora, Colorado, USA

bDepartment of Veterans Affairs, Eastern Colorado Healthcare System, Aurora, Colorado, USA

[email protected]

15.2.1 The Human Skin Microbiota: Composition and Function in Barrier Homeostasis The human skin is a complex physiological barrier designed to maintain internal homeostasis and protect the host from opportunistic pathogens. The epidermis is composed of 4 stratified layers of terminally differentiating keratinocytes and studded with hair follicles and sebaceous glands. Taking these appendages into account, the surface area of the skin is at least 30 m2, which is even larger than

Staphylococcus epidermidis—Skin Friend or Foe?

the surface area of the gut [1]. The skin is densely populated by a diverse resident commensal flora of bacteria, archaea, fungi, and viruses [2]. Both metagenomics sequencing surveys and traditional culture methods have demonstrated that coagulase negative staphylococci (CoNS) are one of the most abundant colonizers of all skin sites [2]. The CoNS are a heterogeneous group of 38 species that are predominantly genetically and functionally uncharacterized. Despite this lack of characterization, there is mounting evidence that CoNS actively contribute to the maintenance of skin integrity and homeostasis by priming cutaneous immunity, controlling other resident flora, and preventing colonization by opportunistic pathogens (i.e., colonization resistance) [3]. Historically, the field has focused on the ubiquitous skin commensal Staphylococcus epidermidis with the assumption that all S. epidermidis strains or all CoNS behave similarly. In fact, current evidence suggests that S. epidermidis skin colonization may be far more nuanced and that colonization by specific strains of S. epidermidis may either help or hurt the skin barrier. Here, we discuss S. epidermidis strain-level diversity on skin, evaluate benefits and costs of S. epidermidis skin colonization, and comment on future directions in skin microbiome research with a focus on understanding how specific organisms, like S. epidermidis, contribute to skin health or disease.

15.2.2 S . epidermidis Strain-Level Diversity Underlies Skin–Microbe Interactions

S. epidermidis is by far the best studied member of the CoNS family and was historically used as a commensal comparator to its more pathogenic cousin, Staphylococcus aureus [4]. S. epidermidis can be been isolated from all skin microenvironments, including dry, moist, sebaceous, and foot regions [2]. However, the significant strain-level diversity among these isolates, especially specific virulence or host modulatory factors, is only beginning to be appreciated. Approximately 80% of the 2.5 Mb S. epidermidis genome is composed of core genes, whereas the remaining 20% is variable, indicating that S. epidermidis has an open pan-genome and a potentially unlimited genetic repertoire [5]. The observation that up to 20% of the genome can be exchanged with a larger pool of genes suggests that S. epidermidis is well poised to rapidly adapt to and thrive in all skin microenvironments. Indeed, a targeted metagenomics study revealed that there is an incredibly high spatiotemporal diversity of healthy skin S. epidermidis isolates between different skin microenvironments and between individuals [6]. Furthermore, these specialized communities are under high selective pressure, undergoing multiple horizontal gene transfer events via plasmid and phage to adapt to and persist in their specific skin niche [6]. One mechanistic example of the significance of S. epidermidis strain-level diversity and its implications for overall skin health is the accessory gene regulator (agr) quorum sensing system. The agr locus (agrBDCA) is conserved across all staphylococci, including S. epidermidis. The S. epidermidis agr regulon controls

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production of a small suite of potential virulence factors like proteases, lipases, and immunomodulatory phenol soluble modulins (PSMs), and retention of the agr system is necessary for skin colonization [7]. Importantly, every S. epidermidis strain is a single agr type (I–IV) determined by a hypervariable region spanning agrBDC [6, 7]. While most individuals are dominantly colonized by a single S. epidermidis agr type, minor subpopulations of nondominant agr types in specific skin sites are also common [6]. Certain S. epidermidis agr types, as well as other CoNS species, make small peptides that inhibit noncognate S. epidermidis agr signaling [7, 8]. This observation suggests that agr heterogeneity in concert with total CoNS diversity may be an important factor in promoting homeostatic S. epidermidis skin colonization and suppressing virulence factor production [6–8]. As agr activity is controlled by bacterial density, low absolute numbers of S. epidermidis on the skin may also contribute to low agr activity under homeostatic conditions [6, 7]. Future work could address these inter- and intraspecies agr interactions and their implications for skin health in larger patient cohorts. Furthermore, S. epidermidis agr is only 1 example of how strain variation may have specific functional outcomes for skin health. Metagenomics analyses to reveal which strains are present, coupled with mechanistic studies to understand strain-level functionality, will likely uncover more intriguing links between S. epidermidis strain-level diversity and skin health status.

15.2.3 Benefits of S. epidermidis Skin Colonization

Significant attention in the field has been given to S. epidermidis and its role as a beneficial skin commensal (Fig. 15.2). For example, S. epidermidis activated distinct innate immune signaling pathways in human keratinocytes to augment antimicrobial peptide (AMP)-mediated killing of S. aureus, though the secreted factor necessary for this enhancement was not elucidated [9]. S. epidermidis PSMs are small, amphipathic α-helical peptides that are abundantly produced on normal epidermis and in hair follicles [10]. PSMs synergized with host AMPs to enhance killing of the pathogen Streptococcus pyogenes [10]. In a mouse model of skin injury, both S. epidermidis lipoteichoic acid [11] and the lipopeptide LP78 [12] attenuated the inflammatory response to accelerate wound healing in a Toll-like receptor (TLR)-3-dependent mechanism. Finally, some strains of S. epidermidis can dampen S. aureus–induced neutrophil recruitment and proinflammatory cytokine production, which could potentially be protective against more severe skin infection [13]. In addition to modulating the innate immune response to skin infection or damage, S. epidermidis colonization contributes to the development and priming of the adaptive immune system. Studies of gnotobiotic mice revealed that S. epidermidis skin colonization is necessary for effector T cell development and function [14] as well as early localization and priming of mucosal-associated invariant T cells (MAIT cells), which are an important component of nonclassical cutaneous immune signaling that mediates distinct patterns of host–commensal

Staphylococcus epidermidis—Skin Friend or Foe?

cross talk [15]. The skin is also home to one of the largest reservoirs of effector T cell subsets, and there is growing appreciation of the depth and complexity of cross talk between these tissue-resident lymphocytes and colonizers like S. epidermidis [16]. Taken together, S. epidermidis is undoubtedly important for priming innate and adaptive defenses against pathogens and promoting homeostasis. However, future work may reveal that other CoNS, in concert with S. epidermidis, contribute more substantially to the full picture of skin development and health than previously appreciated.

Figure 15.2 The ubiquitous skin commensal S. epidermidis positively and negatively impacts barrier homeostasis and integrity. (A) S. epidermidis phenol soluble modulins PSMγ and PSMδ can synergize with keratinocyte-derived AMPs to kill opportunistic skin pathogens like MRSA and Streptococcus pyogenes. S. epidermidis also makes anti-MRSA quorum sensing inhibitor peptides and a variety of small antimicrobials known as lantibiotics to mediate skin colonization resistance. (B) S. epidermidis lipoteichoic acid and some lipopeptides can dampen the inflammatory response to skin injury, accelerating wound healing. Early skin colonization with S. epidermidis is crucial for development of immune cell subsets including effector T cells and MAIT cells, and long-term S. epidermidis skin colonization may help the cutaneous immune system distinguish between commensal and pathogenic bacteria. (C) Certain S. epidermidis strains can “bloom” and exacerbate AD or NS skin lesions through production of the EcpA protease. Inflammatory S. epidermidis biofilms that occlude sweat glands have also been shown to exacerbate some AD lesions. Abbreviations: AD, atopic dermatitis; AMPs, antimicrobial peptides; MAIT cells, mucosalassociated invariant T cells; MRSA, methicillin-resistant Staphylococcus aureus; NS, Netherton

syndrome; PSM, phenol soluble modulins; QS, quorum sensing.

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15.2.4 Costs of S. epidermidis Skin Colonization While widely appreciated as an abundant skin symbiont, emerging evidence suggests that skin colonization by specific strains of S. epidermidis may actually be detrimental to the host under certain conditions. The intact skin is a formidable barrier to pathogens and commensals alike, but disruption of this barrier, through either genetic mutation or physical disruption, can dramatically alter S. epidermidis behavior from benign to pathogenic (Fig. 15.2). For example, murine skin pretreated with S. epidermidis was only resistant to S. aureus challenge when the barrier was intact, not when it was physically disrupted by tape stripping prior to bacterial inoculation [17]. In atopic dermatitis (AD, i.e., eczema), patients are often highly colonized with S. aureus at lesional sites, and this bacterial “bloom” positively correlates with disease severity [18]. Longitudinal metagenomics studies have shown that some AD patients can be highly colonized by S. epidermidis rather than S. aureus at lesional sites. It has been postulated that such outgrowth may similarly correlate with disease severity; however, there have been few investigations of the mechanistic basis of S. epidermidis–mediated AD barrier exacerbation [19, 20]. Recently, the cysteine protease EcpA was identified as a key mediator of S. epidermidis–induced AD barrier degradation [8, 21]. Underscoring the importance of strain-level diversity, EcpA is present in all S. epidermidis strains but only seems to be expressed by a subset [8]. EcpA has significant sequence similarity and protein homology to the well-characterized S. aureus staphopains A and B, which can digest the AMP LL-37 to enhance S. aureus biofilm growth in AD lesions [21, 22]. EcpA degraded multiple components of the skin barrier, including LL-37 as well as desmoglein-1, and significantly contributed to increased inflammation and barrier dysfunction in mouse models of AD [8]. Aside from AD, S. epidermidis overexpansion and EcpA production are also linked to exacerbation of Netherton syndrome (NS), a skin disorder characterized by high levels of serine protease activity caused by a mutation in the gene SPINK5 [21]. Importantly, EcpA production is regulated by the S. epidermidis agr quorum sensing system [7]. These observations suggest a possible mechanism of S. epidermidis exacerbation of AD and NS, where there is some initial dysbiosis of inhibitory S. epidermidis or CoNS strains, followed by deinhibition of S. epidermidis agr signaling. This would facilitate the outgrowth of 1 S. epidermidis agr type (most commonly agr-I) [23] and the up-regulation of virulence factors like EcpA [8]. Such enhanced expression of EcpA and other virulence factors, combined with genetic or environmental barrier disruption in both skin diseases, would provide an ideal environment for S. epidermidis expansion and exacerbation. Finally, the propensity of S. epidermidis to form biofilms may also exacerbate AD, as inflammatory biofilm communities of both S. aureus and S. epidermidis have been documented in some sweat glands at AD lesional sites [24]. However, it is still unclear to what extent S. epidermidis biofilms form on normal or diseased skin, and more work is needed to fully understand the impact of biofilms in AD or other skin diseases.

References

15.2.5 Concluding Remarks and Future Directions Together, these findings demonstrate the variable physiology and contextual control of S. epidermidis on skin and underscore the potential duality of the S. epidermidis lifestyle as colonizer or pathogen. Future work should continue to evaluate (with strain-level resolution) how this complex organism fits into the larger context of skin health. While the field has rapidly shifted to metagenomics analysis of “who’s there” on skin during health or disease, it is imperative to continue to define and understand the specific mechanisms that regulate commensal colonization as well as pathogenicity. This is especially true for the growing demands to utilize commensal bacteria as nonantibiotic treatments for skin diseases such as AD. While there is some successful precedence for using CoNS as an anti-methicillin– resistant S. aureus (MRSA) topical treatment [25], it is imperative to fully appreciate and regulate an organism’s potential for pathogenicity (i.e., S. epidermidis EcpA production) before widespread use as a therapeutic. As for other skin-dominant CoNS like Staphylococcus warneri, Staphylococcus hominis, and Staphylococcus capitis, their roles in colonization resistance or their potential for pathogenicity are even less well defined than S. epidermidis. These highly abundant yet understudied CoNS species, in addition to non-staphylococcal members of the microbiota like Corynebacterium spp. or Cutibacterium spp., represent a potential wealth of mechanistic information on interactions between the microbiota, host epithelia, and opportunistic pathogens that remain to be discovered. In conclusion, we posit that this high-resolution understanding of skin commensals, with an emphasis on benefits and costs of colonization, will fundamentally alter how we manage or treat our skin health. Disclosures and Conflict of Interest

This section was originally published as: Brown, M. M., Horswill, A. R. (2020). Staphylococcus epidermidis—Skin friend or foe? PLoS Pathog., 16(11), e1009026, https://doi.org/10.1371/journal.ppat.1009026, under the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates. Funding: M.M.B. was supported by the NIH T32 AI052066 training grant from the National Institute for Allergy and Infectious Diseases (NIAID). A.R.H. was supported by NIAID grants AI153185 and AI133089 and by a Merit Award (I01 BX002711) from the Department of Veteran Affairs. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of this section. Competing interests: The authors have declared that no competing interests exist.

References

1. Gallo RL. Human skin is the largest epithelial surface for interaction with microbes. J Invest Dermatol. 2017;137(6):1213–1214.

2. Byrd AL, Belkaid Y, Segre JA. The human skin microbiome. Nat Rev Microbiol. 2018;16(3):143–155.

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3. Parlet CP, Brown MM, Horswill AR. Commensal Staphylococci influence Staphylococcus aureus skin colonization and disease. Trends Microbiol. 2019;27(6):497–507.

4. Otto M. Staphylococcus epidermidis—The “accidental” pathogen. Nat Rev Microbiol. 2009;7(8):555–567. 5. Conlan S, Mijares LA, Becker J, Blakesley RW, Bouffard GG, Brooks S, et al. Staphylococcus epidermidis pan-genome sequence analysis reveals diversity of skin commensal and hospital infection-associated isolates. Genome Biol. 2012;13(7):R64.

6. Zhou W, Spoto M, Hardy R, Guan C, Fleming E, Larson PJ, et al. Host-specific evolutionary and transmission dynamics shape the functional diversification of Staphylococcus epidermidis in human skin. Cell. 2020;180(3):454–470.e18. 7. Olson ME, Todd DA, Schaeffer CR, Paharik AE, Van Dyke MJ, Buttner H, et al. Staphylococcus epidermidis agr quorum-sensing system: Signal identification, cross talk, and importance in colonization. J Bacteriol. 2014;196(19):3482–3493.

8. Cau L, Williams MR, Butcher AM, Nakatsuji T, Kavanaugh JS, Cheng JY, et al. Staphylococcus epidermidis protease EcpA can be a deleterious component of the skin microbiome in atopic dermatitis. J Allergy Clin Immunol. 2020. Available at: https://linkinghub.elsevier.com/retrieve/pii/S0091674920309532 (accessed on May 13, 2021).

9. Wanke I, Steffen H, Christ C, Krismer B, Götz F, Peschel A, et al. Skin commensals amplify the innate immune response to pathogens by activation of distinct signaling pathways. J Invest Dermatol. 2011;131(2):382–390.

10. Cogen AL, Yamasaki K, Muto J, Sanchez KM, Alexander LC, Tanios J, et al. Staphylococcus epidermidis antimicrobial δ-toxin (phenol-soluble modulin-γ) cooperates with host antimicrobial peptides to kill group A Streptococcus. PLoS ONE. 2010;5(1):e8557.

11. Lai Y, Di Nardo A, Nakatsuji T, Leichtle A, Yang Y, Cogen AL, et al. Commensal bacteria regulate toll-like receptor 3-dependent inflammation after skin injury. Nat Med. 2009;12(12):1377–1382.

12. Li D, Wang W, Wu Y, Ma X, Zhou W, Lai Y. Lipopeptide 78 from Staphylococcus epidermidis activates β-catenin to inhibit skin inflammation. J Immunol. 2019;202(4): 1219–1228.

13. Bitschar K, Staudenmaier L, Klink L, Focken J, Sauer B, Fehrenbacher B, et al. Staphylococcus aureus skin colonization is enhanced by the interaction of neutrophil extracellular traps with keratinocytes. J Invest Dermatol. 2020;140(5):1054–1065.e4.

14. Naik S, Bouladoux N, Wilhelm C, Molloy MJ, Salcedo R, Kastenmuller W, et al. Compartmentalized control of skin immunity by resident commensals. Science. 2012;337(6098):1115–1119. 15. Constantinides MG, Link VM, Tamoutounour S, Wong AC, Perez-Chaparro PJ, Han SJ, et al. MAIT cells are imprinted by the microbiota in early life and promote tissue repair. Science. 2019;366(6464):eaax6624.

16. Belkaid Y, Segre J. Dialogue between skin microbiota and immunity. Science. 2014;346(6212):954–959.

17. Burian M, Bitschar K, Dylus B, Peschel A, Schittek B. The protective effect of microbiota on S. aureus skin colonization depends on the integrity of the epithelial barrier. J Invest Dermatol. 2017;137:976–979.

When Pigs Fly

18. Geoghegan JA, Irvine AD, Foster TJ. Staphylococcus aureus and atopic dermatitis: a

complex and evolving relationship. Trends Microbiol. 2018;26(6):484–497.

19. Byrd AL, Deming C, Cassidy SKB, Harrison OJ, Ng WI, Conlan S, et al. Staphylococcus aureus and Staphylococcus epidermidis strain diversity underlying pediatric atopic

dermatitis. Sci Transl Med. 2017;9(397):eaal4651.

20. Hon KL, Tsang YCK, Pong NH, Leung TF, Ip M. Exploring Staphylococcus epidermidis in atopic eczema: friend or foe? Clin Exp Dermatol. 2016;41(6):659–663.

21. Williams MR, Cau L, Wang Y, Kaul D, Sanford JA, Zaramela LS, et al. Interplay of Staphylococcal and host proteases promotes skin barrier disruption in netherton syndrome. Cell Rep. 2020;30(9):2923–2933.e7.

22. Sonesson A, Przybyszewska K, Eriksson S, Mörgelin M, Kjellström S, Davies J,

et al. Identification of bacterial biofilm and the Staphylococcus aureus derived

protease, staphopain, on the skin surface of patients with atopic dermatitis. Sci Rep.

2017;7(1):8689.

23. Williams MR, Costa SK, Zaramela LS, Khalil S, Todd DA, Winter HL, et al. Quorum sensing between bacterial species on the skin protects against epidermal injury in atopic dermatitis. Sci Transl Med. 2019;11(490):eaat8329.

24. Allen HB, Vaze ND, Choi C, Hailu T, Tulbert BH, Cusack CA, et al. The presence and impact

of biofilm-producing Staphylococci in atopic dermatitis. JAMA Dermatol. 2014;150(3): 260–265. 25. Nakatsuji T, Chen TH, Narala S, Chun KA, Two AM, Yun T, et al. Antimicrobials from human skin commensal bacteria protect against Staphylococcus aureus and are deficient in atopic dermatitis. Sci Transl Med. 2017;9(378):eaah4680.

15.3 When Pigs Fly: Pandemic Influenza Enters the 21st Century Nídia S. Trovão, PhD, and Martha I. Nelson, PhD Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA [email protected]

15.3.1 Introduction Influenza A viruses (IAVs) are one of the most intensively studied pathogens, due to the severe global mortality and economic disruption associated with influenza pandemics [1]. In addition to annual epidemics, pandemics sporadically occur when a novel IAV host jumps to humans from an animal reservoir [2]. Wild waterfowl have long been considered the most important reservoir host and pandemic risk, but there are indications that mammals are emerging as key reservoirs. Here, we describe how the modernization of swine production during the last half century provided new opportunities for IAVs to become established in swine globally [3–5], resulting in the first influenza pandemic of swine origin in 2009 [6]. A key lesson is that the landscape of pandemic risk is not static but continuously shifting in response to demographic changes in host populations.

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It is therefore vital to track how transformations in the economy and global trade impact animal contact rates, disease dynamics, and pandemic risk among livestock and companion animals.

15.3.1.1 Pigs fly?

For centuries, domestic pigs (Sus scrofa domesticus) were raised on traditional small-scale farms that could not sustain IAV transmission. Whereas horses and people traveled frequently within and between urban areas, causing recurrent influenza outbreaks in both species as far back as the 13th century [7], influenza was not maintained in any country’s swine population until 1918, when the Spanish influenza H1N1 pandemic virus was introduced from humans into swine in the United States [8]. The H1N1 virus circulated in US swine for most of the 20th century without substantially evolving, causing severe disease, or becoming endemic in swine in other countries. In the latter decades of the 20th century, the replacement of small-scale swine farms with larger, more efficient production systems (Fig. 15.3A) had profound effects on disease dynamics. Enhanced biosecurity improved control of important pathogens, such as hog cholera. But modern production systems often require pigs to be transported long distances between multiple locations specialized in different growth stages, facilitating the spread of IAV in swine (IAV-S; commonly known as swine flu) and other pathogens not specifically targeted for eradication. For example, many large breeding operations located in the southern US find it more efficient to transport fattening pigs to the Midwest “corn belt” than to transport the large volumes of feed back to the south. By the 2000s, trucks were transporting millions of pigs’ long distances across North America, facilitating the spread of IAV-S along established “swineways” [9]. In addition, pigs were flying (Figs. 15.3B and 15.3C). To meet the needs of expanding middle classes for animal-sourced protein, many countries imported more productive sows (female breeding pigs) with improved genetics from North America and Europe. Imported swine must be declared free of certain pathogens, such as African swine fever (ASF) or foot-and-mouth disease (FMD). However, influenza is not routinely tested for, and many countries do not quarantine, facilitating the long-distance spread of IAV-S [3, 5] (Fig. 15.4A). Using genetic sequences of IAV-S collected in different countries over time, the direction and timing of viral movements can be inferred from the evolutionary relationships depicted on phylogenetic trees. Trees reveal how international trade of live swine in the 1990s and 2000s facilitated the long-distance dispersal of IAV-S between trade partners, shaping the global spatial distribution of IAV-S lineages that is observed today. Many countries in Asia import swine from different continents (Fig. 15.3C), introducing multiple divergent lineages and creating nexuses for genetic diversity. By the end of the millennium, the high genetic and antigenic diversity of IAV-S made it one of the most intractable diseases for swine producers in the US and present in most swine-producing countries [4].

When Pigs Fly

Figure 15.3 Trends in swine production. (A) Trends in consolidation of swine production in the US, 1964 to 2012 (data available from the US Department of Agriculture and the National Agricultural Statistics Service Quick Stats Database). (B) Growth of global trade (US$) of live animals between all countries, 1961 to 2017 (data available from FAOSTAT). (C) The global distribution and density of swine populations (approximately 1 billion animals) is depicted by points shaded along a gradient from light red (1 to 5 swine per km2) to black (more than 250 swine per km2). Lines with arrows depict the direction and volume of routes of trade (US$) of live swine, summarized by region and over the time period 1996 to 2012. Trade data available from United Nations Comtrade Database. Digital layers from GLW (version 2.01) [38] were downloaded from the publicly available Livestock Geo-Wiki database. Abbreviations: FAOSTAT, Food and Agriculture Organization Statistical Database (United Nations); GLW, Gridded Livestock of the World.

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Figure 15.4 IAV-S evolution. (A) Inferred spatial movements of the major Eurasian lineage of IAV-S (avian-like Eurasian H1N1) between countries, inferred from a time-scaled MCC tree of the N1 segment. Lines represent general directions of movement inferred from available genetic data, and actual paths may differ and include unsampled locations. (B) Genomic reassortment events between the three swine lineages that produced the 2009 H1N1 pandemic virus. Horizontal bars represent the eight individual segments of the IAV genome, ordered from longest (PB2, 2,277 nucleotides) to shortest (NS, 890 nucleotides).

Abbreviations: HA, hemagglutinin; IAV, Influenza A virus; IAV-S, IAV of swine; MCC, maximum clade credibility; MP, matrix protein; NA, neuraminidase; NP, nucleoprotein; NS, nonstructural protein; PA, polymerase acidic protein; PB1, polymerase basic protein 1; PB2, polymerase basic protein 2.

When Pigs Fly

15.3.2 How Did the First Pandemic Virus of Swine Origin Evolve? It is inherently difficult to predict when and where a pandemic virus will evolve. Pandemic viruses have a rare combination of properties, including animal-origin surface proteins that are antigenically divergent from human viruses and thus evade immune detection while retaining a capacity to replicate and transmit in humans. The evolution of such a variant is facilitated by a process termed “reassortment,” in which whole segments of the IAV genome are exchanged between viruses coinfecting a host cell, rapidly repositioning genes into different genetic backgrounds (Fig. 15.4B). Reassortant viruses are therefore more likely to evolve in locations where multiple distinct lineages cocirculate and in hosts with high capacities for reassortment, such as wild birds, poultry, and swine. The pandemic viruses of 1957 (H2N2), 1968 (H3N2), and 2009 (H1N1) were all reassortants with chimeric genomes derived from multiple lineages. The 2009 pandemic was the first to occur in the genomic era, providing large-scale sequence data to understand how globalized swine production and long-distance viral migration contributed to the evolution of a novel reassortant virus. The 2009 pandemic vividly demonstrated both the promise and persisting limitations of outbreak investigation in the genomic era. Genetic sequencing of the first H1N1 pandemic viruses isolated from humans in April 2009 rapidly determined that the reassortant virus was comprised of three genetic lineages of swine origin (Fig. 15.4B) [6]. However, the country of origin was unknown, due to geographical gaps in IAV-S surveillance. Many countries did not consider IAV-S an important clinical disease. To their credit, the 2009 pandemic stimulated an expansion of IAV-S research in many countries, filling gaps in our knowledge of IAV-S diversity and evolution on a global scale [10–15]. Expanded surveillance in Mexico revealed how IAV-S diversity expanded during the 1990s and 2000s, when swine were imported from Europe and the US, introducing the three IAV-S lineages that reassorted to generate the pandemic virus [16]. Going forward, trade flows can identify other countries that import live swine (and potentially IAV-S) from multiple regions and are at higher risk for generating novel reassortant viruses with pandemic potential [5]. An outstanding question is the specific circumstances by which the 2009 H1N1 virus transmitted from swine to humans. Both IAV-S sequence data and epidemiological data in humans support swine-to-human transmission occurring in Central Mexico [16, 17]. However, by the time the first human cases were detected by surveillance, the virus had already diversified genetically, evidence of transmission in humans for several months [18]. There are multiple challenges to understanding the human–animal interface for IAVs: (1) There is a need for coordination between animal health and public health research, (2) the genomics of host switches are too complex to accurately predict zoonotic potential from genetic sequence alone, and (3) spillover events can be rare and difficult to detect via traditional modes of virological surveillance, particularly in developing countries.

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15.3.3 Pigs and Humans: Who Is Infecting Whom? It should be noted that the human–animal interface looks quite different from the perspective of the pig. Only one IAV has successfully transmitted from swine to humans to cause a pandemic (2009) [19]. In comparison, swine are continuously experiencing pandemics of human origin. In the US alone, at least eight genetically distinct IAVs have successfully host jumped from humans to swine (reverse zoonosis). On a global scale, there have been at least 20 successful human-to-swine transmission events, defined by sustained onward transmission in swine [20]. This number could be much greater, as many countries do not routinely test for IAV-S. Tellingly, almost every country’s IAV-S population that has been genetically characterized includes viruses of human origin. Recently, viruses of human origin were identified in swine in Australia and Chile that have circulated undetected for decades [21, 22]. Notably, as soon as the 2009 H1N1 pandemic virus became established in humans, the virus disseminated back to swine in at least 30 countries, spanning Africa, Asia, North and South America, Europe, and Australia [23]. In addition to seeding genetically novel viruses in swine populations globally, the human-origin viruses frequently reassort with other IAV-S lineages. The proliferation of new reassortants complicates control in swine, introducing new strains that poorly match those included in commercially available vaccines, and presents new risks for humans. As a case in point, novel IAV-S reassortants with segments derived from human-origin pandemic viruses have been associated with over 450 zoonotic infections in the US since 2011, largely in the context of agricultural fairs [24].

15.3.4 What Happened to Bird Flu?

All three pandemics of the 20th century (1918, 1957, and 1968) were of avian origin, and birds have been key sources of novel viruses in a range of other mammalian hosts, including swine, equines, canines, and phocines. For decades, the large numbers of human infections in Asia with IAVs of the H5N1 subtype and, more recently, H7N9 subtype were considered indications of impending pandemics, prompting governments to stockpile antivirals and vaccines targeting H5 and H7 antigens. H5N1 and H7N9 viruses continue to infect humans, but the absence of sustained human-to-human transmission to date underscores the difficulty of predicting pandemics, especially as large numbers of human infections and deaths are only one measure of multifaceted pandemic risk [25].

15.3.5  IAV Evolution Never Follows Predictions: What Will the Next Surprise Be?

Expanded surveillance in mammalian hosts has yielded several surprises, including divergent IAVs in bats (H17 and H18) [26], a new genus of

When Pigs Fly

Orthomyxovirus (influenza D) in bovines [27], and the establishment of IAV in canines (CIV) during the 2000s: CIV-H3N8 in the US [28] and CIV-H3N2 in Asia [29]. An outbreak of H7N2 in felines in New York [30] suggests that other mammalian species could potentially be capable hosts for IAV transmission at certain thresholds of animal density and movement. However, IAVs have relatively low reproductive numbers (R), just above the threshold of 1 required for transmission, and require high contact rates between susceptible hosts to maintain transmission. For example, CIV in the US is maintained only in high-turnover animal shelters and dog daycares [31]. In contrast to swine, whose movements are tracked as livestock commodities and can be directly linked to disease spread [5, 9], efforts to understand and predict emerging threats in companion animals are impeded by the low availability of public records and virological surveillance. Counterintuitively, shifting global attitudes towards dogs as domestic companions has facilitated the emergence of CIV. In the US, CIV-H3N2 appears to be maintained in dog daycares, which have proliferated and become mainstream. Furthermore, the recent introduction of CIV-H3N2 into the US spatial-temporally coincides with efforts by animal rights groups to rescue hundreds of Asian meat dogs for US adoption, although to date no direct link has been established between any rescue animals and the appearance of CIV-H3N2 in the US [32]. Dogs entering the US must provide documentation of rabies vaccination but otherwise require no disease testing or quarantine. The most pronounced change in attitudes towards dogs has been in China, where pet dogs are surging in popularity in urban areas after being banned for many decades. High rates of CIV have been observed in pets in Chinese cities [33]. It remains unclear how populations of meat dogs, strays, and pets interact and collectively contribute to CIV emergence and transmission. A diversity of swine-origin [33], human-origin [34], and reassortant IAVs have been isolated from dogs in Asia but with unknown degrees of onward transmission. High contact rates between canines and humans provides frequent opportunities for zoonotic transmission, and further research is greatly needed to understand the extent to which Asia’s rapidly expanding dog populations present a pandemic risk.

15.3.6 Concluding Remarks

Transformations in the movement and care of livestock and companion animal populations have altered animal contact rates, disease dynamics, and pandemic risk. At the same time, however, technological developments are underway that could enhance pandemic response. A commitment of resources has mobilized efforts to develop a universal influenza vaccine that could broadly protect humans against all animal-origin strains [35], as well as technologies to accelerate production of new vaccines during a pandemic. Harnessing new forms of digital, social, and medical claims data could detect outbreaks faster, potentially limiting spread [36]. Portable nanopore sequencing technologies are capable of tracking epidemics on location to guide intervention strategies [37]. Given these advances, it is possible to imagine a future when pandemic influenza no longer presents a threat to global health and security. However, these initiatives still face a host of technological, logistical, and market-related challenges.

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In the race between human ingenuity and pathogen evolution, influenza can never be underestimated. Disclosures and Conflict of Interest

This section was originally published as: Trovão, N. S., Nelson, M. I. (2020). When pigs fly: Pandemic influenza enters the 21st century. PLoS Pathog. 16(3), e1008259, https://doi.org/10.1371/journal.ppat.1008259, under the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates.

Funding: This section was funded by Centers of Excellence for Influenza Research and Surveillance (CEIRS), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), and Department of Health and Human Services (HHS), under contract number HHSN272201400008C. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of this section. Competing interests: The authors have declared that no competing interests exist.

References

1. van Wijhe M, Ingholt MM, Andreasen V, Simonsen L. Loose ends in the epidemiology of the 1918 Pandemic: explaining the extreme mortality risk in young adults. Am J Epidemiol. 2018;187:2503–2510.

2. Webster RG, Bean WJ, Gorman OT, Chambers TM, Kawaoka Y. Evolution and ecology of influenza A viruses. Microbiol Rev. 1992;56:152–179.

3. Vijaykrishna D, Smith GJD, Pybus OG, Zhu H, Bhatt S, Poon LLM, et al. Long-term evolution and transmission dynamics of swine influenza A virus. Nature. 2011;473: 519–522. 4. Lewis NS, Russell CA, Langat P, Anderson TK, Berger K, Bielejec F, et al. The global antigenic diversity of swine influenza A viruses. Elife. 2016;5:e12217. 5. Nelson MI, Viboud C, Vincent AL, Culhane MR, Detmer SE, Wentworth DE, et al. Global migration of influenza A viruses in swine. Nat Commun. 2015;6:6696.

6. Smith GJD, Vijaykrishna D, Bahl J, Lycett SJ, Worobey M, Pybus OG, et al. Origins and evolutionary genomics of the 2009 swine-origin H1N1 influenza A epidemic. Nature. 2009;459:1122–1125.

7. Morens DM, Taubenberger JK. Historical thoughts on influenza viral ecosystems, or behold a pale horse, dead dogs, failing fowl, and sick swine. Influ Other Respi Viruses. 2010;4:327–337.

8. Koen J. A practical method for field diagnosis of swine diseases. Am J Vet Med. 1919;14: 468–470. 9. Nelson MI, Lemey P, Tan Y, Vincent A, Lam TT-Y, Detmer S, et al. Spatial dynamics of human-origin H1 influenza A virus in North American swine. PLoS Pathog. 2011;7: e1002077.

References

10. Mathieu C, Moreno V, Retamal P, Gonzalez A, Rivera A, Fuller J, et al. Pandemic (H1N1) 2009 in breeding turkeys, Valparaiso, Chile. Emerg Infect Dis. 2010;16:709–711. 11. Pereda A, Rimondi A, Cappuccio J, Sanguinetti R, Angel M, Ye J, et al. Evidence of reassortment of pandemic H1N1 influenza virus in swine in Argentina: are we facing the expansion of potential epicenters of influenza emergence? Influenza Other Respi Viruses. 2011;5:409–412.

12. Meseko CA, Odurinde OO, Olaniran BO, Heidari A, Oluwayelu DO. Pandemic influenza A/H1N1 virus incursion into Africa: countries, hosts and phylogenetic analysis. Niger Vet J. 36:1251–1261.

13. Holyoake PK, Kirkland PD, Davis RJ, Arzey KE, Watson J, Lunt RA, et al. The first identified case of pandemic H1N1 influenza in pigs in Australia. Aust Vet J. 2011;89: 427–431. 14. Poonsuk S, Sangthong P, Petcharat N, Lekcharoensuk P. Genesis and genetic constellations of swine influenza viruses in Thailand. Vet Microbiol. 2013;167:314–326.

15. Trevennec K, Leger L, Lyazrhi F, Baudon E, Cheung CY, Roger F, et al. Transmission of pandemic influenza H1N1 (2009) in Vietnamese swine in 2009–2010. Influenza Other Respi Viruses. 2012;6:348–357. 16. Mena I, Nelson MI, Quezada-Monroy F, Dutta J, Cortes-Fernández R, Lara-Puente JH, et al. Origins of the 2009 H1N1 influenza pandemic in swine in Mexico. Elife. 2016;5:e16777.

17. Chowell G, Echevarría-Zuno S, Viboud C, Simonsen L, Tamerius J, Miller MA, et al. Characterizing the epidemiology of the 2009 influenza A/H1N1 pandemic in Mexico. PLoS Med. 2011;8: e1000436. 18. Lemey P, Suchard M, Rambaut A. Reconstructing the initial global spread of a human influenza pandemic: A Bayesian spatial-temporal model for the global spread of H1N1pdm. PLoS Curr. 2009;1:RRN1031.

19. Garten RJ, Davis CT, Russell C a, Shu B, Lindstrom S, Balish A, et al. Antigenic and genetic characteristics of swine-origin 2009 A(H1N1) influenza viruses circulating in humans. Science. 2009;325:197–201.

20. Nelson MI, Vincent AL. Reverse zoonosis of influenza to swine: new perspectives on the human-animal interface. Trends Microbiol. 2015;23:142–153.

21. Nelson M, Culhane MR, Rovira A, Torremorell M, Guerrero P, Norambuena J. Novel human-like influenza A viruses circulate in swine in Mexico and Chile. PLoS Curr. 2015;7: ecurrents.outbreaks.c8b3207c9bad98474eca3013fa933ca6.

22. Wong FYK, Donato C, Deng Y-M, Teng D, Komadina N, Baas C, et al. Divergent humanorigin influenza viruses detected in Australian swine populations. J Virol. 2018;92: e00316-18.

23. Nelson MI, Gramer MR, Vincent AL, Holmes EC. Global transmission of influenza viruses from humans to swine. J Gen Virol. 2012;93:2195–2203.

24. Epperson S, Jhung M, Richards S, Quinlisk P, Ball L, Moll M, et al. Human infections with influenza A(H3N2) variant virus in the United States, 2011–2012. Clin Infect Dis. 2013; 57: S4–S11.

25. Trock SC, Burke SA, Cox NJ. Development of an influenza virologic risk assessment tool. Avian Dis. 2012;56:1058–1061.

289

290

Current Perspectives in Medical Microbiology

26. Tong S, Zhu X, Li Y, Shi M, Zhang J, Bourgeois M, et al. New World bats harbor diverse influenza A viruses. PLoS Pathog. 2013;9: e1003657. 27. Hause BM, Collin EA, Liu R, Huang B, Sheng Z, Lu W, et al. Characterization of a novel influenza virus in cattle and swine: proposal for a new genus in the Orthomyxoviridae family. MBio. 2014;5: e00031–14

28. Crawford PC, Dubovi EJ, Castleman WL, Stephenson I, Gibbs EPJ, Chen L, et al. Transmission of equine influenza virus to dogs. Science. 2005;310:482–485.

29. Song D, Kang B, Lee C, Jung K, Ha G, Kang D, et al. Transmission of avian influenza virus (H3N2) to dogs. Emerg Infect Dis. 2008;14:741–746. 30. Hatta M, Zhong G, Gao Y, Nakajima N, Fan S, Chiba S, et al. Characterization of a feline influenza A(H7N2) virus. Emerg Infect Dis. 2018;24:75–86.

31. Dalziel BD, Huang K, Geoghegan JL, Arinaminpathy N, Dubovi EJ, Grenfell BT, et al. Contact heterogeneity, rather than transmission efficiency, limits the emergence and spread of canine influenza virus. PLoS Pathog. 2014;10: e1004455.

32. Voorhees IEH, Glaser AL, Toohey-Kurth K, Newbury S, Dalziel BD, Dubovi EJ, et al. Spread of canine influenza A(H3N2) virus, United States. Emerg Infect Dis. 2017;23: 1950–1957.

33. Chen Y, Trovão NS, Wang G, Zhao W, He P, Zhou H, et al. Emergence and evolution of novel reassortant influenza a viruses in canines in southern China. MBio. 2018;9(3): e00909–18. 34. Song D, Moon H-J, An D-J, Jeoung H-Y, Kim H, Yeom M-J, et al. A novel reassortant canine H3N1 influenza virus between pandemic H1N1 and canine H3N2 influenza viruses in Korea. J Gen Virol. 2012;93:551–554.

35. Erbelding EJ, Post DJ, Stemmy EJ, Roberts PC, Augustine AD, Ferguson S, et al. A universal influenza vaccine: the strategic plan for the National Institute of Allergy and Infectious Diseases. J Infect Dis. 2018;218:347–354.

36. Simonsen L, Gog JR, Olson D, Viboud C. Infectious disease surveillance in the Big Data era: towards faster and locally relevant systems. J Infect Dis. 2016;214: S380–S385.

37. Quick J, Loman NJ, Duraffour S, Simpson JT, Severi E, Cowley L, et al. Real-time, portable genome sequencing for Ebola surveillance. Nature. 2016;530:228–232.

38. Robinson TP, Wint GRW, Conchedda G, Van Boeckel TP, Ercoli V, Palamara E, et al. Mapping the global distribution of livestock. PLoS ONE. 2014;9: e96084.

15.4  Clostridioides difficile Biofilms: A Mechanism of Persistence in the Gut? Lucy R. Frost, Jeffrey K. J. Cheng, and Meera Unnikrishnan, PhD Division of Biomedical Sciences, Warwick Medical School,

University of Warwick, Coventry, UK [email protected]

Biofilms are structured bacterial communities encased in an extracellular matrix. The structure and complexity of biofilms depend on the microorganism and the

Clostridioides difficile Biofilms

local environment [1, 2]. Biofilms form on tissues and foreign implants during human infections and confer pathogens resistance to drugs and immune responses, making biofilm-associated infections extremely difficult to treat [1]. Clostridioides difficile, a major healthcare-associated gastrointestinal pathogen, causes C. difficile infection (CDI), which is associated with high rates of recurrence, especially in the elderly [3]. CDI is strongly associated with long-term antibiotic therapy, which results in disruption of the native gut microbiota. In recent years, C. difficile biofilms have been considered to be important for persistence of the bacterium in the gut and for recurrent infections. Here we review the current knowledge on C. difficile biofilms in the context of the gut environment and infection.

15.4.1 C. difficile Forms Biofilms in vitro

Biofilm formation by C. difficile was first reported by Donelli and colleagues where they identified the role of polymicrobial biofilms in clogging of biliary stents using confocal and field emission scanning electron microscopy [4]. Soon after, biofilm formation by C. difficile strains of clinical origin (strains 630, R20291) on abiotic surfaces was reported, as quantitated by crystal violet staining [5, 6]. Viable cell counts, as well as LIVE/DEAD viability staining showed that bacterial viability was higher in 1- to 3-day-old biofilms and decreased in 6-day-old biofilms [4–7]. C. difficile biofilms are multilayered, encased in a thick matrix composed of bacterial proteins, extracellular DNA (eDNA), and polysaccharide II; however, it is noteworthy that the composition and structure of biofilms are both time- and strain-dependent [5, 7]. Numerous C. difficile factors which modulate biofilm formation have been identified, including pili, flagella proteins, the S-layer, Cwp84, quorum sensing, germination receptor SleC, and sporulation. Mutants deficient in stress-related proteins including the SOS response regulator, LexA, the RNA chaperone, Hfq, and the heat stress-associated chaperone, DnaK, have been associated with increased biofilm formation [8–10]. Interestingly, the toxins TcdA and TcdB were identified in the biofilm matrix of 3- and 6-day-old biofilms, suggesting that biofilms may play a role in C. difficile virulence [7]. Cyclic di-GMP (c-di-GMP) is thought to play an important role in the motile to sessile biofilm state shift through repression of flagellar synthesis and induction of pili [11]. In a recent global gene expression analysis of microfermentor biofilms, several genes controlled by the SinR-like regulators CD2214 and CD2215, including pilA1, were differentially expressed in biofilms, although pilA1 appeared to contribute to biofilm/aggregate formation only in c-di-GMP overexpressing strains [12]. Thus, C. difficile forms complex biofilms in vitro which involves multiple regulatory pathways and several virulence-associated proteins.

15.4.2 Biofilms—A Niche for C. difficile Spore Formation?

Spores are critical for transmission of CDI, and sporulation is a key pathway in C. difficile pathogenesis which is initiated under conditions of stress. Viable cell

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counts from biofilms formed by the clinically relevant strain, R20291, show that the majority of C. difficile cells are vegetative in 3-day-old biofilms. However, the number of spores increased over time, with spores forming the majority of cells in 6-day-old biofilms [6, 7]. A sporulation-deficient C. difficile strain lacking Spo0A, a master transcriptional regulator which induces the sporulation pathway upon phosphorylation, formed significantly reduced biofilms compared to the wild type [5, 6]. The spo0A mutant biofilms were easily detached and had significantly less resistance to oxygen stress than the wild type [6]. Together, these findings suggest that biofilm formation may be regulated by Spo0A, indicating an intriguing link between spore formation and biofilms. Furthermore, Semenyuk and colleagues found that spores from biofilm cultures had a reduced germination efficiency compared to conventionally cultured spores [7]. Differences observed in the exosporium structure of spores from planktonic and biofilm cultures may contribute to increased thermotolerance and reduced germination efficiency in biofilm-derived spores [7, 13, 14]. Collectively, although we lack direct evidence from infection, these findings suggest that C. difficile biofilms can serve as a niche for generating modified spores, which favour maintenance of a dormant population, aiding bacterial persistence and disease recurrence.

15.4.3 C. difficile Biofilms Protect from Antibiotics

Biofilm-associated antibiotic tolerance is the result of a myriad of factors, including the type of antibiotic, bacterial species, biofilm stage, and availability of resources [1]. The ability to form biofilms allows C. difficile to resist antibiotics and oxidative stresses [2, 5, 6]. When C. difficile is exposed to varying levels of vancomycin, a drug commonly used to treat CDI, bacteria survived better and displayed resistance in a biofilm compared to planktonic culture [5]. In a triplestage human gut model, vancomycin treatment reduced planktonic C. difficile to below the detection limit, while the biofilm population remained unchanged [15]. Utilising a colony biofilm model, treatment of C. difficile biofilms grown on black polycarbonate membranes with 100x minimum inhibitory concentration (MIC) of metronidazole, another drug used to treat CDI, resulted in a significant decrease in bacterial numbers compared to vancomycin at 100x MIC [16]. However, for both vancomycin and metronidazole, the biofilms only delayed killing and neither were successful in reducing viable spores [16]. Fidaxomicin, a newer antibiotic that is effective for recurrent infections, at 25x MIC, was able to reduce biofilm bacterial and spore viability by approximately 2.5- and 1.5-fold, respectively. Surotomycin, a cyclic lipopeptide, showed similar abilities, yielding a 3-fold reduction of vegetative cells and 1.5-fold reduction in spore viability, suggesting a quicker penetration and greater disruptive ability of fluorescently labelled fidaxomicin compared to surotomycin [16]. A recent larger scale study which assayed antimicrobials including thuricin CD, tigecycline, vancomycin, teicoplanin, rifampicin, and nitazoxanide, against a variety of C. difficile strains in sessile and planktonic modes, showed that pairwise combinations of antimicrobials were

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more effective than single antibiotic treatments against R20291 biofilms, except nitazoxanamide, whose potency was reduced when combined with thuricin CD. Sensitivity to drugs or drug combinations was shown to be strain-dependent, with strains producing varied levels of biofilms in vitro [17]. With regard to the mechanisms underlying antibiotic resistance, the dense biofilm matrix can act as a physical barrier, providing resistance to antimicrobial penetration, and disrupted biofilms were more susceptible to antibiotics compared to intact biofilms [1, 5]. Paradoxically, subinhibitory concentrations of metronidazole and vancomycin induced biofilm formation and seemingly reduced antibiotic susceptibility [5, 18]. Therefore, it is possible that low levels of antibiotics could induce C. difficile biofilm production, thus promoting persistence and recurrence of infection.

15.4.4 C. difficile Interactions with the Microbiota

C. difficile establishes itself in the intestine only when the native gut microbiota is altered, usually by treatment with broad spectrum antibiotics like fluoroquinolones. Colonisation resistance provided by intestinal bacteria prevents C. difficile from colonising through different mechanisms, including generation of nutritional niches, production of antimicrobial peptides, metabolites, and quorum sensing [3]. Commensals like Clostridium scindens have been associated with resistance to infection through production of secondary bile acids like deoxycholate, which prevent C. difficile growth [19]. Bacteroides spp. can prevent C. difficile growth, both in vitro and in vivo; interestingly, B. fragilis appears to inhibit C. difficile only when in close contact within mixed biofilms, through an autoinducer-2– mediated mechanism [20]. In a culturomics study, 66 species isolated from microbiota were found to inhibit C. difficile. When bacterial species combinations were tested for their inhibitory effects, species composition and blend size were found to be important for C. difficile inhibition, suggesting that bacterial interactions play a role in the inhibitory effects [21]. However, recent research has indicated that C. difficile also closely interacts with the gut microbiota during colonisation. A recent study showed that deoxycholate from the gut commensal species, C. scindens, induced C. difficile biofilm formation, indicating that deoxycholate effects on C. difficile may be concentrationdependent [22]. C. difficile and the commensal Fusobacterium nucleatum were reported to coaggregate in vitro, increasing biofilm formation and extracellular polysaccharide production [23]. Studies employing a triple stage human gut chemostat model have shown that C. difficile was present within sessile microbiota communities from faecal emulsions [15]; spores were predominantly found in these communities, which appeared to germinate over time. In one of the early in vivo studies to examine the presence of C. difficile multicellular communities during infection, the Driks laboratory showed that low numbers of C. difficile were present within mixed communities containing Bacteroidetes and Firmicutes

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species on the outer mucus layer of the gut in a C. difficile murine infection model [24]. Thus, the data point to C. difficile forming adherent communities in close association with the commensal microbiota species.

15.4.5 C. difficile Biofilms during Infection

During infection, biofilms may serve as reservoirs of C. difficile, which allow bacteria to persist in the gut in the presence of antibiotic therapy, potentially reestablishing infections and resulting in recurrent disease. Nevertheless, a direct role for biofilms in the recurrence of C. difficile infection is yet to be demonstrated. Bacterial factors that are necessary for biofilm formation are essential for colonisation and virulence in many gastrointestinal pathogens. Some C. difficile surface and regulatory factors key for biofilm formation such as flagella, pili, and Spo0A also have colonisation defects in murine models of infection, indicating that adherence to gut surfaces is important during infection [3]. However, a role for these factors in biofilm formation during infection has not been formally demonstrated. A general challenge with defining the role of biofilms during infection by gastrointestinal pathogens is the visualisation of biofilm communities, which is confounded by the microbiota lining the gut. C. difficile microcolonies and filaments were observed on epithelial cells during infection in an in vitro gut infection model [25], and biofilm-like C. difficile cell aggregates have also been reported from hamster and murine infection models [26, 27]. A recent study demonstrated that C. difficile forms mono-species biofilm communities in gnotobiotic mice [2]. Different C. difficile strains were reported to colonise the murine gut similarly with bacterial aggregates associated to the mucus layer rather than with the epithelial cells [2]. Although this study suggests that C. difficile is capable of building communities in vivo, the formation of such communities in the context of the native microbiota needs further study. While communities likely form in conjunction with the microbiota, communities may also form within deeper cell layers during invasive infection. Based on our current knowledge, we propose a model for how C. difficile biofilms may form during infection (Fig. 15.5). C. difficile initially attaches to the mucosal layers in the gut, when the native gut microbiota is disrupted by broad spectrum antibiotics. Increased c-di-GMP levels resulting in decreased bacterial motility enables attachment and establishment of microaggregates or communities. These communities could exist as single species or in close association with the gut microbiota, serve as a niche for production of spores and toxins (toxins A, B, and binary C. difficile toxin), and provide protection from oral antibiotics using for treatment (e.g., vancomycin, metronidazole) in the lumen. Surface factors (e.g., pili, flagella, S-layer), quorum sensing (e.g., LuxS), and regulators (e.g., Spo0A, CD630_2214) control biofilm/aggregate formation. Direct bacterial interactions of C. difficile and action of toxins trigger cell death and disruption of epithelial barrier, allowing bacteria to penetrate the epithelial cell layer to the underlying basement membrane and myofibroblasts. C. difficile may form communities in underlying tissue which may protect bacteria from

Clostridioides difficile Biofilms

oxygen and immune responses. Bacterial communities in the gut mucosa may allow bacterial persistence, and under conducive conditions, bacteria may be dispersed, leading to reseeding and recurrence of infection. Image created with BioRender.com.

Figure 15.5 A model of C. difficile biofilms during infection.

15.4.6 Future Perspectives

C. difficile biofilm communities are likely critical in recurrent CDI. Although we have a good understanding of C. difficile biofilm formation and regulation in vitro, several questions regarding the relevance of such biofilm communities in bacterial persistence and the bacterial and host factors regulating their formation in vivo remain unanswered. New visualisation tools and cutting-edge gut infection models for C. difficile combined with further studies on clinical samples will likely provide better insight into the role of C. difficile biofilms in CDI. Disclosures and Conflict of Interest

This section was originally published as: Frost, L. R., Cheng, J. K. J., Unnikrishnan, M. (2021). Clostridioides difficile biofilms: A mechanism of persistence in the gut? PLoS Pathog. 17(3), e1009348, https://doi.org/10.1371/journal.ppat.1009348, under the Creative Commons Attribution license (http://creativecommons.org/ licenses/by/4.0/), and appears here, with edits and updates.

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Funding: This work was supported by University of Warwick and BBSRC funded MIBTP PhD studentships to Lucy Frost (1782606) and Jeffery Cheng (1897785). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of this section. Competing interests: The authors have declared that no competing interests exist.

References

1. Hall CW, Mah TF. Molecular mechanisms of biofilm-based antibiotic resistance and tolerance in pathogenic bacteria. FEMS Microbiol Rev. 2017;41:276–301.

2. Soavelomandroso AP, Gaudin F, Hoys S, Nicolas V, Vedantam G, Janoir C, et al. Biofilm structures in a mono-associated mouse model of Clostridium difficile infection. Front Microbiol. 2017;8:1–10.

3. Abt MC, McKenney PT, Pamer EG. Clostridium difficile colitis: pathogenesis and host defence. Nat Rev Microbiol. 2016;14:609–20. 4. Donelli G, Vuotto C, Cardines R, Mastrantonio P. Biofilm-growing intestinal anaerobic bacteria. Immunol Med Microbiol. 2012;65:318–25.

5. Dapa T, Leuzzi R, Ng YK, Baban ST, Adamo R, Kuehne SA, et al. Multiple factors modulate biofilm formation by the anaerobic pathogen Clostridium difficile. J Bacteriol. 2013;195:545–55.

6. Dawson LF, Valiente E, Faulds-Pain A, Donahue EH, Wren BW, Popoff MR. Characterisation of Clostridium difficile Biofilm Formation, a Role for Spo0A. PLoS ONE. 2012;7. 7. Semenyuk EG, Laning ML, Foley J, Johnston PF, Knight KL, Gerding DN, et al. Spore Formation and Toxin Production in Clostridium difficile Biofilms. PLoS ONE. 2014;9:e87757.

8. Jain S, Smyth D, O’Hagan BMG, Heap JT, McMullan G, Minton NP, et al. Inactivation of the dnaK gene in Clostridium difficile 630 Δerm yields a temperature-sensitive phenotype and increases biofilm-forming ability. Sci Rep. 2017;7:1–13. 9. Boudry P, Gracia C, Monot M, Caillet J, Saujet L, Hajnsdorf E, et al. Pleiotropic role of the RNA chaperone protein Hfq in the human pathogen Clostridium difficile. J Bacteriol. 2014;196:3234–48.

10. Walter BM, Cartman ST, Minton NP, Butala M, Rupnik M. The SOS response master regulator LexA is associated with sporulation, motility and biofilm formation in Clostridium difficile. PLoS ONE. 2015;10:1–17.

11. Purcell EB, McKee RW, McBride SM, Waters CM, Tamayo R. Cyclic diguanylate inversely regulates motility and aggregation in Clostridium difficile. J Bacteriol. 2012;194:3307–16.

12. Poquet I, Saujet L, Canette A, Monot M, Mihajlovic J, Ghigo JM, et al. Clostridium difficile Biofilm: Remodeling metabolism and cell surface to build a sparse and heterogeneously aggregated architecture. Front Microbiol. 2018;9:1–20.

13. Pizarro-Guajardo M, Calderón-Romero P, Paredes-Sabja D. Ultrastructure variability of the exosporium layer of Clostridium difficile spores from sporulating cultures and biofilms. Appl Environ Microbiol. 2016;82:5892–8.

References

14. Pickering DS, Wilcox MH, Chilton CH. Biofilm-derived spores of Clostridioides (Clostridium) difficile exhibit increased thermotolerance compared to planktonic spores. Anaerobe. 2018;54:169–71. 15. Crowther GS, Chilton CH, Todhunter SL, Nicholson S, Freeman J, Baines SD, et al. Development and Validation of a Chemostat Gut Model To Study Both Planktonic and Biofilm Modes of Growth of Clostridium difficile and Human Microbiota. PLoS ONE. 2014;9.

16. James GA, Chesnel L, Boegli L. Pulcini E de L, Fisher S, Stewart PS. Analysis of Clostridium difficile biofilms: Imaging and antimicrobial treatment. J Antimicrob Chemother. 2018;73:102–8.

17. Mathur H, Rea MC, Cotter PD, Hill C, Ross RP. The efficacy of thuricin CD, tigecycline, vancomycin, teicoplanin, rifampicin and nitazoxanide, independently and in paired combinations against Clostridium difficile biofilms and planktonic cells. Gut Pathog. 2016;8:1–10.

18. Vuotto C, Moura I, Barbanti F, Donelli G, Spigaglia P. Subinhibitory concentrations of metronidazole increase biofilm formation in Clostridium difficile strains. Pathog Dis. 2016;74:1–7.

19. Buffie CG, Bucci V, Stein RR, McKenney PT, Ling L, Gobourne A, et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature. 2015;517:205–8.

20. Slater RT, Frost LR, Jossi S, Millard AD, Unnikrishnan M. Clostridioides difficile LuxS mediates inter-bacterial interactions within biofilms. Sci Rep. 2019;9:9903.

21. Ghimire S, Roy C, Wongkuna S, Antony L, Maji A, Keena C. Identification of Clostridioides difficile-inhibiting gut commensals using culturomics, phenotyping and combinatorial community assembly. mSphere. 2020;5:1–19. 22. Dubois T, Tremblay YDN, Briandet R. Dupuy B. A microbiota-generated bile salt induces biofilm formation in Clostridium difficile. NPJ Biofilms Microbes. 2019;5:1–12.

23. Engevik M, Danhof HA, Auchtung J, Endres BT, Ruan W, Bassères E, et al. Fusobacterium nucleatum adheres to Clostridioides difficile via the RadD adhesin to enhance biofilm formation in intestinal mucus. Gastroenterology. 2021;160(4):1301–314.e8. 24. Semenyuk EG, Poroyko VA, Johnston PF, Jones SE, Knight KL, Gerding DN, et al. Analysis of bacterial communities during Clostridium difficile infection in the mouse. Infect Immun. 2015;83:4383–91.

25. Anonye BO, Hassall J, Patient J, Detamornrat U, Aladdad AM, Schüller S, et al. Probing Clostridium difficile Infection in Complex Human Gut Cellular Models. Front Microbiol. 2019;10:1–15.

26. Lawley TD, Clare S, Walker AW, Goulding D, Stabler RA, Croucher N, et al. Antibiotic treatment of Clostridium difficile carrier mice triggers a supershedder state, sporemediated transmission, and severe disease in immunocompromised hosts. Infect Immun. 2009;77:3661–9.

27. Buckley AM, Spencer J, Candlish D, Irvine JJ, Douce GR. Infection of hamsters with the UK Clostridium difficile ribotype 027 outbreak strain R20291. J Med Microbiol. 2011;60:1174–80.

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15.5 Cesar ean Section and Childhood Infections: Causality

for Concern?

Gordon C. S. Smith, MD, PhD, DSc Department of Obstetrics & Gynaecology, University of Cambridge, Cambridge, UK [email protected]

Human birth where the infant survives without passing through the mother’s genital tract has only been recorded in the past few thousand years, with accounts of cesarean delivery in antiquity. Cases where the mother also survives the surgery are more recent still, with the first authenticated description of successful cesarean delivery in 1610 [1]. The development and implementation of safe cesarean delivery in the late 19th and 20th centuries were transformative for maternal and infant survival in high-income countries. Globally, the availability of cesarean delivery is now 1 of the most important elements of emergency obstetric care to reduce rates of maternal and perinatal death [2]. In the past century, however, cesarean delivery has transformed from occasional lifesaving surgery to the most commonly performed laparotomy, accounting for 1 in 14 of all surgical procedures worldwide in 2012 [3]. The indications for its use, once lethal and absolute, have been supplemented with many relative indications. Maternal preference for the method in the absence of any medical indication is increasing [4]. Given that there is widespread international variation in the proportion of female obstetricians choosing to have elective cesarean deliveries themselves [5], variation in practice reflects differences in interpretation of the same evidence base. Deciding on cesarean section depends on understanding its risks and benefits, and a huge volume of data exists to inform women. Interpretation of elements of the evidence base may not be simple, such as associations with the actual mode of delivery [6], which, unlike the intended mode of delivery, cannot be known when making a decision. While there is extensive information on the short-term associations, the long-term effects on the both the mother and child are less well documented. A recent cohort study in PLOS Medicine reported by Jessica Miller and colleagues, including 7.1 million live births from 4 countries, provided useful new evidence and raises the possibility that cesarean delivery may be associated with the risk of later childhood infectious disease [7]. The authors performed a pooled analysis of retrospective cohort studies using data from Denmark, Scotland, England, and Australia. They defined mode of delivery and assessed potential confounders using birth records and identified subsequent infant infections by record linkage to hospital discharge data from the children. Although the proportional increase in risk is relatively modest (hazard ratio 1.10, 95% confidence interval 1.09 to 1.12), as cesarean delivery is common, even this modest association leads to an attributable fractions of 1.8% to 3.2% across the different countries. However, these calculations assume causality.

Cesarean Section and Childhood Infections

One finding in favour of a causal association is the fact that it was observed with both planned and emergency cesarean delivery, given that the indications for each are very different. However, it is still possible that an unmeasured confounder explains this observation, and the relatively modest hazard ratios are consistent with this. The most common indication for emergency cesarean in first pregnancies is poor progress in labour [8]. Repeat cesarean section, both planned and emergency, accounts for a large proportion of cesarean sections in parous women [8]. Hence, it is plausible that a single unmeasured confounder, associated with both poor progress in labour and the risk of infection in the offspring, could explain the findings. However, the fact that the association was observed across all 4 countries strengthens the argument for causality. It is interesting that the association was observed across a wide range of age windows, from 0 to 3 months through to 2 to 5 years, and involved infection of multiple organ systems. The consistency of association could be interpreted as supporting causation. However, the same pattern could also be interpreted as a lack of specificity. A causal association requires an underlying mechanism, and it is important to consider what type of mechanism might explain a modest increase in risk, but one which is present across a wide range of ages and across multiple organs. An argument in favour of a causal association is the fact that cesarean section represents—literally—an unnatural form of birth. Labour and vaginal delivery are associated with multiple stimuli for the infant, including physical, hormonal, and microbial. Extrauterine life is characterised by colonisation of the infant by the various site-specific microbiomes. Passage through the genital tract exposes the fetus to the mother’s anogenital microbiome and is thought to have an important role in the genesis of the infant’s commensal microbiota. The past 20 years have seen a massive expansion in our understanding of the importance of the various organ-specific microbiomes in the determination of health and disease. Given that mammalian physiology has, for more than 100 million years, involved this exposure of the fetus during transition from the sterile womb to the microbially diverse world, it seems plausible that entering the world through an aseptic opening could have significant effects on the infant. The area is highly controversial. Some authors have applied molecular tests to intrauterine tissues and concluded that the fetus may be colonised before birth [9]. Others have argued that these signals are artefacts, including contamination of laboratory reagents with DNA from environmental bacteria and contamination of tissues by bacteria from the mother’s genital tract during birth [10]. Some have observed prolonged alteration in the fetal intestinal microbiome in relation to cesarean delivery [11], while others have not [12]. However, there is direct experimental evidence in animal models to indicate that cesarean section can lead to altered immune responses through effects on intestinal colonisation [13], and this a candidate mechanism to explain the observations described by Miller and colleagues. Key points for women considering this evidence in their decision-making around mode of delivery could include the following. First, human beings have evolved giving birth vaginally. As we will never have perfect information about the balance of risks and benefits, some prioritisation of what is physiological is

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scientifically reasonable. Second, in relation to the association documented by Miller and colleagues, the absolute risk difference is relatively small. Moreover, it is uncertain whether the actual decision about mode of delivery is causally associated with this outcome. Finally, the individual woman’s choice is not to be delivered by cesarean section or to have a vaginal birth. Rather, the alternative to elective cesarean section is to attempt vaginal birth with the possible outcomes of success or emergency cesarean delivery. For a minority of women with a high prior risk of emergency cesarean, a planned procedure may be associated with lower risks [14]. However, among the majority of women who have a high probability of vaginal delivery, the balance of risks and benefits will favour aiming for a vaginal birth. But calculating the balance of risks and benefits requires knowing both as fully as possible. It is biologically plausible that mode of delivery could have lifelong effects on the mother and baby, and studies such as this one are crucial for women to make fully informed decisions. Disclosures and Conflict of Interest

This section was originally published as: Smith, G. C. S. (2020). Cesarean section and childhood infections: Causality for concern? PLoS Med., 17(11), e1003457, https://doi.org/10.1371/journal.pmed.1003457, under the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates. Funding: The author received no specific funding for this work.

Competing interests: GS is a member of the Editorial Board of PLOS Medicine. GS reports grants and personal fees from GlaxoSmithKline Research and Development, Ltd., grants from Sera Prognostics Inc., nonfinancial support from Illumina, Inc., and personal fees from Roche Diagnostics, Ltd., outside the submitted work. In addition, GS has a patent for a novel predictive test for fetal size pending.

References

1. Low J. Caesarean section—past and present. J Obstet Gynaecol Can. 2009;31(12): 1131–1136.

2. McClure EM, Goldenberg RL, Bann CM. Maternal mortality, stillbirth and measures of obstetric care in developing and developed countries. Int J Gynaecol Obstet. 2007;96(2):139–146.

3. Weiser TG, Haynes AB, Molina G, Lipsitz SR, Esquivel MM, Uribe-Leitz T, et al. Size and distribution of the global volume of surgery in 2012. Bull World Health Organ. 2016;94(3):201–9F.

4. Begum T, Saif-Ur-Rahman KM, Yaqoot F, Stekelenburg J, Anuradha S, Biswas T, et al. Global incidence of Caesarean deliveries on maternal request: a systematic review and meta-regression. BJOG. 2020. doi: 10.1111/1471-0528.16491.

5. Finsen V, Storeheier AH, Aasland OG. Cesarean section: Norwegian women do as obstetricians do—not as obstetricians say. Birth. 2008;35(2):117–120.

Infectious Hypothesis of Alzheimer Disease

6. Lumbiganon P, Laopaiboon M, Gulmezoglu AM, Souza JP, Taneepanichskul S, Ruyan P,

et al. Method of delivery and pregnancy outcomes in Asia: the WHO global survey on

maternal and perinatal health 2007–08. Lancet. 2010;375(9713):490–499.

7. Miller JE, Goldacre R, Moore HC, Zeltzer J, Knight M, Morris C, et al. Mode of birth and risk of infection-related hospitalisation in childhood: A population cohort study of 7.17 million births from 4 high-income countries. PLoS Med. 2020;17(11): e1003429.

8. Brennan DJ, Robson MS, Murphy M, O’Herlihy C. Comparative analysis of international

cesarean delivery rates using 10-group classification identifies significant variation

in spontaneous labor. Am J Obstet Gynecol. 2009;201(3):308.e1–8.

9. Aagaard K, Ma J, Antony KM, Ganu R, Petrosino J, Versalovic J. The placenta harbors

a unique microbiome. Sci Transl Med. 2014;6(237):237ra65.

10. de Goffau MC, Lager S, Sovio U, Gaccioli F, Cook E, Peacock SJ, et al. Human placenta has

no microbiome but can contain potential pathogens. Nature. 2019;572(7769):329–334.

11. Shao Y, Forster SC, Tsaliki E, Vervier K, Strang A, Simpson N, et al. Stunted microbiota

and opportunistic pathogen colonization in caesarean-section birth. Nature.

2019;574(7776):117–121.

12. Chu DM, Ma J, Prince AL, Antony KM, Seferovic MD, Aagaard KM. Maturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery. Nat Med. 2017;23(3):314–326.

13. Zachariassen LF, Krych L, Rasmussen SH, Nielsen DS, Kot W, Holm TL, et al. Cesarean

section induces microbiota-regulated immune disturbances in C57BL/6 Mice. J Immunol. 2019;202(1):142–150.

14. Sovio U, Smith GCS. Blinded ultrasonic fetal biometry at 36 weeks and the risk of

emergency caesarean delivery: a prospective cohort study of 3,047 low risk nulliparous

women. Ultrasound Obstet Gynecol. 2017;52(1):78–86.

15.6 Infectious Hypothesis of Alzheimer Disease Charles E. Seaks and Donna M. Wilcock, PhD Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA [email protected]

15.6.1 Alzheimer Disease Alzheimer disease (AD) is the leading cause of dementia worldwide, accounting for almost 70% of all dementia cases, and is one of the fastest growing healthcare price burdens. Pathologically, AD is characterized by the presence of beta amyloid (Aβ) plaques and neurofibrillary tangles composed of aggregated microtubule associated protein tau. Clinically, AD manifests as progressive cognitive decline and worsening memory deficits [1]. The traditional hypothesis on the progression of AD pathologies states that Aβ plaques appear first, causing hyperphosphorylation of tau, leading to tangles and neurodegeneration. However, with the continued failure of clinical trials aimed at decreasing Aβ plaques, this hypothesis has come under scrutiny while alternative hypotheses

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are being investigated. One of the more controversial emerging hypotheses is the infectious hypothesis.

15.6.2 The Infectious Hypothesis

The infectious hypothesis proposes that a pathogen (virus, bacteria, prion, etc.) is the root cause of AD [2]. The hypothesis is supported by evidence that some pathogens, such as herpesviruses and certain bacterial species, are found more commonly in AD patients. There is some variation within the infectious hypothesis field as to how an infectious pathogen explains the pathological hallmarks of AD. Direct infection and eventual death of central nervous system (CNS) cells by pathogens could explain the cognitive deficits and heightened inflammation found in AD [3]. The relationship between inflammation and the AD hallmarks has long been recognized, with inflammation hypothesized to cause tissue damage, leading to protein aggregates such as Aβ plaques and tangles, which in turn can lead to more inflammation [4]. This cascade could be initiated by a number of endogenous and external factors, including microbial pathogens. Alternatively, Aβ and tau may be the products of normal responses to infection, intended to sequester threats to the CNS [5]. Accumulation of Aβ and tau could then occur when the generation of the aggregates outpaces clearance by the microglia in the brain, a process brought about by the natural process of aging [5]. Such an antimicrobial role–centered model is illustrated in Fig. 15.6. The aggregates themselves have shown to trigger neuroinflammation as well [6]. Recent findings have highlighted a number of pathogens as potential drivers of AD, but the family of pathogens most investigated is the herpesviruses [7].

Figure 15.6 Conceptual framework of the infectious hypothesis as it relates to beta amyloid plaque deposition.

Infectious Hypothesis of Alzheimer Disease

15.6.3 Herpesviruses and Potential Contributions to Alzheimer

Disease: What Is Currently Known? The herpesviruses are a family of enveloped, double-stranded DNA viruses capable of infecting a wide range of mammalian species [8]. The worldwide ubiquity of herpesviruses tends to be high due to their relatively broad tropism, with some species, such as the herpes simplex viruses, reaching over 80% seroprevalence in humans [7]. Herpes simplex virus 1 (HSV-1) is the most commonly studied pathogen in the context of AD, primarily due to identification years ago of HSV-1 DNA in AD patient brains at autopsy [9, 10]. The first of these studies [10] identified latent virus in both normal and AD brains but postulated that differences in viral expression and susceptibility might underlie HSV-1 contribution to AD. Itzhaki and colleagues [9] then demonstrated that the presence of ApoE4, the strongest genetic risk factor for AD, and HSV-1 together was a stronger risk factor for the development of AD than either factor on its own. While a role for HSV-1 has long been suspected in AD, other members of the herpesviruses have recently been implicated, including cytomegalovirus, Epstein–Barr virus, and human herpesvirus 6 (HHV6) [11]. Largely, these studies have consisted of screening patient samples for the presence of these viruses, either through immunoglobulin titers or direct PCR amplification of viral DNA, and then comparing between AD and normal patients. More specific work has focused on HHV6’s ability to seed Aβ plaques in vitro and in vivo [12]. HSV-1 establishes latency in the ganglia of the trigeminal nerve, giving it access to the CNS after initial infection in the peripheral tissue of the lips and mouth [8]. Latency of HSV-1 is maintained by an RNA transcript known as the latency transcript [13]. This prevents the host immune system from identifying mature viral proteins and nucleic acids that would trigger an innate immune response [8]. This latency can be broken by a number of factors, including UV light, physical damage, hormonal changes, and off-target effects of pharmaceuticals, leading to a reestablished active infection [13]. The chief clinical indicator of this reactivation is the presence of the herpetic lesions colloquially known as “cold sores” or “fever blisters.” In a healthy individual, the infection remains in the periphery, as the intrinsic immunity of the brain prevents the further propagation of the virus into the CNS [14]. However, in older or immunocompromised individuals, this intrinsic immunity is impaired, allowing the virus to spread into the rest of the brain [15]. The presence of herpesviruses in the CNS may clinically progress in a number of ways. The most severe, but relatively rare, outcome is an exaggerated immune response leading to herpes simplex encephalitis [16]. The potentially more relevant course for development of AD is a subclinical infection resulting in the virus establishing latency in CNS resident cells [17]. From here, the virus can reactivate periodically, spreading further and establishing more latent populations. This cycle of active and latent states could also explain the aging nature of AD, with a certain number of reactivation cycles to reach a viral load level necessary to overwhelm the response of the brain or cause enough damage to exhibit clinical

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symptoms [17]. De Chiara and colleagues demonstrated that recurrent reactivation of HSV-1 in a wild-type mouse model could produce hallmark AD pathology, accompanied by cognitive deficits, an intriguing result that supports the hypothesis that reactivation is critical in the connection between herpesviruses and AD. Herpesviruses have been implicated in AD through the isolation of viral DNA from AD patients, as well as through epidemiological data [18]. A relatively recent study utilizing national insurance information in Taiwan showed that patients showing infection by herpesviruses were more likely to be diagnosed with multiple forms of dementia, including AD. Specifically, patients infected with HSV-1 had a hazard ratio of 2.564, indicating a 2.56-fold increase in likelihood of developing dementia [18]. Additionally, they showed that treatment with antiherpetic therapeutics, specifically acyclovir, decreased the likelihood of being diagnosed with those same forms of dementia significantly. Use of any antiherpetics resulted in hazard ratios well below 1.0, indicating decreasing risk of developing dementia.

15.6.4 Future Directions of the Infectious Hypothesis

There is mounting support and evidence for the infectious hypothesis. At numerous conferences on AD, the number of abstracts examining the connection between pathogens and the development of AD has increased in recent years, and dedicated sessions and panels have begun to arise. Considerable work is still needed, however, to more convincingly connect AD and infectious agents. Primarily, important questions on the relationship between established risk factors of AD and pathogen infections remain to be addressed. There has always been the lingering question of how pathogens ubiquitous in much of the population are causing a disease in only a subset of that population. The answer to this selective vulnerability likely lies within the infectious hypothesis’s interactions with other theories and risk factors. Could clearance of Aβ and tau, used to stop HSV-1 spread, be impaired due to changes in vascular health? Could breakdown of the blood–brain barrier with age be providing new sites of entry for other pathogens? There is therefore a need for increased research in the AD field with respect to infectious pathogens. Many of the well-known AD risk genes, such as apolipoprotein-E, are related to immunity or, in some cases, life cycle of the pathogens [11]. A review by Dr. Steven Harris and Dr. Elizabeth Harris discussing what we already know about these interactions is recommended to any interested readers (see [13]). Instead of viewing the infectious hypothesis as a standalone entity, it should be investigated through the lens of interactivity with other components of the disease. Such holistic, inclusive approaches are a natural progression toward a larger hypothesis, a process that the Aβ hypothesis has had to undergo, which then leads to another question: why has the process been so slow for the infectious hypothesis?

Infectious Hypothesis of Alzheimer Disease

15.6.5 The Alzheimer Disease Field and Infectious Hypothesis The answer likely stems from the composition of the AD field. Though expanding and diversifying, most AD researchers are not microbiologists or virologists. Typically, they are neuroscientists, biochemists, neuropathologists, neuropsychologists, and pharmacologists. There is a distinct lack of overlap between the disciplines that is sometimes hard to bridge in terms of forming collaborations or productive dialogue. The number of individuals who work on AD and identify as a microbiologist or virologist is extremely small. The longstanding support for Aβ research is overwhelming, despite high failure rates for therapeutics centered on Aβ modulation [19]. NIH funding for AD research as of this section’s writing sits at $2.3 billion, a funding level reflective of the imminent health concern AD represents. Of the NIH AD funding, a negligible amount is used to investigate pathogens in AD [20]. However, the recent announcement of special interest by the National Institute on Aging (NIA) for grants examining a potential connection between pathogens and AD represents an important step in increased funding in this area (Notice #: NOT-AG-19-012). Despite the challenges the infectious hypothesis faces, there are reasons to be hopeful for further research in this area. Recent studies have contributed significant evidence to the infectious hypothesis and have received public attention [12, 21]. Eimer and colleagues [12], as previously mentioned, demonstrated the ability of herpesviruses to seed Aβ plaques and showed that these plaques act in an antiviral manner. This provided a clear mechanistic connection between herpesviruses and the development of pathology. Readhead and colleagues [21] showed significant overlap of affected pathways in HHV6 infection and AD, specifically related to amyloid precursor protein (APP) processing to the Aβ peptide, forming oligomers, and ultimately amyloid plaques. This connection in pathways strongly supports the theory that herpesviruses contribute to AD and fits within the antimicrobial Aβ theory, with the presence of herpesviruses triggering up-regulation of APP metabolism. Organizations related to the infectious hypothesis have been growing as well, including the Alzheimer’s Germ Quest, Inc, founded by Dr. Leslie Norins to provide a challenge award for research identifying a causative pathogen of AD. Representatives from the Human Herpesvirus 6 Foundation have been offering assistance in the form of reagents and funding for groups interested in pursuing the connection of HHV6 to AD. A petition for increased allocations of funds for infectious AD–related research has also been started, and funding opportunities, such as those provided by the NIA, will provide opportunities for further research. Ultimately, the opportunity for meaningful infectious hypothesis research has never been better. Alternative hypotheses of AD are being considered more openly than at any point in the past, given trial failures, and longtime researchers in the field have been fighting for opportunities that are now becoming a reality.

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The next stage in infectious hypothesis research is difficult to predict. Recent work has highlighted the connection between HSV-1 reactivation and AD pathology [7]. Clinical trials are under way by researchers that are looking at the use of antivirals in treating AD (ClincialTrials.gov Identifier: NCT03282916). Gingivitis and chlamydia have also been recently implicated in AD, giving credence to the idea that Aβ plaques are a broad antimicrobial peptide [22, 23]. Determining common characteristics of pathogens that contribute to AD would be a step forward, as it would allow investigators to identify other potential pathogens and winnow the field of potentially relevant interactions with risk factors. There are a large number of identified risk factors for AD, and establishing how pathogens may be interacting with them is an important issue. Ultimately, these questions require answers that will come slowly; however, increased attention, collaboration, and funding will be necessary to answer them. For those with greater interest in specific aspects of the infectious hypothesis, we would direct you to more comprehensive reviews written by Dr. Ruth Itzhaki, who frequently publishes scientific reviews on the topic. Disclosures and Conflict of Interest

This section was originally published as: Seaks, C. E., Wilcock, D. M. (2020). Infectious hypothesis of Alzheimer disease. PLoS Pathog., 16(11), e1008596, https://doi. org/10.1371/journal.ppat.1008596, under the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates. Funding: The authors have received no funding for this work.

Competing interests: The authors have declared that no competing interests exist.

References

1. Mirra SS, Heyman A, McKeel D, Sumi SM, Crain BJ, Brownlee LM, Vogel FS, Hughes JP, van Belle G, Berg L. The consortium to establish a registry for Alzheimer’s disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer’s disease. Neurology. 1991;41(4):479–86.

2. Sochocka M, Zwolinska K, Leszek J. The infectious etiology of Alzheimer’s disease. Curr Neuropharmacol. 2017;15(7):996–1009.

3. Lin WR, Wozniak MA, Cooper RJ, Wilcock GK, Itzhaki RF. Herpesviruses in brain and Alzheimer’s disease. J Pathol. 2002;197(3):395–402.

4. Akiyama H. Inflammation and Alzheimer’s disease. Neurobiol Aging. 2000;21(3): 383–421. 5. Bush AI, Soscia SJ, Kirby JE, Washicosky KJ, Tucker SM, Ingelsson M, Hyman B, Burton MA, Goldstein LE, Duong S, Tanzi RE, Moir RD. The Alzheimer’s disease-associated amyloid β-protein is an antimicrobial peptide. PLoS ONE. 2010;5(3):5(3):e9505. 6. McGeer PL, Rogers J, McGeer EG. Inflammation, antiinflammatory agents, and Alzheimer’s disease: The Last 22 years. J Alzheimers Dis. 2016;54(3):853–7.

References

7. Itzhaki RF. Corroboration of a major role for Herpes simplex virus type 1 in Alzheimer’s disease. Front Aging Neurosci. 2018;10:324.

8. Chayavichitsilp P, Buckwalter JV, Krakowski AC, Friedlander SF. Herpes simplex. Pediatr Rev. 2009;30(4):119–29. 9. Itzhaki RF, Lin W-R, Shang D, Wilcock GK, Faragher B, Jamieson GA. Herpes simplex virus type 1 in brain and risk of Alzheimer’s disease. Lancet. 1997;349(9047):241–4.

10. Jamieson GA, Maitland NJ, Wilcock GK, Craske J, Itzhaki RF. Latent herpes simplex virus type 1 in normal and Alzheimer’s disease brains. J Med Virol. 1991;33(4):224–7. 11. Hemling N, Röyttä M, Rinne J, Pöllänen P, Broberg E, Tapio V, Vahlberg T, Hukkanen V. Herpesviruses in brains in Alzheimer’s and Parkinson’s diseases. Am Neurol Assoc. 2003;54:267–71.

12. Eimer WA, Vijaya Kumar DK, Navalpur Shanmugam NK, Rodriguez AS, Mitchell T, Washicosky KJ, Gyorgy B, Breakefield XO, Tanzi RE, Moir RD. Alzheimer’s diseaseassociated beta-amyloid is rapidly seeded by herpesviridae to protect against Brain infection. Neuron. 2018;99(1):56–63 e3. 13. Harris SA, Harris EA. Molecular mechanisms for herpes simplex virus type 1 pathogenesis in Alzheimer’s disease. Front Aging Neurosci. 2018;10:48.

14. Nair S, Diamond MS. Innate immune interactions within the central nervous system modulate pathogenesis of viral infections. Curr Opin Immunol. 2015;36:47–53. 15. Wozniak MA, Shipley SJ, Combrinck M, Wilcock GK, Itzhaki RF. Productive herpes simplex virus in brain of elderly normal subjects and Alzheimer’s disease patients. J Med Virol. 2005;75(2):300–6.

16. Bradshaw MJ, Venkatesan A. Herpes simplex virus-1 encephalitis in adults: Pathophysiology, diagnosis, and management. Neurotherapeutics. 2016;13(3):493–508.

17. De Chiara G, Piacentini R, Fabiani M, Mastrodonato A, Marcocci ME, Limongi D, Napoletani G, Protto V, Coluccio P, Celestino I, Li Puma DD, Grassi C, Palamara AT. Recurrent herpes simplex virus-1 infection induces hallmarks of neurodegeneration and cognitive deficits in mice. PLoS Pathog. 2019;15(3):e1007617.

18. Tzeng NS, Chung CH, Lin FH, Chiang CP, Yeh CB, Huang SY, Lu RB, Chang HA, Kao YC, Yeh HW, Chiang WS, Chou YC, Tsao CH, Wu YF, Chien WC. Anti-herpetic medications and reduced risk of dementia in patients with herpes simplex virus infections-a nationwide, population-based cohort study in Taiwan. Neurotherapeutics. 2018;15(2): 417–29. 19. Ricciarelli R, Fedele E. The amyloid cascade hypothesis in Alzheimer’s disease: It’s time to change our mind. Curr Neuropharmacol. 2017;15(6):926–35.

20. National Institute of Health. Alzheimer’s disease and related dementias project listing by category—Research Portfolio Online Reporting Tools (RePORT); 2018. Available at: https://report.nih.gov/categorical_spending_project_listing.aspx?FY=2018&ARRA= N&DCat=Alzheimer%27s%20Disease%20including%20Alzheimer%27s% 20Disease%20Related%20Dementias%20(AD/ADRD) (accessed on May 14, 2021).

21. Readhead B, Haure-Mirande JV, Funk CC, Richards MA, Shannon P, Haroutunian V, Sano M, Liang WS, Beckmann ND, Price ND, Reiman EM, Schadt EE, Ehrlich ME, Gandy S, Dudley JT. Multiscale analysis of independent Alzheimer’s cohorts finds disruption of molecular, genetic, and clinical networks by human herpesvirus. Neuron. 2018;99(1): 64–82.e7.

307

308

Current Perspectives in Medical Microbiology

22. Balin BJ, Hammond CJ, Little CS, Hingley ST, Al-Atrache Z, Appelt DM, Whittum-Hudson JA, Hudson AP. Chlamydia pneumoniae: An etiologic agent for late-onset dementia. Front Aging Neurosci. 2018;10:302.

23. Singhrao SK, Olsen I. Assessing the role of Porphyromonas gingivalis in periodontitis to determine a causative relationship with Alzheimer’s disease. J Oral Microbiol. 2019;11(1):1563405.

15.7 Insights into Malaria Pathogenesis Gained from Host Metabolomics Heather N. Colvin, PhDa,b Regina Joice Cordy, PhDa,b aDepartment

of Biology, Wake Forest University, Winston-Salem, North Carolina, USA

of Microbiology and Immunology, Wake Forest School of Medicine,

Winston-Salem, North Carolina, USA bDepartment

[email protected]

15.7.1 Introduction Malaria is a devastating disease caused by the protozoan parasite Plasmodium. The most common Plasmodium species that infect humans are Plasmodium falciparum and Plasmodium vivax, which together cause the vast amount of the disease’s morbidity and mortality worldwide [1]. From a clinical perspective, Plasmodium causes a spectrum of disease ranging from asymptomatic to severe. From a biochemical perspective, malaria involves an interconnection of host and parasite through a shared resource environment, resulting in the exchange of nutrients and signaling molecules. Within the bloodstream of Plasmodium-infected hosts, perturbations in the levels of various metabolites occur, including amino acids, lipids, fatty acids, sugars, and heme metabolites [2]. Metabolomics is a robust tool to study host–pathogen interactions. In-depth analysis of metabolism and the associated by-products and pathways can be viewed in snapshots of time, and the biochemical fingerprints contribute greatly to our understanding of the complex interaction between hosts and pathogen [3]. Metabolomics utilizes methods such as nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography–mass spectrometry (LC–MS), or gas chromatography–mass spectrometry (GC–MS) to identify small weight molecules known as metabolites. Analyses can be performed on biological fluids and tissues (e.g., plasma and urine), volatile organic compounds (VOCs, e.g., odor from breath or skin), and cell cultures as either untargeted or targeted, the former outputting a vast dataset based on chemical features (e.g., mass-to-charge ratio) and the latter including chemical annotations based on reference compounds. Metabolite results can be further analyzed for biochemical involvement using publicly available databases such as Malaria Parasite Metabolic Pathways (MPMP) [4] and Kyoto Encyclopedia of Genes and Genomes (KEGG) [5], to name a few.

Insights into Malaria Pathogenesis Gained from Host Metabolomics

Within the past several years, numerous published works have emerged that employ metabolomics methodology toward the goal of better understanding malaria infection. These metabolome studies have both confirmed previous biological findings that were determined through careful molecular and cellular experimental work as well as shed light on new findings for which the biological underpinnings are still unclear. Prior reviews have provided an overview of the metabolism of Plasmodium from a host–parasite interaction viewpoint [6, 7] as well as covering how host metabolites may contribute to malaria transmission [8]. Here, this section aims to summarize the status of our knowledge about metabolic fluctuations that occur in the host during malaria infection that may relate to malaria pathogenesis, immunity, and diagnosis. In particular, we focus on amino acid, lipid and fatty acid, and red blood cell (RBC)-related alterations in the bloodstream of hosts during malaria infection and how this compares to other diseases. We also discuss metabolites produced by the parasite and by the gut microbiota, respectively, and discuss the potential for metabolite-based biomarkers to aid in malaria diagnostics.

15.7.2 Bloodstream Amino Acid and Glucose Perturbations in Malaria

A significant depletion of amino acids occurs in the bloodstream of Plasmodiuminfected hosts, and a number of studies have characterized these perturbations [9–22]. Of these, arginine, glutamine, and tryptophan have received the most attention in recent studies due to the direct clinical consequences when either are decreased. Low levels of arginine in the bloodstream during malaria may underlie downstream consequences of impaired vasodilation, endothelial disruption, and reduced nitric oxide production [16]. While depletion of host arginine could derive in part from parasite-specific processes (e.g., elevated Plasmodium arginase activity [19]), experiments using murine and nonhuman primate models paired with analyses of human samples have demonstrated simultaneously diminished levels of arginine and its biosynthetic pathway metabolites (e.g., ornithine and citrulline) in the blood of malaria-infected hosts [9, 18, 21] (Fig. 15.7A). This work suggests that arginine depletion results, at least in part, from a block in host production, in addition to parasite arginase activity. So, why is arginine in low supply? A probable cause is the limited bioavailability of precursors for arginine biosynthesis, including glutamine and proline, which decrease in parallel during malaria [21]. Glucose and glutamine are important precursors for energy production by both host and parasite. Plasmodium relies primarily on glycolysis for ATP production, and parasites take up glucose in large amounts during their development, leading to increased lactate production [23]. P. falciparum– infected hosts may have increased lactate in the bloodstream, a condition called metabolic acidosis [24], and glycolysis pathway metabolites have been shown to be particularly perturbed in the bloodstream of P. falciparum malaria patients [25].

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Figure 15.7 Examples of metabolic pathways perturbed in the host during acute malaria. Biochemical pathways of amino acids (yellow), lipids (purple), and heme (green) found to be either increased (red) or decreased (blue) in acute malaria as compared to healthy individuals. (A) Amino acids involved in the de novo biosynthesis of arginine are globally decreased during malaria [9, 18, 21]. Metabolic changes associated with malaria also include (B) elevated conversion of tryptophan to kynurenine via IDO enzyme [9, 11, 20, 28], (C) depleted LPC from phospholipids [9, 11, 32], and (D) increased heme products indicating hemolysis and hemoglobin degradation [9–11]. (E) Lysine catabolism into pipecolic acid is also detected in Plasmodium infections [33–35]. Abbreviations: IDO, indoleamine 2,3-dioxygenase; LPC, lysophosphatidylcholine.

For ATP production, both Plasmodium-infected RBCs and host immune cells also consume glutamine, which is fluxed into the tricarboxylic acid (TCA) cycle [23, 26, 27], and plasma glutamine levels are depleted in the human host in both falciparum and vivax malaria [9, 21, 22, 28]. In addition to impacting arginine biosynthesis (Fig. 15.7A), low plasma glutamine has been associated with severe malarial anemia in children with P. falciparum [29] and with impaired humoral immunity in a murine model of severe malaria [27]. Conversely though, inhibiting glutamine metabolism is associated with increased survival in a murine model of late stage cerebral malaria (CM) via reducing immune-mediated pathology in the brain [30]. Glutamine may therefore be a “double-edged sword” in the pathogenesis of malaria due to its opposing effects on these different manifestations of disease. Also implicated in severe malaria is the enzymatic conversion of tryptophan to kynurenine, which is elevated during severe malaria, resulting in the production of neurotoxic metabolites (e.g., quinolinic and kynurenic acid), which are thought to play a role in CM [11, 20] (Fig. 15.7B). A decrease in indolepropionate, a neuroprotective derivative of tryptophan, is also observed in the bloodstream of humans with CM and may further contribute to neurological dysregulation [11]. Elevated production of kynurenine from tryptophan is not, however, specific to neurological diseases like CM, and similar perturbations have been observed in non-CM malaria [9, 20, 28]. Elevated kynurenine also indicates elevated indoleamine 2,3-dioxygenase (IDO) enzymatic activity and the initiation of the host’s

Insights into Malaria Pathogenesis Gained from Host Metabolomics

immunotolerant responses. While tryptophan catabolites may have a neurotoxic role, the catabolism of tryptophan is likely driven by the host’s acute response to malaria, which includes both pro-inflammatory and anti-inflammatory tolerogenic programs [31].

15.7.3 Bloodstream Lipid and Fatty Acid Perturbations in Malaria

Acute falciparum and vivax malaria infections in humans coincide with a reduction in monounsaturated fatty acid–containing phospholipids, a reduction of lysophosphotidylcholines (LPCs), and an elevation in fatty acyl carnitines [9, 10, 32]. This pattern suggests an increase in beta oxidation of fatty acids in mitochondria as a means of energy production. Phospholipase A2 (PLA2) is a host hydrolytic enzyme that acts on phospholipids to release lysophospholipids and free fatty acids. In humans with falciparum malaria, PLA2 activity has been associated with an enrichment of a particular downstream product, arachidonic acid (AA), which modulates inflammation [11]. Prior metabolomic studies correlated brain volume with downstream PLA2 products, suggesting that this pathway may play a key role in the pathogenesis of CM [12]. LPCs are reduced in humans with acute malaria (Fig. 15.7C) [9, 11, 32], which may result in part from host metabolic processes that are altered during acute infection states. The parasite may also play a role in depleting host plasma LPC, as parasites take up lipids and fatty acids from their environment to build their own membranes. Regardless, low levels of certain glycerophospholipids, such as LPC, in plasma may promote conversion to Plasmodium gametocyte stages, which are required for malaria transmission [33, 34]. This metabolic perturbation may therefore play a critical role in perpetuating the life cycle of the parasite.

15.7.4 Red Blood Cell–Related Alterations in Malaria Metabolome

Malaria is associated with a vast loss of RBCs due, in part, to parasite-mediated lysis, with hemoglobin and free heme being released in the process. Heme containing iron induces oxidative stress on RBCs [35] and likely contributes to further lysis of the host’s uninfected RBCs during malaria infection (e.g., “bystander effect”) [36]. Metabolic processes are subsequently mounted by the host in an attempt to detoxify heme. Free heme converts to bilirubin in the liver, spleen, and bone marrow using biliverdin as an intermediate, or in the intestine, using urobilinogen as an intermediate. Multiple reports document elevated biliverdin, bilirubin, L-urobilin, and I-urobilinogen in the plasma of human malaria and its animal models [9–11], presumably resulting from the host’s response to free heme during malaria infection (Fig. 15.7D).

15.7.5 Metabolic Changes during Mild or Asymptomatic Malaria

Although mild or asymptomatic malaria is generally tolerable compared to its severe form, low-level chronic parasite carriage is still associated with changes

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in host metabolism. Studies of low parasitemia in human falciparum malaria and its nonhuman primate model include altered energy metabolism pathways and reductions in multiple lipids, including sphingomyelins [9]. Sphingolipid metabolism has been thought to play a role in signaling related to immune responses and vascular integrity and possibly aid in controlling the infection [37]. Aside from bloodstream changes, enriched levels of certain VOCs have also been detected in the skin odor of humans with asymptomatic P. falciparum malaria, including ethylbenzene, which has been shown to be a mosquito attractant [8, 38].

15.7.6 Comparable Metabolic Dysregulation in Other Blood Diseases

Some alterations in host bloodstream metabolite levels during malaria are also common among hosts with other diseases. For instance, decreased arginine levels has received much attention in malaria, but this amino acid is also markedly decreased in hemolytic anemia and sepsis, pointing to similar host responses among these conditions [39]. Additionally, altered levels of kynurenine [40], PLA2 [41], and LPC [42] have also been reported in sepsis, highlighting the possibility of common host-mediated metabolic responses across acute bloodstream infections.

15.7.7 Plasmodium Derived Metabolites Identified Using Metabolomics

During its intraerythrocytic development, Plasmodium derives most amino acids from hemoglobin degradation. In the process, Plasmodium produces various byproducts including pipecolic acid, a catabolite of lysine (Fig. 15.7E). Pipecolic acid is detected in in vitro P. falciparum cultures, murine malaria models, and humans with P. falciparum, but not in uninfected RBC cultures or in humans without malaria infection [43–45]. Other metabolites potentially generated by the parasite include VOCs pinene and limonene, which may derive from Plasmodium’s isoprenoid biosynthetic pathways and are detected in P. falciparum cultures and breath of humans with falciparum malaria [46]. Metabolites in the alphalinolenic acid pathway, commonly found in plants, have also been found in both P. falciparum cultures and plasma from infected patients [47]. Additional metabolite signatures have been identified through untargeted metabolomics approaches of P. falciparum in vitro culture, including 3-methylindole, succinylacetone, S-methyl-L-thiocitrulline, and O-arachidonoyl glycidol [48]. While about half of the detectable metabolic features measured through this untargeted approach could be mapped to KEGG metabolic pathways for human and Plasmodium, over 500 metabolic features detected in Plasmodium culture could not be matched to these databases, and many of these metabolite identities are yet to be determined. These may represent interesting candidates for future research.

Insights into Malaria Pathogenesis Gained from Host Metabolomics

15.7.8 Host Gut Microbe Impact on the Metabolome during Malaria

Infection Plasma metabolomics applied to humans experiencing metabolic acidosis during falciparum malaria has revealed the presence of organic acids potentially of bacterial origin, including diaminopimelic acid, a component of gram-negative bacterial cell wall. Elevated diaminopimelic acid was observed concomitant with a depletion in L-citrulline [17], which plays a role in maintaining intestinal barrier function [49]. Gut barrier integrity can be lost during malaria, enabling gut microbes to translocate into the bloodstream. As levels of bacteria-associated metabolites in plasma were associated with elevated disease severity [17], this suggests a potential link between the presence of these metabolites in the bloodstream and pathological processes involving gut bacteria.

15.7.9 Metabolic Biomarkers Provide Potential for Novel Malaria Diagnostic Tests

Implementation of point-of-care diagnostics guided by metabolome findings could aid in the diagnosis and appropriate treatment of malaria and help to differentiate it from non-malarial febrile illnesses. A current rapid diagnostic test (RDT) for malaria includes detection of histidine-rich protein 2 and 3 (hrp2/3), which unfortunately has failed to detect hrp2/3 gene deletion strains of Plasmodium, which may be rising in prevalence [50]. Furthermore, current tests do not indicate disease severity which, if incorporated, could provide prognostic benefit to current RDTs and allow for prompt treatment and better resource allocation for those likely to develop severe disease [51]. Studies comparing the metabolome of malaria with non-malarial febrile illnesses have identified both common and distinct features of malaria [9, 52]. RDTs that include both Plasmodium infection markers (e.g., elevated pipecolic acid [45] and pinene [46]) and disease severity markers (e.g., depleted arginine [16], glutamine [29], and citrulline [17]) could have diagnostic and prognostic benefit.

15.7.10 Conclusion

Dynamic perturbations in host metabolites occur in individuals infected with Plasmodium. Some of these metabolic signatures overlap with other acute infectious and inflammatory responses, such as sepsis, which is characterized by catabolic distress involving a breakdown of carbohydrates, lipids, and protein stores. Overall, a notable dysregulation of amino acids and lipids occurs during Plasmodium infection, likely resulting from catabolic and anabolic processes for immune cells and parasites alike. Although there have been significant metabolic findings to elucidate host and pathogen interactions during malaria as highlighted in this section, there are still many areas to investigate to further understand metabolic roles during infection.

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Disclosures and Conflict of Interest This section was originally published as: Colvin, H.N., Joice Cordy, R. (2020). Insights into malaria pathogenesis gained from host metabolomics. PLoS Pathog., 16(11), e1008930, https://doi.org/10.1371/journal.ppat.1008930, under the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates. Funding: RJC was supported by a grant from the U.S. National Institutes of Health (K01HL143112). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of this section. Competing interests: The authors have declared that no competing interests exist.

Acknowledgments: The authors would like to thank Mary R. Galinski, Charles E. McCall, and Karl B. Seydel for their feedback on this section.

References

1. WHO Global Team. World malaria report 2019. 2019 p. 232. Available at: https://www. who.int/publications-detail/world-malaria-report-2019 (accessed on May 13, 2021).

2. Ghosh S, Pathak S, Sonawat HM, Sharma S, Sengupta A. Metabolomic changes in vertebrate host during malaria disease progression. Cytokine. 2018;112:32–43. 3. Salinas JL, Kissinger JC, Jones DP, Galinski MR. Metabolomics in the fight against malaria. Memorias do Instituto Oswaldo Cruz. 2014;109:589–597. 4. Ginsburg H, Abdel-Haleem AM. Malaria parasite metabolic pathways (MPMP) upgraded with targeted chemical compounds. Trends Parasitol. 2016;32:7–9.

5. Kanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28:27–30.

6. Lakshmanan V, Rhee KY, Daily JP. Metabolomics and malaria biology. Mol Biochem Parasitol. 2011;175:104–111.

7. Kafsack BFC, Llinás M. Eating at the table of another: metabolomics of host-parasite interactions. Cell Host Microbe. 2010;7:90–99.

8. Joice CR. Mining the human host metabolome toward an improved understanding of malaria transmission. Front Microbiol. 2020;11:164.

9. Cordy RJ, Patrapuvich R, Lili LN, Cabrera-Mora M, Chien J-T, Tharp GK, et al. Distinct amino acid and lipid perturbations characterize acute versus chronic malaria. JCI Insight. 2019;4.

10. Gardinassi LG, Cordy RJ, Lacerda MVG, Salinas JL, Monteiro WM, Melo GC, et al. Metabolome-wide association study of peripheral parasitemia in Plasmodium vivax malaria. Int J Med Microbiol. 2017;307:533–541.

11. Gupta S, Seydel K, Miranda-Roman MA, Feintuch CM, Saidi A, Kim RS, et al. Extensive alterations of blood metabolites in pediatric cerebral malaria. Motta A, editor. PLoS One. 2017;12: e0175686.

12. Pappa V, Seydel K, Gupta S, Feintuch CM, Potchen MJ, Kampondeni S, et al. Lipid metabolites of the phospholipase A2 pathway and inflammatory cytokines are associated with brain volume in paediatric cerebral malaria. Malar J. 2015;14:513.

References

13. Surowiec I, Orikiiriza J, Karlsson E, Nelson M, Bonde M, Kyamanwa P, et al. Metabolic signature profiling as a diagnostic and prognostic tool in pediatric Plasmodium falciparum malaria. Open Forum Infect Dis. 2015;2:ofv062. 14. Uppal K, Salinas JL, Monteiro WM, Val F, Cordy RJ, Liu K, et al. Plasma metabolomics reveals membrane lipids, aspartate/asparagine and nucleotide metabolism pathway differences associated with chloroquine resistance in Plasmodium vivax malaria. Hirayama K, editor. PLoS One. 2017;12: e0182819.

15. Lopansri BK, Anstey NM, Stoddard GJ, Mwaikambo ED, Boutlis CS, Tjitra E, et al. Elevated plasma phenylalanine in severe malaria and implications for pathophysiology of neurological complications. Infect Immun. 2006;74:3355–3359.

16. Lopansri BK, Anstey NM, Weinberg JB, Stoddard GJ, Hobbs MR, Levesque MC, et al. Low plasma arginine concentrations in children with cerebral malaria and decreased nitric oxide production. Lancet. 2003;361:676–678.

17. Leopold SJ, Ghose A, Allman EL, Kingston HWF, Hossain A, Dutta AK, et al. Identifying the components of acidosis in patients with severe Plasmodium falciparum malaria using metabolomics. J Infect Dis. 2019;219:1766–1776.

18. Alkaitis MS, Wang H, Ikeda AK, Rowley CA, MacCormick IJC, Chertow JH, et al. Decreased rate of plasma arginine appearance in murine malaria may explain hypoargininemia in children with cerebral malaria. J Infect Dis. 2016;214:1840–1849.

19. Olszewski KL, Morrisey JM, Wilinski D, Burns JM, Vaidya AB, Rabinowitz JD, et al. Host-parasite interactions revealed by Plasmodium falciparum metabolomics. Cell Host Microbe. 2009;5:191–199.

20. Leopold SJ, Apinan S, Ghose A, Kingston HW, Plewes KA, Hossain A, et al. Amino acid derangements in adults with severe Falciparum malaria. Sci Rep. 2019;9:9.

21. Rubach MP, Zhang H, Florence SM, Mukemba JP, Kalingonji AR, Anstey NM, et al. Kinetic and cross-sectional studies on the genesis of hypoargininemia in severe pediatric Plasmodium falciparum malaria. Adams JH, editor. Infect Immun. 2019;87:e00655–18.

22. Cowan G, Planche T, Agbenyega T, Bedu-Addo G, Owusu-Ofori A, Adebe-Appiah J, et al. Plasma glutamine levels and falciparum malaria. Trans R Soc Trop Med Hyg. 1999;93: 616–618.

23. MacRae JI, Dixon MW, Dearnley MK, Chua HH, Chambers JM, Kenny S, et al. Mitochondrial metabolism of sexual and asexual blood stages of the malaria parasite Plasmodium falciparum. BMC Biol. 2013;11:67. 24. Leopold SJ, Ghose A, Allman EL, Kingston HWF, Hossain A, Dutta AK, et al. Identifying the components of acidosis in patients with severe Plasmodium falciparum malaria using metabolomics. J Infect Dis. 2019;219:1766–1776.

25. Na J, Khan A, Kim JK, Wadood A, Choe YL. Walker DI, et al. Discovery of metabolic alterations in the serum of patients infected with Plasmodium spp by high-resolution metabolomics. Metabolomics. 2019;16:9. 26. O’Neill LAJ, Kishton RJ, Rathmell J. A guide to immunometabolism for immunologists. Nat Rev Immunol. 2016;16:553–565. 27. Vijay R, Guthmiller JJ, Sturtz AJ, Surette FA, Rogers KJ, Sompallae RR, et al. Infectioninduced plasmablasts are a nutrient sink that impairs humoral immunity to malaria. Nat Immunol. 2020;21(7):790–801. 28. Gardinassi LG, Arévalo-Herrera M, Herrera S, Cordy RJ, Tran V, Smith MR, et al. Integrative metabolomics and transcriptomics signatures of clinical tolerance to

315

316

Current Perspectives in Medical Microbiology

Plasmodium vivax reveal activation of innate cell immunity and T cell signaling. Redox Biol. 2018;17:158–170.

29. Kempaiah P, Dokladny K, Karim Z, Raballah E, Ong’echa JM, Moseley PL, et al. Reduced Hsp70 and glutamine in pediatric severe malaria anemia: Role of hemozoin in suppressing Hsp70 and NF-κB activation. Mol Med. 2016;22:570–584.

30. Gordon EB, Hart GT, Tran TM, Waisberg M, Akkaya M, Kim AS, et al. Targeting glutamine metabolism rescues mice from late-stage cerebral malaria. Proc Natl Acad Sci. 2015;112:13075–13080.

31. dos Santos RO, da Cruz MGS, Lopes SCP, Oliveira LB, Nogueira PA, Lima ES, et al. A first Plasmodium vivax natural infection induces increased activity of the interferon gamma-driven tryptophan catabolism pathway. Front Microbiol. 2020;11:400.

32. Orikiiriza J, Surowiec I, Lindquist E, Bonde M, Magambo J, Muhinda C, et al. Lipid response patterns in acute phase paediatric Plasmodium falciparum malaria. Metabolomics. 2017;14:13.

33. Brancucci NMB, Gerdt JP, Wang C, De Niz M, Philip N, Adapa SR, et al. Lysophosphatidylcholine regulates sexual stage differentiation in the human malaria parasite Plasmodium falciparum. Cell. 2017;171:1532–1544.e15.

34. Tanaka TQ, Tokuoka SM, Nakatani D, Hamano F, Kawazu S-I, Wellems TE, et al. Polyunsaturated fatty acids promote Plasmodium falciparum gametocytogenesis. Biol Open. 2019;8:bio042259. doi: 10.1242/bio.042259.

35. Mohanty JG, Nagababu E, Rifkind JM, Kaestner L, Bogdanova A. Red blood cell oxidative stress impairs oxygen delivery and induces red blood cell aging. Front Physiol. 2014;5:84.

36. Fonseca LL, Alezi HS, Moreno A, Barnwell JW, Galinski MR, Voit EO. Quantifying the removal of red blood cells in Macaca mulatta during a Plasmodium coatneyi infection. Malar J. 2016;15:410.

37. Finney CA, Hawkes CA, Kain DC, Dhabangi A, Musoke C, Cserti-Gazdewich C, et al. S1P is associated with protection in human and experimental cerebral malaria. Mol Med. 2011;17:717–725.

38. De Moraes CM, Wanjiku C, Stanczyk NM, Pulido H, Sims JW, Betz HS, et al. Volatile biomarkers of symptomatic and asymptomatic malaria infection in humans. Proc Natl Acad Sci. 2018;115:5780–5785.

39. Morris CR, Hamilton-Reeves J, Martindale RG, Sarav M, Ochoa Gautier JB. Acquired amino acid deficiencies: A focus on arginine and glutamine. Nutr Clin Pract. 2017;32: 30S–47S.

40. Ferrario M, Cambiaghi A, Brunelli L, Giordano S, Caironi P, Guatteri L, et al. Mortality prediction in patients with severe septic shock: a pilot study using a target metabolomics approach. Sci Rep. 2016;6:20391.

41. Green J-A, Smith GM, Buchta R, Lee R, Ho KY, Rajkovic IA, et al. Circulating phospholipase A2 activity associated with sepsis and septic shock is indistinguishable from that associated with rheumatoid arthritis. Inflammation. 1991;15:355–367.

42. Drobnik W, Liebisch G, Audebert F-X, Fröhlich D, Glück T, Vogel P, et al. Plasma ceramide and lysophosphatidylcholine inversely correlate with mortality in sepsis patients. J Lipid Res. 2003;44:754–761.

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43. Beri D, Ramdani G, Balan B, Gadara D, Poojary M, Momeux L, et al. Insights into

physiological roles of unique metabolites released from Plasmodium-infected RBCs and their potential as clinical biomarkers for malaria. Sci Rep. 2019;9:9.

44. Tewari SG, Swift RP, Reifman J, Prigge ST, Wallqvist A. Metabolic alterations in

the erythrocyte during blood-stage development of the malaria parasite. Malar J.

2020;19:94.

45. Abdelrazig S, Ortori CA, Davey G, Deressa W, Mulleta D, Barrett DA, et al. A metabolomic

analytical approach permits identification of urinary biomarkers for Plasmodium

falciparum infection: a case-control study. Malar J. 2017;16:229.

46. Schaber CL, Katta N, Bollinger LB, Mwale M, Mlotha-Mitole R, Trehan I, et al.

Breathprinting reveals malaria-associated biomarkers and mosquito attractants. J Infect Dis. 2018;217:1553–1560.

47. Lakshmanan V, Rhee KY, Wang W, Yu Y, Khafizov K, Fiser A, et al. Metabolomic analysis

of patient plasma yields evidence of plant-like α-linolenic acid metabolism in Plasmodium falciparum. J Infect Dis. 2012;206:238–248.

48. Park YH, Shi YP, Liang B, Medriano CAD, Jeon YH, Torres E, et al. High-resolution

metabolomics to discover potential parasite-specific biomarkers in a Plasmodium

falciparum erythrocytic stage culture system. Malar J. 2015;14:122.

49. Chau JY, Tiffany CM, Nimishakavi S, Lawrence JA, Pakpour N, Mooney JP, et al.

Malaria-associated L-arginine deficiency induces mast cell-associated disruption to

intestinal barrier defenses against nontyphoidal Salmonella bacteremia. Infect Immun.

2013;81:3515–3526.

50. Gamboa D, Ho M-F, Bendezu J, Torres K, Chiodini PL, Barnwell JW, et al. A large

proportion of P. falciparum isolates in the Amazon region of Peru lack pfhrp2

and pfhrp3: implications for malaria rapid diagnostic tests. Bjorkman A, editor. PLoS One. 2010;5:e8091. 51. McDonald CR, Weckman A, Richard-Greenblatt M, Leligdowicz A, Kain KC. Integrated fever management: disease severity markers to triage children with malaria and non-malarial febrile illness. Malar J. 2018;17:353.

52. Decuypere S, Maltha J, Deborggraeve S, Rattray NJW, Issa G, Bérenger K, et al.

Towards improving point-of-care diagnosis of non-malaria febrile illness: a metabolomics approach. PLoS Negl Trop Dis. 2016;10:e0004480.

15.8 Japanese Encephalitis Virus and Its Mechanisms of Neuroinvasion Justin T. Hsieh, MS, MD, PhD,a and Ashley L. St. John, PhDa,b,c,d aProgram in Emerging Infectious Diseases, Duke-National University of Singapore Medical School, Singapore,

bDepartment of Microbiology and Immunology, Yong Loo Lin School of Medicine,

National University of Singapore, Singapore cSingHealth Duke-NUS Global Health Institute, Singapore

dDepartment of Pathology, Duke University Medical Center, Durham, North Carolina, USA

[email protected]

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15.8.1 Introduction Japanese encephalitis virus (JEV) is a positive-sense single-stranded RNA virus of the Flavivirus genus that is spread by Culex mosquitos. It is maintained in an enzootic cycle in pigs and wild birds in which humans are dead-end hosts [1]. Despite having effective vaccines, JEV is the leading cause of viral encephalitis in Asia [1]. As a neuroinvasive virus, it can effectively cross the blood–brain barrier (BBB) to cause acute encephalitis. Twenty-five percent to 30% of Japanese encephalitis (JE) cases are fatal, and 50% result in permanent neuropsychiatric complications [2]. There are currently no treatments for JE, partly due to an incomplete understanding of the mechanisms promoting encephalitis.

Figure 15.8 Hypothetical mechanisms of viral entry into the CNS. Diagram depicting 5 mechanisms through which viruses can enter the CNS. (1) Transcellular transport within endothelial cells of the BBB through transcytosis or infection and release of viral particles into the CNS. (2) Passage through the BBB within infected cells such as monocytes in a “Trojan Horse” method. (3) Paracellular trafficking of virus across the BBB at locations where TJs have been cleaved, resulting in permeability. (4) Retrograde neuronal transport where CNS neurons that contact the PNS become infected. (5) Inoculation of the CSF at locations where endothelial cells lack BBB function, such as in circumventricular organs.

Abbreviations: BBB, blood–brain barrier; CNS, central nervous system; CSF, cerebrospinal fluid; JEV, Japanese encephalitis virus; PNS, peripheral nervous system; TJ, tight junction.

Japanese Encephalitis Virus and Its Mechanisms of Neuroinvasion

The central nervous system (CNS) relies on the BBB, a tightly regulated barrier between the peripheral circulation and the CNS, to prevent entry of pathogens, including viruses. Yet, JEV and other neuroinvasive viruses can overcome the BBB, which usually excludes foreign substances. It is formed primarily by tight junctions (TJ) between endothelial cells, comprised of proteins such as claudin-5, zonula occludens (ZO)-1, and occludin. The BBB is sustained by supporting cell types, including astrocytes, pericytes, microglia, and mast cells (MCs) [1, 3]. Together, these cells form neurovascular units that maintain a barrier along the cerebrovascular microvessels to promote immune privilege and CNS homeostasis [3, 4]. Neuroinvasive viruses use several mechanisms to access the CNS: (1) direct infection of endothelial cells and subsequent transcellular release of virus into the brain parenchyma, (2) infection of peripheral immune cells that enter the CNS in a “Trojan Horse” mechanism, (3) paracellular entry following breakdown of the BBB, (4) retrograde transport of virus from the peripheral nervous system (PNS) into the CNS, and (5) translocation from the blood to the cerebrospinal fluid (CSF) [5, 6] (Fig. 15.8). JEV infection through the natural subcutaneous route leads to widespread infection in various parts of the brain [7], suggesting a hematological route of infection, such as would occur for mechanisms 1–3. Here we provide an overview of a few described mechanisms of JEV penetration of the BBB and processes that amplify CNS infection.

15.8.2 Transcellular Infection of Endothelial Cells and Activation of the Neurovascular Unit

Some neurotropic viruses directly infect endothelial cells to reach the brain from the circulation and travel transcellularly to release viruses into the brain parenchyma [6]. JEV has been visualized intracellularly in vesicles using electron microscopy and was suggested to undergo transcytosis across endothelial cells through pericytes into the brain of infected suckling mice [3]. JEV antigens have not been observed in brain endothelial cells in virus-infected adult mice [8] or consistently observed in brain specimens from fatal JE patients [9], but this mechanism of neuroinvasion could potentially contribute to CNS infection. Cell-culture studies have also shown that endothelial cells may transiently propagate JEV to other supporting cells such as astrocytes, which can subsequently be activated [10].

15.8.3 Paracellular Infection Is Promoted by MCs

Neurotropic viruses can gain entry into the brain through compromising the BBB. This occurs when TJs between endothelial cells become leaky, often due to the proinflammatory response. That JEV gains entry into the brain through cleaved TJs is supported by reduced resistance across JEV-exposed cultured brainendothelial-cell membranes in vitro [11] and increased BBB leakiness and TJ protein breakdown in mouse models [12]. MCs are an important population of CNS-resident immune cells recently implicated in BBB compromise during

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JEV infection [12]. Indeed, MC depletion from the CNS reduced BBB breakdown and JEV penetration of the brain in mice [12]. In addition to their presence within the brain, MCs are also one of the first immune cell types JEV encounters in the periphery. JEV causes MC degranulation, which enhances JEV-induced breakdown of the BBB and augments infection in the brain [13]. In particular, MC-derived chymase, a vasoactive protease, played a functionally significant role in breakdown of TJ proteins, including ZO-1, ZO-2, claudin-5, and occludin, ultimately leading to increased BBB compromise (Fig. 15.9). This was restored with either pharmacological inhibition of chymase or genetic knockout of the mouse-chymase MCPT4 [2].

HOMEOSTATIC CONDITIONS

JEV INFECTION

Figure 15.9 MC chymase induces BBB breakdown and paracellular JEV entry into the CNS. JEV activates MCs, leading to degranulation and the release of MC-derived chymase. Chymase cleaves brain endothelial TJ proteins ZO-1, ZO-2, claudin-5, and occludin, compromising the BBB and facilitating JEV penetration into the CNS. Other cells in the neurovascular unit such as astrocytes and pericytes are also activated during JEV infection and release proinflammatory cytokines. Abbreviations: BBB, blood–brain barrier; CNS, central nervous system; JEV, Japanese encephalitis virus; MC, mast cell; MMP, matrix metalloproteinase; ROS, reactive oxygen species; TJ, tight junctions.

Japanese Encephalitis Virus and Its Mechanisms of Neuroinvasion

In vivo, therapeutically inhibiting chymase reduced JEV penetration of the BBB and reduced the morbidity and mortality associated with JE [12]. Thus, MCs facilitate paracellular entry of JEV across the BBB.

15.8.4 Other Peripheral Myeloid Cells May Contribute to Neuroinvasion

Another potential pathway establishing CNS infection is through infection of peripheral immune cells that can subsequently gain entry into the CNS in a “Trojan Horse” mechanism. For JEV infection, macrophages [11] and DCs [14] are suspected peripheral infection cell targets. Furthermore, macrophages are the predominant infiltrated immune cells in fatal JEV brain samples [13]. However, in vivo experiments, thus far, have failed to identify infected macrophages in CNS tissues at the time points tested [11]. This does not rule out the possibility of low titer introduction of a virus into the CNS in this manner, but further investigation is needed. Alternatively, peripheral macrophages may indirectly promote neuroinvasion through the release of other mediators, such as reactive oxygen species (ROS) and MMP-9 [13] (Fig. 15.9). In contrast to the suspected role of macrophages in BBB compromise during JE, JEV-infected DCs promote antiinflammatory regulatory T cells (T-regs) while inhibiting proinflammatory Th-17 T cells and monocyte differentiation [15, 16]. These modulations are thought to result in improved BBB integrity and reduced viral entry into the CNS, demonstrating the dual contributions of peripheral immune responses to BBB integrity and infection control.

15.8.5 Amplification of BBB Leakiness by JEV Infection within the Brain

Once present in the CNS, inflammation due to JEV amplifies BBB breakdown. Pericytes, which are situated within the basement membrane next to endothelial cells, aid in breakdown of the BBB through release of IL-6, which leads to ZO-1 degradation [17]. Coculture of astrocytes and brain endothelial cells increases TJ leakiness through increased release of mediators such as IL-6, CCL5, and CXCL10 [17] (Fig. 15.9). Microglia also release proinflammatory mediators such as TNF-α to promote BBB leakiness [17]. At the latter stages of disease, CNS supporting cells are activated and accentuate BBB leakiness and disease, but this increased permeability may also allow penetration of immune cells that are necessary for infection clearance, such as T cells [18].

15.8.6 Dual Role of Interferons in JEV Protection and BBB Permeability

Another factor that has been implicated in accentuating BBB breakdown during JEV infection are interferons (IFNs). Quick induction of type I IFNs has been associated with protection of astrocytes from apoptosis [19] and protection from lethal infection in mice [20]. Similarly, IFNγ was shown to contribute to protection

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from CNS infection [18]. In contrast, targeting of IFNγ with a neutralizing antibody improved BBB integrity in one study, suggesting its role in enhancing BBB permeability [21].

15.8.7 Concluding Remarks

JEV causes high morbidity and mortality in humans, leading to permanent neurological deficits, even in those who survive. Recent reports have advanced our understanding of the pathophysiological events that allow JEV to traverse the BBB and cause encephalitis. Although multiple routes of CNS entry are plausible, paracellular penetration of viruses resulting from protease and cytokine­ driven breakdown of the BBB appears to be the dominant mechanism for JEV neuropenetration. This new knowledge may aid to the development of therapeutics for the treatment of virally induced encephalitis. Disclosures and Conflict of Interest

This section was originally published as: Hsieh, J. T., St. John, A. L. (2020). Japanese encephalitis virus and its mechanisms of neuroinvasion. PLoS Pathog., 16(4), e1008260, https://doi.org/10.1371/journal.ppat.1008260, under the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates. Funding: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of this section. NRF2016NRF-CRP001-063 and MOE2019-T2-1-146 provided funding to ALS. Competing interests: The authors have declared that no competing interests exist.

Acknowledgments: We thank Abhay Rathore for critical review of this section.

References

1. Campbell GL, Hills SL, Fischer M, Jacobson JA, Hoke CH, Hombach JM, et al. Estimated global incidence of Japanese encephalitis: a systematic review. Bull World Health Organ. 2011;89(10):766–74, 74A-74E. 2. Yun S-I, Lee Y-M. Japanese encephalitis. Human Vaccines Immunotherapeutics. 2014;10(2):263–79.

3. Abbott NJ, Patabendige AAK, Dolman DEM, Yusof SR, Begley DJ. Structure and function of the blood–brain barrier. Neurobiol Disease. 2010;37(1):13–25. 4. Khalil M, Ronda J, Weintraub M, Jain K, Silver R, Silverman AJ. Brain mast cell relationship to neurovasculature during development. Brain Res. 2007;1171:18–29.

5. Phillips AT, Rico AB, Stauft CB, Hammond SL, Aboellail TA, Tjalkens RB, et al. Entry sites of Venezuelan and Western equine encephalitis viruses in the mouse central nervous system following peripheral infection. J Virol. 2016;90(12):5785–96.

6. Cain MD, Salimi H, Diamond MS, Klein RS. Mechanisms of pathogen invasion into the central nervous system. Neuron. 2019;103(5):771–83.

References

7. Johnson RT, Burke DS, Elwell M, Leake CJ, Nisalak A, Hoke CH, et al. Japanese encephalitis:

immunocytochemical studies of viral antigen and inflammatory cells in fatal cases. Ann Neurol. 1985;18(5):567–73.

8. Lai CY, Ou YC, Chang CY, Pan HC, Chang CJ, Liao SL, et al. Endothelial Japanese encephalitis virus infection enhances migration and adhesion of leukocytes to brain microvascular endothelia via MEK-dependent expression of ICAM1 and the CINC and RANTES chemokines. J Neurochem. 2012;123(2):250–61.

9. Liou ML, Hsu CY. Japanese encephalitis virus is transported across the cerebral blood

vessels by endocytosis in mouse brain. Cell Tissue Res. 1998;293(3):389–94.

10. Patabendige A, Michael BD, Craig AG, Solomon T. Brain microvascular endothelialastrocyte cell responses following Japanese encephalitis virus infection in an in vitro human blood-brain barrier model. Mol Cell Neurosci. 2018;89:60–70.

11. German AC, Myint KS, Mai NT, Pomeroy I, Phu NH, Tzartos J, et al. A preliminary neuropathological study of Japanese encephalitis in humans and a mouse model. Trans R Soc Trop Med Hyg. 2006;100(12):1135–45. 12. Hsieh JT, Rathore APS, Soundarajan G, St John AL. Japanese encephalitis virus neuropenetrance is driven by mast cell chymase. Nat Commun. 2019;10(1):706.

13. Dutta K, Mishra MK, Nazmi A, Kumawat KL, Basu A. Minocycline differentially modulates macrophage mediated peripheral immune response following Japanese encephalitis virus infection. Immunobiology. 2010;215(11):884–93.

14. Boothpur R, Brennan DC. Human polyoma viruses and disease with emphasis on clinical BK and JC. J Clin Virol. 2010;47(4):306–12.

15. Kim JH, Choi JY, Kim SB, Uyangaa E, Patil AM, Han YW, et al. CD11c(hi) dendritic cells regulate Ly-6C(hi) monocyte differentiation to preserve immune-privileged CNS in lethal neuroinflammation. Sci Rep. 2015;5:17548. 16. Choi JY, Kim JH, Patil AM, Kim SB, Uyangaa E, Hossain FMA, et al. Exacerbation of Japanese encephalitis by CD11c(hi) dendritic cell ablation is associated with an imbalance in regulatory Foxp3(+) and IL-17(+)CD4(+) Th17 cells and in Ly-6C(hi) and Ly-6C(lo) monocytes. Immune Netw. 2017;17(3):192–200.

17. Lannes N, Summerfield A, Filgueira L. Regulation of inflammation in Japanese encephalitis. J Neuroinflammation. 2017;14(1):158.

18. Larena M, Regner M, Lobigs M. Cytolytic effector pathways and IFN-gamma help protect against Japanese encephalitis. Eur J Immunol. 2013;43(7):1789–98.

19. Lindqvist R, Mundt F, Gilthorpe JD, Wolfel S, Gekara NO, Kroger A, et al. Fast type I interferon response protects astrocytes from flavivirus infection and virus-induced cytopathic effects. J Neuroinflammation. 2016;13(1):277. 20. Han YW, Choi JY, Uyangaa E, Kim SB, Kim JH, Kim BS, et al. Distinct dictation of Japanese encephalitis virus-induced neuroinflammation and lethality via triggering TLR3 and TLR4 signal pathways. PLoS Pathog. 2014;10(9):e1004319.

21. Li F, Wang Y, Yu L, Cao S, Wang K, Yuan J, et al. Viral infection of the central nervous system and neuroinflammation precede blood-brain barrier disruption during Japanese encephalitis virus infection. J Virol. 2015;89(10):5602–14.

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15.9 Bringing the Path toward an HIV-1 Vaccine into Focus Cesar J. Lopez Angel, MD, PhDa,b,c,d,e and Georgia D. Tomaras, PhDb,c,d,e,f,g aDepartment

of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA bDuke Center for Human Systems Immunology, Duke University School of Medicine, Durham, North Carolina, USA cDuke Human Vaccine Institute, Duke University School of Medicine, Durham, North Carolina, USA dDepartment of Surgery, Duke University School of Medicine, Durham, North Carolina, USA eDepartment of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA fDepartment of Immunology, Duke University School of Medicine, Durham, North Carolina, USA gDepartment of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, North Carolina, USA [email protected]

15.9.1 Why Do We Need a Human Immunodeficiency Virus (HIV-1) Vaccine in the Age of Antiretroviral Therapy and Preexposure Prophylaxis? In the nearly four decades since its identification, HIV-1 has infected more than 70 million individuals worldwide, one-half of whom have succumbed to HIV-1-related illness [1]. Despite concerted preclinical studies and clinical trials by the scientific community during this time, the development of an HIV-1 vaccine remains elusive. Meanwhile, progress in antiretroviral therapy (ART) has rendered HIV-1 a chronic disease, with life expectancy approaching that of the general population when adequately treated [2]. These therapeutic advances led to “test and treat” guidelines to immediately start patients on ART upon diagnosis [3], with the goal of lowering viral loads to undetectable levels and effectively eliminating the risk of HIV-1 transmission to HIV-1–negative sexual partners [1, 4]. The “treatment as prevention” paradigm has resulted in approximately 80% of ARTtreated individuals achieving undetectable viremia [1] and is now recommended for HIV-1–uninfected individuals at high risk of infection, in the form of daily prophylactic ART known as preexposure prophylaxis (PrEP). However, the efficacy of these interventions is pragmatically limited by the requirements for readily accessible HIV-1 testing, longitudinal clinical monitoring, availability of ART for the approximately 15 million individuals living with HIV-1 and not currently on treatment [1], and daily adherence to treatment for prolonged periods: a lack of which could potentially lead to drug-resistant strains. As a result, approximately 2 million individuals acquire HIV-1 globally every year despite our current prevention strategies [1]. Historically, vaccination represents the most costeffective, scalable, and lasting public health intervention for the eradication of infectious disease; thus, developing a safe and effective HIV-1 vaccine is a global health imperative [5]. Importantly, an HIV-1 vaccine will be part of a multimodal array of HIV-1 prevention tools, and work on alternative preventive approaches

Bringing the Path toward an HIV-1 Vaccine into Focus

should be extended and further developed until an effective vaccine becomes available.

15.9.2 How Close Are We to an HIV-1 Vaccine?

Most clinically approved vaccines confer immunity by inducing protective antibody responses. As the only viral determinants on the surface of HIV-1, the trimeric gp120 and gp41 HIV-1 envelope glycoproteins (Env), which mediate entry, are the primary targets of humoral immunity. Env trimers range from a metastable closed state to an open state when fully bound to CD4. Following CD4 binding, gp120 subunits undergo conformational changes that transiently expose coreceptor binding sites and lead to its dissociation from gp41. Subsequently, gp41 undergoes a step-wise transition that drives fusion of viral and target cell membranes. This metastability and conformational flexibility of Env, in conjunction with its tremendous genetic diversity and dynamic glycosylation states, allow HIV-1 to evade antibody neutralization and have frustrated vaccine development efforts. To date, seven HIV-1 vaccine efficacy trials have been completed [6, 7]. The first two efficacy trials, VAX003 and VAX004, tested whether vaccine-induced antibodies against recombinant monomeric gp120 antigens could be protective or correlate with protection in injection drug users (VAX003) or in men who have sex with men (MSM) and women at high risk for infection (VAX004). Though these vaccines elicited high titers of anti-Env antibodies, they failed to induce antibodies capable of neutralizing a wide range of HIV-1 variants (i.e., broadly neutralizing antibodies [bNAbs]) or protect against HIV-1 acquisition. With greater appreciation for the role of T cells in controlling HIV-1, subsequent trials tested whether protection or reduced viral loads postinfection could be achieved by inducing anti–HIV-1 cellular immunity with vaccine formulations comprised of recombinant viral vectors encoding key HIV-1 antigens. The Step and closely related Phambili trials tested recombinant adenovirus serotype 5 (rAd5) vectors encoding HIV-1 Gag, Pol, and Nef proteins in MSM and women at high risk of infection (Step) or heterosexual men and women in South Africa (Phambili). The HIV Vaccine Trials Network 505 (HVTN 505) trial aimed to elicit both humoral and cellular responses by priming with DNA plasmids encoding gag/pol/ nef/env, followed by a boost with rAd5 encoding a Gag-Pol fusion and Env proteins in men or transgender persons who have sex with men. These regimens showed no overall protection or reduction in viral load [8, 9], and a subset of vaccinees in the Step trial with preexisting immunity to Ad5 saw increased rates of HIV-1 infection [8]. Yet, Step also offered the first evidence that a viral vector vaccine could impose a selective immune pressure on transmitted virus [10]. The vaccine-induced sieve effect (determined by measuring genetic distance between transmitted and vaccine-encoded viral sequences) was observed in HIV-1 T cell epitopes encoded by the rAd5 vector in the Step trial [10]. The sieve effect in the HVTN 505 trial primarily focused on Env regions associated with infectivity, namely the CD4 binding site [11], and may have been mediated by humoral and/or cellular pressure [11–14].

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In 2009, the RV144 “Thai trial” of a recombinant canarypox vector prime (ALVAC) and recombinant gp120 boost (AIDSVAX) tested in individuals at risk of heterosexual transmission became the first trial to show efficacy against HIV-1 infection by demonstrating 60.5% efficacy in the first year [6] that waned to 31.2% by three years postvaccination [15]. The rapid ebb of the immune response is an important shortcoming of this vaccination approach that has proven challenging to overcome. Nevertheless, models estimate that a vaccine with more than 50% efficacy for at least two years could significantly reduce the incidence of HIV-1 in high prevalence areas [5], so the modest efficacy of the immunization strategy used in the RV144 trial is an important benchmark that encourages cautious optimism that the path toward an HIV-1 vaccine is in view. The Uhambo trial tested in South Africa was a poxvirus prime, protein boost regimen like the RV144 vaccine regimen but with a different adjuvant, different envelope sequences [16, 17] and additional gp120 protein boosting [18, 19] with a goal of improving the breadth of immunity to subtype C and durability of vaccine-elicited antibody responses [20, 21]. The Uhambo trial was recently stopped after an interim analysis found that the regimen did not prevent HIV-1 infection [7], highlighting the need for systems immunology studies to explain the different results of the two trials and better understand the balance of immunity needed to achieve protection in different populations.

15.9.3 What Are the Humoral Correlates of Protective HIV-1 Vaccination?

Vaccine-induced humoral responses are broadly binned into neutralizing antibody functions mediated by the antibody antigen-binding fragment (Fab) and antibody effector functions mediated by an antibody’s fragment crystallizable (Fc) region engaging its receptor (FcR) on innate immune cells, i.e., antibodydependent cellular cytotoxicity (ADCC), antibody dependent cellular phagocytosis (ADCP), complement activation, and innate immune cell activation. Most chronically infected individuals generate antibody responses with a modest degree of crossneutralization breadth and potency. After years of sustained viremia, 10% to 20% of these individuals develop highly potent bNAbs that, despite their broad neutralization activity, are incapable of controlling the host’s infection due to viral Env evolution outpacing the adaptive response. Production of these naturally occurring bNAbs is influenced by transmitted viral antigens [22] and is due to extensive somatic hypermutation of rare, low affinity, naïve B cell receptors (BCRs). Notably, BCRs that ultimately result in bNAbs exhibit atypical structural and binding characteristics that are frequently negatively selected during B cell development (reviewed in [23]). Immunogens tested in vaccine candidates to date have failed to overcome these challenges to elicit bNAbs, but the protective potential of bNAbs has been repeatedly demonstrated by passive immunization and challenge of nonhuman primates (NHPs) (reviewed [23]). The ongoing Antibody Mediated Prevention (AMP) trial is the first efficacy study testing passive immunization in humans [24] and will provide the first insight as to whether

Bringing the Path toward an HIV-1 Vaccine into Focus

immunoprophylaxis of a single bNAb targeting the CD4bs region of the HIV-1 envelope can also protect humans. The results from the AMP trial will inform future plans to test combinations of different bNAbs designed to have improved potency and breadth against circulating viruses. In contrast, NHP protection studies [23, 25] and clinical trials have underscored the important role of antibodies with Fc effector functions in protective vaccination. Case-control analysis of the RV144 trial vaccines revealed that the magnitude of immunoglobulin G (IgG) antibodies targeting Env variable regions 1 and 2 (V1V2), correlated with decreased HIV-1 risk, while the titer of specific serum immunoglobulin A (IgA) antibodies correlated with increased risk of infection [26]. Moreover, antibodies elicited in the RV144 trial were polyfunctional and promoted ADCC, ADCP, and complement activation [23, 27]. Similar analyses of the HVTN 505 trial also revealed strong inverse correlations between HIV-1 acquisition and Env-IgG binding [12] and Env IgG3 breadth, FcγRIIa binding, and ADCP [14]. In both trials, the efficacy of the vaccine was modified by the presence of single nucleotide polymorphisms at the FcR locus, further supporting FcR-mediated protective mechanisms [14, 28].

15.9.4 Are There Cellular Immune Correlates of Protective HIV-1 Vaccination?

With the development of a new computational tool for combinatorial polyfunctionality analysis of antigen-specific T-cell subsets (COMPASS), an Envspecific polyfunctional CD4+ T cell signature was identified in the RV144 trial that correlated with decreased HIV-1 risk and matched the significance of the primary V1V2 IgG correlate of decreased HIV-1 risk in that trial [29]. Considering that the development and maturation of the optimal antibody responses associated with protective vaccination described above require help from CD4+ T cells, this finding suggests that a coordinated humoral and cellular response may be necessary for protection. Conversely, while CD8+ T cells effect many antiviral functions, the CD8+ T cell responses in the RV144 trial were limited [15]. In the HVTN 505 trial, however, COMPASS helped identify a strong inverse correlation between HIV-1 acquisition and the abundance and polyfunctionality of Env-specific CD8+ T cells [13]. These findings highlight the importance of sophisticated analytical approaches to dissect complex single-cell data to aid the search for cellular immune correlates of protective vaccination.

15.9.5 What Does the Path to An HIV-1 Vaccine Look Like from Here?

The HIV-1 vaccine field sits at a nexus where immunology, virology, genetics, translational medicine, computational analytics, and community engagement converge (Fig. 15.10). Ongoing vaccine trials are guided by the immune correlates of protection defined above and include combination efficacy studies that incorporate PrEP alongside vaccine regimens (PrEPVacc) and tests of new viral vectors.

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Leading vector candidates include modified vaccinia Ankara (MVA) [6, 21], cytomegalovirus (CMV) vectors that induce persistent novel CD8+ T cell responses [30], and rAd26 vectors that differ from previous rAd5 vectors in part by lower preexisting antivector neutralizing antibody titers in human populations [23]. The ongoing Imbokodo (HVTN 705/HPX2008; NCT03060629) and Mosaico (HVTN 706/HPX3002/Mosaico; NCT03964415) efficacy trials utilize rAd26 vectors encoding epitope sequences computationally optimized to capture the diversity of multiple global HIV-1 strains, i.e., mosaic immunogens, followed by Env protein boosting [18].

Figure 15.10 Focusing diverse efforts to achieve an HIV-1 vaccine. The development of a safe and effective HIV-1 vaccine is a confluence of scientific efforts in immunology (orange), virology (green), and genetics (blue), is boosted by advanced analytical pipelines (purple), and is inextricably linked to community implementation and reliant on partnerships with key stakeholders (red). Knowledge of the protective immune and genetic correlates and crucial viral targets yielded from previous efficacy trials is combined with basic concepts in immunology, viral epitope expression, and genetic modulators of HIV-1 acquisition to test novel vaccines in clinical and experimental medicine trials. Analyses of the immune responses elicited in the global clinical efficacy trials that are ongoing, and recently completed (Uhambo), will inform our understanding of the balance of immunity that favors long-lasting protective immunity. Looking forward, there is a robust pipeline of innovative immunogen and trial design strategies to build upon the knowledge gained from the outcomes of these trials.

Bringing the Path toward an HIV-1 Vaccine into Focus

Vaccine immunogen design strategies have increasingly focused on novel methods to systematically train the immune system to elicit bNAbs, such as shepherding the production of bNAbs from germline BCRs, antibody lineagebased immunogen designs, strategies to elicit multi-epitope bNAb responses, and epitope focusing strategies [24, 31]. These efforts have been enhanced by SOSIP immunogens: stabilized Env trimers engineered to mimic the native virion trimer [24], with the goal of eliciting more physiologically relevant, potent bNAbs, in comparison to gp120 monomers. While SOSIPs have yielded some success in animal models [24, 32], the immunological differences across species compel proof of concept experimental medicine trials to directly test the capacity of the human immune system to be “trained” to elicit protective immunity. To that end, a leading SOSIP candidate, SOSIP.664 based on HIV-1 strain BG505, advanced to human trials in 2018 [24]. A major challenge for HIV-1 vaccinologists has been incorporating the interconnectivity and heterogeneity of the human immune system into evaluations of vaccine efficacy and the search for correlates of protection. Novel analytical tools have bolstered this search by dissecting heterogenous host genetics and vaccination responses to identify characteristics predictive of outcome. Such systems vaccinology approaches integrate systems biology methodologies into the historically empirical field of vaccinology and have already identified genetic correlates of HIV-1 vaccine efficacy (e.g., HLA haplotypes [6, 33] and FcR polymorphisms [6, 14, 28, 34]) and genetic modulators of HIV-1 vaccination responses, e.g., Ig allotypes [35]. Analyses at the intersection of immunology, host genetics, and viral genetics will likely uncover new insights into human immunity that could be leveraged for vaccine development more broadly and enable vaccinologists to precisely guide the administration of these vaccines to individuals most likely to benefit. Indeed, identifying communities most likely to benefit from an HIV-1 vaccine, such as populations with high prevalence of HIV-1 and Mycobacterium tuberculosis coinfection, is important when designing and recruiting for future trials. Similarly, a proven efficacious vaccine that can be safely administered early in life may offer the best opportunity for prevention [36]. Integrative data analysis from knowledge gained from the intersections of immunology, virology, and host genetics, along with an informed understanding of implementation strategies tuned specifically for optimal effectiveness in the affected communities [37], is on the horizon. Disclosures and Conflict of Interest

This section was originally published as: Lopez Angel, C. J., Tomaras, G. D. (2020). Bringing the path toward an HIV-1 vaccine into focus. PLoS Pathog., 16(9), e1008663, https://doi.org/10.1371/journal.ppat.1008663, under the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates. Funding: This work is supported by the National Institutes of Health (NIH), National Institute of Allergy and Infectious Diseases (NIAID), HIV Vaccine Trials Network (HVTN) grants UM1 AI068614; UM1 AI068618, P01 AI120756, and AI064518 (Duke

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Center for AIDS Research). CJLA is also supported by an HVTN RAMP Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of this section.

Competing interests: GST has patent applications for HIV immunogen design and HIV incidence assays. GST received a research contract through Duke University from GSK.

Acknowledgments: We thank members of the Tomaras lab for insightful discussions and Steven Wakefield, Peter Gilbert, David Montefiori, Larry Corey, Glenda Gray, Susan Buchbinder, Scott Hammer, and Guido Ferrari for critical reading of this section. We thank Lauren Halligan for assistance with the illustration in Fig. 15.10 (copyrighted by Duke University and used with permission under a CC BY-ND 4.0 license).

References

1. Global Health Observatory (GHO) Data on HIV/AIDS. World Health Organization. 2018. Available at: http://www.who.int/gho/hiv/en/ (accessed on May 13, 2021).

2. Antiretroviral Therapy Cohort C. Survival of HIV-positive patients starting antiretroviral therapy between 1996 and 2013: a collaborative analysis of cohort studies. Lancet HIV. 2017;4(8):e349–e56.

3. Ford N, Mayer KH, World Health Organization Postexposure Prophylaxis Guideline Development G. World Health Organization guidelines on postexposure prophylaxis for HIV: recommendations for a public health approach. Clin Infect Dis. 2015;60 Suppl 3:S161–4.

4. Cohen MS, Chen YQ, McCauley M, Gamble T, Hosseinipour MC, Kumarasamy N, et al. Prevention of HIV-1 infection with early antiretroviral therapy. N Engl J Med. 2011;365(6):493–505.

5. Medlock J, Pandey A, Parpia AS, Tang A, Skrip LA, Galvani AP. Effectiveness of UNAIDS targets and HIV vaccination across 127 countries. Proc Natl Acad Sci U S A. 2017;114(15):4017–22. 6. Tomaras GD, Plotkin SA. Complex immune correlates of protection in HIV-1 vaccine efficacy trials. Immunol Rev. 2017;275(1):245–61.

7. Experimental HIV vaccine regimen ineffective in preventing HIV: National Institute of Health. 2020. Available at: https://www.nih.gov/news-events/news-releases/ experimental-hiv-vaccine-regimen-ineffective-preventing-hiv (accessed on May 13, 2021). 8. Gray G, Buchbinder S, Duerr A. Overview of STEP and Phambili trial results: two phase IIb test-of-concept studies investigating the efficacy of MRK adenovirus type 5 gag/pol/nef subtype B HIV vaccine. Curr Opin HIV AIDS. 2010;5(5):357–61.

9. Hammer SM, Sobieszczyk ME, Janes H, Karuna ST, Mulligan MJ, Grove D, et al. Efficacy trial of a DNA/rAd5 HIV-1 preventive vaccine. N Engl J Med. 2013;369(22):2083–92.

10. Rolland M, Tovanabutra S, deCamp AC, Frahm N, Gilbert PB, Sanders-Buell E, et al. Genetic impact of vaccination on breakthrough HIV-1 sequences from the STEP trial. Nat Med. 2011;17(3):366–71.

References

11. deCamp AC, Rolland M, Edlefsen PT, Sanders-Buell E, Hall B, Magaret CA, et al. Sieve analysis of breakthrough HIV-1 sequences in HVTN 505 identifies vaccine pressure targeting the CD4 binding site of Env-gp120. PLoS ONE. 2017;12(11):e0185959.

12. Fong Y, Shen X, Ashley VC, Deal A, Seaton KE, Yu C, et al. Modification of the association between T-cell immune responses and human immunodeficiency virus type 1 infection risk by vaccine-induced antibody responses in the HVTN 505 trial. J Infect Dis. 2018;217(8):1280–8.

13. Janes HE, Cohen KW, Frahm N, De Rosa SC, Sanchez B, Hural J, et al. Higher T-cell responses induced by DNA/rAd5 HIV-1 preventive vaccine are associated with lower HIV-1 infection risk in an efficacy trial. J Infect Dis. 2017;215(9):1376–85. 14. Neidich SD, Fong Y, Li SS, Geraghty DE, Williamson BD, Young WC, et al. Antibody Fc effector functions and IgG3 associate with decreased HIV-1 risk. J Clin Invest. 2019;129(11):4838–49. 15. Rerks-Ngarm S, Pitisuttithum P, Nitayaphan S, Kaewkungwal J, Chiu J, Paris R, et al. Vaccination with ALVAC and AIDSVAX to prevent HIV-1 infection in Thailand. N Engl J Med. 2009;361(23):2209–20. 16. Shen X, Laher F, Moodie Z, McMillan AS, Spreng RL, Gilbert PB, et al. HIV-1 Vaccine sequences impact V1V2 antibody responses: A comparison of two poxvirus prime gp120 boost vaccine regimens. Sci Rep. 2020;10(1):2093.

17. Bekker LG, Moodie Z, Grunenberg N, Laher F, Tomaras GD, Cohen KW, et al. Subtype C ALVAC-HIV and bivalent subtype C gp120/MF59 HIV-1 vaccine in low-risk, HIVuninfected, South African adults: a phase 1/2 trial. Lancet HIV. 2018;5(7):e366–e78.

18. Barouch DH. A step forward for HIV vaccines. Lancet HIV. 2018;5(7):e338–e9.

19. Laher F, Moodie Z, Cohen KW, Grunenberg N, Bekker L-G, Allen M, et al. Safety and immune responses after a 12-month booster in healthy HIV-uninfected adults in HVTN 100, a randomized double blind placebo-controlled trial of ALVAC-HIV (vCP2438) and Bivalent Subtype C gp120/MF59 vaccines in South Africa. PLoS Med. 2020;17(2):e1003038.

20. Lewis GK, DeVico AL, Gallo RC. Antibody persistence and T-cell balance: two key factors confronting HIV vaccine development. Proc Natl Acad Sci U S A. 2014;111(44): 15614–21.

21. Corey L, Gilbert PB, Tomaras GD, Haynes BF, Pantaleo G, Fauci AS. Immune correlates of vaccine protection against HIV-1 acquisition. Sci Transl Med. 2015;7(310):310rv7.

22. Kouyos RD, Rusert P, Kadelka C, Huber M, Marzel A, Ebner H, et al. Tracing HIV-1 strains that imprint broadly neutralizing antibody responses. Nature. 2018;561(7723):406–10. 23. Alter G, Barouch D. Immune correlate-guided HIV vaccine design. Cell Host Microbe. 2018;24(1):25–33.

24. Burton DR. Advancing an HIV vaccine; advancing vaccinology. Nat Rev Immunol. 2019;19(2):77–8.

25. Crowley AR, Ackerman ME. Mind the gap: How interspecies variability in IgG and its receptors may complicate comparisons of human and non-human primate effector function. Front Immunol. 2019;10:697.

26. Haynes BF, Gilbert PB, McElrath MJ, Zolla-Pazner S, Tomaras GD, Alam SM, et al. Immunecorrelates analysis of an HIV-1 vaccine efficacy trial. N Engl J Med. 2012;366(14): 1275–86.

331

332

Current Perspectives in Medical Microbiology

27. Yates NL, Liao HX, Fong Y, deCamp A, Vandergrift NA, Williams WT, et al. Vaccine-induced Env V1-V2 IgG3 correlates with lower HIV-1 infection risk and declines soon after vaccination. Sci Transl Med. 2014;6(228):228ra39.

28. Li SS, Gilbert PB, Carpp LN, Pyo CW, Janes H, Fong Y, et al. Fc gamma receptor polymorphisms modulated the vaccine effect on HIV-1 risk in the HVTN 505 HIV vaccine trial. J Virol. 2019;93(21):e02041-18. 29. Lin L, Finak G, Ushey K, Seshadri C, Hawn TR, Frahm N, et al. COMPASS identifies T-cell subsets correlated with clinical outcomes. Nat Biotechnol. 2015;33(6):610–6.

30. Hansen SG, Marshall EE, Malouli D, Ventura AB, Hughes CM, Ainslie E, et al. A liveattenuated RhCMV/SIV vaccine shows long-term efficacy against heterologous SIV challenge. Sci Transl Med. 2019;11(501):eaaw2607.

31. Haynes BF, Mascola JR. The quest for an antibody-based HIV vaccine. Immunol Rev. 2017;275(1):5–10.

32. Saunders KO, Wiehe K, Tian M, Acharya P, Bradley T, Alam SM, et al. Targeted selection of HIV-specific antibody mutations by engineering B cell maturation. Science. 2019;366(6470):eaay7199.

33. Prentice HA, Tomaras GD, Geraghty DE, Apps R, Fong Y, Ehrenberg PK, et al. HLA class II genes modulate vaccine-induced antibody responses to affect HIV-1 acquisition. Sci Transl Med. 2015;7(296):296ra112.

34. Li SS, Gilbert PB, Tomaras GD, Kijak G, Ferrari G, Thomas R, et al. FCGR2C polymorphisms associate with HIV-1 vaccine protection in RV144 trial. J Clin Invest. 2014;124(9): 3879–90.

35. Kratochvil S, McKay PF, Chung AW, Kent SJ, Gilmour J, Shattock RJ. Immunoglobulin G1 allotype influences antibody subclass distribution in response to HIV gp140 vaccination. Front Immunol. 2017;8:1883. 36. Permar S, Levy O, Kollman TR, Singh A, De Paris K. Early Life HIV-1 Immunization: Providing a window for protection before sexual debut. AIDS Res Hum Retroviruses. 2018;34(10):823–7.

37. Bekker LG, Tatoud R, Dabis F, Feinber M, Kaleebu P, Marovich M et al. The complex challenges of HIV vaccine development require renewed and expanded global commitment. Lancet. 2020;395(10221):384–388.

15.10 Quorum Sensing across Bacterial and Viral Domains Olivia P. Duddya and Bonnie L. Bassler, PhDa,b aDepartment bHoward

of Molecular Biology, Princeton University, Princeton, New Jersey, USA

Hughes Medical Institute, Chevy Chase, Maryland, USA

[email protected]

15.10.1 Introduction Quorum sensing (QS) is a process of cell-to-cell communication that bacteria use to orchestrate collective behaviors in response to changes in cell population

Quorum Sensing across Bacterial and Viral Domains

density and species composition of the community [1]. QS relies on the production, release, and group-wide detection of and response to extracellular signaling molecules called autoinducers (AIs) [1]. Recent studies demonstrate that bacteriainfecting viruses, called phages, also employ chemical communication to regulate collective activities. Phages can encode exclusive phage QS-like systems, or they can tune into and manipulate their host bacterial QS-mediated communication pathways to optimize the timing of the lysis–lysogeny switch. These research advances suggest that phage-mediated QS signaling and phage eavesdropping on bacterial QS signaling drive bacteria–phage interactions, possibly contributing to mechanisms that shape both phage and bacterial biology [2–6]. Here, we briefly review QS in bacteria, and we summarize recent highlights in chemical communication among phages and across the bacterial and phage domains.

15.10.2 The Bacterial Chemical Lexicon

QS-mediated communication systems are widespread in the bacterial world. QS controls group behaviors including bioluminescence, competence for DNA uptake, virulence factor production, biofilm formation, and the regulation of antiphage defense strategies [1, 7, 8]. Commonly, bacteria integrate information encoded in multiple AIs, enabling intraspecies, intragenera, and interspecies cell–cell communication (Fig. 15.11, top). Gram-negative bacteria typically use acylhomoserine lactones (AHLs) as AIs [1]. AHLs are usually produced by LuxI-type synthases and are detected by partner LuxR-type cytoplasmic receptor-transcription factors. Gram-positive bacteria predominantly use oligopeptides as AIs, which are detected by membrane-bound two-component sensor histidine kinases, and the information is relayed to cognate cytoplasmic response regulators [9]. New AIs continue to be discovered expanding our knowledge of the bacterial chemical lexicon. For example, a family of AIs based on rearranged forms of 4,5-dihydroxy2,3-pentanedione, collectively referred to as autoinducer 2 (AI-2) [10–12], and the pyrazine AI 3,5-dimethyl-pyrazin-2-ol (DPO) [13] are broadly produced among gram-negative and gram-positive bacteria and enable interspecies communication. AI-2 AIs are detected by periplasmic binding proteins homologous to the first known AI-2 receptor, LuxP [12, 14–16], and DPO is detected by a cytoplasmic transcription factor called VqmA [13]. Curiously, some bacterial QS systems appear to foster “one-way” conversations (Fig. 15.11, top). In one scenario, bacteria cannot produce an AI but can detect it. For example, neither Escherichia coli nor Salmonella enterica possess a LuxI-type AI synthase, and therefore, they make no AHL AIs [17]. However, both E. coli and S. enterica encode the SdiA LuxR-type receptor that detects exogenously supplied AHLs [17]. Thus, collective behaviors in these bacteria are presumed to be driven by other AHL-producing bacteria in the vicinal community. In a second scenario, bacteria can produce an AI but do not possess an apparent partner AI receptor. This arrangement is relevant to both the AI-2 and DPO AIs.

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Figure 15.11 QS-mediated communication. Shown are representative chemical communication systems highlighted in the text that occur between top, bacteria–bacteria; middle, phage–phage; and bottom, bacteria–phage. In each case, the low- and high-cell density or low- and high-phage infection states are shown on the left and right sides, respectively. In each panel, dashed arrows represent release and uptake of AIs, solid arrows represent peptide/protein production or gene regulation, and the horizontal line represents the bacterial membrane. In the middle panel, the scissors signify processing of the signaling peptide. See text for details about each system. Abbreviations: AI, autoinducer; QS, quorum sensing.

Quorum Sensing across Bacterial and Viral Domains

The capacity to produce these AIs is widespread among bacteria; however, few receptors have been shown capable of AI-2 recognition [10, 12, 15, 16], and to our knowledge, among bacteria, only Vibrios possess VqmA DPO-receptors [7]. Thus, presumably, only select bacteria can garner information from these two AI inputs. It remains possible that bacteria make AIs (i.e., AHLs) by atypical routes, and/or they possess unconventional AI-2 and DPO receptors. Alternatively, these asymmetric AI production and detection patterns could confer particular advantages exclusively to subsets of bacteria existing in mixed-species communities.

15.10.3 Phage Lingo

Phages employ two strategies to control their proliferation: dissemination and persistence. Lytic phages, upon entering the bacterial host, replicate and lyse the infected host cells [18]. By contrast, lysogenic or temperate phages can remain dormant in host cells and are passed down via the host cells’ progeny [18]. Importantly, temperate phages can harbor the ability to convert from the lysogenic mode to the lytic mode [18, 19]. Seminal studies of phage lambda from E. coli have guided our understanding of the lysis–lysogeny lifestyle switch [19]. Common to many phages is that inhibition of the phage lytic repressor, called cI, is crucial for launching the phage lytic cascade that drives host cell killing. Coordination of group behaviors among viruses is far less understood than is the choreography of collective traits in bacteria. Recently, a small-molecule QS-like phage communication process was discovered, termed the “arbitrium system” (Fig. 15.11, middle) [20]. Following phage phi3T infection of Bacillus species, a phage-encoded precursor peptide called AimP is produced and secreted. AimP is processed by extracellular proteases into the final arbitrium signaling peptide. The mature peptide is internalized by bacteria, and if they are phage infected, the peptide is detected by the phage AimR receptor, which is a transcription factor. In the unliganded state, AimR binds DNA and activates transcription of the gene encoding the AimX small RNA. AimX represses expression of the arbitrium cI repressor gene, and subsequently, the lytic cascade is deployed [21]. At sufficient concentration, the AimP peptide binds and inactivates AimR. Consequently, aimX is not expressed, cI is made and represses lytic development, and lysogeny is established. Thus, newly infecting phages can avoid triggering the lytic cascade when there is low availability of uninfected hosts in the vicinity [20, 21]. Arbitrium-like systems exist among numerous phage groups and in conjugative elements, with the majority identified in Bacillus species [21]. The native Bacillus subtilis conjugation plasmids pLS20 and ICEBs1 use peptide-based signaling systems to regulate expression of plasmid genes [22, 23]. Analogous to the phage arbitrium system, accumulation of the plasmid-produced signaling peptide represses conjugation. Thus, DNA transfer is suppressed under conditions when few non-plasmid carrying (i.e., “uninfected”) cells are present.

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15.10.4 A Shared Bacterial–Phage Vocabulary The potential for QS-like chemical communication between bacteria and phages emerged from the discovery that phages can encode homologs of QS components. Specifically, sequencing of the Clostridrium difficile temperate phage phiCDHM1 revealed genes homologous to the bacterial accessory gene regulator (Agr) QS system, a peptide-based QS system used by gram-positive bacteria [24]. Phage phiCDHM1 possesses genes encoding predicted homologs of AgrD, AgrB, and AgrC, which are required to produce and secrete the Agr autoinducing peptide [9]. The phage lacks a gene specifying the QS receptor-transcription factor AgrA. The hypothesis is that the phage-produced signal could be detected by the C. difficile community [24]. Thus, infection of only a few host cells could drive communitywide collective bacterial behaviors. Similarly, DNA sequencing shows that an uncharacterized Myoviridae phage encodes a predicted LuxI–LuxR QS pair [25]. While verification is needed, this arrangement could enable two-way interdomain communication: the phage-produced AI could be detected by the bacterial LuxR, and/or the host-produced AI could be detected by the phage LuxR. If so, each entity could control the other’s behavior. Our early knowledge of possible bacteria– phage QS interactions relies primarily on genomic sequencing data. As more viral genomes are sequenced, additional assemblies of phage-encoded QS components are being revealed [25, 26]. We anticipate future identification of the outputs controlled by these systems. A concrete link between host QS and the control of the phage lysis–lysogeny transition is established via the example of Vibriophage VP882 (Fig. 15.11, bottom). Specifically, phage VP882 encodes a homolog of the bacterial VqmA DPO-binding QS receptor and transcription factor [27, 28]. The phage homolog is called VqmAPhage [28]. When the bacterial-produced DPO AI accumulates at high cell density, VqmAPhage binds DPO. Subsequently, DPO-bound VqmAPhage activates transcription of the phage gene qtip, encoding a novel antirepressor, Qtip. Qtip binds and sequesters the phage VP882 repressor, called cIVP882, to the cell poles [28, 29]. The consequence of Qtip-directed inactivation of cIVP882 is derepression of the lytic gene activator Q and expression of genes required for host cell lysis [28]. The notion is that by monitoring the host-produced QS AI, phage VP882 is able to tune the timing of lysis to conditions of high host cell density [28]. Thus, phage VP882 exclusively triggers dissemination from its current host when the probability of infection of the next Vibrio cell is maximized [28, 30]. Finally, while phage VP882 does not possess the capacity to synthesize DPO, VqmAPhage can activate expression of host-encoded vqmR, the transcriptional target of bacterial VqmA [13, 28, 31]. VqmR is a small RNA that, in Vibrio cholerae, regulates genes required for pathogenicity [13, 31]. Thus, phage VP882, beyond connecting its own biology to host QS, directly regulates host gene expression, and specifically, host QS-controlled genes. Observations analogous to those regarding phage VP882 and DPO were recently reported for the E. coli phage T1 and for Enterococcus faecalis temperate

Quorum Sensing across Bacterial and Viral Domains

phages. Specifically, the administration of synthetic AI-2 to cell cultures induced phage lytic development [32, 33]. How the AI-2 input drives phage induction is unknown, and the phage T1 and the E. faecalis phage genomes harbor no obvious AI-2 receptors. Finally, in Vibrio anguillarum, QS represses φH20-like phage p41 lytic development at high cell density, again by an unknown mechanism [34]. We speculate that many more phages can derive information from host-produced QS signals to regulate their lysis–lysogeny transitions.

15.10.5 Concluding Remarks

Here, we have focused on newly discovered QS-mediated chemical interactions between phages and bacteria. These studies reveal that phages, like bacteria, have mechanisms that foster collective processes. From the phage side, using or eavesdropping on QS provides an insidious strategy for phages to optimally prey on bacterial hosts. From the bacterial side, QS-controlled antiphage defense mechanisms provide bacteria enhanced tactics for combatting these very same predators. In particular, at high cell density, QS represses production of cell surface phage receptors [8, 35], activates transcription of CRISPR antiphage systems [7, 36], and induces phage-inactivating proteases [37, 38], all of which defend bacteria against their viral foes. Given that the risk of phage infection escalates with increasing bacterial cell density, placing antiphage defense mechanisms under QS control presumably enables those defenses to be deployed precisely when vulnerability to phage infection is high. We note that examples also exist of QS-mediated interdomain communication between bacteria and eukaryotes. Specifically, fungi, plant cells, and mammalian cells can synthesize AI mimics that modulate bacterial QS-controlled behaviors [39–42]. Eukaryotic host factors can likewise modulate QS via inactivation or sequestration of bacterial AIs [43–46]. The role of phages in phage–bacterial relationships and in three-way phage–bacterial–eukaryotic partnerships, both harmful and beneficial, represents an exciting research frontier. Given the prevalence of phages in bacterial communities combined with the prevalence of microbiome bacteria in and/or on eukaryotic hosts, defining the contributions of phages to QS could prove central to a comprehensive understanding of the functioning of QS in natural settings. Disclosures and Conflict of Interest

This section was originally published as: Duddy, O. P., Bassler, B. L. (2021). Quorum sensing across bacterial and viral domains. PLoS Pathog., 17(1), e1009074, https:// doi.org/10.1371/journal.ppat.1009074, under the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates.

Funding: This work was supported by the Howard Hughes Medical Institute, NIH grant R37GM065859, National Science Foundation grant MCB-1713731 (to B.L.B.) and the NIGMS grant T32GM007388 (to O.P.D.). The funders had no role in

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study design, data collection and analysis, decision to publish, or preparation of this section. Competing interests: The authors have declared that no competing interests exist.

References

1. Papenfort K, Bassler BL. Quorum sensing signal-response systems in Gram-negative bacteria. Nat Rev Microbiol. 2016;14:576–588.

2. Seed KD, Lazinski DW, Calderwood SB, Camilli A. A bacteriophage encodes its own CRISPR/Cas adaptive response to evade host innate immunity. Nature. 2013;494: 489–491.

3. Waldor MK. Bacteriophage biology and bacterial virulence. Trends Microbiol. 1998;6: 295–297.

4. O’Hara BJ, Barth ZK, McKitterick AC, Seed KD. A highly specific phage defense system is a conserved feature of the Vibrio cholerae mobilome. PLoS Genet. 2017;13:e1006838.

5. McKitterick AC, Seed KD. Anti-phage islands force their target phage to directly mediate island excision and spread. Nat Commun. 2018;9:1–8.

6. Waldor MK, Mekalanos JJ. Lysogenic conversion by a filamentous phage encoding cholera toxin. Science. 1996;272:1910–1913. 7. Høyland-Kroghsbo NM, Paczkowski J, Mukherjee S, Broniewski J, Westra E, BondyDenomy J, et al. Quorum sensing controls the Pseudomonas aeruginosa CRISPRCas adaptive immune system. Proc Natl Acad Sci U S A. 2017;114:131–135.

8. Høyland-Kroghsbo NM, Mærkedahl RB, Svenningsen S Lo. A quorum-sensinginduced bacteriophage defense mechanism. MBio. 2013;4:e00362–12.

9. Novick RP, Geisinger E. Quorum sensing in Staphylococci. Annu Rev Genet. 2008; 42:541–564.

10. Chen X, Schauder S, Potier N, Van Dorsselaer A, Pelczer I, Bassler BL, et al. Structural identification of a bacterial quorum-sensing signal containing boron. Nature. 2002;415:545–549.

11. Schauder S, Shokat K, Surette MG, Bassler BL. The LuxS family of bacterial autoinducers: biosynthesis of a novel quorum-sensing signal molecule. Mol Microbiol. 2001;41: 463–476.

12. Miller ST, Xavier KB, Campagna SR, Taga ME, Semmelhack MF, Bassler BL, et al. Salmonella typhimurium recognizes a chemically distinct form of the bacterial quorum-sensing signal AI-2. Mol Cell. 2004;15:677–687.

13. Papenfort K, Silpe JE, Schramma KR, Cong JP, Seyedsayamdost MR, Bassler BL. A Vibrio cholerae autoinducer-receptor pair that controls biofilm formation. Nat Chem Biol. 2017;13:551–557.

14. Bassler BL, Wright M, Silverman MR. Multiple signalling systems controlling expression of luminescence in Vibrio harveyi: sequence and function of genes encoding a second sensory pathway. Mol Microbiol. 1994;13:273–286.

15. Shao H, James D, Lamont RJ, Demuth DR. Differential interaction of Aggregatibacter (Actinobacillus) actinomycetemcomitans LsrB and RbsB proteins with autoinducer 2. J Bacteriol. 2007;189:5559–5565.

References

16. Armbruster CE, Pang B, Murrah K, Juneau RA, Perez AC, Weimer KED, et al. RbsB (NTHI-0632) mediates quorum signal uptake in nontypeable Haemophilus influenzae strain 86-028NP. Mol Microbiol. 2011;82:836–850.

17. Ahmer BMM. Cell-to-cell signalling in Escherichia coli and Salmonella enterica. Mol Microbiol. 2004;52:933–945.

18. Little JW. Lysogeny, Prophage induction, and lysogenic conversion. In Phages: Their Role in Bacterial Pathogenesis and Biotechnology, Waldor MK, Friedman DI, Adhya S, eds. (ASM Press, Washington, D.C.), 2014, chapter 3, pp. 37–54. 19. Ptashne M. A Genetic Switch, Third Edition, Phage Lambda Revisited. Cold Spring Harb Lab Press. 2004.

20. Erez Z, Steinberger-Levy I, Shamir M, Doron S, Stokar-Avihail A, Peleg Y, et al. Communication between viruses guides lysis-lysogeny decisions. Nature. 2017;541: 488–493. 21. Stokar-Avihail A, Tal N, Erez Z, Lopatina A, Sorek R. Widespread utilization of peptide communication in phages infecting soil and pathogenic bacteria. Cell Host Microbe. 2019;25:746–755. 22. Singh PK, Ramachandran G, Ramos-Ruiz R, Peiró-Pastor R, Abia D, Wu LJ, et al. Mobility of the native Bacillus subtilis conjugative plasmid pLS20 is regulated by intercellular signaling. PLoS Genet. 2013;9:e1003892.

23. Auchtung JM, Lee CA, Monson RE, Lehman AP, Grossman AD. Regulation of a Bacillus subtilis mobile genetic element by intercellular signaling and the global DNA damage response. Proc Natl Acad Sci U S A. 2005;102:12554–12559.

24. Hargreaves KR, Kropinski AM, Clokie MRJ. What does the talking? Quorum sensing signalling genes discovered in a bacteriophage genome. Plos One. 2014;9:e1003892.

25. Silpe JE, Bassler BL. Phage-encoded LuxR-type receptors responsive to host-produced bacterial quorum-sensing autoinducers. MBio. 2019;10:e00638–19.

26. Hargreaves KR, Kropinski AM, Clokie MR. Bacteriophage behavioral ecology. Bacteriophage. 2014;4:e29866.

27. Lan SF, Huang CH, Chang CH, Liao WC, Lin IH, Jian WN, et al. Characterization of a new plasmid-like prophage In a pandemic Vibrio parahaemolyticus O3:K6 strain. Appl Environ Microbiol. 2009;75:2659–2667.

28. Silpe JE, Bassler BL. A host-produced quorum-sensing autoinducer controls a phage lysis-lysogeny decision. Cell. 2019;176:268–280.e13. 29. Silpe JE, Bridges AA, Huang X, Coronado DR, Duddy OP, Bassler BL. Separating functions of the phage-encoded quorum-sensing-activated antirepressor qtip. Cell Host Microbe. 2020;27:629–641.e4.

30. Ghosh D, Roy K, Williamson KE, Srinivasiah S, Wommack KE, Radosevich M. Acylhomoserine lactones can induce virus production in lysogenic bacteria: an alternative paradigm for prophage induction. Appl Environ Microbiol. 2009;75:7142–7152.

31. Papenfort K, Förstner KU, Cong JP, Sharma CM, Bassler BL. Differential RNA-seq of Vibrio cholerae identifies the VqmR small RNA as a regulator of biofilm formation. Proc Natl Acad Sci U S A. 2015;112:E766–E775.

32. Laganenka L, Sander T, Lagonenko A, Chen Y, Link H, Sourjik V. Quorum sensing and metabolic state of the host control lysogeny-lysis switch of bacteriophage T1. MBio. 2019;10:e01884–19.

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33. Rossmann FS, Racek T, Wobser D, Puchalka J, Rabener EM, Reiger M, et al. Phagemediated dispersal of biofilm and distribution of bacterial virulence genes is induced by quorum sensing. PLoS Pathog. 2015;11:e1004653.

34. Tan D, Hansen MF, de Carvalho LN, Røder HL, Burmølle M, Middelboe M, et al. High cell densities favor lysogeny: induction of an H20 prophage is repressed by quorum sensing and enhances biofilm formation in Vibrio anguillarum. ISME J. 2020;14: 1731–1742.

35. Tan D, Svenningsen S Lo, Middelboe M, Bassler B. Quorum sensing determines the choice of antiphage defense strategy in Vibrio anguillarum. Am Soc Microbiol. 2015;6:e00627–15.

36. Patterson AG, Jackson SA, Taylor C, Evans GB, Salmond GPC, Przybilski R, et al. Quorum sensing controls adaptive immunity through the regulation of multiple CRISPR-cas systems. Mol Cell. 2016;64:1102–1108.

37. Hoque MM, Naser I Bin, Bari SMN, Zhu J, Mekalanos JJ, Faruque SM. Quorum regulated resistance of Vibrio cholerae against environmental bacteriophages. Sci Rep. 2016;6:37956.

38. Castillo D, Rørbo N, Jørgensen J, Lange J, Tan D, Kalatzis PG, et al. Phage defense mechanisms and their genomic and phenotypic implications in the fish pathogen Vibrio anguillarum. FEMS Microbiol Ecol. 2019;95:fiz004.

39. Teplitski M, Chen H, Rajamani S, Gao M, Merighi M, Sayre RT, et al. Chlamydomonas reinhardtii secretes compounds that mimic bacterial signals and interfere with quorum sensing regulation in bacteria. Plant Physiol. 2004;134:137–146.

40. Teplitski M, Robinson JB, Bauer WD. Plants secrete substances that mimic bacterial N-acyl homoserine lactone signal activities and affect population density-dependent behaviors in associated bacteria. Mol Plant Microbe Interact. 2000;13:637–648.

41. Ismail AS, Valastyan JS, Bassler BL. A host-produced autoinducer-2 mimic activates bacterial quorum sensing. Cell Host Microbe. 2016;19:470–480.

42. Tourneroche A, Lami R, Hubas C, Blanchet E, Vallet M, Escoubeyrou K, et al. Bacterial– fungal interactions in the kelp endomicrobiota drive autoinducer-2 quorum sensing. Front Microbiol. 2019;10:1693.

43. Pietschke C, Treitz C, Forêt S, Schultze A, Künzel S, Tholey A, et al. Host modification of a bacterial quorum-sensing signal induces a phenotypic switch in bacterial symbionts. Proc Natl Acad Sci U S A. 2017;114:E8488–E8497.

44. Peterson MM, Mack JL, Hall PR, Alsup AA, Alexander SM, Sully EK, et al. Apolipoprotein B is an innate barrier against invasive Staphylococcus aureus infection. Cell Host Microbe. 2008;4:555–566.

45. Smith AC, Rice A, Sutton B, Gabrilska R, Wessel AK, Whiteley M, et al. Albumin inhibits Pseudomonas aeruginosa quorum sensing and alters polymicrobial interactions. Infect Immun. 2017;85:e00116–17.

46. Stoltz DA, Ozer EA, Taft PJ, Barry M, Liu L, Kiss PJ, et al. Drosophila are protected from Pseudomonas aeruginosa lethality by transgenic expression of paraoxonase-1. J Clin Invest. 2008;118:3123–3131.

Coinfections in Wildlife

15.11 Coinfections in Wildlife: Focus on a Neglected Aspect of Infectious Disease Epidemiology Axel O. G. Hoarau, Patrick Mavingui, PhD, and Camille Lebarbenchon, PhD Université de La Réunion, Processus Infectieux en Milieu Insulaire Tropical, INSERM, Réunion Island, France [email protected]

15.11.1 Are Coinfections Common? Coinfection (or co-infection), which can refer to simultaneous infection, mixed infection, multiple infections, concomitant infection, concurrent infection, polyinfection, polyparasitism, and multiple parasitisms [2], defines the occurrence of at least two genetically different infectious agents in the same host (Fig. 15.12A) [3]. This definition, therefore, includes infectious agents of different taxonomic levels (e.g., bacterium and virus) and also genetic variants of the same infectious agent (e.g., virus genotypes) [3]. Coinfections have been mainly studied in humans, with a particular emphasis on macroparasite helminths [3, 4].

Figure 15.12 The importance of coinfection in wild hosts. (A) Coinfections are defined by the presence of at least two genetically different infectious agents in the same host: infectious agents of different taxonomic levels (e.g., bacterium and virus) but also genetic variants of the same infectious agent (e.g., virus genotypes). (B) Coinfection is the rule in all living communities and prevalence can be high in vertebrate animals. (C) Coinfection can impact host fitness. (D) The importance of coinfection in zoonoses emergence processes remains to be fully assessed.

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The presence of helminth eggs from multiple species has even been reported in human remains and coprolites recovered from many prehistoric sites and analyzed by microscopy [5]. About 30% of human infections may actually be coinfections, and this rate could reach up to 80% in some human communities [4, 6]. Recent studies have focused on other living organisms such as plants, vertebrate, and invertebrate animals and have demonstrated that coinfection is indeed the rule rather than the exception (Fig. 15.12B) [2–4, 7]. For example, coinfection by protozoa (Eimeria sp., Entamoeba sp., Giardia sp. and Cryptosporidium sp.) and by protozoa and helminths (e.g., Ancylostomatidae, Vampirolepsis nana) in Brazilian bats can reach 22% in Molossus molossus, 25% in Myotis lavali, and 36% in Noctilio albiventris [8]. In field voles (Microtus agrestis), the prevalence of coinfected individuals with Babesia microti, Cowpox virus, Anaplasma phagocytophilum, and Bartonella spp., can reach up to 79% of tested animals [9].

15.11.2 How Do Infectious Agents Interact?

Coinfection not only reflects the simultaneous presence of multiple infectious agents in a given host but also involves complex interactions between them. The type of interactions within a community of infectious agents exploiting the same host can be direct, for example, via physical interference or competition for resources, or indirect, such as through immunological pathways or the production of chemical compounds [10–17]. The outcome of these interactions can be positive (synergistic), in which the presence of one infectious agent may facilitate infection by other infectious agents; negative (antagonistic), when the presence of one infectious agent inhibits infection or replication; or neutral when the presence of one infectious agent does not affect the infection by other infectious agents [6, 14, 16]. In field voles, infection patterns have been shown to be highly conditioned by a complex web of interactions, depending on the presence or absence of the other infectious agents [9]. Both positive interactions (e.g., Cowpox virus and Bartonella bacteria) and negative interactions (e.g., Anaplasma phagocytophilum and Babesia microti) contribute to the global web of relationships. Some infectious agents seem to directly compete for blood as a resource (Babesia microti and Bartonella bacteria) and others indirectly through immunomodulatory effects (e.g., Anaplasma phagocytophilum and Cowpox virus) [9]. The cocirculation of paramyxovirus species in Australian flying foxes (Genus Pteropus) also seems to be driven by conditional associations. Indeed, at the sample level, several commonly detected species in natural populations seem to positively interact (e.g., Teviot virus and Hendra virus or Yeppoon virus) and may suggest immunomodulatory effects, such as activation of latent infections; whereas other species rarely detected together could interact negatively (e.g., Hendra virus and Yeppoon virus) and may suggest competition [18].

Coinfections in Wildlife

15.11.3 What Are the Effects of Coinfections on Host Fitness? Coinfections have a large range of effects on host fitness, and this emanates from the interactions taking place among the infectious agents (Fig. 15.12C) [6, 14, 16, 17]. The consequences are not simply the sum of the effects caused by each infectious agent, but the result of a complex combination of known and novel effects affecting key epidemiological parameters, often resulting in more pronounced effects than the individual infections alone [6, 11, 19]. Coinfections can have negative consequences, from abnormal symptoms for a given disease to accelerated death [6]. For example, mice (Mus musculus) coinfected by gastrointestinal helminths and respiratory bacteria are able to chronically shed a larger number of helminth eggs than monoinfected individuals [20]. African buffaloes (Syncerus caffer) coinfected by gastrointestinal nematodes and Mycobacterium bovis face an accelerated mortality [21]. Coinfections can also have beneficial effects for the host, for instance, by taking advantage of antagonistic interactions occurring between infectious agents. For example, gray treefrogs (Hyla versicolor), Northern leopard frogs (Lithobates pipiens), and spring peepers (Pseudacris crucifer) coinfected with helminth Echinoparyphium and Ranavirus, have lower viral load than individuals only infected by the virus, suggesting that macroparasite infection can reduce microparasite infection, possibly through cross-reactive immunity [22]. Pekin ducks (Anas platyrhynchos var. domestica) coinfected with Newcastle disease virus and low-pathogenic avian influenza virus, face a decrease of influenza virus shedding and transmission, suggesting viral interference between the two infectious agents [23]. “Forced” coinfection can also be beneficial for the host, for example, phage therapy, defined as the use of bacteriophage as treatment against targeted bacteria, is based on antagonistic interactions [6, 24]. More broadly, beyond the direct consequences on host fitness, simultaneous infections can strongly affect the dynamics of infectious agents by modifying host susceptibility, infection probabilities, or transmission rates [6].

15.11.4 Why Should We Investigate Coinfection in Wild Hosts?

Disease ecology studies have highlighted the key role of environmental and biological factors in spatial and temporal infection dynamics. For example, seasonal changes in social behavior (e.g., grouping of animals at water sources during dry season) or changes in population structure (e.g., increase of the amount of immunologically naïve juveniles during breeding season) affect transmission opportunities and, therefore, infection dynamics in many species [25]. However, although coinfections are widespread in living communities, and demonstrate epidemiological consequences, most studies have been limited to the investigation of a one host and one infectious agent system, largely because of the complexity of natural systems [13]. Although challenging, future studies need to integrate the natural diversity of infectious agents among living communities and fully

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investigate interaction networks, by considering infectious agents from different taxonomic levels (e.g., virus, bacteria, protozoa, and helminths) and by investigating a broad variety of samples for each host (e.g., feces, blood, and urine) through longitudinal studies. It has been illustrated that considering only a part of the infectious agents’ community when assessing the effects on host fitness is biased [26]. These investigations can be performed at individual, population, or community levels in order to identify the outcomes on infectious agent dynamics [13]. Given that two-thirds of emerging infectious diseases are zoonoses, with nearly 70% originating from wildlife, better knowledge of the interactions of infectious agents in wild reservoirs will provide key insight for the understanding and management of spillover processes [27, 28]. In particular, the role of coinfections in wild and also domestic hosts (e.g., livestock), in the emergence of zoonoses, remains to be fully assessed (Fig. 15.12D).

15.11.5 How Coinfections in Wild Animals Can Be Studied?

Coinfections in wild animals can be investigated using classical approaches based on sample collection, infectious agent detection, and analysis of the results. Sample collection can be achieved through cross-sectional or longitudinal studies [6]. Cross-sectional studies provide information on the co-occurrence of infectious agents at the time of sampling, whereas longitudinal studies provide a more detailed information in the infection dynamics in individual hosts and communities over time. However, most studies designed to investigate coinfection in wildlife are restricted to limited “niches” (e.g., blood, saliva, urine, and feces), and collected samples are analyzed with targeted assays (e.g. PCR and serology), therefore, offering few opportunities to investigate generic coinfection [4, 11]. The development and improvement of new approaches such as metagenomics, next generation sequencing, and bioinformatics, provides a method to simultaneously describe a large number of pathogens without previous knowledge and a priori [13, 29, 30] and allow to share an increasing amount of data with the scientific community, therefore, offering new insights compared to traditional methodologies. Regarding the analytical approach, statistical tests such as chi-squared test can be used to quickly examine co-occurrence but often with limited assumptions concerning interactions and their consequences [6]. Many other statistical tests, mathematical models, and ecological theories have been developed to better infer interactions, although approaches vary depending on study designs and infectious agents [10, 14–16]. Field studies can also be associated with experimental approaches such as captive studies (with wild animals) or mesocosms (artificial ecosystems) [14, 22].

15.11.6 Conclusion

Coinfections are recognized to be the rule in all living communities and have consequences on both host fitness and infectious agent epidemiology. The

References

emergence of infectious diseases associated with wild hosts highlights the need for a better knowledge on the factors and mechanisms involved in the epidemiology of infectious diseases in natural populations. Although challenging, both theoretical and experimental tools are available. This provides a unique opportunity to gain better fundamental knowledge on infectious agent interactions and also to investigate the role and consequences of coinfections in the emergence of zoonoses. Disclosures and Conflict of Interest

This section was originally published as: Hoarau, A. O. G., Mavingui, P., Lebarbenchon, C. (2020). Coinfections in wildlife: Focus on a neglected aspect of infectious disease epidemiology. PLoS Pathog., 16(9), e1008790, https://doi.org/10.1371/ journal.ppat.1008790, under the Creative Commons Attribution license (http:// creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates. Funding: AH was supported by a “Ministère de L’Enseignement supérieur, de la Recherche et de l’Innovation” PhD fellowship. This work was funded by the VIROPTERE program (INTERREG V Océan Indien). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of this section. Competing interests: The authors have declared that no competing interests exist.

Emerging infectious diseases represent a global and major health problem. The understanding of biological processes involved in the transmission and evolution of infectious agents in host reservoirs is critical [1]. Disease ecology has, therefore, become an important area of research, aiming at investigating the interactions between infectious agents, their hosts, and environments. Interactions between infectious agents exploiting the same vertebrate host at the same time (coinfections) can affect disease outcomes and transmissibility. In this section, we review this point, as this could have significant consequences on zoonoses emergence.

References

1. Wilcox BA, Gubler DJ. Disease ecology and the global emergence of zoonotic pathogens. Environ Health Prev Med. 2005;10:263–272. 2. Rigaud T, Perrot-Minnot M-J, Brown MJF. Parasite and host assemblages: embracing the reality will improve our knowledge of parasite transmission and virulence. Proc R Soc B Biol Sci. 2010;277:3693–3702.

3. Cox FEG. Concomitant infections, parasites and immune responses. Parasitology. 2001;122: S23–S38. 4. Petney TN, Andrews RH. Multiparasite communities in animals and humans: frequency, structure and pathogenic significance. Int J Parasitol. 1998;28:377–393.

5. Cockburn TA, Cockburn E, Reyman TA, editors. Mummies, Disease and Ancient Cultures. 2nd ed. Cambridge: Cambridge University Press; 1998.

6. Vaumourin E, Vourc’h G, Gasqui P, Vayssier-Taussat M. The importance of multiparasitism: examining the consequences of co-infections for human and animal health. Parasit Vectors. 2015;8:545.

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7. Moutailler S, Valiente Moro C, Vaumourin E, Michelet L, Tran FH, Devillers E, et al. Co-infection of ticks: the rule rather than the exception. PLoS Negl Trop Dis. 2016;10: e0004539. 8. Santana Lima VF, Rocha PA, Dias Silva MA, Beltrão-Mendes R, Ramos RAN, Giannelli A, et al. Survey on helminths and protozoa of free-living Neotropical bats from Northeastern Brazil. Acta Trop. 2018;185:267–272.

9. Telfer S, Lambin X, Birtles R, Beldomenico P, Burthe S, Paterson S, et al. Species interactions in a parasite community drive infection risk in a wildlife population. Science. 2010;330:243–246.

10. Pedersen AB, Fenton A. Emphasizing the ecology in parasite community ecology. Trends Ecol Evol. 2007;22:133–139.

11. Bordes F, Morand S. The impact of multiple infections on wild animal hosts: a review. Infect Ecol Epidemiol. 2011;1:7346.

10. Knowles SCL, Fenton A, Petchey OL, Jones TR, Barber R, Pedersen AB. Stability of within-host-parasite communities in a wild mammal system. Proc R Soc B Biol Sci. 2013;280:20130598.

11. Hellard E, Fouchet D, Vavre F, Pontier D. Parasite–parasite interactions in the wild: How to detect them? Trends Parasitol. 2015;31:640–652. 12. Ezenwa VO. Helminth-microparasite co-infection in wildlife: lessons from ruminants, rodents and rabbits. Parasite Immunol. 2016;38:527–534.

13. Pedersen AB, Fenton A. Wild rodents as a natural model to study within host parasite interactions. In: Wilson K, Fenton A, Tompkins D, editors. Wildlife Disease Ecology: Linking Theory to Data and Application. 1st ed. Cambridge University Press; 2019.

14. Ezenwa VO, Jolles AE, Beechler BR, Budischak SA, Gorsich EE. The cause and consequences of parasite interactions: African buffalo as a case study. In: Wilson K, Fenton A, Tompkins D, editors. Wildlife Disease Ecology: Linking Theory to Data and Application, 1st ed. Cambridge University Press; 2019. 15. Graham AL, Cattadori IM, Lloyd-Smith JO, Ferrari MJ, Bjørnstad ON. Transmission consequences of coinfection: cytokines writ large? Trends Parasitol. 2007;23:284–291.

16. Peel AJ, Wells K, Giles J, Boyd V, Burroughs A, Edson D, et al. Synchronous shedding of multiple bat paramyxoviruses coincides with peak periods of Hendra virus spillover. Emerg Microbes Infect. 2019;8:1314–1323. 17. Alizon S, de Roode JC, Michalakis Y. Multiple infections and the evolution of virulence. Ecol Lett. 2013;16:556–567.

18. Lass S, Hudson PJ, Thakar J, Saric J, Harvill E, Albert R, et al. Generating super-shedders: co-infection increases bacterial load and egg production of a gastrointestinal helminth. J R Soc Interface. 2012;10:20120588.

19. Jolles AE, Ezenwa VO, Etienne RS, Turner WC, Olff H. Interactions between macroparasites and microparasites drive infection patterns in free-ranging African buffalo. Ecology. 2008;89:2239–2250. 20. Wuerthner VP, Hua J, Hoverman JT. The benefits of coinfection: trematodes alter disease outcomes associated with virus infection. J Anim Ecol. 2017;86:921–931.

21. Pantin-Jackwood MJ, Costa-Hurtado M, Miller PJ, Afonso CL, Spackman E, Kapczynski RD, et al. Experimental co-infections of domestic ducks with a virulent Newcastle

Isoniazid-Resistant Tuberculosis

disease virus and low or highly pathogenic avian influenza viruses. Vet Microbiol. 2015; 177:7–17.

22. Reindel R, Fiore CR. Phage therapy: considerations and challenges for development. Clin Infect Dis. 2017;64:1589–1590.

23. Altizer S, Dobson A, Hosseini P, Hudson P, Pascual M, Rohani P. Seasonality and the dynamics of infectious diseases. Ecol Lett. 2006;9:467–484.

24. Serrano E, Millán J. What is the price of neglecting parasite groups when assessing the cost of co-infection? Epidemiol Infect. 2014;142:1533–1540. 25. Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL, et al. Global trends in emerging infectious diseases. Nature. 2008;451:990–993. 26. Daszak P. Emerging infectious diseases of wildlife—Threats to biodiversity and human health. Science. 2000;287:443–449.

27. Belak S, Karlsson OE, Leijon M, Granberg F. High-throughput sequencing in veterinary infection biology and diagnostics. Rev Sci Tech OIE. 2013;32:893–915.

28. Razzauti M, Galan M, Bernard M, Maman S, Klopp C, Charbonnel N, et al. A comparison between transcriptome sequencing and 16s metagenomics for detection of bacterial pathogens in wildlife. PLoS Negl Trop Dis. 2015;9: e0003929.

15.12 Isoniazid-Resistant Tuberculosis: A Problem We Can No Longer Ignore Giorgia Sulis, MD, PhD,a,b and Madhukar Pai, MD, PhD a,b,c aDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada bMcGill International TB Centre, McGill University, Montreal, Quebec, Canada cManipal McGill Program for Infectious Diseases, Manipal Centre for Infectious Diseases, Manipal Academy of Higher Education, Manipal, Karnataka, India

[email protected]

For decades, people working in tuberculosis (TB) knew that monoresistance to isoniazid (INH) was common. INH has been in clinical use since the 1950s, and drug resistance was expected because its use became widespread. But this knowledge did not necessarily lead to testing for INH-resistant, rifampicin-susceptible tuberculosis (Hr-TB) or to the use of special drug regimens for this form of TB. Indeed, for decades, no drug-susceptibility testing (DST) for any drug was done unless patients failed first-line therapy or had risk factors for drug-resistant TB (DR-TB). Simply put, we chose to ignore the problem. When the TB world woke up to the need for universal DST and included it as a key goal in the End TB Strategy released in 2015, the focus became rapid testing for rifampicin resistance (RR) as a means of achieving universal DST. Novel technologies such as Xpert MTB/RIF (Cepheid, Sunnyvale, CA, USA) were rolled out in 2010, but the technology did not include INH-resistance testing [1]. Even today, access to any DST remains low, and when performed, DST is often limited to RR [2].

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In 2020, we can no longer hide from this worrisome problem because Hr-TB is much more common than RR and could seriously jeopardize progress in the fight against TB. This is confirmed by an analysis of aggregated drug resistance data from 2003 to 2017 across 156 countries presented in a research study by Dean and colleagues in PLOS Medicine, showing that—on average—7.4% (95% CI 6.5–8.4) of new cases and 11.4% (9.4–13.4) of previously treated patients have Hr-TB [3]. The overall prevalence of INH resistance (with or without concomitant RR) ranged between 10.7% (9.6–11.9) and 27.2% (24.6–29.9) depending on the treatment history and reached even more alarming levels in certain countries, particularly in the European and Western Pacific regions. The analysis by Dean and colleagues highlights major flaws in national surveillance systems, which go hand in hand with limited laboratory capacity. The small sample sizes available from some countries make national prevalence estimates imprecise. Furthermore, the diversity of detection methods employed across settings along with the widespread lack of quality control underscores the need for improved surveillance by countries. From a clinical standpoint, if INH resistance is not detected, new patients are managed as if they had pansusceptible TB, with a substantially increased risk of treatment failure or relapse and a greater propensity to acquire further resistance [4]. Yet, most research and policy efforts so far have been focused solely on RR as a proxy for multidrug-resistant tuberculosis (MDR-TB). This means that hundreds of thousands of patients with Hr-TB are staying in the shadows, not receiving appropriate care, and all too often ending up developing MDR-TB. Thankfully, some progress has been made in the last couple of years. In 2018, the World Health Organization (WHO) issued new treatment recommendations for Hr-TB, replacing the previous 9-month course of rifampicin, pyrazinamide, and ethambutol (RZE) with a 6-month regimen based on levofloxacin plus RZE [5, 6]. This recommendation is reflected in the updated guideline for DR-TB jointly released in 2019 by the American Thoracic Society, Centers for Disease Control and Prevention, European Respiratory Society, and Infectious Disease Society of America, which also suggests reducing the duration of pyrazinamide to 2 months in case of noncavitary and lower-burden disease or if a high risk of pyrazinamide­ induced toxic effects are anticipated [7]. However, high-TB-burden countries will struggle to routinely implement these new guidelines for Hr-TB because easy access to INH-resistance testing is a challenge. Although there are WHO-endorsed technologies (such as line-probe assays and liquid cultures) that can detect INH resistance, these tools are limited to centralized or reference laboratories [8]. Also, even in settings where the prevalence of Hr-TB is the highest, starting a quinolone-containing regimen empirically requires caution, in spite of the low levels of resistance to levofloxacin and/or pyrazinamide, as documented by a recent surveillance project [9]. The year 2020 might bring some hope, as the Xpert MTB/XDR cartridge (Cepheid, Sunnyvale, CA, USA) is expected to be released and will include

Disclosures and Conflict of Interest

resistance testing for INH, fluoroquinolones, and second-line injectables [10]. The TB diagnostics pipeline also includes several next-generation, high-throughput molecular tests that are able to simultaneously detect rifampicin and INH resistance in centralized laboratories [11, 12]. Whole-genome sequencing (WGS) data continue to shed light on the wide range of mutations associated with drug resistance, thus not only improving our understanding of transmission dynamics but also helping to refine therapeutic choices for the individual patient [13]. Importantly, a deeper examination of the genotypic diversity of INH resistance would be of great benefit to inform treatment guidelines that are currently based on low-quality evidence [6]. At present, nationwide scale-up of WGS for routine TB diagnosis may not seem within reach in most high-TB-burden countries owing to cost and infrastructure requirements. However, this technology is becoming cheaper and easier and offers an incredible opportunity to generate better quality information on INH resistance, thanks to its ability to detect clinically relevant mutations that are not captured by conventional rapid tests, which usually target only the most common katG and inhA mutations [13, 14]. Recently, Unitaid and the Foundation for Innovative New Diagnostics (FIND) have partnered to launch the Seq&Treat project that will pilot next-generation genome sequencing, an innovation that will enable fast, accurate diagnosis of DR-TB [15]. Beyond technologies and guidelines, we need to acknowledge the human impact of DR-TB, which imposes a tremendous physical, mental, financial, and social burden on patients. In addition to the 3 million TB patients who fail to get diagnosed or notified [11], there are large numbers of people with Hr-TB who are misdiagnosed and consequently mismanaged. Therefore, access to quality TB care is a human rights issue. In 2020, quality TB care must include universal DST—not just RR testing—for all individuals with TB, followed by individualized therapy, based on DST results. Disclosures and Conflict of Interest

This section was originally published as: Sulis, G., Pai, M. (2020). Isoniazid-resistant tuberculosis: A problem we can no longer ignore. PLoS Med., 17(1), e1003023, https:// doi.org/10.1371/journal.pmed.1003023, under the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates. Funding: The authors received no specific funding for this work.

Competing interests: The authors of this section have the following competing interests: MP serves on the editorial boards of PLoS Medicine and PLoS One and is a co-editor of the PLOS Tuberculosis Channel. He has no financial or industry interests to disclose.

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References 1. Automated Real-Time Nucleic Acid Amplification Technology for Rapid and Simultaneous Detection of Tuberculosis and Rifampicin Resistance: Xpert MTB/RIF Assay for the Diagnosis of Pulmonary and Extrapulmonary TB in Adults and Children: Policy Update. Geneva: World Health Organization (WHO), 2013. Available at: https:// apps.who.int/iris/handle/10665/112472 (accessed on May 17, 2021)

2. Kendall EA, Sahu S, Pai M, Fox GJ, Varaine F, Cox H, et al. What will it take to eliminate drug-resistant tuberculosis? Int J Tuberc Lung Dis. 2019;23(5):535–46.

3. Dean AS, Zignol M, Cabibbe AM, Falzon D, Glaziou P, Cirillo DM, et al. Prevalence and genetic profiles of isoniazid resistance in tuberculosis patients: a multicountry analysis of cross-sectional data. PLoS Med. 2020;17(1): e1003008.

4. Gegia M, Winters N, Benedetti A, van Soolingen D, Menzies D. Treatment of isoniazidresistant tuberculosis with first-line drugs: a systematic review and meta-analysis. Lancet Infect Dis. 2017;17(2):223–34.

5. WHO treatment guidelines for isoniazid-resistant tuberculosis: Supplement to the WHO treatment guidelines for drug-resistant tuberculosis. Geneva: World Health Organization (WHO), 2018. Available at: https://www.who.int/tb/publications/2018/ WHO_guidelines_isoniazid_resistant_TB/en/ (accessed on May 17, 2021). 6. Fregonese F, Ahuja SD, Akkerman OW, Arakaki-Sanchez D, Ayakaka I, Baghaei P, et al. Comparison of different treatments for isoniazid-resistant tuberculosis: an individual patient data meta-analysis. Lancet Respir Med. 2018;6(4):265–75.

7. Nahid P, Mase SR, Migliori GB, Sotgiu G, Bothamley GH, Brozek JL, et al. Treatment of drug-resistant tuberculosis. An Official ATS/CDC/ERS/IDSA Clinical Practice Guideline. Am J Respir Crit Care Med. 2019;200(10):e93–e142.

8. Talbot EA, Pai M. Tackling drug-resistant tuberculosis: we need a critical synergy of product and process innovations. Int J Tuberc Lung Dis. 2019;23(7):774–82.

9. Zignol M, Dean AS, Alikhanova N, Andres S, Cabibbe AM, Cirillo DM, et al. Populationbased resistance of Mycobacterium tuberculosis isolates to pyrazinamide and fluoroquinolones: results from a multicountry surveillance project. Lancet Infect Dis. 2016;16(10):1185–92.

10. Chakravorty S, Roh SS, Glass J, Smith LE, Simmons AM, Lund K, et al. Detection of isoniazid-, fluoroquinolone-, amikacin-, and kanamycin-resistant tuberculosis in an automated, multiplexed 10-color assay suitable for point-of-care use. J Clin Microbiol. 2017;55(1):183–98.

11. Global tuberculosis report 2019. Geneva: World Health Organization (WHO), 2019. Available at: https://www.who.int/teams/global-tuberculosis-programme/tb-reports/ global-report-2019 (accessed on May 17, 2021). 12. Pipeline Report 2019. Tuberculosis diagnostics. New York: Treatment Action Group (TAG); 2019. Available at: https://www.treatmentactiongroup.org/resources/pipelinereport/2019-pipeline-report/ (accessed on May 17, 2021). 13. Temesgen Z, Cirillo DM, Raviglione MC. Precision medicine and public health interventions: tuberculosis as a model? Lancet Public Health. 2019;4(8):e374.

References

14. Meehan CJ, Goig GA, Kohl TA, Verboven L, Dippenaar A, Ezewudo M, et al. Whole genome sequencing of Mycobacterium tuberculosis: current standards and open issues. Nat Rev Microbiol. 2019;17(9):533–45. 15. Unitaid’s investment in tuberculosis hits record levels with new grant for diagnostic technologies. Unitaid. July 4, 2019. Available at: https://unitaid.org/news-blog/ unitaids-investment-in-tuberculosis-hits-record-levels-with-new-grant-for-diagnostic­ technologies/#en (accessed on May 17, 2021).

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Chapter 16

Bacterial Virulence Plays a Crucial Role in Methicillin-Resistant S. aureus (MRSA) Sepsis Gordon Y. C. Cheung, PhD,a Justin S. Bae,a Ryan Liu, MSc, Rachelle L. Hunt,a,b Yue Zheng,a and Michael Otto, PhDa aPathogen Molecular Genetics Section, Laboratory of Bacteriology,

National Institute of Allergy and Infectious Diseases, National Institutes of Health,

Bethesda, Maryland, USA

bCurrent address: Yale University, New Haven, Connecticut, USA

[email protected]

Keywords: sepsis, bacteremia, Staphylococcus aureus, methicillin-resistant S. aureus, quorum-sensing system accessory gene regulator, phenol-soluble modulin, neutropenia, cytokine storm, bacterial virulence, leukopenia, pathogenesis, pro-inflammatory cytokines, biofilms

16.1 Introduction Sepsis results from bacteremia, the invasion of bacteria into the bloodstream, and represents one of the most frequent causes of death. Globally, an estimated ~50 million cases of sepsis are reported with ~11 million fatalities [1]. In the United States, sepsis is the predominant cause of death in hospitalized patients and costs the healthcare system ~ $24 million annually [2, 3]. Sepsis is a life-threatening organ dysfunction syndrome that traditionally is believed to result from an overwhelming and dysregulated immune response to bacterial intruders [4–6]. One of the hallmarks of the inflammatory septic response is the production of specific pro-inflammatory cytokines, such as IL-6 or

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TNF-α, while anti-inflammatory cytokines, such as IL-4, are also produced to keep the inflammatory reaction under control [7]. However, the pathophysiology of sepsis is still not completely understood and there is considerable controversy regarding the role of immune suppression during sepsis [8–13] and whether cytokines are appropriate sepsis biomarkers [14–17]. Phylogenetically widely conserved bacterial surface structures, such as lipopolysaccharide (LPS) in Gram-negative and lipoteichoic acid and lipopeptides in Gram-positive bacteria, are commonly regarded as the primary triggers of the host inflammatory response to bacterial infection [18, 19]. However, this model cannot explain why some bacteria cause more severe and frequent cases of sepsis than others; and more recent reports have suggested a role of toxin-mediated bacterial virulence in the progression and outcome of sepsis [20–22]. Staphylococcus aureus is the leading cause of blood infections in industrialized nations and the pathogen causing the highest mortality [23, 24]. Antibiotic resistance as present in methicillin-resistant S. aureus (MRSA) leads to worsened clinical outcome of staphylococcal bacteremia [25]. Importantly, most MRSA blood infections originate from infections on indwelling medical devices, such as in the case of intravascular catheter-related S. aureus bacteremia [26, 27], and characteristically involve biofilm formation on the infected devices [28]. The quorum-sensing system Agr (accessory gene regulator) controls most virulence determinants in S. aureus, including toxins that lyse leukocytes and other immune evasion factors [29]. For that reason and in the light of frequent antibiotic resistance in S. aureus, quorum-quenching approaches that target Agr functionality are often proposed as potential alternatives to antibiotic-based treatment of S. aureus infection [30]. However, clinical and experimental data regarding the impact of Agr quorum-sensing on sepsis are somewhat contradictory. Most [31–34], yet not all [35], studies using isogenic S. aureus agr mutants in experimental bacteremia indicate a strong impact of Agr on mortality. Then again, there is increased incidence and association with disease severity of naturally occurring functionally Agr-deficient isolates from clinical cases of bacteremia [36–38]. What further complicates the matter is the fact that Agr also impacts biofilm formation via strict control of the phenol-soluble modulin (PSM) surfactant peptides [39, 40], which leads to increased biofilm extension and resistance to immune clearance of Agr-deficient strains during infection of indwelling medical devices [41–43]. Thus, as is the case for most bacteria causing bloodstream infections, the role of virulence and quorum-sensing in S. aureus sepsis remains incompletely understood [44]. This is to a large part due to the lack of detailed investigation of the events accompanying bacterial sepsis, such as interaction with leukocytes and cytokine production. Such investigation–particularly during early stages of sepsis—is hardly possible in the clinic and requires animal models. Furthermore, despite recently obtained insight into the role of quorum-sensing in S. aureus biofilm-associated infection [29, 41, 42, 45], biofilm-associated infection models

Results

have not yet been used to investigate the role of quorum-sensing in sepsis as its main clinically important complication. As a consequence, the often-claimed potential of quorum-sensing blockers for systemic S. aureus and MRSA infection lacks thorough experimental confirmation. Here, to understand the role of virulence and quorum-sensing in S. aureus sepsis, including sepsis of device-associated origin, we performed animal models of non-catheter-associated and catheter-associated sepsis using the MRSA strain USA300 (LAC), the most frequent cause of S. aureus infections in the Unites States [46]. In contrast to most previously published S. aureus infection studies, which were performed in mice, we focused on a rabbit model for the following reasons. First, only use of a larger animal allows repeated drawing of sufficient blood to monitor the development of sepsis-related parameters. Second, mice–in contrast to rabbits—are not sensitive to several S. aureus leukocidins [47], making rabbits a better choice for the analysis of the general impact of Agr, Agr-regulated virulence factors, and overall S. aureus virulence during systemic infection. Our results demonstrate a critical role of bacterial virulence, and particularly of S. aureus-neutrophil interaction, for the outcome of sepsis and highlight fundamental differences between device- and non-device-related origins of sepsis regarding the role of the impact of quorum-sensing. Overall, they discourage the use of quorum-quenching but underline the potential of approaches directly targeting leukocyte-bacteria interaction for the treatment of S. aureus sepsis.

16.2 Results

16.2.1 The Quorum-Sensing Virulence Regulator Agr Strongly Impacts Mortality in a Mouse Sepsis Model Mouse models of acute, systemic S. aureus infection that produce a sepsis scenario and pronounced mortality within days post infection have frequently been used before. This includes studies that compared agr mutants and corresponding isogenic parental strains [31–34]. To study the role of virulence and quorum-sensing in S. aureus sepsis, we here also first performed a mouse study comparing mortality due to infection with wild-type LAC versus isogenic Δagr bacteria. Additionally, we tested the impact of cyclophosphamide (CY), which depletes animals of leukocytes [48]. This analysis specifically addresses the impact of S. aureus virulence, because (i) phagocytosis by leukocytes, and among them particularly neutrophils, is known to represent the primary factor controlling S. aureus infection [49] and (ii) a wide array of S. aureus cytolytic toxins that target leukocytes, such as the bicomponent leukocidins, α-toxin and PSMs, are Agr-controlled and represent major virulence determinants of S. aureus, as demonstrated by the significant effects they have on the outcome of S. aureus infection [47, 50–52].

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Mice inoculated with equal amounts of the S. aureus wild-type strain LAC succumbed to infection much more rapidly than those infected with the isogenic Dagr mutant ( p < 0.0001) (Fig. 16.1A). In CY-treated mice, in contrast to nontreated mice, there was no significant difference in mortality between wild-type and Dagr-infected animals (Fig. 16.1B), confirming the idea of a major impact of bacteria-induced leukocyte lysis on mortality in this model. These results indicate that bacterial virulence and resulting leukocyte numbers have a determining impact on the outcome of S. aureus sepsis.

Figure 16.1 Mouse sepsis model. (A) Scheme of experimental setup and survival experiment. Survival of female C57BL/6NCrl mice following intravenous challenge with ~2–3 × 108 CFU of S. aureus LAC or its isogenic agr deletion strain (n = 10/group) was recorded. Animals were monitored for survival up to 52 h. Statistical analysis is by log-rank (Mantel-Cox) test. (B) Survival experiment under CY treatment. Scheme of experimental setup and survival data. Survival of female C57BL/6NCrl mice following intravenous challenge with 106 CFU of S. aureus LAC or its isogenic Dagr deletion strain (n = 10/group) was recorded. Statistical analysis is by log-rank (Mantel-Cox) test. Abbreviation: CY, cyclophosphamide. Mouse picture is from openclipart.org.

16.2.2 The Impact of Agr on Sepsis-Related Mortality Is Strongly Dependent on Whether the Infection Is Device-Associated

As already mentioned, the mouse is a suboptimal model organism to study the impact of virulence on sepsis caused by S. aureus. We therefore performed all following experimentation in rabbits to confirm and further elaborate on the findings we had achieved in mice. In these experiments, we included a catheterassociated model to assess the specific impact of device-associated infection as the most frequent type of staphylococcal blood infection.

Results

Figure 16.2 Rabbit sepsis models–experimental setup and survival. (A) Experimental setup of catheter- and non-catheter-associated model. The models were set up essentially in the same fashion, except that in the catheter-associated model, a silastic CVC was inserted 7 days before injection of 108 CFU of S. aureus LAC or its isogenic Dagr deletion strain and additional 16- and 20 h samples (dashed circles) were only taken in the catheterassociated model. (B) Survival curve in the non-catheter associated (n = 9/group) and (C) of the catheter-associated model (n = 12/group). The non-catheter-associated model was performed two independent times (n = 4/group and n = 5/group, respectively; total, n = 9 animal/group). The catheter-associated model was performed two independent times with n = 6 animals/group each (total, n = 12 animals/group). Data obtained with the two batches were combined in both models. Statistical analysis is by the indicated tests. Rabbit picture is from openclipart.org.

In a non-catheter-related infection setup, essentially reproducing the above mouse model in rabbits (Fig. 16.2A), we observed a slightly increased mortality rate in animals infected with the wild-type as compared to those infected with the Dagr strain (Fig. 16.2B). This difference was only statistically significant in the Gehan-Breslow-Wilcoxon analysis, which attributes more weight to early time points (p = 0.038), but not in the usual log-rank test (p = 0.15). In contrast, in a

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setup with an inserted silastic central venous catheter (CVC), the Dagr strain produced significantly higher mortality using both statistical analysis methods (p = 0.0011, log-rank; p = 0.0013, Gehan-Breslow-Wilcoxon) (Fig. 16.2C). CFU over the time course of infection in the blood and organs reflected the mortality results, inasmuch as they were (i) increased in moribund animals, at least in the blood and some organs, (ii) similar between wild-type and Dagr-infected mice in the non-catheter-associated model, and (iii) overall higher in Dagr-infected rabbits in the catheter-associated model (Figs. 16.3A, 16.3B, 16.4A and 16.4B). There were no apparent differences in weight loss but we observed decreased rectal temperature in moribund animals (Fig. 16.S1). Notably, most (10/12) rabbits infected with the Δagr strain developed biofilms on the device and of these

Figure 16.3 Rabbit non-catheter-associated sepsis model–CFU in blood and organs. (A) Bacterial CFU in the blood over time. (B) Bacterial CFU at the time of death or the end of the experiment in the indicated organs. Empty symbols represent animals that died during the observation period.

Results

Figure 16.4 Rabbit catheter-associated sepsis model–CFU in blood, organs, and on catheters. (A) Bacterial CFU in the blood over time. (B) Bacterial CFU at the time of death or the end of the experiment in the indicated organs. (C) Bacterial CFU in the lumina of implanted catheters. Data are from the second batch of rabbits only (n = 6/group). The dotted line depicts the threshold above which CFU values were considered to reflect biofilm formation in both batches of rabbits. (A–C) Rabbits that developed catheter biofilms are marked by open symbols and light-colored borders.

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rabbits, almost all (9/10) showed high blood and organ CFU and succumbed to the infection, whereas only one (1/12) rabbit infected with the wild-type strain died (Fig. 16.4A and 16.4C). (The latter finding contrasts the results in the non-catheter-associated model considering that the same inoculum was used, but is probably to be explained by the catheters sequestering the bacteria and thus initially removing them from the bloodstream.) Only one rabbit infected with the wild-type strain developed a biofilm. There were highly significant correlations between negative Agr status and death (Fisher’s exact test, p = 0.0028) and between biofilm formation and death (Fisher’s exact test, p = 0.0005). Together, these results show that (i) in rabbits the observed impact of Agr on the outcome of sepsis is much weaker than in mice and (ii) the effect is reversed in device-associated infection, where Agr deficiency causes higher mortality in apparent association with biofilm formation on the device.

16.2.3 Early Neutropenia Is Associated with a Fatal Outcome of Sepsis

Leukocyte numbers decreased in the early hours of infection in the non-catheterrelated infection model, while they increased again in survivors (Figs. 16.5A, 16.5B and 16.S2). This was most pronounced in neutrophils (Fig. 16.5B), but less obvious in the catheter-related model, probably due to attachment of bacteria to the device and slow release from the formed biofilm (Fig. 16.5C and 16.5D). We then analyzed whether early leukopenia and specifically neutropenia is correlated with outcome of infection and Agr status. Numbers of total leukocytes and all leukocyte types except monocytes were significantly lower in animals that ultimately died during early infection in the catheter-related model, and neutrophil numbers were significantly lower in both models (Figs. 16.5E–5H and 16.S2). The most important conclusion from this analysis is that early neutropenia is correlated with fatality in both device- and non-device associated sepsis. Notably, confirming the opposite impact of Agr status on infection outcome in catheter- versus non-catheter-related infection, total leukocyte and neutrophil numbers early during infection were significantly lower in the wild-typeas compared to Dagr-infected animals in the non-catheter-related model, while they were significantly higher in the catheter-related model (Fig. 16.5I–16.5L). A “cytokine storm” is often regarded a hallmark of sepsis [7]. We therefore analyzed the concentration over time of specific cytokines often reported to change in concentration during sepsis. IL-6 concentrations were very strongly increased over the course of infection in both infection models and were generally higher in moribund animals than in those that recovered (Figs. 16.6A and 16.6G). Concentrations of TNF-α followed a similar pattern but with much lower overall changes, and this was only observed in the non-catheter-associated model (Figs. 16.6B and 16.6H). In contrast, concentrations of the anti-inflammatory cytokine IL-4 were overall reduced compared to baseline (Figs. 16.6C and 16.6I). These findings reflect previous reports on cytokine concentrations during sepsis [7].

Results

Figure 16.5 Rabbit sepsis models–leukocyte numbers. (A–D) WBC and neutrophil numbers over time in the non-catheter-associated (A, B) and catheter-associated (C, D) model. (C, D) Rabbits that developed catheter biofilms are marked by open symbols and lightcolored borders. (E–H) Analysis of death versus survival outcome based on the averages of leukocyte numbers of every animal in the time window 6–24 h (grey shading) in panels A–D. (I–L) Analysis of impact of Agr status on the averages of WBC and neutrophil numbers in the time window 6–24 h (grey shading) in panels A through D. (E–L) Statistical analysis is by unpaired two-tailed t-tests. Error bars show the mean ± SD. Abbreviation: NS, not significant; WBC, white blood cells (p ≥ 0.05). See Fig. 16.S2 for monocyte and lymphocyte analyses.

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However, cytokine concentrations during the analyzed initial time window were not correlated with infection outcome, indicating that they are not an early predictor of infection outcome (Figs. 16.6D, 16.6E, 16.6F, 16.6J, 16.6K and 16.6L). Consistently with the lack of correlation between cytokine concentration and death, there was also no significant correlation with Agr status (Fig. 16.S3).

Figure 16.6 Rabbit sepsis model–cytokine levels. (A–C) Fold-changes of the indicated cytokines over time in the non-catheter-associated model. (D–F) Analysis of death versus survival outcome based on average cytokine fold-changes in the time window 6–24 h (grey shading) in panels A–C. (G–I) Fold-changes of the indicated cytokines over time in the catheterassociated model. (J–L) Analysis of death versus survival outcome based on average cytokine fold-changes in the time window 6–24 h (grey shading) in panels G–I. (D–F, J–L) Statistical analysis is by unpaired two-tailed t-tests. Error bars show the geometric mean and geometric SD. Abbreviation: NS, not significant (p ≥ 0.05). See Fig. 16.S3 for the corresponding analysis of Agr status for both rabbit models.

16.3 Discussion

Bacterial virulence has not traditionally been regarded as a primary factor determining the outcome of sepsis. This notion is based on conventional models

Discussion

that attribute sepsis to an overwhelming immune reaction to widely conserved bacterial surface structures [4–6] or more recent reports that emphasize the role of a later stage of immune suppression [8–13]. These models, which explain differences in sepsis outcome predominantly by host factors, can hardly explain the clinically observed increased morbidity and mortality that is associated with specific pathogens such as S. aureus [53]. In this study we hypothesized that bacterial virulence significantly affects sepsis development and outcome. To evaluate that hypothesis, we analyzed the role of the global virulence regulator Agr and the association of leukopenia with mortality in acute blood infection caused by MRSA as a leading cause of sepsis. Agr and leukocyte killing are considered major parameters of S. aureus and MRSA virulence: Agr is a major regulator of virtually all S. aureus toxins, among them many leukocidins [29, 52], and while only directly demonstrated in a pneumonia model [54], leukopenia is the assumed in-vivo consequence of their activity. We chose these parameters as they allow for a better general evaluation of our hypothesis than the analysis of specific toxins, given the plethora of S. aureus toxins and their functional redundancy [55]. Our findings on mortality being associated with the virulence regulator Agr in addition to the absence of any effect under CY treatment indicate a significant impact of bacterial virulence on the outcome of sepsis that occurs via leukocyte killing. This notion is also supported by the kinetics of the observed leukopenia, because overall leukocyte and neutrophil numbers first increased, but then rapidly decreased, strongly suggesting that low leukocyte concentrations in moribund animals were a result of lysis rather than leukocyte migration into organs, which happens much earlier, at ~ 5 min post infection [56]. This migration-dependent leukopenia is short-timed and leukocyte numbers return to normal at 60 min, which is long before the effects we measured [56]. Our study revealed differences in mouse versus rabbit test animals regarding the relative impact of Agr during non-catheter-associated infection, inasmuch as the relative impact of Agr was stronger in mice. We believe this to be due to the fact that mice are immune to many leukocidins owing to the lack of responsive receptors [47]. While rabbits are therefore overall more sensitive to infection by S. aureus, the relative impact of Agr–which increases with the relative Agr dependence of toxin expression–is greater in mice, as in that species it is mostly dependent on PSMs, which are under much stricter Agr control than the bicomponent leukocidins [40, 52]. As rabbits more closely resemble humans in their responsiveness to leukocidins, this finding indicates that the impact of Agr on systemic infection may be overestimated in the mouse infection model. Furthermore, we are aware of the fact that these toxins not only have cytolytic, but also pro-inflammatory capacities, often detected at concentrations much lower than those that are cytolytic [57, 58]. Moreover, some toxins, such as PSMs and LukAB/GH, are assumed to contribute to leukocyte lysis after phagocytosis rather than by toxin release into the bloodstream [59, 60]. Which specific leucocidin activities contribute to pathogenesis, including during different experimental setups that may differ for example in the initial bacterial inoculum, is not understood.

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While our study was focused on assessing the role of bacterial virulence, it gave important information also regarding immune regulation in sepsis. Notably, in our animal studies we analyzed very early stages of sepsis, which is not commonly possible in the clinic. We found that early leukopenia (particularly neutropenia), but not early increase of pro-inflammatory cytokines, was strongly correlated with mortality. Our results indicate that immune suppression early during infection plays a critical role for the development of sepsis. This highlights the importance of different stages of sepsis regarding inflammation and immune suppression. Of note, the early immune suppression we observed, which is mediated by bacterial virulence, is different in character and timing from the previously described late immune suppression that is considered a host effect [8–13]. While many animal models of device-associated infection have been performed, particularly using S. aureus as pathogen [28, 41, 42, 61–64], we here also performed what to the best of our knowledge represents the first experimental assessment of device-associated sepsis. As mentioned above, this is of particular importance given that most cases of staphylococcal sepsis originate from the infection of indwelling medical devices, which due to the constant seeding from the bacterial biofilm on the infected device represents a completely different scenario [26, 27]. Results of the device-associated infection model generally reflected those of the non-device-associated model as for bacteria-leukocyte interaction, but were opposite regarding the role of Agr. Due to complete absence of the PSMs, which are important for biofilm structuring and turnover [39, 42, 65], Agr-deficient S. aureus are known to form extended biofilms in vitro [43] and on indwelling medical devices in vivo [41, 42]. However, it has not been previously investigated how this situation impacts the development of sepsis as the most severe complication of device-associated infection. Our results demonstrated increased mortality, accompanied by increased bacterial presence in blood and organs, in rabbits infected with Agr-deficient as compared to those infected with wild-type MRSA, effects that were more pronounced in animals in which biofilms had formed on the device. Only one animal infected with wild-type bacteria developed a biofilm on the device, but did not show comparably severe disease parameters, in accordance with the notion that in-vivo biofilms formed by wildtype S. aureus are less extended and less resistant to removal by phagocytes than those formed by Agr-deficient strains [41]. These results show that Agr dysfunction and concomitant biofilm formation on the indwelling device causes increased development of sepsis, most likely caused by constant seeding of bacteria from the device. Notably, they help to explain the clinical observation of a high percentage of functionally Agr-deficient isolates found in cases of S. aureus blood infection [36]. Our results have important implications for the suggested application of quorum-sensing blockers for the treatment of S. aureus systemic infection. Such drugs have often been proposed and reported to have efficacy in local and occasionally systemic S. aureus infections [66–68]. An inherent problem with

Materials and Methods

the interpretation of results achieved with quorum-sensing blockers is the frequently narrow window between genuine quorum-sensing blocking efficacy and bactericidal effects. The comparison of wild-type with isogenic Δagr S. aureus thus gives a better idea than the use of quorum-sensing blocking drugs about whether Agr is a promising target. The frequent involvement of devices in S. aureus bacteremia [26, 27], the fact that clinical treatment even of sepsis without a device origin often requires placement of devices such as catheters, and the increased pathogenesis associated with Dagr S. aureus in device-associated systemic infection that we found argue against using quorum-sensing blockers for systemic S. aureus infections. Because strict control of PSMs by Agr is the underlying cause for these phenomena [40, 42, 65], specifically targeting PSMs also does not appear to be a promising drug development strategy for blood infections. Our results on the importance of leukocyte-bacteria interaction for the outcome of sepsis rather suggest that protection of leukocytes from bacterial attacks, such as by direct interference with non-PSM bacterial leukocidins [69–71], bears promise for the treatment of device- or non-device-associated S. aureus blood infection. In conclusion, in this study we provide evidence for a major contribution of bacterial virulence to sepsis and show that early neutropenia is a predictor for sepsis outcome. These results call for an adjustment of the traditional notion that the severity of bacterial sepsis is predominantly attributable to host factors. In addition, our results give important mechanistic information regarding deviceassociated staphylococcal sepsis to guide the development of alternative antivirulence strategies.

16.4 Materials and Methods 16.4.1 Ethics Statement

Mouse and rabbit experiments were performed under NIAID animal study protocols LB1E and LB2E, respectively. Both protocols were reviewed and approved by the Animal Care and Use Committee (NIAID, NIH) and are in accordance with the Animal Welfare Act of the United States (7 U.S.C. 2131 et. seq.).

16.4.2 Bacterial Strains and Growth Conditions

S. aureus strains used in this study were LAC (CA-MRSA, pulsed-field type USA300) [72] and its isogenic agr mutant [57]. Bacteria were prepared for infections as described previously [20].

16.4.3 Mouse Model of Non-Catheter-Associated Blood Infection

The mouse sepsis model was performed using female, 8–10 week-old, C57BL/6NCrl mice (Charles River Laboratories) as described previously [73] using tail vein injection with the following modifications. Mice received ~ 2–3 × 108 CFUs of live

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S. aureus. The health status of challenged mice was recorded every 2 h for the first 12 h, every 4 h for the following 12 h, and every 8 h thereafter until the study ended (52 h). Mice that prematurely reached the end point (defined as having a head tilt, microphthalmia, lack of response to prodding, immobility, or >20% weight loss from the pre-infection baseline) during the study and those that survived to the end of the study were anesthetized with isoflurane. For depletion of leukocytes in naïve mice, cyclophosphamide monohydrate (CY, Sigma), diluted in sterile PBS, was injected intraperitoneally at 250 mg/kg each day for 3 days prior to intravenous infection with 1 × 106 CFUs of bacteria on day 4. This lower dose of bacteria was chosen because of generally increased sensitivity to infection of CY-treated animals. CBC analyses of blood from CY-treated mice confirmed significantly reduced numbers of WBCs (~20/ml) compared to untreated mice (~8000/ml). The health status of challenged mice was recorded as described above until the study ended.

16.4.4 Rabbit Models of Systemic Catheter- and Non-CatheterAssociated Blood Infection

Female, 2–3 kg, New Zealand White rabbits (Charles River Laboratories) were housed singly and allowed to acclimatize in an American Association for the Accreditation of Laboratory Animal Care (AAALAC)–approved NIH facility for a minimum of 7 days prior to introduction of catheters and/or infection with S. aureus. Vascular catheterization was performed using in-house made sterilized CVCs, composed of silastic catheters (1.02 × 2.16 mm, Dow Corning) attached to 18gauge intramedic Luer stub adapters (Becton Dickenson). CVCs were sterilized with ethylene oxide gas and then cut to length for each rabbit. Hair, over the right cervical, right shoulder and scapular region for each rabbit was removed. Under anesthesia, a 2- to 3-cm-long skin incision was made on the right anterolateral cervical region to expose the external jugular vein. A small incision was then made through the vein wall and a sterile-saline filled CVC was threaded into the vein and forwarded until the catheter cuff was contiguous with the vein. The proximal end of the CVC was exteriorized by a subcutaneous tunnel created by threading it through a sterile trocar (1 × 34 cm) underneath the skin from the external jugular incision site to an exit site through the intrascapular skin. The skin incision over the external jugular vein was then closed and sutured. The proximal part of the catheter was attached to the metal end of the Luer stub and a needleless injection cap, which was secured to the skin exit with 2–0 silk sutures and tied together to prevent displacement [74]. Two-way flow was performed to confirm integrity of the catheter with up to 1 ml of sterile pharmaceutical-grade heparin in physiological saline solution (100 units/ml). Catheterized rabbits were placed on post-operative observation for 7 days post-surgery prior to intravenous infection with S. aureus.

Materials and Methods

For pre-infection baselines, weights and rectal temperatures were taken prior to infection. Additionally. blood samples were drawn from the ear artery or via the catheter of non-catheterized and catheterized rabbits, respectively. Blood samples were drawn from the ear artery and weights and rectal temperatures were taken prior to infection for pre-infection baselines. Rabbits (with and without catheters) were then infected via the marginal ear vein with 1 ml of bacteria at a concentration of 1 × 108 CFUs/ml in sterile PBS. Approximately 0.5-ml volumes of blood were sampled 2, 4, 6, 8, 12, and 24 h post infection from the marginal ear vein of non-catheterized rabbits, and 2, 4, 6, 8, 12, 16, 20 and 24 h post infection via the catheter of catheterized rabbits, for quantitative blood culture, cytokine measurements and CBC analyses. The health status, weights and rectal temperatures were recorded every 2 h post infection for the first 8 h, every 4 h for the following 16 h, and then every 8 h thereafter until the end of the study (96 h). Rabbits prematurely reaching end-point (defined as having neurological signs, abdominal breathing, cyanosis, a lack of response to prodding, immobility, or >15% weight loss from the pre-infection baseline for two consecutive days) during the study and those that survived to the end of the study, were anesthetized with ketamine and xylazine and terminally exsanguinated. Rabbits were then euthanized with Beuthanasia D (Schering-Plough Animal Health Corp) and the livers, kidneys, spleens, lungs and any catheters were surgically removed. Enumeration of CFUs from organ homogenates was performed as previously described [75]. The catheter lumina were flushed several times with a total of 0.5 ml of sterile PBS each, homogenized, and serial dilutions of the contents were plated onto TSA plates for enumeration of CFU. Biofilm formation was defined as a result of > 7500 CFU/ml. No biofilm formation was defined as a result of < 200 CFU/ml (detection limit). There were no catheters with CFU between these two values.

16.4.5 CFU Enumeration, Cytokine Measurements and Complete Blood Count Analyses from Blood

Blood samples were distributed into tubes containing serum clotting gel, heparin, and ethylenediaminetetraacetic acid (EDTA) (Sarstedt) and then inverted immediately. The total volume of blood draws did not exceed those determined by Comparative Medicine Branch Standard Operating Protocol guidelines. The concentrations of IL-4, IL-6, and TNF-α in rabbit sera (stored at –20°C immediately after collection) were determined using Quantikine ELISA kits (R&D Systems) according to the manufacturer’s instructions. CFUs were enumerated from blood samples collected in heparin-containing tubes as described previously (20). CBCs were assessed in blood samples collected in EDTA-containing tubes within 3 h of collection using a ProCyte Dx Hematology Analyzer (IDEXX technologies). Automatic gating, defined by the software algorithms, was used to identify white blood cell differentials on all uninfected samples. For blood samples collected from infected animals, manual gating was applied to identify white blood cell differentials. Identical gates were applied across samples collected at each time point.

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16.4.6 Statistics Statistical analysis was performed using Graph Pad Prism for MAC version 8.3.0. Statistical analysis is by Log-rank (Mantel-Cox) and/or Gehan-Breslow-Wilcoxon tests for survival curves. For the comparison of two groups, unpaired two-tailed t-tests were used. All error bars depict the mean and standard deviation for non-logarithmic, or the geometric mean and geometric standard deviation for logarithmic scales.

16.5 Supporting Information

Figure 16.S1 Physiological parameters in the rabbit sepsis models. Weight change and rectal temperature over time in the non-catheter-associated and the catheter-associated sepsis models. The transient dip at ~ 48 h in weight for some animals in the catheterassociated model was due to those rabbits not eating. After critical care (forced feeding) was activated, they recovered.

Supporting Information

Figure 16.S2 Rabbit sepsis models–leukocyte numbers (monocytes, lymphocytes). (A–D) Monocyte and lymphocyte numbers over time in the non-catheter-associated (A, B) and catheter-associated (C, D) model. (C, D) Rabbits that developed catheter biofilms are marked by open symbols and light-colored borders. (E–H) Analysis of death versus survival outcome based on average monocyte and lymphocyte numbers for every animal in the time window 6–24 h (grey shading) in panels A–D. (I–L) Analysis of impact of Agr status on average monocyte and lymphocyte numbers for every animal in the time window 6–24 h (grey shading) in panels A through D. (C–L) Statistical analysis is by unpaired two-tailed t-tests. Error bars show the mean ± SD. Abbreviation: NS, not significant (p ≥ 0.05).

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Figure 16.S3 Impact of Agr status on cytokine levels early during infection. Fold-changes in average cytokine levels for every animal in the rabbit infection models in the early 6–24 h infection window were analyzed dependent on infection group (wild-type versus Dagr-infected animals). Statistical analysis is by unpaired two-tailed t-tests. Error bars show the geometric mean and geometric SD. Abbreviation: NS, not significant (p ≥ 0.05).

Abbreviations CVC: CY: EDTA: LPS:

central venous catheter cyclophosphamide ethylenediaminetetraacetic acid lipopolysaccharide

References

MRSA: PSM: WBC: NS:

methicillin-resistant S. aureus phenol-soluble modulin white blood cells not significant

Disclosures and Conflict of Interest This chapter was originally published as: Cheung, G. Y. C., Bae, J. S., Liu, R., Hunt, R. L., Zheng, Y., Otto, M. (2021). Bacterial virulence plays a crucial role in MRSA sepsis. PLoS Pathog., 17(2), e1009369, https://doi.org/10.1371/journal.ppat.1009369, under the Creative Commons Attribution license (http://creativecommons.org/ licenses/by/4.0/), and appears here, with edits and updates. Data Availability Statement: All relevant data are within the chapter and its Supporting Information.

Funding: This study was supported by the Intramural Research Program of

the National Institute of Allergy and Infectious Diseases (NIAID), U.S. National Institutes of Health (NIH), project number ZIA AI000904 (to M.O.). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the chapter. Competing interests: The authors have declared that no competing interests exist.

Acknowledgments: We would like to thank Dr. Binh A. Diep (UCSF, CA) for providing advice on the rabbit experiments, Dr. Marvin L. Thomas, Dr. Holly Habbershon, Mr. Keith Johnson, and Mr. John DeLeonardis at the Division of Veterinary Resources (DVR), NIH, for the surgical placement of catheters in rabbits and their invaluable technical expertise, and the technical staff at the DVR and Comparative Medicine Branch (CMB), NIH, for excellent technical support.

References

1. Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395(10219):200–11.

2. Liu V, Escobar GJ, Greene JD, Soule J, Whippy A, Angus DC, et al. Hospital deaths in patients with sepsis from 2 independent cohorts. JAMA. 2014;312(1):90–2.

3. Torio CM, Moore BJ. National inpatient hospital costs: the most expensive conditions by payer, 2013: statistical brief #204. Available at: https://pubmed.ncbi.nlm.nih. gov/27359025/ (accessed on June 2, 2021). 4. Cohen J. The immunopathogenesis of sepsis. Nature. 2002;420(6917):885–91.

5. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801–10.

6. Rossaint J, Zarbock A. Pathogenesis of multiple organ failure in sepsis. Crit Rev Immunol. 2015;35(4):277–91.

371

372

Methicillin-Resistant S. aureus Sepsis

7. Schulte W, Bernhagen J, Bucala R. Cytokines in sepsis: potent immunoregulators and potential therapeutic targets—an updated view. Mediators Inflamm. 2013;2013:165974.

8. Boomer JS, Green JM, Hotchkiss RS. The changing immune system in sepsis: is individualized immuno-modulatory therapy the answer? Virulence. 2014;5(1):45–56.

9. Boomer JS, To K, Chang KC, Takasu O, Osborne DF, Walton AH, et al. Immunosuppression in patients who die of sepsis and multiple organ failure. JAMA. 2011;306(23):2594–605.

10. Germain RN. Maintaining system homeostasis: the third law of Newtonian immunology. Nat Immunol. 2012;13(10):902–6. 11. Hotchkiss RS, Monneret G, Payen D. Sepsis-induced immunosuppression: from cellular dysfunctions to immunotherapy. Nat Rev Immunol. 2013;13(12):862–74.

12. van der Poll T, van de Veerdonk FL, Scicluna BP, Netea MG. The immunopathology of sepsis and potential therapeutic targets. Nat Rev Immunol. 2017;17(7):407–20.

13. van Dissel JT, van Langevelde P, Westendorp RG, Kwappenberg K, Frolich M. Antiinflammatory cytokine profile and mortality in febrile patients. Lancet. 1998;351 (9107):950–3. 14. Davies R, O’Dea K, Gordon A. Immune therapy in sepsis: Are we ready to try again? J Intensive Care Soc. 2018;19(4):326–44.

15. Faix JD. Biomarkers of sepsis. Crit Rev Clin Lab Sci. 2013;50(1):23–36.

16. Fjell CD, Thair S, Hsu JL, Walley KR, Russell JA, Boyd J. Cytokines and signaling molecules predict clinical outcomes in sepsis. PLoS One. 2013;8(11):e79207. 17. Gentile LF, Cuenca AG, Vanzant EL, Efron PA, McKinley B, Moore F, et al. Is there value in plasma cytokine measurements in patients with severe trauma and sepsis? Methods. 2013;61(1):3–9.

18. Bochud PY, Calandra T. Pathogenesis of sepsis: new concepts and implications for future treatment. BMJ. 2003;326(7383):262–6.

19. Nguyen MT, Gotz F. Lipoproteins of Gram-positive bacteria: key players in the immune response and virulence. Microbiol Mol Biol Rev. 2016;80(3):891–903.

20. Qin L, Da F, Fisher EL, Tan DC, Nguyen TH, Fu CL, et al. Toxin mediates sepsis caused by methicillin-resistant Staphylococcus epidermidis. PLoS Pathog. 2017;13(2):e1006153.

21. Choi VM, Herrou J, Hecht AL, Teoh WP, Turner JR, Crosson S, et al. Activation of Bacteroides fragilis toxin by a novel bacterial protease contributes to anaerobic sepsis in mice. Nat Med. 2016;22(5):563–7. 22. Johnsen N, Hamilton ADM, Greve AS, Christensen MG, Therkildsen JR, Wehmoller J, et al. alpha-Haemolysin production, as a single factor, causes fulminant sepsis in a model of Escherichia coli-induced bacteraemia. Cell Microbiol. 2019;21(6):e13017.

23. Laupland KB. Incidence of bloodstream infection: a review of population-based studies. Clin Microbiol Infect. 2013;19(6):492–500.

24. Shorr AF, Tabak YP, Killian AD, Gupta V, Liu LZ, Kollef MH. Healthcare-associated bloodstream infection: A distinct entity? Insights from a large U.S. database. Crit Care Med. 2006;34(10):2588–95.

25. van Hal SJ, Jensen SO, Vaska VL, Espedido BA, Paterson DL, Gosbell IB. Predictors of mortality in Staphylococcus aureus bacteremia. Clin Microbiol Rev. 2012;25(2):362–86.

References

26. Yousif A, Jamal MA, Raad I. Biofilm-based central line-associated bloodstream infections. Adv Exp Med Biol. 2015;830:157–79.

27. Fowler VG Jr., Olsen MK, Corey GR, Woods CW, Cabell CH, Reller LB, et al. Clinical identifiers of complicated Staphylococcus aureus bacteremia. Arch Intern Med. 2003;163(17): 2066–72. 28. Otto M. Staphylococcal biofilms. Microbiol Spectr. 2018;6(4):GPP3-0023-2018.

29. Le KY, Otto M. Quorum-sensing regulation in staphylococci-an overview. Front Microbiol. 2015;6:1174.

30. Dickey SW, Cheung GYC, Otto M. Different drugs for bad bugs: antivirulence strategies in the age of antibiotic resistance. Nat Rev Drug Discov. 2017;16(7):457–71. 31. Alonzo F 3rd, Benson MA, Chen J, Novick RP, Shopsin B, Torres VJ. Staphylococcus aureus leucocidin ED contributes to systemic infection by targeting neutrophils and promoting bacterial growth in vivo. Mol Microbiol. 2012;83(2):423–35.

32. Altman DR, Sullivan MJ, Chacko KI, Balasubramanian D, Pak TR, Sause WE, et al. Genome plasticity of agr-defective Staphylococcus aureus during clinical infection. Infect Immun. 2018;86(10):e00331-18.

33. Heyer G, Saba S, Adamo R, Rush W, Soong G, Cheung A, et al. Staphylococcus aureus agr and sarA functions are required for invasive infection but not inflammatory responses in the lung. Infect Immun. 2002;70(1):127–33.

34. Maurer K, Reyes-Robles T, Alonzo F 3rd, Durbin J, Torres VJ, Cadwell K. Autophagy mediates tolerance to Staphylococcus aureus alpha-toxin. Cell Host Microbe. 2015;17(4):429–40.

35. Rom JS, Atwood DN, Beenken KE, Meeker DG, Loughran AJ, Spencer HJ, et al. Impact of Staphylococcus aureus regulatory mutations that modulate biofilm formation in the USA300 strain LAC on virulence in a murine bacteremia model. Virulence. 2017;8(8): 1776–90.

36. Fowler VG Jr., Sakoulas G, McIntyre LM, Meka VG, Arbeit RD, Cabell CH, et al. Persistent bacteremia due to methicillin-resistant Staphylococcus aureus infection is associated with agr dysfunction and low-level in vitro resistance to thrombin-induced platelet microbicidal protein. J Infect Dis. 2004;190(6):1140–9.

37. Kang CK, Kim YK, Jung SI, Park WB, Song KH, Park KH, et al. agr functionality affects clinical outcomes in patients with persistent methicillin-resistant Staphylococcus aureus bacteraemia. Eur J Clin Microbiol Infect Dis. 2017;36(11):2187–91. 38. Schweizer ML, Furuno JP, Sakoulas G, Johnson JK, Harris AD, Shardell MD, et al. Increased mortality with accessory gene regulator (agr) dysfunction in Staphylococcus aureus among bacteremic patients. Antimicrob Agents Chemother. 2011;55(3):1082–7.

39. Cheung GY, Joo HS, Chatterjee SS, Otto M. Phenol-soluble modulins—critical determinants of staphylococcal virulence. FEMS Microbiol Rev. 2014;38(4):698–719.

40. Queck SY, Jameson-Lee M, Villaruz AE, Bach TH, Khan BA, Sturdevant DE, et al. RNAIIIindependent target gene control by the agr quorum-sensing system: insight into the evolution of virulence regulation in Staphylococcus aureus. Mol Cell. 2008;32(1):150–8.

41. He L, Le KY, Khan BA, Nguyen TH, Hunt RL, Bae JS, et al. Resistance to leukocytes ties benefits of quorum sensing dysfunctionality to biofilm infection. Nat Microbiol. 2019;4(7):1114–9.

373

374

Methicillin-Resistant S. aureus Sepsis

42. Periasamy S, Joo HS, Duong AC, Bach TH, Tan VY, Chatterjee SS, et al. How Staphylococcus aureus biofilms develop their characteristic structure. Proc Natl Acad Sci USA. 2012;109(4):1281–6. 43. Vuong C, Saenz HL, Gotz F, Otto M. Impact of the agr quorum-sensing system on adherence to polystyrene in Staphylococcus aureus. J Infect Dis. 2000;182(6):1688–93.

44. Painter KL, Krishna A, Wigneshweraraj S, Edwards AM. What role does the quorumsensing accessory gene regulator system play during Staphylococcus aureus bacteremia? Trends Microbiol. 2014;22(12):676–85.

45. Otto M. Staphylococcal infections: mechanisms of biofilm maturation and detachment as critical determinants of pathogenicity. Annu Rev Med. 2013;64:175–88.

46. Diekema DJ, Richter SS, Heilmann KP, Dohrn CL, Riahi F, Tendolkar S, et al. Continued emergence of USA300 methicillin-resistant Staphylococcus aureus in the United States: results from a nationwide surveillance study. Infect Control Hosp Epidemiol. 2014;35(3):285–92.

47. Spaan AN, van Strijp JAG, Torres VJ. Leukocidins: staphylococcal bi-component poreforming toxins find their receptors. Nat Rev Microbiol. 2017;15(7):435–47. 48. Cox PJ, Phillips BJ, Thomas P. The enzymatic basis of the selective action of cyclophosphamide. Cancer Res. 1975;35(12):3755–61.

49. Rigby KM, DeLeo FR. Neutrophils in innate host defense against Staphylococcus aureus infections. Semin Immunopathol. 2012;34(2):237–59.

50. Berube BJ, Bubeck Wardenburg J. Staphylococcus aureus alpha-toxin: nearly a century of intrigue. Toxins (Basel). 2013;5(6):1140–66.

51. Peschel A, Otto M. Phenol-soluble modulins and staphylococcal infection. Nat Rev Microbiol. 2013;11(10):667–73. 52. Cheung GY, Wang R, Khan BA, Sturdevant DE, Otto M. Role of the accessory gene regulator agr in community-associated methicillin-resistant Staphylococcus aureus pathogenesis. Infect Immun. 2011;79(5):1927–35.

53. Naber CK. Staphylococcus aureus bacteremia: epidemiology, pathophysiology, and management strategies. Clin Infect Dis. 2009;48 Suppl 4:S231–7.

54. Diep BA, Chan L, Tattevin P, Kajikawa O, Martin TR, Basuino L, et al. Polymorphonuclear leukocytes mediate Staphylococcus aureus Panton-Valentine leukocidin-induced lung inflammation and injury. Proc Natl Acad Sci USA. 2010;107(12):5587–92.

55. Otto M. Staphylococcus aureus toxins. Curr Opin Microbiol. 2014;17:32–7.

56. Rogers DE, Melly MA. Studies on bacteremia. II. Further observations on the granulocytopenia induced by the intravenous injection of Staphylococci. J Exp Med. 1957;105(2):99–112.

57. Wang R, Braughton KR, Kretschmer D, Bach TH, Queck SY, Li M, et al. Identification of novel cytolytic peptides as key virulence determinants for community-associated MRSA. Nat Med. 2007;13(12):1510–4.

58. Holzinger D, Gieldon L, Mysore V, Nippe N, Taxman DJ, Duncan JA, et al. Staphylococcus aureus Panton-Valentine leukocidin induces an inflammatory response in human phagocytes via the NLRP3 inflammasome. J Leukoc Biol. 2012;92(5):1069–81.

References

59. Surewaard BG, Nijland R, Spaan AN, Kruijtzer JA, de Haas CJ, van Strijp JA. Inactivation of staphylococcal phenol soluble modulins by serum lipoprotein particles. PLoS Pathog. 2012;8(3):e1002606. 60. DuMont AL, Yoong P, Surewaard BG, Benson MA, Nijland R, van Strijp JA, et al. Staphylococcus aureus elaborates leukocidin AB to mediate escape from within human neutrophils. Infect Immun. 2013;81(5):1830–41.

61. Kadurugamuwa JL, Sin L, Albert E, Yu J, Francis K, DeBoer M, et al. Direct continuous method for monitoring biofilm infection in a mouse model. Infect Immun. 2003;71(2): 882–90. 62. Prabhakara R, Harro JM, Leid JG, Harris M, Shirtliff ME. Murine immune response to a chronic Staphylococcus aureus biofilm infection. Infect Immun. 2011;79(4):1789–96.

63. Wang Y, Cheng LI, Helfer DR, Ashbaugh AG, Miller RJ, Tzomides AJ, et al. Mouse model of hematogenous implant-related Staphylococcus aureus biofilm infection reveals therapeutic targets. Proc Natl Acad Sci USA. 2017;114(26):E5094–E102. 64. Heim CE, Hanke ML, Kielian T. A mouse model of Staphylococcus catheterassociated biofilm infection. Methods Mol Biol. 2014;1106:183–91.

65. Joo HS, Otto M. Molecular basis of in vivo biofilm formation by bacterial pathogens. Chem Biol. 2012;19(12):1503–13.

66. Sully EK, Malachowa N, Elmore BO, Alexander SM, Femling JK, Gray BM, et al. Selective chemical inhibition of agr quorum sensing in Staphylococcus aureus promotes host defense with minimal impact on resistance. PLoS Pathog. 2014;10(6):e1004174.

67. Parlet CP, Kavanaugh JS, Crosby HA, Raja HA, El-Elimat T, Todd DA, et al. Apicidin attenuates MRSA virulence through quorum-sensing inhibition and enhanced host defense. Cell Rep. 2019;27(1):187–98.e6.

68. Greenberg M, Kuo D, Jankowsky E, Long L, Hager C, Bandi K, et al. Small-molecule AgrA inhibitors F12 and F19 act as antivirulence agents against Gram-positive pathogens. Sci Rep. 2018;8(1):14578.

69. Chan R, Buckley PT, O’Malley A, Sause WE, Alonzo F 3rd, Lubkin A, et al. Identification of biologic agents to neutralize the bicomponent leukocidins of Staphylococcus aureus. Sci Transl Med. 2019;11(475):eaat0882.

70. Rouha H, Badarau A, Visram ZC, Battles MB, Prinz B, Magyarics Z, et al. Five birds, one stone: neutralization of alpha-hemolysin and 4 bi-component leukocidins of Staphylococcus aureus with a single human monoclonal antibody. MAbs. 2015;7(1): 243–54.

71. Alonzo F 3rd, Kozhaya L, Rawlings SA, Reyes-Robles T, DuMont AL, Myszka DG, et al. CCR5 is a receptor for Staphylococcus aureus leukotoxin ED. Nature. 2013;493(7430):51–5.

72. Centers for Disease C, Prevention. Outbreaks of community-associated methicillinresistant Staphylococcus aureus skin infections—Los Angeles County, California, 2002–2003. MMWR Morb Mortal Wkly Rep. 2003;52(5):88. 73. Cheung GY, Kretschmer D, Duong AC, Yeh AJ, Ho TV, Chen Y, et al. Production of an attenuated phenol-soluble modulin variant unique to the MRSA clonal complex 30 increases severity of bloodstream infection. PLoS Pathog. 2014;10(8):e1004298.

375

376

Methicillin-Resistant S. aureus Sepsis

74. Walsh TJ, Bacher J, Pizzo PA. Chronic silastic central venous catheterization for induction, maintenance and support of persistent granulocytopenia in rabbits. Lab Anim Sci. 1988;38(4):467–71. 75. Diep BA, Stone GG, Basuino L, Graber CJ, Miller A, des Etages SA, et al. The arginine catabolic mobile element and staphylococcal chromosomal cassette mec linkage: convergence of virulence and resistance in the USA300 clone of methicillinresistant Staphylococcus aureus. J Infect Dis. 2008;197(11):1523–30.

Chapter 17

Where Cancer and Bacteria Meet Alexandra Merlos, PhD,a,b Ricardo Perez-Tomás, PhD,c José López-López, PhD,b and Miguel Viñas, PhDa aLaboratory of Molecular Microbiology & Antimicrobials,

Department of Pathology and Experimental Therapeutics, Medical School, Campus Bellvitge,

University of Barcelona, Hospitalet, Barcelona, Spain

bOral Medicine Section, Medical School, Campus Bellvitge, University of Barcelona,

Feixa Llarga s/n. Pavelló de Govern, Hospitalet, Barcelona, Spain

cCancer Cell Biology Research Group, Department of Pathology & Experimental Therapeutics,

Medical School, Campus Bellvitge, University of Barcelona, Hospitalet, Barcelona, Spain

[email protected]

Keywords: Helicobacter pylori, gastric cancer, colonizing strain, carcinogen, neoplasia, lipopolysaccharide, Gram-negative, Gram-positive, microbiota, microbial populations, bacterial adhesin, reactive oxygen species, cyclin-dependent kinases, tumorigenesis, inflammatory mechanisms, biofilms, molecular pathways, oral squamous cell carcinoma, biotic factors, prodiginines, immunosuppressive agent, tambjamines, metastasis, apoptosis

17.1 Introduction More than 10 years ago, the Australian microbiologists Barry J. Marshall and J. Robin Warren became Nobel laureates for their work demonstrating that infection by the bacterium Helicobacter pylori is closely related to several gastric pathologies, specifically gastritis, peptic ulcer, and gastric carcinoma. In fact, the two researchers succeeded in demonstrating that H. pylori is the etiologic agent of these diseases. H. pylori is a Gram-negative microaerophilic bacterium that induces a chronic infectious state as a precursor of chronic gastritis, peptic ulcer, gastric and duodenal cancer, and mucosa-associated lymphoid tissue lymphoma [1]. Furthermore, a role for H. pylori in other diseases, including cardiovascular disease, liver and biliary tract diseases, and colorectal cancer, has been studied and reported. In these cases, H. pylori is not the etiological agent but seems to act synergistically to significantly contribute to the development of Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

Copyright © 2022 Jenny Stanford Publishing Pte. Ltd.

ISBN 978-981-4877-84-8 (Hardcover), 978-1-003-18043-2 (eBook)

www.jennystanford.com

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the pathology. Roughly half of the human population, at some point, becomes infected with H. pylori, but only a relatively small number of individuals will develop gastric ulcer and even fewer will develop gastric cancer or one of the other aforementioned diseases. This has been attributed to the genetic diversity of H. pylori, such that the risk of disease depends on the colonizing strain of the bacterium [2–5]. The well-established relationship between cancer and infection has given rise to new perspectives on antimicrobials, which are now being used to prevent cancer, and also to a closer examination of the relationship between bacterial infection and cancer in general. However, despite intensive research, H. pylori is the only bacterium currently considered by the WHO as a class I human carcinogen, that is, as an etiologic agent of a specific form of cancer. Moreover, the relationship between cancer and infection is complex, since bacteria may contribute to or even be the main cause of cancer, but they and their products are also being effectively used in cancer treatment. This diverse and contradictory relationship is discussed in this review.

17.2 Infection and Neoplasia

Infection-mediated carcinogenesis has been confirmed through a series of long and complex epidemiological studies and meta-analyses. Indeed, ~20% of the worldwide cancer burden can be attributed to microbial-mediated oncogenesis. The recognition of the potential relationship between microbial prevalence and the incidence of cancer has revealed several previously hidden associations besides H. pylori and gastric cancer, some of which were later also determined to be causative. The most well-known examples include hepatitis B and C viruses, schistosomiasis, and Epstein–Barr virus, causing liver, bladder, and nasopharyngeal cancer, respectively (Table 17.1) [6]. However, most of the work linking cancer and infection has been based on epidemiological studies, some of which have become outdated. New studies, especially those concerning mechanisms of carcinogenesis, are therefore needed. The classical division of bacteria into Gram-positive and Gram-negative is mostly based on the strong differences in the bacterial wall. Gram-negative bacteria are surrounded by a double membrane: a plasma membrane that is very similar to the plasma membranes of all cell systems and an additional outer membrane. This external lipid bilayer is atypical because its external leaflet is mostly (if not exclusively) formed by lipopolysaccharides (LPSs) instead of phospholipids. Nevertheless, from a physiological point of view, the permeability barrier formed by the outer membrane is similar to that of other lipid bilayers. However, unlike the plasma membrane, the outer membrane lacks biological energy because energetic metabolism is restricted to the cytoplasm, with which the outer membrane does not have contact. These differences have conditioned the evolution of bacterial strategies of infection. Thus, whereas the pathogenicity mechanisms of Gram-negative bacteria are generally based on invasive

Infection and Neoplasia

processes, those of Gram-positive species involve virulence factors, which in most cases are secreted proteins that include a wide variety of toxins and enzymes with broad-ranging effects on cell integrity and metabolism. Both Gram-negative and Gram-positive bacteria have been linked, directly or indirectly, to cancer.

Table 17.1 The main bacterial species, viruses, chemical agents, and predisposing conditions involved in oncogenesis Infectious/inflammatory agent

Cancer type

Schistosomiasis

Bladder cancer

Helicobacter pylori–induced gastritis H. pylori H. pylori Porphyromonas gingivalis P. gingivalis Fusobacterium nucleatum Bacteroides species Hepatitis virus (B and C) HHV8 Silica/Cigarette smoke Inflammatory bowel disease Barrett’s metaplasia Prostatitis Thyroiditis Asbestos Salpingitis/talc/ovulation/ endometriosis Pelvic inflammatory disease/tissue remodeling Papillomavirus

Gastric cancer

Mucosa-associated lymphoid tissue lymphoma Pancreatic cancer Pancreatic cancer Oral cancer

Oral, pancreatic, and colorectal cancer Colon cancer

Hepatocellular carcinoma Kaposi’s sarcoma

Bronchial carcinoma Colorectal cancer

Esophageal cancer Prostate cancer

Papillary thyroid carcinoma Mesothelioma

Ovarian cancer Cervical cancer

Note: Approximately 20% of the total cancer burden may be mediated by microorganisms, with inflammation as the initial and main process involved in oncogenesis [7]. Chronic inflammation caused by chemical and physical agents [8] as well as by autoimmune and inflammatory reactions of uncertain etiology [7–10] also increase the risk of malignancy.

17.2.1 Gram-Negative Bacteria

In addition to H. pylori, several other species of Gram-negative bacteria are associated with cancer. Salmonella, like H. pylori, is a Gram-negative facultative anaerobe with the epidemiologically demonstrated ability to promote chronic inflammation. For example, after episodes of active Salmonella infection the gallbladder frequently remains colonized by the bacterium. Thus, individuals

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carrying Salmonella in the gallbladder become Salmonella carriers. While the carrier state is nonsymptomatic, the persisting infection produces chronic inflammation, which has been shown to directly influence the emergence of gallbladder cancer [11, 12]. The healthy human stomach contains several bacterial genera, among them Haemophilus, Prevotella, Streptococcus, Veillonella, and Rothia. Nevertheless, the composition of the gastric microbial populations is diverse and mutable and is strongly influenced by factors such as antibiotic use, alcohol ingestion, diet, and disease state. The interaction between the “normal” microbiota and H. pylori infection may play a key role in determining the risk of gastric diseases and, subsequently, cancer. This would imply relevant roles in carcinogenicity for several other facultative Gram-negative bacteria [13]. Among aerobic Gram-negative bacteria, only one species, Neisseria gonorrhoeae, has thus far been epidemiologically related to a specific cancer, namely prostate cancer, demonstrated in a population of Mexican males [14]. The authors of that study inferred a key etiological role for sexually transmitted diseases, particularly gonorrhea, in prostate cancer, consistent with previous publications. The main mechanism was suggested to involve the direct action of either the bacterium or its products on host DNA and the subsequent deregulation of oncogenic proteins, such as p21, p27, and p53. Anaerobic Gram-negative bacteria have also been implicated in several forms of cancer. One of the best studied species is Fusobacterium nucleatum, representative of the genus Fusobacterium. This spindle-shaped strict anaerobe is commonly found both in the oral cavity and in the gut. F. nucleatum induces permanent chronic inflammation in the colon and has thus been associated with colorectal cancer. However, the mechanism is still unclear. Rubinstein et al. [15] showed that F. nucleatum adheres to and invades tissues, which activates inflammatory responses and stimulates the growth of colorectal cells. The underlying mechanism involves the interaction of colonic cells with the bacterial adhesin FadA, which binds to E-cadherin. The same authors showed 100 times higher fadA gene levels in the colon tissue from patients with adenomas and adenocarcinomas than in healthy individuals, thus identifying FadA as a potential diagnostic and therapeutic target for colorectal cancer. Another strictly anaerobic Gram-negative bacterium associated with cancer is Bacteroides fragilis. Carbohydrate metabolism by B. fragilis produces numerous organic acids as metabolic waste. A relationship between B. fragilis and colorectal cancer has been suggested on the basis of the induced production of reactive oxygen species (ROS), which in turn causes DNA damage, although there is also evidence of direct activation [12]. Consistent with these observations, in an animal model antibiotic treatment was shown to reduce colon tumorigenesis [16]. Among the anaerobic Gram-negative bacteria associated with cancer, those in the oral microbiota merit special mention. They include some whose existence is known only from molecular data as they have not yet been cultured. Other species, such as Porphyromonas gingivalis, have been very well described, mainly

Infection and Neoplasia

because they cause periodontal diseases. Bacteriologically, P. gingivalisis closely related to Bacteroides, with which it shares many physiological features, but P. gingivalis is an oral microbe whereas Bacteroides is not. Epidemiological studies have established an association between P. gingivalis and both oral cancer and squamous carcinoma of the esophagus. Thus, while P. gingivalis is an oral microbe, its presence in the esophagus is common; it is also found in the gut. The carcinogenicity of P. gingivalis in the esophagus is thought to involve the microbe’s direct inhibition of apoptosis and the modification of cyclins and cyclindependent kinases (CDKs), with the latter inducing drastic changes in the cell cycle and therefore in cell progression [17]. In addition, a growing number of studies suggest an underlying infectious component to pancreatic cancer [6] involving two of the usual suspects in infectionmediated carcinogenesis, H. pylori and P. gingivalis. LPS produced by Gram-negative bacteria can be inhaled through cigarette smoke. In an animal model, it plays a key role in the development of lung carcinoma [18]. The triggering of innate immunity and the mounting of an acute response are generally followed by a late phase, in which regulatory mechanisms, tissue repair, and remodeling prevail. Lung tumorigenesis in response to 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone had a more significant outcome in mice when LPS was also administered; both a higher number of and more histologically advanced tumors were observed, as was observed evidence of inflammation, including macrophage recruitment and cell proliferation [18]. Acute inflammation triggered by the exogenous administration of tumor necrosis factor (TNF)-α, interleukin (IL)-1, and LPS may, under certain conditions, promote malignancy and metastasis [9]. In mice, inflammatory mechanisms account for the tumor-promoting effect of tobacco smoke on lung cancer [19], but in humans most neoplastic conditions are provoked by chronic local inflammatory reactions, with few systemic manifestations [20].

17.2.2 Gram-Positive Bacteria

Two examples of the association of Gram-positive bacteria and cancer are briefly described as follows: Propionibacterium acnes is an anaerobic rod-shaped species and a common inhabitant of human skin. It produces propionic acid as a normal metabolite. However, its presence in the prostate causes inflammation that has been linked to prostate cancer [21], although direct evidence is lacking. Bacillus is a highly diverse, heterogeneous, and complex genus. Its virulence and pathogenic characteristics account for the ability of member species to easily colonize, grow, and develop in any tissue. Spore formation, a biofilm life style, and toxin production are three of the most relevant characteristics by which Bacillus causes sustained chronic irritation and/or inflammation that gradually gives way to oncogenesis. Bacillus species are recognized as the causative agent of several infections, but the number of isolates in cancers, wounds, or infections has probably been underestimated. This is because in clinical specimens Bacillus

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is often considered as a contaminant colony rather than as a contributing etiologic agent. Most members of this genus and related taxa form stable biofilms, which in combination with sporulation ensure cell survival within the tissue confines. Moreover, cytotoxic molecules produced by Bacillus cells and secreted into the extracellular space are taken up by erythrocytes and fibroblasts and in some host species by cells of the colon, causing partial or total lysis. The pore-forming proteins of Bacillus enable the entry of surrounding molecules and play a key role in necrosis, inflammation, and possibly cancer development, by inducing the synthesis of ROS and other carcinogenic compounds. The Bacillus species isolated from different tobacco brands include B. amyloliquefaciens, B. cereus, B. subtilis, B. methylotrophicus, B. pumilus, Oceanobacillus chungangensis, and B. licheniformis, whereas B. subtilis, B. pumilus, B. methylotrophicus, and B. licheniformis have been identified in lung cancer biopsies. Tobacco flakes carrying bacteria and trapped within the cigarette filters provide the main entrance for the pathogen into the pulmonary airways [22]. The powerful colonization ability of the isolates has been demonstrated in adhesion assays, as has the secretion of pore-forming toxins with high cytotoxicity in cytotoxicity assays using planar lipid bilayers and cell cultures [23]. Molecular pathways involving mycobacteria in carcinogenesis have been identified as well [24].

17.3 Head and Neck Cancers and Bacterial Oral Microbiota

Over 700 bacterial species inhabit the oral cavity, with most living in a more or less symbiotic relationship with host cells and the host immune system [25, 26]. However, there is increasing evidence of a link between the oral microbiota and several systemic pathologies, including cancer and especially oral squamous cell carcinoma (OSCC). Between 350,000 and 400,000 new cases of head and neck cancer are diagnosed every year throughout the world, and the incidence is rising as tobacco and alcohol consumption is increasing, particularly among young populations [27]. Nonetheless, although smoking and alcohol are the main factors contributing to the development of carcinoma in the oral cavity, their effects do not suffice to explain “field cancerization” [28, 29]. Furthermore, oral cancer is frequently diagnosed in non-smoking, non-alcohol-consuming individuals [30]. At the molecular level, the pathogenesis of oral cancer has been attributed to the deregulation of cellular pathways such that carcinogenesis is promoted and to the expression of tumor suppressors, such as p53 and CDKN2A. The cancerprovoking molecules in head and neck cancers include carcinogenic substances and their metabolites, via a chronic inflammatory process [31, 32] involving myeloid and nonmyeloid cells (polymorphonuclear neutrophils, oral keratinocytes, macrophages, osteoclasts, osteoblasts, and dendritic cells). The membraneassociated receptors, secreted pattern recognition receptors, nucleotidebinding oligomerization domain-like receptors, toll-like receptors, RIG-I-like

Head and Neck Cancers and Bacterial Oral Microbiota

receptors, and C-type lectin receptors on these cells interact with periodontal microbial components that include microbe-associated molecular patterns (MAMPs), such as fimbriae, BspA (Bacteroides surface protein A), lipoproteins, LPS, and nucleic acids. These interactions may stimulate the production of damage/danger-associated molecules, such as fibrinogen, heat-shock proteins, and nucleic acids [33]. In this context, infectious agents, mostly bacteria, influence cancer emergence and progression [34] but do not induce cancer directly. For example, long-term infection of oral cancer cells by P. gingivalis leads to the increased expression of the malignant stem cell markers CD44 and CD133 and exacerbates the tumorigenic properties of infected versus noninfected cancer cells [35]. Moreover, P. gingivalis is a noncanonical activator of b-catenin, inducing the disassociation of the b-catenin destruction complex by gingipain-dependent proteolytic processing. b-Catenin activation in epithelial cells by P. gingivalis may thus contribute to a proliferative phenotype [36]. A relationship between other oral bacteria, such as the above-mentioned F. nucleatum, and cancer has also been proposed on the basis of the observed stimulation of human OSCC proliferation and the expression of key molecules involved in tumorigenesis [37]. Several periodontal pathogens, particularly P. gingivalis, F. nucleatum, and Prevotella intermedia, are associated with OSCC, as per a model similar to that of gastric cancer and H. pylori [37]. For example, as shown in Fig. 17.1, F. nucleatum activates p38, leading to the secretion of MMP-9 and MMP-13 (collagenase 3). The effects of F. nucleatum LPS are similar to those of P. gingivalis LPS on epithelial cells. F. nucleatum LPS activates inflammatory cytokines, such as TNF-α, IL-1b, and IL-6. These events become cyclic, leading to periodontal attachment and tissue damage. However, F. nucleatum LPS differs from the LPS of Escherichia coli because of its higher content of 2-keto-3-deoxyoctonate and heptoses, both of which play a relevant role in inducing cell injury and in cytokine-mediated inflammation, through the modulation of several apoptotic pathways [37–39]. Other suspected culprit bacterial species are Prevotella melaninogenica, Streptococcus mitis, and Capnocytophaga gingivalis, high levels of which have been proposed as OSCC markers [40] on the basis of the following capabilities of different serotypes (Fig. 17.2): (i) epithelial colonization; (ii) carcinogen production (nitrosamines, acetaldehyde, and 4-nitroquinoline-1-oxide, among others) [41, 42]; (iii) their ability to metabolize procarcinogens, such as acetaldehyde and hydroethyl radicals, ethoxy radicals, and tobacco, to acetaldehyde; and (iv) their modification of chronic inflammation, for example, by suppressing the activation of activator protein-1. Periodontal pathogenic species may also induce anticardiolipin antibodies in periodontitis patients in response to the antigenically similar bacterial proteins glycoprotein I and serum protein β-2. Because the induction of antiphospholipid antibodies is closely related to the infectious process and thus to chronic inflammation [43], chronic periodontal disease and the presence of subgingival plaques containing anaerobic bacteria have been implicated in head-and-neck squamous cell carcinoma.

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Figure 17.1 The possible role of F. nucleatum in the transformation of an epithelial cell into an oncogenic phenotype involves FadA (encoding a critical host colonization factor), which promotes the internalization of b-catenin and therefore cell survival and proliferation. Intracellular Fusobacterium activates P38, leading to the secretion of MMP-9 and MMP-13, both of which play a key role in the malignant transformation of squamous cells.

Poor oral health has also been linked to pancreatic, intestinal, and esophageal cancers. A reduction in tobacco consumption delays the clinical manifestations of chronic inflammation, and smoking cessation enhances gingival health [44]. Apart from bacteria other microbes, such as yeasts, have been also more or less implicated in some kinds of cancer. There is a lack of evidence to support the relation between chronic candidiasis and cervix cancer. Concerning oral chronic candidiasis, it has been shown that in immunocompromised patients, esophagus cancer is frequently associated with chronic mucocutaneous candidiasis and infrequently with autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy [45]. In fact, it has been suggested that the participation of nitrosamine compounds produced by chronic Candida infections could be a risk factor for esophageal cancer [45]. Candida albicans is the most typical Candida species present in leukoplakia and chronic hyper plastic candidiasis (CHC) [42, 46, 47]. Moreover, it has been reported that oral carriage of Candida albicans is higher in patients with OSCC or leukoplakia than in patients without oral pathology; and that the degree of epithelial dysplasia present in these patients also correlates with higher titles of yeasts in the oral cavity [48, 49].

Bacteria and Bacterial Products in Cancer Treatment

Figure 17.2 Pathways involved in the relationship between chronic periodontal disease and the induction of head-and-neck cancer. Adapted from [43].

17.4 Bacteria and Bacterial Products in Cancer Treatment

While bacteria have been implicated as causative agents in several cancers, they and their products have also been used to therapeutic effect. Over the last decade, there has been an explosion in the discovery and development of small organic molecule as anticancer drugs. Several Gram-negative aerobes have been shown to exert inhibitory effects on cancer cells and are being explored as promising anticancer agents. For example, Pseudomonas aeruginosa fragments (mannose­ sensitive fimbriae) inhibit pancreatic cancer growth [50] by inducing apoptosis and inhibiting tumor cell proliferation. This activity is mediated by hindering epidermal growth factor receptor signaling and by activation of the caspase pathway. A diverse resource for new anticancer drugs is the marine environment, with its abundance of aquatic plants and animals, many of which have been screened for antifungal, antimicrobial, anti-inflammatory, anticancer, and analgesic properties. Among the more than 13,000 molecules described thus far, active properties were determined for ~3000 [51]. Of these, >590 are currently in the pipeline of pharmaceutical-discovery programs or phase III clinical trials [51–53]. Ionophores promote ion transport across lipid bilayers and include a large number of naturally occurring molecules. External disruption of the ion permeability of membranes disturbs the normal ion balance, a property that can

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be exploited in the induction of cell death (apoptosis, autophagy, or necrosis), including that of tumor cells, or the elimination of harmful microorganisms. While anion-selective ionophores (anionophores) are much less common than cationophores, they have attracted intense interest over the last two decades. Anionophores are natural products that facilitate transmembrane anion transport across phospholipid bilayers [54]. This capability is crucial for the maintenance of the concentration gradients that form the basis for signaling and cellular regulation. A few examples of anionophores with anticancer activity are provided below.

17.4.1 Prodiginines

One of the first microorganisms to be used in cancer therapy was Serratia marcescens, a microorganism characterized by the production of prodigiosin [55]. The microbe and its secondary metabolites were first assayed to experimentally treat cancer in studies performed almost 70 years ago [56]. Soon thereafter, studies on the effect of bacterial polysaccharide fractions on tissue cultures of normal chicken fibroblasts and mouse sarcoma cells showed that while these fractions are not directly toxic for the tissues, they enhance the areal increase of both tissue types [57]. These results provided slight evidence that Serratia marcescens, while in principle useful as an anticancer agent, is also able to induce the abnormal growth of human cells. More recently, however, prodigiosin has been extensively tested in experimental oncology (for a review, see Ref. [58]).

Figure 17.3 Molecular structure of three natural anionophores.

One of the most important families of anionophores consists of prodiginines, produced by microorganisms such as Streptomyces sp. and Serratia marcescens [59]. Prodigiosin (2-methyl-3-pentyl-6-methoxyprodiginine), within the redpigmented prodiginines family, is an alkaloid secondary metabolite synthesized by Serratia marcescens, among other microorganisms [55], and located in the bacterial inner membrane [60]. The structure of prodigiosin (Fig. 17.3a) (C20H25N3O) was clarified in the early 1960s, when partial and total chemical synthesis

Bacteria and Bacterial Products in Cancer Treatment

revealed a pyrrolyl-dipyrromethene core skeleton. Prodigiosin occurs in solution in interconverting cis (or β) and trans (or α) conformations. The equilibrium between the two is dependent on the solution pH. The biological role of prodigiosin in producer organisms remains unclear [61], although the alkaloid is a very efficient anion exchanger that facilitates Cl−/HCO3− exchange across lipid bilayers. The antifungal, immunosuppressive, and anticancer activities of prodigiosin have been described in several studies [62, 63]. Its structural analog obatoclax mesylate (GX15-070MS) is currently in phase II clinical trials to test its efficacy in combination with first-line drugs for the treatment of hematological malignancies and solid tumors [64–66].

17.4.1.1 Properties and mechanism of action of prodigiosin

The wide-ranging pharmacological activities of the tripyrrole prodigiosin have been the focus of increasing interest, including as a potent antimalarial drug with high toxicity against the causative agent Plasmodium falciparum [67); as an inhibitor of the growth of Gram-positive and Gram-negative bacteria [68] as well as fungi [69]; and as an immunosuppressive agent, based on the ability of prodigiosin to block T lymphocyte activation, primarily by inhibiting IL-2receptor-α expression [70]. Of relevance to this review, however, is the capacity of prodigiosin to trigger apoptosis in malignant cancer cells. This small molecule is now generally recognized as an anticancer compound, an activity demonstrated both in vitro and in vivo [71, 72].

Figure 17.4 Prodigiosin activates pathways that promote the inhibition of cancer development.

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The mechanism of action by which prodigiosin exerts its cytotoxicity in cancer cells has yet to be fully elucidated, but it being a small molecule multiple processes are likely to be involved. Indeed, prodigiosin has multiple cellular targets, summarized in Fig. 17.4. The induction of cell stress is one of the mechanisms that may account for the anticancer activity of prodigiosin, specifically, uncoupling of the vacuolar-type ATPase by promoting H+/Cl− symport in lysosomes and subsequent disruption of the pH gradient [73, 74]. This can lead to cell-cycle blockage or cell death by apoptosis [75, 76] (Fig. 17.5). In addition, prodigiosin intercalates within double-stranded DNA, leading to the latter’s copper-mediated cleavage. Prodigiosin also inhibits topoisomerases I and II activity, resulting in DNA rupture and therefore apoptosis [58, 76, 77].

Figure 17.5 The inhibition of RhoA and MMP-2 has a result of inhibition of metastasis. The activation of cyclin E, CDK2, P27 leads to the arrest of cell cycle. In both cases these constitute anticancer mechanisms.

GX15-070MS inhibits the binding of antiapoptotic Bcl-2 to the proapoptotic proteins Bax and Bak, thus triggering the apoptosis pathway in Bcl-2overexpressing cancer cells [78]. It also promotes other forms of programmed cell death, including autophagic cell death [79–82] and necroptosis [83].

17.4.2 Tambjamines

Tambjamines are derived from bacteria of the genus Pseudoalteromonas and from a large group of marine invertebrates, including bryozoans, nudibranchs, and ascidians [84]. In the latter group, they serve as chemical defense compounds that protect against predators. The 4-methoxy-2,2¢-bipyrrolenamine structure (Fig. 17.6) of tambjamines is clearly related to the structure of prodiginines, including prodigiosin (Fig. 17.3a). Like these compounds, tambjamines are alkaloids with intriguing biological activities, including as antimicrobial, cytotoxic, and antitumor agents [85–88].

Bacteria and Bacterial Products in Cancer Treatment

Figure 17.6 Structures of the tambjamines.

17.4.2.1 Properties and mechanism of action The biological potential of tambjamines was confirmed in studies of tambjamines A–D, which demonstrated their antimicrobial effect [89]. As natural anionophores tambjamines facilitate the permeabilization of cell membranes and thus upset the normal intracellular ionic balance. Spectroscopic analysis of the structure of tambjamines revealed strong hydrogen bonding between these compounds and the chloride anion [87]. The two heterocycles and the enamine moiety are essentially coplanar. A chloride anion interacts with the pyrrole and enamine N−H groups, whereas the indole moiety is rotated 180° and interacts with a second chloride anion. However, in their preferred conformation tambjamines bind with the anion through the hydrogen bond cleft involving the three N−H groups of the molecule [90].

17.4.2.2 Synthetic analogs of tambjamines

In the search for compounds with efficient transport capacity, 11 derivatives of naturally occurring tambjamines have been synthesized (Fig. 17.7).

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Figure 17.7 Tambjamine analogs.

These indole-based tambjamine analogs have been used to demonstrate that anion transport induces cell membrane hyperpolarization and cell death in cancer stem cells [91]. The mechanisms responsible for the cytotoxicity of these tambjamine analogs have been studied by one of the authors (Ricardo Pérez-Tomás). The results revealed both a decrease in the intracellular pH (acidification) and the induction of apoptosis through p38 mitogen–activated protein kinase activation [92].

Abbreviations CDKs: CHC: IL: LPSs: MAMPs: OSCC: ROS: TNF:

cyclin-dependent kinases chronic hyper plastic candidiasis interleukin lipopolysaccharides microbe-associated molecular patterns oral squamous cell carcinoma reactive oxygen species tumor necrosis factor

References

Disclosures and Conflict of Interest This chapter was originally published as: Merlos, A., Perez-Tomás, R., López-López, J., Viñas, M. (2019). Where cancer and bacteria meet. In: Chakrabarty, A. M., and Fialho, A. M., eds. Microbial Infections and Cancer Therapy: Recent Advances, Jenny Stanford Publishing, Singapore, pp. 411–436, and appears here, with edits and updates, by kind permission of the publisher.

References

1. Marshall B. J., Warren J. R. Unidentified curved bacilli in the stomach of patients with gastritis and peptic ulceration. Lancet, 16 (1984) 1311–1315.

2. Zhu R. M., Qin T. S., Jin X. X., Xu W. A., Wang Z. K., Wang L. Helicobacter pylori typing and upper digestive tract disease relationship. Yixue Yanjiusheng Xuebao, 18 (2005) 911017. 3. Shen J., Lu M., Feng C. W. Helicobacter pylori infection and diseases of liver and gall of progress. Gastroenterology Hepatology Journal, 22 (2013) 123–125.

4. Venkateshwari A., Krishnaveni D., Venugopal S., Shashikumar P., Vidyasagar A., Jyothy A. Helicobacter pylori infection in relation to gastric cancer progression. Indian Journal of Cancer, 48 (2011) 94–98. 5. Sachs G., Wen Y., Scott D. R. Gastric infection by Helicobacter pylori. Current Gastroenterology Reports, 11 (2009) 455–461. 6. Michaud D. S. Role of bacterial infections in pancreatic cancer. Carcinogenesis, 34 (2013) 2193–2197. 7. Pisani S., Gallinelli C., Seganti L., Lukic A., Nobili F., Vetrano G., Imperi M., Degener A. M., Chiarini F. Detection of viral and bacterial infections in women with normal and abnormal colposcopy. European Journal of Gynaecological Oncology, 20 (1999) 69–73.

8. Gulumian M. The role of oxidative stress in diseases caused by mineral dusts and fibres: current status and future of prophylaxis and treatment. Molecular and Cellular Biochemistry, 196 (1999) 69–77.

9. Balkwill F., Mantovani A. Inflammation and cancer: Back to Virchow? Lancet, 357 (2001) 539–545.

10. Ekbom A., Helmick C., Zack M., Adami HO. Increased risk of large-bowel cancer in Crohn’s disease with colonic involvement. Lancet, 336 (1990) 357–359.

11. Ye Z., Petrof E. O., Boone D., Claud E. C., Sun J. Salmonella effector AvrA regulation of colonic epithelial cell inflammation by deubiquitination. American Journal of Pathology, 17 (2007) 882–892. 12. Wu S., Ye Z., Liu X., Zhao Y., Xia Y., Steiner A., et al. Salmonella typhimurium infection increases p53 acetylation in intestinal epithelial cells. American Journal of Physiology. Gastrointestinal and Liver Physiology, 298 (2010) G784–G794.

13. Nardone G., Compare D. The human gastric microbiota: Is it time to rethink the pathogenesis of stomach diseases? United European Gastroenterology Journal, 3 (2015) 255–260.

391

392

Where Cancer and Bacteria Meet

14. Vázquez-Salas R. A., Torres-Sánchez L., López-Carrillo L., Romero-Martínez M., Manzanilla-García H. A., Cruz-Ortíz C. H., et al. History of gonorrhea and prostate cancer in a population-based case-control study in Mexico. Cancer Epidemiology, 40 (2016) 95–101.

15. Rubinstein M. R., Wang X., Liu W., Hao Y., Cai G., Han Y. W. Fusobacterium nucleatum promotes colorectal carcinogenesis by modulating E-cadherin/β-catenin signaling via its FadA adhesin. Cell Host & Microbe, 14 (2013) 195–206.

16. DeStefano Shields C. E., Van Meerbeke S. W., Housseau F., Wang H., Huso D. L., Casero R. A. Jr., et al. Reduction of murine colon tumorigenesis driven by enterotoxigenic bacteroides fragilis using cefoxitin treatment. Journal of Infectious Diseases, 214 (2016) 122–129. 17. Gao S., Li S., Ma Z., Liang S., Shan T., Zhang M., et al. Presence of Porphyromonas gingivalis in esophagus and its association with the clinicopathological characteristics and survival in patients with esophageal cancer. Infectious Agents and Cancer, 11 (2016) 3.

18. Melkamu T., Qian X., Upadhyaya P., O’Sullivan M. G., Kassie F. Lipopolysaccharide enhances mouse lung tumorigenesis: A model for inflammation-driven lung cancer. Veterinary Pathology, 50 (2013) 895–902.

19. Takahashi H., Ogata H., Nishigaki R., Broide D. H., Karin M. Tobacco smoke promotes lung tumorigenesis by triggering IKKbeta-and JNK1-dependent inflammation. Cancer Cell, 19 (2010) 89–97. 20. Balkwill F., Charles K. A., Mantovani A. Smoldering and polarized inflammation in the initiation and promotion of malignant disease. Cancer Cell, 7 (2005) 211–217.

21. Sutcliffe S., Platz E. A. Inflammation and prostate cancer: A focus on infections. Current Urology Reports, 9 (2008) 243–249.

22. Pauly J. L., Waight J. D., Paszkiewicz G. M. Tobacco flakes on cigarette filters grow bacteria: A potential health risk to the smoker? Tobacco Control, 17(Suppl 1) (2008) i49–i52.

23. Merlos A., Rodríguez P., Bárcena-Uribarri I., Winterhalter M., Benz R., Vinuesa T., et al. Toxins secreted by bacillus isolated from lung adenocarcinomas favor the penetration of toxic substances. Frontiers in Microbiology, 6 (2015) 1301. 24. Holla S., Ghorpade D. S., Singh V., Bansal K., Balaji K. N. Mycobacterium bovis BCG promotes tumor cell survival from tumor necrosis factor-α-induced apoptosis. Molecular Cancer, 13 (2014) 210. 25. Wade W. G. The oral microbiome in health and disease. Pharmacological Research, 69 (2013) 137–143.

26. Duran-Pinedo A. E., Chen T., Teles R., Starr J. R., Wang X., Krishnan K., et al. Community-wide transcriptome of the oral microbiome in subjects with and without periodontitis. ISME Journal, 8 (2014) 1659–1672.

27. López-López J., Omaña-Cepeda C., Jané-Salas E. Oral precancer and cancer. Medicina Clinica (Barc), 145 (2015) 404–408.

28. Chimenos-Küstner E., Font-Costa I., López-López J. Oral cancer risk and molecular markers. Medicina Oral Patolologia Oral Y Cirugia Bucal, 9 (2004) 377–384.

References

29. Sahingur S. E., Yeudall W. A. Chemokine function in periodontal disease and oral cavity cancer. Frontiers in Immunology, 6 (2015) 214. 30. Albuquerque R. P., López-López J., Jané-Salas E., Rosa-Santos J., Ibrahim C. A pioneering epidemiological study investigating the incidence of squamous cell carcinoma of tongue in a Portuguese population. Medicina Oral Patologia Oral Y CirugíaBucal, 17 (2012) e550–e554.

31. Meurman J. H. Oral microbiota and cancer. Journal of Oral Microbiology, 2 (2010) 5195.

32. Pereira A. C., Dias do Carmo E., Dias da Silva M. A., Blumer Rosa L. E. Matrix metalloproteinase gene polymorphisms and oral cancer. Journal of Clinical and Experimental Dentistry, 4 (2012) e297–e301. 33. Hajishengallis G. Immunomicrobial pathogenesis of periodontitis: pathobionts, and host response. Trends in Immunology, 35 (2014) 3–11.

Keystones,

34. Gholizadeh P., Eslami H., Yousefi M., Asgharzadeh M., Aghazadeh M., Kafil H. S. Role of oral microbiome on oral cancers, a review. Biomedicine & Pharmacotherapy, 84 (2016) 552–558.

35. Ha N. H., Woo B. H., Kim D. J., Ha E. S., Choi J. I., Kim S. J., et al. Prolonged and repetitive exposure to Porphyromonas gingivalis increases aggressiveness of oral cancer cells by promoting acquisition of cancer stem cell properties. Tumor Biology, 36 (2015) 9947–9960. 36. Zhou Y., Sztukowska M., Wang Q., Inaba H Potempa J., Scott D. A., Wang H., et al. Noncanonical activation of β-catenin by Porphyromonas gingivalis. Infection and Immunity, 83 (2015) 3195–3203.

37. Binder Gallimidi A., Fischman S., Revach B., Bulvik R., Maliutina A., Rubinstein A. M., et al. Periodontal pathogens Porphyromonas gingivalis and Fusobacterium nucleatum promote tumor progression in an oral-specific chemical carcinogenesis model. Oncotarget, 6 (2015) 22613–22623.

38. Hamada S., Koga T., Nishihara T., Fujiwara T., Okahashi N. Characterization and immunobiologic activities of lipopolysaccharides from periodontal bacteria. Advances in Dental Research, 2 (1988) 284–289.

39. Yu T., Wu Y., Helman J. I., Wen Y., Wang C., Li L. CXCR4 promotes oral squamous cell carcinoma migration and invasion through inducing expression of MMP-9 and MMP-13 via the ERK signaling pathway. Molecular Cancer Research, 9 (2011) 161–172.

40. Mager D. L., Haffajee A. D., Devlin P. M., Norris C. M., Posner M. R., Goodson J. M. The salivary microbiota as a diagnostic indicator of oral cancer: A descriptive, nonrandomized study of cancer-free and oral squamous cell carcinoma subjects. Journal of Translational Medicine, 3 (2005) 27.

41. Hooper S. J., Wilson M. J., Crean S. J. Exploring the link between microorganisms and oral cancer: A systematic review of the literature. Head & Neck, 31 (2009) 1228–1239. 42. Dwivedi P. P., Mallya S., Dongari-Bagtzoglou A. A novel immuno-competent murine model for Candida albicans-promoted oral epithelial dysplasia. Medical Mycology, 47 (2009) 157–167. 43. Han Y. W., Houcken W., Loos B. G., Schenkein H. A., Tezal M. Periodontal disease, atherosclerosis, adverse pregnancy outcomes, and head-and-neck cancer. Advances in Dental Research, 26 (2014) 47–55.

393

394

Where Cancer and Bacteria Meet

44. Nair P., Sutherland G., Palmer R. M., Wilson R. F., Scott D. A. Gingival bleeding on probing increases after quitting smoking. Journal of Clinical Periodontology, 30 (2003) 435–437.

45. Domingues-Ferreira M., Grumach A. S., Duarte A. J., De Moraes-Vasconcelos D. Esophageal cancer associated with chronic mucocutaneous candidiasis. Could chronic candidiasis lead to esophageal cancer? Medical Mycology, 47 (2009) 201–205. 46. Jepsen A., Winther J. Mycotic infection in oral leukoplakia. Acta Odontologica Scandinavica, 23 (1965) 239–256.

47. Van der Waal I. Potentially malignant disorders of the oral and oropharyngeal mucosa; terminology, classification and present concepts of management. Oral Oncology, 45 (2009) 317–323.

48. McCullough M., Jaber M., Barrett A. W., Bain L., Speight P. M., Porter S. R. Oral yeast carriage correlates with presence of oral epithelial dysplasia. Oral Oncology, 38 (2002) 391–393. 49. Spolidorio L. C., Martins V. R., Nogueira R. D., Spolidorio D. M. The frequency of Candida sp. in biopsies of oral mucosal lesions. Pesquisa Odontologica Brasileira, 17 (2003) 89–93.

50. Cheng X., Wang B., Jin Z., Ma D., Yang W., Zhao R., et al. Pseudomonas aeruginosamannose-sensitive hemagglutinin inhibits pancreatic cancer cell proliferation and induces apoptosis via the EGFR pathway and caspase signaling. Oncotarget, 7(47) (2016) 77916–77925.

51. Harshad M. Exploring the ocean for new drug developments: Marine pharmacology. Journal of Pharmacy and Bioallied Sciences, 8 (2016) 83–91.

52. Mayer A. M., Glaser K. B., Cuevas C., Jacobs R. S., Kem W., Little R. D., et al. The odyssey of marine pharmaceuticals: A current pipeline perspective. Trends in Pharmacological Sciences, 31 (2010) 255–265. 53. Montaser R., Luesch H. Marine natural products: A new wave of drugs? Future Medicinal Chemistry, 3 (2011) 1475–1489.

54. Davis J. T., Okunola O., Quesada R. Recent advances in the transmembrane transport of anions. Chemical Society Reviews, 39 (2010) 3843–3862. 55. Williams R. P. Biosynthesis of prodigiosin, a secondary metabolite of Serratia marcescens. Applied Microbiology, 25 (1973) 396–402.

56. Creech H. J., Hamilton M. A., Diller I. C. Comparative studies of the immunological, toxic and tumor-necrotizing properties of polysaccharides from Serratia marcescens (Bacillus prodigiosus). Cancer Research, 8 (1948) 318–329.

57. Lasfargues E. Y., Wharton D. R., Difine J. C. Some effects of polysaccharide preparations from Serratia marcescens and Aerobacter aerogenes on cells in tissue culture. Cancer Research, 11 (1951) 425–427.

58. Pérez-Tomás R., Viñas M. New insights on the antitumoral properties of prodiginines. Current Medicinal Chemistry, 17 (2010) 2222–2231.

59. Bennett J., Bentley R. Seeing red: The story of prodigiosin. Advances in Applied Microbiology, 47 (2000) 1–32.

60. Viñas M., Loren J., Guinea J. Particulate-bound pigment of Serratia marcescens and its association with the cellular envelopes. Microbios Letters, 93 (1983) 19–26.

References

61. Darshan N., Manonmani H. K. Prodigiosin and its potential applications. Journal of Food Science and Technology, 52 (2015) 5393–5407.

62. Soto-Cerrato V., Llagostera E., Montaner B., Scheffer G. L., R. Pérez-Tomás. Mitochondriamediated apoptosis operating irrespective of multidrug resistance in breast cancer cells by the anticancer agent prodigiosin. Biochemical Pharmacology, 68 (2004) 1345–1352.

63. Davis J. T., Gale P. A., Okunola O. A., Prados P., Iglesias-Sanchez J. C., Torroba T., et al. Using small molecules to facilitate exchange of bicarbonate and chloride anions across liposomal membranes. Nature Chemistry, 1 (2009) 138–144.

64. Chiappori A., Williams C., Northfelt D. W., Adams J. W., Malik S., Edelman M. J., et al. Obatoclax mesylate, a Pan–Bcl-2 inhibitor, in combination with docetaxel in a phase 1/2 trial in relapsed non– small-cell lung cancer. Journal of Thoracic Oncology, 9 (2014) 121–125. 65. Schimmer A. D., Raza A., Carter T. H., Claxton D., Erba H., DeAngelo D. J., et al. A multicenter phase I/II study of obatoclax mesylate administered as a 3- or 24hour infusion in older patients with previously untreated acute myeloid leukemia. PLOS One, 9 (2014) e108694.

66. Langer C. J., Albert I., Ross H. J., Kovacs P., Blakely L. J., Pajkos G., et al. Randomized phase II study of carboplatin and etoposide with or without obatoclax mesylate in extensive-stage small cell lung cancer. Lung Cancer, 85 (2014) 420–428. 67. Castro A. J. Antimalarial activity of prodigiosin. Nature, 213 (1967) 903–904.

68. Darah. I., The, F., Jain K., Sheh-Hong L. Prodigiosin-an antibacterial red pigment produced by Serratia marcescens IBRL USM 84 associated with a marine sponge Xestospongia testudinaria. Journal of Applied Pharmaceutical Science, 4 (2014) 1–6.

69. Berg G. Diversity of antifungal and plant-associated Serratia plymuthica strains. Journal of Applied Microbiology, 88 (2000) 952–960.

70. Han S. B., Kim H. M., Kim Y. H., Lee C. W., Jang E. S., Son K. H., et al. T-cell specific immunosuppression by prodigiosin isolated from Serratia marcescens. International Journal of Immunopharmacology, 20 (1998) 1–13.

71. Manderville R. A. Synthesis, proton-affinity and anti-cancer properties of the prodigiosin-group natural products. Current Medicinal Chemistry–Anti-Cancer Agents, 1 (2001) 195–218.

72. Pérez-Tomás R., Montaner B., Llagostera E., Soto-Cerrato V. The prodigiosins, proapoptotic drugs with anticancer properties. Biochemical Pharmacology, 66 (2003) 1447–1452.

73. Ohkuma S., Sato T., Okamoto M., Matsuya H., Arai K., Kataoka T., et al. Prodigiosins uncouple lysosomal vacuolar-type ATPase through promotion of H+/Cl− symport. Biochemical Journal, 334 (1998) 731–741.

74. Sessler J. L., Eller L. R., Cho W. S., Nicolaou S., Aguilar A., Lee J. T., et al. Synthesis, anion-binding properties, and in vitro anticancer activity of prodigiosin analogues. Angewandte Chemie International Edition, 44 (2005) 5989–5992. 75. Montaner B., Pérez-Tomás R. The Prodigiosins: A new family of anticancer drugs Current Cancer Drug Targets, 3 (2003) 57–65.

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Where Cancer and Bacteria Meet

76. Castillo-Avila W., Abal M., Robine S., Pérez-Tomás R. Non-apoptotic concentrations of prodigiosin (H+/Cl− symporter) inhibit the acidification of lysosomes and induce cell cycle blockage in colon cancer cells. Life Sciences, 78 (2005) 121–127.

77. Montaner B., Pérez-Tomás R. Prodigiosin-induced apoptosis in human colon cancer cells. Life Sciences, 68 (2001) 2025–2036.

78. Montaner B., Martinell M., Öllinger E., Giralt E., Pérez-Tomás R. DNA interaction and dual topoisomerase I and II inhibition properties of the antitumor drug prodigiosin. Toxicological Sciences, 85 (2005) 870–879. 79. Goard C., Aaron D. An evidence-based review of obatoclax mesylate in the treatment of hematological malignancies. Core Evidence, 8 (2013) 15–26.

80. Pan J., Cheng C., Verstovsek S., Chen Q., Jin Y., Cao Q. The BH3-mimetic GX15-070 induces autophagy, potentiates the cytotoxicity of carboplatin and 5-fluorouracil in esophageal carcinoma cells. Cancer Letters, 293 (2010) 167–174. 81. Heidari N., Hicks M., Harada H. GX15-070 (obatoclax) overcomes glucocorticoid resistance in acute lymphoblastic leukemia through induction of apoptosis and autophagy. Cell Death & Disease, 1 (2010) e76.

82. McCoy F., Hurwitz J., McTavish N., Paul I., Barnes C., O’Hagan B., et al. Obatoclax induces Atg7-dependent autophagy independent of beclin-1 and BAX/BAK. Cell Death & Disease, 1 (2010) e108.

83. Espona-Fiedler M., Soto-Cerrato V., Hosseini A., Lizcano J. M., Guallar V., Quesada R., et al. Identification of dual mTORC1 and mTORC2 inhibitors in melanoma cells: Prodigiosin vs. obatoclax. Biochemical Pharmacology, 83 (2012) 489–496.

84. Basit F., Cristofanon S., Fulda S. Obatoclax (GX15-070) triggers necroptosis by promoting the assembly of the necrosome on autophagosomal membranes. Cell Death & Differentiation, 20 (2013) 1161–1173. 85. Soliev A., Kakushi H., Keiichi E. Bioactive pigments from marine bacteria: Applications and physiological roles. Evidence-Based Complementary and Alternative Medicine, 2011 (2011) 670349.

86. Carbone M., Irace C., Costagliola F., Castelluccio F., Villani G., Calado G., et al. A new cytotoxic tambjamine alkaloid from the Azorean nudibranch Tambjaceutae. Bioorganic & Medicinal Chemistry Letters, 20 (2010) 2668–2670.

87. Pinkerton D. M., Banwell M. G., Garson M. J., Kumar N., de Moraes M. O., Cavalcanti B. C., et al. Antimicrobial and cytotoxic activities of synthetically derived tambjamines C and E-J, BE-18591, and a related alkaloid from the marine bacterium Pseudoalteromonas tunicata. Chemistry & Biodiversity, 7 (2010) 1311–1324.

88. Iglesias P., Moreno D., Javier A. A., Torroba T., Pérez-Tomás R., Quesada R. Tambjamine alkaloids and related synthetic analogs: Efficient transmembrane anion transporters. Chemical Communications, 48 (2012) 1556–1558. 89. Hernando E., Soto-Cerrato V., Cortes-Arroyo S., Pérez-Tomás R., Quesada R. Transmembrane anion transport and cytotoxicity of synthetic tambjamine analogs Organic & Biomolecular Chemistry, 12 (2014) 1771–1778. 90. Carte B., Faulkner J. Defensive metabolites from three nembrothid nudibranchs. The Journal of Organic Chemistry, 48 (1983) 2314–2318.

References

91. Gale P., Perez-Tomas R., Quesada R. Anion transporters and biological systems. Accounts of Chemical Research, 46 (2013) 2801–2813.

92. Soto-Cerrato V., Manuel-Manresa P., Hernando E., Calabuig-Farinas S., Martinez-Romero A., Fernandez-Duenas V., et al. Facilitated anion transport induces hyperpolarization of the cell membrane that triggers differentiation and cell death in cancer stem cells. Journal of the American Chemical Society, 137 (2015) 15892–15898.

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Chapter 18

Fungal Diseases as Neglected Pathogens: A Wake-Up Call to Public Health Officials Marcio L. Rodrigues, PhD,a,b and Joshua D. Nosanchuk, MD, PhDc aInstituto

Carlos Chagas, Fundação Oswaldo Cruz, Curitiba, Brazil de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil cDepartment of Medicine (Division of Infectious Diseases) and Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, USA bInstituto

[email protected], [email protected]

Keywords: pathogens, fungi, antifungal compounds, immunocompromised patients, histoplamosis, HIV, cryptococcosis, meningitis, income-related infection rates, mycoses, World Health Organization, social integration, eukaryotic, neglected populations, amphotericin B, diagnostic tests, antimicrobial resistance, zoonotic fungi, emerging infections, morbidity, mortality

18.1 Fungal Diseases: A Real Threat to Public Health Human fungal diseases differ fundamentally from other infections in diverse ways. As eukaryotic pathogens, fungi share many similarities with their host cells, which impairs the development of antifungal compounds. Fungal tropism is highly variable, as pathogens infect a wide range of cell types. A single fungal pathogen can infect multiple tissues in the same patient (depending on the host’s immunological status) and can undergo morphogenic shifts during infection. Fungi are still underappreciated as major pathogens by both the public and public health officials. Diseases caused by protozoa, bacteria, and viruses have been recognized as important public health issues for centuries. For instance, syphilis, influenza, and Chagas disease have been documented for over 100 Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa Copyright © 2022 Jenny Stanford Publishing Pte. Ltd. ISBN 978-981-4877-84-8 (Hardcover), 978-1-003-18043-2 (eBook) www.jennystanford.com

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years [1], while invasive mycoses were only widely acknowledged as medically important pathogens in the 1980s [2]. Viral diseases of major population impact (such as smallpox, influenza and—more recently—dengue, Zika, Chikungunya, and coronavirus) have affected millions of people with significant effects on human health in developed, developing, and less developed nations [3, 4]. These conditions have boosted the generation of knowledge, which has led to the eradication of smallpox [5], the wide availability of effective vaccines [6], and the development of diagnostic and preventive tools against influenza [7] and, more recently, Zika [8]. Bacterial diseases have profoundly impacted human health at different times in history, and although the phenomenon of antimicrobial resistance is a matter of extreme concern, there are several effective tools for the prevention, treatment, and diagnosis of bacterial infections [9]. Human parasitosis have been recognized to negatively impact public health in different parts of the globe for decades, which has stimulated the ongoing development of vaccines, new drugs, and diagnostic tests for malaria, sleeping sickness, leishmaniasis, filariasis, and Chagas disease [10]. Fungal infections, however, are part of a different scenario. These diseases, for most of recorded history as well as the majority of the last century, have been rare or had a low impact on human health. The increase in the number of immunocompromised patients, some of whom are highly susceptible to fungal infections, has totally changed this picture. The invasive diseases caused by fungi, the so-called systemic mycoses, profoundly impact human health. Moreover, the Global Action Fund for Fungal Infections (GAFFI) also highlights the devastating impact of focal fungal diseases in individuals who often have intact immune systems. GAFFI estimates that more than 1 million eyes go blind each year due to fungal keratitis [11]. Nearly one billion people have skin mycoses, which makes this disease only slightly less common on the planet than headaches and dental caries. Fungal spores contribute to significant reactive airway diseases in over 10 million individuals. In total, the GAFFI estimates that over 300 million people of all ages suffer from a serious fungal infection each year globally [11]. Notably, over 1.5 million of these individuals are estimated to die from their fungal disease [12]. Individual fungal diseases have profound impacts on human health. Around 220,000 new cases of cryptococcal meningitis occur worldwide each year, resulting in 181,000 deaths concentrated in sub-Saharan Africa [13]. More than 400,000 people develop Pneumocystis pneumonia annually and die without access to therapy [11]. In Latin America, histoplasmosis is one of the most common opportunistic infections among people living with HIV/AIDS, and approximately 30% of patients diagnosed with histoplasmosis in that region die from this disease [12]. Morbidity rates linked to fungal infections also represent an important health issue. For example, diseases such as chromoblastomycosis and eumycetoma lead to destructive deformations and debilitating conditions of the subcutaneous tissues, skin, and underlying bones, which result in social exclusion [14].

Systemic Mycoses Are Neglected Diseases

18.2 AIDS and Opportunistic Fungal Diseases: Problem Solved or Current Threat? Along with patients on anticancer therapies and other immunosuppressive medications, individuals with advanced HIV have dramatically contributed to the excess numbers of deaths due to fungal diseases. The implementation of new therapeutic strategies has had an unquestionably positive impact on the health of individuals with HIV and, as a result, AIDS-related deaths have fallen by more than 50% since their peak in 2004. The global number of people living with HIV ranged from 32.7 million to 44 million in 2018. In this group, up to 23.3 million people had access to antiretroviral therapy. In 2017, about 1.7 million new HIV infections were reported, and about 770,000 people died from this condition. It is noteworthy that up to 75 million people have been infected with HIV since the start of the pandemic, resulting in approximately 32 million AIDS-related deaths [15]. Hence, there remain large numbers of individuals who are not in care or whose immune systems are compromised by HIV. These compromised HIV-infected individuals, particularly those with CD4+ cell counts less than 200/mm3, are at high risk for invasive fungal diseases. Thus, the spread or control of AIDS is directly linked to the impact of invasive mycoses on public health. Tuberculosis remains the leading cause of death among people living with HIV, accounting for about 1 in 3 AIDS-related deaths [15]. By the end of 2016, 1.2 million people living with HIV developed tuberculosis. However, it is important to reinforce that invasive mycoses have a similarly close relationship to AIDS. According to the Centers for Disease Control and Prevention (CDC), fungi are among the leading causes of opportunistic infections affecting patients with HIV/AIDS [16]. Even with the increasing availability of anti-HIV treatment in less developed countries, fungal infections, particularly cryptococcosis and histoplasmosis [12, 13], are still a major problem for people living with HIV/AIDS. For example, meningitis caused by the genus Cryptococcus is (after tuberculosis) the second leading cause of death in people living with HIV [13]. Importantly, cryptococcal meningitis is a brain infection that, if left untreated, results in an agonizing death for people living with HIV [17].

18.3 Systemic Mycoses Are Neglected Diseases

Despite their alarming impact on human health, fungal diseases have been continually neglected over the years. According to Molyneux [18], neglected tropical diseases have particular characteristics. Firstly, they afflict the poorest people without access to safe drinking water, sanitation, and basic health services. Secondly, they are usually chronic and slowly developing, becoming progressively worse if left undiagnosed and untreated. The damage these diseases cause can be irreversible. Finally, neglected tropical diseases can cause severe pain and disability throughout life, with long-term consequences for patients

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and families of the affected person. People with neglected tropical diseases are often stigmatized and socially excluded, which can affect their mental health. High-income groups are rarely affected. The number of diseases that meet the above criteria is regrettably higher than would be expected for the second decade of the current millennium. This number, however, is underestimated, as several important syndromes fit these criteria but are not formally recognized as such, including systemic mycoses. Indeed, most high-mortality mycoses remain ignored by public health authorities and decisionmakers. The financial support for fungal disease research is incredibly lower than the funding available for other infectious diseases that cause similar mortality [19, 20]. For instance, for each human individual dying from malaria, US$1,315 are invested in research and development. Investment per death corresponds to US$334 for tuberculosis, US$276 for diarrheal diseases, and only US$31 for cryptococcal meningitis [19]. Still, there is no clear recognition of the importance of fungal diseases by international health agencies. For example, the World Health Organization (WHO) has recently included mycetoma, chromoblastomycosis, and “other deep mycoses” in the list of neglected tropical diseases [21], but specific information on WHO plans to combat fungal diseases is not yet available. Research on histoplasmosis, paracoccidioidomycosis, and sporotrichosis receives negligible funding [19]. Although these diseases are associated with high rates of mortality or the generation of conditions that hinder the performance of professional functions and social integration [14], none of them has been formally recognized as neglected diseases by WHO. According to Morel [22], neglected diseases persist due to failures in science, market, and public health. Science failures occur when there is insufficient knowledge on the pathophysiology of infectious agents and the host response. Market failures are usually observed in diseases against which medicines or vaccines exist but at a prohibitive cost. Finally, public health failures occur in syndromes against which low cost or even free prophylactic tools and medicines are available but their use is limited by poor logistics and lack of governmental support. Fungal diseases are clearly affected by the three types of failures described above. In this field, there has been a significant failure in science compared to diseases of medical importance recognized for decades or centuries, as previously mentioned. Of course, significant gaps in knowledge generation rates exist. Fungal infections consist of pathogenic processes triggered by eukaryotic microorganisms, which hinders the development of drugs that are toxic to the pathogen without affecting host tissues. The fact that there are no licensed antifungal vaccines underscores another clear failure in science. Similarly, reliable diagnostic methods are available for a very limited number of mycoses [23], and therapeutic options are restricted to a few classes of drugs that too frequently are associated to both intrinsic and acquired resistance [24], toxic, and expensive [25]. In fact, innovative tools to combat invasive mycoses are rare and of slow development. For illustration, the

Systemic Mycoses Are Neglected Diseases

most recently developed antifungals (echinocandins) were approved for clinical use in 2002 [26], reinforcing a major science failure in the area. It is noteworthy that this class of drugs is ineffective against various high-mortality mycoses [25]. Market failures have a profound impact on the control of fungal diseases. The deadliest fungal infections affect neglected populations, which results in a reduced market for drug commercialization and lack of interest from the pharmaceutical sector in the development of medicines, vaccines, and diagnostic tests for human mycoses. The main drug historically used for the treatment of severe disseminated mycoses is amphotericin B (AmB), whose discovery dates to 1955 [27], and it remains the standard first-line medication for certain fungal infections, such as cryptococcal meningitis. AmB formulations used for invasive fungal infections vary greatly in efficacy, safety, and cost. Conventional formulations are usually affordable but include significant side effects. The most effective and least toxic formulation is liposomal AmB, which can generate costs of up to US$100,000 per patient in different parts of the globe, including developing countries [28]. Liposomal AmB is highly effective when used in combination with other drugs. This pharmaceutical preparation was recommended by WHO as the preferred treatment for cryptococcal meningitis [29]. However, the high prices and unavailability of liposomal AmB in several countries have created major barriers to access to the most recommended treatment—as recognized by WHO itself—in developing countries. Liposomal AmB is registered and available for use (at high cost) in only 6 of 116 developing countries where fungal meningitis is a public health problem [11]. Prices are impeditive in many countries, revealing an unquestionable market failure. Public health failures also impact fungal diseases negatively. According to GAFFI [11], several major antifungals are not available or registered in various regions where fungal diseases are most lethal. 5-Fluorocytosine, a low-cost antimetabolite that is beneficial to a number of patients with systemic mycoses when used in combination with other antifungal drugs, is not available and/or registered in many countries, including those highly affected by systemic mycoses [11]. Given the intrinsic difficulties and high costs of drug development and the evident market and public health failures in this field, it is more realistic and impactful to make rational use of the diagnostic and antifungal tests already available to minimize the number of deaths caused by fungal diseases. In a recent study, Denning [30] proposed actions to reduce deaths from fungal diseases on the basis of currently available diagnostic tests and generic antifungals. Assuming that diagnostic tests would be properly applied and that antifungal therapy would be administered promptly and following current international guidelines, it was estimated that by 2020 annual deaths from cryptococcal meningitis could fall from 180,000 to 70,000. Deaths due to Pneumocystis pneumonia would fall from 400,000 annually to 162,500. The 80,000 annual deaths attributable to disseminated histoplasmosis could be reduced by 60%. Annual deaths due to chronic pulmonary aspergillosis (56,288) could fall by 33,500. These actions

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would thus result in a total of 1 million lives saved over 5 years. Of course, the effective implementation of AIDS control and prevention campaigns in areas lacking such programs would also positively impact the reduction in deaths caused by fungal infections. Such actions have the potential to minimize a clear public health failure on the basis of the use of existing tools for diagnosing and treating invasive mycoses.

18.4 Present and Future Problems: The Unknown

The epidemiology of fungal diseases is dynamic, and changes are difficult to predict. In 2012, the CDC reported an outbreak of fungal infections of the central nervous system that occurred among patients who received epidural or paraspinal injections of methylprednisolone. The majority of affected patients had meningitis caused by an extremely rare cause of fungal disease, namely Exserohilum rostratum [31]. This fungus is an example of an unexpected, emergent fungal disease, and it reinforced the perception of the pathogenic potential inherent in the fungal kingdom. The E. rostratum outbreak killed over 60 people out of 750 infected patients [32]. There is also a growing perception that climate change directly impacts the ability of fungi to cause damage to the human host. Recently, the multiresistant pathogen Candida auris has emerged as a serious global threat to human health, causing infections resistant to all major classes of antifungal drugs in immunocompromised patients [33]. C. auris differs from most other Candida species in several aspects. As recently reviewed by Lockhart, C. auris colonizes the skin rather than the gastrointestinal tract and is extremely resilient in the environment [34]. This resiliency has led to the fungus being associated with healthcare outbreaks, which have been exceedingly difficult to control due to the remarkable difficulty in eradicating the fungus from both patients and the environment [33]. Also of great concern, antimicrobial resistance in C. auris is more common than susceptibility to antifungals [34]. The spread of C. auris disease is linked to clonal isolates recovered from India, Venezuela, and South Africa between 2012 and 2015. Widespread use of antifungal drugs has been suggested as a determining factor for the emergence of C. auris [35]. Another hypothesis for the emergence of C. auris suggests that the fungus has recently acquired the virulence characteristics required to cause damage to human hosts. Although these explanations cannot be ruled out, it is unlikely that these changes occurred simultaneously on 3 continents. In this sense, it has recently been proposed that isolates of C. auris adapted to the human body temperature through selection from high-temperature regions [35]. Thus, this would be the first example of a novel human fungal pathogen that emerged as a result of global warming, which would explain several of its pathogenic characteristics. This observation demonstrates an important link between climate change and infectious diseases. Importantly, new threats to human health might still occur through climate adaptation mechanisms of zoonotic fungi, as proposed for C. auris.

The Need for Improved Diagnosis of Fungal Infections

Diseases that are known for decades still raise concerns. The city of Rio de Janeiro, Brazil, currently faces the largest sporotrichosis epidemic in history from a species, Sporothrix brasiliensis, that emerged locally [36]. Paracoccidioidomycosis is still one of the most important systemic mycoses in Latin America and the leading cause of mycosis mortality in immunocompetent individuals in Brazil [37]. Globally, the latest estimates suggest an annual occurrence of approximately 3 million cases of chronic pulmonary aspergillosis, over 200,000 cases of cryptococcal meningitis, 700,000 cases of invasive candidiasis, 500,000 cases of Pneumocystis jirovecii pneumonia, 250,000 cases of invasive aspergillosis, 100,000 cases of histoplasmosis, over 10 million cases of fungal asthma, and 1 million cases of fungal keratitis [12].

18.5 The Need for Improved Diagnosis of Fungal Infections

Early diagnosis of mycoses is decisive to efficient therapy. Common methods for the laboratory diagnosis of fungal infections include direct microscopic examination of human or animal samples, histopathology, microbial culture, antigen detection, serology, and, in a few cases, molecular tests [38]. These tests are relatively efficient at identifying well-known pathogens as causative agents of human and animal syndromes. Difficulties are nevertheless present as fungi typically reproduce slowly, and culture methods may take as long as a month to identify common species such as Histoplasma sp. Moreover, susceptibility testing to guide clinicians is also problematic, and breakpoints are not available for several important human pathogenic fungi, including C. auris [39]. Moreover, as reviewed by Wickes and Wiederhold [23], detection of less frequently encountered fungi is considerably more complex because routine clinical laboratories may lack the expertise and appropriate equipment to identify pathogenic agents. In fact, a recent survey involving 129 major laboratory centers in 24 countries of Latin America and the Caribbean revealed that only 9% of these centers appear to potentially meet the minimum European Confederation of Medical Mycology standards for fungal laboratory diagnostics [40]. Furthermore, in the national laboratories of developing countries, there is an enormous demand for the diagnostics of other infectious (quite often epidemic) diseases, and the lack of trained personnel is an additional limitation. The impact of this condition on human health is clear. For example, Exserohilum diseases before the 2012 United States outbreak manifested as rare systemic, cutaneous, corneal, and subcutaneous infections [41]. As the causative agent of the meningitis outbreak, E. rostratum was identified 1 month after the first meningitis case was reported, when the CDC announced that E. rostratum was recovered from unopened vials of steroid injections [42]. Similar problems occurred with necrotizing mucormycosis, a devastating complication of wounds caused by Apophysomyces sp., Saksenaea sp., and Lichtheimia [43]. Late laboratory diagnosis of cases of necrotizing mucormycosis caused by Apophysomyces trapeziformis resulted in 5 deaths in Missouri in 2011 [44]. Addressing the epidemic of C. auris has also been impeded by inherent deficiencies

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with classical laboratory methods utilized by many clinical laboratories as well as public health institutions [39]. These cases reinforce the notion that tests rapidly identifying infecting fungi have the potential to impact the course of fungal diseases beneficially.

18.6 Funding for Research and Innovation in Fungal Diseases

Funding for research on fungal diseases is unquestionably small compared to funding available for other infectious diseases that cause similar mortality [19, 20]. As an illustration, funding for research on cryptococcal meningitis, the fifth deadliest infectious disease, receives 4.3-fold less funding than the disease caused by the bacterial pathogen Neisseria meningititis [19]. Of concern, reports submitted between 2008 and 2017 on funding for neglected diseases show that cryptococcal meningitis was the only measurably funded fungal disease, accounting for 0.5% of the total invested [45]. Tuberculosis, for comparison, had a 34-fold higher investment. Other fungal diseases were not even included in these reports. Specifically, neglected mycoses of unquestionable clinical importance—such as paracoccidioidomycosis, mycetoma, sporotrichosis, and chromoblastomycosis— have not even been mentioned in the report, suggesting that these research areas have received negligible funding. These observations were fully confirmed by direct analysis of scientific articles declaring financial support from major international agencies with a history of supporting neglected disease research [45]. Reduced support for research and innovation in fungal diseases impacts knowledge generation directly. For example, tuberculosis and malaria were the focus of 8,827 and 5,687 scientific articles published in 2017, respectively. Fungal diseases, on the other hand, were much less investigated, with 213 articles on cryptococcosis, 80 on paracoccidioidomycosis, 51 on chromoblastomycosis, 53 on mycetoma, and 56 on sporotrichosis produced in the same period [45]. These numbers are probably linked to alarming facts, such as the aforementioned lack of vaccines capable of preventing fungal disease, less effective diagnostics, and a dearth of antifungal drugs in development.

18.7 Perspectives

There are ongoing initiatives to develop antifungal vaccines and drugs with the potential to control invasive mycoses [46]. However, the distance between promising laboratory results and the translation of knowledge into benefits to the general population is unquestionably long. In fungal diseases, this distance is apparently longer, considering the lack of investment in science and technology in association with the science, market, and public health failures discussed here. The situation is even more complex if one considers the emergence of multiresistant and still largely unknown pathogens such as C. auris. The impact

Perspectives

of emerging infections of this nature on human health is still hard to predict, but, as C. auris is now spread across the globe, the reality is that such infections can lead to significant morbidity and mortality as well as have vast economic consequences. Thus, it seems clear that public health authorities and decisionmakers need to more thoughtfully and closely consider invasive fungal diseases as a real and contemporary problem to avoid disasters historically observed in other models of infectious diseases. The fact that fungal diseases are not spread at the same rate as other microbial transmissible diseases causing epidemics does not mean they are less relevant in terms of the number of attained individuals. Furthermore, the fact that they are less studied represents an enormous risk given the new potential threats as a consequence of environmental deterioration and global warming. Realistic discussions about how prevention, diagnosis, and control of fungal diseases will improve outcomes demand a separation between concrete actions using currently available tools and future preventive actions. Of course, prophylactic actions against as yet unknown conditions are complex and difficult to develop, but the recent history of emerging fungal diseases reveals a clear need for knowledge generation on fungal pathogens. The attention to emerging fungal pathogens is important because even in the case they do not cause disease to humans due to new and as yet unknown zoonosis, they can affect animal health with an impact in the economy. Also, they can affect wild animals with an unpredictable ecological impact on biodiversity. Stimulating basic science and innovative activities in the area is therefore essential to reduce the impact of poorly known or yet unknown fungal diseases on human health. On the basis of currently available therapeutic and diagnostic tools, shortand medium-term impact actions also need to be implemented. For example, in 2019, WHO reinforced the need to use the Histoplasma capsulatum antigen detection test to diagnose histoplasmosis [47]. This test allows the diagnosis of the disease in more than 85% of patients within 48 hours, which would hasten the implementation of lifesaving antifungal therapy. Without proper diagnosis, patients are usually treated for tuberculosis, which has similar clinical symptoms. Under these conditions, patients usually die within 1 to 3 weeks. It is estimated that 48,000 lives could be saved over 5 years if appropriate diagnosis and treatment approaches are implemented for histoplasmosis. The above examples illustrate the complexity behind well-known and still poorly known fungal diseases. In both scenarios, concrete actions can be implemented, Support for basic research and technological development is obviously important, but making health professionals and decision-makers aware of the profound and ongoing impact of fungal diseases on human health is essential. The current situation, however, raises serious concerns, considering the funding limitations in the area and lack of public programs for prevention and control of fungal diseases. The high incidence of invasive mycoses in AIDS patients and the recent examples of C. auris and E. rostratum demonstrate that, without game-changing actions, the perspective on how fungal diseases will impact human health in the coming decades is extremely negative.

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Abbreviations AIDS: CDC: GAFFI: HIV:

Acquired Immunodeficiency Syndrome

US Centers for Disease Control and Prevention Global Action Fund for Fungal Infections

Human Immunodeficiency Virus

Disclosures and Conflicts of Interest

This chapter was originally published as: Roidrigues, M. L., Nosanchuk, J. D. (2020). Fungal diseases as neglected pathogens: A wake-up call to public health officials. PLoS Negl. Trop. Dis., 14(2), e0007964. https://doi.org/10.1371/journal.pntd.0007964, under the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), and appears here, with edits and updates, by kind permission of the copyright holders.

Acknowledgements: We thank Samuel Goldenberg (Fiocruz, Brazil) for critical reading and pertinent suggestions, in addition to the discussion on the importance of fungal diseases to human health. We are grateful to Carlos M. Morel and José Noronha (Fiocruz, Brazil) for supporting the inclusion of fungal infections as neglected diseases.

Funding: MLR is supported by grants from the Brazilian agency Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, grants 405520/2018-2, 440015/2018-9/Ministry of Health and 301304/2017-3) and Fiocruz (grants VPPCB-007-FIO-18 and VPPIS-001-FIO18). We acknowledge support from the Instituto Nacional de Ciência e Tecnologia de Inovação em Doenc¸as de Populações Negligenciadas (INCT-IDPN). JDN is supported in part by NIH R01AI052733. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of this chapter. The authors declare no competing financial interests.

References

1. Brachman PS. Infectious diseases—Past, present, and future. Int J Epidemiol. 2003; 32(5): 684–686. 2. Nucci M, Marr KA. Emerging fungal diseases. Clin Infect Dis. 2005; 41(4): 521–526.

3. Woolhouse MEJ, Howey R, Gaunt E, Reilly L, Chase-Topping M, Savill N. Temporal trends in the discovery of human viruses. Proc R Soc B Biol Sci. 2008; 275(1647): 2111–2115.

4. Woolhouse M, Scott F, Hudson Z, Howey R, Chase-Topping M. Human viruses: Discovery and emeraence. Philos Trans R Soc B Biol Sci. 2012;367(1604): 2864–2871.

5. Strassburg MA. The global eradication of smallpox. Am J Infect Control. 1982; 10(2): 53–59.

6. CDC. Recommended Vaccines by Age. 2016. Available at: https://www.cdc.gov/vaccines/ vpd/vaccines-age.html (accessed on July 6, 2021).

References

7. Marzoratti L, Iannella HA, Gómez VF, Figueroa SB. Recent advances in the diagnosis and treatment of influenza pneumonia. Curr Infect Dis Rep. 2012; 14(3): 275–283.

8. Barrett ADT. Current status of Zika vaccine development: Zika vaccines advance into clinical evaluation. npj Vaccines. 2018; 3: 24. 9. Aminov RI. A brief history of the antibiotic era: Lessons learned and challenges for the future. Front Microbiol. 2010; 1: 134.

10. Sacks DL. Vaccines against tropical parasitic diseases: A persisting answer to a persisting problem. Nat Immunol. 2014; 15(5): 403–405.

11. Global Fund for Fungal Infections. Available at: https://www.gaffi.org (accessed on July 6, 2021).

12. Bongomin F, Gago S, Oladele RO, Denning DW. Global and multi-national prevalence of fungal diseases—estimate precision. J Fungi. 2017; 3(4): 57.

13. Rajasingham R, Smith RM, Park BJ, Jarvis JN, Govender NP, Chiller TM, et al. Global burden of disease of HIV-associated cryptococcal meningitis: an updated analysis. Lancet Infect Dis. 2017; 17: 873–881.

14. Queiroz-Telles F, Fahal AH, Falci DR, Caceres DH, Chiller T, Pasqualotto AC. Neglected endemic mycoses. Lancet Infect Dis. 2017; 17(11), E367–E377.

15. UNAIDS. Global HIV & AIDS statistics—2019 fact sheet.

16. CDC. Fungal Diseases. 2017 Available at: https://www.cdc.gov/fungal/infections/hivaids.html (accessed on July 6, 2021).

17. Colombo AC, Rodrigues ML. Fungal colonization of the brain: Anatomopathological aspects of neurological cryptococcosis. An Acad Bras Cienc. 2015; 87: 1293–1309.

18. Molyneux David. Neglected tropical diseases. Commun Eye Heal. 2013; 26: 21–24.

19. Rodrigues ML, Albuquerque PC. Searching for a change: The need for increased support for public health and research on fungal diseases. PLoS Negl Trop Dis. 2018; 12: 1–5.

20. Rodrigues ML. Funding and innovation in diseases of neglected populations: The paradox of cryptococcal meningitis. PLoS Negl Trop Dis. 2016; 10(3): e0004429.

21. WHO. Neglected tropical diseases. 2017. Available at: https://www.who.int/neglected_ diseases/diseases/en/ (accessed on July 6, 2021).

22. Morel CM. [Innovation in health and neglected diseases]. Cad Saude Publica. 2006; 22(8): DOI: https://doi.org/10.1590/S0102-311X2006000800001. 23. Wickes BL, Wiederhold NP. Molecular diagnostics in medical mycology. Nat Commun. 2018; 9: 5135.

24. Robbins N, Caplan T, Cowen LE. Molecular evolution of antifungal drug resistance. Annu Rev Microbiol. 2017; 71: 753–775.

25. Mourad A, Perfect JR. The war on cryptococcosis: A review of the antifungal arsenal. Mem Inst Oswaldo Cruz. 2018; 113(7): e170391.

26. Denning DW. Echinocandins: a new class of antifungal. J Antimicrob Chemother. 2002; 49(6): 889–891. 27. Oura M, Sternberg TH, Wright ET. A new antifungal antibiotic, amphotericin B. Antibiot Annu. 1955; 3: 566–573.

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28. Borba HHL, Steimbach LM, Riveros BS, Tonin FS, Ferreira VL, Bagatim BA de Q, et al. Cost-effectiveness of amphotericin B formulations in the treatment of systemic fungal infections. Mycoses. 2018; 61(10): 754–763. 29. WHO. Guidelines for the diagnosis, prevention and management of cryptococcal disease in HIV-infected adults, adolescents and children. Supplement to the 2016 consolidated guidelines on the use of antiretroviral drugs for treating and preventing HIV infection. 2018. pp. 1–45. Available at: https://apps.who.int/iris/ handle/10665/260399?locale-attribute=pt& (accessed on July 6, 2021).

30. Denning DW. Minimizing fungal disease deaths will allow the UNAIDS target of reducing annual AIDS deaths below 500 000 by 2020 to be realized. Philos Trans R Soc B Biol Sci. 2016; 371(1709): 20150468.

31. Smith RM, Schaefer MK, Kainer MA, Wise M, Finks J, Duwve J, et al. Fungal infections associated with contaminated methylprednisolone injections. N Engl J Med. 2013; 369: 1598–1609. 32. CDC. Multistate outbreak of fungal meningitis and other infections–case count. 2015 Available at: https://www.cdc.gov/hai/outbreaks/meningitis-map-large.html (accessed on July 6, 2021).

33. Clancy CJ, Nguyen MH. Emergence of Candida auris: An international call to arms. Clin Infect Dis. 2017; 64(2): 141–143.

34. Lockhart SR. Candida auris and multidrug resistance: Defining the new normal. Fungal Genet Biol. 2019; 131: 103243.

35. Casadevall A, Kontoyiannis DP, Robert V. On the Emergence of Candida auris: Climate change, azoles, swamps, and birds. MBio. 2019; DOI: 10.1128/mBio.01397-19.

36. Gremião IDF, Miranda LHM, Reis EG, Rodrigues AM, Pereira SA. Zoonotic epidemic of sporotrichosis: Cat to human transmission. PLoS Pathog. 2017; 13(1): e1006077.

37. Giacomazzi J, Baethgen L, Carneiro LC, Millington MA, Denning DW, Colombo AL, et al. The burden of serious human fungal infections in Brazil. Mycoses. 2016; 59: 145–150.

38. Backx M, White PL, Barnes RA. New fungal diagnostics. Br J Hosp Med. 2014; 75(5): 271–276.

39. Kordalewska M, Perlin DS. Identification of drug resistant Candida auris. Front Microbiol. 2019; 10: 1918. 40. Falci DR, Pasqualotto AC. Clinical mycology in Latin America and the Caribbean: A snapshot of diagnostic and therapeutic capabilities. Mycoses. 2019; 62: 368–373.

41. Katragkou A, Pana ZD, Perlin DS, Kontoyiannis DP, Walsh TJ, Roilides E. Exserohilum infections: Review of 48 cases before the 2012 United States outbreak. Med Mycol. 2014; 52(4): 376–386.

42. Larone DH, Walsh TJ. Exserohilum rostratum: Anatomy of a national outbreak of fungal meningitis. Clin Microbiol Newsl. 2013; 35(23): 185–193.

43. Walsh TJ, Hospenthal DR, Petraitis V, Kontoyiannis DP. Necrotizing mucormycosis of wounds following combat injuries, natural disasters, burns, and other trauma. J Fungi. 2019; 5(3): 57. 44. Fanfair RN, Benedict K, Bos J, Bennett SD, Lo YC, Adebanjo T, et al. Necrotizing cutaneous mucormycosis after a tornado in Joplin, Missouri, in 2011. N Engl J Med. 2012; 367: 2214–2225.

References

45. Rodrigues ML, Albuquerque PC. Searching for a change: The need for increased support for public health and research on fungal diseases. PLoS Negl Trop Dis. 2018; 12(6): e0006479. 46. McCarthy MW, Walsh TJ. Drug development challenges and strategies to address emerging and resistant fungal pathogens. Expert Rev Anti Infect Ther. 2017; 15: 577–584. 47. WHO. Second WHO Model List of Essential In Vitro Diagnostics. 2019.

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Chapter 19

Catch the Wave: Metabolomic Analyses in Human Pathogenic Fungi Philipp Brandt, Enrico Garbe, and Slavena Vylkova, PhD Septomics Research Center, Friedrich Schiller University and Leibniz Institute for Natural Product Research and Infection Biology–Hans Knöll Institute, Jena, Germany [email protected]

Keywords: pathogenic fungi, host-fungal interactions, mass spectrometry, gas chromatography, liquid chromatography, matrix-associated laser desorption ionization, nuclear magnetic resonance, selective reagent ionization, metabolomics, antifungal agents, biofilms

19.1 Introduction The ability of fungi to colonize and persist within the human host is accompanied by an adaptation of fungal metabolism that allows them to withstand stress conditions, contend with the immune response, acquire nutrients, or simply secure a competitive edge during infection. Metabolites are the end products of cellular functions, and their levels reflect the fungal response to genetic or environmental changes. Despite the importance of metabolism for fungal fitness and pathogenicity, a comprehensive understanding of its impact on host-fungal interactions is still missing. Metabolomics, defined here as the simultaneous identification and quantification of the complete set of metabolites in a biological specimen, not only represents the chemical phenotype of an organism but also allows identification and interpretation of associations between genotype and

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phenotype. Investigations of the unique metabolic fingerprints of pathogenic microorganisms and the infection-associated changes to the host’s metabolism can provide a more complete impression of the infection process. Yet the utilization of metabolomics approaches to study host-fungal interactions are still few and far between. In this Pearl, we present an overview of the metabolome analyses in human pathogenic fungi to date, give examples of new discoveries made by such approaches, and discuss future research directions.

19.2 What Methods Are Available to Study Metabolomics?

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) are the analytical tools of choice in most metabolomic studies, with a plethora of substrate- and study-specific variations available [1–3]. An overview of the most commonly used techniques and critical points to consider during study design is presented in Fig. 19.1. Such approaches have been utilized in fungal research to perform untargeted monitoring of primary and secondary metabolites (SMs), targeted search for biomarkers (e.g., lipidomics), and in vivo measurement of metabolic fluxes [4–7]. For downstream analysis of metabolomic data, an increasing number of databases, software, and tools are available (summarized in [8, 9]).

19.3 How to Get the Most Out of Your Metabolomics Data? 19.3.1 Metabolomic Databases and Mathematical Models

The required combination of analytical methods, complex data interpretation, and limited number of relevant studies in pathogenic fungi are the main reasons why researchers may hesitate to undertake metabolomic studies. However, the field is rapidly expanding due to the implementation of new methods, streamlined data analyses, and lower experimental costs. For instance, several fungal-specific metabolomic databases were recently launched, such as A2MDB, a comprehensive Aspergillus fumigatus SM repository [10]. A2MDB contains catalogued and annotated experimental metabolomics data, information about metabolic pathways, and molecular docking models of metabolite–protein target interactions. YMDB (http://www.ymdb.ca) is a database compiling information about metabolites found in or produced by Saccharomyces cerevisiae, which could be applied toward understanding metabolic responses of pathogenic yeasts (e.g., Candida glabrata). Another database collected curated literature information for 2,240 metabolic reactions in Aspergillus niger. The data were used to generate a mathematical metabolic model that was further validated with experimentally obtained transcriptional and metabolomic data [11]. Such databases and models are invaluable tools for making predictions and evaluating results, which increases the approachability of metabolomics research in pathogenic fungi.

Figure 19.1 Overview of metabolomic approaches and typical workflow. The method of choice is shaped by the rationale of the experiment, the amount and type of the sample material (single or mixed cellular samples, tissues, supernatants/fluids, etc.), the decision for single measurements versus time course analyses, and whether the focus is on known or unknown (global) metabolic pathways/cellular processes (targeted versus untargeted approach, respectively). NMR is frequently used for the detection of biomarkers. Although this method provides high reproducibility and the option of in vivo metabolomics, it is less sensitive compared to MS techniques. Commonly used MS-based methods are MRM and SRI, usually performed with tandem MS. Typically, prior to MS measurements, a separation of the extracted metabolites is needed to increase sensitivity and specificity. Sample separation is done by either GC, more favorable for volatile compounds, or LC. HILIC is an LC technique optimized for separation of hydrophilic polar compounds, like carbohydrates. MALDI is a surface-based MS approach used in single-cell metabolomics, and for the determination of the spatial distribution of metabolites within a specimen via MALDI imaging. However, MALDI-based approaches are limited to abundant metabolites. The raw data processing includes method-dependent quality controls, as well as normalization and identification steps. The absolute quantification of the measured metabolites is only possible in targeted approaches. Finally, the processed data can be compared to existing databases, used for modeling of metabolic fluxes, or integrated with other OMICs datasets. Abbreviations: GC, gas chromatography; HILIC, hydrophilic interaction chromatography; LC, liquid chromatography; MALDI, matrix-associated laser desorption ionization; MRM, multiple reaction monitoring; MS, mass spectrometry; NMR, nuclear magnetic resonance; SRI, selective reagent ionization.

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19.4 Integrated OMICs Approaches A few studies have linked metabolomics with other OMICs data, expanding our understanding of biological functions in a way that was not possible in singleOMICs studies. For example, comparative genomic analysis of Cryptococcus neoformans var. grubii versus C. neoformans var. neoformans and Cryptococcus gattii revealed a genomic translocation that disrupts TGR1, a gene encoding a newly described protein involved in metabolism. Metabolomic and subsequent phenotypic analyses showed that the deletion of TGR1 leads to an accumulation of intracellular trehalose that is critical for protection against a variety of hostassociated stresses [12]. Burgain and colleagues evaluated Candida albicans growth under hypoxia and combined transcriptomics and metabolomics data to show that oxygen limitation stimulates lipid biosynthesis, resulting in structural rearrangements of the cell membrane [13]. Another study linked proteomic and metabolomic data to gain insight into the drug resistance mechanisms of the emerging fungal pathogen Candida auris [14]. These are just a few examples that highlight the importance of multi-OMICs platforms that comprehensively utilize systems biology to draw more accurate conclusions about biological processes.

19.5 What Have We Learned from Metabolomics of Human Pathogenic Fungi to This Date? 19.5.1 Identification of Biomarkers

One key biological application of metabolomics is the identification of disease signatures and biomarkers. The routine measurement of single molecules or a pattern of several molecules as a part of fungus-specific metabolite imprints is rather inexpensive and could be used in diagnosis and therapeutic monitoring. Several metabolites have already shown promising characteristics for improved early Aspergillus detection [4]. As an example, testing of serum levels of gliotoxin, an SM with immunosuppressive properties, has been applied in the clinics [15]. A study searching for novel aspergillosis-specific biomarkers compared the secreted metabolites from 30 strains of several pathogenic fungi and identified a novel Aspergillus-specific linear tetrapeptide named aspergitide [16]. Another work utilized metabolomic-based approaches to improve the taxonomical identification of common human fungal pathogens [17]. A total of 45 primary metabolites from A. pallidofulvus, Fusarium oxysporum, and Geothrichum candidum could clearly differentiate between the species. Further, Ahmed and colleagues developed sampling methods for volatile organic compounds (volatome) in A. fumigatus [5], which were enriched in pyrazines and terpene. Others used a similar approach to identify species-specific volatomes of several Candida spp. and found that the C. albicans volatome was marked by increased 3-methyl-2-butanone and styrene,

What Have We Learned from Metabolomics of Human Pathogenic Fungi to This Date?

a feature absent in the other species [18]. These studies could lead to the development of breath or blood-based tests for the detection of fungal infections. An important and thus far understudied aspect of fungal metabolomics is the ex vivo and in vivo metabolic sampling of the pathogen and the host during infection. The limited number of such studies already has revealed novel virulence characteristics. For example, metabolic profiles from meningitis rat model of cryptococcosis showed increased amounts of lactate, citrate, and polyols (mannitol and glycerol) and a decrease of glucose in the central spinal fluid [19]. Others showed that infection of lung epithelial cells with C. neoformans increases the secretion of pantothenic acid, previously found to stimulate fungal growth [20]. The implementation of newly identified metabolites and patterns into diagnostics or therapy will depend on several critical aspects, including early detection, reliability, low invasiveness, and costs.

19.5.2 Effect of Antifungal Agents on Fungal Metabolism

Another major aim of metabolic studies in pathogenic fungi is to gain deeper insight in the effect of antifungal agents. Metabolomics-based approaches have been used, for example, to examine the effects of fluconazole on C. albicans metabolism [21, 22]. The drug increased the abundance of central carbon metabolism intermediates (e.g., glucose-6-phosphate, phenylpyruvate, α-ketoglutarate), whereas intermediates of amino acid and purine metabolism were decreased [21]. Beside fluconazole, the effects of other antifungal agents on the metabolism of C. albicans [23–25] and other pathogenic fungi [26, 27] have also been investigated. Targeted metabolomic analyses of clinically relevant Mucorales species following exposure to sublethal concentrations of posaconazole revealed significant alterations in ergosterol biosynthesis compared to A. fumigatus, e.g., accumulation of the toxic sterol 14methylergosta-8,24-diene-3,6-diol [26]. Ergosterol biosynthesis, together with glycolysis and inositol biosynthesis, were among the iron-dependent pathways affected by the loss of the C. neoformans iron regulatory protein Cir1 [27], critical for fungal virulence. The knowledge acquired about drug-specific metabolic signatures and effects can help target vulnerable spots amenable to therapeutic intervention in the fungus.

19.5.3 Virulence Traits

The expression of virulence traits in human pathogenic fungi is frequently accompanied by distinct changes in metabolism. For example, metabolomic analysis of C. albicans revealed that the transition from yeast to hyphae, a crucial virulence factor, is accompanied by impaired central carbon and nitrogen metabolism [28]. Unlike yeast, hyphal cells had low intracellular ATP levels, whereas the levels of aromatic amino acids, proline, and fatty acids were increased. In accordance, the quorum sensing molecules farnesol or phenylethyl alcohol that suppresses hyphae formation stimulated the central carbon and energy metabolism [29, 30].

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Hyphal-inducing compounds, such as the monosaccharide N-acetyl-D-glucosamine (GlcNAc), also affect C. albicans metabolism. GlcNAc-grown cells had low intracellular levels of amino acids compared to cells grown in glucose. This led to an amino acid starvation response, a known trigger of hyphal morphogenesis [31]. Another study revealed that hyphal growth requires a functional glutamate dehydrogenase, an enzyme that links amino acid metabolism with the tricarboxylic acid (TCA) cycle [32]. Further, treatment of C. albicans with mitochondrial inhibitors led to the suppression of hyphae formation, which correlated with changes in the redox state, decreased TCA cycle activity, and increased catabolism of fatty acids compared to nontreated cells [6]. A combination of transcriptomic and metabolomic approaches defined the importance of C. albicans Snf5, a subunit of the SWI/SNF chromatin remodeling complex, in controlling metabolic flexibility and fungal fitness specifically under hypoxia [33]. Central carbon metabolism was also found to play a critical role in other pathogenic fungi. Metabolomics was used to characterize the function of a newly identified gene, HVA1, in C. neoformans. The mutant strain had increased levels of phosphoenolpyruvate and decreased levels of 2-ketoglutarate relative to the wild type, suggestive of a block in the TCA cycle and lowered ATP production. Further investigations showed that HVA1 coordinates cell fitness (and thus virulence), likely via regulation of cellular NADPH levels [34]. Thus, metabolomic approaches aided in an understanding of stimulus-driven phenotypes and the construction of a more detailed framework of host-pathogen interactions.

19.5.4 Biofilm Formation

Several fungal pathogens can form robust biofilms on biotic surfaces and medical devices, which is a major health issue due to their reduced antifungal susceptibility. Metabolomic analyses of different stages of C. albicans biofilm formation showed that mature biofilms are characterized by low TCA cycle and mitochondrial activity, whereas the intracellular levels of several amino acids and glycerol (cellular response to osmotic stress) are elevated [35]. Moreover, trehalose that accumulated in the first 24 h of biofilm formation was critical for resistance to the antifungal drug amphotericin B [35]. Thus, metabolomic approaches revealed that both conservation of energy and increased production of stress-protective metabolites contributes to the antifungal resistance of cells within a biofilm. Host-associated biofilms occur mostly as multispecies entities, which show different virulence characteristics compared to single-species biofilms. Metabolomic analyses of Staphylococcus aureus and C. albicans mixed biofilms showed that the symbiotic coexistence of the two species is signified by the high abundance of sedoheptulose-7-phosphate, an intermediate of the pentose phosphate pathway [36]. By contrast, the antagonistic effect of Proteus mirabilis on C. albicans growth resulted in slower metabolism and energy consumption by the fungus within the mixed biofilm [37]. Thus, metabolomics has an enormous potential to

Conclusions and Perspectives

define interspecies interactions, which might be difficult or even impossible to achieve with other approaches.

19.5.5 Interspecies Interactions within the Host

Besides in biofilms, interspecies interactions take place en masse in the human body (e.g., in the gut). Both metabolic modifications of the microbiome following antibiotic treatment and infection-associated changes to the host gut metabolome involving fungi have been investigated [38, 39]. Specifically, C. albicans–colonized mice had minimal changes in the cecum metabolites compared to the untreated animals [38]. However, mice treated with the antibiotic cefoperazone showed significant alterations in the microbiome and metabolome [39]. Intestinal levels of metabolites that promote C. albicans growth and morphogenesis, including carbohydrates, sugar alcohols, and primary bile acids, were increased after treatment with this antibiotic, whereas the levels of growth-inhibiting carboxylic acids and secondary bile acids were decreased [39]. Thus, metabolomicbased approaches are valuable tools for understanding the complex interactions between microbes and their host.

19.6 Conclusions and Perspectives

In conclusion, metabolomics and the respective bioinformatics tools and databases are rapidly evolving and have the potential to reveal novel aspects of metabolic adaptations in fungal pathogens. In contrast to transcriptomics, which measures changes in gene expression that might lead to metabolic rearrangements, metabolomics reveals the most downstream effects of cellular activity. Therefore, this technique brings more concrete insights into metabolic regulation and adaptation to changing environments. However, since metabolomics provides only a snapshot of the organism’s physiological state at the moment of sampling, the examination of metabolic fluxes or a time course of sample collection should be considered to fully understand metabolic dynamics and reprograming. In contrast to other well-established and widely used OMICs approaches, metabolomics is still a new tool. The studies performed in the field of fungal research to this date illustrate the broad applicability of the technique (Fig. 19.2). However, several improvements are required for better utilization of this methodology, including optimization of the standard sampling and extraction protocols, the generation of user-friendly multi-OMICs databases, and mathematical prediction models of metabolic fluxes. For example, incorporating metabolomic datasets to established fungal resources, such as FungiDB, would improve data mining and interpretation. Further, new advances in the metabolomics field should be considered. For instance, Judge and colleagues used Neurospora crassa as a model organism for continuous in vivo monitoring of fungal metabolism [7]. In another study, antifungal drug activity was monitored in vivo and experimentally validated

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in A. nidulans [40]. Barkal and colleagues applied a micrometabolomic approach, in which an open microfluidic channel was used to collect SM from A. fumigatus incubated in culture media, blood, or coculture with bacteria [41]. Additionally, single-cell metabolomics was performed with S. cerevisiae to investigate potentially heterogeneous adaptations within a population to certain environmental conditions [42]. Moreover, matrix-associated laser desorption ionization (MALDI) imaging MS could be utilized to identify the spatial distribution of metabolites in a sample—for example, in infected patient tissue. Ultimately, there are many exciting possibilities in metabolomics research that can move the field of host-fungal interactions forward and toward an improvement of disease prevention and treatment.

Figure 19.2 Metabolomics approaches in human pathogenic fungi. An overview of the metabolomics approaches utilized to date in human pathogenic fungi, with a primary focus on Aspergillus, Candida, Cryptococcus, and Mucor spp. In Aspergillus, metabolomic studies were used to identify secondary metabolites and biomarkers as a means to improve earlier detection and diagnosis of aspergillosis [4, 5, 10, 11, 15–17, 40, 41]. To date, metabolomics approaches have not been utilized to investigate the primary metabolism of Aspergillus spp., which has been the main focus of C. albicans and C. neoformans research. Metabolomic approaches brought insight into C. albicans virulence mechanisms, antifungal effects, and biofilm growth [6, 13, 18, 21–25, 28–33, 35–39]. Although one study focused on secreted metabolites and biomarkers in C. auris and used metabolomics to study resistance mechanisms in this organism [14, 43], metabolomics approaches in other pathogenic Candida spp. are still lacking. Further, metabolomic-based ex vivo, in vivo, and secondary metabolites/biomarker identification in Candida spp. are either scarce or lacking. In Cryptococcus spp. metabolomics was utilized in the identification of biomarkers, resistance mechanisms to antifungal drugs, and in vivo infection models [12, 19, 20, 27, 34]. In Mucor spp., the only metabolomic approach undertaken has examined the metabolic effect of antifungal agents [26].

References

Abbreviations GC: GlcNAc: HILIC: LC: MALDI: MRM: MS: NMR: SMs: SRI: TCA:

gas chromatography N-acetyl-D-glucosamine hydrophilic interaction chromatography liquid chromatography matrix-associated laser desorption ionization multiple reaction monitoring mass spectrometry nuclear magnetic resonance secondary metabolites selective reagent ionization tricarboxylic acid

Disclosures and Conflict of Interest

This chapter was originally published as: Brandt, P., Garbe, E., Vylkova, S. (2020). Catch the wave: Metabolomic analyses in human pathogenic fungi. PLoS Pathog., 16(8), e1008757, https://doi.org/10.1371/journal.ppat.1008757, under the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates. Funding: This work was supported by the German Research Foundation (DFG) through the TRR 124 FungiNet, “Pathogenic fungi and their human host: Networks of Interaction,” DFG project number 210879364, Project C2 (to SV) and by the German Ministry for Education and Science in the program Unternehmen Region (BMBF 03Z22JN11) (to SV). These funders played no role in study design, data collection and analysis, decision to publish, or preparation of the chapter. Competing interests: The authors have declared that no competing interests exist.

Acknowledgments: We thank Amelia Barber, Bettina Böttcher, Franziska Gerwien, and Lysett Wagner for the critical reading of the chapter and all members of the research group Host Fungal Interfaces at ZIK Septomics for the helpful discussions.

References

1. Amberg A, Riefke B, Schlotterbeck G, Ross A, Senn H, Dieterle F, et al. NMR and MS methods for metabolomics. In: Gautier J-C, editor. Drug Safety Evaluation: Methods and Protocols. Springer New York; 2017. p. 229–58.

2. Wang S, Blair IA, Mesaros C. Analytical methods for mass spectrometry-based metabolomics studies. In: Woods AG, Darie CC, editors. Advancements of Mass Spectrometry in Biomedical Research. Cham: Springer International Publishing; 2019. p. 635–47.

3. Patejko M, Jacyna J, Markuszewski MJ. Sample preparation procedures utilized in microbial metabolomics: an overview. J Chromatogr B. 2017;1043:150–7.

421

422

Metabolomic Analyses in Human Pathogenic Fungi

4. Savelieff MG, Pappalardo L. Novel cutting-edge metabolite-based diagnostic tools for aspergillosis. PLoS Pathog. 2017;13(9):e1006486.

5. Ahmed WM, Geranios P, White IR, Lawal O, Nijsen TM, Bromley MJ, et al. Development of an adaptable headspace sampling method for metabolic profiling of the fungal volatome. Analyst. 2018;143(17):4155–62.

6. Grahl N, Demers EG, Lindsay AK, Harty CE, Willger SD, Piispanen AE, et al. Mitochondrial activity and Cyr1 are key regulators of Ras1 activation of C. albicans virulence pathways. PLoS Pathog. 2015;11(8):e1005133.

7. Judge MT, Wu Y, Tayyari F, Hattori A, Glushka J, Ito T, et al. Continuous in vivo metabolism by NMR. Front Mol Biosci. 2019;6:26. 8. Cambiaghi A, Ferrario M, Masseroli M. Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration. Brief Bioinformatics. 2016;18(3):498–510.

9. O’Shea K, Misra BB. Software tools, databases and resources in metabolomics: updates from 2018 to 2019. Metabolomics. 2020;16(3):36.

10. Vadlapudi V, Borah N, Yellusani KR, Gade S, Reddy P, Rajamanikyam M, et al. Aspergillus Secondary Metabolite Database, a resource to understand the Secondary metabolome of Aspergillus genus. Sci Rep. 2017;7(1):7325. 11. Andersen MR, Nielsen ML, Nielsen J. Metabolic model integration of the bibliome, genome, metabolome and reactome of Aspergillus niger. Mol Syst Biol. 2008;4(1):178.

12. Morrow CA, Lee IR, Chow EWL, Ormerod KL, Goldinger A, Byrnes EJ, et al. A unique chromosomal rearrangement in the Cryptococcus neoformans var. grubii type strain enhances key phenotypes associated with virulence. mBio. 2012;3(2):e00310–11.

13. Burgain A, Tebbji F, Khemiri I, Sellam A. Metabolic reprogramming in the opportunistic yeast Candida albicans in response to hypoxia. mSphere. 2020;5(1):e00913–19.

14. Zamith-Miranda D, Heyman HM, Cleare LG, Couvillion SP, Clair GC, Bredeweg EL, et al. Multi-omics signature of Candida auris, an emerging and multidrug-resistant pathogen. mSystems. 2019;4(4):e00257–19.

15. Vidal-García M, Domingo MP, De Rueda B, Roc L, Delgado MP, Revillo MJ, et al. Clinical validity of bis(methylthio)gliotoxin for the diagnosis of invasive aspergillosis. Appl Microbiol Biotechnol. 2016;100(5):2327–34. 16. Lee K-C, Tam EWT, Lo K-C, Tsang AKL, Lau CCY, To KKW, et al. Metabolomics analysis reveals specific novel tetrapeptide and potential anti-inflammatory metabolites in pathogenic Aspergillus species. Int J Mol Sci. 2015;16(6):13850–67.

17. Ząbek A, Klimek-Ochab M, Jawień E, Młynarz P. Biodiversity in targeted metabolomics analysis of filamentous fungal pathogens by 1H NMR-based studies. World J Microbiol Biotechnol. 2017;33(7):132.

18. Hertel M, Hartwig S, Schütte E, Gillissen B, Preissner R, Schmidt-Westhausen AM, et al. Identification of signature volatiles to discriminate Candida albicans, glabrata, krusei and tropicalis using gas chromatography and mass spectrometry. Mycoses. 2016;59(2):117–26. 19. Himmelreich U, Malik R, Kühn T, Daniel H-M, Somorjai RL, Dolenko B, et al. Rapid etiological classification of meningitis by NMR spectroscopy based on metabolite profiles and host response. PLoS ONE. 2009;4(4):e5328.

References

20. Liew KL, Jee JM, Yap I, Yong PVC. In vitro analysis of metabolites secreted during infection of lung epithelial cells by Cryptococcus neoformans. PLoS ONE. 2016;11(4):e0153356.

21. Katragkou A, Alexander EL, Eoh H, Raheem SK, Roilides E, Walsh TJ. Effects of fluconazole on the metabolomic profile of Candida albicans. J Antimicrob Chemother. 2015;71(3):635–40. 22. Li L, Liao Z, Yang Y, Lv L, Cao Y, Zhu Z. Metabolomic profiling for the identification of potential biomarkers involved in a laboratory azole resistance in Candida albicans. PLoS ONE. 2018;13(2):e0192328.

23. Katragkou A, Williams M, Sternberg S, Pantazatos D, Roilides E, Walsh TJ. Micafungin alters the amino acid, nucleic acid and central carbon metabolism of Candida albicans at subinhibitory concentrations: novel insights into mechanisms of action. J Antimicrob Chemother. 2016;72(3):712–6.

24. Hosseinzadeh A, Stylianou M, Lopes JP, Müller DC, Häggman A, Holmberg S, et al. Stable redox-cycling nitroxide tempol has antifungal and immune-modulatory properties. Front Microbiol. 2019;10:1843.

25. Corte L, Tiecco M, Roscini L, Germani R, Cardinali G. FTIR analysis of the metabolomic stress response induced by N-alkyltropinium bromide surfactants in the yeasts Saccharomyces cerevisiae and Candida albicans. Colloids Surf B Biointerfaces. 2014;116:761–71. 26. Müller C, Neugebauer T, Zill P, Lass-Flörl C, Bracher F, Binder U. Sterol composition of clinically relevant mucorales and changes resulting from posaconazole treatment. Molecules. 2018;23(5):1218.

27. Choi JN, Kim J, Kim J, Jung WH, Lee CH. Influence of iron regulation on the metabolome of Cryptococcus neoformans. PLoS ONE. 2012;7(7):e41654.

28. Han T-l, Cannon RD, Villas-Bôas SG. Metabolome analysis during the morphological transition of Candida albicans. Metabolomics. 2012;8(6):1204–17.

29. Han T-L, Cannon RD, Villas-Bôas SG. The metabolic response of Candida albicans to farnesol under hyphae-inducing conditions. FEMS Yeast Research. 2012;12(8):879–89.

30. Han T-L, Tumanov S, Cannon RD, Villas-Boas SG. Metabolic response of Candida albicans to phenylethyl alcohol under hyphae-inducing conditions. PLoS ONE. 2013;8(8):e71364.

31. Kamthan M, Mukhopadhyay G, Chakraborty N, Chakraborty S, Datta A. Quantitative proteomics and metabolomics approaches to demonstrate N-acetyl-d-glucosamine inducible amino acid deprivation response as morphological switch in Candida albicans. Fungal Genet Biol. 2012;49(5):369–78.

32. Han T-L, Cannon RD, Gallo SM, Villas-Bôas SG. A metabolomic study of the effect of Candida albicans glutamate dehydrogenase deletion on growth and morphogenesis. npj Biofilms Microbiomes. 2019;5(1):13. 33. Burgain A, Pic É, Markey L, Tebbji F, Kumamoto CA, Sellam A. A novel genetic circuitry governing hypoxic metabolic flexibility, commensalism and virulence in the fungal pathogen Candida albicans. PLoS Pathog. 2019;15(12):e1007823.

34. McClelland EE, Ramagopal UA, Rivera J, Cox J, Nakouzi A, Prabu MM, et al. A small protein associated with fungal energy metabolism affects the virulence of Cryptococcus neoformans in mammals. PLoS Pathog. 2016;12(9):e1005849.

35. Zhu Z, Wang H, Shang Q, Jiang Y, Cao Y, Chai Y. Time Course Analysis of Candida albicans Metabolites during biofilm development. J Proteome Res. 2013;12(6):2375–85.

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Metabolomic Analyses in Human Pathogenic Fungi

36. Weidt S, Haggarty J, Kean R, Cojocariu CI, Silcock PJ, Rajendran R, et al. A novel targeted/untargeted GC-Orbitrap metabolomics methodology applied to Candida albicans and Staphylococcus aureus biofilms. Metabolomics. 2016;12(12):189.

37. Kart D, Yabanoglu Ciftci S, Nemutlu E. Altered metabolomic profile of dual-species biofilm: Interactions between Proteus mirabilis and Candida albicans. Microbiol Res. 2020;230:126346. 38. Bratburd JR, Keller C, Vivas E, Gemperline E, Li L, Rey FE, et al. Gut microbial and metabolic responses to Salmonella enterica Serovar Typhimurium and Candida albicans. mBio. 2018;9(6):e02032–18.

39. Gutierrez D, Weinstock A, Antharam VC, Gu H, Jasbi P, Shi X, et al. Antibiotic-induced gut metabolome and microbiome alterations increase the susceptibility to Candida albicans colonization in the gastrointestinal tract. FEMS Microbiol Ecol. 2019;96(1):fiz187.

40. Forgue P, Halouska S, Werth M, Xu K, Harris S, Powers R. NMR metabolic profiling of Aspergillus nidulans to monitor drug and protein activity. J Proteome Res. 2006;5(8): 1916–23. 41. Barkal LJ, Theberge AB, Guo C-J, Spraker J, Rappert L, Berthier J, et al. Microbial metabolomics in open microscale platforms. Nat Commun. 2016;7:10610.

42. Ibáñez AJ, Fagerer SR, Schmidt AM, Urban PL, Jefimovs K, Geiger P, et al. Mass spectrometry-based metabolomics of single yeast cells. PNAS. 2013;110(22):8790–4.

43. Semreen MH, Soliman SSM, Saeed BQ, Alqarihi A, Uppuluri P, Ibrahim AS. Metabolic profiling of Candida auris, a newly-emerging multi-drug resistant Candida species, by GC-MS. Molecules. 2019;24(3):399.

Chapter 20

The Unmet Medical Need for Trypanosoma cruzi-Infected Patients: Monitoring the Disease Status Maan Zrein, PhD,a and Eric Chatelain, PhDb aInfYnity bDrugs

Biomarkers, France for Neglected Diseases Initiative, Switzerland

[email protected]

Keywords: Chagas disease, zoonosis, World Health Organization, Trypanosoma cruzi, neglected tropical disease, trypanosomiasis, chagoma, immunoassay, persistence, clearance, biomarkers, seroreversion, machine learning, pattern markers, genetic markers, inflammatory markers

20.1 Introduction Chagas disease (CD), a zoonosis affecting humans in endemic areas, remains neglected after more than a century of its discovery by Carlos Chagas. CD is recognized by the World Health Organization as one of the most neglected tropical diseases. An estimated eight million individuals are affected in endemic areas and more than twenty million are at risk of exposure. The incidence of mortality in CD patients is over 10,000 deaths annually [1]. The disease is endemic in the majority of Latin American countries and, due to population migration, it has been reported in the United States, Canada, many European and some Western Pacific countries. The vector of this parasitic disease belongs to the Triatomine family, is present in a large animal and sylvatic reservoir spanning from Argentina to the Northern part of Mexican border and is impossible to fully control. Trypanosoma cruzi Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

Copyright © 2022 Jenny Stanford Publishing Pte. Ltd.

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(T. cruzi) is a flagellar protozoan that can lead to American trypanosomiasis also known as Chagas disease if the infection is not resolved spontaneously or by medication. The vector acquires T. cruzi during a blood meal, it then transmits the infective form of the parasite to humans during future blood meals. Beside vector transmission, CD may also be transmitted through blood transfusion, organ transplantation, congenitally or by the oral route through consumption of unsterilized insect-infested fruit by-products. Despite extensive technological progress, current serodiagnostic methods have not greatly contributed to resolving this major health problem in Latin America. Chagas cardiomyopathy is the primary cause of morbidity and mortality of infected patients [2]. The focus of this review is serodiagnosis, with the spotlight on an unmet medical need: monitoring T. cruzi infection. We further emphasize that the humoral immune response can be more easily translated to simple routine assays, as compared to cellular assays, in low-resource countries. Although cellular immunity and genetic parameters are equally important, intensive research efforts have not been translated into concrete applications. Therefore, they are not considered in this review due to the complexity of implementation in ordinary laboratory settings. This review represents a synopsis of some immunological characteristics of T. cruzi and, an attempt to elucidate what is still hampering methodological developments to address the unmet medical need of disease monitoring. The authors express their personal thoughts and not necessarily those of their respective institutions. After entering the human host, the parasite rapidly establishes an acute infection, occasionally identified by an apparent clinical symptom (chagoma) or other non-specific symptoms such as fever, headache, muscle pain, swelling and abdominal pain. A massive and specific immune-response takes place against the majority of immunodominant proteins that are expressed in the trypomastigote forms of the parasite. Due to multiple homologies in T. cruzi protein sequences with mammalian proteins, part of this immune response may involve an autoimmunity that plays a significant role in the pathogenesis of CD. The acute infection might resolve spontaneously and if treated in its very early stage the parasite may be prevented from invading various tissues to establish a chronic infection. Some studies have shown that early trypanocidal treatment can reduce the progression rate to cardiomyopathy [3]. This is particularly the case in infants who seem more responsive to trypanocidal medication [4]; it is expected that parasite elimination results in a reduction of disease progression rate but followup for many years is required to ascertain this hypothesis. However, the majority of infected individuals living in remote geographical areas, and those who do not have access to efficient healthcare systems, will progress to the chronic stage with clinically indeterminate or less perceptible symptoms. Ultimately, about 30%–40% of patients will experience more severe clinical symptoms with either cardiac, gastrointestinal or neurological disorders. The remaining patients may stay in the indeterminate phase for the rest of their lives without showing any apparent life-threatening condition although still infected with T. cruzi [2, 5].

Introduction

Diagnosis of patients in the chronic phase (indeterminate or symptomatic form) is currently based on serological assays that detect specific antibodies to T. cruzi antigens. There are several immunoassays marketed with various technological platforms that have acceptable performance. All existing immunoassay techniques have been designed and optimized for screening for anti-T. cruzi antibodies at a very high sensitivity and the best possible specificity. Obviously, the sensitivity and specificity balance depends, to a large extent, on the nature and quality of antigens and reagents used in the assays. Indeed, T. cruzi-strains are classified into seven different genomic variants DTU I-DTU VI and DTU bat. Multiple sources of antigen variation exist in wild type T. cruzi and are related to the different geographical strains [6]. In the early days of Chagas immunoassays, antigens were extracted from in vitro culture of parasites. Cultured parasites express different antigens compared to those expressed in natural infective cycles by amastigotes (intracellular replicating form) and trypomastigotes (circulating flagellar form). Various antigen compositions are responsible for a significant amount of falseand cross-reactivity, which can only be sorted out by a complex interpretation algorithm involving several technologies [7]. For instance, a large evaluation study by Saez-Alquézar et al. compared three different screening strategies applied in blood bank settings, namely: immunofluorescence, haemagglutination and enzyme-linked immunosorbent assay (ELISA) to confirm or rule-out a positive serology result. More recently, the use of recombinant proteins and synthetic peptide antigens has significantly improved serodiagnostic performance [8]. Nevertheless, differences between immunoassay methods persist, and extensive comparison studies can classify the assays into the most sensitive or most specific for anti-T. cruzi antibody screening [9]. Unfortunately, research and development efforts practiced by the in vitro diagnostics industry are often guided by the largest available market size, in this case blood bank screening. Such organizations require highly sensitive immunoassays to pick up a tiny number of anti-T. cruzi antibodies, irrespective of their clinical relevance. Accordingly, all in vitro diagnostic manufacturers have boosted the sensitivity parameters to a level that causes poor and unrelated specificity when it comes to disease monitoring. This strategy unavoidably detects clinically irrelevant levels of anti-T. cruzi antibodies in blood samples of already “cured” individuals. Detecting residual traces of specific antibodies may be helpful for epidemiological studies and for an ultrasecure blood supply, but not for resolving clinical situations. The particular challenge for T. cruzi clinical diagnostics is monitoring the infection and identifying patients that have cleared the infection and those that still carry the pathogen. This goal is virtually impossible to accomplish with classical single immunoassays that use a mixture of antigens such as those assays designed for blood bank screenings. Many research groups have shown interest in and continue to strive for the identification of biomarkers that can discriminate between patients with active infection (parasite persistence) and those who have resolved the infection (parasite clearance). The latter would have a better prognosis with regard to

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CD evolution [10]. Furthermore, stratification of patients that still carry the pathogen represents a better strategy to assess therapeutic efficacy of existing trypanocidal drugs or drugs in ongoing clinical trials. A new generation of immunoassays is being developed to address the clinical need for T. cruzi and CD monitoring. These assays focus on candidate biomarkers with different properties to those employed for screening purposes.

20.2 Direct Biomarkers

In the very early days of Chagas diagnostics, xenodiagnosis was the method of choice for establishing a specific and direct diagnosis of T. cruzi-infected patients. Naïve vectors were nurtured on the skin of a patient suspected of infection and after several blood meals the insect was sacrificed for microscopic examination. Although xenodiagnosis is highly specific, this method lacked sensitivity and required skilled operators throughout the cumbersome, long and error-prone procedure. As more advanced molecular technologies appeared, xenodiagnosis methods were widely abandoned to the benefit of immunoassays. Direct parasite detection methods in the blood (such as microscopy or polymerase chain reaction (PCR)) lack sensitivity due to the very low and fluctuating parasitemia during the chronic indeterminate phase. It is obvious that microscopy lacks sensitivity, given the limitations of observing blood smears in the case of low parasitemia. However, PCR techniques are reputed to be of high sensitivity and able to pick up a single parasite in 10 mL of blood; so the main cause of poor sensitivity is not the PCR technique itself, but rather wavering parasitic cycles in the blood stream. Nevertheless, treated patients that are persistently positive by PCR are clearly treatment failures. Indeed, sustained PCR positive results can indicate unsuccessful parasite elimination post-medication. Negative PCR results cannot confirm whether the parasite is eliminated since a negative result could be due to a “valley” in the parasitemia cycle. These limitations have guided research and development efforts towards indirect biomarkers for maintaining the same objective: monitoring the infection for improving medical care.

20.3 Indirect Biomarkers

A large amount of research work has been dedicated to indirect non-specific biomarkers such as cytokines, transcriptomic and inflammatory biomarkers [18]. Attempts to associate such biomarkers to disease staging have not yet resulted in major successes. The number of patients that can be enrolled to be able to derive statistically sound conclusions is limited because of the complexity and high-costs of such studies. Consequently, validation studies to determine a robust set of biomarkers that can irrevocably indicate parasitological cure or persistence are still lacking.

Indirect Biomarkers

It is more relevant to investigate indirect but specific biomarkers, such as anti-T. cruzi antibodies, because they have specific features that link their presence to the parasite. Such biomarkers are derived either from T. cruzi-specific antibodies or proteomic signature studies. There is a large consensus among the community of Chagas experts that converting serological status from seropositive to seronegative (seroreversion) provides solid evidence of parasite clearance [11, 12]. Patients who, in the indeterminate stage, convert from positive to negative serology, most probably succeeding parasite clearance, are less susceptible to develop clinical Chagas disease. The pathogenic mechanisms underlying cardiac and/or gastrointestinal clinical symptoms development are intimately associated with both the parasitic status and the patient’s immune response. This can be easily understood since the immune system needs continuous antigenic stimulation to maintain the necessary oligoclonal activation of the lymphoid cellular repertoire to produce specific antibodies. Patient seroconversion from negative to positive status after parasite acquisition is a steady and cumulative buildup for implementing a robust immune response; the humoral component of this response cannot vanish as soon as the parasite has disappeared, whether spontaneously or subsequent to medication. “Rewinding” the immune response occurs once antigenic stimulation terminates (e.g., parasite clearance); polyclonal reactivity will then gradually decrease [10] and may ultimately reach full seronegative status decades later. This is a consequence of B-cell anergy and occurs at a different pace depending on the individual, and probably on the infecting strain and the duration of infection since its onset. One of the most remarkable facets of antibody-related detection approaches is, in addition to its intrinsic specificity, its power to unmask hidden parasites. While direct detection methods are limited to parasitemia, that is the actual presence of the parasite in the blood stream, immunological methods can indirectly reveal parasites hidden in the tissues. Indeed, the immune system acts as a potent and specific sensor throughout the body tissues and is able to reveal cryptic parasites by stimulating the secretion of specific antibodies. However, in the context of monitoring a therapy, the major drawback of antibody-related biomarkers resides in its relatively slow kinetics. The massive number of memory cells in the T. cruzispecific B-cell repertoire continues to secrete antibodies years after the parasite has been eliminated from an individual. Another drawback is related to the autoimmune nature of certain antibody specificities; the inflammatory process and induced autoimmunity may partially withstand the stimulation of lymphocytes even after the parasite has been eliminated from an individual. Under the strict regulatory and technical requirements for safety in blood banks and routine screening, the vast majority of immunoassays available on the market have been essentially designed to detect tiny amounts of T. cruzi-specific antibodies. Therefore, currently existing conventional serological assays cannot address the question of infection monitoring by measuring antibodies. While negligible amounts of antibodies reveal a past-infection for blood screening

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purposes, they are insignificant for clinical practice. Nevertheless, low amounts of anti-T. cruzi antibodies during the indeterminate asymptomatic stage may point towards a favorable prognosis for the disease. Countless efforts have been made to identify biomarkers that indicate therapeutic efficacy (lytic antibodies, anti-Tc24, anti-F29) [11], or more global host responses (T-cell responses, transcriptomics and differential gene expression) or even cytokine profiling in chronic Chagas patients [13]. However, none of these efforts have resulted in a significant breakthrough so far. Consequently, seroreversion, as measured by conventional serology tests, remains the standard for monitoring parasitological cure. Yet, this approach is not suitable for routine testing nor for clinical trials, since it requires multi-year follow-up. As a matter of fact, full seroreversion measured by conventional serology may take up to twenty years and differs between individuals and possibly regions [14, 15]. Hence, there is a need for methods that can capture the trend to seroreversion as soon as possible after parasitic cure. It has become clear that antibody patterns reflecting the diversity of immune responses can more easily identify patients that undergo major changes in their immune status [10]. Such changes can be induced by anti-parasitic medication or simply after spontaneous elimination of the parasite. To study antibody diversity, new multiparametric assays needed to be designed. This has been recently achieved where fifteen different antigens selected for their individual sensitivity and specificity balance were used in a single microplate [16]. Figure 20.1 illustrates the changes that can be observed in the B-cell response in situations of parasite persistence (Fig. 20.1a) or parasite clearance (Fig. 20.1b). Both patients’ samples are reported clearly seropositive in conventional screening assays. Only by looking at the antibody patterns in terms of diversity and signal intensity one can distinctly notify the two different clinical states. Pattern recognition can provide an impressive performance after an appropriate training of computerized image analysis using machine learning techniques. Major changes in the pattern may be recognized by naked-eyes of a well-trained human being. However, machines learning techniques enable a computerized analysis to recognize subtle changes in the patterns that are more complex to observe by naked-eyes of a laboratory operator. Deep learning consists of training a computer for recognizing a large number of images (over 3000) obtained from well categorized samples (training dataset). The interpretation algorithm can be finetuned with an independent validation dataset of new images. This iterative process is then challenged and finalized when test images are perfectly recognized in the correct classification category. Besides the antibody pattern markers, other biomarkers identified from comparative proteomic studies can provide indications about the presence of the parasite. In fact, the enzymatic machinery of the parasite is able to process certain endogenous proteins in the patients’ blood stream. This is the case for two common proteins: fibronectin and A1-Apolipoprotein (Apo-A1). These endogenous proteins are cleaved by the overload of parasitic enzymes to produce fragments of a defined

Indirect Biomarkers

size. Ndao et al. have identified such fragments in the blood of T. cruzi-infected patients. The fragments were identified by mass spectrometry studies and the relative ratios of cleaved fragment and native protein can be correlated to the presence of the parasite [19]. However, the mass spectrometry approach is not suitable for testing in the field. Novel immunoassays are under development to specifically measure Apo-A1 fragments without interference with the full-length Apo-A1 protein.

Figure 20.1 Real images of serological patterns obtained with seropositive samples representative of parasite persistence (A) and parasite clearance (B) conditions. Both samples are seropositive in conventional screening assays. Technical details are described in Zrein et al. [16].

Notwithstanding the controversial findings of the Benznidazole Evaluation for Interrupting Trypanosomiasis (BENEFIT) trial [17], successful treatment of T. cruzi-infected patients in the chronic indeterminate phase, if it results in parasite elimination, significantly increases the chance of preventing evolution to Chagas cardiomyopathy. However, there are three major limitations to using current medication to treat T. cruzi-infected patients (i) the relatively lengthy therapy protocols (60 days for benznidazole and 90 days for nifurtimox), (ii) the severe side effects which often lead to a premature termination of treatment and, (iii) lack of reliable tools to assess treatment efficacy after a reasonably short period of follow-up. New drugs and drug regimens are being developed to shorten treatment duration, and thus reduce related side effects. When it comes to drug development, it is of utmost importance to identify patients in need of medication and to be able to measure drug efficacy within a period of time that doesn’t exceed the timeline of the clinical trial. Significant research efforts have been made to develop simple laboratory methods that help with patient stratification and follow-up. Many unspecific inflammatory markers have

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been investigated in isolation or in combination with genetic markers. These investigations have contributed to the understanding of inflammatory mechanisms of cardiomyopathy but have not yet resulted in a clear-cut methodology to monitor the disease. Therefore, seroreversion remains the most solid evidence of parasite clearance. Multiparametric assays, as opposed to conventional serology, can capture the trend to seroreversion sufficiently early and provide valuable information about the parasite clearance in progress.

20.4 Conclusion

Despite impressive efforts in basic research to help with CD management, there has been little translation into products available in the clinic. The complexity of the parasite in vivo life cycle and its pathogenic relationship to severe clinical symptoms in CD patients, combined with a lack of methods to reliably assess treatment efficacy, makes disease control extremely challenging. Because T. cruziinfected patients experience different outcomes, a novel strategy is required for patient stratification and disease monitoring. This strategy should tackle two issues (i) parasite elimination and, (ii) clinical evolution to symptomatic CD; these two sides of the disease are consecutively related. Thus, novel serological methods based on pattern recognition could pave the way for a better assessment of trypanocidal drugs and/or spontaneous parasite clearance. Indeed, a favorable decrease in the serological pattern acts as a surrogate marker for treatment efficacy and could also serve as guidance for adequate medical decisions. Furthermore, when it comes to vaccination research programs, the change in serology paradigm, from a simple “Yes/No” answer to a pattern recognition, will be instrumental to distinguishing adaptive immunity from vaccine-induced immunity. The pattern recognition approach represents a major step towards personalized medicine, which is precisely what all Chagas patients need.

Abbreviations Apo-A1: BENEFIT: CD: ELISA: PCR:

A1-Apolipoprotein

Benznidazole Evaluation for Interrupting Trypanosomiasis

Chagas disease enzyme-linked immunosorbent assay

polymerase chain reaction

Disclosures and Conflict of Interest

This chapter was originally published as: Zrein, M., Chatelain, E. (2020).The unmet medical need for Trypanosoma cruzi-infected patients: Monitoring the disease status, Biochim. Biophys. Acta, 1866(3), 165628, https://doi.org/10.1016/j. bbadis.2019.165628, and appears here, with edits and updates, by kind permission of the copyright holders and the publisher, Elsevier.

References

Declaration of competing interest: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. Maan Zrein acts as Chief Scientific Officer employed by Infynity Biomarkers; this employment does not interfere with the ethical quality of the chapter nor with any present or future interests. Eric Chatelain is employed by Drugs for Neglected Diseases initiative (DNDi) as director of biomarkers and discovery; this employment does not interfere with the quality of the chapter and its compliance to ethical considerations. Acknowledgements: The Drugs for Neglected Diseases initiative (DNDi) is grateful to its donors, public and private, who have provided funding to DNDi since its inception in 2003. A full list of DNDi’s donors can be found at http://www.dndi.org/ donors/donors/.

References

1. WHO, Chagas disease in Latin America: An epidemiological update based on 2010 estimates, Wkly. Epidemiol. Rec. 90(6), 2015, 33–43. 2. Maria Carmo Pereira Nunes, et al., Chagas cardiomyopathy: An update of current clinical knowledge and, a scientific statement from the American Heart Association, Circulation, 138, 2018, e169–e209.

3. R. Viotti, C. Vigliano, B. Lococo, G. Bertocchi, M. Petti, M.G. Alvarez, et al., Long-term cardiac outcomes of treating chronic Chagas disease with benznidazole versus no treatment: A nonrandomized trial, Ann. Intern. Med. 144(10), 2006, 724–734.

4. M.C. Albareda, M.A. Natale, A.M. De Rissio, M. Fernandez, A. Serjan, M.G. Alvarez, G. Cooley, H. Shen, R. Viotti, J. Bua, M.D. Castro Eiro, M. Nuñez, L.E. Fichera, B. Lococo, K. Scollo, R.L. Tarleton, S.A. Laucella, Distinct treatment outcomes of antiparasitic therapy in Trypanosoma cruzi-infected children is associated with early changes in cytokines, chemokines, and T-cell phenotypes, Front. Immunol. 13(9), 2018, 1958. 5. J.A. Pérez-Molina, I. Molina, Chagas disease, Lancet. 391(10115), 2018, 82–94.

6. P.L. Dorn, A.G. McClure, M.D. Gallaspy, E. Waleckx, A.S. Woods, M.C. Monroy, L. Stevens, The diversity of the Chagas parasite, Trypanosoma cruzi, infecting the main Central American vector, Triatoma dimidiata, from Mexico to Colombia, PLoS Negl. Trop. Dis. 11(9), 2017, e0005878.

7. Z. Moure, E. Sulleiro, L. Iniesta, C. Guillen, M. M. Alcover, I. Molina, C. Riera, T. Pumarola, R. Fisa, The challenge of discordant serology in Chagas disease: The role of two confirmatory techniques in inconclusive cases, Acta Tropica 185(2018), 2018, 144–148. 8. S.-A. A1, E.C. Sabino, N. Salles, D.F. Chamone, F. Hulstaert, H. Pottel, E. Stoops, M. Zrein, 2000. Serological confirmation of Chagas’ disease by a recombinant and peptide antigen line immunoassay: INNO-LIA chagas, J. Clin. Microbiol. 38(2), 2000, 851–854.

9. M. E. Villagrán-Herrera, M. Sánchez-Moreno, A. J. Rodríguez-Méndez, H. L. HernándezMontiel, F. de J. Dávila-Esquivel, G. González-Pérez, J. A. Martínez-Ibarra, J. A. de DiegoCabrera, Comparative serology techniques for the diagnosis of Trypanosoma cruzi

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infection in a rural population from the state of Querétaro, Mexico, Mem. Inst. Oswaldo Cruz 109(7), 2014, 964–969.

10. M.G. Alvarez, G.L. Bertocchi, G. Cooley, M.C. Albareda, R. Viotti, D.E. Perez-Mazliah, B. Lococo, M. Castro Eiro, S.A. Laucella, R.L. Tarleton, Treatment success in Trypanosoma cruzi infection is predicted by early changes in serially monitored parasite-specific T and B cell responses, PLoS Negl. Trop. Dis. 10(4), 2016, e0004657. 11. M.-J. Pinazo, M.-C. Thomas, J. Bustamante, I.C. de Almeida, M.-C. Lopez, J. Gascon, Biomarkers of therapeutic responses in chronic Chagas disease: State of the art and future perspectives, Mem. Inst. Oswaldo Cruz 110(3), 2015, 422–432.

12. E. Chatelain, Chagas disease research and development: Is there light at the end of the tunnel?, Comput. Struct. Biotechnol. J. 15, 2016, 98–103.

13. Y. Sguassero, et al., Course of serological tests in treated subjects with chronic Trypanosoma cruzi infection: A systematic review and meta-analysis of individual participant data, Int. J. Infect. Dis. 73, 2018 93–101.

14. E. Ruiz-Lancheros, M. Golizeh, M. Ndao, 2019. Apolipoprotein A1 and fibronectin fragments as markers of cure for the Chagas disease, Methods Mol. Biol. 1955, 2019, 263–273. 15. O. Yun, M.A. Lima, T. Ellman, W. Chambi, S. Castillo, L. Flevaud, P. Roddy, F. Parreño, P. Albajar Viñas, P.P. Palma, Feasibility, drug safety, and effectiveness of etiological treatment programs for Chagas disease in Honduras, Guatemala, and Bolivia: 10-year experience of Médecins Sans Frontières, PLoS Negl. Trop. Dis. 3(7), 2009, e488.

16. M. Zrein, E. Granjon, L. Gueyffier, J. Caillaudeau, P. Liehl, H. Pottel, C.S. Cardoso, C.D.L. Oliveira, L.C. de Oliveira, T.-H. Lee, A.M. Ferreira, A.L.P. Ribeiro, M.P. Busch, E.C. Sabino, A novel antibody surrogate biomarker to monitor parasite persistence in Trypanosoma cruzi-infected patients, PLoS Negl. Trop. Dis. 12(2), 2018, e0006226.

17. C.A. Morillo, J.A. Marin-Neto, A. Avezum, S. Sosa-Estani, A. Rassi Jr., F. Rosas, E. Villena, R. Quiroz, R. Bonilla, C. Britto, F. Guhl, E. Velazquez, L. Bonilla, B. Meeks, P. Rao-Melacini, J. Pogue, A. Mattos, J. Lazdins, A. Rassi, S.J. Connolly, S. Yusuf, BENEFIT Investigators, Randomized trial of benznidazole for chronic Chagas’ cardiomyopathy, N. Engl. J. Med. 373(14), 2015, 1295–1306.

18. M.C. Albareda, D. Perez-Mazliah, M.A. Natale, M. Castro-Eiro, M.G. Alvarez, R. Viotti, G. Bertocchi, B. Lococo, R.L. Tarleton, S.A. Laucella, Perturbed T cell IL-7 receptor signaling in chronic Chagas disease, J. Immunol. 194(8), 2015, 3883–3889. 19. E. Ruiz-Lancheros, A. Rasoolizadeh, E. Chatelain, F. Garcia-Bournissen, S. Moroni, G. Moscatelli, J. Altcheh, M. Ndao, Validation of apolipoprotein A-1 and fibronectin fragments as markers of parasitological cure for congenital Chagas disease in children treated with benznidazole, Open Forum Infect. Dis. 11, 2018, 1–10.

Chapter 21

Present and Future of Carbapenem-Resistant Enterobacteriaceae Infections Beatriz Suay-García, PhD, and María Teresa Pérez-Gracia, PhD Área de Microbiología, Departamento de Farmacia, Instituto de Ciencias Biomédicas, Facultad de Ciencias de la Salud, Universidad Cardenal Herrera-CEU, Valencia, Spain [email protected]

Keywords: Enterobacteriaceae, carbapenem-resistant, carbapenemases, β-lactamases, carbapenems, Escherichia, Citrobacter, Proteus, Serratia, Klebsiella oxacillinase, World Health Organization, imipenem, minimum inhibitory concentration, Klebsiella pneumoniae, carbapenemase, extended-spectrum β-lactamases

21.1 Introduction Antibiotic resistance occurs when bacteria causing an infection survive after being exposed to a drug that, under normal conditions, would kill it or inhibit its growth [1]. As a result, these surviving strains multiply and spread due to the lack of competition from other strains sensitive to the same drug. Due to the inappropriate prescription and administration of antibiotics, resistant bacteria have become a public health threat worldwide [2]. In fact, the issue of antibiotic-resistant bacteria is such that, according to the World Health Organization (WHO) predictions, if antibiotic resistance continues to increase at this rate, infections caused by resistant bacteria will become the top cause of death worldwide, ahead of cancer, diabetes and cardiovascular diseases [3].

Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

Copyright © 2022 Jenny Stanford Publishing Pte. Ltd.

ISBN 978-981-4877-84-8 (Hardcover), 978-1-003-18043-2 (eBook)

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Present and Future of Carbapenem-Resistant Enterobacteriaceae Infections

In 2017, the WHO published a list of antibiotic-resistant bacteria against which there is an urgent need to develop new antibiotics [4]. This list is divided into three categories depending on the urgency with which new antibiotics are needed: critical, high and medium priority. Within the critical priority group are carbapenem and 3rd generation cephalosporin-resistant Enterobacteriaceae. These bacteria are common pathogens causing severe infections such as bloodstream infections, pneumonia, complicated urinary tract infections and complicated intra-abdominal infections. As a result, antibiotic resistance in Enterobacteriaceae has significant clinical and socioeconomic consequences [5, 6]. Initially, Enterobacteriaceae posed a threat to the public health due to their ability to become resistant to antibiotics by producing extended-spectrum β-lactamases (ESBLs) [7]. To fight this threat, the medical community turned to drugs such as carbapenems as first-line empirical treatments [8]. This new treatment for resistant bacteria had an unexpected result, as it led to a more serious problem, the emergence of carbapenem-resistant Enterobacteriaceae (CRE) [9]. In particular, CRE refer to bacteria belonging to the Enterobacteriaceae family that have the ability to survive and grow in the presence of clinically relevant concentrations of carbapenems [10]. Specifically, the Centers for Disease Control and Prevention (CDC) defines CRE as enterobacteria non-susceptible to any carbapenem or documented to produce carbapenemases [11]. This chapter analyzes the epidemiology of CRE as well as current and future treatment options against these increasingly resistant bacteria. Furthermore, it provides an extensive review of the different mechanisms by which Entero­ bacteriaceae develop resistance against carbapenems. The presence of these three aspects in one article could be used as a key tool for a better understanding of this emerging problem and as guidance to elaborate plans to manage the CRE crisis and develop new active drugs more efficiently.

21.2 Mechanisms of Drug Resistance

There are three major mechanisms by which Enterobacteriaceae become resistant to carbapenems: enzyme production, efflux pumps and porin mutations [12]. Of these, enzyme production is the main resistance mechanism. Gram-negative bacteria generally develop resistances through the production of ß-lactam-hydrolyzing enzymes [13]. Initially, these enzymes inactivated penicillin; however, as different types of antibiotics were introduced in the treatment of infectious diseases, their spectra extended. Thus, cephalosporinases, ESBLs, metallo-ß-lactamases (MBLs) and other carbapenemases appeared [14]. Generally, CRE are divided into two main subgroups: carbapenemase-producing CRE (CP-CRE) and noncarbapenemase-producing CRE (non-CP-CRE) (Fig. 21.1) [15].

21.2.1 Carbapenemase-Producing CRE

CP-CRE can produce a large variety of carbapenemases which can be divided in three groups according to the Ambler classification: class A, class B and class D

Mechanisms of Drug Resistance

ß-lactamases [16]. There is a fourth class, Ambler class C; however, its clinical relevance remains unknown [17].

Figure 21.1 Classification of the different mechanisms of drug resistance in CRE (light grey: Ambler class A, white: Ambler class B, dark grey: Ambler class D). Abbreviations: CRE, Carbapenem-resistant Enterobacteriaceae; CP, carbapenemase producing; KPC, Klebsiella pneumoniae carbapenemase; IMI, imipenem-hydrolyzing ß-lactamase; GES, Guiana extendedspectrum ß-lactamase; MBLs, metallo-ß-lactamase; OXA, oxacillinase; NDM, New Delhi metallo-ß-lactamase; VIM, Verona integron-borne metallo-ß-lactamase; IMP, imipenem­ resistant Pseudomonas carbapenemase; SMP, Sao Paulo metallo-ß-lactamase; GIM, German imipenemase; SIM, Seoul imipenemase; AmpC, Type C ampicillinase; ESBLs, extended-spectrum ß-lactamase.

Within class A carbapenemases is the clinically relevant Klebsiella pneumoniae carbapenemase (KPC) [18]. This is a plasmid encoded enzyme which actively hydrolyzes carbapenems and is partially inhibited by clavulanic acid [19]. Its clinical relevance is due to the fact that it is the most prevalent and most widely spread worldwide [20] Enterobacteriaceae producing KPCs have acquired multidrug resistance to ß-lactams, which limits the therapeutic options to treat infections caused by these bacteria [21]. KPC were originally found in K. pneumoniae isolates; however, clinical isolates of KPC-producing Escherichia

437

438

Present and Future of Carbapenem-Resistant Enterobacteriaceae Infections

coli, Klebsiella oxytoca, Salmonella enterica, Citrobacter freundii, Enterobacter aerogenes, Enterobacter cloacae, Proteus mirabillis and Serratia marcescens have been identified [22–26] (Table 21.1). According to a study by Perez et al. [27], a total of 12 blaKPC gene variants exist globally. Table 21.1 Carbapenemases detected in different species belonging to the Enterobacteriaceae family Species

Class A Class B (MBLs)

Class D

Ref.

Klebsiella pneumoniae

KPC-3

NDM-1, VIM-1

OXA-48

Okoche et al. [23] Boutal et al. [24]

KPC

NDM-1, NDM-5, NDM-9, VIM

KPC

VIM

Klebsiella oxytoca Escherichia coli Proteus mirabilis Serratia marcescens

KPC

Enterobacter cloacae KPC, IMI-1 Enterobacter aerogenes

Citrobacter freundii

KPC

Citrobacter koseri Salmonella enterica Morganella morganii Providencia stuartii Providencia rettgeri

OXA-48, OXA-181

Okoche et al. [23] Boutal et al. [24]

OXA-48

Okoche et al. [23] Boutal et al. [24]

VIM-4

OXA-48

Okoche et al. [23] Boutal et al. [24]

VIM

OXA-48

OXA-48, OXA-181, Okoche et al. [23] Boutal et al. [24] OXA-244

OXA-48

Okoche et al. [23] Boutal et al. [24] Okoche et al. [23] Boutal et al. [24] Okoche et al. [23] Boutal et al. [24]

OXA-48

Okoche et al. [23] Boutal et al. [24]

NDM-1

OXA-48

Boutal et al. [24]

IMP-1

OXA-72

KPC-2

OXA-48 NMD-1, NMD-5, VIM-1, VIM-2, IMP-4

KPC-2

VIM-1

Fernández et al. [25]

Abdallah et al. [26]

Abdallah et al. [26]

Another major carbapenemase family belonging to class A are MBLs. These enzymes depend on the interaction with zinc ions in the active site of the enzyme [28]. These enzymes are particularly problematic as they have a high potential for horizontal transfer, they lack clinically useful inhibitors, and they have broad hydrolytic properties that affect most ß-lactam antibiotics except for monobactams [29]. However, MBL resistance is usually associated with multidrugresistance, with MBL-producing isolates often co-expressing ESBLs, which inactivate monobactams [13]. The most common families of MBLs found in Enterobacteriaceae were acquired [17]. These families are the New Delhi metallo-ß-

Mechanisms of Drug Resistance

lactamase 1 (NDM-1), Imipenem-resistant Pseudomonas (IMP)-type carbapenemases and the Verona integron-encoded metallo-ß-lactamases (VIM) [14]. IMP-type carbapenemases were first detected in Japan during the 1990s and have up to 18 varieties [30]. Similarly, VIM was first isolated in Verona, Italy, in 1997 and consists of 14 members [31]. Both MBLs originated in P. aeruginosa and were transferred to Enterobacteriaceae. In fact, these MBLs share similarities regarding the plasmids they are carried on and their mechanism of action, as both hydrolyze all ß-lactams except for monobactams and are susceptible to all ß-lactam inhibitors [32]. Regarding NDM-1, it is the most recently discovered MBL. It was isolated in India, which is considered the main reservoir of NDM-producing bacteria [33]. Since then, it has spread worldwide, reaching Europe and the United States through tourists [34]. Currently, NDM is predominant in K. pneumoniae and E. coli [34]. Studies suggest that most plasmids containing blaNDM also harbor other resistance determinants encoding different ß-lactamases, quinolone resistance and 16S rRNA methylases which confer resistance to aminoglycosides [35]. The third clinically relevant group of carbapenemases are OXA-48-like, which belong to Ambler class D. Six OXA-48-like variants have been identified, OXA-48 being the most widespread [36]. The remaining variants are: OXA-162, OXA163, OXA-181, OXA-204 and OXA-232. They are all grouped within the OXA-48like category because they only differ on one to five amino acid substitutions or deletions [36]. These plasmid-mediated enzymes are primarily found in K. pneumoniae, E. coli, C. freundii and E. cloacae [37]. A major concern with these carbapenemases is that no existing inhibitors work against them and they have an extraordinary ability to mutate and expand their activity spectrum [38]. These enzymes are highly active against penicillins, have low activity against carbapenems and intermediate activity against broad-spectrum cephalosporins [17].

21.2.2 Non-Carbapenemase-Producing CRE

Besides carbapenemase production, Enterobacteriaceae have alternative mechanisms by which they can present carbapenem resistance. These are unspecific mechanisms which can result in multi-drug resistance, such as the production of other ß-lactamases, porin loss and efflux pump overexpression [14]. These mechanisms generally appear paired among themselves or with carbapenemase-production [39]. In fact, while carbapenemases specifically target carbapenems and other ß-lactam antibiotics, efflux pump expression or porin changes are associate with multidrug resistance [40]. All three alternative mechanisms aim to block the penetration of the antibiotic within the bacterial cell. Firstly, Enterobacteriaceae can produce different types of ß-lactamases, such as AmpC-type ß-lactamases. These enzymes do not degrade carbapenems [41] but they form a bond with the carbapenem molecule, preventing it from accessing its target [42]. Specifically, the plasmid-encoded AmpC CMY-2 is frequently found in E. coli and other Enterobacteriaceae worldwide, causing resistance to carbapenems [43].

439

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Present and Future of Carbapenem-Resistant Enterobacteriaceae Infections

Secondly, resistance-nodulation-division (RND) efflux pumps are a major mechanism of multi-drug resistance in Enterobacteriaceae [44]. Among the different efflux systems, the AcrAB-TolC RND system is the most common [44]. This RND efflux pump, along with the CusABC efflux complex, belongs to E. coli [45]. Similarly, Campylobacter jejuni presents multi-drug resistance through the expression of the CmeABC complex [45]. These resistant genes can be easily transmitted from one microorganism to another through plasmids [46]. Lastly, alterations of porin synthesis also contribute to blocking penetration of carbapenems into the bacterial cell [47]. These alterations have been described in AmpC- and carbapenemase-producing K. pneumoniae, which suggests that changes in porin expression play a key role in the ß-lactam resistance displayed by multi-drug-resistant bacteria [48]. Studies suggest that strains that have their porins mutated or their expression modulated typically do not have potential for mobilization into community settings but may proliferate locally within hospitals [49].

21.3 Current Resistance Status

Since the detection of the first strain of CRE in the 1980s [50], CRE has rapidly spread worldwide. Epidemiology studies suggest that different carbapenemases predominate in different areas of the world. For that matter, NDM-1 is the main carbapenemase producing resistance in India, Pakistan and Sri Lanka. On the other hand, KPC-producing Enterobacteriaceae are endemic in the United States, Colombia, Argentina, Greece and Italy, while OXA-48-like enzyme-producers are endemic in Turkey, Malta, the Middle-East and North Africa [51] (Fig. 21.2). As mentioned earlier, the first case of CP-CRE was isolated in Japan and corresponded to an IMP-producing Serratia marcescens [50]. This strain caused a plasmid-mediated outbreak in seven Japanese hospitals, followed by a widespread dissemination of blaIMP-1-harboring Enterobacteriaceae throughout Japan. Since then, 52 variants of IMP genes have been identified and have their endemicity limited to Japan and Taiwan [52]. VIM-type MBLs were described shortly after in P. aeruginosa strains [53]. By the early 2000s, cases of VIM-producing Enterobacteriaceae were already being reported [17]. K. pneumoniae and E. coli strains producing VIM-type carbapenemases have their endemicity peak in Greece [28]. However, the major threat of MBL-producing Enterobacteriaceae appeared with the discovery of an ST14 K. pneumoniae strain producing the NDM enzyme from a Swedish patient who received healthcare in New Delhi, India [54]. Bacteria producing this enzyme is endemic in the Indian subcontinent and generally appears as sporadic cases in the rest of the world [55]. NDM-1 producing Enterobacteriaceae have been reported in both hospital- and communityacquired infections, including urinary tract infections, septicemia, pulmonary infections, peritonitis, device-associated infections and soft tissue infections [56]. An additional issue with NDM-producing bacteria is their ability to spread via environmental sources in community settings of lower-income countries. In fact, studies carried out in India found that 4% of the drinking water and 30% of seepage samples contained NDM-1-producing bacteria [33].

Figure 21.2 Timeline representing the introduction of carbapenems and the appearance of carbapenemases worldwide.

Current Resistance Status 441

442

Present and Future of Carbapenem-Resistant Enterobacteriaceae Infections

KPC-producing Enterobacteriaceae are categorized as one of the most successful pandemics in the history of Gram-negative bacteria, particularly due to K. pneumoniae ST258 [57]. This strain has been reported as endemic in Greece, Israel, Latin America and the United States [39]. The endemic state of KPC-producing Enterobacteriaceae is not surprising, seeing as the first case of K. pneumoniae producing this enzyme was reported in a patient in a North Carolina hospital in 1996 [18]. Only five years later, an outbreak of KPC-producing bacteria took place throughout northeastern United States within hospitalized patients [58]. On the other hand, Greece has one of the highest CRE rates worldwide. Initially, this resistance was due to VIM enzymes; however, in 2007, a rapid dissemination of KPC-producing bacteria made KPC the main mechanism of resistance against carbapenems in the country [39]. Current studies suggest that around 40% of the carbapenemase-resistant K. pneumoniae harbor blaKPC in Greece [59]. Colombia was the first country within Latin America to report an outbreak of KPC-producing K. pneumoniae, which originated from a patient who had travelled to Israel [60]. Since then, Argentina, Chile, Mexico and Brazil have also reported the detection of KPC-producing CRE [39]. Finally, regarding OXA-48-like-producing CRE, outbreaks caused by these bacteria have been reported in several countries; however, only Turkey, Japan and Taiwan have reported endemicity [61].

21.4 Treatment Options

Carbapenems continue to be used for the treatment of infections caused by Enterobacteriaceae as suggested by both, EUCAST (European Committee on Antimicrobial Susceptibility Testing) and CLSI (Clinical and Laboratory Standards Institute) guidelines [62, 63]. The clinical breakpoints of the carbapenems currently used are presented in Table 21.2. It must be noted that doripenem has been removed from 2019 EUCAST guidelines due to the lack of availability of this drug in most countries. In those countries where doripenem is still available, 2018 EUCAST guidelines must be used as a reference [64]. However, CRE are an increasingly common issue in the clinical practice, rendering carbapenems useless. Bearing in mind the different mechanisms by which Enterobacteriaceae can become resistant to carbapenems, there are different approaches to treat infections caused by these bacteria. These treatment options include the repurposing of already existing antibiotics, dual therapies with these antibiotics and the development of new ß-lactamase inhibitors and antibiotics [65] (Table 21.3). Firstly, certain “old antibiotics” which have been included in the therapeutic arsenal for years are still effective against CRE. For example, fosfomycin, frequently used to treat urinary tract infections (UTIs), continues to be effective against approximately 80% of CRE [66]. Similarly, aminoglycosides are still considered first-line therapy for the treatment of carbapenem-resistant K. pneumoniae infections [6]. While gentamicin is the most frequently used aminoglycoside,

Treatment Options

studies report cases where amikacin was the only active molecule [67]. Colistin also remains as a key drug in the treatment of CRE infections [65]. However, CRE, and more particularly K. pneumoniae, have started to develop resistance against this drug, decreasing its efficiency as a monotherapy treatment [68]. As a result, colistin has been included as part of a dual therapy with meropenem, which results in a significant reduction of mortality, especially in patients with septic shock, high mortality score or rapidly fatal underlying diseases [69]. Moreover, polymyxins continue to be considered last resort drugs due to their adverse effects, which include nephrotoxicity, neurotoxicity and skin pigmentation [65]. Table 21.2 Breakpoints for carbapenems against Enterobacteriaceae family Antibiotic

Disk Guidelines content (µg)

Disk diffusion (mm) S

I

R

S

I

R

— 19–21

≤25 ≤18

≤0.5 ≤0.5

— 1

0.5 ≥2

16 ≤19

≤2 ≤1

3–7 2

8 ≥4

EUCAST 1 CLSI 2

10

≥25 ≥23

Meropenem EUCAST 1 CLSI 2

10

22 ≥23

Ertapenem Imipenem

EUCAST 1 CLSI 2

Doripenem

EUCAST 3 CLSI 2

10

10 10

Dilution (µg/mL)

22 ≥23

21–18 20–22

≤17 ≤19

22 ≥23

21–17 20–22

≤16 ≤19

21–17 20–22

≤2 ≤1

≤1 ≤1

3 2

2–3 2

4 ≥4

4 ≥4

1The European Committee on Antimicrobial Susceptibility Testing. Breakpoint tables for interpretation of minimum inhibitory concentrations (MICs) and zone diameters. Version 9.0, 2019. Available on: http// www.eucast.org [62].

2CLSI. Performance Standards for Antimicrobial Susceptibility Testing. 29th ed. CLSI supplement M100.

Wayne, PA: Clinical and Laboratory Standards Institute; 2019 [63]. 3The

European Committee on Antimicrobial Susceptibility Testing. Breakpoint tables for interpretation of

MICs and zone diameters. Version 8.1, 2018. Available on: http//www.eucast.org [64].

Tigecycline also remains as an option for CRE treatment in certain cases [70]. The particularity with this drug is that it displays low serum concentrations in the approved dosing regimen for the treatment of community-acquired and nosocomial-acquired pneumonia, which hampers clinical outcomes [71]. As a result, a high-dose tigecycline regimen has been investigated and is being used to treat CRE infections. This therapy consists of a 200 mg initial dose and a maintenance dose of 100 mg every 12 h [65]. This high-dose is particularly effective for the treatment of ventilator-associated pneumonia caused by CRE [72]. Furthermore, a systematic review comprising 25 studies reporting the efficacy and safety of tigecycline-based regimens for treating CRE infections concluded that a much lower mortality rate resulted from high-dose tigecycline than standard-dose tigecycline [70].

443

444

Present and Future of Carbapenem-Resistant Enterobacteriaceae Infections

Table 21.3 Current and future treatment options for infections caused by CRE Drug (pharma­ ceutical company) Cell

“Old Antibiotics”

Action

mechanism wall Cell wall Cell wall synthesis Fosfomycin Cell wall synthesis Cell wall Cell wall synthesis (Merck) synthesis inhibitor synthesis inhibitor synthesis inhibitor inhibitor Cell wall inhibitor

inhibitor synthesis Aminoglycosides Protein inhibitor

synthesis Protein Protein Protein inhibitor Protein

Protein synthesis synthesis synthesis synthesis synthesis inhibitor inhibitor

Structure

Limitations

Ref.

Appearance of resistance

Vardakas et al. [66]

Appearance of resistance

RodriguezBano et al. [6] Satlin et al. [67]

Nephrotoxicity and other severe adverse effects

Karaiskos et al. [65] Daikos et al. [69]

inhibitor Protein inhibitor inhibitor synthesis inhibitor

Colistin Cell Cell (Kobayashi membrane membrane Cell disruptor Cell Bacteriological Cell Cell disruptor membrane Laboratory) membrane

membrane membrane Cell disruptor disruptor disruptor membrane disruptor

disruptor Protein synthesis Protein inhibitor Tigecycline Protein

Protein synthesissynthesis Protein Protein synthesis inhibitor Protein inhibitor synthesis synthesis inhibitor synthesis inhibitor inhibitor inhibitor

(Pfizer)

Dual Therapies

Ertapenem Cell wall Cell wall + synthesis synthesis Meropenem/ inhibitor inhibitor Cell wall Doripenem

Low Ni concentration et al. [70] in tissue

—

Bulik et al. [73]

Appearance of resistance

De Jonge et al. [77]

synthesis

Cell wall inhibitor Cellwall wall Cell Cell wall synthesis synthesis synthesis synthesis inhibitor inhibitor inhibitor inhibitor

Cell wall synthesis inhibitor/ Cell wall ß-lactamase Ceftazidime/ inhibitor synthesisCell wall

Avibactam synthesis inhibitor/ (Allergan) inhibitor/ß­ Cell ß-lactamase Cell wallwall lactamase synthesis inhibitor Cell wall inhibitor synthesis

Cell wall inhibitor/ synthesis inhibitor/

synthesis Cell wall ß-lactamase inhibitor/ ß-lactamase synthesis inhibitor/ inhibitor ß-lactamase inhibitor inhibitor/ ß-lactamase inhibitor ß-lactamase Cell wall inhibitor inhibitor synthesis inhibitor/ ß-lactamase Cell wall inhibitor synthesis

Cell wall inhibitor/ Cell wall synthesis Cell wall ß-lactamase synthesis inhibitor/ inhibitor synthesis inhibitor/ ß-lactamase inhibitor/ ß-lactamase inhibitor

Cell wall synthesis inhibitor/ ß-lactamase Druginhibitor (pharma­ ceutical company)

Treatment Options

Action mechanism Structure

Limitations

Ref.

Insufficient clinical data

Karaiskos et al. [65]

Ineffective against MBLproducers

Landman et al. [85]

Currently in clinical trials

Zhanel et al. [94]

Imipenem/ Cell wall Cell wall synthesis Relebactam Cell wall synthesis (Merck) inhibitor/ Cell wall synthesis ß-lactamase Cell wall inhibitor/ synthesis inhibitor/ Cell synthesis Cell wall inhibitor

Currently in clinical trials

Blizzard et al. [93]

Cefiderocol (Shionogi)

Currently in clinical trials

Saisho et al. [96]

Currently in clinical trials

Karaiskos et al. [99]

Currently in clinical trials

PappWallace et al. [100]

Meropenem/ Cell wall Vaborbactam synthesis (Melinta) Cell wall inhibitor/ Action ß-lactamase synthesis Action inhibitor

Action Mechanism inhibitor/ Mechanism Mechanism ß-lactamase Action Action Action inhibitor Mechanism Mechanism Mechanism

Novel Drugs

Protein

Protein Plazomicin

Protein synthesis Protein (Achaogen)

synthesis synthesis inhibitor Protein inhibitor Protein

Protein synthesis inhibitor synthesis synthesis synthesis inhibitor inhibitor inhibitor inhibitor

Protein Protein synthesis Eravacycline Protein synthesis Protein inhibitor Protein (Tetraphase) synthesis Protein inhibitor Protein synthesis inhibitor synthesis synthesis inhibitor synthesis inhibitor inhibitor inhibitor

ß-lactamase inhibitor/ ß-lactamase inhibitor/ synthesis inhibitor synthesis ß-lactamase inhibitor ß-lactamase inhibitor/ inhibitor/ inhibitor inhibitor ß-lactamase ß-lactamase inhibitor inhibitor

Cell wall synthesis Cell wall inhibitor Cell wall

synthesis Cell wall synthesis Cell wall inhibitor synthesis inhibitor synthesis inhibitor Cell wall inhibitor

Cell wall synthesis inhibitor synthesis inhibitor β-lactamase Zidebactam (Wockhardt)

inhibitor

ß-lactamase ß-lactamase ß-lactamase inhibitor ß-lactamase inhibitor inhibitor inhibitor

ß-lactamase

Nacubactam inhibitor β-lactamase (Roche) inhibitor ß-lactamase

ß-lactamase ß-lactamase ß-lactamase inhibitor inhibitor ß-lactamase inhibitor inhibitor inhibitor

ß-lactamase inhibitor

ß-lactamase inhibitor

Structure Structure Structure Structure Structure Structure

445

446

Present and Future of Carbapenem-Resistant Enterobacteriaceae Infections

Lastly, carbapenems continue to be used for the treatment of CRE infections. This is done through the combination of two different carbapenems, which is known as “double carbapenems”. Generally, the combination consists of an initial dose of ertapenem followed by a prolonged infusion of meropenem or doripenem over 3 or 4 h with additional 2 g doses of meropenem every 8 h [73]. This therapy is effective against CRE because the greater affinity of ertapenem to KPC makes it play a “sacrificial role”, meaning that it is preferentially hydrolyzed by the carbapenemase, allowing the concomitant administration of the second carbapenem to sustain a high concentration [74]. Comparator studies such as those by Oliva et al. [75] and Venugopalan et al. [76] confirm the efficacy of dual carbapenem therapy, reporting clinical success rates of more than 70% in both cases. Regarding novel antibacterial drugs, they can be differentiated in two groups: newly approved antibiotics and molecules in development stages. The latest antibiotics approved and already being used to treat CRE infections are ceftazidime/ avibactam, meropenem/vaborbactam, plazomicin and eravacycline. Ceftazidime/avibactam (Allergan) is a novel ß-lactam/ß-lactamase inhibitor combination. The novelty of this combination relies on avibactam, which is a synthetic non-ß-lactam ß-lactamase inhibitor active against ß-lactamases from Ambler classes A, C and D [77]. Clinical studies using this combination are still scarce; however, initial results show an improved mortality rate of 9% compared to the 32% obtained when using colistin [78]. Regardless of the promising initial results, ceftazidime/avibactam-resistant strains have already been reported during treatment [79, 80]. The resistance is due to mutations in the blaKPC-2 and blaKPC-3 genes affecting omega loop D179Y, down-regulation of ompk35/36 and increase in efflux, which could decrease meropenem activity [81]. This should be taken into account by clinicians when prescribing this treatment. Similarly, meropenem/vaborbactam (Melinta) is also a new ß-lactam/ßlactamase inhibitor consisting of a carbapenem and a novel boron-containing serine-ß-lactamase inhibitor that potentiates the activity of meropenem [65]. This combination inhibits Ambler classes A and C serine carbapenemases [82]. There are few clinical data with this combination; however, in vivo results showed that, out of 991 clinical isolates of KPC-producing Enterobacteriaceae, 99% were susceptible to meropenem-vaborbactam [83]. Furthermore, results from the Tango II trial, which compared the efficacy and safety of this combination with the best available therapy in CRE infections, showed a higher clinical cure (65.6% vs 33.3%) and 28-day mortality (15.6% vs 33.3%) for meropenem/vaborbactam [84]. Plazomicin (Achaogen) is a next-generation semisynthetic aminoglycoside with activity against bacteria producing aminoglycoside-modifying enzymes [85]. Studies report higher potency of plazomicin compared to other aminoglycosides against KPC-producing Enterobacteriaceae [86]. Along these lines, Endimiani et al. [86] analyzed collections of clinically relevant KPC-producers with resistance to aminoglycosides and observed inhibition using plazomicin, with a minimum inhibitory concentration (MIC90) of ≤2 mg/L [87]. Plazomicin has shown broad-

Treatment Options

spectrum activity against Gram-positive cocci and Gram-negative bacilli [87]; however, MBL-producers are resistant to this antibiotic due to the methyltransferase enzymes which are commonly found, especially in NDM-producers [87]. Aminoglycosides are not generally used as monotherapy; however, the broad spectrum of activity along with the low renal toxicity of plazomicin make it an option for a targeted monotherapy against extensively drug-resistant Enterobacteriaceae causing urinary tract infections [88]. Lastly, eravacycline (Tetraphase) is a synthetic fluorocycline with broadspectrum antimicrobial activity against Gram-positive, Gram-negative and anaerobic bacteria, regardless of resistance to other antibiotic classes [89]. This antibiotic has several potential advantages over tigecycline, which include a more potent in vitro antibacterial activity, excellent oral bioavailability, lower potential for drug interactions and superior activity in biofilm [90]. This drug was also studied in cUTI (complicated urinary tract infection) in two Phase 3 trials (IGNITE 2/3), failing to meet endpoints in both studies, which could be explained by an erratic pharmacokinetic in urine [91]. However, eravacycline did meet endpoints in the IGNITE 4 Phase 3 study, in which it demonstrated similar activity to ertapenem (100% cure rate for eravacycline vs 92.3% for ertapenem) in the treatment of complicated intra-abdominal infections [92]. In addition to these already approved drugs, there are six molecules in early developmental stages: imipenem/cilastatin and relebactam (Merck), cediferocol (Shionogi), SPR741 (SperoTherapeutics), zidebactam (Wockhardt), nacubactam (Roche) and VNRX 5133 (VenatoRx Pharmaceuticals). Firstly, imipenem/cilastatin and relebactam shares similarities with previously discussed combinations in that it combines an approved carbapenem with a novel ß-lactamase inhibitor. In fact, the inhibitory mechanism of relebactam is similar to that of avibactam, as it covalently and reversibly binds to classes A and C ß-lactamases [93]. By including relebactam, the activity of imipenem increases considerably against carbapenemase-producing bacteria, up to >16-fold [94]. In fact, the RESTORE-IMI 1 study proved this combination to be as effective and better tolerated than colistin/imipenem for the treatment of infections caused by KPC-producing Enterobacteriaceae [95]. Regarding cefiderocol, it is the first siderophore-conjugated cephalosporin antibiotic to advance into late-stage development. This drug has a novel mechanism of action in which the cathecol substituent forms a chelating complex with iron, acting as a trojan horse by using iron active transport systems in Gram-negative bacteria to bypass the other membrane permeability barrier [96]. This molecule demonstrates potent in vitro and in vivo activity against a variety of Gram-negative bacteria, including CRE [96]. A study analyzed the activity of cefiderocol and comparative agents against 1,022 isolates of carbapenemnonsusceptible Enterobacteriaceae, obtaining MIC50 and MIC90 for cefiderocol of 1 and 4 µg/mL, respectively [97]. SPR741 is in very early stages of the development process. This molecule is a polymyxin B potentiator that increases ceftazidime and piperazine/tazobactam activity against CRE and ESBLs including OXA-48 [98]. The remaining molecules under development are ß-lactamase inhibitors. Firstly, zidebactam and nacubactam have high affinity to Ambler classes A and C

447

448

Present and Future of Carbapenem-Resistant Enterobacteriaceae Infections

ß-lactamases [99]. Moreover, they also have affinity to PBPs as well as ß-lactam enhancer activity [100]. The cefepime/zidebactam combination is currently in phase 2 clinical trials for the treatment of Gram-negative bacteria. This combination showed potent in vitro activity against carbapenemase-producing Enterobacteriaceae, with MIC50 of 0.25 mg/L for KPC-producers and 0.5 mg/L for MBL-producers [101]. On the other hand, nacubactam in combination with meropenem is currently in phase 1 trials against Gram-negative bacteria causing UTI infections [102]. Results from this study show improved MIC values for the meropenem/nacubactam combination in comparison with meropenem alone. Furthermore, this combination was active against ceftazidime/avibactam-resistant isolates. Lastly, VNRX 5133 is a cyclic boronate broad spectrum ß-lactamase inhibitor in clinical development with cefepime for the treatment of multidrug­ resistant bacteria [103].

21.5 Conclusions

As highlighted by the Global Priority List published by WHO, carbapenemresistant Enterobacteriaceae pose an exponentially increasing threat for the public health worldwide. These bacteria possess diverse and versatile mechanisms of drug resistance, which makes control and early detection of infections caused by CRE difficult. As a result, a joint effort must be made between the scientific and medical community to slow down the appearance of resistances. Along these lines, there is an urgent need to develop new therapeutic guidelines to treat CRE infections. This includes the repurposing of already existing antibiotics such as fosfomycin, aminoglycosides and colistin and the development of novel drugs such as plazomicin, eravacycline or cefiderocol among others.

Abbreviations

AmpC: CDC: CLSI: CP-CRE: CRE: cUTI: ESBLs: EUCAST: GES: GIM: IMI: IMP: KPC: MBLs:

Type C ampicillinase Centers for Disease Control and Prevention Clinical and Laboratory Standards Institute carbapenemase-producing CRE carbapenem-resistant Enterobacteriaceae complicated urinary tract infection extended-spectrum ß-lactamases European Committee on Antimicrobial Susceptibility Testing Guiana extended-spectrum ß-lactamase German imipenemase imipenem-hydrolyzing ß-lactamase imipenem-resistant Pseudomonas carbapenemase Klebsiella pneumoniae carbapenemase metallo-ß-lactamases

References

MICs: NDM: OXA: RND: SIM: SMP: VIM: WHO:

minimum inhibitory concentrations New Delhi metallo-ß-lactamase oxacillinase resistance-nodulation-division Seoul imipenemase Sao Paulo metallo-ß-lactamase Verona integron-borne metallo-ß-lactamase World Health Organization

Disclosures and Conflict of Interest

This chapter was originally published as: Suay-García, B., and Pérez-Gracia, M. T. (2019). Present and future of carbapenem-resistant Enterobacteriaceae (CRE) infections. Antibiotics, 8, 122, https://doi.org/10.3390/antibiotics8030122, under the Creative Commons Attribution license (http://creativecommons.org/licenses/ by/4.0/). It appears here, with edits and updates, by kind permission of the copyright holders. Both B.S.-G. and M.T.P.-G. conceived and wrote this chapter. This research received no external funding. The authors declare no conflict of interest.

References

1. Centers for Disease Control and Prevention. About antimicrobial resistance. Available at: https://www.cdc.gov/drugresistance/about.html (accessed on May 26, 2021).

2. Niu, G., Li, W. Next-generation drug discovery to combat antimicrobial resistance. Trends Biochem. Sci. 2019, 44, 961–972.

3. O’Neill, J. Antimicrobial resistance: Tackling a crisis for the health and wealth of nations, 2014. Available at: https://amr-review.org/sites/default/files/AMR%20Review%20 P aper%20-%20Tackling%2 0 a%20crisis%20for%20the%20health%20and%20 wealth%20of%20nations_1.pdf (accessed on May 15, 2021). 4. World Health Organization. WHO priority pathogens list for R&D of new antibiotics, 2017. Available at: https://www.who.int/news/item/27-02-2017-who-publishes-list-of­ bacteria-for-which-new-antibiotics-are-urgently-needed (accessed on May 15, 2021). 5. Lee, C., Lai, C.C., Chiang, H.T., Lu, M.C., Wang, L.F., Tsai, T.L., Kang, M.Y., Jan, Y.N., Lo, Y.T., Ko, W.C., et al. Presence of multidrug-resistant organisms in the residents and environments of long-term care facilities in Taiwan. J. Microbiol. Immunol. Infect. 2017, 50, 133–144. 6. Rodriguez-Bano, J., Gutierrez-Gutierrez, B., Machuca, I., Pascual, A. Treatment of infections caused by extended-spectrum-beta-lactamase-, AmpC-, and carbapenemase­ producing Enterobacteriaceae. Clin. Microbiol. Rev. 2018, 31, e00079-17.

7. Pana, Z.D., Zaoutis, T. Treatment of extended-spectrum ß-lactamase-producing Enterobacteriaceae (ESBLs) infections: What have we learned until now? F1000Res 2018, 7, F1000.

449

450

Present and Future of Carbapenem-Resistant Enterobacteriaceae Infections

8. D’Angelo, R.G., Johnson, J.K., Bork, J.T., Heil, E.L. Treatment options for extendedspectrum beta-lactamase (ESBL) and AmpC-producing bacteria. Expert Opin. Pharmacother. 2016, 17, 953–967.

9. Sheu, C.C., Lin, S.Y., Chang, Y.T., Lee, C.Y., Chen, Y.H., Hsueh, P.R. Management of infections caused by extended-spectrum beta-lactamase-producing Enterobacteriaceae: Current evidence and future prospects. Expert Rev. Anti Infect. Ther. 2018, 16, 205–218.

10. Durante-Mangoni, E., Andini, R., Zampino, R. Management of carbapenem-resistant Enterobacteriaceae infections. Clin. Microbiol. Infect. 2019, 25, 943–950.

11. Centers for Disease Control and Prevention. Healthcare Facilities: Information about CRE. Available at: https://www.cdc.gov/hai/organisms/cre/Cre-toolkit/index.html (accessed on May 26, 2021).

12. Haidar, G., Clancy, C.J., Chen, L., Samanta, P., Shields, R.K., Kreiswirth, B.N., Nguyen, M.H. Identifying specra of activity and therapeutic niches for ceftazidime-avibactam and imipenem-relebactam against carbapenem-resistant Enterobacteriaceae. Antimicrob. Agents Chemother. 2017, 61, e00642-17.

13. Tooke, C.L., Hinchliffe, P., Bragginton, E.C., Colenso, C.K., Hirvonen, V.H.A., Takebayashi, Y., Spencer, J. ß-Lactamases and ß-lactamase inhibitors in the 21st century. J. Mol. Biol. 2019, 431, 3472–3500. 14. Codjoe, F.S., Donkor, E.S. Carbapenem resistance: A review. Med. Sci. 2018, 6, 1.

15. Lutgring, J.D., Limbago, B.M. The problem of carbapenemase-producing-carbapenemresistant-Enterobacteriaceae detection. J. Clin. Microbiol. 2016, 54, 529–534.

16. Ambler, R.P. The structure of ß-lactamases. Philos. Trans. R. Soc. Lond. B 1980, 289, 321–331. 17. Queenan, A.M., Bush, K. Carbapenemases: The versatile ß-lactamases. Clin. Microbiol. Rev. 2007, 20, 440–458.

18. Yigit, H., Queenan, A.M., Anderson, G.J., Domenech-Sanchez, A., Biddle, J.W., Steward, C.D., Alberti, S., Bush, K., Tenover, F.C. Novel carbapenem-hydrolyzing beta-lactamase, KPC-1, from a carbapenem-resistant strain of Klebsiella pneumoniae. Antimicrob. Agents Chemother. 2001, 45, 1151–1161. 19. Ji, S., Lv, F., Du, X., Wei, Z., Fu, Y., Mu, X., Jiang, Y., Yu, Y. Cefepime combined with amoxicillin/clavulanic acid: A new choice for the KPC-producing K. pneumoniae infection. Int. J. Infect. Dis. 2015, 38, 108–114.

20. Porreca, A.M., Sullivan, K.V., Gallagher, J.C. The epidemiology, evolution, and treatment of KPC-producing organisms. Curr. Infect. Dis. Rep. 2018, 20, 13.

21. Nordmann, P., Cuzon, G., Naas, T. The real threat of Klebsiella pneumoniae carbapenemase-producing bacteria. Lancet Infect. Dis. 2009, 9, 228–233.

22. Miriagou, V., Cornaglia, G., Edelstein, M., Galani, I., Giske, G. Acquired carbapenemases in Gram-negative bacterial pathogens: Detection and surveillance issues. Clin. Microbiol. Infect. 2010, 16, 112–122.

23. Okoche, D., Asiimwe, B.B., Katabazi, F.A., Kato, L., Najjuka, C.F. Prevalence and characterization of carbapenem-resistant Enterobacteriaceae isolated from Mulago National Referral Hospital, Uganda. PLoS One 2015, 10, e0135745. 24. Boutal, H., Vogel, A., Bernabeu, S., Devilliers, K., Creton, E., Cotellon, G., Plaisance, M., Oueslati, S., Dortet, L., Jousset, A., et al. A multiplex lateral flow immunoassay

References

for the rapid identification of NDM-, KPC-, IMP- and VIM-type and OXA-48-like carbapenemase-producing Enterobacteriaceae. J. Antimicrob. Chemother. 2018, 73, 909–915.

25. Fernández, J., Guerra, B., Rodicio, M.R. Resistance to carbapenems in non-typhoidal Salmonella enterica serovars from humans, animals and food. Vet. Sci. 2018, 5, 40. 26. Abdallah, M., Balshi, A. First literature review of carbapenem-resistant Providencia. New Microb. New Infect. 2018, 25, 16–23.

27. Perez, F., van Duin, D. Carbapenem-resistant Enterobacteriaceae: A menace to our most vulnerable patients. Clevel Clin. J. Med. 2013, 80, 225–233.

28. Walsh, T.R., Toleman, M.A., Piorel, L., Nordman, P. Metallo-b-lactamases: The quiet before the storm? Clin. Microbiol. Rev. 2005, 18, 306–325.

29. Van Duin, D., Doi, Y. The global epidemiology of carbapenemase-producing Enterobacteriaceae. Virulence 2017, 8, 460–469. 30. Livermore, D.M., Woodford, N. Carbapenemases: A problem in waiting? Curr. Opin. Microbiol. 2000, 3, 489–495.

31. Gupta, V. Metallo-b-lactamases in Pseudomonas aeruginosa and Acinetobacter species. Expert Opin. Investig. Drugs 2008, 17, 131–143.

32. Bush, K. Past and present perspectives on ß-lactamases. Antimicrob. Agents Chemother. 2018, 62, e01076–18.

33. Walsh, T.R., Weeks, J., Livermore, D.M., Toleman, M.A. Dissemination of NDM-1 positive bacteria in the New Delhi environment and its implications for human health: An environmental point prevalence study. Lancet Infect. Dis. 2011, 11, 355–362. 34. Poirel, L., Hombrouck-Alet, C., Freneaux, C., Bernabeu, S., Nordmann, P. Global spread of New Delhi metallo-ß-lactamase 1. Lancet Infect. Dis. 2010, 10, 832.

35. Ivanov, I., Sabtcheva, S., Dobreva, E., Todorova, B., Velinov, T.Z., Borissova, V., Kantardijev, T. Prevalence of carbapenemase genes among 16S rRNA methyltransferase-producing Enterobacteriaceae isolated for cancer patients. Probl. Infect. Parasit. Dis. 2014, 42, 10–13. 36. Poirel, L., Potron, A., Nordmann, P. OXA-48-like carbapenemases: The phantom menace. J. Antimicrob. Chemother. 2012, 67, 1597–1606. 37. Rasmussen, J.W., Hoiby, N. OXA-type carbapenemases. J. Antimicrob. Chemother. 2006, 57, 373–383.

38. Stewart, A., Harris, P., Henderson, A., Paterson, D. Treatment of Infections by OXA-48Producing Enterobacteriaceae. Antimicrob. Agents Chemother. 2018, 62, e01195-18.

39. Logan, L.K., Weinstein, R.A. The epidemiology of carbapenem-resistant Entero­ bacteriaceae: The impact and evolution of a global menace. J. Infect. Dis. 2017, 215 (Suppl. 1), S28–S36.

40. Tzouvelekis, L.S., Marjogiannakis, A., Psichogiou, M., Tassios, P.T., Daikos, G.L. Carbapenemases in Klebsiella pneumoniae and other Enterobacteriaceae: An evolving crisis of global dimensions. Clin. Microbiol. Rev. 2012, 25, 682–707.

41. Queenan, A.M., Shang, W., Flamm, R., Bush, K. Hydrolysis and inhibition profiles of ß-lactamases from molecular classes A to D with doripenem, imipenem, and meropenem. Antimicrob. Agents Chemother. 2010, 54, 565–569.

451

452

Present and Future of Carbapenem-Resistant Enterobacteriaceae Infections

42. Goessens, W.H.F., van der Bij, A.K., van Boxtel, R., Pitout, J.D.D., van Ulsen, P., Melles, D.C., Tommassen, J. Antibiotic trapping by plasmid-encoded CMY-2 ß-lactamase combined with reduced outer membrane permeability as a mechanism of carbapenem resistance in Escherichia coli. Antimicrob. Agents Chemother. 2013, 57, 3941–3949. 43. Philippon, A., Arlet, G., Jacoby, G.A. Plasmid-determined AmpC-type ß-lactamases. Antimicrob. Agents Chemother. 2002, 46, 1–11.

44. Weston, N., Sharma, P., Ricci, V., Piddock, L.J.V. Regulation of the AcrAB-TolC efflux pump in Enterobacteriaceae. Res. Microbiol. 2018, 169, 425–431.

45. Routh, M.D., Zalucki, Y., Su, C.C., Zhang, Q., Shager, W.M., Yu, E.W. Efflux pumps of the resistance-nodulation-division family: A perspective of their structure, function, and regulation in Gram-negative bacteria. Adv. Enzymol. Relat. Areas Mol. Biol. 2011, 77, 109–146.

46. Courvalin, P. Transfer of antibiotic resistance genes between Gram-positive and Gram-negative bacteria. Antimicrob. Agents Chemother. 1994, 38, 1447–1451.

47. Bialek-Davenet, S., Mayer, N., Vergalli, J., Duprilot, M., Brisse, S., Pagès, J.M., NicolasChanoine, M.H. In-vivo loss of carbapenem resistance by extensively drug-resistant Klebsiella pneumoniae during treatment via porin expression modification. Sci. Rep. 2017, 7, 6722. 48. Masi, M., Réfrigiers, M., Pos, K.M., Pagès, J.M. Mechanisms of envelope permeability and antibiotic influx and efflux in Gram-negative bacteria. Nat. Microbiol. 2017, 2, 17001. 49. Walsh, C. Molecular mechanisms that confer antibacterial drug resistance. Nature 2000, 406, 775–781.

50. Ito, H., Arakawa, Y., Oshuka, S., Wacharotayankun, R., Kato, N., Ohta, M. Plasmidmediated dissemination of the metallo-beta-lactamase gene blaIMP among clinically isolated strains of Serratia marcescens. J. Antimicrob. Chemother. 1995, 50, 503–511.

51. Nordmann, P., Poirel, L. The difficult-to-control spread of carbapenemase producers among Enterobacteriaceae worldwide. Clin. Microbiol. Infect. 2014, 20, 821–830.

52. Nordmann, P., Naas, T., Poirel, L. Global spread of carbapenemase-producing Enterobacteriaceae. Emerg. Infect. Dis. 2011, 17, 1791–1798.

53. Lauretti, L., Riccio, M.K., Mazzariol, A., Cornaglia, G., Amicosante, G., Fontana, R., Rossolini, G.M. Cloning and characterization of blaVIM, a new integron-borne metallo­ beta-lactamase gene from a Pseudomonas aeruginosa clinical isolate. Antimicrob. Agents Chemother. 1999, 43, 1584–1590.

54. Yong, D., Toleman, M.A., Giske, C.G., Cho, H.S., Sundman, K., Lee, K., Walsh, T.R. Characterization of new metallo-beta-lactamase gene, bla(NDM-1), and a novel erythromycin esterase gene carried on a unique genetic structure in Klebsiella pneumoniae sequence type 14 from India. Antimicrob. Agents Chemother. 2009, 53, 5046–5054.

55. Dortet, L., Poirel, L., Nordmann, P. Worldwide dissemination of the NDM-type carbapenemases in Gram-negative bacteria. Biomed. Res. Int. 2014, 2014, 249856.

56. Poirel, L., Savov, E., Nazli, A., Trifonova, A., Todorova, I., Gergova, I., Nordmann, P. Outbreak caused by NDM-1- and RmtB- producing Escherichia coli in Bulgaria. Antimicrob. Agents Chemother. 2012, 58, 2472–2474.

References

57. Patel, G., Bonomo, R.A. Stormy waters ahead: Global emergence of carbapenemases. Front. Microbiol. 2013, 4, 1–17.

58. Bradford, P.A., Bratu, S., Urban, C., Visalli, M., Mariano, N., Landman, D., Rahal, J.J., Brooks, S., Cebular, S., Quale, J. Emergence of carbapenem-resistant Klebsiella species possessing the class a carbapenem-hydrolyzing KPC-2 and inhibitor-resistant TEM-30 ß-lactamases in New York City. Clin. Infect. Dis. 2004, 39, 55–60.

59. Munoz-Price, L.S., Poirel, L., Bonomo, R.A., Schwaber, M.J., Daikos, G.L., Cormican, M., Cornaglia, G., Garau, J., Gniadkowski, M., Hayden, M.K. Clinical epidemiology of the global expansion of Klebsiella pneumoniae carbapenemases. Lancet Infect. Dis. 2013, 13, 785–796.

60. Maya, J.J., Ruiz, S.J., Blanco, V.M., Gotuzzo, E., Guzman-Blanco, M., Labarca, J., Salles, M., Quinn, J.P., Villegas, M.V. Current status of carbapenemases in Latin America. Expert Rev. Anti Infect. Ther. 2013, 11, 657–667.

61. Poirel, L., Bonnin, R.A., Nordmann, P. Genetic features of the widespread plasmid coding for the carbapenemase OXA-48. Antimicrob. Agents Chemother. 2012, 56, 559–562.

62. The European Committee on Antimicrobial Susceptibility Testing. Breakpoint tables for interpretation of MICs and zone diameters, version 9.0. 2019. Available at: http//www.eucast.org (accessed on May 15, 2021).

63. CLSI. Performance Standards for Antimicrobial Susceptibility Testing, 29th ed., CLSI supplement M100, Clinical and Laboratory Standards Institute: Wayne, PA, USA, 2019.

64. The European Committee on Antimicrobial Susceptibility Testing. Breakpoint tables for interpretation of MICs and zone diameters, version 8.1. 2018. Available at: http//www.eucast.org (accessed on May 15, 2021).

65. Karaiskos, I., Lagou, S., Pontikis, K., Rapti, V., Poulakou, G. The “old” and the “new” antibiotics for MDR Gram-negative pathogens: For whom, when and how. Front. Public Health 2019, 7, 151. 66. Vardakas, K.Z., Legakis, N.J., Triarides, N., Falagas, M.E. Susceptibility of contemporary isolates to Fosfomycin: A systematic review of the literature. Int. J. Antimicrob. Agents 2016, 47, 269–285.

67. Satlin, M.J., Kubin, C.J., Blumental, J.S., Cohen, A.B., Furuya, E.Y., Wilson, S.J., Jenkins, S.G., Calfee, D.P. Comparative effectiveness of aminoglycosides, polymyxin B and tigecycline for clearance of carbapenem resistant Klebsiella pneumoniae from urine. Antimicrob. Agents Chemother. 2011, 55, 2528–2531. 68. Sader, H.S., Castanheira, M., Duncan, L.R., Flamm, R.K. Antimicrobial susceptibility of Enterobacteriaceae and Pseudomonas aeruginosa isolates from United States medical centers stratified by infection type: Results from the international network for optimal resistance monitoring (INFORM) surveillance program, 2015–2016. Diagn. Microbiol. Infect. Dis. 2018, 92, 69–74.

69. Daikos, G.L., Tsaousi, S., Tzouvelekis, L.S., Anyfantis, I., Psichogiou, M., Argyropoulou, A., Stefanou, I., Sypsa, V., Miriagou, V., Nepka, M. Carbapenemase-producing Klebsiella pneumoniae bloodstream infections: Lowering mortality by antibiotic combination schemes and the role of carbapenems. Antimicrob. Agents Chemother. 2014, 58, 2322–2328.

453

454

Present and Future of Carbapenem-Resistant Enterobacteriaceae Infections

70. Ni, W., Han, Y., Liu, J., Wei, C., Zhao, J., Cui, J., Wang, R., Liu, Y. Tigecycline treatment for carbapenem-resistant Enterobacteriaceae infections: A systematic review and meta-analysis. Medicine 2016, 95, e3126.

71. Giamarellou, H., Poulakou, G. Pharmakokinetic and pharmacodynamic evaluation of tigecycline. Expert Opin. Drug Metab. Toxicol. 2011, 7, 1459–1470. 72. De Pascale, G., Montini, L., Pennisi, M., Bernini, V., Maviglia, R., Bello, G., Spanu, T., Tumbarello, M., Antonelli, M. High dose tigecycline in critically ill patients with severe infections due to multidrug-resistant bacteria. Crit. Care 2014, 18, R90.

73. Bulik, C.C., Nicolau, D.P. Double-carbapenem therapy for carbapenemase-producing Klebsiella pneumoniae. Antimicrob. Agents Chemother. 2011, 55, 3002–3004.

74. Anderson, K.F., Lonsway, D.R., Rasheed, J.K., Biddle, J., Jensen, B., McDougal, L.K., Carey, R.B., Thompson, A., Stocker, S., Limbago, B., et al. Evaluation of methods to identify the Klebsiella pneumoniae carbapenemase in Enterobacteriaceae. J. Clin. Microbiol. 2007, 45, 2723–2725.

75. Oliva, A., Scorzolini, L., Castaldi, D., Gizzi, F., De Angelis, M., Sotrto, M., D’Abramo, A., Aloj, F., Mascellino, M.T., Mastroianni, C.M., et al. Double-carbapenem regimen, alone or in combination with colistin, in the treatment of infections caused by carbapenemresistent Klebsiella pneumoniae (CR-Kp). J. Infect. 2017, 74, 103–106.

76. Venugopalan, V., Nogid, B., Le, T.N., Rahman, S.M., Bias, T.E. Double carbapenem therapy (DCT) for bacteremia due to carbapenem-resistant Klebsiella pneumoniae (CRKP): From the test tube to clinical practice. Infect. Dis. 2017, 49, 867–870.

77. De Jonge, B.L., Karlowsky, J.A., Kazmierczak, K.M., Biedenbach, D.J., Sahm, D.F., Nichols, W.W. In vitro susceptibility to ceftazidime-avibactam of carbapenem-nonsusceptible Enterobacteriaceae isolates collected during the INFORM global surveillance study (2012 to 2014). Antimicrob. Agents Chemother. 2016, 60, 3163–3169.

78. Van Duin, D., Lok, J.J., Earley, M., Cober, E., Richter, S.S., Perez, F., Salata, R.A., Kalayjian, R.C., Watkins, R.R., Doi, Y., et al. Colistin versus ceftazidime-avibactam in the treatment of infections due to carbapenem-resistant Enterobacteriaceae. Clin. Infect. Dis. 2018, 66, 163–171.

79. Nelson, K., Hemarajata, P., Sun, D., Rubio-Aparicio, D., Tsivokovski, R., Yang, S., Sebra, R., Kasarskis, A.M., Nguyen, H., Hanson, B.M., et al. Resistance to ceftazidime-avibactam is due to transposition of KPC in a porin-deficient strain of Klebsiella pneumoniae with increased efflux activity. Antimicrob. Agents Chemother. 2017, 61, e00989-17.

80. Venditti, C., Nisii, C., D’Arezzo, S., Vulcano, A., Capone, A., Antonini, M., Ippolito, G., Di Caro, A. Molecular and phenotypical characterization of two cases of antibioticdriven ceftazidime-avibactam resistance in blaKPC-3-harboring Klebsiella pneumoniae. Infect. Drug Resist. 2019, 12, 1935–1940. 81. Humphries, R.M. Mechanisms of Resistance to Ceftazidime-Avibactam, 28th ed., European Congress of Clinical Microbiology & Infectious Diseases (ECCMID): Madrid, Spain, 2018.

82. Petty, L.A., Henig, O., Patel, T.S., Pogue, J.M., Kaye, K.S. Overview of meropenemvaborbactam and newer antimicrobial agents for the treatment of carbapenem­ resistant Enterobacteriaceae. Infect. Drug Resist. 2018, 11, 1461–1472.

83. Lomovskaya, O., Sun, D., Rubio-Aparicio, D., Nelson, K., Tsivkovski, R., Griffith, D.C., Dudley, M.N. Vaborbactam: Spectrum of beta-lactamase inhibition and impact of resistance

References

mechanisms on activity in Enterobacteriaceae. Antimicrob. Agents Chemother. 2017, 61, 1–15.

84. Wunderink, R.G., Giamarellos-Bourboulis, E.J., Rahav, G., Mathers, A.J., Bassetti, M., Vazquez, J., Cornely, O.A., Solomkin, J., Bhowmick, T., Bishara, J., et al. Effect and safety of meropenem-vaborbactam versus best-available therapy in patients with carbapenem­ resistant Enterobacteriaceae infections: The TANGO II randomized clinical trial. Infect. Dis. Ther. 2018, 7, 439–455.

85. Landman, D., Babu, E., Shah, N., Kelly, P., Backer, M., Bratu, S., Quale, J. Activity of a novel aminoglycoside, ACHN-490, against clinical isolates of Escherichia coli and Klebsiella pneumoniae from New York City. J. Antimicrob. Chemother. 2010, 65, 2123–2127.

86. Endimiani, A., Hujer, K.M., Hujer, A.M., Armstrong, E.S., Choudhary, Y., Aggen, J.B., Bonomo, R.A. ACHN-490, a neoglycoside with potent in vitro activity against multidrugresistant Klebsiella pneumoniae isolates. Antimicrob. Agents Chemother. 2009, 53, 4504–4507.

87. Walkty, A., Adam, H., Baxter, M., Denisuik, A., Lagace-Wiens, P., Karlowsky, J.A., Hoban, D.J., Zhanel, G.G. In vitro activity of plazomicin against 5,015 Gram-negative and Gram-positive clinical isolates obtained from patients in Canadian hospitals as part of the CANWARD study, 2011–2012. Antimicrob. Agents Chemother. 2014, 58, 2554–2563.

88. Castanheira, M., Deshpande, L.M., Woosley, L.N., Serio, A.W., Krause, K.M., Flamm, R.K. Activity of plazomicin compared with other aminoglycosides against isolates from European and adjacent countries, including Enterobacteriaceae molecularly characterized for aminoglycoside-modifying enzymes and other resistance mechanisms. J. Antimicrob. Chemother. 2018, 73, 3346–3354. 89. Zhanel, G.G., Cheung, D., Adam, H., Zelenitsky, S., Golden, A., Schweizer, F., Gorityala, B., Lagacé-Wiens, P.R., Walkty, A., Gin, A.S., et al. Review of eravacycline, a novel fluorocycline antibacterial agent. Drugs 2016, 76, 567–588.

90. Bassetti, M., Righi, E. Eravacycline for the treatment of intra-abdominal infections. Expert Opin. Investig. Drugs 2014, 23, 1575–1584. 91. XERAVA (Eravacycline) for Injection. XERAVA (eravacycline) IGNITE1 and IGNITE4 trial results. 2019. Available at: https://www.xerava.com/efficacy (accessed on May 15, 2021).

92. Solomkin, J.S., Ramesh, M.K., Cesnauskas, G., Novikovs, N., Stefanova, P., Sutcliffe, J.A., Walpole, S.M., Horn, P.T. Phase 2, randomized, double-blind study of the efficacy and safety of two dose regimens of eravacycline versus ertapenem for adult communityacquired complicated intra-abdominal infections. Antimicrob. Agents Chemother. 2014, 58, 1847–1854. 93. Blizzard, T.A., Chen, H., Kim, S., Wu, J., Bodner, R., Gude, C., Imbriglio, J., Young, K., Park, Y.W., Ogawa, A., et al. Discovery of MK-7655, a beta-lactamase inhibitor for combination with Primaxin®. Bioorg. Med. Chem. Lett. 2014, 24, 780–785.

94. Zhanel, G.G., Lawrence, C.K., Adam, H., Schweizer, F., Zelenitsky, S., Zhanel, M., LagacéWiens, P.R.S., Walkty, A., Denisuik, A., Golden, A., et al. Imipenem-relebactam and meropenem-vaborbactam: Two novel carbapenem-ß-lactamase inhibitor combinations. Drugs 2018, 78, 65–98. 95. Motsch, J., Oliveira, C., Stus, V., Koksal, I., Lyulko, O., Boucher, H.W., Kaye, K.S., File, T.M., Brown, M.L., Khan, I., et al. RESTORE-IMI 1: A Multicenter, Randomized, Double-Blind,

455

456

Present and Future of Carbapenem-Resistant Enterobacteriaceae Infections

Comparator-Controlled Trial Comparing the Efficacy and Safety of Imipenem/Relebactam Versus Colistin Plus Imipenem in Patients with Imipenem-Non-Susceptible Bacterial Infections, 28th ed., ECCMID: Madrid, Spain, 2018.

96. Saisho, Y., Katsube, T., White, S., Fukase, H., Shimada, J. Pharmacokinetics, safety, and tolerability of cefiderocol, a novel siderophore cephalosporin for Gram-negative bacteria, in healthy subjects. Antimicrob. Agents Chemother. 2018, 62, e-02163-17.

97. Hackel, M.A., Tsuji, M., Yamano, Y., Echols, R., Karlowsky, J.A., Sahm, D.F. In vitro activity of the siderophore cephalosporin, cediferocol, against carbapenem-nonsusceptible and multidrug-resistant isolates of Gram-negative bacilli collected worldwide in 2014 to 2016. Antimicrob. Agents Chemother. 2018, 62, e01968-17. 98. Corbett, D., Wise, A., Langley, T., Skinner, K., Trimby, E., Birchall, S., Dorali, A., Sandiford, S., Williams, J., Warn, P., et al. Potentiation of antibiotic activity by a novel cationic peptide: Potency and spectrum of activity of SPR741. Antimicrob. Agents Chemother. 2017, 61, e00200-17.

99. Karaiskos, I., Galani, I., Souli, M., Giamarellou, H. Novel ß-lactam-ß-lactamase inhibitor combinations: Expectations for the treatment of carbapenem-resistant Gram-negative pathogens. Expert Opin. Drug Metab. Toxicol. 2019, 15, 133–149.

100. Papp-Wallace, K.M., Nguyen, N.Q., Jacobs, M.R., Bethel, C.R., Barnes, M.D., Kumar, V., Bajaksouzian, S., Rudin, S.D., Bhavsar, S., et al. Strategic approaches to overcome resistance against Gram-negative pathogens using ß-lactamase inhibitors and ß-lactam enhancers: Activity of three novel diazabicyclooctanes WCK 5153, zidebactam (WCK 5107), and WCK 4234. J. Med. Chem. 2018, 61, 4067–4086.

101. Sader, H.S., Rhomberg, P.R., Flamm, R.K., Jones, R.N., Castanheira, M. WCK 5222 (cefepime/zidebactam) antimicrobial activity tested against Gram-negative organisms producing clinically relevant ß-lactamases. J. Antimicrob. Chemother. 2017, 72, 1696–1703. 102. Monogue, M.L., Giovagnoli, S., Bissantz, C., Zampaloni, C., Nicolau, D.P. In vivo efficacy of meropenem with a novel non-ß-lactam-ß-lactamase inhibitor, nacubactam, against Gram-negative organisms exhibiting various resistance mechanisms in a murine complicated urinary tract infection model. Antimicrob. Agents Chemother. 2018, 62, e02596-17.

103. Daigle, D., Hamrick, J., Chatwin, C., Kurepina, N., Kreiswirth, B.N., Shields, R.K., Oliver, A., Clancy, C.J., Nguyen, M.H., Pevear, D., et al. Cefepime/VNRX-5133 borad-spectrum activity is mantained against emerging KPC- and PDC-variants in multidrug-resistant K. pneumoniae and P. aeruginosa. Open Forum Infect. Dis. 2018, 5 (Suppl. 1), S419–S420.

Chapter 22

Human Plague: An Old Scourge That Needs New Answers Xavier Vallès, PhD,a Nils Chr. Stenseth, PhD,b,c Christian Demeure, PhD,d Peter Horby, MD, PhD,e Paul S. Mead, MD,f Oswaldo Cabanillas, PhD,g Mahery Ratsitorahina, PhD,a Minoarisoa Rajerison, PhD,h Voahangy Andrianaivoarimanana, PhD,h,i Beza Ramasindrazana, PhD,h Javier Pizarro-Cerda, PhD,d Holger C. Scholz, PhD,j Romain Girod, PhD,k B. Joseph Hinnebusch, PhD,l Ines Vigan-Womas, PhD,m,n Arnaud Fontanet, PhD,o,p David M. Wagner, PhD,q Sandra Telfer, PhD,r Yazdan Yazdanpanah, MD, PhD,s,t Pablo Tortosa, PhD,u Guia Carrara, PhD,s Jane Deuve, PhD,v Steven R. Belmain, PhD,w Eric D’Ortenzio, MD,s,t and Laurence Baril, MD, PhDa aEpidemiology

and Clinical Research Unit, Institut Pasteur de Madagascar, Antananarivo, Madagascar for Ecological and Evolutionary Synthesis, Department of Biosciences, University of Oslo, Oslo, Norway cKey Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, China dYersinia Research Unit, National Reference Centre “Plague & Other Yersinioses,” WHO Collaborating Research and Reference Centre for Yersinia, Institut Pasteur, Paris, France eCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK

fBacterial Diseases Branch, Division of Vector Borne Diseases,

Centers for Disease Control and Prevention, Fort Collins, Colorado, USA

gControl de Epidemia Desastres y Otras Emergencias Sanitarias,

Oficina General de Epidemiologia, Ministerio de Salud, Perúu hPlague Unit, Central Laboratory for Plague, Institut Pasteur de Madagascar, Antananarivo, Madagascar iSurveillance and Epidemiology Directorate at the Ministry of Public Health, Antananarivo, Madagascar jReference Laboratory for Plague, Bundeswehr Institute of Microbiology, Munich, Germany kMedical Entomology Unit, Institut Pasteur de Madagascar, Antananarivo, Madagascar lRocky Mountain Laboratories, National Institute of Health, National Institutes of Allergy and Infectious Diseases, Hamilton, Montana, USA mImmunology of Infectious Diseases Unit, Institut Pasteur de Madagascar, Antananarivo, Madagascar nImmunology of Infectious Diseases Unit, Institut Pasteur de Dakar, Dakar, Sénégal oEmerging Diseases Epidemiology Unit, Institut Pasteur, Paris, France pPACRI unit, Conservatoire National des Arts et Métiers, Paris, France qThe Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA rSchool of Biological Sciences, University of Aberdeen, Aberdeen, UK sREACTing, Inserm, Université Paris-Diderot, Sorbonne Paris Cité, Paris, France tService de Maladies Infectieuses et Tropicales, Hôpital Bichat-Claude Bernard, AP-HP, Paris, France uUniversité de La Réunion, Unité Mixte de Recherche Processus Infectieux en Milieu Insulaire Tropical, La Réunion, France vDepartment of International Affairs, Institut Pasteur, Paris, France wNatural Resources Institute, University of Greenwich, Chatham Maritime, Kent, UK bCentre

[email protected]

Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

Copyright © 2022 Jenny Stanford Publishing Pte. Ltd.

ISBN 978-981-4877-84-8 (Hardcover), 978-1-003-18043-2 (eBook)

www.jennystanford.com

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Keywords: plague, epidemiology, plague vector, plague hosts, Yersinia pestis, control

and prevention, zoonosis, bubonic plague, pneumonic plague, rapid diagnostic test, point of care, sensitivity, specificity, serology, translational research

22.1 Introduction Plague is a bacterial rodent-borne disease caused by Yersinia pestis, a gramnegative bacillus member of the Enterobacteriaceae family. As a zoonosis, it is first and foremost a rodent disease with complex zoonotic/epizootic cycles that may occasionally be transmitted to humans, whereby it can cause sporadic cases, outbreaks, or even large epidemics (Fig. 22.1). Bubonic plague is the most common clinical presentation among humans. Bubonic forms may evolve to septicaemic disease, and 1% to 3% of cases develop a secondary pneumonic plague [1–3]. Secondary pneumonic plague forms may be transmitted from person to person through respiratory droplets, which can result in primary pneumonic plague. Without intensive treatment, the lethality of septicaemic and pneumonic plague is almost 100% between 1 and 4 days following the onset of symptoms [1–3].

Figure 22.1 Epizootic/enzootic cycle of Y. pestis. Adapted with permission from Ben Ari et al., PLoS Pathog. 2011; 7: e1002160 [49].

Introduction

Plague has marked human history in a unique way during at least 3 historical pandemics. The first described pandemic was the Justinian epidemic (6th–7th centuries), whereas the second spanned from the 14th century to the 19th century in Europe [4, 5], including the Black Death period (1,347–1,351 BC) that wiped out an estimated 30% to 40% of the European population, constituting its deadliest recorded epidemic. The third pandemic of plague emerged from its natural cradle in the Yunnan province (China) in the mid-19th century [6]. Because of the expansion of the shipping industry, this third wave established sustained Y. pestis epizootic cycles worldwide, including in the United States, South America, Madagascar, and other areas previously free of plague. Plague is currently endemic in restricted areas where it has been present for several hundred or even thousands of years (China, Kyrgyzstan, Kazakhstan, Russia, and Mongolia), or just a hundred years (Peru, the US, Madagascar, and some areas in Africa) [5, 7, 8]. Although in the mid-20th century human cases were mostly reported from Asia, with a steadily decreasing trend, a sharp increase of cases in Africa was observed during the 1980s to the 2000s, which now make up the majority of total cases [4]. Between 2013 and 2018, 2,886 cases and 504 deaths have been notified to WHO (with a reported case fatality of 17.5%), of which 95% derive from sub-Saharan Africa, mainly in Madagascar and the Ituri region of the Democratic Republic of Congo (DRC) [8]. Occasional scattered cases of human plague are regularly declared in China, Mongolia, the Russian Federation, Kyrgyzstan, Peru, Bolivia, Uganda, Tanzania, and the US [7–9]. Nowadays, Y. pestis remains present in at least 33 countries (where signs of activity have been detected in the last 30 years). More than 30 different flea vectors have been suspected to play a role in transmission, and over 200 mammals (mainly rodents and lagomorphs) have been reported to be infected by Y. pestis, including potential reservoir hosts, in different parts of the world. Table 22.1 exemplifies this diversity. However, the commensal rats (Rattus rattus and R. norvegicus) and their flea (Xenopsylla cheopis) are currently considered the most important hosts and vector involved in human outbreaks. Other hosts and vectors are involved in human transmission in different regions. Overall, plague is largely a disease of wild mammals, killing susceptible rodent species, cats, camels, and other mammals when the disease spills over from rodent reservoir host species. The maintenance of Y. pestis in nature, and from where it spreads to cause human outbreaks, renders it almost impossible to eradicate. Plague once again grasped the attention of the scientific community when an outbreak of pneumonic plague was declared in Madagascar in September 2017, primarily striking the capital Antananarivo and the main seaport of Toamasina. A total of 1,878 clinically suspected pneumonic plague cases were identified [10]. This episode should be considered an echo of the third pandemic, which first established plague in Madagascar [11]. Pneumonic plague outbreaks with human-to-human transmission are fortunately relatively rare, probably due to the estimated low basic reproduction number (R0) between 1.2 and 1.4 [12, 13]. Besides the recent outbreak in

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Madagascar, minor episodes of pneumonic plague have been described in recent years in the US [13], Madagascar [14, 15], India [16], DRC [17,18], Uganda [19], Peru [20], and China [21]. However, large and deadly outbreaks of pneumonic plague have occurred, most notably in Manchuria, where more than 60,000 estimated deaths occurred between 1910 and 1911 [3, 22], highlighting that R0 of pneumonic plague depends on the specific situation and interaction of vulnerable population as has been showed during the analysis of more recent outbreaks [15] and past outbreaks [3]. Therefore, given the high lethality, the capacity for social disruption, increasing connectivity between endemic and rural areas, and international transport, plague should be considered a neglected threat that needs renewed attention. This article is based on the conclusions of a workshop held by international plague experts in Paris in July 2018 that had the main goal of drafting a roadmap on plague research priorities, expanding the scope and contents of a short report published elsewhere [23]. With this purpose, we summarize the current knowledge about plague and identify the priority gaps to be filled based on the exchange of knowledge and experiences during the meeting and a systematic review of published plague literature.

22.2 Which Hosts and Vectors Should Be Targeted for Human Plague Control?

It is important to point out that the true number of species implicated in plague transmission—the true composition of reservoirs and accidental hosts—is unknown [24]. Therefore, one major challenge in the management and prevention of plague is to identify the flea vector(s) and small mammal reservoir(s) in different parts of the world where plague has long been established. In most plague foci, several vectors and reservoirs are implicated in plague transmission and persistence; however, in some plague foci, there is limited research and only partial knowledge of the vector and reservoir species involved. Different vectors and hosts (including intermediary hosts between wild and domestic animals) have been described in disparate settings and ecological conditions: from equatorial forests in the DRC to arid regions in central Asia. Since the management priority has normally been to control human plague by preventing transmission to humans, researchers have tended to focus on the interface between humans and flea vectors, while the interactions between sylvatic and commensal small mammals, vectors, and landscape ecology have received relatively little attention. Factors responsible for epizootic spread (episodic amplification within and between mammal species) vary in different settings, and reservoirs may be multiple or unique. Domestic mammals and some atypical vectors could play a bridge role between wild hosts and the human environment. In the US, the increase of intermediary hosts populations of the grasshopper mouse (Onychomys leucogaster)

Which Hosts and Vectors Should Be Targeted for Human Plague Control?

is thought to enhance the connectivity between primary hosts populations of prairie dogs (Cynomys ludovicianus), the risk of plague outbreaks in the wild [25], and in turn the chances of human transmission. Although fleas have been assumed to constitute the main drivers of host-to-human transmission, this may depend on different contexts. In Madagascar, X. cheopis is predominantly found on black rats living in houses, while Synopsyllus fonquerniei is found on the fur or within burrows of black rats living outside houses but also in open biotopes and forests, where it may parasitize endemic insectivores and rodents [26], with a synergistic role on plague persistence [27]. Similar situations may be found across other plague-endemic areas, thus highlighting the diversity of hosts and vectors as a key factor for plague persistence in the wild. In fact, Y. pestis has been detected in a large number of fleas (Table 22.1) and other ectoparasites; their roles throughout plague-endemic regions of the world remain unclear. A recent study suggests that inter-human transmission through ectoparasites (Pulex irritans and Pediculus humanus) may have played a predominant role during the historical Black Death Pandemic [28]. In fact, both of these ectoparasites are capable of carrying Y. pestis [29, 30]. Furthermore, it has been pointed out that specific so-called vectors are acting as true hosts, since they are able to carry Y. pestis for weeks. Current data indicate that host and vector population structures and hotspots of plague have a crucial influence on plague epidemiology. It has been suggested that the balance between resistant hosts (those able to harbour Y. pestis with no apparent ill effect) and susceptible host (those able to tolerate Y. pestis for only short periods and eventually succumb to the effects of the bacteria) is important for plague persistence in the wild or resurgence among humans, and susceptible hosts [24]. For instance, modelling and empirical data from Madagascar indicate that rat genetic population structure (the balance between susceptible and resistant hosts) plays a crucial role [31–35] as modelled by topography (see below). This model may also be applied between different species, including multi-host and intra-host interactions. Finally, the implication of amoebae as biotic reservoirs has recently received some experimental and field-based support and therefore deserves consideration [36]. The persistence of plague in the wild or the onset of plague among humans may be related to the internal dynamics of enzootic (maintenance in the wild)/ epizootic cycles. This has been well documented in Kazakhstan, where threshold densities of great gerbils (Rhombomys opimus) and fleas are correlated with persistence of plague in the wild or resurgence of plague in humans, respectively [37, 38]. Empirical observations elsewhere corroborate this complex dynamic, which should in turn help us to understand the ability of Y. pestis to regularly emerge in areas where plague has been silent for years. A further question to explore is the capacity of Y. pestis to survive in soil or post-mortem tissues and contribute to further transmission, as a potential link to enzootic and even epizootic cycles [39, 40].

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Table 22.1 Main vectors and hosts identified in countries where human plague has been declared between 2013 and 2018* Country

Declared human plague cases 2013–2018**

Recognized main host(s)

Described vectors involved in zoonotic/ epizootic cycles

Democratic Republic of Congo [7]

410

Arvicanthis abyssinicus Mastomys natalensis Lemniscomys striatus Rattus rattus Mus minutoides

Dinopsyllus lypusus Ctenophthalmus cabirus Ctenophthalmus phyris Xenopsylla brasiliensis

Madagascar [7, 100, 108]

2,323

R. rattus R. norvegicus Suncus murinus

X. cheopis X. brasiliensis Synopsyllus fonquerniei

Uganda [7, 56]

22

X. cheopis X. brasiliensis C. cabirus D. lypusus

United Republic of Tanzania [7]

36

Arvicanthis niloticus Mastomys spp. Crocidura spp.

R. rattus M. natalensis A. abyssinicus

X. cheopis X. brasilensis D. lypusus

Bolivia [7, 98]

3

R. Rattus Graomys griseoflavus Galea musteloides

X. cheopis

Peru [98]

40

R. rattus R. norvegicus Sciurus stramineus Akodon mollis Cavia porcellus Aegialomys xanthaeolus Orzomys andinus

Polygenis litargus X. cheopis Hectopsylla spp. Tiamastus cavicola

US [25, 109]

40

Cynomys gunnisoni Cynomys ludovicianus Onychomys leucogaster Otospermophilus variegatus Otospermophilus beecheyi Callospermophilus lateralis Urocitellus beldingi Eutamias spp. Microtus californicus

Oropsylla hirsuta Oropsylla montana Poisocrostis spp. Diamanus montanus Hoplopsyllus anomaluse

China [6, 7]

5

Marmota himalayana Marmota caudata Rattus flavipectus Urocitellus undulatus Spermophilus dauricus Eothenomys miletus Apodemus chevrieri Meriones unguiculatus Microtus brandti Microtus fustus

Callopsylla dolabris Oropsylla silantiewi

Which Hosts and Vectors Should Be Targeted for Human Plague Control?

Country

Declared human plague cases 2013–2018**

Kyrgystan [6, 110]

1

Marmota baibacina Microtus gregalis Microtus carruthersi M. caudata

Callopsylla caspia O. silantiewi Citellophyllus tesquorom

Russia Federation [110]

1

Spermophilus pygmaeus Meriones meridianus Ochotona pallasi pricei U. undulatus S. dauricus Spermophilus musicus Microtus arvalis

C. caspia Neopsylla setosa Neopsylla laeviceps X. conformis Paradoxopsyllus scorodumovi

Mongolia [7, 110–113]

5

Marmota sibirica Rhombomys opimus M. unguiculatus Allactaga sibirica Cardiocranius paradoxus

O. silantiewi

Recognized main host(s)

Described vectors involved in zoonotic/ epizootic cycles

*A comprehensive review of hosts and vectors involved or suspected to be involved in plague transmission would need an entire article or chapter of a book. The aim of this table is to highlight the outstanding ability of Y. pestis to evolve in different hosts and vectors. **From: Bertherat E. Plague around the world in 2019. Weekly Epidemiol Rep. 2019;25:289–292 [8].

The understanding of the biology of host-vector-pathogen interaction and proximity to humans would aid development of specific control strategies, and to address apparently simple but not well-answered questions, such as the relative efficacy of using domestic flea control on its own or in combination with rodent control or reservoir host vaccination. Mechanisms of host and vector control more commonly involve the combined use of flea and rodent control, or community-based interventions such as encouraging behavioural changes or environmental interventions (e.g., improved hygiene and sanitation). The use of chemicals to eliminate fleas or rodents requires the surveillance of physiological resistance mechanisms in flea/rodent populations and the identification of biochemical or genetic markers of resistance as well as the characterization of mechanisms involved and their environmental and genetic determinants. Studies on X. cheopis insecticide resistance have already been carried out in different situations [41, 42]. However, information on other flea species is most often missing. Beyond insecticides, rodenticides, and the problem of resistance, innovative tools and methods for flea and rodent control must be proposed, tested, and validated in the field, including nontoxic solutions and strategies (i.e., physical methods like cellulose glue-based products for flea control). The implementation of rodent or vector control measures should also be carefully evaluated, given the complexity of rodent/vector interactions in different settings. For instance, experience in Peru has suggested that reducing the population of particular rodent species may in turn act as a risk factor for

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increasing intra- and interspecific contact rates among rodent species and with the human population, leading to a resurgence in human plague cases. Similar observations have been made with other rodent/mammal-borne diseases, such as leptospirosis [43] and bovine tuberculosis [44], whereby host control (rats and badgers, respectively) can lead to increased movement by those animals remaining, facilitating disease spread and higher disease prevalence. This strongly suggests that applying universal measures may be counterproductive in different scenarios. Besides the use of insecticides or rodenticides, there are specific human behaviours that could be modified to avoid proliferation of domestic hosts or the chances of contact with infected vectors. Some behavioural factors that favour the transmission of Y. pestis to humans are well known in on-going plague surveillance programmes. In Peru, plague cases have been correlated to human activities, e.g., domestic breeding of guinea pigs within people’s homes, the storage of harvest in unsealed conditions, and the poor hygienic living conditions of temporary workers [45]. In Madagascar, a close link exists between seasonality of human plague and the movement of rats between houses and rice fields related to the harvesting season and the use of slash-and-burn cultivation [26, 46]. During the 2004 outbreak in DRC [47], inter-human pneumonic plague transmission could be favoured by the housing conditions of gold and diamond mine workers.

22.3 What Are the Drivers of Human Plague?

Drivers of human plague include contextual factors that influence host and vector population dynamics and human behaviours that may increase the chances of exposure and infection. Such factors mainly include altitude, temperature, rainfall, biome, geography, and anthropogenic environmental changes through deforestation, agricultural expansion, cropping systems, activities and patterns, climate change, urbanisation, and the introduction of non-native invasive species. For instance, in the DRC an increase of plague was observed after the introduction of Rattus spp. into the wild combined with the introduction of new crops to replace cattle farming [47]. In Madagascar, R. rattus was introduced centuries before the arrival of Y. pestis, most probably through the Arabian trade network in the Indian Ocean, which was flourishing from the middle of the first millennium [48]. Climate is considered to have a major impact on plague incidence [49]. Rainfall and temperature have correlated with the increase or decrease of gerbils or vector populations in Kazakhstan [37], and rainfall was linked to human plague occurrence in Uganda [50], which, in turn, strongly influences the abundance of small mammals [51], thus increasing the probability of epizootic cycles. Studies in the US have shown relationships with El Niño episodes and the effects of rainfall on host populations and temperature on vector survival [52, 53]. Similarly, El Niño episodes have been considered an early warning in Peru [45] and Madagascar [54]. Temperature anomalies and altitude

Which New Diagnostic Tools for Plague Are Needed?

have been related to plague occurrence in Madagascar [55]. Temperature and humidity influence the differential development and survival of the 2 main flea vectors (X. cheopis and S. fonquerniei) involved in plague transmission in Madagascar (where plague shows a clear seasonal pattern) [27]. There is a clear interplay between plague occurrence, human behaviours, climate, and landscape in a number of contexts [56–59]. Data from Madagascar suggest that geography shaped the genetic diversity of hosts (rats) and Y. pestis as a result of the relative population isolation, which may play an important role for Y. pestis persistence, through the reintroduction of new strains of hosts and/or the pathogen between different areas [60–62]. Furthermore, geography and landscape may influence the risk and directionality of plague spread through their hosts [63]. The confirmation of the drivers of plague should help to better map the risk of plague, to improve surveillance, preparedness, and focused interventions. The connection between rural and urban areas is a major concern to avoid urban outbreaks. The reduction or discontinuation of surveillance programmes and the exacerbation of poverty and poor sanitation are important factors in the emergence of human cases. The 2017 outbreak in Madagascar raises the question of whether specific determinants exist for bubonic or pneumonic outbreaks: climatic oscillations with earlier seasonal slash-and-burn farming practices, a lack of vigilance from the health authorities leading to the absence of efficient communication to the population and lower healthcare standards and surveillance systems. However, only human population density has been consistently related to the risks of human to human transmission of pneumonic plague. No specific strains of Y. pestis have been associated with particular virulence factors or tropism for pneumonic forms.

22.4 Which New Diagnostic Tools for Plague Are Needed?

The WHO gold standard for plague diagnosis remains the bacterial culture. However, culture is not feasible in most of the settings in which plague occurs as proper handling of samples is sometimes difficult, and the results are not immediately available (minimum 4 days). In addition, isolation of Y. pestis is hampered by administration of antibiotics prior to sampling. Therefore, case management is essentially based upon symptomatic diagnosis, which is specific to endemic areas with bubonic forms. Pneumonic cases pose particular diagnostic challenges, since it is rarely suspected during the initial stages of an outbreak. Initial symptoms of pneumonic plague without an evident bubo (lymph node swelling) are nonspecific. Collection of high-quality sputum from suspected pneumonic plague patients is particularly difficult to obtain, more so than aspirates of bubo, and even if good quality sputum is obtained, the processing stage of this material, polymicrobial, and complex specimen is technically problematic. As a result, the yield from sputum examination and cultivation is very low.

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The rapid diagnostic test (RDT) as point of care (POC) strategy was implemented in Madagascar in 2002 in primary care facilities based in the “endemic districts” together with information provided to community health workers. This RDT has been widely used and improved the specificity of diagnosis, resulting in a drop in the number of suspected cases of bubonic plague [64]. The RDT is based on monoclonal antibodies against the F1 capsular antigen of Y. pestis. F1 detection in samples collected directly from bubonic aspirate are processed and interpreted by the healthcare workers. The combination of bacteriological methods and F1 ELISA have positive and negative predictive values of 90.6% and 86.7%, respectively [65]. However, this RDT was evaluated during the Madagascar pneumonic plague outbreak of 2017, and the sensitivity and specificity for sputum samples was limited; further analysis is on-going. Conventional PCR targeting pla [66, 67] and caf1 genes [68, 69] has also been used for investigations in the past. In the context of the Madagascar outbreak, a new algorithm based on real time (targeting pla, caf1 genes) and conventional PCR (targeting inv1100, caf1 and pla) has been implemented to ensure a rapid and reliable diagnostic [10]. Serology based on anti-F1 capsular antigen-specific immunoglobulins (IgG/IgM) [70] is used for prevalence surveys among humans, mammals, and post-outbreak investigations, but it may have questionable value in pneumonic plague. Therefore, rapid and reliable diagnostic tests are necessary for both bubonic and pneumonic plague for an efficient outbreak response. Different plague diagnostic options have been proposed, from improvement of culture methods to appropriate molecular methodologies. Proposed methods are summarized in Box 22.1. Box 22.1 Proposed improved plague diagnostic methods

1. Improve the yield of Y. pestis culture (i) Better sample collection, transport, and processing (homogenization) (ii) Developing more selective media for cultivation from sputum and bubo aspirates 2. To improve the sensitivity and specificity of RDT (i) More adequate protocols for more efficient F1 antigen release (ii) To develop a second generation of RDT through the inclusion of additional antigens 3. New nucleic acid–based tools (i) Quantification of DNA in sputum samples (ii) PCR-based detection in blood (iii) PCR for saliva

4. Upgrade serological testing methods through the inclusion of more antigens and/or alternative to current ELISA methods In addition, especially for pneumonic plague, capabilities for differential diagnostic should be improved, and the use of new techniques such as metagenomics in sputum could help to estimate the true magnitude of an outbreak. The use of these diagnostic tools among animals (domestic or wild) as sentinel surveillance mechanisms should also be considered.

How Can Plague Surveillance and Case Management Be Improved?

22.5 How Can Plague Surveillance and Case Management Be Improved? Surveillance of plague is of utmost importance in endemic regions, first for early case detection and treatment (which is related to favourable outcome); second, for early warning of human plague occurrence or outbreaks; and, third, for more accurate quantification of the disease burden and geographical distribution. Most human plague cases are currently occurring in underpopulated and remote areas with particularly difficult access. True case numbers of human plague may be underestimated in some areas, especially in areas with weak health systems and unstable social and political situations, like in the DRC. Therefore, the accuracy of current surveillance systems for human plague cases is still uncertain, to which is added the existence of pauci-symptomatic or asymptomatic cases suggested by some sero-surveillance studies [71]. However, plague surveillance should be integrated into regular surveillance systems of common diseases in impoverished regions, and therefore general improvement of the Health Information System is needed, since plague is not usually the leading health issue. Plague surveillance should integrate human, climate, and animal surveillances as well as a combination of passive or active case detection depending on the scenarios, the season, or the early warning signs. A central question regarding surveillance is identifying which reservoir hosts and vectors are the most appropriate for sentinel surveillance. Once again, the answer may be different in different settings. Classical pre-outbreak deaths among hosts (rats or other rodents), which typically indicates an explosive spread of an epizootic cycle, is not systematically observed and may even be an exception perpetuated by classical accounts [72]. Experiences in Peru pointed out the utility of some domestic animals, like dogs or cats, as sentinel indicators of an increased risk of infection. Some studies found a predictive plague occurrence value of seroprevalence among certain mammals [73, 74]. Cases may arise in a community with a high degree of stigma linked to plague and therefore be hidden from local authorities. Changing deeply rooted cultural behaviours may be difficult and increase the risk of outbreak by delaying health care-seeking behaviours and making access to healthcare centres difficult. Community-based surveillance systems by community health workers or networks of community-based key informants have been used to increase awareness and as early warning systems for different diseases, including plague in Madagascar. As with other infectious diseases, when cases arise in areas or countries where plague is not usually reported or with uncommon clinical forms (i.e., pneumonic plague), a lack of suspicion may lead to a critical delay in notification, as it was reported during the early stages of the 2017 outbreak in Madagascar. Early detection, diagnosis, and treatment are crucial for a favourable outcome and depend on an efficient surveillance system. A number of treatment protocols have been used to treat plague (Table 22.2). However, only a small randomized trial including gentamycin and doxycycline has been conducted [75].

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Antibiotic

Dose

Duration

Remarks

Way of administration

Streptomycin

15 mg/kg/dose

7 days

Maximum 1 g per day

IM

Doxycycline

100 mg/12 h

7 to 10 days Initially a loading dose of 200 mg/12 h the first day

Gentamycin

Tetracycline

Levofloxacin

Ciprofloxacin

5 mg/kg/d 2 g/d

7 days

Once daily

7 to 10 days Initially loading dose of 2 g

500 to 750 mg daily 7 to 10 days Once-daily special indication for high tissue penetration 400 mg/12 h

7 to 10 days

Chloramphenicol

50 to 100 mg/ kg/d

10 days

Indicated in cases for high tissue penetration

Cotrimoxazole

1 g/4–6 h

10 days

Not indicated as first choice

Approved combinations

Cotrimoxazole Ciprofloxacin

IV or PO IV or PO

PO

IV or PO PO

See above

Gentamycin + levofloxacin Doxycycline

Initial loading dose of 2–4 g

IV or PO

See above

Gentamycin + doxycycline Postexposure prophylaxis

Dose may be reduced to 25 to 30 mg/kg/d depending on clinical response to reduce the risk of bone marrow suppression

IM or IV

100 mg/12 h

ND

500 mg/12 h

PO

Modified from: Mead PS. Yersinia species (including plague). In: Mandell, Douglas, and Bennett’s. Principles and Practice of Infectious Diseases, 8th ed; 2015. pp. 2607–2618, Elsevier Health, USA [2].

Abbreviations: CDC, Centers for Disease Control and Prevention; IM, intramuscular; IV, intravenous; ND, not defined; PO, by mouth.

Human Plague

Table 22.2 Current treatment protocol (WHO and CDC recommendations)*

How Can Plague Surveillance and Case Management Be Improved?

Different therapeutic choices have been based on empirical observations or animal models. The most widely recommended treatment is a high dose of streptomycin, resulting in effective clinical responses in both bubonic and pneumonic plague [1, 2]. However, streptomycin has side effects such as deafness and renal toxicity, and the administration to children and pregnant women is problematic. The administration protocol (intramuscular) remains a logistical and material challenge in resource-poor settings, as well as in the context of an outbreak. In addition, streptomycin is often not available as it is no longer used as a firstline treatment of tuberculosis. As shown during the recent plague outbreak in Madagascar, the lack of specific clinical and laboratory diagnostics for pneumonic plague was responsible for an important number of misdiagnoses leading to an overestimate of the magnitude of the outbreak [10] and, consequently, inadequate treatment of severe community-acquired respiratory infections with streptomycin. The situation was worsened by the lack of resources to carry out rapid and specific exclusion diagnostics for other pathogens. During an investigation of pneumonic plague in Ituri, DRC, it was found that leptospirosis cases were included as suspected plague cases [76]. On the other hand, healthcare providers should be trained to provide chemoprophylaxis to decrease secondary cases and should be equipped to avoid nosocomial infections. These experiences indicate that validation of therapeutic alternatives are needed. A number of different alternatives to streptomycin have been empirically suggested (see Table 22.2). Clinical trials need to be set up during outbreaks to evaluate these therapeutic alternatives. New guidelines from the Ministry of Public Health of Madagascar are now recommending the combination of levofloxacin and gentamycin for hospitalized pulmonary cases. They also give the possibility to switch to other treatments after the acute phase (switch to ciprofloxacin after the early phase of streptomycin treated cases). Gentamycin, fluoroquinolones, chloramphenicol, doxycycline, and trimethoprim-sulfamethoxazole are different alternatives used in different settings, which generally result in good clinical outcomes [1, 7, 77]. A well-established first-line treatment for suspected pneumonic plague needs to be established that is affordable, is orally administered, has few side effects, is continuously available, and has a wide spectrum of activity to target other community-acquired respiratory diseases. The possibility of Y. pestis to exchange resistant coding plasmids with other members of the Enterobacteriaceae family, which can easily carry almost any sort of drug resistance, is a serious threat [78]. Y. pestis has been shown to be sensitive to antibiotics in strains isolated from humans; however, 3 unrelated strains have been isolated from hosts and vectors in Madagascar which carried plasmid transmitted resistance to streptomycin and other drugs [79, 80]. The extreme virulence of Y. pestis further stresses the need to set up reliable monitoring of susceptibility to treatment.

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22.6 What Are the Gaps in Knowledge about Y. pestis Biology? Although much is known about specific virulence factors of Y. pestis [81, 82], further technical development regarding specific prevention or therapeutic tools or drugs has not occurred, which would require a deeper understanding of the underlying biology of Y. pestis. For instance, current data indicate that the degree of blockage of the biofilm in the flea’s gut is vector specific and is responsible for the biofilm-dependent transmission efficiency of infected vectors—or the ability to carry Y. pestis over days or weeks—and a key step in Y. pestis transmissibility [83, 84]. Drugs targeting the biofilm formation in the guts of flea vectors could be a promising strategy. One interesting avenue of research is genetic expression studies of Y. pestis, which may help to understand pathogen-vector-host interactions and their persistence [85]. An example of this approach is the identification of 10 conserved genes that lead Y. pestis to survive in soil and post-mortem tissues. This suggests that this pathway plays a relevant role in Y. pestis persistence [86]. In fact, the study of the interplay of pathogenicity and evolution has given important insights about Y. pestis biology. The combination of gene acquisition (through horizontal exchange) and loss in natural populations of Y. pestis explains their outstanding adaptability to different hosts, vectors, and environments [6, 87]. The high virulence of Y. pestis could be a result of an adaptive response to generate particularly high levels of bacteraemia in order to ensure transmissibility between hosts and vectors [88]. With respect to hosts and vectors, an interesting area to explore is to ascertain the selective process that plague has exerted upon mammal populations [89], including humans [90]. The selection of resistant hosts or intermediate-susceptible hosts in a suitable region is a mechanism of establishment and long-term persistence of Y. pestis. In turn, this may provide insights on the pathophysiology of plague, through the determination of specific alleles or genotypes, selected by natural pressure of plague which may confer natural resistance. The natural cellular immune response to human pulmonary cases during acute, early, and convalescent phases of the infection in particular is not well understood. Mice models suggest a local immunosuppression effect that may explain the lack of immune response in early stages and the rapid progression of this clinical form [91]. The practical results of this research could be specific drugs that may prevent Y. pestis spread in the wild or humans, new antibiotics or better shaped control, and prevention measures based on the knowledge of their intimate biological cycle, e.g., the development of more adequate therapies or prophylaxis strategies through the development of vaccines. In this sense, vaccines against Y. pestis have been developed since the early identification of the pathogen. A live attenuated vaccine (EV76) was developed and used in Madagascar and introduced in Vietnam, Indonesia, and in the former USSR [92]. Due to the significant side effects and the need of revaccination, its use was abandoned once

Discussion

the number of cases fell. Its Russian EV-NIIEG derivative is still used in Asia and Russia [93]. A number of other candidates are under investigation, including whole-cell–based or subunit-based DNA vaccines—attenuated or with live carriers—combining different recombinant antigens and with molecular adjuvants [94]. To date, no phase III clinical trial has been carried out. However, rF1V and SV1 vaccines successfully passed phase II trials; WHO recently provided guidance for phase III evaluation in the field [95]. Given the current extent of human plague, a human vaccine candidate should be effective for immediate distribution in an outbreak context and be effective against pulmonary forms. One promising strategy could be reservoir host vaccination campaigns particularly as vaccination of reservoir hosts is a tried and tested strategy for many zoonotic diseases such as wild foxes for rabies in Europe [96] and livestock for leptospirosis in New Zealand [97]. To sum up, for human plague control, 3 operational targets have been defined for Y. pestis biology research: (1) identify molecular targets susceptible for drug development in addition to the currently used antibiotics, (2) improve our understanding of the underlying molecular mechanisms of Y. pestis ability to spread and persist over centuries in different eco-epidemiological settings, and (3) improve our understanding of the immune response to plague that may pave the way for vaccine development and better diagnostic tools.

22.7 Discussion

The horrifying impact of plague during the 14th century is still hard to forget as it is reflected in European folklore, language, and literature. The 2017 Madagascar outbreak should be taken as a serious warning and confirms that plague remains a human threat. However, given this fatal past background and recognized virulence, it is striking to realize that plague is mostly seen as an historical curiosity. It is alarming, indeed, that the most basic tools for control and prevention of plague, such as curative and preventive treatments and vaccines, are still pending adequate validation. This is not related to the lack of robust candidates but to the fact that plague has been a neglected disease during recent decades. Peculiarly, recent plague vaccine development and its funding has long been motivated more by the desire to fight plague as a potential bioterrorism weapon rather than as a public health problem in endemic countries. This is in stark contrast with the accelerated development of Ebola and SARS-CoV-2 vaccines. Nevertheless, given the diversity of hosts and vectors involved in different parts of the world where plague is endemic, context-specific research is still lacking, particularly in plague-affected low- and middle-income countries. The contrasts in ecological dynamics of vectors and reservoir hosts involved in different locations where plague circulates are remarkable. For instance, in Madagascar, R. rattus has been recognized as the only relevant host during regular and sustained zoonotic and epizootic cycles, whereas in South America, a surprisingly large range of small mammals has been described to carry

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Y. pestis [98]. It is clear that the knowledge of the dynamics of enzootic and epizootic cycles remains fragmented and poorly understood. Indeed, while animal–human transmission is usually investigated following emergence episodes, it is most important to understand the biological cycle of Y. pestis in the environment in between 2 successive outbreaks. The seasonality of plague in Madagascar makes this environmental setup well-adapted to conduct such research programs. Plague foci contexts may be rather different between those where the disease occurs or has until recently occurred in a regular and rather predictable pattern (Madagascar, DRC, Peru, Kazakhstan, etc.) or where it is a mostly incidental phenomenon (US, China, Libya, Zambia, Algeria). Furthermore, there are scenarios in which sporadic cases emerge from a contact within a current epizootic focus, or those of a major outbreaks or epidemics. Indeed, plague has a natural tendency to progress, disperse, and eventually, to expand to other regions, as it was observed during the 1990s in Madagascar when plague was established in the city harbour of Mahajanga [99]. This urban focus was sustained by the Asian house shrew (Suncus murinus) but transmitted to humans through the black rat (R. rattus), which exemplifies the surprising adaptability of Y. pestis [100]. In this sense, it is important to note the nature of Y. pestis as a “generalist” pathogen, demonstrated by its ability to evolve across different environments, vectors, and hosts, facilitating its capacity to move from one region, context, or scenario to another, or even the emergence or re-emergence in previously free areas or where human plague has not been observed for decades, as it has been already observed in North and East Africa [101–103]. The apparent unpredictability of plague re-occurrence and emergence highlights how much about the eco-epidemiology of plague remains poorly understood, and this should be a matter of great concern for public health officials. Studies during on-going outbreaks may provide important insights about the epidemic dynamics of human plague. More context-specific research should be framed in a translational strategy in order to produce appropriate tools, devices, and policies. Translational research should be considered in their 2 widely used meanings: the “bench-to-bedside” process—which involves applying knowledge from basic sciences to produce new medicines, diagnostic tools, and treatment options for patients—and from a public health perspective whereby work focuses on healthcare delivery systems to improve health services research and to have a primary outcome of new policies and practices. In the case of plague, new drugs, vaccines, and diagnostic devices are clearly needed. Furthermore, more research on the surveillance, ecology, and human behavioural interventions is urgently required. Special efforts should be made to obtain community engagement towards the public health response. To this end, social scientists and anthropologists can provide invaluable insights that can mitigate distrust, increase cooperation, and improve community communication. This cross-cutting research should be linked to the paradigm of One Health (Fig. 22.2), which is grounded in the recognition that human, animal, and

Discussion

environmental health are interdependent. Human plague is a good example of this interdependence where improving human living conditions in plague-endemic areas through environmental and behavioural interventions might be the most effective way to avoid human plague cases as well as reduce environmental degradation to improve ecosystem resilience. The Ebola crisis in West Africa in 2013–2015 shows the capacity of deadly diseases, like human plague, to disrupt societies and health systems [104, 105], as we are experiencing with the current COVID-19 pandemic. Growing urbanization, environmental/climate change, and population mobility increase the chances of such phenomena. In summary, we ascertained the main axes of research that should be prioritized for plague prevention and control: (i) an improved understanding of the ecological interactions among the reservoir, vector, pathogen, and environment; (ii) human and societal responses; (iii) improved diagnostic tools and case management; and (iv) vaccine development.

Figure 22.2 “One Health” integrative approach. Abbreviation: KAP, Knowledge, Attitudes and Practices. Adapted from UC Davis.

Accordingly, as a final point highlighted from the Paris workshop, plague research needs an inter- and transdisciplinary approach that involves molecular biologists, immunologists, clinicians, epidemiologists, public health specialists, veterinarians, zoologists, entomologists, mathematical modelling specialists, ecologists, anthropologists, and social scientists. Even in emergency situations,

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the involvement of anthropologists and social scientists has been considered increasingly relevant [106, 107]. An example of such a cross-cutting research could be the identification of reservoir hosts to be targeted for the development and implementation of wildlife vaccination programmes. As was suggested during the workshop, to make human plague history has 2 complementary meanings: a history with a happy end or to make plague a relic of the past.

Abbreviations CDC: COVID-19: DRC: ELISA: KAP: PCR: POC: RDT: WHO:

US Centers for Disease Control and Prevention coronavirus disease 2019 Democratic Republic of Congo

enzyme-linked immunosorbent assay

knowledge, attitudes and practices

polymerase chain reaction

point of care rapid diagnostic test World Health Organization

Disclosures and Conflict of Interest

This chapter was originally published as: Vallès, X., Stenseth, N. C., Demeure, C., Horby, P., Mead, P. S., Cabanillas, O., et al. (2020). Human plague: An old scourge that needs new answers. PLoS Negl. Trop. Dis., 14(8), e0008251, https://doi.

org/10.1371/journal.pntd.0008251, under the Creative Commons Attribution

license (http://creativecommons.org/licenses/by/4.0/), and appears here, with

edits and updates, by kind permission of the copyright holders.

X.V., N.C.S., and L.B. contributed equally to this work.

Acknowledgments: List of the other participants to the Plague Workshop (by

alphabetical order): Fabrice Biot, Institut de Recherche Biomédicale des Armées,

France; Carine Brouat, IRD, France; Simon Cauchemez, Infectious Diseases

Mathematical Modelling Unit, Institut Pasteur, Paris, France; Rob Cohen, USAID,

Washington, DC, USA; Koussay Dellagy, Department of International Affairs, Institut Pasteur, Paris, France; Nathalie Denoyes, Institut Pasteur, Paris, France; Hebert Echenique-Rivera, Yersinia Unit, Institut Pasteur, Paris, France; Florence Fouque, TDR, WHO, Lyon, France; Stephen Francesconi, Defence Threat Reduction Agency, Washington, DC, USA; Anna Funk, Emerging Diseases Epidemiology Unit, Institut Pasteur, Paris, France; Finnian Hanrahan, DG Research, European Commission, Brussels, Belgium; Mireille Harimalala, Medical Entomology Unit, Institut Pasteur de Madagascar, Antananarivo, Madagascar; Nadia Khellef, Institut Pasteur, Paris, France; Anne-Sophie Le Guern, Yersinia Unit, Institut Pasteur, Paris, France; Nadine Lemaitre, Bacteriology Department, CHU Lille, France; Jean-Claude Manuguerra, CIBU, Institut Pasteur, Paris, France; Jodie Mac Vernon, GLoPID-R, University of Melbourne, Australia; Serge Morand, CIRAD, Bangkok, Thailand;

References

Birgit Nikolay, Infectious Diseases Mathematical Modelling Unit, Institut Pasteur, Paris, France; Juliette Paireau, Infectious Diseases Mathematical Modelling Unit, Institut Pasteur, Paris, France; Anna Paoletti, Ministry for Higher Education and Research, Paris, France; Feno MJ Rakotoarimanana, Epidemiology and Clinical Research Unit, Institut Pasteur de Madagascar, Antananarivo, Madagascar; Zely Randriamanatany, Ministry of Public Health, Antananarivo, Madagascar; Laurent Raskine, Fondation Mérieux; Stéphanie Simon, CEA de Saclay, France; Cathy Roth, Department for International Development, London, UK; Alex Salam, Epidemic Diseases Research Group Oxford (ERGO), Nuffield Department of Medicine, University of Oxford, Oxford, UK; Florent Sebbane, Institut Pasteur de Lille, France; Christophe Shako, Department of Disease Control, Ministry of Health, Democratic Republic of Congo; Quirine Ten Bosch, Epidemiology and Clinical Research Unit, Institut Pasteur de Madagascar, Antananarivo, Madagascar; Kathleen Victoir, Department of International Affairs, Institut Pasteur, Paris, France.

References

1. Pollitzer R. Plague studies. VIII. Clinical aspects. Bull World Health Organ. 1953; 9(1): 59–129.

2. Mead PS. Yersinia species (including plague). In: Mandell, Douglas, and Bennett’s Principles and Practice of Infectious Diseases, 8th ed.; 2014. pp. 2607–2618, Elsevier Health, USA. 3. Wu L., League of Nations. A Treatise on Pneumonic Plague. 1926. Berger-Levrault, Paris.

4. Stenseth NC, Atshabar BB, Begon M Belmain SR, Bertherat E, Carniel E, et al. Plague: past, present, and future. PLoS Med. 2008; 5(1):e3.

5. Bramanti B, Stenseth NC, Walløe L, Lei X. Plague: A disease which changed the path of human civilization. Adv Exp Med Biol. 2016; 918:1–26.

6. Zhou D, Han Y, Song Y, Tong Z, Wang J, Guo Z et al. DNA microarray analysis of genome dynamics in Yersinia pestis: insights into bacterial genome microevolution and niche adaptation. J Bacteriol. 2004; 186:5138–5146. 7. Denis DT, Gage KL, Gratz N, Poland JD, Tikhomirov E. Plague manual: epidemiology, distribution, surveillance and control, WHO 1999. Available at: http://www.who.int/ csr/resources/publications/plague/whocdscsredc992a.pdf?ua=1 (accessed on May 15, 2021).

8. Bertherat E. Plague around the world in 2019. Weekly Epidemiol Rep. 2019; 25:289–292. 9. Balakhonov SV, Popova AY, Mishchenko AI, Mikhailov EP, Ezhlova EB, Demina YV et al. A case of human infection with plague in the Kosh-Agach region of the Republic of Altai in 2015. Communication 1. Clinical-epidemiological and epizootiological aspects. Problemy Osobo Opasnykh Infektsii. 2016; 1:55–60.

10. Randremanana R, Andrianaivoarimanana V, Nikolay B, Ramasindrazana B, Paireau J, Ten Bosch QA et al. Epidemiological characteristics of an urban plague epidemic in Madagascar, August-November, 2017: an outbreak report. Lancet Infect Dis. 2019; 19(5):P537–545.

475

476

Human Plague

11. Morelli G, Song Y, Mazzoni CJ, Eppinger M, Roumagnac P, Wagner DM et al. Yersinia pestis genome sequencing identifies patterns of global phylogenetic diversity. Nat Genet. 2010; 42:1140–3.

12. Gani R, Leach S. Epidemiologic determinants for modeling pneumonic plague outbreaks. Emerg Infect Dis. 2004; 10:608–614.

13. Hinckley AF, Biggerstaff BJ, Griffith KS, Mead PS. Transmission dynamics of primary pneumonic plague in the USA. Epidemiol Infect. 2012; 140:554–560. 14. Ramasindrazana B, Andrianaivoarimanana V, Rakotondramanga JM, Birdsell DN, Ratsitorahina M, Rajerison M. Pneumonic plague transmission, Moramanga, Madagascar, 2015. Emerg Infect Dis. 2015; 23:521–524.

15. Richard V, Riehm JM, Herindrainy P, Soanandrasana R, Ratsitoharina M, Rakotomanana F et al. Pneumonic plague outbreak, Northern Madagascar, 2011. Emerg Infect Dis. 2015; 21(1):8–15. 16. Gupta ML, Sharma A. Pneumonic plague, northern India, 2002. Emerg Infect Dis. 2007; 13:664–666. 17. Bertherat E, Lamine KM, Formenty P, Thuier P, Mondonge V, Mitifu A, Rahalison L. Major pulmonary plague outbreak in a mining camp in the Democratic Republic of Congo: brutal awakening of an old scourge. Méd Trop Rev Corps Santé Colon. 2005; 65:511–514.

18. Mondonge V, Shako-Lomami JC, Mossoko GM, Kabengele-Obel B, Lobho R, KambaleKapund L. Investigation report and support for epidemic of pneumonic plague in HZs of Linga and Rethy. Administrative district of Ituri, Orientale Province, Democratic Republic of the Congo. 2006.

19. Begier EM, Asiki G, Anywaine Z, Yockey B, Schriefer ME, Aleti P et al. Pneumonic plague cluster, Uganda, 2004. Emerg Infect Dis. 2006; 12:460–467.

20. Donaires LF, Céspedes M, Valencia P, Salas JC, Luna ME, Castañeda A et al. Primary pneumonic plague with nosocomial transmission in La Libertad, Peru 2010. Rev Peru Med Exp Salud Publica. 2010; 27:326–336. 21. Luo H, Dong X, Li F, Xie X, Song Z, Shao Z et al. Cluster of primary pneumonic plague transmitted in a truck cab in a new enzootic focus in China. Am J Trop Med Hyg. 2013; 88:923–928.

22. Gamsa M. The epidemic of pneumonic plague in Manchuria 1910–1911. Past Present. 2006; 190:147–184.

23. Baril L, Vallès X, Stenseth NC, Rajerison M, Ratsitorahina M, Pizarro-Cerdá J et al. Can we make human plague history? A call to action. BMJ Glob Health.2019; 4:e001984.

24. Kl Gage, Kosoy MY. Natural history of plague: perspectives from more than a century of research. Annu Rev Entomol.2005; 50:505–528.

25. Salkeld DJ, Salathé M, Stapp P, Jones JH. Plague outbreaks in prairie dog populations explained by percolation thresholds of alternate host abundance. Proc Natl Acad Sci U S A. 2010; 107:14247–14250.

26. Andrianaivoarimanana V, Kreppel K, Elissa N, Duplantier JM, Carniel E, Rajerison M et al. Understanding the persistence of plague foci in Madagascar. PLoS Negl Trop Dis. 2013; 7:e2382.

27. Kreppel KS, Telfer S, Rajerison M, Morse A, Baylis M. Effect of temperature and relative humidity on the development times and survival of Synopsyllus fonquerniei and

References

Xenopsylla cheopis, the flea vectors of plague in Madagascar. Parasites Vectors. 2016; 9:82.

28. Dean KR, F. Krauer F, Walløe L, Lingjærde OC, Bramanti B, Stenseth NC et al. Human ectoparasites and the spread of plague in Europe during the Second Pandemic. Proc Natl Acad Sci U S A. 2018; 115:1304–1309.

29. Piarroux R, Abedi AA, Shako JC, Kebela B, Karhemere S, Diatta G et al. Plague epidemics and lice, Democratic Republic of the Congo. Emerg Infect Dis. 2013; 19:505–506. 30. Ratovonjato J, Rajerison M, Rahelinirina S, Boyer S. Yersinia pestis in Pulex irritans fleas during plague outbreak, Madagascar. Emerg. Infect. 2014; 20:1414–1415.

31. Rahalison L, Ranjalahy M, Duplantier JM, Duchemin JB, Ravelosaona J, Ratsifasoamanana L et al. Susceptibility to plague of the rodents in Antananarivo, Madagascar. Adv Exp Med Biol. 2003; 529:439–42.

32. Tollenaere C, Rahalison L, Ranjalahy M, Duplantier JM, Rahelinirina S, Telfer S et al. Susceptibility to Yersinia pestis experimental infection in wild Rattus rattus, reservoir of plague in Madagascar. Ecohealth. 2010; 7:242–247. 33. Gilabert A, Loiseau A, Duplantier JM, Rahelinirina S, Rahalison L, Chanteau S et al. Genetic structure of black rat populations in a rural plague focus in Madagascar. Can J Zool. 2007; 85:965–972. 34. Gascuel F, Choisy M, Duplantier JM, Débarre F, Brouat C. Host resistance, population structure and the long-term persistence of bubonic plague: contributions of a modelling approach in the Malagasy focus. PLoS Comput Biol. 2013; 9:e1003039.

35. Andrianaivoarimanana V, Telfer S, Rajerison M, Ranjalahy MA, Andriamiarimanana F, Rahaingosoamamitiana C et al. Immune responses to plague infection in wild Rattus rattus, in Madagascar: a role in foci persistence? PLoS ONE. 2012; 7:e38630.

36. Markman DW, Antolin MF, Bowen RA, Wheat WH, Woods M, Gonzalez-Juarrero M et al. Yersinia pestis survival and replication in potential ameba reservoir. Emerg Infect Dis. 2018; 24:294–302.

37. Schmid BV, Jesse M, Wilschut LI, Viljugrein H, Heesterbeek JA. Local persistence and extinction of plague in a metapopulation of great gerbil burrows, Kazakhstan. Epidemics. 2012; 4:211–218.

38. Samia NI, Kausrud KL, Heesterbeek H, Ageyev V, Begon M, Chan KS et al. Dynamics of the plague-wildlife-human system in Central Asia are controlled by two epidemiological thresholds. Proc Natl Acad Sci U S A. 2011; 108:14527–14532.

39. Ayyadurai S, Houhamdi L, Lepidi H, Nappez C, Raoult D, Drancourt M. Long-term persistence of virulent Yersinia pestis in soil. Microbiology. 2008; 9:2865–2871. 40. Eisen RJ, Petersen JM, Higgins CL, Wong D, Levy CE, Mead PS et al. Persistence of Yersinia pestis in soil under natural conditions. Emerg Infect Dis. 2008; 14:941–943.

41. Miarinjara A, Boyer S. Current perspectives on plague vector control in Madagascar: susceptibility status of Xenopsylla cheopis to 12 insecticides. PLoS Negl Trop Dis. 2016; 10:e0004414.

42. Miarinjara A, Vergain J, Kavaruganda JM, Rajerison M, Boyer S. Plague risk in vulnerable community: assessment of Xenopsylla cheopis susceptibility to insecticides in Malagasy prisons. Infect Dis Poverty. 2017; 6:141. 43. Lee MJ, Byers KA, Donovan CM, Bidulka JJ, Stephen C, Patrick DM et al. Effects of culling on Leptospira interrogans carriage by rats. Emerg Infect Dis. 2018; 24:356–360.

477

478

Human Plague

44. Bielby J, Vial F, Woodroffe R, Donnelly CA. Localised badger culling increases risk of herd breakdown on nearby, not focal, land. PLoS ONE. 2016; 11:e0164618.

45. Rivière-Cinnamond A, Santandreu A, Luján A, Mertens F, Espinoza JO, Carpio Y et al. Identifying the social and environmental determinants of plague endemicity in Peru: insights from a case study in Ascope, La Libertad. BMC Public Health. 2018; 18:220.

46. Rahelinirina S, Duplantier JM, Ratovonjato J, Ramilijaona O, Ratsimba M, Rahalison L. Study on the movement of Rattus rattus and evaluation of the plague dispersion in Madagascar. Vector Borne Zoonotic Dis. 2010; 10:77–84.

47. Abedi AA, Shako JC, Gaudart J, Sudre B, Ilunga BK, Shamamba SKB et al. Ecologic features of plague outbreak areas, Democratic Republic of the Congo. Emerg Infect Dis. 2018; 24:210–220. 48. Brouat C, Tollenaere C, Estoup A, Loiseau A, Sommer S, Soanandrasana R et al. Invasion genetics of a human commensal rodent: the black rat Rattus rattus in Madagascar. Mol Ecol. 2014; 23:4153–4167.

49. Ben Ari MT, Neerinckx S, K. L. Gage KL, Kreppel K, Laudisoit A, Leirs H et al. Plague and climate: scales matter. PLoS Pathog. 2011; 7:e1002160.

50. MacMillan K, Monaghan AJK, Apangu T, Griffith KS, Mead PS, Acayo S et al. Climate predictors of the spatial distribution of human plague cases in the West Nile region of Uganda. Am J Trop Med Hyg. 2012; 86:514–523.

51. Moore SM, Monaghan A, Borchert JN, Mpanga JT, Atiku LA, Boegler KA et al. Seasonal fluctuations of small mammal and flea communities in a Ugandan plague focus: evidence to implicate Arvicanthis niloticus and Crocidura spp. as key hosts in Yersinia pestis transmission. Parasit Vectors. 2015; 8:11.

52. Ari TB, Gershunov A, Tristan R, Cazelles B, Gage K, Stenseth NC. Interannual variability of human plague occurrence in the western United States explained by tropical and North Pacific Ocean climate variability. Am J Trop Med Hyg. 2010; 83:624–632.

53. Enscore RE, Biggerstaff BJ, Brown TL, Fulgham RE, Reynolds PJ, Engelthaler DM et al. Modelling relationships between climate and the frequency of human plague cases in the south-western United States, 1960–1997. Am J Trop Med Hyg. 2002; 66:186–196.

54. Kreppel KS, Caminade C, Telfer S, Rajerison M, Rahalison L, Morse A et al. A nonstationary relationship between global climate phenomena and human plague incidence in Madagascar. PLoS Negl Trop Dis. 2014; 8:e3155.

55. Giorgi E, Kreppel K, Diggle PJ, Caminade C, Ratsitorahina M, Rajerison M et al. Modelling of spatio-temporal variation in plague incidence in Madagascar from 1980 to 2007. Spat Spatiotemporal Epidemiol. 2016; 19:125–135. 56. MacMillan K, Enscore RE, Ogen-Odoi A, Borchert JN, Babi N, Amatre G et al. Landscape and residential variables associated with plague-endemic villages in the West Nile region of Uganda. Am J Trop Med Hyg. 2011; 84:435–442. 57. Eisen RJ, Griffith KS, Borchert JN, MacMillan K, Apangu T, Owor N et al. Assessing human risk of exposure to plague bacteria in north-western Uganda based on remotely sensed predictors. Am. J Trop Med Hyg. 2010; 82:904–911.

58. Goel S, Kaur H, Gupta AK, Chauhan U, Singh A. Socio-epidemiological determinants of 2002 plague outbreak in Himachal Pradesh, India: a qualitative study. BMC Public Health. 2002; 14:325.

References

59. Hieronimo P, Kimaro DN, Kihupi NI, Gulinck H, Mulungu LS, Msanya BM et al. Land use determinants of small mammal abundance and distribution in a plague endemic area of Lushoto District, Tanzania. Tanzan J Health Res. 2014; 16:219–28. 60. Vogler AJ, Andrianaivoarimanana V, Telfer S, Hall CM, Sahl JW, Hepp CM et al. Temporal phylogeography of Yersinia pestis in Madagascar: insights into the long-term maintenance of plague. PLoS Negl Trop Dis. 2017; 11:e0005887.

61. Brouat C, Rahelinirina S, Loiseau A, Rahalison L, Rajerison M, Laffly D et al. Plague circulation and population genetics of the reservoir Rattus rattus: the influence of topographic relief on the distribution of the disease within the Madagascar focus. PLoS Negl Trop Dis. 2013; 7:e2266.

62. Vogler AJ, Chan F, Wagner DM, Roumagnac P, Lee J, Nera R et al. Phylogeography and molecular epidemiology of Yersinia pestis in Madagascar. PLoS Negl Trop Dis. 2011; 5:e1319. 63. Wilschut LI, Addink EA, Heesterbeek H, Heier L, Laudisoit A, Begon M et al. Potential corridors and barriers for plague spread in Central Asia. Int J Health Geogr. 2013; 12:49.

64. Andrianaivoarimanana V, Piola P, Wagner DM, Rakotomanana F, Maheriniaina V, Andrianalimanana S et al. Trends of Human Plague, Madagascar, 1998–2016. Emerg Infect Dis. 2019; 25:220–228.

65. Chanteau S, Rahalison L, Ralafiarisoa L, Foulon J, Ratsitorahina M, Ratsifasoamanana L et al. Development and testing of a rapid diagnostic test for bubonic and pneumonic plague. Lancet. 2003; 361:211–216. 66. Hinnebusch J, Schwan TG. New method for plague surveillance using polymerase chain reaction to detect Yersinia pestis in fleas. J Clin Microbiol. 1993; 31:1511–1514. 67. Higgins JA, Ezzell J, Hinnebusch BJ, Shipley M, Henchal EA, Ibrahim MS. 5′ nuclease PCR assay to detect Yersinia pestis. J Clin Microbiol. 1998; 36:2284–2288.

68. Loiez C, Herwegh S, Wallet F, Armand S, Guinet F, Courcol RJ. Detection of Yersinia pestis in sputum by real-time PCR. J Clin Microbiol. 2003; 41:4873–4875. 69. Rahalison L, Vololonirina E, Ratsitorahina M, Chanteau SJ. Diagnosis of bubonic plague by PCR in Madagascar under field conditions. J Clin Microbiol. 2000; 38:260–263. 70. Neubauer H, Rahalison L, Brooks TJ, Aleksic S, Chanteau S, Splettstösser WD. Serodiagnosis of human plague by an anti-F1 capsular antigen specific IgG/IgM ELISA and immunoblot. Epidemiol Infect. 2000; 125:593–597.

71. Ratsitorahina M, Rabarijaona L, Chanteau S, Boisier P. Seroepidemiology of human plague in the Madagascar highlands. Trop Med Int Health. 2000; 5:94–8.

72. Sodikoff GM. Zoonotic semiotics: plague narratives and vanishing signs in Madagascar. Med Anthropol Q. 2019; 33:42–59.

73. Li B, Guo Y, Guo Z, Liang Y, Zhu Z, Zhou Q et al. Serologic survey of the sentinel animals for plague surveillance and screening for complementary diagnostic markers to F1 antigen by protein microarray. Am J Trop Med Hyg. 2008; 79:799–802 (2008).

74. Brown HE, Levy CE, Enscore RE, Schriefer ME, DeLiberto TJ, Gage KL et al. Annual seroprevalence of Yersinia pestis in coyotes as predictors of interannual variation in reports of human plague cases in Arizona, United States.Vector Borne Zoonotic Dis. 2011; 11:1439–1446.

479

480

Human Plague

75. Mwengee W, Butler T, Mgema S, Mhina G, Almasi Y, Bradley C et al. Treatment of plague with gentamicin or doxycycline in a randomized clinical trial in Tanzania. Clin Infect Dis. 2006; 42:614–621.

76. Bertherat E, Mueller MJ, Shako JC, Picardeau M. Discovery of a leptospirosis cluster amidst a pneumonic plague outbreak in a miners’ camp in the Democratic Republic of the Congo. Int J Environ Res Public Health. 2014; 11:1824–1833. 77. Apangu T, Griffith K, Abaru J, Candini G, Apio H, Okoth F et al. Successful treatment of human plague with oral ciprofloxacin. Emerg Infect Dis. 2017; 23:553–555. 78. Welch TJ, Fricke WF, McDermott PF, White DG, Rosso ML, Rasko DA et al. Multiple antimicrobial resistance in plague: an emerging public health risk. PLoS ONE. 2007; 21:e309.

79. Galimand M, Carniel E, Courvalin P. Resistance of Yersinia pestis to antimicrobial agents. Antimicrob Agents Chemother. 2006; 50:3233–3236.

80. Cabanel N, Bouchier C, Rajerison M, Carniel E. Plasmid-mediated doxycycline resistance in a Yersinia pestis strain isolated from rat. Int J Antimicrob Agents. 2018; 51:249–254.

81. Atkinson S, Williams PF. Yersinia virulence factors—a sophisticated arsenal for combating host defences. F1000Res. 2016; 5:1370. 82. Zhou D, Yank R. Molecular Darwinian evolution of virulence in Yersinia pestis. Infect Immun. 2009; 77:2242–2250.

83. Hinnebusch BJ, D.L. Erickson DL. Yersinia pestis biofilm in the flea vector and its role in the transmission of plague. Curr Top Microbiol Immunol. 2008; 322:229–248. 84. Chouikha I, Hinnebusch BJ. Yersinia—flea interactions and the evolution of the arthropod-borne transmission route of plague. Curr Opin Microbiol. 2012; 15:239–246.

85. Han Y, Fang H, Liu L, Zhou D. Genetic regulation of Yersinia pestis. Adv Exp Med Biol. 2016; 918:223–256. 86. Easterday WR, Kausrud KL, Star B, Heier L, Haley BJ, Ageyev V et al. An additional step in the transmission of Yersinia pestis? ISME J. 2012; 6:231–236.

87. Sun YC, Jarrett CO, Bosio CF, Hinnebusch BJ. Retracing the evolutionary path that led to flea-borne transmission of Yersinia pestis. Cell Host Microbe. 2014; 15:578–586.

88. Hinnebusch BJ. The Evolution of Flea-borne transmission in Yersinia pestis. Curr Issues Mol Biol. 2005; 7:197–212.

89. Lewnard JA, Townsend JP. Climatic and evolutionary drivers of phase shifts in the plague epidemics of colonial India. Proc Natl Acad Sci U S A. 2016; 113:14601–14608.

90. Laayouni H, Oosting M, Luisi P, Ioana M, Alonso S, Ricaño-Ponce I et al. Convergent evolution in European and Roma populations reveals pressure exerted by plague on Toll-like receptors. Proc Natl Acad Sci U S A. 2014; 111:2668–2673. 91. Price PA, Jin J, Goldman WE. Pulmonary infection by Yersinia pestis rapidly establishes a permissive environment for microbial proliferation. Proc Natl Acad Sci U S A. 2012; 109:3083–3088. 92. Butler T. Plague history: Yersin’s discovery of the causative bacterium in 1894 enabled, in the subsequent century, scientific progress in understanding the disease and the development of treatments and vaccines. Clin Microbiol Infect. 2014; 20:202–209.

References

93. Feodorova VA, Sayapina LV, Corbel MJ, Motin VL. Russian vaccines against especially dangerous bacterial pathogens. Emerg Microbes Infect. 2014; 3:e86. 94. Verma SK, Tuteja U. Plague vaccine development: current research and future trends. Front Immunol. 2016; 7:602. 95. WHO (2018). Plague vaccines workshop, April 23, 2018. Available at: https://www.who. int/blueprint/what/norms-standards/Plague_vaccines_workshop-23-april-2018/en/ (accessed on May 15, 2021).

96. Pastoret PP, Brochier B. The development and use of a vaccinia-rabies recombinant oral vaccine for the control of wildlife rabies: a link between Jenner and Pasteur. Epidemiol Infect. 1996; 116:230–240. 97. Vallée E, Ridler AL, Heuer C, Collins-Emerson JM, Benschop J, Wilson PR. Effectiveness of a commercial leptospiral vaccine on urinary shedding in naturally exposed sheep in New Zealand. Vaccine. 2017; 35:1362–1368. 98. Bonvicino CR, Oliveira JA, Corderio-Estrela P, D’andrea PS, Almeida AM. A taxonomic update of small mammal plague reservoirs in South America. Vector Borne Zoonotic Dis. 2015; 15:571–579. 99. Vogler AJ, Chan F, Nottingham R, Andersen G, Drees K, Beckstrom-Sternberg SM et al. A decade of plague in Mahajanga, Madagascar: insights into the global maritime spread of pandemic plague. MBio. 2013; 4:e00623–12.

100. Rahelinirina S, Rajerison M, Telfer S, Savin C, Carniel E, Duplantier JM. The Asian house shrew Suncus murinus as a reservoir and source of human outbreaks of plague in Madagascar. PLoS Negl Trop Dis. 2017; 11:e0006072. 101. Bertherat E, Bekhoucha S, Chougrani S, Razik F, Duchemin JB, Houti L. Plague reappearance in Algeria after 50 years. Emerg Infect Dis. 2007; 13:1459–1462.

102. Cabanel N, Leclercq A, Chenal-Francisque V, Annajar B, Rajerison M, Bekkhoucha S et al. Plague outbreak in Libya, 2009, unrelated to plague in Algeria. Emerg Infect Dis. 2013; 19:230–236.

103. Neerinckx S, Bertherat E, Leirs H. Human plague occurrences in Africa: an overview from 1877 to 2008. Trans Royal Soc Trop Med Hyg. 2010; 104:97–103.

104. Nuriddin A, Jalloh MF, Meyer E, Bunnell R, Bio FA, Jalloh MB et al. Trust, fear, stigma and disruptions: community perceptions and experiences during periods of low but ongoing transmission of Ebola virus disease in Sierra Leone, 2015. BMJ Glob Health. 2018; 3:e000410.

105. Wagenaar BH, Augusto O, Beste J, Toomay SJ, Wickett E, Dunbar N et al. The 2014–2015 Ebola virus disease outbreak and primary healthcare delivery in Liberia: time-series analyses for 2010–2016. PLoS Med. 2018; 15:e1002508.

106. Sams K, Desclaux A, Anoko J, Akindès F, Egrot M, Sow K et al. Mobilising experience from Ebola to address plague in Madagascar and future epidemics. Lancet. 2017; 390: 2624–2625. 107. Stellmach D, Beshar I, Bedford J, du Cros P, Stringer B. Anthropology in public health emergencies: what is anthropology good for? BMJ Glob. Health. 2018; 3:e000534.

108. Mirarinjara A, Rogier C, Harimalala M, Ramihangihajason TR, Boyer S. Xenopsylla brasiliensis fleas in plague focus areas. Madagascar. Emerg Infect Dis. 2016; 22: 2207–2208.

481

482

Human Plague

109. Ruiz A. Plague in the Americas. Emerg Infect Dis. 2001; 7:539–540.

110. Anisimov AP, Lindler LE, Pier GB. Intraspecific diversity of Yersinia pestis. Clin Microbiol Rev. 2004; 17:434–464.

111. Riehm JM, Tserennorov D, Kiefer D, Stuermer IW, Tomaso H, Zoller L et al. Yersinia pestis in small rodents, Mongolia. Emerg Infect Dis. 2011; 17:1320–1322.

112. Ebright JR, Altantsetseg T, Oyungerel R. Emerging infectious diseases in mongolia. Emerg Infect Dis. 2003; 9:1509–1515.

113. Galdan B, Baatar U, Molotov B, Dashdavaa O. Plague in Mongolia. Vector Borne Zoonotic Dis. 2010; 10:69–75.

SECTION 3

BIG DATA AND ARTIFICIAL INTELLIGENCE

Chapter 23

Human Brain/Cloud Interface Nuno R. B. Martins, PhD,a,b Amara Angelica, BGE,c Krishnan Chakravarthy, PhD, MD,d,e Yuriy Svidinenko, MS,f Frank J. Boehm,g Ioan Opris, PhD,h,i Mikhail A. Lebedev, PhD,j,k,l Melanie Swan, MS,m Steven A. Garan, PhD,a,b Jeffrey V. Rosenfeld, PhD, MD,n,o,p,q Tad Hogg, PhD,r and Robert A. Freitas Jr., JDr aLawrence

Berkeley National Laboratory, Berkeley, California, USA for Research and Education on Aging, University of California, Berkeley and LBNL, Berkeley, California, USA cKurzweil Technologies, Newton, Massachusetts, USA dUC San Diego Health Science, San Diego, California, USA eVA San Diego Healthcare System, San Diego, California, USA fNanobot Medical Animation Studio, San Diego, California, USA gNanoApps Medical, Inc., Vancouver, British Columbia, Canada hMiami Project to Cure Paralysis, University of Miami, Miami, Florida, USA iDepartment of Biomedical Engineering, University of Miami, Coral Gables, Florida, USA jCenter for Neuroengineering, Duke University, Durham, North Carolina, USA kCenter for Bioelectric Interfaces of the Institute for Cognitive Neuroscience of the National Research University Higher School of Economics, Moscow, Russia lDepartment of Information and Internet Technologies of Digital Health Institute, I.M. Sechenov First Moscow State Medical University, Moscow, Russia mDepartment of Philosophy, Purdue University, West Lafayette, Indiana, USA nMonash Institute of Medical Engineering, Monash University, Clayton, Victoria, Australia oDepartment of Neurosurgery, Alfred Hospital, Melbourne, Victoria, Australia pDepartment of Surgery, Monash University, Clayton, VIC, Australia qDepartment of Surgery, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA rInstitute for Molecular Manufacturing, Palo Alto, California, USA bCenter

[email protected], [email protected]

Keywords: brain/cloud interface, brain-computer interface, brain-to-brain interface, brain-machine interface, transparent shadowing, neuralnanorobots, neuralnanorobotics, nanomedicine, endoneurobots, gliabots, synaptobots, neurons, blood–brain barrier, synapses, nanoparticles, nanoelectronics

The Internet comprises a decentralized global system that serves humanity’s collective effort to generate, process, and store data, most of which is handled by Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa Copyright © 2022 Jenny Stanford Publishing Pte. Ltd. ISBN 978-981-4877-84-8 (Hardcover), 978-1-003-18043-2 (eBook) www.jennystanford.com

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Human Brain/Cloud Interface

the rapidly expanding cloud. A stable, secure, real-time system may allow for interfacing the cloud with the human brain. One promising strategy for enabling such a system, denoted here as a “human brain/cloud interface” (“B/CI”), would be based on technologies referred to here as “neuralnanorobotics.” Future neuralnanorobotics technologies are anticipated to facilitate accurate diagnoses and eventual cures for the ∼400 conditions that affect the human brain. Neuralnanorobotics may also enable a B/CI with controlled connectivity between neural activity and external data storage and processing, via the direct monitoring of the brain’s ∼86 × 109 neurons and ∼2 × 1014 synapses. Subsequent to navigating the human vasculature, three species of neuralnanorobots (endoneurobots, gliabots, and synaptobots) could traverse the blood–brain barrier (BBB), enter the brain parenchyma, ingress into individual human brain cells, and autoposition themselves at the axon initial segments of neurons (endoneurobots), within glial cells (gliabots), and in intimate proximity to synapses (synaptobots). They would then wirelessly transmit up to ∼6 × 1016 bits per second of synaptically processed and encoded human–brain electrical information via auxiliary nanorobotic fiber optics (30 cm3) with the capacity to handle up to 1018 bits/sec and provide rapid data transfer to a cloud based supercomputer for real-time brain-state monitoring and data extraction. A neuralnanorobotically enabled human B/CI might serve as a personalized conduit, allowing persons to obtain direct, instantaneous access to virtually any facet of cumulative human knowledge. Other anticipated applications include myriad opportunities to improve education, intelligence, entertainment, traveling, and other interactive experiences. A specialized application might be the capacity to engage in fully immersive experiential/sensory experiences, including what is referred to here as “transparent shadowing” (TS). Through TS, individuals might experience episodic segments of the lives of other willing participants (locally or remote) to, hopefully, encourage and inspire improved understanding and tolerance among all members of the human family.

23.1 Introduction

“We’ll have nanobots that… connect our neocortex to a synthetic neocortex in the cloud… Our thinking will be a…. biological and non-biological hybrid.” — Ray Kurzweil, TED 2014

There is an incessant drive in medicine toward the development of smaller, more capable, efficacious, and cost-effective devices and systems. The primary driver of this quest relates to the cellular and sub-cellular genesis of human disease, at which scale, nanodevices can directly interact and potentially positively influence disease outcomes or prevent them altogether, particularly in regard to brain disorders (Kandel et al., 2000, Kandel, 2001; Zigmond et al., 2014; Chaudhury et al., 2015; Fornito et al., 2015; Falk et al., 2016). The pursuit of ever smaller tools to treat patients is approaching a pivotal juncture in medical history as advanced nanomedicine — specifically, medical nanorobotics — is expected to serve

Introduction

as a dynamic tool toward addressing most human brain disorders. The goal is to finally empower medical professionals to treat diseases at individual cellular and sub-cellular resolution (Freitas, 1998, 1999b, 2003, 2005a,c, 2007, 2016; Morris, 2001; Astier et al., 2005; Patel et al., 2006; Park et al., 2007; Popov et al., 2007; Mallouk and Sen, 2009; Martel et al., 2009; Kostarelos, 2010; Mavroides and Ferreira, 2011; Boehm, 2013). The application of nanorobots to the human brain is denoted here as “neuralnanorobotics.” This technology may allow for the monitoring, recording, and even manipulation of many types of brain-related information at cellular and organellar levels (Martins et al., 2012, 2015, 2016). Medical neuralnanorobots are expected to have the capacity for real-time, non-destructive monitoring single-neuron and single-synapse neuroelectric activity, local neuropeptide traffic, and other relevant functional data, while also allowing the acquisition of fundamental structural information from neuron surfaces, to enhance the connectome map of a living human brain (Sporns et al., 2005; Lu et al., 2009; Anderson et al., 2011; Kleinfeld et al., 2011; Seung, 2011; Martins et al., 2012, 2015, 2016). Non-destructive neuralnanorobotically mediated whole-brain monitoring coupled with single-cell repair capabilities (Freitas, 2007) is anticipated to provide a powerful medical capability to effectively treat most, or all of the ∼400 known brain disorders, including, most notably: Parkinson’s and Alzheimer’s (Freitas, 2016), addiction, dementia, epilepsy, and spinal cord disorders (NINDS, 2017). Neuralnanorobots are also expected to empower many non-medical paradigm-shifting applications, including significant human cognitive enhancement, by providing a platform for direct access to supercomputing storage and processing capabilities and interfacing with artificial intelligence systems. Since information-based technologies are consistently improving their priceperformance ratios and functional design at an exponential rate, it is likely that once they enter clinical practice or non-medical applications, neuralnanorobotic technologies may work in parallel with powerful artificial intelligence systems, supercomputing, and advanced molecular manufacturing. Furthermore, autonomous nanomedical devices are expected to be biocompatible, primarily due to their structural materials, which would enable extended residency within the human body (Freitas, 1999a, 2002, 2003). Medical neuralnanorobots might also be fabricated in sufficient therapeutic quantities to treat individual patients, using diamondoid materials, as these materials may provide the greatest strength, resilience, and reliability in vivo (Freitas, 2010). An ongoing international “Nanofactory Collaboration” headed by Robert Freitas and Ralph Merkle has the primary objective of constructing the world’s first nanofactory, which will permit the mass manufacture of advanced autonomous diamondoid neuralnanorobots for both medical and non-medical applications (Freitas and Merkle, 2004, 2006; Freitas, 2009, 2010). It is conceivable that within the next 20–30 years, neuralnanorobotics may be developed to enable a safe, secure, instantaneous, real-time interface between

487

488

Human Brain/Cloud Interface

the human brain and biological and non-biological computing systems, empowering brain-to-brain interfaces (BTBI), brain-computer interfaces (BCI), and, in particular, sophisticated brain/cloud interfaces (B/CI). Such human B/CI systems may dramatically alter human/machine communications, carrying the promise of significant human cognitive enhancement (Kurzweil, 2014; Swan, 2016). Historically, a fundamental breakthrough toward the possibility of a B/CI was the initial measurement and recording of the electrical activity of the brain via EEG in 1924 (Stone and Hughes, 2013). At the time, EEG marked a historical advance in neurologic and psychiatric diagnostic tools, as this technology allowed for the measurement of a variety of cerebral diseases, the quantification of deviations induced by different mental states, and detection of oscillatory alpha waves (8–13 Hz), the so-called “Berger’s wave.” The first EEG measurements required the insertion of silver wires into the scalps of patients, which later evolved to silver foils that were adhered to the head. These rudimentary sensors were initially linked to a Lippmann capillary electrometer. However, significantly improved results were achieved through the use of a Siemens double-coil recording galvanometer, which had an electronic resolution of 0.1 mv (Jung and Berger, 1979). The first reported scientific instance of the term “brain–computer interface” dates to 1973, ∼50 years following the first EEG recording, when it was envisioned that EEG-reported brain electrical signals might be employed as data carriers in human–computer communications. This suggestion assumed that mental decisions and reactions might be probed by electroencephalographic potential fluctuations measured on the human scalp, and that meaningful EEG phenomena should be viewed as a complex structure of elementary wavelets that reflected individual cortical events (Vidal, 1973). Currently, invasive1 and non-invasive brain–computer interfaces and noninvasive brain-to-brain communication systems have already been experimentally demonstrated and are the subject of serious research worldwide. Once these existing technologies have matured, they might provide treatments for completely paralyzed patients, eventually permitting the restoration of movement in paralyzed limbs through the transmission of brain signals to muscles or external prosthetic devices (Birbaumer, 2006). The first reported direct transmission of information between two human brains without intervention of motor or peripheral sensory systems occurred in 2014, using a brain-to-brain communication technique referred to as “hyperinteraction” (Grau et al., 2014). The most promising long-term future technology for non-destructive, realtime human–brain–computer interfaces and brain-to-brain communications may

1For the purposes of this chapter, the term “invasive” is defined as a medical procedure or device that imparts quantifiable physiological damage (at any level) to a patient. In the case of the envisaged nanomedically enabled B/CI, the assumption is that millions of micron-scale nanorobots will be noninvasive — i.e., they may ingress into a patient and subsequently auto-situate themselves at various sites within the human brain, with no disruptive functional physiological or experiential effects. Or in some cases, they may be minimally invasive.

Introduction

be neuralnanorobotics (Martins et al., 2016). Neuralnanorobotics, which is the application of medical nanorobots to the human brain, was first envisaged by Freitas, who proposed the use of nanorobots for direct real-time monitoring of neural traffic from in vivo neurons, as well as the translation of messages to neurons (Freitas, 1999b, 2003). Other authors have also envisioned B/CI and predicted that in the future, humans will have access to a synthetic non-biological neocortex, which might permit a direct B/CI. Within the next few decades, neuralnanorobotics may enable a non-destructive, real-time, ultrahigh-resolution interface between the human brain and external computing platforms such as the “cloud.” The term “cloud” refers to cloud computing, an information technology (IT) paradigm and a model for enabling ubiquitous access to shared pools of configurable resources (such as computer networks, servers, storage, applications, and services), that can be rapidly provisioned with minimal management effort, often over the Internet. For both personal or business applications, the cloud facilitates rapid data access, provides redundancy, and optimizes the global usage of processing and storage resources while enabling access from virtually any location on the planet. However, the primary challenge for worldwide global cloud-based information processing technologies is the speed of access to the system, or latency. For example, the current round-trip latency rate for transatlantic loops between New York and London is ∼90 ms (Verizon, 2014). Since there are now more than 4 billion Internet users worldwide, its economic impact on the global economy is increasingly significant. The economic impact of IoT (Internet of Things) applications alone has been estimated by the McKinsey Global Institute to range from $3.9 to $11.1 trillion per year by 2025. The global economic impact of cloud-based information processing over the next few decades may be at least an order of magnitude higher once cloud services are combined in previously unimagined ways, disrupting entire industries (Miraz et al., 2015). A neuralnanorobotics-mediated human B/CI, potentially available within 20–30 years, will require broadband Internet access with extremely high upload and download speeds, compared to today’s rates. Humankind has at its core a potent and ceaseless drive to explore and to challenge itself, to improve its collective condition by relentlessly probing and pushing boundaries while constantly attempting to breach those barriers that tenuously separate the possible from the impossible. The notions of human augmentation and cognitive enhancement are borne of these tenets. This drive includes an incessant quest for exploration and a constant desire for social interaction and communication — both of which are catalysts for rapidly increasing globalization. Consequently, the development of a non-destructive, real-time human B/CI technology may serve as an intimate, personalized conduit through which individuals would have instantaneous access to virtually any facet of cumulative human knowledge and also the optional specialized capacity to engage in myriad real-time fully immersive experiential and sensory worlds.

489

490

Human Brain/Cloud Interface

23.2 The Human Brain 23.2.1 The Quantitative Human Brain The human brain comprises a remarkable information storage and processing system that possesses an extraordinary computation-per-volume efficiency, with an average weight of 1400 g and a volume of ∼1350 cm3, contained within an “average” intracranial volume of ∼1,700 cm3. A brief quantification of the brain’s constituents and operational parameters includes ∼1,350 cm3 (∼75%) brain cells, ∼200 cm3 (15%) blood, and up to ∼150 cm3 (10%) of cerebrospinal fluid (Rengachary and Ellenbogen, 2005). The raw computational power of the human brain has been estimated to range from 1013 to 1016 operations/sec (Merkle, 1989). The human brain’s functional action potential based information is estimated as 5.52 × 1016 bits/sec (Martins et al., 2012), with a brain power output estimated at 15–25 W and a power density of 1.1–1.8 × 104 W/m3 at an operating temperature of 37.3°C (Freitas, 1999b). When considering the human brain at the regional level, an exceptional component is the neocortex (Tables 23.1 and 23.2), which has a highly organized neural architecture that encompasses sensorimotor, cognitive, and emotional domains (Alexander et al., 1986; Fuster and Bressler, 2012). This cortical structure consists of mini-columnar and laminar arrangements of neurons that are linked via afferent and efferent connections distributed across multiple brain regions (Lorento de Nó, 1938; Mountcastle, 1997; Shepherd and Grillner, 2010; Opris, 2013; Opris et al., 2011, 2013, 2014, 2015). Cortical minicolumns consist of chains of pyramidal neurons that are surrounded by a “curtain of inhibition” formed by interneurons (Szentágothai and Arbib, 1975). Table 23.1 Neocortical measures (Pakkenberg and Gundersen, 1997; Stark et al., 2007a,b)

Female Male

Thickness Surface (cm2) (mm)

Volume (cm3)

Neuron number density (106/cm3)

Neurons (N, 109)

1678–1680

440–458

43.1–43.8

19.3–19.7

489

44.0

21.5

1883–1900

Humans 1820

2.61–2.74

2.72–2.79

2.69

517–524

44.0–44.1

22.8–22.9

At the cellular level, the average human brain is estimated to contain (86.06 ± 8.2) × 109 neurons, with ∼80.2% (69.03 ± 6.65 × 109 neurons) located in the cerebellum, ∼19% (16.34 ± 2.17 × 109 neurons) located in the cerebral cortex, and only ∼0.8% (0.69 ± 0.12 × 109 neurons) located throughout the rest of the brain (Azevedo et al., 2009). The human cerebellum and cerebral cortex together hold the vast majority (99.2%) of brain neurons (Azevedo et al., 2009). Another approximation, based on combining estimates for the different brain regions,

The Human Brain

produced a similar value of 94.2 ± 11.3 × 109 neurons for the whole human brain (Martins et al., 2012). Table 23.2 Enumeration of neurons and synapses in the human neocortex (Tang et al., 2001; Sandberg and Bostrom, 2008; Karlsen and Pakkenberg, 2011) Total Number of Glial cell Neocortex neocortex Number of Number of synapses per number region volume (cm3) synapses (1012) neurons (109) neuron (103) (109)

Occipital

Parietal

Temporal

Frontal

Total

69

149

133

239

590

22.0

41.5

42.0

58.9

164.0

3–4.65

4–6.61

4–4.80

6–7.89

17–23.9

4.36

6.33

8.95

7.54

6.93

3

4

5

7

18

Glial cells comprise another brain-cell type (Fig. 23.1). The average number of glial cells in the human brain is estimated to be 84.61 ± 9.83 × 109 (HerculanoHouzel, 2009), with the population of glial cells in the neocortex estimated at from 18.2 to 38.6 × 109 (Karlsen and Pakkenberg, 2011). The ratio of glia to neurons likely has functional relevance (Nedergaard et al., 2003) and varies between different brain regions. While the whole-brain glia/neuron ratio is ∼1:1, there are significant differences between brain domains. For example, the glia/neuron ratio of the cerebral cortex is 3.72:1 (60.84 billion glia; 16.34 billion neurons) but only 0.23:1 (16.04 billion glia; 69.03 billion neurons) in the cerebellum; the basal ganglia, diencephalon, and brainstem have a combined ratio of 11.35:1 (Azevedo et al., 2009). In addition, synapses, numbering (2.42 ± 0.29) × 1014 in the average human brain, are collectively estimated to process information at spiking rates of (4.31 ± 0.86) × 1015 spikes/sec, empowering the human brain to process data at (5.52 ± 1.13) × 1016 bits/sec (Martins et al., 2012). Synapses are elements of the neural network that play a critical role in processing information in the brain, being involved in learning, long-term and short-term memory storage and deletion, and temporal information processing (Black et al., 1990; Bliss and Collingridge, 1993; Kandel, 2001; Fuhrmann et al., 2002; Lee et al., 2008; Holtmaat and Svoboda, 2009; Liu et al., 2012). Synapses are also key effectors for signal transduction and plasticity in the brain. Proper synapse formation during childhood provides a substrate for cognition, whereas improper formation or functionality leads to neuro-developmental disorders including mental retardation and autism (Rollenhagen and Lübke, 2006; Mcallister, 2007; Rollenhagen et al., 2007). Synapse loss, as occurs in Alzheimer’s patients, is intimately associated with cognitive decline (Dekosky and Scheff, 1990; Terry et al., 1991; Scheff and Price, 2006).

491

Figure 23.1 Artistic representation of neurons (with blue processes) and glial (white) cells. Image credit: Yuriy Svidinenko, Nanobotmodels Company.

492 Human Brain/Cloud Interface

The Human Brain

23.2.2 Processing Units

Structural cellular or sub-cellular elements of the human brain are considered as information processing units if they are involved in significant functional input/ output changes in electrochemically based brain-data storage and/or processing systems. There is some disagreement in the current scientific literature regarding the quantification of this “significance” metric. This incongruity has led various authors to consider different cellular and subcellular structures as fundamental elements of human brain storage and its computation system, encompassing (aside from neurons and synapses): dendritic trees, axons, proteins, and even neural microtubules (Koch et al., 1983; Bialek, 1993; Juusola et al., 1996; Zador, 1998; Manwani and Koch, 2001; London and Häusser, 2005; Ford, 2010). Estimates for whole-brain electrical data processing rates range from 1.48 × 1011 bits/sec. to a high of 3.2 × 1029 bits/sec (Sandberg and Bostrom, 2008; Martins et al., 2012). The human brain might even have more than 100 times higher computational capacity than previously thought, based on the discovery that dendrites may generate nearly 10 times as many electrochemical spikes as do neuron soma, and are hybrids that process both analog and digital signals (Moore et al., 2017). This finding may challenge the long-held belief that spikes in the soma (body of the neuron) are the primary means through which perception, learning, and memory formation occur. Dendrites comprise more than 90% of neural tissue, so knowing that they are much more active than the soma would fundamentally alter our understanding of how the brain processes information. As dendrites are ∼100 times larger by volume than neuronal bodies, the immense number of firing dendritic spikes would suggest that the brain may indeed possess significantly higher computational power than earlier estimated. However, there is currently a consensus that neurons and synapses constitute the fundamental electrochemical processing units of the human brain (Gkoupidenis et al., 2017; Jackman and Regehr, 2017). The roles of neurons in electrical information processing include receiving, integrating, generating, and transmitting action-potential-based information (Koch, 1997; Koch and Segev, 2000; Zhang, 2008). However, several neuronal noise sources influence the reliability and precision of neuronal signaling, so stimulusresponse functions are sometimes unreliable and are dissociated from what is being encoded via spike activity (Bialek and Rieke, 1992). The other fundamental consensual processing units of electrochemical information are synapses. Synapses are a core component of the neuron network that process information and are involved in learning and memory, with synapse dimensions and morphologies reported as playing a fundamental role in longand short-term memory storage and deletion. Synapses are also engaged in signal transduction and plasticity, ensuring one-way transmission of signals, and are involved in temporal information processing to allow complex system behaviors, along with acting to decelerate electrical signals (Puro et al., 1977; Black et al., 1990; Bliss and Collingridge, 1993; Kandel, 2001; Rollenhagen and Lübke, 2006;

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Rollenhagen et al., 2007; IBM, 2008; Lee et al., 2008; Holtmaat and Svoboda, 2009). The role of synapses as processing units of the human brain is reinforced by the results of computational simulation, which indicate that the computational power of a network is increased using dynamic synapses. This suggests that emulation of biological synapses is a prerequisite for the development of brainlike computational systems (Maass and Zador, 1999; Fuhrmann et al., 2002; Kuzum et al., 2012). A recently developed ultra-low-power artificial synapse for neural computing has demonstrated the capacity to provide 500 distinct states (Van de Burgt et al., 2017). Real-time monitoring of the whole human brain (by placing neuralnanorobots within each neuron and nearby synaptic connections to record/transmit data from localized neuron and synapse spiking) may provide redundant data that might be employed in the development of validation protocols.

23.3 The Cloud

Due to the immense volume of data involved, data transfer to and from living human brains and the cloud may likely require the use of supercomputers with artificial intelligence algorithms. Current von Neumann-based-architecture supercomputers with massive numbers of processors are either centralized (composed of large numbers of dedicated processors) or distributed (based on a large number of discrete computers distributed across a network, such as the Internet). One estimate of maximum computational speed required to handle the electrical data in the human brain is 5.52 × 1016 bits/sec (Martins et al., 2012). Several centralized and distributed supercomputers have processing speeds that are significantly higher than this estimate (Martins et al., 2012). As of November 2018, the fastest supercomputer worldwide was Summit, developed at the United States Oak Ridge National Laboratory (Tennessee), with 122.3 petaflops on the High Performance Linpack (HPL) benchmark. This computational model may be questionable, however, as computers are based on von Neumann architecture, whereas brain circuits are not; and brains operate in a massively parallel manner, whereas computers do not (Nagarajan and Stevens, 2008; Whitworth and Ryu, 2008). The Internet consists of a decentralized global system, based on von-Neumannarchitecture-based computers and supercomputers, used for data transfer across processing and storage units. The global storage capacity of Internet data centers in 2018 was 1450 exabytes (Statistica, 2018). Van den Bosch et al. (2016) estimate that the storage capacity of the World Wide Web doubles every 3 years, with its computational capacity doubling every 1.5 years. However, once brain data is interfaced with supercomputers in near real-time, the connection to supercomputers in the cloud will be the ultimate bottleneck between the cloud and the human brain (Knapp, 2013). This challenge includes, in particular, the bottleneck of the bandwidth required to transmit data worldwide. According to one study, “Global Internet traffic in 2021 will be equivalent to 127

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times the volume of the entire global Internet in 2005. Globally, Internet traffic will reach 30 GB per capita by 2021, up from 10 GB per capita in 2016” (Cisco, 2017). This speed is forcing innovation to deal with bandwidth constraints. Conventional fiber-optic cables transfer trillions of bits/sec between massive data centers. As of October 2018, the average Internet peak connection speed was 189.33 Mbps in Singapore and 100.07 Mbps in the United States (Kemp, 2018). Several commercial efforts to increase Internet speeds are presently underway, including the recently built $300 million fiber-optic cable between Oregon, Japan, and Taiwan. In 2016, much of the world’s Internet traffic was transmitted via undersea fiber-optic cables; the 6,600 km-long MAREA Facebook/Microsoft-owned cable was estimated to carry 160 Tb/sec of data across the Atlantic Ocean (Hecht, 2016). Current commercial 4G networks provide broadband speeds of up to 100 Mbits/sec. However, United States carriers have stated that they plan to deploy 5G technology in 2020 that will eventually “bring speeds of around 10 gigabits per second to your phone. That’s more than 600 times faster than typical 4G speeds of today’s mobile phones, and 10 times faster than Google Fiber’s standard home broadband service” (Finley, 2018).

23.4 Potential of Current Technologies toward a Brain/Cloud Interface 23.4.1 Nanoparticles, Nanotubes, and Nanodots

One promising near-term technology that may enable an interface with brainbased neural networks is magnetoelectric nanoparticles, which may be employed to enhance coupling between external magnetic fields and localized electric fields that emanate from neural networks (Yue et al., 2012; Guduru et al., 2015). Magnetoelectric nanoparticles might also induce nanoparticles to traverse the blood–brain barrier (BBB) by applying a direct-current magnetic field gradient to the cranial vault. Magnetoelectric nanoparticles have already been utilized to control intrinsic fields deep within the mouse brain and have permitted the coupling of external magnetic fields to neuronal electric fields. A strategy developed for the delivery of nanoparticles to the perineuronal environment is expected to provide a means to access and eventually stimulate selected populations of neurons (Freitas, 1999b). The delivery of nanoparticles into the human brain will indeed pose a formidable challenge. For intravenous injection, at least 90% of nanoparticles have been observed to be sequestered within tissues and organs prior to reaching the brain (Calvo et al., 2001), so intra-arterial injections might be more reliable. Steering nanoparticles to selected brain regions may also be achieved using external magnetic fields (Li et al., 2018). Since it has been shown that certain customized nanoparticles may damage dopaminergic and serotoninergic systems, a further detailed analysis of the biodistribution and metabolism of nanoparticles will be required. Further, the risk of infection, inflammatory reactions, potential

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immunogenicity, cytotoxicity, and tumorigenicity must be effectively addressed prior to the in vivo application of nanoparticles in humans (Cupaioli et al., 2014). The use of carbon-nanotube-based electrical stimulation of targets deep within the brain has been proposed as a novel treatment modality for patients with Parkinson’s disease and other CNS disorders (Srikanth and Kessler, 2012). This strategy utilizes unidirectional electrical stimulation, which is more precise and avoids the surgical risks associated with deep macroelectrode insertion, used with current methods of deep brain stimulation (Mayberg et al., 2005; Taghva et al., 2013) that employ long stereotactically placed quadripolar macroelectrodes through the skull. When intended for use as a component of a B/CI system, carbon-nanotube-based electrical stimulation would also require a two-way information pathway at single-neuron resolution for neuronal electrochemical information recording. Fluorescing carbon nanodots (synthesized using D-glucose and L-aspartic acid) with uniform diameters of 2.28 ± 0.42 nm have been employed to target and image C6 glioma cells in mouse brains. Excellent biocompatibility, tunable fullcolor emission, and the capacity to freely penetrate the BBB might make fluorescing carbon nanodots viable candidates as tagging agents to facilitate the implementation of nanomedical B/CI technologies (Zheng et al., 2015). However, fluorescing carbon nanodots might be problematic, since crossing the BBB is a challenging process for ∼98% of all small molecules (Pardridge, 2005; Grabrucker et al., 2016). This is primarily due to the BBB forming a dynamic, blood-andbrain-regulated, strict physical, transport, metabolic, and immunologic barrier while it is permeable to O2 and CO2 and other gaseous molecules, as well as water and other lipid soluble substances (Serlin et al., 2015), the barrier is very restrictive to large molecules. However, small peptides may cross the BBB by either non-specific fluid-phase endocytosis or receptor-mediated transcytosis (RMT) mechanisms. Optically based nanotechnologies, including optical imaging methods, have demonstrated valuable applications at the cellular level. For example, quantum dot fullerenes have been employed for in vitro and in vivo cellular membrane potential measurements (Nag et al., 2017).

23.4.2 Injectable “Neural Lace”

A recently proposed technology for the potential integration of brain neural networks and computing systems at the microscale is referred to as “neural lace.” This would introduce minimally invasive three-dimensional mesh nanoelectronics, via syringe-injection, into living brain tissue to allow for continuous monitoring and stimulation of individual neurons and neuronal networks. This concept is based on ultraflexible mesh nanoelectronics that permit interfaces with nonplanar topographies. Experimental results have been reported using the injection and unfolding of sub-micrometer-thick, centimeter-scale macroporous mesh nanoelectronics through needles with diameters as small as 100 μm, which

Potential of Current Technologies Toward a Brain/Cloud Interface

were injected into cavities with a >90% device yield (Liu et al., 2015). One of the other potential applications of syringe-injectable mesh nanoelectronics is in vivo multiplexed neural network recording. Plug-and-play input/output neural interfacing has also been achieved using platinum electrodes and silicon nanowire field-effect transistors, which exhibited a low interface contact resistance of ∼3 Ω (Schuhmann et al., 2017). Dai et al. (2018) also demonstrated “stable integration of mesh nanoelectronics within brain tissue on at least 1 year scales without evidence of chronic immune response or the glial scarring characteristic of conventional implants.” This group also showed that the activities of individual neurons and localized neural circuits could be monitored and stimulated over timelines of eight months or more, for applications such as recording of alterations in the activities of specific neurons as the brain ages (Dai et al., 2018).

23.4.3 Neural Dust

Future human B/CI technologies may preferably require long-term, self-implanting in vivo neural interface systems, a characteristic that is absent from most current brain–machine interface (BMI) technologies. This means that the system design should balance the size, power, and bandwidth parameters of neural recording systems. A recent proposal capable of bidirectional communication explored the use of low-power CMOS circuitry coupled with ultrasonic delivery of power and backscatter communications to monitor localized groups of neurons (Seo et al., 2013). The goal was to enable scalability in the number of neural recordings from the brain, while providing a path toward a longer-duration BMI. This technology currently employs thousands of independent free-floating 10–100 μm scale sensor nodes referred to as “neural dust.” These nodes detect and report local extracellular electrophysiological data, while using a subcranial interrogator that establishes power and communications links with each of the neural dust elements. Power transmission is accomplished ultrasonically to enable low-efficiency (7%, 11.6 dB) links, yielding ∼500 μW of received power (>107 higher than the ∼40 pW EM transmission available at a similar-size scale) with a 1 mm2 interrogator, which may eventually provide ∼10 μm sensing nodes.

23.4.4 Brain–Machine Interface

Brain–machine interface technology is currently being pursued via invasive neural interfaces composed of neural microchip sensor arrays that contain a plurality of electrodes that can detect multicellular signals. These are available for several brain areas (e.g., visual cortex, motor cortex neuroprosthetics, hippocampus, and others) (Berger et al., 2005; BrainGate, 2009). There are currently two different types of BMI systems. One type samples the neural activity of a single brain and unidirectionally controls an external device (Lebedev, 2014), while the other type (sensory BMI) includes sensory feedback

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from the device to the brain (O’Doherty et al., 2011). Non-invasive neural BMI interface strategies include the use of EEG, magnetoencephalography (MEG), fMRI (Miyawaki et al., 2008) and optical strategies, including fNIRS (Naseer and Hong, 2015). One 8-channel EEG signal-capture platform, built around Texas Instruments’ ADS1299 analog front-end integrated circuit, may soon be printable at home, thus democratizing low-resolution brain-data-extraction technologies (OpenBCI, 2019). Neurophotonics integrated with prosthetics, which links artificial limbs and peripheral nerves using two-way fiber-optic communications to enable the ability to feel pressure or temperature, is expected to permit high-speed communications between the brain and artificial limbs. Neuralnanorobots are anticipated to optimize interfaces using advanced touch-sensitive limbs that convey real-time sensory information to amputees, via a direct interface with the brain (Tabot et al., 2013). At the cellular level, attempts to achieve a direct junction between individual nerve cells and silicon microstructures are being pursued. Neuron-silicon junctions were spontaneously formed using the nerve cells of a mammalian brain, which permitted direct stimulation of nerve cells (Fromherz and Stett, 1995; Offenhausser, 1996; Vassanelli and Fromherz, 1997; Schätzthauer and Fromherz, 1998). Currently, nanoelectronics devices utilizing carbon nanotubes and silicon nanowires can detect and identify neuronal biomolecular chemical secretions and their bioelectrical activities (Veliev, 2016). An array of nanowire transistors can detect, stimulate, or inhibit nerve impulses and their propagation along individual neurites (Freitas, 1999b; Zeck and Fromherz, 2001; Patolsky et al., 2006). To demonstrate experimental minimally invasive neuron cytosolic recording of action potentials, a nanotransistor device was placed at the tip of a bent silicon nanowire to intracellularly record action potentials (Tian et al., 2010; Duan et al., 2011). Vertically arranged gold nanowire arrays have been used to stimulate and detect electrical activity at the nanoscale from simultaneous locations within neurons (Saha et al., 2008). High-density arrays of nanowire FETs enabled mapping signals at the subcellular level—a functionality that is not possible with conventional microfabricated devices (Timko et al., 2010). In principle, neuralnanorobotics may empower a near-optimal BCI with longterm biocompatibility by incorporating silicon, platinum, iridium, polyesterimideinsulated gold wires, peptide-coated glassy carbon pins, carbon nanotubes, polymer-based electrodes, silicon nitride, silicon dioxide, stainless steel, or nichrome (Niparko et al., 1989a,b; Edell et al., 1992; Yuen and Agnew, 1995; Huber et al., 1998; Malmstrom et al., 1998; Decharms et al., 1999; Normann et al., 1999; Mattson et al., 2000; Kristensen et al., 2001; Parak et al., 2001; Freitas, 2003). Neural electrodes can be implanted without producing any detectable damage beyond the initial trauma and brief phagocytosis, which are typically limited to the edges of the electrode insertion pathway (Babb and Kupfer, 1984) (Freitas, 2003).

Potential of Current Technologies Toward a Brain/Cloud Interface

Several types of neural electrodes are presently employed to interface with the brain via cochlear implants at scala tympani electrode arrays, and in potential CNS auditory prostheses, retinal chip implants, semiconductor-based microphotodiode arrays placed in the subretinal space, visual cortex microelectrode arrays, and other neural implants intended for the mobilization of paraplegics, phrenic pacing, or cardiac assistance (Haggerty and Lusted, 1989; Niparko et al., 1989a,b; Lefurge et al., 1991; Burton et al., 1996; Heiduschka and Thanos, 1998; Guenther et al., 1999; Normann et al., 1999; Peachey and Chow, 1999; Kohler et al., 2001; Mayr et al., 2001; Pardue et al., 2001; Shoham et al., 2001; Freitas, 2003; Mannoor et al., 2013). Each of these electrodes interface with very diminutive and specific brain regions, and are always confined to the surface areas of highly localized domains. Early “neural dust” proposals for providing BCI access to specific human– brain regions (e.g., neocortex) had several inherent limitations (Seo et al., 2013). Conversely, neuralnanorobotics technologies may possess the appropriate scale for optimally enabling BCI, exhibiting suitable mobility, being minimally invasive, imparting negligible localized tissue damage, and possessing robust monitoring capabilities over distinct information channels without requiring conventional surgical implantation. Neuralnanorobotics may also be massively distributed, whereas surgically introduced neural implants must be positioned in one or several specific locations. These shortcomings suggest that neuralnanorobotics may be a preferred solution to the formidable challenges ahead in the development of B/CI technologies.

23.4.5 Brain-to-Brain Interface

A BTBI involves inducing two distinct brains to directly communicate with each other (Pais-Vieira et al., 2015). BTBI systems were initially implemented in humans (Fig. 23.2) using non-invasive recordings and brain stimulation. Information was transferred from the sensorimotor cortex of one participant (recorded via EEG) to the visual (Grau et al., 2014) or motor (Rao et al., 2014) cortex of the second participant (delivered via transcranial magnetic stimulation, or TMS). A number of BTBI’s involving different species have also been recently demonstrated, for example, by linking the brain of a human to the spinal cord of an anesthetized rat (Yoo et al., 2013). In another example of interspecies BTBI, a human brain guided the movements of a Madagascar hissing cockroach along an S-shape track, controlling the cockroach antennae via electrical stimulation (Li and Zhang, 2016). Human brains have also been connected to cell cultures, experimentally demonstrating that brain activity can control gene expression using an EEG-based BMI to trigger optogenetic stimulation of designer cells, thereby mediating their genetic expression (Folcher et al., 2014).

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Figure 23.2 Brain-to-brain interface (BTBI) for information transfer between human subjects. The emitter subject is shown on the left, where sensorimotor cortex activity was recorded using EEG electrodes. The emitter performed an imagery based binary motor task: imagery of the feet (bit value 0) versus imagery of the hands (bit value 1). The receiver subject is shown on the right. The TMS coil was positioned differently over the visual cortex for 1 and 0 bit values, and evoked or did not evoke phosphenes (flashes of light), respectively. An Internet link was used for this brain-to-brain communication. Reproduced from Grau et al. (2014).

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23.4.6 Brainet Systems A particularly intriguing application of BTBI technologies, termed “Brainets,” involve the interfacing and processing of neuronal signals recorded from multiple brains, to enable information exchange between interconnected brains (Pais-Vieira et al., 2015) in order to perform cooperative tasks (Ramakrishnan et al., 2015). While not yet particularly sophisticated, recently demonstrated Brainet systems have already provided several interesting insights, including verification of potential direct communications between the brains of two rats located on different continents, after the rats had been permanently implanted with microelectrodes in the sensorimotor cortex (Pais-Vieira et al., 2013). Experiments have tested three different control systems using 2–3 implanted monkeys that shared BMI-mediated control of a virtual arm (Ramakrishnan et al., 2015). The first type of shared-control, using two subjects, merged recorded neural signals to move a virtual arm on a computer screen. The extracted brain data were summed and observed to improve performance, using noise cancelation. Another system involved two monkeys with partitioned contributions. The first monkey controlled the X-coordinate of the virtual arm, whereas the second monkey controlled the Y-coordinate. The overall task performance was shown to be improved as each monkey made fewer errors. (Interestingly, each monkey brain adapted and responded less to the other coordinate). A third experiment involved three animals, which together operated and controlled the virtual arm in three dimensions. As the monkeys were unaware that their final task was three-dimensional (given that each monkey had a two-dimensional display) this Brainet might be considered as a rudimentary “super-brain,” where the contributions of individual participants gave rise to higher-order operations that were not performable by each individual alone. Several cooperative BMI schemes have also been implemented in humans — for example, cooperative navigation of a spacecraft (Poli et al., 2013), cooperatively enabled decision making (Eckstein et al., 2012; Yuan et al., 2013; Poli et al., 2014), and movement planning (Wang and Jung, 2011). A four-brain Brainet system was dubbed an “organic computer” for mimicking simple computer-like operations, such as information-input retention, in a memory-like buffer composed of four serially connected rat brains (Pais-Vieira et al., 2015). This experimental Brainet system always outperformed singlebrain computation performance, particularly for discrimination tasks, in which the four brains “voted” to generate the response. This comprised an interesting advance toward the potential eventual emergence of very complex operations in systems with massive numbers of Brainet participants. A three-human BTBI system, called “BrainNet,” has been recently developed, which allowed three human subjects to collaboratively solve a task using noninvasive, direct brain-to-brain communication (Jiang et al., 2018). Similar to the twohuman BTBI system, the three-human BTBI system interface used EEG to record brain signals from the “Senders” and TMS to non-invasively deliver information to the brain of the “Receiver.” The two Senders’ brain signals were decoded using real-time EEG data analysis, extracting their decisions to rotate, or not rotate, a

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block in a Tetris-like game. These decisions were then uploaded to the cloud and subsequently downloaded and applied to the Receiver’s brain via magnetic stimulation of the occipital cortex. Once this information was received, the Receiver, who could not see the game screen, integrated the information and decided to rotate, or not rotate, the block. The experiment was repeated with five groups with an average accuracy of 0.813. Such high reliability supports further research to improve multi-person BTBI systems that empower future cooperative multi-human problem solving. Based on current elementary Brainet implementations, it is not yet clear if more complex Brainet systems might be employed for high-throughput information transfer between individual brains, although improved Brainet performance is expected with more advanced Brainet operations. With further progress in the field, the number of information transfer channels may increase, along with the number of subjects involved in each Brainet system. Clinically relevant Brainets that connect patients with therapists, or healthy to unhealthy individuals, would be a particularly interesting application.

23.4.7 Limited Prospects for Current Techniques

Current technological trajectories appear to be converging toward the creation of systems that will have the capacity to empower a human B/CI. However, since the human brain possesses cellular (neuron) and sub-cellular (synapse) processing elements, any technology that is capable of establishing a long-term and non-destructive, real-time human interface with the cloud must embody the following capabilities: (1) ultrahigh-resolution mobility, (2) autonomous or semiautonomous activity, (3) non-intrusive (ideally, physiologically imperceptible) ingress/egress into/from the human body, and (4) supplying sufficient and robust information transfer bandwidth for interfacing with external supercomputing systems. Current techniques, whether in present-day or extrapolated future forms, appear to be unscalable and incapable of fulfilling all of the temporal or spatial resolution requirements necessary for a properly comprehensive fully functional human B/CI.

23.5 Neuralnanorobotic Brain/Cloud Interface

Neuralnanorobotics is expected to provide a non-destructive, real-time, secure, long-term, and virtually autonomous in vivo system that can realize the first functional human B/CI (Martins et al., 2012, 2015, 2016). Neuralnanorobots could monitor relevant functional and structural connectome data, functional- actionpotential-based electrical information processing that occurs within synapses and neurons, and synaptic and neuronal structural changes associated with processing such electrolytic-based functional data (Seung, 2011). Monitoring the intracellular structural and functional connectome may be enabled by three classes of neuralnanorobots, introduced here as endoneurobots, synaptobots, and gliabots (Martins et al., 2016). They also constitute a non-intrusive, self-installed

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in vivo accessory high-speed nanofiber-optic network, which has been described elsewhere (Freitas, 1999b). More specifically, endoneurobots are autonomous neuron-resident neuralnanorobots that interface with all ∼86 × 109 human–brain neurons at the AIS to directly monitor and interact with action-potential-based electrically processed information. Synaptobots are autonomous neuron-resident neuralnanorobots that might employ multiple flexible stalk-mounted nanosensors to interface with each of the ∼2 × 1014 synapses of the human brain to directly monitor and interact with synaptically processed and stored information. Gliabots are glia-resident autonomous neuralnanorobots that are endowed with the capacity to monitor human–brain glial cells and may further serve as supportive infrastructure elements of the system. Subsequent iterations of an initial high-speed nanofiberoptic network may also incorporate wireless transmitters (self-embedded at the periphery of the human brain or within the skull) configured as an evenly distributed network that can wirelessly enable an interface with neurons, axons, and synapses to receive/transmit data from/to the cloud. To achieve a safe, reliable, high-performance B/CI system, a critical mission requirement is the initial establishment of intimate and stable connections to monitor the electrical firing patterns and waveforms of the ∼86 × 109 neurons and the ∼2 × 1014 synapses of the human brain at a suitable repetition rate (400–800 Hz is the reported average maximum range) (Wilson, 1999; Contreras, 2004). Neuralnanorobots themselves, and/or other dedicated nanomedical mapping devices, such as an envisaged Vascular Cartographic Scanning Nanodevice (VCSN) (Domschke and Boehm, 2017) might initially generate an ultra-high-resolution connectome map of the human brain. This would permit the acquisition and storage of detailed structural and functional connectomic data for each unique individual brain and allow for reporting specific spatial coordinates of different classes of neurons, as well as their typical electrophysiological spiking pattern behaviors (i.e., regular-spiking, bursting, or fast-spiking) (Seung, 2011). For the purposes of a B/CI, interfacing with neuronal and synaptically processed action-potential-based electrical brain activity alone (without monitoring chemically based information) may be sufficient to facilitate robust human B/CI systems. For example, one recent study has found that quantum dots can function as voltage-sensitive probes for real-time visualization of cellular membrane potential in neurons (Nag et al., 2017). Optical interrogation of individual cells and organelles with a spatial resolution of ∼100 nm might be enabled through the use of carbon-nanotube-based endoscopes that project from B/CI nanorobots (Singhal et al., 2011). Here, synaptically processed action-potential-based information is regarded as fundamental information (Fuhrmann et al., 2002; Shepherd, 2003; Abbott and Regehr, 2004). Synaptobots would detect virtually all of the synaptically processed action potentials and their waveforms and report synaptically processed spikes into the data handling system. Consequently, neuralnanorobots would assist with the prediction of neurotransmitter bursts that traverse each synaptic gap. All these data would be continually processed at sub-millisecond resolution, enabling a virtually real-time data stream between the human brain and the cloud.

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23.5.1 Endoneurobots and Gliabots Neuralnanorobots might be transdermally injected, after which they would navigate the vasculature and anchor to the endothelial cells of the BBB. A 10 μm3 volume of endoneurobots (Fig. 23.3) would subsequently egress the bloodstream, traverse the BBB by methods that have been extensively reviewed elsewhere (Freitas, 2016), enter the brain parenchyma, and begin to navigate within the neuropil. Subsequently, they would enter the neuron cell soma and position themselves intracellularly within the AIS (Martins et al., 2016). Similarly, a 10 μm3 volume of gliabots (Fig. 23.4) would egress the bloodstream, enter their respective glial cells, and position themselves intracellularly at the most appropriate intra-glial region, which can vary. The synaptobots would also enter the human body via the bloodstream, cross the BBB (possibly assisted by auxiliary transport nanorobots), enter the brain parenchyma, commence navigation within the neuropil, enter the neuron cell soma, and then proceed intracellularly into the pre-synaptic or post-synaptic structure of a synapse.

Figure 23.3 Artistic representation of endoneurobot (left) with diamondoid depiction (right). Grooves and orifices might facilitate propulsion within the neurons. Extendable tendrils could project from a number of these orifices to enable stable anchoring and precise post-anchor positioning. Image credits: (left) Frank Boehm, Nanoapps Medical, Inc.; (right) Yuriy Svidinenko, Nanobotmodels Company. (These conceptual illustrations do not literally represent the actual neuralnanorobot design of the endoneurobots).

The synaptobots would reside in the proper monitoring position within the neurons, in close proximity to presynaptic or postsynaptic structures. Once in place, these neuralnanorobots would monitor the action potentials and the structural changes initiated by the action-potential-based functional data. These data would be transferred from the synaptobots to corresponding endoneurobots (in some cases, with communications and other support from nearby gliabots). Once the data is received by the endoneurobots, it would proceed to the previously installed in vivo high-speed nanofiber-optic network, for subsequent

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transfer to the central units that are responsible for transmitting data to an external supercomputer. The auxiliary nanofiber-optic network system would provide essential support for the data that is transmitted by the endoneurobots and synaptobots, thereby minimizing their onboard data storage capacity requirements. The external supercomputer would communicate with the cloud and handle data post-processing.

Figure 23.4 Artistic representations of gliabots, which would self-migrate to glial cells and position themselves intracellularly at the most appropriate intra-glial regions to perform supportive B/CI operations. Image credits: (A) Frank Boehm, Nanoapps Medical, Inc.; (B) Julia Walker, Department of Chemical Engineering, Monash University. (These conceptual illustrations do not represent the actual neuralnanorobot design of the gliabots.)

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An optimal ingress strategy for all species of neuralnanorobots may employ the most rapid route to the human brain through the vasculature. Injection of the neuralnanorobots into the vasculature would be performed in the clinical environment under the supervision of medical personnel.2 Once injected, the neuralnanorobots would have access to the dense microvasculature of human brain, which is composed of an estimated ∼100 billion capillaries, with a combined surface area of ∼20 m2 and a total length of ∼400 miles. Intercapillary distances in the brain are typically ∼40 μm. Hence, each individual neuron within the human brain is at most 2–3 neurons away from a microcapillary (Pardridge, 2011). The cerebral microvasculature is protected by the BBB, which comprises endothelial cells that are closely abutted as tight junctions. Cumulatively, they form a protective barrier for the human brain that is only naturally crossable by small molecules and lipophilic drugs. Neuralnanorobots can traverse the BBB by methods that have been extensively reviewed elsewhere (Freitas, 2016). For example, the potential uptake of nanoparticles (∼100 nm) through the BBB from the vasculature has been investigated, encompassing numerous strategies including passive diffusion, temporary disruption of tight junctions, receptor mediated endocytosis, transcytosis, and inhibition of p-glycoprotein efflux pumps (Kreuter, 2004; Lockman et al., 2004; Agarwal et al., 2009; Hu and Gao, 2010). Since the BBB consists of the endothelium of cerebral capillaries, the choroid plexus epithelium, and the arachnoid membranes (Talegaonkar and Mishra, 2004), it comprises one of the most impermeable ingress pathways for nanomedical devices (100 nm–1 μm) due to the presence of tight junctions. Once the neuralnanorobots are distributed throughout the brain microvasculature, they could initially seek out any naturally present, randomly placed BBB junctional gaps or imperfections of various dimensions (Freitas, 2003). The BBB is not a perfect barrier, and perijunctional gaps of 0.5 μm have been reported (Stewart et al., 1987; Fraser and Dallas, 1993). Although various strategies exist for the traversal of nanoparticles through the BBB (Freitas, 2003, 2016; Grabrucker et al., 2016), further in-depth study would be required to precisely quantify the population, dimensions, and distribution of naturally occurring perijunctional gaps throughout the BBB network. This would be required if we are to consider passage through the BBB as the most appropriate method of ingress for some B/CI neuralnanorobots. A process akin to “diapedesis” (the movement of leukocytes out of the circulatory system and toward the site of tissue damage or infection) might be employed by B/CI neuralnanorobots to traverse the BBB. As described by Muller, diapedesis is a multistep procedure by which leukocyte cells cross endothelial cell boundaries from within the bloodstream in ameboid fashion to access sites of inflammation within tissues. In humans, leukocyte transmission through interfacial junctions between tight, laterally apposed (≤0.5 μm thick) endothelial cells involves a number of sequential steps, including the organized activity of molecules upon and within the endothelial cells themselves. Additionally, the dual roles that endothelial cells must play, include facilitating the traversal of (∼7–10 μm

2Alternatives to the regular injection of neuralnanorobots into the human vasculature include: intravenously, intranasally in aerosolized form, orally as a pill, via a dermal patch, or topical gel.

Neuralnanorobotic Brain/Cloud Interface

in diameter) leukocytes, while sustaining tight apposing seals at the leading and trailing edges of these “passengers” as they are transferred through the junction to negate the leakage of plasma into the interstitial domain (Boehm, 2013; Muller, 2013). Further, it is conceivable that a certain class of facilitative B/ CI neuralnanorobots with extendable/telescopic tendrils might project their nanoscopic appendages through smaller nanoscale perijunctional gaps to communicate with those neuralnanorobots that reside on the opposite side of the BBB, within the neocortex itself, or other relevant brain structures (Stewart et al., 1987; Fraser and Dallas, 1993; Freitas, 2003, 2016; Schrlau et al., 2008; Orynbayeva et al., 2012; Boehm, 2013). Should large BBB junctional gaps be detected by the neuralnanorobots, they may be exploited to penetrate within the neuropil. However, in cases where there is a complete absence of large BBB junctional gaps, mission-designed strategies, including a combination of cytopenetration, cytolocomotion, and histonatation, would likely permit access to the neuropil (Freitas, 1999b, 2003, 2016). The BBB may also be opened using intravenous mannitol (an old method) and ultrasound, externally delivered (Samiotaki et al., 2017; Wang et al., 2017). In addition, “substances may cross the BBB by passive diffusion, carrier mediated transport, receptor mediated transport, and adsorptive transcytosis” (Grabrucker et al., 2016). Once arrived at their designated neurons, the endoneurobots would autolocate and settle into their monitoring positions, intimately yet unobtrusively. Since action potentials might be initiated in different subcellular compartments, the endoneurobots would be anchored at the AIS (the most likely location for the initiation of action potentials), where they would monitor most action potentials. With some types of neurons, action potentials may be initiated at the first nodes of Ranvier or the axon hillock. Two synaptobots placed at these sites would ensure proper waveform detection of all action potentials. For example, the site of action potential initiation in cortical layer 5 pyramidal neurons is ∼35 μm from the axon hillock (in the AIS). For other classes of neurons, the action potential may be initiated at the first nodes of Ranvier, which for layer 5 pyramidal neurons is ∼90 μm from the axon hillock. The first myelin process is ∼40 μm from soma, whereas the length of the first myelin process is ∼50 μm (Palmer and Stuart, 2006). All three types of neuralnanorobots (endoneurobots, gliabots, and synaptobots) would monitor action potential-based electrical information using the same types of FET-based nanosensors embedded in their surfaces (Martins et al., 2015). For the monitoring of neuronal structural changes (some of these triggered by the processing of action potentials), once they are securely anchored to the internal neuron membrane surface (with “typical” neurons having a “volume of 14,000 μm3 or (∼24 μm)3), endoneurobots and synaptobots might employ a tactile scanning probe to image the surrounding membrane surface area of (1.4 μm)2 in ∼2 sec at ∼1 nm2 resolution (∼1 mm/s tip velocity), or ∼50 s to ∼0.2 nm (i.e., atomic) resolution (∼0.2 mm/s tip velocity), assuming a scan rate of ∼106 pixels/s” (Freitas, 1999b). For their part, gliabots would utilize the same probing strategy.

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23.5.2 Synaptobots Synaptobots (Fig. 23.5), the most diminutive (0.5 μm3) of the three types of neuralnanorobots, are responsible for monitoring synapses, which are relevant sub-cellular structures of the human brain. Synapses (either of the 5–25% electrical or 75–95% chemical variety (DeFelipe and Fariñas, 1992) are key components of the neural network that processes information. They play a crucial role in brain information processing (IBM, 2008) and are involved in learning and memory (Black et al., 1990; Bliss and Collingridge, 1993; Holtmaat and Svoboda, 2009; Liu et al., 2012), long-term and short-term memory storage and deletion (Kandel, 2001; Lee et al., 2008), and temporal information processing (Fuhrmann et al., 2002). They are also the key elements for signal transduction and plasticity in the human brain (Rollenhagen and Lübke, 2006; Rollenhagen et al., 2007). Synapses are so important that proper synapse formation during childhood provides the substrate for cognition, whereas improper formation or malfunction may lead to neurodevelopmental disorders, including various cognitive deficits and autism (Mcallister, 2007). The loss of synapses, as occurs in Alzheimer’s patients, is intimately related to cognitive decline (Dekosky and Scheff, 1990; Terry et al., 1991; Scheff and Price, 2006). The monitoring of synapses is expected to be essential for a stable and robust fully functional real-time B/CI. Synaptobots would be delivered via the brain microvasculature to avoid long-distance navigation within the brain parenchyma. Auxiliary transport nanorobots having a volume of ∼20 μm3 (∼3.2 μm × 2.5 μm × 2.5 μm) might each convey cargos of 24 synaptobots (total of ∼12 μm3) through the circulatory system and into the neuron soma. “The full complement of synaptobots would be transported by a fleet of ∼1 trillion auxiliary transport nanorobots, which perform ∼10 round trips to complete the insertion of all synaptobots” toward the implementation of the neuralnanorobotic system prior to the activation of the B/CI system. Individual neurons, on average, would obtain ∼117 such shipments, for an average overall distribution of 2800 synaptobots (≈2.42 × 1014 synapses/ 86 × 109 neurons), which would assign one nanorobot per synapse (Martins et al., 2012). The protocol for regularly updating the number of synaptobots in the brain (due to nanorobot damage, synapse elimination, neuron death, new synaptic formation, etc.) would be initiated by endoneurobots, which would communicate synaptic requirements to an external supercomputer. About 1 trillion auxiliary transport nanorobots may suffice to accommodate the workload of dynamically adjusting the physical deployment of synaptobots. Auxiliary transport nanorobots (∼2.5 μm) would adhere to a similar transit protocol for crossing the BBB and traversing the neuropil as the endoneurobots and gliabots, which are of comparable size (∼2.2 μm). Once arrived at the neurons, the auxiliary transport nanorobots would release their cargo of 24 synaptobots into the cytoplasms of each neuron. Following deployment, each synaptobot would either remain within the neuron soma, or navigate (utilizing its onboard locomotion system) from the neuron soma along the axon or dendrite into pre-synaptic or post-synaptic structures — the sites at

Neuralnanorobotic Brain/Cloud Interface

which synaptic monitoring would occur. To identify and differentiate presynaptic and postsynaptic structures of synapses, synaptobots would initially map (from within the cell) the surfaces of the axon (for axo-axonic, axo-somatic, and axodendritic synapses), the neuron soma (for somato-axonic, somato-somatic, or somato-dendritic synapses), and dendrites (for dendro-somatic, dendro-axonic, and dendro-dendritic synapses) (Harris, 1999).

Figure 23.5 Artistic representations of synaptobot (left) with diamondoid depiction (right) and calibrating at an axon (below). Oscillating piezo “fins” in conjunction with a central ovoid orifice might enable flow-through propulsion. In one configuration, ultrasensitive extendible/retractable “cuff ” nanosensors might externally encircle synaptic gaps to monitor neurotransmitter traffic. [Image credits: (left) Frank Boehm, Nanoapps Medical, Inc. and (right and below) Yuriy Svidinenko, Nanobotmodels Company. (These conceptual illustrations do not represent the actual neuralnanorobot design of the synaptobots)].

Synaptobots would possess an independent propulsion system for traversing along the axons and dendrites in both directions and may also exploit existing biological neuronal axonic or dendritic transport systems. The process of locomotion may be biomimetically inspired by mitochondrial locomotion strategies within human neurons, to minimize any physiological damage to neuronal processes. Alternatively, oscillating piezo “fins” may operate in

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conjunction with a ovoid orifice to enable flow-through propulsion for synaptobots (Fig. 23.5). The anticipated synaptobot deployment linear density would be ∼0.5 synaptobots/μm-length of axonic or dendritic processes, and the deployment volumetric number density would be ∼0.5 synaptobots/μm3 of axonic or dendritic processes. Maximum synaptobot velocities of ∼1 μm/s may be required to respect biocompatibility requirements, given that the bidirectional movements of mitochondria within axons and dendrites are reported to have velocities of 0.32–0.91 μm/s (Morris and Hollenbeck, 1995; Macaskill et al., 2009), with mitochondrial motility in non-transgenic (NTG) neurons reported as 0.93 ± 0.55 μm/s for anterograde motion and 0.97 ± 0.63 μm/s for retrograde motion (Trushina et al., 2012). Once securely emplaced at the monitoring positions in close proximity to presynaptic or postsynaptic structures, the primary synaptobot mission would be to monitor the exact timing and intensity of the electrical action potential information arriving at the synapses, and regularly monitor associated changes that occur in key structural elements of the synapse. With one synaptobot positioned near each synapse in the human brain, the action potential data might be acquired using ∼3375 nm3 FET-based neuroelectric nanosensors (Martins et al., 2015), enabling monitoring of the synaptically processed 4.31 × 1015 spikes/sec. Data collection would have a temporal resolution of at least 0.1 ms, which is sufficient for waveform characterization, even at the maximum human neuronal firing rate of 800 Hz. Facilitated and mediated by endoneurobots and gliabots, the synaptobots would subsequently transmit 5.52 × 1016 bits/sec of continuous action potential data (Martins et al., 2012) via an in vivo nanofiber-optic network system, as described above (Freitas, 1999b). Protocols for the application of the B/CI should include regular structural scanning of the human–brain connectome. The synaptobots, along with the endoneurobots and gliabots, could map and monitor relevant neuronal and synaptic structural changes using tactile scanning probe nanosensors (Freitas, 1999b) with special scanning tips that permit the synaptic bouton volume and shape to be measured, along with other relevant synaptic structural characteristics. This structural scanning process may include mapping the main ultrastructural components of a chemical synapse (whether located within the presynaptic axon terminal, the synaptic cleft, or post-synaptic terminal), the postsynaptic density (PSD), the active zone (AZ), synaptic vesicles (e.g., coated vesicles, dense core vesicles, and double-walled vesicles), endoplasmic reticulum, mitochondria, and punctum adhaerens (PA). While scrutinizing synaptic structural changes, neuralnanorobots would also detect induced changes via monitoring synaptic plasticity and crosstalk, including long-term synaptic based potentiation (LTP), long-term depression (LTD), short-term plasticity, metaplasticity, and homeostatic plasticity. For instance, the activity-dependent modification of PSD proteins occurring over timescales of seconds to hours is believed to underlie plasticity processes such as LTP and LTD (Sheng and Hoogenraad, 2007). Longer-term changes in the PSD structure and composition (from hours to days) involve altered protein synthesis, either

Neuralnanorobotic Brain/Cloud Interface

within the neuronal cell body, or dendrites (Sheng and Hoogenraad, 2007). The degradation of PSD proteins via the ubiquitin-proteasome system (Bingol and Schuman, 2006) also sculpts the PSD structure and plays a primary role in synaptic plasticity. Remarkably, recent evidence points toward the rapid exchange of PSD proteins, such as AMPARS and PSD-95, even between neighboring synapses under steady-state conditions (Sheng and Hoogenraad, 2007). Neuralnanorobotic monitoring of the PSD appears to be an essential requirement. The PSD is a complex molecular machine that dynamically alters its structure and composition in response to synaptic activity. The PSD dynamically regulates its components through protein phosphorylation, palmitoylation, local protein translation, the ubiquitin-proteasome system for protein degradation, and redistribution of specific proteins (e.g., CaMKIIα, AMPARs) both entering and leaving the PSD (Kim and Ko, 2006; Sheng and Hoogenraad, 2007). Signaling pathways are organized by PSD proteins to coordinate synaptic structural and functional changes. These proteins also regulate the trafficking and recycling of glutamate receptors (which determine synaptic strength and plasticity), promote the formation and maturation of excitatory synapses by co-aggregating with post-synaptic cell adhesion molecules, organize neurotransmitter receptors within the synaptic cleft, serve as a signaling apparatus. These proteins are also an essential component of an extraordinary synaptic signaling and regulatory assemblage. The “typical” PSD consists of a disk-like structure with an average diameter of 300–400 nm (range 200–800 nm), a thickness of 30–60 nm (Baude et al., 1993; Rácz et al., 2004; Okabe, 2007; Sheng and Hoogenraad, 2007), volume of ∼7.5 × 106 nm3, and mass of ∼1.1 GDa (Chen et al., 2005). Events involving LTP and LTD structural changes to dendritic spines can alter spine number, size, shape, and subcellular composition in both immature and mature spines (Bourne and Harris, 2008). The dendritic spine neck serves as a diffusion barrier (controlled by neuronal activity) to current flow and diffusion of molecules between the spine head and the dendrite. The geometry of the spine neck determines the rate of calcium efflux into the dendrite shaft and hence the degree of elevation of calcium concentrations within the spine head, following n-methyl-D-aspartate receptor (NMDAR) activation (Bloodgood and Sabatini, 2005; Alvarez and Sabatini, 2007; Sheng and Hoogenraad, 2007). In experimental work, dendritic spines that received LTP induction increased in volume, from 50 to 200% (Alvarez and Sabatini, 2007), with this increase persisting for more than 1 h following stimulation (Alvarez and Sabatini, 2007). Sustained head enlargement in dendritic spines is induced by LTP, due to F-actin polymerization. LTD causes α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor internalization with spine elongation and/or shrinkage of spine heads, due to actin depolymerization (Bourne and Harris, 2008). There exists a clear and strong association between synapse bouton size/shape and the organellar and macromolecular changes that occur within the bouton. This provides some level of information redundancy and suggests that monitoring all dendritic spine organelles and molecular components is likely unnecessary.

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Synaptobots may deduce a great deal of useful information subsequent to scanning the gross volume and shape of the spine. This information redundancy is expected to significantly reduce synaptobot monitoring tasks. The auxiliary nanofiber-optic system (Fig. 23.6), coupled with endoneurobot and gliabot data transmission support, would likely serve to minimize the onboard data storage requirements for synaptobots. An onboard synaptobot nanocomputer might be manifest as a ∼0.01 μm3 CPU device with ∼100 megaflops processing speed. The total internal volume of onboard synaptobot computation might be 0.11 μm3 to fulfill redundancy requirements. Such volume allocation is similar to other nanorobot designs with comparable degrees of mission design complexity (Freitas, 2005b).

Figure 23.6 Artistic representations of wireless nanoscale transmitter (left), and in its diamondoid form (right), which might interconnect to form an evenly distributed mesh network, subsequent to self-embedding at the periphery of the brain, on or within the skull. Image credits: (left) Frank Boehm, Nanoapps Medical, Inc.; (right) Yuriy Svidinenko, Nanobotmodels Company. (These conceptual illustrations do not represent the actual neuralnanorobot design of the wireless nanoscale transmitter.)

23.5.3 Data Transmission between Neuralnanorobots and the Cloud

Data from the three types of neuralnanorobots would be selected in real time, based on relevance to a specific use (such as auditory or visual content). The data would also be linked to other selected and related network activities, potentially with neurons in the prefrontal cortex and with mixed-selectivity neurons, which have been found to encode distributed information related to task-relevant aspects (Rigotti et al., 2013). Key design goals include: reducing latency, heat buildup, device size, and power for electronics; and tradeoffs for processing and latency between embedded/wearable/portable devices, local processing, and the cloud. One key future technological advance in reducing latency will be 5G mobile telecommunication, expected in the year 2020 (AT&T Business, 2018). 5G promises to ensure a new way for mobile users to experience VR and AR, for example, via the cloud without latency artifacts. “To give you a sense of scale, the typical refresh speeds for a computer screen are approximately 80 ms” (Weldon, 2016).

Neuralnanorobotic Brain/Cloud Interface

“However, for AR/VR, the industry is driving the conversation toward the Vestibulo-Ocular Reflex (VOR) — the neurological process by which the brain coordinates eye and head movements to stabilize images on the retina. This is critical to synchronizing virtual and real objects to create a coherent view. The entire VOR process takes the brain 7 ms, a more than 10× reduction over screento-brain propagation. … Today’s VR systems recommend a latency of 25,000), accuracy (10 Hz). Currently, the most suitable instrument types are hybrid Orbitrap mass spectrometers, certain Q-TOF instruments, and recently developed instruments that combine ion mobility with time of flight [21]. More recently, data independent acquisition (DIA) has been applied in metaproteomics studies [22, 23] with the promise of increasing metaproteome coverage and improving protein quantification. Proteins are usually identified by searching the acquired mass spectra against a protein sequence database. In this strategy, experimental spectra are compared to theoretical spectra predicted from a comprehensive protein database.

What Does a General Metaproteomics Workflow Look Like?

The database should contain the expected protein sequences for a given sample. Research over the last few years has shown that, ideally, the protein sequence database used for metaproteomics should be derived from a metagenomics/ metatranscriptomics sequencing experiment of the same samples used for metaproteomics [24, 25]. The use of protein sequences from reference databases such as Uniprot has been shown to greatly reduce the number of proteins identified [26] and potentially increase rates of false positives and incorrect taxonomic assignment of identified proteins [27]. In special cases, for example, when the study is performed on gnotobiotic animals inoculated with a defined microbial community, a database assembled from reference databases can be used [10, 28].

32.3 What Does a General Metaproteomics Workflow Look Like?

The most common metaproteomics workflow consists of sample collection and preservation, cellular lysis, protein extraction, tryptic digestion of proteins into peptides, peptide separation by LC, and analyses of peptide masses (MS) and their fragments (MS/MS) by mass spectrometry (Fig. 32.1). The success of a metaproteomic study depends on 3 general factors: efficiency of protein extraction, efficiency of separation, and unambiguous identification [29]. The efficiency of protein extraction from tissues or environmental samples is dependent on sample preservation, available sample amount, and composition. Adequate sample preservation during collection is critical to avoid protein degradation during storage. Some preservatives have been tested to ensure sample integrity without freezing when necessary [30], although flash freezing of samples remains a preferred preservation method. Similar to other meta-omics approaches such as metagenomics, much progress has been made in reducing the input amounts needed for sample preparation. Current filter-aided or cartridgebased sample preparation protocols can work with just a few milligrams of sample (e.g., tissue or stool), while also efficiently removing interfering compounds. Some cell types are more easily disrupted, such as animal cells or gram-negative bacteria, whereas others, such as fungi, plant cells, or gram-positive bacteria, require harsher treatments for cell lysis. To reduce bias against specific cell types during extraction, it is critical to optimize protein extraction protocols for specific samples. For example, cell lysis by ultra-sonication in sodium dodecyl sulfate (SDS) lysis buffer has been shown to work well for metaproteome analyses of intestinal communities [31]. To achieve a high number of peptides, and consequently, a high number of proteins identified, sample complexity needs to be reduced by separating either proteins or peptides. The most common separation approach consists of on­ line separation of peptides by nano-LC using a reversed-phase column (RP) and injection into the mass spectrometer. In addition, a second on-line separation step (2D-LC) can be used by adding a second column (e.g., strong cation exchange)

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Figure 32.1 General workflow employed in metaproteomics experiments. (A) Metagenome shotgun sequencing can be used to generate the reference database for metaproteomics. (B) Metaproteomics workflow including generation of peptides and high-resolution MS analysis. Abbreviations: LC, liquid chromatography; MS, mass spectrometry.

652 Five Key Aspects of Metaproteomics

How Much Does It Cost as Compared to Other Meta-Omics Technologies?

upstream of the RP column [32]. Before on-line separations, proteins are usually prepared and digested following the filter-aided sample preparation protocol (FASP) [33]. As an additional step, proteins can be pre-separated by 1D-SDS PAGE gel electrophoresis and peptides prepared by in gel digestion prior to nano-LC (GeLC method). The separation approach of choice will depend on the sample and LC instrumentation available [20]. Each metaproteomics run will generate tens to hundreds of thousands of mass spectra of peptides and their fragments that are then used for peptide and protein identification. For identification, mass spectra are computationally matched to theoretical mass spectra derived from a protein sequence database. Development of efficient search algorithms is a very active research field providing a great diversity of commercial and open-access software. As discussed in question 2, the choice of nano-LC system, MS instrumentation, and reference database will determine the power of protein identification and the ability to discriminate homologous proteins from different organisms.

32.4 How Accessible Is Metaproteomics to the General Scientific Community, and How Much Does It Cost as Compared to Other Meta-Omics Technologies?

Similar to DNA or RNA sequencing, MS-based research is often facilitated by dedicated research service core facilities available at many institutions. Thus, to conduct a metaproteomics experiment, the researcher does not necessarily need a mass spectrometer in their laboratory. Mass spectrometry research centers can provide the analyses as a service; however, many centers currently do not have adequate experience with the preparation, acquisition, and analyses of samples and data for metaproteomics, and thus, it will be up to the researcher to guide the process through frequent communication with facility staff. A frequent mistake is to transfer approaches developed for proteomics of individual organisms or tissues directly to metaproteomics samples, which have additional challenges such as sample matrix, diversity of cell types in the sample, and protein inference issues caused by the presence of large numbers of homologous proteins in the sample and protein sequence database. The costs for metaproteomics analyses per sample are similar to those of metagenomics or metatranscriptomics experiments, and rapid developments in the area of LC and MS are decreasing overall measurement costs. One of the major cost drivers in metaproteomics is the amount of run time needed on the LC-MS/ MS system per sample. The amount of run time needed is changing in recent years, for example, runs in the past would often take 24 hours or more per sample for protein identification, whereas today runs of 2 to 6 hours are often sufficient. The hands-on time for metaproteomic sample preparation is around 1.5 to 2 days, and large numbers of samples can be prepared in parallel [34]; thus, personnel costs associated with sample preparation are also similar to other meta-omics approaches.

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32.5 What Do the Data Look Like, and How Can They Be Analyzed? Many proteomics software packages, such as MaxQuant [35] and Proteome Discoverer (Thermo Fisher Scientific, Bremen, Germany), allow qualitative and quantitative exploration of metaproteomic data. The resulting tables can be exported and further analyzed with other software, including general statistical software such as R [36] and specialized gene expression analysis software such as Perseus [37]. Open-source software dedicated to metaproteomics, such as MetaProteomeAnalyzer [38] and MetaQuantome [39], provide tools for data analyses and interpretation. For a review of specialized metaproteomics software, see [40]. After database searching, the output usually consists of a large table that provides, for each protein, an identity, an annotation, and the number of peptide­ spectrum matches (PSMs), among other features (Fig. 32.2A). Quantitative metaproteomics experiments interrogate the whole metaproteome and identify which proteins show differential abundances between different conditions. For differential metaproteomics, spectral counting approaches seem to be more robust for estimating abundances compared to peptide intensity approaches [16]; however, this has to be tested more extensively. Spectral counting approaches use the number of PSMs mapped to each protein as the quantitative value, which is usually normalized to protein length and total PSM number in the samples [41]. In metaproteomics, the normalized spectral abundance factor (NSAF) is frequently used [42]. Metaproteomic datasets, like most count-based microbiome datasets, are compositional, and thus, appropriate statistical methods should be used to address data compositionality issues [43]. The richness of information provided by the metaproteomic data allow researchers to look at complex biological questions that can be addressed using protein abundances (Fig. 32.2). For example, species abundances can be calculated by summing the relative protein abundances for each species; these estimates can then provide the microbial community composition in terms of biomass contributions of different taxa (Fig. 32.2B). For differential abundance analyses of proteins, various statistical methods with correction for multiple hypothesis testing can be used to identify proteins that differ significantly in abundance between treatments, conditions, and body locations. Abundance differences and significance thresholds can, for example, be displayed using a volcano plot (Fig. 32.2C). Differential abundance analyses are often a critical tool for identifying genes/proteins of particular relevance under a given condition and thus helps to narrow the focus. Understanding how samples/treatments as a whole differ can also help narrow the results. For this, multivariate analyses visualized by principal component analysis (PCA) plots and hierarchical clustering help characterize the differences across samples (Fig. 32.2D). For example, hierarchical clustering identifies similarities and differences among all samples by separating them from different experimental

What Do the Data Look Like, and How Can They Be Analyzed?

states based on their protein abundance values. This enables the identification of protein clusters of similar abundance changes across treatments. These types of tests and visualizations are accessible through R packages or the free GUI-based software Perseus [37] customized for proteomic data analysis.

Figure 32.2 Examples of different analysis approaches to extract and help the interpretation of biological information from metaproteome datasets. (A) Table containing all proteins identified per taxon and associated PSMs. (B) Microbiome composition in terms of biomass contributions can be provided by the summed relative protein abundance of each taxon. (C) Volcano plots display abundance differences and thresholds derived from statistical tests corrected for multiple hypothesis testing and allow for the identification of differentially abundant proteins between treatments/conditions. (D) Multivariate analyses, visualized by PCA plots and hierarchical clustering, help to classify samples according to protein abundance differences. (E) COG represent functional protein groups across different microbes. (F) Pathway reconstruction supported by pathway databases. (G) Analyses of protein–protein interaction networks by mapping protein functional categories. These types of tests and visualizations are available through R packages [30] or the free GUI-based software Perseus [31] customized for proteomic data analysis. Abbreviations: COG, Clusters of Ortholog Groups; PCA, principal component analysis; PSM, peptide-spectrum match.

For functional analyses, proteins can be classified into Clusters of Ortholog Groups (COG). Each COG represents a group of orthologous proteins from different microbes sharing the same functional characteristics (Fig. 32.2E) [44]. Similarly, Gene Ontology [45] and eggNOG [46] provide functional annotations. Furthermore, analyses of specific metabolic pathways through manual reconstruction or use of automated tools, such as Pathway Tools [47], can provide a more in-depth

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visualization of the functional state of the metaproteome. The commonly used pathway databases used to support pathway reconstruction are MetaCyc [48] and KEGG pathways [49] (Fig. 32.2F). Construction of protein–protein interaction networks can give insights about protein function in biological processes. Protein– protein interactions can be visualized by mapping, for example, COG categories against the String database [50]. In addition, a new tool called MicrobioLink [51] offers a pipeline for downstream analyses of host–microbiome functional interactions. Information on additional tools for functional analyses and their validation can be found in a recent comparative study by Sajulga and colleagues [52]. Lastly, the integration of metaproteomics with other “meta-omics” approaches, such as metagenomics and metatranscriptomics, are growing in popularity since it allows the investigation of complex mechanisms across different molecular layers. Several workflow analyses for the integration of meta-omics datasets have been proposed [53–55]. In summary, metaproteomics is a rapidly growing field that allows to characterize microbial communities and host-associated microbiomes on multiple levels. The enabling technologies (LC and MS/MS) see major improvements every year, while also the number of metaproteomics experts is growing, which will make metaproteomics measurements more broadly accessible on the near term. The nascent metaproteomics community has started to organize, and a first set of inter-lab comparison studies is under way to test and validate differing metaproteomics workflows, with the ultimate goal to consolidate and standardize some of the approaches. At the same time, new metaproteomics wet lab and computational methods are continuously being developed to provide additional capabilities. We are confident that metaproteomics will continue to grow in its importance as a tool for the study of host-associated microorganisms.

Abbreviations

BAL: COG: DIA: ETA: EVs: FASP: IBD: LC: MS: NSAF: PCA: PSMs: RP: SDS: VAP:

bronchoalveolar lavage Clusters of Ortholog Groups

data independent acquisition

endotracheal aspirate

extracellular vesicles filter-aided sample preparation protocol

inflammatory bowel disease

liquid chromatography

mass spectrometry normalized spectral abundance factor principal component analysis

peptide-spectrum matches

reversed-phase

sodium dodecyl sulfate

ventilator-associated pneumonia

References

Disclosures and Conflict of Interest This chapter was originally published as: Salvato, F., Hettich, R. L., Kleiner, M. (2021). Five key aspects of metaproteomics as a tool to understand functional interactions in host-associated microbiomes. PLoS Pathog., 17(2), e1009245, doi:10.1371/ journal.ppat.1009245, under the Creative Commons Attribution license (http:// creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates.

Funding: The authors would like to acknowledge support from the following grants: USDA National Institute of Food and Agriculture Hatch project 1014212 (MK), by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number R35GM138362 (MK), the Novo Nordisk Foundation INTERACT project under Grant number NNF19SA0059360 (MK), the Foundation for Food and Agriculture Research Grant ID: 593607 (MK), the NC State Chancellor’s Faculty Excellence Program Cluster on Microbiomes and Complex Microbial Communities (MK), and NIH R01-DK70977 (RHL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the chapter. Competing interests: The authors have declared that no competing interests exist.

Acknowledgments: We are grateful to Dr. Heather Maughan for feedback on the chapter.

References

1. Pickard JM, Zeng MY, Caruso R, Núñez G. Gut microbiota: role in pathogen colonization, immune responses, and inflammatory disease. Immunol Rev. 2017:70–89.

2. Theriot CM, Koenigsknecht MJ, Carlson PE, Hatton GE, Nelson AM, Li B, et al. Antibioticinduced shifts in the mouse gut microbiome and metabolome increase susceptibility to Clostridium difficile infection. Nat Commun. 2014;5:3114. 3. Zhou J, He Z, Yang Y, Deng Y, Tringe SG, Alvarez-Cohen L. High-throughput metagenomic technologies for complex microbial community analysis: open and closed formats. MBio. 2015;6:e02288-14. 4. Mills RH, Vázquez-Baeza Y, Zhu Q, Jiang L, Gaffney J, Humphrey G, et al. Evaluating metagenomic prediction of the metaproteome in a 4.5-year study of a patient with Crohn’s disease. mSystems. 2019;4:e00337-18.

5. Moya A, Ferrer M. Functional edundancy-induced stability of gut microbiota subjected to disturbance. Trends Microbiol. 2016:402–13.

6. Heintz-Buschart A, Wilmes P. Human gut microbiome: function matters. Trends Microbiol. 2018;26:563–74.

7. Kleiner M. Metaproteomics: much more than measuring gene expression in microbial communities. mSystems. 2019;4:e00115-19. 8. Wilmes P, Bond PL. The application of two-dimensional polyacrylamide gel electrophoresis and downstream analyses to a mixed community of prokaryotic micro­ organisms. Environ Microbiol. 2004;6:911–20.

9. Lassek C, Burghartz M, Chaves-Moreno D, Otto A, Hentschker C, Fuchs S, et al. A metaproteomics approach to elucidate host and pathogen protein expression during

657

658

Five Key Aspects of Metaproteomics

catheter-associated urinary tract infections (CAUTIs). Mol Cell Proteomics. 2015;14: 989–1008.

10. Patnode ML, Beller ZW, Han ND, Cheng J, Peters SL, Terrapon N, et al. Interspecies competition impacts targeted manipulation of human gut bacteria by fiber-derived glycans. Cell. 2019;179:59–73.e13.

11. Pathak KV, McGilvrey MI, Hu CK, Garcia-Mansfield K, Lewandoski K, Eftekhari Z, et al. Molecular profiling of innate immune response mechanisms in ventilator-associated pneumonia. Mol Cell Proteomics. 2020;19:1688–705.

12. Belda-Ferre P, Williamson J, Simón-Soro Á, Artacho A, Jensen ON, Mira A. The human oral metaproteome reveals potential biomarkers for caries disease. Proteomics. 2015;15: 3497–507.

13. Gavin PG, Mullaney JA, Loo D, Cao KAL, Gottlieb PA, Hill MM, et al. Intestinal metaproteomics reveals host-microbiota interactions in subjects at risk for type 1 diabetes. Diabetes Care. 2018;41:2178–86. 14. Tanca A, Palomba A, Fraumene C, Manghina V, Silverman M, Uzzau S. Clostridial butyrate biosynthesis enzymes are significantly depleted in the gut microbiota of nonobese diabetic mice. mSphere. 2018;3:e00492-18.

15. Zhang X, Deeke SA, Ning Z, Starr AE, Butcher J, Li J, et al. Metaproteomics reveals associations between microbiome and intestinal extracellular vesicle proteins in pediatric inflammatory bowel disease. Nat Commun. 2018;9:2873. 16. Kleiner M, Thorson E, Sharp CE, Dong X, Liu D, Li C, et al. Assessing species biomass contributions in microbial communities via metaproteomics. Nat Commun. 2017;8:1–38.

17. Kleiner M, Dong X, Hinzke T, Wippler J, Thorson E, Mayer B, et al. A metaproteomics method to determine carbon sources and assimilation pathways of species in microbial communities. Proc Natl Acad Sci U S A. 2018;115:E5576–84.

18. Jehmlich N, Vogt C, Lünsmann V, Richnow HH, von Bergen M. Protein-SIP in environmental studies. Curr Opin Biotechnol. 2016:26–33.

19. Smyth P, Zhang X, Ning Z, Mayne J, Moore JI, Walker K, et al. Studying the dynamics of the gut microbiota using metabolically stable isotopic labeling and metaproteomics. bioRxiv. 2020. doi: 10.1101/2020.03.09.982884.

20. Hinzke T, Kouris A, Hughes R-A, Strous M, Kleiner M. More is not always better: evaluation of 1D and 2D-LC-MS/MS methods for metaproteomics. Front Microbiol. 2019;10:238.

21. Meier F, Brunner AD, Koch S, Koch H, Lubeck M, Krause M, et al. Online parallel accumulation–serial fragmentation (PASEF) with a novel trapped ion mobility mass spectrometer. Mol Cell Proteomics. 2018;17:2534–45.

22. Aakko J, Pietilä S, Suomi T, Mahmoudian M, Toivonen R, Kouvonen P, et al. Dataindependent acquisition mass spectrometry in metaproteomics of gut microbiota— implementation and computational analysis. J Proteome Res. 2020;19:432–6.

23. Long S, Yang Y, Shen C, Wang Y, Deng A, Qin Q, et al. Metaproteomics characterizes human gut microbiome function in colorectal cancer. NPJ Biofilms Microbiomes. 2020;6: 1–10.

24. Tanca A, Palomba A, Fraumene C, Pagnozzi D, Manghina V, Deligios M, et al. The impact of sequence database choice on metaproteomic results in gut microbiota studies. Microbiome. 2016;4:51.

References

25. Rechenberger J, Samaras P, Jarzab A, Behr J, Frejno M, Djukovic A, et al. Challenges in clinical metaproteomics highlighted by the analysis of acute leukemia patients with gut colonization by multidrug-resistant enterobacteriaceae. Proteomes. 2019;7:2.

26. Kumar P, Johnson JE, Easterly C, Mehta S, Sajulga R, Nunn B, et al. A sectioning and database enrichment approach for improved peptide spectrum matching in large, genome-guided protein sequence databases. bioRxiv. 2019. doi: https://doi. org/10.1101/843078.

27. Tanca A, Palomba A, Deligios M, Cubeddu T, Fraumene C, Biosa G, et al. Evaluating the impact of different sequence databases on metaproteome analysis: insights from a lab-assembled microbial mixture. PLoS ONE. 2013;8:e82981.

28. McNulty NP, Wu M, Erickson AR, Pan C, Erickson BK, Martens EC, et al. Effects of diet on resource Utilization by a model human gut microbiota containing Bacteroides cellulosilyticus WH2, a symbiont with an extensive glycobiome. PLoS Biol. 2013;11:e1001637.

29. Hettich RL, Pan C, Chourey K, Giannone RJ. Metaproteomics: harnessing the power of high performance mass spectrometry to identify the suite of proteins that control metabolic activities in microbial communities. Anal Chem. 2013;85:4203–14.

30. Saito MA, Bulygin VV, Moran DM, Taylor C, Scholin C. Examination of microbial proteome preservation techniques applicable to autonomous environmental sample collection. Front Microbiol. 2011;2:215. 31. Zhang X, Li L, Mayne J, Ning Z, Stintzi A, Figeys D. Assessing the impact of protein extraction methods for human gut metaproteomics. J Proteome. 2018;180:120–7.

32. Taylor P, Nielsen PA, Trelle MB, Horning OB, Andersen MB, Vorm O, et al. Automated 2D peptide separation on a 1D nano-LC-MS system. J Proteome Res. 2009;8:1610–6.

33. Wiśniewski JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nat Methods. 2009;6:359–62.

34. Gonzalez CG, Wastyk HC, Topf M, Gardner CD, Sonnenburg JL, Elias JE. High-throughput stool metaproteomics: method and application to human specimens. mSystems. 2020;5:e00200-20.

35. Tyanova S, Temu T, Cox J. The MaxQuant computational platform for mass spectrometrybased shotgun proteomics. Nat Protoc. 2016;11:2301–19.

36. Oxford Protein Informatics Group. Citing R packages in your thesis/paper/ assignments. Available at: https://www.blopig.com/blog/2013/07/citing-r-packagesin-your-thesispaperassignments/ (accessed on April 27, 2021).

37. Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, Geiger T, et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods. 2016;13:731–40.

38. Heyer R, Schallert K, Büdel A, Zoun R, Dorl S, Behne A, et al. A robust and universal metaproteomics workflow for research studies and routine diagnostics within 24 h using phenol extraction, FASP digest, and the MetaProteomeAnalyzer. Front Microbiol. 2019;10:1883.

39. Easterly CW, Sajulga R, Mehta S, Johnson J, Kumar P, Hubler S, et al. MetaQuantome: an integrated, quantitative metaproteomics approach reveals connections between taxonomy and protein function in complex microbiomes. Mol Cell Proteomics. 2019;18:S82–91.

659

660

Five Key Aspects of Metaproteomics

40. Kunath BJ, Minniti G, Skaugen M, Hagen LH, Vaaje-Kolstad G, Eijsink VGH, et al. Metaproteomics: sample preparation and methodological considerations. Adv Exp Med Biol. 2019;1073:187–215. 41. Gokce E, Shuford CM, Franck WL, Dean RA, Muddiman DC. Evaluation of normalization methods on GeLC-MS/MS label-free spectral counting data to correct for variation during proteomic workflows. J Am Soc Mass Spectrom. 2011;22:2199–208.

42. Florens L, Carozza MJ, Swanson SK, Fournier M, Coleman MK, Workman JL, et al. Analyzing chromatin remodeling complexes using shotgun proteomics and normalized spectral abundance factors. Methods. 2006;40:303–11.

43. Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome datasets are compositional: and this is not optional. Front Microbiol. 2017;8:2224.

44. Tatusov RL, Koonin EV, Lipman DJ. A genomic perspective on protein families. Science. 1997;278:631–7. 45. The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 2019;47:D330–8.

46. Huerta-Cepas J, Szklarczyk D, Forslund K, Cook H, Heller D, Walter MC, et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44:D286–93.

47. Karp PD, Paley S, Romero P. The pathway tools software. Bioinformatics. 2002; 18 (suppl 1): S225–S232.

48. Caspi R, Foerster H, Fulcher CA, Kaipa P, Krummenacker M, Latendresse M, et al. The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 2007;36:D623–31.

49. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016;44:D457–62. 50. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al. The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017;45:D362–8.

51. Andrighetti T, Bohar B, Lemke N, Sudhakar P, Korcsmaros T. MicrobioLink: an integrated computational pipeline to infer functional effects of microbiome-host interactions. Cell. 2020;9:1278. 52. Sajulga R, Easterly C, Riffle M, Mesuere B, Muth T, Mehta S, et al. Survey of metaproteomics software tools for functional microbiome analysis. PLoS ONE. 2020;15:e0241503.

53. Heintz-Buschart A, May P, Laczny CC, Lebrun LA, Bellora C, Krishna A, et al. Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes. Nat Microbiol. 2016;2: 16180.

54. Broberg M, Doonan J, Mundt F, Denman S, McDonald JE. Integrated multi-omic analysis of host-microbiota interactions in acute oak decline. Microbiome. 2018;6:21. 55. Delogu F, Kunath BJ, Evans PN, Arntzen M, Hvidsten TR, Pope PB. Integration of absolute multi-omics reveals dynamic protein-to-RNA ratios and metabolic interplay within mixed-domain microbiomes. Nat Commun. 2020;11: 4708.

SECTION 4

SARS-COV-2 AND COVID-19

Chapter 33

COVID-19: Hundred Questions and Answers for Healthcare Providers and the Public Raj Bawa, MS, PhD, MD ‘22 Patent Law Department, Bawa Biotech LLC, Ashburn, Virginia, USA Guanine, Inc., Rensselaer, New York, USA Albany College of Pharmacy and Health Sciences, Pharmaceutical Research Institute, Albany, New York, USA Teva Pharmaceutical Industries, Ltd., Israel [email protected]

Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa Copyright © 2022 Raj Bawa ISBN 978-981-4877-84-8 (Hardcover), 978-1-003-18043-2 (eBook) www.jennystanford.com

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Copyright © 2022 Raj Bawa. All rights reserved. This work is free and may be used by anyone for any purpose. As a service to authors and researchers, as copyright holder, I permit unrestricted use, distribution, online posting and reproduction of this article or unaltered excerpts therefrom, in any medium, provided the original source is clearly identified and properly credited.

This chapter has been organized from information courtesy of the Food and Drug Administration (FDA), the Centers for Disease Control and Prevention (CDC), the National Institutes of Health (NIH), the World Health Organization (WHO), US Department of Agriculture (USDA), among others. Since the information on SARS­ CoV-2 and COVID-19 is rapidly evolving, it is recommended to consult the most recent guidelines from the FDA, CDC, and NIH on these topics. As of June 8, 2021, globally, there have been 174,591,505 coronavirus cases, 3,757,419 deaths and 157,941,391 patients have recovered from COVID-19. See: Worldometer. COVID-19 outbreak live update. Available at: https://www.worldometers.info/coronavirus (accessed on June 8, 2021). Another authoritative source is the Johns Hopkins Coronavirus Resource Center: https://coronavirus.jhu.edu/. Also, refer to the following open access reviews:

• R. Bawa. (2022). The age of COVID-19: medical facts and fiction. In: R. Bawa, E. H. Chang, G. F. Audette, A. Diwan, and S. A. Faiz (eds.). Advances in Medical Biochemistry, Genomics, Physiology, and Pathology, Jenny Stanford Publishing, Singapore, chapter 1, pp. 7–54. • R. Bawa. (2022). SARS-CoV-2 and COVID-19: a perspective. In: R. Bawa (ed.). Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data, Jenny Stanford Publishing, Singapore, chapter 1, pp. 7–38.

Keywords: 3D printing, 3D-printed PPE, a test-negative design study, acute respiratory distress syndrome (ARDS), acute respiratory illness, aerosolization, air filtration protection, alcohol-based hand sanitizers, alveolar edema, anesthesia gas machines, angiotensin converting enzyme 2 (ACE2) receptor, Animal and Plant Health Inspection Service (APHIS), antibodies, antibody test, antigen test, apoptosis, asymptomatic, asymptomatic infection, at-home test, authorized COVID-19 vaccines, bamlanivimab, baricitinib, BEST Initiative, case-control studies, canine infectious respiratory disease, casirivimab, Center for Drug Evaluation and Research (CDER), Center for Veterinary Biologics (CVB), Centers for Disease Control and Prevention (CDC), Centers for Medicare & Medicaid Services (CMS), chimeric viruses, chloroquine phosphate, clinical data, clinical outcome, clinical studies, clinical trial results, cloth face covering, cohort studies, convalescent plasma, convalescent plasma therapy, Coordinated Outbreak Response and Evaluation (CORE) Network, coronae, Coronavirus Disease 2019 (COVID-19), Coronavirus Treatment Acceleration Program (CTAP), COVID-19 Prevention Network, COVID-19 vaccines during pregnancy, cytokine storm,

COVID-19: Hundred Questions and Answers for Healthcare Providers and the Public

delta (δ) coronavirus, deoxyribonucleic acid (DNA), diagnostic test, Direct to Consumer (DTC), discovery and screening phase, disinfectant products, disinfectant sprays, drug sponsor, e-cigarette, ecologic analysis assessments, emergency medical system (EMS), Emergency Use Authorization (EUA), emerging and reemerging infections, endocytosis, endoplasmic reticulum Golgi intermediate (ERGIC), essential workers, expanded access, extracorporeal membrane oxygenation (ECMO), FDA’s Center for Veterinary Medicine, FDA’s MedWatch Adverse Event Reporting, FDA’s Sentinel System, Federal Food, Drug, and Cosmetic (FD&C) Act, Federal Trade Commission (FTC), feline enteric coronavirus, feline infectious peritonitis (FIP), first responders, fluid barrier, Food and Drug Administration (FDA), fraudulent COVID-19 products, frontline workers, “gain-of-function” research, Golgi intermediate, hand sanitizers, healthcare personnel, herd immunity, HHS Protect Public Data Hub, human cells, tissues, or cellular and tissue-based products (HCT/Ps), hydroxychloroquine sulfate, imdevimab, intensive care unit (ICU), invasive mechanical ventilation, Investigational New Drug (IND) Application, investigational treatments, Janssen COVID-19 Vaccine, Johns Hopkins Coronavirus Resource Center, kennel cough, National Animal Health Laboratory Network (NAHLN), laboratory test, live-attenuated viral vaccine, long-standing systemic health, long-term side effects, low vaccination rates, macrophages, major food allergens, major histocompatibility complexes I and II (MHC I and II), medical countermeasures (MCMS), microbiologist, m-RNA, moderate COVID-19, Moderna COVID-19 Vaccine, molecular test, monoclonal antibody, mRNA-based COVID-19 vaccine, N95 respirators, nanoparticle, nasopharyngeal swab, National Infusion Center Association (NICA), National Institutes of Health (NIH), new drug application (NDA), non­ prescription tests, non-surgical face masks, norovirus, hepatitis A, nucleic acid amplification test (NAAT), nucleocapsid protein, nursing homes, “off-label” use, Olumiant, Operation Quack Hack, over-the-counter (OTC), packaging and labeling requirements, pandemic, pathogenesis, personal protective equipment (PPE), plasma, point-of-care (POC), PK and PD data, positive for SARS-CoV-2 antibodies, positive pressure breathing devices, positive test result, post-market authority, preclinical, pregnancy and COVID-19, PREP Act Coverage, prescription tests, priority allergens, prophylaxis, public health emergency, rapid test, realworld assessments, real-world conditions, regulatory mandates, remdesivir, repurposing of existing drugs, respiratory etiquette, rough endoplasmic reticulum (RER), routine blood donor screening, RT-PCR test, saliva tests, SARS-CoV-2, scanning electron micrograph, screening method assessments, sensitivity, serial screening programs, serology test, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), severe COVID-19, severe pneumonia, single-stranded RNA, single-stranded RNA genome, social distancing, social inequities, sodium chlorite products, species-specific coronavirus vaccines, specificity, spikeconverting enzyme 2 (ACE2), spike proteins, supplemental oxygen, supply chain disruptions, surfactant, surgical masks, temporary policy for food labeling, Pfizer-BioNTech COVID19 Vaccine, transmission electron micrograph, tribal nations, turnaround time, type II pneumocytes, underlying medical conditions, unvaccinated individuals, US transportation hubs, USDA’s National Veterinary Services Laboratories (NVSL), Vaccine Adverse Event Reporting System (VAERS), variants of concern, Veklury, viral test, viral vector, virus-neutralizing antibodies, v-safe system, waiting period, World Health Organization (WHO), Wuhan Institute of Virology (WIV)

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100 Questions 1. 2. 3. 4.

5. 6. 7. 8. 9. 10.

11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26.

Overall, how is the FDA addressing the COVID-19 pandemic? What is the FDA’s role in approving vaccines and what is being done to produce a COVID-19 vaccine? What is an Emergency Use Authorization (EUA)? What safety and effectiveness data are required to be submitted to FDA for an EUA request for a vaccine intended to prevent COVID-19? What would involve a full clinical trial for a COVID-19 vaccine? Why should I get a COVID-19 vaccine? Do the COVID-19 vaccines work against the new variants? Is COVID-19 vaccine safety monitored after approval or authorization? Can the vaccine give me COVID-19? If I already had COVID-19 and recovered, do I still need to get vaccinated? Is it safe to get a COVID-19 vaccine if I have an underlying medical condition? Is it safe to get a COVID-19 vaccine if I have allergies? Is it better to get natural immunity to COVID-19 rather than immunity from a vaccine? Will the shot hurt or make me sick? I am pregnant. Is it safe for me to get the COVID-19 vaccine? I am breastfeeding. Is it safe for me to get the COVID-19 vaccine? Are there long-term side effects from the COVID-19 vaccine? How do I know if the COVID-19 vaccine is safe? How do I report problems or bad reactions after getting a COVID-19 vaccine? Do I have to continue to wear a mask and avoid close contact with others after I have been vaccinated? Do clinical trial results show whether vaccines are effective? Why would the effectiveness of vaccines be different after the clinical trials? How will experts evaluate the COVID-19 vaccines in real-world conditions? Will assessments determine if the vaccines protect people from severe COVID-19 illness? Will assessments determine if the vaccines protect people against mild illness? Will assessments determine if the vaccines protect people who are ill with no symptoms at all?

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27. Who will be included in the real-world vaccine assessments? 28. These vaccines were produced so quickly. How do we know they are safe? 29. Will CDC continue to watch for problems with these new vaccines? 30. What are some of the best ways to prevent infection and illness from SARS-CoV-2? 31. Should I wear a face covering or mask when I go out in public? 32. Can I prevent or treat COVID-19 by using disinfectant sprays, wipes, or liquids on my skin? Can I inject, inhale, or ingest disinfectants to prevent or treat COVID-19? 33. Does spraying people with disinfectant lower the spread of COVID-19? 34. Will Miracle Mineral Solution (MMS) cure COVID-19? 35. Is hand sanitizer effective against COVID-19? 36. Why has the FDA placed alcohol-based hand sanitizers from Mexico on import alert? 37. Where can I buy hand sanitizer? Can I make my own hand sanitizer? 38. What do I do if I get a rash or other reaction to hand sanitizer? 39. What is the risk of using a hand sanitizer that contains methanol (wood alcohol) or 1-propanol? 40. Products online claim to prevent or treat COVID-19. Where can I report websites selling products with fraudulent claims? 41. Am I at risk for serious complications from COVID-19 if I smoke cigarettes? 42. If I vape tobacco or nicotine am I at risk for complications from COVID-19? 43. Does COVID-19 present a risk to the safety of the nation’s blood supply? 44. Can SARS-CoV-2 be transmitted by blood transfusion? 45. What steps are being taken to protect the US blood supply from SARS-CoV-2? 46. Why aren’t blood centers testing donors for SARS-CoV-2? 47. Is it safe for me to donate blood during the coronavirus pandemic? 48. Can COVID-19 be transmitted through human cells, tissues, or cellular and tissue-based products (HCT/Ps)? 49. What is convalescent plasma and why is it being investigated to treat COVID-19? 50. I recently recovered from COVID-19; can I donate convalescent plasma? 51. What does it mean to be an FDA-approved drug? 52. What is the FDA’s role in regulating potential treatments during a public health emergency? 53. Are there any FDA-approved (non-vaccine) therapeutics for COVID-19?

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54. Is Veklury (remdesivir) approved by the FDA to treat COVID-19?

55. Is Olumiant (baricitinib) approved by the FDA to treat COVID-19? 56. Is bamlanivimab, a monoclonal antibody, FDA-approved to treat COVID-19? 57. Are the monoclonal antibodies, casirivimab and imdevimab, FDA-approved to treat COVID-19?

58. Where are infusions of monoclonal antibody treatments available?

59. Are chloroquine phosphate or hydroxychloroquine sulfate approved by the FDA to treat COVID-19?

60. Should I take chloroquine phosphate used to treat disease in aquarium fish to prevent or treat COVID-19? 61. Are antibiotics effective in preventing or treating COVID-19? 62. Should I take ivermectin to prevent or treat COVID-19?

63. What is the FDA doing to protect people from products making fraudulent COVID-19 claims? 64. Will there be drug shortages due to COVID-19?

65. Am I at risk for COVID-19 from taking FDA-approved drugs made outside the United States? 66. Is there a test for COVID-19?

67. How are people tested for COVID-19?

68. Are there any at-home tests for COVID-19?

69. When will other diagnostic tests for COVID-19 be authorized? 70. What is the difference between the types of tests available for SARS-CoV-2?

71. Should I purchase PPE such as facemasks or N95 respirators for me and my family?

72. Is there a shortage of PPE such as gloves, masks, and N95 respirators or of ventilators? 73. Can 3D printing be used to make PPE?

74. I built a DIY ventilator using instructions I found on the internet. May I sell it?

75. What is the FDA’s role in helping to ensure the safety of the human and animal food supply? 76. Will there be food shortages?

77. Why is the FDA providing flexibility to food manufacturers, under limited circumstances during the COVID-19 public health emergency, to make minor changes in ingredients without reflecting those changes on the package label?

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78. What do I need to know about the temporary policy for food labeling of minor ingredient changes during the COVID-19 public health emergency if I have food allergies? 725 79. Will there be animal food shortages? 725 80. What are the most important things I need to know to keep myself and others safe when I go to the grocery store during the pandemic?

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82. Can I get the coronavirus from food, food packaging, or food containers and preparation area?

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81. Are food products produced in the United States or other countries affected by COVID-19 a risk for the spread of COVID-19? 83. Is the US food supply safe?

84. Is the US animal food supply safe?

85. Can I get COVID-19 from a food worker handling my food? 86. Should food workers who are ill stay home?

87. Should food facilities (grocery stores, manufacturing facilities, restaurants, etc.) perform any special cleaning or sanitation procedures for COVID-19?

88. What is the FDA doing to respond to foodborne illnesses during the COVID-19 pandemic? 89. What is the FDA’s role in regulating animal drugs, animal food (including pet food), and animal medical devices?

90. Can I give my pet COVID-19? Can I get COVID-19 from my pet or other animals? 91. Is there a test for COVID-19 in pets? If so, has it been approved by the FDA? 92. Should I get my pet tested for COVID-19? 93. What animal species can get COVID-19?

94. Since domestic cats can get infected with the virus that causes COVID-19, should I worry about my cat?

95. Why are animals being tested when many people can’t get tested?

96. Can pets carry the virus that causes COVID-19 on their skin or fur?

97. Are there any approved products that can prevent or treat COVID-19 in animals? 98. Is it true that animals, like dogs, cats, and cattle, get their own different types of coronavirus?

99. If my pet previously had a species-specific coronavirus, does that make them more or less likely to get COVID-19?

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Section 1 FDA, EUA, and COVID-19 Vaccines

1. Overall, how is the FDA addressing the COVID-19 pandemic? The FDA, along with other federal, state, and local agencies and public health officials across the country and internationally, plays a critical role in protecting public health during the COVID-19 pandemic. FDA staff are supporting development of medical countermeasures (MCMS) (Fig. 33.1a and Fig. 33.1b) and are providing regulatory advice, guidance, and technical assistance to advance the development and availability of vaccines, therapies, diagnostic tests and other medical devices for use diagnosing, treating, and preventing this novel virus. The FDA continues to monitor the human and animal food supply and take swift action on fraudulent COVID-19 products.

Figure 33.1a Examples of medical countermeasures include drugs, vaccines, and devices, such as diagnostic tests and personal protective equipment.

2. What is the FDA’s role in approving vaccines and what is being done to produce a COVID-19 vaccine? The FDA regulates vaccines. Vaccines undergo a rigorous review of laboratory, clinical and manufacturing data to ensure the safety, effectiveness, and quality of

FDA, EUA, and COVID-19 Vaccines

Updated June 2020

Figure 33.1b Medical countermeasures.

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these products. Vaccines approved for marketing may also be required to undergo additional studies to further evaluate the vaccine and often to address specific questions about the vaccine’s safety, effectiveness, or possible side effects. On December 11, 2020, the FDA issued an EUA for the use of the Pfizer-BioNTech COVID-19 Vaccine. On December 18, 2020, the FDA issued an EUA for the use of the Moderna COVID-19 Vaccine. And on February 27, 2021, the FDA issued an EUA for the use of the Janssen COVID-19 Vaccine. The issuance of an EUA is different than an FDA approval (licensure) of a vaccine. In determining whether to issue an EUA for a product, the FDA evaluates the available evidence and assesses any known or potential risks and any known or potential benefits. And if the benefit-risk assessment is favorable, the product is made available during the public health emergency. Once a manufacturer submits an EUA request for a COVID-19 vaccine, the FDA then evaluates the request and determines whether the relevant statutory criteria are met, taking into account the totality of the scientific evidence about the vaccine that is available to the agency. In addition to supporting product development for high priority COVID-19 vaccines, the FDA continues to expedite clinical trials for additional vaccine candidates, providing timely advice to and interactions with vaccine developers.

3. What is an Emergency Use Authorization (EUA)?

In certain types of emergencies, the FDA can issue an EUA, to provide more timely access to critical medical products (including medicines and tests) that may help during the emergency when there are no adequate, approved, and available alternative options. The EUA process is different than FDA approval, clearance, or licensing because the EUA standard may permit authorization based on significantly less data than would be required for approval, clearance, or licensing by the FDA. This enables the FDA to authorize the emergency use of medical products that meet the criteria within weeks rather than months to years. EUAs are in effect until the emergency declaration ends but can be revised or revoked as we evaluate the needs during the emergency and new data on the product’s safety and effectiveness, or as products meet the criteria to become approved, cleared, or licensed by the FDA. The EUA is a mechanism to facilitate the availability and use of MCMS, including vaccines, during public health emergencies, such as the current COVID-19 pandemic. Under an EUA, FDA may allow the use of unapproved medical products, or unapproved uses of approved medical products in an emergency to diagnose, treat, or prevent serious or life-threatening diseases or conditions when certain statutory criteria have been met, including that there are no adequate, approved, and available alternatives. Taking into consideration input from the FDA, manufacturers decide whether and when to submit an EUA request to FDA.

FDA, EUA, and COVID-19 Vaccines

Once submitted, FDA will evaluate an EUA request and determine whether the relevant statutory criteria are met, considering the totality of the scientific evidence about the vaccine that is available to FDA.

4. What safety and effectiveness data are required to be submitted to FDA for an EUA request for a vaccine intended to prevent COVID-19?

COVID-19 vaccines are undergoing a rigorous development process that includes tens of thousands of study participants to generate the needed non-clinical, clinical, and manufacturing data. FDA will undertake a comprehensive evaluation of this information submitted by a vaccine manufacturer. For an EUA to be issued for a vaccine, for which there is adequate manufacturing information to ensure quality and consistency, FDA must determine that the known and potential benefits outweigh the known and potential risks of the vaccine. An EUA request for a COVID-19 vaccine can be submitted to FDA based on a final analysis of a phase 3 clinical efficacy trial or an interim analysis of such trial, i.e., an analysis performed before the planned end of the trial once the data have met the pre-specified success criteria for the study’s primary efficacy endpoint. From a safety perspective, FDA expects an EUA submission will include all safety data accumulated from phase 1 and 2 studies conducted with the vaccine, with an expectation that phase 3 data will include a median follow-up of at least 2-months (meaning that at least half of vaccine recipients in phase 3 clinical trials have at least 2 months of follow-up) after completion of the full vaccination regimen. In addition, FDA expects that an EUA request will include a phase 3 safety database of well over 3,000 vaccine recipients, representing a high proportion of participants enrolled in the phase 3 study, who have been followed for serious adverse events and adverse events of special interest for at least one month after completion of the full vaccination regimen. Part of FDA’s evaluation of an EUA request for a COVID-19 vaccine includes evaluation of the chemistry, manufacturing, and controls information for the vaccine. Sufficient data should be submitted to ensure the quality and consistency of the vaccine product. FDA will use all available tools and information, including records reviews, site visits, and previous compliance history, to assess compliance with current good manufacturing practices. On May 25, 21021, the FDA updated its guidance, Emergency Use Authorization for Vaccines to Prevent COVID-19, to include a new section that clarifies how the agency intends to prioritize review of EUA requests for the remainder of the COVID-19 public health emergency. As noted in the guidance, for the remainder of the current pandemic, the FDA may decline to review and process further EUA

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requests other than those for vaccines whose developers have already engaged with the agency as described in the agency’s guidance, “Emergency Use Authorization Vaccines to Prevent COVID-19.”

5. What would involve a full clinical trial for a COVID-19 vaccine?

Clinical trials (Fig. 33.2a and Fig 33.2b) are evaluating investigational COVID-19 vaccines in tens of thousands of study participants to generate the scientific data and other information needed by FDA to determine safety and effectiveness. These clinical trials are conducted according to the rigorous standards set forth by the FDA. For example, both the Pfizer-BioNTech and Moderna have submitted applications to the FDA for full vaccine regulatory approval. Initially, in phase 1, the vaccine is given to a small number of generally healthy people to assess its safety at increasing doses and to gain early information about how well the vaccine works to induce an immune response in people. In the absence of safety concerns from phase 1 studies, phase 2 studies include more people, where various dosages are tested on hundreds of people with typically varying health statuses and from different demographic groups, in randomized-controlled studies. These studies provide additional safety information on common short-term side effects and risks, examine the relationship between the dose administered and the immune response, and may provide initial information regarding the effectiveness of the vaccine. In phase 3, the vaccine is generally administered to thousands of people in randomized, controlled studies involving broad demographic groups (i.e., the population intended for use of the vaccine) and generates critical information on effectiveness and additional important safety data. This phase provides additional information about the immune response in people who receive the vaccine compared to those who receive a control, such as a placebo.

 Figure 33.2a Vaccine development and ultimate delivery is a complex process that requires regulatory approval.

675 EUA, and COVID-19 Vaccines Adding new measures for prevenƟŽŶ͗ FDA, COVID-19 vaccines

Understanding Clinical Trials | NHBLI (nih.gov) Figure 33.2b Adding new measures for prevention: COVID-19 vaccines.

6. Why should I get a COVID-19 vaccine? When you get a COVID-19 vaccine, you are choosing to protect yourself and make a difference for your children, parents, grandparents, and other loved ones. Millions of people in the US have already received a COVID-19 vaccine. For a community to be fully protected, most community members need to get the vaccine. Getting vaccinated against COVID-19 will help protect you from COVID-19, and it may also protect the people around you. For the latest recommendations from the CDC, refer to the new guidance for fully vaccinated people.

7. Do the COVID-19 vaccines work against the new variants?

While each FDA-authorized COVID-19 vaccine is slightly different, available information suggests that the authorized vaccines remain effective in protecting the American public against currently circulating strains of COVID-19. We are already talking with vaccine manufacturers about these new strains and how to quickly and safely make any changes that may be needed in the future. Some variants spread more easily than others. To help slow the spread of COVID-19, get a COVID-19 vaccine when it is available to you. Other ways to slow the spread include: • Wearing a mask • Keeping 6 feet apart from others who don’t live with you • Avoiding crowds and poorly ventilated indoor spaces

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• Washing your hands often with soap and water (use hand sanitizer if soap and water aren’t available)

8. Is COVID-19 vaccine safety monitored after approval or authorization?

Yes. There are several systems in place to continually monitor COVID-19 vaccine safety. These systems, called “passive surveillance” and “active surveillance” systems, rapidly detect and investigate potential safety problems. Systems such as the Vaccine Adverse Event Reporting System (VAERS) (Fig. 33.3) and CDC’s text-based v-safe system, which receive reports of adverse events following vaccination, are examples of passive surveillance systems. The FDA‘s BEST Initiative is an example of an active surveillance system, which can rapidly analyze information occurring in millions of individuals recorded in large data systems to investigate any safety signals that are identified by VAERS or v-safe.





























 











9. Can the vaccine give me COVID-19?  

 No. None of the COVID-19 vaccines currently authorized for use or in development  in the United States use the live virus that causes COVID-19. However, it  typically takes a few weeks for the body to build immunity after vaccination. That means it’s possible you could be infected with the virus that causes COVID-19 just before or just after vaccination and get sick. 



10. If I already had COVID-19 and recovered, do I still need to get vaccinated? 

Yes. CDC recommends that you get vaccinated even if you have already had COVID-19, because you might become infected more than once. While you may have some short-term antibody protection after recovering from COVID-19, we don’t know how long that protection will last.

11. Is it safe to get a COVID-19 vaccine if I have an underlying medical condition?

Yes. COVID-19 vaccination is especially important for people with underlying health conditions like heart disease, lung disease, diabetes, or obesity. People with these conditions are more likely to get very sick from COVID-19.



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Vaccine Adverse Event Repor ting System A N a t i o n a l P r o g r a m f o r M o n i t o r i n g Va c c i n e S a f e t y

Vaccine Adverse Event Reporting System (VAERS) The Vaccine Adverse Event Reporting System (VAERS), is a national program managed by the U.S. Centers for Disease Control and Prevention (CDC) and the U.S. Food and Drug Administration (FDA) to monitor the safety of all vaccines licensed in the United States. VAERS collects and reviews reports of adverse events that occur after vaccination. An “adverse event” is any health problem or “side effect” that happens after a vaccination. VAERS cannot determine if a vaccine caused an adverse event, but can determine if further investigation is needed.

VAERS provides valuable information

VAERS is an early-warning system that detects problems possibly related to vaccines. The system relies on reports from healthcare providers*, vaccine manufacturers, and the general public. Reporting gives CDC and FDA important information to identify health concerns and ensure vaccines are safe in order to protect the public’s health.

VAERS staff evaluate reports of adverse events

VAERS defines a “serious adverse event” as life-threatening illness, hospitalization, prolongation of an existing hospitalization, permanent disability or death. Once adverse events are identified using VAERS, they may be monitored in other immunization safety systems to confirm if a particular adverse event is related to a vaccination and identify any specific risk factors.

Anyone can report to VAERS

Anyone can submit a report to VAERS, including patients, family members, healthcare providers, vaccine manufacturers and the general public. CDC and FDA encourage anyone who experiences an adverse event after receiving a vaccine to report to VAERS.

How to report to VAERS

You can report to VAERS online at https://vaers.hhs.gov/index.

For more information about VAERS: E-mail: [email protected] Phone: 1-800-822-7967 Web site: www.vaers.hhs.gov

For further assistance reporting to VAERS, visit https://vaers.hhs.gov/index or contact VAERS directly at [email protected] or 1-800-822-7967.

VAERS data are available to the public

VAERS data can be downloaded at https://vaers.hhs.gov/data/index or searched at http://wonder.cdc.gov/vaers.html. Privacy is protected and personal identifying information (such as name, date of birth and address) is removed from the public data.

*Healthcare providers are encouraged to report all clinically significant adverse events after vaccination to VAERS even if it is uncertain whether the vaccine caused the event. They are also required to report to VAERS adverse events found in the Reportable Events Table (RET) at https://vaers.hhs.gov/resources/VAERS_Table_of_Reportable_Events_Following_Vaccination.pdf

F AC T S H E E T Figure 33.3 Vaccine adverse event reporting system.

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12. Is it safe to get a COVID-19 vaccine if I have allergies? For most people with allergies, yes. However, if you have ever had a severe allergic reaction to any ingredient in a COVID-19 vaccine, you should not get that vaccine or any COVID-19 vaccine. We can find the list of ingredients on CDC’s website together. If you have had an immediate allergic reaction of any severity to other vaccines or injectable therapies, I can help you decide if it is safe for you to get vaccinated. You may still get vaccinated if you have severe allergies to oral medications, food, pets, insect stings, latex, or environmental irritants like pollen or dust.

13. Is it better to get natural immunity to COVID-19 rather than immunity from a vaccine?

No. COVID-19 is new and so are the vaccines to prevent it. We don’t know how long protection lasts for those who get infected or for those who are vaccinated. What we do know is that COVID-19 has caused very serious illness and death for a lot of people. If you get COVID-19, you also risk giving it to loved ones who may get very sick. Getting a COVID-19 vaccine is a safer choice.

14. Will the shot hurt or make me sick?

The vaccine will not make you sick. There may be side effects, but they should go away within a few days. Possible side effects include a sore arm, headache, fever, or body aches. This does not mean you have COVID-19. These side effects are signs that the vaccine is working to build immunity. If they don’t go away in a week, or you have more serious symptoms, call the office.

15. I am pregnant. Is it safe for me to get the COVID-19 vaccine?

Based on what we know about how these vaccines work, experts believe they are unlikely to pose a risk for pregnant women. However, there is limited information about the safety of COVID-19 vaccines during pregnancy. You may choose to get vaccinated if you are part of a group that is recommended for COVID-19 vaccine. We can talk through this decision together.

16. I am breastfeeding. Is it safe for me to get the COVID-19 vaccine?

Based on what we know about how these vaccines work, experts don’t think that COVID-19 vaccines pose risks to breastfeeding babies. However, there are no safety data related to COVID-19 vaccination and breastfeeding. You may choose to get vaccinated if you are part of a group that is recommended for COVID-19 vaccine. We can talk through this decision together.

FDA, EUA, and COVID-19 Vaccines

17. Are there long-term side effects from the COVID-19 vaccine? Because all COVID-19 vaccines are new, it will take more time and more people getting vaccinated to learn about very rare or possible long-term side effects. At least 8-weeks of safety data were gathered in the clinical trials for all the authorized vaccines, and it’s unusual for vaccine side effects to appear more than 8 weeks after vaccination.

18. How do I know if the COVID-19 vaccine is safe?

All COVID-19 vaccines were tested in clinical trials involving tens of thousands of people to make sure they meet safety standards and protect adults of different ages, races, and ethnicities. There were no serious safety concerns. These trials were very similar to trials done for other licensed vaccines, but were done more quickly due to the urgent need to reduce illnesses during the pandemic. CDC and the FDA will keep monitoring the vaccines to look for safety issues after they are authorized and in use.

19. How do I report problems or bad reactions after getting a COVID-19 vaccine?

I am encouraging all recipients who receive the vaccine to enroll in v-safe. This is a smartphone tool you can use to tell CDC if you have any side effects after getting a COVID-19 vaccine. If you report serious side effects, someone from CDC will call to follow up. I will give you instructions for how to enroll.

20. Do I have to continue to wear a mask and avoid close contact with others after I have been vaccinated?

Yes, it is important to keep covering your mouth and nose with a mask, washing hands often, and staying at least 6 feet away from others even after you have been vaccinated. We don’t yet know if the vaccine reduces transmission of the virus. Also, there is not enough information currently available to say if or when CDC will stop recommending that people wear masks and avoid close contact with others to help prevent the spread of the virus that causes COVID-19. Experts need to understand more about the protection that COVID-19 vaccines provide before making that decision. Other factors, including how many people get vaccinated and how the virus is spreading in communities, will also affect this decision.

21. Do clinical trial results show whether vaccines are effective?

Yes. Clinical trials provide data and information about how well a vaccine prevents an infectious disease and about how safe it is. The FDA evaluates these

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data, along with information from the manufacturer, to assess the safety and effectiveness of a vaccine. FDA then decides whether to approve a vaccine or authorize it for emergency use in the United States. After a vaccine is either approved or authorized for emergency use by FDA, more assessments are done before a vaccine is recommended for public use. The goal of these assessments is to understand more about the protection a vaccine provides under real-world conditions, outside of clinical trials. After COVID-19 vaccines are approved or authorized for emergency use by FDA and recommended for public use, CDC will further assess their effectiveness. These real-world assessments will compare groups of people who do and don’t get vaccinated and people who do and don’t get COVID-19 to find out how well COVID-19 vaccines are working to protect people.

22. Why would the effectiveness of vaccines be different after the clinical trials?

Many factors can affect a vaccine’s effectiveness in real-world situations. These factors can include things such as how a vaccine is transported and stored or even how patients are vaccinated. Vaccine effectiveness can also be affected by differences in the underlying medical conditions of people vaccinated as compared to those vaccinated in the clinical trials. Assessments of vaccine effectiveness can also provide important information about how well a vaccine is working in groups of people who were not included or were not well represented in clinical trials.

23. How will experts evaluate the COVID-19 vaccines in real-world conditions?

Experts are working on many types of real-world studies to determine vaccine effectiveness, and each uses a different method:



• Case-control studies will include cases (people who have the virus that causes COVID-19) and controls (people who do not have the virus that causes COVID-19). People who agree to participate in a case-control study will provide information on whether they received a COVID-19 vaccine or not. Experts will look to see if the cases were less likely to have received the vaccine than controls, which would show that the vaccine is working.

• A test-negative design study will enroll people who are seeking medical care for symptoms that could be due to COVID-19. In this special type of case-control study, experts will compare the COVID-19 vaccination status of those who test positive (meaning they have COVID-19) to those who test negative (meaning they do not have COVID-19).

FDA, EUA, and COVID-19 Vaccines







• Cohort studies will follow people who have and haven’t had a COVID-19 vaccine for several months to see if getting vaccinated protects them from getting the disease. This can be done in real time (prospectively) or by looking back in time (retrospectively) using data that were already collected, such as information in participants’ medical records.

• Screening method assessments look at vaccination status among a group of cases (for example, cases detected through ongoing COVID-19 surveillance) and compares those cases with vaccination coverage among the overall population where those cases come from (for example, people from the same state). By comparing coverage between these two groups, researchers can get an early estimate of whether a vaccine is working as expected. • Ecologic analysis assessments look at groups of people—such as those in different geographic locations or at different times—to find out how many were vaccinated and how many were diagnosed with COVID-19. These analyses may be hard to interpret because the number of COVID-19 illnesses has changed rapidly over time and in different places. CDC will use several methods because they can all contribute different information about how the vaccine is working.

24. Will assessments determine if the vaccines protect people from severe COVID-19 illness?

Yes. Severe illness from COVID-19 is defined as needing care in a hospital or intensive care unit (ICU), needing to be on a ventilator, or dying due to COVID-19. Experts will assess how well COVID-19 vaccines protect people against severe illness using case-control studies among hospitalized patients. Experts also will use cohort studies of electronic health records to see if people hospitalized with COVID-19 received the vaccine or not.

25. Will assessments determine if the vaccines protect people against mild illness?

Yes. CDC will use case-control studies to assess how well COVID-19 vaccines protect people against less severe forms of COVID-19—for example, people with COVID-19 who need to visit a doctor but don’t need to be hospitalized.

26. Will assessments determine if the vaccines protect people who are ill with no symptoms at all?

Yes. Some people can be infected with or “carry” the virus that causes COVID-19, but they don’t feel sick or have any symptoms. Experts call this

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asymptomatic infection. It is important to know whether COVID-19 vaccines can help lower the number of people who have asymptomatic infection. People with asymptomatic infection can unknowingly spread the virus to others. A special type of cohort study will find out how effective the vaccine is when people are asymptomatic. People who agree to participate will be tested for COVID-19 every week whether they have symptoms or not. Experts will then compare the proportion of people with infection who were vaccinated to the proportion of people with infection who were not vaccinated.

27. Who will be included in the real-world vaccine assessments?

CDC is working to make sure real-world vaccine assessments include diverse groups of people including the following:

Healthcare personnel and essential workers: Experts will rapidly assess vaccine effectiveness among healthcare personnel working in hospitals, long term care/ skilled nursing facilities, or nursing homes in selected sites across the United States. These assessments will show how well COVID-19 vaccines protect healthcare personnel from getting sick or having severe illness. Assessments among healthcare personnel and essential workers will also inform how well COVID-19 vaccines protect them against getting infected, regardless of whether they have symptoms or not.

Older adults and those living in nursing homes: The risk for severe illness from COVID-19 increases with age, so making sure these vaccines protect older adults is critical. People living in nursing homes and long-term care facilities are at especially high risk of getting COVID-19 and severe disease. The FDA and the Centers for Medicare and Medicaid Services (CMS) will effectiveness among older adults, including those living in nursing homes and long-term care facilities. These data will include information about whether people received a COVID-19 vaccine, whether they got sick with COVID-19, and if they needed hospital care. This information will help inform how well the vaccine works in preventing COVID-19 and severe illness among older adults. Experts will also use data from CDC and CMS to conduct a case-control assessment. Experts will identify older adults hospitalized for COVID-19 and older adults hospitalized for other reasons. They will then compare how many cases and controls received a COVID-19 vaccine to estimate vaccine effectiveness. People with underlying medical conditions: To better understand how well COVID-19 vaccines protect people with underlying medical conditions who may be at increased risk for severe illness. Experts are working to make sure various real-world vaccine assessments will include adults with heart conditions, obesity, and diabetes. The real-world vaccine effectiveness assessments will also collect information about other underlying medical conditions. This information will be used to better understand how well COVID-19 vaccines protect people with underlying medical conditions.

FDA, EUA, and COVID-19 Vaccines

People in racial and ethnic minority groups: Long-standing systemic health and social inequities have put many people from racial and ethnic minority groups at increased risk of getting sick and dying from COVID-19. CDC is working to ensure that real-world assessments of vaccine effectiveness include diverse populations, such as people from racial and ethnic minority groups disp­ roportionately affected by COVID-19. CDC also is working with the Indian Health Service (IHS), tribal nations, and other partners to ensure that these real-world assessments include American Indian and Alaska Native populations who have been disproportionately affected by COVID-19. This is important to ensure that COVID-19 vaccines can help achieve health equity, so everyone has a fair opportunity to be as healthy as possible.

28. These vaccines were produced so quickly. How do we know they are safe?

It is the US vaccine safety system’s job to make sure that all vaccines are as safe as possible. Safety has been a top priority while federal partners have worked to make COVID-19 vaccines available for use in the United States. The new COVID-19 vaccines have been evaluated in tens of thousands of individuals, who volunteered to be vaccinated and to participate in clinical trials. The information from these clinical trials allowed the FDA to determine the safety and effectiveness of the vaccines. These clinical trials were conducted according to rigorous standards set forth by FDA. FDA has determined that the newly authorized COVID-19 vaccines meet its safety and effectiveness standards. Therefore, FDA has made these vaccines available for use in the United States under what is known as an EUA.

29. Will CDC continue to watch for problems with these new vaccines?

Yes. Even though no safety issues arose during the clinical trials, CDC and other federal partners will continue to monitor the new vaccines for serious side effects (known as adverse events) using many vaccine safety monitoring systems. This continued monitoring can pick up on side effects that may not have been seen in clinical trials. If an unexpected side effect with the new COVID-19 vaccines is seen, experts can quickly study it further to determine if it is a true safety concern. Monitoring vaccine safety is critical to help ensure that the benefits of the COVID-19 vaccines continue to outweigh the risks for people who are vaccinated. The current vaccine safety system is strong and robust, with the capacity to monitor COVID-19 vaccine safety effectively. Existing data systems can rapidly detect if a vaccine has any possible safety problems. These systems are being scaled up to fully meet the needs of the nation. Additional systems and data sources are also being developed to further enhance safety monitoring capabilities.

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Section 2 General Information on Safety and Prevention

30. What are some of the best ways to prevent infection and illness from SARS-CoV-2? The best way to prevent illness is to avoid being exposed to the virus. The CDC recommends everyday preventive actions to help prevent the spread of respiratory diseases. They include:

• Wash your hands often with plain soap and water. The CDC recommends washing your hands often with soap and water for at least 20 seconds, especially after you have been in a public place, or after blowing your nose, coughing, or sneezing. If soap and water are not available, the CDC recommends using an alcohol-based hand sanitizer (Fig. 33.4) that contains at least 60 percent alcohol.

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• Cover your mouth and nose with a cloth face covering or non-surgical mask when around others. • Get the COVID-19 vaccine when it is offered to you. Once you are fully vaccinated, you may be able to start doing some things that you had stopped doing because of the pandemic. • Follow CDC guidance on large gatherings, social distancing and mask wearing, based on if you are fully vaccinated or not.

Figure 33.4 Safely using hand sanitizer is critical.

31. Should I wear a face covering or mask when I go out in public? The CDC recommends wearing masks in public when other social distancing measures are difficult to maintain. Effective February 2, 2021, masks are required on planes, buses, trains, and other forms of public transportation traveling into, within, or out of the United States and in US transportation hubs such as airports and stations. The FDA has authorized the emergency use of face masks (Fig. 33.5), including cloth face coverings, that meet certain criteria for use as source control by the general public and health care personnel in accordance with CDC recommendations during the COVID-19 public health emergency. The FDA also regulates other medical devices, including personal protective equipment (PPE) such as surgical masks and N95 respirators (Fig. 33.6). The CDC recommends that PPE should be reserved for use by health care workers, first responders, and other frontline workers whose jobs put them at much greater risk of acquiring COVID-19. Read more about types of face masks and the FDA’s emergency use authorization for non-surgical face masks. It is important to know how to properly use a face mask (Fig. 33.7).



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Figure 33.5 Many continue to use face masks indoors. 2021 has been a year of mixed messages pertaining to masking from health experts and a maze of regulatory mandates from governmental agencies like the CDC and WHO. The author agrees that it is best to continue to use face masks under all circumstances while indoors, whether they are mandated or not. This is especially important for unvaccinated individuals, the immuno­ comprised, infants, pregnant women, and the elderly. Herd immunity in the world is most likely never possible given the low vaccination rates, inequitable distribution of the vaccine and continuous emergence of variants of concern.

32. Can I prevent or treat COVID-19 by using disinfectant sprays, wipes, or liquids on my skin? Can I inject, inhale, or ingest disinfectants to prevent or treat COVID-19? No. Disinfectants should not be used on human or animal skin. Disinfectants may cause serious skin and eye irritation. Disinfectants are dangerous for people to inject, inhale, or ingest. If you breathe, inject, or swallow disinfectants you may be seriously hurt or die. If someone near you swallows, injects, or breathes a disinfectant, call poison control or a medical professional immediately. Disinfectant products such as sprays, mists, wipes, or liquids are only to be used on hard, non-porous surfaces (materials that do not absorb liquids easily) such as floors and countertops, or on soft surfaces such as mattresses, sofas, and beds. View the current list of disinfectants that meet EPA’s criteria for use against SARS-CoV-2, the virus that causes COVID-19.

General Information on Safety and Prevention

Figure 33.6 Surgical masks and N95 respirators: Understanding the difference.

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WEAR YOUR MASK CORRECTLY • • • • •

Wash your hands before putting on your mask Put it over your nose and mouth and secure it under your chin Try to fit it snugly against the sides of your face Make sure you can breathe easily Do not place a mask on a child younger than 2

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USE A MASK TO HELP PROTECT OTHERS • Wear a mask over your nose and mouth to help prevent getting and spreading COVID-19 • Wear a mask in public settings when around people who don’t live in your household, especially when indoors and when it may be difficult for you to stay six feet apart from people who don’t live with you • Don’t put the mask around your neck or up on your forehead • Don’t touch the mask, and, if you do, wash your hands or use hand sanitizer

FOLLOW EVERYDAY HEALTH HABITS • • • •

PHARMACY

Stay at least 6 feet away from others Avoid contact with people who are sick Avoid crowds and places with poor ventilation Wash your hands often

TAKE OFF YOUR MASK CAREFULLY, WHEN YOU’RE HOME • • • • •

Untie the strings behind your head or stretch the ear loops Handle only by the ear loops or ties Fold outside corners together Place mask in the washing machine Wash your hands with soap and water

Figure 33.7 How to safely wear and take off a mask.

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33. Does spraying people with disinfectant lower the spread of COVID-19? Currently there are no data showing that spraying people with aerosolized dis­ infectants, or having people walk through tunnels or rooms where disinfectant is in the air, can treat, prevent, or lower the spread of COVID-19. Surface disinfectants should not be used on people or animals. Disinfectant products, such as sprays, mists, wipes, or liquids are only to be used on hard, non-porous surfaces (materials that do not absorb liquids easily) such as floors and countertops, or on soft surfaces such as mattresses, sofas, and beds. CDC provides information regarding disinfectant practices for surfaces in the Reopening Guidance for Cleaning and Disinfecting Public Spaces, Workplaces, Businesses, Schools, and Homes. Human antiseptic drugs, such as hand sanitizers, are intended for use on human skin, but are not intended for aerosolization (to be sprayed in the air in very small droplets). Due to serious safety concerns, including the risk of inhalational toxicity and flammability, the FDA’s temporary policies for alcoholbased hand sanitizers during the COVID-19 public health emergency specifically do not apply to aerosol sprays. In addition, hand sanitizers are intended for use on the hands, and should never be used over larger body surfaces, swallowed, or inhaled.

34. Will Miracle Mineral Solution (MMS) cure COVID-19?

No. MMS does not cure COVID-19 and has not been approved by the FDA for any use (Fig. 33.8). The solution, when mixed as directed, forms industrial bleach that may cause serious and potentially life-threatening side effects. FDA took action against Genesis II Church of Health and Healing for unlawfully distributing MMS for the treatment of COVID-19 and other diseases.

Figure 33.8 The FDA warns you not to drink sodium chlorite products such as Miracle Mineral Solution.



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35. Is hand sanitizer effective against COVID-19? The best way to prevent the spread of infections and decrease the risk of getting sick is by washing your hands with plain soap and water, advises the CDC. Washing hands often with soap and water for at least 20 seconds is essential, especially after going to the bathroom; before eating; and after coughing, sneezing, or blowing one’s nose. If soap and water are not available, CDC recommends consumers use an alcohol-based hand sanitizer (Fig. 33.9) that contains at least 60% alcohol.

Figure 33.9 Keeping hands clean is especially important to help prevent the virus from  spreading.

36. Why has the FDA placed alcohol-based hand sanitizers from Mexico on import alert? During the coronavirus pandemic, the FDA has seen a sharp increase in hand sanitizer products from Mexico that were labeled to contain ethanol (also called ethyl alcohol) but, among other concerns, tested positive for methanol contamination. Methanol (or wood alcohol) can be toxic when absorbed through the skin and life-threatening when ingested. Methanol is not an acceptable ingredient in hand sanitizer. Recently, the FDA placed all alcohol-based hand sanitizers from Mexico on a country-wide import alert to help prevent entry into the US of potentially dangerous products until we can review the product’s safety. An import alert informs field staff the FDA has sufficient evidence to detain products (at the border/import site) that appear to violate laws and regulations without physical examination. FDA analyses of alcohol-based hand sanitizers from Mexico found that 84 percent of the samples we analyzed from April through December 2020 were not in compliance with FDA regulations. Import alerts:

• Help prevent potentially violative products from being distributed in the United States;

General Information on Safety and Prevention

• Help free up agency resources to examine other shipments; • Provide uniform information to FDA field offices across the country; • Provide the importer the opportunity to show that the products being imported into the United States are in compliance with the FDA’s laws and regulations.

To stay informed, visit the FDA’s Hand Sanitizers and COVID-19 page. Refer to the flowchart (Fig. 33.10) to determine if your hand sanitizer is on the FDA list.

37. Where can I buy hand sanitizer? Can I make my own hand sanitizer?

Many retail stores and pharmacies sell hand sanitizers. However, we understand that many stores may not have hand sanitizers available to buy. To help increase the availability of hand sanitizers, the FDA has issued guidance for the temporary preparation of alcohol-based hand sanitizers by some companies and pharmacies during the COVID-19 public health emergency. The FDA does not recommend that consumers make their own hand sanitizer. If made incorrectly, hand sanitizer can be ineffective, and there have been reports of skin burns from homemade hand sanitizer. The agency lacks verifiable information on the methods being used to prepare hand sanitizer at home and whether they are safe for use on human skin.

38. What do I do if I get a rash or other reaction to hand sanitizer?

Call your doctor if you experience a serious reaction to hand sanitizer. The FDA encourages consumers and health care professionals to report adverse events experienced with the use of hand sanitizers to the FDA’s MedWatch Adverse Event Reporting program.

39. What is the risk of using a hand sanitizer that contains methanol (wood alcohol) or 1-propanol?

Methanol exposure can result in nausea, vomiting, headache, blurred vision, permanent blindness, seizures, coma, permanent damage to the nervous system or death. Although people using these products on their hands are at risk for methanol poisoning, young children who accidentally swallow these products and adolescents and adults who drink these products as an alcohol (ethanol) substitute are most at risk. Swallowing or drinking a hand sanitizer with 1-propanol can result in decreased breathing and heart rate, among other serious symptoms, and can lead to death. Hand sanitizer with 1-propanol contamination can irritate your skin (or eyes, if exposed). Although it is rare, some people have reported allergic skin reactions.

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the hand 1 Find sanitizer label.

EXAMPLE BUSINESS NAME

3

Go to www.fda.gov/ handsanitizerlist and click on the red button at the top of the page.

v

the: 2 Locate • Product Name • Manufacturer • Distributor • National Drug Code or NDC number

VERY GOOD

Active Ingredient:

*

BRAND

L ETHYL ALCOHO

NDC 53598-007-01

4

5

Search list.

• Type the product name, manufacturer, distributor, or National Drug Code or NDC number in the search box. OR • Sort the columns alphabetically. OR • Find your product on the correct page of results (1, 2, 3, …).

t

Look for your hand sanitizer on the list. • Do not use a hand sanitizer made by a manufacturer on the list. • If the manufacturer is not on the label, contact the distributor for more information. • Check back often. As FDA test results are released, we add products to the list.

t

Put contaminated hand sanitizer

6 into hazardous waste collection.

• Contact your local government or trash collection agency and ask about hazardous waste disposal. • Do not pour the hand sanitizer down the drain, mix it with other liquids, or put it in your regular trash.

Figure 33.10 Flowchart*Sample to determine if a hand sanitizer approved by the FDA. Sample hypothetical product label for explanatory not purposes only. hypothetical label shown.

General Information on Safety and Prevention

40. Products online claim to prevent or treat COVID-19. Where can I report websites selling products with fraudulent claims?1 The FDA advises consumers to be beware of websites and stores selling products that claim to prevent, treat, or cure COVID-19. If you have a question about a product sold online that claims to treat, prevent, or cure COVID-19, talk to your health care provider or doctor. Please report websites selling products with fraudulent claims about treatment or prevention of COVID-19. If you have experienced a bad reaction to a product sold with COVID-19 claims, report it to the FDA’s MedWatch Adverse Event Reporting program: • Complete and submit the report online; or • Download and complete the form, then submit it via fax at 1-800-FDA-0178.

Include as much information as you can about the product that caused the reaction, including the product name, the manufacturer, and the lot number (if available).

41. Am I at risk for serious complications from COVID-19 if I smoke cigarettes?

Yes. Data shows that when compared to never smokers, cigarette smoking increases the risk of more severe illness from COVID-19, which could result in hospitalization, the need for intensive care, or even death. Smoking cigarettes can cause inflammation and cell damage throughout the body, and can weaken your immune system, making it less able to fight off disease. There’s never been a better time to quit smoking. If you need resources to help you quit smoking, the FDA’s Every Try Counts campaign has supportive tips and tools to help you get closer to quitting for good.

 1As

of May 6, 2021, the FDA has received more than 1375 reports of fraudulent products related to COVID-19. To proactively identify and neutralize threats to consumers, the FDA launched Operation Quack Hack in March 2020. The Operation Quack Hack team has reviewed thousands of websites, social media posts, and online marketplace listings, resulting in more than 170 warning letters to sellers, more than 305 reports sent to online marketplaces, and more than 299 abuse complaints sent to domain registrars to date.

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42. If I vape tobacco or nicotine am I at risk for complications from COVID-19? E-cigarette use can expose the lungs to toxic chemicals, but whether those exposures increase the risk of COVID-19 or the severity of COVID-19 outcomes is not known. However, many e-cigarette users are current or former smokers, and cigarette smoking increases the risk of respiratory infections, including pneumonia.

Biologics, Human Tissues, and Blood Products

Section 3 Biologics, Human Tissues, and Blood Products 43. Does COVID-19 present a risk to the safety of the nation’s blood supply? In general, respiratory viruses are not known to be transmitted by blood transfusion, and there have been no reported cases of transfusion-transmitted coronavirus.

44. Can SARS-CoV-2 be transmitted by blood transfusion?

In general, respiratory viruses are not known to be transmitted by blood transfusion, and there have been no reported cases of transfusion-transmitted coronavirus.

45. What steps are being taken to protect the US blood supply from SARS-CoV-2?

Blood donors must be healthy and feel well on the day of donation. Routine blood donor screening measures that are already in place should prevent individuals with respiratory infections from donating blood. For example, blood donors must be in good health and have a normal temperature on the day of donation. Donors are instructed to contact the donor center if they become ill after donation, so that their blood or plasma will not be used. Even when a donor develops COVID-19 after donation, however, there have been no cases of COVID-19 linked to donor blood or products made from blood.

46. Why aren’t blood centers testing donors for SARS-CoV-2?

At this time, the FDA does not recommend using laboratory tests to screen blood. Someone who has symptoms of COVID-19, including fever, cough, and shortness of breath, is not healthy enough to donate blood. Standard screening processes already in place will mean that someone with these symptoms will not be allowed to donate. The blood establishment’s responsible physician must evaluate prospective donors and determine eligibility. The donor must be in good health and meet all donor eligibility criteria on the day of donation. The responsible physician may wish to consider the following: • individuals diagnosed with COVID-19 or who are suspected of having COVID-19, and who had symptomatic disease, refrain from donating blood for at least 14 days after complete resolution of symptoms,

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• individuals who had a positive diagnostic test for SARS-CoV-2 (e.g., nasopharyngeal swab), but never developed symptoms, refrain from donating at least 14 days after the date of the positive test result, • individuals who are tested and found positive for SARS-CoV-2 antibodies, but who did not have prior diagnostic testing and never developed symptoms, can donate without a waiting period and without performing a diagnostic test (e.g., nasopharyngeal swab), • individuals who received a nonreplicating, inactivated, or mRNA-based COVID-19 vaccine can donate blood without a waiting period, • individuals who received a live-attenuated viral COVID-19 vaccine, refrain from donating blood for a short waiting period (e.g., 14 days) after receipt of the vaccine, • individuals who are uncertain about which COVID-19 vaccine was administered, refrain from donating for a short waiting period (e.g., 14 days) if it is possible that the individual received a live-attenuated viral vaccine.

47. Is it safe for me to donate blood during the coronavirus pandemic? If you are healthy and interested in donating blood, the FDA encourages you to contact a local donation center to make an appointment. One way to make a difference during a public health emergency is to donate blood if you are able.

• AABB: www.aabb.org; +1.301.907.6977 • America’s Blood Centers: www.americasblood.org • American Red Cross: www.redcrossblood.org; +1.800.RED CROSS (+1.800.733.2767) • Armed Services Blood Program: www.militaryblood.dod.mil; +1.703.681.8024 • Blood Centers of America: www.bca.coop

48. Can COVID-19 be transmitted through human cells, tissues, or cellular and tissue-based products (HCT/Ps)2?

Respiratory viruses, in general, are not known to be transmitted by implantation, transplantation, infusion, or transfer of human cells, tissues, or cellular or tissuebased products (HCT/Ps). The potential for transmission of COVID-19 by HCT/Ps is unknown at this time. There have been no reported cases of transmission of COVID-19 via HCT/Ps. Routine screening measures are already in place for evaluating clinical evidence of infection in HCT/P donors. The HCT/P establishment’s responsible person must determine and document the eligibility of a cell or tissue donor (21 CFR 1271.50). Based on information available at this time, establishments may wish to consider, whether, in the 28 days prior to HCT/P recovery, the donor 2Human

cells, tissues, or cellular or tissue-based products (HCT/Ps) means articles containing or consisting of human cells or tissues that are intended for implantation, transplantation, infusion, or transfer into a human recipient.

Biologics, Human Tissues, and Blood Products

• cared for, lived with, or otherwise had close contact with individuals diagnosed with or suspected of having COVID-19 infection; or • had been diagnosed with or suspected of having COVID-19 infection; or • had a positive diagnostic test (e.g., nasopharyngeal swab) for SARS-CoV-2 but never developed symptoms.

49. What is convalescent plasma and why is it being investigated to treat COVID-19?

Convalescent refers to anyone recovering from a disease. Plasma is the yellow, liquid part of blood that contains antibodies. Antibodies are proteins made by the body in response to infections. Convalescent plasma (Fig. 33.11) from patients who have already recovered from COVID-19 may contain antibodies against COVID-19. The FDA has issued an EUA for the use of convalescent plasma in hospitalized patients. It is being investigated for the treatment of COVID-19 patients. Based on scientific evidence available, the FDA concluded this product may be effective in treating COVID-19 and that the known and potential benefits of the product outweigh the known and potential risks of the product for patients hospitalized with COVID-19.

Figure 33.11 Overview of the use and applications of convalescent plasma therapy. Virus-neutralizing antibodies in the plasma of a patient who recovered from COVID-19 can be administered prophylactically to prevent infection in vulnerable individuals and those with known exposure to the virus (prophylaxis). Convalescent plasma can also be administered to infected individuals to improve the clinical outcome (treatment). Source: D. Montelongo-Jauregui, et al. (2020). Convalescent serum therapy for COVID-19: A 19th century remedy for a 21st century disease. PLoS Pathog. 16(8):e1008735.

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Figure 33.12 Key information regarding convalescent plasma.

Biologics, Human Tissues, and Blood Products

Figure 33.12 (Continued)

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50. I recently recovered from COVID-19; can I donate convalescent plasma? COVID-19 convalescent plasma must only be collected from recovered individuals if they are eligible to donate blood. Individuals must have had a prior diagnosis of COVID-19 documented by a laboratory test and meet other laboratory criteria. Individuals must have fully recovered from COVID-19, with complete resolution of symptoms for at least 14 days before donation of convalescent plasma. You can ask your local blood center if there are options to donate convalescent plasma in your area (Fig. 33.12).

Development and Use of FDA-approved Drugs for COVID-19

Section 4 Development and Use of FDA-approved Drugs for COVID-19 51. What does it mean to be an FDA-approved drug? FDA approval of a drug (Figs. 33.13 and 33.14) means that the agency has determined, based on substantial evidence, that the drug is effective for its intended use, and that the benefits of the drug outweigh its risks when used according to the product’s approved labeling. The drug approval process takes place within a structured framework that includes collecting clinical data and submitting an application to the FDA.

52. What is the FDA’s role in regulating potential treatments during a public health emergency? The FDA carries out many activities to protect and promote public health during a public health emergency, including helping to accelerate the development and availability of potential treatments, protecting the security of drug supply chains, providing guidance to food and medical device manufacturers, advising developers on clinical trial issues, and keeping the public informed with authoritative health information. The FDA is committed to supporting the development of new drugs, and the potential repurposing of existing drugs, to address COVID-19 by working with potential drug makers and sponsors to rapidly move products into clinical trials, helping to ensure that trials are properly designed and safe, and protecting the public from potentially unsafe products.

53. Are there any FDA-approved (non-vaccine) therapeutics for COVID-19?

Yes, the FDA has approved Veklury (remdesivir) for certain COVID-19 patients. Researchers are studying new drugs, and medicines that are already approved for other health conditions, as possible treatments for COVID-19. The FDA created the Coronavirus Treatment Acceleration Program (CTAP) to use every available method to move new treatments to patients. Additionally, the FDA is working with the NIH, drug manufacturers, researchers, and other partners to accelerate the development process for COVID-19 treatments. FDA’s Sentinel System is being used to monitor the use of drugs, describe the course of illness among hospitalized patients, and evaluate the treatment impact of therapies actively being used under real-world conditions. For information about clinical trials for COVID-19 treatments visit clinicaltrials.gov3 and the COVID-19 Prevention Network. 3The information on clinicaltrials.gov is provided by the sponsor or principal investigator of a clinical trial.

The listing of a study on the site does not reflect evaluation or endorsement of the trial by the Federal government.

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Figure 33.13a Drug Sponsor’s Discovery and Screening Phase (Preclinical). Modified by the author, original courtesy of the FDA.

Development and Use of FDA-approved Drugs for COVID-19

Figure 33.13b Drug Sponsor’s Clinical Studies/Trials. Modified by the author, original . courtesy of the FDA.

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Figure 33.14 FDA’s New Drug Application (NDA) Review. Modified by the author, original courtesy of the FDA.

Development and Use of FDA-approved Drugs for COVID-19

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Understanding the Regulatory Terminology of Potential Preventions and Treatments for COVID-19 https://www.fda.gov/consumers/ consumer-updates/understanding­ regulatory-terminology-potential­ preventions-and-treatments-covid -19 There's a lot of confusion about which medical products might work to prevent or treat coronavirus disease 2019 (COVID-19). Scientists are working hard to develop a number of potential drugs for the prevention or treatment of coronavirus. The FDA recently approved the first treatment for COVID-19, the antiviral drug remdesivir. Some other investigational drugs are already in clinical trials. In so me cases, scientists are testing whether drugs that are already approved for a different disease are safe and effective against COVID-19. As studies continue, these drugs are sometimes made available to patients through the FDA's Expanded Access Program, or under an Emergency Use Authorization. Health care providers mal also decide to treat a patient with a drug that has been approved by the FDA for one use, but not for the patient's disease or condition (sometimes called "off-label" use). If you think you have, or have had, COVID-19, your health care provider has a complete picture of your health and health history and can help you make the best decisions for your care. The language used to describe potential therapies can be confusing, and there's public interest around the FDA's work to ensure access to potentially life-saving treatments. Here's what those terms mean.

 ´ Means What ³FDA Approved U.S. consumers rely on the FDA to provide independent scientific reviews of medical products, including drugs and vaccines. During this public health emergency, there is an urgent need for products to treat or prevent the virus that causes COVID-19. Before the FDA can approve a drug, the agency must determine whether the clinical data and other information show that the drug is safe and effective for its intended use (for example, to prevent or treat a certain disease),

and that the product can be made according to federal quality standards. When the FDA approves a drug, it means the agency has determined, based on substantial evidence, that the drug is effective for its intended use, and that the benefits of the drug outweigh its risks when used according to the product’s approved labeling. The FDA is working with manufacturers and researchers to make sure the agency is getting the information needed to complete that evaluation for drugs to treat or prevent COVID-19 as quickly as possible.

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Investigational Treatments An investigational drug can also be called an experimental drug. Scientists conduct clinical trials to study investigational drugs to see if they can safely and effectively prevent or treat a specific disease or condition. As part of those clinical trials, they might try to discover: • How the drug might be used for that disease

or condition.

• If the drug is safe for people. • How much of the drug is needed. • Information about whether it works against the

disease and the potential benefits and risks of taking the drug.

Expanded Access Sometimes called “compassionate use,” expanded access is a potential pathway for a patient with a serious or immediately life-threatening disease or condition to gain access to an investigational medical product (drug, biological product, or medical device) for treatment outside of clinical trials when there is no comparable or satisfactory alternative therapy.  Currently, expanded access is one pathway for use of COVID-19 convalescent plasma for patients with serious or immediately life-threatening COVID-19 disease who are not eligible for or who are unable to participate in randomized clinical trials. Limited information suggests that convalescent plasma – an antibody-rich product made from blood donated by people who have recovered from the virus – may help COVID-19 patients. Because current information is limited, it’s important to evaluate this therapy in the context of a clinical trial.

Emergency Use Authorization (EUA) An Emergency Use Authorization (EUA) is one of several tools the FDA is using to help make certain medical products available quickly during the COVID-19 pandemic. In certain emergencies, the FDA can issue an EUA to provide access to medical products that may potentially be used when there are no adequate, approved, and available options. The EUA process is different than an FDA approval or clearance. Under an EUA, in an emergency, the FDA

makes a product available to the public based on the best available evidence, without waiting for all the evidence that would be needed for FDA approval or clearance. When evaluating an EUA, we carefully balance the potential risks and benefits of the products based on the data currently available. EUAs are effective until the emergency declaration ends. EUAs can also be revised or revoked by the FDA at any time as we continue to evaluate the available data and patient needs during the public health emergency. The FDA has granted EUAs to a few possible COVID-19 therapies. Learn more about EUAs in this video.

³Off-Label´Use: Unapproved Uses of Approved Drugs Once the FDA has approved a drug for a disease or medical condition, health care providers generally may prescribe or administer the drug in clinical practice for an unapproved use not described in the approved labeling (i.e., “off-label”) based on their medical judgment, recognizing that the FDA has not assessed the safety or effectiveness of such use.

Development and Use of FDA-approved Drugs for COVID-19

54. Is Veklury (remdesivir) approved by the FDA to treat COVID-19? Yes, on October 22, 2020, the FDA approved Veklury (remdesivir) for certain COVID-19 patients.

55. Is Olumiant (baricitinib) approved by the FDA to treat COVID-19?

No. Olumiant is not FDA-approved for the treatment of COVID-19. However, the FDA issued an EUA authorizing Olumiant for emergency use by healthcare providers, in combination with Veklury (remdesivir), for the treatment of suspected or laboratory-confirmed COVID-19 in hospitalized adults and pediatric patients 2 years of age or older requiring supplemental oxygen, invasive mechanical ventilation, or extracorporeal membrane oxygenation (ECMO).

56. Is bamlanivimab, a monoclonal antibody,4 FDA-approved to treat COVID-19?

No. Bamlanivimab is not FDA-approved to treat any diseases or conditions, including COVID-19. On November 9, 2020, FDA issued an EUA for bamlanivimab for the treatment of mild to moderate COVID-19 in adults and pediatric patients with positive results of direct SARS-CoV-2 viral testing who are 12 years and older weighing at least 40kg, and who are at high risk for progressing to severe COVID-19 and/or hospitalization. Since the initial authorization of bamlanivimab administered alone for emergency use, there has been a sustained increase in SARS-CoV-2 viral variants across the US that are resistant to bamlanivimab alone. Given the frequency of these particular viral variants, and since current testing technologies are not available to ascertain whether a particular patient who has tested positive for COVID-19 is infected with a viral variant prior to initiation of treatment, there is an increased risk of treatment failure when bamlanivimab is administered alone. Based on the totality of scientific evidence available, the Agency concluded that the known and potential benefits of bamlanivimab administered alone no longer outweigh the known and potential risks for the product. Therefore, FDA determined that the criteria for issuance of an EUA are no longer met and revoked the EUA for bamlanivimab administered alone for the treatment of COVID-19 on April 16, 2021.

57.  Are the monoclonal antibodies, casirivimab and imdevimab, FDAapproved to treat COVID-19?

No. Casirivimab and imdevimab are not FDA-approved to treat any diseases or conditions, including COVID-19. However, the FDA issued an EUA for casirivimab 4Monoclonal

antibodies are laboratory-produced molecules that act as substitute antibodies that can restore, enhance or mimic the immune system’s attack on cells. Monoclonal antibodies for COVID-19 may block the virus that causes COVID-19 from attaching to human cells, making it more difficult for the virus to reproduce and cause harm. Monoclonal antibodies may also neutralize a virus.

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and imdevimab to be administered together for the treatment of mild to moderate COVID-19 in adults and pediatric patients (12 years of age or older weighing at least 40 kilograms [about 88 pounds]) with positive results of direct SARS-CoV-2 viral testing and who are at high risk for progressing to severe COVID-19. This includes those who are 65 years of age or older or who have certain chronic medical conditions.

58. Where are infusions of monoclonal antibody treatments available?

The following websites contain information regarding access to monoclonal antibody treatments for COVID-19: • HHS Protect Public Data Hub – Therapeutics Distribution • National Infusion Center Association (NICA)

Monoclonal antibody treatments for COVID-19 may only be administered in settings in which health care providers have immediate access to medications to treat a severe infusion reaction, such as anaphylaxis, and have the ability to activate the emergency medical system (EMS), if necessary. Please speak with your doctor or contact your local or state public health department for more information.

59. Are chloroquine phosphate or hydroxychloroquine sulfate approved by the FDA to treat COVID-19?

No. Hydroxychloroquine sulfate and some versions of chloroquine phosphate are FDA-approved to treat malaria. Hydroxychloroquine sulfate is also FDA-approved to treat lupus and rheumatoid arthritis. On March 28, 2020, the FDA issued an EUA for chloroquine phosphate and hydroxychloroquine sulfate to treat adults and adolescents hospitalized with COVID-19 for whom a clinical trial was not available or participation was not feasible. Based on FDA’s continued review of the scientific evidence available, the criteria for an EUA for chloroquine phosphate and hydroxychloroquine sulfate as outlined in Section 564(c)(2) of the FD&C Act are no longer met. As a result, the EUA for these two drugs was revoked on June 15, 2020.

60. Should I take chloroquine phosphate used to treat disease in aquarium fish to prevent or treat COVID-19?

No. Products marketed for veterinary use, “for research only,” or otherwise not for human consumption have not been evaluated for safety or effectiveness and should never be used by humans. The FDA is aware that chloroquine phosphate

Development and Use of FDA-approved Drugs for COVID-19

is marketed to treat disease in aquarium fish, but these products have not been evaluated by the FDA to determine if they are safe, effective, properly manufactured, and adequately labeled. The agency continues to work with online marketplaces to remove these items, and many have been removed based on these efforts. Patients should not take any form of chloroquine unless it has been prescribed by a licensed health care provider. Chloroquine products also should not be given to pets or livestock unless prescribed by a veterinarian.

61. Are antibiotics effective in preventing or treating COVID-19?

No. Antibiotics do not work against viruses; they only work on bacterial infections. Antibiotics do not prevent or treat COVID-19, because COVID-19 is caused by a virus, not bacteria. Some patients with COVID-19 may also develop a bacterial infection, such as pneumonia. In that case, a health care professional may treat the bacterial infection with an antibiotic.

62. Should I take ivermectin to prevent or treat COVID-19?

No. While there are approved uses for ivermectin in people and animals, it is not approved for the prevention or treatment of COVID-19. You should not take any medicine to treat or prevent COVID-19 unless it has been prescribed to you by your health care provider and acquired from a legitimate source. Additional testing is needed to determine whether ivermectin might be appropriate to prevent or treat coronavirus or COVID-19.

63. What is the FDA doing to protect people from products making fraudulent COVID-19 claims? We have established a cross-agency team dedicated to closely monitoring for fraudulent COVID-19 products. In response to internet scammers, the FDA has taken—and continues to take—actions to stop those selling unapproved products that fraudulently claim to prevent, treat, diagnose or cure COVID-19. The FDA and the Federal Trade Commission (FTC) issue warning letters to companies and individuals that are unlawfully selling unapproved products with fraudulent COVID-19 claims. The FDA also has taken enforcement action against certain sellers that continued to illegally market products for prevention or treatment of COVID-19. Additionally, the FDA also has reached out to major retailers to ask for their help in monitoring online marketplaces for fraudulent COVID-19 products. You can report websites selling fraudulent medical products to the FDA through our website, by phone at 1-800-332-1088, or email to FDA-COVID-19­ [email protected].

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64. Will there be drug shortages due to COVID-19? The FDA has been closely monitoring the supply chain with the expectation that the COVID-19 outbreak would likely impact the medical product supply chain, including potential disruptions to supply or shortages of critical medical products in the US We have been reaching out to manufacturers as part of our approach to identifying potential disruptions or shortages. We will use all available tools to react swiftly and mitigate the impact to US patients and health care professionals when a potential disruption or shortage is identified. Find real-time information about drug shortages.

65. Am I at risk for COVID-19 from taking FDA-approved drugs made outside the United States?

Currently, there is no evidence to support transmission of COVID-19 associated with imported goods, including food and drugs for humans and pets. There have not been any cases of COVID-19 in the United States associated with imported goods.

Diagnostic Testing for SARS-CoV-2

Section 5 Diagnostic Testing for SARS-CoV-25 66. Is there a test for COVID-19? Yes, the FDA has issued EUAs for different types of COVID-19 tests (Figure 33.15a and 33.15b and Table 33.1.). Some tests are used to diagnose the virus that causes COVID-19 infection whereas other tests are used to detect a recent or prior COVID-19 infection. There are 2 different types of COVID-19 diagnostic tests — molecular tests and antigen tests. Molecular tests detect the virus that causes COVID-19, SARS-CoV-2. Antigen tests detect specific proteins made by the virus. Tests that detect recent or prior COVID-19 infection are called antibody or serology tests. The EUAs allow the emergency use of tests during the COVID-19 emergency when the FDA determines certain criteria are met. These criteria include that the test may be effective at diagnosing COVID-19 and that the known and potential benefits outweigh the known and potential risks.

67. How are people tested for COVID-19?

Most tests to diagnose COVID-19 require a swab of your nose, or the part of the throat behind the nose, by a health care provider. Some tests use saliva (spit) or other types of samples. For most FDA-authorized tests, the swab or sample must be sent to a lab for analysis. Some tests allow the patient to collect the sample at home and then send it to a lab for analysis. Some tests can be analyzed at the point-of­ care, such as in a doctor’s office or health clinic. The FDA has also authorized some at-home tests that allow a person to collect their sample and run the test completely at home without sending anything to a lab. Some tests can be purchased online or in a store without a prescription, but they may not be available everywhere. 5As

of May 7, 2021, there were 370 tests and sample collection devices authorized by the FDA under Emergency Use Authorizations (EUAs). These included 270 molecular tests and sample collection devices, 76 antibody and other immune response tests, and 24 antigen tests. There are 49 molecular authorizations and one antibody authorization that can be used with home collected samples. There is one molecular prescription at-home test, two antigen prescription at-home tests, four antigen over-the-counter (OTC) at-home tests, and two molecular OTC at-home tests. The FDA has authorized 9 antigen tests and 3 molecular tests for serial screening programs. The FDA has also authorized 483 revisions to EUA authorizations. The FDA continues to monitor authorized tests and emerging scientific evidence and may revise or revoke an EUA, when appropriate, including when a test’s benefits no longer outweigh its risks. The FDA provides continuous updates to make clear which tests have been issued EUAs by the agency, and which tests should not be used.

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68. Are there any at-home tests for COVID-19? Yes. There are now FDA-authorized COVID-19 tests available for purchase online or in a store that can be used completely at home. At-home tests allow you to collect your own sample and test it with a system that gives you results in minutes at home. Additionally, the FDA has authorized some tests that can be purchased online or in a store that allow you to collect your own sample and then send it to a laboratory for analysis.

69. When will other diagnostic tests for COVID-19 be authorized?

The FDA is actively working with test developers and issues EUAs frequently for EUA requests with sufficient supporting data.

Table 33.1 COVID-19 testing basics: comparing the diagnostic and antibody tests. Courtesy of the FDA.

Diagnostic Testing for SARS-CoV-2

Figure 33.15a Diagnostic tests with alternative options. Courtesy of the FDA.

70. What is the difference between the types of tests available for SARS-CoV-2? There are two different types of tests—diagnostic tests and antibody tests.

1. A diagnostic test can show if you have an active coronavirus infection and should take steps to quarantine or isolate yourself from others. Currently there are two types of diagnostic tests—molecular (RT-PCR) tests that detect the virus’s genetic material, and antigen tests that detect specific proteins on the surface of the virus. Samples are typically collected with a nasal or throat swab, or saliva collected by spitting into a tube.

2. An antibody test looks for antibodies that are made by the immune system in response to a threat, such as a specific virus. Antibodies can help fight infections. Antibodies can take several days or weeks to develop after you have an infection and may stay in your blood for several weeks after recovery. Because of this, antibody tests should not be used to diagnose an active coronavirus infection. At this time, researchers do not know if the presence of antibodies means that you are immune to the coronavirus in the future. While there is a lot of uncertainty with this new virus, it is also possible that, over time, broad use of antibody tests and clinical follow-up will provide the medical community with more information on whether or not, and how long, a person who has recovered from the virus is at lower risk of infection if they are exposed to the virus again. Samples are typically blood from a finger stick or blood draw.

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The FDA has worked around the clock to help increase the availability of critical medical products, including diagnostic tests, to fight the coronavirus disease 2019 (COVID-19) pandemic. A patient and consumer overview1 of COVID-19 testing has plain language information about both diagnostic and antibody testing for COVID-19. This companion resource takes a closer look at diagnostic testing for COVID-19 and may be of interest to health care providers, test purchasers, and other public health professionals.

COVID-19 Diagnostic Tests

use of medical products that meet the criteria within days or weeks rather than months to years. The FDA has prioritized review of EUA requests for tests where authorization would increase testing accessibility (such as point-of-care (POC) tests, home collection tests, and at-home tests) or would significantly increase testing capacity (such as tests that reduce reliance on test supplies and high-throughput, widely distributed tests).

In certain types of emergencies, the FDA can issue an emergency use authorization, known as an EUA, to provide more timely access to critical medical products (including medicines and tests) that may help during the emergency when there are no adequate, approved, and available alternative options. The FDA has authorized for emergency use tests2 that can diagnose infection with the virus that causes COVID-19, severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2.

Test performance

No test is 100% accurate, and test performance can vary based on the prevalence of disease in the population being tested. COVID-19 diagnostic tests may be less accurate in populations with a low prevalence of disease and in asymptomatic individuals, individuals who shed little virus, or individuals who are early or late in the course of illness. Tests are assessed based on their sensitivity and specificity. A test’s sensitivity is the fraction of positive cases that the test correctly identifies as positive, and a test’s specificity is the fraction of negative cases that the test correctly identifies as negative.

Emergency Use Authorization

• A highly sensitive test will generally have a low false negative rate but will run a risk of false positives if the test’s specificity is low.

The EUA process is different than FDA approval, clearance, or licensing because the EUA standard requires less evidence than the full approval, clearance, or licensing standard. Under an EUA, the data must show that a product may be effective and that the known and potential benefits outweigh the known and potential risks. This enables the FDA to authorize the emergency

• A highly specific test will generally have a low false positive rate but will run a risk of false negatives if the test’s sensitivity is low. To help mitigate the impact of false results, most

1

https://www.fda.gov/consumers/consumer-updates/coronavirus-testing-basics https://www.fda.gov/medical-devices/coronavirus-disease-2019-covid-19-emergency-use-authorizations-medical-devices/vitro­ diagnostics-euas 2

Figure 33.15b A closer look at COVID-19 diagnostic testing. 1

Diagnostic Testing for SARS-CoV-2

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MOLECULAR TEST

ANTIGEN TEST

Detects

Viral genetic material, through multiple amplification cycles in PCR testing

Protein(s) from a virus particle

Sample type

Nasal swab, nasopharyngeal swab, mid-turbinate swab, respiratory aspirate/lavage, or saliva sample, depending on the test

Nasal swab or nasopharyngeal swab, depending on the test

Laboratory, point-of-care, or at-home

Most tests are authorized for use in traditional laboratories. A few tests are authorized for use at the point-of-care. Some tests are authorized for at-home sample collection and the patient mails the sample to a traditional laboratory for analysis. Some tests are authorized to be used completely at home with results provided in minutes.

Most tests are authorized for use at the point-of-care.

Turnaround time

Several hours to days for traditional laboratory tests; less than an hour for point-of-care tests; minutes for at-home tests

Less than an hour

Sensitivity and specificity

Generally highly sensitive (especially traditional laboratory PCR tests) and highly specific

Generally highly specific, but less sensitive than molecular tests

even low levels of viral genetic material in a patient’s sample, so these tests tend to be highly sensitive (especially traditional laboratory PCR tests).

COVID-19 tests are prescription-only, so that clinicians order and can then interpret results for patients. Any tests authorized for non-prescription use state in the authorization that they are for “direct-to-consumer” (DTC) or “over-the-counter” (OTC) use, and the patient labeling directs patients to consult their health care provider.

• A PCR test can be authorized to run batched or “pooled” patient samples if the developer demonstrates that the test meets the EUA standard for pooled testing. If a pooled sample tests positive, the samples that were combined then need to be tested individually to identify the positive case(s). When there is a low prevalence of cases (and a high number of negative results is therefore expected), pooling samples may result in fewer tests needing to be run and fewer testing supplies being required.

Molecular versus antigen tests

Currently authorized SARS-CoV-2 diagnostic tests operate using one of two different underlying technological principles. These two diagnostic test types are molecular tests and antigen tests. Each type of test detects a different part of the SARS-CoV-2 virus particle. 1. Molecular tests detect the genetic material or nucleic acid present inside a virus particle. The FDA has authorized molecular tests for use in a traditional laboratory setting and authorized some for use in a point-of-care (POC) setting. Most molecular tests are polymerase chain reaction (PCR) tests, also called nucleic acid amplification tests (NAAT). In PCR testing, a machine located in a traditional laboratory or a POC setting, depending on the test, runs a series of reactions. These reactions first convert the virus’s ribonucleic acid (RNA), if present, into deoxyribonucleic acid (DNA) and then amplify it (make millions of copies of the DNA); the test then detects this DNA. By running multiple amplification cycles, a PCR test can sense

2. An antigen test detects one or more specific proteins from a virus particle. Most currently-authorized antigen tests are POC tests and provide results in less than an hour. Antigen tests tend to be highly specific but are typically less sensitive than molecular tests. However, because antigen tests can generally be produced at a lower cost than molecular tests and have a simpler design, antigen tests could scale to test millions of individuals per day.

Authorized uses and condidtions of authorization When authorizing a product for emergency use, the FDA issues a Letter of Authorization.3 Each Letter of

3 https://www.fda.gov/medical-devices/coronavirus-disease-2019-covid-19-emergency-use-authorizations-medical-devices/vitro­ diagnostics-euas

Figure 33.15b (Continued) 2

February 2021

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Authorization issued for a COVID-19 product is available on FDA’s website. These letters set forth the terms and conditions of authorization, including the setting for conducting the test, the indications for using the test, and the responsibilities of the test’s developer, distributors, and laboratories performing the test.

or less. Tests authorized to be performed completely at home may be prescription or over-the-counter (OTC), which will be indicated in the authorization. These do not require the individual to send a sample to a laboratory for processing.

Traditional laboratory versus point-of-care tests versus at-home tests

A SARS-CoV-2 diagnostic test is authorized for use for the specific indications included in the EUA. To assist in the collection and submission of data and information needed to support an EUA request for a test, the FDA has provided templates on the agency’s website. The authorized indications for use vary from test to test. For example, a test may be authorized for one of the following categories of indications for use:

The vast majority of SARS-CoV-2 diagnostic tests with EUAs are authorized for use in traditional laboratories certified under the Clinical Laboratory Improvement Amendments of 1988 (CLIA), 41 U.S.C. §263a, to perform high or moderate complexity tests. Laboratory tests include some tests for which an individual’s nasal swab or saliva sample can be self-collected at home and then mailed to an authorized laboratory for analysis. These “home collection” tests may be prescription or non-prescription (direct-to-consumer (DTC)), which will be indicated in the authorization. Laboratory tests are authorized either for the specific laboratory in which the test was developed or for any laboratory that meets certain requirements set forth in the EUA. Several SARS-CoV-2 diagnostic tests are authorized to be conducted entirely at the point-of-care (POC) without a sample being sent to a separate laboratory for analysis. The term “point-of- care” refers to a patient care setting, such as any of the following that meets certain requirements under CLIA:

Indications

• Authorized for use with certain specimen types collected from symptomatic individuals suspected of having COVID-19 by their health care provider, provided that the individuals have experienced the onset of symptoms of COVID-19 within a prespecified number of days prior to administration of the test.

• Doctors’ offices • Nursing homes • Urgent care centers • Pharmacies • School nurse offices • Workplace health clinics For POC tests, each test authorized to date has been authorized for use in patient care settings operating with a Certificate of Waiver, a Certificate of Compliance, or a Certificate of Accreditation under CLIA. The Centers for Medicare & Medicaid Services (CMS) issues CLIA Certificates4 and enforces compliance with CLIA regulatory requirements. An at-home test is a test where a person can collect a sample, test the sample at home, and receive results directly from the test in a short time, such as 30 minutes

4

• Authorized for use with certain specimen types collected from individuals who are suspected of having COVID-19 by their health care provider, even if the individuals lack symptoms of COVID-19. • Authorized for use with certain specimen types collected from any individual, including individuals

https://www.cms.gov/files/document/laboratory-quick-start-guide-cms-clia-certification.pdf

Figure 33.15b (Continued)

Diagnostic Testing for SARS-CoV-2

may consider use of less sensitive POC tests, even if they are not specifically authorized for this indication (commonly referred to as “off-label”). For congregate care settings, like nursing homes or similar settings, repeated use of rapid POC testing may be superior for overall infection control compared to less frequent, highly sensitive tests with prolonged turnaround times. When using tests for general asymptomatic screening, health care providers should be aware of the performance of the tests and may want to consider different testing approaches, such as a predefined serial testing plan or directed testing of high-risk individuals. “Negative” results should be considered as “presumptive negative,” and health care providers should consider them in the context of clinical observations, patient history, and epidemiological information. Thus, if there is a significant new outbreak in a congregate care facility or high clinical suspicion of an infection in an individual resident, a negative POC test should be confirmed with a highly sensitive molecular test (refer to CDC guidelines). It is not necessary to perform confirmatory high sensitivity molecular tests on individuals with negative antigen test or other POC test results if they are obtained during routine screening or surveillance.

without symptoms or any other reasons to suspect COVID-19 infection. Tests with this authorization are referred to as tests authorized for use in “asymptomatic screening.”

Screening Asymptomatic Patients For licensed health care practitioners who are prescribing or administering an authorized SARSCoV-2 diagnostic test for asymptomatic individuals in congregate facilities who are not suspected of having COVID-19, we recommend they consider the information below, as well as the HHS guidance on PREP Act coverage.5 Although the current available literature suggests that symptomatic individuals with COVID-19 and asymptomatic individuals without known exposure may have similar levels of viral genetic material, there is limited data on the distribution of viral loads in individuals with and without symptoms across demographics, different settings, and specimen types. Therefore, when screening asymptomatic individuals, health care providers should consider using a highly sensitive test, especially if rapid turnaround times are available. If highly sensitive tests are not feasible, or if turnaround times are prolonged, health care providers 5

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https://www.hhs.gov/sites/default/files/prep-act-coverage-for-screening-in-congregate-settings.pdf

Figure 33.15b (Continued) 4

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COVID-19: Hundred Questions and Answers for Healthcare Providers and the Public

Section 6 Pregnancy and COVID-19

Pregnant? Take these steps to protect yourself and your baby Take these steps to protect yourself and your baby from COVID-19 (Fig. 33.16). from COVID-19 Accessible Link:

Pregnant people with COVID-19 are at an increased risk for severe illness or death from COVID-19 compared to people with COVID-19 who are not pregnant. Severe illness means that a person with COVID-19 may need: • Hospitalization • Intensive care • A ventilator to help them breathe Pregnant people with COVID-19 might also be more likely to have a baby that is born premature.

If you are pregnant, here’s what you can do to protect yourself: Avoid interacting with people who might have been exposed to or infected with COVID-19 as much possible, including people that live with you. If you do go out or interact with people who don’t live with you, you should: • Wear a mask. • Stay at least 6 feet away from anyone who doesn’t live with you. • Wash your hands frequently with soap and water for at least 20 seconds. If soap and water are not available, use a hand sanitizer with at least 60% alcohol. • Avoid crowds where social distancing can’t be maintained and indoor spaces that do not offer fresh air from the outdoors. Keep all of your recommended healthcare appointments during and after your pregnancy including your prenatal care appointments. • Some of these appointments can be done virtually, like on a phone or on a computer. Get recommended vaccines, including the flu vaccine and the whooping cough (Tdap) vaccine. If you are part of a group recommended to receive the COVID-19 vaccine, you may choose to get vaccinated. • Talk to your healthcare provider to help you make an informed decision. Ask your healthcare provider if you can get a 30-day (or longer) supply of your medicines, so you can make fewer trips to the pharmacy. • If possible, ask someone to go to the pharmacy for you. Call your healthcare provider if you have any health concerns. • If you need emergency help, call 911 right away. Don’t delay getting emergency care because of COVID-19.

Figure 33.16 Protection from COVID-19 during pregnancy.

cdc.gov/coronavirus

CS 321872-A 01/25/2021

Personal Protective Equipment

Section 7 Personal Protective Equipment 71. Should I purchase PPE such as facemasks or N95 respirators for me and my family? No. Surgical masks (Fig. 33.17) and N95s (Fig. 33.18) need to be reserved for use by health care workers, first responders, and other frontline workers whose jobs put them at much greater risk of acquiring COVID-19. The cloth face coverings recommended by CDC are not surgical masks or N95 respirators. Surgical masks and N95s are critical supplies that must continue to be reserved for health care workers and other medical first responders, as recommended by CDC.



Figure 33.17 A surgical mask is a loose-fitting, disposable device that creates a physical barrier between the mouth and nose of the wearer and potential contaminants in the immediate environment. These are often referred to as face masks, although not all face masks are regulated as surgical masks. Note that the edges of the mask are not designed to form a seal around the nose and mouth.



Figure 33.18 An N95 respirator is a respiratory protective device designed to achieve a very close facial fit and very efficient filtration of airborne particles. Note that the edges of the respirator are designed to form a seal around the nose and mouth.

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COVID-19: Hundred Questions and Answers for Healthcare Providers and the Public

72. Is there a shortage of PPE such as gloves, masks, and N95 respirators or of ventilators? The FDA has been working closely with PPE and ventilator (Fig. 33.19) manufacturers to understand their supply capabilities during this pandemic. The agency is also aware of challenges throughout the supply chain that are presently impacting the availability of PPE products and is taking steps to mitigate shortages that health care facilities are already experiencing. The FDA issued new guidance to give ventilator manufacturers and non-medical device manufacturers more flexibility to start making new ventilators and parts. We adjusted our screening of PPE and medical devices at US ports of entry to expedite imports of legitimate products into the US With CDC we took action to make more respirators, including certain N95s, available to health care personnel for use in health care settings. The FDA encourages manufacturers and health care facilities to report any supply disruptions to the device shortages mailbox at [email protected].



Figure 33.19 A ventilator in use.

73. Can 3D printing be used to make PPE? PPE includes protective clothing, gowns, gloves, face shields, goggles, face masks, and respirators or other equipment designed to protect the wearer from injury or the spread of infection or illness. While it is possible to use 3D printing to make certain PPE, there are technical challenges. 3D-printed PPE may provide a physical barrier, but 3D-printed PPE are unlikely to provide the same fluid barrier and air filtration protection as FDA-cleared surgical masks and N95 respirators. The CDC has recommendations for how to optimize the supply of face masks. Find more information about 3D printing during the COVID-19 pandemic.

Personal Protective Equipment

74. I built a DIY ventilator using instructions I found on the internet. May I sell it? DIY ventilator makers may request that their product be added to the EUA that the FDA issued on March 24, 2020, to legally market the product in the US. Instructions on how to do so, and the criteria for ventilator safety, performance and labeling, may be found on the FDA’s website related to ventilators, anesthesia gas machines modified for use as ventilators, positive pressure breathing devices modified for use as ventilators, ventilator tubing connectors, and ventilator accessories.

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COVID-19: Hundred Questions and Answers for Healthcare Providers and the Public

Section 8 Food Products



75. What is the FDA’s role in helping to ensure the safety of the human and animal food supply? To protect public health, the FDA monitors domestic firms and the foods that they produce. The FDA also monitors imported products and foreign firms exporting to the United States. The FDA protects consumers from unsafe foods through research and methods development; inspection and sampling; and regulatory and legal action.

76. Will there be food shortages?

In some cases, the inventory of certain foods at your grocery store might be temporarily low before stores can restock. Food production and manufacturing generally are widely dispersed throughout the US, however; there is a significant shift in where consumers are buying food during the pandemic. While food use in large-scale establishments, such as hotels, restaurants, sports arenas/stadiums and universities suddenly declined, the demand for food at grocery stores increased. The FDA has issued temporary guidance to provide flexibility in packaging and labeling requirements to support food supply chains and get foods to the consumer retail marketplace. The FDA is closely monitoring the food supply chain for any shortages in collaboration with industry and our federal and state partners. We are in regular contact with food manufacturers and grocery stores.

77. Why is the FDA providing flexibility to food manufacturers, under limited circumstances during the COVID-19 public health emergency, to make minor changes in ingredients without reflecting those changes on the package label? Due to limited shortages of specific ingredients and foods, or unexpected supply chain disruptions in some industries, food manufacturers may need to make

Food Products

small changes to some ingredients during the COVID-19 public health emergency. Manufacturers may not be able to relabel their products to reflect these minor changes on the food label without slowing down the processing or distribution of the food. To avoid slowing down food processing or distribution during the coronavirus pandemic, the FDA issued a guidance available on the FDA’s website. The temporary policy provides food manufacturers with flexibility to make minor formulation changes in certain, limited circumstances without making conforming label changes on packages as long as any substitutions or omissions of ingredients do not pose a health or safety issue (such as allergens), and do not cause significant changes in the finished product.

78. What do I need to know about the temporary policy for food labeling of minor ingredient changes during the COVID-19 public health emergency if I have food allergies?

Although the temporary policy allows some flexibility, the eight major food allergens under the Food Allergen Labeling and Consumer Protection Act (FALCPA) of 2004 cannot be substituted for labeled ingredients by manufacturers without a corresponding label change. While the temporary policy does not list all ingredients known to cause sensitivities in some people, manufacturers should avoid substituting ingredients with major food allergens or with ingredients recognized as priority allergens (such as sesame, celery, lupin, buckwheat, molluscan shellfish, and mustard) in other parts of the world without a label change. These flexibilities are intended to remain in effect only for the duration of the COVID-19 public health emergency in the United States. However, when this public health emergency is over, extensions may be needed if the food and agriculture sectors need additional time to bring supply chains back into regular order.

79. Will there be animal food shortages?

There are no nationwide shortages of animal food, although in some cases the inventory of certain foods at your grocery store might be temporarily low before stores can restock. Animal food production and manufacturing are widely dispersed throughout the United States and no widespread disruptions have been reported in the supply chain.

80. What are the most important things I need to know to keep myself and others safe when I go to the grocery store during the pandemic? There are steps you can take to help protect yourself, grocery store workers and other shoppers, such as wearing a face covering, practicing social distancing, and using wipes on the handles of the shopping cart or basket. Review Fig. 33.20 for more tips on shopping for food during the COVID-19 pandemic.

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COVID-19: Hundred Questions and Answers for Healthcare Providers and the Public

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COVID-19

Information for Consumers — Shopping for Food

As grocery shopping remains a necessity during this pandemic, many people have questions about how to shop safely. We want to reassure consumers that there is currently no evidence of human or animal food or food packaging being associated with transmission of the coronavirus that causes COVID-19. This particular coronavirus causes respiratory illness and is spread from person-to-person, unlike foodborne gastrointestinal or GI viruses, such as norovirus and hepatitis A that often make people ill through contaminated food. Although your grocery store may be temporarily out of certain products, there are no nationwide shortages of food. Food production and manufacturing are spread throughout the United States. During this pandemic, consumers are getting most of their food from grocery stores, and many stores have modified their operating hours to allow for more time to restock shelves and clean. In addition, many stores are providing special hours for seniors or other high-risk individuals to shop and are offering pick-up and delivery services. Check the store’s website or call the store to learn more. To help protect yourself, grocery store workers, and other shoppers, it is important to keep a few things in mind:

1.

Prepare a shopping list in advance. Buy just 1 to 2 weeksworth of groceries at a time. Buying more than you need can create unnecessary demand and temporary shortages.

2.

Wear a face covering or mask while you are in the store. Some stores and localities may require it. Check your state, county, or city guidelines for any other requirements.

3.

Carry your own wipes, or use one provided by the store to wipe down the handles of the shopping cart or basket. If you use reusable shopping bags, ensure they are cleaned or washed before each use.

4.

Practice social distancing while shopping – keeping at least 6 feet between you, other shoppers, and store employees. Keep your hands away from your face.

5.

Wash your hands with warm water

and soap for at least 20 seconds

when you return home and again

after you put away your groceries.

6.

Again, there is no evidence of food

packaging being associated with

the transmission of COVID-19.

However, if you wish, you can wipe down product packaging and allow

it to air dry, as an extra precaution. ­

As always, it is important to follow these food safety practices to help prevent foodborne illness:

7.

Before eating, rinse fresh fruits and vegetables under running tap water, including those with skins and rinds that are not eaten. Scrub firm produce with a clean produce brush. For canned goods, remember to clean lids before opening.

8.

When unpacking groceries, refrigerate or freeze meat, poultry, eggs, seafood, and other perishables—like berries, lettuce, herbs, and mushrooms—within 2 hours of purchasing.

2

HOURS

Regularly clean and sanitize kitchen counters using a commercially available disinfectant product or a DIY sanitizing solution with 5 tablespoons (1/3rd cup) unscented liquid chlorine bleach to 1 gallon of water or 4 teaspoons of bleach per quart of water. WARNING: Do not use this solution or other disinfecting products on food.

9.

10.

1 CLEAN 2 SEPARATE 3 COOK 4 CHILL

Always keep in mind the basic 4 food safety steps — Clean, Separate, Cook, and Chill.

Food is a source of comfort, as well as nourishment for you and your family – especially now – and we hope this advice will help you continue to buy groceries with care and confidence. www.fda.gov

April 2020

Figure 13.20 COVID-19 information for consumers: shopping for food.

Food Products

81. Are food products produced in the United States or other countries affected by COVID-19 a risk for the spread of COVID-19? There is no evidence to suggest that food produced in the United States or imported from countries affected by COVID-19 can transmit COVID-19.

82. Can I get the coronavirus from food, food packaging, or food containers and preparation area?

Currently there is no evidence of food, food containers, or food packaging being associated with transmission of COVID-19. Like other viruses, it is possible that the virus that causes COVID-19 can survive on surfaces or objects. If you are concerned about contamination of food or food packaging, wash your hands after handling food packaging, after removing food from the packaging, before you prepare food for eating and before you eat. Consumers can follow CDC guidelines on frequent hand washing with soap and water for at least 20 seconds (Fig. 33.21); and frequently clean and disinfect surfaces. It is always important to follow the 4 key steps of food safety—clean, separate, cook, and chill.

 Figure 33.21 During the COVID-19 pandemic, keeping hands clean is especially important to help prevent the virus from spreading.

83. Is the US food supply safe? Currently there is no evidence of food or food packaging being associated with transmission of COVID-19. Unlike foodborne gastrointestinal (GI) viruses like norovirus and hepatitis A that often make people ill through contaminated food, SARS-CoV-2, which causes COVID-19, is a virus that causes respiratory illness and not gastrointestinal illness, and foodborne exposure to this virus is not known to be a route of transmission. It may be possible that a person can get COVID-19 by touching a surface or object that has the virus on it and then touching their own mouth, nose, or possibly their eyes, but this is not thought to be the main way the virus spreads.

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COVID-19: Hundred Questions and Answers for Healthcare Providers and the Public

84. Is the US animal food supply safe? Currently there is no evidence of animal food or food packaging being associated with transmission of COVID-19. Foodborne exposure to the virus that causes COVID-19 is not known to be a route of transmission.

85. Can I get COVID-19 from a food worker handling my food?

Currently, there is no evidence of food or food packaging being associated with transmission of COVID-19. However, the virus that causes COVID-19 is spreading from person-to-person in some communities in the US. The CDC recommends that if you are sick, stay home until you are better and no longer pose a risk of infecting others. Anyone handling, preparing and serving food should always follow safe food handling procedures, such as washing hands and surfaces often.

86. Should food workers who are ill stay home?

CDC recommends that employees who have symptoms of acute respiratory illness stay home and not come to work until they are free of fever (100.4° F [37.8° C] or greater using an oral thermometer), signs of a fever, and any other symptoms for at least 24 hours, without the use of fever-reducing or other symptom-altering medicines (e.g. cough suppressants). Employees should notify their supervisor and stay home if they are sick. We recommend that businesses review CDC’s interim guidance for businesses and employers for planning and responding to coronavirus disease. Also see the FDA’s Retail Food Protection: Employee Health and Personal Hygiene Handbook.

87. Should food facilities (grocery stores, manufacturing facilities, restaurants, etc.) perform any special cleaning or sanitation procedures for COVID-19?

CDC recommends routine cleaning of all frequently touched surfaces in the workplace, such as workstations, countertops, and doorknobs. Use the cleaning agents that are usually used in these areas and follow the directions on the label. CDC does not recommend any additional disinfection beyond routine cleaning at this time. View the current list of products that meet EPA’s criteria for use against SARS-CoV-2. Restaurants and retail food establishments are regulated at the state and local level. State, local, and tribal regulators use the Food Code published by the FDA to develop or update their own food safety rules. Generally, FDA-regulated food manufacturers are required to maintain clean facilities, including, as appropriate, clean and sanitized food contact surfaces, and to have food safety plans in place. Food safety plans include a hazards analysis and risk-based preventive controls and include procedures for maintaining clean and sanitized facilities and food contact surfaces.

Food Products

88. What is the FDA doing to respond to foodborne illnesses during the COVID-19 pandemic? The virus that causes COVID-19 is a virus that causes respiratory illness. Viruses like norovirus and hepatitis A that can make people sick through contaminated food usually cause gastrointestinal or stomach illness. Currently there is no evidence of food, food containers, or food packaging being associated with transmission of COVID-19. The CDC, FDA, and USDA continue to work with state and local partners to investigate foodborne illness and outbreaks during the COVID-19 pandemic. The FDA’s Coordinated Outbreak Response and Evaluation (CORE) Network manages outbreak response, as well as surveillance and post-response activities related to incidents involving multiple illnesses linked to FDA-regulated human food products. During this coronavirus outbreak, CORE’s full-time staff will continue to operate to prepare for, coordinate and carry out response activities to incidents of foodborne illnesses. The FDA’s Center for Veterinary Medicine manages outbreak response for animal food and is similarly staffed and prepared to respond to incidents of foodborne illness in animals.

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COVID-19: Hundred Questions and Answers for Healthcare Providers and the Public

Section 9 Animals, Pets, and Animal Drug Products

89. What is the FDA’s role in regulating animal drugs, animal food (including pet food), and animal medical devices? The FDA approves and regulates animal drugs to ensure they are safe and effective. In addition, the FDA helps ensure that animal food (including pet food) is safe and truthfully labeled. The FDA has post-market authority over veterinary medical devices.

90. Can I give my pet COVID-19? Can I get COVID-19 from my pet or other animals?

There is a very small number of pets around the world reported to be infected with the virus that causes COVID-19 after having contact with a person with COVID-19. Based on the limited information currently available, the risk of animals spreading COVID-19 to people is considered low. Until we learn more about how this virus affects animals, treat pets as you would other human family members to protect them from a potential infection. • Do not let pets interact with people outside the household. • Keep cats indoors when possible to prevent them from interacting with other animals or people. • Walk dogs on a leash, maintaining at least 6 feet (2 meters) from other people. • Avoid dog parks or public places where a large number of people gather.

Talk to your veterinarian if your pet gets sick or if you have any concerns about your pet’s health. Learn more about Pet Safety & COVID-19.

Animals, Pets, and Animal Drug Products

91. Is there a test for COVID-19 in pets? If so, has it been approved by the FDA? Certain veterinary diagnostic laboratories have developed diagnostic tests for

SARS-CoV-2 for use in pets if needed. Diagnostic tests for animals are regulated

differently than those for humans. The FDA does not require approval or clearance of a 510(k), PMA, or any other pre-market submission for devices, including diagnostic tests, intended for animal use. The FDA does, however, have post-market regulatory oversight over devices intended for animal use and can take appropriate regulatory action if an animal device is misbranded or adulterated. Certain private, state, and university veterinary diagnostic laboratories have developed diagnostic tests for SARS-CoV-2 for use in dogs and cats. The FDA is also aware of at least two veterinary tests for COVID-19 in pets developed by commercial laboratories initially for internal surveillance, but the agency has not evaluated the validity of these tests. The tests are not currently available for routine testing. The decision to test pets should be made collaboratively between local, state, or federal public and animal health officials.

92. Should I get my pet tested for COVID-19?

Routine testing of pets for COVID-19 is not recommended at this time. There is currently no evidence that animals are a source of COVID-19 infection in the United States. Based on the limited information available to date, the risk of pets spreading the virus is considered to be low. If your pet is sick, consult your veterinarian. Animal testing is reserved for situations when the results may affect the treatment or management of people and animals. If your veterinarian thinks your pet is a candidate for testing, they will consult the state veterinarian and public health officials. Do not contact your state veterinarians directly: they do not have the client/patient-veterinarian relationship that would allow them to fully understand the situation and they are also actively involved in other animal disease-related emergencies as well as response to COVID-19.

93. What animal species can get COVID-19?

We currently don’t fully understand how COVID-19 affects different animal species. We are aware of a very small number of pets, including dogs, cats and a ferret reported to be infected with the virus that causes COVID-19 after close contact with people with COVID-19. Large cats in captivity, including several lions and tigers in a New York zoo, a puma in South Africa, and tigers in a Tennessee zoo have tested positive for SARS-CoV-2, as have several gorillas at the San Diego zoo, after showing signs of respiratory illness. It is suspected these animals became sick after being exposed to zoo employees with COVID-19. The virus that causes COVID-19 has been reported in minks on farms in the Netherlands, Denmark,

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Spain, Italy, Sweden and the United States. Once the virus is introduced on a farm, spread can occur between mink as well as from mink to other animals on the farm (dogs, cats). Because some workers on these farms had COVID-19, it is likely that infected farm workers were the initial source of mink infections. Recent research shows that ferrets, cats, fruit bats, and golden Syrian hamsters can be experimentally infected with the virus and can spread the infection to other animals of the same species in laboratory settings. Mice, pigs, chickens, and ducks did not become infected or spread the infection based on results from these studies. Data from one study suggest that dogs are not as likely to become infected with the virus as cats and ferrets. These findings were based upon a small number of animals and do not indicate whether animals can spread infection to people. For any animal that tests positive for SARS-CoV-2 at a private or state laboratory, USDA’s National Veterinary Services Laboratories performs additional testing to confirm the infection and posts the results on its website.

94. Since domestic cats can get infected with the virus that causes COVID-19, should I worry about my cat?

We are still learning about this virus and how it spreads, but it appears it can spread from humans to animals in some situations. The FDA is aware of a very small number of pets, including cats, reported to be infected with the virus that causes COVID-19. The majority of these cases were linked to close contact with people who tested positive for COVID-19. At this time, there is no evidence that pets, including cats and dogs, play a role in spreading COVID-19 to people. The virus that causes COVID-19 spreads mainly from person to person, typically through respiratory droplets from coughing, sneezing, or talking. People sick with COVID-19 should isolate themselves from other people and animals, including pets, during their illness until we know more about how this virus affects animals. If you must care for your pet or be around animals while you are sick, wear a cloth face covering and wash your hands before and after you interact with pets.

95. Why are animals being tested when many people can’t get tested?

The FDA, USDA and CDC recommend that any testing of animals should be conducted using kits not required when testing people. USDA’s National Veterinary Services Laboratories (NVSL) and the laboratories of the National Animal Health Laboratory Network (NAHLN) use tests developed for animal testing that are not used for testing in people. This avoids placing additional stresses on human testing resources while also recognizing the potential importance of animal testing to supporting public health. Although animal and human tests are generally similar, this type of testing has to be adjusted in each species and for each sample type (blood, feces, nasal swab). Human and animal tests are not intended to be interchangeable. Some testing performed on animals is based on

Animals, Pets, and Animal Drug Products

the published tests used in people, but animal testing is not likely to reduce the availability of tests for people if labs follow recommendations from the FDA, USDA, and CDC that animal testing be conducted using tests developed for animals.

96. Can pets carry the virus that causes COVID-19 on their skin or fur?

Although we know certain bacteria and fungi can be carried on fur and hair, there is no evidence that viruses, including the virus that causes COVID-19, can spread to people from the skin, fur, or hair of pets. However, because animals can sometimes carry other germs that can make people sick, it’s always a good idea to practice healthy habits (Fig 33.22) around pets and other animals, including washing hands before and after interacting with them and especially after cleaning up their waste. There are no products that are FDA-approved to disinfect the hair or coats of pets, but if you do decide to bathe or wipe off your pet, first talk to your veterinarian about suitable products. Never use hand sanitizer, countercleaning wipes or other industrial or surface cleaners, as these can penetrate the skin or be licked off and ingested by your pet. If you have recently used any of these products on your pet, or your pet is showing signs of illness after use, contact your veterinarian and rinse or wipe down your pet with water.

97. Are there any approved products that can prevent or treat COVID-19 in animals? No. Under the Federal Food, Drug, and Cosmetic (FD&C) Act, “articles intended for use in the diagnosis, cure, mitigation, treatment, or prevention of disease in man or other animals” are drugs. The FDA has not approved any drugs for the diagnosis, cure, mitigation, treatment, or prevention of COVID-19 in animals. The USDA’s Animal and Plant Health Inspection Service (APHIS) Center for Veterinary Biologics (CVB) regulates veterinary biologics, including vaccines, diagnostic kits, and other products of biological origin. Similarly, APHIS CVB has not licensed any products to treat or prevent COVID-19 in animals. The FDA has taken action against unapproved products claiming to prevent or cure COVID-19. The public can help safeguard human and animal health by reporting any products claiming to do so to [email protected] or 1-888-INFO-FDA (1-888-463-6332).

98. Is it true that animals, like dogs, cats, and cattle, get their own different types of coronavirus?

Yes. Coronaviruses are a large family of viruses. Some coronaviruses like COVID-19 cause cold-like illnesses in people, while others cause illness in certain types of animals, such as cattle, camels, and bats. Some coronaviruses, such as canine and feline coronaviruses, only infect animals and do not infect humans.

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For example, bovine coronavirus causes diarrhea in young calves, and pregnant cows are routinely vaccinated to help prevent infection in calves. This vaccine is only licensed for use in cattle for bovine coronavirus and is not licensed to

Figure 33.22 How to stay healthy around pets.

Animals, Pets, and Animal Drug Products

prevent COVID-19 in cattle or other species, including humans. Dogs can get a respiratory coronavirus, which is part of the complex of viruses and bacteria associated with canine infectious respiratory disease, commonly known as “kennel cough.” While this virus is highly contagious among both domestic and wild dogs, it is not transmitted to other animal species or humans. Most strains of feline enteric coronavirus, a gastrointestinal form, are fought off by a cat’s immune system without causing disease. However, in a small proportion of these cats, the virus can cause feline infectious peritonitis (FIP), a disease that is almost always fatal. Other species, like horses, turkeys, chickens, and swine, can contract their own species-specific strains of coronavirus but, like the other strains mentioned above, they are not known to be transmissible to humans. More information is available in the American Veterinary Medical Association’s fact sheet about coronaviruses in domestic species.

99. If my pet previously had a species-specific coronavirus, does that make them more or less likely to get COVID-19? There are no data to suggest that current or previous infection with another strain of coronavirus would make your pet more or less likely to get COVID-19.

100. If my pet has been vaccinated for species-specific coronavirus, does that make them more or less likely to get COVID-19?

Species-specific coronavirus vaccines are unlikely to work against this type of coronavirus because it is a new virus that is different from the species-specific strains of coronavirus targeted by the vaccine.

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Supplementary Information Box 33.1 What’s in a name? Importance of nomenclature It is important to distinguish the SARS-CoV-2 virus from the disease it causes, namely, COVID-19. The disease is caused by a coronavirus, the same class of virus that causes the common cold. During the initial outbreak various names were used for the virus, including, the “coronavirus,” “Wuhan coronavirus,” or “the Chinese virus.” In February 2020, the International Committee on Taxonomy of Viruses adopted the official name “Severe Acute Respiratory Syndrome Coronavirus 2” (SARS-CoV-2). This name comes from the disease it causes, namely, severe acute respiratory syndrome; CoV stands for coronavirus, and the number 2 was added because it is the second coronavirus that causes a serious respiratory disease (in 2019). Some publications and articles in the media refer to SARS-CoV-2 as “the COVID-19 virus” or “HCoV-19 virus.”

 A virus is an entity that infects living organisms/cells and requires a host for survival and multiplication. The term “pandemic,” on the other hand, refers to the outbreak, occurrence, and spread of a particular disease. In that sense, it has a much more prominent social connotation. The layperson often calls both the virus and the disease it causes, “coronavirus,” often omitting the numerical designation at the end. As a microbiologist, I will not use the two terms SARS-CoV-2 (the virus) and COVID-19 (the disease) interchangeably because they are distinct. In this chapter, I will use the terms SARS-CoV-2 virus, CoV-2 virus, and coronavirus 2 interchangeably to refer to the virus that causes COVID-19.

Supplementary Information

Box 33.2 Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)

Courtesy of NIH.

The first coronavirus, avian infectious bronchitis virus, was discovered in 1937. Forty years later when electron microscopy was performed on specimens from cultures of viruses known to cause colds in humans, the particles were and identified as avian infectious bronchitis virus. The term “coronavirus,” is from the Latin word, corona (“crown”) because the glycoprotein spikes of these viruses create an image similar to a solar corona (Box 33.1, the figure on the next page, and also Figs. 33.25a,b and 33.26). Coronaviruses belong to the Coronaviridae family in the Nidovirales order and are in the nanoparticle size range (65–125 nm in diameter) and contain a single-stranded RNA as a nucleic material, ranging from 26 to 32 kbs in length. The subgroups of coronaviruses family are alpha (α), beta (β), gamma (γ), and delta (δ) coronavirus. SARS-CoV, H5N1 influenza A, H1N1 2009, and MERS-CoV cause acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) which leads to pulmonary failure and result in fatality.

The Chinese government informed the WHO in December 2019 that SARS­ CoV-2 originated in Wuhan. However, circumstantial evidence now (in May 2021) points to its emergence months earlier in 2019, with the finger pointing to a possible lab leak. The WHO declared it as Public Health Emergency of International Concern (PHEIC) in January 2020, and finally a pandemic in March 2020. According to one study, of the individuals infected by the virus, about 80% had mild to moderate disease and among those with severe disease, 5% develop critical illness (JAMA 2020; 323:1239–1242). Those infected with SARS-CoV-2 virus generally develop symptoms 4–5 days postexposure and include fever, throat pain, cough, muscle or body aches, loss of taste or smell, and diarrhea. Recovery from mild infection commonly resolves within 7–10 days after the onset of symptoms but can take 3–6 weeks in severe/critical illness (Report of the WHO-China joint mission on coronavirus disease 2019). However, continued follow-up of patients who recovered from COVID-19 showed that one or more symptoms persist in a substantial percentage of people, even weeks or months after COVID-19. Coronavirus can also cause forgetfulness, psychosis, mania, or a stutter (Scientific American Health & Medicine 2021; 3(2):15–18).

(Continued)

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Box 33.2 (Continued)

Along with the pneumonia, blood clots, and other serious health concerns caused by SARS-CoV-2, some studies have now also identified another troubling connection: the virus can target and impair cells in the pancreas causing Type 1 diabetes (Cell Metab 2021; S1550–4131(21)00232-1; Cell Metab. 2021; S1550–4131(21)00230-8.). A few of those who recovered from COVID-19 develop persistent or new symptoms lasting weeks or months; this is referred to as “long COVID,” “Long Haulers,” or “Post COVID syndrome.” Long COVID can be continuous or relapsing and remitting in nature (BMJ (2020). 370:m3489, http://dx.doi.org/10.1136/bmj.m3489). Majority of patients with post-COVID syndrome are polymerase chain reaction (PCR) negative. Genomic analysis revealed that SARS-CoV-2 is phylogenetically related to SARS-like bat viruses, leading to the theory that this novel virus is also of bat origin with a currently unknown animal species potentially acting as an intermediate host between bats and humans. The highly infectious virus mainly targets pulmonary epithelial cells via its spike protein that binds to the host’s angiotensin converting enzyme 2 (ACE2) receptor. There is data to show that besides human ACE2 (hACE2), SARS-CoV-2 also recognizes ACE2 from pig, ferret, rhesus monkey, civet, cat, pangolin, rabbit, and dog. An important conclusion of this broad receptor usage of SARS-CoV-2 implies that it may have a wide host range, and the varied efficiency of ACE2 usage in different animals may indicate their different susceptibilities to SARS-CoV-2 infection. I am certain that studies will confirm this.

SARS-CoV 2 Structure. Courtesy of Dr. R. B. Singh.

Supplementary Information

Box 33.3 Fact sheet: activity at the Chinese Wuhan Institute of Virology

For more than a year, the Chinese Communist Party (CCP) has systematically prevented a transparent and thorough investigation of the COVID-19 pandemic’s origin, choosing instead to devote enormous resources to deceit and disinformation. Nearly two** million people have died. Their families deserve to know the truth. Only through transparency can we learn what caused this pandemic and how to prevent the next one. **As of June 8, 2021, this number is ~3.76 million.

The U.S. government does not know exactly where, when, or how the COVID-19 virus—known as SARS-CoV-2—was transmitted initially to humans. We have not determined whether the outbreak began through contact with infected animals or was the result of an accident at a laboratory in Wuhan, China. The virus could have emerged naturally from human contact with infected animals, spreading in a pattern consistent with a natural epidemic. Alternatively, a laboratory accident could resemble a natural outbreak if the initial exposure included only a few individuals and was compounded by asymptomatic infection. Scientists in China have researched animal-derived coronaviruses under conditions that increased the risk for accidental and potentially unwitting exposure.

The CCP’s deadly obsession with secrecy and control comes at the expense of public health in China and around the world. The previously undisclosed information in this fact sheet, combined with open-source reporting, highlights three elements about COVID-19’s origin that deserve greater scrutiny: 1. Illnesses inside the Wuhan Institute of Virology (WIV):

• The U.S. government has reason to believe that several researchers inside the WIV became sick in autumn 2019, before the first identified case of the outbreak, with symptoms consistent with both COVID-19 and common seasonal illnesses. This raises questions about the credibility of WIV senior researcher Shi Zhengli’s public claim that there was “zero infection” among the WIV’s staff and students of SARS-CoV-2 or SARS-related viruses. (Continued)

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Box 33.3 (Continued)

• Accidental infections in labs have caused several previous virus outbreaks in China and elsewhere, including a 2004 SARS outbreak in Beijing that infected nine people, killing one. • The CCP has prevented independent journalists, investigators, and global health authorities from interviewing researchers at the WIV, including those who were ill in the fall of 2019. Any credible inquiry into the origin of the virus must include interviews with these researchers and a full accounting of their previously unreported illness.

2. Research at the WIV:

• Starting in at least 2016—and with no indication of a stop prior to the COVID-19 outbreak—WIV researchers conducted experiments involving RaTG13, the bat coronavirus identified by the WIV in January 2020 as its closest sample to SARS-CoV-2 (96.2% similar). The WIV became a focal point for international coronavirus research after the 2003 SARS outbreak and has since studied animals including mice, bats, and pangolins. • The WIV has a published record of conducting “gain-of-function” research to engineer chimeric viruses. But the WIV has not been transparent or consistent about its record of studying viruses most similar to the COVID-19 virus, including “RaTG13,” which it sampled from a cave in Yunnan Province in 2013 after several miners died of SARS-like illness. • WHO investigators must have access to the records of the WIV’s work on bat and other coronaviruses before the COVID-19 outbreak. As part of a thorough inquiry, they must have a full accounting of why the WIV altered and then removed online records of its work with RaTG13 and other viruses.

3. Secret military activity at the WIV:

• Secrecy and non-disclosure are standard practice for Beijing. For many years the United States has publicly raised concerns about China’s past biological weapons work, which Beijing has neither documented nor demonstrably eliminated, despite its clear obligations under the Biological Weapons Convention. • Despite the WIV presenting itself as a civilian institution, the United States has determined that the WIV has collaborated on publications and secret projects with China’s military. The WIV has engaged in classified research, including laboratory animal experiments, on behalf of the Chinese military since at least 2017. • The United States and other donors who funded or collaborated on civilian research at the WIV have a right and obligation to determine whether any of our research funding was diverted to secret Chinese military projects at the WIV.

Supplementary Information

Today’s revelations just scratch the surface of what is still hidden about

COVID-19’s origin in China. Any credible investigation into the origin of

COVID-19 demands complete, transparent access to the research labs in

Wuhan, including their facilities, samples, personnel, and records.

As the world continues to battle this pandemic—and as WHO investigators

begin their work, after more than a year of delays—the virus’s origin remains

uncertain. The United States will continue to do everything it can to support

a credible and thorough investigation, including by continuing to demand

transparency on the part of Chinese authorities.

Courtesy of the US Department of State, Office of the Spokesperson, January 15, 2021.

Box 33.4 China’s push to control Americans’ health-care future

For all the polarization that grips Washington, here’s a source of rare consensus: the emerging threat of China’s push to acquire our health-care data, including the DNA of American citizens. US officials tell us the communist regime’s aggressive collection of our most personal information presents a danger both to national security and our economy. As alarm bells ring across agencies, parties, and presidential administrations, different branches of government have taken action over the past year to stem the tide of our medical data flowing to China. The quest to control our biodata—and, in turn, control health care’s future—has become the new space race, with more than national pride in the balance. Our investigation begins with an unsolicited and surprising proposal that came from overseas at the onset of the COVID crisis … It’s not just China that’s recognized what a valuable commodity your DNA can be. As you’ll hear: some of the fastest-growing US tech companies are in this space, as well. In fact, you may have already surrendered your DNA by spitting in a tube.

Questions regarding relationships between US firms and foreign entities can be directed to the National Counterintelligence and Security Center (NCSC) and the Office of the Director of National Intelligence (ODNI). Courtesy of CBS News, January 31, 2021.

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Total confirmed COVID-19 deaths and cases per million people

The confirmed counts shown here are and lowerAnswers than thefor total counts. The main reason for Public this is limited testing and 742 COVID-19: Hundred Questions Healthcare Providers and the challenges in the attribution of the cause of death.

No data 0

5

10

25

50

100

250

Source: Johns Hopkins University CSSE COVID-19 Data – Last updated 19 June, 06:02 (London time)

500

1,000

>5,000

OurWorldInData.org/coronavirus ‡ CC BY

Figure 33.23 World map of total confirmed COVID-19 cases per million people. The confirmed counts shown here are lower than the total counts. The main reason for this is limited testing and challenges in the attribution of the cause of death. Source: Our World In Data (CC BY 4.0).

Figure 33.24 Global examples of emerging and reemerging infections. Courtesy of Dr. Anthony S. Fauci and NIH.

Supplementary Information

Figure 33.25a The peplomers of a SARS-CoV-2. This illustration reveals the surface morphology/topography of the virus nanoparticle. Note the spikes that adorn the outer surface of the virus, which impart the look of a corona surrounding it, when viewed electron microscopically. A peplomer (Greek: peplos, ‘robe’, ‘[woman’s] dress’ + meros, ‘part’) is one of the knoblike spike structures (red, orange, yellow), generally composed of glycoproteins (spike protein) and projecting from the lipid bilayer of the surface envelope of an enveloped virus. Peplomers play important roles in the infection process. Courtesy of the CDC.

Figure 33.25b Structural view of a coronavirus. Source: https://commons.wikimedia.org/ wiki/File:3D_medical_animation_coronavirus_structure.jpg (CC BY-SA 4.0).

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Figure 33.26 Digitally colorized transmission electron micrograph of SARS-CoV-2. Virions with visible coronae (bluish) are emerging from human cells cultured in a laboratory. The virus shown was isolated from a patient in the US. Courtesy of NIH.

Figure 33.27 Digitally colorized scanning electron micrograph of SARS-CoV-2. Virions (yellow) are emerging from human cells cultured in a laboratory. The virus shown was isolated from a patient in the US. Courtesy of NIH.

Figure 33.28 Transmission and life-cycle of SARS-CoV-2 causing COVID-19. SARS-CoV-2 is transmitted via respiratory droplets of infected cases to oral and respiratory mucosal cells. The virus, possessing a single-stranded RNA genome wrapped in nucleocapsid (N) protein and three major surface proteins: membrane (M), envelope (E) and spike, replicates and passes to the lower airways potentially leading to severe pneumonia. The gateway to host cell entry (magnified view) is via Spike-converting enzyme 2 (ACE2) interaction with cleavage of spike in the prefusion state by proteases TMPRSS-2/furin. A simplified depiction of the life cycle of the virus is shown along with potential immune responses elicited. Source: C. D. Funk, C. Laferrière and A. Ardakani (2020). A snapshot of the global race for vaccines targeting SARS-CoV-2 and the COVID-19 pandemic. Front. Pharmacol. 11:937.

Supplementary Information 745

Figure 33.29 COVID-19 pathogenesis. 1. A. SARS-CoV-2 enters the epithelial cell either via endocytosis or by membrane fusion through binding to ACE2 receptor and releasing its RNA into the cytoplasm. B. Viral RNA uses the cell’s machinery to translate its viral non-structural and structural proteins and replicate its RNA. C. Viral structural proteins S, E, and M assemble in the rough endoplasmic reticulum (RER). D. Viral structures and nucleocapsid subsequently assemble in the endoplasmic reticulum Golgi intermediate (ERGIC). E. New virion packed in Golgi vesicles fuse with the plasma membrane and get released via exocytosis. 2. SARS-CoV-2 infection induces inflammatory factors that lead to activation of macrophages and dendritic cells. 3. Antigen presentation of SARS-CoV-2 via major histocompatibility complexes I and II (MHC I and II) stimulates humoral and cellular immunity resulting in cytokine and antibody production. 4. In severe COVID-19 cases, the virus reaches the lower respiratory tract and infects type II pneumocytes leading to apoptosis and loss of surfactant. The influx of macrophages and neutrophils induces a cytokine storm. Leaky capillaries lead to alveolar edema. Hyaline membrane is formed. All of these pathological changes result in alveolar damage and collapse, impairing gas exchange. Source: N. Chams, et al. (2020). COVID-19: A multidisciplinary review. Front. Public Health 8:383.

746 COVID-19: Hundred Questions and Answers for Healthcare Providers and the Public

Supplementary Information

Figure 33.30 False-color transmission electron micrograph of SARS-CoV-2, B.1.1.7 variant. The variant’s increased transmissibility is believed to be due to changes in the structure of the spike proteins, shown here in green. Transmission electron micrograph of a SARS-CoV-2 virus particle (UK B.1.1.7 variant), isolated from a patient sample and cultivated in cell culture. The prominent projections (green) seen on the outside of the virus particle (yellow) are spike proteins. This fringe of proteins enables the virus to attach to and infect host cells and then replicate. Image captured at the NIAID Integrated Research Facility in Fort Detrick, Maryland. Courtesy of NIH.

Figure 33.31 SARS-CoV-2 Symptoms. Courtesy of WHO.

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Figure 33.32 Understanding the protein structure of SARS-CoV-2. Courtesy of Argonne National Laboratory.

748 COVID-19: Hundred Questions and Answers for Healthcare Providers and the Public

Supplementary Information

749

Choosing Safer Activities Accessible link: https://www.cdc.gov/coronavirus/2019-ncov/daily-life-coping/participate-in-activities.html

Fully Vaccinated People

Examples of Activities

Unvaccinated People

Outdoor

Safest

Walk, run, wheelchair roll, or bike outdoors with members of your household Attend a small, outdoor gathering with fully vaccinated family and friends

Less Safe

Dine at an outdoor restaurant with friends from multiple households

Least Safe

Attend a small, outdoor gathering with fully vaccinated and unvaccinated people, particularly in areas of substantial to high transmission

Attend a crowded, outdoor event, like a live performance, parade, or sports event

Indoor

Safest

Less Safe

Visit a barber or hair salon Go to an uncrowded, indoor shopping center or museum Attend a small, indoor gathering of fully vaccinated and unvaccinated people from multiple households Go to an indoor movie theater

Least Safe

Attend a full-capacity worship service Sing in an indoor chorus Eat at an indoor restaurant or bar Participate in an indoor, high intensity exercise class

Get a COVID-19 vaccine Prevention measures not needed

• Safety levels assume the recommended prevention measures are followed, both by the individual and the venue (if applicable).

Take prevention measures

• CDC cannot provide the specific risk level for every activity in every community. It is important to consider your own personal situation and the risk to you, your family, and your community before venturing out.

Wear a mask, stay 6 feet apart, and wash your hands.

cdc.gov/coronavirus CS324153K

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Supplementary Information

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Chapter 34

SARS-CoV-2 Tropism, Entry, Replication, and Propagation: Considerations for Drug Discovery and Development Nicholas Murgolo, PhD,a Alex G. Therien, PhD,b Bonnie Howell, PhD,c Daniel Klein,

PhD,d Kenneth Koeplinger, PhD,e Linda A. Lieberman, PhD,b Gregory C. Adam,

PhD,f Jessica Flynn, PhD,c Philip McKenna, PhD,c Gokul Swaminathan, PhD,b

Daria J. Hazuda, PhD,g and David B. Olsen, PhDc

aDepartment

of Genetics and Pharmacogenomics, Merck & Co., Inc., Kenilworth, New Jersey, USA Science Center, Merck & Co., Inc., Cambridge, Massachusetts, USA cDepartment of Infectious Diseases and Vaccines, Merck & Co., Inc., West Point, Pennsylvania, USA dDepartment of Computational and Structural Chemistry, Merck & Co., Inc., West Point, Pennsylvania, USA eDepartment of Pharmacokinetics, Merck & Co., Inc., West Point, Pennsylvania, USA fDepartment of Quantitative Biosciences, Merck & Co., Inc., West Point, Pennsylvania, USA gDiscovery Biology & Translational Medicine, Merck & Co., Inc., West Point, Pennsylvania, USA bExploratory

[email protected]

Keywords: SARS-CoV-2, coronaviruses, severe acute respiratory syndrome coronavirus, Middle East respiratory syndrome coronavirus, severe acute respiratory syndrome coronavirus 2, remdesivir, hydroxychloroquine, lopinavir, interferon β 1a, C-reactive protein, African green monkey, non-small cell lung cancer, cytopathic effect, angiotensin converting enzyme 2, trypsin-like protease, cathepsin L, neuropilin 1, RNA-dependent RNA polymerase

34.1 Introduction Coronaviruses, named for their crown-like spiked surface, are genetically diverse and can infect multiple animal species, including bats, pigs, cats, rodents, and humans [1]. Coronaviruses are divided into 4 genera: alpha, beta, gamma, and delta. Only alpha and beta coronaviruses are known to infect humans, resulting in Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

Copyright © 2022 Jenny Stanford Publishing Pte. Ltd.

ISBN 978-981-4877-84-8 (Hardcover), 978-1-003-18043-2 (eBook)

www.jennystanford.com

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SARS-CoV-2 Tropism, Entry, Replication, and Propagation

pathology ranging from upper respiratory symptoms typical of the common cold to life-threatening lower respiratory disease. The common cold-causing coronaviruses 229E and OC43 were first discovered in the mid-1960s, with 2 additional coronaviruses, NL63 and HKU1, identified in 2004 and 2005, respectively. All are ubiquitous human pathogens [2]. From 2003 to mid-2019, 2 beta coronaviruses of zoonotic origin have caused outbreaks of severe respiratory disease: Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV). SARS-CoV emerged in Asia in February 2003 and spread to 26 countries before the outbreak was contained [3, 4]. Over 8,000 people were infected with a case fatality rate of approximately 10% [5]. MERS-CoV first appeared in 2012 with early cases emanating from Saudi Arabia and Jordan. Infections are still occurring and have been reported in 27 countries, with the majority of cases isolated to the Arabian Peninsula [6]. While human-to-human transmission for MERS-CoV is rare, the case fatality rate is greater than 30% [3, 7]. In December 2019, an outbreak of fever and respiratory illness of unknown cause was reported in Wuhan, China [8], and by mid-January 2020, the etiologic agent had been identified as another newly emergent beta coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) [9, 10]. While many infected with SARS-CoV-2 are asymptomatic or develop mild disease, for others, COVID-19 may have potential long-term sequelae; and in vulnerable populations like the elderly and those with underlying medical conditions, it may cause significant morbidity and result in severe respiratory distress, hospitalization, and even death [11]. Since that time, SARS-CoV-2 has spread globally, prompting the World Health Organization (WHO) to declare the novel coronavirus disease, Coronavirus Disease 2019 or COVID-19, a pandemic in March 2020. In just 12 months, the virus has resulted in a major global health crisis with over 81 million COVID-19 cases across 190 countries, over 1,777,000 deaths, and an estimated case fatality rate of approximately 2.6% [12]. Aside from the intravenously administered antiviral drug remdesivir in patients with severe COVID-19 illness, there are no therapeutic agents approved for treatment of SARS-CoV-2 infection or disease [13]. A multicenter evaluation of 4 repurposed antiviral drugs (remdesivir, hydroxychloroquine, lopinavir, and interferon β 1a) reported by WHO noted no effect on overall mortality initiation of ventilation and duration of hospital stay [14]. A recent subgroup analyses suggested that early glucocorticoid use in patients with markedly elevated C-reactive protein levels (≥20 mg/dL) was associated with a significant reduction in mortality or mechanical ventilation, whereas glucocorticoid treatment in patients with lower C-reactive protein levels was associated with worse outcomes [15]. As the SARS-CoV-2 pandemic continues, there is an urgent need to develop effective therapeutics to limit further spread. Early attempts to identify efficacious therapeutics for COVID-19 have mainly focused on drug repurposing efforts wherein existing clinically advanced or marketed drugs are screened for antiviral activity against SARS-CoV-2 in vitro in cellular infection systems. While such screens have yielded intriguing hits, questions have arisen around the physiological

Entry Mechanisms and Proteases

and pathological relevance of infecting immortalized cell lines derived from non-pulmonary or gastrointestinal origins. Specific questions have arisen around the mechanisms of viral attachment and entry into human cells which may vary in cells from different tissue origins. In addition, screening cell lines may have limited intracellular machinery, such as catabolizing enzymes, which are a key component of the primary cell of infection in human patients. It is therefore of paramount importance to enhance our understanding of the key molecular and cellular interactions involved in SARS-CoV-2 infection in order to develop appropriate in vitro tools to support current and future drug discovery efforts.

34.2 Scope/Prior Reviews

The purpose of this chapter is to review key aspects of SARS-CoV-2 biology, including determinants of virus entry into multiple cell lines and systems permissive for virus growth, tissue tropism, and host genes affecting viral entry, propagation, and nucleotide prodrug import and conversion for SARS-CoV-2, with the purpose of facilitating and enabling drug discovery efforts. For a more comprehensive review of entry mechanisms and proteases processing in coronaviruses, the reader is directed to other recent articles [16–19]. Extensive reviews are available on MERS-CoV [20], SARS-CoV [21, 22], and SARSCoV-2 [19] inhibitors. We discuss SARS-CoV-2 repurposing screens in the “Entry mechanisms and proteases” section.

34.3 Entry Mechanisms and Proteases

To date, initial screening to identify SARS-CoV-2 antivirals has largely utilized the Vero E6 African green monkey kidney cell line as the host cell for cytopathic effect (CPE) inhibition assays. In addition to being deficient in expression of angiotensin converting enzyme 2 (ACE2) and TMPRSS2, intrinsic nonspecific endocytic viral uptake mechanisms are responsible for viral entry in Vero E6 and a variety of other cell types [23–26]. Thus, a wide variety of molecules that modulate endosomal–lysosomal maturation and autophagy pathways have been found to exhibit potent antiviral activity in Vero E6 cells that may not translate to primary lung epithelial cells. This raises the possibility that the antiviral inhibition exhibited by many of these compounds against SARS-CoV-2 in Vero E6 screens could be an in vitro activity specific to highly endocytic cells and may not be relevant nor translatable as SARS-CoV-2 therapeutics. As further elaborated below, the SARS-CoV-2 virus has been demonstrated to deliver viral RNA directly across the plasma membrane without significant involvement of endocytic uptake. Infection of mammalian lung epithelial cells by SARS-CoV-2, as well as by other coronaviruses, starts with binding of the virus through its spike protein to a specific receptor on the cell surface. The cellular receptor for SARS-CoV-2, and for the related virus SARS-CoV, is human ACE2 [16, 27, 28], which is expressed on

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epithelial cells of the lung and intestine, and to a lesser extent, in the kidney, heart, adipose, and both male and female reproductive tissues [29–35]. Binding to the receptor is followed by activation of the spike protein through proteolytic cleavage by a host protease near the junction between its S1 and S2 domains [17, 36]. Insertion of the newly liberated S2 domain N-terminus into the cell membrane leads to fusion of the viral and cellular membranes, resulting in transfer of the viral RNA into the host cell cytoplasm where viral replication can occur [17, 36].

Figure 34.1 SARS-CoV-2 entry mechanisms. Viral coat spike protein binds to ACE2, and in some cases, perhaps NRP1, on responsive cells. Virus spike protein is either processed by TMPRSS2 and other serine proteases facilitating cell surface entry or endocytosed into endosomes where spike is processed by CTSL in the lysosome. Viral RNA released from TMPRSS2-mediated entry or endosome release is replicated as partial and complete genome copies and translated in the ER to form new SARS-CoV-2 virions. Processing of spike protein by furin occurs prior to release of new viruses into the extracellular environment. Abbreviations: ACE2, angiotensin converting enzyme 2; CTSL, cathepsin L; ER, endoplasmic reticulum; NRP1, Neuropilin 1; SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2.

While the steps outlined above are considered essential for infection of host cells by coronaviruses, this description omits key aspects of the viral entry process, namely the nature of the host protease(s) responsible for activating (or priming) the spike protein for fusion and the cellular location of the fusion event itself. Work on multiple coronaviruses over the last 20 years, and largely confirmed for SARS-CoV-2 within the last several months, shows that coronaviruses enter host cells through 1 of 2 distinct pathways: (i) the cell surface pathway following activation by serine proteases such as TMPRSS2; or (ii) the endocytic pathway within the endosomal–lysosomal compartments including processing by lysosomal cathepsins (Fig. 34.1) [16–18]. A new study argues that coronaviruses use a lysosomal exocytosis pathway for release [37]. The contribution of each pathway in a given cell type depends largely on the expression of proteases, in particular

Entry Mechanisms and Proteases

TMPRSS2. When TMPRSS2 (or other serine proteases such as TMPRSS4 or human airway trypsin-like protease [HAT]) is expressed, the early entry pathway is preferred, whereas in the absence of this protease, the virus relies on the late pathway involving endocytosis and activation by cathepsin L (CTSL) [27, 38]. An understanding of which viral entry pathway is prevalent in specific cell types is paramount not only to understanding coronavirus biology, but also to the proper interpretation of cell-based genetic and small-molecule screens. To date, screens aimed at identifying marketed or clinically advanced drugs for potential repurposing for the treatment of COVID-19 have relied on the use of immortalized kidney epithelial cell lines such as Vero E6 (African green monkey) and 293T (embryonic human) [39–42]. Such screens have yielded multiple hits known to block or impair endocytosis or endosomal maturation [39, 40, 42, 43]. This is not surprising since Vero E6 and 293T cells do not express the cell surface protease TMPRSS2; therefore, coronavirus infection in these cells is dependent on the late endocytic/cathepsin-mediated entry pathway. Thus, agents that interfere with this pathway, including cathepsin inhibitors, agents that prevent endosomal acidification (upon which the cathepsins are dependent for activity), and inhibitors of endosomal maturation (e.g., the PIKfyve inhibitor apilimod) can block viral entry in these cells [38, 44, 45]. Importantly, heterologous expression of TMPRSS2 in Vero E6 and HeLa cells abrogates the pharmacological efficacy of cathepsin inhibitors by enabling the cell surface viral entry pathway [38, 46]. Similarly, pre-activation of virus with the serine protease trypsin also enables the cell surface entry pathway, even in cells that do not express TMPRSS2 [45, 47, 48]. Consistent with these observations, cells that express TMPRSS2 endogenously, including cells derived from the lung and intestinal epithelium, are also susceptible to coronavirus entry via the cell surface pathway. In these cells, cathepsin inhibitors are only partially effective, TMPRSS2 inhibitors such as camostat and nafamostat are significantly more effective, and a combination of the 2 types of protease inhibitors is maximally effective in preventing coronavirus infection [27, 38, 49–52]. These data suggest that coronaviruses can enter these cells via both pathways, with a preference for the cell surface pathway. These cellular data notwithstanding, it is still a matter of debate whether individual inhibition of the cell surface or endocytic pathways will provide therapeutic benefit against COVID-19. SARS-CoV-2 infects a broad variety of cell types [46, 53, 54], although it seems reasonable to surmise that epithelial cells of the respiratory and gastrointestinal tracts are among the first to encounter the virus in a clinical setting. In vivo studies with the related coronavirus SARS-CoV supports the notion that inhibitors of the cell surface entry pathway may be at least partially protective. Genetic [55] and pharmacological [56] blockade of TMPRSS2 provided partial protection against SARS-CoV in mouse models of infection. In the latter study, the broad cathepsin inhibitor SMDC256160 provided no benefit, either alone or in combination with camostat. Further studies will be required, particularly in SARS-CoV-2 infection models, to further define the contribution of each viral entry pathway to COVID-19 pathology. Notably, it has recently been proposed that SARS-CoV-2 entry blockade may require inhibition of both the cell surface and endocytic pathways [57].

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34.4 TMPRSS2 and Furin in Cell Surface Entry The TMPRSS2 gene encodes a type II transmembrane serine protease (TTSP) that was originally discovered more than 2 decades ago [58] and subsequently shown to be regulated by androgen and highly expressed in prostate epithelium [59]. TMPRSS2 is localized to the plasma membrane via a single-pass transmembrane helix near its N-terminus. The enzyme can also undergo autocleavage at Arg255 yielding a 32-kDa secreted form of the protease [60]. Mice lacking the TMPRSS2encoded protease (TMPRSS2[-/-]) show no discernable abnormal phenotype, suggesting that the protease does not serve an essential nonredundant function [61], an observation which enabled later investigation of TMPRSS2’s role in mouse models of coronavirus disease. Our current understanding of the relationship between TMPRSS2 and SARS-CoV-2 infection has seen rapid progress in the first few months since the emergence of SARS-CoV-2. Much of this advancement was built on more than a decade of studies beginning from the first evidence that TMPRSS2 expression correlates with SARS-CoV infection in lung tissue and leads to activation of the spike protein, enabling membrane fusion [62]. Studies using cell fusion assays indicated that TMPRSS2 expression on target cells, rather than virus producing cells, is critical for spike protein activation, supportive of spatial and temporal constraints on TMPRSS2 action at the plasma membrane during the early steps of cell surface viral entry. Two sites of cleavage on SARS-CoV spike—R667, located at the S1/S2 cleavage site, and R797, located at the S2’ cleavage site—have been suggested to be relevant sites of action of TMPRSS2, as well as other serine proteases that can prime the spike protein in cell culture, such as trypsin and HAT. Mutation of R797 at S2’ abrogates TMPRSS2-dependent activation of the spike protein, and this site is highly conserved across coronaviruses, suggesting that is functionally relevant to TMPRSS2-dependent cell surface entry in SARSCoV-2. The pro-protein convertase furin has long been known to play a role in viral entry, and recent data support a role of this enzyme, specifically in TMPRSS2mediated cell surface entry. Processing of the spike protein by furin at the S1/S2 cleavage site is thought to occur following viral replication in the endoplasmic reticulum Golgi intermediate compartment (ERGIC) [63]. Underscoring the complex role of furin in viral fusion and entry, the S1/S2 furin cleavage site is present in SARS-CoV-2 and MERS but not in SARS-CoV [64]. Furthermore, the SARSCoV-2 furin S1/S2 site is rapidly lost upon passaging in Vero E6 cells [65], and a milder SARS-CoV-2 strain isolate ZJ01 was demonstrated to have lost this site as well [30]. Furin can also cleave the spike protein at the fusion activating the S2’ site during biogenesis in MERS-CoV and SARS-CoV [30, 36, 65–67]. Overall, current available data support a plausible model for SARS-CoV-2 spike processing wherein furin-mediated cleavage at the S1/S2 site pre-primes the spike protein during biogenesis, facilitating subsequent activation for membrane fusion by a second cleavage event at S2’ by TMPRSS2 following ACE2 receptor binding on target cells [46, 50].

Cell Line Tropism/Expression

The VEGF-A receptor Neuropilin 1 (NRP1) was quite recently shown to be a host factor receptor for furin-cleaved SARS-CoV-2 spike peptide [68–71]. Blockade of NRP1 reduces infectivity and entry, and alteration of the furin site leads to loss of NRP1 dependence. Deletion of the furin peptide in spike leads to reduced replication in Calu3, augmented replication and improved fitness in Vero E6, and attenuated disease in a hamster pathogenesis disease model [70]. More recently, in vivo evidence for the relevance of TMPRSS2 in mouse models for SARS-CoV and MERS-CoV infection was provided from TMPRSS2(-/-) mice showing reduced disease severity [55]. TMPRSS2 shRNA knockdown studies provide evidence for a specific and nonredundant role in SARS-CoV-2 infection [72].

34.5 Lysosomal Cathepsins and Endocytosis

While the evidence outlined above makes clear the role of TMPRSS2 and other serine proteases in activating the coronavirus spike protein for plasma membrane fusion, in vitro studies using various cell culture systems have demonstrated an alternative endosomal–lysosomal pathway for viral entry. Early studies demonstrated the sensitivity of SARS-CoV replication in cell culture to lysosomotropic agents, followed by additional studies dissecting the role of cathepsins for processing and activating the spike for membrane fusion. The availability of highly potent and specific small-molecule cathepsin inhibitors was key to dissecting the molecular events involved in this pathway and ascribing the relevant functional effect to CTSL, 1 of 11 cathepsins in humans [73, 74]. Additional studies localized the site of CTSL cleavage on SARS-CoV spike protein to T678, 11 amino acids carboxyl terminus to R667 of the furin cleavage site. The observation that the CTSL cleavage site is located 120 amino acids upstream of the S2’ site cleaved by TMPRSS2 exposes a gap in our understanding of the molecular events involved in the endosomal entry pathway. Previous studies suggested the possibility of another protease that may cleave at S2’ in the low pH environment of endosomes/lysosomes to fully activate the membrane fusion potential of the spike [36], or alternatively, differences in lipid composition between plasma membranes and endosomal/lysosomal membranes may render the latter more amenable to viral fusion [75]. Despite this discrepancy in our current understanding of endocytic viral entry, it is clear that coronaviruses in general, and SARS-CoV-2 in particular, are capable of establishing robust infection through endosomal entry in commonly used in vitro cell culture systems.

34.6 Cell Line Tropism/Expression

With the complexity of multiple entry mechanisms of SARS-CoV-2, selection of optimal cell line(s) for compound evaluation and screening is imperative, especially in studies looking for broad antiviral mechanisms of action beyond inhibition of the viral nonstructural proteins. Toward this end, studies to better

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understand the cellular tropism of the virus have been reported. A Vesicular Stomatitis Virus (VSV) pseudotyped virus bearing SARS-CoV-2 spike protein or SARS-CoV spike protein and a panel of well-characterized human and animal cell lines was used to determine the broader tropism of the virus [46]. The spectrum of cell lines susceptible to virus infection was similar for SARS-CoV and SARS-CoV-2 spike protein with entry supported in A549, BEAS-2B, Calu-3, H1299, 293T, Huh7, Caco-2, Vero E6, and MDCK2 cell types [27]. Additional efforts to understand viral tropism included monitoring the infectivity of virus for 120 hours with 0.1 multiplicity of infection (MOI) of SARS-CoV-2 HKU-001a across a panel of 9 human and 11 nonhuman cell lines [53]. Similar to the results observed with a pseudotyped virus, the most robust infection among the human cell lines was observed for Calu-3 (pulmonary) and Caco-2 (intestinal) cells with infection also observed in Huh7 (hepatic), 293T (renal), and to a lesser extent, U251 (neuronal) cells. Among the 11 cell types found permissive to infection in the study, CPE was observed 120 hours postinfection only in Vero E6 and FRhK4 (rhesus kidney), with damage including cell rounding, detachment, degeneration, and syncytium formation. Even up to 7 days postinfection, CPE was not observed in Calu-3, Caco 2, LLCMK2, PK-15, and RK-13 cells with SARS-CoV-2. At a higher MOI of one, a study of proteomics kinetic changes on infection showed both viral replication and CPE in Caco-2 cells [76]. By monitoring protein expression levels, the authors of this study observed that infection results in the reshaping of central cellular pathways, such as translation, splicing, carbon metabolism, and nucleic acid metabolism. This assay was modified by lowering the MOI from 1 to 0.01 and increasing incubation time from 24 to 48 hours to allow for several cycles of viral replication to screen for antivirals in a high-content imaging assay [77]. From a screen of 5,632 compounds including greater than 3,400 clinical candidates, the authors highlighted 6 compounds which had previously been reported to be active against SARS-CoV-2, SARS-CoV, or MERS in antiviral assays in Vero E6, Calu-3, or BHK21 (hamster kidney fibroblast) cells, with the pattern of inhibition matching more closely to results in human Calu-3 cells than in nonhuman Vero E6 and BHK21 cells. For example, camostat and nafamostat, inhibitors of TMPRSS2, which plays a role in the cell surface pathway for viral cellular entry, were measured to be 100x more potent against SARS-CoV-2 in Caco-2 cells than in Vero E6 cells. However, compounds like remdesivir, lopinavir, and mefloquine were relatively consistent across cell types. Further delineating the different mechanisms involved in SARS-CoV-2 entry and infection, Dittmar and colleagues applied a high-content imaging assay to compare the antiviral activity of a panel of approximately 3,000 compounds [39]. Less than 1% infection was observed in several cell types including A549, Calu-1, Huh7, HepG2, HaCaT, IMR90, NCI-H292, CFBE41o, and U2OS cells; however, robust infection was observed in the human hepatocyte cell line Huh7.5, lungderived Calu-3 cells, and Vero CCL81 cells. Among the 3 cell types, compounds with different mechanisms of action were observed to have variable antiviral activity.

Table 34.1 Cell line expression level of ACE2 receptor and 5 proteases. Cell line

Species Tissue

NCBI GEO GSM

Vero E6

AGM

Kidney

GSM758842

Caco2

Human

Colorectal adenocarcinoma

GSM24832

T84

Calu3 Huh7 293T

U251

FRhK4

LLCMK2

BEAS-2B H1299 A549

Calu1

HepG2 CRFK

16HBE

HUVEC

Human

Human

Human

Human AGM

AGM

Human

Human

Human

Human

Human

Human Cat

Human

Human

Colon

Lung epithelial adenocarcinoma Liver carcinoma

Kidney embryonic Glioblastoma Kidney

Kidney

Lung bronchial epithelial Lung NSCLC metastasis

Lung epithelial adenocarcinoma Lung bronchial epithelial Liver carcinoma Lung fibroblast Kidney

Bronchial epithelial Vein epithelial

Expression

GSM24844

GSM98974

GSM523816

GSM871735

GSM803750

GSM871739

GSM871737

GSM157034 GSM98990

GSM2359842

GSM1374431 GSM720616

GSM654536

GSM3553405 GSM417467

GSM385333 High

12.00 9.60

7.20

4.80

2.40

0.00

Note: Values are normalized log 2 expression with data obtained from NCBI Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/) [93]. Abbreviations: ACE2, angiotensin converting enzyme 2; AGM, African green monkey; CTSL, cathepsin L; NSCLC, non-small cell lung cancer.

Low

Cell Line Tropism/Expression

MRC5

Human

ACE2 Furin TMPRSS2 CTSL Elastase Trypsin

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For example, inhibitors of endosomal entry, such as the cathepsin inhibitor Z-FA-FMK and PIKfyve inhibitor apilimod, were active in Huh7.5 and Vero E6 cells but not Calu-3 cells. In contrast, camostat, an inhibitor of the plasma membrane protease TMPRSS2, was active in Calu-3 cells but not Huh7.5 and Vero E6 cells, further highlighting the importance of understanding the translatability of a cellular model of infection. Viral tropism of SARS-CoV-2 for various cell types likely reflects differential expression of key host proteins involved in viral attachment and entry. Table 34.1 summarizes publicly available expression data for the SARS-CoV-2 cellular receptor ACE2 and for 5 proteases involved in viral fusion and entry (furin, TMPRSS2, CTSL, elastase, and trypsin). While CTSL is expressed in all lines examined, ACE2 and TMPRSS2 levels vary, explaining the use of heterologous expression of these genes in prior studies involving Vero E6 and other cell lines.

34.7 Nucleotide/Side Import and Conversion

Screening cell lines for evaluation of nucleotide prodrugs and nucleosides as inhibitors of viral RNA-dependent RNA polymerase (RdRp) must consider their ability to import and convert compounds to active nucleoside triphosphate (NTP) metabolites [78]. Remdesivir [79–81] is a phosphoramidate protide drug [82] requiring import and conversion in host cells to inhibit the replication essential SARS-CoV-2 RdRp.

Figure 34.2 Nucleotide phosphorylation. The 24-hour intracellular NTP concentration of adenosine antiviral drug leads (MK-0608, Gilead Sciences remdesivir prodrug GS-5734, and Gilead Sciences remdesivir parent nucleoside GS-441524) after incubation at 1 μM illustrates the apparent deficiency of Vero E6 cells with regard to conversion to the active triphosphate form as compared to other cell lines studied. Abbreviation: NTP, nucleoside triphosphate.

Primary Cells/Model Systems

Cellular uptake of modified nucleosides largely depends on expression of concentrative membrane transporters (cNTs) or equilibrative/facilitative membrane transporters (ENTs) [83, 84]. Intracellular conversion to nucleosides to nucleotides is mediated by cellular kinase phosphorylation, and deficient expression can reduce antiviral potency [39, 85, 86]. Some prodrugs require expression and enzymatic conversion by esterases and lysosomal cathepsin A. We therefore tested 5 cell lines for ability to phosphorylate adenosine ribonucleosides MK-0608 and GS-441524 (parent nucleoside of remdesivir) compared with remdesivir prodrug GS-5734 and demonstrated that host cells have differential capability for drug activation (Fig. 34.2). Hepatocyte cell lines HepG2 and Huh7 exhibited significantly better activation of monophosphate phosphoramidite prodrug GS-5734 than African green monkey Vero E6 kidney cells, Crandell-Rees feline kidney (CRFK) cells, or human Calu-3 lung cells due to increased cathepsin A expression in hepatocytes compared with extrahepatic cells [87].

34.8 Primary Cells/Model Systems

Cell lines provide a rapid method to screen and gather data in a high-throughput manner, but there are limitations in their translatability. Primary cell culture systems may more accurately model relevant cellular physiology. Insight into the SARS-CoV-2 course of infection has been gleaned using single cell sequencing results from the Human Atlas (noninfected individuals) to identify transcriptional expression of viral receptors. Protein staining of these receptors is a better indicator of actual surface expression, but such data are currently sparse. Analysis of RNA from infected patient tissue samples provides a snapshot in time for which we often do not know the timing of the onset of infection. Primary in vitro model systems allow for the testing of hypotheses in human tissues and allow us to gather temporal information about gene/protein expression. Many studies have reported high levels of ACE2 and TMPRSS2 co-expression in lung AT2 cells utilizing data from public databases and lung resections [29, 88, 89]. Lung air–liquid interface (ALI) cultures derived from various regions of the lung from healthy individuals showed the greatest co-expression of ACE2 and TMPRSS2 in transient secretory cells of the bronchial branches. It was also shown that there is triple expression of ACE2, TMPRSS2, and FURIN when looking across lung tissue, with a preference for co-expression of at least 2 of the receptors [29]. In a recent study, single cell (sc) RNA sequencing (seq) was used to follow viral RNA on a per cell basis over a time course of infected bronchial epithelial cells (ALI model) and found that the primary target of SARS-CoV-2 in the upper airway is ciliated lung cells, the cells responsible for removing mucus and foreign particles from the lung. As infection progresses, the virus infected basal and club cells over time, which they confirmed using electron microscopy. Additionally, an up-regulation was observed of type I and type III interferons (IFNs), IL-6, and the

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chemokines CXCL9, CXCL10, and CXCL11 (important for recruitment of T/NK cells) in infected cells and broad interferon-stimulated gene (ISG) up-regulation in infected cells as well as bystander cells [90]. While these single cell sequencing and single nucleus sequencing data provide much detail about the expression of RNA transcripts, an older study confirms co-staining of ACE2 and TMPRSS2 protein in lung sections by immunohistochemistry [91]. In contrast, others have reported that nasal epithelial cells have the highest expression of ACE2 in the respiratory tract. Additionally, by combing publicly available databases, it was found that the upper airway has the highest expression of ACE2-correlated genes, and genes with innate immune function are disproportionately represented [92]. Specifically, they reported that the nasal goblet cells have the highest expression of these immune genes. While lung ALI cultures represent a more physiologically relevant primary model due to the exposure of the apical membrane to air, lung organoids have utility in studying SARS-CoV-2. Similar to lung ALI cultures, lung organoids are derived from basal stem cells of the lung (adult) or induced pluripotent stem cells (embryonic) and are grown in a 3D supportive matrix [93]. A benefit of using organoid cultures is that the stem cells can be propagated continuously and then terminally differentiated into more mature cell types of the epithelium allowing for more rapid expansion and differentiation of primary cells to numbers high enough to support higher throughput screening compared with ALI cultures, which take 21 to 28 days to reach maturity. One group reported using lung organoids to successfully screen the Food and Drug Administration (FDA)-approved Prestwick drug library with a luciferase-expressing SARS-CoV-2 pseudovirus [94]. Additionally, they confirmed 3 out of 4 hits in hPSC-derived colonic organoids. While the mucosal surface is likely the entry site for SARS-CoV-2, examination of other tissues has revealed that high levels of ACE2/TMPRSS2 co-expression are present in the enterocytes of the ileum and colon, and the gut may have higher expression of these receptors than the lung [30, 31]. The observation that some COVID-19 patients have gastrointestinal distress prior to developing respiratory symptoms as well as during disease progression may suggest a fecal–oral route of transmission [95, 96]; evidence supporting this has been recently reviewed [97, 98]. Similarly, gastrointestinal infection/distress was previously observed for the closely related SARS and MERS coronaviruses [99, 100]. The transcriptional data are supported by endoscopic samples from a COVID-19 patient (esophagus, stomach, duodenum, and rectum) that displayed staining of the ACE2 receptor in the gastrointestinal epithelial cells, but not in the esophageal epithelium [101]. Studies have linked the observed gastrointestinal phenotype to the expression of ACE2+/TMPRSS2+ cells in the gut, with highest expression levels in the ileum, followed by the colon. More conclusively, infection of intestinal organoids with SARS-CoV-2 was successful, and they supported replication of the virus with enterocytes being the primary cell type targeted [31]. Interestingly, it was observed that type III IFN, but not type I IFN, was up-regulated following infection of colonoids with SARS-CoV-2, suggesting that this cytokine may play a protective role in this infection [102].

Innate Immune Cells

The intestinal organoid model has allowed further characterization of viral entry. Using a VSV-SARS-CoV-2 pseudovirus (expressing the SARS-CoV-2 spike protein), it was demonstrated that TMPRSS2 and TMPRSS4 are both necessary for fusogenic entry into duodenal enterocytes [103]. Additionally, the authors observed preferential infection of the apical membrane of the enterocytes, and imaging suggested polarized viral assembly and release from the apical membrane. These data demonstrate how data from primary cells/tissue can be further dissected when replicated in a physiologically relevant primary model system. The use of primary lung and gut model systems is proving to be very relevant for the study for SARS-CoV-2 as they more accurately replicate receptor expression and possibly the physiological responses seen in vivo. It should be noted that other ex vivo systems, such as tissue explants, have been used to study viral respiratory infections and could likely be used to study SARS-CoV-2. Multispecies in vivo models are also rapidly being developed to better understand host responses to this virus. In vivo and ex vivo models that have been or are under development have recently been reviewed, including mouse, ferret, hamster, and nonhuman primate animal models [104, 105].

34.9 Innate Immune Cells

Immune dysfunction has been extensively characterized in COVID-19 patients, such as dysregulation of T cells, B-cells, and innate immune cells [106, 107]. Specifically, increased prevalence and activation of innate immune cells has been observed in COVID-19 patients with severe disease [108]. Interestingly, it was reported that there are significant morphological and functional differences in monocytes derived from COVID-19 patients compared with healthy human donors [109]. Postmortem analyses of secondary lymphoid organs from COVID-19 patients have confirmed the expression of ACE2 receptor and the presence of SARS-CoV-2 nucleoprotein in CD169+ macrophages [110]. However, it is unclear if SARS-CoV-2 can infect and replicate in innate immune cells. Varying degrees of viral entry and replication have been observed in primary innate immune cells infected with SARS-CoV-1, MERS-CoV, HCoV-OC43, and HCoV 229E [111]. Minimal expression of key receptors required for SARS-CoV-2 entry, including ACE2 and TMPRSS2 in innate immune cells such as monocytes and macrophages, has been recently reported in nonhuman primates [112] and humans [30]. In addition, monocytes can express high levels of CD147 [113], a potential receptor for SARS-CoV-2 entry [114]. Two recent publications have identified that CD14+ primary monocytes from healthy human donors are readily susceptible to SARS-CoV-2 infection. Pontelli and colleagues demonstrated this by infecting primary human monocytes ex vivo with SARS-CoV-2 and measuring viral antigens and dsRNA entities in infected cells [115]. They observed substantial reduction in viral entry when monocytes were infected in the presence of ammonium chloride, which suggested that the

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endocytic pathway is likely important. Similarly, Codo and colleagues observed infection of SARS-CoV-2 in primary human monocytes based on measurement of viral transcripts by qPCR [116]. Furthermore, they observed that SARS-CoV-2 infection of monocytes results in induction of glycolysis and increase in transcript levels of several genes including the main viral entry receptor ACE2. Given the minimal expression of ACE2 receptor in monocytes, these observations are rather surprising. More in-depth virologic analyses of productive replication in monocytes will greatly bolster the observations in these 2 aforementioned publications. Furthermore, additional studies are needed to better understand if (i) monocytes/macrophages are exposed to SARS-CoV-2 via direct infection via ACE2 binding or through phagocytosis; (ii) ACE2 is induced in infected monocytes and bystander cells through secretion of inflammatory mediators; and (iii) expression of additional host entry receptors such as CD147 and Neuropilin-1 may be expressed in monocytes to facilitate infection in an ACE2-independent manner [69]. Continued evaluation of the susceptibility of innate immune cells to SARS-CoV-2 will greatly improve our current understanding of SARS-CoV-2-host interactions as well as the immunopathogenesis observed in COVID-19 patients.

34.10 Concluding Remarks

Phenotypic screening to identify novel inhibitors for infectious diseases is a costly and time-consuming endeavor. The most important decisions are the choice of cell line or model systems in which to conduct the screen and the availability of relevant secondary screens to eliminate irrelevant hits and prioritize those of potential interest. In this regard, assessing antiviral potency for SARS-CoV-2 viral replication should take into account the following key considerations. Expression of the ACE2 receptor and key host proteases will influence the nature of hits identified [117]. Screens in Vero E6 cells have identified agents affecting the endocytic pathway as having antiviral activity, but blockade of both endocytic and cell surface viral entry pathways may be required in the context of clinical infection. In addition to understanding the viral entry pathway(s) which SARS-CoV-2 uses in the specific cell types used for screening, consideration should be given to the degree of nucleotide prodrug activation and phosphorylation observed in the in vitro screening system, as clearly demonstrated recently [39]. Emerging model systems including ALI and organoid cultures are promising host mimetic systems for evaluating candidates, although these have their own limitations. Finally, the potential role of innate immune cells in infection is an additional important consideration. Selecting appropriate in vitro model systems reflecting multiple aspects of SARS-CoV-2 biology will be essential to optimize success in current and future drug discovery efforts.

Disclosures and Conflict of Interest

Abbreviations ACE2: ALI: cNTs: CPE: CRFK: CTSL: ENTs: ER: ERGIC: FDA: IFNs: ISG: MERS-CoV: MOI: NRP1: NSCLC: NTP: RdRp: SARS-CoV: SARS-CoV-2: TTSP: VSV: WHO:

angiotensin converting enzyme 2 air–liquid interface concentrative membrane transporters cytopathic effect Crandell-Rees feline kidney cathepsin L equilibrative/facilitative membrane transporters endoplasmic reticulum endoplasmic reticulum Golgi intermediate compartment Food and Drug Administration interferons interferon-stimulated gene Middle East Respiratory Syndrome Coronavirus multiplicity of infection Neuropilin 1 non-small cell lung cancer nucleoside triphosphate RNA-dependent RNA polymerase Severe Acute Respiratory Syndrome Coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 type II transmembrane serine protease Vesicular Stomatitis Virus World Health Organization

Disclosures and Conflict of Interest

This chapter was originally published as: Murgolo, N., Therien, A. G., Howell, B., Klein, D., Koeplinger, K., Lieberman, L. A., et al. (2021). SARS-CoV-2 tropism, entry, replication, and propagation: Considerations for drug discovery and development. PLoS Pathog., 17(2), e1009225, https://doi.org/10.1371/journal.ppat.1009225, under the Creative Commons Attribution license (http://creativecommons. org/licenses/by/4.0/), and appears here, with edits and updates.

Funding: This work was supported by Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA. The authors are employees of Merck and Co., Inc., and were responsible for the study design, data collection and analysis, decision to publish, and preparation of the chapter. Competing interests: All authors are employees of Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA and may own stock or hold stock options in Merck & Co., Inc., Kenilworth, NJ, USA. DBO is the senior author.

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Acknowledgments: Mary E. Hanson, PhD of Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, New Jersey, USA, provided medical writing support; and Carol Zecca and Karyn Davis, of Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, New Jersey, USA, provided editorial and submission support.

References

1. Weiss SR. Forty years with coronaviruses. J Exp Med. 2020;217:e20200537.

2. Corman VM, Muth D, Niemeyer D, Drosten C. Hosts and sources of endemic human coronaviruses. Adv Virus Res. 2018;100:163–88.

3. Graham RL, Baric RS. Recombination, reservoirs, and the modular spike: mechanisms of coronavirus cross-species transmission. J Virol. 2010;84:3134–46. 4. Li W, Shi Z, Yu M, Ren W, Smith C, Epstein JH, et al. Bats are natural reservoirs of SARS-like coronaviruses. Science. 2005;310:676–9. 5. Kamps BS, Hoffmann C. COVID Reference. Steinhäuser Verlag, Germany.

6. WHO. Coronavirus disease (COVID-19) pandemic. 2020. Available at: https://www.who. int/emergencies/diseases/novel-coronavirus-2019 (accessed on May 6, 2021).

7. Cui J, Li F, Shi ZL. Origin and evolution of pathogenic coronaviruses. Nat Rev Microbiol. 2019;17:181–92. 8. Zhou Z, Ren L, Zhang L, Zhong J, Xiao Y, Jia Z, et al. Heightened innate immune responses in the respiratory tract of COVID-19 patients. Cell Host Microbe. 2020;27(6):883–890.e2.

9. Coronaviridae Study Group of the International Committee on Taxonomy of Viruses. The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol. 2020;5:536–44.

10. Hui DS, Azhar EI, Madani TA, Ntoumi F, Kock R, Dar O, et al. The continuing 2019nCoV epidemic threat of novel coronaviruses to global health—The latest 2019 novel coronavirus outbreak in Wuhan. China Int J Infect Dis. 2020;91:264–6.

11. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497–506. 12. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020;20:533–4.

13. Srinivas P, Sacha G, Koval C. Antivirals for COVID-19. Cleve Clin J Med. 2020. Available at: https://www.ccjm.org/content/early/2020/09/30/ccjm.87a.ccc030 (accessed on May 6, 2021).

14. WHO Solidarity Trial Consortium, Pan H, Peto R, Henao-Restrepo A-M, Preziosi M-P, Sathiyamoorthy V, et al. Repurposed antiviral drugs for COVID-19—interim WHO Solidarity trial results. N Engl J Med. 2021;384(6):497–511 .

15. Keller MJ, Kitsis EA, Arora S, Chen J-T, Agarwal S, Ross MJ, et al. Effect of systemic glucocorticoids on mortality or mechanical ventilation in patients with COVID-19. J Hosp Med. 2020; 15(8):489–493. 16. Yang N, Shen HM. Targeting the endocytic pathway and autophagy process as a novel therapeutic strategy in COVID-19. Int J Biol Sci. 2020;16:1724–31.

17. Millet JK, Whittaker GR. Physiological and molecular triggers for SARS-CoV membrane fusion and entry into host cells. Virology. 2018;517:3–8.

References

18. Tang T, Bidon M, Jaimes JA, Whittaker GR, Daniel S. Coronavirus membrane fusion mechanism offers a potential target for antiviral development. Antivir Res. 2020;178:104792. 19. Xiu S, Dick A, Ju H, Mirzaie S, Abdi F, Cocklin S, et al. Inhibitors of SARS-CoV-2 entry: current and future opportunities. J Med Chem. 2020;63(21):12256–74. 20. Liang R, Wang L, Zhang N, Deng X, Su M, Su Y, et al. Development of small-molecule MERS-CoV inhibitors. Viruses. 2018;10:721.

21. Tong TR. Therapies for coronaviruses. Part I of II—viral entry inhibitors. Expert Opin Ther Pat. 2009;19:357–67. 22. Tong TR. Therapies for coronaviruses. Part 2: Inhibitors of intracellular life cycle. Expert Opin Ther Pat. 2009;19:415–31.

23. Mercer J, Helenius A. Virus entry by macropinocytosis. Nat Cell Biol. 2009;11:510–20.

24. Aleksandrowicz P, Marzi A, Biedenkopf N, Beimforde N, Becker S, Hoenen T, et al. Ebola virus enters host cells by macropinocytosis and clathrin-mediated endocytosis. J Infect Dis. 2011;204(Suppl 3):S957–67.

25. Saeed MF, Kolokoltsov AA, Albrecht T, Davey RA. Cellular entry of ebola virus involves uptake by a macropinocytosis-like mechanism and subsequent trafficking through early and late endosomes. PLoS Pathog. 2010;6:e1001110.

26. de Vries E, Tscherne DM, Wienholts MJ, Cobos-Jiménez V, Scholte F, García-Sastre A, et al. Dissection of the influenza A virus endocytic routes reveals macropinocytosis as an alternative entry pathway. PLoS Pathog. 2011;7:e1001329.

27. Hoffmann M, Kleine-Weber H, Schroeder S, Krüger N, Herrler T, Erichsen S, et al. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell. 2020;181:271, e278–80.

28. Wrapp D, Wang N, Corbett KS, Goldsmith JA, Hsieh C-L, Abiona O, et al. Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science. 2020;367:1260–3.

29. Lukassen S, Chua RL, Trefzer T, Kahn NC, Schneider MA, Muley T, et al. SARS-CoV-2 receptor ACE2 and TMPRSS2 are primarily expressed in bronchial transient secretory cells. EMBO J. 2020;39:e105114. 30. Zhang H, Kang Z, Gong H, Xu D, Wang J, Li Z, et al. Digestive system is a potential route of COVID-19: an analysis of single-cell coexpression pattern of key proteins in viral entry process. Gut. 2020;69:1010–8.

31. Lamers MM, Beumer J, van der Vaart J, Knoops K, Puschhof J, Breugem TI, et al. SARSCoV-2 productively infects human gut enterocytes. Science. 2020;369(6499):50–4.

32. Chen L, Li X, Chen M, Feng Y, Xiong C. The ACE2 expression in human heart indicates new potential mechanism of heart injury among patients infected with SARS-CoV-2. Cardiovasc Res. 2020;116:1097–100.

33. Jing Y, Run-Qian L, Hao-Ran W, Hao-Ran C, Ya-Bin L, Yang G, et al. Potential influence of COVID-19/ACE2 on the female reproductive system. Mol Hum Reprod. 2020;26: 367–73. 34. Subramanian A, Vernon KA, Slyper M, Waldman J, Luecken MD, Gosik K, et al. RAAS blockade, kidney disease, and expression of ACE2, the entry receptor for SARS-CoV-2, in kidney epithelial and endothelial cells. bioRxiv. 2020.

769

770

SARS-CoV-2 Tropism, Entry, Replication, and Propagation

35. Kachuri L, Francis SS, Morrison M, Wendt GA, Bossé Y, Cavazos TB, et al. The landscape of host genetic factors involved in infection to common viruses and SARS-CoV-2. medRxiv. 2020.

36. Hoffmann M, Hoffman-Winkler H, Pohlmann S. In: Activation of Viruses by Host Proteases. Böttcher-Friebertshäuser E, Garten W, Klenk HD, editors. Springer; 2018. p. 71–98.

37. Ghosh S, Dellibovi-Ragheb TA, Kerviel A, Pak E, Qiu Q, Fisher M, et al. β-Coronaviruses use lysosomes for egress instead of the biosynthetic secretory pathway. Cell. 2020;183(6):1520–35.e14.

38. Kawase M, Shirato K, van der Hoek L, Taguchi F, Matsuyama S. Simultaneous treatment of human bronchial epithelial cells with serine and cysteine protease inhibitors prevents severe acute respiratory syndrome coronavirus entry. J Virol. 2012;86: 6537–6545.

39. Dittmar M, Lee JS, Whig K, Segrist E, Li M, Jurado K, et al. Drug repurposing screens reveal FDA approved drugs active against SARS-Cov-2. bioRxiv. 2020.

40. Heiser K, McLean PF, Davis CT, Fogelson B, Gordon HB, Jacobson P, et al. Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2. bioRxiv. 2020.

41. Mirabelli C, Wotring JW, Zhang CJ, McCarty SM, Fursmidt R, Frum T, et al. Morphological cell profiling of SARS-CoV-2 infection identifies drug repurposing candidates for COVID-19. bioRxiv. 2020. 42. Riva L, Yuan S, Yin X, Martin-Sancho L, Matsunaga N, Burgstaller-Muehlbacher S, et al. A large-scale drug repositioning survey for SARS-CoV-2 antivirals. bioRxiv. 2020.

43. Mirabelli C, Wotring JW, Zhang CJ, McCarty SM, Fursmidt R, Frum T, et al. Morphological cell profiling of SARS-CoV-2 infection identifies drug repurposing candidates for COVID-19. bioRxiv. 2020.

44. Kang Y-L, Chou Y-Y, Rothlauf PW, Liu Z, Soh TK, Cureton D, et al. Inhibition of PIKfyve kinase prevents infection 1 by EBOV and SARS2-CoV-2. bioRxiv. 2020. 45. Ou X, Liu Y, Lei X, Li P, Mi D, Ren L, et al. Characterization of spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV. Nat Commun. 2020;11:1620. 46. Hoffmann M, Kleine-Weber H, Pohlmann S. A multibasic cleavage site in the spike protein of SARS-CoV-2 is essential for infection of human lung cells. Mol Cell. 2020;78(4):779–84.e5.

47. Menachery VD, Dinnon KH, Yount BL Jr, McAnarney ET, Gralinski LE, Hale A, et al. Trypsin treatment unlocks barrier for zoonotic bat coronavirus infection. J Virol. 2020;94(5):e01774-19.

48. Simmons G, Bertram S, Glowacka I, Steffen I, Chaipan C, Agudelo J, et al. Different host cell proteases activate the SARS-coronavirus spike-protein for cell-cell and virus-cell fusion. Virology. 2011;413:265–74.

49. Yamamoto M, Kiso M, Sakai-Tagawa Y, Iwatsuki-Horimoto K, Imai M, Takeda M, et al. The anticoagulant nafamostat potently inhibits SARS-CoV-2 S protein-mediated fusion in a cell fusion assay system and viral infection in vitro in a cell-type-dependent manner. Viruses. 2020;12:629.

References

50. Shang J, Wan Y, Luo C, Ye G, Geng Q, Auerbach A, et al. Cell entry mechanisms of SARS-CoV-2. Proc Natl Acad Sci U S A. 2020;117(21):11727–34.

51. Shirato K, Kawase M, Matsuyama S. Middle East respiratory syndrome coronavirus infection mediated by the transmembrane serine protease TMPRSS2. J Virol. 2013;87:12552–61.

52. Shirato K, Kawase M, Matsuyama S. Wild-type human coronaviruses prefer cell-surface TMPRSS2 to endosomal cathepsins for cell entry. Virology. 2018;517:9–15.

53. Chu H, Chan JF-W, Yuen TT-T, Shuai H, Yuan S, Wang Y, et al. Comparative tropism, replication kinetics, and cell damage profiling of SARS-CoV-2 and SARS-CoV with implications for clinical manifestations, transmissibility, and laboratory studies of COVID-19: an observational study. Lancet Microbe. 2020;1:e14–23.

54. Chan JF, Chan K-H, Choi GK-Y, To KK-W, Tse H, Cai J-P, et al. Differential cell line susceptibility to the emerging novel human betacoronavirus 2c EMC/2012: implications for disease pathogenesis and clinical manifestation. J Infect Dis. 2013;207:1743–52. 55. Iwata-Yoshikawa N, Okamura T, Shimizu Y, Kotani O, Sato H, Sekimukai H, et al. Acute respiratory infection in human dipeptidyl peptidase 4-transgenic mice infected with Middle East respiratory syndrome coronavirus. J Virol. 2019;93:e01818-18.

56. Zhou Y, Vedantham P, Lu K, Agudelo J, Carrion R Jr, Nunneley JW, et al. Protease inhibitors targeting coronavirus and filovirus entry. Antivir Res. 2015;116:76–84.

57. Bittmann S, Weissenstein A, Villalon G, Moschuring-Alieva E, Luchter E. Simultaneous treatment of COVID-19 with serine protease inhibitor camostat and/or cathepsin L inhibitor? J Clin Med Res. 2020;12:320–2.

58. Paoloni-Giacobino A, Chen H, Peitsch MC, Rossier C, Antonarakis SE. Cloning of the TMPRSS2 gene, which encodes a novel serine protease with transmembrane, LDLRA, and SRCR domains and maps to 21q22.3. Genomics. 1997;44:309–20. 59. Lin B, Ferguson C, White JT, Wang S, Vessella R, True LD, et al. Prostate-localized and androgen-regulated expression of the membrane-bound serine protease TMPRSS2. Cancer Res. 1999;59:4180–4. 60. Afar D, Vivanco I, Hubert RS, Kuo J, Chen E, Saffran DC, et al. Catalytic cleavage of the androgen-regulated TMPRSS2 protease results in its secretion by prostate and prostate cancer epithelia. Cancer Res. 2001;61:1686–92.

61. Kim TS, Heinlein C, Hackman RC, Nelson PS. Phenotypic analysis of mice lacking the Tmprss2-encoded protease. Mol Cell Biol. 2006;26:965–75.

62. Glowacka I, Bertram S, Müller MA, Allen P, Soilleux E, Pfefferle S, et al. Evidence that TMPRSS2 activates the severe acute respiratory syndrome coronavirus spike protein for membrane fusion and reduces viral control by the humoral immune response. J Virol. 2011;85:4122–34.

63. Hasan A, Paray BA, Hussain A, Qadir FA, Attar F, Aziz FM, et al. A review on the cleavage priming of the spike protein on coronavirus by angiotensin-converting enzyme-2 and furin. J Biomol Struct Dyn. 2020:1–9.

64. Coutard B, Valle C, de Lamballerie X, Canard B, Seidah NG, Decroly E. The spike glycoprotein of the new coronavirus 2019-nCoV contains a furin-like cleavage site absent in CoV of the same clade. Antivir Res. 2020;176:104742–2.

771

772

SARS-CoV-2 Tropism, Entry, Replication, and Propagation

65. Ogando NS, Dalebout TJ, Zevenhoven-Dobbe JC, Limpens RWAL, van der Meer Y, Caly L, et al. SARS-coronavirus-2 replication in Vero E6 cells: replication kinetics, rapid adaptation and cytopathology. bioRxiv. 2020.

66. Kleine-Weber H, Elzayat MT, Hoffmann M, Pohlmann S. Functional analysis of potential cleavage sites in the MERS-coronavirus spike protein. Sci Rep. 2018;8:16597.

67. Follis KE, York J, Nunberg JH. Furin cleavage of the SARS coronavirus spike glycoprotein enhances cell-cell fusion but does not affect virion entry. Virology. 2006;350:358–69.

68. Cantuti-Castelvetri L, Ojha R, Pedro LD, Djannatian M, Franz J, Kuivanen S, et al. Neuropilin-1 facilitates SARS-CoV-2 cell entry and infectivity. Science. 2020;370:856–60.

69. Daly JL, Simonetti B, Klein K, Chen K-E, Kavanagh Williamson M, Antón-Plágaro C, et al. Neuropilin-1 is a host factor for SARS-CoV-2 infection. Science. 2020;370:861–5.

70. Johnson BA, Xie X, Kalveram B, Lokugamage KG, Muruato A, Zou J, et al. Furin Cleavage Site Is Key to SARS-CoV-2 Pathogenesis. bioRxiv. 2020. 71. Moutal A, Martin LF, Boinon L, Gomez K, Ran D, Zhou Y, et al. SARS-CoV-2 Spike protein co-opts VEGF-A/Neuropilin-1 receptor signaling to induce analgesia. Pain. 2020; 162(1):243–52.

72. Bestle D, Heindl MR, Limburg H, Van Lam van T, Pilgram O, Moulton H, et al. TMPRSS2 and furin are both essential for proteolytic activation 1 and spread of SARS2 CoV-2 in human airway epithelial cells and provide promising drug targets. bioRxiv. 2020.

73. Rossi A, Deveraux Q, Turk B, Sali A. Comprehensive search for cysteine cathepsins in the human genome. Biol Chem. 2004;385:363–72.

74. Simmons G, Gosalia DN, Rennekamp AJ, Reeves JD, Diamond SL, Bates P. Inhibitors of cathepsin L prevent severe acute respiratory syndrome coronavirus entry. Proc Natl Acad Sci U S A. 2005;102:11876–81.

75. Matsuyama S, Taguchi F. Two-step conformational changes in a coronavirus envelope glycoprotein mediated by receptor binding and proteolysis. J Virol. 2009;83:11133–41.

76. Bojkova D, Klann K, Koch B, Widera M, Krause D, Ciesek S, et al. Proteomics of SARSCoV-2-infected host cells reveals therapy targets. Nature. 2020;583(7816):469–72. 77. Ellinger B, Bojkova D, Zaliani A, Cinatl J, Claussen C, Westhaus S, et al. Identification of inhibitors of SARS-CoV-2 in-vitro cellular toxicity in human (Caco-2) cells using a large scale drug repurposing collection. 2020. Available at: https://www.researchsquare.com/ article/rs-23951/v1 (accessed on May 6, 2021).

78. Wang G, Deval J, Hong J, Dyatkina N, Prhavc M, Taylor J, et al. Discovery of 4’-chloromethyl-2’-deoxy-3’,5’-di-O-isobutyryl-2’-fluorocytidine (ALS-8176), a firstin-class RSV polymerase inhibitor for treatment of human respiratory syncytial virus infection. J Med Chem. 2015;58:1862–78.

79. Siegel D, Hui HC, Doerffler E, Clarke MO, Chun K, Zhang L, et al. Discovery and synthesis of a phosphoramidate prodrug of a Pyrrolo[2,1-f][triazin-4-amino] adenine C-nucleoside (GS-5734) for the treatment of Ebola and emerging viruses. J Med Chem. 2017;60: 1648–61. 80. Pardo J, Shukla AM, Chamarthi G, Gupte A. The journey of remdesivir: from Ebola to COVID-19. Drugs Context. 2020;9: 9:2020-4-14. 81. Remdesivir Emergency Use Authorization Fact Sheet. Gilead Sciences, Inc; 2020.

References

82. Mehellou Y, Rattan HS, Balzarini J. The ProTide prodrug technology: from the concept to the clinic. J Med Chem. 2018;61:2211–26.

83. Boswell-Casteel RC, Hays FA. Equilibrative nucleoside transporters: a review. Nucleosides Nucleotides Nucleic Acids. 2017;36:7–30.

84. Pastor-Anglada M, Perez-Torras S. Who is who in adenosine transport. Front Pharmacol. 2018;9:627. 85. Smee DF, McKernan PA, Nord LD, Willis RC, Petrie CR, Riley TM, et al. Novel pyrazolo [3,4-d]pyrimidine nucleoside analog with broad-spectrum antiviral activity. Antimicrob Agents Chemother. 1987;31:1535–41.

86. Taylor R, Kotian P, Warren T, Panchal R, Bavari S, Julander J, et al. BCX4430—A broadspectrum antiviral adenosine nucleoside analog under development for the treatment of Ebola virus disease. J Infect Public Health. 2016;9:220–6. 87. Dousson CB. Current and future use of nucleo(s)tide prodrugs in the treatment of hepatitis C virus infection. Antivir Chem Chemother. 2018;26:2040206618756430.

88. Qi F, Qian S, Zhang S, Zhang Z. Single cell RNA sequencing of 13 human tissues identify cell types and receptors of human coronaviruses. Biochem Biophys Res Commun. 2020;526:135–40.

89. Zou X, Chen K, Zou J, Han P, Hao J, Han Z. Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection. Front Med. 2020;14:185–92.

90. Ravindra NG, Alfajaro MM, Gasque V, Habet V, Wei J, Filler RB, et al. Single-cell longitudinal analysis of SARS-CoV-2 infection in human bronchial epithelial cells. bioRxiv. 2020. 91. Bertram S, Heurich A, Lavender H, Gierer S, Danisch S, Perin P, et al. Influenza and SARS-coronavirus activating proteases TMPRSS2 and HAT are expressed at multiple sites in human respiratory and gastrointestinal tracts. PLoS ONE. 2012;7:e35876.

92. Sungnak W, Huang N, Bécavin C, Berg M, Queen R, Litvinukova M, et al. SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes. Nat Med. 2020;26:681–7.

93. Clevers H. Modeling development and disease with organoids. Cell. 2016;165:1586–97.

94. Han Y, Yang L, Duan X, Duan F, Nilsson-Payant BE, Yaron TM, et al. Identification of candidate COVID-19 therapeutics using hPSC-derived lung organoids. bioRxiv. 2020.

95. Wan YL, Li J, Shen L, Zou Y, Hou L, Zhu L, et al. Enteric involvement in hospitalised patients with COVID-19 outside Wuhan. Lancet Gastroenterol Hepatol. 2020;5:534–5. 96. Lin L, Jiang X, Zhang Z, Huang S, Zhang Z, Fang Z, et al. Gastrointestinal symptoms of 95 cases with SARS-CoV-2 infection. Gut. 2020;69:997–1001.

97. Mohapatra S, Menon NG, Mohapatra G, Pisharody L, Pattnaik A, Menon NG, et al. The novel SARS-CoV-2 pandemic: possible environmental transmission, detection, persistence and fate during wastewater and water treatment. Sci Total Environ. 2021;765:142746.

98. Zhong P, Xu J, Yang D, Shen Y, Wang L, Feng Y, et al. COVID-19-associated gastrointestinal and liver injury: clinical features and potential mechanisms. Signal Transduct Target Ther. 2020;5:256.

773

774

SARS-CoV-2 Tropism, Entry, Replication, and Propagation

99. Leung WK, To K-F, Chan PKS, Chan HLY, Wu AKL, Lee N, et al. Enteric involvement of severe acute respiratory syndrome-associated coronavirus infection. Gastroenterology. 2003;125:1011–7.

100. Zhou J, Li C, Zhao G, Chu H, Wang D, Yan HH-N, et al. Human intestinal tract serves as an alternative infection route for Middle East respiratory syndrome coronavirus. Sci Adv. 2017;3:eaao4966. 101. Xiao F, Tang M, Zheng X, Liu Y, Li X, Shan H. Evidence for gastrointestinal infection of SARS-CoV-2. Gastroenterology. 2020;158:1831–1833.

102. Stanifer ML, Kee C, Cortese M, Zumaran CM, Triana S, Mukenhirn M, et al. Critical role of type III interferon in controlling SARS-CoV-2 infection, replication and spread in primary human intestinal epithelial cells. bioRxiv. 2020.

103. Zang R, Gomez Castro MF, McCune BT, Zeng Q, Rothlauf PW, Sonnek NM, et al. TMPRSS2 and TMPRSS4 promote SARS-CoV-2 infection of human small intestinal enterocytes. Sci Immunol. 2020;5:eabc3582. 104. Johansen MD, Irving A, Montagutelli X, Tate MD, Rudloff I, Nold MF, et al. Animal and translational models of SARS-CoV-2 infection and COVID-19. Mucosal Immunol. 2020;13:877–91.

105. Pandey K, Acharya A, Mohan M, Ng CL, Reid SP, Byrareddy SN. Animal models for SARS-CoV-2 research: a comprehensive literature review. Transbound Emerg Dis. 2020.

106. Tay MZ, Poh CM, Renia L, MacAry PA, Ng LFP. The trinity of COVID-19: immunity, inflammation and intervention. Nat Rev Immunol. 2020;20:363–74.

107. Kuri-Cervantes L, Pampena MB, Meng W, Rosenfeld AM, Ittner CAG, Weisman AR, et al. Comprehensive mapping of immune perturbations associated with severe COVID-19. Sci Immunol. 2020;5:eabd7114.

108. Lee S, Channappanavar R, Kanneganti TD. Coronaviruses: innate immunity, inflammasome activation, inflammatory cell death, and cytokines. Trends Immunol. 2020;41(12):1083–99.

109. Zhang D, Guo R, Lei L, Liu H, Wang Y, Wang Y, et al. COVID-19 infection induces readily detectable morphological and inflammation-related phenotypic changes in peripheral blood monocytes, the severity of which correlate with patient outcome. medRxiv. 2020.

110. Feng Z, Diao B, Wang R, Wang G, Wang C, Tan Y, et al. The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) directly decimates human spleens and lymph nodes. medRxiv. 2020.

111. Yilla M, Harcourt BH, Hickman CJ, McGrew M, Tamin A, Goldsmith CS, et al. SARScoronavirus replication in human peripheral monocytes/macrophages. Virus Res. 2005;107:93–101.

112. Ziegler CGK, Allon SJ, Nyquist SK, Mbano IM, Miao VN, Tzouanas CN, et al. SARS-CoV-2 receptor ACE2 is an interferon-stimulated gene in human airway epithelial cells and is detected in specific cell subsets across tissues. Cell. 2020;181:1016, e1019–35.

113. Kim JY, Kim WJ, Kim H, Suk K, Lee WH. The stimulation of CD147 induces MMP-9 expression through ERK and NF-kappaB in macrophages: implication for atherosclerosis. Immune Netw. 2009;9:90–7.

References

114. Xi J, Xu K, Jiang P, Lian J, Hao S, Yao H, et al. Virus strain of a mild COVID-19 patient in Hangzhou representing a new trend in SARS-CoV-2 evolution related to Furin cleavage site. medRxiv. 2020.

115. Pontelli MC, Castro IA, Martins RB, Veras FP, La Serra L, Nascimento DC, et al. Infection of human lymphomononuclear cells by SARS-CoV-2. bioRxiv. 2020.

116. Codo AC, Gastão Davanzo G, de Brito Monteiro L, de Souza GF, Muraro SP, Virgilioda-Silva JV, et al. Elevated glucose levels favor SARS-CoV-2 infection and monocyte response through a HIF-1alpha/glycolysis-dependent axis, e435. Cell Metab. 2020;32: 437–46.

117. Xie X, Muruato AE, Zhang X, Lokugamage KG, Fontes-Garfias CR, Zou J, et al. A nanoluciferase SARS-CoV-2 for rapid neutralization testing and screening of antiinfective drugs for COVID-19. bioRxiv. 2020.

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Chapter 35

Presence of Antibody-Dependent Cellular Cytotoxicity against SARS-CoV-2 in COVID-19 Plasma For Yue Tso,a,b Salum J. Lidenge,a,b,c,d Lisa K. Poppe,a,b Phoebe B. Peña,a,b Sara R. Privatt,a,b Sydney J. Bennett,a,b John R. Ngowi,c Julius Mwaiselage,c,d Michael Belshan,a,e Jacob A. Siedlik,f Morgan A. Raine,e Juan B. Ochoa,g Julia Garcia-Diaz,h Bobby Nossaman,h Lyndsey Buckner,h W. Mark Roberts,h Matthew J. Dean,i Augusto C. Ochoa,i,j John T. West,a,k and Charles Wooda,b,k aNebraska

Center for Virology, University of Nebraska-Lincoln, Lincoln, Nebraska, USA of Biological Sciences, University of Nebraska-Lincoln, Lincoln, Nebraska, USA cOcean Road Cancer Institute, Dar es Salaam, Tanzania, dMuhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania eDepartment of Medical Microbiology & Immunology, Creighton University, Omaha, Nebraska, USA fDepartment of Exercise Science and Pre-Health Professions, Creighton University, Omaha, Nebraska, USA gDepartment of Surgery, Ochsner Medical Center, New Orleans, Louisiana, USA hDepartment of Internal Medicine Ochsner Medical Center, New Orleans, Louisiana, USA iLouisiana State University Cancer Center, New Orleans, Louisiana, USA jDepartment of Pediatrics LSU Health, New Orleans, Louisiana, USA kDepartment of Biochemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, USA bSchool

[email protected]

Keywords: antibody-dependent cellular cytotoxicity, antibody-dependent cellular phagocytosis, asymptomatic individual, complement-dependent cytotoxicity, coronavirus disease of 2019, Dulbecco’s modified Eagle medium, immunofluorescence assay, natural killer cells, neutralization assay, nucleocapsid proteins, receptor binding domain, SARS-CoV-2 virus, seropositive individual, spike glycoprotein, viral RNA titer

35.1 Introduction

The coronavirus disease of 2019 (COVID-19) pandemic is caused by the novel SARS-CoV-2 virus [1, 2]. According to the latest report from the Johns Hopkins

Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

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Presence of Antibody-Dependent Cellular Cytotoxicity against SARS-CoV-2 in COVID-19 Plasma

Coronavirus Resource Center, as of Feb 5, 2021, SARS-CoV-2 has infected >100 million individuals worldwide, and >26 million in the U.S. alone, leading to >450 thousand deaths [3]. With a wide variety of vaccine candidates currently in various stages of clinical trials worldwide, it is important to consider what vaccine correlates are likely to promote responses of sufficient magnitude and durability to impart protection. Antibody responses develop against SARS-CoV-2 during the infection in many subjects tend to increase over the course of disease and correlate with viral RNA titer [4]. Neutralizing antibody (nAb) responses have been shown to preferentially target the receptor binding domain (RBD) of the SARS-CoV-2 spike glycoprotein (S), but the levels of nAb were variable in infected subjects and can undergo fairly rapid decay kinetics [5, 6]. Other non-RBD-specific Ab which target the SARS-CoV-2 S could be less apt to neutralization, but nevertheless have important roles in viral control by coupling adaptive humoral responses to natural killer (NK) cells through the mechanism of Ab-dependent cellular cytotoxicity (ADCC). Convalescent plasma has been used successfully against other infectious diseases such as influenza and SARS and remains among the potential COVID-19 therapies producing efficacy against COVID-19 in several small-scale studies [7–10]. Neutralization is considered a mechanism of action for SARS-CoV-2 convalescent plasma. Additionally, other non-neutralizing antibody-dependent effector mechanisms such as antibody-dependent cellular phagocytosis (ADCP), complement-dependent cytotoxicity (CDC) and ADCC have been shown to play a role in protection against other viruses [11–14]. For ADCC, NK cells recognize and bind to Ab opsonized (targeted) cells using their FcγRIII receptor, CD16, leading to perforin and granzyme degranulation-mediated cytotoxicity of the infected target cells. Since other humoral effector mechanisms have not been investigated for efficacy in SARS-CoV-2 infection, we sought to explore whether ADCC was evident in plasma from recovered or recovering COVID-19 patients in this study.

35.2 Materials and Methods 35.2.1 Study Cohort

This study comprised of 23 consenting subjects, ≥18 years of age and of both genders from U.S. and Sub-Saharan Africa (SSA). The SSA samples included 2 plasma samples from confirmed COVID-19 individuals (SSA1 and SSA2), 1 COVID19 exposed but unconfirmed by SARS-CoV-2 RT-PCR individual (SSA3) and 3 pre-pandemic voluntary blood donor plasma samples (N1, N2 and N3). The pre-pandemic samples were collected in SSA between March and May of 2019. Seventeen COVID-19 plasma samples (US1 to US17) were obtained from U.S. All COVID-19 diagnoses were determined by local health providers with RT-PCR of SARS-CoV-2 in the buccal and/or nasopharyngeal swabs. All study procedures

Materials and Methods

were approved by the institutional review board at the University of Nebraska– Lincoln.

35.2.2 Cell Lines

HEK-293T cells (CRL-3216, ATCC, Manassas, VA, USA) were cultured in Dulbecco’s modified Eagle medium (DMEM) with 10% fetal bovine serum (FBS) and 1% penicillin–streptomycin (P/S). HEK-293T-hACE2 cells (HEK-293T cells expressing the human angiotensinconverting enzyme 2) (NR-52511, BEI Resources, Manassas, VA, USA) were cultured in DMEM with 10% FBS and 1% P/S. NK92.05-CD16-176V, a natural killer cell line engineered to express the high affinity FcγRIII (generously provided by Dr. Kerry Campbell at Fox Chase Cancer Center) were maintained in αMEM complete media: MEM (M0644, Sigma, Burlington, MA, USA) supplemented with 2.2g/L sodium bicarbonate (25080094, ThermoFisher Scientific, Waltham, MA), 0.1mM 2-mercaptoethanol (31350010, ThermoFisher Scientific, Waltham, MA), 2mM L-glutamine (25005CI, Corning, NY, USA), 0.2mM myo-inositol (I5125, Sigma, Burlington, MA, USA), 0.02mM folic acid (F7876, Sigma, Burlington, MA, USA), 1% non-essential amino acids (11140050, ThermoFisher Scientific, Waltham, MA), 1% sodium pyruvate (11360070, ThermoFisher Scientific, Waltham, MA), 1% P/S, 12.5% FBS, and 12.5% horse serum (H1138, Sigma, Burlington, MA, USA). The cells were passaged every 4 days in the presence of 2.5–5% freshly thawed J558L supernatant (see Section 35.2.3). J558L Hu-IL-2 cells, a mouse myeloma cell line that expresses human IL-2 (provided by Dr. Kerry Campbell at Fox Chase Cancer Center) were cultured in RPMI media with 10% FBS, 1% P/S, 2mM L-glutamine, 1% sodium pyruvate, 0.1mM 2-mercapotethanol, and 1% HEPES (25060CI, ThermoFisher Scientific, Waltham, MA). All cells were maintained in 5% CO2 incubator at 37°C.

35.2.3 Human IL-2 Production

J558L Hu-IL-2 cells were used to produce human IL-2 as a growth supplement for NK92.05-CD16-176V cells. The cells were initiated in 10 ml culture media (see above) in a T25 flask. The day after thaw, the culture was transferred to a T75 flask and brought to a total volume of 30 ml. The cells were then expanded every 2–3 days until the desired volume was achieved. The cells were allowed to grow for about one week, until the media turned yellow. The culture was then centrifuged for 3 min at 1300 rpm. Supernatant was 0.22 μm filtered, aliquoted and frozen at –80°C.

35.2.4 Cells Expressing SARS-CoV-2 Spike and Nucleocapsid Proteins

Cells expressing either the SARS-CoV-2 spike or nucleocapsid proteins were generated for use in the immunofluorescence assay (IFA) and as target cells in

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Presence of Antibody-Dependent Cellular Cytotoxicity against SARS-CoV-2 in COVID-19 Plasma

the ADCC assay. At 24 h before transfection, 8 × 105 HEK-293T cells per well were seeded into a 6-well plate with DMEM, 20% FBS without P/S. The cells in each well were transfected with 2 μg of either SARS-CoV-2 spike mammalian expression plasmid pcDNA3.1-SARS2-S (a gift from Dr. Fang Li, 145032, Addgene, Watertown, MA, USA) [15] or SARS-CoV-2 nucleocapsid mammalian expression plasmid pGBW-m4134903 (a gift from Ginkgo Bioworks, 151951, Addgene, Watertown, MA, USA), using Fugene 6 (E2692, Promega, Madison, WI, USA) in Opti-MEM reduced serum medium (31985070, ThermoFisher Scientific, Waltham, MA). The transfected cells were incubated at 37°C in a 5% CO2 incubator for 48 h.

35.2.5 Immunofluorescence Assay against SARS-CoV-2

To generate the microscopy slides for IFA, at 48 h post-transfection, the transfected HEK-293T cells expressing either the SARS-CoV-2 spike or nucleocapsid proteins were harvested without trypsin, fixed with 4% PFA and seeded onto 12-well polytetrafluoroethylene (PTFE) printed slides (6342505, Electron Microscopy Sciences, Hatfield, PA, USA). Each well contained either mock, spike or nucleocapsid transfected cells. The cells were then permeabilized with 0.3% H2O2 methanol solution, washed with 1X PBS, air-dried and stored at -80°C. The donor plasmas were first heat-inactivated at 56°C for 1 h, spun at 12,000 × g for 5 min to remove any debris or aggregates, diluted at 1:20 with 1X PBS containing 0.1% Tween-20 and incubated at room temperature for 30 min. The IFA slides were thawed and incubated with 1X PBS containing 0.1% Tween-20 for 30 min at 37°C in a humidity chamber. Fifteen microliters of the diluted plasmas were then added onto each well and incubated for 1 h at 37°C in a humidity chamber. After washing with 1X PBS, the secondary mouse monoclonal anti-human IgG antibody (CRL-1786, ATCC, Manassas, VA, USA) was added and incubated for 1 h at 37°C in a humidity chamber. After washings with 1X PBS, the slides were incubated with the tertiary CY2 conjugated donkey anti-mouse IgG (715225150, Jackson ImmunoResearch, West Grove, PA, USA) for 1 h at 37°C in a humidity chamber. Finally, the slides were washed, stained with 0.004% Evans Blue solution for 30 s and washed to remove excess staining solution. The slides were air-dried and protected by a cover slip with Fluoromount Aqueous Mounting Medium (F4680, Sigma, Burlington, MA, USA). The slides were examined by three independent readers using Nikon Eclipse 50i fluorescence microscope. A sample was only considered positive if the results from at least two readers concurred.

35.2.6 Neutralization Assay

SARS-CoV-2 spike glycoprotein pseudotyped lentivirus were generated by co­ transfection of HEK-293T cells with SARS-CoV-2 spike mammalian expression plasmid (pcDNA3.1-SARS2-S, a gift from Dr. Fang Li, 145032, Addgene, Watertown, MA, USA) [15], 3rd generation lentiviral plasmid encoding EGFP (FUGW, a gift from

Materials and Methods

Dr. David Baltimore, 14883, Addgene, Watertown, MA, USA) [16] and the packaging plasmid (psPAX2, a gift from Dr. Didier Trono, 12260, Addgene, Watertown, MA, USA). The culture supernatant containing the pseudotyped virus was collected at 72 h post-transfection and concentrated by ultracentrifugation. At 24 h before the neutralization assay, 1 × 104 HEK-293T-hACE2 cells per well were seeded into a 96-well plate. The donor plasmas were heat inactivated at 56°C for 1 h and diluted at 1:40 with culture medium and 25 μl of the SARS-CoV-2 Spike pseudotyped virus for a total volume of 200 μl per well. The plasma-virus mixtures were then incubated at 37°C for 1 h. The old culture medium of the pre-plated HEK-293T-hACE2 cells was then replaced with the plasma-virus mixture, spun at 400 × g for 20 min and incubated at 37°C in a 5% CO2 incubator for 72 h. The level of infection was determined by quantification of the GFP signal using BD Accuri C6 Plus flow cytometer (BD Biosciences, San Jose, CA, USA) and flow data analyzed by FlowJo software (BD Life Sciences, San Jose, CA, USA). Each plasma sample was tested in triplicate. Each set of experiment contained mock-only cells and virus-only cells. The percent GFP in mock cells was subtracted from all other samples, including virus-only cells. To calculate the final percent neutralization, the following equation was used: (Virus only–Sample)/Virus only × 100%. The data were then plotted and statistical analysis was conducted using GraphPad Prism version 5.05 (GraphPad Software, San Diego, CA, USA).

35.2.7 Antibody-Dependent Cellular Cytotoxicity Assay

ADCC activity was assessed with a calcein release assay [17, 18]. Target cells (HEK293T, either mock transfected or expressing SARS-CoV-2 spike) were labeled with 2 μg/ml Calcein-AM (C3099, ThermoFisher Scientific, Waltham, MA) at a concentration of 106 cells/ml for 30 min at 37°C, 5% CO2. After labeling, excess dye was removed by washing the cells twice with DMEM plus 10% FBS and cells were resuspended at a final concentration of 106 cells/ml. The labeled target cells (105 cells) were aliquoted at 100 μl per well into a 96-well v-bottom plate. Sample plasma at a final dilution of 1:50 (4 μl) were added to each experimental well and allowed to incubate at room temperature for 15 min while the effector cells were prepared. NK92.05-CD16-176V effector cells were resuspended at 5 × 106 cells/ml in αMEM complete media and 100 μl (5 × 105 cells) were added to all wells, except the spontaneous and maximum release control wells (Target cells only), for an effector to target ratio of 5:1. Maximum release was achieved through the addition of 104 μl of 0.1% Triton-X-100, while 104 μl of αMEM complete media were added to spontaneous release control wells. The 96-well v-bottom plate was then spun at 100 × g for 2 min to increase cell interactions and incubated at 37°C, 5% CO2 for 4 h. Following incubation, the wells were mixed via gentle pipetting, and spun at 400 × g for 2 min to pellet the cells and debris. The supernatant (150 μl) was transferred to a black-walled

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Presence of Antibody-Dependent Cellular Cytotoxicity against SARS-CoV-2 in COVID-19 Plasma

96-well clear bottom plate and fluorescence was determined with Victor3V plate reader (PerkinElmer, Waltham, MA, USA). Each plate contained the following controls: target cells only (spontaneous release); target cells plus Trition-X-100 (maximum release); target cells and effectors, no plasma (TE). To calculate the percent ADCC activity, this formula was used: [(Experimental-Spontaneous)—(TE-Spontaneous)]/(MaximumSpontaneous)*100%. Data is presented as the change in ADCC (ΔADCC), where ADCC against mock is subtracted from ADCC against spike expressing cells (i.e. ADCC of spike–ADCC of mock). All samples were tested in quintuplicate and data shown is the mean with standard deviation of the centroid three replicates. The data were then plotted and statistical analysis (Mann Whitney) was performed using GraphPad Prism version 5.05 (GraphPad Software, San Diego, CA, USA).

35.3 Results

To evaluate humoral neutralizing and ADCC responses against SARS-CoV-2 and to investigate potential relationships between the two, we obtained plasmas from 18 confirmed COVID-19 symptomatic individuals, 1 confirmed COVID-19 asymptomatic individual, 1 presumed highly exposed but asymptomatic SARS-CoV-2 seropositive individual and 3 SARS-CoV-2 seronegative blood donors from the U.S. and sub-Saharan Africa (SSA) (Table 35.1). Among the 17 U.S. COVID-19 samples, 5 individuals (US1, US2, US3, US16 and US17) had recovered from COVID-19 when the plasma samples were collected. The remaining 12 U.S. samples were individuals with severe COVID-19 infection that required hospitalization. Two RT-PCR confirmed cases SSA1 and SSA2, symptomatic and asymptomatic, respectively, as well as an exposed but non-RT-PCR confirmed seropositive individual (SSA3) (cohabitating household member of SSA2 as well as a confirmed COVID-19 spouse) were from SSA. These individuals all recovered from COVID-19. The three negative control cases (N1, N2 and N3) were prepandemic healthy blood donors from SSA. To determine if plasma from these COVID-19 cases contained SARS-CoV-2 specific IgG antibodies, we developed an in-house immunofluorescence assay (IFA) using HEK293T cells expressing either SARS-CoV-2 S or nucleocapsid (N) proteins [19]. The assay detects binding of SARS-CoV-2 specific IgG in the plasma to the viral proteins expressed in these cells. After the addition of a secondary anti-human IgG antibody followed by a fluorescently tagged tertiary antibody, SARS-CoV-2 positive plasma produced a green color in epifluorescence microscopy (Fig. 35.1). The expression of SARS-CoV-2 S and N proteins in HEK293T cells, 57.6% and 58.4% respectively, were assessed via IFA with convalescent COVID-19 plasma. Using this method, we detected strong IgG antibody responses

Results

against the SARS-CoV-2 S glycoprotein in all COVID-19 individuals, and against the N protein in 18/20 COVID-19 individuals. Surprisingly, COVID-19 individuals US13 and SSA3 only showed strong responses against the S glycoprotein, but no response against N. This result reinforces the concept that SSA3 was sufficiently exposed to SARS-CoV-2 to generate an Ab response to a viral surface glycoprotein, without generating a N protein response or exhibiting symptoms. No fluorescent staining (green) was observed against SARS-CoV-2 S- and N-expressing cells by negative control plasmas. Likewise, no staining (green) was evident when SARS-CoV-2 plasma was applied to mock transfected cells in parallel (Fig. 35.1). Table 35.1 Study cohort information Sample ID

Area of origin Age

Gender

COVID-19 diagnosis Symptomatic/ Disease by RT-PCR asymptomatic severity

US1

US

32

Male

Confirmed

Symptomatic

Convalescent

US3

US

22

Female

Confirmed

Symptomatic

Convalescent

US2

US4

US5

US6

US7

US8

US9

US10

US11

US12

US13

US14

US15

US16

US17 SSA1

SSA2

SSA3

*

US

US

US

US

US

US

US

US

US

US

US

US

US

US

US

SSA

SSA

SSA

N1 to N3 SSA

35

61

76

51

60

61

81

79

72

59

76

79

85

31

59

26

31

32

N/A

Male

Female Male

Female Male

Female Male

Male

Male

Male

Female

Female Male

Female

Female Male

Female Male

N/A

Confirmed

Confirmed

Confirmed

Confirmed

Confirmed

Confirmed

Confirmed

Confirmed

Confirmed

Confirmed

Confirmed

Confirmed

Confirmed

Confirmed

Confirmed

Confirmed

Confirmed N/A

N/A

“*” denotes pre-pandemic negative control plasmas.

Symptomatic

Symptomatic

Symptomatic

Symptomatic

Symptomatic

Symptomatic

Symptomatic

Symptomatic

Symptomatic

Symptomatic

Symptomatic

Symptomatic

Symptomatic

Symptomatic

Symptomatic

Symptomatic

Convalescent Hospitalized

Hospitalized

Hospitalized

Hospitalized

Hospitalized

Hospitalized

Hospitalized

Hospitalized

Hospitalized

Hospitalized

Hospitalized

Hospitalized

Convalescent

Convalescent Mild

Asymptomatic N/A

Asymptomatic N/A

N/A

N/A

Abbreviations: US, United States of America; SSA, sub-Saharan Africa; N/A, not-applicable or not-available.

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Presence of Antibody-Dependent Cellular Cytotoxicity against SARS-CoV-2 in COVID-19 Plasma

Figure 35.1 Immunofluorescence assay (IFA) against SARS-CoV-2 proteins. Representative pictures of IFA against either the mock, SARS-CoV-2 spike or nucleocapsid expressing HEK-293T cells. The upper row shows IFA with negative control plasma collected before the COVID-19 pandemic. The lower row shows IFA with COVID-19 plasma, where strong green color positive cells (indicated by white arrows) were only observed in cells expressing either SARS-CoV-2 spike or nucleocapsid proteins. The lack of green color positive cells with negative control plasma and mock cells demonstrated the specificity of the IFA. All pictures were taken at 20× magnification with Nikon Eclipse 50i fluorescence microscope. The white scale bars indicate 50 μm.

Since the detection of SARS-CoV-2 specific IgG does not guarantee the presence of neutralizing antibodies, we used a SARS-CoV-2 S-pseudotyped virus neutralization assay to evaluate the presence of such antibodies. We detected significantly elevated neutralization at 84–97% (p < 0.05) in 17/20 COVID-19 plasmas compared to plasma from negative controls (Fig. 35.2). This finding is consistent with other studies showing nAb in the majority of COVID-19 infected individuals [20]. It was surprising, however, that there was no significant nAb activity in the plasma from COVID-19 individuals US4, US15 and SSA3, despite the fact that they had detectable anti-S glycoprotein antibodies. It is possible that at the time of sample collection these individuals were in early stages of infection and had experienced insufficient time or viremia to develop detectable levels of nAb. In addition, these individuals may have other factors that weaken their humoral responses. Given that SSA3 remained asymptomatic despite two sources of household exposure, it is also plausible that T-cell responses, or alternatively, non-neutralizing Ab effector responses, such as ADCC (targeted against the S glycoprotein), may have provided protection against symptomatic COVID-19.

Results

Figure 35.2 SARS-CoV-2 spike pseudotyped virus neutralization assay. SARS-CoV-2 spike pseudotyped lentivirus virus encoding EGFP were tested against negative control prepandemic plasmas (N1, N2 and N3) and COVID-19 plasmas (US1 to US17 and SSA1 to SSA3) at 1:40 plasma dilution. At 72 h post-infection, percentage of GFP positive cells were quantified with BD Accuri C6 Plus flow cytometer. The mean of triplicate wells was shown with error bars representing SEM. P-values were calculated via one-way ANOVA and “*” denotes p < 0.05 relative to negative control plasmas.

To test for non-neutralizing responses, we performed a calcein-release ADCC assay with NK92.05-CD16-176V effector cells to measure SARS-CoV-2 specific responses against mock-transfected cells and cells expressing S protein (Fig. 35.3). Target cells for ADCC were produced by transfecting HEK-293T cells with mammalian CMV-promoter SARS-CoV-2 S expression plasmid and 57.6% of the cells express the S protein after transfection as shown in Fig. 35.1. The target cells were then labeled with non-fluorescent Calcein-AM, a dye that is intracellularly converted to green fluorescent calcein. The NK92.05-CD16-176V effector cells will recognized these dye-labeled and Ab decorated antigen expressing target cells via its FcγRIII receptor that binds to the Fc region of the bound antibody. This recognition activates the NK cell which then disrupts the plasma membrane of target cells and release calcein into the culture supernatant, where it was quantified by a fluorescent plate reader. We found that plasma in 19/20 SARS-CoV-2 infected/exposed individuals induced ADCC activity against S glycoprotein-expressing targets (Fig. 35.3A) that was significantly higher

785

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Presence of Antibody-Dependent Cellular Cytotoxicity against SARS-CoV-2 in COVID-19 Plasma

Figure 35.3 Antibody-dependent cellular cytotoxicity (ADCC) assay. The ADCC activity of COVID-19 plasmas were tested against HEK-293T cells expressing SARS-CoV-2 spike protein, which served as the target cells. After incubation with plasma, Calcein-AM labeled target cells were incubated with NK cells (NK92.05-CD16-176V) which served as the effector cells. The amount of fluorescent calcein released into the medium was then measured with Victor3V plate reader. (A) changes in ADCC (ΔADCC) activity against the spike protein, relative to their respective activity against mock cells. N denotes pre-pandemic negative control plasmas. US and SSA denote COVID-19 samples from USA and sub-Saharan Africa, respectively. (B) Comparison of ΔADCC against the spike protein between COVID-19 and negative control plasmas. P-values were calculated via Mann Whitney test.

Discussion

(P = 0.0011) compared to negative controls (Fig. 35.3B). All individuals with nAb also demonstrated ADCC activity. Of the three individuals without nAb, sample US4 was the only individual who had SARS-CoV-2 spike specific antibodies that were non-neutralizing and unable to induce ADCC activity. The reason for the absence of ADCC activity in US4 is not clear, even though there are antibodies that bind to both spike and nucleocapsid proteins. One possible explanation is the lack of anti-SARS-CoV-2 antibody subclasses that can bind the FcγRIII receptor with high affinity to mediate ADCC in our assay [21]. The remaining two individuals, US15 and SSA3 had SARS-CoV-2 spike specific antibodies as detected by IFA but lacked nAb. Yet, their plasma was still able to induce significant ADCC activity against the S-expressing target cells, strongly indicating that non-neutralizing antibodies might play a role against SARS-CoV-2 via ADCC.

35.4 Discussion

Our study demonstrated that specific SARS-CoV-2 S glycoprotein targeting antibodies from COVID-19 plasma can induce ADCC killing via NK cells in vitro. Although speculation as to the presence of ADCC against SARS-CoV-2 has been reported, there has been little direct experimental evidence [22, 23]. Several studies showed human and murine mAb against SARS-CoV displayed crossreactive ADCC responses against SARS-CoV-2, but no study has examined COVID­ 19 plasma for its capacity to directly mediate ADCC [24, 25]. Despite our findings and those in previous reports, whether SARS-CoV-2 specific ADCC actually occurs in vivo, will require further investigation with a larger sample size and functional testing of NK cells from the infected individuals in conjunction with autologous plasma. Several studies have suggested dysfunction and decreased number of NK cells in patients with severe COVID-19 disease, which may undermine the role that NK cells and ADCC play in disease recovery [26, 27]. Nevertheless, ADCC may still be an important factor in vaccine efficacy. Moreover, in addition to NK cells, other FcγRIII receptor-expressing cells, such as macrophages, neutrophils, and eosinophils, could also mediate ADCC. The detection of ADCC inducing-antibodies in COVID-19 individuals also supports the exploration of other antibody-dependent effector mechanisms, such as ADCP and CDC, and their role in COVID-19 pathogenesis and recovery. The major implication of our findings is that efficacy of COVID-19 vaccine candidates should not be evaluated solely based on the level of nAb elicited, but rather the totality of SARS-CoV-2 specific humoral responses elicited. The potential contributions and durability of non-neutralizing antibody effector mechanisms need to be included in such assessments. Therefore, in addition to T-cell responses, a qualitative examination of other immune cells such as NK cells and macrophages should also be taken into consideration. Clearly, given the importance and urgency of developing an effective COVID-19 vaccine, a larger more in-depth study will be warranted to dissect the role of non-neutralizing Ab more completely in COVID-19 patients and vaccinated individuals.

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Presence of Antibody-Dependent Cellular Cytotoxicity against SARS-CoV-2 in COVID-19 Plasma

Abbreviations ADCC: ADCP: CDC: COVID-19: DMEM: FBS: IFA: nAb: NK: P/S: PTFE: RBD:

antibody-dependent cellular cytotoxicity antibody-dependent cellular phagocytosis complement-dependent cytotoxicity

coronavirus disease of 2019

Dulbecco’s modified Eagle medium fetal bovine serum immunofluorescence assay neutralizing antibody

natural killer

penicillin–streptomycin

polytetrafluoroethylene

receptor binding domain

Disclosures and Conflict of Interest

This chapter was originally published as: Tso, F. Y., Lidenge, S. J., Poppe, L. K., Peña P. B., Privatt, S. R., Bennett, S. J., et al. (2021). Presence of antibody-dependent cellular cytotoxicity (ADCC) against SARS-CoV-2 in COVID-19 plasma. PLoS ONE, 16(3), e0247640, https://doi.org/10.1371/journal.pone.0247640, under the Creative Commons Attribution license (http://creativecommons.org/licenses/ by/4.0/), and appears here, with edits and updates.

Data Availability: All relevant data are within the chapter.

Funding: The work in this chapter was supported in part by the US National Institute of Health (NIH), National Cancer Institute U54 CA190155 (CW); Fogarty International Center K43TW011418 (SJL) and D43 TW010354 (CW); National Institute of Allergy and Infectious Diseases Ruth L. Kirschstein National Research Service Award T32 AI125207 (CW); LKP, SJB and SRP were a Ruth L Kirchstein fellows and PBP is an INBRE scholar supported by National Institute of General Medical Sciences P20 GM103427. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the chapter. Competing interests: The authors have declared that no competing interests exist.

Acknowledgments: The authors would like to thank all patients for providing plasma to be used in this study.

References

1. Wu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG, et al. A new coronavirus associated with human respiratory disease in China. Nature. 2020;579(7798):265–9.

2. Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020;579(7798):270–3.

References

3. Johns Hopkins Coronavirus Resource Center: Johns Hopkins University; 2020. Available at: https://coronavirus.jhu.edu/ (accessed on May 8, 2021).

4. Chen Y, Li L. SARS-CoV-2: virus dynamics and host response. Lancet Infect Dis. 2020;20(5):515–6. 5. Jiang S, Hillyer C, Du L. Neutralizing antibodies against SARS-CoV-2 and other human coronaviruses. Trends Immunol. 2020;41(5):355–9.

6. Ibarrondo FJ, Fulcher JA, Goodman-Meza D, Elliott J, Hofmann C, Hausner MA, et al. Rapid decay of Anti-SARS-CoV-2 antibodies in persons with mild COVID-19. N Engl J Med. 2020;383(11):1085–7.

7. Duan K, Liu B, Li C, Zhang H, Yu T, Qu J, et al. Effectiveness of convalescent plasma therapy in severe COVID-19 patients. Proc Natl Acad Sci U S A. 2020;117(17):9490–6.

8. Shen C, Wang Z, Zhao F, Yang Y, Li J, Yuan J, et al. Treatment of 5 critically ill patients with COVID-19 with convalescent plasma. JAMA. 2020; 323(16):1582–9. 9. Cheng Y, Wong R, Soo YO, Wong WS, Lee CK, Ng MH, et al. Use of convalescent plasma therapy in SARS patients in Hong Kong. Eur J Clin Microbiol Infect Dis. 2005;24(1):44–6.

10. Hung IF, To KK, Lee CK, Lee KL, Chan K, Yan WW, et al. Convalescent plasma treatment reduced mortality in patients with severe pandemic influenza A (H1N1) 2009 virus infection. Clin Infect Dis. 2011;52(4):447–56.

11. Gorander S, Ekblad M, Bergstrom T, Liljeqvist JA. Anti-glycoprotein g antibodies of herpes simplex virus 2 contribute to complete protection after vaccination in mice and induce antibody-dependent cellular cytotoxicity and complement-mediated cytolysis. Viruses. 2014;6(11):4358–72.

12. Jenks JA, Goodwin ML, Permar SR. The roles of host and viral antibody Fc Receptors in herpes simplex virus (HSV) and human cytomegalovirus (HCMV) infections and immunity. Front Immunol. 2019;10:2110.

13. Moraru M, Black LE, Muntasell A, Portero F, Lopez-Botet M, Reyburn HT, et al. NK cell and Ig interplay in defense against herpes simplex virus type 1: epistatic interaction of CD16A and IgG1 allotypes of variable affinities modulates antibody-dependent cellular cytotoxicity and susceptibility to clinical reactivation. J Immunol. 2015;195(4):1676–84. 14. Forthal DN, Finzi A. Antibody-dependent cellular cytotoxicity in HIV infection. AIDS. 2018;32(17):2439–51.

15. Shang J, Ye G, Shi K, Wan Y, Luo C, Aihara H, et al. Structural basis of receptor recognition by SARS-CoV-2. Nature. 2020;581(7807):221–4.

16. Lois C, Hong EJ, Pease S, Brown EJ, Baltimore D. Germline transmission and tissue-specific expression of transgenes delivered by lentiviral vectors. Science. 2002;295(5556):868–72.

17. Neri S, Mariani E, Meneghetti A, Cattini L, Facchini A. Calcein-acetyoxymethyl cytotoxicity assay: standardization of a method allowing additional analyses on recovered effector cells and supernatants. Clin Diagn Lab Immunol. 2001;8(6):1131–5.

18. Somanchi SS, McCulley KJ, Somanchi A, Chan LL, Lee DA. A novel method for assessment of natural killer cell cytotoxicity using image cytometry. PLoS One. 2015;10(10):e0141074.

789

790

Presence of Antibody-Dependent Cellular Cytotoxicity against SARS-CoV-2 in COVID-19 Plasma

19. Tso FY, Lidenge SJ, Pena PB, Clegg AA, Ngowi JR, Mwaiselage J, et al. High prevalence of pre-existing serological cross-reactivity against SARS-CoV-2 in sub-Sahara Africa. Int J Infect Dis. 2020;102:577–83.

20. Robbiani DF, Gaebler C, Muecksch F, Lorenzi JCC, Wang Z, Cho A, et al. Convergent antibody responses to SARS-CoV-2 in convalescent individuals. Nature. 2020;584(7821):437–42.

21. de Taeye SW, Bentlage AEH, Mebius MM, Meesters JI, Lissenberg-Thunnissen S, Falck D, et al. FcgammaR binding and ADCC activity of human IgG allotypes. Front Immunol. 2020;11:740. 22. Market M, Angka L, Martel AB, Bastin D, Olanubi O, Tennakoon G, et al. Flattening the COVID-19 curve with natural killer cell based immunotherapies. Front Immunol. 2020;11:1512.

23. Masselli E, Vaccarezza M, Carubbi C, Pozzi G, Presta V, Mirandola P, et al. NK cells: A double edge sword against SARS-CoV-2. Adv Biol Regul. 2020;77:100737.

24. Pinto D, Park YJ, Beltramello M, Walls AC, Tortorici MA, Bianchi S, et al. Crossneutralization of SARS-CoV-2 by a human monoclonal SARS-CoV antibody. Nature. 2020;583(7815):290–5. 25. Yasui F, Kohara M, Kitabatake M, Nishiwaki T, Fujii H, Tateno C, et al. Phagocytic cells contribute to the antibody-mediated elimination of pulmonary-infected SARS coronavirus. Virology. 2014;454–455:157–68.

26. Qin C, Zhou L, Hu Z, Zhang S, Yang S, Tao Y, et al. Dysregulation of immune response in patients with coronavirus 2019 (COVID-19) in Wuhan, China. Clin Infect Dis. 2020;71(15):762–8. 27. Zheng M, Gao Y, Wang G, Song G, Liu S, Sun D, et al. Functional exhaustion of antiviral lymphocytes in COVID-19 patients. Cell Mol Immunol. 2020;17(5):533–5.

Chapter 36

Performance of SARS-CoV-2 Serology Tests: Are They Good Enough? Isabelle Piec, PhD,a Emma English,b Mary Annette Thomas, MPhil,c Samir Dervisevic, MD,d William D. Fraser, MD,a,e and William Garry Johnb,e aBioAnalytical

Facility, Faculty of Medicine, University of East Anglia, Norwich, UK of Medicine and Health, University of East Anglia, Norwich, UK cWEQAS, Cardiff and Vale University Health Board, Cardiff, UK dVirology Department, Norfolk and Norwich University Hospitals, Norwich, UK eClinical Biochemistry Department, Norfolk and Norwich University Hospitals, Norwich, UK bFaculty

[email protected]

Keywords: SARS-CoV-2 virus, lateral flow immunoassays, anti-cyclic citrullinated peptide, antibodies, thyroid stimulating immunoglobulin, rheumatoid arthritis, thyroid stimulating immunoglobulin, seasonal coronaviruses, response intensity, specificity, sensitivity, crossreactivity, autoantibodies, immunoassays, cross-reactivity, spike proteins, nucleocapsid, neutralizing antibodies, nucleocapsid assays

36.1 Introduction The scientific community has had to rapidly develop and manufacture tests for the new SARS-CoV-2 pandemic at unprecedented speed, taking three months to develop assays that would ordinarily take three years. Serology testing, that can identify those who have previously been exposed to the SARS-CoV-2 virus and have mounted an immune response, has been hailed as key to managing the pandemic however controversy remains over both the accuracy and utility of serology testing in disease management. Structural proteins, including the spike (essential for viral infection) and the nucleocapsid (important for viral RNA transcription), are both potential targets for early detection of infection and known to elicit an Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

Copyright © 2022 Jenny Stanford Publishing Pte. Ltd.

ISBN 978-981-4877-84-8 (Hardcover), 978-1-003-18043-2 (eBook)

www.jennystanford.com

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Performance of SARS-CoV-2 Serology Tests

immune response in the host [1] with antibodies detectable within 20 days of disease onset [2–4]. Systematic reviews [5, 6] challenged the diagnostic accuracy of serological tests, particularly when using lateral flow immunoassays (LFIAs). Public Health England (PHE) showed only the Siemens and the Roche Diagnostics assays met the minimum UK Medicines and Healthcare products Regulatory Agency Target Product Profile criteria for sensitivity [7] after the threshold of positivity was adjusted to 0.128. Assays from DiaSorin and Abbott Diagnostics [8] also provided acceptable diagnostic results. These evaluations did not address cross-reactivity. To our knowledge little has been done regarding interference from antibodies produced during other viral infection and autoimmune disorders. Additionally, with the focus on diagnostic sensitivity and specificity, little has been done to evaluate the analytical accuracy, which if poor, has the potential to negate all of these study findings. Indeed in the editorial, Duong and colleagues clearly states that there is a need for critical independent evaluations of these tests, using the same specimen panels [9]. This study provides a head-to-head evaluation of the diagnostic and analytical performance of four commercially available IgG based serology assays for SARSCoV-2 and a diagnostic accuracy study of one point of care LFIA.

36.2 Materials and Methods

36.2.1 Specimen Collection and Storage Patients were not involved in any part of the work. All samples were from archived specimens and were fully anonymized before we accessed them. Therefore, our study is in accordance with the blanket Ethical standards of University of East Anglia on de-identified samples for method development. Moreover, using the UK NHS Research Ethics Committee decision toolkit (http://www.hra-decisiontools. org.uk/ethics/) we confirmed that separate ethical review is not required for this study which is in concordance with the Helsinki Declaration. All serum samples were collected between April and June 2020, anonymized, aliquoted and stored at –80°C until analyzed. SARS-CoV-2 PCR-positive patients (AusDiagnostics platform, Chesham, UK) were of both genders, age range 66 to 93 and hospitalized at the Norfolk and Norwich University Hospital (NNUH) or Queen Elizabeth Hospital in King Lynn (QEH). Samples were taken 8–44 days after testing positive for SARS-CoV-2. Negative control samples were collected in 2018 from patients with no history of infection or immune disorder. Pre-pandemic samples from patients who had a range of confirmed respiratory infections (including Influenza A, B and seasonal coronaviruses [Table 36.1]), samples collected from patients with inflammatory polyarthritis positive for anti-cyclic citrullinated peptide antibodies (anti-CCP) along with samples positive for thyroid stimulating immunoglobulin (TSI) were used to test the non-specific binding of non-SARS-CoV-2 antibodies. These groups of samples are referred to as N (negative

Materials and Methods

control), CR (cross-reactivity), RA (Rheumatoid Arthritis), TSI (patients with thyroid stimulating immunoglobulin) and P (SARS-CoV-2 Positive). A total of 195 individual serum samples (43 P, 50 N, 50 CR, 22 RA and 30 TSI) were analyzed for SARS-CoV-2 IgG antibodies. For a subset of patients, samples were available for a series of time-points thus allowing for a time course analysis (43 patients, 142 samples). Table 36.1 Respiratory infections tested for cross reactivity in the SARS-CoV-2 IgG immunoassays Infection

No. of patients

Epstein-Barr virus

8

Respiratory syncytial virus

7

Influenza A virus

Seasonal Coronaviruses Borrelia burgerdorferii Cytomegalovirus

Varicellazoster virus Bordella Pertussis Hepatitis B

Human immunodeficiency virus Adenovirus

Mycoplasma

ParaInfluenza Rhinovirus

36.2.2 Study Design

8 7 4 3 3 2 2 2 1 1 1 1

SARS-CoV-2 IgG immunoassays were from 1) Epitope Diagnostics Inc. (EDI, San Diego, CA, USA) performed using the Agility ELISA automate (Dynex Technologies, Chantilly, VA, USA), 2) EuroImmun UK ITC (UK) performed manually, 3) Abbott Diagnostics (Maidenhead, UK) on the Alinity™ i analyzer and 4) DiaSorin (London, UK) on the Liaison XL analyzer. A subset of samples was also tested using the point of care testing (POCT) device SARS-CoV-2 IgG/IgM rapid test from Healgen (Houston, TX, USA). Due to a limited number of cassettes available, 49 samples from 27 P were analyzed along with 3 N, 8 CR, 4 RA and 4 TSI. Cross-reactive and negative samples were primarily chosen from patient samples proven positive for seasonal coronaviruses and influenza A or a false positive result in one or more of the immunoassays. We focused on the IgG results in order to compare with the immunoassays. Assays were performed by trained biomedical scientists using manufacturer’s instructions. The SARS-CoV-2 Abbott assay was performed in the clinical biochemistry

793

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Performance of SARS-CoV-2 Serology Tests

department at NNUH and the other SARS-CoV-2 assays were performed at the University of East Anglia. All other non-SARS-CoV-2 related tests were performed at the NNUH virology department. DiaSorin SARS-Cov-2 is a quantitative assay and antibody concentrations are expressed in AU/mL. The Abbott, EDI and EuroImmun are qualitative assays for which the result is calculated using the ratio of the sample optical density (OD) against the negative or calibrator control (Table 36.S1). EuroImmun and DiaSorin assays detect antibodies to, respectively, recombinant S1 and S1/S2 domains of the SARS-CoV-2 spike protein while both the EDI and Abbott assay detect antibodies to the nucleocapsid. The POCT from Healgen is a solid phase lateral flow immunochromatographic assay (LFIA) for detection of SARS-CoV-2 of IgG and IgM, antigen not specified.

36.2.3 Imprecision

As the results are expressed with a values correlating with the amount of antibody detectable, imprecision was assessed using a Clinical and Laboratory Standards Institute (CLSI) EP-15 based protocol on the automated clinical laboratory analyzers protocol [10]. Positive and negative patient pools and/or controls of different concentrations were prepared and frozen as aliquots and assayed as 5 replicates per day on 5 different days. For the plate-based assays, inter- and intraassay CVs were calculated. Intra-assay was determined using the CV of the optical density (OD) of duplicated samples. Inter-assay was determined using the CV obtained from the sample pool and the kit positive control across the plates.

36.2.4 Statistics

Using IBM SPSS Statistics 25.0.0.1, Mann-Whitney and Cohen’s Kappa tests were used to compare OD results between groups and to determine the concordance between the assays, respectively. Analysis of EP15 was performed using EP evaluator. Variation was estimated on calculated values (R) or response (Relative Light Unit, RLU) as intra and inter-assay coefficient of variation (CV). Graphical representations were conducted with GraphPad Prism version 8.0 (GraphPad Software, Inc., USA). Throughout the tables, figures, and legends, the following terminology is used to show statistical significance: *P < 0.05; **P < 0.01 and ***P < 0.001.

36.3 Results

36.3.1 Imprecision 36.3.1.1 Abbott EP15 and was performed on two Alinity analyzers (Table 36.2). Overall, negative pool imprecision was CV = 8.1% and 6.8% on equipment 1 and 2 respectively. Positive pool imprecisions were CV = 2.3% and 1.1% respectively.

Results

Table 36.2 EP15 analysis on two Abbott Alinity, DiaSorin Liaison XL and ELISAs imprecision tests Intra-assay imprecision Inter-assay imprecision

EURO IMMUN

EDI

DIASORIN

ABBOTT

Sample Alinity 1 (Neg)

Alinity 1 (Pos)

Alinity 2 (Neg)

n 25

25

25

Alinity 2 (Pos)

25

Kit Positive control

25

Kit Negative control Level 1 (Neg pool) Level 2 (Pool 1) Level 3 (Pool 2) Level 4 (pool 3)

Kit Negative control Kit positive control Duplicate samples

Kit Negative control

Kit positive control Calibrator

Duplicate samples

20

25 25

Mean 0.136 (R)

7.254 (R)

0.143 (R)

58662 (RLU)

4815

2457 (RLU)

3

3

44

8.1

2.3

5.2

5608

0.074 (OD)

—

56802

55667 —

11128

13.4

—

0.014

10.9

13.8

10.0 —

0.277 (OD)

—

—

—

—

6.7

6.8

1.1

13.4

3.3

1.169 (OD)

8.1

2.3

116.4

3.8 —

%CV

2860

57.6

—

0.074 (OD)

0.010

8.2

11128

557660 (RLU)

0.011

0.170

0.082

83236 (RLU)

4003

SD

1.1

70.4

0.482 (OD)

3

%CV

1730

6945 (RLU)

9

308

0.007

0.081

410600 (RLU)

27

0.011

0.167

7.242 (R)

25

25

SD

5887

56802

58092 0.068 —

—

0.003

6.1

—

—

0.15

0.027

84.8 9.6

13.8

10.4

14.2 —

3.7

2.9

9.5 —

Note: For the DiaSorin, negative samples (QC or pools) results were typically below the limit of detection of 3.8 AU/mL and variation was estimated on the response in relative light units (RLU).

36.3.1.2 DiaSorin

EP15 imprecision was estimated based on response intensity (RLU). Positive control imprecision was between 8.2% and 13.8% (Table 36.2). The negative quality control material results were consistently below the lower limit of detection of 3.8AU/mL and the negative pool concentration was consistently below 10AU/mL, the resulting calculated imprecision was therefore expectedly elevated.

36.3.1.3 EDI and EuroImmun

Intra-assay imprecision on duplicate samples (Table 36.2) was on average CV = 3.3±3.8% and 6.1±6.7% respectively. Inter-assay imprecision of EDI was CV = 14.2% for the kit positive pool and 16.5% for the negative pool. Baseline OD varied between the plates increasing the inter-assay variations, however, the ratio positive/cut-off was on average 1.43 ±0.16 (CV = 11.1%). Inter-assay of EuroImmun was evaluated using the positive kit QC, the calibrator and the negative kit control. Coefficient of variation were CV = 12.9%, 9.5% and 3.7% respectively.

36.3.2 Specificity and Sensitivity

A total of 43 individual P was analyzed for SARS-CoV-2 IgG antibodies. Of these, twenty had samples taken at least 14 days after a positive PCR result (P ≥ 14) and

795

20 20 20 20 20 50 50 50 50 4 50 50 50 50 9 22 22 22 22 4 30 30 30 30 4

EDI EuroImmun Abbott DiaSorin Healgen Pre-pandemic controls (N) EDI EuroImmun Abbott DiaSorin Healgen Other Respiratory Infection EDI EuroImmun (CR) Abbott DiaSorin Healgen Rheumatoid Arthritis (RA) EDI EuroImmun Abbott DiaSorin Healgen Thyroid Disorder (TSI) EDI EuroImmun Abbott DiaSorin Healgen 20 20 20 19 20 0 2 0 2 0 0 3 0 2 1 0 0 0 1 0 1 0 0 1 1

30 27

35 35 35

SARS-CoV-2 IgG Positive

0 0 0 1 0 50 46 50 48 4 50 47 50 48 8 22 22 22 21 4 29 28 30 29 3

12 0

8 8 8

SARS-CoV-2 IgG Negative

n/a 0 n/a 0 n/a n/a 2 n/a 0 n/a n/a 0 n/a 0 n/a n/a 0 n/a 0 n/a n/a 2 n/a 0 n/a

n/a 0 n/a 0 n/a

Equivocal result

100 (80–100) 100 (80–100) 100 (80–100) 95 (73–100) 100 (80–100) 100 (91–100) 92 (79–97) 100 (91–100) 96 (85–99) — 100 (91–100) 94 (82–98) 100 (91–100) 96 (85–99) — 92 (72–99) 92 (72–99) 100 (82–100) 95 (75–100) — 97 (81–100) 93 (76–99) 100 (85–100) 97 (81–100) —

71 (55–84) 100 (84–100)

81 (66–91) 81 (66–91) 81 (66–91)

Result (95% CI)

Note: Specificity was estimated on pre-2020 samples (N) from healthy individuals and patients with disorders that induce the production of potentially interfering substances. n/a = no equivocal range available.

SARS-CoV-2 Positive ≥14 days post PCR

DiaSorin Healgen 42 27

Total Tested

43 43 43

SARS-CoV-2 Positive all time points

EDI EuroImmun Abbott

Assay

Table 36.3 Sensitivity of the assays was estimated on all time points and including only samples >14 days post PCR

SENSITIVITY

SPECIFICITY

796 Performance of SARS-CoV-2 Serology Tests

Results

23 were taken prior (P < 14). All P ≥ 14 had detectable antibodies in the EDI, EuroImmun, Abbott and Healgen assays. However, one sample returned a negative result using the DiaSorin assay. These results suggest a true positive rate of 100% with EDI, EuroImmun, Abbott and Healgen assays and 95% for the DiaSorin assay. Amongst the 23 P < 14 samples, antibodies were detected for 65% (Abbott & EuroImmun), 61% (EDI) and 43% (DiaSorin) of the samples. Two samples R were close to the threshold in EDI and Abbott (EDI: 0.8 and Abbott 1.9; EDI: 1.0 and Abbott 0.8) resulting in one being positive in one assay and negative in the other (and vice-versa). All 50 N were negative on the Abbott and EDI. Two samples were positive and 48 were negative on the EuroImmun (although 2 were equivocal). Two false positive samples were also observed on the DiaSorin, one being positive on both DiaSorin and EuroImmun assays. The IgG kits showed a very good diagnostic ability to differentiate between P and N (Table 36.3). Overall, EuroImmun and DiaSorin showed lower sensitivity and specificity than EDI and Abbott. Sensitivity ranged between 81–100% on all time points for EDI, EuroImmun and Abbott. DiaSorin sensitivity was 71% on all time points and 95% for P ≥ 14. Specificity was consistently 100% for the Abbott while it ranged between 92 to 100% for the other assays.

36.3.3 Cross-Reactivity

There were no SARS-CoV-2 IgG positive results from patients with non-SARS-CoV-2 infection (CR, n = 50, including seasonal flu (n = 7)), anti-CCP positive (RA, n = 22) nor TSI positive (n = 30) using the Abbott and the EDI assays. Overall, DiaSorin showed the highest (4%) cross-reactivity (2CR, 1 RA and 1 TSI), followed by EuroImmun (3%–3CR) and EDI (1%–1 TSI). The Mann-Whitney test showed that on the EDI only, the R value of samples used to test cross-reactivity (RA and TSI) was significantly elevated, however only one sample was falsely positive for SARS-CoV-2 (Fig. 36.1). Any sample that gave a false positive result in any of the immunoassays was also tested on the Healgen POCT and none were IgG positive. However, a very weak signal could be detected on one TSI sample and one sample from a patient with seasonal flu. Because of the very small number of samples tested; specificity calculation was not performed for the rapid test.

36.3.4 Time Course Analysis

We analyzed 1 to 13 data points for 43 P. We observed an increase of the signal for presence of IgG over time going from negativity to positivity and reaching a plateau (Fig. 36.2). Sigmoid curve-fitting indicated a time from PCR to seroconversion at 9.8 days (95% CI 10.7–13.7), 10.2 (95% CI 8.5–11.8), 12.2 days (95% CI 10.7–13.7) and 10.4 days (95% CI 7.9–12.9) for EDI, Abbott, DiaSorin and EuroImmun assays

797

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Performance of SARS-CoV-2 Serology Tests

Figure 36.1 Dot-plots of R values for each condition (N, P, CR, RA and TSI) for the (A) EDI, (B) Abbott, (C) EuroImmun and (D) DiaSorin tests. Mann-Whitney analysis demonstrated a significant increase in the R value for the positive samples. Mann-Whitney statistical significance *p < 0.05; **p < 0.01 and ***p < 0.001. Dotted line represents the positive cut-off for each assay.

Figure 36.2 Seropositivity in specimen with PCR positive relative to day of PCR. Dashed line represents the cut-off ratio for each assay. Solid black line and dotted lines represent the 4-parameter logistic curve-fit of the points with confidence interval. Time to PCR onset is calculated as curve inflection point.

Discussion

respectively. Note that due to a limited number of EuroImmun tests available, we only had measurements for 56 (of 142) data points. One data point was missing for Abbott and 4 were missing for DiaSorin due to insufficient sample volume. We tested 48 samples from 27 P patients using the Healgen rapid test. Ninety four percent (n = 45) displayed a positive test for IgG. Samples showed positive results with POCT from day 7 post PCR although these were still negative in the other immunoassays (SARS-CoV-2 positive at day 12).

36.3.5 Assay Concordance

Abbott and EDI had the greatest concordance with Cohen’s Kappa of 0.957 and 97.9% agreement between the all results (Table 36.4). DiaSorin was the most different, with agreements below 95%. The Healgen POCT concordance with the other assays was low (below 90%) but reflect a limited number of samples and may not be representative. Modifying the threshold to 0.8 for EDI would allow the detection of 2 more P < 14 without increasing the rate of false positive. No change in threshold in the other assay would reclassify any results without dramatically affecting the specificity to either have a high rate of false positive or false negative.

Table 36.4 Cohen’s Kappa concordance analysis of the assays and overall (all samples included) agreement of results given as % Abbott

Euroimmun

0.950 (0.019) 0.907 (0.034) 97.9% 96.6%

0.892 (0.037) 96.1%

Cohen’s Kappa (±SD) % greement

DiaSorin

Healgen

0.892 (0.027) 94.8%

0.745 (0.083) 88.2%

EDI

0.852 (0.043) 94.7%

0.631 (0.111) 82.7%

EuroImmun

0.891 (0.027) 94.8%

0.777 (0.078) 89.6%

0.485 (0.109) 76.1%

Abbott

DiaSorin

Note: Equivocal results were considered negative.

36.4 Discussion 36.4.1 Statement of Principal Findings In this head to head study we demonstrated the good performance of four commercially available serologic assays for SARS-CoV-2 and one POCT. Abbott, Epitope Diagnostics Ltd and EuroImmun demonstrated higher sensitivity and specificity than the DiaSorin assay on the same specimens. The Abbott assay showed no cross-reactivity to any other potential interfering substances tested while EDI, EuroImmun and DiaSorin cross-reacted in 1%, 3% and 4% of the sample tested.

799

800

Performance of SARS-CoV-2 Serology Tests

However, no assay cross-reacted with Influenza A and B or other coronaviruses. The analytical performance was deemed acceptable although it varied considerably between the different methods. It is estimated that there are nearly 300 different SARS-CoV-2 antibody tests in development globally ranging from POCT through to assays on large clinical laboratory analyzers. Whilst data is accruing on the sensitivity and specificity of a number of these assays [5, 6] there are still many with little or no published, independent performance evaluations. Whilst there is a focus on the diagnostic accuracy of these tests, much less is understood about the analytical performance of these devices such as imprecision and cross reactivity with common respiratory illnesses or immunoassay interferences. Without this knowledge the sensitivity and specificity data is brought into question and it is important that the limitations of assay are fully understood before applying the results in clinical practice. The Food and Drug Administration and European Medicines Agency acceptance criteria for biological assays typically define the required between-run and within-run precision as CV≤15% for positive samples and ≤20% for samples at the lower limit of quantification [11, 12]. All immunoassays passed the criteria for positive samples. Published median seroconversion time for IgG is around 14 days post symptoms [13–15]. As we did not have access to symptom onset for most patients, we used PCR day to date the samples, before and after day 14. We included in the positive group only one sample per patient, thus limiting our sample size. However, our results are not biased by repeat measurements. All samples post day 14 were positive in all assay except DiaSorin, which returned one false negative (day 39). Positivity prior to day 14 was consistent between EDI, EuroImmun and Abbott. These results are differing from those published by PHE who observed more false negative results in the Abbott than the DiaSorin (92.7% sensitivity vs 95% sensitivity, respectively) [8]. We estimated seroconversion post PCR positivity to be between 9 and 12 days on these assays. Although we couldn’t do a full comparison of the POCT with the immunoassays, 100% of the P ≥ 14 samples were IgG positive. More samples were also positive with POCT prior day 14 than in the other assays. In regard to the POCT, our study showed excellent sensitivity and specificity. We observed no false negative results on P ≥ 14 after a positive SARS-CoV-2 PCR and more samples were IgG positive P < 14 than the other immunoassays. Two potential false positive were detected (including seasonal flu) but the signal was very weak and confirmation would be necessary. The results of systematic reviews on point-of care serological tests for SARS-CoV-2 suggest discontinuing the use of the devices due to low sensitivity [5]. Our results tend to reveal a different pattern however we only performed a limited number of tests. We analyzed 50 samples collected in 2018 from patients with no known infection as negative controls. Both the EDI and the Abbott showed 100% specificity. However, EuroImmun and DiaSorin produced false positives (n = 4 and 2, respectively). Only one of these samples was common between both assays. PHE

Discussion

also showed lower specificity of the DiaSorin assay (vs Abbott). We analyzed 50 samples from patients (pre-pandemic) presenting with respiratory infection. Among those 7 had the seasonal flu, 8 had influenza A., other viruses included EBV, Varicellazoster virus, parainfluenza, Adenovirus. EDI and Abbott showed 100% specificity with no false positive; however, we observed 3 positive results with the EuroImmun, two of these also being positive with the DiaSorin. These samples were from patients with EBV (n = 1) and RSV (n = 2). Our results on EuroImmun differ slightly from a previous evaluation [16], where specificity of the assay was excellent as early as 4 days after positive PCR and only 2 of 28 samples showed borderline cross-reactivity to common human coronaviruses. None of the assays showed cross-reactivity either to the seasonal CoV flu or to Influenza A. Although it is based on a small number of sample (n = 7 for each), it brings confidence that assays will be able to discriminate SARS-CoV-2 antibodies during the next seasonal flu. Tang et al. showed similar results on 5 patients using EuroImmun and Abbott Assay [17]. A great variety of endogenous substances such as polyreactive antibodies or autoantibodies, can interfere with the reaction between analyte and reagent antibodies in immunoassays. Assays for SARS-CoV-2 are no exception. Manufacturers, and evaluation studies to date, offer a limited insight into crossreactivity of other antibodies in particular to other SARS-CoV antibodies [18–22]. A small independent study showed no cross-reactivity was seen for patients with Influenza A (n = 2), Influenza B (n = 2) and other coronaviruses (n = 5) [17]. Samples with potentially interfering antibodies did not cross-react in the Abbott Diagnostics assay, and a limited number cross-reacted in the other assays. Using the EDI assay, the signal obtained for both RA and TSI samples is significantly higher than the negative controls however all but one TSI sample remain below the cut-off of positivity. The cut-off is therefore appropriate for use with the assay with potentially cross-reactive substances. None of these samples was common between the different assays and modification of the various threshold would not improve performance of any assay. Successful attempts to treat SARS-CoV-2 patients with blood from convalescent individuals suggest antibodies against SARS-CoV-2 may have the ability to confer protective immunity to the disease [23–28]. Spike proteins are the most likely target for neutralizing antibodies are displayed on the surface of the virus whereas the nucleocapsid is contained within the viral envelope [29, 30]. Antibodies against the nucleocapsid have been shown to appear first [31, 32], followed by the production of antibodies against the spike protein [13, 14]. Therefore, assays based on the nucleocapsid detection appear to be more sensitive early on in the disease recovery but presence of anti-S1/S2 antibodies may indicate presence of neutralizing antibodies. Both the EuroImmun and the DiaSorin are targeted the spike protein of SARS-Cov-2 while the EDI and Abbott are targeted to the nucleocapsid protein of the virus. We observe a highest specificity of both nucleocapsid assays (EDI and Abbott, 100% (91–100%)) compared to the two spike assays (DiaSorin (96% (85–99%)) and EuroImmun (92% (79–97%)). Although the EuroImmun assay had the same sensitivity (all time points to PCR) as the EDI

801

802

Performance of SARS-CoV-2 Serology Tests

and Abbott, the DiaSorin assay was less sensitive (71% (73–100%) vs (81% (66–91%)), potentially supporting this hypothesis. Overall, the assays had high concordance, DiaSorin being the least identical to the others, with higher false negative and false positive, and lower performance. This is in accordance with the high false positive rate observed by Boukli et al. [33] with the DiaSorin Liaison SARS-CoV-2 IgG assay on patients with non-SARS-CoV-2 acute infections. In April 2020 both the DiaSorin assay and the Abbott assays were authorized by Public Health England for emergency use in the clinical setting; 15 sites using the Abbott method reported to the WEQAS scheme in October while 84 (Abbott Architect and Alinity) reported to the UKNEQAS scheme in November. However, 3 sites using the DiaSorin method reported to WEQAS and 9 to UKNEQAS for the same period. The same samples were analyzed on the different platforms and therefore the direct comparison is possible. However, one needs to consider the potential variance in antigen as the Wuhan strain has evolved as geographic spread has occurred between the different regions of the globe (GISAID) [34] and it is possible that these differences will not be seen on a different set of samples. Harmonization of the assays is necessary but will be near impossible with such variation between assay designs (spike vs nucleocapsid). The Wales External Quality Assessment Scheme (WEQAS, UK, https://www.weqas.com/) and the UK NEQAS (https://ukneqas.org. uk/) are now offering a SARS-CoV-2 antibody external quality assessment (EQA) program for laboratories which will reduce uncertainty associated with different methods.

36.4.2 Strengths and Limitations of This Study

The main strength of our study is the direct comparison (same specimens) of five SARS-CoV-2 assays and the analysis of potentially cross-reactive substances produced during other respiratory infections and disorders such as rheumatoid arthritis and thyroid imbalance which are known to affect immunoassays. Limitations include the limited number of positive samples due to the UK East Anglian region’s low prevalence and unavailability of onset date of SARS-CoV-2 symptoms. PCR may have been done from symptom onset day to several days post symptom; therefore, we based our seroconversion on PCR-positive date. The severity of symptoms was not available for all patients however these patients were hospitalized and we cannot comment on whether the production of antibodies correlate with the severity of symptoms.

36.4.3 Conclusion and Policy Implications

The role of serology testing in the management of people with SARS-CoV-2 infection will remain controversial until we have clear data that enables an understanding of how production of IgG relates to immunity over time and whether or not the presence or absence of antibodies can inform risk of future infection. Whilst the

Supporting Information

clinical utility of serology tested is debated, it is important that the diagnostic and analytical performance of these tests is understood and adequate for need so that there can be confidence in the results when a meaningful clinical use is determined. Without high quality analytical testing the clinical application of serology testing in the future is not viable. This study examines the performance of four commercially available serologic assays for SARS-CoV-2 in a head to head study. Our study demonstrated good analytical performance for all of the assays, however we observed Abbott, EDI and EuroImmun demonstrated higher sensitivity and specificity than the DiaSorin assay in this study. Whilst a full evaluation was not possible the P14+ samples from the main study were used in a sub analysis using the Healgen POCT device which showed 100% specificity, this contradicts earlier studies [5, 6] and indicates that the evolution of the quality of POC devices has been rapid and some may now demonstrate adequate performance for antibody detection. Assays showed 0–4% cross-reactivity, however none with Influenza viruses. This may give increase confidence of the test during the seasonal flu period. We observed differences between the assay responses with DiaSorin being the most different from the other three. We hypothesize that these differences may be linked to the design of the assay themselves (spike glycoprotein or nucleocapsid) and the timeline of production of antibodies for either antigen. We also suggested the possibility that the antigen plasticity and the antigen used when the manufacturer set up the test may influence the sensitivity of the CoV-2 assays. These findings highlight the importance of following the evolution of the antibody production and evolution of the virus over time. But it also highlights how harmonization of the assays will be complex.

36.5 Supporting Information

Table 36.S1 Characteristics of the immunoassays evaluated, as provided by the manufacturers Assay and manufacturer

Analyser used Viral target and (if any) antibody type Manufacturer’s thresholds (R)

Epitope Diagnostics Agility (Dynex) Nucleocapsid protein, IgG Ltd. SARS-CoV-2 ELISA

R = ODsample/1.1 × (NC) + 0.18) NC = mean of negative control OD R < 1 : negative

EuroImmun SARS-CoV-2 ELISA

Manual

Spike protein S1 IgG

R ≥ 1 : positive ODsample/ODcal

R < 0.8 : negative

0.8 ≤ R < 1.1: equivocal R ≥ 1 : positive

(Continued)

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Table 36.S1 (Continued) Assay and manufacturer

Analyser used Viral target and (if any) antibody type Manufacturer’s thresholds (R)

DiaSorin Laison SARS-CoV-2 S1/S2 IgG Immunoassay

Liaison XL

Abbott Diagnostics SARS-CoV-2 Immunoassay

Alinity

Healgen COVID-19 IgG/IgM Rapid Test

Cassette provided

Abbreviations anti-CCP: CLSI: CR: CV: EBV: EQA: IgG: LFIAs: N: OD: P: POCT: R: RA: RLU: S1/S2: TSI:

Spike protein S1/S2 IgG Nucleocapsid protein, IgG

3-points calibration curve

R < 12 AU/mL: negative

12 ≤ R < 15 AU/mL: equivocal ≥15: positive

ODsample/ODcal

R < 1.4: negative R ≥ 1.4: positive

IgG band present = G positive IgG/IgM– specificity of IgM band present = M positive antigen not given

cyclic citrullinated peptide antibodies Clinical and Laboratory Standards Institute cross-reactivity coefficient of variation expressed as percentage Epstein Barr Virus external quality assessment immunoglobulin G lateral flow immunoassays negative control optical density SARS-CoV-2 positive point of care testing threshold of positivity rheumatoid arthritis Relative Light Unit spike protein 1 and 2 thyroid stimulating immunoglobulin

Disclosures and Conflict of Interest

This chapter was originally published as: Piec, I., English, E., Thomas, M. A., Dervisevic, S., Fraser, W .D., John, W. G. (2021). Performance of SARS-CoV-2 serology tests: Are they good enough? PLoS ONE, 16(2): e0245914, https://doi. org/10.1371/journal.pone.0245914, under the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates.

References

Data Availability: All relevant data are within the chapter.

Funding: The authors received no specific financial support for this study. They

acknowledge the Norfolk, Suffolk, Essex and Bedfordshire Freemasons for their

generous material support in providing funding for some equipment used in this

study. Author Contributions:

Conceptualization: Isabelle Piec, Emma English, Samir Dervisevic, William D.

Fraser, William Garry John; Data curation: Isabelle Piec; Formal analysis: Isabelle Piec; Funding acquisition: Samir Dervisevic, William Garry John; Investigation: Isabelle Piec; Methodology: Isabelle Piec, Emma English, William D. Fraser, William Garry John; Project administration: Isabelle Piec, Emma English, Samir Dervisevic; Resources: Isabelle Piec, Mary Annette Thomas, Samir Dervisevic, William Garry John; Supervision: William Garry John; Validation: Isabelle Piec; Writing—original draft: Isabelle Piec; Writing—review & editing: Emma English, Mary Annette Thomas, Samir Dervisevic, William D. Fraser, William Garry John. Competing interests: The authors acknowledge the Norfolk, Suffolk, Essex and Bedfordshire Freemasons for their material support in providing funding for some equipment used in this study. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Acknowledgments: The authors would like to thank the staff at the Norfolk and Norwich University Hospital and the Queen Elizabeth Hospital, Kings Lynn who collected the samples from SARS-CoV-2 patients, in particular Christopher Jeanes, and the Norfolk Arthritis Register (NOAR) for kindly providing historical samples of patients with inflammatory polyarthritis. We would also like to express our gratitude to Myra Del Rosario and Simon Clements who analyzed the samples on the Abbott Alinity, and Christopher McDonnell and Reenesh Prakash who procured EuroImmun kits.

References

1. Chang MS, Lu YT, Ho ST, Wu CC, Wei TY, Chen CJ, et al. Antibody detection of SARS-CoV spike and nucleocapsid protein. Biochem Biophys Res Commun. 2004;314(4):931–6.

2. Woelfel R, Corman VM, Guggemos W, Seilmaier M, Zange S, Mueller MA, et al. Clinical presentation and virological assessment of hospitalized cases of coronavirus disease 2019 in a travel-associated transmission cluster. medRxiv. 2020;2020.03.05.20030502.

3. Long QX, Liu BZ, Deng HJ, Wu GC, Deng K, Chen YK, et al. Antibody responses to SARS-CoV-2 in patients with COVID-19. Nat Med. 2020;26(6):845–8.

4. Okba NMA, Müller MA, Li W, Wang C, GeurtsvanKessel CH, Corman VM, et al. Severe acute respiratory syndrome coronavirus 2-specific antibody responses in coronavirus disease patients. Emerg Infect Dis. 2020;26(7):1478–88.

5. Deeks JJ, Dinnes J, Takwoingi Y, Davenport C, Spijker R, Taylor-Phillips S, et al. Antibody tests for identification of current and past infection with SARS-CoV-2. Cochrane Database Syst Rev. 2020;6(6):CD013652.

805

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Performance of SARS-CoV-2 Serology Tests

6. Lisboa Bastos M, Tavaziva G, Abidi SK, Campbell JR, Haraoui L-PP, Johnston JC, et al. Diagnostic accuracy of serological tests for covid-19: systematic review and metaanalysis. BMJ. 2020;370:m2516. Available at: http://www.bmj.com/lookup/doi/10.1136/ bmj.m2516 (accessed on April 28, 2021).

7. UK Medicines and Healthcare products Regulatory Agency (MHRA). Target product profile: enzyme immunoassay (EIA) antibody tests to help determine if people have antibodies to SARS-CoV-2. Available at: https://www.gov.uk/government/publications/ how-tests-and-testing-k its-for-coronavirus-covid-19-work /target -product-profil e enzyme-immunoassay-eia-antibody-tests-to-help-determine-if-people-have-antibodiesto-sars-cov-2 (accessed on June 2, 2021).

8. Public Health England (London). Evaluation of sensitivity and specificity of four commercially available SARS-CoV-2 antibody immunoassays. 2020. Available at: https:// assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_ data/file/8 98437/Evaluation_ _of_sensitivity_ and _specificity_ of_ 4_ commercially_ available_SARS-CoV-2_antibody_immunoassays.pdf (accessed on May 6, 2021).

9. Duong YT, Wright CG, Justman J. Antibody testing for coronavirus disease 2019: Not ready for prime time. BMJ. 2020;370:m2655.

10. Neill Carey R, Paul Durham A, Hauck WW, Kallner A, Kondratovich MV., Guy Middle J, et al. EP15-A3 User Verification of Precision and Estimation of Bias, 3rd ed. Clinical and Laboratory Standards Institute, Maryland, USA 2014.

11. Kaza M, Karaźniewicz-Łada M, Kosicka K, Siemiątkowska A, Rudzki PJ. Bioanalytical method validation: new FDA guidance vs. EMA guideline. Better or worse? J Pharm Biomed Anal. 2019;165:381–5.

12. Center for Drug Evaluation and Research. Bioanalytical method validation guidance for industry bioanalytical method validation. FDA Guid Ind. 2018;(May):1–22. Available at: http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ default.htm and/orhttp://www.fd a.gov/AnimalV eter inar y/Guid anceC ompliance Enforcement/GuidanceforIndustry/default.htm (accessed on April 28, 2021).

13. Guo L, Ren L, Yang S, Xiao M, Chang D, Yang F, et al. Profiling early humoral response to diagnose novel coronavirus disease (COVID-19). Clin Infect Dis. 2020;71(15):778–85.

14. Zhao J, Yuan Q, Wang H, Liu W, Liao X, Su Y, et al. Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019. Clin Infect Dis. 2020;71(16):2027–34.

15. Burbelo PD, Riedo FX, Morishima C, Rawlings S, Smith D, Das S, et al. Sensitivity in detection of antibodies to nucleocapsid and spike proteins of severe acute respiratory syndrome coronavirus 2 in patients with coronavirus disease 2019. J Infect Dis. 2020;222(2):206–13.

16. Beavis KG, Matushek SM, Abeleda APF, Bethel C, Hunt C, Gillen S, et al. Evaluation of the EUROIMMUN anti-SARS-CoV-2 ELISA assay for detection of IgA and IgG antibodies. J Clin Virol. 2020;129:104468. 17. Tang MS, Hock KG, Logsdon NM, Hayes JE, Gronowski AM, Anderson NW, et al. Clinical performance of two SARS-CoV-2 serologic assays. Clin Chem. 2020;66(8):1055-62.

18. Zheng Z, Monteil VM, Maurer-Stroh S, Yew CW, Leong C, Mohd-Ismail NK, et al. Monoclonal antibodies for the S2 subunit of spike of SARS-CoV-1 cross-react with the newly-emerged SARS-CoV-2. Euro Surveill. 2020;25(28):2000291.

References

19. Patrick DM, Petric M, Skowronski DM, Guasparini R, Booth TF, Krajden M, et al. An outbreak of human coronavirus OC43 infection and serological cross-reactivity with SARS coronavirus. Can J Infect Dis Med Microbiol. 2006;17(6):330–6.

20. Lv H, Wu NC, Tsang OTY, Yuan M, Perera RAPM, Leung WS, et al. Cross-reactive antibody response between SARS-CoV-2 and SARS-CoV infections. Cell Rep. 2020;31(9):107725. 21. Che XY, Qiu LW, Liao ZY, Di Wang Y, Wen K, Pan YX, et al. Antigenic cross-reactivity between severe acute respiratory syndrome-associated coronavirus and human coronaviruses 229E and OC43. J Infect Dis. 2005;191(12):2033–7.

22. Chan KH, Cheng VCC, Woo PCY, Lau SKP, Poon LLM, Guan Y, et al. Serological responses in patients with severe acute respiratory syndrome coronavirus infection and cross-reactivity with human coronaviruses 229E, OC43, and NL63. Clin Diagn Lab Immunol. 2005;12(11):1317–21.

23. Hegerova L, Gooley T, Sweerus KA, Maree CL, Bailey N, Bailey M, et al. Use of convalescent plasma in hospitalized patients with COVID-19—case series. Blood. 2020;136(6): 759–62.

24. Duan K, Liu B, Li C, Zhang H, Yu T, Qu J, et al. Effectiveness of convalescent plasma therapy in severe COVID-19 patients. Proc Natl Acad Sci U S A. 2020;117(17):9490–6. 25. Shen C, Wang Z, Zhao F, Yang Y, Li J, Yuan J, et al. Treatment of 5 Critically Ill Patients with COVID-19 with Convalescent Plasma. JAMA. 2020;323(16):1582–9.

26. Zhang B, Liu S, Tan T, Huang W, Dong Y, Chen L, et al. Treatment with convalescent plasma for critically ill patients with severe acute respiratory syndrome coronavirus 2 infection. Chest. 2020;158(1):e9–13.

27. Ahn JY, Sohn Y, Lee SH, Cho Y, Hyun JH, Baek YJ, et al. Use of convalescent plasma therapy in two COVID-19 patients with acute respiratory distress syndrome in Korea. J Korean Med Sci. 2020;35(14):e149.

28. Ye M, Fu D, Ren Y, Wang F, Wang D, Zhang F, et al. Treatment with convalescent plasma for COVID-19 patients in Wuhan, China. J Med Virol. 2020;92(10):1890–1901. 29. Wang C, Li W, Drabek D, Okba NMA, van Haperen R, Osterhaus ADME, et al. A human monoclonal antibody blocking SARS-CoV-2 infection. Nat Commun. 2020;11, 2251.

30. Ni L, Ye F, Cheng ML, Feng Y, Deng YQ, Zhao H, et al. Detection of SARS-CoV-2-specific humoral and cellular immunity in COVID-19 convalescent individuals. Immunity. 2020;52(6):971–7.e3.

31. To KKW, Tsang OTY, Leung WS, Tam AR, Wu TC, Lung DC, et al. Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study. Lancet Infect Dis. 2020;20(5):565–74.

32. Zou L, Ruan F, Huang M, Liang L, Huang H, Hong Z, et al. SARS-CoV-2 viral load in upper respiratory specimens of infected patients. New Engl J Med. 2020;382(12):1177–9.

33. Boukli N, Le Mene M, Schnuriger A, Cuervo NS, Laroche C, Morand-Joubert L, et al. High incidence of false positive results in patients with other acute infections, using the LIAISON® SARS-CoV-2 commercial chemiluminescent micro-particle immunoassay for detection of IgG anti SARS-CoV-2 antibodies. J Clin Microbiol. 2020;58(11):10.1128/ jcm.01352-20.

807

808

Performance of SARS-CoV-2 Serology Tests

34. GISAID. Genomic epidemiology of hCoV-19. Available at: https://www.gisaid.org/ phylodynamics/ (accessed on June 2, 2021).

Chapter 37

Forecasting the Novel Coronavirus COVID-19 Fotios Petropoulos, DEng,a and Spyros Makridakis, PhDb aSchool

of Management, University of Bath, Bath, UK for the Future, University of Nicosia, Nicosia, Cyprus

bInstitute

[email protected]

Keywords: forecasting, diagnosis, epidemic, predictions, modeling, statistics, coronavirus, COVID-19, cognitive bias, uncertainty, decision making

37.1 Introduction The accuracy of traditional forecasting largely depends on the availability of data to base its predictions and estimates of uncertainty. In outbreaks of epidemics there is no data at all in the beginning and then limited as time passes, making predictions widely uncertain. Last February, a New York Times article [1] cautioned against excessive optimism about the crisis peaking, even though there were close to 50 days since the virus had been identified. Besides, there are concerns that the data may not be reliable, as was the case of bird flu and SARS when the number of affected people and deaths were misreported to hide the extent of the epidemic. Similarly, in the case of coronavirus disease 2019 (COVID-19), the reporting did not reflect the correct numbers as well when on the February 13 a new category of “clinically diagnosed” was added to “lab-confirmed” ones [2]. Such problems decrease forecasting accuracy and increase uncertainty, making the drawing of definite conclusions more difficult. Related to forecasting accuracy and uncertainty, there is a more severe problem that has to do the perception of epidemics and pandemics. Politicians are Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

Copyright © 2022 Jenny Stanford Publishing Pte. Ltd.

ISBN 978-981-4877-84-8 (Hardcover), 978-1-003-18043-2 (eBook)

www.jennystanford.com

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concerned with regard to the measures to be taken while the general population fears about the impact on the epidemic on their health/lives. Furthermore, the pharmaceutical firms are working on vaccinations for the new virus with considerable commercial interest. This was the case with SARS when governments persuaded on the severity of the virus bought large numbers of vaccines that were never used as its spread stopped without the need to vaccinate people. Of course, the big problem is the asymmetry of risks and the irrational fear of a pandemic with its possible catastrophic consequences, as happened with the 1918 Spanish flu that killed an estimated 50 million worldwide. In contrast, the SARS killed a total of 774 in 2003 and the bird flu around 100 in 1997. COVID-19 has resulted in an estimated 1,839,660 deaths as of January 4, 2021. At the same time, there is much less concern over the seasonal flu that kills about 646,000 people worldwide each year [3]. Medical predictions are often not accurate while their uncertainty is seriously underestimated [4]. Predicting the future of epidemics and pandemics is much more difficult as the number of cases to be studied can be measured in one hand. At one end of the scale is the case of SARS where the fear of becoming a pandemic was overblown, resulting in overspending and the application of restrictive measures to be contained that it turned out to be unnecessary. At the other end is the Spanish flu that turned out to be a serious pandemic with catastrophic consequences, arguably in a different era when communication and the ability to raise public awareness (and possibly exaggerated fear) were limited. Despite the inaccuracies associated with medical predictions, still forecasting is invaluable in allowing us to better understand the current situation and plan for the future. In this chapter, we provide statistical forecasts for the confirmed cases of COVID-19 using robust time series models, and we analyse the trajectory of recovered cases.

37.2 Analysis and Forecasting

We focus on the cumulative daily figures aggregated globally of the three main variables of interest: confirmed cases, deaths and recoveries. These were retrieved by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19 accessed on 12/03/2020) and are presented in Fig. 37.1. The data refer to daily cumulative cases and cover the period from January 22, 2020 until March 11, 2020. We include both “lab-confirmed” and “clinically diagnosed” cases. We emphasise the importance of the recovered cases, which is not covered in media as widely as the confirmed cases or the deaths. While all three data patterns show an exponential increase, the trends of both the confirmed cases and the deaths were reduced in the mid of February; a second exponential increase is observed in late February and March as a result of the increased number of cases in South Korea, Iran, and Europe. At the same time, the number of recovered cases is steadily increasing.

Analysis and Forecasting

Figure 37.1  Daily  cumulative  confirmed,  deaths  and  recovered  cases  from  COVID-19  from  a  span of a few months in 2020.

To  forecast  confirmed  cases  of  COVID-19,  we  adopt  simple  time  series  forecasting  approaches.  We  produce  forecasts  using  models  from  the  exponential  smoothing  family  [5,  6].  This  family  has  shown  good  forecast  accuracy  over  several  forecasting  competitions  [7–9]  and  is  especially  suitable  for  short  series.  Exponential  smoothing  models  can  capture  a  variety  of  trend  and  seasonal  forecasting  patterns  (such  as  additive  or  multiplicative)  and  combinations  of  those.  We  limit  our  attention  to  trended  and  non-seasonal  models,  given  the  patterns  observed  in  Fig.  37.1.  Note  that  we  follow  a  pragmatic  approach  in  that  we  assume  that  the  trend  will  continue  indefinitely  in  the  future.  This  approach  contradicts  other  modelling  approaches  to  COVID-19  using  an  S-Curve  model  (logistics curve) that assumes convergence. While  statistical  approaches  to  model  selection  (such  as  information  criteria,  which  measure  the  maximum  likelihood  of  a  model  while  penalising  for  its  complexity)  could  be  used,  we  judgmentally  select  a  model  [10]  to  better  reflect  the  nature  of  the  data.  We  opt  for  an  exponential  smoothing  model  with  multiplicative  error  and  multiplicative  trend  components.  Even  if  in  some  cases  an  additive  trend  model  gave  lower  information  criteria  values,  we  opted  for  the  multiplicative  trend  model  given  the  asymmetric  risks  involved  as  we  believe  that it is better to err to the positive direction. We  produce  10-days-ahead  point  forecasts  and  prediction  intervals  and  update  our  forecasts  every  ten  days.  Please  note  that  this  is  not  an  ex-post  analysis, but  a real, live forecasting  exercise.  We  have  been posting and evaluating  our  forecasts  publicly  in  social  media  (please,  refer  to  the  Twitter  accounts  of  the authors, @fotpetr and @spyrosmakrid).

First round of forecasts: 01/02/2020 till 10/02/2020

We  first  started  at  the  end  of  January  31,  2020  and  only  had  ten  actual  data points  in  hand.  We  decided  to  use  a  multiplicative  trend  exponential  smoothing  model.  The  forecasts  (and  90%  prediction  intervals)  produced  at  the  end  of  31/01/2020  are  presented  in  Fig.  37.2  with  red  (and  pink)  colour. 

811

Figure 37.2 Cumulative actual confirmed cases of COVID-19, together with forecast and prediction intervals produced over several origins. The y-axis is log-scaled.

812 Forecasting the Novel Coronavirus COVID-19

Analysis and Forecasting

Note that the vertical axis is log-scaled. The mean estimate (point forecast) for the confirmed cases 10-days-ahead was 209 thousand with the 90% prediction intervals ranging from about 38 to 534 thousand cases. The actual confirmed cases on 10/02/2020 were just under 43 thousand. We observe a considerable forecast error from the mean estimate equal to 166 thousand cases (an absolute percentage error of 388%), with the forecasts being extremely positively biased. Still, the actual cases lie within the prediction intervals.

Second round of forecasts: 11/02/2020 till 20/02/2020

Then, we increased the historical number of our data to include cases up to the end of February 10, 2020 (20 data points). We once again produced 10-days-ahead predictions. The forecasts and prediction intervals are depicted in Fig. 37.2 with blue colour. We observe that the actual values for the period 11/02/2020 until 20/02/2020 closely follow the mean estimate. The forecast error on 20/02/2020 was 5.8 thousand cases (an absolute percentage error of 7.7%). This was despite the change that was made on 13/02/2020 with regard to how confirmed cases are recorded to now include “clinically diagnosed” instances as opposed to exclusively lab-confirmed. One crucial observation is that this more accurate forecast came with a significant decrease in the steepness of the slope compared to the forecast for the previous ten-day period. Another observation is that at the end of 20/02/2020, we were still over-forecasting the number of confirmed cases. Finally, all actual values lied well inside the prediction intervals range.

Third round of forecasts: 21/02/2020 till 01/03/2020

We produced a third set of forecasts and prediction intervals using the data up until 20/02/2020. The forecasts are presented in Fig. 37.2 with green colour. The mean estimate for ten days ahead (01/03/2020) was 83 thousand cases. The slope of the forecasts was, once again, lower compared to the previous two sets of forecasts, confirming the fact that the observed confirmed cases (up until 20/02/2020) show a steady decrease. We also observed a significant decrease in the associated forecast uncertainty, with the prediction intervals being much tighter compared to our past forecasts. The 90% prediction intervals worst-case scenario was about 600 thousand cases, which is halved compared to that of the last round of forecasts (1.2 million cases). The actual confirmed cases at the end of 01/03/2020 were 88 thousand. At the end of this third round of forecasts, we recorded an error of 5.5 thousand cases (6.2%). While this error was lower than the previous round (in both absolute and percentage terms), it was the first time that our 10-day ahead forecast were below the actual values (under-forecasting). This was because the virus had been spreading in three countries outside Mainland China (South Korea, Iran, and Italy).

Fourth round of forecasts: 02/03/2020 till 11/03/2020

Our fourth round of forecasts is shown in Fig. 37.2 with purple colour. The mean estimate for 11/03/2020 was 112 thousand confirmed cases, with the uncertainty

813

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Forecasting the Novel Coronavirus COVID-19

levels being similar to the previous round: There was a 5% chance that they would exceed 613 thousand by the end of 11/03/2020. The observed actual confirmed cases at the end of this period were almost 127 thousand. The absolute forecast error at the end of the last period (11/03/2020) was 15.4K (12.1%), higher compared to the previous set of forecasts but still well within the prediction intervals. For the second round in a row, we were consistently under-forecasting the actual cases. This was due to the exponential increase of the confirmed cases mostly in Europe, Iran and the US, with South Korea managing to decrease the number of new daily cases significantly.

Fifth round of forecasts: 12/03/2020 till 21/03/2020

We produced a final set of forecasts and prediction intervals using the most recent data, up until 11/03/2020. These are presented in Fig. 37.2 with yellow/ gold colour. Note that we estimated three levels of uncertainty (50, 70 and 90%). The trend of our forecasts is much increased compared to the last two rounds: We predict 83 thousand new cases for this round (a total of 210 thousand cases). The associated levels of uncertainty are also increased: There is a 25% chance that the total confirmed cases will exceed 413 thousand by the end of 21/03/2020; and a 5% chance that they will exceed 1.19M. We also attempted to produce forecasts by splitting the recorded confirmed cases into two groups: cases within Mainland China and cases anywhere else, as the trends into these two groups are different. We fitted independent exponential smoothing models, and then we summed up the forecasts (bottom-up hierarchical forecasting). We notice that using this approach, the mean estimate is close to that if all data are considered together (207 versus 210 thousand cases). However, the estimated uncertainty by splitting the data is considerably lower, possibly since the confirmed cases outside Mainland China have significantly increased only recently.

Recovered cases

We next turn our attention to the recovered cases that have received little attention until now. We focus on the number of the recovered cases as a percentage of the total confirmed cases as well as the ratio of recovered cases versus deaths. We are particularly interested in the trajectory of these two ratios. Fig. 37.3 presents this analysis. First, we observe the solid relationship between the two curves. Second, we notice that despite the very small percentages of recovered cases until the end of January (less than 2%), currently, about 1 out of 2 confirmed cases have recovered (52.8% of the total confirmed cases). Moreover, the ratio of recovered cases versus deaths is currently above 14:1. Despite this, we observe a reverse of both curves since 08/03/2020, which is associated with the increasing number of cases outside Mainland China.

Discussion and Conclusion

Figure 37.3 Recoveries as a percentage of the total confirmed cases and recovered cases per death over time.

37.3 Discussion and Conclusion

The uncertainty surrounding an unknown, novel coronavirus can spark a global alarm, leading a Harvard Professor stating that 40-70% of the global population might be infected in the coming year [11] which matches Chancellor Angela Merkel’s warning regarding the effects of the novel coronavirus in Germany [12]. Norman, Bar-Yam and Taleb [13] discuss the systemic risk of pandemics, the existence of fat-tailed processes due to global interconnectivity and the negatively biased estimates of spread, reproduction and mortality rates. On the opposite side, others are arguing about people overly panicking [14] and neglecting the probabilities [15, 16] with the new virus being the first “infodemic” as a result of the hyper-connectivity offered by today’s social media [17, 18]. The polarisation of the opinions globally can be summarised by the quotes of three renowned personalities: • Elon Musk: “The coronavirus panic is dumb” [19]. • Nassim Nicholas Taleb: “Saying the coronavirus panic is dumb is dumb” [20]. • Bill Gates: “I hope it’s not that bad, but we should assume it will be until we know otherwise.” [21].

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Regardless of what one’s beliefs are, we believe that forecasts and their associated uncertainty can and should be an integral part of the decision-making process, especially in high-risk cases. Apart from the significant public health concerns, the dangers imposed on global supply chains and the economy as a whole are also considerable [22, 23]. Risk-averse people can focus on the worst-casescenarios and act accordingly. Deciding to discard any formal, statistical forecasts and acting conservatively, still implies an underlying forecasting process, even if this process is not formalised (personal judgment/belief). In this exercise, we used univariate time series models, which assume that the data is accurate and past patterns (including precautionary measures) will continue to apply. Significant, consistent forecast errors (potentially spanning outside the prediction intervals) should be associated with changes in the observed patterns and the need for additional actions and measures in the case of negatively biased forecasts. We believe that the significant forecast error at the end of the first forecast period (from 01/02/2020 to 10/20/2020) as depicted in Fig. 37.2 could be the result of two factors:

• While the forecasts that we produced using the data up until 31/01/2020 would be a good estimate in the scenario of “business-as-usual” (nothing changes), they disregard the fact that the world will act to get the virus under control. The Chinese authorities managed to rapidly construct two new hospitals, in Huoshenshan and Leishenshan areas in Wuhan, that opened on 03/02/2020 and 08/02/2020 respectively. Multiple commuting restrictions were applied both within China and internationally. The World Health Organisation helped in creating awareness of the novel virus. So, the decline in the spread of the COVID-19 during this first round could well be linked with these attempts from local and global authorities. • There may be a “garbage-in, garbage-out” situation. As mentioned above, our analysis and forecasts assumed that the data are accurate. It could be the case that the positive bias of the first-period forecasts is not as significant as it seems dues to potential inaccuracies in the actual data and the underaccounting of confirmed cases. This is especially true given the delay effects in diagnosing COVID-19 cases [24].

Our second and third sets of forecasts that cover the period 11/02/2020 to 01/03/2020 were very close to the recorded confirmed cases (the forecast error was lower than 6 thousand cases at the end of each 10-day period). The slowing down of the trend during this period suggested that COVID-19 would not cause any serious problems, particularly outside of Mainland China. Unfortunately, that was not the case. The last two sets of forecasts that cover the period 02/03/2020 to 21/03/2020 show a significant increase in the trend of cases globally coupled with an increase in the associated uncertainty. We hope that our forecasts will be a useful tool for governments and individuals towards making decisions and taking the appropriate actions to contain the spreading of the virus to the degree possible.

References

Disclosures and Conflict of Interest This chapter was originally published as: Petropoulos, F., Makridakis, S. (2020). Forecasting the novel coronavirus COVID-19. PLoS ONE 15(3), e0231236, https:// doi.org/10.1371/journal.pone.0231236, under the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). It appears here with edits and updates, by kind permission of the copyright holder. Data Availability Statement: The data were retrieved by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University: https://github.com/ CSSEGISandData/COVID-19 (accessed on 12/03/2020). Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Author contributions: conceptualization: Fotios Petropoulos, Spyros Makridakis; data curation: Fotios Petropoulos; formal analysis: Fotios Petropoulos; funding acquisition: Spyros Makridakis; investigation: Fotios Petropoulos, Spyros Makridakis; methodology: Fotios Petropoulos, Spyros Makridakis; project administration: Spyros Makridakis; software: Fotios Petropoulos; validation: Fotios Petropoulos; visualization: Fotios Petropoulos; writing—original draft: Fotios Petropoulos, Spyros Makridakis; writing—review & editing: Fotios Petropoulos, Spyros Makridakis.

References

1. Wang V. Coronavirus epidemic keeps growing, but spread in China slows. New York Times. Available at: https://www.nytimes.com/2020/02/18/world/asia/chinacoronavirus-cases.html?referringSource=articleShare (accessed on January 4, 2021).

2. BBC. Coronavirus: Sharp increase in deaths and cases in Hubei. Available at: https://www.bbc.co.uk/news/world-asia-china-51482994 (accessed on January 4, 2021).

3. Medicine Net. Flu kills 646,000 people worldwide each year: Study finds. Available at: https://www.medicinenet.com/script/main/art.asp?articlekey=208914 (accessed on January 4, 2021).

4. Makridakis S, Wakefield A, Kirkham R. Predicting medical risks and appreciating uncertainty. Foresight: The International Journal of Applied Forecasting. 2019; 52: 28–35.

5. Hyndman RJ, Koehler AB, Snyder RD, Grose S. A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting. 2002; 18(3): 439–454.

6. Taylor JW. Exponential smoothing with a damped multiplicative trend. International Journal of Forecasting. 2003; 19(4): 715–725.

7. Makridakis S, Andersen A, Carbone R, Fildes R, Hibon M, Lewandowski R, et al. The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting. 1982; 1(2): 111–153.

8. Makridakis S, Hibon M. The M3-Competition: results, conclusions and implications. International Journal of Forecasting. 2000; 16(4): 451–476.

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9. Makridakis S, Spiliotis E, Assimakopoulos V. The M4 competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting. 2020; 36(1): 54–74.

10. Petropoulos F, Kourentzes N, Nikolopoulos K, Siemsen E. Judgmental selection of forecasting models. Journal of Operations Management. 2018; 60: 34–46.

11. Nash C. Harvard professor sounds alarm on ‘likely’ coronavirus pandemic: 40% to 70% of world could be infected this year. Mediaite. Available at: https://www.mediaite.com/ ne ws/harvard-pr ofessor-sound s-alar m-on-li kely-c o r onav irus-pandemic-40-to-70of-world-could-be-infected-this-year/ (accessed on January 4, 2021).

12. BBC News. Coronavirus: Up to 70% of Germany could become infected—Merkel. Available at: https://www.bbc.co.uk/news/world-us-canada-51835856 (accessed on January 4, 2021).

13. Norman J, Bar-Yam Y, Taleb NN. Systemic risk of pandemic via novel pathogenscoronavirus: A note. New England Complex Systems Institute (January 26, 2020). Available at: https://necsi.edu/systemic-risk-of-pandemic-via-novel-pathogenscoronavirus-a-note (accessed January 4, 2021).

14. Salzberg S. Forbes. Coronavirus: There are better things to do than panic. Available at: https://www.for bes.com/sites/stevensalzber g/2020/02/29/coronavirus-time-to panic-yet (accessed on January 4, 2021). 15. Sunstein CR. The cognitive bias that makes us panic about coronavirus. Bloomberg. Available at: https://www.bloomberg.com/opinion/articles/2020-02-28/coronaviruspanic-caused-by-probability-neglect (accessed on January 4, 2021).

16. della Cava M. Coronavirus and its global sweep stokes fear over facts. Experts say it’s unlikely to produce ‘apocalyptic scenario’. USA Today. Available at: https://eu.usatoday. com/story/news/nation/2020/02/27/coronavirus-experts-urge-sharing-facts covid-19-spreads-worldwide/4892422002/ (accessed on January 4, 2021).

17. BBC News. Coronavirus in Europe: Epidemic or ‘infodemic’?. Available at: https://www. bbc.co.uk/news/world-europe-51658511 (accessed on January 4, 2021).

18. Hao K, Basu T. The coronavirus is the first true social-media “infodemic”. MIT Technology Review. Available at: https://www.technologyreview.com/s/615184/thecoronavirus-is-the-first-true-social-media-infodemic/ (accessed on January 4, 2021).

19. Bariso J. Inc. Bill Gates and Elon Musk just issued very different responses to the coronavirus. It’s a lesson in emotional intelligence. Available at: https://www.inc.com/ justin-bariso/bill-gates-elon-musk-just-issued-very-different-responses-to-coronavirusits-a-lesson-in-emotional-intelligence.html (accessed on January 4, 2021). 20. Yahoo Finance. ‘The Black Swan’ author to Elon Musk: ‘Saying the coronavirus panic is dumb is dumb’. Available at: https://finance.yahoo.com/news/black-swan-authorelon-musk-055555539.html (accessed on January 4, 2021).

21. Gates B. Responding to Covid-19—A once-in-a-century pandemic? The New England Journal of Medicine. 2020; 382(18): 1677–1679.

22. Haren P, Simchi-Levi D. How coronavirus could impact the global supply chain by mid-March. Harvard Business Review. Available at: https://hbr.org/2020/02/howcoronavirus-could-impact-the-global-supply-chain-by-mid-march. (accessed on January 4, 2021).

References

23. Winck B. JPMorgan officially forecasts a coronavirus-driven recession will rock the US and Europe by July. Markets Insider. Available at: https://markets.businessinsider.com/ news/stocks/coronavirus-fuel-recession-forecast-us-europe-economic-july-marketjpmorgan-2020-3-1028994637 (accessed on January 4, 2021).

24. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China. Journal of the American Medical Association. 2020. Available at: https://jamanetwork.com/journals/jama/fullarticle/2762130 (accessed on January 4, 2021).

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Chapter 38

Pandemic Responses: Planning to Neutralize SARS-CoV-2 and Prepare for Future Outbreaks The PLOS Medicine Editors Public Library of Science, San Francisco, California, USA, and Cambridge, UK [email protected]

Keywords: Ebola, Middle East Respiratory Syndrome, SARS-CoV-2, severe acute respiratory syndrome, Severe Acute Respiratory Syndrome Coronavirus 2, Zika virus

In February 2020 in the course of discussing possible challenges to international health plans for the 2020s, we noted in passing that “a new coronavirus outbreak [was] emerging in Asia at the time of writing” [1]. Vividly illustrating the pace at which an infectious disease outbreak can progress among a highly interlinked and susceptible global population, in the intervening few weeks a pandemic has not only taken hold but already reached virtually every country. As of March 30, 2020, almost 700,000 cases of COVID-19 disease had been confirmed worldwide, with the severity of disease highlighted by the more than 33,000 deaths reported among those infected [2]. The severe and growing consequences of the pandemic are likely to have transformational effects on people, societies and health systems worldwide, and highlight the degree of interconnection of individuals and countries—calling for intensified and coordinated preparation and action in the future. The new pathogen, Severe Acute Respiratory Syndrome Coronavirus 2 (SARSCoV-2), is closely related to other coronaviruses, including the causative agent of the more limited SARS outbreak that occurred principally in China and Hong Kong during 2002–04 [3]. The new virus emerged in a cluster of pneumonia cases detected in Wuhan, central China in December 2019. The virus had been identified in early January and, by the time that cases had been documented in several other Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

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countries, on 20 January, the risk of a serious disease outbreak was already clear [4]. The shocking speed with which SARS-CoV-2 has spread indicates not only the vulnerability of people and communities to threats posed by infectious diseases, but the challenges of mounting swift and effective responses. As has been widely reported, the city of Wuhan and its 11 million people were “locked down” in late January, although this dramatic action was evidently too late to limit the infection’s breakneck global expansion [5]. Much remains to be learned about SARS-CoV-2 and its effects. We can surmise that the virus is readily transmitted from person to person, in a similar manner to other respiratory pathogens, before symptoms are pronounced. Disease severity seems to vary markedly, with serious respiratory diseases, including pneumonia, developing in a proportion of patients, often elderly people and those with comorbidities. It is evident from the scholarly publications so far that available treatments, including some antiviral drugs, are being employed in patients; although various clinical studies have been done and reported impressively quickly, specific drug effects are difficult to assess [6]. PLOS and other publishers have played a part in encouraging early communication of research findings, and the relevant literature is growing. It is to be hoped that clinical and supportive care is developed to a point that the uncertain but apparently substantial death toll in infected people—estimated at 0.25–3% [7]—can be mitigated in settings with sufficient health provision. Development of vaccines is, unfortunately, likely to lag behind the outbreak by a year at least. Could individual national authorities and agencies have responded better? There has been no lack of major infectious disease outbreaks in recent years, recalling SARS (2002–04), Middle East Respiratory Syndrome (MERS; 2012–19), Ebola (2013–2016 in West Africa and 2018–20 in the Democratic Republic of the Congo), Zika virus (2015–16) and others. Therefore, it has to be acknowledged that COVID-19 is, albeit disastrous, a valuable alarm call to the effect that no constituency or person can now be reliably protected from an emerging and unstudied infectious disease. It would be unwise to make specific predictions about the course and outcome of, or efforts to combat, the ongoing SARS-CoV-2 outbreak, but some initial conclusions can be made. WHO—an organization that has received harsh criticism in the past for its perceived passivity in the face of disease outbreaks—is essential and has performed creditably against the current challenge. The responses of individual national governments have received some very trenchant commentary, and to a degree this criticism can be explained by local political factors, the intense and sometimes distorting lens of news and social media, and the luxury of hindsight. Most organizations and their staff will have learnt important lessons over the course of the current outbreak, however, and there will be much debate and planning to come. Those health workers in emergency medicine and other areas must surely feel that they are working in systems thoroughly unprepared for the current crisis, and deserve enormous credit. Looking to the design of an outbreak-ready future,

References

early inferences would suggest that decisive and permanent changes in animal handling practices are needed to control, and as far as possible prevent, further zoonotic transmission events. Throughout the outbreak, there has been the impression that governments are unsure about or unprepared for the public health responses needed, and hesitant in communicating with their countries’ populations. Greater international cooperation seems essential in outbreak science and public health, and in actions to prevent disease movement between regions and countries; here, the resources and reputation of WHO can probably achieve more to foster coordinated thinking and action. The biggest challenge is that of uncertainty—how will a new virus move and manifest itself in different countries, how can potentially contradictory data and advice from models and researchers be reconciled and implemented, and how will governments manage conflicting political, economic and health priorities? SARS-CoV-2 is an unprecedented challenge, and one in a sense created by the modern world. The impact in many low- and middle-income countries and on key population groups is yet to be judged. How people and health systems respond to the current outbreak will be key not only to planning for the unknown pathogens of the future but to maintaining a stable environment for the global community’s threatened, but hopefully not erased, health plans for the 2020s and beyond [1].

Disclosures and Conflict of Interest

This chapter was originally published as: The PLOS Medicine Editors (2020). Pandemic responses: Planning to neutralize SARS-CoV-2 and prepare for future outbreaks. PLoS Med., 17(4), e1003123, https://doi.org/10.1371/journal.pmed.1003123, under the Creative Commons Attribution license (http://creativecommons.org/licenses/ by/4.0/), and appears here, with edits and updates. Funding: The authors received no specific funding for this work.

Competing interests: The authors’ individual competing interests are at http:// journals.plos.org/plosmedicine/s/staff-editors. PLOS is funded partly through manuscript publication charges, but the PLOS Medicine Editors are paid a fixed salary (their salaries are not linked to the number of papers published in the journal).

The PLOS Medicine editors are Artur Arikainen, Louise Gaynor-Brook, Thomas McBride, Adya Misra, Caitlin Moyer, Clare Stone, and Richard Turner.

References

1. The PLOS Medicine Editors. Plans and prospects for the 2020s: Beyond peak health? PLoS Med. 2020;17(2):e1003075

2. WHO. Situation report—70. Available at: https://www.who.int/docs/default-source/ coronaviruse/situation-reports/20200330-sitrep-70-covid-19.pdf?sfvrsn=7e0fe3f8_4 (accessed on May 8, 2021).

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3. WHO. Severe acute respiratory syndrome. Available at: https://www.who.int/csr/sars/ en/ (accessed on May 8, 2021).

4. WHO. Situation report—1. Available at: https://www.who.int/docs/default-source/ coronaviruse/situation-reports/20200121-sitrep-1-2019-ncov.pdf?sfvrsn=20a99c10_4 (accessed on May 8, 2021).

5. Crossley G. Wuhan lockdown ‘unprecedented’, shows commitment to contain virus: WHO representative in China. January 23, 2020. Available at: https://www.reuters.com/ article/us-china-health-who-idUSKBN1ZM1G9 (accessed on May 8, 2021). 6. Mahase E. COVID-19: what treatments are being investigated? BMJ. 2020;12:m1252.

7. Wilson N, Kvalsvig A, Telfar Barnard L, Baker MG. Case-fatality risk estimates for COVID-19 calculated by using a lag time for fatality. Emerg Infect Dis. 2020. Available at: https://wwwnc.cdc.gov/eid/article/26/6/20-0320_article (accessed on May 8, 2021).

Chapter 39

Pandemic Preparedness and Responses: WHO to Turn to in a Crisis?* The PLOS Medicine Editors Public Library of Science, San Francisco, California, USA, and Cambridge, UK [email protected]

In the thick of a global pandemic, it should be straightforward to appreciate the role and responsibilities of the World Health Organization (WHO). With a newly emerged coronavirus, SARS-CoV-2, exerting an appalling global toll in terms of lives lost, ill-health, and societal and economic disruption, the organization is a fulcrum on which all efforts to combat the COVID-19 outbreak and manage its consequences must be based. As of April 30, 2020, in excess of 3.2 million cases had been recorded, with more than 227,000 deaths attributed to the disease [1]. WHO Director-General Tedros Adhanom Ghebreyesus, who has led the agency since 2017, has been prominent in the response to the coronavirus outbreak, not least in his authoritative public appearances. Yet the rapidity of the pandemic’s growth, and the diverse and apparently tentative responses in certain countries, have created concerns in some quarters about the agency’s capabilities to advise on and respond to disease outbreaks. Indeed, on April 14, 2020 ,a short suspension of US funding for WHO was announced, prompted by alleged suppression of information about the COVID-19 outbreak during its early stages in China [2]. Little is certain about the course and possible conclusion of the current outbreak, save that the actions and attributes of WHO, its structures, and its people will be scrutinized in the minutest possible detail.

*Note from Dr. Raj Bawa, Series Editor: Although this editorial appeared in summer 2020, the perspectives continue to be critical to combating COVID-19 and other emerging microbial diseases. Hence, in this context, it is timely and of major significance. Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

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WHO is tasked with forcefully representing international resolutions, creating confidence in its unparalleled technical capacity, and acting fairly and responsibly to promote health and wellbeing in all countries, as far as is possible. These countries vary enormously in population size and structure, wealth, political objectives and many other characteristics, of course. Consequently, the element of WHO’s 1946 Constitution that posits achievement of “the enjoyment of the highest attainable state of health … [by] every human being without distinction” may—perversely given its universal appeal—be subordinated to other, more shortterm, factors [3]. It is easy to imagine situations in which attaching blame to an international body might be an attractive route to political or economic advantage. WHO’s revenue was about $2.9 billion in 2018 [4], made up of assessed and voluntary contributions, the latter category often linked to specific programmes or aims by donors. Large donors and countries are therefore likely to have, or be perceived to have, influence over its actions and announcements. WHO’s physical and human footprint is also criticized from time to time, and even ardent supporters would concede that, were the agency to be designed and launched today, its Swiss headquarters, along with 6 regional fiefdoms and 150 country offices, could well be reimagined in a much more streamlined fashion. As we discussed in an Editorial in 2016 [5], WHO’s record in previous infectious disease outbreaks has not always met with unqualified approval. The agency’s response to the 2012–2014 Ebola outbreak in West Africa, under previous Director-General Margaret Chan, was seen to have been plagued by delay and dysfunction. In the subsequent reports that investigated WHO’s perceived failings, it was noted that the organization had, for example, previously cut a substantial proportion of its emergency response capability, and “lacked the governance needed to coordinate multiple stakeholders” in the response to a disease outbreak [6]. Essentially, there was a sense that the organization had been trying to do too many things with too few resources, and making questionable strategic decisions in the process. The recent suspension of US funding for WHO has elicited criticism from many in the health arena, including PLOS [7]. Additional political manoeuvring has followed [8], and subsequently China has trumped the announcements by pledging an additional $30 million in funding, noting that WHO had been “actively fulfilling its duties and upholding an objective, scientific and impartial stance” on the disease outbreak [9]. These opportunistic political gambits could well continue in longer campaigns seeking to acquire plaudits for perceived (but at this stage perhaps ephemeral) successes in addressing the continuing outbreak, alongside creative attribution of responsibility for early, and possibly ongoing, errors and omissions in country-specific pandemic responses; wilful misinformation must also be considered as a factor. We contacted a number of commentators for their views on the emerging debate around WHO’s role in the current outbreak, and Margaret Kruk, of the Harvard T.H. Chan School of Public Health, argues that “WHO plays an indispensable

References

role in our shared health and it is one of the few institutions that is seen as credible in countries at a time that health and science are increasingly politicized. But it is hamstrung by insufficient, strings-tied funding and a governance structure that precludes its ability to speak uncomfortable truths for fear of offending member countries. The goal of reforms should be to build a technically stronger, better funded, and more independent WHO”. Although far too early to make definitive judgments about individual country or agency actions during the current pandemic, we can anticipate a frank debate about the capabilities and actions of WHO throughout this extraordinary time. Among thoughts that come to mind are, first, that political involvement with or by WHO is regrettable, with its parent organization, the United Nations, the forum for this purpose. Second, the experiences of the current pandemic need to be put to good use to prepare WHO and countries for future disease outbreaks—how do the agency’s capabilities and infrastructure, and indeed those of country public health bodies, need to be strengthened and adapted to this end? It may be that the function of WHO needs to be refocused on convening expertise and providing normative guidance for health goals, with a distinct entity, akin to UNAIDS, adopting responsibility for outbreak surveillance and responses. Finally, a global agency for health will remain essential, and all governments should seek to work with rather than counter to WHO as an essential partner in promoting the increasingly interconnected state of the world’s health.

Disclosures and Conflict of Interest

This chapter was originally published as: The PLOS Medicine Editors (2020). Pandemic preparedness and responses: WHO to turn to in a crisis? PLoS Med., 17(5), e1003167, https://doi.org/10.1371/journal.pmed.1003167, under the Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/ by/4.0/), and appears here, with edits and updates. Funding: The authors received no specific funding for this work.

Competing interests: The authors’ individual competing interests are at http:// journals.plos.org/plosmedicine/s/staff-editors. PLOS is funded partly through manuscript publication charges, but the PLOS Medicine Editors are paid a fixed salary (their salaries are not linked to the number of papers published in the journal).

The PLOS Medicine editors are Artur Arikainen, Louise Gaynor-Brook, Thomas McBride, Adya Misra, Caitlin Moyer, Clare Stone, and Richard Turner.

References

1. Johns Hopkins Coronavirus Resource Center. COVID-19 Case Tracker. Available at: https://coronavirus.jhu.edu/ (accessed on May 11, 2021).

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2. Gearan A. Trump announces cutoff of new funding for the World Health Organization over pandemic response. Washington Post, April 15, 2020. Available at: https://www. was h ingto npo s t.co m/po li tics /tr ump-announces -cuto ff -o f-ne w-f und i ng-f o r -th e wo rld -health -o r gani zatio n-o ve r-pand emic-respo ns e/20 2 0/0 4/1 4/f 1d f 101 e- 7 e9 f11ea-a3ee-13e1ae0a3571_story.html (accessed on May 11, 2021).

3. WHO Constitution. Available at: https://apps.who.int/gb/bd/pdf_files/BD_49th-en. pdf#page=7 (accessed on May 11, 2021). 4. WHO Budget. Available at: https://www.who.int/about/finances-accountability/ reports/A72_36-en.pdf?ua=1 (accessed on May 11, 2021).

5. The PLOS Medicine Editors. A global champion for health—WHO’s next? PLoS Med. 2016;13(6):e1002059.

6. Gostin LO, Tomori O, Wibulpolprasert S, Jha AK, Frenk J, Moon S, et al. Toward a common secure future: four global commissions in the wake of Ebola. PLoS Med. 2016;13(5): e1002042.

7. The PLOS Medicine Editors. We need to support the WHO, not stop its funding in the middle of a pandemic. Speaking of Medicine, April 16, 2020. Available at: https:// blogs.pl os.org/speakingofmedicine/2020/04/16/we-need-to-support-the-who not-stop-its-funding-in-the-middle-of-a-pandemic/ (accessed on May 11, 2021).

8. Wintour P, Harvey F, Beaumont P. US scuppers G20 coronavirus statement on strengthening WHO. The Guardian, April 20, 2020. Available at: https://www.theguardian. com/world/2020/apr/20/us-scuppers-g20-coronavirus-statement-on-strengtheningwho (accessed on May 11, 2021).

9. Shih G. China pledges additional $30 million funding for World Health Organization. Washington Post, April 23, 2020. Available at: https://www.washingtonpost.com/ world/asia_pacific/china-pledges-additional-30-mil lion-funding-for-world-heal thorganization/2020/04/23/24f9b680-8539-11ea-81a3-9690c9881111_st ory.htm l (accessed on May 11, 2021).

Chapter 40

Links between Integrin αvβ3 and COVID-19: Impact on Vascular and Thrombotic Risk Marwa S. Hamza, PhD,a,b and Shaker A. Mousa, PhD, MBAc aClinical Pharmacy Practice Department, Faculty of Pharmacy,

The British University in Egypt, El-Sherouk City, Cairo, Egypt

bThe Center for Drug Research and Development, Faculty of Pharmacy,

The British University in Egypt, El-Sherouk City, Cairo, Egypt

cThe Pharmaceutical Research Institute, Albany College of Pharmacy and Health Sciences,

Rensselaer, New York, USA

[email protected]

Keywords: coagulation, coagulopathy, coronavirus, coronavirus disease, severe acute respiratory syndrome coronavirus 2 virus, COVID-19, D-dimer, disseminated intravascular coagulation, integrin receptors, integrin αvβ3, L-thyroxine RGD, RGD-disintegrins, thrombosis, thyroid hormone, venous thromboembolism, vein thrombosis, pulmonary embolism, pathogen pattern recognition receptors, complement receptors, immunoglobulin Fc receptors

In Wuhan, China, a severe acute respiratory syndrome coronavirus 2 virus (SARS­ CoV-2) caused a novel coronavirus disease (COVID-19) in December 2019, which rapidly spread in China and all over the world [1, 2]. As of May 24, 2021, globally, there have been 167,836,626 coronavirus cases, 3,484,202 deaths and 149,018,862 patients have recovered [3]. Patients usually have fever, cough, myalgia, or tiredness [1, 4]. Males with a median age of 50 years are most commonly affected [1, 4, 5]. The full presentation of COVID-19 symptoms and complications is not entirely explained. Clinical manifestations include asymptomatic or extremely mild disease to severe illness, sepsis, and mortality. While the current evidence suggests that most COVID-19 infections are mild, Guan et al. (2020) indicate that 16% of cases with COVID-19 are suffering from severe illness [4] and all severe and critically ill COVID-19 patients have a high risk of venous thromboembolism (VTE) [6]. Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

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Venus thromboembolism includes deep vein thrombosis (DVT) and pulmonary embolism (PE) [7]. There are several predisposing environmental and genetic factors underlying VTE such as reduction in blood flow, vascular injury, or hypercoagulability [8]. The most important clinical risk factors for VTE include age [9], male gender [10], obesity [11], malignant disease [12], surgery, trauma, and immobilization [13]. VTE is associated with a number of potentially serious complications such as post-thrombotic syndrome, the development of PE after DVT, chronic pulmonary hypertension, bleeding, and drug-induced thrombocytopenia [14]. The dysregulation of the coagulation cascade and subsequent intra-alveolar or systemic fibrin coagulation may be due to prothrombotic response, which prevents diffuse alveolar hemorrhage and leads to hypercoagulation with negative effects in patient recovery and survival [15]. These thrombotic complications tend to be a major problem in COVID-19 patients. Preliminary COVID-19 pandemic tests have shown that infected patients typically develop thrombocytopenia with higher D-dimer levels, whereas the rate of developing thrombocytopenia in patients with severe COVID-19 disease is even higher [4]. Emerging data support the risk of occurrence of disseminated intravascular coagulation (DIC) in patients with COVID-19 [4, 13]. Tang et al. (2020) reported that 15 out of 21 non-survivor patients developed DIC, which results in abrupt coagulation and influences the prognosis of COVID-19 [13]. Viral infections cause systemic inflammatory responses and interfere with the balance of procoagulants and anticoagulants [16]. In severe or critically ill patients, large amounts of inflammatory mediators, hormones, and immunoglobulin are released, leading to blood hypercoagulability. Especially increased in patients with COVID-19 are levels of interleukins IL-6, IL-7, IL-2, interferon-γ inducible protein 10 (IP-10), granulocyte colony stimulating factor, monocyte chemoattractant protein-1 (MCP-1), macrophage inflammatory proteins-1-alpha (MIP1-a) and tumor necrosis factor-alpha (TNFα) [17]. Multiple pathogenetic pathways are involved in COVID-19 infection such as von Willebrand factor (VWF) elevation, endothelial dysfunction, tissue-factor pathway activation, and toll-like receptor activation [16, 18]. After antigen recognition, platelets become activated and interact with white blood cells to clear pathogen by white blood cells’ activation and clot formation [19]. Platelets are key mediators of inflammation and infectious agents through the interaction of cell receptors and pathogens (pathogen pattern recognition receptors) or immune system (complement receptors and immunoglobulin Fc receptors). The activation and the interactions between monocytes, macrophages, platelets, endothelial cells, and lymphocytes have a crucial role in the procoagulant effect of viral infections [20, 21]. In addition, vascular endothelial damage may be caused by mechanical ventilation, central venous catheterization, and surgery [22]. The combination of these factors can lead to VTE due to thrombus migration. Integrins are heterodimeric proteins. They are formed from α and β subunits on the cell surface [23]. Integrins are common receptors for many viruses. They affect cell adhesion, cell migration, and cell signaling [24]. Many integrins identify RGD and KGD motifs that are presented on the loops of proteins. RGD could

Links between Integrin αvb3 and COVID-19

accomplice some integrins such as αvβ1, αvβ3, αvβ5, αMβ2, αvβ6, αvβ8, α3β1, αIIbβ3, and αLβ2. KGD identifies other integrins: αIIbβ3, αvβ5, αvβ6, and αvβ8 [23]. Integrin αvβ3 is the cellular receptor for some viruses such as rotavirus, human cytomegalovirus, West Nile virus, herpes simplex virus type 1, and porcine epidemic diarrhea virus (PEDV) infection [25–29]. Multiple in vitro studies showed that the integrin αvβ3 functions as the main receptor for entry of pathogenic hantaviruses and suggested its role in the pathogenesis of the disease [30–32]. Furthermore, HCV replication is regulated through integrin αvβ3 [33]. Sigrist et al. (2020) suggested that SARS-CoV-2 may enter the host through integrins and that it binds with RGD motif in the receptor domain of the spike proteins of the virus [34] with integrin heterodimers of the host [35] leading to the activation of the transducing pathways, including phosphatidylinositol-3 kinase (PI–3K) and mitogen-activated protein kinase (MAPK). All of that endorses the virus entry and infection of the host [34]. Luan et al. (2020) reported that RGD/KGD motif presents in both S protein and its receptor angiotensin I converting enzyme 2 (ACE2). Therefore, they suggested that integrins have inhibitory roles in the entry of both SARS-CoV-2 and SARS-CoV [36]. Integrin αvβ3 and other integrins are highly plastic molecules that can alter their molecular postures with the binding of specific protein ligands and in response to Ca2+ and Mn2+. That may play a role in integrin αvβ3 activation [37]. Additionally, integrin αvβ3 binding facilitates thyroid hormone’s proliferative action on blood vessel cells [38]. The activation and deactivation of thyroid hormones may also stimulate angiogenesis. Various mechanisms stimulate angiogenesis through the non-genomic actions of T4 and T3 on integrin αvβ3. They enable tumor cell proliferation, tumor-linked angiogenesis, tumor cell anti-apoptosis, radioresistance, and chemoresistance [39]. Therefore, agents that block integrin binding may provide a promising avenue of research. Integrin αvβ3 is highly expressed on tumor cells, dividing blood vessel cells and on osteoclasts [40–43]. Integrin αvβ3 is also present on neurons that may affect the basal state of ion transport in excitable cells [44]. CCL20 and CCL26 are two examples for homeostatic chemokines in the CNS whose gene transcription is regulated from the cell surface integrin αvβ3 [45], and their expressions at these sites will respond to proinflammatory cytokines [46]. On hepatic stellate cells, integrin αvβ3 appears to mediate a pro-fibrotic action of thyroid hormone in the liver. The activity of the Na+/H+ exchanger in mouse myoblasts is stimulated by T3 and inhibited by tetraiodothyroacetic acid (tetrac) [47]. Integrin αvβ3 is also represented on human platelets [48]. It has been shown to mediate fibrin clot retraction when expressed in mammalian cell lines [49, 50]. Therefore, we would like to throw light on the effect of integrin αvβ3 on platelet aggregation and endothelial cells on hemostasis and thrombosis. When the virus binds to the integrin on the platelets both in the extracellular matrix (ECM) and plasma, the morphology, activation, and binding of platelets are changed. After platelet–collagen adhesion, interactions between virus and receptor endorse platelet aggregation and thrombus formation [51, 52]. Clotted fibrin binds to platelets due to interaction between each fiber and clusters of integrin receptors

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on the surface of activated platelets [53]. The key integrin in thrombus formation is αIIbβ3, which is restricted to platelets and megakaryocytes and is a receptor for a number of proteins that are responsible for adhesion such as VWF, fibrinogen, and fibronectin [51]. Fibronectin is widely present in the plasma and it promotes thrombus formation [54]. Then thrombin cleavage enhances the accessibility of RGD motif binding to integrin αvβ3 on osteopontin, which is a component of blood vessels’ subendothelial matrix [55]. These data suggest that thrombin cleavage makes the RGD motif more accessible [56]. RGD has three types of homologous repeating modules, and the RGD sequence in one of its types is responsible for interactions with its integrin receptors, namely αIIbβ3, αvβ3, and αvβ1 [57, 58], leading to activation-dependent integrin αvβ3-mediated platelet and lymphocyte adhesion [55]. It was reported that SARS-CoV-2 infects the host through ACE2 receptor [59]. ACE2 receptor is expressed in several organs, including the lung, heart, kidney, and intestine, and in endothelial cells [60]. Endothelial cells are related to the vascular beds of different organs in a series of patients with COVID-19 [59]. It is known that endothelial cells express large amounts of integrin αvβ3, and its expression increases after vascular injury [61]. Integrin αvβ3 is also involved in endothelial cell-mediated clot retraction [42]. Thus, integrin αvβ3 can mediate fibrin clot retraction, especially at the boundaries of vascular injury [62]. In cancer, metastasis is mediated through integrin αvβ3 and depends on integrin activation, suggesting that integrin αvβ3 function is controlled on the transcriptional as well as post translational level [63]. Coagulation can affect cancer metastasis. Knowles et al. (2013) reported that the initial phase of lung colonization depends on integrin αvβ3-mediated functions because transient knockdown of integrin αvβ3 reduced lung metastasis in fibrosarcoma cell line [64]. Therefore, blockage of integrin αvβ3 can be beneficial and decrease the clotting effect that occurs due to integrin αvβ3 activation. As mentioned earlier, integrin αvβ3 is not normally expressed on resting endothelial cells but it is highly expressed on activated endothelial cells and tumor cells. Therefore, it can be used in targeted anti-angiogenic therapy and targeted tumor cell therapy. Monoclonal antibodies, RGD containing peptides, siRNA, combination therapy, and targeted anticancer therapy are examples for angiogenic therapies acting via integrin αvβ3 antagonism [60, 61]. As mentioned before, the plasma membrane integrin αvβ3 acts as a membrane receptor for thyroid hormone [38]. The primary thyroid hormone ligand of this receptor is L-thyroxine (T4) [65]. The activation and deactivation of thyroid hormones may also stimulate angiogenesis associated with primary or metastatic tumors. These problems may be avoided by using tetrac, an analog of T4, to prevent the binding of iodothyronines to integrin αvβ3 [66]. Tetrac blocks the pro-angiogenic activity of vascular endothelial growth factor (VEGF) basic fibroblast growth factor (b-FGF), and platelet-derived growth factor (PDGF), and other pro-angiogenic factors in the chick chorioallantoic membrane (CAM) model and in an endothelial cell microtubule formation assay [67]. Another non-peptide

Abbreviations

compound that blocks thyroid hormone on integrin αvβ3 is compound XT199, which is an integrin antagonist that can block aggregatory effects on platelets. It acts at the cell surface via interaction with integrin αvβ3 and thyroid hormone receptors [68]. Furthermore, K-RAS oncogene and p53 tumor suppressor gene are reported to link between integrin αvβ3 and p53 activity where blockade of integrin αvβ3 suppresses the expression and/or activity of p53 [69]. K-RAS alters the levels of crucial angiogenic mediators such as VEGF [70]. RGD cyclic peptides are examples of antibodies that inhibit ligand binding to integrin αvβ3 and prevent angiogenesis [71]. Disintegrins are a group of small proteins rich in cysteine and have the adhesive RGD motif [72]. They are potent antagonists of integrin αvβ3 and α5β1 and have anti-tumor as well as antiangiogenic actions [73]. Bothrops alternatus snake venom is another example of RGD-disintegrin. It has a potent integrin αvβ3 and αIIbβ3 antagonistic effect, anti-thrombotic and anti-platelet effects [74]. Other molecules isolated from Macrovipera lebetina venom, such as lebectin [75], phospholipases [76], and serine-protease inhibitor [77], inhibit integrins α5β1 and αvβ3. Another example of disintegrin is Kistrin, which selectively binds to integrin αvβ3, blocking its interaction with ECM proteins [78]. All these examples endowed with integrin­ antagonist capacity block different pathological processes including angiogenesis [79] and endothelial dysfunction that may lead to atherogenesis [80]. This review emphasized integrin αvβ3-targeted therapies and addressed the most recent developments. We summarized the integrin αvβ3-targeted therapies that might solve the problem of the scarceness of anti-viral molecules acting against SARS-CoV-2. Anti-integrin αvβ3 therapy may be a solution for this pandemic. To that end and to find a novel and effective treatment for COVID-19 pandemic, more in-depth knowledge is required concerning the molecular mechanisms of the action of integrin αvβ3. Novel orally active, potent, and selective integrin antagonists deserve to be tested and transferred from preclinical to clinical trials.

Abbreviations

ACE2: b-FGF: CAM: COVID-19: DIC: DVT: ECM: IP-10: MAPK: MCP-1: MIP1-a: PDGF: PE:

angiotensin I converting enzyme 2 basic fibroblast growth factor chick chorioallantoic membrane coronavirus disease 2019 disseminated intravascular coagulation deep vein thrombosis extracellular matrix interferon-γ inducible protein 10 mitogen-activated protein kinase monocyte chemoattractant protein-1 macrophage inflammatory proteins-1-alpha platelet-derived growth factor pulmonary embolism

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PEDV: PI–3K: SARS-CoV-2: T4: TNFα: VEGF: VTE: VWF:

porcine epidemic diarrhea virus

phosphatidylinositol-3 kinase

severe acute respiratory syndrome coronavirus 2 virus

L-thyroxine tumor necrosis factor-alpha

vascular endothelial growth factor

venous thromboembolism

von Willebrand factor

Disclosures and Conflict of Interest

The authors declare no conflict of interest. No writing assistance was utilized in the production of this chapter and the authors have received no payment for its preparation.

References

1. Huang, C., et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet, 2020. 395(10223): 497–506.

2. Zhu, N., et al., A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med, 2020. 382(8): 727–733.

3. Worldometer. COVID-19 outbreak live update. Available at: https://www.worldometers. info/coronavirus (accessed on May 24, 2021).

4. Guan, W.J., et al., Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med, 2020. 382: 1708–1720.

5. Han, H., et al., Prominent changes in blood coagulation of patients with SARS-CoV-2 infection. Clin Chem Lab Med, 2020. 58(7): 1116–1120. 6. Zhai, Z., et al., Prevention and treatment of venous thromboembolism associated with coronavirus disease 2019 infection: A consensus statement before guidelines. Thromb Haemost, 2020. 120(6): 937–948.

7. Jaffer, A.K., An overview of venous thromboembolism: impact, risks, and issues in prophylaxis. Cleve Clin J Med, 2008. 75 (Suppl 3): S3–6. 8. Shapiro, N.L. and S.H. Bhatt, Critical review and update on the treatment of acute and chronic pulmonary embolism. J Pharm Pract, 2016. 29(1): 35–45.

9. Silverstein, M.D., et al., Trends in the incidence of deep vein thrombosis and pulmonary embolism: A 25-year population-based study. Arch Intern Med, 1998. 158(6): 585–593.

10. Naess, I.A., et al., Incidence and mortality of venous thrombosis: A population-based study. J Thromb Haemost, 2007. 5(4): 692–699.

11. Allman-Farinelli, M.A., Obesity and venous thrombosis: a review. Semin Thromb Hemost, 2011. 37(8): 903–907.

12. Wik, H.S., et al., Long-term mortality and incidence of cancer after pregnancyrelated venous thrombosis: results of a population-based cohort study. Thromb Res, 2013. 131(6): 497–501.

References

13. Tang, N., et al., Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost, 2020. 18(4): 844–847. 14. Ruppert, A., M. Lees, and T. Steinle, Clinical burden of venous thromboembolism. Curr Med Res Opin, 2010. 26(10): 2465–2473.

15. Giannis, D., I.A. Ziogas, and P. Gianni, Coagulation disorders in coronavirus infected patients: COVID-19, SARS-CoV-1, MERS-CoV and lessons from the past. J Clin Virol, 2020. 127: 104362.

16. Subramaniam, S. and I. Scharrer, Procoagulant activity during viral infections. Front Biosci, 2018. 23: 1060–1081. 17. Aggarwal, S., S. Gollapudi, and S. Gupta, Increased TNF-alpha-induced apoptosis in lymphocytes from aged humans: Changes in TNF-alpha receptor expression and activation of caspases. J Immunol, 1999. 162(4): 2154–2161.

18. Key, N.S., et al., Infection of vascular endothelial cells with herpes simplex virus enhances tissue factor activity and reduces thrombomodulin expression. Proc Natl Acad Sci U S A, 1990. 87(18): 7095–7099. 19. Guo, L. and M.T. Rondina, The era of thromboinflammation: Platelets are dynamic sensors and effector cells during infectious diseases. Front Immunol, 2019. 10: 2204. 20. van Gorp, E.C., et al., Review: Infectious diseases and coagulation disorders. J Infect Dis, 1999. 180(1): 176–186.

21. Neumann, F.J., et al., Induction of cytokine expression in leukocytes by binding of thrombin-stimulated platelets. Circulation, 1997. 95(10): 2387–2394.

22. Wang, X., et al., Expert consensus on the nursing management of critically ill elderly patients with coronavirus disease 2019. Aging Med, 2020. 3(2): 74–81.

23. Kuo, Y.-J., C.-H. Chung, and T.-F. Huang, From discovery of snake venom disintegrins to a safer therapeutic antithrombotic agent. Toxins, 2019. 11(7): 372.

24. Hynes, R.O., Integrins: Bidirectional, allosteric signaling machines. Cell, 2002. 110(6): 673–687. 25. Guerrero, C.A., et al., Integrin alpha(v)beta(3) mediates rotavirus cell entry. Proc Natl Acad Sci, 2000. 97(26): 14644–14649.

26. Chu, J.J. and M.L. Ng, Interaction of West Nile virus with alpha(v)beta(3) integrin mediates virus entry into cells. J Biol Chem, 2004. 279(52): 54533–54541.

27. Parry, C., et al., Herpes simplex virus type 1 glycoprotein H binds to alpha(v)beta(3) integrins. J Gen Virol, 2005. 86(Pt 1): 7–10. 28. Wang, X., et al., Integrin alphavbeta3 is a coreceptor for human cytomegalovirus. Nat Med, 2005. 11(5): 515–521.

29. Sun, D., et al., Analysis of protein expression changes of the Vero E6 cells infected with classic PEDV strain CV777 by using quantitative proteomic technique. J Virol Methods, 2015. 218: 27–39.

30. Matthys, V.S., et al., Andes virus recognition of human and Syrian hamster beta3 integrins is determined by an L33P substitution in the PSI domain. J Virol, 2010. 84(1): 352–360.

31. Gavrilovskaya, I.N., et al., Hantaviruses direct endothelial cell permeability by sensitizing cells to the vascular permeability factor VEGF, while angiopoietin 1 and

835

836

Links between Integrin αvb3 and COVID-19

sphingosine 1-phosphate inhibit hantavirus-directed permeability. J Virol, 2008. 82(12): 5797–5806.

32. Gavrilovskaya, I.N., et al., Pathogenic hantaviruses selectively inhibit beta3 integrin directed endothelial cell migration. Arch Virol, 2002. 147(10): 1913–1931.

33. Iqbal, J., et al., Mechanism of hepatitis C virus (HCV)-induced osteopontin and its role in epithelial to mesenchymal transition of hepatocytes. J Biol Chem, 2013. 288(52): 36994–37009.

34. Sigrist, C.J., A. Bridge, and P. Le Mercier, A potential role for integrins in host cell entry by SARS-CoV-2. Antiviral Res, 2020. 177: 104759.

35. Hussein, H.A., et al., Beyond RGD: virus interactions with integrins. Arch Virol, 2015. 160(11): 2669–2681.

36. Luan, J., et al., A potential inhibitory role for integrin in the receptor targeting of SARS-CoV-2. J Infect, 2020. 37. Plow, E.F., et al., Ligand binding to integrins. J Biol Chem, 2000. 275(29): 21785–21788.

38. Davis, P.J., F. Goglia, and J.L. Leonard, Nongenomic actions of thyroid hormone. Nat Rev Endocrinol, 2016. 12(2): 111–121. 39. Cayrol, F., et al., Integrin αvβ3 acting as membrane receptor for thyroid hormones mediates angiogenesis in malignant T cells. Blood, 2015. 125(5): 841–851.

40. Dijkgraaf, I., A.J. Beer, and H.J. Wester, Application of RGD-containing peptides as imaging probes for alphavbeta3 expression. Front Biosci, 2009. 14: 887–899.

41. Dimastromatteo, J., et al., In vivo molecular imaging of myocardial angiogenesis using the alpha(v)beta3 integrin-targeted tracer 99mTc-RAFT-RGD. J Nucl Cardiol, 2010. 17(3): 435–443. 42. Yoshimoto, M., et al., alpha(v)beta(3) Integrin-targeting radionuclide therapy and imaging with monomeric RGD peptide. Int J Cancer, 2008. 123(3): 709–715.

43. Nakamura, I., et al., Involvement of alpha(v)beta3 integrins in osteoclast function. J Bone Miner Metab, 2007. 25(6): 337–344.

44. Yonkers, M.A. and A.B. Ribera, Sensory neuron sodium current requires nongenomic actions of thyroid hormone during development. J Neurophysiol, 2008. 100(5): 2719–2725. 45. Davis, P.J., G.V. Glinsky, and H.Y. Lin, Actions of thyroid hormone analogues on chemokines. J Immunol Res, 2016. 2016: 3147671. 46. Arima, Y., et al., Regional neural activation defines a gateway for autoreactive T cells to cross the blood-brain barrier. Cell, 2012. 148(3): 447–457.

47. D’Arezzo, S., et al., Rapid nongenomic effects of 3,5,3¢-triiodo-L-thyronine on the intracellular pH of L-6 myoblasts are mediated by intracellular calcium mobilization and kinase pathways. Endocrinology, 2004. 145(12): 5694–5703.

48. Bennett, J.S., B.W. Berger, and P.C. Billings, The structure and function of platelet integrins. J Thromb Haemost, 2009. 7 Suppl 1: 200–205.

49. Chen, Y.P., et al., Beta 3 integrin-mediated fibrin clot retraction by nucleated cells: Differing behavior of alpha IIb beta 3 and alpha v beta 3. Blood, 1995. 86(7): 2606–2615.

50. Schaffner-Reckinger, E., N.H. Brons, and N. Kieffer, Evidence from site-directed mutagenesis that the cytoplasmic domain of the beta3 subunit influences the

References

conformational state of the alphaVbeta3 integrin ectodomain. Thromb Haemost, 2001. 85(4): 716–723.

51. Ruggeri, Z.M., Platelets in atherothrombosis. Nat Med, 2002. 8(11): 1227–1234.

52. Jackson, S.P., W.S. Nesbitt, and S. Kulkarni, Signaling events underlying thrombus formation. J Thromb Haemost, 2003. 1(7): 1602–1612.

53. Hantgan, R.R. and S.A. Mousa, Inhibition of platelet-mediated clot retraction by integrin antagonists. Thromb Res, 1998. 89(6): 271–279. 54. Ni, H., et al., Plasma fibronectin promotes thrombus growth and stability in injured arterioles. Proc Natl Acad Sci U S A, 2003. 100(5): 2415–2419.

55. Helluin, O., et al., The activation state of alphavbeta 3 regulates platelet and lymphocyte adhesion to intact and thrombin-cleaved osteopontin. J Biol Chem, 2000. 275(24): 18337–18343. 56. Senger, D.R., and C.A. Perruzzi, Cell migration promoted by a potent GRGDS-containing thrombin-cleavage fragment of osteopontin. Biochim Biophys Acta, 1996. 1314(1–2): 13–24.

57. Bowditch, R.D., et al., Integrin alpha IIb beta 3 (platelet GPIIb-IIIa) recognizes multiple sites in fibronectin. J Biol Chem, 1991. 266(34): 23323–23328.

58. Altroff, H., et al., The eighth FIII domain of human fibronectin promotes integrin alpha5beta1 binding via stabilization of the ninth FIII domain. J Biol Chem, 2001. 276(42): 38885–38892.

59. Varga, Z., et al., Endothelial cell infection and endotheliitis in COVID-19. Lancet, 2020. 395(10234), 1417–1418.

60. Ferrario, C.M., et al., Effect of angiotensin-converting enzyme inhibition and angiotensin II receptor blockers on cardiac angiotensin-converting enzyme 2. Circulation, 2005. 111(20): 2605–2610. 61. Sajid, M. and G.A. Stouffer, The role of alpha(v)beta3 integrins in vascular healing. Thromb Haemost, 2002. 87(2): 187–193.

62. Mor-Cohen, R., et al., Disulfide bond exchanges in integrins alphaIIbbeta3 and alphavbeta3 are required for activation and post-ligation signaling during clot retraction. Thromb Res, 2014. 133(5): 826–836.

63. Felding-Habermann, B., et al., Integrin activation controls metastasis in human breast cancer. Proc Natl Acad Sci U S A, 2001. 98(4): 1853–1858. 64. Knowles, L. M., Gurski, L. A., Engel, C., Gnarra, J. R., Maranchie, J. K., Pilch, J., Integrin αvβ3 and fibronectin upregulate Slug in cancer cells to promote clot invasion and metastasis. Cancer Res, 2013. 73(20): 6175–6184.

65. Lee, Y.S., et al., Thyroid hormone promotes beta-catenin activation and cell proliferation in colorectal cancer. Horm Cancer, 2018. 9(3): 156–165.

66. Chang, T.C., et al., Enhancement by nano-diamino-tetrac of antiproliferative action of gefitinib on colorectal cancer cells: Mediation by EGFR sialylation and PI3K activation. Horm Cancer, 2018. 9(6): 420–432.

67. Davis, P.J., et al., Cancer cell gene expression modulated from plasma membrane integrin αvβ3 by thyroid hormone and nanoparticulate tetrac. Front Endocrinol (Lausanne), 2014. 5: 240.

837

838

Links between Integrin αvb3 and COVID-19

68. Bishop, G.G., et al., Selective αvβ3-receptor blockade reduces macrophage infiltration and restenosis after balloon angioplasty in the atherosclerotic rabbit. Circulation, 2001. 103(14): 1906–1911.

69. Strömblad, S., et al., Suppression of p53 activity and p21WAF1/CIP1 expression by vascular cell integrin alphaVbeta3 during angiogenesis. J Clin Invest, 1996. 98(2): 426–433. 70. Rak, J. and G. Klement, Impact of oncogenes and tumor suppressor genes on deregulation of hemostasis and angiogenesis in cancer. Cancer Metastasis Rev, 2000. 19(1–2): 93–96.

71. Swenson, S., S. Ramu, and F.S. Markland, Anti-angiogenesis and RGD-containing snake venom disintegrins. Curr Pharm Des, 2007. 13(28): 2860–2871.

72. Niewiarowski, S., et al., Disintegrins and other naturally occurring antagonists of platelet fibrinogen receptors. Semin Hematol, 1994. 31(4): 289–300.

73. Calvete, J.J., et al., The presence of the WGD motif in CC8 heterodimeric disintegrin increases its inhibitory effect on αIIbβ3, αvβ3, and α5β1 integrins. Biochemistry, 2002. 41(6): 2014–2021.

74. Danilucci, T.M., et al., Recombinant RGD-disintegrin DisBa-01 blocks integrin α(v)β(3) and impairs VEGF signaling in endothelial cells. Cell Commun Signal, 2019. 17(1): 27. 75. Sarray, S., et al., Lebectin and lebecetin, two C-type lectins from snake venom, inhibit alpha5beta1 and alphaV-containing integrins. Matrix Biol, 2007. 26(4): 306–313.

76. Bazaa, A., et al., MVL-PLA2, a phospholipase A2 from Macrovipera lebetina trasmediterranea venom, inhibits tumor cells adhesion and migration. Matrix Biol, 2009. 28(4): 188–193.

77. Morjen, M., et al., PIVL, a new serine protease inhibitor from Macrovipera lebetina transmediterranea venom, impairs motility of human glioblastoma cells. Matrix Biol, 2013. 32(1): 52–62.

78. Oliva, I.B., et al., Effect of RGD-disintegrins on melanoma cell growth and metastasis: involvement of the actin cytoskeleton, FAK and c-Fos. Toxicon, 2007. 50(8): 1053–1063. 79. Kumar, C.C., et al., Inhibition of angiogenesis and tumor growth by SCH221153, a dual alpha(v)beta3 and alpha(v)beta5 integrin receptor antagonist. Cancer Res, 2001. 61(5): 2232–2238.

80. Porter, K.M. and R.L. Sutliff, HIV-1, reactive oxygen species, and vascular complications. Free Radic Biol Med, 2012. 53(1): 143–159.

Chapter 41

The Ocular Surface and the Coronavirus Disease 2019: Does a Dual ‘Ocular Route’ Exist? Pietro Emanuele Napoli, MD,a Matteo Nioi, MD,b Ernesto d’Aloja, MD,b and Maurizio Fossarelo, MDa,c aDepartment

of Surgical Science, University of Cagliari, Eye Clinic, Cagliari, Italy of Clinical Sciences and Public Health, University of Cagliari, Forensic Medicine Unit, Cagliari, Italy cClinica Oculistica, San Giovanni di Dio Hospital, Azienda Ospedaliera Universitaria di Cagliari, Cagliari, Italy bDepartment

[email protected]

Keywords: coronavirus disease 2019, coronavirus, severe acute respiratory syndrome coronavirus 2, angiotensin converting enzyme 2 receptor, Middle East Respiratory Syndromerelated coronavirus, conjunctivitis, ocular surface, cornea, dual ocular route, reverse transcriptase-polymerase chain reaction, tear film, tear meniscus, pandemic, RNA virus, angiotensin converting enzyme 2

41.1 Introduction Coronavirus disease 2019 (COVID-19) is an important health problem that was defined as a pandemic by the World Health Organization (WHO) on 11 March 2020 [1, 2]. This global epidemic is determined by a novel betacoronavirus named “severe acute respiratory syndrome coronavirus 2” (SARS-CoV-2), whose ease of interpersonal transmission is one of the crucial factors in the spread of the disease [3]. SARS-CoV-2 belongs to the same betacoronavirus family as SARS-CoV and Middle East respiratory syndrome-related coronavirus (MERS-CoV), the other two viruses that caused outbreaks in the past two decades. The main routes of transmission of the infection are considered as follows: airborne dissemination (nose/throat or ‘respiratory route’ through aerosols, dust or liquids), direct or indirect contact (e.g., face/eye touching in case of conjunctivitis), and oral-fecal. Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

Copyright © 2022 Jenny Stanford Publishing Pte. Ltd.

ISBN 978-981-4877-84-8 (Hardcover), 978-1-003-18043-2 (eBook)

www.jennystanford.com

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41.2 Ocular Surface Findings in Case of COVID-19 and Controversial Issues SARS-CoV-2 conjunctivitis has been described as a mild follicular conjunctivitis otherwise indistinguishable from other viral causes, and be potentially transmitted by aerosol contact with conjunctiva [4]. Other features of ocular surface involvement include unilateral or bilateral bulbar conjunctiva hyperemia alone or in association with chemosis, follicular reaction of the palpebral conjunctiva, watery discharge, epiphora, and mild eyelid edema. There have been no reports of COVID-19 patients experiencing blurred vision. The prevalence of conjunctivitis in patients with COVID-19 is controversial. Although it has been reported that only 0.9% developed signs of conjunctivitis [5], other report indicates that up to 31.6% of hospitalized patients had conjunctivitis [6]. However, in the latter study, only about 5% of patients with positive findings for COVID-19 on reverse transcriptase-polymerase chain reaction (RT-PCR) from nasopharyngeal swabs showed a positive conjunctival swab. In no cases was a positive conjunctival swab associated with a negative nasopharyngeal swab. Moreover, only one patient out of 38 presented with conjunctivitis as the first symptom. Patients with ocular symptoms were more likely to have higher white blood cell and neutrophil counts, as well as higher levels of procalcitonin, C-reactive protein, and lactate dehydrogenase than patients without ocular symptoms. In one patient, RT-PCR assay demonstrated the presence of viral RNA in conjunctival specimen 13 days after onset. The conjunctival swab specimens remained positive for SARS-CoV-2 on 14 and 17 days after onset. On day 19, RT-PCR result was negative for SARS-CoV-2 [4]. In another report, one hospitalized patient showed SARS-CoV-2 positive conjunctival swabs up to 21 days from symptom onset, a few days after the virus was undetectable in nasal swabs. Five days after, the virus resulted undetectable in the conjunctival swab, it was detected again at day 27, suggesting sustained replication of the virus in conjunctiva [7].

41.3 Ocular Transmission and the ACE2 Receptors in the Ocular Surface

Although great concern has been expressed about COVID-19 infection acquired through ocular transmission, its underlying mechanism has not yet been clarified [8]. SARS-CoV-2, like the others betacoronaviruses [9], entry into target cells by the binding of the viral Spike (S) protein to a specific cell-surface receptor, called angiotensin converting enzyme 2 (ACE2), followed by its priming by host cell transmembrane protease, serine 2 (TMPRSS2) [10]. Although much is still to be learned about the factors involved in SARS-CoV-2 infection of human cells, ACE2

Ocular Transmission and the ACE2 Receptors in the Ocular Surface

and TMPRSS2 are currently believed to represent the major players during cell entry [10, 11]. The presence of ACE2, which normally helps regulate blood pressure, is widespread throughout the body, and the eye is not excluded [12, 13]. The human ocular globe has its own intraocular renin-angiotensin system, which is present not only on the surface of the eye (e.g., conjunctiva and cornea), but also inside the eye (trabecular meshwork, aqueous humor, iris, ciliary body, non-pigmented ciliary epithelium, and retina) [14]. TMPRSS2 is also highly expressed in various tissues, including cornea limbal stem cells, substantiating that in the cornea ACE2 and TMPRSS2 are coexpressed in the same cell [15]. Accordingly, the two main elements that should be taken into account to understand the “ocular route”, from a clinical and molecular point of view, are the dynamism of the ocular surface system [16] and the distribution of ACE2 receptors and TMPRSS2 protein. In the first place, the dynamism of the tear film is the factor supporting SARS-CoV-2 to pass from the infected ocular surface to the respiratory and digestive tract through the lacrimal canaliculi (that drain tears from the eye surface into the nasal cavity), regardless of a more or less significant presence of ACE2 receptors on the cornea and conjunctiva [17, 18]. The opposite passage of viruses from nasal mucosa to conjunctiva seems unlikely, but cannot be excluded, as well as haematogenous infection of the lacrimal gland. Clearly, the lacrimal drainage from conjunctival sac into the nasal cavity is not or only partially operating in people with dry eye, for which lacrimal substitutes are highly recommended. On the other hand, the obstruction (complete or partial) of lacrimal drainage pathways may play a role in retaining the coronavirus on the ocular surface regardless of its presence in the nasal cavity, thus promoting periocular/face skin contamination by means of epiphora (i.e., the excessive watering of the eye). Secondly, the presence of the ACE2 receptors and TMPRSS2 protein on the corneal limbal stem cells [15] may theoretically allow the betacoronavirus to cross the ocular surface, and then spread from the eye to other parts of the body through the blood stream and/or the nervous system (ophthalmic branch of trigeminal nerve) [19]. Although there are no evidences at the moment that COVID-19 virus, in humans, can enter inside the eye or spread to the brain through corneal nerves, in some animal models (feline and murine), betacoronaviruses can cause several ocular affections (e.g., conjunctivitis, uveitis, retinitis and optic neuritis), thus suggesting that they are able, in some mammals, to penetrate inside the ocular globe [20]. Overall, preliminary studies reveal abundant gene expression of ACE2 receptor in the human conjunctiva and cornea [18] together with TMPRSS2 protein [15]. The coexpression in the corneal limbal cells of ACE2 and TMPRSS2 [15] substantiates the high affinity of this tissue for SARS-CoV-2 and its presence in tears. Therefore, in eyes with normal tearing (typically in young subjects) the dispersion of the viral load in the tear film may convey the virus into the respiratory or digestive tract, while in a dry eye the corneal and conjunctival epithelium may more easily retain

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the virus (for example in elderly people, where this disorder is very frequent), favoring a reactive/infective keratoconjunctivitis.

41.4 Discussion

A number of experimental and clinical evidences support the hypothesis that SARS-CoV-2 can be found in tears, and that it can therefore be received and transmitted through this route. Although it seems at the moment that the virus cannot be detected in the conjunctival sac of infected patients without conjunctivitis [21], and that there is a low risk of coronavirus spreading through tears [22], nevertheless SARS-CoV-2 may survive for a long time or replicate in the conjunctiva, after conjunctivitis signs vanished. Therefore, eye protection (protective goggles alone or in association with face shield) are necessary to prevent eye exposure to contaminated droplets and bioaerosol emitted by patients not only by cough and sneeze, but also by breathing [23–25]. Obviously, eye care providers and technicians may be more susceptible to infection due to the nature and proximity of the ophthalmic examination [26]. Eye care providers are encouraged to use slit lamp breath shields and should counsel patients to speak as little as possible when sitting at the slit lamp to reduce the risk of virus transmission. Disinfection and sterilization practices should be employed for shared clinic equipment such as tonometers, trial frames, pinhole occluders, near vision cards, B-scan probes, and contact lenses for laser procedures. Clearly, the use of disposable material (e.g., tonometer tips or gloves), as far as possible, would also be desirable during eye examinations (together with all other recommended personal protective equipment). However, eye protection is recommended not only for all health care workers, but also for various categories of people at risk (e.g., immunocompromised patients located in small rooms with poor ventilation, or older people living in close communities, such as health care residences) [27–29], and for individuals with ocular surface diseases (e.g., dry eye) or at risk of corneal ulcer (e.g., contact lens wearers). In this respect, personal eyeglasses and contact lenses do not qualify as personal protective equipment, although contact lenses remain a perfectly acceptable form of vision correction during the coronavirus pandemic, as long as people practice good hand hygiene and follow appropriate wear-and-care directions [20]. In conclusion, scientific literature on human ocular SARS-CoV-2 infection is growing rapidly, and this will help to clarify the role played by specific ocular routes in the transmission of COVID-19.

Abbreviations ACE2: COVID-19: MERS-CoV: RT-PCR:

angiotensin converting enzyme 2 coronavirus disease 2019 Middle East respiratory syndrome-related coronavirus reverse transcriptase-polymerase chain reaction

References

SARS-CoV-2: severe acute respiratory syndrome coronavirus 2 TMPRSS2: transmembrane protease, serine 2 WHO: World Health Organization

Disclosures and Conflict of Interest

This chapter was originally published as: Napoli, P. E., Nioi, M., d’Aloja, E., and Fossarello, M. (2020). The ocular surface and the coronavirus disease 2019: Does a dual ‘ocular route’ exist? J. Clin. Med., 9(5), 1269, https://doi.org/ 10.3390/jcm9051269, under the Creative Commons Attribution license (http:// creativecommons.org/licenses/by/4.0/), and appears here, with edits and updates, by kind permission of the copyright holders. Funding: This research received no external funding.

Conflicts of Interest: The authors declare no conflict of interest.

References

1. Eurosurveillance Editorial Team. Note from the editors: World Health Organization declares novel coronavirus (2019-nCoV) sixth public health emergency of international concern. Eurosurveillance 2020, 25, 200131e.

2. Napoli, P.E., Nioi, M. Global spread of coronavirus disease 2019 and malaria: An epidemiological paradox in the early stage of a pandemic. J. Clin. Med. 2020, 9(4), 1138.

3. Liu, J., Liao, X., Qian, S., Yuan, J., Wang, F., Liu, Y., Wang, Z., Wang, F.S., Liu, L., Zhang, Z. Community transmission of severe acute respiratory syndrome coronavirus 2, Shenzhen, China, 2020, Emerg. Infect. Dis. 2020, 26(6), 1320–1323.

4. Chen, L., Liu, M., Zhang, Z., Qiao, K., Huang, T., Chen, M., Xin, N., Huang, Z., Liu, L., Zhang, G., Wang, J. Ocular manifestations of a hospitalised patient with confirmed 2019 novel coronavirus disease. Br. J. Ophthalmol. 2020, 104(6), 748–751.

5. Guan, W.J., Ni, Z.Y., Hu, Y., Liang, W.H., Ou, C.Q., He, J.X., Liu, L., Shan, H., Lei, C.L., Hui, D.S., et al. Clinical characteristics of coronavirus disease 2019 in China. N. Engl. J. Med. 2020, 382, 1708–1720.

6. Wu, P., Duan, F., Luo, C., Liu, Q., Qu, X., Liang, L., Wu, K. Characteristics of ocular findings of patients with coronavirus disease 2019 (COVID-19) in Hubei Province, China. JAMA Ophthalmol. 2020, 138(5), 575–578.

7. Colavita, F., Lapa, D., Carletti, F., Lalle, E., Bordi, L., Marsella, P., Nicastri, E., Bevilacqua, N., Giancola, M.L., Corpolongo, A., et al. SARS-CoV-2 isolation from ocular secretions of a patient with COVID-19 in Italy with prolonged viral RNA detection. Ann. Intern. Med. 2020, 173(3), 242–243.

8. Li, J.O., Lam, D.S.C., Chen, Y., Ting, D.S.W. Novel Coronavirus disease 2019 (COVID-19): The importance of recognising possible early ocular manifestation and using protective eyewear. Br. J. Ophthalmol. 2020, 104, 297–298. 9. Tang, X., Wu, C., Li, X., Song, Y., Yao, X., Wu, X., Duan, Y., Zhang, H., Wang, Y., Qian, Z., et al. On the origin and continuing evolution of SARS-CoV-2. Natl. Sci. Rev. 2020, 7(6), 1012–1023.

843

844

The Ocular Surface and the Coronavirus Disease 2019

10. Hoffmann, M., Kleine-Weber, H., Schroeder, S., Krüger, N., Herrler, T., Erichsen, S., Schiergens, T.S., Herrler, G., Wu, N.H., Nitsche, A., et al. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically-proven protease inhibitor. Cell 2020, 181(2), 271–280.e8.

11. Fodoulian, L., Tuberosa, J., Rossier, D., Boillat, M., Kan, C., Pauli, V., Egervari, K., Lobrinus, J.A., Landis, B.N., Carleton, A., Rodriguez, I. SARS-CoV-2 receptor and entry genes are expressed by sustentacular cells in the human olfactory neuroepithelium. BioRxiv 30 May 2020. Available at: https://www.biorxiv.org/content/10.1101/2020.03.31.013268 v2 (accessed on January 4, 2021). 12. Baig, A.M., Khaleeq, A., Ali, U., Syeda, H. Evidence of the COVID-19 virus targeting the CNS: Tissue distribution, host-virus interaction, and proposed neurotropic mechanisms. ACS Chem. Neurosci. 2020, 11, 995–998.

13. Yan, R., Zhang, Y., Li, Y., Xia, L., Guo, Y., Zhou, Q. Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2. Science 2020, 367, 1444–1448. 14. Holappa, M., Vapaatalo, H., Vaajanen, A. Many faces of renin-angiotensin system-focus on eye. Open Ophthalmol. J. 2017, 11, 122–142.

15. Sungnak, W., Huang, N., Bécavin, C., Berg, M., Rachel, Q., Litvinukova, M., Talavera-López, C., Maatz, H., Reichart, D., Sampaziotis, F., et al. SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes. Nat. Med. 2020, 26, 681–687.

16. Napoli, P.E., Nioi, M., Mangoni, L., Gentile, P., Braghiroli, M., d’Aloja, E., Fossarello, M. Fourier-domain OCT imaging of the ocular surface and tear film dynamics: A review of the state of the art and an integrative model of the tear behavior during the interblink period and visual fixation. J. Clin. Med. 2020, 9(3), 668.

17. Sun, Y., Liu, L., Pan, X., Jing, M. Mechanism of the action between the SARS-CoV S240 protein and the ACE2 receptor in eyes. Int. J. Ophthalmol. 2006, 6, 783–786.

18. Sun, K., Gu, L., Ma, L., Duan, Y. Atlas of ACE2 gene expression in mammals reveals novel insights in transmission of SARS-Cov-2. BioRxiv. 31 March 2020. Available at: https://www.biorxiv.org/content/10.1101/2020.03.30.015644v1 (accessed on January 4, 2021).

19. Li, Y.C., Bai, W.Z., Hashikawa, T. The neuroinvasive potential of SARS-CoV2 may play a role in the respiratory failure of COVID-19 patients. J. Med. Virol. 2020, 92(6), 552–555.

20. Seah, I., Agrawal, R. Can the coronavirus disease 2019 (COVID-19) affect the eyes? A review of coronaviruses and ocular implications in humans and animals. Ocular Immunol. Inflamm. 2020, 28, 391–395.

21. Xia, J., Tong, J., Liu, M., Shen, Y., Guo, D. Evaluation of coronavirus in tears and conjunctival secretions of patients with SARS-CoV-2 infection. J. Med. Virol. 2020, 92(6), 589–594.

22. Seah, I.Y.J., Anderson, D.E., Kang, A.E.Z., Wang, L., Rao, P., Young, B.E., Lye, D.C., Agrawal, R. Assessing viral shedding and infectivity of tears in coronavirus disease 2019 (COVID-19) Patients. Ophthalmology 2020, 127(7), 977–979.

23. Leung, N.H., Chu, D.K., Shiu, E.Y., Chan, K.H., McDevitt, J.J., Hau, B.J., Yen, H.L., Li, Y., Ip, D.K., Peiris, J.M., et al. Respiratory virus shedding in exhaled breath and efficacy of face masks. Nat. Med. 2020, 26, 676–680.

Supplemental Readings

24. Asadi, S., Bouvier, N., Wexler, A.S., Ristenpart, W.D. The coronavirus pandemic and aerosols: Does COVID-19 transmit via expiratory particles? Aerosol. Sci. Technol. 2020, 1–4.

25. Guzman, M. An overview of the effect of bioaerosol size effect in coronavirus disease 2019 transmission. Int. J. Health Plann. Mgmt. 2020, 1–10.

26. Seah, I., Su, X., Lingam, G. Revisiting the dangers of the coronavirus in the ophthalmology practice. Eye (Lond.) 2020, 34, 1155–1157.

27. World Health Organization. Rational Use of Personal Protective Equipment for Coronavirus Disease (COVID-19) and Considerations during Severe Shortages: Interim Guidance, 6 April 2020, No. WHO/2019-nCov/IPC_PPE_use/2020.3. Available at: https : //www. who.int/publi cati ons/i/item/rati onal-use-of-personal- protectiv eequ ipment -for-coronavirus-disease-(covid-19)-a nd-considerat ions-during-severe shortages (accessed on January 4, 2021).

28. Chen, X., Shang, Y., Yao, S., Liu, R., Liu, H. Perioperative care provider’s considerations in managing patients with the COVID-19 infections. Transl. Perioper Pain Med. 2020, 15, 216–224. 29. Jones, L., Walsh, K., Willcox, M., Morgan, P., Nichols, J. The COVID-19 pandemic: Important considerations for contact lens practitioners. Cont. Lens Anterior Eye 2020, 43(3), 196–203.

Supplemental Readings

1. Napoli, P.E., Mangoni, L., Gentile, P., Braghiroli, M., Fossarello, M. A panel of broad-spectrum antivirals in topical ophthalmic medications from the drug repurposing approach during and after the coronavirus disease 2019 era. J. Clin. Med. 2020, 9(8), 2441. 2. Zhou, L., et al. ACE2 and TMPRSS2 are expressed on the human ocular surface, suggesting susceptibility to SARS-CoV-2 infection. Ocul. Surf. 2020, 18(4), 537–544.

3. Willcox, M.D., et al. The ocular surface, coronaviruses and COVID-19. Clin. Exp. Optom. 2020, 103(4), 418–424.

4. Napoli, P.E., Nioi, M., d’Aloja, E., Fossarello, M. Safety recommendations and medical liability in ocular surgery during the COVID-19 pandemic: An unsolved dilemma. J. Clin. Med. 2020, 9(5), 1403.

5. Garbutcheon-Singh, K.B., et al. A review of the cytokine IL-17 in ocular surface and corneal disease. Curr. Eye Res. 2019, 44(1), 1–10.

6. Chu, Derek K., et al. Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: A systematic review and meta-analysis. Lancet 2020, 395(10242), P1973–1987.

7. Napoli, P.E., Nioi, M., d’Aloja, E., Fossarello, M. The bull’s eye pattern of the tear film in humans during visual fixation on en-face optical coherence tomography. Sci. Rep. 2019, 9(1), 1–9.

8. Napoli, P.E., et al. Ocular surface and respiratory tract damages from occupational, sub-chronic exposure to fluorspar: Case report and other considerations. Int. Ophthalmol. 2019, 39(5), 1175–1178.

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9. Coroneo, M.T. The eye as the discrete but defensible portal of coronavirus infection. Ocul. Surf. 2020. doi: 0.1016/j.jtos.2020.05.011 (accessed on January 4, 2021).

10. Ali M.J. The SARS-CoV-2, tears, and ocular surface debate: What we know and what we need to know. Indian J. Ophthalmol. 2020, 68(7), 1245–1246.

11. Nioi, M., Napoli, P.E., Demontis, R., Locci, E., Fossarello, M., d’Aloja, E. Morphological analysis of corneal findings modifications after death: A preliminary OCT study on an animal model. Exp. Eye Res. 2018, 169, 20–27.

Chapter 42

Exploring Links between Vitamin D Deficiency and COVID-19 Mradul Mohan, MS, PhD,a Jerin Jose Cherian, MD, MBA,b and Amit Sharma, PhDa,c aParasite-Host

Biology Group, National Institute of Malaria Research, New Delhi, India of Basic Medical Sciences, Indian Council of Medical Research, New Delhi, India cStructural Parasitology Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, India bDivision

[email protected]

Keywords: acute respiratory distress syndrome, angiotensin converting enzyme-2 receptor, cathelicidin, Coronavirus Disease 2019, cytochrome P450, cytokine storm, hyper-inflammatory cytokine storm, immunomodulator therapies, melanin, nuclear vitamin D receptors, pro-inflammatory cytokines, retinoid X receptors, SARS-CoV-2, severe acute respiratory syndrome coronavirus 2, T helper type 1 cells, T helper type 2 cells, vitamin D deficiency

42.1 SARS-CoV-2 Infection and the Cytokine Storm There are two critical questions that emanate from the title of this article. The first one is whether there is an association between vitamin D deficiency and susceptibility to Coronavirus Disease 2019 (COVID-19). The second is whether vitamin D administration to deficient individuals can prevent infection or alter the course of disease severity. Here, we have collated available evidence that addresses both these aspects of vitamin D in relation to COVID-19. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects pulmonary epithelial cells using the angiotensin converting enzyme-2 (ACE-2) receptor [1]. Besides pulmonary epithelial damage, SARS-CoV-2 also infects macrophages through ACE-2 receptors and activates them [2]. Macrophages, neutrophils, and T cells get activated through sustained elevation of cytokines including interleukin (IL)-1, IL-6, and tumor necrosis factor (TNF) alpha, resulting Advances in Clinical Immunology, Medical Microbiology, COVID-19, and Big Data Edited by Raj Bawa

Copyright © 2022 Jenny Stanford Publishing Pte. Ltd.

ISBN 978-981-4877-84-8 (Hardcover), 978-1-003-18043-2 (eBook)

www.jennystanford.com

Figure 42.1 (A) The basic pathophysiology of COVID-19 and the development of ARDS. (B) Vitamin D’s dual action on immune response and inflammation. Vitamin D is capable of modulating the expression of various genes which results in augmented innate immune response and lower acquired immune response. Abbreviations: ACE 2, angiotensin-converting enzyme 2; ARDS, acute respiratory distress syndrome; COVID-19, Coronavirus Disease 2019; IL, interleukin; NFκB, nuclear factor kappa-light-chain-enhancer of activated B cells; NO, nitric oxide; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; TH1, T helper type 1; TH2, T helper type 2; TH17, T helper type 17; TLR, toll-like receptor; TNFα, tumor necrosis factor alpha; TREG, regulatory T cell; UVB, ultraviolet B rays; VDR, vitamin D receptor; VDRE, vitamin D receptor element.

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Vitamin D and the Host Immune System

in type 2 pneumocyte apoptosis, and in some patients a path that leads to acute respiratory distress syndrome (ARDS) [2]. The host responses are sometimes amplified by an overwhelming expression of pro-inflammatory cytokines [3]. This ‘cytokine storm’ is responsible for some of the serious manifestations of COVID-19 such as ARDS (Fig. 42.1A) [3]. Hypoxemia and bilateral lung infiltration are features reminiscent of severe viral pneumonia that result from endothelial injury, excessive cytokines, and immune overkill [3, 4].

42.2 Vitamin D and the Host Immune System

Vitamin D can be classified as a steroid hormone and is produced in human skin from 7-dehydrocholesterol due to exposure to ultraviolet B rays (UVB; 280–315 nm range) from sunlight [5]. The subcutaneous production by UVB exposure is the principle source of vitamin D, whilst dietary sources include dairy products or fish liver oil (Fig. 42.1A) [6]. Melanin is an indole-containing polymer produced in melanocytes which offer epidermis and hair their pigmentation. Melanin reduces the penetration of UVB, resulting in decreased vitamin D production in the skin [5]. For established genetic and environmental reasons, melanin expression in some ethnic groups like whites is reduced, and this has been linked to populationwide differences in vitamin D synthesis despite exposure to UVB [7]. Inside the cell, vitamin D binds to nuclear vitamin D receptors (VDRs) and the subsequently activated VDRs dimerize with themselves or with retinoid X receptors (RXR) and translocate to the nucleus to engage the vitamin D receptor element (VDRE) (Fig. 42.1B). The VRDE regulates the expression of a numerous host genes like beta defensin and cathelicidin [8]. In addition, vitamin D levels can influence the expression of toll-like receptors that pivot the innate immune response as they recognize pathogenic proteins [9]. Other important genes regulated by vitamin D include beta defensins that can directly cleave the membrane of a virus and cathelicidins that are involved in the activation of macrophages, dendritic cells, and neutrophils [10]. For example, activated VDR can bind to the VDRE of the cathelicidin gene promoter and can lead to initiation of host defense against some viral infections [10]. Vitamin D also influences the innate immune system via expression of lysosomal enzymes and the release of nitric oxide wherein both contribute towards combating infection (Fig. 42.1B) [11]. Vitamin D also plays an immune regulatory role via suppression of the adaptive immune responses in respiratory epithelial cells during viral infections [5, 10]. This is manifested predominantly via dampening T cell proliferation and the resultant shift from T helper type 1 (Th1) cells to T helper type 2 (Th2) [12]. A reduced Th1 proliferation can be argued to result in lower levels of proinflammatory cytokines and diminished acquired immune responses, and these may be counterproductive in mounting a successful immune response against a virus (Fig. 42.1B). Vitamin D also influences T cell maturation and can divert the development of inflammatory T helper type 17 (Th17) cell mass towards anti-

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inflammatory regulatory T cell (T-reg cell) populations [5, 12]. In this manner, vitamin D can reduce the ‘in milieu’ levels of pro-inflammatory cytokines including IL-1, IL-6, IL-12, TNF alpha, and IL-17 whilst augmenting the anti-inflammatory IL-10 [5, 10]. Reduced expression of pro-inflammatory cytokines restrains the differentiation and activation of various immune cell types and can prevent immune-mediated injury [5]. Additionally, vitamin D directly inhibits the nuclear factor kappa-light-chain-enhancer of activated B cells (NFκB) pathway, thus reducing the expression of pro-inflammatory cytokines [13]. Hence, via its opposing actions on cytokine regulation and T cell differentiation, vitamin D plays a complex dual role in immunopathology (Fig. 42.1B) [5, 10]. Some authors have also postulated that vitamin D may down-regulate ACE-2 receptors and thus can have protective effects in COVID-19 [14].

42.3 Vitamin D Deficiency and COVID-19

Lips and colleagues have recently evaluated the mean levels of vitamin D in populations across approximately 40 countries and have shown >50% deficiency, especially amongst the care home residents (mostly the elderly) [15]. As the COVID-19 pandemic count continues to rise in many countries including India, it is noteworthy that a substantial population of India (approximately >70%) are vitamin D deficient (