Multiplex Biomarker Techniques: Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification 9781071623954, 1071623958

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Methods in Molecular Biology 2511

Paul C. Guest Editor

Multiplex Biomarker Techniques Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

Multiplex Biomarker Techniques Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification

Edited by

Paul C. Guest Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, University of Campinas, Campinas, Sao Paulo, Brazil

Editor Paul C. Guest Laboratory of Neuroproteomics Department of Biochemistry and Tissue Biology University of Campinas Campinas, Sao Paulo, Brazil

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-2394-7 ISBN 978-1-0716-2395-4 (eBook) https://doi.org/10.1007/978-1-0716-2395-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface Due to continuous technical developments and new insights into the high complexity of many diseases, there is an increasing need for multiplex biomarker readouts for improved clinical management and to support the development of new and existing drugs by pharmaceutical companies. This has become increasingly evident in light of the ongoing COVID19 pandemic which has currently affected almost 4% of the world population. This disease requires rapid diagnosis and stratification of patients for enabling the most appropriate treatment course for the best possible outcomes. Diagnosis should be rapid, allowing a positive of negative result in rapid time. In addition, these tests should be specific by ruling out other potential causes of illness through simultaneous testing for the presence of other viruses or pathogens. In terms of stratification, elevated levels of specific biomarkers at the molecular and imaging levels can be used to establish risk of a more serious disease course so that patients can be placed on the right medication at the right time to minimize risk. This book is an extension of the successful previous volume, Multiplex Biomarker Techniques: Methods and Applications, as it describes state-of-the-art technologies in the fields of genomics, proteomics, transcriptomics, metabolomics, and imaging, which are currently the methods of choice in multiplex biomarker research. It also describes the application of these methods in the critical area of COVID-19 research and highlights areas where the greatest progress has been made as well as those where further resources are required. This book includes a series of protocols employing multiplex molecular approaches, which can be applied to accelerate progress in the research of COVID-19 and other infectious illnesses. The authors in this series come from the six habitable continents, from countries such as Australia, Brazil, China, Germany, India, Iran, Japan, Oman, Saudi Arabia, South Africa, Spain, the United Kingdom, and the United States of America. The book will be of high interest to researchers in the areas of virology, metabolic diseases, and respiratory disorders, as well as to clinical scientists, physicians, the major drug companies, and the healthcare services. Campinas, Sao Paulo, Brazil

Paul C. Guest

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Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

PART I

REVIEWS

1 Multiplex Technologies in COVID-19 Research, Diagnostics, and Prognostics: Battling the Pandemic. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paul C. Guest, Fatemeh Zahedipour, Muhammed Majeed, Tannaz Jamialahmadi, and Amirhossein Sahebkar 2 Multivalent Vaccine Strategies in Battling the Emergence of COVID-19 Variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paul C. Guest 3 Challenges of Multiplex Assays for COVID-19 Research: A Machine Learning Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paul C. Guest, David Popovic, and Johann Steiner

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4 Multiplex Quantitative Polymerase Chain Reaction Diagnostic Test for SARS-CoV-2 and Influenza A/B Viruses . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Steve F. C. Hawkins and Paul C. Guest 5 Multiplex Quantitative Polymerase Chain Reaction Test to Identify SARS-CoV-2 Variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Steve F. C. Hawkins and Paul C. Guest 6 NIRVANA for Simultaneous Detection and Mutation Surveillance of SARS-CoV-2 and Co-infections of Multiple Respiratory Viruses . . . . . . . . . . . 79 Chongwei Bi, Gerardo Ramos-Mandujano, and Mo Li 7 Quantitative Real-Time RT-PCR Systems to Detect SARS-CoV-2 . . . . . . . . . . . . 89 Sumino Yanase, Hiroyoshi Sasahara, Momoko Nabetani, Kensuke Yamazawa, Keisuke Aoyagi, Akiko Mita, Yuichi Honma, and Yasuhiko Chiba 8 Guidance for SARS-CoV-2 RNA-Based Molecular Assay Analytical Performance Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Lara Noble, Lesley Scott, Riffat Munir, Kim Steegen, Mignon du Plessis, Lucia Hans, and Wendy Stevens 9 A Rapid User-Friendly Lab-on-a-Chip Microarray Platform for Detection of SARS-CoV-2 Variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Emily Mattig, Paul C. Guest, and Harald Peter 10 SARS-CoV-2 Host Immunogenetic Biomarkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Maemu P. Gededzha, Nakampe Mampeule, Anastasia Gandini, and Elizabeth S. Mayne

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FnCas9 Editor Linked Uniform Detection Assay for COVID-19 . . . . . . . . . . . . . Rhythm Phutela, Sneha Gulati, Manoj Kumar, Souvik Maiti, and Debojyoti Chakraborty Mass Spectrometry Multiplexed Detection of SARS-CoV-2 . . . . . . . . . . . . . . . . . . Luciana Godoy Viana, Adriana Lebkuchen, Rodrigo Andrade Schuch, Guilherme Gonc¸alves Okai, Jessica Silva Salgueiro, Karina Helena Morais Cardozo, and Valdemir Melechco Carvalho Identification of Circulating Biomarkers of COVID-19 Using MALDI-TOF Mass Spectrometry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lucas C. Lazari, Livia Rosa-Fernandes, and Giuseppe Palmisano Antibody-Based Affinity Capture Combined with LC-MS Analysis for Identification of COVID-19 Disease Serum Biomarkers . . . . . . . . . . . . . . . . . . Paul C. Guest and Hassan Rahmoune Liquid Chromatography-Mass Spectrometry Analysis of Peripheral Blood Mononuclear Cells from SARS-CoV-2 Infected Patients. . . . . . . . . . . . . . . Paul C. Guest and Hassan Rahmoune Assay of Fatty Acids and Their Role in the Prevention and Treatment of COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tharusha Jayasena, Sonia Bustamante, Anne Poljak, and Perminder Sachdev Lab-on-a-Chip Immunoassay for Prediction of Severe COVID-19 Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Harald Peter, Emily Mattig, Paul C. Guest, and Frank F. Bier Multiplex Immunoassay for Prediction of Disease Severity Associated with the Cytokine Storm in COVID-19 Cases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paul C. Guest, Mitra Abbasifard, Tannaz Jamialahmadi, Muhammed Majeed, Prashant Kesharwani, and Amirhossein Sahebkar Detection of IgG Antibodies to SARS-CoV-2 and Neutralizing Capabilities Using the Luminex® xMAP® SARS-CoV-2 Multi-Antigen IgG Assay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abbe King, Gregory King, Christy Weiss, Sherry Dunbar, and Shubhagata Das Multiplex Testing of the Effect of Statins on Disease Severity Risk in COVID-19 Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fatemeh Zahedipour, Paul C. Guest, Muhammed Majeed, Khalid Al-Rasadi, Tannaz Jamialahmadi, and Amirhossein Sahebkar Evaluating the Effects of Curcumin on the Cytokine Storm in COVID-19 Using a Chip-Based Multiplex Analysis. . . . . . . . . . . . . . . . . . . . . . . Fatemeh Zahedipour, Paul C. Guest, Muhammed Majeed, Seyed Adel Moallem, Prashant Kesharwani, Tannaz Jamialahmadi, and Amirhossein Sahebkar COVID-19 Detection Using the NHS Lateral Flow Test Kit. . . . . . . . . . . . . . . . . Paul C. Guest and Hassan Rahmoune

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Evaluation Protocol for SARS-CoV-2 Serological Assays. . . . . . . . . . . . . . . . . . . . . Maemu P. Gededzha, Sarika Jugwanth, Nakampe Mampeule, Nontobeko Zwane, Anura David, Lesley Scott, Wendy Stevens, and Elizabeth S. Mayne Measurement of Mitochondrial Respiration in Cryopreserved Human Peripheral Blood Mononuclear Cells (PBMCs). . . . . . . . . . . . . . . . . . . . . . Keiko Iwata, Min-Jue Xie, Paul C. Guest, Takaharu Hirai, and Hideo Matsuzazki Multiplex Testing of Oxidative-Reductive Pathway in Patients with COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paul C. Guest, Mitra Abbasifard, Tannaz Jamialahmadi, Muhammed Majeed, Prashant Kesharwani, and Amirhossein Sahebkar Point-of-Care Device for Assessment of Blood Coagulation Status in COVID-19 Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paul C. Guest and Hassan Rahmoune COVID-19 and the Assessment of Coenzyme Q10 . . . . . . . . . . . . . . . . . . . . . . . . . Nadia Turton, Robert A. Heaton, and Iain P. Hargreaves Isolation and Cell Culture of Human Nasopharyngeal Cells: A Model for Testing Immune Response and Antiviral Treatment . . . . . . . . . . . . . . . . . . . . . . Krist Helen Antunes and Ana Paula Duarte de Souza Machine Learning Approaches to Analyze MALDI-TOF Mass Spectrometry Protein Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lucas C. Lazari, Livia Rosa-Fernandes, and Giuseppe Palmisano A Deep Learning Approach to Identify Chest Computed Tomography Features for Prediction of SARS-CoV-2 Infection Outcomes . . . . . . . . . . . . . . . . . Amirhossein Sahebkar, Mitra Abbasifard, Samira Chaibakhsh, Paul C. Guest, Mohamad Amin Pourhoseingholi, Amir Vahedian-Azimi, Prashant Kesharwani, and Tannaz Jamialahmadi

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Genomic Surveillance for Monitoring Variants of Concern: SARS-CoV-2 Delta, Omicron, and Beyond . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 Paul C. Guest

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors MITRA ABBASIFARD • Immunology of Infectious Diseases Research Center, Research Institute of Basic Medical Sciences, Rafsanjan University of Medical Sciences, Rafsanjan, Iran; Department of Internal Medicine, Ali-Ibn Abi-Talib Hospital, School of Medicine, Rafsanjan University of Medical Sciences, Rafsanjan, Iran KHALID AL-RASADI • Medical Research Centre, Sultan Qaboos University, Muscat, Oman KRIST HELEN ANTUNES • Laboratory of Clinical and Experimental Immunology, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil KEISUKE AOYAGI • Clinical Laboratory and Transfusion Department at the Yokohama Municipal Citizen’s Hospital, Yokohama, Japan CHONGWEI BI • Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia; Shanghai ZhiYu Bio-technology Co., LTD, Shanghai, China FRANK F. BIER • Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany; Institute of Molecular Diagnostics and Bioanalysis (IMDB gGmbH), Berlin, Germany SONIA BUSTAMANTE • Bioanalytical Mass Spectrometry Facility, University of New South Wales, Sydney, NSW, Australia KARINA HELENA MORAIS CARDOZO • Division of Research and Development, Fleury Group, Sa˜o Paulo, SP, Brazil VALDEMIR MELECHCO CARVALHO • Division of Research and Development, Fleury Group, Sa˜o Paulo, SP, Brazil SAMIRA CHAIBAKHSH • Eye Research Center, The five Senses Institute, Rassoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran DEBOJYOTI CHAKRABORTY • CSIR-Institute of Genomics & Integrative Biology, New Delhi, India; Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, India YASUHIKO CHIBA • Clinical Laboratory and Transfusion Department at the Yokohama Municipal Citizen’s Hospital, Yokohama, Japan SHUBHAGATA DAS • Luminex Corporation, A DiaSorin Company, Austin, TX, USA ANURA DAVID • Department of Molecular Medicine and Haematology, School of Pathology, Faculty of Health Science, University of Witwatersrand, Johannesburg, South Africa ANA PAULA DUARTE DE SOUZA • Laboratory of Clinical and Experimental Immunology, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil SHERRY DUNBAR • Luminex Corporation, A DiaSorin Company, Austin, TX, USA MIGNON DU PLESSIS • National Institute for Communicable Diseases, A Division of the National Health Laboratory Service, Johannesburg, Gauteng, South Africa; School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa ANASTASIA GANDINI • Department of Immunology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; National Health Laboratory Services, Johannesburg, South Africa

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MAEMU P. GEDEDZHA • Department of Immunology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; National Health Laboratory Services, Johannesburg, South Africa PAUL C. GUEST • Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil SNEHA GULATI • CSIR-Institute of Genomics & Integrative Biology, New Delhi, India; Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, India LUCIA HANS • National Priority Programme, National Health Laboratory Service, Johannesburg, Gauteng, South Africa IAIN P. HARGREAVES • School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK STEVE F. C. HAWKINS • Meridian Bioscience, Unit 16, The Edge Business Centre, London, UK ROBERT A. HEATON • School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK TAKAHARU HIRAI • Department of Psychiatric and Mental Health Nursing, School of Nursing, University of Fukui, Fukui, Japan YUICHI HONMA • Clinical Laboratory and Transfusion Department at the Yokohama Municipal Citizen’s Hospital, Yokohama, Japan KEIKO IWATA • Division of Development of Mental Functions, Research Center for Child Mental Development, University of Fukui, Fukui, Japan; United Graduate School of Child Development, Osaka University, Osaka, Japan; Life Science Innovation Center, University of Fukui, Fukui, Japan TANNAZ JAMIALAHMADI • Surgical Oncology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Surgical Oncology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran THARUSHA JAYASENA • Centre for Healthy Brain Ageing, University of New South Wales, Sydney, NSW, Australia SARIKA JUGWANTH • Department of Immunology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; National Health Laboratory Services, Johannesburg, South Africa PRASHANT KESHARWANI • Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India ABBE KING • Luminex Corporation, A DiaSorin Company, Austin, TX, USA GREGORY KING • Luminex Corporation, A DiaSorin Company, Austin, TX, USA MANOJ KUMAR • CSIR-Institute of Genomics & Integrative Biology, New Delhi, India; Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, India LUCAS C. LAZARI • GlycoProteomics Laboratory, Department of Parasitology, ICB, University of Sa˜o Paulo, Sa˜o Paulo, Brazil ADRIANA LEBKUCHEN • Division of Research and Development, Fleury Group, Sa˜o Paulo, SP, Brazil MO LI • Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia SOUVIK MAITI • CSIR-Institute of Genomics & Integrative Biology, New Delhi, India; Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, India MUHAMMED MAJEED • Sabinsa Corporation, East Windsor, NJ, USA

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NAKAMPE MAMPEULE • Department of Immunology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; National Health Laboratory Services, Johannesburg, South Africa HIDEO MATSUZAZKI • Division of Development of Mental Functions, Research Center for Child Mental Development, University of Fukui, Fukui, Japan; United Graduate School of Child Development, Osaka University, Osaka, Japan; Life Science Innovation Center, University of Fukui, Fukui, Japan EMILY MATTIG • Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses (IZI-BB), Potsdam, Germany ELIZABETH S. MAYNE • Department of Immunology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; National Health Laboratory Services, Johannesburg, South Africa; Division of Immunology, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa AKIKO MITA • Clinical Laboratory and Transfusion Department at the Yokohama Municipal Citizen’s Hospital, Yokohama, Japan SEYED ADEL MOALLEM • Department of Pharmacology and Toxicology, College of Pharmacy, Al-Zahraa University for Women, Karbala, Iraq; Department of Pharmacodynamics and Toxicology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran RIFFAT MUNIR • Department of Molecular Medicine and Haematology, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa MOMOKO NABETANI • Clinical Laboratory and Transfusion Department at the Yokohama Municipal Citizen’s Hospital, Yokohama, Japan LARA NOBLE • Department of Molecular Medicine and Haematology, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa GUILHERME GONC¸ALVES OKAI • Division of Research and Development, Fleury Group, Sa˜o Paulo, SP, Brazil GIUSEPPE PALMISANO • GlycoProteomics Laboratory, Department of Parasitology, ICB, University of Sa˜o Paulo, Sa˜o Paulo, Brazil HARALD PETER • Fraunhofer Institute for Cell Therapy and Immunology, Branch Bioanalytics and Bioprocesses (IZI-BB), Potsdam, Germany RHYTHM PHUTELA • CSIR-Institute of Genomics & Integrative Biology, New Delhi, India; Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, India ANNE POLJAK • Centre for Healthy Brain Ageing, University of New South Wales, Sydney, NSW, Australia; Bioanalytical Mass Spectrometry Facility, University of New South Wales, Sydney, NSW, Australia DAVID POPOVIC • Section of Forensic Psychiatry, Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany; International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany MOHAMAD AMIN POURHOSEINGHOLI • Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran HASSAN RAHMOUNE • Department of Chemical Engineering & Biotechnology, University of Cambridge, Cambridge, UK GERARDO RAMOS-MANDUJANO • Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia LIVIA ROSA-FERNANDES • GlycoProteomics Laboratory, Department of Parasitology, ICB, University of Sa˜o Paulo, Sa˜o Paulo, Brazil

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PERMINDER SACHDEV • Centre for Healthy Brain Ageing, University of New South Wales, Sydney, NSW, Australia; Neuropsychiatric Institute, Euroa Centre, Prince of Wales Hospital, Sydney, NSW, Australia AMIRHOSSEIN SAHEBKAR • Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; School of Medicine, The University of Western Australia, Perth, Australia; Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Biotechnology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran JESSICA SILVA SALGUEIRO • Division of Chromatography, Fleury Group, Sa˜o Paulo, SP, Brazil HIROYOSHI SASAHARA • Clinical Laboratory and Transfusion Department at the Yokohama Municipal Citizen’s Hospital, Yokohama, Japan RODRIGO ANDRADE SCHUCH • Division of Research and Development, Fleury Group, Sa˜o Paulo, SP, Brazil LESLEY SCOTT • Department of Molecular Medicine and Haematology, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; National Health Laboratory Services, Johannesburg, South Africa; Department of Molecular Medicine and Haematology, School of Pathology, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa KIM STEEGEN • National Priority Programme, National Health Laboratory Service, Johannesburg, Gauteng, South Africa JOHANN STEINER • Laboratory of Translational Psychiatry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany; Department of Psychiatry, Otto-von-GuerickeUniversity Magdeburg, Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Germany; German Center for Mental Health (DZP), Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-IR-C), Site Jena-Magdeburg-Halle, Magdeburg, Germany WENDY STEVENS • Department of Molecular Medicine and Haematology, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; National Priority Programme, National Health Laboratory Service, Johannesburg, Gauteng, South Africa NADIA TURTON • School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK AMIR VAHEDIAN-AZIMI • Trauma Research Center, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran LUCIANA GODOY VIANA • Division of Research and Development, Fleury Group, Sa˜o Paulo, SP, Brazil CHRISTY WEISS • Luminex Corporation, A DiaSorin Company, Austin, TX, USA MIN-JUE XIE • Division of Development of Mental Functions, Research Center for Child Mental Development, University of Fukui, Fukui, Japan; United Graduate School of Child Development, Osaka University, Osaka, Japan; Life Science Innovation Center, University of Fukui, Fukui, Japan KENSUKE YAMAZAWA • Clinical Laboratory and Transfusion Department at the Yokohama Municipal Citizen’s Hospital, Yokohama, Japan SUMINO YANASE • Department of Health Science, Daito Bunka University School of Sports & Health Science, Saitama, Japan; Department of Molecular Life Science, Tokai University School of Medicine, Isehara, Kanagawa, Japan

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FATEMEH ZAHEDIPOUR • Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran NONTOBEKO ZWANE • Department of Immunology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; National Health Laboratory Services, Johannesburg, South Africa

Part I Reviews

Chapter 1 Multiplex Technologies in COVID-19 Research, Diagnostics, and Prognostics: Battling the Pandemic Paul C. Guest, Fatemeh Zahedipour, Muhammed Majeed, Tannaz Jamialahmadi, and Amirhossein Sahebkar Abstract Due to continuous technical developments and new insights into the high complexity of infectious diseases such as COVID-19, there is an increasing need for multiplex biomarkers to aid clinical management and support the development of new drugs and vaccines. COVID-19 disease requires rapid diagnosis and stratification to enable the most appropriate treatment course for the best possible outcomes for patients. In addition, these tests should be rapid, specific, and sensitive. They should rule out other potential causes of illness with simultaneous testing for other diseases. Elevated levels of specific biomarkers can be used to establish severity risks of chronic diseases so that patients can be provided the proper medication at the right time. This review describes the state-of-the-art technologies in proteomics, transcriptomics, and metabolomics, for multiplex biomarker approaches in COVID-19 research. Key words SARS-CoV-2, COVID-19, Biomarkers, Prognostic markers, RT-PCR, Mass spectrometry, Multiplex immunoassay, NMR metabolomics

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Introduction As of August 5, 2021, more than 2.5% of the world population has been infected by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for the ongoing COVID-19 disease [1, 2]. Although vaccine development and rollout have progressed at a rapid rate, with 29.4% of the world population having received at least one dose of a COVID-19 vaccine, there is still a need to manage the effects of this disease and minimize transmission so that normalcy can be established as early as possible (see Fig. 1) [3]. SARS-CoV-2 infection can lead to symptoms typically associated with other viruses that cause respiratory tract infections. The symptoms are moderate in most people and include high body temperature, cough, sore throat, shortness of breath, and general weakness [4–6]. However, this can progress in severe cases to

Paul C. Guest (ed.), Multiplex Biomarker Techniques: Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification, Methods in Molecular Biology, vol. 2511, https://doi.org/10.1007/978-1-0716-2395-4_1, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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pneumonia, acute respiratory distress syndrome (ARDS), multiple organ failure, and sometimes death [5, 7, 8]. In light of this, the World Health Organization (WHO) has emphasized the need for biomarker tests for improved diagnostics and to help stratify

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patients according to risk for the most appropriate treatment course [9]. Furthermore, increasing numbers of the scientific and medical communities have developed a keen interest in enhancing our knowledge about SARS-CoV-2 and other coronaviruses to enhance our chances of being ready when the next pandemic occurs. Given the potential multi-organ system effects in severe COVID-19 cases, biomarker tests can provide information on multiple pathways that can beneficially impact the disease outcomes. The lungs are the primary site of infection, although other organ systems and tissues can come under assault as the effects of the disease spread throughout the body. These include the liver [10], kidneys [11], heart [12], brain [13], and skin [14]. Thus, biomarker tests targeting effects on these tissues might have disease risk or prognostic value. One major pathway that has received considerable attention in the COVID-19 era is the “cytokine storm” response seen in some patients [15–19]. The existing evidence shows that patients with more severe forms of the disease tend to have higher pro-inflammatory cytokine concentrations than those who have milder forms of the illness. This hyper-activation of the inflammatory system indicates poorer prognoses and may be linked with higher mortality rates [20, 21]. In addition to the above needs, the sheer number of respiratory viruses that circulate the globe each year with similar symptoms requires that diagnostic tests for COVID-19 are highly specific. In the coronavirus family alone, 229E, OC43, HKU1, and NL63 are responsible for around one-quarter of the respiratory tract infections each year [22]. In addition to the coronaviruses, other annual respiratory viruses include the various influenza viruses, parainfluenza viruses, respiratory syncytial viruses, and adenoviruses [23, 24]. The COVID-19 diagnostics is an even greater challenge with the emergence of the B.1.1.7 (alpha), B.1.351 (beta), P1 (gamma), B.1.617.2 (delta), and other SARS-CoV-2 variants, all of which may have increased transmissibility and potential resistance to existing vaccines [25]. Given the multifaceted nature of COVID-19 disease and the urgent need to develop new tools for improved disease detection and monitoring, this review focuses on the multiplex biomarker technologies in current use, which enable the scrutiny of multiple targets simultaneously which accommodate the need for high throughput and speed. Here we focus on using these techniques to increase our understanding of the disease pathology and enable more precise diagnostics, prognostics, and treatment response monitoring in COVID-19 cases.

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SARS-CoV-2 Before beginning with our coverage of multiplex biomarker technologies, it is essential to provide a background on coronaviruses. The structure and function of the SARS-CoV-2 virus is similar to the earlier outbreak of coronaviruses such as SARS-CoV-1, which spread across 29 countries from 2002 to 2004 [26, 27], and Middle East respiratory syndrome (MERS)-CoV, which emerged in 2012 and has resulted in reported cases in 26 countries and is still present today [28, 29]. SARS-CoV-2 is an enveloped, spherical virus approximately 100 nm in diameter [30, 31]. As with the other coronaviruses, it can be easily characterized using electron microscopy by the presence of the protruding spike proteins, which give rise to the appearance of a corona (crown) or pin cushion-like effect around the viral core (see Fig. 2). The envelope contains a membrane glycoprotein which fortifies the membrane. The envelope also contains the envelope protein, which is involved in the assembly and release of new viral particles. Within the envelope, the nucleocapsid protein binds to viral RNA and forms the nucleocapsid structure. The SARS-CoV-2 RNA genome has an approximate length of 30,000 bases. Following infection of a host respiratory cell, the virus hijacks the cellular machinery to reproduce itself and facilitate the release of new viral particles. The virus thus spreads to other cells within the same host and eventually spreads to other hosts. The viral RNA encodes the spike, envelope, membrane, nucleocapsid proteins, and other accessory proteins required to maintain its life cycle. The rest of the essential components required in the process come from the host.

100 nm Fig. 2 Basic structure of the SARS-CoV-2 virus

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Multiplex Molecular Analysis of SARS-CoV-2 Several molecular profiling technologies can be used to enhance diagnostic accuracy, disease characterization, risk prediction, and treatment monitoring related to SARS-CoV-2 infections. All of these capitalize on either the viral RNA sequence, the encoded proteins, or the physiological pathways and relevant tissues affected within the host. In the next sections, we describe the leading technologies that can be used to achieve this.

3.1 Quantitative Polymerase Chain Reaction (qPCR)

The most accepted diagnostic technique for COVID-19 is qPCR, although this has been contended. This technique allows the observation of PCR tests in real time by using a nucleotide sequencespecific probe containing a fluorescent dye and quencher and sequence-specific forward and reverse primers (see Fig. 3) [32]. During PCR, probe cleavage by the Taq polymerase 50 to 30 exonuclease activity removes the quencher effects and allows the fluorescent signal to be detected in proportion to the amount of the targeted amplicon. The availability of multiple reporter dyes that fluoresce at distinct wavelengths allows the multiplexing capability. Lee et al. developed a PCR-based method to detect mutational hotspots in the SARS-CoV-2 spike protein, which have given rise to most of the variants of concern across the world [33]. As of September 2020, one of these variants predominated, increasing the infectivity and transmission by enhancing SARS-CoV-2 binding

Fig. 3 Principles behind quantitative PCR analysis using multiple fluorophores for simultaneous detection of different viral sequences

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to the angiotensin-converting enzyme 2 (ACE2) receptor on host cells. In another study, Hernandez et al. described a new sensitive PCR/matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS)-based assay for detection of the virus in saliva specimens from symptomatic and asymptomatic people in community settings as a potential means of increasing screening speed and capacity of the general population [34]. This method had a high diagnostic performance compared to upper respiratory specimens from the same patients, with sensitivities comparable to detection of SARS-CoV-2 RNA in saliva by the Roche cobas® 6800/8800 SARS-CoV-2 RT-PCR Test (Roche). Another group evaluated 11 primer and probe sets to identify SARS-CoV-2 variants of concern [35]. However, some of the variant sequences were contained within the primer annealing sites, which highlights the constant need to re-evaluate these primer sequences. Along the same lines, Vogels et al. described a multiplex qPCR test that discriminates the B.1.1.7 variant, which targets a deletion in the spike protein sequence at amino acids 69–70 (Δ69–70) [36]. To distinguish this from the B.1.351 and the P.1 variants, the authors also targeted deletion in the open reading frame 1a (ORF1a) gene (Δ3675–3677) present in all three variants not in other SARS-CoV-2 lineages. 3.2

MS Proteomics

3.2.1 MS-Based Techniques to Identify Diagnostic/Prognostic Biomarkers

MS measures the mass/charge (m/z) ratio of ionized particles such as peptides, lipids, and metabolites, which can be used for identification and quantitation purposes [37]. In the study of proteins (proteomics), digestion with an enzyme such as trypsin is required as intact proteins are too large and complex to analyze in this way [38]. After this, an electric charge is applied to transform the peptides into a charged plasma state. Next, the peptide ions are introduced into the mass spectrometer, where magnets accelerate them toward a detector at a rate that is inversely proportional to their m/z ratios. The amount of peptide striking the detector over a set unit of time is proportional to its quantity, which can be extrapolated to the quantity of the corresponding parent protein (see Fig. 4). Also, the amino acid sequence of each peptide can be derived from the m/z values of fragmented ions produced using a bombarding gas such as nitrogen in a process called collisioninduced dissociation. Using the mass spectrometry approach, thousands of peptides corresponding to hundreds of proteins can be analyzed simultaneously. Several mass spectrometry analyses have been carried out to increase our knowledge and identify new targets for COVID-19 patients. Rajczewski et al. amassed a list of more than 600 peptides identified through analyses of SARS-CoV-2-infected samples via MS [39]. They next analyzed these peptides using the automated tools PepQuery, BLAST-P, the Multi-omic Visualization Platform,

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Fig. 4 Mass spectrometry workflow using (a) ESI or (b) MALDI ionization approaches for identification and quantitation of multiple peptides simultaneously

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MetaTryp, and Proteomics Data Viewer (PDV) and narrowed the list down to 87 peptides that could be robustly detected and specific to the SARS-CoV-2 virus. After this, they applied a stringent p-value cutoff and manual quality checks of peptide spectra, which finally identified four peptides from the nucleocapsid phosphoprotein and membrane protein. The authors proposed that these peptides would offer the most value for clinical proteomics approaches to detect COVID-19 from patient samples. The findings also suggested that samples harvested from the upper respiratory tract and oral cavity have the highest potential for diagnosing SARS-CoV-2 infection. Another study used a combined MALDITOF MS machine learning approach as a rapid high-throughput approach to COVID-19 testing [40]. They used nasopharyngeal swab samples from adult volunteers for testing this system and carried this out compared to the standard RT-PCR tests. This led to the identification of two optimized models with 98.3% and 96.6% accuracy, which made this comparable to existing commercial SARS-CoV-2 tests. Another study developed a similar method that combined human nasopharyngeal samples analysis using MALDI-TOF MS [41]. The samples were analyzed after minimal sample preparation, and the resulting mass spectra were used to build different machine learning models. The most sensitive model showed performance greater than 90%. In another approach, Suvarna et al. used a labelfree quantitative MS analysis of 71 patients to compare plasma samples from moderate and severe SARS-CoV-2 patients to noninfected controls [42]. The analysis identified 38 proteins that were present at different quantities between the moderate and severe patients. These proteins were involved in peptidase activity, regulated exocytosis, blood coagulation, complement activation, immune response, and glucocorticoid biological processes. They also used a supervised machine learning approach with linear support vector machine modeling to identify the best classifiers of the moderate and severe patients. Based on the results, they selected a 20-plex panel of proteins with a classification accuracy of 0.84. MS-based biomarker signatures for SARS-CoV-2 have also been identified in other body fluids such as urine and saliva [43]. The Critical Course Complications in Patients with SARSCoV-2 Infection Study (CRIT-COV-U) investigators identified a 50-plex urinary proteomic biomarker panel using a capillary electrophoresis (CE)-MS analysis of samples from 228 patients as a training set with replication using 99 patients as a validation cohort. Using this panel, they found an area under the curve (AUC) of 0.80 for mortality and 0.74 for the World Health Organization clinical progression score. Another study used tandem MS and other proteomic approaches to generate a library of approximately 40,000 synthetic peptides linked with SARS-CoV-2 infections [44]. One

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study compared a new MS-based SARS-CoV-2 panel with RT-PCR diagnosis and found discordance between these analyses in approximately 10% of the nasopharyngeal swabs. The proteomic approach showed higher positivity even in patients found to be suspected or negative for COVID-19 using the RT-PCR system [45]. Thus, further work is required to establish the best practices and workflows for the use of each system. A review of the recent proteome, phosphoproteome, and interactome datasets led to identifying common signaling proteins associated with the cellular response to SARS-CoV-2 infection [46]. One of the most robust of these proteins was the tyrosine kinase ephrin receptor A2. 3.2.2 MS-Based Proteomic Approaches to Monitor the Development of New COVID-19 Treatments

MS techniques can also be used to identify new treatment approaches for COVID-19. Ishmail et al. tested the molecular docking of Clinacanthus nutans compounds, which block the interaction of the virus with the ACE2 receptor using gas chromatography (GC) MS-based approach using the AutoDock 4.2 tool [47]. They identified 14 compounds that had an excellent inhibitory effect against the SARS-CoV-2 main protease and the ACE2 receptor. Their analysis showed that glyceryl 2-linolenate had the most potent binding to both proteins. In another study, Morsy et al. developed a rapid tandem MS method in a pharmacokinetic analysis of the antiviral drug favipiravir (FAV) in plasma from healthy volunteers as a potential new treatment for COVID-19 [48]. This method was validated in accordance with US Food and Drug Administration (FDA) guidelines to demonstrate the bioequivalence of this new compound.

3.3 Multiplex Immunoassay

Body fluids such as serum and plasma contain many molecules, including cytokines, hormones, and growth factors, at very low concentrations and require sensitive detection methods. One of the most sensitive approaches for this is multiplex immunoassay using target-specific high-affinity antibodies (see Fig. 5) [38, 49]. This assay is performed using distinct batches of dye-encoded microbeads. A specific capture antibody is attached to the beads so that each has a specific fluorescent signature comprised of differing ratios of red/infrared dyes. The target molecule in the sample binds to the relevant antibody-bead conjugate. After this, a fluorescent-labeled detection antibody is added, which binds specifically to the same target molecule in a “sandwich” configuration. Finally, the detection antibody/antigen/capture antibodybead conjugates are analyzed in a reader using the fluorescent signals from the beads and detection antibodies for simultaneous identification and quantitation, respectively.

3.3.1 Multiplex Immunoassay for Diagnosis of COVID-19

Borena et al. evaluated the performance of an 11-plex blood-based assay for detection of antibodies against the spike protein, subunit 1 of the spike protein, the spike protein receptor-binding domain, and nucleocapsid protein of SARS-CoV-2, and to subunit 1 of the

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Fig. 5 Multiplex immunoassay workflow using dye-coded microbead-capture antibody complexes and fluorescently labeled detection antibodies, and analysis on a fluorescence-activated cell sorting (FACS)-like instrument

spike proteins of SARS-CoV, MERS-CoV, NL63, HKU1, 229E, and OC43 coronaviruses [50]. The sensitivity for detecting SARSCoV-2 antibodies was 83–94%, with 98–100% specificity. In addition, there was no cross-reaction between SARS-CoV-2 and other coronavirus antibodies apart from SARS-CoV. Another study used a multiplex immunoassay to measure SARS-CoV-2 antibody responses during the first week after symptom onset in COVID19 patients who were either eventually discharged or died from the disease [51]. This analysis showed significantly lower levels of SARS-CoV-2 spike protein antibodies in the patients who died. Woudenberg et al. measured antibody responses to SARS-CoV2 and the NL63, HKU1, 229E, and OC43 coronaviruses using a combined multiplex immunoassay luciferase-linked immunosorbent assay and a pseudotype neutralization assay [52]. Interestingly, they found significant cross-reactivity between antibodies to SARSCoV-2 and the other coronaviruses, but there was no evidence for immunity crossover. Another interesting approach was the application of a multiplex immunoassay in the analysis of dried blood spots from COVID-19 patients [53]. This assay detected immunoglobulin G (IgG) antibodies to the nucleocapsid and spike 1 subunit proteins using a 384-well plate format. The authors stated that this test format is suitable for large-scale community-based screening.

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Metabolomics

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MS can also analyze small molecules such as amino acids, lipids, and metabolites without digesting the samples ahead of time. In addition, a technique called proton nuclear magnetic resonance (1H-NMR) can also be used for the analysis of small molecules [38, 54, 55]. This approach has advantages over the MS-based techniques in that it does not typically require separation or fractionation before sample analysis. 1H-NMR spectroscopy provides structural information of molecules and is therefore valuable for studies aimed at identification or detection. This is accomplished by tracking the behavior of the protons on these molecules in a high intensity magnetic field. In such situations, the proton nuclei align with the field in the same way that a compass needle aligns with the Earth’s magnetic field. The NMR procedure’s initiation occurs by applying different radiofrequency pulses, which induce the nuclei to alter their rotation around the axis of the applied magnetic field. Structural information can be obtained as the rotational frequency is related to the position of the proton within the molecule (see Fig. 6).

Fig. 6 Proton (1H) NMR principle for multiplex detection of small molecules, metabolites, lipids, and amino acids

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3.4.1 Metabolomic Studies of COVID-19

A metabolomic study was carried out to identify potential biomarkers in COVID-19 patients associated with mild, moderate, and severe forms of the disease [56]. The study resulted in the identification of 77 molecules, including amino acids, lipids, polyamines, and sugars, that were capable of distinguishing severe from mild COVID-19 patients. Out of the moderate group of patients who received tocilizumab, ten metabolites were found that discriminated individuals who had a favorable outcome compared to those who required treatment in an intensive care unit (ICU). One of the metabolites, anthranilic acid, was correlated significantly with a poor prognosis. A similar metabolomic study attempted to identify serum metabolites associated with mild and severe COVID-19 patients [57]. This analysis found differences in metabolites associated with altered amino acid catabolism under hypoxic conditions. In particular, elevations of α-hydroxyl acids were correlated with disease severity, oxygen saturation levels, and lung damage biomarkers. From these data, the authors suggested that conversion of α-keto acids to α-hydroxyl acids may benefit nicotinamide adenine dinucleotide (NAD) cycling in patients with altered oxygen levels highlighting the potential utility of amino acid supplementation in cases of SARS-CoV-2 infection. A GC-MS metabolomic study by Shi and co-workers identified a 3-plex of d-fructose, citric acid, and 2-palmitoyl-glycerol that could distinguish COVID-19 patients from patients with other respiratory disorders, such as influenza, and healthy controls, with an AUC > 0.92 [58]. Furthermore, they showed that a 7-plex consisting of 2-hydroxy-3-methylbutyric acid, 3-hydroxybutyric acid, cholesterol, succinic acid, L-ornithine, oleic acid, and palmitelaidic acid could be applied to identify those patients most at risk of progressing from mild to severe COVID-19, with an AUC of 0.97.

3.4.2 Metabolomic Identification of COVID-19 Treatment-Related Biomarkers

Another metabolomic study aimed to identify metabolites and xenobiotics capable of binding to the active site of the SARSCoV-2 main protease as a potential starting point in developing new antiviral therapies [59]. Their follow-up in silico and in vitro analyses showed that the food-derived natural compounds silybin and silymarin inhibited the main protease and may therefore be useful as a novel therapeutic approach against COVID-19 disease. In an attempt to identify biomarkers of drug response, Meoni and colleagues used a non-targeted nuclear magnetic resonance (NMR)-based metabolomics and lipidomics approach to screening COVID-19 patients with and without treatment with tocilizumab [60]. This showed that patients had more common plasma metabolomic and lipidomic signatures than controls, and the tocilizumab treatment resulted in at least partial reversion of this signature.

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3.5 Other Approaches

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A number of other techniques have been developed either for detection of the SARS-CoV-2 virus or for studies of its effects inside the host. Lee et al. developed a ribonucleoprotein capture technique followed by transcriptomic analysis, which led to the identification of 109 host factors that bind directly to SARS-CoV2 RNA, and many of these were conserved in other coronaviruses [30]. These proteins included both antiviral and proviral proteins involved in multiple steps of mRNA replication. The findings suggested these proteins as potential new targets for therapeutic intervention. One method that has emerged for more rapid testing for COVID-19 infections is the lateral flow immunoassay technique. A lateral flow test works according to the same principle as an immunoassay. However, in these tests, the applied sample is carried along the surface of a reagent-embedded pad by capillary action to zones where it reacts with antibody-reporter conjugates to show a positive or negative result (see Fig. 7) [61]. A lateral flow immunoassay biosensor was developed for the diagnosis of COVID-19 using four SARS-CoV-2 nucleocapsid-specific variable fragment fusion antibodies [62]. The authors showed that these antibodies explicitly bound to the SARS-CoV-2 nucleocapsid protein and not to the nucleocapsid proteins of MERS-CoV, SARS-CoV, or influenza H1N1 viruses. Another study described the use of clustered regularly interspaced short palindromic repeat (CRISPR/Cas) proteins in a lateral flow assay for SARS-CoV-2 RNA sequences with single base specificity [63]. The same study also developed a multiplex fluorescence CRISPR/Cas9 nuclease cleavage assay capable of distinguishing SARS-CoV-2, influenza A, influenza B, and respiratory syncytial virus that would be useful in identifying respiratory viral pathogens with similar symptomology. Finally, a review by

Fig. 7 Lateral flow assay format for detection of SARS-CoV-2 and other viruses in a multiplex format

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Costa et al. suggested the use of an analytical platform composed of multiple platforms such as infrared spectroscopy, MALDI-TOF mass spectrometry, and qPCR to identify individuals suffering from SARS-CoV-2 or the dengue, Zika, or chikungunya arboviruses to increase diagnostic accuracy [64]. Klu¨pfel et al. developed an automated, microfluidics-based immunoassay for blood-based multiplex detection to reduce assay times for more rapid diagnosis of COVID-19 infections. The detection of IgG antibodies against the SARS-CoV-2 spike 1 protein, receptor-binding domain, and nucleocapsid protein could be accomplished in less than 8 min [65]. They found sensitivities and specificities both of 100% comparing 65 SARS-CoV-2 positive and negative samples. Another group used a multiplex circular flow immunoassay test strip with you only look once (YOLO)-based object recognition to quantitate and distinguish antibodies against the SARS-CoV-2 membrane glycoprotein and the hemagglutinin of influenza A (H1N1) virus in mice [66]. This approach also showed promise in studies of SARS-CoV-2 infections in humans. Since co-circulation of COVID-19 and dengue fever occurs, Lee et al. developed a microchannel multiplex immunoassay with a novel pixel intensity-based image analysis setup which allowed simultaneous testing for SARS-CoV-2, dengue, and other arboviruses within 30 min [67]. In addition, Kumar et al. recently developed a FnCas9-based CRISPR lateral flow assay to detect SARS-CoV-2 spike N501Y mutation by visual readout within one hand, which can be quantified using a smartphone-based app [68]. The N501Y mutation appears to enhance the binding ability of the spike protein to the ACE2 receptor on host cells. It is present in the alpha, beta, and gamma variants and not the delta form. The authors stated that this system can also be adapted for the detection of other mutations such as T478K, which appears to present only in the spike protein of the delta variant [69]. These capabilities highlight the advantages of the CRISPR system for monitoring the emergence of new coronavirus variants in the current and future pandemics.

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Summary and Future Perspectives Despite all of the studies carried out on the SARS-CoV-2 virus over the last year and a half, we are still plagued by the COVID-19 pandemic, with continuous new waves and new variants emerging worldwide. Nevertheless, considerable progress has been made in the development and deployment of diagnostic/prognostic platforms, which have increased our understanding of the multifaceted nature of the disease and provided a means of diagnosing this disease with greater accuracy and in real time. We now also have a way of determining the patients who are most at risk of suffering a

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more severe disease course and place them on the most appropriate treatment course in a timely manner to improve their chances of recovery. In this way, the disease can be handled more effectively in a predictive, preventative, and personalized medicine approach [70, 71]. One of the positive outcomes of this pandemic has been the virtual global cooperative response in developing diagnostics, new treatments, repurposed treatments, and vaccines to help curb the spread of this virus. This has not only led to better disease outcomes in countries where these approaches have been employed, but it has also helped to pave the way for how we deal with future pandemics. In the case of the current pandemic, these approaches are critical to improving disease outcomes during our endeavors to bridge the vaccination gap across the entire world. References 1. https://www.worldometers.info/coronavirus/ 2. Johns Hopkin’s Coronavirus Resource Center. https://coronavirus.jhu.edu/map.html. Accessed 11 Aug 2021 3. Our world in data. Coronavirus (COVID-19) vaccinations. https://ourworldindata.org/ covid-vaccinations. Accessed 5 Aug 2021 4. Krumm ZA, Lloyd GM, Francis CP et al (2021) Precision therapeutic targets for COVID-19. Virol J 18(1):66. https://doi. org/10.1186/s12985-021-01526-y 5. Budinger GRS, Misharin AV, Ridge KM et al (2021) Distinctive features of severe SARSCoV-2 pneumonia. J Clin Invest 131(14): e 1 4 9 4 1 2 . h t t p s : // d o i . o r g / 1 0 . 1 1 7 2 / JCI149412 6. Rufaida, Mahmood T, Kedwai I et al (2021) A dossier on COVID-19 chronicle. J Basic Clin Physiol Pharmacol. https://doi.org/10.1515/ jbcpp-2020-0511. Online ahead of print 7. Rudrapal M, Khairnar SJ, Borse LB et al (2020) Coronavirus disease-2019 (COVID-19): an updated review. Drug Res (Stuttg) 70(9): 389–400 8. Cobb NL, Sathe NA, Duan KI et al (2021) Comparison of clinical features and outcomes in critically ill patients hospitalized with COVID-19 versus influenza. Ann Am Thorac Soc 18(4):632–640 9. Diagnostic testing for SARS-CoV-2: interim guidance, 11 September 2020. https://apps. who.int/iris/handle/10665/334254. Accessed 5 Aug 2021 10. Samidoust P, Samidoust A, Samadani AA et al (2020) Risk of hepatic failure in COVID-19 patients. A systematic review and meta-analysis. Infez Med 28(suppl 1):96–103

11. Jafari-Oori M, Fiorentino M, Castellano G et al (2021) Acute kidney injury and Covid-19: a scoping review and meta-analysis. Adv Exp Med Biol 1321:309–324 12. Sahranavard M, Akhavan Rezayat A, Zamiri Bidary M et al (2021) Cardiac complications in COVID-19: a systematic review and metaanalysis. Arch Iran Med 24(2):152–163 13. Bandeira IP, Schlindwein MAM, Breis LC et al (2021) Neurological complications of the COVID-19 pandemic: what have we got so far? Adv Exp Med Biol 1321:21–31 14. Burger B, Rodrigues HG (2021) Cutaneous manifestations of COVID-19: early diagnosis and prognostic information. Adv Exp Med Biol 1327:119–127 15. Del Valle DM, Kim-Schulze S, Huang HH et al (2020) An inflammatory cytokine signature predicts COVID-19 severity and survival. Nat Med 26(10):1636–1643 16. Mehta P, Fajgenbaum DC (2021) Is severe COVID-19 a cytokine storm syndrome: a hyperinflammatory debate. Curr Opin Rheumatol. https://doi.org/10.1097/BOR. 0000000000000822. Online ahead of print 17. George JA, Mayne ES (2021) The novel coronavirus and inflammation. Adv Exp Med Biol 1321:127–138 18. Hargreaves IR, Mantle D (2021) COVID-19, coenzyme Q10 and selenium. Adv Exp Med Biol 1327:161–168 19. Tavasolian F, Hatam GR, Mosawi SH et al (2021) The immune response and effectiveness of COVID-19 therapies. Adv Exp Med Biol 1321:115–126 20. Ramasamy S, Subbian S (2021) Critical determinants of cytokine storm and type I interferon

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response in COVID-19 pathogenesis. Clin Microbiol Rev 34(3):e00299–e00220. https://doi.org/10.1128/CMR.00299-20 21. Melo AKG, Milby KM, Caparroz ALMA et al (2021) Biomarkers of cytokine storm as red flags for severe and fatal COVID-19 cases: a living systematic review and meta-analysis. PLoS One 16(6):e0253894. https://doi.org/ 10.1371/journal.pone.0253894 22. Fehr AR, Perlman S (2015) Coronaviruses: an overview of their replication and pathogenesis. Methods Mol Biol 1282:1–23 23. Boncristiani HF (2009) Respiratory viruses. In: Encyclopedia of microbiology. Academic/ Elsevier, Cambridge, MA, pp 500–518. ASIN: B01NGZYXVF 24. Audi A, AlIbrahim M, Kaddoura M et al (2020) Seasonality of respiratory viral infections: will COVID-19 follow suit? Front Public Health 8:567184. https://doi.org/10.3389/ fpubh.2020.567184 25. Public Health England (2021) SARS-CoV2 variants of concern and variants under investigation in England. Technical briefing 14 (3 June 2021). https://assets.publishing. service.gov.uk/government/uploads/system/ uploads/attachment_data/file/991343/ Variants_of_Concern_VOC_Technical_Brief ing_14.pdf. Accessed 11 Aug 2021 26. Qin E, Zhu Q, Yu M et al (2003) A complete sequence and comparative analysis of a SARSassociated virus (Isolate BJ01). Chin Sci Bull 48(10):941–948 27. Tam T (2018) Fifteen years post-SARS: key milestones in Canada’s public health emergency response. Can Commun Dis Rep 44(5):98–101 28. de Groot RJ, Baker SC, Baric RS et al (2013) Middle East respiratory syndrome coronavirus (MERS-CoV): announcement of the coronavirus study group. J Virol 87(14):7790–7792. https://doi.org/10.1128/JVI.01244-13 29. Khan S, El Morabet R, Khan RA et al (2020) Where we missed? Middle East Respiratory Syndrome (MERS-CoV) epidemiology in Saudi Arabia; 2012-2019. Sci Total Environ 747:141369. https://doi.org/10.1016/j. scitotenv.2020.141369 30. Kandeel M, Ibrahim A, Fayez M et al (2020) From SARS and MERS CoVs to SARS-CoV-2: moving toward more biased codon usage in viral structural and nonstructural genes. J Med Virol 92(6):660–666 31. Lee S, Lee YS, Choi Y et al (2021) The SARSCoV-2 RNA interactome. Mol Cell 81(13): 2838–2850 32. Hawkins SFC, Guest PC (2017) Multiplex analyses using real-time quantitative PCR. Methods Mol Biol 1546:125–133

33. Lee S, Lee MK, Na H et al (2021) Comparative analysis of mutational hotspots in the spike protein of SARS-CoV-2 isolates from different geographic origins. Gene Rep 23:101100. https://doi.org/10.1016/j.genrep.2021. 101100 34. Hernandez MM, Banu R, Shrestha P et al (2021) RT-PCR/MALDI-TOF mass spectrometry-based detection of SARS-CoV2 in saliva specimens. J Med Virol. https:// doi.org/10.1002/jmv.27069. Online ahead of print 35. Gand M, Vanneste K, Thomas I et al (2021) Deepening of in silico evaluation of SARSCoV-2 detection RT-qPCR assays in the context of new variants. Genes (Basel) 12(4):565. https://doi.org/10.3390/genes12040565 36. Vogels CBF, Breban MI, Ott IM et al (2021) Multiplex qPCR discriminates variants of concern to enhance global surveillance of SARSCoV-2. PLoS Biol 9(5):e3001236. https:// doi.org/10.1371/journal.pbio.3001236 37. Martins-de-Souza D, Guest PC, VanattouSaifoudine N et al (2011) Proteomic technologies for biomarker studies in psychiatry: advances and needs. Int Rev Neurobiol 101: 65–94 38. Guest PC (2016) Biomarkers and mental illness: it’s not all in the mind. In: Chapter 3 – the importance of biomarkers: the required tools of the trade, 1st edn. Copernicus, Go¨ttingen. ISBN-13: 978-3319460871 39. Rajczewski AT, Mehta S, Nguyen DDA et al (2021) A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of coronavirus disease 2019 (COVID-19). Clin Proteomics 18(1):15. https://doi.org/10. 1186/s12014-021-09321-1 40. Tran NK, Howard T, Walsh R et al (2021) Novel application of automated machine learning with MALDI-TOF-MS for rapid high-throughput screening of COVID-19: a proof of concept. Sci Rep 11(1):8219. https://doi.org/10.1038/s41598-02187463-w ˜ a-Me´ndez 41. Deulofeu M, Garcı´a-Cuesta E, Pen EM et al (2021) Detection of SARS-CoV2 infection in human nasopharyngeal samples by combining MALDI-TOF MS and artificial intelligence. Front Med (Lausanne) 8:661358. h t t p s : //d o i . o r g / 1 0 . 3 3 8 9 / f m e d. 2 0 2 1. 661358 42. Suvarna K, Biswas D, Pai MGJ et al (2021) Proteomics and machine learning approaches reveal a set of prognostic markers for COVID-19 severity with drug repurposing potential. Front Physiol 12:652799. https:// doi.org/10.3389/fphys.2021.652799

Multiplex Biomarkers and COVID-19 43. Wendt R, Thijs L, CRIT-COV-U investigators (2021) A urinary peptidomic profile predicts outcome in SARS-CoV-2-infected patients. EClin Med:100883. https://doi.org/10. 1016/j.eclinm.2021.100883. Online ahead of print 44. Li C, Revote J, Ramarathinam SH et al (2021) Resourcing, annotating, and analysing synthetic peptides of SARS-CoV-2 for immunopeptidomics and other immunological studies. Proteomics 2:e2100036. https://doi.org/10. 1002/pmic.202100036. Online ahead of print 45. Rybicka M, Miosz E, Bielawski KP (2021) Superiority of MALDI-TOF mass spectrometry over real-time PCR for SARS-CoV-2 RNA detection. Viruses 13(5):730. https://doi. org/10.3390/v13050730 46. Ahsan N, Rao RSP, Wilson RS et al (2021) Mass spectrometry-based proteomic platforms for better understanding of SARS-CoV2 induced pathogenesis and potential diagnostic approaches. Proteomics 21(10):e2000279. https://doi.org/10.1002/pmic.202000279 47. Ismail NZ, Adebayo IA, Mohamad Zain NN et al (2021) Molecular docking of compounds from Clinacanthus nutans extract detected by GC-MS analysis with the SARS-CoV-2 main protease and ACE2 protein. Nat Prod Res:1–5. https://doi.org/10.1080/14786419.2021. 1919104. Online ahead of print 48. Morsy MI, Nouman EG, Abdallah YM et al (2021) A novel LC-MS/MS method for determination of the potential antiviral candidate favipiravir for the emergency treatment of SARS-CoV-2 virus in human plasma: application to a bioequivalence study in Egyptian human volunteers. J Pharm Biomed Anal 199: 114057. https://doi.org/10.1016/j.jpba. 2021.114057 49. Stephen L (2017) Multiplex immunoassay profiling. Methods Mol Biol 1546:169–176 50. Borena W, Kimpel J, Gierer M et al (2021) Characterization of immune responses to SARS-CoV-2 and other human pathogenic coronaviruses using a multiplex bead-based immunoassay. Vaccines (Basel) 9(6):611. https://doi.org/10.3390/vaccines9060611 51. Zhou ZH, Dharmarajan S, Lehtimaki M et al (2021) Early antibody responses associated with survival in COVID19 patients. PLoS Pathog 17(7):e1009766. https://doi.org/10. 1371/journal.ppat.1009766 52. Woudenberg T, Pelleau S, Anna F, Attia M et al (2021) Humoral immunity to SARS-CoV2 and seasonal coronaviruses in children and adults in North-Eastern France. EBioMedicine 70:103495. https://doi.org/10.1016/j. ebiom.2021.103495. Online ahead of print

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53. Styer LM, Hoen R, Rock J et al (2021) Highthroughput multiplex SARS-CoV-2 IgG microsphere immunoassay for dried blood spots: a public health strategy for enhanced serosurvey capacity. Microbiol Spectr: e0013421. https://doi.org/10.1128/Spec trum.00134-21. Online ahead of print 54. Barbosa BS, Martins LG, Costa TBBC et al (2018) Qualitative and quantitative NMR approaches in blood serum lipidomics. Methods Mol Biol 1735:365–379 55. Danlos FX, Grajeda-Iglesias C, Durand S et al (2021) Metabolomic analyses of COVID-19 patients unravel stage-dependent and prognostic biomarkers. Cell Death Dis 12(3):258. https://doi.org/10.1038/s41419-02103540-y 56. Pontes JGM, Brasil AJM, Cruz GCF et al (2017) 1H NMR metabolomic profiling of human and animal blood serum samples. Methods Mol Biol 1546:275–282 57. Pa´ez-Franco JC, Torres-Ruiz J, Sosa-Herna´ndez VA et al (2021) Metabolomics analysis reveals a modified amino acid metabolism that correlates with altered oxygen homeostasis in COVID-19 patients. Sci Rep 11(1):6350. https://doi.org/10.1038/s41598-02185788-0 58. Shi D, Yan R, Lv L et al (2021) The serum metabolome of COVID-19 patients is distinctive and predictive. Metabolism 118:154739. https://doi.org/10.1016/j.metabol.2021. 154739 59. Sardanelli AM, Isgro` C, Palese LL (2021) SARS-CoV-2 main protease active site ligands in the human metabolome. Molecules 26(5): 1 4 0 9 . h t t p s : // d o i . o r g / 1 0 . 3 3 9 0 / molecules26051409 60. Meoni G, Ghini V, Maggi L et al (2021) Metabolomic/lipidomic profiling of COVID-19 and individual response to tocilizumab. PLoS Pathog 17(2):e1009243. https://doi.org/10. 1371/journal.ppat.1009243 61. Somborac Bacura A, Dorotic M, Grosˇic L et al (2021) Current status of the lateral flow immunoassay for the detection of SARS-CoV-2 in nasopharyngeal swabs. Biochem Med (Zagreb) 31(2):020601. https://doi.org/10.11613/ BM.2021.020601 62. Kim HY, Lee JH, Kim MJ et al (2021) Development of a SARS-CoV-2-specific biosensor for antigen detection using scFv-Fc fusion proteins. Biosens Bioelectron 175:112868. https://doi.org/10.1016/j.bios.2020. 112868 63. Osborn MJ, Bhardwaj A, Bingea SP et al (2021) CRISPR/Cas9-based lateral flow and

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Chapter 2 Multivalent Vaccine Strategies in Battling the Emergence of COVID-19 Variants Paul C. Guest Abstract The emergence of new SARS-CoV-2 variants has led to increased transmission and more severe cases of COVID-19, with some having the ability to escape the existing vaccines. This review discusses the importance of developing new vaccine strategies to keep pace with these variants to more effectively manage the pandemic. Many of the new vaccine approaches include multivalent display of the most highly mutated regions in the SARS-CoV-2 spike protein such that they resemble a virus particle and can stimulate an effective neutralization response. It is hoped that such approaches help to manage the existing pandemic and provide a robust infrastructure toward fast tracking responses across the world in case of future pandemics. Key words COVID-19, SARS-CoV-2, Pandemic, Variant, Vaccine, Multivalent, Antibody

1

Introduction The SARS-CoV-2 virus responsible for COVID-19 disease has led to a global pandemic of proportions that have not been seen since the Spanish flu outbreak of 1918–1920 [1–3]. Most people infected by the SARS-CoV-2 virus experience a mild form of the disease although some develop a more serious illness and, in approximately 2% of the cases, this can be life-threatening and lead to death [4]. As the pandemic evolved, a series of variants began to emerge due the high mutation rate of RNA viruses in general [5–7]. These variants have perpetuated and enhanced the spread of this virus and may have also decreased the protective effect of some of the existing vaccines [8]. Thus, a major objective in this stage of the pandemic is one of increased vigilance to track new and emerging variants and to pave the way for more effective means of managing the virus spread. The most promising approaches so far have been the social distancing and lockdown

Paul C. Guest (ed.), Multiplex Biomarker Techniques: Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification, Methods in Molecular Biology, vol. 2511, https://doi.org/10.1007/978-1-0716-2395-4_2, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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Fig. 1 Histogram showing a range of countries and their rates of vaccination. UAE United Arab Emirates, UK United Kingdom, USA United States of America

measures, as well as the worldwide effort to develop effective treatments and vaccines [9–11]. As of October 25, 2021, almost 49% of the world population has received at least one dose of COVID-19 vaccine, although this is as low as 3.1% in some low-income countries [12]. When assessing the countries according to percentages of population that are fully vaccinated, Portugal and the United Arab Emirates rank the highest (both over 86%) followed by Spain, Singapore, Cambodia, and Chile (all over 75%). However, there is a large disparity as less than 2% of the populations in some African countries such as Ethiopia, Nigeria, and Tanzania have been fully vaccinated (see Fig. 1). It is now an accepted fact that this imbalance has to be corrected in order to stop the virus from spreading further and allow some aspects of normal life such as world travel to resume to near normal levels [13]. This will require increased vaccine production and distribution capacities, political acceptance of the need to distribute vaccines around the world more equitably, as well as the development of new vaccination approaches to keep pace effectively with the constantly emerging variants. There are a number of strategies involved in the worldwide vaccine production endeavor to bring the ongoing SARS-CoV2 pandemic down to manageable levels. These include both traditional and novel approaches including inoculation with inactivated or attenuated virus, as well as vector-, nucleic acid-, and proteinbased versions [14]. Multiple clinical studies have shown that many

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of these vaccines are comparable in their ability to mount an immune response against existing SARS-CoV-2 variants, but there is a realistic probability that some of the emerging variants may be able to escape the existing vaccines [15–17]. In addition, more variants are likely to emerge as long as the virus continues to spread across the globe. This leads to the potential need to constantly update the existing vaccines to target the relevant version of the virus in a given area or to construct multivalent vaccines to extend the coverage more broadly [14, 18]. This review describes the recent efforts to control the COVID19 pandemic through the development of vaccination approaches, with a focus on multivalent designs. It should be stressed again that such approaches alone will not lead to better control of SARS-CoV2 until the current inequities in distribution to low-income countries are addressed. The driving force for this comes from the fact that the continued existence of this deadly virus anywhere is a risk to the entire world.

2

Traditional Vaccine Strategies As of October 25, 2021, seven vaccines have been approved for use by the World Health Organization [19] (Table 1). Most of these vaccines target sequences in the SARS-CoV-2 spike proteins which cover the surface of the virus like a crown (hence the name coronavirus) and acts as the predominant factor in host cell interaction and viral entry (see Fig. 2) [20]. The spike protein has two subunits, with the S1 subunit containing the receptor binding domain (RBD) which binds to the angiotensin-converting enzyme

Table 1 List of current WHO-approved COVID-19 vaccines

Vaccine

WHO approval (no. countries)

Oxford/AstraZeneca AZD1222

124

Non-replicating viral vector

Pfizer/BioNTech BNT162b2

103

RNA

Moderna mRNA-1273

76

RNA

Mechanism

Janssen (Johnson & Johnson) Ad26.COV2.S 75

Non-replicating viral vector

Sinopharm (Beijing) BBIBP-CorV (Vero Cells)

68

Inactivated

Covishield (Oxford/AstraZeneca formulation)

46

Non-replicating viral vector

Sinovac-CoronaVac

42

Inactivated

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SARS-CoV-2

A) Spike protein

ACE2 receptor

TMPRSS2

Host cell

RNA

B)

SARS-CoV-2 spike protein Receptor binding domain 1

331

S1 domain

528

1273

TMPRSS2 cleavage site

S2 domain

Fig. 2 (a) The SARS-CoV-2 enters host cells by binding of the spike protein to the host angiotensin-converting enzyme 2 (ACE2) receptor and priming by the TMPRSS2 protease. (b) Schematic of SARS-CoV-2 spike protein (1273 amino acids in length) with highlighted receptor binding domain, S1 and S2 domains, and S1–S2 cleavage site

2 (ACE2) receptor of host cells. Following this attachment, the spike protein is cleaved by cell surface enzyme transmembrane protease serine 2 (TMPRSS2), which exposes the S2 subunit, to allow viral fusion and release of the SARS-CoV-2 RNA into the host cell. The RNA then hijacks the cellular machinery to replicate itself and to assemble new viral particles. These are then transported out of the cell for infection of neighboring cells. 2.1 Non-replicating (Inactivated) Viral Vector

The approach of the University of Oxford/AstraZeneca vaccine uses an inactivated adenovirus which has been genetically modified to express the nucleic acid code of the SARS-CoV-2 spike protein [9, 21–23]. This is used for vaccination and the host cells produce the spike protein which primes the immune system to recognize the virus (see Fig. 3a). Then, when the vaccinated person encounters

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A) Oxford/AstraZeneca AZD1222 SARS-CoV-2 Spike protein gene

Vaccination

Host cells synthesize spike protein

Spike protein vaccine

Host produces antibodies and cellular response against spike protein

Deactivated adenovirus vector

B) Pfizer/BioNTech BNT162b2

Vaccination

Synthesis of mRNA for spike protein

Host produces antibodies and cellular response against spike protein

Packaging of mRNA into lipid nanoparticles (NLPs)

C) Sinovac/CoronaVac Antigen presenting cell

Inactivated SARS-CoV-2 Vaccination

Activated T cell

Host produces antibodies and cellular response against antigens

Fig. 3 (a) Flow diagram showing the Oxford/AstraZeneca vaccine production and vaccination procedure, as an example of a viral vector-based immunization. (b) Strategy of the Pfizer/BioNTech vaccination procedure, as an example of an RNA-based vaccine. (c) Approach of the Sinovac-CoronaVac vaccine production and vaccination procedure, as an example of an inactivated vaccine

the actual virus, their immune system actively produces spike protein antibodies which will hopefully neutralize the virus. The Janssen (Johnson & Johnson) Ad26.COV2.S and Serum Institute of India Covishield (Oxford/AstraZeneca formulation) are further examples of non-replicating viral vector type vaccines which have been approved for use by the World Health Organization [19].

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2.2

RNA-Based

The Pfizer/BioNTech BNT162b2 is an example of the new mRNA-based vaccines [9, 23–25]. This approach works by packaging the mRNA coding for the SARS-CoV-2 spike protein into lipidbased nanoparticles (LNPs). Once this is injected into the recipient, the LNP acts as a delivery vehicle to introduce the spike mRNA into the host’s cells. Once inside the cells, the mRNA is translated to produce the spike protein which is transported to the cell surface and primes the immune response, in a similar manner as the University of Oxford/AstraZeneca vaccine (see Fig. 2b). The Moderna mRNA-1273 vaccination approach is essentially the same as the Pfizer/BioNTech one, via the use of LNPs to deliver the spike protein mRNA into host cells. This vaccine has also been approved by the World Health Organization [19].

2.3

Inactivated Virus

The Sinovac vaccine serves as an example of an inactivated virus as the immunizing agent [9, 23, 26]. In this approach, the SARSCoV-2 RNA is inactivated so that it cannot replicate inside the host. Once vaccinated, the host immune system processes the inactivated virus by absorption into antigen-presenting cells, which then interact with T helper cells to present viral protein sequences on their cell surface. In turn, this activates B cells to secrete antibodies against the virus (see Fig. 2c). The Sinopharm (Beijing) BBIBPCorV (Vero Cells) vaccine is also an inactivated SARS-CoV-2 vaccine approved by the World Health Organization [19].

3

The Problem with the SARS-COV-2 Variants Although the spread of the SARS-CoV-2 virus appeared to be diminishing by September 2020, a number of variants began to emerge which caused new waves of infections and deaths in several countries. Several of these have been categorized as variants of concern with evolutionary advantages of greater virulence thought to be due mainly to mutations in key amino acids in the spike protein receptor binding domain (RBD) [5, 27]. The first of these variants (B.1.1.7) emerged in September 2020 in the United Kingdom and has since been termed the alpha variant [28]. Shortly afterward, the beta variant (B.1.351) was reported in South Africa [29], and this was followed by gamma (P.1) in Brazil [30] and delta (B.1.617) in India (see Fig. 4) [31]. The fact that many of the mutations occur in the spike protein has raised alarm since many of the existing vaccines were designed against this part of the virus [27, 32] and some reports have shown that these vaccines may be less effective against the variant strains.

3.1 Alpha Variant (B.1.1.7)

The B.1.1.7 variant was first detected in the United Kingdom on September 20, 2020, and 5 months later, it accounted for more 90% of the SARS-CoV-2 cases there [33]. Compared to the original

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Fig. 4 Schematic of SARS-CoV-2 spike proteins showing mutations in the alpha, beta, gamma, and delta variants which may affect vaccine efficacy

strain, B.1.1.7 has undergone nine mutations within the spike protein, consisting of two deletions (Δ69–70HV and Δ144Y) and seven amino acid changes (N501Y, D614G, A570D, P681H, T716I, S982A, and D1118H) [27]. The N501Y mutation has been linked to a higher transmission rate [34, 35]. In addition, patients who are more than 60 years old and infected with this strain have a higher hospitalization and intensive care unit (ICU) admission rate [36], with a longer duration of infection [37], and a higher risk of mortality compared to the original virus [38, 39]. The B.1.1.7 variant may be partially resistant to neutralization by some of antibodies raised against the original SARS-CoV2 strain [40], although studies of the N501Y substitution have shown that this is not substantial [27, 41]. 3.2 Beta Variant (B.1.351)

The B.1.351 SARS-CoV-2 variant emerged in South Africa following the first wave of the pandemic there in October 2020. After just 1 month, it became the major virus strain there [27], and, by January 2021, it emerged in several other countries, including Botswana, the United Kingdom, multiple European countries, South Korea, and Australia [42]. This variant contains nine amino acid substitutions and three deletions in the spike protein, and two of these are in common with the alpha variant (N501Y and D614G)

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[43]. The B.1.351 variant has higher transmission, hospitalization, deaths, and vaccine evasion capability, compared to the original strain [27, 44]. As with the alpha variant, the increased transmission of the beta variant is caused by the N501Y mutation due to increased affinity for the ACE2 receptor [44, 45]. The B.1.351 variant appears to be resistant to neutralization by some antibodies against the spike protein due to the E484K amino acid substitution [27]. Other B.1.351 mutations of concern include N501Y and K417N, as these may relate to the increased transmission and vaccine escape properties [27, 29]. 3.3 Gamma Variant (P.1)

The P.1 variant was first detected in early December 2020 in Manaus, Brazil [27], although it may have emerged a few weeks earlier in Sa˜o Paulo, Brazil [46], and in Japan [47]. In less than 2 months, the P.1 variant accounted for almost 90% of the infections in Manaus [46]. This variant contains multiple mutations in the spike protein, including K417T, E484K, and N501Y, which are thought to affect biological function [48]. The biologically important mutations in P.1 include N501Y, E484K, and K417T. Several studies have found that transmissibility of this variant may be increased between two- and threefold [46, 49, 50] (references [49] and [50] are still under peer review as of October 31, 2021). In addition, the ability of this variant to partly evade the immune system is thought to be mainly imparted by the E484K mutation, which is also present in the B.1.351 variant [27].

3.4 Delta Variant (B.1.617)

The SARS-CoV-2 B.1.617 variant has already emerged as several subtypes. The B.1.617.1 and B.1.617.2 variants were detected in India in December 2020, and the B.1.617.3 subtype arose in February 2021 [51]. The B.1.617.2 variant has been found to be 60% more transmissible than the alpha (B.1.1.7) variant with a higher risk of hospitalization [52]. The L452R, T478K, and P681R mutations found in the B.1.617.2 variant have been linked with the increased transmissibility and vaccine evasion [53]. Furthermore, a recent study found that delta SARS-CoV-2 can evade neutralizing antibodies from a previous COVID-19 infection or from a single dose immunization with some of the major vaccines [54]. The delta variant (mostly the B.1.617.2 version or close relatives) has currently spread to more than 160 countries or territories and now accounts for most of the COVID-19 cases around the world [55].

4

Multivalent Approaches to SARS-CoV-2 Vaccine Production The idea of multivalent vaccination approaches is not a new one. For instance, the current vaccination program for individuals over 50 years old in the United Kingdom uses a tetravalent vaccine called Flucelvax(R) which contains proteins from inactivated H1N1 and

Multivalent COVID-19 Vaccines

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H3N2 influenza A and two influenza B virus strains [56]. It follows that a similar approach could be used against spike RBD sequences from multiple SARS-CoV-2 variants of concern. In line with this objective, a number of approaches are currently in development with the main objective of providing a means of coping with the ongoing emergence of SARS-CoV-2 variants which may result in diminished responses to current vaccines [18]. As described above, the virus has continued to evolve rapidly, with multiple variants having emerged in different countries of the world, and it is not clear if the existing vaccines will remain effective against these [5]. The following sections describe approaches for development of multivalent vaccines for SARS-CoV-2 which may be required to supplant or supplement the first-generation vaccines in case of diminished efficacy. It should be noted that the existing vaccines are actually multivalent considering the multi-epitope nature of the antigen and the polyclonal antibody responses to these epitopes. However, these do not account for the plasticity of antigenic sites in new variants. 4.1 Constructs Using Ferritin-Based Nanoparticles

As noted above, mutations in the spike RBD can lead to enhanced interactions with the ACE2 receptor in the host. Due to its important role in virus transmission, the spike protein has been elected as the key target for vaccine development. Different to most vaccines, those raised against antigens coupled to nanoparticle carriers are efficient in targeting antigens to lymph nodes for potent immune cell activation. Kalathiy et al. carried out a computational study in the design of multiple fusion protein constructs of the spike RBD coupled to different L-ferritin and H-ferritin subunits as a nanocage scaffold [57]. They found that a 5-amino acid linker was the most effective for orientating the spike protein sequence of the ferritin nanocage and suggested this as a new therapeutic approach in the development of vaccine therapeutics against SARS-CoV2 and other viruses. Another group developed a similar RBD-ferritin nanoparticle-based vaccine which elicited similar responses in immunized mice [58]. In this case, a ferritin-like DPS (DNA protection during starvation) from hyperthermophilic Sulfolobus islandicus was covalently coupled with SARS-CoV2 RBD using the SpyCatcher system to immunize mice [59]. They found that a single dose of the vaccine in mice expressing the human ACE2 receptor resulted in a higher antibody titer and neutralizing antibody response compared to a RBD monomer vaccination. Another study used a nanoparticle-based approach for multivalent display of the spike RBD [60]. In this approach, the authors coupled multiple copies of the RBD sequence to the SpyCatchermi3 protein nanoparticle. They found that this elicited multiple cross-reacting antibodies against SARS-CoV, as well as the original SARS-CoV-2 virus and the alpha, beta, and gamma variants of

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concern. In addition, they found that immunization of mice with this nanoparticle construct elicited neutralizing antibodies against the above variants as well as the delta SARS-CoV-2 virus. Similarly, Zhang and co-workers developed a platform using the SpyCatcher system for display of SARS-CoV-2 spike-glycoprotein trimers on self-assembling 60-subunit Aquifex aeolicus lumazine synthase (LuS) nanoparticles containing an N-linked glycan with good yield and antigenicity [61]. Immunization of mice with this construct elicited a 25-fold higher neutralizing response against SARSCoV-2 compared to a native spike protein immunogen. Another study found that an engineered SARS-CoV-2 RBD containing four novel glycosylation sites was more highly expressed and generated more potent neutralizing responses, compared to unmodified SARS-CoV-2 RBD or full-length spike protein immunogens [62]. Furthermore, the neutralizing response was even greater when the glyco-engineered RBD was fused to a multivalent carrier like the ferritin 24-polymer. 4.2 Other Multivalent Vaccine Approaches

Volpatti et al. developed a strategy to cover the surface of polymersomes with multiple SARS-CoV-2 spike protein RBD sequences to simulate the form of the virus particle [63]. This led to the production of neutralizing antibodies in immunized mice, comparable to those in convalescent plasma, along with robust T cell immunity. Walls and colleagues described development of a multivalent SARSCoV-2 spike RBD nanoparticle vaccine in an immunogenic array [64]. The elicited polyclonal antibodies were resistant to many amino acid substitutions tested, although mutations near position 484 showed reduced neutralization capability. This is the same site that appears to confer resistance to neutralization in the gamma [48] and delta [53] variants. To address the challenges involved with the need for wide distribution and administration of SARS-CoV-2 vaccines, Chiba and colleagues engineered the coat protein of the MS2 bacteriophage to generate nanoparticles presenting multiple copies of the spike protein [65]. A single immunization of Syrian hamsters resulted in high neutralizing antibody titers and protection from SARS-CoV-2 infection. In addition, a number of studies have now demonstrated that variable heavy chain domains of heavy chain antibodies (VHH; also known as nanobodies) raised in camelids can recognize epitopes that are sometimes inaccessible to conventional antibodies [66–68]. Ma et al. applied this strategy using alpacas and identified seven RBD-specific nanobodies with high stability and RBD affinities using phage display [69]. They fused these nanobodies to the immunoglobulin 1 fragment crystallizable region (IgG1 Fc) and found that the resulting constructs bound to the SARS-CoV-2 RBD with affinities of 72.7 pM to 4.5 nM and blocked the interaction of the RBD with the ACE2 receptor with nanomolar affinity.

Multivalent COVID-19 Vaccines

4.3 Multivalent Vaccine Constructs Aimed at Reducing Allergenic and Toxic Side Effects

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To address potential concerns that vaccine-induced immune responses may worsen COVID-19 disease symptoms and outcomes in some patients, another study attempted to identify the minimal epitopes on the SARS-CoV-2 spike protein RBD for eliciting an effective antibody response with minimal risk of immunologicalbased side effects [70]. For this they used a series of overlapping peptides spanning the RBD and tested which of these were recognized by plasma taken from COVID-19 patients in the convalescent stage. Following this, the positive epitopes were used to vaccinate mice as diphtheria-toxoid conjugates and the resulting immune sera tested for binding to the SARS-CoV-2 RBD and for blocking interactions with the ACE2 receptor. This led to identification of seven epitopes and six of these induced RBD antibodies, while three generated antibodies that partially blocked RBD-ACE2 interactions. These effects were significantly enhanced using paired combinations of sera. It may also be of significance that two of the sequences used to generate the sera are conserved between the original SARS-CoV-2 isolate and the alpha and beta variants. In an immune-informatics approach, Rencilin and co-workers attempted to identify potential multivalent subunit cytotoxic T lymphocyte (CTL) vaccine candidates against SARS-CoV2 [71]. Using this method, they identified 18 sequences consisting of nine amino acids (9-mers) predicted to be strong binders to major histocompatibility complex class 1 (MHC-1) molecules, with antigenic and antigen processing properties, and which were present in all tested SARS-CoV-2 variants. In addition, these 18 peptides were predicted to have low allergenicity and toxicity and no MHC-2 binding potential to minimize chances of inducing hyperinflammatory responses in vaccinated individuals. Another in silico study designed a multivalent subunit vaccine targeting S2 subunit of the SARS-CoV2 spike protein to comprise epitopes capable of inducing T cells, B cells, and gamma interferon (IFγ), and predicted to be immunogenic, nonallergenic, and nontoxic [72]. Such computational studies may speed the process of developing therapeutics and vaccines to help control the SARS-CoV2 pandemic.

Conclusions and Future Perspectives The SARS-CoV-2 virus has persisted due the ongoing emergence of several variants of concern. This has occurred due to the natural ability of RNA viruses to mutate in available hosts. This calls attention to the fact that vaccination of a significant proportion of the world population is the most effective weapon toward stopping this pandemic. Thus, vaccination does more than just protect the individual from contracting the virus or prevent severe symptoms; it also helps in protecting the population as a whole. This

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SARS-CoV-2

Nanoparticle Nanoparcle

Spike protein

Spike protein RBD

Fig. 5 Similarity in structure and size of the SARS-CoV-2 virus with surface spike proteins and nanoparticles displaying the SARS-CoV-2 spike protein RBD (both ~60–100 nm)

argument alone should be enough to convince those with vaccine hesitancy that it is their civic duty to get vaccinated, as pointed out by former Prime Minister of the United Kingdom Tony Blair (concerning the importance of getting a third “booster” jab of a COVID-19 vaccine) [73]. A study in Israel showed that administration of booster dose of SARS-CoV-2 vaccine to more than one million Israelis over the age of 60 who had received their first two doses at least 5 months earlier were almost 20 times less likely to develop severe COVID-19 symptoms, compared to those who had received the first two jabs only [74]. This review has focused on the importance of developing new vaccine strategies to cope with the persistence of the SARS-CoV2 virus via the emergence of variants which show varying degrees of resistance to the existing vaccines. Many of these approaches include multivalent display of the most highly mutated regions in the RBD of the spike protein on the surface of nanocarriers, such that they resemble a virus particle (see Fig. 5) and can stimulate an effective neutralization response with reduced chances of unwanted allergenicity or toxicity. It is hoped that such approaches will help to reduce the transmission and severe disease outcomes associated with the SARS-CoV-2 virus. In addition, the steep learning curve associated with our ongoing struggle against this virus and our development of effective coping strategies should help to build a framework to fast-track worldwide responses in the likely event of future pandemics.

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21, 2021. https://assets.publishing.service. g ov.uk/g over nment/uploads/s ystem/ uploads/attachment_data/file/961037/ NERVTAG_note_on_B.1.1.7_severity_for_ SAGE_77__1_.pdf. Accessed 8 Oct 2021 38. Challen R, Brooks-Pollock E, Read JM et al (2021) Risk of mortality in patients infected with SARS-CoV-2 variant of concern 202012/1: matched cohort study. BMJ 372: n579. https://doi.org/10.1136/bmj.n579 39. Grint DJ, Wing K, Williamson E et al (2021) Case fatality risk of the SARS-CoV-2 variant of concern B. 1.1. 7 in England, 16 November to 5 February. Euro Surveill 26(11):2100256. https://doi.org/10.2807/1560-7917.ES. 2021.26.11.2100256 40. Wang P, Nair MS, Liu L et al (2021) Antibody resistance of SARS-CoV-2 variants B.1.351 and B.1.1.7. Nature 593(7857):130–135 41. Emary K, Golubchik T, Aley PK et al (2021) Oxford COVID-19 Vaccine Trial Group (2021). Efficacy of ChAdOx1 nCoV-19 (AZD1222) vaccine against SARS-CoV-2 variant of concern 202012/01 (B.1.1.7): an exploratory analysis of a randomised controlled trial. Lancet 397(10282):1351–1362 42. Tang JW, Toovey OT, Harvey KN et al (2021) Introduction of the South African SARS-CoV2 variant 501Y. V2 into the UK. J Inf Secur 82(4):e8–e10 43. Boehm E, Kronig I, Neher RA et al (2021) Novel SARS-CoV-2 variants: the pandemics within the pandemic. Clin Microbiol Infect 27(8):1109–1117. https://doi.org/10.1016/ j.cmi.2021.05.022 44. Abdool Karim SS, de Oliveira T (2021) New SARS-CoV-2 variants – clinical, public health, and vaccine implications. N Engl J Med 384(19):1866–1868 45. Starr TN, Greaney AJ, Hilton SK et al (2020) Deep mutational scanning of SARS-CoV2 receptor binding domain reveals constraints on folding and ACE2 binding. Cell 182(5): 1295–1310.e1220 46. Faria NR, Mellan TA, Whittaker C et al (2021) Genomics and epidemiology of the P. 1 SARSCoV-2 lineage in Manaus, Brazil. Science 372(6544):815–821 47. Fujino T, Nomoto H, Kutsuna S et al (2021) Novel SARS-CoV-2 variant in travelers from Brazil to Japan. Emerg Infect Dis 27(4):1243. https://doi.org/10.3201/eid2704.210138 48. Dejnirattisai W, Zhou D, Supasa P (2021) Antibody evasion by the P. 1 strain of SARSCoV-2. Cell 184(11):2939–2954.e9 49. Coutinho RM, Marquitti FMD, Ferreira LS et al (2021) Model-based evaluation of

Multivalent COVID-19 Vaccines transmissibility and reinfection for the P. 1 variant of the SARS-CoV-2. medRxiv. https:// doi.org/10.1101/2021.03.03.21252706 50. Naveca F, Nascimento V, Souza V et al (2021) COVID-19 epidemic in the Brazilian state of Amazonas was driven by long-term persistence of endemic SARS-CoV2 lineages and the recent emergence of the new Variant of Concern, P.1. Res Square. https://doi.org/10. 21203/rs.3.rs-275494/v1. https://www. researchsquare.com/article/rs-275494/v1. Accessed 31 Oct 2021 51. European Centre for Disease Prevention and Control. Emergence of SARS-CoV-2 B.1.617 variantsin India and situation in the EU/EEA (2021). https://www.ecdc.europa.eu/sites/ default/files/documents/Emergence-ofSARS-CoV-2-B.1.617-variants-in-India-andsituation-in-the-EUEEA_0.pdf. Accessed 31 Oct 2021 52. Mahase E (2021) Delta variant: what is happening with transmission, hospital admissions, and restrictions? BMJ 373:n1513. https://doi. org/10.1136/bmj.n1513 53. Adam D (2021) What scientists know about new, fast-spreading coronavirus variants. Nature 594(7861):19–20 54. Planas D, Veyer D, Baidaliuk A et al (2021) Reduced sensitivity of SARS-CoV-2 variant Delta to antibody neutralization. Nature 596(7871):276–280 55. https://www.statista.com/statistics/124 5971/number-delta-variant-worldwide-bycountry/. Accessed 1 Nov 2021 56. European Medicines Agency. Flucelvax Tetra (influenza vaccine). https://www.ema.europa. eu/en/documents/overview/flucelvax-tetraepar-medicine-overview_en.pdf. Accessed 1 Nov 2021 57. Kalathiya U, Padariya M, Fahraeus R et al (2021) Multivalent display of SARS-CoV2 spike (RBD domain) of COVID-19 to nanomaterial, protein ferritin nanocages. Biomol Ther 11(2):297. https://doi.org/10.3390/ biom11020297 58. Sun W, He L, Zhang H et al (2021) The selfassembled nanoparticle-based trimeric RBD mRNA vaccine elicits robust and durable protective immunity against SARS-CoV-2 in mice. Signal Transduct Target Ther 6(1):340. https://doi.org/10.1038/s41392-02100750-w 59. Salzer R, Clark JJ, Vaysburd M et al (2021) Single-dose immunisation with a multimerised SARS-CoV-2 receptor binding domain (RBD)

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induces an enhanced and protective response in mice. FEBS Lett 595(18):2323–2340 60. Halfmann PJ, Castro A, Loeffler K et al (2021) Potent neutralization of SARS-CoV-2 including variants of concern by vaccines presenting the receptor-binding domain multivalently from nanoscaffolds. Bioeng Transl Med 6(3): e10253. https://doi.org/10.1002/btm2. 10253 61. Zhang B, Chao CW, Tsybovsky Y et al (2020) A platform incorporating trimeric antigens into self-assembling nanoparticles reveals SARSCoV-2-spike nanoparticles to elicit substantially higher neutralizing responses than spike alone. Sci Rep 10(1):18149. https://doi.org/ 10.1038/s41598-020-74949-2 62. Quinlan BD, He W, Mou H et al (2020) An engineered receptor-binding domain improves the immunogenicity of multivalent SARSCoV-2 vaccines. bioRxiv:2020.11.18.388934. https://doi.org/10.1101/2020.11.18. 388934. Preprint 63. Volpatti LR, Wallace RP, Cao S et al (2021) Polymersomes decorated with the SARS-CoV2 spike protein receptor-binding domain elicit robust humoral and cellular immunity. ACS Central Sci 7(8):1368–1380 64. Walls AC, Miranda MC, Sch€afer A et al (2021) Elicitation of broadly protective sarbecovirus immunity by receptor-binding domain nanoparticle vaccines. Cell 184(21):5432–5544 65. Chiba S, Frey SJ, Halfmann PJ et al (2021) Multivalent nanoparticle-based vaccines protect hamsters against SARS-CoV-2 after a single immunization. Commun Biol 4(1):597. https://doi.org/10.1038/s42003-02102128-8 66. Lu Q, Zhang Z, Li H et al (2021) Development of multivalent nanobodies blocking SARS-CoV-2 infection by targeting RBD of spike protein. J Nanobiotechnol 19(1):33. https://doi.org/10.1186/s12951-02100768-w 67. Chouchane L, Grivel JC, Farag EABA et al (2021) Dromedary camels as a natural source of neutralizing nanobodies against SARSCoV-2. JCI Insight 6(5):e145785. https:// doi.org/10.1172/jci.insight.145785 68. Xu J, Xu K, Jung S et al (2021) Nanobodies from camelid mice and llamas neutralize SARSCoV-2 variants. Nature 595(7866):278–282 69. Ma H, Zeng W, Meng X et al (2021) Potent neutralization of SARS-CoV-2 by heterobivalent alpaca nanobodies targeting the spike receptor-binding domain. J Virol 95(10):

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e02438–e02420. https://doi.org/10.1128/ JVI.02438-20. Online ahead of print 70. Pandey M, Ozberk V, Eskandari S et al (2021) Antibodies to neutralising epitopes synergistically block the interaction of the receptorbinding domain of SARS-CoV-2 to ACE 2. Clin Transl Immunol 10(3):e1260. https://doi.org/10.1002/cti2.1260 71. Rencilin CF, Rosy JC, Mohan M et al (2021) Identification of SARS-CoV-2 CTL epitopes for development of a multivalent subunit vaccine for COVID-19. Infect Genet Evol 89: 104712. https://doi.org/10.1016/j.meegid. 2021.104712

72. Bhatnager R, Bhasin M, Arora J et al (2021) Epitope based peptide vaccine against SARSCOV2: an immune-informatics approach. J Biomol Struct Dyn 39(15):5690–5705 73. Sky News; Tony Blair: getting vaccine is ‘civic duty’ and govt should bump boosters to 500,000 a day. https://news.sky.com/story/ tony-blair-getting-vaccine-is-civic-duty-andgovt-should-bump-boosters-to-500-000-aday-12440200. Accessed 3 Nov 2021 74. Kozlov M (2021) COVID-vaccine booster shot shows promise in Israeli study. Nature. https://doi.org/10.1038/d41586-02102516-4. Online ahead of print

Chapter 3 Challenges of Multiplex Assays for COVID-19 Research: A Machine Learning Perspective Paul C. Guest, David Popovic, and Johann Steiner Abstract Multiplex assays that provide simultaneous measurement of multiple analytes in biological samples have now developed into widely used technologies in the study of diseases, drug discovery, and other medical areas. These approaches span multiple assay systems and can provide readouts of specific assay components with similar accuracy as the respective single assay measurements. Multiplexing allows the consumption of lower sample volumes, lower costs, and higher throughput compared with carrying out single assays. A number of recent studies have demonstrated the impact of multiplex assays in the study of the SARS-CoV2 virus, the infectious agent responsible for the current COVID-19 pandemic. In this respect, machine learning techniques have proven to be highly valuable in capturing complex disease phenotypes and converting these insights into models which can be applied in real-world settings. This chapter gives an overview of opportunities and challenges of multiplexed biomarker analysis, with a focus on the use of machine learning aimed at identification of biological signatures for increasing our understanding of COVID-19 disease, and for improved diagnostics and prediction of disease outcomes. Key words SARS-CoV-2, COVID-19, Biomarker discovery, Machine learning, Confounding factor, Bias, Multiplex assay

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Introduction Technological advancements over the last two decades have led the development and implementation of high-throughput medical and scientific research platforms which allow the acquisition of highdimensional data [1]. This enables researchers to carry out hypothesis-free, data-driven searches for biomarker patterns that can be used for diagnosis or classification, treatment response monitoring, or clinical outcome prediction. This approach is particularly useful when applied to complex clinical conditions in which individual biomarkers may have low effect sizes, and therefore combinations of multiple features are needed to achieve higher predictive accuracy [2]. However, high-throughput multiplex biomarker platforms can be affected by substantial measurement

Paul C. Guest (ed.), Multiplex Biomarker Techniques: Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification, Methods in Molecular Biology, vol. 2511, https://doi.org/10.1007/978-1-0716-2395-4_3, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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variability, and development of accurate algorithms is dependent on longitudinal stability of the individual features. Thus, a key objective of these studies is the reduction of measurement noise and bias, which can lead to high false discovery rates [3–6]. Overcoming this can be achieved with systematically designed biomarker discovery and validation phases with a clear-cut interpretation of the highdimensional datasets. This is an important requirement to avoid obscuring a potentially useful biomarker signature with experimental variability and noise. A powerful approach to reduce noise and extract patterns of interest from multiplex platforms is through the application of machine learning techniques [7]. Machine learning relies on algorithms that learn from input data and make decisions or predictions using multivariate pattern recognition. These techniques combine mathematical methods and computing power to draw conclusions from data and develop models, which, in contrast to classical statistics, can then be applied to new datasets. Machine learning has transformed many industries such as drug discovery, medicine, healthcare, engineering, marketing, and computing, by providing us a deeper insight into large-scale datasets. Machine learning algorithms interrogate a certain dataset and explore how specific feature patterns are related to a label of interest such as disease state, therapeutic outcome, or drug response. The relationship between these predictive patterns and the labels is then deployed into a standalone model, which can then be used to predict these labels in new and previously unseen datasets. In the case of COVID-19 disease, this could mean using information such as oxygen saturation levels, radiographic chest image features, and blood biomarker levels to predict disease severity and prognosis in patient cohorts. This information could potentially enable healthcare professionals in their decision-making on treatment and care options. Another advantage of machine learning is the dynamic nature of the models. These models can continuously be retrained with new input information so that the model can be refined and adapted to new conditions, eventually enhancing its prediction accuracy and applicability. In this chapter, we review machine learning approaches which have been applied to bettering the lives of people suffering from COVID-19 disease. Most of these studies have focused on the application of laboratory, clinical, and imaging biomarkers as predictor data with the aim of developing models for improved diagnosis, risk prediction, disease staging, and prognosis. It is anticipated that such approaches will help healthcare staff in clinical decision-making and therefore improve patient outcomes.

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Types of Machine Learning Machine learning algorithms can be classified into supervised, unsupervised, and reinforcement learning [7]. In supervised machine learning, the model is trained on a sample, in which the predictor feature set and the associated label is presented to the algorithm so that it learns to extract a predictor pattern related to the label of interest. An unsupervised machine learning algorithm is given a dataset without any predefined labels. Hence, this algorithm recognizes latent structures in an unlabeled dataset and then creates new groupings of individuals in that dataset (i.e., clusters), or it extracts previously unknown multivariate signatures from the dataset. Reinforcement learning does not require labels or input data to start with. Rather, reinforcement learning algorithms learn dynamically as new data are fed into the algorithm. Contrary to supervised and unsupervised learning, reinforcement learning is a continuous process, which is constantly updated and optimized in a trial-and-error manner. This makes these algorithms particularly suitable for complex control problems, such as software in selfdriving cars or air conditioner control. With regard to multiplex assay-based prediction methods for COVID-19-related purposes, supervised machine learning algorithms are the most suitable since they allow us to define a specific target or label. We can then build models that help us predict these variables of interest. Supervised machine learning algorithms can be divided into shallow and deep learning techniques. Shallow learning techniques, such as support vector machines, are less convoluted and already work robustly in lower sample sizes. Deep learning techniques, such as neural networks, are built in a much more complex manner and possess the ability to predict with higher accuracy than shallow learning techniques. However, since their models are complex and multilayered, they also require much larger datasets to function in a robust manner. Since higher accuracy is particularly important in a critical situation such as COVID-19 disease and large datasets on related issues are readily available, deep learning techniques will be at the forefront of machine learning-based COVID-19 prediction issues. In most cases, deep learning utilizes so-called neural networks. Neural networks are supposed to work in a way similar to the human brain, in which a magnitude of neurons is interconnected, and predictions or decisions are based on a network voting process of neurons. Therefore, neural networks always consist of multiple layers and nodes (see Fig. 1). The layers are organized into input layers, hidden layers, and output layers. Each node represents the conversion of an input into a calculated output to the next layer and node. Calculations consist of multiplication of all the inputs by the related weight (importance), and then an activation function

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Layers Input

Hidden 1

Hidden 2

Hidden 3

Ou t p u t

High weight

Low weight

= node Fig. 1 Deep learning neural network scheme consisting of input, hidden, and output layers. Each layer is comprised of nodes. The thickness of lines between nodes and layers indicate features with the highest weight and therefore importance to that layer and ultimately the final output

defines how the weighted sum of the inputs is transformed into the output, which is forwarded in the network. The network structure comes from the inputs consisting of outputs from the nodes in the prior layer, and this is passed on from layer to layer. The input layer of neurons is used for data input. The data is then fed forward from the input layer to a varying number of hidden layers, in which complex decisions and alterations are made along the way. The hidden layers eventually relay their decisions to the output layer in which all decisions and data from the hidden layers converge and a final prediction, e.g., yes or no, is made. The decision of the output layer is the result or the answer, which is visible for the user, while in most cases, the hidden part of the neural network, in most cases, remains a “black box” for the user [8]. This process is repeated in multiple training steps via back-propagation, until the best combination of inputs into each node that gives the correct answer is determined [9]. This training begins by assigning random weights to the input features and then running the data through the network. The network output is compared to the actual answer and the difference between these is calculated as the error. The error of each observation can be combined to give the total error, and optimizing the performance of the model involves minimizing the error by back-propagation. This is achieved by adjustment of all model

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weights to lower the error. Eventually this will give a small error using the training set. The model training is complemented with model testing. The testing of the model is used to assess the generalizability of the model to new datasets. Generalizability reflects how good a certain model captured a specific effect, which can translate to other datasets. The counterpart to generalizability is overfitting. Overfitting means that the algorithm was optimized to a large extent on the specific characteristics of the study sample, so that it aligns with the sample to a high degree, but in fact, failed to extract the actual pattern of interest. Therefore, models which are optimized too extensively on a certain sample can be overfitted and perform poorly on new datasets. To assess generalizability and prevent overfitting, cross-validation techniques are used. In summary, the study sample is divided into a number of folds (usually 10), of which all but one are used iteratively for training the model. The trained model is then applied to the held-out fold to assess its performance. This process is then repeated until all folds have been used for training and testing. The final model is then generated by computing the mean feature weight values across all created models, and the model’s performance is also calculated as the mean performance of all models. This technique is referred to as repeated nested crossvalidation and is particularly useful to maximize training and testing capabilities. An even higher level of robustness can be achieved via out-of-sample cross-validation. In this case, the final model is applied to a completely new dataset, a so-called validation cohort, which has not been seen by the algorithm before [10, 11]. If the model from the training set has been optimized correctly, this should yield good prediction of the validation cohort. A critical factor required for the model to generalize well to new samples necessitates that the data used for algorithm training should reflect the characteristics of the target population [12, 13]. In general, the performance of a machine learning model should be assessed via balanced accuracy (BAC), which represents the mean of sensitivity and specificity. It is particularly robust when dealing with imbalanced class labels (i.e., on label being much more frequent than the other), which could distort sensitivity and specificity and give a false indication of performance quality.

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SARS-CoV-2 The SARS-CoV-2 virus which causes COVID-19 disease has now infected more than 3% of the world population with more than five million deaths, as of November 24, 2021 [14]. Thus far, there have been four to five waves of the COVID-19 pandemic, and it is apparent that another wave of unknown magnitude is now occurring. In addition, due to the high mutation rate of RNA viruses in

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general, the SARS-CoV-2 virus has already emerged as multiple variants of concern which are more infective and transmissible than the original strain [15]. These variants include the alpha, beta, gamma, and delta strains which were first reported in the United Kingdom, South Africa, Brazil, and India, respectively. Despite the rapid development and deployment of vaccines, the disease is still causing significant strains on the healthcare systems in many countries and territories of the world. Thus, effective disease management schemes are still urgently required. This includes the use of biomarker tests which can be used to assess disease risk, to diagnose patients, to predict disease outcomes, and to help place patients on the most effective treatments for the best possible outcome. Artificial intelligence and machine learning are now playing a key role in these efforts by rapid and efficient identification of patterns in the data to increase our understanding how the virus is transmitted, identifying those patients who are most at risk and by accelerating research and discovery of new treatment approaches [16].

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Models Using Multiplex Profiling Data As stated above, the most prominent and best indicator of a model’s performance is the BAC approach. However, most studies on COVID-19 classifiers have used mixed reporting methods. A recent study used an in silico pathway analysis method to analyze proteomic, metabolomic, and transcriptomic profiling COVID-19 datasets [17]. Combined analysis of the proteomic and metabolomic datasets resulted in ten signatures which yielded an area under the receiver operating characteristic curve (AUC) of 0.840 for distinguishing COVID-19 patients with severe and non-severe forms of the disease. Separate analyses of the transcriptomic dataset identified ten signatures with an AUC of 0.914 for separating patients with COVID-19 from those with other acute respiratory illnesses, and two signatures were identified which gave an AUC of 0.967 in identifying COVID-19 patients from noninfected individuals. All these signatures still yielded high AUCs in testing of the validation sets. Lazari et al. carried out a matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) plasma proteome mass fingerprinting analysis combined with machine learning for prediction of high- and low-risk cases of COVID-19, which resulted in a sensitivity of 0.93 and specificity of 0.92 [18]. A study which used immunological profiling and unsupervised artificial intelligence found that severely ill COVID-19 patients had significantly higher levels of lactate dehydrogenase (LDH), interleukin-6 (IL-6), (MIG), D-dimer, fibrinogen, and glucose compared to patients with moderate disease [19]. Application of artificial intelligence analysis to the data identified a signature

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comprised of creatinine, glucose, monocyte count, fibrinogen, macrophage-derived chemokine (MDC), monokine induced by gamma interferon (MIG), C-reactive protein (CRP), and IL-6 that could predict development of severe disease with accuracies ranging from 0.83 to 0.87. A study by Karami et al. used a weighted gene co-expression network analysis and local interpretable model-agnostic explanations (LIME) to characterize transcriptional changes in SARS-CoV-2-infected bronchial epithelium and transformed lung alveolar cells based on identified hub genes [20]. This analysis revealed a novel hub gene signature, and enrichment analysis showed that the most relevant biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were type I interferon, IL-17, and cytokine-mediated signaling pathways, as well as defense response to virus. Their study also led to identification of 17 Food and Drug Administration (FDA)approved candidate drugs targeting these pathways. Another investigation developed artificial intelligence-based prediction algorithms for prediction of COVID-19 survival outcomes using a publicly available dataset of COVID-19-hospitalized patients, consisting of clinical and proteomic data [21]. The models were based separately on 9 clinical parameters and 45 protein biomarkers, and both yielded an overall accuracy of 0.89 for prediction of survival or death outcomes. Sindelar and co-workers used plasma metabolomics data collected at six different time points of SARS-CoV2 infection combined with machine learning to build a predictive classification model of disease severity [22]. Interestingly, they found that a model built using metabolites measured at the time of study entry was successful in determining disease severity. This highlights the point that informative biomarkers can be identified early in the disease course, paving the way for a rapid and targeted intervention response as appropriate.

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Models Incorporating Laboratory Data Lou and co-workers aimed to identify key indicators from laboratory test results taken on admission for prediction of COVID-19 severity [23]. They first used age as a predictor which gave an AUC of 0.92. Then they improved the accuracy using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and naı¨ve Bayes (NB) classifiers taken from the laboratory test results. This procedure selected three main feature subsets, variously consisting of age and white blood cell, lymphocyte, and neutrophil counts which yielded an AUC of 0.93. Lin et al. applied an artificial neural network from routine clinical laboratory data taken at the time of admission to predict mortality outcomes in COVID-19 patients [24]. This resulted in an AUC of 0.96, and the authors

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developed a publicly available app to predict mortality of patients at the time of hospital admission, and which may help healthcare providers make informed decisions regarding treatment options. Ye et al. constructed a machine learning model using laboratory test results (CRP, procalcitonin, neutrophil count, hemoglobin, neutrophils, and platelet distribution width) combined with age, which yielded an AUC of 0.978 for prediction of risk stratification of COVID-19 outcomes [25]. A similar model was constructed by Karthikeyan et al. using a combination of neutrophils, lymphocytes, LDH, CRP, and age to predict COVID-19 survival/mortality with an accuracy of 0.90, up to 16 days beforehand [26]. Another study constructed a machine learning model for COVID-19 diagnosis based and cross-validated using routine blood tests of more than 5000 patients with different bacterial and viral infections, as well as COVID-19 disease [27]. This resulted in a cross-validated AUC of 0.97 using mean cell hemoglobin concentration, eosinophil count, albumin, international normalized ratio (INR) for blood clotting, and percentage prothrombin activity as the most important features. Rahman and co-workers built a model using two datasets from hospitals in two different countries for COVID-19-related mortality prediction which gave AUCs for the development, internal, and external validation cohorts of 0.987, 0.999, and 0.992, respectively [28]. The key predictive parameters were identified using a nomogram-based scoring technique (age, lymphocyte count, D-dimer, CRP, and creatinine). Blagojevic´ and colleagues used an artificial intelligence approach for classification of patients into four COVID-19 disease categories with an average accuracy of 0.94 [29]. The biomarkers with the greatest influence were identified using a K highest scores approach as LDH, urea, creatinine, CRP, white blood cell count, albumin, percentage lymphocytes, hemoglobin, red cell distribution width, and mean corpuscular hemoglobin concentration. Lee et al. developed a model for identification of COVID-19 patients with a high risk for requiring oxygen support based on CRP levels, age, blood pressure readings, and neutrophil and lymphocyte counts [30]. They found that a logistic regression model yielded the best performance in validation sets with a sensitivity of 0.93 and specificity of 0.81. Pulgar-Sa´nchez et al. used a combined data mining and machine learning approach to predict severe COVID-19 disease using laboratory and clinical test data [31]. The best fit of a feed-forward neural network algorithm identified severe disease with a precision of 0.96, and another supervised learning algorithm produced a decision tree with 0.89 precision. They also used a bivariate Pearson’s correlation matrix and hierarchical clustering which showed that severe COVID-19 disease development involved a shift in the lymphocyte/CRP and leukocyte/CRP ratios, as well as a change in the percentage of neutrophils, blood pH, and partial pressure of carbon dioxide

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(pCO2). Lombardi and colleagues investigated the role of routinely measured biomarkers with respect to preexisting comorbidities in eight different machine learning models for mortality prediction in COVID-19 patients [32]. The most robust model for prediction of 60-day mortality had an AUC of 0.79 in an external validation dataset. The features with the greatest impact on prediction were age, LDH, platelets, and percentage lymphocytes, which exceeded the effect of comorbidities or inflammation markers.

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Models Incorporating Clinical and Imaging Data Multiple studies have built machine learning models based on the use of chest computed tomography (CT) features for diagnostic and prediction of disease severity purposes [33–58]. One investigation built prognosis models consisting of COVID-19 patient vital signs and laboratory data with deep learning and chest CT scan features for prediction of severity outcomes [59]. In the first step, they built a CT database from institutions worldwide which extracted total opacity and consolidation ratios from the images. Next, they obtained prognosis information on the datasets from three of these institutions and built a generalized linear model for prognosis prediction. The AUC of the model was 0.85–0.93 across three validation cohorts. For all three cohorts, the model consisted of CT total opacity and consolidation ratios; in two cohorts, it consisted of age, white blood cell, and platelet counts; and oxygen saturation was a predictor in one cohort. Jafari et al. investigated the chest CT features and risk factors associated with severe and non-severe COVID-19 disease [60]. The CT scans showed bilateral and multifocal involvement in 97.6 and 84.3% of the severe and non-severe patients, respectively, and the features of pure consolidation, mixed ground-glass opacities, consolidation, pleural effusion, and intralesional traction bronchiectasis were significantly higher in the severe patients. The total opacity scores of the severe patients were also significantly higher with an AUC of 0.91. To follow this up, we carried out a deep learning analysis for predicting the outcome of patients with COVID-19 using the same data [61]. Deep learning neural network analysis was used to classify patients as critical or noncritical according to their chest CT results. The presence of diffuse opacities and lesion distribution gave the best prediction accuracies (both ¼ 0.91), lesion distribution yielded the highest sensitivity (0.74), and the largest specificity was found for the presence of diffuse opacities (0.95). The combined model showed an accuracy of 0.89. We suggest that the accuracies would improve significantly through incorporation of other biomarkers, such as laboratory analytes and demographic characteristics. Lassau and colleagues used a multimodal severity score of five clinical and biological variables (age, sex, oxygenation, urea,

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platelets) combined with chest CT deep learning model for increased accuracy in prediction of COVID-19 disease severity [62]. Xia et al. applied a deep neural network with features derived from chest X-ray and clinical findings for COVID-19 diagnosis prediction compared with influenza [63]. The COVID-19 patients experienced lower fever, with more diarrhea and hypercoagulability, and classifiers built on these clinical features or chest X-ray features had AUCs of 0.909 and 0.919, respectively. Notably, the diagnostic accuracy of a combined model had an improved AUC of 0.95 with sensitivities and specificities of 0.92 and 0.81, respectively. Another study showed that while demographic parameters could not predict ICU requirement, CT metrics and laboratory measures were good classifiers with AUCs of 0.88 and 0.86, respectively [64]. Importantly, improved performance was achieved using a combination of demographic parameters, CT metrics, and laboratory measures, as this yielded an AUC of 0.91. A similar result was achieved by Shiri and co-workers who developed a model which combined lung, lesion, and clinical data (AUC ¼ 0.95) [65], and Purkayastha et al. developed a model based on the combination of CT and clinical data, which gave AUCs of 0.90–0.93 for prediction of progression to severe disease on days 3–7 after COVID-19 diagnosis [66]. Another study applied machine learning to detect SARS-CoV-2 infection using a combination of laboratory markers and radiological chest images [67]. Again this yielded a higher sensitivity (>0.90) and specificity in validation cohorts than could be achieved with either measurement alone.

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Conclusions and Future Perspectives The application of high-dimensional data in medicine has revolutionized the field by providing new insights into diseases plaguing humanity. However, such data can be affected by substantial measurement variability and noise which could generate misleading interpretation. Overcoming this problem and generating more accurate and generalizable models can be achieved through the application of machine learning techniques. This review has described the use of multiplex assay and high-dimensional data in combination with machine learning approaches to increase our understanding of the current COVID-19 pandemic facing the world and to develop tools for improved diagnostics or risk prediction to stem the progression of this terrible disease. Models can be developed using single types of data such as proteomic profiling, clinical laboratory scans, and lung imaging features, and these have resulted in good accuracy and generalizability in external validation studies. However, this review has presented findings from multiple studies showing how both aspects can be enhanced using models

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developed on combinations of data types, which increases both the number and nature of features employed. In addition to the above applications, machine learning can be used to aid in the identification of new treatment approaches and in vaccine design. For example, machine learning approaches have been used in the identification of combination therapies for COVID-19 [68–70]. It is anticipated that in a multidrug approach, the compounds identified by machine learning could target different stages of the COVID-19 life cycle, including cellular docking, entry, replication, and exocytosis, to enhance the efficacy beyond what any single drug could achieve. In order to aid interpretations and cross-study comparisons, we suggest that the performance of machine learning models in COVID-19 research should be assessed via the BAC approach. Such approaches are expected to have beneficial impacts on COVID-19 treatment outcomes due to synergistic effects leading to improved efficacy and reduced toxicity. On top of this, drug repurposing appears to be the most effective and fastest strategy in responding to public health crises, such as the COVID19 pandemic [71, 72]. In conclusion, the concerted global response to the COVID19 crisis has resulted in the fastest development and worldwide deployment of effective vaccines in history. There is now realistic hope that through the combined efforts in diagnostics and risk prediction, drug repurposing, and vaccine development, we will see the effects of this pandemic begin to ease in 2021. These efforts will also have the added benefit of providing a worldwide infrastructure to effectively combat future potentially devastating outbreaks of other pathogens. References 1. Guest PC (2017) Multiplex biomarker techniques: methods and applications, Methods in molecular biology, 1546. Humana Press/ Springer, New York. ISBN-13: 978-1493967292 2. Xu T, Fang Y, Rong A et al (2015) Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy. BMC Med Res Methodol 15:94. https://doi.org/10.1186/ s12874-015-0085-z 3. Ransohoff DF (2005) Bias as a threat to the validity of cancer molecular-marker research. Nat Rev Cancer 5(2):142–149 4. Fontela PS, Pai NP, Schiller I et al (2009) Quality and reporting of diagnostic accuracy studies in TB, HIV and malaria: evaluation using QUADAS and STARD standards. PLoS One 4(11):e7753. https://doi.org/10.1371/ journal.pone.0007753 5. Ko¨hler K, Seitz H (2012) Validation processes of protein biomarkers in serum–a cross

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Commun 12(1):634. https://doi.org/10. 1038/s41467-020-20657-4 63. Xia Y, Chen W, Ren H et al (2021) A rapid screening classifier for diagnosing COVID-19. Int J Biol Sci 17(2):539–548 64. Weikert T, Rapaka S, Grbic S et al (2021) Prediction of patient management in COVID19 using deep learning-based fully automated extraction of cardiothoracic CT metrics and laboratory findings. Korean J Radiol 22(6): 994–1004 65. Shiri I, Sorouri M, Geramifar P et al (2021) Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients. Comput Biol Med 132:104304. https://doi.org/10.1016/j.compbiomed. 2021.104304 66. Purkayastha S, Xiao Y, Jiao Z et al (2021) Machine learning-based prediction of COVID-19 severity and progression to critical illness using CT imaging and clinical data. Korean J Radiol 22(7):1213–1224 67. Du R, Tsougenis ED, Ho JWK et al (2021) Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph. Sci Rep 11(1):14250. https://doi.org/10.1038/s41598-02193719-2 68. Poduri R, Joshi G, Jagadeesh G (2020) Drugs targeting various stages of the SARS-CoV-2 life cycle: exploring promising drugs for the treatment of Covid-19. Cell Signal 74:109721. https://doi.org/10.1016/j.cellsig.2020. 109721 69. Jin W, Stokes JM, Eastman RT et al (2021) Deep learning identifies synergistic drug combinations for treating COVID-19. Proc Natl Acad Sci U S A 118(39):e2105070118. https://doi.org/10.1073/pnas.2105070118 70. Caires Silveira E (2021) Screening antiinflammatory, anticoagulant, and respiratory agents for SARS-CoV-2 3CL(pro) inhibition from chemical fingerprints through a deep learning approach. Rev Investig Clin. https:// doi.org/10.24875/RIC.21000282. Online ahead of print 71. Liang H, Zhao L, Gong X et al (2021) Virtual screening FDA approved drugs against multiple targets of SARS-CoV-2. Clin Transl Sci 14(3):1123–1132 72. Yousefi H, Mashouri L, Okpechi SC et al (2021) Repurposing existing drugs for the treatment of COVID-19 /SARS-CoV-2 infection: a review describing drug mechanisms of action. Biochem Pharmacol 183:114296. https://doi.org/10.1016/j.bcp.2020.114296

Part II Protocols

Chapter 4 Multiplex Quantitative Polymerase Chain Reaction Diagnostic Test for SARS-CoV-2 and Influenza A/B Viruses Steve F. C. Hawkins and Paul C. Guest Abstract COVID-19 disease caused by the novel SARS-CoV-2 virus represents a new challenge for healthcare systems. The molecular confirmation of infection is crucial to guide public health decision-making. This task could be made more difficult during the next influenza season. Thus, a rapid and user-friendly diagnostic test to discriminate SARS-CoV-2 from influenza viruses is urgently needed. Here, we present a multiplex quantitative polymerase chain reaction (qPCR) assay capable of distinguishing SARS-CoV2 from influenza A and B cases. This assay benefits from the use of an inhibitor tolerant PCR mix which obviates the need for the rate-limiting extraction step, allowing for a more rapid and accurate analysis. Key words COVID-19, Quantitation

1

SARS-CoV-2,

Influenza,

qPCR,

Extraction,

Inhibitor-tolerant,

Introduction The coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARSCoV-2), has threatened public health worldwide for more than a year with more than 144 million cases and three million deaths by April 2021 [1, 2]. SARS-CoV-2 infection can lead to symptoms similar to those caused by other viruses causing respiratory tract infections, such as influenza type A and type B [3, 4]. The World Health Organization (WHO) has stressed the urgent need for tools, such as affordable point-of-care testing, to detect SARSCoV-2 and other viruses for improved prevention and control, and to help stratify patients for the most appropriate treatment by healthcare teams [5]. Such treatments could include antiviral drugs, anti-inflammatory agents, and monoclonal antibody cocktails. Therefore, an assay which allows multiplex detection of both SARS-CoV-2 and influenza virus would offer significant benefits in the form of rapid and effective patient care [6–8].

Paul C. Guest (ed.), Multiplex Biomarker Techniques: Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification, Methods in Molecular Biology, vol. 2511, https://doi.org/10.1007/978-1-0716-2395-4_4, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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Testing for CDC-N1 sequence in SARS-CoV-2 Correct identification of SARS-CoV-2 using CDC-N1 probe Primer

TACGTT ATGCAA Primer

TACGTT ATGCAA Primer

ATGCAA

Probe cleaved = signal

No identification using CDC-N1 probe in samples not containing SARS-CoV-2 Primer

TACGTT

X Primer

TACGTT

X Primer

TACGTT

X Probe displaced = no signal (failure)

Fig. 1 Schematic diagram showing how SARS-CoV-2 can be detected by multiplex qPCR. The 50 nuclease activity of Taq polymerase produces a fluorescent signal during PCR using a probe that contains a complementary sequence to match that in the native virus as well as fluorescent reporter and quencher dyes. In intact probes, fluorescence of the reporter is suppressed by proximity of the quencher. During the annealing stage of PCR, the probe hybridizes to the target site. During extension stage, the reporter and quencher are released by the Taq polymerase, resulting in a fluorescent signal. Release occurs only in the case of perfectly matched probes and will therefore fail in the case of samples not containing SARS-CoV-2. The top panel shows cleavage of the probe fluorophore, resulting in detection of fluorescent signal samples containing SARS-CoV-2 with an intact sequence complementary to the probe. The bottom panel shows failure to detect a fluorescent signal in the case of samples negative for SARS-CoV-2

Quantitative polymerase chain reaction (qPCR) is the method of choice for sensitive detection of SARS-CoV-2 and other viruses (see Fig. 1). qPCR capitalizes on the 50 nuclease activity of Taq polymerase to produce a fluorescent signal during the amplification stage of the analysis [9]. In addition to standard primers, qPCR also uses a probe that contains a complementary sequence to that in the

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55

CDC-N1 28,309-28,332 266 5’Papain-like protease

3CL- protease

29674 -3’ Nucleocapsid

Spike

RNA-dependent RNA polymerase Helicase

Envelope Membrane

Fig. 2 Diagram of the SARS-CoV-2 sequence showing the target position of the qPCR probe used. The CDC-N1 probe targets bases 28,309–28,332 in the nucleocapsid protein sequence. As the probe is designed to detect SARS-CoV-2 at this position, failure of the qPCR signal would mean that the sequence is absent

1.0-

FAM HEX

Cy5

0.9-

Intensity (normalized)

0.80.70.60.50.40.30.20.10.0400

450

500

550

600

650

700

750

800

Wavelength (nm)

Fig. 3 Emission spectra of fluorophores used for multiplexing in qPCR experiments. FAM ¼ 6-Carboxyfluorescein; HEX ¼ Hexachlorofluorescein; Cy5 ¼ Cyanine 5

target molecule, and it also contains a distinct 50 fluorescent reporter dye and 30 quencher dye. The fluorescence of the reporter dye is suppressed when the probe is intact due to proximity of the quencher. In the annealing stage of PCR, the probe hybridizes to the site in question. During the extension stage, the reporter and quencher are released by the Taq polymerase 50 nuclease activity, resulting in a fluorescent signal. However, cleavage only occurs in the case of probes that match the target sequence perfectly. Thus, if the probe is designed to recognize a specific sequence in SARSCoV-2, as for the CDC-N1 primer, this will fail in cases where this virus is not present or in cases where other viruses are there (see Fig. 2). The multiplexing capability comes from the use of multiple reporter dyes that are spectrally distinct and the signals can therefore be analyzed at specific wavelengths (see Fig. 3).

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However, the entire qPCR procedure can be laborious and time-consuming as it typically involves multiple steps from acquiring nasopharyngeal swab samples to nucleic acid extraction and amplification [10–12]. Furthermore, nucleic acid extraction may be less efficient in samples with low virus titers. Other challenges such as shortages in the reagents required for RNA extraction have led to bottlenecks, and many labs are searching for solutions that provide flexibility in the face of these shortages. Recent efforts have focused on optimizing the procedure to enable more rapid and accurate analysis. Typically, extraction is a time-limiting step that can result in RNA being lost during the process, reducing assay performance. This step is usually required to remove inhibitors which would otherwise impact assay sensitivity and accuracy. Assays that could use alternative sample types such as saliva or sputum would be simpler and quicker to use and, at the same time, safer for healthcare workers. In order to address this challenge, Meridian Bioscience developed an Inhibitor-Tolerant RT-qPCR Mix capable of rapid and sensitive detection, even in the presence of inhibitors such as those found in sputum and saliva [13] (see Fig. 4). This approach enables new features such as extraction-free amplification, and each master mix contains a hot-start polymerase, dNTPs, buffer, and other components optimized for adaptability. The simplified procedure involves sampling, sample pre-treatment, and preparation of the crude sample, master mix, and primer/probe sets, followed by the PCR stage (see Fig. 5). Here, we present a protocol describing the sampling of virus from infected individuals, the primer and probe sets used, and the qPCR analysis using the Inhibitor-Tolerant RT-qPCR kit. The protocol specifically allows multiplex detection of SARS-CoV-2, influenza type A, and influenza type B. It is hoped that this approach can be most useful during the winter months when influenza outbreaks typically occur and another wave of COVID19 is anticipated [14, 15].

2

Materials

2.1 Sample Collection

1. Personal protective equipment (PPE) consisting of FFP2 (N95) mask, disposable cap, goggles, gown, apron, latex gloves, and shoe covers. 2. Sterile nasopharyngeal swabs and 10 mL collection tube containing 2 mL viral nucleic acid sample preservation fluid (see Note 1). 3. Saliva/sputum collection tube with funnel top (see Note 2).

Multiplex qPCR of SARS-CoV-2 and Influenza Viruses

A)

57

200 175

10% saliva

150

RFU

125 100 75 50 25 0

10

15

20

25

30

35

40

45

30

35

40

45

Cycles

200

B)

175

20% saliva

150

RFU

125 100 75 50 25 0 10

15

20

25 Cycles

Fig. 4 qPCR analysis of influenza A spike from (a) 10% and (b) 20% saliva obtained from patient swabs. The results show earlier cycle threshold (Ct) and higher relative fluorescence units (RFU) using the Inhibitor-Tolerant RT-qPCR Mix (red) compared to a standard RT-qPCR mix (black). The figure was adapted from the COVID-19 Reagent Solutions for Molecular and Immunological SARS-CoV2 Diagnostic Assays flyer (meridianbioscience.com) 2.2

qPCR

1. Nuclease-free water. 2. SARS-CoV-2 positive control MN908947.3) (see Note 3).

plasmid

(GenBank:

3. 20 μM working stock of control CDC-N1 primer set in nuclease-free water (see Note 4). (a) Forward primer: GACCCCAAAATCAGCGAAAT. (b) Reverse primer: TCTGGTTACTGCCAGTTGAATCTG. (c) Probe: FAM- ACCCCGCATTACGTTTGGTGGACC BHQ1.

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A)

STANDARD qPCR RNA/DNA EXTRACTION REQUIRED LYSIS

COLLECT PATIENT SAMPLE

WASH

ELUTION

SAMPLE EXTRACTION 20-60 MIN

PRIMERS MASTER PROBES MIX RT EXTRACTED RNA/DNA

RT-qPCR

EXTRACTION-FREE qPCR

B)

NO RNA/DNA EXTRACTION NEEDED CRUDE LYSATE

COLLECT PATIENT SAMPLE

SAMPLE PRE-TREATMENT 5 MIN

INHIBITOR TOLERANT MIX CRUDE PRIMERS LYSATE PROBES

RT-qPCR

Fig. 5 (a) Standard qPCR. qPCR analysis of viral RNA normally requires extraction which is a time-limiting step and can result in RNA being lost and limit the performance of the assay. However, extraction is typically required to remove inhibitors which would otherwise impact assay sensitivity and accuracy. (b) Extraction-free qPCR. Use of the Inhibitor-Tolerant RT-qPCR Mix allows sensitive multiplex detection, even in the presence of inhibitors found in body fluids and excretions. This prevents sample loss and saves time and labor, which are critical factors in viral screening assays that require rapid detection. The figure was adapted from the COVID19 Reagent Solutions for Molecular and Immunological SARS-CoV-2 Diagnostic Assays flyer (meridianbioscience.com)

4. 20 μM working stock of influenza A primer set in nuclease-free water (see Note 5). (a) Forward primer: GGAATGGCTAAAGACAAGACCAAT. (b) Reverse primer: GGGCATTTTGGACAAAGCGTCTAC. (c) Probe: HEX- AGTCCTCGCTCACTGGGCACGGTG BHQ1. 5. 20 μM working stock of influenza B primer set in nuclease-free water (see Note 6). (a) Forward primer: CCAGGGATTGCAGACATTGA. (b) Reverse primer: GAG.

ACAGGTGTTGCCATATTGTAAA

(c) Probe 1: Cy5- TTGTTAGGCCCTCTGTGGCRAGCABHQ2. (d) Probe 2: Cy5- TTGTTAGACCTTCTGTGGCRAGCABHQ2.

Multiplex qPCR of SARS-CoV-2 and Influenza Viruses

59

Table 1 Preparation of multiplex primer probe mixture Primer set

Component

Concentration

SARS-CoV-2 CDC-N1

Forward primer Reverse primer Probe

1 μM 1 μM 0.5 μM

Influenza A

Forward primer Reverse primer Probe

2 μM 2 μM 1 μM

Influenza B

Forward primer Reverse primer Probe 1 Probe 2

2 μM 2 μM 0.5 μM 0.5 μM

6. 400 μL multiplex primer-probe mixture (enough for 100 reactions (Table 1). 7. 4X Inhibitor-Tolerant RT-qPCR kit, containing hot-start polymerase, dNTPs, and MgCl2 (Meridian Bioscience; London, United Kingdom) (see Note 7). 8. qPCR thermocycler (see Note 8).

3

Methods

3.1 Nasopharyngeal Sample Collection

1. Perform sampling in a dedicated room with strict environmental sterilization, the test personnel wearing full PPE, and the test subject wearing a mask other than the time of sampling to minimize chances of virus spread [16] (see Note 9). 2. Have the test subject assume a seated position with the head held straight (see Fig. 6) (see Note 10). 3. Position yourself on the side of the test subject to limit exposure to any droplet projections that might occur. 4. Lift the tip of the nose to identify the area where the swab should be inserted. 5. Hold the swab like a pen and insert gently between the nasal floor and septum of the test subject until resistance is met by the rear wall of the nasopharynx (see Note 10). 6. Once in position, gently rub and roll the swab and then leave in place for approximately 10 s to absorb the secretions. 7. Slowly and gently remove the swab while continuing the rotation.

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Fig. 6 Diagram showing the ideal position of the swab and test subject for obtaining a nasopharyngeal sample

8. Completely remove the swab, check to see that it contains secretions, and insert into the sample tube. 9. Snap the shaft of the swab off as necessary and close the tube. 3.2 Saliva Sample Collection

1. Ask the subject to wash their hands thoroughly for 20 s and to dry them before starting the collection (see Note 11). 2. Wearing full PPE, place the packaged saliva collection tube with funnel on a clean, dry surface. 3. Ask the subject to remove the collection tube and funnel from the packaging. 4. Ask them to spit into the tube until the amount of saliva (without bubbles) reaches the fill line. 5. Retrieve the tube from the test subject and close the lid tightly until a click is heard. 6. Unscrew the funnel from the tube. 7. Use the small cap to close the tube tightly and mix the tube for 5 s.

3.3 qPCR (See Note 12)

1. Suspend all samples by gentle mixing and briefly centrifuge to settle all contents into the bottom of the tube (see Note 13).

Multiplex qPCR of SARS-CoV-2 and Influenza Viruses

61

Table 2 qPCR conditions Step

Temperature 

Time

Cycles

Polymerase activation

95 C

3 min

1

Denaturation

95  C

10 s

45

Annealing/extension

60  C

30 s

Read samples and analyze results

2. Add 5 μL of 4X qPCR mix from the Inhibitor-Tolerant RT-qPCR kit to each well of a sterile 96-well plate. 3. Add 4 μL of the primer probe mix to each well (see Note 14). 4. Add 1 μL each sample or positive control to specified wells in triplicate. 5. Add 10 μL nuclease-free water to wells containing no sample and 9 μL to wells containing sample (see Note 15). 6. Perform qPCR as outlined in Table 2 (see Note 16). 7. Analyze and interpret the results (see Fig. 7 and Table 3) (see Note 17).

4

Notes 1. For example, one can use nasopharyngeal swabs with 2 mL viral nucleic acid sample preservation fluid in a 10 mL tube from suppliers such as Alpha Labs. 2. For example, the tube contained in the P23 At-Home Covid19 Test Collection Kit can be used (P23 Labs; Little Rock, AR, USA). 3. The plasmid contains the complete nucleocapsid gene of SARS-CoV-2 and can therefore be used as a positive control in the current assay. 4. The probe from this set aligns with nucleotides 21,755–21,779 of the native SARS-CoV-2 sequence within the nucleocapsid protein sequence. 5. The probe from this set aligns with nucleotides 221–198 (reverse) of the influenza A virus (A/Indiana/08/2011 (H3N2)) segment 7 matrix protein 2 (M2) and matrix protein 1 (M1) genes [17]. 6. The probe from this set aligns with nucleotides 968–991 of the influenza B virus (B/Singapore/1/2011) nucleoprotein (NP) gene [18]. In 2011, a novel influenza B variant (B/Singapore/1/2011, GenBank accession number: CY093580) was

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Steve F. C. Hawkins and Paul C. Guest

A)

900 800 CDC-N1

700

Influenza A

RFU

600

Influenza B

Double target failure by influenza A and influenza B probes

500 400 300 200 100 0 5

10

15

20

25

30

35

40

45

50

Cycles

B)

900 800 CDC-N1

700

Influenza A

RFU

600

Influenza B

Target failure by CDC-N1 and influenza B probes

500 400 300 200 100 0 5

10

15

20

25

30

35

40

45

50

Cycles

C)

900 800 CDC-N1

700

Influenza A

RFU

600

Influenza B

500

Target failure by influenza B probe

400 300 200 100 0 5

10

15

20

25

30

35

40

45

50

Cycles

Fig. 7 Hypothetical results showing (a) double target failure by influenza A and B probes, suggestive of a positive result for SARS-CoV-2 only; (b) double target failure for SARS-CoV-2 CDC-N1 and influenza B probes suggestive of a positive result for influenza A only; and (c) target failure of influenza B probes suggestive of a positive result for both SARS-CoV-2 and influenza A. RFU relative fluorescence units

Multiplex qPCR of SARS-CoV-2 and Influenza Viruses

63

Table 3 Results interpretation CDC-N1 CT < 35 Fail Fail Fail CT < 35 CT < 35 CT < 35

Primer-probe set used Influenza A Influenza B Fail Fail CT < 35 Fail Fail CT < 35 CT < 35 CT < 35 CT < 35 Fail Fail CT < 35 CT < 35 CT < 35

Interpretaon Potenal SARS-CoV-2 Potenal influenza A Potenal influenza B Potenal influenza A and B Potenal SARS-CoV-2 and influenza A Potenal SARS-CoV-2 and influenza A Potenal SARS-CoV-2 and influenza A and B

CT cycle threshold

detected by direct immunofluorescence staining which tested negative in the original qPCR assay due to two single nucleotide substitutions in the probe region [19]. Thus, an additional probe that fully complements the influenza B variant was designed and included in equimolar amounts with the original probe. 7. Many other kits can be used although the one we have used here does not require RNA extraction. This should add recovery and make the process more user friendly and faster than other systems that require extraction. 8. Many instruments can be used for qPCR from multiple suppliers. The user should check with the manufacturer for compatibility with the required experimental conditions, reagents, and setup. 9. A brief explanation of the upcoming process should be given to the test subject and a medical history obtained. The explanation should also inform the test subject about the potentially uncomfortable nature of the procedure. 10. This position allows the tester to more easily follow nasal floor. To guide the insertion, the swab should be inclined in the same plane of the nose and ear. The distance between the nostril and rear wall of the nasopharynx is typically 8–10 cm in adults. 11. Ensure that the test subject does not eat or drink anything for 30 min prior to collecting the saliva sample. Note that spitting could conceivably transmit coronavirus, so ensure that you are distant and protected by full PPE when the saliva is produced. 12. Clean room conditions should be used when carrying qPCR to help avoid any contamination of the samples. This could occur due to the high sensitivity of qPCR in general. 13. Be sure to mix the reagents when setting up reactions since the components can settle or separate during cold storage. Avoid the use of sonication for mixing as this can lead to shearing of the template.

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14. The length, sequence, and concentration of primers and probes are critical factors to enable specific PCR amplification. For new targets, we recommend the use of a dedicated software for primer/probe design. For obtaining the best results, primers and probes should have calculated melting temperatures of approximately 60  C and amplicon length should be 80–200 base pairs. In addition, the forward and reverse primers should be present in equimolar amounts, and titration over the range of 0.2–1 μM should be applied to identify optimal concentrations. 15. We recommend including a non-template control consisting of nuclease-free water to check for cross-contamination. 16. Cycling conditions can be varied to suit machine-specific protocols. We recommend initially running 30 cycles using a 30 s extension period and then add 5 cycles for each new run as needed. Longer extension times may be needed for large amplicons up to 45 cycles when low levels of template are present. 17. Figure 6 shows the RT-qPCR for detection of SARS-CoV-2, influenza A, and influenza B viruses. The top panel shows amplification using the CDC-N1 target and failure of both the influenza A and B targets, potentially indicative of SARSCoV-2. The middle panel shows amplification using the influenza A target and failure of the SARS-CoV-2 and influenza B targets, potentially indicative of the presence of influenza A. The bottom panel shows amplification of the SARS-CoV2 and influenza A targets, with failure of the influenza B target, indicating the potential presence of both SARS-CoV-2 and influenza A.

Conflict of Interest Statement SFH is an employee of Meridian Bioscience. References 1. https://www.worldometers.info/coronavirus/ 2. https://coronavirus.jhu.edu/map.html 3. Seki M (2021) Trends in the management of infectious disease under SARS-CoV-2 era: from pathophysiological comparison of COVID-19 and influenza. World J Virol 10(2):62–68. https://doi.org/10.5501/wjv. v10.i2.62 4. Liu L, Zeng F, Rao J et al (2021) Comparison of clinical features and outcomes of medically

attended COVID-19 and influenza patients in a defined population in the 2020 respiratory virus season. Front Public Health 9:587425. https://doi.org/10.3389/fpubh.2021. 587425 5. https://www.who.int/emergencies/diseases/ novel-coronavirus-2019/strategies-and-plans 6. Ni M, Xu H, Luo J et al (2021) Simultaneous detection and differentiation of SARS-CoV-2, influenza A virus and influenza B virus by one-step quadruplex real-time RT-PCR in

Multiplex qPCR of SARS-CoV-2 and Influenza Viruses patients with clinical manifestations. Int J Infect Dis 103:517–524 7. Pabbaraju K, Wong AA, Ma R et al (2021) Development and validation of a multiplex reverse transcriptase-PCR assay for simultaneous testing of influenza A, influenza B and SARS-CoV-2. J Virol Methods 293:114151. https://doi.org/10.1016/j.jviromet.2021. 114151. Online ahead of print 8. No¨rz D, Hoffmann A, Aepfelbacher M et al (2021) Clinical evaluation of a fully automated, laboratory-developed multiplex RT-PCR assay integrating dual-target SARS-CoV-2 and influenza A/B detection on a high-throughput platform. J Med Microbiol 70(2). https://doi. org/10.1099/jmm.0.001295 9. Hawkins SFC, Guest PC (2017) Multiplex analyses using real-time quantitative PCR. Methods Mol Biol 1546:125–133 10. Calvez R, Taylor A, Calvo-Bado L et al (2020) Molecular detection of SARS-CoV-2 using a reagent-free approach. PLoS One 15(12): e0243266. https://doi.org/10.1371/journal. pone.0243266 11. Rodrı´guez Flores SN, Rodrı´guez-Martı´nez LM et al (2021) Comparison between a standard and SalivaDirect RNA extraction protocol for molecular diagnosis of SARS-CoV-2 using nasopharyngeal swab and saliva clinical

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samples. Front Bioeng Biotechnol 9:638902. h ttps://d oi.or g/10 .3 38 9/f bioe.2 02 1. 638902 12. Fassy J, Lacoux C, Leroy S et al (2021) Versatile and flexible microfluidic qPCR test for high-throughput SARS-CoV-2 and cellular response detection in nasopharyngeal swab samples. PLoS One 16(4):e0243333. https:// doi.org/10.1371/journal.pone.0243333 13. https://meridianlifescience.com/documents/ Molecular/Meridian%20COVID%20flyer.pdf 14. https://www.theguardian.com/society/2021 /mar/07/uk-hard-winter-flu-respiratoryviruses-top-medic-population-immunity-covid 15. https://www.telegraph.co.uk/global-health/ science-and-disease/suppressed-seasonal-flumay-come-back-vengeance-next-winter/ 16. https://www.cdc.gov/coronavirus/2019ncov/lab/guidelines-clinical-specimens.html 17. https://www.ncbi.nlm.nih.gov/nuccore/ JN638729 18. https://www.ncbi.nlm.nih.gov/nuccore/CY0 93580 19. Lee HK, Lee CK, Loh TP et al (2011) Missed diagnosis of influenza B virus due to nucleoprotein sequence mutations, Singapore, April 2011. Euro Surveill 16(33):19943

Chapter 5 Multiplex Quantitative Polymerase Chain Reaction Test to Identify SARS-CoV-2 Variants Steve F. C. Hawkins and Paul C. Guest Abstract Quantitative polymerase chain reaction (qPCR) is a routinely used method for detection and quantitation of gene expression in real time. This is achieved through the incorporation and measurement of fluorescent reporter probes in the amplified cDNA strands, since the fluorescent signals increase as the reaction progresses. The availability of multiple probes which fluoresce at different wavelengths allows for multiplexing as this gives rise to amplicons with unique fluorescent signatures. Here we describe a method using the Inhibitor-Tolerant RT-qPCR kit, developed by Meridian Bioscience kit which allows simultaneous realtime quantitation of the UK, South Africa, and Brazil SARS-CoV-2 variants. Key words COVID-19, SARS-CoV-2, Variant, PCR, Fluorescent dye, Taq polymerase, Quantitation

1

Introduction The severe acute respiratory syndrome coronavirus-2 (SARS-CoV2), the causative agent of COVID-19 disease, has led to the most severe global pandemic since the Spanish flu of 1918 [1, 2]. One year following the declaration of COVID-19 as a pandemic on March 11, 2020 [3], several waves of the virus have now occurred in most countries of the world, and cases have surpassed 139 million persons (almost 1.8% of the world population) with more than three million deaths [4]. COVID-19 has been responsible for overwhelming the healthcare systems, shutting down of schools and gatherings, lockdowns, and driving most of the planet into an economic recession. As of March 11, 2021, the integrated effort to develop vaccines for COVID-19 has progressed rapidly with the authorization and distribution of more than one dozen vaccines globally [5]. Multiple countries have now vaccinated more than 25% of their populations, such as Israel, United Arab Emirates, the United Kingdom, Bahrain, the United States, Chile, and Serbia, and the World Health

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Organization (WHO) has implemented a global alliance called COVAX to facilitate development, manufacturing, and distribution of SARS-CoV-2 vaccines around the world [6]. However, this progress may be hindered with the emergence of multiple variants of SARS-CoV-2, some of which are more infective and some capable of at least partially evading the existing vaccines [7–9]. The B.1.1.7 variant of SARS-CoV-2 was first of these strains to be detected in the United Kingdom in September 2020 [10]. This appeared to be responsible for the rapid progress of a third wave of the virus to hit the country in December 2020 through March 2021. The B.1.1.7 variant contains 17 polymorphisms, with 8 of these located in the 1273 amino acid SARS-CoV-2 spike protein (see Figs. 1 and 2). This variant is associated with increased transmissibility and risk of death compared with other variants. However, no evidence has been found to suggest an impact on vaccine efficacy [11]. The increased transmission has been attributed to an asparagine to tyrosine mutation at amino acid 501 (N501Y) within the receptor binding site [12]. Two of the mutations in this variant are accounted for by deletions (Δ69–70 and Δ144–145). The SARS-CoV-2 B.1.351 variant emerged in South Africa in mid-December 2020 [13]. This variant also contains the N501Y mutation in the receptor binding site, which has been linked to a higher viral load and increased transmission rates (see Fig. 2) [14]. In addition, a glutamate to lysine mutation at position 484 (E484K) and a lysine to asparagine mutation at amino acid 417 (K417N) may enable this variant to escape immune system response and thereby affect how well the current vaccines work [14, 15]. Another concerning variant emerged in Brazil (P.1) which contains the N501Y mutation of the UK variant, as well as the E484K and K417T mutations of the South Africa variant, along with multiple other mutations (see Fig. 2) [16]. By the end of March 2021, there were more than 300 cases of the Brazil variant in the United States and 30 in the United Kingdom [17]. Not only has the Brazil variant spread rapidly across Brazil, but it has also been detected in multiple European countries as well as in Canada, India, South Korea, and Japan [18]. The current emergence of SARS-CoV-2 variants is a matter of great concern across the world as the increase in transmission generates more chances for the eruption of new SARS-CoV-2 variants. Therefore, it is important to track the emergence and spread of these SARS-CoV-2 variants to help contain the pandemic to more manageable levels. For this purpose, we present a multiplex quantitative polymerase chain reaction (qPCR) assay capable of simultaneously detecting the UK, South Africa, and Brazil variants. The protocol is similar to that described by Vogels et al. [19]. For this assay, we used a TaqMan® SNP genotyping approach, which employs the 50 nuclease activity of Taq polymerase to

qPCR Analysis of SARS-CoV-2 Variants

69

Deleon 69-70 Deleon 144-145

Y

N-501-Y D

A-570-D

G

D-614-G H

P-681-H I

T-716-I

A

S-982-A

H

D-1118-H

Fig. 1 Schematic diagram of the SARS-CoV-2 spike protein showing deletions (red boxes) and mutations (green boxes) that occur in the UK Δ69–70 variant. The numbers refer to the amino acid sequences in the native spike protein

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Fig. 2 Schematic diagram of SARS-CoV-2 spike protein showing deletions (red font) and mutations (green font) in the UK, South Africa, and Brazil variants. The numbers refer to the affected amino sequences in the native protein

produce a fluorescent signal during the PCR stage of the analysis [20]. In these kinds of assays, a probe can be used that contains a complementary sequence to match that in the native molecule. The probe also contains a distinct 50 fluorescent reporter dye and a 30 quencher dye. The fluorescence of the reporter dye is suppressed in intact probes due to the proximity of the quencher. In the annealing stage of PCR, the probe hybridizes to the site in question. During the extension stage, the reporter and quencher are released by the 50 nuclease activity of Taq polymerase, resulting in fluorescence of the reporter dye. Each reporter dye is spectrally distinct, which allows the multiplexing capability (Table 1). However, cleavage of the probe occurs only in the case of perfectly matched probes since only these will be recognized by the polymerase. Thus, if the probe is designed to recognize a specific sequence in the original SARS-CoV-2 virus, such as CATGTC encoding histidine-valine (HV) at amino acids 69–70, this will fail in the case of the Δ69–70 deletion in the spike protein of the SARS-CoV-2 variant (see Fig. 3). However, failure at this site alone would not be enough to distinguish the UK variant from other variants containing this same deletion. Instead, a multiplex assay would be required which assesses multiple different regions of SARS-CoV-2 that have shown mutational changes. Here, we present a protocol describing the sampling of virus from infected individuals, the primer and probe sets used, and the

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Table 1 Fluorophores and quenchers used for qPCR probes in the current study Fluorophore

Absorption

Emission

Quencher

6-Carboxyfluorescein (FAM)

495 nm

517 nm

BHQ-1

Hexachlorofluorescein (HEX)

537 nm

553 nm

BHQ-1

Cyanine 5 (Cy5)

650 nm

667 nm

BHQ-2

Fig. 3 Schematic diagram showing how deletion of six nucleotides (CATGTC) in the UK spike Δ69–70 SARSCoV-2 variant can be detected by multiplex qPCR. This approach takes advantage of the 50 nuclease activity of Taq polymerase to produce a fluorescent signal during PCR and by using a probe that contains a complementary sequence to match that in the native virus as well as fluorescent reporter and quencher dyes. In intact probes, fluorescence of the reporter is suppressed by proximity of the quencher. During the annealing stage of PCR, the probe hybridizes to the target site. During extension stage, the reporter and quencher are released by the Taq polymerase, resulting in a fluorescent signal. Release occurs only in the case of perfectly matched probes and will therefore fail in the case of the spike Δ69–70 SARS-CoV-2 variant. The top panel shows cleavage of the probe fluorophore, resulting in detection of a fluorescent signal in native SARS-CoV-2 which contains an intact CATGTC sequence. The bottom panel shows failure to detect a fluorescent signal in the case of Δ69–70 SARS-CoV-2 variant as the CATGTC sequence is absent

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qPCR analysis using the Inhibitor-Tolerant RT-qPCR kit, developed by Meridian Bioscience. The main objective was to describe a procedure that would distinguish the UK SARS-CoV-2 variant from the native virus, as well as from the South Africa and Brazil variants.

2

Materials

2.1 Sample Collection

1. Personal protective equipment (PPE) consisting of FFP2 (N95) mask and disposable cap, goggles, gown, apron, latex gloves, and shoe covers. 2. Sterile swabs and sample tubes (see Note 1).

2.2

qPCR

1. Nuclease-free water. 2. 20 μM working stock of control CDC-N1 primer set in nuclease-free water (see Note 2). (a) Forward primer: GACCCCAAAATCAGCGAAAT. (b) Reverse primer: TCTGGTTACTGCCAGTTGAATCTG. (c) Probe: FAM- ACCCCGCATTACGTTTGGTGGACC BHQ1. 3. 20 μM working stock of spike Δ69/70 primer set in nucleasefree water (see Note 3). (a) Forward primer: TCAACTCAGGACTTGTTCTTACCT. (b) Reverse primer: TGGTAGGACAGGGTTATCAAAC. (c) Probe: HEX- TTCCATGCTATACATGTCTCTGGGABHQ1. 4. 20 μM working stock of ORF1a Δ3675–3677 primer set in nuclease-free water (see Fig. 4) (see Note 4). (a) Forward primer: TGCCTGCTAGTTGGGTGATG. (b) Reverse primer: TGCTGTCATAAGGATTAGTAACACT. (c) Probe: Cy5GTTTGTCTGGTTTTAAGCTAAAA GACTGTG- BHQ2. 5. 400 μL multiplex primer-probe mixture (enough for 100 reactions (Table 2). 6. 2X Inhibitor-Tolerant RT-qPCR kit, containing hot-start polymerase, dNTPs, and MgCl2 (Meridian Bioscience; London, United Kingdom) (see Note 5). 7. qPCR thermocycler (see Note 6). 8. Data analysis software.

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Fig. 4 Diagram of the SARS-CoV-2 sequence showing the target positions of the qPCR probes used. The ORF1a Δ3675–3677 probe aligns with nucleotides11,283–11,312 on the 30 side of the sequence encoding the 3CL protease in the ORF1a region. The spike Δ69–70 probe aligns with bases 21,755–21,779 in the spike protein sequence. The CDC-N1 probe targets bases 28,309–28,332 in the nucleocapsid protein sequence. In all cases, the probes are designed to detect the intact sequence at these positions. Thus, failure of qPCR signal would mean that the sequence is absent Table 2 Preparation of multiplex primer-probe mixture in nuclease-free water Primer set

Component

Concentration

CDC-N1

Forward primer Reverse primer Probe

1 μM 1 μM 0.5 μM

Spike Δ69/70

Forward primer Reverse primer Probe

2 μM 2 μM 1 μM

Orf-1a Δ3675–3677

Forward primer Reverse primer Probe

2 μM 2 μM 1 μM

3

Methods

3.1 Sample Collection

1. Perform sampling in a dedicated room with strict environmental sterilization, and wearing full PPE. 2. Ensure that the test subject is wearing a mask other than the time of sampling to minimize chances of virus spread (see Note 7). 3. Acquire the nasopharyngeal sample from the test subject as described [21] (see Notes 8 and 9). 4. Once acquired, completely remove the swab, check to see that it contains secretions, and insert into the sample tube. 5. Snap the shaft of the swab off as necessary and close the tube.

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1. Resuspend all reagents by gentle mixing and then centrifuge briefly so that all contents can be collected from the bottom of the tube (see Note 11).

3.2 qPCR (See Note 10)

2. Add 10 μL of 2X qPCR mix to each well of a sterile 96-well plate. 3. Add 4 μL of the primer-probe mix to each well (see Note 12). 4. Then add 1 μL each sample to specified wells in triplicate. 5. Add 5 μL nuclease-free water to wells containing no sample and 4 μL to wells containing sample (see Note 13). 6. Set the thermal cycler for detection of FAM, HEX, and Cy5 fluorophores. 7. Perform qPCR using the conditions outlined in Table 3 (see Note 14). 8. Interpret the results as described [22] (Table 4 and see Fig. 5) (see Note 15).

Table 3 qPCR conditions Step

Temperature

Time

Cycles

Polymerase activation

95  C

3 min

1 45



Denaturation

95 C

10 s

Annealing/extension

60  C

30 s

Read samples

Table 4 Results interpretation Primer-probe set used CDC-N1

Spike Δ69/70

Orf-1a Δ3675–3677

Interpretation

CT < 35

Fail

Fail

Potential B.1.1.7

CT < 35

CT < 35

Fail

Potential B.1.351/P.1

CT < 35

CT < 35

CT < 35

Potential other variants

CT cycle threshold B.1.1.7 ¼ UK variant B.1.135 ¼ South Africa variant P.1 ¼ Brazil variant

qPCR Analysis of SARS-CoV-2 Variants

A)

75

900 800 CDC-N1

700

Spike Δ60-70

RFU

600

ORF1a Δ3675-3677

Double target failure by ORF1a Δ3675-3677 and spike Δ60-70 probes

500 400 300 200 100 0 5

10

15

20

25

30

35

40

45

50

Cycles

B)

900 800 CDC-N1

700

Spike Δ60-70

RFU

600

ORF1a Δ3675-3677

Target failure by ORF1a Δ3675-3677 probe

500 400 300 200 100 0 5

10

15

20

25

30

35

40

45

50

Cycles

C)

900 800 CDC-N1

700

Spike Δ60-70

RFU

600

ORF1a Δ3675-3677

500

No target failure

400 300 200 100 0 5

10

15

20

25

30

35

40

45

50

Cycles

Fig. 5 Hypothetical results showing (a) double target failure by ORF1a Δ3675–3677 and spike Δ60–70 probes, suggestive of the presence of the UK B.1.1.7 SARS-CoV-2 variant; (b) ORF 1a Δ3675–3677 target failure indicating potential South Africa B.135 and Brazil P.1 SARS-CoV-2 variants; and (c) no target failure indicative of native SARS-CoV-2 or lineages other than the B.1.1.7, B.1.135, or P.1 variants. RFU relative fluorescence units

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Notes 1. For example, nasopharyngeal swabs with 2 mL viral nucleic acid sample preservation fluid in a 10 mL tube from suppliers such as Alpha Labs. 2. The CDC-N1 primers and probe are used as a control to ensure that any failure to amplify a target is due to the deletions in the spike and ORF1a genes and not to insufficient SARS-CoV2 RNA. The probe from this set aligns with nucleotides 28,309–28,332 of the native SARS-CoV-2 sequence. 3. This primer and probe set would show a target failure when testing viruses like B.1.1.7 with the Δ69/70 HV deletion. However, this is not definitive without the incorporation of other primers. The probe from this set aligns with nucleotides 21,755–21,779 of the native SARS-CoV-2 sequence. 4. This primer and probe set targets a serine-glycine-phenylalanine (SGF) deletion in the open reading frame 1a (ORF1a) gene, which occurs in the B.1.1.7, B.1.351, and P1 SARSCoV-2 lineages. Thus, the combine targeting of the HV deletion in the spike gene and the SGF deletion in the ORF1a gene assay can distinguish B.1.1.7 from B.1.351 and P.1. The probe from this set aligns with nucleotides 11,283–11,312 of the native SARS-CoV-2 sequence. 5. Other kits can be used although the one we have used here does not require RNA extraction which can result in loss or destruction of sample and instead saves time and labor which are vitally important in viral screening assays that require rapid detection for infection control. 6. Many instruments can be used for qPCR such as from Applied Biosystems, Stratagene/Agilent, Bio-Rad, Roche, and Qiagen. However, it is important to check with the manufacturer for compatibility with the required experimental conditions and setup. 7. Before beginning, a brief explanation of the process should be given to the test subject, and a brief medical history should be obtained to identify any potential obstacles to the test (such as high risk of nosebleed). During the explanation, the test subject should also be informed of the uncomfortable nature of the procedure. 8. This position allows greater ease of following the nasal floor. 9. As a guide, the inclination of the swab should be in the same plane as that of the nose and the ear, and the distance between the nostril and rear wall of the nasopharynx is 8–10 cm in adults.

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10. Clean room conditions should be used to avoid any contamination of the samples, given the high sensitivity of qPCR. 11. Always mix reagents well when setting up reactions as they can settle or partition during storage. For mixing, sonication should not be used considering the risk of shearing the template. 12. The length, sequence, and concentration of the primers and probes are critical for specific amplification. For user-defined targets that are not commercially available, we recommend the use of primer-design software, such as Primer3 (http://frodo. wi.mit.edu/primer3/) or visual OMP™ (http://dnasoftware. com/). For the best results, primers and probes should have melting temperatures of approximately 60  C, and the targeted amplicon length should be 80–200 base pairs. Forward and reverse primers should be equimolar, and titration in the range of 0.2–1 μM should be used to find the ideal concentration. 13. We recommend including a non-template control consisting of nuclease-free water to check for cross-contamination. 14. Cycling conditions can be varied to suit machine-specific protocols. We recommend initially running 30 cycles with a 30 s extension and then adding cycles in increments of 5, if needed. Normally, longer extension times may be required for amplicons larger than 200 base pairs, and, for low concentrations of template, up to 45 cycles may be required. However, it is not recommended to exceed a total of 45 cycles. 15. Figure 5 shows the RT-qPCR for detection of the B.1.1.7, B.1.135, and P.1 SARS-CoV-2 variants. The top panel shows amplification using the CDC-N1 target and failure of both the spike Δ60–70 and ORF1a Δ3675–3677 targets, potentially indicative of B.1.1.7 (UK) variant. The middle panel shows amplification using the CDC-N1 and spike Δ60–70 targets, and failure of the ORF1a Δ3675–3677 target, potentially indicative of the B.1.135 (South Africa) or P.1 (Brazil) variant. The bottom panel shows the case of no target failure, indicating the presence of either native SARS-CoV-2 or other variants of concern.

Conflict of Interest Statement SFH is an employee of Meridian Bioscience.

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References 1. Elderfield R, Barclay W (2011) Influenza pandemics. Adv Exp Med Biol 719:81–103 2. Pandey S, Yadav B, Pandey A et al (2020) Lessons from SARS-CoV-2 pandemic: evolution, disease dynamics and future. Biology (Basel) 9(6):141. https://doi.org/10.3390/ biology9060141 3. https://www.who.int/director-general/ speeches/detail/who-director-general-s-open ing-remarks-at-the-media-briefing-on-covid-1 9%2D%2D-11-march-2020 4. https://www.worldometers.info/corona virus/ 5. https://our worldindata.org/covid-vac cinations 6. https://www.who.int/initiatives/act-accelera tor/covax 7. Hoffmann M, Arora P, Groß R et al (2021) SARS-CoV-2 variants B.1.351 and P.1 escape from neutralizing antibodies. Cell 20:S00928674(21)00367-6. https://doi.org/10. 1016/j.cell.2021.03.036. Online ahead of print 8. Singh J, Samal J, Kumar V et al (2021) Structure-function analyses of new SARSCoV-2 variants B.1.1.7, B.1.351 and B.1.1.28.1: clinical, diagnostic, therapeutic and public health implications. Viruses 13(3): 439. https://doi.org/10.3390/v13030439 9. Dejnirattisai W, Zhou D, Supasa P et al (2021) Antibody evasion by the P.1 strain of SARSCoV-2. Cell 30:S0092-8674(21)00428-1. https://doi.org/10.1016/j.cell.2021.03.055. Online ahead of print 10. Leung K, Shum MH, Leung GM et al (2021) Early transmissibility assessment of the N501Y mutant strains of SARS-CoV-2 in the United Kingdom, October to November 2020. Euro Surveill 26(1):2002106. https://doi.org/10. 2807/1560-7917.ES.2020.26.1.2002106

11. https://www.cdc.gov/coronavirus/2019ncov/science/science-briefs/scientific-briefemerging-variants.html 12. Gu H, Chen Q, Yang G et al (2020) Adaptation of SARS-CoV-2 in BALB/c mice for testing vaccine efficacy. Science 369(6511): 1603–1607 13. https://www.who.int/csr/don/31-decem ber-2020-sars-cov2-variants/en/ 14. https://www.sciencemediacentre.org/expertreaction-to-the-south-african-variant/ 15. Callaway E (2021) Could new COVID variants undermine vaccines? Labs scramble to find out. Nature 589(7841):177–178 16. Nascimento VAD, Corado ALG, Nascimento FOD et al (2020) Genomic and phylogenetic characterisation of an imported case of SARSCoV-2 in Amazonas State, Brazil. Mem Inst Oswaldo Cruz 115:e200310. https://doi. org/10.1590/0074-02760200310 17. https://www.theguardian.com/world/2021/ mar/01/brazil-covid-variant-p1-britain 18. https://www.theguardian.com/world/2021/ mar/01/p1-brazil-variant-found-countriesnot-on-uk-travel-red-list 19. https://virological.org/t/multiplexed-rtqpcr-to-screen-for-sars-cov-2-b-1-1-7variants-preliminary-results/588 20. Glaab WE, Skopek TR (1999) A novel assay for allelic discrimination that combines the fluorogenic 50 nuclease polymerase chain reaction (TaqMan) and mismatch amplification mutation assay. Mutat Res 430:1–12 21. https://www.cdc.gov/coronavirus/2019ncov/lab/guidelines-clinical-specimens.html 22. https://www.protocols.io/view/multiplexedr t-qpcr-to-screen-for-sars-cov-2-b-1-1br9vm966?step¼1

Chapter 6 NIRVANA for Simultaneous Detection and Mutation Surveillance of SARS-CoV-2 and Co-infections of Multiple Respiratory Viruses Chongwei Bi, Gerardo Ramos-Mandujano, and Mo Li Abstract Detection and mutation surveillance of SARS-CoV-2 are crucial for combating the COVID-19 pandemic. Here we describe a lab-based method for multiplex isothermal amplification-based sequencing and realtime analysis of multiple viral genomes. It can simultaneously detect SARS-CoV-2, influenza A, human adenovirus, and human coronavirus and monitor mutations for up to 96 samples in real time. The method proved to be rapid and sensitive (limit of detection: 29 viral RNA copies/μL of extracted nucleic acid) in detecting SARS-CoV-2 in clinical samples. We expect it to offer a promising solution for rapid fielddeployable detection and mutational surveillance of pandemic viruses. Key words RPA, Multiplexing, SARS-CoV-2, Virus detection, Mutation surveillance

1

Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive-sense RNA beta-coronavirus. It is the seventh coronavirus known to infect human and has led to the coronavirus disease 2019 (COVID-19) pandemic. Early diagnosis of SARS-CoV2 infections is vital to combat the COVID-19 pandemic. To date, multiple diagnostic assays have been developed, and they fall into two broad categories: nucleic acid-based and antibody-based tests. Antibody-based SAR-CoV-2 detection requires virus-specific immunoglobulin (Ig)M and IgG proteins. However, studies have shown that the production of detectable SAR-CoV-2 IgM and IgG generally starts 2 weeks after infection [1–3]. This limits the ability of antibody-based test in early SARS-CoV-2 detection. Currently, the nucleic acid-based test is the gold standard of SARS-CoV2 diagnosis. Real-time reverse transcription polymerase chain reaction (rRT-PCR) is the most widely performed assay with decent sensitivity and accuracy. The choice of target genes has a significant

Paul C. Guest (ed.), Multiplex Biomarker Techniques: Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification, Methods in Molecular Biology, vol. 2511, https://doi.org/10.1007/978-1-0716-2395-4_6, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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Fig. 1 Genome structure of SARS-CoV-2. (Adapted from [6]). The genome of SARS-CoV-2 encodes four structural proteins (S, E, M, and N) and several replication-related nonstructural proteins (NSPs)

impact on the performance of rRT-PCR assays. Early studies showed that primers targeting the spike (S) gene are specific for detecting SARS-CoV-2, but with limited sensitivity [4] (see Fig. 1). The sensitivity of detection is improved when changing the targets to the nucleocapsid (N) and envelope (E) genes [5]. Early symptoms of most SARS-CoV-2 infections are fever and dry cough, which are common for other respiratory viruses. This makes it challenging to distinguish SARS-CoV-2 and co-infecting respiratory viruses by clinical features. The co-infection rate of SARS-CoV-2 has been found to be higher in cases leading to death compared to surviving cases in northeastern Iran [7]. In the Iranian study, 22.3% of the death cases were found to have influenza A co-infections as compared with 19.3% for surviving cases. This suggests that the co-infection with SARS-CoV-2 and other viruses could potentially increase the severity of clinical symptoms. Since current detection of SARS-CoV-2 co-infection requires additional rRT-PCR assays [8], there is a need for a high-throughput co-infection detection method to benefit the treatment of patients. As a single-strand RNA virus, the genome of SARS-CoV-2 frequently acquires mutations with an estimated rate of 1.12  10 3 mutations per site-year [9]. These mutations are important to monitor the virus spread and evolution, and to validate the

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detection assays and vaccines. However, most of the current SARSCoV-2 detection assays can only provide a positive or negative answer of infection, rather than showing any genetic information of the viral genome. To survey the SARS-CoV-2 genome, positive samples can be subjected to a separate workflow which normally involves whole genome amplicon sequencing or targeted sequencing by next-generation sequencing (NGS) [10]. Though targeted sequencing can provide high-accuracy variant detection [11], such experiments require high capital instruments and complex molecular biology procedures. Thus, they cannot be performed routinely. Here, we describe an isothermal amplification-based method to do real-time simultaneous detection and mutation surveillance of SARS-CoV-2 and co-infections of multiple respiratory viruses, termed Nanopore sequencing of Isothermal Rapid Viral Amplification for Near real-time Analysis (NIRVANA) [12] (see Fig. 2). In summary, clinical samples were subjected to RNA extraction and reverse transcription to produce cDNA templates. Next, multiplex recombinase polymerase amplification (RPA) was performed for isothermal amplification of five loci in the SARS-CoV-2 genome (see Fig. 3), along with one human housekeeping gene, and three co-infecting viruses. The sequencing of RPA amplicons was performed in the portable Nanopore MinION sequencer, and a realtime data analysis tool was provided to report the sample identity and mutations on the fly.

SARS-CoV-2+ cDNA

9-amplicon primer mix

RPA isothermal amplificaon

MinION sequencing

coverage

Respiratory21+ cDNA

Read alignment 5 SARS-CoV-2 amplicon

ACTB

FluA

HAdVs HCoV

Fig. 2 Schematic representation of NIRVANA. RNA samples were subjected to reverse transcription, followed by multiplex RPA to amplify multiple regions of the SARS-CoV-2 genome. The amplicons were purified and prepared to the Nanopore library using an optimized barcoding library preparation protocol. In the end, the sequencing was performed in the pocket-sized Nanopore MinION sequencer, and sequencing results were analyzed by our algorithm termed RTNano on the fly (adapted from [12])

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11083 G>T/A

14408 C>T

23403 A>G

28144 T>C 28688 T>C 28311 C>T 28657 C>T/G

Fig. 3 The RPA primers used in this study were plotted in the SARS-CoV-2 genome. The corresponding prevalent variants were labeled under the genome. (Adapted from [12])

2 2.1

Materials RNA Extraction

1. Clinical samples in TRIzol (see Note 1). 2. Direct-Zol RNA Miniprep kit (Zymo Research). 3. Ethanol (95–100%). 4. DNase-/RNase-free water. 5. DNAZap (Invitrogen). 6. RNase AWAY (Invitrogen).

2.2 Reverse Transcription

1. Invitrogen SuperScript IV reverse transcriptase. 2. RNase H. 3. Random hexamers. 4. 10 mM dNTP mix. 5. RNaseOUT™ recombinant ribonuclease inhibitor. 6. PCR-clean grade tubes.

2.3 RPA and DNA Purification

1. TwistAmp® basic kit. 2. QIAquick PCR purification kit. 3. Qubit 4 fluorometer. 4. Qubit dsDNA HS assay kit.

2.4 Agarose Gel Electrophoresis

1. 2% agarose gel in 100 mM Tris–HCl (pH 8.3), 50 mM acetic acid, 1 mM EDTA (TAE) buffer containing SYBR Safe DNA gel stain. 2. 1 Kb Plus DNA Ladder and 6 DNA gel loading dye.

2.5 Library Preparation and Sequencing

1. Agencourt AMPure XP beads. 2. Native Barcoding Expansion 96 kit (Oxford Nanopore Technologies). 3. MinION Flow Technologies).

Cell

(R9.4.1)

4. NEBNext FFPE DNA repair mix.

(Oxford

Nanopore

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83

5. NEBNext Ultra II End Repair/dA-tailing module. 6. NEBNext Quick ligation module.

3 3.1

Methods RNA Extraction

1. Decontaminate the bench with 70% ethanol, DNAZap, and RNase AWAY before and after use. 2. Follow the manufacturer’s protocol for a standard RNA extraction. 3. Elute RNA in 50 μL of DNase-/RNase-Free water and store on ice (see Note 2).

3.2 Reverse Transcription

1. Transfer 2.5 μL of extracted RNA to PCR-clean grade 0.2 mL tubes. 2. Follow the manufacturer’s manual to set up the reaction of reverse transcription. 3. Incubate the combined reaction mixture in a thermocycler at 53  C for 10 min, followed by 80  C for 10 min to inactivate the reaction. 4. Add 0.5 μL RNase H to the reaction, incubating at 37  C for 20 min to remove RNA (see Note 3).

3.3

Multiplex RPA

1. Dilute the reaction mixture of reverse transcription five times with DNase-/RNase-free water, and use it as template in RPA. 2. Prepare the primer mixture by adding different volumes of 10 μM primers according to Table 1. 3. Mix 4.8 μL primer mixture, 29.5 μL rehydration buffer, 2.5 μL diluted reaction mixture, and 10.7 μL DNase-/RNase-free water in 1.5 mL PCR tubes (see Note 4). 4. Add the reaction mix to TwistAmp Basic reaction 8-strip tube and use the pipette to mix. 5. Add 2.5 μL of 280 mM magnesium acetate (supplied in the kit) to the lid of TwistAmp Basic reaction 8-strip tube and close the lid. 6. Briefly centrifuge the TwistAmp Basic reaction 8-strip tube to introduce the magnesium acetate to the reaction. 7. Invert the tube several times to mix and briefly centrifuge again. 8. Incubate the reaction in a thermocycler at 39  C for 4 min, take the tube out and invert several times to mix, and centrifuge as above (see Note 5).

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Table 1 Primer sequences and volumes used in NIRVANA Primer

Sequence

Amplicon size Primer amount

pair4-F pair4-R

GCTGGTTCTAAATCACCCATTCAGT TCTGGTTACTGCCAGTTGAATCTG

273 bp

6 μL

pair5-F pair5-R

TTGGGATCAGACATACCACCCA CAACACCTAGCTCTCTGAAGTGG

194 bp

9 μL

pair9-F pair9-R

CCAGCAACTGTTTGTGGACCT AGCAACAGGGACTTCTGTGC

309 bp

12 μL

pair10-F pair10-R

GACCCCAAAATCAGCGAAAT TGTAGCACGATTGCAGCATTG

394 bp

12 μL

pair13-F pair13-R

CCAGAGTACTCAATGGTCTTTGTTC ACCCAACTAGCAGGCATATAGAC

195 bp

6 μL

ACTB-F ACTB-R

CCCAGCCATGTACGTTGCTATCCAGGC ACAGCTTCTCCTTAATGTCACGCACGAT

263 bp

4 μL

influA-F influA-R

ATGAGYCTTYTAACCGAGGTCGAAACG TGGACAAANCGTCTACGCTGCAG

244 bp

12 μL

HAdVs-F GCCGAGAAGGGCGTGCGCAGGTA HAdVs-R TACGCCAACTCCGCCCACGCGCT

161 bp

9 μL

HCoV-F ATGGTCAAGGAGTTCCCATTGCTTTCGGAGTA HCoV-R GGGCCGGTACCGAGATAGTAGAAATACCATC TCG

151 bp

9 μL

9. Replace the TwistAmp Basic reaction 8-strip tube in the thermocycler to continue to incubate at 39  C for 16 min. 10. Store the reaction at 4  C before DNA purification. 3.4

DNA Purification

1. Follow the manufacturer’s protocol for standard DNA purification. 2. Elute the DNA with 30 μL DNase-/RNase-free water. 3. Estimate the concentration of extracted DNA using the Qubit dsDNA HS assay kit on the Qubit fluorometer (see Note 6). 4. For verification of RPA products, carry out an agarose gel electrophoresis on 2% agarose gels in TAE buffer using 10 μL of the PCR product (see Fig. 4 as an example for samples with all targeted RPA amplicons).

3.5 Library Preparation and Sequencing

1. Follow the Nanopore protocol of PCR tiling of COVID-19 virus with Native Barcoding Expansion 96 for the library preparation, starting the procedure from the End-prep step.

Mutation Surveillance of SARS-CoV-2

Sample

M

500 bp – 400 bp – 300 bp – 200 bp –

85

Multi NTC -RPA

* * * **

100 bp – Fig. 4 Agarose gel electrophoresis results of multiplex RPA. All five amplicons were shown in the gel with correct size (*, note that pairs 5 and 13 have similar sizes). The no template control (NTC) showed a different pattern of nonspecific amplicons. M molecular size marker. (Adapted from [12])

2. For each sample, add 5 μL purified DNA, 7.5 μL DNase-/ RNase-free water, 1.75 μL reaction buffer, and 0.75 μL enzyme mix (from the Ultra II End-prep kit) in a 0.2 mL PCR tube. 3. Mix gently by pipetting and spin down. 4. Using a thermocycler, incubate at 20  C for 5 min and 65  C for 5 min. 5. Follow the rest of the Nanopore protocol to complete the library preparation. 6. Follow the Nanopore protocol to prime and load the R9.4.1 flow cell and start the sequencing. 3.6 Real-Time Analysis

1. Open the terminal application in the sequencing computer, and download and install the RTNano package as follows (see Note 7): (a) git clone https://github.com/milesjor/RTNano.git (b) cd ./RTNano/ (c) conda env create --name rtnano --file ./conda_env.yaml (d) conda activate rtnano (e) python -m pip install pandas

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MinION output folder

monitor

analyzed directory

fastq_pass

analyzing

time point 1

time point 2

barcode01

barcode01

barcode01

barcode01

barcode02

barcode02

barcode02

transfer .fastq files

barcode02

transfer folder

barcode03

barcode03

barcode03

barcode03

…

…

…

…

…

result_pool optional:

reads statistics

alignment

demultiplexing trim adapters

read number base number

- filter alignment record by percentage identity and covered region - count alignment hits for each amplicon

combine results

generate reports

print on screen

Fig. 5 Workflow of RTNano real-time analysis. RTNano monitors the Nanopore MinION sequencing output folder. Once newly generated fastq files are detected, it moves the files to the analyzing folder and makes a new folder for each sample. If the Nanopore demultiplexing tool guppy is provided, RTNano will do additional demultiplexing to make sure reads are correctly classified. The analysis will align reads to the SARS-CoV2 reference genome, filter, and count alignment records and assign result mark (POS, NEG, or UNK) for each sample. As sequencing proceeds, RTNano will merge the newly analyzed results with existing ones to update the current sequencing statistics. (Adapted from [12])

2. Check the full usage of RTNano using the following command (see Fig. 5 as the demonstration of the workflow of data analysis): python ./rt_nano.py -h 3. Start the real-time analysis of sequencing output using the following command (see Note 8): python ./rt_nano.py -p /path/to/nanopore_result_folder/ -g / path/to/ont-guppy-cpu/bin/guppy_barcoder -k "EXPNBD196" 4. Check the variant information of samples using the following command (see Note 9): python ./rt_nano.py -p /path/to/nanopore_result_folder/ -call_variant

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Notes 1. Oropharyngeal and nasopharyngeal swabs were used to obtain samples by physicians. The samples were steeped in 1 mL of TRIzol to inactivate virus during transportation. 2. The concentration of extracted RNA can be too low to be quantified by a UV/Vis spectrophotometer. 3. For the rapid protocol, the RNase H digestion can be omitted and the inactivated reaction mixture was used directly for RPA amplification. 4. The reaction mix can be prepared in 0.2 mL PCR tubes and transferred to TwistAmp Basic reaction tubes using multichannel pipettes for large-scale sample processing. 5. Set up the program of thermocycler to incubate at 39  C for 20 min, take the tube out after 4 min, and pause the program, and later replace the tube in the thermocycler and continue the program. 6. For the rapid protocol, the purified DNA were used directly for library preparation without quantification and agarose gel electrophoresis. 7. RTNano was tested in macOS Catalina and Ubuntu 18.04.6 LTS. The Anaconda3 package was required for the installation of RTNano and can be installed using the following command: wget https://repo.anaconda.com/archive/Anaconda3-201 9.10-Linux-x86_64.sh bash Anaconda3-2019.10-Linux-x86_64.sh 8. It is strongly recommended to download guppy from Nanopore community and provide guppy barcoder for RTNano to ensure a confident demultiplexing. 9. The variant detection command was run in a new terminal tab and will call variants using all of the accumulated sequencing reads. The results were saved in the subfolder under the rtnano result folder.

References 1. Caruana G, Croxatto A, Coste AT et al (2020) Diagnostic strategies for SARS-CoV-2 infection and interpretation of microbiological results. Clin Microbiol Infect 26(9):1178–1182 2. Zhao J, Yuan Q, Wang H et al (2020) Antibody responses to SARS-CoV-2 in patients with novel coronavirus disease 2019. Clin Infect Dis 71(16):2027–2034 3. Suhandynata RT, Hoffman MA, Kelner MJ et al (2020) Longitudinal monitoring of

SARS-CoV-2 IgM and IgG seropositivity to detect COVID-19. J Appl Lab Med 5(5): 908–920 4. Zhou P, Yang XL, Wang XG et al (2020) A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579(7798):270–273 5. Corman VM, Landt O, Kaiser M et al (2020) Detection of 2019 novel coronavirus (2019nCoV) by real-time RT-PCR. Euro Surveill

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25(21):2001035. https://doi.org/10.2807/ 1560-7917.ES.2020.25.21.2001035 6. Alanagreh L, Alzoughool F, Atoum M (2020) The human coronavirus disease COVID-19: its origin, characteristics, and insights into potential drugs and its mechanisms. Pathogens 9(5): 3 3 1 . h t t p s : // d o i . o r g / 1 0 . 3 3 9 0 / pathogens9050331 7. Hashemi SA, Safamanesh S, GhasemzadehMoghaddam H et al (2020) High prevalence of SARS-CoV-2 and influenza A virus (H1N1) coinfection in dead patients in Northeastern Iran. J Med Virol 93(2):1008–1012 8. Kim D, Quinn J, Pinsky B et al (2020) Rates of co-infection between SARS-CoV-2 and other respiratory pathogens. JAMA 323(20): 2085–2086

9. Koyama T, Platt D, Parida L (2020) Variant analysis of SARS-CoV-2 genomes. Bull World Health Organ 98(7):495–504 10. Zhang YZ, Holmes EC (2020) A genomic perspective on the origin and emergence of SARSCoV-2. Cell 181(2):223–227 11. Bi C, Wang L, Yuan B et al (2020) Long-read individual-molecule sequencing reveals CRISPR-induced genetic heterogeneity in human ESCs. Genome Biol 21(1):213. https://doi.org/10.1186/s13059-02002143-8 12. Bi C, Ramos-Mandujano G, Tian Y et al (2021) Simultaneous detection and mutation surveillance of SARS-CoV-2 and multiple respiratory viruses by rapid field-deployable sequencing. Med (NY) 2(6):689–700.e4. https://doi. org/10.1016/j.medj.2021.03.015

Chapter 7 Quantitative Real-Time RT-PCR Systems to Detect SARS-CoV-2 Sumino Yanase, Hiroyoshi Sasahara, Momoko Nabetani, Kensuke Yamazawa, Keisuke Aoyagi, Akiko Mita, Yuichi Honma, and Yasuhiko Chiba Abstract Since the outbreak of coronavirus disease 2019 (COVID-19) on the Diamond Princess cruise ship docked at Yokohama Port on February 3, 2020, real-time reverse transcription-polymerase chain reaction (RT-PCR) testing using nasopharyngeal swab samples from symptomatic and asymptomatic COVID-19 individuals has been the main way to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in almost all clinical laboratories in Japan. With the diffusion of sets containing the primers and probe, the gold standard real-time RT-PCR test has permeated throughout the country. To prevent the spread of infection, real-time RT-PCR testing is important to confirm whether people are positive, asymptomatic, or negative for COVID-19. Now, in addition to pharyngeal swab, saliva and blood samples can be used to detect SARS-CoV-2 RNA. Here, we introduce a clinical laboratory test performed using the High Pure viral nucleic acid kit and subsequent real-time RT-PCR system to detect SARS-CoV-2 RNA in serum, plasma, or whole blood in a hospital in Yokohama, Japan. Key words Asymptomatic COVID-19 infection, SARS-CoV-2 RNA, Real-time RT-PCR, Blood sample

1

Introduction In February 2020, an outbreak of coronavirus disease 2019 (COVID-19) was detected among the crew and passengers of the Diamond Princess cruise ship docked at Yokohama Port in Japan. The ship was immediately quarantined and all 3711 crew and passengers were tested using real-time reverse transcriptionpolymerase chain reaction (RT-PCR) to detect the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA on the ship [1–3]. In total, 696 symptomatic or asymptomatic people were positive for viral RNA and diagnosed as having COVID-19. Ultimately, Japanese government officials performed

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more than 3000 RT-PCR tests on the 3711 crew and passengers in combination with rapid antigen tests based on immunochromatographic spot checking [1, 3]. As the gold standard clinical test for determining infection with the SARS-CoV-2 virus, real-time RT-PCR is usually applied in Japan in combination with rapid antigen tests and rapid amplicon sequencing of the viral RNA. Hitherto, rapid antigen tests have been used as the main clinical test for detecting respiratory viruses such as influenza virus in individual specimens. Rapid antigen testing has a major impact on pandemic or endemic population-wide surveillance due to its simplicity and availability [4]. However, immunochromatographic antigen tests generally have limited diagnostic usage due to low sensitivity for detecting viral surface molecules compared with the more sensitive and accurate real-time PCR test [5]. In particular, real-time RT-PCR testing is effective at diagnosing infection in individuals with COVID-19 who have a lower SARS-CoV-2 viral load or are asymptomatic. Thus, both realtime PCR and rapid antigen testing can indicate new infection in such cases. In contrast, rapid antibody tests can reveal the presence of a previous viral infection. Recently, rapid amplicon sequencing has become important to monitor viral variants and trace the origins of endemic COVID-19 outbreaks [6, 7]. During the initial pandemic, these tests to detect and identify SARS-CoV-2 RNA were performed mainly using nasal or pharyngeal swabs from patients. However, there were frequent instances of medical staff infection with the virus linked to patient specimen collection. Thus, clinical samples such as saliva and blood were subsequently used instead of swabs [8, 9]. Based on the viral genome sequence of Wuhan-Hu-1 (GenBank accession number: MN908947) isolated in December 2019 in China, various primers and probes for real-time RT-PCR detection of SARS-CoV-2 RNA have been designed and validated, such as those in the LightMix® modular coronavirus E-gene and N-gene kit (TIB Molbiol Syntheselabor GmbH, Berlin, Germany) [10]. This kit contains the sets of primers and probe for detecting the specific envelope protein (E) gene and nucleocapsid protein (N) gene of bat-associated SARS-related viruses (Sarbecovirus) and the SARS-CoV-2-specific RNA-dependent RNA polymerase (RdRp) gene (see Fig. 1) [10, 11]. Here, to detect SARS-CoV-2 in serum, plasma, or whole blood, we describe the preparation of viral RNA from patients’ specimens using a High Pure viral nucleic acid kit and the subsequent real-time RT-PCR using LightMix® modular coronavirus kit and a cobas® z480 RT-PCR system in the medical laboratory of a hospital in Yokohama, Japan.

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a

Wuhan-Hu-1 ORF1a

ORF1ab

Spike

E M

E gene

RdRp gene

(76 bp)

N

N gene (126 bp)

probe

b

forward primer 5’ SARS-CoV-2

3’

FAM

Q

5’

3’ 5’

3’ 5’ 3’ 3’ 5’ reverse primer

Fig. 1 Relative genomic positions of the amplicons amplified by each primer set in the LightMix® modular coronavirus E- and N-genes of the SARS-CoV-2 genome (based on [10]). (a) Specific primer sets targeting each gene in the LightMix modular coronavirus kit were designed based on [10], but their genomic positions have not been released to the public. ORF1a and 1ab and spike indicate two open reading frames and the spike protein gene, respectively. E, M, and N indicate the envelope protein gene, membrane protein gene, and nucleocapsid protein gene, respectively. RdRp is the RNA-dependent RNA polymerase gene. The numbers below the primers and amplicon show the length (bp) of each PCR product. (b) A set of the non-labeled forward and reverse primers and a probe binds to each complementary region on the SARS-CoV-2 genome DNA. FAM and Q indicate a fluorescein for fluorescent labeling and a quencher at the 50 - and 30 -ends of the probe, respectively

2

Materials

2.1 Human Clinical Samples and RNA Extraction

1. High Pure viral nucleic acid kit stored at 15–25
C (Roche Diagnostics), containing (see Notes 1, 2, and 3): (a) Binding buffer: 10 mM Tris-HCl (pH 4.4), 6 M guanidine hydrochloride, 10 mM urea, and 20% (v/v) Triton X-100. (b) Lyophilized poly [A] carrier RNA. (c) Lyophilized proteinase K. (d) Inhibitor removal buffer: 20 mM Tris-HCl (pH 6.6) and 5 M guanidine hydrochloride in 20 mL ethanol. (e) Wash buffer: 2 mM Tris-HCl (pH 7.5) and 20 mM NaCl and in 40 mL ethanol. (f) Nuclease-free elution buffer.

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(g) High Pure filter tubes assembly containing glass fibers to bind nucleic acids [12]. (h) 2 mL collection tubes 2. Human clinical samples: serum, plasma, or whole blood from COVID-19 patients and controls (see Note 4). 3. LightMix® Modular EAV RNA extraction control (Roche Diagnostics) (see Notes 5 and 6). 4. Absolute ethanol stored at room temperature for use as a dilution reagent of the inhibitor removal buffer and wash buffer. 5. Centrifuges: (a) Centrifuge with swinging bucket rotor capable of 5000  g centrifugal force for 50 mL polypropylene tubes. (b) Tabletop micro-centrifuge capable of 13,000  g centrifugal force. (c) Tabletop micro-centrifuge for micro-well plates. 6. Sterile nuclease-free 1.5 mL micro-tubes. 2.2 Real-Time One-Step RT-PCR

1. LightMix® modular coronavirus E-gene and N-gene kit (Roche Diagnostics) containing: (a) Specific non-labeled forward and reverse primers. (b) FAM-labeled probe. (c) SARS-CoV-2 viral RNA positive control containing diagnostic targets of envelope (E), nucleocapsid (N), and RdRp genes (see Fig. 1) [10] (see Notes 7 and 8), stored at 4–25
C. 2. LightCycler® multiplex RNA virus master, containing (see Note 9): (a) 200x RT-enzyme solution, containing a recombinant reverse transcriptase (RTase) (b) 5x RT-PCR reaction mix, containing aptamer-mediated hot start AptaTaq™ Fast DNA Polymerase and PCR master mix (c) Sterile PCR-grade water stored at room temperature. (d) Ribonuclease (RNase)/deoxyribonuclease (DNase)free 10 mM Tris buffer (pH 8–8.5) for dilution of RNA positive control. 3. Micro-well plate for cobas® 4800 System (Roche Diagnostics) stored at room temperature. 4. Sealing film (Roche Diagnostics) for sealing the top of the micro-well plate while running the PCR.

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5. Adhesive film applicator. 6. Roche cobas z 480 RT-PCR system with hardware and software components.

3

Methods

3.1 Extraction of Viral RNA from Human Clinical Samples

1. Add 200 μL clinical samples, 200 μL freshly prepared working solution (see Note 2), and 50 μL of proteinase K in elution buffer into a nuclease-free 1.5 mL micro-tube. 2. Mix immediately and then incubate at 72
C for 10 min. 3. After the incubation, keep at room temperature for 5 min. 4. Add 100 μL binding buffer, and mix. 5. Add 10 μL extraction control target RNA and mix (see Note 10). 6. Transfer the sample from step 5 to a High Pure filter tube by pipetting into the upper reservoir of the assembly. 7. Insert the entire assembly into a standard tabletop centrifuge with a rotor and centrifuge at 8000  g for 1 min. 8. After centrifugation, remove the tube from the assembly and discard the collection tube including the flow-through liquid. 9. Combine the filter tube with a new collection tube. 10. Add 500 μL of the inhibitor removal buffer to the upper reservoir of the filter tube. 11. Centrifuge at 8000  g for 1 min. 12. Remove the filter tube from the collection tube and discard the latter including the flow-through liquid. 13. Combine the filter tube with a new collection tube. 14. After the removal of inhibitors, add 450 μL wash buffer to the upper reservoir of the filter tube. 15. Centrifuge at 8000  g for 1 min. 16. After the first wash and centrifugation, remove the filter tube from the collection tube and discard the latter including the flow-through liquid. 17. Combine the filter tube with a new collection tube. 18. Add 450 μL wash buffer to the upper reservoir of the filter tube. 19. Centrifuge at 8000  g for 1 min. 20. After centrifugation, discard the flow-through liquid. 21. Leave the filter tube-collection tube assembly in the centrifuge and centrifuge again at maximum speed (approximately 13,000  g) for 10 s to remove any residual wash buffer.

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22. Discard the collection tube with the flow-through liquid. 23. Insert the filter tube into a sterile nuclease-free 1.5 mL microtube. 24. To elute the viral RNA, add 50 μL elution buffer to the upper reservoir of the filter tube. 25. Incubate at room temperature for 1 min and centrifuge the tube assembly at 8000  g for 1 min. 26. Use the eluted viral RNA (< 50 μL) directly in RT-PCR (i.e., 3.5 μL of the eluted viral RNA) or store the eluted viral RNA at 80
C for later analysis. 3.2

RT-PCR

1. Add 320 μL of RNase-/DNase-free 10 mM Tris buffer (pH 8–8.5) to the vial of the RNA positive control. 2. Dissolve by pipetting up and down and leave on ice (see Note 9). 3. Prepare each primer and probe set of the coronavirus E, N, or RdRp genes for 96 reactions by checking for the colored pellet and adding 50 μL PCR-grade water, followed by vortexmixing. 4. Centrifuge briefly and leave on ice. 5. Prepare each primer and probe set of the RNA extraction control as in steps 3–4. 6. Add 4.9 μL PCR-grade water; 0.5 μL of each primer/probe set for the E, N, and RdRp genes and RNA extraction control; 4.0 μL of RT-PCR reaction mix; and 0.1 μL of RT-enzyme solution in a total volume of 20 μL per well of the micro-well plate (see Note 11). 7. Mix gently and spin-down. 8. Transfer 10 μL of the RT-PCR reaction mix into each well of the micro-well plate and set on ice. 9. Add 10 μL of the eluted viral RNA from clinical samples, the RNA positive control or a no-template control into each well of the micro-well plate on ice. 10. Seal the top of the 96-well reaction plate with sealing film using the adhesive film applicator. 11. Centrifuge at 1500  g for 2 min to ensure the reaction mixture is positioned correctly in the bottom of each well. 12. Keep the 96-well reaction plate on ice until it can be loaded into the RT-PCR system. 13. Before loading the 96-well reaction plate into the RT-PCR instrument, create a new plate document on the system.

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Table 1 Reading the results of multiplex assays using a cobas z480 real-time PCR system Channel 530 Sample

Channel 660 RNA extraction Channel 530 No-template control control Result

No amplification Detectable

Negative

SARS-CoV-2 negative

Amplification Cp Detectable 95% as acceptable and >98% as desirable [12]. 23. A κ above 0.80 is preferred and above 0.70 required [11, 15]. 24. At least three valid results at each dilution are required to calculate standard deviation. 25. A mean SD  0.3, %CV  5%, and R2  0.95 are suggested for the assay to meet acceptance criteria. 26. In Fig. 5, SARS-CoV-2 negative specimens are shown in green, SARS-CoV-2 positive specimens are shown in red, and inconclusive or invalid results or errors are shown in yellow. 27. The limit of the blank (carryover) should be negative (i.e., no carryover in any negative specimen). If carryover is observed, the specificity will decrease below acceptable levels. 28. An error rate of 5%.

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30. This is included in the precision analysis, but the AccuPlex specimens are excluded from the precision analysis if poorly detected. The manufacturer LOD is reported if included in the IFU. The observed LOD is reported if available. The majority of assays should easily detect AccuPlex dilutions of 1000 cp/ mL, with most also detecting this material at 500 cp/mL and 250 cp/mL. The 100 cp/mL dilution is frequently not detected across the triplicate testing. 31. Criteria include experience level and training required, handson time, daily maintenance needs, and requirements for non-standard, assay-specific consumables and waste generated. Type, number, inclusion, and availability of controls (internal control, positive control, negative control) are also included in this section. The TPP [11, 12] referred to in Subheading 3.5, 1 and Note 21 can be used to guide users in assessing this. 32. Time to result, specimens processed per run, and apparent availability of the assay in the country will be included in this. Prior linkage of the platforms for existing laboratory systems will be advantageous. 33. The panels are designed for use with open platforms requiring purified RNA input. 34. For example, cobas® SARS-CoV-2. In this case, sufficient volume must be available for use as the source specimen will be placed on the platform. The results can be directly compared to the SOC results, but it is likely that only one closed system can be evaluated per panel. It is recommended that the full 96-specimen panel is used for high- and medium-throughput assays, but the panel can be adapted for low-throughput assays designed for near-patient use (e.g., Xpert® Xpress SARS-CoV2). Near-patient assays are often costlier due to the design complexities of bringing laboratory-quality assays to the field, and thus fewer may be available for evaluation. A minimum of 24 residual patient specimens and 12 reference specimens (four specimens repeated in triplicate) should be evaluated, although increased numbers will improve the robustness of the precision analysis. 35. For example, if the commercial reference material is unavailable, specimens with a low Ct could be selected and serially diluted for the precision analysis. Similarly, if culture specimens cannot be grown in-house, inactivated culture specimens could be sourced from collaborators. If no reference material is available, evaluations could be performed using only residual patient specimens. Precision through serial dilution of selected specimens is still recommended. 36. This enables the user to identify specimens that should be excluded from the final analysis. For example, a specimen that

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is consistently SARS-CoV-2 negative across test assays, even with a positive SARS-CoV-2 result on the original platform, should be excluded from the analysis as it is likely that the specimen has degraded, the extraction has failed, or a specimen mix-up has occurred. Such results could skew the data and thus should be excluded. 37. It is possible that the assay under evaluation is more sensitive than the SOC assay, and this assay could thus potentially be used. This would generally be indicated by a limited number of false positives among the expected SARS-CoV-2 negative specimens, usually with a Ct near the assay limit of detection. However, if there are multiple false positive results, this is indicative of true poor specificity. The emergence of VOC, which have been seen to drive waves of infection, could potentially affect assay sensitivity as the majority were designed based on the Wuhan strain genetic sequence. If possible, it is useful to include limited numbers of relevant SARS-CoV-2 VOC specimens into the panel to determine performance with known VOCs. These could be stored residual specimens or reference materials. A good example of changed assay performance is the spike (S)-gene target failure observed for the TaqPath assay for both the alpha and omicron VOC. This can however only be done once VOC are identified. It is also recommended that assays in use are rapidly evaluated using a limited number of VOC specimens if a novel VOC is identified locally. 38. Our laboratory usually prepares the panels with a 10 μL specimen input (five to six paired evaluations), although the panels can be designed for any volume of RNA (e.g., if the user is planning to evaluate one panel with a 20 μL RNA input, one with a 15 μL RNA input, and three with a 5 μL RNA input, the panels can be prepared at these volumes). A disadvantage of this type of panel is that exogenous internal controls (e.g., MS2-bacteriophage) are not added to these extractions as their presence may interfere with other kits. These will thus be negative in the evaluations and results must be excluded, which may affect analysis software. The analysis can still be done manually using Ct values. If this is not feasible, a specific panel may be required for a specific assay.

Acknowledgments We gratefully acknowledge the National Health Laboratory Service of South Africa and the National Institute for Communicable Diseases for the provision of SARS-CoV-2 residual patient specimens and SOC results. We also wish to acknowledge Prof. Bavesh Kana and Prof. Bhavna Gordhan (University of the Witwatersrand) and

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Prof. Wolfgang Preiser and Dr. Tasnim Suliman (University of Stellenbosch) for the viral culture supernatants supplied representing local variants of concern over time (to date: wild type, beta, delta, and omicron). Funding WS, LS, and LN are supported by funding received from the Bill & Melinda Gates Foundation through the Innovation in Laboratory Engineered Accelerated Diagnostics investment (grant number OPP1171455). RM is supported by funding received from EDCTP through the Foundation of Innovative Diagnostics.

References 1. Zhu N, Zhang D, Wang W et al (2020) A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 382(8):727–733 2. World Health Organisation. WHO DirectorGeneral’s opening remarks at the media briefing on COVID-19, 11 March 2020. Available from: https://www.who.int/director-general/ speeches/detail/who-director-general-s-open ing-remarks-at-the-media-briefing-on-covid-1 9%2D%2D-11-march-2020. Accessed 2 Apr 2020 3. Lu R, Zhao X, Li J, Niu P et al (2020) Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 395(10224):565–574 4. Chan JF, Kok KH, Zhu Z et al (2020) Genomic characterization of the 2019 novel humanpathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan. Emerg Microbes Infect 9(1):221–236 5. Centre for Disease Control (CDC). https:// www.cdc.gov/coronavirus/2019-ncov/lab/ testing.html. Accessed 9 Nov 2020 6. Corman VM, Landt O, Kaiser M et al (2020) Detection of 2019 novel coronavirus (2019nCoV) by real-time RT-PCR. Euro Surveill 25(3). https://doi.org/10.2807/1560-7917. ES.2020.25.3.2000045 7. Xu Y, Cheng M, Chen X et al (2020) Current approach in laboratory testing for SARSCoV-2. Int J Infect Dis 100:7–9 8. FIND Dx. SARS-CoV-2 molecular assay evaluation results, 3 July 2020. https://www. finddx.org/covid-19/sarscov2-eval-molecu lar/molecular-eval-results/. Accessed 27 July 2020

9. Scott L, Noble L, Singh-Moodley A et al (2021) Challenges and complexities in evaluating SARS-CoV-2 molecular diagnostics during the COVID-19 pandemic. Afr J Lab Med. (in press) 10. Kahamba TR, Noble L, Stevens W, Scott L. Comparison of three nasopharyngeal swab types and the impact of physiochemical properties for optimal SARS-CoV-2 detection. medRxiv preprint. https://doi.org/10.1101/2020. 10.21.20206078 11. South African Health Products Regulatory Authority. MD018: Specification criteria for COVID-19 molecular test kits. http://www. sahpra.org.za/wp-content/uploads/2020/0 7/MD018-Specifications-Molecular-Test-kitsv1-22072020.pdf. Accessed 14 Sept 2020 12. World Health Organisation. COVID-19 Target product profiles for priority diagnostics to support response to the COVID-19 pandemic v.0.1. https://www.who.int/publications/m/ item/covid-19-target-product-profilesfor-priority-diagnostics-to-support-response-tothe-covid-19-pandemic-v.0.1. Accessed 8 Aug 2020 13. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20(1): 37–46. Chapman and Hall/CRC; 1st edn (November 22, 1990); London. ISBN-13: 978-0412276309 14. Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174 15. World Health Organisation. Tracking SARSCoV-2 variants. Available at: https://www. who.int/en/activities/tracking-SARS-CoV-2variants/. Accessed 17 Dec 2021

Chapter 9 A Rapid User-Friendly Lab-on-a-Chip Microarray Platform for Detection of SARS-CoV-2 Variants Emily Mattig, Paul C. Guest, and Harald Peter Abstract Since the original SARS-CoV-2 virus emerged from Wuhan, China, in late December 2019, a number of variants have arisen with enhanced infectivity, and some may even be capable of escaping the existing vaccines. Here we describe a rapid automated nucleic acid microarray hybridization and readout in less than 15 min using the Fraunhofer lab-on-a-chip platform for identification of bacterial species and antibiotic resistance. This platform allows a fast adaptation of new biomarkers enabling identification of different genes and gene mutations, such as those seen in the case the SARS-CoV-2 variants. Key words SARS-CoV-2, COVID-19, Variant, Biomarker, Lab-on-a-chip, LOC, Microarray

1 1.1

Introduction Background

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to over 230 million confirmed cases and 4.7 million deaths in less than 2 years, as of September 27, 2021 [1]. One of the greatest challenges has been to adapt to the emergence of new SARS-CoV-2 variants [2]. Four of these have been classified by the World Health Organization (WHO) as variants of concern, and these have been designated alpha (B.1.1.7), beta (B.1.351) (Beta), gamma (P.1), and delta (B.1.617.2) [3]. These variants have been associated with one or more of the following properties at a level of global public health significance [4]: (a) An increase in transmission or a detrimental change in epidemiology. (b) An increase in virulence or change in clinical presentation. (c) A decrease in effectiveness of public health or social measures, diagnostics, vaccines, or therapeutics.

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The SARS-CoV-2 virus encodes 29 genes with a combined length of 29.9 thousand nucleotides [5, 6]. As with most RNA viruses, mutation of the SARS-CoV-2 genome is driven by multiple processes, such as errors in replication, transcription, and translation, as well as proofreading and recombination, and populationwide natural selection [7]. It is known that the properties in the variants stated above are mostly driven by mutations in the spike proteins, which are the key viral components that bind to angiotensin-converting enzyme 2 (ACE2) receptors on host cells, permitting access to the cellular machinery for viral replication [8– 10]. One of the most dominant spike protein mutations is an aspartate-to-glycine change at amino acid 614 (D614G) [11]. This occurs in all current variants of concern which are circulating the world, including the alpha and delta variants. At the same time, the sequence-specific differences in these variants pave the way for molecular tests such as reverse transcriptionquantitative polymerase chain reaction (RT-qPCR) to distinguish these from one another. For example, a primer or probe targeting the Δ69–70 deletion in the alpha variant would distinguish this from the delta variant as the latter contains an intact sequence at this position (see Fig. 1). Likewise, a primer or probe which recognizes the threonine-to-lysine substitution at position 478 (T478K) in the delta variant would only recognize this and not the alpha variant which contains the normal sequence at this position. Although the vaccine rollout for the COVID-19 pandemic has progressed well in many countries of the world [12], there is still an urgent need for rapid diagnosis to identify infected patients and to enable fast and appropriate decisions to be made by clinicians and intensive care unit staff. Traditional PCR and genomic sequencing methods require several hours to days for diagnostics and variant typing [13]. Molecular tests on multiparameter lab-on-a-chip (LOC) platforms, such as the Fraunhofer in vitro diagnostic device, have been used to decrease the time taken between sampling and

Fig. 1 SARS-CoV-2 spike protein amino acid sequence showing mutations present in the alpha and delta variants compared to the original virus. The single amino acid codes are used and Δ indicates deletion

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results. This multiparameter LOC diagnostic platform can be used for fully automated DNA microarray analysis and allows rapid identification and genotyping of infectious agents [14–20]. This makes these assays highly useful for COVID-19 diagnostics and variant tracking. The LOC platforms also overcome some of the disadvantages of current DNA microarray protocols that reduce the impact of this approach in routine analysis. These disadvantages include long hybridization times and complex laboratory-based procedures, as well as the need for skilled laboratory analysts. The LOC-based systems obviate these limitations using a more rapid and automated solution combining microarray technology and microfluidics, which integrate many of the laboratory-based steps [21–23]. The Fraunhofer LOC platform consists of a microfluidic cartridge around the size of credit card and a base unit. The cartridge contains reservoirs for all of essential reagents, integrated pumping systems, a microarray, integrated temperature control for hybridization, and an optical transducer for sensing. The base unit contains the essential electronics to control the cartridge, an optical readout for analysis of the microarray after hybridization, and a touch screen control. With this setup, the total time to generation of results is less than 15 min following the sample preparation steps (see Fig. 2). 1.2

Aims

Collect sample

Here, we describe the steps from sample preparation to microarray analysis which could be used for diagnosis or genotyping of SARSCoV-2. As an example, we use a microarray for detection of methicillin-resistant Staphylococcus aureus (MRSA). Specifically, this microarray was used for identification of single-nucleotide polymorphisms (SNPs). The assay can be performed using standard hybridization or automatically run using the Fraunhofer LOC system. The use of the LOC platform shortens the hybridization, washing, and readout times from approximately 2 h to less than

PCR

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(60 min)

(5 min)

Fraunhofer LOC platform ( 1286.7 (doubled charged light DGIIWVATEGALNTPK, 25 eV) (b) 852.4 > 1013.5, 852.4 > 1113.6, and 852.4 > 1301.6 (double charged heavy DGIIWVATEGALNTPK, 25 eV)

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2

3

4

5

6

7

8

9

10

11

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1.2

0.5

0.5

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Ramp 72.5 27.5

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Ramp 75.5 24.5

Step

Step

Step

Step

Step

flow (mL/min) Grad

Loop

%D

Tee

%C

Flow (mL/min) Grad %A

%B

Valve interface module Eluting pump

Loading pump

Table 1 Chromatographic parameters for turbulent flow chromatography loading and eluting pumps for analysis of SARS-CoV-2 nucleoprotein target peptides

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(c) 1013.0 > 1394.7, 1013.0 > 1495.8, and 1013.0 > 1594.8 (doubled charged light IGMEVTPSGTWLTYTGAIK, 31 eV). 9. Select mass transitions for endogenous peptide (from beta actin) as follows: (a) 895.9 > 901.5, 895.9 > 1201.6, and 895.9 > 1298.7 (double charged SYELPDGQVITIGNER) (see Note 10). 10. Inject 25 μL of the tryptic digest sample at LC-MS/MS for a total run time of 10 min, with fourfold multiplexing enabled and a MS acquisition time per sample of 2.5 min. 3.5

Data Processing

1. Use Skyline in all steps of data processing. 2. Import and process raw data in Skyline with no transformation (i.e., smoothing) and review peak integration individually. 3. Set the 15N-labeled nucleoprotein from 2019-nCoV-N as isotope standard and used to assist retention time detection and peak integration. 4. Create a Skyline report containing replicate name, peaks areas, and signal-to-background ratios and export this to an Excel spreadsheet. 5. Analyze peak areas, ion ratios for the three acquired transitions, and signal-to-background ratios for peptides DGIIWVATEGALNTPK and IGMEVTPSGTWLTYTGAIK (see Fig. 2). 6. Confirm a positive detection by identification of both peptides according to previously established limits of detection (see Note 8). 7. Assess the quality of the analysis by inspecting isotope standard peptide retention times and peak areas. 8. Verify quality control material and compare with established parameters. 9. Verify the endogenous peptide SYELPDGQVITIGNER (see Fig. 2) (if absent, sample collection may have failed) (see Note 11). 10. Reinject samples that followed high viral load samples because carryover may contribute with 1.2x peptide area in the next injection and 0.2x in a subsequent injection (see Note 12).

4

Notes 1. GenScript provides synthetic gene service with the option to optimize codons for expression in E. coli in a variety of expression vectors. We used the nucleoprotein (2019-nCoV-N, NCAP_WCPV) gene subcloned into at SacI and NotI restriction sites of pET28a(+).

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Fig. 2 Typical LC-MS/MS chromatogram from a SARS-CoV-2 sample. (a–d) SARS-CoV-2 nucleoprotein peptides (UniProt accession number P0DTC9). (a) DGIIWVATEGALNTPK (light), (b) DGIIWVATEGALNTPK (heavy), (c) IGMEVTPSGTWLTYTGAIK (light), and (d) IGMEVTPSGTWLTYTGAIK (heavy). (e) Human beta action peptide used to evaluate specimen collection (UniProt accession number P60709) SYELPDGQVITIGNER

2. Respiratory tract samples are intrinsically heterogeneous compared to biological matrices such as plasma and urine. Several factors influence viral load in respiratory tract samples such as method of collection, anatomical collection site (e.g., nasopharyngeal and/or oropharyngeal), type of swab, sampling at a given diagnostic time window, and individual variability [18, 19]. Therefore, standardized collection procedure executed by a trained healthcare provider is one of the foremost prerequisites [20]. 3. The composition of virus transport medium (VTM) is critical for achieving high sensitivity. VTM containing fetal bovine serum (FBS) [21] is not appropriate for MS-based SARSCoV-2 testing and should be replaced by sterile saline. Our laboratory VTM recipe replaces FBS by dry meat extract for microbiology. To prepare VTM (without FBS), combine 900 mL Hank’s balanced salt solution (140 mM NaCl, 5 mM KCl, 1 mM CaCl2, 0.4 mM MgSO4, 0.5 mM MgCl2, 0.3 mM

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Na2HPO4, 0.4 mM KH2PO4, 25 mol/mM NaHCO3, 6 mM D-Glucose, and 10 mg/L phenol red), 100 mL glucose broth (3 g/L meat extract for microbiology, 5 g/L dextrose, 10 g/L NaCl, and 10 g/L tryptone), 1 mL amphotericin solution (1 g/L), and 1 mL gentamicin solution (1 g/L). Sterilize into a 1 L sterile bottle using the vacuum filtration system. 4. For a negative pool, combine previously analyzed specimens by RT-PCR, collected as part of standard diagnostic protocols that otherwise would be discarded. 5. For a positive pool, combine previously analyzed specimens by RT-PCR, collected as part of standard diagnostic protocols that otherwise would be discarded. Analyze the pool in triplicate by LC-MS/MS and dilute with the negative pool to obtain a tenfold IGMEVTPSGTWLTYTGAIK peak area compared with the limit of detection. Aliquot and store at 80
C. 6. The limit of detection is system-dependent, must be verified experimentally, and could be established by signal-to-noise ratio. With the use of isotope internal standard to confirm retention time and product ions ratio, a signal-to-noise ratio between 3:1 and 2:1 is acceptable for estimating the detection limit. 7. For all specimen handling steps, be careful to use appropriate PPE while working in the lab and handle the specimens in a laminar flow hood/biosafety cabinet. Clean the laminar flow hood/biosafety cabinet surfaces with 2% hypochlorite solution and 70% ethanol and turn on the UV light for at least 10 min before starting the work. Homogenize each specimen for approximately 10 s by vortexing. Remove the lids from tubes and swabs carefully, assemble the sample rack in the laminar flow hood/biosafety cabinet and cover it with parafilm strips when taking it to the robotic pipettor deck. 8. Analyze samples by LC-MS/MS to have the limit of detection checked by having a pool of positive samples diluted in a pool of negative samples in tenfold increments. 9. The robotic liquid handler (Microlab STARlet) had a Star UV Light module (Hamilton Company) and exhaust flange with HEPA filter (in house adaptation) added for safety measures while working with infectious pathogens. 10. MS parameters were optimized to give the highest response according to the compounds of interest. 11. Some variation is expected in the endogenous peptide areas in samples due to intrinsic variabilities related to the collecting technique, each person’s anatomy, virus shedding, and depending on whether nostrils were clogged.

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12. As viral load can range over 8 orders of magnitude and carryover is an ever-present phenomenon in liquid chromatography whereby the analytes from a previous injection are retained by adsorption in the flow path within the LC-MS system and are detected in the subsequent injections. Injecting one or two blank samples is necessary to clean the chromatographic channel after a high viral load sample is processed.

Acknowledgments We thank Erico Santana Gomes Ribeiro for excellent engineering assistance. References 1. Guglielmi G (2020) The explosion of new coronavirus tests that could help to end the pandemic. Nature 583(7817):506–509 2. Wang C, Horby PW, Hayden FG et al (2020) A novel coronavirus outbreak of global health concern. Lancet 395(10223):470–473 3. Lalli MA, Langmade SJ, Chen X et al (2020) Rapid and extraction-free detection of SARSCoV-2 from saliva with colorimetric LAMP. medRxiv. https://doi.org/10.1101/2020.05. 07.20093542. Preprint 4. Esbin MN, Whitney ON, Chong S et al (2020) Overcoming the bottleneck to widespread testing: a rapid review of nucleic acid testing approaches for COVID-19 detection. RNA 26(7):771–783 5. No authors listed (2013) Method of the year 2012. Nat Methods 10(1):1. https://doi.org/ 10.1038/nmeth.2329 6. Cardozo KHM, Lebkuchen A, Okai GG et al (2020) Establishing a mass spectrometry-based system for rapid detection of SARS-CoV-2 in large clinical sample cohorts. Nat Commun 11(1):6201. https://doi.org/10.1038/ s41467-020-19925-0 7. Zecha J, Lee CY, Bayer FP et al (2020) Data, reagents, assays and merits of proteomics for SARS-CoV-2 research and testing. Mol Cell Proteomics 19(9):503–1522 8. Nikolaev EN, Indeykina MI, Brzhozovskiy AG et al (2020) Mass-spectrometric detection of SARS-CoV-2 virus in scrapings of the epithelium of the nasopharynx of infected patients via Nucleocapsid N protein. J Proteome Res 19(11):4393–4397 9. Gouveia D, Miotello G, Gallais F et al (2020) Proteotyping SARS-CoV-2 virus from

nasopharyngeal swabs: a proof-of-concept focused on a 3 min mass spectrometry window. J Proteome Res 19(11):4407–4416 10. Ihling C, T€anzler D, Hagemann S et al (2020) Mass spectrometric identification of SARSCoV-2 proteins from gargle solution samples of COVID-19 patients. J Proteome Res 19(11):4389–4392 11. Renuse S, Vanderboom PM, Maus AD et al (2021) A mass spectrometry-based targeted assay for detection of SARS-CoV-2 antigen from clinical specimens. EBioMedicine 69: 103465. https://doi.org/10.1016/j.ebiom. 2021.103465 12. Saadi J, Oueslati S, Bellanger L et al (2021) Quantitative assessment of SARS-CoV-2 virus in nasopharyngeal swabs stored in transport medium by a straightforward LC-MS/MS assay targeting Nucleocapsid, membrane, and spike proteins. J Proteome Res 20(2): 1434–1443 13. Carvalho VM (2012) The coming of age of liquid chromatography coupled to tandem mass spectrometry in the endocrinology laboratory. J Chromatogr B Analyt Technol Biomed Life Sci 883–884:50–58 14. Arora A, Somasundaram K (2019) Targeted proteomics comes to the Benchside and the bedside: is it ready for us? BioEssays 41: e1800042. https://doi.org/10.1002/bies. 201800042 15. Sobsey CA, Ibrahim S, Richard VR et al (2020) Targeted and untargeted proteomics approaches in biomarker development. Proteomics 20(9):e1900029. https://doi.org/10. 1002/pmic.201900029

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16. Wo¨lfel R, Corman VM, Guggemos W et al (2020) Virological assessment of hospitalized patients with COVID-2019. Nature 581(7809):465–469 17. Widders A, Broom A, Broom J (2020) SARSCoV-2: the viral shedding vs infectivity dilemma. Infect Dis Health 25(3):210–215 18. Tang YW, Schmitz JE, Persing DH et al (2020) The laboratory diagnosis of COVID-19 infection: current issues and challenges. J Clin Microbiol 58(6):e00512–e00520. https:// doi.org/10.1128/JCM.00512-20 19. Lippi G, Simundic AM, Plebani M (2020) Potential preanalytical and analytical

vulnerabilities in the laboratory diagnosis of coronavirus disease 2019 (COVID-19). Clin Chem Lab Med 58(7):1070–1076 20. Centers for Disease Control and Prevention (2020) Interim Guidelines for collecting, handling and testing clinical specimens for COVID-19. Updated Oct 8, 2020. https:// www.cdc.gov/coronavirus/2019-ncov/lab/ guidelines-clinical-specimens.html 21. Centers for Disease Control and Prevention (2021) Preparation of viral transport medium. SOP DSR-052-05. https://www.cdc.gov/ coronavirus/2019-ncov/downloads/ViralTransport-Medium.pdf

Chapter 13 Identification of Circulating Biomarkers of COVID-19 Using MALDI-TOF Mass Spectrometry Lucas C. Lazari, Livia Rosa-Fernandes, and Giuseppe Palmisano Abstract Matrix-assisted laser desorption/ionization source coupled with time-of-flight mass analyzer mass spectrometry (MALDI-TOF MS) is being widely used to obtain proteomic profiles for clinical purposes, as a fast, low-cost, robust, and efficient technique. Here we describe a method for biofluid analysis using MALDI-TOF MS for rapid acquisition of proteomic signatures of COVID-19 infected patients. By using solid-phase extraction, the method allows the analysis of biofluids in less than 15 min. Key words MALDI-TOF, COVID-19, Biofluids, Proteomics

1

Introduction The development of mass spectrometry (MS)-based analytical workflows has allowed the identification and quantification of proteins, posttranslational modifications, interaction partners, and 3D structures in complex biological samples including cells, tissues, and biofluids [1]. Each workflow has distinct characteristics and performances that suit different scientific purposes. A matrixassisted laser desorption/ionization (MALDI) source coupled with a time-of-flight (TOF) mass analyzer is a fast, low-cost, highthroughput, and efficient mass spectrometry method [2] that is in current widespread use for clinical purposes. This technique requires minimum sample handling, reducing considerably the time and steps for sample preparation. The ionization and direct measure of the intensity of peptides, proteins, lipids, glycoconjugates, and small molecules can be used as a molecular fingerprint of a particular sample. A common use of MALDI-TOF MS is the characterization of microorganisms, such as viruses, bacteria, parasites, and fungi, applying this technique for diagnosis, monitoring, quality control, and biodefense [3–5]. This technique is also used for diagnosis in cancer research, being robust and reliable in

Paul C. Guest (ed.), Multiplex Biomarker Techniques: Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification, Methods in Molecular Biology, vol. 2511, https://doi.org/10.1007/978-1-0716-2395-4_13, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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biomarker discovery and analysis [6]. Through the analysis of intact proteins or protein digests (peptides), MALDI-TOF MS has been shown to be efficient in identifying proteomic signatures for early diagnosis of colorectal, bladder, or gastric cancers [7–9], and for many other malignancies such as breast, ovarian, and prostate cancers and leukemia [10–13]. Since late 2019, COVID-19 has imposed a great public health challenge, with more than 275 million confirmed cases and more than 5.3 million deaths reported (World Health Organization, WHO). Therefore, developing new diagnostic and prognostic methods is necessary, and fast, robust, low-cost, and reliable techniques are desirable for clinical application. MALDI-TOF MS has already been demonstrated as useful for diagnosis of infectious diseases [5], and recently it is been applied for COVID-19 diagnosis and prognosis in serum, plasma, and nasal swab samples [14– 19]. Here, we detail the sample preparation steps to extract and acquire the profile of biofluids such as plasma and saliva, using MALDI-TOF MS. The method requires only 1 μL of sample and can be performed in less than 10 min per sample. A detailed protocol related to the data analysis step using machine learning is provided in see Chapter 28 of this book.

2

Materials (See Fig. 1) (See Note 1) 1. 10% trifluoroacetic acid (TFA) stock solution in ultrapure water. 2. 0.1% TFA solution in ultrapure water. 3. Acetonitrile (99% purity). 4. C18 47 mm solid-phase extraction discs. 5. p200 tips and 200 μL microtubes for StageTip preparation. 6. Benchtop centrifuge. 7. Matrix solution: 10 mg/mL α-cyano-4-hydroxycinnamic acid (HCCA) matrix in 50% acetonitrile, and 2.5% TFA in ultrapure water (see Note 2). 8. Protein standards for MALDI-TOF calibration: (a) Insulin [M+H]+ ¼ 5734.52 (b) Cytochrome C [M+ 2H]2+ ¼ 6181.05 (c) Myoglobin [M+ 2H]2+ ¼ 8476.66 (d) Ubiquitin I [M+H]+ ¼ 8565.76 (e) Cytochrome C [M+H]+ ¼ 12,360.97 (f) Myoglobin [M+H]+ ¼ 16,952.31 9. Stainless steel MALDI-TOF target metal plate.

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Fig. 1 Tools and steps required to assemble a StageTip for protein purification. (a) Adapted syringe consisting of a regular syringe with a string and a metal stem added in the middle to push the resin out to the tip. The waste can be created using a regular microtube with a hole placed in the lid. The elution syringe must have a rubber tip to seal the contact between the syringe and the tip. (b) Demonstration on how to take the resin discs. (c) Demonstration of the tip stuck in the needle after step described in b. (d) Demonstration on how to place the resin disc into the tip using the metal stem of the adapted syringe. (e) Demonstration of the StageTip placed in the waste. (f) Demonstration on how to assemble the elution syringe for the sample spotting. (g) Spotting of the sample after the addition of the matrix solution into the StageTip. (h) Demonstration of how to fold the resin in order to take two discs at once

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10. MALDI-TOF Autoflex speed smartbeam mass spectrometer using FlexControl software (version 3.3; Bruker Daltonics).

3

Methods

3.1 StageTip Assembly

1. Detach the lid from a 2 mL microtube (see Note 3). 2. With a sharp tool, place a hole in the lid to create a support for a p200 tip (see Note 4 and Fig. 1e). 3. With an adapted syringe (see Fig. 1a), remove two discs of the C8 or C18 resin either separately (see Fig. 1b) or by folding the resin and removing two discs at once (see Fig. 1h) and place in a non-filtered p200 tip (see Fig. 1b, c, and d) (see Note 5). 4. Place the tip onto the adapted microtube (see Fig. 1e) and assemble a StageTip for each sample (see Fig. 1 for entire procedure and Note 6).

3.2 Sample Preparation

1. Add 50 μL pure acetonitrile to the StageTip and centrifuge for 2.5 min at 2000  g (see Note 7). 2. Check if all solvent passed through the resin. 3. Add 100 μL of 0.1% TFA and centrifuge for 2.5 min at 2000  g. 4. Check if the solution passed through completely. 5. Empty the microtube prior to sample addition. 6. Perform a quick centrifugation of the plasma samples and keep them on ice. 7. Add 1 μL sample to 9 μL 0.1% TFA and 1 μL 10% TFA. 8. Mix gently with a micropipette and add the solution to the StageTip (see Note 8). 9. Centrifuge for 2.5 min at 2000  g. 10. Check if the samples passed through completely (see Note 9). 11. Add 100 μL of TFA 0.1% and centrifuge 2.5 min at 2000 g (see Note 10). 12. Remove the tip from the support. 13. Add 10 μL HCCA solution and use a syringe (see Fig. 1a) to elute the samples directly onto the MALDI target plate in two different spots per sample (see Fig. 1f, g). 14. After spotting of five samples, apply 1 μL of the protein standard as a calibration spot. 15. Add 1 μL of HCCA on top of the protein standard drop. 16. Let all spots dry at room temperature in the dark before MALDI-TOF MS acquisition.

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Fig. 2 Spectra obtained from the MALDI-TOF acquisition of the samples prepared using the method described in this chapter. The image demonstrates a clear difference between the proteomic profiles obtained from plasma and saliva samples 3.3 MALDI-TOF Acquisition

1. Acquire the spectra using the MALDI-TOF MS and the FlexControl software. 2. Record spectra in positive linear mode under the following conditions: (a) Laser frequency – 500 Hz (b) Extraction delay time – 390 ns (c) Ion source 1 – 19.5 kV (d) Ion source 2 – 18.4 kV (e) Lens voltage – 8.5 kV (f) Mass range – 2400–20,000 Da 3. Acquire spectra using the automatic run mode to avoid subjective interference with the data acquisition. 4. Perform 2500 shots in 500-shot steps for each sample. 5. Calibrate all spectra using the protein calibration standard (see Fig. 2) (see Note 11).

4

Notes 1. All solutions must be prepared with ultrapure water with resistivity of 18.2 MΩ.cm (at 25  C) and a total organic carbon value below 5 ppb. The glassware must be treated for proteomic procedures. Laboratory masks and caps should be used to prevent keratin contamination. All centrifugation steps should be performed at room temperature. For each sample, it is estimated a total of 50 μL of acetonitrile, 209 μL of TFA

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0.1% and 1 μL of TFA 10%, and 10 μL of α-cyano-4-hydroxycinnamic acid (HCCA) matrix for each sample. Thus, volumes should be prepared according to the number of samples that will be processed. 2. The solution will be saturated with excess of HCCA. Thus, centrifuge the solution for 15 min at 10,000  g, transfer the supernatant to another microtube, and keep the solution covered from light with an aluminum foil. 3. The method presented here is based on protein/peptide purification using microcolumns/StageTips [20–22]. However, different column formats are available depending on the sample amount. Alternatively, 96-well plates can be used to increase the sample preparation throughput. In this protocol, the column purification is presented since it was applied to COVID19 samples. 4. The p200 tip must be supported in a way that its end does not contaminate with the reagents and solutions that will be passed through the StageTip. Thus, it is recommended that the tip end stay above the microtube 300 μL mark. 5. Different functionalized membrane discs with hydrophobic/ hydrophilic, ion-exchange or chelating properties can be used to obtain a biofluid peptide/protein profile [23–25]. Moreover, different column format solid-phase extraction (SPE) or batch-mode purification can be applied. 6. Do not apply too much force while placing the C8 or C18 resin discs into the tip to avoid blocking and excessive back pressure. Instead, gently push out the resin from the syringe and gently add this to the tip making sure that no space is left on the sides to prevent liquid leakage. 7. In some centrifuges, the lid cannot be closed due to the StageTip assembly. If this is the case, you can still centrifuge without the lid if the assembly was performed correctly. However, care must be taken to avoid sample loss and contamination of the centrifuge. 8. After mixing the sample with the TFA solution, use approximately 0.1 μL to check the pH using pH paper. A pH lower than 3 should be achieved to allow protein binding to the resin. 9. Plasma samples, especially without minimum pre-treatment, may have different levels of viscosity, which can result in obstruction of the resin and make the liquid passage through the resin more difficult. If this occurs, increase the centrifugation time or pass these manually using the elution syringe (see Fig. 1e, f). 10. If any samples obstruct the resin and the step described above was performed to pass the sample, the passage of TFA 0.1%

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through the resin can also be more difficult. As above, this will require increased centrifugation time or manual passing. 11. Figure 2 contains two examples of spectra acquired using the method described here. These were obtained using human plasma and saliva.

Acknowledgements We are grateful for the financial support provided by the Sa˜o Paulo Research Foundation (FAPESP, grants processes n 2018/182571, 2018/15549-1, 2020/04923-0 (GP); by the Conselho Nacional de Desenvolvimento Cientı´fico e Tecnolo´gico (“Bolsa de Produtividade” (GP)); and by the Coordenac¸˜ao de Aperfeic¸oamento de Pessoal de Nı´vel Superior (CAPES 88887.510020/2020-00 to LCL and bolsa PNPD 88887372048/2019-00 to LRF). References 1. Domon B, Aebersold R (2006) Mass spectrometry and protein analysis. Science 312(5771):212–217 2. Hou TY, Chiang-Ni C, Teng SH (2019) Current status of MALDI-TOF mass spectrometry in clinical microbiology. J Food Drug Anal 27(2):404–414. https://doi.org/10.1016/J. JFDA.2019.01.001 3. Croxatto A, Prod’hom G, Greub G (2012) Applications of MALDI-TOF mass spectrometry in clinical diagnostic microbiology. FEMS Microbiol Rev 36(2):380–407 4. Avila CC, Almeida FG, Palmisano G (2016) Direct identification of trypanosomatids by matrix-assisted laser desorption ionizationtime of flight mass spectrometry (DIT MALDI-TOF MS). J Mass Spectrom 51(8): 549–557. https://doi.org/10.1002/JMS. 3763 5. Patel R (2015) MALDI-TOF MS for the diagnosis of infectious diseases. Clin Chem 61(1): 100–111 6. Merlos Rodrigo MA, Zitka O, Krizkova S et al (2014) MALDI-TOF MS as evolving cancer diagnostic tool: a review. J Pharm Biomed Anal 95:245–255 7. Zhu D, Wang J, Ren L et al (2013) Serum proteomic profiling for the early diagnosis of colorectal cancer. J Cell Biochem 114(2): 448–455 8. Aquino PF, Fischer JSG, Neves-Ferreira AGC et al (2012) Are gastric cancer resection margin proteomic profiles more similar to those from

controls or tumors? J Proteome Res 11(12): 5836–5842 9. Zhang M, Meng Q, Lei T et al (2013) Identification of proteins differentially expressed in adriamycin-resistant (pumc-91/ADM) and parental (pumc-91) human bladder cancer cell lines by proteome analysis. J Cancer Res Clin Oncol 139(3):509–519 10. Teiten MH, Gaigneaux A, Chateauvieux S et al (2012) Identification of differentially expressed proteins in curcumin-treated prostate cancer cell lines. OMICS 16(6):289–300 11. Kaz´mierczak M, Luczak M, Lewandowski K et al (2013) Esterase D and gamma 1 actin level might predict results of induction therapy in patients with acute myeloid leukemia without and with maturation. Med Oncol 30(4): 725. https://doi.org/10.1007/s12032-0130725-2 12. Ween MP, Lokman NA, Hoffmann P et al (2011) Transforming growth factor-betainduced protein secreted by peritoneal cells increases the metastatic potential of ovarian cancer cells. Int J Cancer 128(7):1570–1584 13. Velstra B, Van Der Burgt YEM, Mertens BJ et al (2012) Improved classification of breast cancer peptide and protein profiles by combining two serum workup procedures. J Cancer Res Clin Oncol 138(12):1983–1992 14. Yan L, Yi J, Huang C et al (2021) Rapid detection of COVID-19 using MALDI-TOF-based serum peptidome profiling. Anal Chem 93(11):4782–4787

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15. Rybicka M, Miłosz E, Bielawski KP (2021) Superiority of MALDI-TOF mass spectrometry over Real-Time PCR for SARS-CoV2 RNA detection. Viruses 13:730. https:// doi.org/10.3390/V13050730 16. Lazari LC, de Rose GF, Rosa-Fernandes L et al (2021) Prognostic accuracy of MALDI-TOF mass spectrometric analysis of plasma in COVID-19. Life Sci Alliance 4(8): e202000946. https://doi.org/10.26508/lsa. 202000946 ˜ a-Me´ndez 17. Deulofeu M, Garcı´a-Cuesta E, Pen EM et al (2021) Detection of SARS-CoV2 infection in human nasopharyngeal samples by combining MALDI-TOF MS and artificial intelligence. Front Med 8:661358. https:// doi.org/10.3389/fmed.2021.661358 18. Rocca MF, Zintgraff JC, Dattero ME et al (2020) A combined approach of MALDITOF mass spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs. J Virol Methods 286:113991. https://doi.org/ 10.1016/j.jviromet.2020.113991 19. Nachtigall FM, Pereira A, Trofymchuk OS et al (2020) Detection of SARS-CoV-2 in nasal swabs using MALDI-MS. Nat Biotechnol 2020 3810 38(10):1168–1173 20. Gobom J, Nordho E, Mirgorodskaya E et al (1999) Sample purification and preparation technique based on nano-scale reversed-phase columns for the sensitive analysis of complex

peptide mixtures by matrix-assisted laser desorption/ionization mass spectrometry. J MASS Spectrom J Mass Spectrom 34(2): 105–116 21. Rappsilber J, Mann M, Ishihama Y (2007) Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat Protoc 2(8): 1896–1906 22. Rappsilber J, Ishihama Y, Mann M (2002) Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal Chem 75(3):663–670 23. Callesen AK, Mohammed S, Bunkenborg J et al (2005) Serum protein profiling by miniaturized solid-phase extraction and matrixassisted laser desorption/ionization mass spectrometry. Rapid Commun Mass Spectrom 19(12):1578–1586 24. Callesen AK, Christensen RDP, Madsen JS et al (2008) Reproducibility of serum protein profiling by systematic assessment using solidphase extraction and matrix-assisted laser desorption/ionization mass spectrometry. Rapid Commun Mass Spectrom 22(3): 291–300 25. Boccardi C, Rocchiccioli S, Cecchettini A et al (2012) An automated plasma protein fractionation design: high-throughput perspectives for proteomic analysis. BMC Res Notes 5:612. https://doi.org/10.1186/1756-0500-5-612

Chapter 14 Antibody-Based Affinity Capture Combined with LC-MS Analysis for Identification of COVID-19 Disease Serum Biomarkers Paul C. Guest and Hassan Rahmoune Abstract Blood serum or plasma proteins are potentially useful in COVID-19 research as biomarkers for risk prediction, diagnosis, stratification, and treatment monitoring. However, serum protein-based biomarker identification and validation is complicated due to the wide concentration range of these proteins, which spans more than ten orders of magnitude. Here we present a combined affinity purification-liquid chromatography mass spectrometry approach which allows identification and quantitation of the most abundant serum proteins along with the nonspecifically bound and interaction proteins. This led to the reproducible identification of more than 100 proteins that were not specifically targeted by the affinity column. Many of these have already been implicated in COVID-19 disease. Key words COVID-19, SARS-CoV-2, Affinity capture, Antibody, Liquid chromatography, Mass spectrometry, LC-MS, Biomarker

1

Introduction The SARS-CoV-2 virus responsible for COVID-19 disease has infected more than 220 million people and caused more than 4.5 million deaths around the world in just 20 months (as of September 3, 2021) [1]. In addition, a number of variants of concern (alpha, beta, gamma, delta) and interest (eta, iota, kappa, lambda, and mu) have arisen, which may be more virulent compared to the original virus and potentially capable of escaping the current vaccines (Table 1) [2]. The main symptoms include fever, cough, dyspnea, malaise, loss of taste and smell, diarrhea, and headache, and this can progress to more severe complications such as acute respiratory distress syndrome (ARDS), thromboembolisms, total organ failure, and death [3]. Hyperinflammation is common in COVID-19 disease and is characterized by infiltration of inflammatory cells and release of

Paul C. Guest (ed.), Multiplex Biomarker Techniques: Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification, Methods in Molecular Biology, vol. 2511, https://doi.org/10.1007/978-1-0716-2395-4_14, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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Table 1 List of variants (as of September 9, 2021) [2] Name

Lineage

Earliest detection (country)

Designation date

Variants of concern Alpha

B.1.1.7

September 2020 (United Kingdom)

December 18, 2020

Beta

B.1.351

May 2020 (South Africa)

December 18, 2020

Gamma

P.1

November 2020 (Brazil)

January 11, 2021

Delta

B.1.617.2

October 2020 (India)

April 4–May 11, 2021

Variants of interest Eta

B.1.525

December 2020 (multiple locations)

March 17, 2021

Iota

B.1.526

November 2020 (United States of America)

March 24, 2021

Kappa

B.1.617.1

October 2020 (India)

April 4, 2021

Lambda

C.37

December 2020 (Peru)

June 14, 2021

Mu

B.1.621

January 2021 (Columbia)

August 30, 2021

pro-inflammatory cytokines into the lungs and other organs [4– 6]. Many components of the inflammatory can be detected in blood plasma or serum such as α1-antitrypsin, which plays a role in modulation of inflammation [7], plasminogen and fibrinogen which are components of the clotting cascade [8, 9], transthyretin which functions as a transport protein [10–12], and immunoglobulin A (IgA), IgG, and IgM which play a role in the adaptive immune response to infections [13, 14]. The complement system has also been implicated in progression of the inflammatory process via assembly of a terminal complex that damages the vascular endothelium and drives thrombus formation in the hosts [15, 16]. Decreased circulating complement C1q has been identified in patients with severe COVID-19 disease [17, 18], with increased deposition of this factor observed in the lungs [19] and kidneys [20, 21] of some patients. Thus, all of the above factors are involved in the pathogenesis of COVID-19 and can therefore be used as biomarkers of disease severity or for treatment response. An increasing interest has been shown in the blood serum/ plasma proteome for identification of biomarkers and targets of COVID-19 disease [22–27]. Blood contains the largest representation of the human proteome due to the presence of secreted, damage, and leaked biomarkers from virtually all cell types along with copious transport proteins and immunoglobulin sequences [28]. It is also one of the most accessible sources of biomarkers. However, identification of blood-based biomarkers is complicated due to the wide range in concentrations of the resident proteins, which spans more than ten orders of magnitude [28–31]. This

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H H L H

H

L H

H H H L

L H

L L L

H

H

L H L H

L L L Flow through fraction with abundant proteins depleted

H H

Eluted fraction with abundant and strongly interacting proteins

Fig. 1 Scheme showing the principle of the antibody-based affinity capture for enrichment of both low- and high-abundance serum/plasma biomarkers

means that the low-abundance proteins will be difficult to analyze as they may be obscured by proteins of higher abundance such as albumin and the immunoglobulins [32]. Partial or complete depletion of these high-abundance proteins using immunoaffinity chromatography has now been used routinely in a number studies to increase sample loading and allow greater sensitivity in detection and quantitation of the low-abundance proteins (see Figs. 1 and 2) [30–35]. However, nonspecific binding and direct interaction of some low-abundance proteins with the high-abundance components has resulted in

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Sample collection Affinity enrichment Buffer exchange Protein quantitation Reduction/alkylation Digestion Liquid chromatography mass spectrometry Protein reduction Protein identification Fig. 2 Workflow showing the affinity purification-mass spectrometry procedure for analysis of the abundance and interacting protein fraction

many of non-targeted proteins being removed in a reproducible manner [29, 36–38]. Considering the importance of many of these proteins as a readout of physiological function, inflammatory status, and tissue damage, we suggest that characterization of this fraction might lead to identification of useful biomarkers of viral infection. This chapter describes the characterization of the affinity purified fraction from blood serum using label-free LC-MSE. The main objective was to identify the targeted proteins as well as retained non-targeted proteins, which are not detected in the depleted flowthrough fraction. We reported previously that this resulted in identification of 147 proteins [29]. Many of these have properties which make them appropriate for further investigation as biomarkers of COVID-19 disease.

2

Materials

2.1 Clinical Samples (See Note 1)

1. Personnel protective equipment (PPE) consisting of FFP2 (N95) mask, disposable cap, goggles, gown, apron, latex gloves, and shoe covers.

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2. PCR-diagnosed COVID-19 patients (n ¼ 20). 3. Control patients matched for age, gender, and body mass index with COVID-19 patients. 4. Sterile blood draw needles, holders/adapters, and other phlebotomy equipment (see Note 2). 5. 70% isopropyl alcohol and sterile wipes 6. Evacuated serum tubes (see Note 3). 7. Storage tubes (LoBind) or cryovials. 8. 1% sodium hypochlorite. 2.2 Affinity Purification/Depletion

1. Protein assay kit (see Note 4). 2. ProteoPrep-20 Plasma Immunodepletion spin columns targeting the proteins indicated in Table 2 (see Note 5). Table 2 High-abundance proteins in serum/plasma directly targeted in the ProteoPrep-20 Plasma Immunodepletion kit Targeted protein Albumin Apolipoprotein A1 Apolipoprotein A2 Apolipoprotein B Ceruloplasmin Complement C1q Complement C3 Complement C4 Fibrinogen Haptoglobulin Immunoglobulin A Immunoglobulin D Immunoglobulin G Immunoglobulin M Plasminogen Serotransferrin Transthyretin α1 acid glycoprotein α1 antitrypsin α2 macroglobulin

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3. Equilibration buffer: 40 mM sodium phosphate (pH 7.4), 0.5 M NaCl, 0.02% sodium azide. 4. Elution buffer: 2 M urea, 0.5 M glycine (pH 2.25). 5. Luer Lock caps and syringes. 6. 0.2 μm centrifuge tube filter. 7. 2 mL capacity collection tubes. 8. Benchtop microcentrifuge. 9. 1 M Trizma base. 10. Acetone. 11. 15 mL-capacity acetone-compatible centrifuge tube. 2.3

Trypsin Digestion

1. 50 mM ammonium bicarbonate (pH 7.5). 2. 100 mM dithiothreitol (DTT) in 50 mM ammonium bicarbonate. 3. 300 mM iodoacetamide (IAA) in 50 mM ammonium bicarbonate. 4. Sequencing grade modified trypsin (0.2 μg/μL) in 50 mM ammonium bicarbonate. 5. 12 N hydrochloric acid (HCl).

2.4 Liquid ChromatographyMass Spectrometry (LC-MS)

1. Trypsinized yeast enolase. 2. Separation solution A: high-performance liquid chromatography (HPLC) pure water, 0.1% formic acid. 3. Separation solution B: HPLC grade acetonitrile, 0.1% formic acid. 4. Waters ACQUITY UPLC PST C18 nanoACQUITY liquid chromatography instrument 10 K psi (Waters Corporation). 5. Reverse phase C18 trapping column (Waters Corporation; 180 μm i.d., 20 mm length, 5 μm particle size) (see Note 6). 6. C18 BEH nano-Column (Waters Corporation; 75 μm i.d., 200 mm length, 1.7 μm particle size) (see Note 6). ABOVE. 7. Quadrupole Time-Of-Flight (Q-TOF) Premier mass spectrometer with a 7 cm nano-ESI online emitter (10 μm tip). 8. 100 fmol/uL human glu-fibrinopeptide B. 9. ProteinLynx Global Server (PLGS) v.2.3 (also known as IdentityE) (see Note 7). 10. Rosetta Elucidator (Ceiba Solutions; Boston, MA, USA) (see Note 8).

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189

Methods Samples

1. Obtain ethical approval from the appropriate institution and informed written consent from all participants. 2. Carry out procedures involving sample collection, preparation, and storage in a BSL2 or BSL3 facility as required. 3. Collect blood from all subjects into serum tubes according to the World Health Organization (WHO) guidelines [39]. 4. Place samples at room temperature for 2 h to allow the blood to clot (see Note 9). Centrifuge at 4000  g for 5 min. 5. Determine total protein concentration of each sample using the protein assay kit. 6. Store the resulting supernatants at 80  C in LoBind Eppendorf tubes.

3.2 Affinity Purification

1. Remove the bottom plug and loosen the upper cap of the immuno-depletion spin column and place in a 2 mL-capacity collection tube. 2. Centrifuge at 2000  g for 30 s. 3. Remove the cap and replace with the Luer Lock cap. 4. Add 4 mL equilibration buffer using the Luer Lock syringe by gently pushing through the resin into a suitable collection tube. 5. Repeat step 3.2, 4 and remove the syringe and cap. 6. Place the column in a 2 mL collection tube. 7. Replace the original cap on the column so that it fits loosely. 8. Centrifuge as in step 3.2, 2. 9. Discard the collected buffer and place the column into a fresh 2 mL collection tube. 10. Thaw serum samples slowly in a room temperature water bath. 11. Gently mix to suspend any proteins which may have partitioned or precipitated in cold storage. 12. Dilute 8 μL with 92 μL equilibration buffer and pass through a 0.2 μm centrifuge tube filter by centrifugation at 2000  g for 1 min. 13. Add 100 μL of diluted and filtered serum to the top of the packed resin and incubate at room temperature for 20 min. 14. Centrifuge as above and save the flow-through. 15. Remove any residual depleted serum proteins by adding 100 μL equilibration buffer to the top of resin and centrifuge as above. 16. Collect the flow-through in the same tube and repeat steps 3.2, 14 and 3.2, 15.

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17. Store the combined flow-through fractions at 20  C (see Note 10). 18. Elute the bound proteins by adding 2 mL elution buffer using the Luer Lock cap and syringe as above. 19. Allow this to drip through into a 5 mL-capacity collection tube to yield the bound protein fraction (see Note 11). 20. Neutralize by adding 100 μL 1 M Trizma base per 2 mL eluate. 21. Transfer the 2.1 mL neutralized eluate to a 15 mL-capacity centrifuge tube. 22. Add 10.5 mL 20  C acetone and invert the tube for gentle mixing. 23. Leave the tube at 20  C overnight. 24. Centrifuge at 15,000  g for 30 min at 4  C and remove the supernatant by gently decanting this into a collection tube. 25. Suspend the pellets by vortexing in 10.5 mL 20  C 50% acetone and centrifuge as in step 3.2, 24. 26. Repeat steps 24 and 25 and allow the final pellet to air-dry at room temperature. 27. Immediately re-equilibrate column for future use by repeating steps 3.2, 3 to 3.2, 9. 3.3

Trypsin Digestion

1. Resuspend the final pellet form from step 3.2, 26 in 200 μL ammonium bicarbonate. 2. Determine protein concentration as above and adjust by addition of 50 mM ammonium bicarbonate so that the final concentration is 1 μg/μL. 3. Add 50 μL of the sample to a 1.5 μL microcentrifuge tube. 4. Add 2.5 μL 100 mM DTT and homogenize (see Note 12). 5. Incubate the samples at 60  C for 30 min. 6. Remove the samples from the heating device and cool at room temperature. 7. Add 2.5 μL IAA and vortex (see Note 13). 8. Incubate the samples 30 min at room temperature in the dark. 9. Add 5 μL of trypsin solution and gently mix (see Note 14). 10. Incubate overnight or 16 h at 37  C. 11. Stop the digestions by addition of 2.1 μL 12 N HCl and store the samples at -80  C prior to analysis.

3.4

LC-MS

1. Spike each sample with 25 fmol/μL trypsinized yeast enolase (see Note 15). 2. Add formic acid to a final concentration of 0.1% to samples.

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3. Inject 0.5 μg of each sample in triplicate into the liquid chromatography instrument coupled to the mass spectrometer by desalting in 100% buffer A in the trapping column at a flow rate of 300 nL/min. 4. Switch to the BEH nanoColumn and apply the following gradient at 300 nL/min: 3–30% B over 80 min; 30–90% B in 10 min; 90% B for 13 min and then resetting to initial conditions of 3% B (see Note 16). 5. Acquire data in MSE mode (data-independent analysis) as described previously [40] (see Note 17). 6. Set the instrument to infuse glu-fibrinopeptide B using the LockSpray with scans every 30 s to maintain mass accuracy during analysis. 7. Analyze a blank containing the trypsinized yeast enolase after each triplicate sample run. 3.5

Data Analysis

1. Carry out MSE data processing, analyze the resulting mass spectra, and carry out database searching and expression analyses with PLGS and Rosetta Elucidator, using default parameters (see Note 18): 2. Reduce fragmented ions for charge state by deisotoping and mass correction with their corresponding precursor peptides, based on mass and retention time measurements in the low and high collision energy channels. 3. Set the low energy ion detection threshold to 250 counts and the high energy ion detection threshold set to 100 counts. 4. Extract and integrate aligned peaks using the threedimensional ion volume (time, m/z, intensity). 5. Use the total ion current to normalize feature intensities. 8. Set the automatic data processing, using the following criteria for acceptance: (a) A minimum of three fragment ion matches per peptide ion. (b) A minimum of seven fragment ion matches per protein. (c) A minimum of two peptide ion matches per protein. (d) A maximum of one missed cleavage. (e) Detection of peptides in at least two out of three replicates and 60% of the samples to ensure biological reproducibility. (f) A false positive rate of less than 4% (see Note 19). 9. Use the UniProt database, appended with the sequence of yeast enolase and porcine trypsin for searches using the following parameters allowing carbamidomethylation of cysteines and oxidation of methionines.

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Common Unbound

137

Bound

43

147

Fig. 3 Venn diagram showing the number of proteins in the flow-through (unbound) and purified (bound fraction)

10. Perform database searches using the ion accounting algorithm of PLGS as described previously [41] (see Fig. 3 and Table 3) (see Note 20). 11. Use the intensity sums of non-posttranslationally modified peptides corresponding to an identified protein to produce the total protein intensity across all samples in which these were detected. 12. Import the search results into Elucidator to annotate the aligned features and produce a matrix incorporating peptide intensity for each sample and peptide (see Note 21).

4

Notes 1. All patient and control materials should be handled according to standard COVID-19 practices to help minimize risks of spreading the infection [39]. In addition, all materials should be stored or disposed of according to local regulations in a biosafety level (BSL) 2 or 3 laboratory as appropriate. 2. The evacuated systems come with a syringe and single-draw or butterfly attachments. 3. Some proteins in blood-based proteomic studies can be present at significantly different concentrations in comparing serum and plasma. Here, we describe the procedure for preparation of serum. 4. Ensure that the chosen kit is compatible with all reagents/ buffers containing proteins. 5. This kit is available from multiple suppliers. 6. Other similar columns can be used but should be optimized for instrumentation and experimental compatibility. 7. Version 3.0. 3 is now available.

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Table 3 Complete list of unique proteins identified by at least two unique peptides in the bound eluted fraction. The UniProt identification, gene, and protein name are indicated. Proteins targeted for depletion by the immunodepletion kit are indicated in bold font UniProt

Gene

Protein name

P01009

A1AT

Alpha-1-antitrypsin

P21399

ACOC

Cytoplasmic aconitate hydratase

O00116

ADAS

Alkyl-dihydroxyacetone phosphate synthase, peroxisomal

Q96PN6

ADCYA

Adenylate cyclase type 10

Q02952

AKA12

A-kinase anchor protein 12

O75969

AKAP3

A-kinase anchor protein 3

Q9Y243

AKT3

RAC-gamma serine/threonine-protein kinase

Q86TB3

ALPK2

Alpha-protein kinase 2

Q01433

AMPD2

AMP deaminase 2

Q92974

ARHG2

Rho guanine nucleotide exchange factor 2

Q96PE2

ARHGH

Rho guanine nucleotide exchange factor 17

O14497

ARI1A

AT-rich interactive domain-containing protein 1A

O95376

ARI2

Protein ariadne-2 homolog

Q9UKP4

ATS7

A disintegrin and metalloproteinase, thrombospondin motifs 7

Q15582

BGH3

Transforming growth factor-beta-induced protein ig-h3

Q6RI45

BRWD3

Bromodomain and WD repeat-containing protein 3

A6QL63

BTBDB

Ankyrin repeat and BTB/POZ domain-containing protein

P02746

C1QB

Complement C1q subcomponent subunit B

P02747

C1QC

Complement C1q subcomponent subunit C

Q6PIY5

CA228

Uncharacterized protein C1orf228

Q502W7

CCD38

Coiled-coil domain-containing protein 38

Q8TEP8

CE192

Centrosomal protein of 192 kDa

P00450

CERU

Ceruloplasmin

Q14008

CKAP5

Cytoskeleton-associated protein 5

Q9NWS1

CL048

UPF0419 protein C12orf48

Q01955

CO4A3

Collagen alpha-3(IV) chain

O75128

COBL

Protein cordon-bleu

Q2NKJ3

CQ068

Uncharacterized protein C17orf68

Q96SW2

CRBN

Protein cereblon

Q86T65

DAAM2

Disheveled-associated activator of morphogenesis 2 (continued)

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Table 3 (continued) UniProt

Gene

Protein name

Q5JSL3

DOC11

Dedicator of cytokinesis protein 11

P50570

DYN2

Dynamin-2

O43491

E41L2

Band 4.1-like protein 2

P01133

EGF

Pro-epidermal growth factor

Q14152

EIF3A

Eukaryotic translation initiation factor 3 subunit A

Q8IYF1

ELOA2

RNA polymerase II transcription factor SIII subunit A2

Q8NEV8

EXPH5

Exophilin-5

Q9H4H8

FA83D

Protein FAM83D

O15287

FANCG

Fanconi anemia group G protein

Q9C0D6

FHDC1

FH2 domain-containing protein 1

P02675

FIBB

Fibrinogen beta chain

O95302

FKBP9

FK506-binding protein 9

Q15007

FL2D

Pre-mRNA-splicing regulator WTAP

Q68DA7

FMN1

Formin-1

Q2WGJ9

FR1L6

Fer-1-like protein 6

Q9Y2L6

FRM4B

FERM domain-containing protein 4B

Q92538

GBF1

Golgi-specific brefeldin A-resistance guanine exchange factor 1

Q96RT8

GCP5

Gamma-tubulin complex component 5

Q9Y625

GPC6

Glypican-6

Q8IUY3

GRAM2

GRAM domain-containing protein 2

Q14687

GSE1

Genetic suppressor element 1

P68871

HBB

Hemoglobin subunit beta

P02042

HBD

Hemoglobin subunit delta

P02100

HBE

Hemoglobin subunit epsilon

Q8IVU3

HERC6

Probable E3 ubiquitin-protein ligase HERC6

Q86YM7

HOME1

Homer protein homolog 1

P01763

HV302

Ig heavy chain V-III region WEA

P01765

HV304

Ig heavy chain V-III region TIL

P01767

HV306

Ig heavy chain V-III region BUT

P01774

HV313

Ig heavy chain V-III region POM

P01776

HV315

Ig heavy chain V-III region WAS (continued)

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Table 3 (continued) UniProt

Gene

Protein name

P01777

HV316

Ig heavy chain V-III region TEI

P01781

HV320

Ig heavy chain V-III region GAL

Q8NAC3

I17RC

Interleukin-17 receptor C

Q9HBG6

IF122

Intraflagellar transport protein 122 homolog

Q96RY7

IF140

Intraflagellar transport protein 140 homolog

P01877

IGHA2

Ig alpha-2 chain C region

P01880

IGHD

Ig delta chain C region

P01591

IGJ

Immunoglobulin J chain

Q14005

IL16

Pro-interleukin-16

P27987

IP3KB

Inositol-trisphosphate 3-kinase B

P78412

IRX6

Iroquois-class homeodomain protein IRX-6

O75153

K0664

Protein KIAA0664

Q9ULI1

K1239

Leucine-rich repeat, WD repeat-containing protein KIAA1239

Q2M2I5

K1C24

Keratin, type I cytoskeletal 24Type I keratin-24

P35527

K1C9

Keratin, type I cytoskeletal 9

P35908

K22E

Keratin, type II cytoskeletal 2 epidermal

Q9Y597

KCTD3

BTB/POZ domain-containing protein KCTD3

Q8NHM5

KDM2B

Lysine-specific demethylase 2B

Q6ZMV9

KIF6

Kinesin-like protein KIF6

P01598

KV106

Ig kappa chain V-I region EU

P01614

KV201

Ig kappa chain V-II region cum

P01617

KV204

Ig kappa chain V-II region TEW

P01622

KV304

Ig kappa chain V-III region Ti

O00515

LAD1

Ladinin-1

Q008S8

LFDH

Putative guanine nucleotide exchange factor LFDH

Q8N0V4

LGI2

Leucine-rich repeat LGI family member 2

P15018

LIF

Leukemia inhibitory factor

Q8ND23

LR16B

Leucine-rich repeat-containing protein 16B

P01714

LV301

Ig lambda chain V-III region SH

P80748

LV302

Ig lambda chain V-III region LOI

Q9Y2H9

MAST1

Microtubule-associated serine/threonine-protein kinase 1 (continued)

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Table 3 (continued) UniProt

Gene

Protein name

O60318

MCM3A

80 kDa MCM3-associated protein

Q5JRA6

MIA3

Melanoma inhibitory activity protein 3

Q96J65

MRP9

Multidrug resistance-associated protein 9

Q7L0Y3

MRRP1

Mitochondrial ribonuclease P protein 1

P35580

MYH10

Myosin-10

P35749

MYH11

Myosin-11

Q9Y623

MYH4

Myosin-4

P13533

MYH6

Myosin-6

P12883

MYH7

Myosin-7

B0I1T2

MYO1G

Myosin-Ig

Q9NZM1

MYOF

Myoferlin

P46934

NEDD4

E3 ubiquitin-protein ligase NEDD4

O94856

NFASC

Neurofascin

Q8N4C6

NIN

Ninein

Q12879

NMDE1

Glutamate [NMDA] receptor subunit epsilon-1

Q08J23

NSUN2

tRNA (cytosine-5-)-methyltransferase NSUN2

Q7Z4N8

P4HA3

Prolyl 4-hydroxylase subunit alpha-3

Q96JP9

PCD21

Protocadherin-21

Q8NCN5

PDPR

Pyruvate dehydrogenase phosphatase regulatory subunit

O15534

PER1

Period circadian protein homolog 1

P16234

PGFRA

Alpha-type platelet-derived growth factor receptor

Q00722

PLCB2

1-Phosphatidylinositol-4,5-bisphosphate phosphodiesterase

P00747

PLMN

Plasminogen

Q8WUA2

PPIL4

Peptidyl-prolyl cis-trans isomerase-like 4

Q9NQV8

PRDM8

PR domain zinc finger protein 8

Q8IZ41

RASEF

RAS and EF-hand domain-containing protein

Q9UFD9

RIM3A

RIMS-binding protein 3A

A6NJZ7

RIM3C

RIMS-binding protein 3C

Q96EP0

RNF31

RING finger protein 31

Q9Y2J0

RP3A

Rabphilin-3A

Q5T5P2

SKT

Sickle tail protein homolog (continued)

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Table 3 (continued) UniProt

Gene

Protein name

P28370

SMCA1

Probable global transcription activator SNF2L1

Q92673

SORL

Sortilin-related receptor

O43295

SRGP2

SLIT-ROBO rho GTPase-activating protein 3

Q9H2K8

TAOK3

Serine/threonine-protein kinase TAO3

Q9Y2I9

TBC30

TBC1 domain family member 30

Q96RS0

TGS1

Trimethylguanosine synthase homolog

Q96J01

THOC3

THO complex subunit 3

Q13009

TIAM1

T-lymphoma invasion and metastasis-inducing protein 1

Q86XT4

TRI50

E3 ubiquitin-protein ligase TRIM50

Q15643

TRIPB

Thyroid receptor-interacting protein 11

Q8IYL2

TRM44

Probable tRNA (uracil-O(2)-)-methyltransferase

Q9NZQ8

TRPM5

Transient receptor potential cation channel subfamily M 5

P02766

TTHY

Transthyretin

Q8N2N9

U634B

UPF0634 protein B

Q9NPG3

UBN1

Ubinuclein-1

O60701

UGDH

UDP-glucose 6-dehydrogenase

P52735

VAV2

Guanine nucleotide exchange factor VAV2

P17948

VGFR1

Vascular endothelial growth factor receptor 1

P35968

VGFR2

Vascular endothelial growth factor receptor 2

Q5TAQ9

WD42A

WD repeat-containing protein 42A

Q6AWC2

WWC2

Protein WWC2

Q6ZU21

YB019

Uncharacterized protein FLJ44048

Q14929

ZN169

Zinc finger protein 169

Q9C0G0

ZN407

Zinc finger protein 407

8. This is a comprehensive solution for proteomics research to support mass spectrometry-based biomarker discovery. It consists of raw data management and processing for quantitative analyses, and protein identification studies at the peptide and protein level. 9. It is important to use the same timing and conditions for each sample to allow consistent clotting efficiency. 10. This comprises the depleted fraction. This is the fraction that is usually analyzed in proteomic studies. However, this misses the

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specifically bound and interacting proteins found in the affinity purified fraction (termed the depletome [29]). 11. There should be approximately 1 drop/s and take approximately 1 min to complete. 12. This reagent reduces inter- and intramolecular disulfide bonds in peptide chains. 13. DTT treatment is typically followed by alkylation of the free sulfhydryl groups with a reagent like IAA to prevent reformation of disulfide bonds. 14. In all of these steps with small volumes, the samples should be centrifuged briefly after gentle mixing to ensure that all of liquid volume is collected in the bottom of the tube. 15. This is used as a peptide source for normalization during data processing. 16. The counter buffer was A, such that 3% B also comprises 97% A, 30% B also comprises 70% A, etc. 17. This mode of analysis describes the transfer of all ions by the quadrupole as the collision cell switches throughout acquisition from low (5 eV) to high (17–40 eV) energy with a cycle time of 1.25 s. 18. MSE enables accurate mass measurement of eluting peptides at a high sampling and fragmentation rate compared to datadependent analysis methods. It also conserves the chromatographic profile for both intact peptides and fragments. Use of the Elucidator system facilitates alignment of raw MS data in time and mass/charge (m/z) dimensions. 19. The false positive rate can be expressed as a percentage based on the ratio false positive rate/true positive rate  100. 20. The flow-through fraction was also analyzed in this study to identify the unique proteins found in each fraction (see Fig. 3). The proteins unique to the affinity purified fraction are shown in Table 3. 21. This can be applied in studies in comparative proteomic studies such as looking at the effects of a specific disease. References 1. https://www.worldometers.info/coronavirus/ 2. https://www.who.int/en/activities/trackingSARS-CoV-2-variants/ ¨ ztu¨rk R, Tas¸ova Y, Ayaz A (2020) COVID3. O 19: pathogenesis, genetic polymorphism, clinical features and laboratory findings. Turk. J Med Sci 50(SI-1):638–657 4. Rodrı´guez Y, Novelli L, Rojas M et al (2020) Autoinflammatory and autoimmune conditions at the crossroad of COVID-19. J

Autoimmun 114:102506. https://doi.org/ 10.1016/j.jaut.2020.102506 5. Vardhana SA, Wolchok JD (2020) The many faces of the anti-COVID immune response. J Exp Med 217(6):e20200678. https://doi. org/10.1084/jem.20200678 6. Zhou Y, Zhang J, Wang D et al (2021) Profiling of the immune repertoire in COVID-19 patients with mild, severe, convalescent, or retesting-positive status. J

COVID-19 Serum Biomarkers Autoimmun 118:102596. https://doi.org/ 10.1016/j.jaut.2021.102596 7. Hogan G, Geoghegan P, Carroll TP et al (2021) Alpha1-antitrypsin: key player or bystander in acute respiratory distress syndrome? Anesthesiology 134(5):792–808 8. Gando S, Wada T (2021) Thromboplasminflammation in COVID-19 coagulopathy: three viewpoints for diagnostic and therapeutic strategies. Front Immunol 12:649122. https://doi.org/10.3389/fimmu.2021. 649122 9. Semeraro N, Colucci M (2021) The Prothrombotic state associated with SARS-CoV2 infection: pathophysiological aspects. Mediterr J Hematol Infect Dis 13(1):e2021045. https://doi.org/10.4084/MJHID.2021.045 10. Pascolo S (2021) Synthetic messenger RNA-based vaccines: from scorn to hype. Viruses 13(2):270. https://doi.org/10. 3390/v13020270 11. Koike H, Okumura T, Murohara T et al (2021) Multidisciplinary approaches for transthyretin amyloidosis. Cardiol Ther 4:1–23. https://doi. org/10.1007/s40119-021-00222-w. Online ahead of print 12. Akbar MR, Pranata R, Wibowo A et al (2021) The association between serum prealbumin and poor outcome in COVID-19 - systematic review and meta-analysis. Eur Rev Med Pharmacol Sci 25(10):3879–3885 13. Liu K, Yang T, Peng XF et al (2021) A systematic meta-analysis of immune signatures in patients with COVID-19. Rev Med Virol 31(4):e2195. https://doi.org/10.1002/rmv. 2195 14. Pang NY, Pang AS, Chow VT et al (2021) Understanding neutralising antibodies against SARS-CoV-2 and their implications in clinical practice. Mil Med Res 8(1):47. https://doi. org/10.1186/s40779-021-00342-3 15. Ricklin D, Hajishengallis G, Yang K, Lambris JD (2010) Complement: a key system for immune surveillance and homeostasis. Nat Immunol 11(9):785–797 16. Merle NS, Church SE, Fremeaux-Bacchi V et al (2015) Complement system part I—molecular mechanisms of activation and regulation. Front Immunol 6:262. https://doi.org/10.3389/ fimmu.2015.00262 17. Wu Y, Huang X, Sun J et al (2020) Clinical characteristics and immune injury mechanisms in 71 patients with COVID-19. mSphere 5(4): e00362-20. https://doi.org/10.1128/ mSphere.00362-20 18. Tang Y, Sun J, Pan H et al (2021) Aberrant cytokine expression in COVID-19 patients:

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associations between cytokines and disease severity. Cytokine 143:155523. https://doi. org/10.1016/j.cyto.2021.155523 19. Macor P, Durigutto P, Mangogna A et al (2021) Multi-organ complement deposition in COVID-19 patients. medRxiv. https://doi. org/10.1101/2021.01.07.21249116. Preprint 20. Pfister F, Vonbrunn E, Ries T et al (2021) Complement activation in kidneys of patients with COVID-19. Front Immunol 11:594849. https://doi.org/10.3389/fimmu.2020. 594849 21. Jamaly S, Tsokos MG, Bhargava R et al (2021) Complement activation and increased expression of Syk, mucin-1 and CaMK4 in kidneys of patients with COVID-19. Clin Immunol 229: 108795. https://doi.org/10.1016/j.clim. 2021.108795 22. Messner CB, Demichev V, Wendisch D et al (2020) Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection. Cell Syst 11(1):11–24.e4. https://doi.org/10. 1016/j.cels.2020.05.012 23. Hou X, Zhang X, Wu X et al (2020) Serum protein profiling reveals a landscape of inflammation and immune signaling in early-stage COVID-19 infection. Mol Cell Proteomics 19(11):1749–1759 24. Liu X, Cao Y, Fu H et al (2021) Proteomics analysis of serum from COVID-19 patients. ACS Omega 6(11):7951–7958 25. Lazari LC, Ghilardi FR, Rosa-Fernandes L et al (2021) Prognostic accuracy of MALDI-TOF mass spectrometric analysis of plasma in COVID-19. Life Sci Alliance 4(8): e202000946. https://doi.org/10.26508/lsa. 202000946 26. Laudanski K, Jihane H, Antalosky B et al (2021) Unbiased analysis of temporal changes in immune serum markers in acute COVID-19 infection with emphasis on organ failure, antiviral treatment, and demographic characteristics. Front Immunol 12:650465. https://doi. org/10.3389/fimmu.2021.650465 27. Memon D, Barrio-Hernandez I, Beltrao P (2021) Individual COVID-19 disease trajectories revealed by plasma proteomics. EMBO Mol Med 13(8):e14532. https://doi.org/10. 15252/emmm.202114532 28. Anderson NL, Anderson NG (2002) The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics 1(11):845–867 29. Koutroukides TA, Guest PC, Leweke FM et al (2011 Jul) (2011) characterization of the human serum depletome by label-free shotgun proteomics. J Sep Sci 34(13):1621–1626

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30. Fioramonte M, Guest PC, Martins-de-Souza D (2017) LC-MSE for qualitative and quantitative proteomic studies of psychiatric disorders. Adv Exp Med Biol 974:115–129 31. Garcia S, Silva-Costa LC, Reis-de-Oliveira G et al (2017) Identifying biomarker candidates in the blood plasma or serum proteome. Adv Exp Med Biol 974:193–203 32. Urbas L, Brne P, Gabor B et al (2009) Depletion of high-abundance proteins from human plasma using a combination of an affinity and pseudo-affinity column. J Chromatogr A 1216(13):2689–2694 33. Ahmed N, Rice GE (2005) Strategies for revealing lower abundance proteins in two-dimensional protein maps. J Chromatogr B Analyt Technol Biomed Life Sci 815(1–2): 39–50 34. Tam SW, Pirro J, Hinerfeld D (2004) Depletion and fractionation technologies in plasma proteomic analysis. Expert Rev Proteomics 1(4):411–420 35. Pernemalm M, Lehtio¨ J (2014) Mass spectrometry-based plasma proteomics: state of the art and future outlook. Expert Rev Proteomics 11(4):431–448

36. Echan LA, Tang HY, Ali-Khan N et al (2005) Depletion of multiple high-abundance proteins improves protein profiling capacities of human serum and plasma. Proteomics 5(13): 3292–3303 37. Liu T, Qian WJ, Mottaz HM (2006) Evaluation of multiprotein immunoaffinity subtraction for plasma proteomics and candidate biomarker discovery using mass spectrometry. Mol Cell Proteomics 5(11):2167–2174 38. Bellei E, Bergamini S, Monari E et al (2011) High-abundance proteins depletion for serum proteomic analysis: concomitant removal of non-targeted proteins. Amino Acids 40(1): 145–156 39. https://www.euro.who.int/__data/assets/ pdf_file/0005/268790/WHO-guidelineson-drawing-blood-best-practices-in-phlebot omy-Eng.pdf 40. Levin Y, Schwarz E, Wang L et al (2007) Labelfree LCMS/MS quantitative proteomics for large-scale biomarker discovery in complex samples. J Sep Sci 30(14):2198–2203 41. Silva JC, Denny R, Dorschel CA et al (2005) Quantitative proteomic analysis by accurate mass retention time pairs. Anal Chem 77(7): 2187–2200

Chapter 15 Liquid Chromatography-Mass Spectrometry Analysis of Peripheral Blood Mononuclear Cells from SARS-CoV-2 Infected Patients Paul C. Guest and Hassan Rahmoune Abstract COVID-19 disease is caused by infection with the SARS-CoV-2 virus and is associated with a cytokine storm effect in some patients. This can lead to decreased ability of the host to cope with the infection and result in severe disease outcomes. Here, we present a protocol for isolation of peripheral blood mononuclear cells (PBMCs) from COVID-19 patients followed by liquid chromatography-mass spectrometry (LC-MS) profiling to identify the affected molecules and molecular pathways. It is hoped that this will lead to the identification of potential biomarkers for monitoring the disease as well as treatment responses. This approach could also be used in the study of other respiratory viruses. Key words COVID-19, SARS-CoV-2, Mass spectrometry, LC-MS, Biomarkers, Cytokine storm, Cytokine

1

Introduction COVID-19 disease is caused by the SARS-CoV-2 virus which first erupted in Wuhan, China, in late 2019 [1, 2] and has now become a pandemic of global proportions [3, 4]. Although infection by the SARS-CoV-2 virus causes mild symptoms in most people and approximately 30% of people are asymptomatic [3, 4], some can suffer a more severe course with an increased chance of a death outcome [5, 6]. Due the unprecedented worldwide effort to develop and distribute vaccines against this virus [7], more than half of the global population has now received at least one dose of a World Health Organization-approved COVID-19 vaccine (as of November 11, 2021) [8]. Although this has led to reduced case numbers and decreased disease severity in some countries, the inequalities in distribution, vaccine hesitancy, and anti-vaccine sentiments and the increasing appearance of more virulent SARS-

Paul C. Guest (ed.), Multiplex Biomarker Techniques: Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification, Methods in Molecular Biology, vol. 2511, https://doi.org/10.1007/978-1-0716-2395-4_15, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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SARS-CoV-2 Epithelium

Multi-organ failure

Brain

Heart

Kidney

Immune cells Liver Lungs Acute respiratory distress syndrome

Cytokine storm

Disseminated intravascular coagulation

Fig. 1 Cytokine storm resulting in severe SARS-CoV-2 infection

CoV-2 variants of concern have resulted in perpetuation of the pandemic [9–12]. Severe cases of COVID-19 disease are often accompanied by a cytokine storm or hyper-inflammatory effect in the patients (see Fig. 1) [13–15]. These is marked by increased production of inflammation-related molecules including interleukin-6 (IL-6), IL-1β, and tumor necrosis factor alpha (TNF-α) and activation of macrophages [14, 16]. This effect can trigger other damaging effects on organs and tissues in severe COVID-19 disease, including endothelial cell dysfunction, a hyper-coagulation state, and pathological angiogenesis [14–17]. These conditions can lead to overproduction of mitochondrial reactive oxygen species (ROS), which has been linked to lower antioxidant defenses and conditions that favor viral replication in the host [18–20]. A number of studies have now demonstrated that severe COVID-19 cases are marked by a heightened inflammatory response with immune and cytotoxic cell exhaustion [21]. In

LC-MS Analysis of PBMCs

203

addition, it has been demonstrated that SARS-CoV-2 infection of peripheral blood mononuclear cells (PBMCs) isolated from patients resulted in vulnerability of B cells and T cells with an increase in monocyte infection with time [22]. Manunta et al. found that PBMCs isolated from newly infected COVID-19 patients had decreased lymphocyte and monocyte populations, and increased levels of immature neutrophils [23]. Another study showed that incubation of PBMCs isolated from recovered COVID-19 patients with the SARS-CoV-2 virus resulted in decreased effector memory CD8+ T cells but no effect on CD4+ T cells [24]. The effect on CD8+ T cells was confirmed in moderate COVID-19 and convalescent patients, with reduced levels of cytotoxic molecules in the convalescent group [25]. Taken together, these findings indicate that isolated PBMCs from patients with COVID-19 disease would represent a useful cellular model for studying the effects on inflammation and immune cell dysfunction as well as potential therapeutic approaches. Here we present a protocol for preparation and subcellular fractionation of PBMCs from whole blood from COVID-19 patients for use in liquid chromatography-mass spectrometry (LC-MS) profiling studies. It is hoped that application of this approach will lead to identification of potential biomarkers for tracking COVID-19 disease severity and for treatment monitoring. Such biomarkers may also be useful in the study of additional diseases associated with a cytokine storm effect, such as those caused by other respiratory viruses.

2

Materials

2.1 Sample Collection

1. Patients with SARS-CoV-2 infections (see Note 1). 2. Personal protective equipment (PPE) (see Note 1). 3. 18 gauge sterile needles for blood draw and other standard phlebotomy equipment. 4. Alcohol wipes (70% isopropyl alcohol). 5. Latex, rubber or vinyl gloves. 6. Evacuated 9 mL ethylenediaminetetraacetic acid (EDTA) blood collection tubes (see Note 2).

2.2 PBMC Preparation and Fractionation

1. Dulbecco’s phosphate-buffered saline (DPBS). 2. Ficoll-Paque Plus 1.077 (see Note 3). 3. Sterile 15 and 50 mL centrifuge tubes. 4. Benchtop centrifuge with swinging bucket sleeves and rotor. 5. Cryopreservation solution (optional): 10% DMSO/90% fetal bovine serum (FBS) (see Note 4).

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6. Thawing solution: RPMI 1640 (optional), 10% fetal calf serum (FCS), 1% glutamine, 1% penicillin, 1% streptavidin, and 1% DNAse (see Note 4). 7. ProteoExtract Subcellular Proteome Extraction Kit components (see Note 5): (a) Wash buffer. (b) Extraction buffer 1. (c) Extraction buffer 2. (d) Protease inhibitor cocktail. (e) Benzonase nuclease. 2.3

LC-MS

1. Acetone. 2. 50 mM ammonium bicarbonate (pH 7.5). 3. DC protein assay kit or similar. 4. Reduction buffer: 100 mM dithiothreitol (DTT) in 50 mM ammonium bicarbonate (pH 7.5). 5. Alkylation buffer: 300 mM iodoacetamide (IAA) in 50 mM ammonium bicarbonate (pH 7.5). 6. Digest solution: sequencing grade modified trypsin (0.2 μg/μ L) in 50 mM ammonium bicarbonate (pH 7.5). 7. Stop solution: 12 N hydrochloric acid (HCl). 8. Trypsinized yeast enolase. 9. Split-less nano Ultra Performance LC system with following components (see Note 6): (a) C18 trapping column (180 μm i.d., 20 mm length, 5 μm particle size). (b) BEH C18 separation nanocolumn (75 μm i.d., 200 mm length, 1.7 μm particle size). (c) NanoESI online emitter (7 cm length with 10 μm tip). 10. Q-TOF Premier mass spectrometer or similar (see Note 6). 11. Separation buffer A: HPLC grade water containing 0.1% formic acid. 12. Separation buffer B: HPLC grade acetonitrile containing 0.1% formic acid. 13. 100 fmol/uL human glu-fibrinopeptide B in water/acetonitrile/formic acid (77/25/0.1%). 14. MassLynx software. 15. ProteinLynx Global Server software. 16. UniProt database. 17. R software package [26].

LC-MS Analysis of PBMCs

3

205

Methods

3.1 Venipuncture (See Note 1)

1. Before drawing blood, record all personal details, physiological data, and laboratory data of each donor using a coded worksheet (Table 1) (see Note 7). 2. Select to draw blood from the most appropriate arm vein of the donor [27] (see Fig. 2) (see Note 8). 3. Clean the arm of the patient with an alcohol wipe using a circular motion and beginning at the intended site of blood draw. 4. After allowing the area to air-dry, insert the needle at a 15–20
angle of the plane of the vein, taking care to minimize trauma. 5. Draw 8–10 mL blood from the vein into the collection tube.

3.2 PBMC Preparation

1. Warm the Ficoll-Paque solution to room temperature and gently invert the tube so that it is homogeneous (see Note 9). 2. Add 8 mL Ficoll-Paque to a 50 mL centrifuge tube. 3. Add 8 L DPBS to 8 mL blood and mix gently by inversion. 4. Layer the blood/DPBS mixture gently on top of the FicollPaque solution taking care not to mix the layers. 5. Centrifuge 30 min at 750  g in the benchtop centrifuge with the brakes turned off to decrease chances of layer mixing during the deceleration phase. 6. Remove the upper plasma layer with a sterile pipette taking care not to disturb the lower layer. 7. Collect the PBMCs from the plasma/Ficoll-Paque interface using a sterile pipette (see Fig. 2) (see Note 10).

Table 1 Concentrations (%) of buffers of A and B used during the LC stage of the LC-MS analysis A/B (%)

Total time (min)

Desalting

97/3

–

Initial

97/3

0

Step 1

70/30

90

Step 2

10/90

115

Step 3

5/95

120

Step 4 (hold)

5/95

125

Step 5 (reset)

97/3

126

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A) Blood sample

Plasma Platelets

Layer onto Ficoll

Ficoll

Collect PBMCs

Centrifuge RBCs

Suspend PBMCs in extraction buffer 1

B)

Centrifuge 10 min at 6000 x g

Suspend pellet in extraction buffer 2

Centrifuge 10 min at 1000 x g

Collect fraction 2 (membrane/organelle proteins)

Protein digestion

C)

LC

Proteins digested ionization to peptides

Electrospray

Quadruple mass analyzer Detector

Separation of peptides by LC Low energy gas

Intact peptide ion

Peak height = abundance High energy gas

Fragment peptide ion

Mass = sequence = identity

Fig. 2 Flow diagram of experimental procedure showing (a) isolation and (b) subcellular fractionation of PBMCs followed by (c) LC-MS analysis of the PBMC proteome

LC-MS Analysis of PBMCs

207

8. Transfer the PBMCs into a new sterile 50 mL centrifuge tube. 9. Add 30 mL DPBS and centrifuge 5 min at 350  g to wash the cells. 10. Gently suspend the pellet using 10 mL DPBS and transfer to a sterile 15 mL centrifuge tube. 11. Centrifuge 5 min at 350  g. 12. Remove the supernatant and gently suspend the pellet in 10 mL DPBS. 13. Repeat steps 11 and 12 and proceed immediately to LC-MS or preserve the cells under liquid nitrogen in cryopreservation solution (see Note 11). 3.3

Cell Culture

1. Suspend 7  106 cells in thawing solution and culture overnight at 37
C in 5% CO2/95% air. 2. Collect the cells by centrifugation for 4 min at 1000  g at 4
C. 3. Suspend 7  106 cells in thawing solution and culture overnight at 37
C in 5% CO2/95% air (see Note 12). 4. Collect the cells by centrifugation for 4 min at 1000  g at 4
C. 5. Wash the pellets twice by addition of ice-cold DPBS and centrifugation at 1000  g between washes. 6. Remove the media and store the pellets at 80
C before use.

3.4 PBMC Fractionation (See Fig. 2)

1. Using reagents from the ProteoExtract kit, gently suspend cells in wash buffer. 2. Centrifuge 10 min at 300  g at 4
C. 3. Aspirate and discard supernatant. 4. Repeat steps 1–3 and suspend cells in 1 mL extraction buffer 1 containing 5 μL protease inhibitor cocktail. 5. Centrifuge 10 min at 1000  g at 4
C. 6. Transfer the supernatant to a fresh tube to yield a fraction enriched in cytosolic proteins [28]. 7. Suspend remaining cellular material in the pellet from step 5 in 1 mL extraction buffer 2 containing 5 μL protease inhibitor cocktail. 8. Centrifuge 10 min at 6000  g at 4
C. 9. Transfer the supernatant to a fresh tube to generate a fraction enriched in membrane and organelle proteins. 10. Determine the protein concentration of both fractions using the DC assay or similar protein determination technique.

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LC-MS

1. Transfer each fraction to separate 15 mL-capacity centrifuge tubes. 2. Add 5 mL 20
C acetone and mix contents by gentle inversion. 3. Leave the tubes at 20
C overnight. 4. Centrifuge 30 min at 15,000  g at 4
C. 5. Discard the supernatant by decanting into a collection tube and making sure not to disturb the pellet. 6. Suspend the pellets by vortexing in 5 mL 20
C 50% acetone and centrifuge as above. 7. Repeat steps 4–6 and leave the pellet at room temperature to dry. 8. Dissolve the dried samples in 50 μL 50 mM ammonium bicarbonate and determine the protein concentration. 9. Adjust the final concentration to 2 μg/μL and add 50 μL of this to a fresh microcentrifuge tube. 10. Add 2.5 μL 100 mM DTT and incubate 30 min at 60
C. 11. Cool to room temperature and add 2.5 μL alkylation solution. 12. Incubate the samples 30 min at room temperature in the dark. 13. Add 5 μL trypsin, mix gently, and incubate overnight at 37
C. 14. Add 2.1 μL 12 N HCl to stop the digests and store the samples at 80
C until ready for LC-MS analysis. 15. Spike samples with trypsin-digested yeast enolase so that the final concentration of this is 6 pmol/μL. 16. Set the desalting phase using 97% buffer A/3% buffer B on the C18 trapping column. 17. Set the chromatographic separation of the peptides using the BEH C18 nanocolumn at 0.3 μL/min coupled online to the mass spectrometer via the nanoESI emitter with the A/B gradient indicated in Table 1. 18. Infuse the glu-fibrinopeptide B using the LockSpray and scan every 30 s. 19. Acquire data in positive V mode with a full width half mass (FWHM) resolution of 10,000 using the MassLynx software in alternative scanning mode with the settings shown in Table 2. 20. Process data using the PLGS software, align the raw data in time, and search the UniProt database with parameters set as shown in Table 3. 21. Employ the ion previously [29].

accounting

algorithm

described

22. Compile a list of all identified proteins as described [30, 31] (see Note 13).

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Table 2 Settings for LC-MSE analysis in alternative scanning mode Parameter

Setting

Low collision energy

5 eV

High collision energy

15–42 eV (ramped)

Acquisition time

0.6 s

Mass correction

Glu-fibrinopeptide B [monoisotopic mass (m/z), 785.8426]

Table 3 Parameters used for database searching Enzyme

Trypsin

Fixed modifications

Carbamidomethylation of cysteine

Variable modifications

Oxidation of methionine Phosphorylation of serine, threonine, or tyrosine

Criteria for peptide identification

fragment ions/peptide  fragment ions/protein  peptides/protein Detection in 2/3 replicates Detection in 60% of the samples

4

Notes 1. SARS-CoV-2 is a dangerous pathogen and full PPE should be worn by all workers in contact with patients and all safety aspects considered as a priority. Likewise, all biological samples and associated materials should be handled with the possibility that these can transmit the disease and disposed of in a manner similar to other potentially biohazardous waste in a biosafety level (BSL) 2 or 3 facility. 2. The presence of EDTA prevents the blood from clotting, which is essential in blood cell isolation procedures. 3. The number 1.077 refers to the density of the solution. 4. This will only be needed if PBMCs require freezing under liquid nitrogen. It will not be necessary if the cells are to be used fresh after isolation. 5. We extracted the cells into two fractions (soluble proteins and membrane bound/organelle proteins) to increase the total number of identifiable proteins in the LC-MS step. 6. Similar instrumentation can be used but this may require different conditions from the indicated protocol.

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7. This can be used to add information to biomarker data resulting from the LC-MS analysis. For example, a deep learning strategy could be employed to identify which of the input data contributes most to severe disease outcomes. 8. This is typically the median cubital vein as it is the largest and most stable vein in the arm for venipuncture. 9. This is important as the solution may settle or partition in cold storage. 10. Approximately 6 mL PBMCs should be collected from the interface using this procedure. The bottom layer will contain red blood cells. 11. If possible, it is advantageous to proceed immediately to the next step to avoid loss of specific cell types. 12. Culturing overnight should remove any apoptotic cells and, therefore, increase overall cell viability. This is especially important if the cells have been stored frozen. Death of cells during thawing can cause leakage of DNA and cell aggregation. The addition of DNase in the initial resuspension media will help to improve cell recovery. 13. Previously, we carried out two LC-MSE analyses of the PMBC cytosolic fraction, which resulted in identification of 1436 [Herberth 1] and 7713 [Herberth 2] peptides, which translated into 295 [Herberth 1] and 441 [Herberth 2] proteins by searching the UniProt database with the parameters listed in Table 3. References 1. Huang C, Wang Y, Li X et al (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223):497–506 2. Zhu N, Zhang D, Wang W et al (2020) A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 382(8):727–733 3. https://www.worldometers.info/coronavirus/ 4. https://coronavirus.jhu.edu/map.html 5. Guest PC (2021) Clinical, biological and molecular aspects of COVID-19, Advances in experimental medicine and biology 1321. Springer, New York. ISBN-13: 978-3030592608 6. Jafari-Oori M, Ghasemifard F, Ebadi A et al (2021) Acute respiratory distress syndrome and COVID-19: a scoping review and metaanalysis. Adv Exp Med Biol 1321:211–228 7. Guest PC, Ozanne SE (2021) The worldwide effort to develop vaccines for COVID-19. Adv Exp Med Biol 1327:215–223

8. Our World in Data; Coronavirus Vaccinations. https://ourworldindata.org/covid-vaccinations. Accessed 11 Nov 2021 9. Singh B, Chattu VK (2021) Prioritizing ‘equity’ in COVID-19 vaccine distribution through Global Health Diplomacy. Health Promot Perspect 11(3):281–287 10. Muric G, Wu Y, Ferrara E (2021) COVID-19 vaccine hesitancy on social media: building a public twitter dataset of anti-vaccine content, vaccine misinformation and conspiracies. JMIR Public Health Surveill. https://doi.org/10. 2196/30642. Online ahead of print 11. Lu F, Sun Y (2022) COVID-19 vaccine hesitancy: the effects of combining direct and indirect online opinion cues on psychological reactance to health campaigns. Comput Human Behav 127:107057. https://doi.org/ 10.1016/j.chb.2021.107057 12. Thye AY, Law JW, Pusparajah P et al (2021) Emerging SARS-CoV-2 variants of concern

LC-MS Analysis of PBMCs (VOCs): an impending global crisis. Biomedicine 9(10):1303. https://doi.org/10.3390/ biomedicines9101303 13. Azar MM, Shin JJ, Kang I et al (2020) Diagnosis of SARS-CoV-2 infection in the setting of the cytokine release syndrome. Expert Rev Mol Diagn 20(11):1087–1097 14. Morris G, Bortolasci CC, Puri BK et al (2021) The cytokine storms of COVID-19, H1N1 influenza, CRS and MAS compared. Can one sized treatment fit all? Cytokine 144(155593). https://doi.org/10.1016/j.cyto.2021. 155593 15. Ahmad F, Kannan M, Ansari AW (2021) Role of SARS-CoV-2 -induced cytokines and growth factors in coagulopathy and thromboembolism. Cytokine Growth Factor Rev: S1359-6101(21)00079-4. https://doi.org/ 10.1016/j.cytogfr.2021.10.007. Online ahead of print 16. Pasrija R, Naime M (2021) The deregulated immune reaction and cytokines release storm (CRS) in COVID-19 disease. Int Immunopharmacol 90:107225. https://doi.org/10. 1016/j.intimp.2020.107225 17. Norooznezhad AH, Mansouri K (2021) Endothelial cell dysfunction, coagulation, and angiogenesis in coronavirus disease 2019 (COVID19). Microvasc Res 137:104188. https://doi. org/10.1016/j.mvr.2021.104188 18. Polonikov A (2020) Endogenous deficiency of glutathione as the Most likely cause of serious manifestations and death in COVID-19 patients. ACS Infect Dis 6(7):1558–1562 19. Muhammad Y, Kani YA, Iliya S et al (2021) Deficiency of antioxidants and increased oxidative stress in COVID-19 patients: a crosssectional comparative study in Jigawa, Northwestern Nigeria. SAGE Open Med 9: 2050312121991246. https://doi.org/10. 1177/2050312121991246 20. Singh SP, Amar S, Gehlot P et al (2021) Mitochondrial modulations, autophagy pathways shifts in viral infections: consequences of COVID-19. Int J Mol Sci 22(15):8180. https://doi.org/10.3390/ijms22158180 21. Vigo´n L, Fuertes D, Garcı´a-Pe´rez J et al (2021) Impaired cytotoxic response in PBMCs from patients with COVID-19 admitted to

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the ICU: biomarkers to predict disease severity. Front Immunol 12:665329. https://doi.org/ 10.3389/fimmu.2021.665329 22. Pontelli MC, Castro IA, Martins RB et al (2020) Infection of human lymphomononuclear cells by SARS-CoV-2. bioRxiv. https://doi.org/10. 1101/2020.07.28.225912. Preprint 23. Manunta MDI, Lamorte G, Ferrari F et al (2021) Impact of SARS-CoV-2 infection on the recovery of peripheral blood mononuclear cells by density gradient. Sci Rep 11(1):4904. https://doi.org/10.1038/s41598-02183950-2 24. Matyushenko V, Isakova-Sivak I, Kudryavtsev I et al (2021) Detection of IFNgamma-secreting CD4(+) and CD8(+) memory T cells in COVID-19 convalescents after stimulation of peripheral blood mononuclear cells with live SARS-CoV-2. Viruses 13(8):1490. https:// doi.org/10.3390/v13081490 25. Singh Y, Trautwein C, Fendel R et al (2021) SARS-CoV-2 infection paralyzes cytotoxic and metabolic functions of the immune cells. Heliyon 7(6):e07147. https://doi.org/10.1016/j. heliyon.2021.e07147 26. http://cran.rproject.org 27. Guest PC, Rahmoune H (2017) Blood sampling and preparation procedures for proteomic biomarker studies of psychiatric disorders. Adv Exp Med Biol 974:141–147 28. https://www.merckmillipore.com/GB/en/ product/ProteoExtract-Subcellular-Prote ome-Extraction-Kit,EMD_BIO-539790? bd¼1#anchor_TI 29. Silva JC, Denny R, Dorschel CA et al (2005) Quantitative proteomic analysis by accurate mass retention time pairs. Anal Chem 77(7): 2187–2200 30. Herberth M, Koethe D, Levin Y et al (2011) Peripheral profiling analysis for bipolar disorder reveals markers associated with reduced cell survival. Proteomics 11(1):94–105 31. Herberth M, Koethe D, Cheng TM et al (2011) Impaired glycolytic response in peripheral blood mononuclear cells of first-onset antipsychotic-naive schizophrenia patients. Mol Psychiatry 16(8):848–859

Chapter 16 Assay of Fatty Acids and Their Role in the Prevention and Treatment of COVID-19 Tharusha Jayasena, Sonia Bustamante, Anne Poljak, and Perminder Sachdev Abstract Since the emergence of COVID-19, concerted worldwide efforts have taken place to minimize global spread of the contagion. Its widespread effects have also facilitated evolution of new strains, such as the delta and omicron variants, which emerged toward the end of 2020 and 2021, respectively. While these variants appear to be no more deadly than the previous alpha, beta, and gamma strains, and widespread population vaccinations notwithstanding, greater virulence makes the challenge of minimizing spread even greater. One of the peculiarities of this virus is the extreme heath impacts, with the great majority of individuals minimally affected, even sometimes unaware of infection, while for a small minority, it is deadly or produces diverse long-term effects. Apart from vaccination, another approach has been an attempt to identify treatments, for those individuals for whom the virus represents a threat of particularly severe health impact(s). These treatments include anti-SARS-CoV-2 monoclonal antibodies, anticoagulant therapies, interleukin inhibitors, and anti-viral agents such as remdesivir. Nutritional factors are also under consideration, and a variety of clinical trials are showing promise for the use of specific fatty acids, or related compounds such as fat-soluble steroid vitamin D, to mitigate the more lethal aspects of COVID-19 by modulating inflammation and by anti-viral effects. Here we explore the potential protective role of fatty acids as a potential prophylactic as well as remedial treatment during viral infections, particularly COVID-19. We present a multiplexed method for the analysis of free and phospholipid bound fatty acids, which may facilitate research into the role of fatty acids as plasma biomarkers and therapeutic agents in minimizing pre- and post-infection health impacts. Key words COVID-19, Fatty acid, Mass spectrometry, Quantitation, Plasma

1

Introduction COVID-19 variants have emerged since 2019, giving rise to the most recent viral pandemic to cause health concerns and disruption of economies and to usurp research and medical resources in the feverish attempt to stop spread. It is far from being the most deadly of recent global viral outbreaks (Table 1), with estimates of the infected who have health consequences serious enough to require

Paul C. Guest (ed.), Multiplex Biomarker Techniques: Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification, Methods in Molecular Biology, vol. 2511, https://doi.org/10.1007/978-1-0716-2395-4_16, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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Table 1 Demographics of global viral outbreaks:1900–2021 Worldwide Repro. number number R0 infected

Estimated number of worldwide deaths and (% rel. to number infected)

References

17–100 million (3.5–20%)

[1–3]

Virus

Date

Spanish flu (H1N1)

1918–2020 1.80

500 million

Asian flu (H2N2)

1957–1958 1.65

> 500 million 1–4 million ( 500 million 1–4 million (90% [20]. Its high level of infectiousness and transmissibility has led to worldwide deaths exceeding by far the total numbers (if not mortality rate) of the SARS outbreaks of 2002–2004 (Table 1). As with all infectious diseases, the immune system is at the forefront of protection against disease. Free fatty acids involvement in COVID-19 pathogenesis is linked to their actions as important modulators of the inflammatory response. For example, arachidonic acid is converted to eicosanoids including prostaglandins, thromboxanes, and leukotrienes which regulate a large

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number of pathways that control the immune response [26]. Plasma levels of arachidonic, oleic, and linoleic acids have been reported to be elevated in COVID-19 patients and correlate with COVID-19 disease severity [27–29]. Palmitic and stearic acids which are the major saturated fatty acids which make up the phospholipids (PL) of the cell membrane were found to be significantly reduced in plasma from COVID-19 patients even though total PL levels were elevated [29]; in contrast these fatty acids were elevated in red blood cells of COVID-19 patients [30]. Therefore, alterations to fatty acid metabolism may play important roles in the pathogenesis of COVID-19. An immune system overreaction (i.e., cytokine storm, sometimes leading to pulmonary and cardiac inflammatory damage) has been found to occur in COVID-19 infection and is linked to a worst prognosis [31]. Severe COVID-19 disease is thought to be mediated by rapid elevations of inflammatory cytokines including tumor necrosis factor-alpha (TNF-α), interleukin 1 beta (IL-1β), and interleukin 6 (IL-6), leading to a cytokine release syndrome, which is also known as a cytokine storm [32]. A recent study found that patients with severe COVID-19 have higher plasma plasminogen activator inhibitor 1 (PAI-1) levels [33, 34]. PAI-1 can be induced by a wide range of pro-inflammatory mediators including IL-6 and TNF-α, which are known as components of the cytokine release syndrome that is observed in many patients with severe COVID-19 [35]. Another study also reported elevated levels of the PAI-1/tissue plasminogen activator complex and its correlation with disease severity. Elevated PA1 is also a risk factor for thrombosis, and therefore PA1 may be a potential risk factor for death in patients with COVID-19 [36]. It has also been estimated 10–30% of infected individuals suffer prolonged symptoms of 2 months or more (long COVID) [37, 38]. Individual genetic or lifestyle factors may account for degree of health impact and speed of recovery from COVID-19 infection. Recent work suggests that blood lipid profiles may give insight into which individuals are at higher risk of the more serious COVID-19 effects, with several studies showing associations between dyslipidemia and serious COVID-19 disease [39, 40]. For example, hypertriglyceridemia is associated with higher mortality in COVID-19 patients [41], low vitamin D levels, and high cholesterol increase risk of COVID-19 infection in older individuals (> 48 years) [42], and higher blood polyunsaturated free fatty acid levels and lower apolipoprotein E and HDL levels are associated with greater COVID-19 severity [28]. Although pro-inflammatory lipid profiles may pose a risk of severe COVID-19, the heterogeneous, Janus-faced lipid family also includes mitigators of inflammation [43]. Long-chain essential fatty acids, such as omega 3 fatty

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acids eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and linoleic acids, may offer protection [32, 44]. Metabolic analyses of the serum of COVID-19 patients have found increased levels of metabolites of the kynurenine pathway (which plays an important role in the modulation of the inflammatory response) [45]. Increased levels of most fatty acids measured were also observed in patients and metabolites correlated with serum markers of inflammation [45], again indicating that fatty acids may serve as potential biomarker of disease severity and potential therapeutic targets for COVID-19 treatment or prevention. Arachidonic acid is a polyunsaturated fatty acid covalently bound in esterified form in the cell membranes of most body cells. Following irritation or injury, arachidonic acid is released and oxygenated by enzyme systems leading to the formation of an important group of inflammatory mediators, the eicosanoids [46]. Several studies using targeted lipidomic analysis of bronchoalveolar lavages using mass spectrometry from severe COVID-19 patients have found increased fatty acids and inflammatory lipid mediators including eicosanoids. One study found that the cytokine storm occurring in severe COVID-19 involves both pro- and anti-inflammatory lipids. These results indicate that the increased inflammation found in the lungs of patients with severe COVID-19 is exacerbated by the elevated production of eicosanoids [47, 48]. Fatty acids are important signaling molecules for many physiological processes, and alterations in the levels of free fatty acids have been found to result in disease, and altered levels are also reported in COVID-19 patient plasma, serum, red blood cells, and lungs [27–30, 32, 48]. EPA and DHA contribute to the production of less inflammatory eicosanoids and thus supplementation which these fatty acids may be beneficial in reducing inflammation and thus potentially contribute to a faster recovery of patients infected with COVID-19 [49]. Fatty acids are important structural elements of biological membranes and have a variety of roles in different metabolic pathways. They can exist in two forms: free fatty acids also known as non-esterified fatty acids or bound into more complex lipids (such as phospholipids, di- and triglycerides, etc.), also known as esterified fatty acids. Fatty acids can also be categorized into saturated, monosaturated, and polyunsaturated fatty acids (PUFAs). The PUFAs can be further divided into omega 3 and omega 6 subgroups. These different chemical structures, of which fatty acids are essential components, give rise to a plethora of functionally and biologically important lipid structures. In this chapter, we outline a multiplexed method for assaying 29 plasma fatty acids in free and bound fatty acid fractions and report ranges expected in a population of normal healthy older individuals. The following protocol describes adaptation of the standard method for extracting and

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quantifying fatty acids in plasma using negative ion chemical ionization gas chromatography mass spectrometry (GCMS) approach using selected ion monitoring (SIM) as originally reported by Quehenberger et al. [50, 51] who developed a rapid protocol for the extraction of free and bound fatty acids from complex biological samples including plasma, cultured cells, and animal tissues. Fatty acids are quantified using the stable isotope dilution method, which is based on the principle that each target fatty acid is compared to a deuterated analog with similar chemical and structural properties, which is added to all samples and standards in identical amounts. This allows for accurate quantification and compensates for any losses during sample preparation and analysis. Enrichment and extraction of FFAs are achieved through addition of acidified methanol and iso-octane and deuterated fatty acids which act as internal standards for quantification and to compensate for any losses during sample preparation. Samples are shaken, and the two resulting solvent phases separated. The upper iso-octane layer containing the FFA fraction is removed and extraction steps repeated and resulting extractions pooled. Samples are then evaporated to dryness. Hydrolysis of the remaining methanol fraction containing the esterified or bound fatty acid fraction is completed using potassium hydroxide (KOH) and incubation for 1 h at 37
C, followed by extraction of the released fatty acid using the iso-octane procedure described above. All samples are then derivatized to pentafluorobenzyl esters and reconstituted in iso-octane before analysis. Standard curves of primary (unlabeled) fatty acid standards, with added stable-isotope labeled internal standards (for normalization), are run together with samples, allowing peak area-based absolute quantification following normalization of all standards and samples. The ratios of unlabeled to labeled standards from the standard curves are used to quantify levels of fatty acids in plasma samples, also known as the stable-isotope dilution, peak area ratio quantification method.

2

Materials

2.1 Samples and Reagents

1. Fasting ethylenediaminetetraacetic acid (EDTA) plasma samples from adults aged 70–90 years (see Note 1). 2. Phosphate buffered saline (PBS) (see Note 2). 3. Internal standard mix (Table 2). 4. 1% pentafluorobenzyl bromide in acetonitrile. 5. 1% di-isopropylethylamine in acetonitrile. 6. Methanol. 7. Hydrochloric acid (HCL).

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Table 2 List of fatty acids and internal standards (ISTD) with ions, retention times, and standard curve concentration ranges

Name

Chain Length Manufacturer

Standard curve concentrations (ng/μL Type of SIM Retention injected ¼ ng on standard Ion time (min) column)

Caprylic acid

C8:0

NuChek Prep, MN, USA

Target

143

6.88

0, 0.02, 0.05, 0.2, 0.5, 2, 5, 20

Capric acid

C10:0 NuChek Prep, MN, USA

Target

171

8.83

0, 0.06, 0.15, 0.6, 1.5, 6, 15, 60

Lauric acid

C12:0 NuChek Prep, MN, USA

Target

199 10.52

0, 0.08, 0.2, 0.8, 2, 8, 20, 80

Myristic acid

C14:0 NuChek Prep, MN, USA

Target

227 12.04

0, 0.1, 0.25, 1, 2.5, 10, 25, 100

Myristoleic acid

C14:1 NuChek Prep, MN, USA

Target

225 11.92

0, 0.008, 0.02, 0.08, 0.2, 0.8, 2, 8

Palmitic acid)

C16:0 NuChek Prep, MN, USA

Target

255 13.43

0, 0.14, 0.35, 1.4, 3.5, 14, 35, 140

Palmitoleic acid

C16:1 NuChek Prep, MN, USA

Target

253 13.24

0, 0.08, 0.2, 0.8, 2, 8, 20, 80

Stearic acid

C18:0 NuChek Prep, MN, USA

Target

283 14.86

0, 0.14, 0.35, 1.4, 3.5, 14, 35, 140

Oleic acid

C18:1 NuChek Prep, MN, USA

Target

281 14.90

0, 0.1, 0.25, 1, 2.5, 10, 25, 100

Linoleic acid

C18:2 Novachem, VIC, Australia

Target

279 14.52

0, 0.08, 0.2, 0.8, 2, 8, 20, 80

α linolenic acid

C18:3 NuChek Prep, MN, USA

Target

277 14.70

0, 0.06, 0.15, 0.6, 1.5, 6, 15, 60

γ linolenic acid

C18:3 NuChek Prep, MN, USA

Target

277 14.50

0, 0.0016, 0.004, 0.016, 0.04, 0.16, 0.4, 1.6

Stearidonic acid

C18:4 Novachem, VIC, Australia

Target

275 14.33

0, 0.0008, 0.002, 0.008, 0.02, 0.08, 0.2, 0.8

Arachidic acid

C20:0 Novachem, VIC, Australia

Target

311 16.90

0, 0.09, 0.225, 0.9, 2.25, 9, 22.5, 90

Gondolic acid

C20:1 NuChek Prep, MN, USA

Target

309 16.46

0, 0.05, 0.125, 0.5, 1.25, 5, 12.5, 50

Eicosadienoic acid

C20:2 NuChek Prep, MN, USA

Target

307 16.60

0, 0.04, 0.1, 0.4, 1, 4, 10, 40 (continued)

Quantifying Fatty Acids Role in COVID19

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Table 2 (continued)

Name

Chain Length Manufacturer

Standard curve concentrations (ng/μL Type of SIM Retention injected ¼ ng on standard Ion time (min) column)

Dihomo-γ-linolenic C20:3 NuChek Prep, acid MN, USA

Target

305 16.42

0, 0.02, 0.05, 0.2, 0.5, 2, 5, 20

Arachidonic acid

C20:4 NuChek Prep, MN, USA

Target

303 16.10

0, 0.02, 0.05, 0.2, 0.5, 2, 5, 20

Eicosapentaenoic acid (EPA)

C20:5 NuChek Prep, MN, USA

Target

301 16.20

0, 0.016, 0.04, 0.16, 0.4, 1.6, 4, 16

Behenic acid

C22:0 NuChek Prep, MN, USA

Target

339 18.01

0, 0.004, 0.01, 0.04, 0.1, 0.4, 1, 4

Erucic acid

C22:1 NuChek Prep, MN, USA

Target

337 17.94

0, 0.0008, 0.002, 0.008, 0.02, 0.08, 0.2, 0.8

Docosadienoic acid

C22:2 NuChek Prep, MN, USA

Target

335 17.89

0, 0.0004, 0.001, 0.004, 0.01, 0.04, 0.1, 0.4

Docosatrienoic acid C22:3 NuChek Prep, MN, USA

Target

333 17.92

0, 0.0004, 0.001, 0.004, 0.01, 0.04, 0.1, 0.4

Adrenic acid

C22:4 NuChek Prep, MN, USA

Target

331 17.58

0, 0.00016, 0.0004, 0.0016, 0.004, 0.016, 0.04, 0.16

Docosapentaenoic acid (DPA)

C22:5 NuChek Prep, MN, USA

Target

329 17.57

0, 0.008, 0.02, 0.08, 0.2, 0.8, 2, 8

Docosahexaenoic acid (DHA)

C22:6 NuChek Prep, MN, USA

Target

327 17.38

0, 0.02, 0.05, 0.2, 0.5, 2, 5, 20

Lignoceric acid

C24:0 NuChek Prep, MN, USA

Target

367 19.05

0, 0.004, 0.01, 0.04, 0.1, 0.4, 1, 4

Nervonic acid

C24:1 NuChek Prep, MN, USA

Target

365 18.93

0, 0.0004, 0.001, 0.004, 0.01, 0.04, 0.1, 0.4

Cerotic acid

C26:0 NuChek Prep, MN, USA

Target

395 19.89

0, 0.002, 0.005, 0.02, 0.05, 0.2, 0.5, 2

d15-C8:0

Toronto Research Chemicals, ON, Canada

ISTD

158

6.76

5

d19-C10:0

Toronto Research Chemicals, ON, Canada

ISTD

190

8.70

1

d3-C12:0

Toronto Research Chemicals, ON, Canada

ISTD

202 10.49

1

(continued)

220

Tharusha Jayasena et al.

Table 2 (continued)

Name

Chain Length Manufacturer

Standard curve concentrations (ng/μL Type of SIM Retention injected ¼ ng on standard Ion time (min) column)

d27-C14:0

Novachem, VIC, Australia

ISTD

254 11.88

5

d4-C16:0

Toronto Research Chemicals, ON, Canada

ISTD

259 13.39

10

d35-C18:0

Toronto Research Chemicals, ON, Canada

ISTD

318 14.67

12.5

d2-C18:1

Novachem, VIC, Australia

ISTD

283 14.86

25

d5-C18:3

Toronto Research Chemicals, ON, Canada

ISTD

282 14.80

2.5

d4-C20:0

Toronto Research Chemicals, ON, Canada

ISTD

315 16.72

1

d5-C20:5

Novachem, VIC, Australia

ISTD

306 15.76

1

d4-C22:0

Novachem, VIC, Australia

ISTD

343 18.08

2

d5-C22:6

Novachem, VIC, Australia

ISTD

332 17.36

1

d47-C24:0

Toronto Research Chemicals, ON, Canada

ISTD

414 18.70

1

d4-C26:0

Toronto Research Chemicals, ON, Canada

ISTD

399 19.88

1

Quantifying Fatty Acids Role in COVID19

221

8. KOH. 9. Iso-octane. 10. Acetonitrile. 11. Isopropyl alcohol. 2.2 Instruments and Supplies

1. 9 mL borosilicate disposable glass culture tubes with screw caps. 2. 300 μL fused insert glass vials. 3. 9 mm blue silicone/polytetrafluoroethylene septa screw caps. 4. Corning disposable Pasteur pipettes. 5. Nichipet Eco pipette and glass tips, volume range 20–200 μL. 6. Nichipet Eco 100–1000 μL.

pipette

and

glass

tips,

volume

range

7. Vortexer. 8. Tube rotator shaker/sample mixer. 9. Incubator/oven. 10. Speedvac. 11. Zebron ZB-1 column (15 m  0.25 mm  0.25 μm, Phenomenex, Lane Cove, NSW Australia). 12. Agilent 5973 Gas Chromatograph Mass Spectrometer (see Note 3). 13. Agilent MassHunter Workstation Quantitative Analysis software (version 10.0).

3

Methods

3.1 Fatty Acid Extraction, Hydrolysis, and Derivatization

1. Add 50 μL plasma into 9 mL borosilicate culture tubes using plastic pipette tip (see Note 4). 2. Add 75 μL PBS to all samples. 3. Add 50 μL internal standard mix to all samples. 4. Add 125 μL methanol to all samples. 5. Add 100 μL of 100 mM HCL (final concentration 25 mM) to all samples. 6. Add 1 mL iso-octane to all samples. 7. Cap, vortex, and then shake for 2 min at 35 RPM on the benchtop shaker. 8. Spin in a speedvac with no vacuum for 1 min to separate the phases.

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9. Transfer the top phase (organic solvent iso-octane layer) into a new labeled 9 mL glass culture tube using glass Pasteur pipette. 10. Repeat steps 6–8 and pool the top iso-octane layers (as the free fatty acid fraction). 11. To the bottom methanol fraction containing esterified fatty acids, add another aliquot of 50 μL of internal standard mix. 12. Then add 500 μL of 1 N KOH, cap, vortex, shake, and incubate at 37
C for 1 h. 13. Add 1.5 mL of 1 N HCL and check that the pH is below 5 using pH strip with accuracy to 0.5 pH units. 14. Repeat steps 6–9 above to extract the released fatty acids, following hydrolysis of the bound fatty acids, into the iso-octane (as the bound fatty acid fraction). 15. Add 100 μL of iso-propyl alcohol to all samples after adding 900 μL of iso-octane at repeat of step 6 (see Note 5). 16. Dry down both free and bound fractions in the speedvac under vacuum (see Note 6). 17. Derivatize samples by adding 50 μL of 1% pentafluorobenzyl bromide in acetonitrile and 50 μL of 1% di-isopropylethylamine in acetonitrile, in a fumehood (see Note 7). 18. Cap tubes, vortex, and leave at ambient temperature for 20 min. 19. Dry all samples in a speedvac under vacuum (see Note 6). 20. Reconstitute samples in 50 μL iso-octane and transfer into labeled glass 300 μL GCMS vials with insert. 3.2 Standard Curve Preparation

1. Add appropriate volumes of primary standard mix (0–200 μL) into glass 300 μL GCMS glass vials (see Table 2 for concentration ranges). 2. Add 50 μL internal standard mix to all vials (see Table 2 for final concentration in vial). 3. Dry all samples under vacuum in a speedvac. 4. Derivatize samples by adding 50 μL of 1% pentafluorobenzyl bromide in acetonitrile and 50 μL of 1% di-isopropylethylamine in acetonitrile to all vials (see Note 7). 5. Cap vials, vortex, and leave at ambient temperature for 20 min. 6. Dry in speedvac under vacuum (see Note 6). 7. Reconstitute in 50 μL iso-octane. 8. Cap, vortex, and inject 1 μL of samples and standards into the GCMS instrument for analysis using the following settings:

Quantifying Fatty Acids Role in COVID19

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Table 3 SIM groups used in method setup with time windows and ions SIM group number

Start time (min)

Ions (m/z)

1 (C8 carbons)

4.00

143, 158

2 (C10-C12 carbons)

7.50

171, 190, 199, 202

3 (C14 carbons)

11.00

225, 227, 254

4 (C16-C18 carbons)

12.50

253, 255, 259, 275, 277, 279, 281, 282, 283, 318

5 (C20 carbons)

15.20

301, 303, 305, 306, 307, 309, 311, 315

6 (C22 carbons)

17.00

327, 329, 331, 332, 333, 335, 337, 339, 343

7 (C24-C26 carbons)

18.40

365, 367, 395, 399, 414

(a) Negative ion mode. (b) Selected ion monitoring (SIM). (c) 100 ms dwell time. (d) Use the following oven temperature gradient on the GCMS instrument: (e) Initial ramp from 100
C to 120
C at 5
C/min. (f) Ramp to 235
C at 12
C/min. (g) Ramp to 315
C at 20
C/min. (h) Hold at 315
C for 3 min for a total run time of 22.83 min. 9. Acquire data divided into seven time-resolved groups for SIM to achieve high sensitivity (as outlined in Table 3) (see Note 8 and Note 9). 3.3

Data Analysis

1. Following GCMS analysis, transfer raw data (*.dat) files for data analysis using the MassHunter Workstation software setting up the following parameters: (a) Compound: name, scan type, mass/charge (m/z), and polarity. (b) Retention time: retention time with 0.5 min left and right delta window. (c) Internal standard: compound name, concentration, and corresponding target ion. (d) Calibration curve: concentration of fatty acid at each calibration level. 2. After quantification method setup, quantify each batch of raw files using the feature of the software.

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3. Manually check the integration of all peaks and adjust if required (see Note 10). 4. Export target and internal standard response values to a spreadsheet and calculate ratios of unlabeled to labeled responses. 5. For quantitative assessment of fatty acids in each plasma sample, compare the mass spectrometric ion signal of the target molecule with that of an identical standard (see Note 9).

4

Notes 1. Plasma samples were obtained from the Memory and Ageing Study (MAS), a population-based longitudinal study of older adults aged 70–90 [52]. All participants at baseline were non-demented, cognitively normal controls, and these samples were used to quantify fatty acid levels. The MAS study was approved by the Ethics Committees of the University of New South Wales and the South Eastern Sydney and Illawarra Area Health Service. Fasting EDTA plasma samples were collected, aliquoted, and stored at 80
C prior to analysis. 2. Ensure PBS and all other solvents and buffers used are made up and stored in glass bottles to minimize contamination from exogenous fatty acids, some of which are found in abundance in common laboratory plastics. 3. Similar instruments can be used from other suppliers but settings and running conditions should be adjusted to maintain optimal chromatographic separation and detection sensitivity. 4. Plastic pipette tips (pre-washed with methanol) were used for transfer of plasma into glass tubes. Make sure to use only glass pipette tips for all subsequent steps to minimize contamination from exogenous fatty acids. 5. Addition of 10% iso-propyl alcohol is added to help remove/ prevent the emulsification of sample which was found to occur after hydrolysis. 6. Ensure to not over-dry samples in the speedvac. Solvents are volatile so check samples after 10–15 min. 7. GCMS is a routine and reliable tool for the quantitative analysis of complex mixtures of fatty acids. Fatty acids readily undergo fragmentation under hard EI conditions, which results in lower levels of sensitivity. Soft ionization techniques such as negative chemical ionization allow quantifiable amounts of fragments and substantially improve detection limits. Limits of detection can be further improved by using halogenated derivatizing

Quantifying Fatty Acids Role in COVID19

225

agents such as pentafluorobenzyl bromide that have been shown to increase electron affinity and enhance sensitivity for NCI-MS [53]. 8. Due to the large number of analytes (29 unlabeled fatty acids and 14 isotope-labeled internal standards) included in this protocol, data acquisition must be divided into groups for selected ion monitoring (SIM) to achieve high sensitivity. The SIM parameters used and ions allocated to each group are shown in Table 3. 9. As indicate above, fatty acid standard curves are prepared from serial dilutions of a standard mixture of unlabeled quantitative fatty acid standards at precise concentrations. See Tables 2 and 4 for the concentration of standards used, retention times, ions, limit of quantification, and coefficient of variances for each fatty acid. A standard curve is generated using the ratio between the ion yields of the quantitative standard and internal standard plotted versus the concentration of the quantitative standard. See Figs. 1 and 2 for representative chromatograms and standard curves, respectively. The fatty acid concentration in the sample is then calculated from the standard curve using analyte/internal standard ion ratios. See Table 5 for reference values for all plasma fatty acids in both males and females of healthy older adults. Total fatty acid concentration was calculated as the sum of free and bound concentrations. 10. Same integration method parameters should be used for all standards and samples.

Table 4 Fatty acid internal standards, limit of quantification (signal to noise ratio > 10), and coefficients of variation (CVs)

Name

SIM group

Internal standard

LOQ (ng/mL)

Inter-assay CV (%)

Intra-assay CV (%)

Caprylic acid

1

d15-C8:0

20

23.23

0.31

Capric acid

2

d19-C10:0

60

15.71

1.14

Lauric acid

2

d3-C12:0

80

7.20

1.18

Myristic acid

3

d27-C14:0

100

16.58

0.96

Myristoleic acid

3

d27-C14:0

100

10.02

1.26

Palmitic acid

4

d4-C16:0

350

11.87

0.35 (continued)

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Table 4 (continued)

Name

SIM group

Internal standard

LOQ (ng/mL)

Inter-assay CV (%)

Intra-assay CV (%)

Palmitoleic acid

4

d4-C16:0

80

17.51

0.26

Stearic acid

4

d35-C18:0

350

18.50

0.99

Oleic acid

4

d2-C18:1

250

10.85

0.36

Linoleic acid

4

d5-C18:3

80

12.11

4.30

α linolenic acid

4

d5-C18:3

60

15.06

1.91

γ linolenic acid

4

d5-C18:3

4

6.75

1.90

Stearidonic acid

4

d5-C18:3

0.8

13.05

2.13

Arachidic acid

5

d4-C20:0

90

5.58

0.16

Gondolic acid

5

d4-C20:0

50

13.07

1.03

Eicosadienoic acid

5

d4-C20:0

40

8.50

1.32

Dihomo-γ-linolenic acid

5

d4-C20:0

20

13.14

1.91

Arachidonic acid

5

d4-C20:0

50

5.73

2.48

Eicosapentaenoic acid (EPA)

5

d5-C20:5

40

11.46

0.75

Behenic acid

6

d4-C22:0

10

4.92

1.07

Erucic acid

6

d4-C22:0

2

12.24

1.14

Docosadienoic acid

6

d4-C22:0

0.4

13.76

0.58

Docosatrienoic acid

6

d4-C22:0

40

16.64

0.44

Adrenic acid

6

d4-C22:0

1.6

8.75

6.80

Docosapentaenoic acid (DPA)

6

d5-C22:6

8

8.41

1.01

Docosahexaenoic acid (DHA)

6

d5-C22:6

20

12.30

1.70

Lignoceric acid

7

d47-C24:0

4

13.05

0.85

Nervonic acid

7

d47-C24:0

1

5.60

1.66

Cerotic acid

7

d4-C26:0

2

12.45

0.28

Quantifying Fatty Acids Role in COVID19

227

Fig. 1 Representative chromatograms for a selection of fatty acid standards (top) with corresponding internal standard (bottom)

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Tharusha Jayasena et al.

Fig. 1 (continued)

250

25

Standard/Internal Standard Response Rao

Standard/Internal Standard Response Rao

Quantifying Fatty Acids Role in COVID19

R² = 0.9998

200

R² = 1

20

150

15

R² = 0.9997

100

10

5

R² = 0.9863 R² = 0.98

0 0

5000

10000

15000

20000

25000

50

R² = 0.9967

0 0

20000

40000

-50

Standard/Internal Standard Response Rao

R² = 0.9998

25 20

R² = 0.9979

15 10 5

Standard/Internal Standard Response Rao

Concentraon (ng/mL)

30

0 0

5000

10000

15000

20000

80000

100000

25 R² = 0.9999 20

15

10 R² = 0.9925 5

R² = 0.9966 R² = 0.9731

0 0

25000

20000

40000

60000

80000

100000

Concentraon (ng/mL)

Concentraon (ng/mL)

70 90

R² = 0.9999 60

80

Standard/Internal Standard Response Ratio

Standard/Internal Standard Response Ratio

60000

Concentraon (ng/mL)

50 R² = 0.9937

40 30 20 10

R² = 0.9979

70 60 50 40 30 20 R² = 1

10

R² = 0.9998

0 -10 0

R² = 0.9998

0 0

10000

20000

30000

40000

50000

60000

5000

10000

15000

20000

Concentration (ng/mL) 70000

5

100 R² = 0.9151

90 80 70 60 50 40 30

R² = 0.9999

20

R² = 0.9572

10

R² = 0.9824

0 0

500000

1000000

1500000

Standard/Internal Standard Response Ratio

Standard/Internal Standard Response Ratio

Concentration (ng/mL)

4.5

R² = 0.9998

4 3.5 R² = 1

3 2.5

R² = 0.9983

2 1.5

R² = 0.9997

1 R² = 0.9999

0.5 0 -0.5 0

Concentration (ng/mL)

Fig. 2 Representative standard curves for individual fatty acids

200

400

600

Concentration (ng/mL)

800

1000

229

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Tharusha Jayasena et al.

Table 5 Plasma fatty acid concentrations in cognitively healthy older adults Plasma concentration (ng/mL) Average  SEM Fatty acid name

Age 70–79 years Male n ¼ 120

Age 70–79 years Female n ¼ 151

Age 80–90 years Male n ¼ 49

Age 80–90 years Female n ¼ 69

Caprylic acid

Free: 864  100 Free: 754  80 Bound: 939  178 Bound: Total: 1659  237 1575  311 Total: 2439  390

Capric acid

Free: 338  24 Bound: 369  39 Total: 707  58

Lauric acid

Free: 807  47 Free: 844  37 Bound: 796  54 Bound: 753  59 Total: 1628  112 Total: 1586  88

Free: 686  57 Free: 920  70 Bound: 488  65 Bound: 746  92 Total: 1174  105 Total: 1666  143

Myristic acid

Free: 3577  245 Free: 3894  230 Bound: 5342  537 Bound: Total: 9237  725 4997  456 Total: 8574  640

Free: 3526  364 Free: 9237  725 Bound: Bound: 4737  629 4549  903 Total: 8610  910 Total: 8005  1179

Myristoleic acid

Free: 455  28 Bound: 426  52 Total: 881  67

Free: 432  46 Bound: 310  55 Total: 742  83

Free: 591  45 Bound: 505  75 Total: 1089  103

Palmitic acid

Free: 37083  1229 Free: 39745  1653 Bound: 70206  4614 Bound: 75078  5311 Total: Total: 107290  5235 114823  6165

Free: 35779  2380 Bound: 58404  5756 Total: 94183  7302

Free: 37160  2334 Bound: 64615  6069 Total: 101775  7678

Palmitoleic acid

Free: 15313  880 Bound: 29731  3242 Total: 45044  3597

Free: Free: 16779  658 13821  1382 Bound: Bound: 28009  2699 20433  3483 Total: 44770  3069 Total: 34254  4279

Free: 16659  1146 Bound: 26821  3772 Total: 43479  4429

Stearic acid

Free: 38820  1908 Bound: 58615  4984 Total: 97435  5677

Free: 37130  1450 Free: 44907  3319 Bound: 59499  4380 Bound: Total: 96755  4910 53206  4373 Total: 98113  5905

Free: 47355  2622 Bound: 56955  4012 Total: 104310  5215

Oleic acid

Free: 68536  2935 Bound: 82377  5523

Free: 72433  2545 Bound: 78803  4587 Total: 150709  5494

Free: 78804  3576 Bound: 76627  5982

Free: 307  13 Bound: 317  28 Total: 617  36

Free: 580  27 Bound: 447  51 Total: 1023  69

Free: 402  93 Free: 572  102 Bound: 776  388 Bound: 607  226 Total: 1178  475 Total: 1179  290 Free: 253  24 Bound: 210  39 Total: 462  58

Free: 73065  4324 Bound: 69708  6059

Free: 282  19 Bound: 232  27 Total: 514  41

(continued)

Quantifying Fatty Acids Role in COVID19

231

Table 5 (continued) Plasma concentration (ng/mL) Average  SEM Fatty acid name

Age 70–79 years Male n ¼ 120

Age 70–79 years Female n ¼ 151

Total: 150913  6589

Age 80–90 years Male n ¼ 49

Age 80–90 years Female n ¼ 69

Total: Total: 142772  7469 155431  7024

Linoleic acid

Free: 6643  430 Free: 7045  295 Free: 7424  277 Free: 7951  285 Bound: Bound: 15592  663 Bound: Bound: 13784  800 14283  1063 Total: 23544  855 15369  774 Total: Total: Total: 20829  966 20926  1422 22793  990

α linolenic acid

Free: 613  28 Bound: 766  50 Total: 1378  56

Free: 672  26 Bound: 762  49 Total: 1431  62

Free: 597  39 Free: 651  42 Bound: 749  114 Bound: 840  101 Total: 1345  121 Total: 1491  110

γ linolenic acid

Free: 54  3.0 Bound: 482  60 Total: 536  61

Free: 76  4.1 Bound: 527  59 Total: 613  60

Free: 80  18 Bound: 497  97 Total: 577  99

Free: 75  5.4 Bound: 577  98 Total: 653  99

Stearidonic acid

Free: 35  2.2 Bound: 170  26 Total: 205  27

Free: 44  2.7 Bound: 176  21 Total: 222  22

Free: 30  3.2 Bound: 128  26 Total: 158  28

Free: 42  4.0 Bound: 182  33 Total: 224  35

Arachidic acid

Free: 121  10.9 Bound: 347  73 Total: 468  75

Free: 110  9.2 Bound: 412  79 Total: 522  80

Free: 76  7.7 Bound: 115  13 Total: 191  19

Free: 84  7.9 Bound: 190  54 Total: 274  57

Gondolic acid

Free: 730  47 Bound: 794  77 Total: 1524  94

Free: 711  43 Bound: 717  68 Total: 1420  91

Free: 649  56 Free: 637  47 Bound: 559  84 Bound: 551  65 Total: 1208  112 Total: 1189  92

Eicosadienoic acid Free: 374  22 Bound: 765  81 Total: 1138  88

Free: 385  21 Bound: 756  87 Total: 1139  94

Free: 302  25 Bound: 591  82 Total: 892  95

Free: 332  22 Bound: 615  68 Total: 947  78

Arachidonic acid

Free: 1550  109 Free: 1971  176 Free: 2349  258 Free: 2235  183 Bound: Bound: Bound: Bound: 16873  1430 20146  1378 19248  1353 17077  1355 Total: 22519  1376 Total: Total: Total: 18423  1424 21597  1376 19048  1339

Eicosapentaenoic acid (EPA)

Free: 734  42 Bound: 10356  837 Total: 11089  855

Free: 553  48 Free: 733  41 Bound: 10748  811 Bound: 8633  1177 Total: 11661  826 Total: 9186  1207

Behenic acid

Free: 32  3.0 Bound: 105  22 Total: 137  22

Free: 35  4.8 Bound: 108  21 Total: 143  22

Free: 20  1.4 Bound: 27  2.6 Total: 47  3.5

Free: 857  116 Bound: 10841  1354 Total: 11698  1416 Free: 24  4.0 Bound: 45  14 Total: 69  15 (continued)

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Tharusha Jayasena et al.

Table 5 (continued) Plasma concentration (ng/mL) Average  SEM Age 70–79 years Male n ¼ 120

Age 70–79 years Female n ¼ 151

Age 80–90 years Male n ¼ 49

Age 80–90 years Female n ¼ 69

Erucic acid

Free: 69  4.2 Bound: 65  5.8 Total: 134  8.2

Free: 59  3.1 Bound: 57  3.9 Total: 115  5.8

Free: 66  5.5 Bound: 60  13 Total: 126  15

Free: 56  3.6 Bound: 52  6.3 Total: 108  8.4

Docosadienoic acid

Free: 3.34  0.23 Free: 3.1  0.14 Bound: 6.56  0.9 Bound: 6.8  1.0 Total: 9.9  0.97 Total: 9.8  1.1

Free: 2.6  0.22 Bound: 6.0  1.2 Total: 7.5  1.0

Free: 2.6  0.2 Bound: 4.9  1.0 Total: 7.5  1.0

Adrenic acid

Free: 85  6.1 Bound: 218  23 Total: 303  23

Free: 78  8.8 Bound: 203  29 Total: 281  30

Free: 91  8.8 Bound: 197  25 Total: 289  27

Fatty acid name

Free: 92  5.9 Bound: 220  19 Total: 314  20

Free: 606  34 Docosapentaenoic Free: 655  41 Bound: 3280  299 acid (DPA) Bound: Total: 3884  319 3833  373 Total: 4488  393

Free: 691  81 Free: 510  60 Bound: Bound: 3729  560 2440  422 Total: 2949  460 Total: 4410  602

Docosahexaenoic acid (DHA)

Free: 2209  180 Free: 2979  257 Free: 3207  347 Free: 2792  142 Bound: Bound: 15979  924 Bound: Bound: 16625  1436 12765  1321 16457  1043 Total: 18854  985 Total: Total: Total: 19604  1567 14973  1446 19664  1224

Lignoceric acid

Free: 76  5.4 Bound: 185  33 Total: 261  36

Free: 85  12 Bound: 163  24 Total: 247  27

Free: 59  4.0 Bound: 75  3.9 Total: 133  6.2

Free: 70  9.7 Bound: 92  12 Total: 162  15

Nervonic acid

Free: 142  7.9 Bound: 65  3.2 Total: 207  9.6

Free: 116  4.6 Bound: 60  2.9 Total: 174  6.3

Free: 122  8.5 Bound: 59  4.6 Total: 181  10

Free: 121  5.8 Bound: 57  3.2 Total: 178  7.6

Cerotic acid

Free: 24  4.1 Bound: 43  8.7 Total: 67  10

Free: 34  7.8 Bound: 35  5.6 Total: 68  9.8

Free: 12  2.0 Free: 18  6.3 Bound: 12  0.97 Bound: 16  2.6 Total: 24  2.3 Total: 34  7.1

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43. Brennan E, Kantharidis P, Cooper ME et al (2021) Pro-resolving lipid mediators: regulators of inflammation, metabolism and kidney function. Nat Rev Nephrol 17(11):725–739 44. Arnardottir H, Pawelzik SC, Artiach G et al (2020) Stimulating the resolution of inflammation through Omega-3 polyunsaturated fatty acids in COVID-19: rationale for the COVID-omega-F trial. Front Physiol 11: 624657. https://doi.org/10.3389/fphys. 2020.624657 45. Thomas T, Stefanoni D, Reisz JA et al (2020) COVID-19 infection alters kynurenine and fatty acid metabolism, correlating with IL-6 levels and renal status. JCI. Insight 5(14). https://doi.org/10.1172/jci.insight.140327 46. Hoxha M (2020) What about COVID-19 and arachidonic acid pathway? Eur J Clin Pharmacol 76(11):1501–1504 47. Archambault AS, Zaid Y, Rakotoarivelo V et al (2021) High levels of eicosanoids and docosanoids in the lungs of intubated COVID-19 patients. FASEB J 35(6):e21666. https://doi. org/10.1096/fj.202100540R 48. Zaid Y, Dore E, Dubuc I et al (2021) Chemokines and eicosanoids fuel the hyperinflammation within the lungs of patients with severe COVID-19. J Allergy Clin Immunol 148(2): 368–380 e3 49. Hathaway D, Pandav K, Patel M et al (2020) Omega 3 fatty acids and COVID-19: a comprehensive review. Infect Chemother 52(4): 478–495 50. Quehenberger O, Armando AM, Brown AH et al (2010) Lipidomics reveals a remarkable diversity of lipids in human plasma. J Lipid Res 51(11):3299–3305 51. Quehenberger O, Armando AM, Dennis EA (2011) High sensitivity quantitative lipidomics analysis of fatty acids in biological samples by gas chromatography-mass spectrometry. Biochim Biophys Acta 1811(11):648–656 52. Sachdev PS, Brodaty H, Reppermund S et al (2010) The Sydney Memory and Ageing Study (MAS): methodology and baseline medical and neuropsychiatric characteristics of an elderly epidemiological non-demented cohort of Australians aged 70-90 years. Int Psychogeriatr 22(8):1248–1264 53. Kawahara FK (1968) Microdetermination of derivatives of phenols and mercaptans by means of electron capture gas chromatography. Anal Chem 40(6):1009–1010

Chapter 17 Lab-on-a-Chip Immunoassay for Prediction of Severe COVID-19 Disease Harald Peter, Emily Mattig, Paul C. Guest, and Frank F. Bier Abstract Most people infected by the SARS-CoV-2 virus which causes COVID-19 disease experience mild or no symptoms. Severe forms of the disease are often marked by a hyper-inflammatory response known as a cytokine storm. Thus, biomarker tests which can identify these patients and place them on the appropriate treatment regime at the earliest possible phase would help to improve outcomes. Here we describe an automated microarray-based immunoassay using the Fraunhofer lab-on-a-chip platform for analysis of C-reactive protein due to its role in the hyper-inflammatory response. Key words COVID-19, SARS-CoV-2, Cytokine storm, Biomarker, Lab-on-a-chip, LOC

1

Introduction COVID-19 disease is caused by the SARS-CoV-2 virus (originally named 2019-nCoV) [1]. Most patients experience only mild symptoms or are asymptomatic, but some are struck by a more severe form of the disease [2, 3]. The minor symptoms include fever, cough, shortness of breath, diarrhea, and pneumonia, and severe disease can be marked by acute respiratory distress syndrome (ARDS), thrombosis, respiratory, gastrointestinal, hepatic, and neurological complications [4–7]. The severe form can require patient hospitalization for oxygen supplementation and mechanical ventilation and, in some cases, can result in death [3, 8]. As with other respiratory viruses, severe COVID-19 disease has been linked to a hyper-inflammatory response of the host, known as a cytokine storm [8–12]. A number of multiplex tests have been developed which can be used to identify individuals who are at risk of developing severe COVID-19 disease, so they can be placed on the appropriate treatment regime at the earliest possible opportunity [13– 17]. However, many of these assays typically take several days for

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sampling, testing, and readout, and the costs could be relatively high. There are other disadvantages regarding current multiplex immunoassay protocols that render the use of these approaches impractical in routine analysis, including the relatively long experimental times and the need for intricate laboratory procedures, and appropriately skilled personnel. Lab-on-a-chip (LOC) systems can bypass some of these limitations given their use of rapid, user-friendly automated platforms which can incorporate many of the above steps. In this chapter, we describe the application of the Fraunhofer integrated LOC in vitro diagnostic device. This can be used to analyze both single- and multiplex immunoassays to determine a disease “score” in body fluid samples [18]. This incorporates an antibody microarray in combination with a microfluidic system in a credit card-sized cartridge to give an efficient system to automate and increase multiplex immunoassay speeds. The LOC platform consists of a microfluidic cartridge and a base unit. The cartridge contains all of the essential components to allow antibody attachment to target specific proteins of interest, as well as the reagent reservoirs, integrated pumping system, and optical transducer to allow integrated sensing (see Fig. 1) [18–20]. The cartridge works in combination with a base unit consisting of the electronics that control the cartridge, an optical system, and touch screen for monitoring and analysis of the results, all of which can be achieved in less than 15 min, after application of the sample. Here, we present an automated microarray-based sandwich immunoassay using the Fraunhofer LOC platform for analysis of C-reactive protein (CRP) due to its role in the cytokine storm effect [21–23]. We immobilized a polyclonal antibody against CRP in a 10  10 grid on an acyclic olefin polymer slide using a piezo spotter (see Fig. 2). Other antibodies can be immobilized to expand the detection repertoire including those against other inflammatory biomarkers such as interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α). Non-specific antibodies can also be applied as a negative control. Following this preparatory step, the sample is applied to the slide, and the sensor field is washed with a buffer to detach any non-specifically bound molecules. In the next step, the microarray is pumped with the CRP antibody, and this is followed by pumping through another washing step. In the last step, the fluorescently labeled secondary antibody is flushed over the sensor field to allow quantitation of the CRP levels in the sample, based on the optical readout.

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Fig. 1 Fraunhofer LOC diagnostic platform for multiplex immunoassay analysis from a drop of blood or serum/ plasma sample in less than 15 min. (a) Credit card-sized cartridge containing 9 reservoirs, micropumps, microfluidic channels, thermal control elements, electronics, and sensor area. (b) Base-unit for control and analysis of the cartridge data Alexa Fluor

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Fig. 2 Diagram showing microarray-based immunoassay for determining CRP levels in patient samples. (a) Laboratory-based protocol (3 h duration) using glass slides for the microarray, including washing steps and data output using a microarray scanner or the Fraunhofer base unit (3 h processing time). (b) Fully automated lab-on-a-chip immunoassay for detection of CRP in blood or serum/plasma samples with automatic pumping of all solutions within the cartridge and data analysis using the base unit

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Materials

2.1 Participants and Samples (See Note 1)

1. COVID-19 patients and control subjects. 2. Personal protective equipment (PPE): (a) FFP2 (N95) mask (b) Disposable cap (c) Disposable gown (d) Disposable apron (e) Goggles (f) Latex gloves and shoe covers 3. Sterile phlebotomy needles with holder/adapter (see Note 2). 4. Tourniquet. 5. Alcohol wipes. 6. Serum collection tubes. 7. Storage tubes. 8. 1% sodium hypochlorite.

2.2

Microarray

1. Square tissue culture dish. 2. 384-well microtiter plate. 3. Scienion sciFLEXARRAYER SX non-contact microarray spotter (see Note 3). 4. 3D-epoxy polymer slides. 5. Phosphate-buffered saline (PBS): 1.5 mM KH2PO4, 8 mM Na2HPO4, 137 mM NaCl, 2.7 mM KCl, (pH 7.4). 6. PBSIT: PBS containing 0.004% IGEPAL non-denaturing detergent and 1% trehalose.

nonionic,

7. PBST: PBS containing 0.05% Tween-20. 8. Capture antibody: goat anti-human CRP. 9. Control antibody: goat anti-rabbit IgG (Alexa Fluor 647) (see Note 4). 10. Internal control antibody: rabbit anti-goat IgG (see Note 5). 11. Blocking buffer: 1% bovine serum albumin (BSA) containing 0.05% Tween-20 and 0.02% NaN3 (see Note 6). 2.3

Immunoassay

1. ProPlate multi-array system with adhesive seal strips (Grace Bio-Labs; Bend, OR, USA) (see Note 3). 2. Human CRP standard. 3. Primary antibody: mouse anti-human CRP monoclonal antibody.

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4. Secondary antibody: goat anti-mouse IgG conjugated with Alexa Fluor 647. 5. CRP-depleted human serum control. 6. Tris-buffered saline (TBS): 20 mM Tris base (pH 7.4), 150 mM NaCl. 7. TBST: TBS containing 0.05% Tween-20. 2.4

Equipment

1. Bench-top centrifuge. 2. Tecan LS Reloaded laser scanner (see Note 3). 3. Tecan Array-Pro Analyzer or other quantification software. 4. Microfluidic cartridge (Fraunhofer IZI-BB, Potsdam; BiFlow Systems, Chemnitz, Germany). 5. Base unit (Fraunhofer IZI-BB). 6. Microsoft Excel or other spreadsheet software.

3 3.1

Methods Samples

1. Collect blood from overnight fasted patients if possible into serum tubes following World Health Organization (WHO) phlebotomy guidelines for samples containing infectious pathogens [24] (see Note 7). 2. Make sure that patients are wearing masks and that social distance (>1 m) is maintained for as long as possible prior to and after sampling. 3. Make sure that the phlebotomist appropriate PPE. 4. Sanitize the phlebotomy area before and after each patient is sampled. 5. Upon completion, decontaminate the phlebotomy room. 6. Allow all blood samples to sit at room temperature for 90 min for coagulation, (see Note 8). 7. Centrifuge 15 min at 10,000  g to pellet the clotted material. 8. Collect the upper serum layer and store each sample in 0.5 mL aliquots at 20  C until required. 9. After completion, clean all equipment with the 1% sodium hypochlorite, autoclave sample tubes and PPE, and dispose of all additional waste using standard biohazardous waste observances.

3.2 Microarray Fabrication

1. Dilute the capture antibody to 0.1 μg/μL in PBSIT buffer. 2. Dilute the internal spotting control and the internal control to 75 μg/mL in PBSIT buffer.

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3. Add 30 μL spotting solutions into designated wells of the microtiter plate. 4. Spot the probes onto the slides using the spotter with the following settings (see Note 9): (a) Number of dots: 3 (b) Volume/dot: 0.5 nL and the grid to (c) Distance between spots: 500 μm 5. Upon completion of spotting, leave the slides in a humidified chamber with saturated NaCl at room temperature in the dark (see Note 10). 6. Leave the slide to dry for 30–60 min at room temperature in the dark. 7. Immerse the slide in blocking solution for 30 s with up and down agitation. 8. Incubate the slide in blocking solution for about 1 h at room temperature while shaking gently in the dark. 9. Wash the slides 10 s in TBST with up and down agitation. 10. Wash the slide as above two further times in TBST and once in TBS each for 10 min. 11. Wash the slide 5 s in de-ionized water as above. 12. Dry the slide carefully under a flow of nitrogen (see Note 11). 3.3 Immunoassay (See Note 12)

1. Prepare serial dilutions of the CRP standard from 100 to 0.001 μg/mL in CRP-depleted serum or blocking solution for use as a standard curve (see Note 13). 2. Assemble the multi-array system on the slides. 3. Add 70 μL of each CRP dilution or blood sample into wells taking care to avoid introduction of air bubbles. 4. Seal with adhesive strips and incubate 1 h at room temperature with gentle shaking (see Note 14). 5. Dilute both the mouse anti-CRP and goat anti-mouse (Alexa Fluor 647) antibodies to 10 μg/mL in blocking solution. 6. Perform the following washing procedures between the incubation periods by removing the previous solution each time with a multi-channel aspirator. 7. Wash the arrays three times for 30 s with 250 μL TBST with gentle shaking and removing the solution each time with a multi-channel aspirator. 8. Add 70 μL mouse anti-CRP antibody into each well ensuring no air bubbles form.

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9. Seal the chamber with adhesive seal strips and incubate 1 h at room temperature with gentle shaking. 10. Wash the arrays as above in step 3.3, 7. 11. Add 70 μL secondary antibody into each well and incubate 1 h as above. 12. Wash the arrays as above in step 3.3, 7. 13. Remove the multi-array system from the slides, and wash 10 s in TBS and 5 s in de-ionized water with up and down agitation. 14. Remove liquids carefully from the slide with a flow of nitrogen but do not allow the droplets to dry on the surface. 15. Image the slides using a laser scanner at 633 nm with the appropriate filter and photomultiplier tube (PMT) gain setting based on signal/noise. 16. Use the fluorescent signals of the scanner software for quantitation according to the manufacturer’s instructions. 3.4 Automated Immunoassay Procedure with the Fraunhofer LOC Platform

1. Prepare the samples as described above and transfer these to the corresponding sample reservoir of the cartridge (see Note 15).

3.5

1. After image acquisition and fluorescent signal quantification, subtract the local background of each spot from the raw spot intensity value and calculate the mean net signal intensity (NI) and standard deviation (SD) of the replicates.

Data Analysis

2. Insert the cartridge into the base unit and initiate the immunoassay program. 3. Analyze the image automatically within the base unit or export for external analysis (see Note 16).

2. Analyze the data depending on the clinical question (see Note 17).

4

Notes 1. SARS-CoV-2, like most viruses, is a potentially deadly pathogen with high transmission rates. All biological samples and materials should be handled with appropriate precautions under the guidance of preset risk assessments. Likewise, all materials should be disposed of carefully in accordance with local regulations in a biosafety level (BSL) 2 or 3 laboratory. 2. A number of options are available including syringes with single-draw or butterfly configurations. 3. Equipment from other manufacturers can also be used. However, materials and reagent compatibility should be optimized.

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4. Many antibodies can be used here. The main prerequisite is that the chosen antibody does not bind to human IgG. 5. As above, many antibodies can be used here. These should not react with human serum proteins. 6. Sodium azide (NaN3) is a preservative which prevents microbial growth in long-term storage conditions. 7. Blood can either be analyzed directly on the LOC platform or can be processed to produce serum or plasma. The latter approaches would allow long-term storage of the samples. 8. Consistency in clotting times and room conditions should be applied to increase accuracy in comparisons across samples. In addition, collection tubes should be opened in a biosafety cabinet, and splashing, agitation, or leakage of samples should be avoided or cleaned immediately should any of these occur. 9. Using these settings, the resulting spots should have a diameter of ~150 μm. 10. Once this point has been reached, the slides should be processed within ~1 week. 11. Proteins can easily be removed or blown away if too much nitrogen pressure is applied without allowing droplets to dry on the surface. 12. All solutions containing proteins should be kept on ice until ready for use. 13. The use of human blood serum would provide a more realistic matrix and should be used if possible (see Fig. 3a). However, using the blocking solution would give a lower standard deviation. 14. Make sure the slides are protected from light during all incubation periods as the fluorophores are light sensitive. 15. All reservoirs are prefilled with the necessary buffers and solutions. If an empty cartridge is being used, each reservoir can also be filled with custom buffers and reagents. 16. A fluorescent image from a CRP microarray within the flow channel of the cartridge is shown in Fig. 3b, as an example. 17. The analysis of 20 clinical samples is shown in comparison to the Roche Cobas/Hitachi immunoturbidimetric assay platform in Fig. 3c.

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A) Relative fluorescence

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Fig. 3 (a) Serum calibration curve for quantification of CRP in the range of 3.5–1000 μg/L using the LOC system. (b) Fluorescent image of a 64 spot microarray in the flow channel of the LOC platform. The spots consisted of 46 CRP and positive-, negative- and spotting-control samples. (c) Validation analysis, showing the results of measurements with the LOC platform in comparison to the Roche Cobas/Hitachi immunoturbidimetric assay system. Twenty samples were analyzed from patient sera with CRP in the range of 7–100 μg/mL. For analysis with the LOC platform, samples were diluted 300-fold References 1. Kannan S, Shaik Syed Ali P, Sheeza A et al (2020) COVID-19 (Novel Coronavirus 2019) – recent trends. Eur Rev Med Pharmacol Sci 24(4):2006–2011 2. Wang J, Jiang M, Chen X, Montaner LJ (2020) Cytokine storm and leukocyte changes in mild

versus severe SARS-CoV-2 infection: review of 3939 COVID-19 patients in China and emerging pathogenesis and therapy concepts. J Leukoc Biol 108(1):17–41 3. Ballow M, Haga CL (2021) Why do some people develop serious COVID-19 disease

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after infection, while others only exhibit mild symptoms? J Allergy Clin Immunol Pract 9(4): 1442–1448 4. Cyprian F, Sohail MU, Abdelhafez I et al (2021) SARS-CoV-2 and immunemicrobiome interactions: lessons from respiratory viral infections. Int J Infect Dis 105: 540–550 5. Van Vo G, Bagyinszky E, Park YS et al (2021) SARS-CoV-2 (COVID-19): beginning to understand a new virus. Adv Exp Med Biol 1321:3–19 6. Bandeira IP, Schlindwein MAM, Breis LC et al (2021) Neurological complications of the COVID-19 pandemic: what have we got so far? Adv Exp Med Biol 1321:21–31 7. Mezoh G, Crowther NJ (2021) Endothelial dysfunction as a primary consequence of SARS-CoV-2 infection. Adv Exp Med Biol 1321:33–43 8. Abiri B, Guest PC, Vafa M (2021) Obesity and risk of COVID-19 infection and severity: available evidence and mechanisms. Adv Exp Med Biol 1321:97–107 9. George JA, Mayne ES (2021) The novel coronavirus and inflammation. Adv Exp Med Biol 1321:127–138 10. Wiggill TM, Mayne ES, Vaughan JL (2021) Overview of the haematological effects of COVID-19 infection. Adv Exp Med Biol 1321:163–172 11. Louw S, Jacobson BF, Mayne ES et al (2021) The novel coronavirus and haemostatic abnormalities: pathophysiology, clinical manifestations, and treatment recommendations. Adv Exp Med Biol 1321:173–180 12. Jafari-Oori M, Ghasemifard F, Ebadi A et al (2021) Acute respiratory distress syndrome and COVID-19: a scoping review and metaanalysis. Adv Exp Med Biol 1321:211–228 13. Zhao Y, Qin L, Zhang P et al (2020) Longitudinal COVID-19 profiling associates IL-1RA and IL-10 with disease severity and RANTES with mild disease. JCI Insight 5(13):e139834. https://doi.org/10.1172/jci.insight.139834 14. Spadaro S, Fogagnolo A, Campo G et al (2021) Markers of endothelial and epithelial pulmonary injury in mechanically ventilated COVID-19 ICU patients. Crit Care 25(1):74. https://doi.org/10.1186/s13054-02103499-4 15. Liu BM, Martins TB, Peterson LK et al (2021) Clinical significance of measuring serum

cytokine levels as inflammatory biomarkers in adult and pediatric COVID-19 cases: a review. Cytokine 142:155478. https://doi.org/10. 1016/j.cyto.2021.155478 16. Syed F, Li W, Relich RF et al (2021) Excessive matrix metalloproteinase-1 and hyperactivation of endothelial cells occurred in COVID19 patients and were associated with the severity of COVID-19. J Infect Dis 224(1):60–69 17. Egorov AI, Griffin SM, Fuzawa M et al (2021) A multiplex noninvasive salivary antibody assay for SARS-CoV-2 infection and its application in a population-based survey by mail. Microbiol Spectr Sep 15:e0069321. https://doi. org/10.1128/Spectrum.00693-21. Online ahead of print 18. Schumacher S, Nestler J, Otto T, Wegener M, Ehrentreich-Fo¨rster E, Michel D et al (2012) Highly-integrated lab-on-chip system for point-of-care multiparameter analysis. Lab Chip 12:464–473 19. Schumacher S, Ludecke C, EhrentreichFo¨rster E, Bier FF (2013) Platform technologies for molecular diagnostics near the patient’s bedside. Adv Biochem Eng Biotechnol 133: 75–87 20. Streit P, Nestler J, Shaporin A, Schulze R, Gessner T (2016) Thermal design of integrated heating for lab-on-a-chip systems. In: Proceedings of the 17th international conference on thermal, mechanical and multi-physics simulation and experiments in microelectronics and microsystems (EuroSimE), April 18–20, pp 1–6 21. Potempa LA, Rajab IM, Hart PC et al (2020) Insights into the use of C-reactive protein as a diagnostic index of disease severity in COVID19 infections. Am J Trop Med Hyg 103(2): 561–563 22. Liu F, Li L, Xu M et al (2020) Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19. J Clin Virol 127:104370. https://doi.org/10.1016/ j.jcv.2020.104370 23. Terpos E, Ntanasis-Stathopoulos I, Elalamy I et al (2020) Hematological findings and complications of COVID-19. Am J Hematol 95(7): 834–847 24. https://www.euro.who.int/__data/assets/ pdf_file/0005/268790/WHO-guidelineson-drawing-blood-best-practices-in-phlebot omy-Eng.pdf

Chapter 18 Multiplex Immunoassay for Prediction of Disease Severity Associated with the Cytokine Storm in COVID-19 Cases Paul C. Guest, Mitra Abbasifard, Tannaz Jamialahmadi, Muhammed Majeed, Prashant Kesharwani, and Amirhossein Sahebkar Abstract Severe cases of SARS-CoV-2 and other pathogenic virus infections are often associated with the uncontrolled release of proinflammatory cytokines, known as a “cytokine storm.” We present a protocol for multiplex analysis of three cytokines, tumor necrosis factor-alpha (TNF-a), interleukin 6 (IL-6), and IL-10, which are typically elevated in cytokine storm events and may be used as a predictive biomarker profile of disease severity or disease course. Key words SARS-CoV-2, COVID-19, Cytokine storm, Multiplex immunoassay, TNF-α, IL-6, IL-10

1

Introduction Multiple studies have shown a link between the occurrence of a “cytokine storm” and clinical outcomes in patients infected with the SARS-CoV-2 virus, the causative agent of COVID-19 [1– 6]. The cytokine storm is a physiological reaction involving the uncontrolled and excessive release of proinflammatory cytokine molecules in response to an invading pathogen [7, 8]. Many pathogenic viruses induce a cytokine storm effect, a hyperactive immune-inflammatory response [9–15]. The heightened release of proinflammatory cytokines is injurious to the host. The shift from a beneficial to a destructive effect by the increased production of proinflammatory molecules such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) can cause hypotension, fever, and edema and lead to vascular hyperpermeability, hypercoagulation, organ damage, and death [14–17]. Although the majority of patients infected with the SARSCoV-2 virus experience mild to moderate symptoms, 10–15% suffer from a more severe form of the disease, and a fatality occurs in approximately 2% of the cases [18–20]. These more severe disease

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courses have been linked directly to the cytokine storm effect [21, 22]. In particular, meta-analyses have found a direct association of three cytokines with the severe form of COVID-19: TNF-α, IL-6, and IL-10 [22, 23]. Severe COVID-19 infection causes diffuse alveolar, thrombotic, and epithelial barrier damage in the lungs, manifesting as acute respiratory distress syndrome (ARDS) [21, 24]. As a result, damaging reactive oxygen species (ROS) are produced, which causes cell death and can stimulate the synthesis of NOD, LRR-, and pyrin domain-containing protein 3 (NLRP3) and nuclear factor kappa B (NF-κB). The increased activation of the NLRP3 and NF-κB pathways contributes to cytokine levels observed in the cytokine storm. The exacerbation of inflammation leads to further lung cell damage, potentially spreading to other organs, including the kidneys, heart, and brain (see Fig. 1). This study describes the setup of trials in patients with COVID19 for the multiplex detection of serum or plasma TNF-α, IL-6, and IL-10. In this assay, samples are incubated with fluorescenceencoded microbeads conjugated to capture antibodies against the target biomarkers [25] (see Fig. 2). Next analyte-specific biotinylated reporter antibodies are added and developed using streptavidin-phycoerythrin. Finally, particle flow cytometry analysis is performed to read the fluorescence code of the microbead complexes for biomarker identification and determine the reporter signal’s strength for quantitation of each target biomarker. This multiplex procedure allows higher sample throughput and crosscomparability within and across experiments because each assay is simultaneously processed, read, and analyzed under identical conditions.

2

Materials

2.1 Participants and Samples (See Note 1)

1. COVID-19 patients and controls are as follows: (a) PCR-diagnosed COVID-19 patients with cytokine storm signs (n ¼ 50) (b) PCR-diagnosed COVID-19 patients with a mild or asymptomatic disease (n ¼ 50) (c) PCR-diagnosed COVID-19 free health controls (n ¼ 50) 2. Personnel protective equipment (PPE) consisting of FFP2 (N95) mask, disposable cap, goggles, gown, apron, latex gloves, and shoe covers. 3. 14–20 gauge sterile needles for drawing blood. 4. Holder/adapter (see Note 2). 5. Tourniquet.

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Lung epithelium ROS

TNFD

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Fig. 1 SARS-CoV-2 infection of lung epithelial cells leading to increased generation of reactive oxygen species (ROS) and the cytokine storm associated with more severe COVID-19 outcomes

6. Alcohol wipes (70% isopropyl alcohol). 7. Evacuated serum collection tubes (see Note 3). 8. Storage tubes or cryovials. 9. 1% sodium hypochlorite.

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Fig. 2 Multiplex immunoassay flow chart. In the example, a multiplex assay is created targeting three cytokines: TNF-α, IL-6, and IL-10 2.2 Microbead Conjugation

1. Magnetic plate separator (see Note 4). 2. 4 mL magnetic microbeads (5  107 beads). 3. Capture antibodies: 125 μg/mL monoclonal antibodies against TNF-α, IL-6, and IL-10 (see Note 5). 4. N-Hydroxysulfosuccinimide (sulfo-NHS) (see Note 6). 5. N-(3-Dimethylaminopropyl)-N0 -ethylcarbodiimide (see Note 7).

(EDCI)

6. Activation solution: 88 mM NaH2PO4/12 mM Na2HPO4 (pH 6.0). 7. 0.05 M 2-morpholino-ethane-sulfonic acid mono-hydrate (MES) (pH 5.0) (see Note 8).

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8. Blocking solution: 10 mM sodium phosphate (pH 7.4), 150 mM NaCl, 0.02% Tween 20, 0.1% bovine serum albumin (BSA), 0.05% sodium azide. 9. Sonicator. 2.3 Detection Antibodies

1. Antibodies against the same proteins as above but which target distinct epitopes (see Note 5). 2. Freshly prepared sulfo-NHS biotin (Thermo Fisher Scientific) (see Note 9). 3. 16.7 mM Na2HPO4, 1.4 mM KH2PO4, 13.7 mM NaCl, pH 7.4 (phosphate buffered saline, PBS).

2.4 Multiplex Development

1. Assay buffer: PBS containing 1% BSA. 2. Wash buffer: PBS containing 0.02% Tween-20. 3. 100 μg/mL streptavidin R-phycoerythrin (SPE) (see Note 10). 4. 96-well microtiter plates. 5. Recombinant or native protein standards for TNF-α, IL-6, and IL-10. 6. Meridian Life Sciences Tru-Block or other blocking buffer. 7. Luminex 100 or MAGPIX analyzer. 8. Double-deionized water. 9. 70% ethanol. 10. Hemocytometer.

3

Methods

3.1 Sample Collection

1. Collect fasting blood into serum tubes according to World Health Organization (WHO) and governmental phlebotomy guidelines for drawing and processing samples containing infectious pathogens [26]. 2. Ensure patients are masked and maintain >1 m distance while waiting. 3. Ensure the phlebotomist wears the appropriate PPE. 4. Use disposable tourniquets. 5. Sanitize phlebotomy chairs after each patient is sampled. 6. Decontaminate the phlebotomy room.

3.2 Sample Processing

1. Ensure that all researchers processing the samples wear appropriate PPE. 2. Disinfect containers using 1% sodium hypochlorite solution. 3. Leave samples at room temperature for 90 min to allow clotting (see Note 11).

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7. Centrifuge at 10,000  g for 15 min and collect the upper serum layer (see Note 12). 4. Store each sample in 0.5 mL aliquots at 20  C until ready for analysis. 5. After processing COVID-19 suspected/confirmed samples, clean all equipment with 1% sodium hypochlorite solution. 8. Autoclave the sample tubes and PPE, followed by incineration. 9. Dispose of all other waste from suspected/confirmed COVID19 specimens as with other biohazardous waste in the laboratory. 3.3 AntibodyMicrobead Conjugation (See Fig. 3)

1. Collect microbeads using the magnetic separator or any other device following the manufacturer’s instructions. 2. Remove the solution, add 0.5 mL activation buffer and sonicate to form a suspension. 3. Collect and resuspend the beads as above in 0.4 mL activation buffer. 4. Add activation buffer to the sulfo-NHS to give a final 50 mg/ mL concentration. 5. Add 50 uL of the activated sulfo-NHS to the microbeads and vortex to yield a suspension. 6. Add activation buffer to EDCI to give a final concentration of 10 mg/mL. 7. Add 50 uL of activated EDCI to the microbeads and vortex to give a suspension. 8. Mix by rotation for 20 min in the dark at room temperature. 9. Collect the microbeads as above and wash twice with 0.5 mL MES.

EDCI

Carboxylated microbead

Sulfo-NHS

Reactive O-acylisourea ester

Antibody

Amine reactive NHS ester

Fig. 3 Procedure for conjugating capture antibodies to microbeads

Antibody-conjugated microbead

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10. Leave the beads suspended in 0.45 mL coupling reagent and add 0.2 mL capture antibodies against TNF-α, IL-6, and IL-10 to separate batches of the dye-coded microbeads (see Note 13). 11. Mix by rotation for 2 h in the dark at room temperature. 12. Collect the microbeads as above, resuspend in 1 mL blocking solution, and mix by rotation for 30 min. 13. Collect the microbeads as above and wash twice in 0.25 mL blocking buffer. 14. Count the microspheres using a hemocytometer or similar device. 15. Bring the concentration of each microbead-antibody conjugate to 5  107/mL and leave at 4  C until ready for use. 3.4 Biotinylation of Detection Antibodies (See Fig. 4)

1. Add 10 mM sulfo-NHS biotin to detect antibodies at a 20:1 molar ratio. 2. Incubate 2 h on ice or 30 min at room temperature. 3. Remove surplus against PBS.

sulfo-NHS

biotin

through

dialysis

4. Add BSA to a final concentration of 1%. 3.5 Assay (See Fig. 5)

1. Combine 5 μL each microbead solution into one tube to create a pool of the desired different capture antibody-microbead complexes and make up to 1.4 mL in assay buffer. 2. Create seven tenfold serial dilutions of 1 μg/mL for each standard protein to generate an 8-point standard curve. 3. Create a 5 μg multiplex of all three biotinylated antibody solutions in 5 mL assay buffer. 4. Dilute serum 1:5 in assay buffer (see Note 14). 5. Add 30 μL diluted samples (or standard solutions) to designated wells of the microtiter plate. 6. Add 10 μL blocking buffer. 7. Add 10 μL multiplex capture microbeads and incubate 1 h at room temperature (see Note 15). 8. Wash 3 times with 100 μL wash buffer. 9. Add 40 μL multiplex biotinylated antibody solution to each well and incubate 1 h on the shaker at room temperature. 10. Add 20 μL SPE to each sample and incubate 30 min on the shaker at room temperature. 11. Wash 3 times with 100 μL wash buffer using the separator or similar device as above and leave in 100 μL assay buffer on the shaker for 5 min. 12. Analyze on the MagPix [27] or Luminex 200 [28] instruments according to the manufacturer’s instructions.

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Sulfo-NHS biotin

B

B

Biotynylated antibody

Antibody with primary amine

+ Sulfo-NHS byproduct

Fig. 4 Procedure for biotinylated detection antibodies

Capture antibody- microbead

Cytokine target

B

B

Biotinylated detection antibody

S PE

B

S PE

Streptavidin-phycoerythrin

Fig. 5 Assay procedure. The capture antibody-microbead binds the target protein which is next bound by the biotinylated detection antibody, and streptavidin-phycoerythrin is added as the detection reagent. The target protein (in this case a cytokine) can be identified using the Luminex reader by a red laser which reads the identity of the capture antibody-microbead and a green laser determines the amount of bound biotinylated detection antibody

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13. Follow instrument instructions for setting the assay protocol to account for sample volume, microbead type, and gating. 14. Enter the volume per well to be aspirated. 15. Enter the analysis type as Quantitative Protocol. 16. Enter the information as prompted for a number of standards (not including blank) and microbead locations on the plate. 17. Enter sample dilution factor and run the analysis. 3.6

Data Analysis

1. Carry out data analyses and determine the levels of each cytokine in each sample (see Note 16). 2. Identify significant differences (two-sided p < 0.05) between experimental and control samples for each cytokine measurement and using an algorithm for all three cytokines (see Note 17).

4

Notes 1. Remember, safety comes first, and bio-samples and materials should be handled as contagious materials, and assessments should be in place to help minimize risks. In addition, sampling materials should be disposed of with adequate precautions, according to state and local regulations in a biosafety level (BSL) 2 or 3 laboratory as appropriate. 2. Evacuated systems are available with syringe, single draw, or butterfly attachments. 3. Blood-based biomarker analyses can be influenced by the use of serum compared to plasma, due to variations in content and stability of the resident molecules and to matrix differences. Here, we describe the procedure for the preparation of serum using 10 mL BD Vacutainer and plastic serum tubes. 4. We describe the use of a modified plate magnet for the separation of microbeads in a 96-well plate during assay preparation steps. Other instruments such as a plate washer can be used for this purpose, although these are not as efficient as the magnetic system. 5. Each analyte requires distinct antibodies for capture and detection. Each antibody pair should recognize a distinct epitope on the target analyte for maximum selectivity and minimize steric obstruction chances. 6. Sulfo-NHS converts carboxyl groups to NHS esters for crosslinking. 7. EDCI activates carboxyl groups for the formation of bonds with primary amines.

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8. MES is a buffer used in cross-linking reactions. 9. Sulfo-NHS biotin is a water-soluble biotinylation reagent for labeling molecules via primary amines. 10. SPE is used to detect biotinylated molecules such as secondary antibodies. 11. Coagulation times can be reduced to 30 min when using tubes that contain a clotting activator. Consistent clotting times and room conditions should be used for all samples for increased accuracy of biochemical comparisons across samples. 12. Collection tubes should be opened in a biosafety cabinet. Avoid splash, agitation, or leakage of samples. 13. The use of the different dye mixtures allows each capture antibody-microbead conjugate to be spectrally distinguished. 14. These cytokines are low in abundance in serum. Therefore, a low dilution factor is used. In contrast, more abundant serum proteins would need larger dilutions. For example, accurate detection of serotransferrin and ferritin would require a 1  106 fold dilution. The readings can be optimized by varying the sample dilutions if needed. More accurate readings will be obtained if sample readings are occurring within the linear region of standard curves. 15. The incubation time is dependent on the affinity of each capture antibody. Antibodies with higher affinity require shorter incubation times. It is important that these are closely matched across all antibodies used in the multiplex. 16. Studies from several groups have found increased levels of TNF-α, IL-6, and IL-10 in patients experiencing a cytokine storm, and this signature can be used as a predictor of disease severity [22, 23, 29]. 17. For example, comparisons could be made across control, non-symptomatic, and severe COVID-19 groups or in testing the effects of specific treatments on the levels of these cytokines. An algorithm could also be developed to incorporate the cytokine measurements as well as demographic or radiographic information to produce a disease severity score [30–32]. References 1. Alijotas-Reig J, Esteve-Valverde E, Belizna C et al (2020) Immunomodulatory therapy for the management of severe COVID-19. Beyond the anti-viral therapy: a comprehensive review. Autoimmun Rev 19(7):102569. https://doi.org/10.1016/j.autrev.2020. 102569

2. Tufan A, Avanog˘lu Gu¨ler A, Matucci-Cerinic M (2020) COVID-19, immune system response, hyper inflammation and repurposing antirheumatic drugs. Turk J Med Sci 50(SI-1): 620–632 3. Soy M, Keser G, Atagu¨ndu¨z P et al (2020) Cytokine storm in COVID-19: pathogenesis

Multiplex Immunoassay of Cytokine Storm and overview of anti-inflammatory agents used in treatment. Clin Rheumatol 39(7): 2085–2094 4. Bindoli S, Felicetti M, Sfriso P et al (2020) The amount of cytokine-release defines different shades of Sars-Cov2 infection. Exp Biol Med (Maywood) 245(11):970–976 5. Rabaan AA, Al-Ahmed SH, Muhammad J et al (2021) Role of inflammatory cytokines in COVID-19 patients: a review on molecular mechanisms, immune functions, immunopathology and immunomodulatory drugs to counter cytokine storm. Vaccines (Basel) 9(5): 4 3 6 . h t t p s : // d o i . o r g / 1 0 . 3 3 9 0 / vaccines9050436 6. Ramasamy S, Subbian S (2021) Critical determinants of cytokine storm and type I interferon response in COVID-19 pathogenesis. Clin Microbiol Rev 34(3):e00299–e00220. https://doi.org/10.1128/CMR.00299-20 7. Peiris JS, Hui KP, Yen HL (2010) Host response to influenza virus: protection versus immunopathology. Curr Opin Immunol 22(4):475–481 8. Teijaro JR (2015) The role of cytokine responses during influenza virus pathogenesis and potential therapeutic options. Curr Top Microbiol Immunol 386:3–22 9. Us D (2008) Cytokine storm in avian influenza. Mikrobiyol Bul 42(2):365–380 10. Mares CA, Ojeda SS, Morris EG et al (2008) Initial delay in the immune response to Francisella tularensis is followed by hypercytokinemia characteristic of severe sepsis and correlating with upregulation and release of damageassociated molecular patterns. Infect Immun 76(7):3001–3010 11. de Castro IF, Guzman-Fulgencio M, GarciaAlvarez M et al (2010) First evidence of a pro-inflammatory response to severe infection with influenza virus H1N1. Crit Care 14(1): 115. https://doi.org/10.1186/cc8846 12. Tisoncik JR, Korth MJ, Simmons CP et al (2012) Into the eye of the cytokine storm. Microbiol Mol Biol Rev 76(1):16–32 13. Perrone LA, Plowden JK, Garcia-Sastre A et al (2008) H5N1 and 1918 pandemic influenza virus infection results in early and excessive infiltration of macrophages and neutrophils in the lungs of mice. PLoS Pathog 4(8): e1000115. https://doi.org/10.1371/journal. ppat.1000115 14. Wang SY, Le TQ, Kurihara N et al (2010) Influenza virus-cytokine-protease cycle in the pathogenesis of vascular hyperpermeability in severe influenza. J Infect Dis 202(7):991–1001

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15. Cheng XW, Lu JA, Wu CL et al (2011) Three fatal cases of pandemic 2009 influenza A virus infection in Shenzhen are associated with cytokine storm. Respir Physiol Neurobiol 175(1): 185–187 16. Sharma J, Mares CA, Li Q et al (2011) Features of sepsis caused by pulmonary infection with Francisella tularensis type A strain. Microb Pathog 51(1–2):39–47 17. Sharma J, Mares CA, Li Q, Morris EG, Teale JM (2011) Features of sepsis caused by pulmonary infection with Francisella tularensis type A strain. Microb Pathog 51(1–2):39–47 18. https://www.cdc.gov/coronavirus/2019ncov/hcp/clinical-guidance-managementpatients.html 19. https://www.worldometers.info/coronavi rus/#countries 20. https://coronavirus.jhu.edu/map.html 21. Bhaskar S, Sinha A, Banach M et al (2020) Cytokine storm in COVID-19-immunopathological mechanisms, clinical considerations, and therapeutic approaches: the REPROGRAM consortium position paper. Front Immunol 11:1648. https://doi.org/10. 3389/fimmu.2020.01648 22. Mulchandani R, Lyngdoh T, Kakkar AK (2021) Deciphering the COVID-19 cytokine storm: systematic review and meta-analysis. Eur J Clin Investig 51(1):e13429. https:// doi.org/10.1111/eci.13429 23. Udomsinprasert W, Jittikoon J, Sangroongruangsri S et al (2021) Circulating levels of interleukin-6 and Interleukin-10, but not tumor necrosis factor-alpha, as potential biomarkers of severity and mortality for COVID19: systematic review with meta-analysis. J Clin Immunol 41(1):11–22 24. Morris G, Bortolasci CC, Puri BK et al (2020) The pathophysiology of SARS-CoV-2: a suggested model and therapeutic approach. Life Sci 258:118166. https://doi.org/10.1016/j. lfs.2020.118166 25. Stephen L (2017) Multiplex immunoassay profiling. Methods Mol Biol 1546:169–176 26. https://www.euro.who.int/__data/assets/ pdf_file/0005/268790/WHO-guidelineson-drawing-blood-best-practices-in-phlebot omy-Eng.pdf 27. https://www.fresnostate.edu/csm/rimi/ documents/equipment/Magpix.pdf 28. https://www.luminexcorp.com/download/ manual-luminex-200-user-ivd-english/ 29. Han H, Ma Q, Li C et al (2020) Profiling serum cytokines in COVID-19 patients reveals

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IL-6 and IL-10 are disease severity predictors. Emerg Microbes Infect 9(1):1123–1130 30. Kwon YJF, Toussie D, Finkelstein M et al (2020) Combining initial radiographs and clinical variables improves deep learning prognostication in patients with COVID-19 from the emergency department. Radiol Artif Intell 3(2):e200098. https://doi.org/10.1148/ ryai.2020200098 31. Akhtar S, Ahamad MM, Rashed-Al-Mahfuz M et al (2021) Machine learning approach to

predicting COVID-19 disease severity based on clinical blood test data: statistical analysis and model development. JMIR Med Inform 9(4):e25884. https://doi.org/10.2196/ 25884 32. Jiao Z, Choi JW, Halsey K et al (2021) Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study. Lancet Digit Health 3(5):e286–e294

Chapter 19 Detection of IgG Antibodies to SARS-CoV-2 and Neutralizing Capabilities Using the Luminex® xMAP® SARS-CoV-2 Multi-Antigen IgG Assay Abbe King, Gregory King, Christy Weiss, Sherry Dunbar, and Shubhagata Das Abstract Serological assays have been a useful tool for detection of antibodies to SARS-CoV-2 during the COVID19 pandemic. These assays are used for epidemiology and serosurveillance to monitor the progression of the pandemic, to identify and differentiate individuals who have developed antibodies from natural infection versus vaccine-induced immunity, and to identify potential donors of convalescent plasma for therapeutic purposes. In this chapter, we describe a commercially available bead-based serological assay, the Luminex® xMAP® SARS-CoV-2 Multi-Antigen IgG Assay, that detects and identifies antibodies against three SARSCoV-2 antigens. In addition to the assay principle and workflow, we describe modifications that may be used to evaluate alternate sample types, antibody isotypes, and potential neutralizing antibody responses. Key words COVID-19, SARS-CoV-2, Serology, Antibody testing, Luminex, xMAP, Multiplex

1

Introduction During the early months of the coronavirus disease 2019 (COVID19) pandemic, nucleic acid tests for rapid identification of the SARS-CoV-2 viral RNA were critical for diagnosing acute infection and informing patient management, infection control practices, and minimizing potential transmission. However, these tests cannot detect past infection or possible immunity, nor can they determine the efficacy of vaccines. Therefore, as the pandemic progressed, serology or antibody testing became important as an essential tool for monitoring seroprevalence in various populations [1–3]. Serology testing is primarily recommended for epidemiology and serosurveillance, to estimate the proportion of the population exposed to SARS-CoV-2, and is also used clinically as a supplementary or confirmatory test for patients who test negative

Paul C. Guest (ed.), Multiplex Biomarker Techniques: Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification, Methods in Molecular Biology, vol. 2511, https://doi.org/10.1007/978-1-0716-2395-4_19, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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by molecular tests or antigen-based tests, but are strongly suspected of having SARS-CoV-2 infection [4, 5]. Serological assays can also identify potential donors of convalescent plasma, measure immune response to vaccines, and assist public health in the implementation of safety policies, including social distancing, quarantine, and travel restrictions [6, 7]. Multiplex serological assays that can simultaneously analyze different antigens for large-scale serological screening are of particular interest during this pandemic as they have the potential to replace traditional singleplex ELISA [8, 9]. Compared to singleplex ELISA, multiplex serological assays can reduce the amount of testing and sample volume required, decrease the time to results, and reduce labor and associated costs. Several serological assays have received Emergency Use Authorization (EUA) from the US Food and Drug Administration (FDA) for the detection of SARS-CoV-2 antibodies, including the Luminex® xMAP® SARS-CoV2 Multi-Antigen IgG Assay [10]. The xMAP SARS-CoV-2 MultiAntigen IgG Assay is a multiplex, microsphere-based, highly sensitive, and specific assay that detects the presence or absence of antibodies against three SARS-CoV-2 antigens: the spike 1 antigen (S1); the nucleocapsid antigen (N); and the S1 receptor binding domain (RBD) antigen. Based on Luminex’s proprietary xMAP bead-based technology, the xMAP SARS-CoV-2 Multi-Antigen IgG Assay is designed for use with both human serum and plasma sample types and has been validated on three Luminex xMAP instruments: MAGPIX®; Luminex 200™; and FLEXMAP 3D®. The assay methodology and workflow is described in Fig. 1. In addition to the assay principle and standard workflow, we also describe modifications to the protocol that may be used to evaluate dried blood spot (DBS) samples, measure different antibody isotypes, and detect potential neutralizing antibody responses. DBS samples have been shown to be a suitable specimen for xMAP Technology in both protein and nucleic acid assays [11– 14]. More recently, investigators at the Kungliga Tekniska ho¨gskolan (KTH) Royal Institute of Technology used in-home sampled DBS with a multiplexed, multianalyte xMAP-based assay to accurately determine seroprevalence of SARS-CoV-2 in Stockholm during the first wave of the COVID-19 pandemic [15]. Additionally, Turgeon et al. (2021) recently reported their validation of the Luminex xMAP SARS-CoV-2 Multi-Antigen assay for DBS specimens. They tested 159 paired DBS and serum specimens and compared these with serum tested by one of two reference assays and found an overall concordance of 96.9%. Furthermore, when they analyzed the results using a multivariate pattern recognition software, the overall concordance was increased to 99.4%, demonstrating DBS as a reliable specimen type for the assay [16].

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Fig. 1 xMAP SARS-CoV-2 Multi-Antigen IgG Assay workflow

2

Materials

2.1 xMAP® SARSCoV-2 Multi-Antigen IgG Assay Kit Components

1. USB flash drive with the following contents: (a) xMAP SARS-CoV-2 Multi-Antigen IgG Assay software (MULTI IgG CoV-2) (b) xMAP SARS-CoV-2 Multi-Antigen IgG Assay software user manual (c) Three xPONENT Software Data Acquisition protocol files (one for each xMAP system MAGPIX, Luminex® 200, and FLEXMAP 3D) (d) MAGPIX Serology Post Clean Routine (e) xMAP SARS-CoV-2 Multi-Antigen IgG Assay package insert (f) xMAP SARS-CoV-2 IgG Control Kit package insert (g) Product fact sheets 2. xMAP® SARS-CoV-2 Multi-Antigen IgG Assay Kit sufficient for 96 tests. 3. xMAP® SARS-CoV-2 Multi-Antigen IgG Assay Microsphere Mix (6 mL). 4. xMAP® SARS-CoV-2 Multi-Antigen IgG Assay IgG Detection Reagent (6 mL). 5. xMAP® SARS-CoV-2 Multi-Antigen IgG Assay Wash Buffer (125 mL). 6. White 96-well round bottom plate. 7. Two clear Mylar (5.25
3.25 cm). 8. Threshold card.

seal

Thermowell®

96-Well

Plates

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2.2 Samples and Reagents

1. Serum or plasma samples from patients and controls (see Note 1). (a) Sera collected more than 14 days after onset of symptoms or after a positive SARS-CoV-2 test (when date of symptom onset is unknown) (n ¼ 53) (b) Plasma specimens as above (n ¼ 30) (c) Sera collected over days 8–14 (n ¼ 21) and before day 7 (n ¼ 38) after symptom onset (d) Plasma specimens as above (n ¼ 11) (e) Control sera (n ¼ 308) and plasma samples (n ¼ 133) collected prior to December 2019 and presumed to be SARS-CoV-2 IgG antibody negative 2. xMAP SARS-CoV-2 IgG positive and negative controls (included in the xMAP SARS-CoV-2 IgG Control Kit). 3. Deionized (DI) water. 4. 0.1 N NaOH. 5. 10% bleach solution (see Note 2). 6. Phosphate buffer saline containing 0.05% Tween-20, 1.0% bovine serum albumin (BSA), 0.1% sodium azide (PBS-TBN). 7. Protein A Sepharose. 8. R-Phycoerythrin goat anti-human IgM. 9. R-Phycoerythrin goat anti-human serum IgA.

2.3

Equipment

1. Luminex 200, MAGPIX (all configurations), or FLEXMAP 3D (with MFI divider enabled), including xPONENT 4.3 software: calibrators, verifiers, and controls. 2. Computer with Windows 10 operating system and PC specifications (as stated in the xPONENT 4.3 release notes). 3. Enzyme-linked immunoadsorbent assay (ELISA) washer with magnetic base plate or a magnetic plate separator and manual washing by pipette. 4. Vortex mixer and orbital plate shaker. 5. Thermowell® seals. 6. Serum/plasma collection tubes. 7. Whatman 903 filter cards.

3

Methods

3.1 Assay Procedure (See Note 3)

1. Dilute the serum or plasma sample 1:400 by performing two 1: 20 dilutions in PBS-TBN.

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2. Dilute each serum or plasma (dipotassium EDTA) control in PBS-TBN so that the final concentration added to the plate well is 0.25% serum or plasma (see Note 4). 3. If using the 5% serum controls from the xMAP SARS-CoV2 IgG control kit, separately dilute each control 1:20 in PBS-TBN. 4. If using 100% serum or plasma controls, dilute the controls 1: 400 in PBS-TBN. 5. Add 50 μL 1:400 diluted (0.25% serum or plasma) sample or control to the appropriate wells of the plate. 6. Vortex microsphere mix 30 s and add 50 μL to each well immediately after vortexing. 7. Close the plate with a Thermowell seal (see Note 5). 8. Cover the plate with foil to protect from light and shake on an orbital plate shaker at 800 rpm for 60 min at room temperature (25 +/ 5  C). 9. Turn off the plate shaker, remove the plate, and place on a magnetic separator for 120 s to allow the microspheres to separate (see Note 6). 10. Remove the foil seal. 11. With the plate still positioned on the magnetic separator, pipette and remove the supernatant from each sample well, taking care not to disturb the microspheres. 12. Remove the plate from the magnetic separator and add 150 μL PBS-TBN to the sample wells for washing. 13. Place the plate on the magnetic separator for 120 s to allow the microspheres to separate. 14. With the plate still positioned on the magnetic separator, pipette and remove the supernatant from each sample well, being careful to not disturb the microspheres. 15. Repeat steps 12–14 to perform a total of two washes (see Note 7). 16. Vortex the detection reagent for 30 s. 17. Remove the plate from the magnetic separator, and add 50 μL of detection reagent to each sample well, and close the plate with a Thermowell seal. 18. Cover the plate with foil to protect from light. 19. Shake the plate as above in step 8. 20. Turn off the plate shaker, remove the plate, and place on a magnetic separator for 120 s to separate the microspheres (see Note 6).

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21. Remove the foil seal. 22. With the plate still positioned on the magnetic separator, remove the supernatant from each sample well, taking care not to disturb the microspheres. 23. Remove the plate from the magnetic separator and add 150 μL PBS-TBN to the sample wells for washing. 24. Place the plate on the magnetic separator for 120 s to allow the microspheres to separate. 25. With the plate still positioned on the magnetic separator, pipette and remove the supernatant from each sample well, making sure not to disturb the microspheres. 26. Repeat steps 23–25 to perform a total of two washes (see Note 7). 27. Remove the plate from the magnetic separator. 28. Add 100 μL PBS-TBN to each well and mix by gently pipetting up and down to resuspend the microspheres. 3.2 System Software Setup (See Note 8)

1. Insert the USB flash drive with the protocol files into the PC (see Note 9). 2. On the PC desktop, double-click the Luminex xPONENT icon. 3. Enter your on the tab. 4. Enter your if you are using a secure version of the software. 5. Click to display the home page. 6. Navigate to the page. 7. Click . 8. In the box, navigate to the folder. (a) Luminex 200: double-click the file. (b) MAGPIX: lxt2> file.

double-click

the

14

TP FN PPA

95% CI

27 11 71.1% 55.0–83.0% 16 4 80.0% 58.0–92.0% 52 1 98.1% 90.0–100.0%

TN

FP NPA

95% CI

Total

100.0% 99.0–100.0% 418

307 0

TP true positive, FN false negative, PPA positive percent agreement, CI confidence interval, TN true negative, FP false positive, NPA negative percent agreement

Table 2 Agreement of SARS-CoV-2 Multi-Antigen IgG Assay on the FLEXMAP 3D system in plasma samples Days from symptom onset (or days from molecular positive) Plasma 7 8–14 >14

TP FN PPA 9 0 9 1 27 1

95% CI

100% 70.0–100% 90.0% 60.0–98.0% 96.4% 82.0–99.0%

TN

FP NPA

132 1

95% CI

Total

99.2% 96.0–100.0% 180

TP true positive, FN false negative, PPA positive percent agreement, CI confidence interval, TN true negative, FP false positive, NPA negative percent agreement

24. All 100% of the pre-pandemic controls were negative for IgG, and 94% of PCR-positive samples from 21 days after symptom onset were positive for IgG using the standard xMAP SARSCoV-2 IgG assay. Most samples showed a significant response to SARS-CoV-2 antigens compared to pre-pandemic negative controls. IgG titers rose for all three antigens, peaking at 29–41 days. These retained moderately high titers at day 60 and beyond (see Fig. 3). The average IgM response reached a maximum at 8–14 days for the N antigen and at 15–21 days for the RBD and S1 antigens. A significant drop in signal occurred after day 60, declining to levels more comparable to negative samples. The IgA response peaked at 8–14 days for N and within 29–41 days for RBD and S1. 25. As shown in Fig. 4, no or low MFI signal was obtained for the pre-pandemic samples (orange), and antibody responses acquired by natural infection (blue) and vaccination with an RBD-based vaccine (pink) can be clearly distinguished. High MFI signal for N is only observed in convalescent samples, whereas high MFI signal for RBD is seen in both convalescent and post-vaccination samples.

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Average MFI x 103

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10 8 6 4 2 0 Negative 0-7

0 8-14

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0 Negative 0-7

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15-21 22-28 29-41 42-60

60+

Negative 0-7

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60+

Days post symptom onset Antigen N RBD S1

Fig. 3 Results from xMAP SARS-CoV-2 Multi-Antigen assay modified to detect specific IgG, IgM, and IgA responses. Average MFI measurements of IgG, IgM, and IgA against SARS-CoV-2 N, RBD, and S1 antigens in pre-pandemic negative controls and PCR-positive SARS-CoV-2 infected subjects by days post symptom onset are shown

Fig. 4 Detection of neutralizing antibody responses using the xMAP SARS-CoV-2 Multi-Antigen IgG Assay. MFI results from the xMAP SARS-CoV-2 Multi-Antigen IgG Assay were compared to results obtained from a live virus micro-neutralization assay

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References 1. Ferna´ndez-Barat L, Lo´pez-Aladid R, Torres A (2020) The value of serology testing to manage SARS-CoV-2 infections. Eur Respir J 56(2): 2 0 0 2 4 1 1 . h t t p s : // d o i . o r g / 1 0 . 1 1 8 3 / 13993003.02411-2020 2. Xiang F, Wang X, He X et al (2020) Antibody detection and dynamic characteristics in patients with coronavirus disease 2019. Clin Infect Dis 71(8):1930–1934 3. Zhang W, Du RH, Li B et al (2020) Molecular and serological investigation of 2019-nCoV infected patients: implication of multiple shedding routes. Emerg Microbes Infect 9(1): 386–389 4. Kucirka LM, Lauer SA, Laeyendecker O et al (2020) Variation in false-negative rate of reverse transcriptase polymerase chain reaction–based SARS-CoV-2 tests by time since exposure. Ann Intern Med 173(4):262–267 5. Centers for Disease Control and Prevention. Interim guidelines for COVID-19 antibody testing. https://www.cdc.gov/coronavi rus/2019-ncov/lab/resources/antibodytests-guidelines.html. Accessed 11 Nov 2021 6. Balcerek J, Trejo E, Levine K et al (2021) Hospital-based donor recruitment and predonation serologic testing for COVID-19 convalescent plasma. Am J Clin Pathol 155(4): 515–521 7. Winter AK, Hegde ST (2020) The important role of serology for COVID-19 control. Lancet Infect Dis 20(7):758–759 8. Tighe PJ, Ryder RR, Todd I et al (2015) ELISA in the multiplex era: potentials and pitfalls. Proteomics Clin Appl 9(3–4):406–422 9. Elshal MF, McCoy JP (2006) Multiplex bead array assays: performance evaluation and comparison of sensitivity to ELISA. Methods 38(4):317–323 10. U.S. Food & Drug Administration. EUA authorized serology test performance. https://

www.fda.gov/medical-devices/coronavirus-dis ease-2019-covid-19-emergency-useauthorizations-medical-devices/euaauthorized-serology-test-performance. Accessed 11 Nov 11. Bellisario R, Colinas RJ, Pass KA (2000) Simultaneous measurement of thyroxine and thyrotropin from newborn dried blood-spot specimens using a multiplexed fluorescent microsphere immunoassay. Clin Chem 46(9): 1422–1424 12. Bellisario R, Colinas RJ, Pass KA (2001) Simultaneous measurement of antibodies to three HIV-1 antigens in newborn dried blood-spot specimens using a multiplexed microspherebased immunoassay. Early Hum Dev 64(1): 21–25 13. Colinas RJ, Bellisario R, Pass KA (2000) Multiplexed genotyping of β-globin variants from PCR-amplified newborn blood spot DNA by hybridization with allele-specific oligodeoxynucleotides coupled to an array of fluorescent microspheres. Clin Chem 46(7):996–998 14. Skogstrand K, Thorsen P, Nørgaard-Pedersen B et al (2005) Simultaneous measurement of 25 inflammatory markers and neurotrophins in neonatal dried blood spots by immunoassay with xMAP technology. Clin Chem 51(10): 1854–1866 15. Roxhed N, Bendes A, Dale M et al (2021) Multianalyte serology in home-sampled blood enables an unbiased assessment of the immune response against SARS-CoV-2. Nat Commun 12(1):1–9 16. Turgeon CT, Sanders KA, Rinaldo P et al (2021) Validation of a multiplex flow immunoassay for detection of IgG antibodies against SARS-CoV-2 in dried blood spots. PLoS One 16(5):e0252621. https://doi.org/10.1371/ journal.pone.0252621

Chapter 20 Multiplex Testing of the Effect of Statins on Disease Severity Risk in COVID-19 Cases Fatemeh Zahedipour, Paul C. Guest, Muhammed Majeed, Khalid Al-Rasadi, Tannaz Jamialahmadi, and Amirhossein Sahebkar Abstract Statins have pleiotropic effects on inflammatory responses in addition to their lipid-lowering action, which contributes to their favorable effect on cardiovascular disorders. Statins affect adhesion, migration, antigen presentation, and cytokine generation of immune cells. Pre-clinical and clinical studies suggest that statin intervention targeted early in the infection might help COVID-19 patients to reduce the effects of acute respiratory distress syndrome (ARDS), the cytokine storm, and vascular collapse by modulating harmful pathogenic mechanisms. This chapter presents a protocol for measuring blood-based biomarkers predictive of these responses in COVID-19 patients using two specific multiplex immunoassays that target proteins that differ widely in concentration. Key words Statins, Simvastatin, SARS-CoV-2, COVID-19, Multiplex immunoassay, TNF-α, IL-6, IL-8, Von Willebrand factor, C-reactive protein

1

Introduction The cytokine storm is a severe systemic inflammatory reaction characterized by an apparent increase in a large number of cytokines that can occur in various pathophysiological processes such as SARS-CoV-2 infections, which causes COVID-19 [1]. The virus attaches to the alveolar epithelium causing significant tissue damage, triggering the host’s uncontrolled innate and adaptive immune responses. T-helper 1 (Th1) cell activation is the most prominent of the inflammatory response. Cytokines that inhibit the inflammatory response, such as interleukin-4 (IL-4) and IL-10, can also be produced by T-helper 2 (Th2) cells [2]. In individuals with COVID-19, elevated serum levels of IL-2 receptor (IL-2R), IL-6, and tumor necrosis factor-α (TNF-α) have been linked to disease severity. High levels of IL-6 are frequently observed in severe

Paul C. Guest (ed.), Multiplex Biomarker Techniques: Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification, Methods in Molecular Biology, vol. 2511, https://doi.org/10.1007/978-1-0716-2395-4_20, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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COVID-19 cases compared to mild cases [1, 3]. Cytokine storm and elevation of proinflammatory cytokines are linked to the severity of the infection. According to a recent analysis of in-hospital mortality, statin administration was associated with a better prognosis among more than 8000 COVID-19 patients across the world [4]. Statins are prescribed to reduce cholesterol levels in patients and therefore minimize the risk of them developing atherosclerosis and cardiovascular disorders. They act by inhibiting the rate-limiting step in cholesterol synthesis via suppression of 3-hydroxy-3methylglutarylcoenzyme A reductase (HMG-CoA reductase) activity [5]. The two main groups of statins are the lipophilic and hydrophilic varieties. Lipophilic statins include lovastatin, simvastatin, cerivastatin, fluvastatin, pitavastatin, and atorvastatin, while pravastatin is a hydrophilic statin. The structural differences of statins can have an impact on their activity profiles [6, 7]. Besides their well-known cholesterol-lowering activity, statins can exert a multiple lipid-independent effects [8–17]. The antiinflammatory effects of statins were initially demonstrated in studies of patients with cardiovascular disease [18]. In COVID-19 patients, statins have been shown to have three primary effects: (1) endothelial injury protection; (2) regulation and suppression of the coagulation cascade; and (3) suppression of cytokine storm formation and progression [19]. Statins can also synergistically enhance the impact of traditional immunosuppressive therapies by modulating T-cell proliferation and generating proinflammatory cytokines [20]. In addition, they can have a critical impact on the immune system by decreasing antigen presentation via interferongamma (IFN-γ) inhibition, which is necessary for developing MHC class II and suppressing the production of the proinflammatory Th1 subtype via protein geranylation. In turn, this inhibits the production of the proinflammatory cytokines TNF-α, IL-1, IL-6, and IL-8 [21] (see Fig. 1). Statins can improve the vascular redox balance by decreasing reactive oxygen species, increasing antioxidants, ameliorating nitric oxide bioavailability, and improving endothelial function and integrity through other mechanisms. Most of these effects are due to downregulation of redox-sensitive proinflammatory transcriptional factors such as nuclear factor-KB (NF-KB) [22]. Observational studies and meta-analyses showed that in-hospital use of statins is associated with a reduced risk of mortality in COVID-19 patients [23–26]. Furthermore, statin treatment was found to reduce the probability of patients’ intensive care unit (ICU) admission by 22–30%. It has been proposed that statins may improve patient survival by modulating cytokine overexpression, angiotensin-converting enzyme 2 (ACE2) expression, and the immunological response in COVID-19 patients [27–32].

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Fig. 1 Potential role of statins in alleviation of alveolar cytokine storm effects in COVID-19 patients

Ongoing technical developments and new insights into the high complexity of many diseases have sparked the need for multiplex biomarker readouts for improved clinical management of conditions such as COVID-19. Multiplexing facilitates higher sample throughput with greater cross-comparability within and across experiments as each assay component is simultaneously processed, read, and analyzed under identical conditions. One of the most widely used approaches for accomplishing this is through the use of antibody-based approaches like the multiplex immunoassay technique [33]. These assays involve incubation of samples with fluorescence-tagged microbeads conjugated to capture antibodies that target specific biomarkers, followed by the addition of biotinylated reporter antibodies and analysis by flow cytometry for identification and quantitation of each target biomarker (see Fig. 2). One prerequisite of these assays is that analytes in the body fluid of interest are present at approximately equal concentrations. However, the dynamic range of serum and plasma proteins spans more than ten orders of magnitude, making it impossible to measure all analytes in a single assay. One way of overcoming this is by using two or more multiplexes that target molecules of different concentration ranges and by adding the appropriate amounts of the sample to these. Thus, a multiplex assay targeting high abundance proteins such as the apolipoproteins would require samples to be diluted by a factor of approximately 106, and one aimed at detecting low abundance molecules, such as polypeptide hormones and cytokines

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Data analysis

Add biotinylated detection antibody

Add serum

Read in detector

Streptavidin-PE Biotin Detection antibody Analyte 1 Capture antibody

Analyte 1 antibodyconjugated microbead

Analyte 3 antibodyconjugated microbead

Analyte 2 antibodyconjugated microbead

Fig. 2 Flow chart of multiplex immunoassay procedure

would need a dilution of one order of magnitude or less. This would increase the accuracy as sample readings can be taken within the linear region of standard curves in both cases. In this chapter we present a protocol to evaluate the clinical effect of statins on the cytokine storm of COVID-19 patients via multiplex detection of the low abundance cytokines TNF-α, IL-6, and IL-8. In addition, we address the dynamic range issue through a parallel multiplex analysis of the medium abundance proteins C-reactive protein (CRP) and Von Willebrand factor (vWF), which can be used as biomarkers of disease severity (see Fig. 3) [34–37].

2

Materials

2.1 Participants and Samples (See Note 1)

1. Patients with a polymerase chain reaction (PCR) confirmation of COVID-19 infection admitted into the ICU. Exclusion criteria: l l

Patients 6
normal)

l

Patients using any of the following medicines: – Cyclosporine – HIV protease inhibitors – Hepatitis C protease inhibitors – Fabric acid derivatives – Niacin

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– Azole antifungals – Clarithromycin – Colchicine l

Patients who are unable to take medicines orally

l

Pregnancy or active breastfeeding

l

Inability to provide written informed consent for any reason

l

Treatment withdrawal imminent within 24 h

l

Determination by a physician that a statin is needed for a confirmed indication

2. Simvastatin capsules (80 mg). 3. Placebo capsules matched in shape, size, color, and texture to the simvastatin capsules. 4. Personnel protective equipment (PPE) [38]. 5. Sterile blood draw needles (14–20 gauge) and other phlebotomy equipment. 6. Isopropyl alcohol (70%) wipes. 7. Evacuated serum collection tubes with syringe, single draw or butterfly attachments. 8. Disinfectant: 1% sodium hypochlorite. 2.2 Preparation of Capture AntibodyBead Conjugates

1. Magnetic microbeads (1
108) (Luminex Corp.; Austin, TX, USA) (see Note 3). 2. Magnetic separator (Luminex Corp.) (see Note 4). 3. Capture antibodies: 125 μg/mL monoclonal antibodies against TNF-α, IL-6, IL-8, CRP, and vWF. 4. N-Hydroxysulfosuccinimide (sulfo-NHS). 5. N-(3-Dimethyl aminopropyl)-N0 -ethylcarbodiimide (EDCI). 6. Activation solution: 88 mM monosodium phosphate/12 mM disodium phosphate (pH 6.0). 7. 50 mM 2-morpholino-ethane-sulfonic acid mono-hydrate (MES) (pH 5.0). 8. Blocking solution: 10 mM sodium phosphate (pH 7.4), 150 mM sodium chloride, 0.02% Tween-20, 0.1% bovine serum albumin (BSA). 9. Sonicator.

2.3 Preparation of Detection Antibodies

1. Detection antibodies: 100 μg/mL antibodies against TNF-α, IL-6, IL-8, CRP, and vWF that target distinct epitopes from the capture antibodies (see Note 5). 2. Sulfo-NHS biotin (see Note 6).

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3. Phosphate buffered saline (PBS): 16.7 mM disodium phosphate, 1.4 mM monopotassium phosphate, 13.7 mM sodium chloride, pH 7.4. 2.4 Multiplex Development

1. Assay buffer: PBS, 1% BSA. 2. Wash buffer: PBS, 0.02% Tween-20. 3. 100 μg/mL streptavidin R-phycoerythrin (SPE) (see Note 7). 4. 96-well microtiter plates. 5. Recombinant or native protein standards for TNF-α, IL-6, IL-8, CRP, and vWF. 6. Meridian Life Sciences Tru Block or other blocking buffers. 7. MagPix or Luminex 200 analyzer (see Note 8). 8. Double-deionized water. 9. 70% ethanol. 10. Hemocytometer.

3

Methods

3.1 Sample Collection

1. Conduct a prospective randomized, double-blind, placebocontrolled trial as described [39]. (a) Ensure the study is approved by a national research ethics committee and the research governance department at each study site. (b) Perform randomization to the study groups in a 1:1 ratio: l

Study group 1 – COVID-19 patients with cytokine storm signs to receive a capsule of simvastatin (80 mg) once daily.

l

Study group 2 – COVID-19 patients with cytokine storm signs to receive a placebo capsule once daily.

(c) Carry out treatment protocol for 28 days or until: l

Discharge from ICU or high-dependency unit

l

Death

l

Development of a comorbid condition requiring other treatment

l

Withdrawal of the patient from the study

l

The study drug is stopped on safety grounds

2. Collect blood into serum tubes before and after the final day of treatment in accordance with World Health Organization (WHO) and governmental phlebotomy standards for obtaining and processing samples containing dangerous pathogens [40].

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3. Make sure patients are adequately masked and keep a distance of at least 1 m while waiting. 4. Make sure the phlebotomist wears the appropriate PPE and disposable tourniquets are used. 5. After each patient is sampled, sanitize the phlebotomy seats and room. 3.2 Sample Processing

1. Make sure that all researchers working with suspected COVID19 samples are wearing proper PPE. 2. Wipe the outside surface of sample tubes using the disinfectant. 3. Allow samples to clot 90 min at room temperature (see Note 9). 4. Centrifuge at 10,000
g for 15 min and collect the upper serum layer, taking care to open tubes in a biosafety cabinet, and avoiding splash, agitation, or leakage of samples. 5. Store each sample in 0.5 mL aliquots at 20  C until ready for analysis. 6. When finished, clean all equipment with disinfectant and autoclave the sample tubes and PPE. 6. Dispose of all additional waste as for other biohazardous materials.

3.3 AntibodyMicrobead Conjugation (See Note 10)

1. Activate the microbeads as described previously [33] using the activation buffer, sulfo-NHS, and EDCI, with washing steps carried out using the magnetic separator according to the manufacturer’s instructions. 2. Collect the microbeads using the separator and wash twice with 0.5 mL MES. 3. Leave the beads suspended in 0.45 mL MES and add 0.2 mL capture antibodies against TNF-a, IL-6, IL-8, CRP, and vWF to separate batches of microbeads. 4. Mix by rotation 2 h in the dark at room temperature, collect the microbeads using the separator, and wash 3 times in blocking buffer. 5. Count the microspheres using a hemocytometer, and make the concentration of each microbead-antibody conjugate 5
107/ mL, and leave at 4  C until ready for use.

3.4 Biotinylation of Detection Antibodies

1. Add 10 mM sulfo-NHS biotin to detection antibodies at a 20:1 molar ratio. 2. Incubate 2 h on ice or 30 min at room temperature. 3. Remove surplus sulfo-NHS biotin by dialyzing in PBS. 4. Add BSA to a final concentration of 1%.

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Assay

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1. Combine the TNF-α, Il-6, and IL-8 microbead solutions in one tube and make up to 1.4 mL in assay buffer to generate a pool of the required distinct capture antibody-microbead complexes. 2. Combine the CRP and vWF microbead solutions and make up to 1.4 mL in assay buffer as above. 3. Create seven tenfold serial dilutions of 1 μg/mL each standard protein to generate five 8-point standard curves. 4. Add 5 μg of the TNF-α, Il-6, and IL-8 biotinylated antibody solutions in 5 mL assay buffer to create a detection multiplex. 5. Create a detection multiplex of the CRP and vWF biotinylated antibody solutions as above. 6. Dilute serum 1:5 in assay buffer for use with the TNF-α/Il-6/ IL-8 multiplex (see Note 11). 7. Dilute serum 1:5000 in assay buffer for use with the CRP/ vWF multiplex (see Note 12). 8. Add 30 μL diluted samples (or standard solutions) and 10 μL blocking buffer to designated wells of the microtiter plate. 9. Add 10 μL each multiplex and incubate 1 h at room temperature. 10. After washing 3
with 100 μL wash buffer, add 40 μL the appropriate multiplex biotinylated antibody solution to each well and mix 1 h at room temperature. 11. Add 20 μL SPE to each sample and incubate 30 min on the shaker at room temperature. 12. Wash as above and leave in 100 μL assay buffer on the shaker for 5 min. 13. Analyze using the MagPix [41] or Luminex 200 [42] instruments following the manufacturer’s protocol and as described in Chapter 15 of this volume.

3.6

Data Analysis

1. Analyze the data and determine the amounts of each biomarker in each sample (see Note 13). 2. Identify significant differences in the levels of each biomarker (two-sided p < 0.05) between the placebo and statin treatment groups. 3. Potentially develop an algorithm for all five measurements combined with demographic or radiographic information to produce a disease severity score (see Note 14).

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Notes 1. All samples should be handled and disposed of with appropriate precautions in accordance with biosafety level (BSL) 3 laboratory requirements. 2. This trial is testing the use of statins in-hospital only since a recent meta-analysis found that this resulted in improved patient outcomes compared to pre-hospital use [43]. 3. Other microbeads can be used from other suppliers. We used the MagPix magnetic beads for use with the magnetic plate separator. 4. This is a magnetic system designed for rapid and efficient separation of magnetic beads during washing and buffer exchange steps. Other systems such as a standard plate washer can be used for non-magnetic beads. 5. As this is a basic sandwich design immunoassay, the secondary (detection) antibodies should target a distinct epitope from the capture antibodies to avoid steric hindrance effects. 6. This reagent should be prepared freshly before use. It is used for labeling antibodies and other proteins via primary amine residues. 7. SPE is used to detect biotin-conjugated molecules in immunofluorescence applications. 8. Other analyzers can also be used, such as the Luminex 200. However, the MagPix analyzer is designed for use with the magnetic beads as we have used here. 9. Use consistent clotting times for all samples. 10. For details, see Chapter 15 of this volume. 11. The high concentration used will allow greater ease of detection of the low abundance cytokines. 12. The medium concentration used will allow greater ease of detection of these medium abundance proteins. 13. The concentrations can be derived from the standard curves of each standard analyte, and the readings are most accurate if they appear in the linear range of the curve. The readings can be optimized by varying the sample dilutions if needed. 14. This kind of algorithm has been described by Kwon et al. [44] and Jiao et al. [45].

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References 1. Huang C, Wang Y, Li X et al (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223):497–506 2. Halacli B, Topeli A (2020) Treatment of the cytokine storm in COVID-19. J Crit Intensive Care 11(Suppl 1):36–40 3. Sun X, Wang T, Cai D et al (2020) Cytokine storm intervention in the early stages of COVID-19 pneumonia. Cytokine Growth Factor Rev 53:38–42 4. Mehra MR, Desai SS, Kuy S et al (2020) Retraction: cardiovascular disease, drug therapy, and mortality in Covid-19. New Eng J Med 382(25):e102. https://doi.org/10. 1056/NEJMoa2007621 5. Bedi O, Dhawan V, Sharma P et al (2016) Pleiotropic effects of statins: new therapeutic targets in drug design. Naunyn Schmiedeberg’s Arch Pharmacol 389(7):695–712 6. Davignon J (2012) Pleiotropic effects of pitavastatin. Br J Clin Pharmacol 73(4):518–535 7. Viola G, Grobelny P, Linardi MA et al (2012) Pitavastatin, a new HMG-CoA reductase inhibitor, induces phototoxicity in human keratinocytes NCTC-2544 through the formation of benzophenanthridine-like photoproducts. Arch Toxicol 86(3):483–496 8. Afshari AR, Mollazadeh H, Henney NC et al (2021) Effects of statins on brain tumors: a review. Semin Cancer Biol 73:116–133 9. Bahrami A, Parsamanesh N, Atkin SL et al (2018) Effect of statins on toll-like receptors: a new insight to pleiotropic effects. Pharmacol Res 135:230–238 10. Dehnavi S, Kiani A, Sadeghi M et al (2021) Targeting AMPK by statins: a potential therapeutic approach. Drugs 81(8):923–933 11. Gorabi AM, Kiaie N, Pirro M et al (2021) Effects of statins on the biological features of mesenchymal stem cells and therapeutic implications. Heart Fail Rev 26(5):1259–1272 12. Sahebkar A, Kiaie N, Gorabi AM et al (2021) A comprehensive review on the lipid and pleiotropic effects of pitavastatin. Prog Lipid Res 84: 101127. https://doi.org/10.1016/j.plipres ˇ , Hatamipour M, Banach M, Pirro M, 13. Reiner Z Al-Rasadi K, Jamialahmadi T, Radenkovic D, Montecucco F, Sahebkar A (2020) Statins and the Covid-19 main protease: In silico evidence on direct interaction, Archives of Medical Science 16(2):490–496. https://doi.org/10. 5114/aoms.2020.94655 14. Shakour, N., Ruscica M, Hadizadeh F, Cirtori C, Banach M, Jamialahmadi T, Sahebkar A

(2020) Statins and C-reactive protein: In silico evidence on direct interaction. Archives of Medical Science 16(6):1432–1439. https:// doi.org/10.5114/AOMS.2020.100307 15. Khalifeh, M., Penson PE, Banach M, Sahebkar A (2021) Statins as anti-pyroptotic agents. Archives of Medical Science 17 (5):1414–1417. https://doi.org/10.5114/ aoms/141155 16. Sohrevardi SM, Nasab FS, Mirjalili MR, Bagherniya M, Tafti AD, Jarrahzadeh MH, Azarpazhooh MR, Saeidmanesh M, Banach M, Jamialahmadi T, Sahebkar A (2021) Effect of atorvastatin on delirium status of patients in the intensive care unit: A randomized controlled trial. Archives of Medical Science 17 (5):1423. https://doi.org/10.5114/aoms. 2019.89330 ˇ , Banach M, Sahebkar 17. Amin F, Fathi F, Reiner Z A (2022) The role of statins in lung cancer. Archives of Medical Science 18(1):141–152. https://doi.org/10.5114/aoms/123225 18. Albert MA, Danielson E, Rifai N et al (2001) Effect of statin therapy on C-reactive protein levels: the pravastatin inflammation/CRP evaluation (PRINCE): a randomized trial and cohort study. JAMA 286(1):64–70 19. Castiglione V, Chiriaco` M, Emdin M et al (2020) Statin therapy in COVID-19 infection. Eur Heart J Cardiovasc Pharmacother 6(4): 258–259 20. Jameel A, Ooi KG-J, Jeffs NR et al (2013) Statin modulation of human T-cell proliferation, IL-1 and IL-17 production, and IFN-T cell expression: synergy with conventional immunosuppressive agents. Int J Inflamm 2013:434586. https://doi.org/10.1155/ 2013/434586 21. Stu¨ve O, Youssef S, Slavin AJ et al (2002) The role of the MHC class II transactivator in class II expression and antigen presentation by astrocytes and in susceptibility to central nervous system autoimmune disease. J Immunol 169(12):6720–6732 22. Nagashima T, Okazaki H, Yudoh K et al (2006) Apoptosis of rheumatoid synovial cells by statins through the blocking of protein geranylgeranylation: a potential therapeutic approach to rheumatoid arthritis. Arthritis Rheum 54(2):579–586 23. Zeiser R (2018) Immune modulatory effects of statins. Immunology 154(1):69–75 24. Permana H, Huang I, Purwiga A et al (2021) In-hospital use of statins is associated with a reduced risk of mortality in coronavirus-2019

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(COVID-19): systematic review and metaanalysis. Pharmacol Rep 73(3):769–780 25. Chow R, Im J, Chiu N et al (2021) The protective association between statins use and adverse outcomes among COVID-19 patients: a systematic review and meta-analysis. PLoS One 16(6):e0253576. https://doi.org/10. 1371/journal.pone.0253576 26. Vahedian-Azimi A, Mohammadi SM, Beni FH et al (2021) Improved COVID-19 ICU admission and mortality outcomes following treatment with statins: a systematic review and meta-analysis. Arch Med Sci 17(3):579–595 27. Gupta A, Madhavan MV, Poterucha TJ et al (2021) Association between antecedent statin use and decreased mortality in hospitalized patients with COVID-19. Nat Commun 12(1):1–9 28. Teoh N, Farrell G (2020) Statins as early therapy to mitigate COVID-19 (SARS-CoV-2)associated ARDS and cytokine storm syndrome–time is of the essence. J Clin Transl Res 5(5):227–229 29. Bhaskar S, Sinha A, Banach M et al (2020) Cytokine storm in COVID-19 – immunopathological mechanisms, clinical considerations, and therapeutic approaches: the REPROGRAM consortium position paper. Front Immunol 11:1648. https://doi.org/10. 3389/fimmu.2020.01648 30. Gorabi AM, Kiaie N, Bianconi V et al (2020) Antiviral effects of statins. Prog Lipid Res 79: 101054. https://doi.org/10.1016/j.plipres. 2020.101054 31. Ganjali S, Bianconi V, Penson PE et al (2020) Commentary: statins, COVID-19, and coronary artery disease: killing two birds with one stone. Metabolism 113:154375. https://doi. org/10.1016/j.metabol.2020.154375 32. Olszewska-Parasiewicz J, Szarpak Ł, Rogula S et al (2021) Statins in COVID-19 therapy. Life (Basel) 11(6):565. https://doi.org/10.3390/ life11060565 33. Stephen L (2017) Multiplex immunoassay profiling. Methods Mol Biol 1546:169–176 34. Rauch A, Labreuche J, Lassalle F et al (2020) Coagulation biomarkers are independent predictors of increased oxygen requirements in COVID-19. J Thromb Haemost 18(11): 2942–2953 35. Hoechter DJ, Becker-Pennrich A, Langrehr J et al (2020) Higher procoagulatory potential

but lower DIC score in COVID-19 ARDS patients compared to non-COVID-19 ARDS patients. Thromb Res 196:186–192 36. Hardy M, Michaux I, Lessire S et al (2021) Prothrombotic hemostasis disturbances in patients with severe COVID-19: individual daily data. Data Brief 33:106519. https://doi. org/10.1016/j.dib.2020.106519 37. Marchetti M, Gomez-Rosas P, Sanga E et al (2021) Endothelium activation markers in severe hospitalized COVID-19 patients: role in mortality risk prediction. TH Open 5(3): e253–e263 38. Public Health England. Coronavirus (COVID19): personal protective equipment (PPE) hub. h t t p s : // w w w. g o v. u k / g o v e r n m e n t / c o l lections/coronavirus-covid-19-personal-pro tective-equipment-ppe 39. McAuley DF, Laffey JG, O’Kane CM et al (2014) Simvastatin in the acute respiratory distress syndrome. N Engl J Med 371(18): 1695–1703 40. World Health Organization. Interim guidance 19 March, 2020. Laboratory testing for coronavirus disease (COVID-19) in suspected human cases. https://apps.who.int/iris/ bitstream/handle/10665/331501/WHOCOVID-19-laborator y-2020.5-eng.pdf? sequence¼1&isAllowed¼y 41. https://www.fresnostate.edu/csm/rimi/ documents/equipment/Magpix.pdf 42. https://www.luminexcorp.com/download/ manual-luminex-200-user-ivd-english/ 43. Vahedian-Azimi A, Mohammadi SM, Heidari Beni F et al (2021) Improved COVID-19 ICU admission and mortality outcomes following treatment with statins: a systematic review and meta-analysis. Arch Med Sci 17(3):579–595 44. Kwon YJF, Toussie D, Finkelstein M et al (2021) Combining initial radiographs and clinical variables improves deep learning prognostication in patients with COVID-19 from the emergency department. Radiol Artif Intell 3(2):e200098. https://doi.org/10.1148/ ryai.2020200098 45. Jiao Z, Choi JW, Halsey K et al (2021) Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study. Lancet Digital Health 3(5):e286–e294

Chapter 21 Evaluating the Effects of Curcumin on the Cytokine Storm in COVID-19 Using a Chip-Based Multiplex Analysis Fatemeh Zahedipour, Paul C. Guest, Muhammed Majeed, Seyed Adel Moallem, Prashant Kesharwani, Tannaz Jamialahmadi, and Amirhossein Sahebkar Abstract SARS-CoV-2 can stimulate the expression of various inflammatory cytokines and induce the cytokine storm in COVID-19 patients leading to multiple organ failure and death. Curcumin as a polyphenolic compound has been shown to have anti-inflammatory properties and inhibit the release of numerous pro-inflammatory cytokines. We present multiplex analysis using the Evidence Investigator biochip system to determine the effect of curcumin on serum level of cytokines which are typically elevated in cytokine storm events, including tumor necrosis factor (TNF-α), interleukin 6 (IL-6), and IL-10. Key words Curcumin, SARS-CoV-2, COVID-19, Multiplex analysis, TNF-α, IL-6, and IL-10

1

Introduction Coronavirus disease 2019 (COVID-19) is a fast-spreading infectious and inflammatory disease that has led to a global pandemic. Severe COVID-19 patients frequently develop uncontrolled inflammatory responses and cytokine storm-like syndromes, which is the most life-threatening aspect of this infection [1– 3]. The cytokine storm is characterized by an inappropriate increase in the pro-inflammatory cytokines and chemokines generated by a dysregulated immune response and can ultimately lead to multiorgan failure and death [4]. COVID-19 patients may experience different clinical disease statuses, including asymptomatic incubation status, disease onset with respiratory symptoms, disease progression, and a severe disease phase [5]. Severe COVID-19 patients frequently require mechanical ventilators and intensive care unit (ICU) admission [1]. The infected patients respond to SARSCoV-2 infection in three main immune and inflammatory phases.

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Incubation Detecting infection

Onset of the disease Antiviral defense

Progression of the disease Local and systemic immune responses

Clinical phases

Severe disease Cytokine storm

ICU care ARDS and multi-organ failure

Immune and inflammatory response

Fig. 1 Clinical phases of COVID-19 and the immune and inflammatory responses of the host

First, by sensing infection, the antiviral innate immune defense in lungs is activated. Second, a local or systemic immune response phase is triggered. Third, an uncontrolled inflammatory response and cytokine storm-like syndrome is initiated [5, 6]. Acute respiratory distress syndrome (ARDS) and multi-organ failure may occur in certain severe patients [7] (see Fig. 1). As a result, hyper-inflammatory responses and cytokine storm-like syndrome are primary causes of severe disease and mortality in COVID-19 individuals, and early detection and decrease of cytokine levels may be essential to improve the chances of survival of these patients [8]. Several cytokines are overexpressed in COVID-19 such as tumor necrosis factor alpha (TNF-α), interferon-gamma (IFN-γ), nuclear factor-кB (NF-кB), interleukin (IL)-1β, IL-2, IL-6, IL-8, and IL-10. Meta-analyses have discovered a direct link between TNF-α, IL-6, and IL-10 and the severe type of COVID-19 [9, 10]. Curcumin is a bioactive component of turmeric that is extracted from the Curcuma longa plant. Curcumin is a polyphenol with the chemical formula 1,7-bis (4-hydroxy-3-methoxyphenyl)1,6-heptadiene-3,5-dione [11]. It has been utilized in medical care for many years because of its anti-inflammatory, antioxidant, and anticancer properties linked to the action of the methoxy groups [12–14]. Curcumin has been found to have beneficial effects in different disorders, such as Alzheimer’s disease, cardiovascular disease, diabetes, multiple sclerosis, rheumatoid arthritis, cancer, and psoriasis because of its anti-inflammatory properties [15–19]. As an immunomodulatory drug, curcumin can stop tissue damage, progression, and development of inflammation. It also has effects on

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Fig. 2 The effect of curcumin in decreasing the level of pro-inflammatory cytokines and inhibiting the cytokine storm

the immune system via reducing the formation of reactive oxygen species (ROS) in macrophages, modulating T cells activity and proliferation, overexpression of CD80 and CD86 on dendritic cells [20], controlling the production of pro-inflammatory cytokines and adhesion molecules, and therefore influencing immune response patterns [21, 22]. Curcumin regulates the expression of various pro-inflammatory cytokines such as NF-κB, transforming growth factor-β (TGF-β), and mitogen-activated protein kinase (MAPK) pathways [23, 24]. The anti-inflammatory and antifibrotic effects of curcumin are driven mainly via reducing the expression of important cytokines (see Fig. 2). Given that inflammation strikes at the core of COVID-19 etiopathogenesis, it is possible that curcumin can help to reduce inflammatory responses while also alleviating disease symptoms [25–27]. Here, we describe a clinical trial on COVID-19 patients treated with curcumin to evaluate its effect on cytokine storm using a multiplex analysis. We describe the detection of 12 circulating analytes [IL-1α, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IFNγ, TNF-α, vascular endothelial growth factor (VEGF), epidermal growth factor (EGF), and monocyte chemotactic protein 1 (MCP-1)] is described in the Randox Evidence Investigator biochip array system (see Fig. 3). The Evidence Investigator uses the sandwich chemiluminescent immunoassay technique for simultaneous multipleanalyte detection [28, 29]. This sensitivity of this method improves the real-time detectability of numerous cytokines in blood samples while maintaining an excellent upper-limit concentration range for each cytokine without losing precision.

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Fig. 3 A Randox biochip array consists of an array of discrete test regions with a different test located at each region. The biochip detection method is based on a chemiluminescent signal

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Materials

2.1 Participants and Samples (See Note 1)

1. Set up a randomized, double-blind, placebo-controlled trial with COVID-19 patients aged 20–75 years as follows (see Note 2): (a) PCR-diagnosed COVID-19 patients admitted in ICU with cytokine storm signs to receive a capsule of curcumin plus piperine or matching placebo (n ¼ 50). (b) PCR-diagnosed COVID-19 patients with mild or asymptomatic disease to receive a capsule of curcumin plus piperine or matching placebo (n ¼ 50). Exclusion criteria: l

Age less than 20 and more than 75 years.

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Current use of warfarin or other anticoagulant drugs.

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Sensitivity to herbal products such as turmeric and pepper.

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The occurrence of any side effects (see Note 3).

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Consumption of less than 90% of curcumin-piperine supplement.

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2. Curcumin capsules containing 500 mg curcuminoids and 5 mg piperine (Sami-Sabinsa Group Limited; Bangalore, India). 3. Placebo capsules matched in shape, size, color, and texture to the curcumin capsules (see Note 4). 4. Personnel protective equipment (PPE) consisting of FFP2 (N95) mask, disposable cap, goggles, gown, apron, latex gloves, and shoe covers. 5. 14–20 gauge sterile blood draw needles. 6. Holder/adapter (see Note 5). 7. Tourniquet. 8. Alcohol wipes (70% isopropyl alcohol). 9. Evacuated serum or plasma collection tubes. 10. Storage tubes or cryovials. 11. 1% sodium hypochlorite. 2.2 Multiplex Development

1. Diluent solution: 20 mM tris-buffered saline (TBS; pH 7.2) containing protein (stored at 4  C). 2. Conjugate solution: 20 mM TBS (pH 7.5) containing protein, surfactant, preservatives, and assay-specific antibodies labeled with horseradish peroxidase (HRP) (stored at 4  C, protected from light). 3. Evidence Investigator biochips: solid-phase substrate containing discrete test regions of immobilized antibodies stored at 4  C and protected from light (Randox Laboratories; Kearneysville, WV, USA) (see Note 6). 4. Evidence Investigator thermoshaker unit. 5. Calibrators: 9 vials of a lyophilized base material containing analytes for the entire panel. 6. Signal Reagent-EV701:luminol-EV701 (10 mL). 7. Peroxide (10 mL). 8. Wash buffer: 20 mM TBS (pH 7.4), containing surfactant and preservatives. 9. Evidence Investigator™ analyzer. 10. Calibrator concentration disc and barcodes. 11. Evidence Investigator cytokine multianalyte controls. 12. Evidence Investigator ziplock bags. 13. Double deionized water.

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Methods

3.1 Preparation of Working Reagents

1. Handle all reagents with caution to avoid contamination. 2. Add 1 mL double deionized water to each bottle of calibrator before use. 3. Mix the bottles by rolling 30 min, avoiding the formation of foam (see Note 7). 4. Mix the luminol-EV701 and peroxide in a ratio of 1:1 to form the working signal reagent (see Note 8). 5. Calculate the amount of working signal reagent required based on 250 μL/well. 6. Dilute the wash buffer 31.25-fold in water to give a final volume of 1 L just before use.

3.2 Sample Collection

1. Ensure that all collection devices and instruments are durable, leak-proof, and constructed of non-absorbing materials. 2. Ensure patients are masked and maintain >1 m distance while waiting for blood draw. 3. Ensure the phlebotomist wears the appropriate PPE. 4. Collect fasting blood into serum or plasma tubes consistent with World Health Organization (WHO) and governmental phlebotomy guidelines for drawing and processing samples potentially containing infectious microorganisms [30]. 5. For serum, leave at room temperature 90 min, centrifuge at 2000  g for 5 min, and store the supernatant in aliquots at 80  C until ready for use. 6. For plasma leave on ice 5 min, centrifuge at 2000  g for 5 min, and store the supernatant in aliquots at 80  C until ready for use. 7. Ensure that samples are non-hemolyzed and non-lipemic. 8. Sanitize phlebotomy chairs after each patient is sampled and decontaminate the phlebotomy room.

3.3 Sample Processing

1. Thaw sample aliquots as required. 2. Dilute serum samples 1:2 using the working strength wash buffer (see Note 9). 3. Sanitize all equipment after the experiment with 1% sodium hypochlorite solution and autoclave the sample tubes before disposal. 4. Dispose of all other waste from suspected/confirmed COVID19 specimens as with other biohazardous waste under standard laboratory practices.

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Assay

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1. Remove the required number of biochip carriers from the packaging. 2. Place the handling tray supplied with the thermoshaker unit onto the working surface. 3. Insert each carrier into the handling tray, making sure that they are flat and secure by clicking into position (see Note 10). 4. Set the thermoshaker to 37  C for 30 min prior to use. 5. Perform installation, calibration, and quality control of the Evidence Investigator device according to the manufacturer’s instructions (see Note 11). 6. Add the samples with the tip of the pipette pointing toward the back of each well (see Note 12). 7. Add 200 μL of assay diluent per well as above. 8. Add 100 μL calibrator, sample or control per well as above. 9. Mix the contents by gently tapping all sides of the handling tray. 10. Put the handling tray on the base plate of the thermoshaker and incubate 1 h at 37  C and 370 rpm. 11. Remove the handling tray containing the carriers from the thermoshaker and discard the reagents. 12. Immediately carry out two quick wash cycles by adding approximately 350 μL diluted wash buffer to each well, gently tapping all edges of the handling tray and flick to waste with a sharp action (see Note 13). 13. Repeat with four more wash cycles with careful tapping of the sides of the handling tray for 10–15 s each time. 14. Soak the biochips in wash buffer for 2 min. 15. After the final wash, tap the carrier gently onto lint-free tissue to remove any residual wash buffer. 16. Immediately add 300 μL conjugate into each well as above. 17. To mix reagents, gently tap all sides of the handling tray. 18. Place the handling tray on the base plate of the thermoshaker and incubate 1 h, as above. 19. Remove the handling tray containing carriers from the thermoshaker and discard reagents using a quick flick as above. 20. Immediately carry out two quick wash cycles as above. 21. Repeat with four more wash cycles as above and then soak the biochips in wash buffer for 2 min. 22. Fill the wells with wash buffer after the last wash and let to soak until just before imaging (see Note 14).

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3.5 Imaging (See Note 15)

1. Remove the first carrier to be imaged from the handling tray. 2. Remove wash buffer using a sharp, flicking action, and tap the carrier onto lint-free tissue to remove any residual wash buffer immediately before addition of the signal reagent. 3. Add 250 μL of working signal reagent-EV701 to each well and cover to protect from light. 4. Place the carrier into the Evidence Investigator device after exactly 2 min (see Notes 16 and 17).

3.6

Data Analysis

1. Perform data analyses and determine the levels of each cytokine in each sample. 2. Calculate the analyte concentration from the calibration curve. 3. Identify significant differences (two-sided p < 0.05) between placebo and test samples in each group for each cytokine measurement and using an algorithm for all three cytokines (see Note 18).

4

Notes 1. All samples should be disposed of with adequate precautions in accordance with the state and local regulations in a biosafety level (BSL) 2 or 3 laboratories as appropriate. 2. Over the course of 2 weeks, fifty outpatients can be randomly assigned to receive a curcumin-piperine capsule containing 500 mg curcumin plus 5 mg piperine or a matched placebo containing 500 mg maltodextrin in a 1:1 ratio. Likewise, 50 inpatients admitted to hospital wards other than ICU can be randomly allocated to receive a curcumin-piperine capsule or a placebo capsule (provided by the Sami Labs company) in a 1:1 ratio twice a daily, after lunch and dinner [27]. 3. Side effects that can cause the subject to withdraw from the study after taking the supplement include: (a) Any unwanted digestive problems, including heartburn, nausea, vomiting, diarrhea, etc. (b) Any allergies, whether skin, respiratory, or others. (c) Any side effects that did not exist before taking the supplement. 4. The curcuminoid and placebo capsules should be matched in shape, size, and color. Also, the color of the placebo powder should match that of the curcuminoids. The oral bioavailability of curcuminoids is enhanced in this study by addition of 5 mg piperine to each 500 mg curcuminoid capsule.

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5. Evacuated systems can be used consisting of a syringe, 1-draw or butterfly attachments. 6. When open, the biochips can be stored in closed ziplock bags with a desiccant at room temperature, and they remain stable for 7 days. Mark the biochip orientation based on the well prior to removal from the biochip carrier. 7. Calibrators are stable at 20  C for 2 weeks. In the original vial, reconstituted calibrators are stable for up to 12 h at 2–8  C. Only the necessary amount of material should be removed. Do not replace material back into the original vial. 8. Approximately 3 mL working signal reagent-EV701 is required for a 9-well carrier plus excess carrier. 9. For measuring IL-2, a maximum dilution of 1:2 for serum samples or 1:8 for plasma samples is suggested. It is recommended to use a maximum dilution of 1:4 for serum samples or use 1:2 for plasma samples for MCP-1 analysis, and EGF samples should not be diluted. Since we are using a 1:2 dilution here, some of the analytes may not be ideally located in the linear range of the standard curve, but all should read above the limit of detection. 10. The handling tray is used for all sample and reagent additions, washing, and incubations, and carriers should only be removed for the operation’s final signal addition and imaging step. 11. Use a 9-point calibration curve of the cytokine, and growth factor calibrators span the measuring range of all assays. A maximum of six biochip carriers can be tested at the same time, and it is suggested that a new calibration curve is prepared for each assay series. Furthermore, it is suggested to use the cytokine multianalyte controls (Randox Laboratories) for quality control for accuracy and precision monitoring [31]. 12. Make sure not to touch the biochip surface. 13. Do not overfill wells during the wash to avoid the potential for well-to-well contamination. 14. Do not soak for more than 30 min. 15. Process carriers for imaging individually and protect carriers awaiting imaging from light. 16. It is recommended to use a timer in order to ensure imaging occurs at the correct time. 17. The capture of pictures will be begun automatically as determined by dedicated software according to the operator’s manual [32]. 18. For example, comparisons could be made across severe COVID-19 groups that use placebo and curcumin on the levels of these cytokines. An algorithm could also be developed

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to incorporate the cytokine measurements as well as demographic or radiographic information to produce a disease severity score [33, 34].

Conflict of Interest Muhammed Majeed is the CEO of Sabinsa Corporation and SamiSabinsa Group Limited. References 1. Ragab D, Salah Eldin H, Taeimah M et al (2020) The COVID-19 cytokine storm; what we know so far. Front Immunol 11:1446. https://doi.org/10.3389/fimmu.2020. 01446 2. Huang HZ, Qiu M, Lin JZ et al (2021) Potential effect of tropical fruits Phyllanthus emblica L. for the prevention and management of type 2 diabetic complications: a systematic review of recent advances. Eur J Nutr 60(7):3525–3542 3. Liu BM, Martins TB, Peterson LK et al (2021) Clinical significance of measuring serum cytokine levels as inflammatory biomarkers in adult and pediatric COVID-19 cases: a review. Cytokine 142:155478. https://doi.org/10.1016/ j.cyto.2021.155478 4. Li G, Fan Y, Lai Y et al (2020) Coronavirus infections and immune responses. J Med Virol 92(4):424–432 5. Hall MW, Joshi I, Leal L et al (2020) Immune modulation in COVID-19: strategic considerations for personalized therapeutic intervention. Clin Infect Dis:ciaa904. https://doi.org/10. 1093/cid/ciaa904. Online ahead of print 6. Shi Y, Wang Y, Shao C et al (2020) COVID-19 infection: the perspectives on immune responses. Cell Death Differ 27(5):1451–1454 7. Zaim S, Chong JH, Sankaranarayanan V et al (2020) COVID-19 and multiorgan response. Curr Probl Cardiol 45(8):100618. https:// doi.org/10.1016/j.cpcardiol.2020.100618 8. Del Valle DM, Kim-Schulze S, Huang HH et al (2020) An inflammatory cytokine signature predicts COVID-19 severity and survival. Nat Med 26(10):1636–1643 9. Mulchandani R, Lyngdoh T, Kakkar AK (2021) Deciphering the COVID-19 cytokine storm: systematic review and meta-analysis. Eur J Clin Investig 51(1):e13429. https:// doi.org/10.1111/eci.13429 10. Morris G, Bortolasci CC, Puri BK et al (2020) The pathophysiology of SARS-CoV-2: a

suggested model and therapeutic approach. Life Sci 258:118166. https://doi.org/10. 1016/j.lfs.2020.118166 11. Gupta SC, Patchva S, Aggarwal BB (2013) Therapeutic roles of curcumin: lessons learned from clinical trials. AAPS J 15(1):195–218 12. Aggarwal BB, Kumar A, Bharti AC (2003) Anticancer potential of curcumin: preclinical and clinical studies. Anticancer Res 23(1a):363–398 13. Ghandadi M, Sahebkar A (2017) Curcumin: an effective inhibitor of interleukin-6. Curr Pharm Des 23(6):921–931 14. Ghasemi F, Shafiee M, Banikazemi Z et al (2019) Curcumin inhibits NF-kB and Wnt/β-catenin pathways in cervical cancer cells. Pathol Res Pract 215(10):152556. https://doi.org/10.1016/j.prp.2019.152556 15. Dolati S, Ahmadi M, Aghebti-Maleki L et al (2018) Nanocurcumin is a potential novel therapy for multiple sclerosis by influencing inflammatory mediators. Pharmacol Rep 70(6):1158–1167 16. Hewlings SJ, Kalman DS (2017) Curcumin: a review of its effects on human health. Foods 6 ( 1 0 ) : 9 2 . h t t p s : // d o i . o r g / 1 0 . 3 3 9 0 / foods6100092 17. Panahi Y, Khalili N, Sahebi E et al (2018) Effects of Curcuminoids plus Piperine on glycemic, hepatic and inflammatory biomarkers in patients with type 2 diabetes mellitus: a randomized double-blind placebo-controlled trial. Drug Res (Stuttg) 68(7):403–409 ´ vila-Rodriguez MF 18. Mohajeri M, Bianconi V, A et al (2020) Curcumin: a phytochemical modulator of estrogens and androgens in tumors of the reproductive system. Pharmacol Res 156: 104765. https://doi.org/10.1016/j.phrs. 2020.104765 19. Sadeghian M, Rahmani S, Jamialahmadi T et al (2021) The effect of oral curcumin supplementation on health-related quality of life: a

Curcumin Effect on Cytokine Storm in COVID-19 systematic review and meta-analysis of randomized controlled trials. J Affect Disord 278: 627–636 20. Bose S, Panda AK, Mukherjee S et al (2015) Curcumin and tumor immune-editing: resurrecting the immune system. Cell Div 10:6. https://doi.org/10.1186/s13008-0150012-z 21. Afolayan FID, Erinwusi B, Oyeyemi OT (2018) Immunomodulatory activity of curcumin-entrapped poly d,l-lactic-co-glycolic acid nanoparticles in mice. Integr Med Res 7(2):168–175 22. Trivedi MK, Mondal SC, Gangwar M et al (2017) Immunomodulatory potential of nanocurcumin-based formulation. Inflammopharmacology 25(6):609–619 23. Ferreira VH, Nazli A, Dizzell SE et al (2015) The anti-inflammatory activity of curcumin protects the genital mucosal epithelial barrier from disruption and blocks replication of HIV-1 and HSV-2. PLoS One 10(4): e0124903. https://doi.org/10.1371/journal. pone.0124903 24. Avasarala S, Zhang F, Liu G, Wang R et al (2013) Curcumin modulates the inflammatory response and inhibits subsequent fibrosis in a mouse model of viral-induced acute respiratory distress syndrome. PLoS One 8(2):e57285. https://doi.org/10.1371/journal.pone. 0057285 25. Zahedipour F, Hosseini SA, Sathyapalan T et al (2020) Potential effects of curcumin in the treatment of COVID-19 infection. Phytother Res 34(11):2911–2920 26. Askari G, Alikiaii B, Soleimani D et al (2021) Effect of curcumin-pipeine supplementation on clinical status, mortality rate, oxidative stress, and inflammatory markers in critically ill ICU patients with COVID-19: a structured summary of a study protocol for a randomized

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Chapter 22 COVID-19 Detection Using the NHS Lateral Flow Test Kit Paul C. Guest and Hassan Rahmoune Abstract Approximately one in three people infected with the SARS-CoV-2 virus have mild symptoms or are asymptomatic. However, these individuals can still spread the virus. Regular self-testing can help to detect these individuals and thereby slow the spread and protect the more vulnerable members of society. Here, we present a protocol for use of the COVID-19 rapid antigen test which was made freely available to residents of the United Kingdom in April of this year. This using the lateral flow technique for detection of antigens and is amenable to multiplexing. Key words COVID-19, SARS-CoV-2, Lateral flow, Antigen test, Sandwich assay, Immunoassay

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Introduction The SARS-CoV-2 virus is the causative agent of COVID-19 which was declared a pandemic on March 11, 2020. It has since spread across the globe and has infected more than 2% of the world population (as of May 12, 2021) [1, 2]. The initial form of the SARS-CoV-2 virus had a high capacity for rapid spread, with a basic reproduction number greater than three (R>3) and mean incubation period of approximately 6 days [3–5]. This high R number meant that it had the capability of infecting more than three individuals from each infected patient. Furthermore, a number of SARS-CoV-2 variants have emerged as the virus has continued to circulate around the world [6, 7]. Emerging evidence has led to the suggestion that some of these variants may have alterations in specific traits such as transmissibility, pathogenicity, and resistance to vaccines, which may alter the trajectory of the pandemic for the worse. The viral particles replicate mainly in the upper and lower respiratory tract and can be transmitted via droplets and aerosols from the infected persons who may be sneezing, coughing, or even talking loudly. Most of the COVID-19 cases are mild without

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complications, but 5–10% can develop a more serious disease course, and the current death rate can be estimated at approximately 2% [1, 8]. The most frequent manifestations that can lead to hospitalization include pneumonia, acute respiratory distress syndrome, cardiac involvement, and thromboembolism [9, 10], and the risk factors that can lead to a more severe disease course are high age, hypertension, diabetes mellitus, cardiovascular diseases, and immunodeficiency syndromes [11–13]. Considering the high capacity for spreading, one of the most worrying aspects is that disease transmission can still occur from asymptomatic people [14– 16]. Therefore, increased mass testing and surveillance methods should be deployed to help control the disease spread. This virus is a single-stranded positive-sense RNA virus, with four major structural proteins termed the envelope, membrane, nucleocapsid, and spike proteins (see Fig. 1) [17–19]. The membrane and envelope proteins are involved virus assembly process. The spike protein is critical for gaining entry into host cells via binding to angiotensin-converting enzyme 2 receptors, and the nucleocapsid protein is important for viral genome packing and replication. The latter two proteins are the most commonly used in immunological-based diagnostic methods for COVID-19 [20–22].

Membrane protein Spike protein

Envelope protein

Nucleocapsid protein

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Envelope protein Membrane envelope

Fig. 1 Structure of the SARS-CoV-2 virus

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In the United Kingdom, the COVID-19 self-test (rapid antigen test) was made freely available to residents from April 9, 2021 [23, 24]. In addition to a successful vaccine rollout, it is anticipated that use of this test will be one of the most effective weapons in reducing the spread of the virus and thereby allowing the gradual reopening of the economy and other aspects of society. This approach leverages the lateral flow device technology is relatively inexpensive, does not require a laboratory, and can provide results rapidly [25, 26]. Basically, lateral flow devices can be used to confirm the presence or absence of one or more analytes in a biological sample. They usually contain one or more test (T) zones which target the analyte and a control (C) line to confirm the test is working properly. The lateral flow cassette for COVID-19 contains a nitrocellulose membrane with a mouse monoclonal SARS-CoV-2 nucleocapsid protein antibody coated on the T line and secondary antibody which targets the primary antibody coated on the C line (see Fig. 2). In addition, the strip contains a SARS-CoV-2 nucleocapsid protein antibody conjugated with color particles which is used as a detector.

Fig. 2 The lateral flow cassette for detection of the SARS-CoV-2 virus

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In a positive test, the virus in the sample interacts with the detector antibody, forming a labeled complex (see Fig. 3). This is transported along the membrane by capillary action to the T line, where it is captured by the bound nucleocapsid protein antibody. This leads to formation of a colored T line, indicating a positive result. The intensity of the colored line will correlate with the amount of

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Fig. 3 Schematic showing testing biological samples on a lateral flow device for (a) the presence or (b) the absence of the SARS-CoV-2 virus

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SARS-CoV-2 virus. If the virus is not present in the sample, the labeled antibodies will pass by the T region and be captured by the secondary antibody bound there. This results in formation of a colored line only in the C region, indicating a negative result. Here, we present a protocol for home use of the COVID-19 self-test which includes the sampling procedure, application of the sample to the device, interpretation of the results, and the reporting procedure.

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Materials (See Note 1) 1. Lateral flow test cassette inside sealed packaging (see Fig. 4). 2. Sterile swab inside sealed packaging. 3. Extraction buffer in plastic sachet (see Note 2). 4. Extraction buffer tube. 5. Tube holder (see Note 3). 6. Plastic waste bag.

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3.1 Preparation for the Home Test (See Note 4)

1. Clean the surface that you will conduct the test on. 2. Wash your hands with soap or a hand sanitizer. 3. Check that nothing in the test kit is damaged and the expiration date is valid. 4. Place all of the test kit items on the clean surface.

3.2 Taking the Throat/Nasal Sample (See Note 5)

1. Take the test cassette out of the sealed packaging. 2. Carefully twist or snap open the sachet containing the extraction fluid (see Note 6). 3. Carefully squeeze all of the fluid into the collection tube and place the empty sachet in the waste disposal bag.

Barcode Control line Test line Sample well

Fig. 4 Lateral flow cassette showing the sample well, the T and C lines, and barcode

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Fig. 5 Sampling procedure for the throat and nose using the swab

4. Place the filled collection tube in the holder. 5. Fill the tube in the test kit with the liquid provided and close the lid. 6. Blow your nose and throw the tissue into a closed waste bin. 7. Wash your hands again as above. 8. Peel open the packet containing the swab from the end opposite the fabric tip and remove the swab (see Note 7). 9. Open your mouth wide and rub the swab over the area where your tonsils are, avoiding your teeth, tongue, or gum area (see Fig. 5) (see Note 8). 10. Put the same swab approximately 2.5 cm inside one of your nostrils or until you feel some resistance (see Fig. 5) (see Note 8). 11. Roll the swab around the inside of the nostril 10 times firmly. 3.3 Processing the Sample

1. Remove the swab from your nostril and place into the extraction tube so that the felt tip is immersed in the extraction fluid. 2. Press the tip against the side of the tube and rotate the tip for 15 s (see Note 9). 3. Pinch the extraction tube against as you remove the swab to collect any remaining sample from the tip. 4. Dispose the swab in the waste bag. 5. Press the cap tightly on the tube.

3.4 Testing the Sample and Reading the Result

1. Position the extraction tube upside down so that the nozzle is over the sample well of the test cassette. 2. Gently squeeze the tube so that two drops are administered to the sample well (see Fig. 6). 3. Read the results in 30 min (see Fig. 7) (see Notes 10 and 11). 4. Report the results as indicated on the test (see Note 12).

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Apply two drops of sample

Fig. 6 Application of the extracted sample to the sample well on the test cassette

Negative result

positive result

C

C

T

T

Fig. 7 Example results. One line will appear in the control (C) zone if the sample does not contain virus, and an additional line will occur in test (T) zone if SARSCoV-2 is present

4

Notes 1. In the United Kingdom, these tests are free and can be ordered online and through some pharmacies, workplaces, schools, and colleges (from April 9, 2021). Similar tests are offered in other countries although there may be small cost. 2. The extraction buffer is used to break up the virus particles to make the viral proteins more easily recognizable by the antibodies on the test strip. 3. The kit provides a box with a hole to support the tube. However, anything can be used here which offers vertical support

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for the tube (e.g., a small cup or a standard laboratory tube rack). 4. The following steps are for adults over 18 years old. Those aged 12–17 should be supervised by an adult, and those under 12 years old should be tested by the adult. The test sub subject should not eat for 30 min prior to conducting the test as this may affect the result. The test should be halted if the subject feels any pain during the sampling stage with the swab. 5. Start the test within 30 min of opening the kit. 6. Open the sachet away from your face and try not to spill any of the liquid. 7. Do not touch the fabric tip with your hands. 8. Using a mirror would be helpful in finding the correct spot and probe depth. This procedure may cause some discomfort but should not be painful. 9. This will transfer the sample into the liquid. 10. You will see a red indication appear at the control line (C) after 4 min. It is important to wait the full 30 min before reading the final result as a positive result on the test line (T) may take longer to appear. 11. If you get see a positive result, you are currently infected with the SARS-CoV-2 virus and at risk of infecting others. You and any of your co-inhabitants and close contacts must self-isolate in accordance with current national and local guidance. If you get a negative result, it is likely that you were not infected at the time the test was taken. However, a negative result is not a guarantee, and you should continue to follow national and local rules to stop the spread of the virus. 12. In the case of the free NHS test kit, report your result as indicated within the kit so the NHS can track the spread of the SARS-CoV-2 virus. This can be done using the QR code or the ID number on the test cassette and reporting the results online at www.gov.uk/report¼covid-19-result. For similar tests in other countries, follow the reporting instructions in your test kit. References 1. https://www.worldometers.info/coronavirus/ 2. https://coronavirus.jhu.edu/map.html 3. Manigandan S, Wu MT, Ponnusamy VK et al (2020) A systematic review on recent trends in transmission, diagnosis, prevention and imaging features of COVID-19. Process Biochem 98:233–240

4. Hussein M, Toraih E, Elshazli R et al (2020) Meta-analysis on serial intervals and reproductive rates for SARS-CoV-2. Ann Surg 273(3): 416–423 5. Salian VS, Wright JA, Vedell PT et al (2021) COVID-19 transmission, current treatment, and future therapeutic strategies. Mol Pharm 18(3):754–771

COVID-19 Lateral Flow Test 6. Go´mez CE, Perdiguero B, Esteban M (2021) Emerging SARS-CoV-2 variants and impact in global vaccination programs against SARSCoV-2/COVID-19. Vaccines (Basel) 9(3): 2 4 3 . h t t p s : // d o i . o r g / 1 0 . 3 3 9 0 / vaccines9030243 7. Peacock TP, Penrice-Randal R, Hiscox JA et al (2021) SARS-CoV-2 one year on: evidence for ongoing viral adaptation. J Gen Virol 102(4). https://doi.org/10.1099/jgv.0.001584 8. Salzberger B, Buder F, Lampl B et al (2021) SARS-CoV-2/COVID-19-epidemiology and prevention. Nephrologe 16:1–7. https://doi. org/10.1007/s11560-020-00472-0. Online ahead of print 9. Brosnahan SB, Jonkman AH, Kugler MC et al (2020) COVID-19 and respiratory system disorders: current knowledge, future clinical and translational research questions. Arterioscler Thromb Vasc Biol 40(11):2586–2597 10. Castro RA, Frishman WH (2021) Thrombotic complications of COVID-19 infection: a review. Cardiol Rev 29(1):43–47 11. Li X, Wang L, Yan S et al (2020) Clinical characteristics of 25 death cases with COVID19: a retrospective review of medical records in a single medical center, Wuhan, China. Int J Infect Dis 94:128–132 12. Qiu P, Zhou Y, Wang F et al (2020) Clinical characteristics, laboratory outcome characteristics, comorbidities, and complications of related COVID-19 deceased: a systematic review and meta-analysis. Aging Clin Exp Res 32(9):1869–1878 13. Rashedi J, Mahdavi Poor B, Asgharzadeh V et al (2020) Risk factors for COVID-19. Infez Med 28(4):469–474 14. Mahalmani VM, Mahendru D, Semwal A et al (2020) COVID-19 pandemic: a review based on current evidence. Indian J Pharmacol 52(2): 117–129 15. Esakandari H, Nabi-Afjadi M, Fakkari-Afjadi J et al (2020) A comprehensive review of COVID-19 characteristics. Biol Proced Online 22:19. https://doi.org/10.1186/s12575020-00128-2

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16. Augustine R, Das S, Hasan A et al (2020) Rapid antibody-based COVID-19 mass surveillance: relevance, challenges, and prospects in a pandemic and post-pandemic world. J Clin Med 9(10):3372. https://doi.org/10.3390/ jcm9103372 17. Malik YA (2020) Properties of Coronavirus and SARS-CoV-2. Malays J Pathol 42(1):3–11 18. Satarker S, Nampoothiri M (2020) Structural proteins in severe acute respiratory syndrome Coronavirus-2. Arch Med Res 51(6):482–491 19. Rehman MFU, Fariha C, Anwar A et al (2021) Novel coronavirus disease (COVID-19) pandemic: a recent mini review. Comput Struct Biotechnol J 19:612–623 20. Cui F, Zhou HS (2020) Diagnostic methods and potential portable biosensors for coronavirus disease 2019. Biosens Bioelectron 165: 112349. https://doi.org/10.1016/j.bios. 2020.112349 21. Martı´n J, Tena N, Asuero AG (2021) Current state of diagnostic, screening and surveillance testing methods for COVID-19 from an analytical chemistry point of view. Microchem J 167:106305. https://doi.org/10.1016/j. microc.2021.106305 22. Shah J, Liu S, Potula HH et al (2021) IgG and IgM antibody formation to spike and nucleocapsid proteins in COVID-19 characterized by multiplex immunoblot assays. BMC Infect Dis 21(1):325. https://doi.org/10.1186/ s12879-021-06031-9 23. https://www.gov.uk/government/news/ new-campaign-urges-public-to-get-testedtwice-a-week 24. https://assets.publishing.service.gov.uk/gov ernment/uploads/system/uploads/attach ment_data/file/957271/COVID-19-selftest-instructions.pdf 25. Yetisen AK, Akram MS, Lowe CR (2013) Paper-based microfluidic point-of-care diagnostic devices. Lab Chip 13(12):2210–2251 26. Koczula KM, Gallotta A (2016) Lateral flow assays. Essays Biochem 60(1):111–120

Chapter 23 Evaluation Protocol for SARS-CoV-2 Serological Assays Maemu P. Gededzha, Sarika Jugwanth, Nakampe Mampeule, Nontobeko Zwane, Anura David, Lesley Scott, Wendy Stevens, and Elizabeth S. Mayne Abstract Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has been identified as the causative agent of COVID-19. Accurate detection of SARS-CoV-2 infection is not only important for management of infected individuals but also to break the chain of transmission. Although the polymerase chain reaction (PCR) is the gold standard for diagnosis of acute SARS-CoV-2 infection, there are a number of limitations of these assays, which include the inability to detect past infection and decline in sensitivity 14 days postsymptom onset. There are several serology tests developed for the detection of SARS-CoV-2 antibodies including high-throughput serology platforms and lateral flow immunoassays. These tests should be evaluated for their performance to meet local regulations acceptance criteria. To optimize the diagnostic algorithm for SARS-CoV-2, this protocol describes the evaluation of serological antibody testing using various automated serology platforms and lateral flow immunoassays. This protocol was evaluated in both serum and plasma samples. The sample preparation, procedure, and data analysis are described. The protocol can be adapted for any serological testing. Key words SARS-CoV-2 serology test, Automated serology Platform, Lateral flow immunoassay, Precision

1 1.1

Introduction Background

Validation is the process of evaluating analytical performance of a clinical test. This includes analytical precision (repeatability) and accuracy. Validation ensures that a diagnostic test is reliable and that the test performs in accordance with the claims of the manufacture. Viral serology (or antibody) assays are important ancillary tests. They are used to diagnose retrospective infection and may indicate immunity. They are also useful in determining the infection rates within a defined population in seroprevalence surveys [1, 2]. For severe acute respiratory coronavirus syndrome-2 (SARS-CoV2) and the associated coronavirus disease 2019 (COVID-19), serology assays have been used primarily for seroprevalence work and for

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retrospective diagnosis, particularly in patients with prolonged symptomatology (long COVID) or children with multiinflammatory syndrome of coronavirus (MIS-C) [3, 4]. Clinical presentation and severity does appear to show a relationship with the extent of the humoral response. Another potential but controversial use for the assay is to monitor post-vaccine responses [1, 5]. Prior to selection and validation, there are three important factors that should be considered: 1. The requirements for analytical performance. 2. The antigen requirements of the assay. 3. The antibody isotype to be measured. Sensitivity and specificity are measures of analytical performance of a qualitative assay. Sensitivity refers to the likelihood of a false-negative result, and specificity addresses the likelihood of a false-positive result. For screening and diagnostic purposes, a high level of sensitivity (a low false-negative rate) is desirable, but for seroprevalence surveys, higher levels of specificity may be required. In addition to the requirements for sensitivity and specificity, selection of an appropriate antigen is key. Early high-throughput platforms developed for SARS-CoV-2 measured antibody responses against the nucleocapsid (N) antigen. These tests are highly specific but may lack sensitivity at early time points postinfection [6–8]. In addition, although specific for retrospective diagnosis, these assays do not show a direct relationship with protective immunity, although the presence of a robust anti-N antibody response is an indicator of wider and potentially more protective immune responses. The final important aspect is the antibody isotype picked. Immunoglobulin G (IgG) is often measured in routine serology since this is the primary antibody of memory [5]. IgG responses are often robust and sustained. IgG in SARS-CoV-2 develops after about 10–14 days after symptom onset. IgM and IgA are produced earlier making them attractive for acute diagnosis although both isotypes have disadvantages [5]. IgM is produced in only approximately 76% of infected patients. IgA is produced by a larger percentage of patients and is highly correlated with neutralization and local protective responses. There is, however, potential crossreactivity with other coronaviruses which may reduce test specificity [9]. A final consideration is the relationship between widely available commercial serology tests and post-vaccine monitoring. Protective antibodies are neutralizing antibodies which bind to the spike (S)-protein and prevent cellular entry. Although no test has been developed commercially to detect these antibodies, a number of pseudo-neutralization assays are available [10, 11].

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Ease of Use

1.3 Special Considerations

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Lateral flow immune assays (LFIAs)/rapid tests require minimal laboratory space and equipment, can be easily interpreted, and can be performed with minimal training by testing personnel often at or near the patient. Automated serology requires a qualified trained technician, technologist, or scientist to perform the assay but require less manual input and generally have high throughput (more than 500 samples per day). The results are automatically calculated by the instrument in AU/mL, S/CO, etc. The automated serology can be easily integrated with a lab information system. These tests should be performed according to the national testing protocol. Formal automated serology testing should be performed in a laboratory by trained laboratory personnel on a specific instrument as provided by the manufacture. Lateral flow immunoassays require minimal laboratory space and equipment and can be performed with minimal training by testing personnel [12]. Serological tests results should be interpreted in conjunction with the clinical presentations of the patients and the results of other tests. This test should not be used to exclude acute/recent infection with SARS-CoV-2. False-positive results may occur due to cross-reactivity from pre-existing antibodies or other infections. Negative results do not exclude acute SARS-CoV-2 infection. Negative results may occur if serum is collected prior to the development of a humoral response or with low titer antibodies or in immunosuppressed patients [13]. It may also occur in patients with minimally symptomatic or localized infections. For correlates of protection, a viral neutralization assay should be performed [14]. The serological assay may be used for monitoring, confirmation, or surveillance purposes. Evaluations performed by our lab include those using the following platforms: ALINITY SARS-CoV-2-IgG, SARS-CoV-2IgG II Quant, and SARS-CoV-2-IgM (Abbott Diagnostics, Abbott Park, IL, USA); ARCHITECT SARS-CoV-2-IgG, SARS-CoV-2IgG II Quant, and SARS-CoV-2-IgM (Abbott Diagnostics); Roche Elecsys SARS-CoV-2-IgG, EUROIMMUN SARS-CoV2 IgG, and IgA (EUROIMMUN Medizinische Labor diagnostika AG, Lu¨beck, Germany); Beckman Coulter kit Access SARS-CoV2 IgG (Beckman Coulter, Brea, California, USA); VITROS Immunodiagnostic Products Anti-SARS-CoV-2 Total and IgG (VITROS Immuno, Raritan, NJ, USA); and over 30 lateral flow immunoassays rapid kits including Zheihang Orient Gene COVID-19 IgG/IgM, Genrui Novel Coronavirus (2019-nCoV) IgG/IgM and Biosynex COVID-19 BSS, and Boson Biotech 2019-nCoV IgG/IgM. However, any suitable serology method approved for use in human serum or plasma would be acceptable. The formal serology assays require 20 min. 23. Precision refers to the similarity of values when a test is repeated under the same conditions on the same sample. 24. More than two operators should run the precision to consider reading results variability. 25. Samples with variable relative antibody titers should be selected once the initial analysis of samples is completed. 26. The coefficient of variation (CV) measures precision from repeated measurements. The CV is used to determine how reliable assays are by determining the ratio of the standard deviation to the mean. 27. The titer at which the test becomes negative should be recorded. 28. Samples should be analyzed according to manufacturer’s instructions acceptance criteria. 29. Negative results do not exclude acute infection with SARSCoV-2. 30. In this case, collect a new sample 1 or 2 weeks later and retest. 31. “DIAGT” is a STATA module to report summary statistics for diagnostic tests compared to a patient’s true disease status.

Acknowledgments We would like to acknowledge staff members from the Department of Immunology, National Health Laboratory Service, Braamfontein. Funding Validation studies were funded as follows: sample collection was supported through a grant provided by the Bill and Melinda Gates Foundation iLEAD grant OPP1171455 awarded to Professors Wendy Stevens and Lesley Scott; and sample storage and processing was partially supported by an NIH grant number 5U24HG007438-09 awarded to Professor Elizabeth Mayne.

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References 1. Bobrovitz N, Arora RK, Cao C et al (2021) Global seroprevalence of SARS-CoV-2 antibodies: a systematic review and metaanalysis. PLoS One 16(6):e0252617. https://doi.org/ 10.1371/journal.pone.0252617 2. Mu¨ller SA, Wood RR, Hanefeld J et al (2021) Seroprevalence and risk factors of COVID-19 in healthcare workers from 11 African countries: a scoping review and appraisal of existing evidence. Health Policy Plan: czab133. https://doi.org/10.1093/heapol/ czab133 3. Riollano-Cruz M, Akkoyun E, Briceno-Brito E et al (2021) Multisystem inflammatory syndrome in children related to COVID-19: a New York City experience. J Med Virol 93(1): 424–433 4. Whittaker E, Bamford A, Kenny J et al (2020) Clinical characteristics of 58 children with a pediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2. JAMA 324(3):259–269 5. Mayne ES, Scott HL, Semete B et al (2020) The role of serological testing in the SARSCoV-2 outbreak. S Afr Med J 110(9):842–845 6. Fenwick C, Croxatto A, Coste AT et al (2021) Changes in SARS-CoV-2 spike versus nucleoprotein antibody responses impact the estimates of infections in population- based seroprevalence studies. J Virol 95(3): e01828–e01820. https://doi.org/10.1128/ JVI.01828-20 7. Grove JS, Mayne ES, Burgers WA et al (2021) Validation of Roche immunoassay for severe acute respiratory coronavirus 2 in South Africa. S Afr J Infect Dis 36(1). https://doi.org/10.4102/sajid.v36i1.286

8. Gededzha MP, Mampeule N, Jugwanth S et al (2021) Performance of the EUROIMMUN anti-SARSCoV-2 ELISA Assay for detection of IgA and IgG antibodies in South Africa. PLoS One 16(6):e0252317. https://doi.org/ 10.1371/journal.pone.0252317 9. Guo L, Ren L, Yang S et al (2020) Profiling early humoral response to diagnose novel coronavirus disease (COVID-19). Clin Infect Dis 71(15):778–785 10. Bosˇnjak B, Stein SC, Willenzon S et al (2021) Low serum neutralizing anti-SARS-CoV-2 S antibody levels in mildly affected COVID-19 convalescent patients revealed by two different detection methods. Cell Mol Immunol 18(4): 936–944 11. Valcourt EJ, Manguiat K, Robinson A et al (2021) Evaluation of a commercially-available surrogate virus neutralization test for severe acute respiratory syndrome coronavirus2 (SARS-CoV-2). Diagn Microbiol Infect Dis 99(4):115294. https://doi.org/10.1016/j. diagmicrobio.2020.115294 12. David A, Scott L, Jugwanth S et al (2021) Operational characteristics of 30 lateral flow immunoassays used to identify COVID-19 immune response. J Immunol Methods 496: 113096. https://doi.org/10.1016/j.jim. 2021.113096 13. Caturegli G, Materi J, Howard BM et al (2020) Clinical validity of serum antibodies to SARSCoV-2: a case-control study. Ann Intern Med 173(8):614–622 14. Khoury DS, Cromer D, Reynaldi A et al (2021) Neutralizing antibody levels are highly predictive of immune protection from symptomatic SARS-CoV-2 infection. Nat Med 27(7): 1205–1211

Chapter 24 Measurement of Mitochondrial Respiration in Cryopreserved Human Peripheral Blood Mononuclear Cells (PBMCs) Keiko Iwata, Min-Jue Xie, Paul C. Guest, Takaharu Hirai, and Hideo Matsuzazki Abstract Inflammatory diseases caused by infectious agents such as the SARS-CoV-2 virus can lead to impaired reductive-oxidative (REDOX) balance and disrupted mitochondrial function. Peripheral blood mononuclear cells (PBMCs) provide a useful model for studying the effects of inflammatory diseases on mitochondrial function but can be limited by the need to store these cells by cryopreservation prior to assay. Here, we describe a method for improving and determining PBMC viability with normalization of values to number of living cells. The approach can be applied not only to PBMC samples derived from patients with diseases marked by an altered inflammatory response such as viral infections. Key words Inflammatory disease, COVID-19, SARS-CoV-2, PBMCs, Cryopreservation, Mitochondrial respiration

1

Introduction At the global level, there have been more than 236 million cases and 4.8 million deaths attributed to the SARS-CoV-2 virus responsible for coronavirus 19 (COVID-19) disease, as of October 6, 2021 [1, 2]. This makes the COVID-19 pandemic the second most infectious in recorded history after the 1918–1920 influenza pandemic which was estimated to have infected more than 500 million people (approximately one quarter of the world’s 1.9 billion population at the time) [3]. COVID-19 has now affected virtually every country and territory in the world, and it is still uncertain when the infection rate will decrease to near baseline levels. The disease causes mild symptoms in most people although some suffer a more severe course which requires hospitalization and can lead to

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acute respiratory distress, intensive care unit (ICU) admission, multi-organ failure, and sometimes death [4, 5]. One positive outcome has been the successful worldwide effort to develop and distribute anti-COVID-19 vaccines across the globe [5, 6]. This has led to reduced cases and lower death rates in most countries where the vaccines have been widely distributed. As with many other respiratory viruses, severe cases of COVID19 are often marked by a hyper-inflammatory effect known as a cytokine storm [7, 8]. These is seen as a heightened activation of inflammatory molecules such as interleukin-6 (IL-6), IL-2 R, IL-10, interferon gamma-induced protein 10 (IP-10), and monocyte chemoattractant protein 1 (MCP-1). This increased secretion of inflammatory molecules can trigger other damaging effects on organs, tissues, and physiological processes such as an impairment of the reductive-oxidative (REDOX) system in severe COVID-19 cases [9–11]. Impairments in REDOX balance can lead to excessive production of reactive oxygen species (ROS), which has been attributed to diminished antioxidant defenses in viral infections [12, 13]. This appears to occur due to increased stress placed on the antioxidant defense system in counteracting the destructive effects of ROS. Viruses such as SARS-COV-2 can also alter the mitochondrial dynamics seen as a decrease in supply of energy which generates further ROS and organ damage [14]. This can also alter peripheral immune cell profiles and their ability to generate protective immunity, leading to conditions that favor viral replication [15, 16]. Several studies have reported effects on characteristics of circulating immune cells in COVID-19 patients. Vignon et al. analyzed factors associated with the cellular immune response in blood samples from COVID-19 patients and showed that more severe cases had a strong inflammatory response with immune and cytotoxic exhaustion [17]. Pontelli et al. showed that SARS-CoV-2 infection of human peripheral blood mononuclear cells (PBMCs) resulted in monocyte, B and T cell susceptibility, and the infection of monocytes increased with time [18]. Manunta et al. isolated PBMCs from early-stage COVID-19 patients and found reduced lymphocytes and monocytes, with high levels of immature neutrophils [19]. Matyushenko and colleagues stimulated PBMCs with live SARS-CoV-2 and found a decline in the pool of effector memory CD8+ T cells with no effect on CD4+ T cells in recovered COVID19 patients [20]. A flow cytometric study of PBMCs from COVID19 patients with different degrees of symptom severity found that OX40+CD137+CD4+ T cells and CD69+CD137+CD8+ T cells persisted for 8 months after symptom onset and memory CD4+ T-cell responses were higher in patients with severe illness compared to those with mild or no symptoms [21]. Another flow cytometric study found that CD8+ T cells were significantly

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reduced in moderate COVID-19 and convalescent patients, with significantly lower expression of the cytotoxic molecules perforin and granzyme A in the convalescent patients [22]. The same study used proton-nuclear magnetic resonance (1H-NMR) spectroscopy analysis and found elevated fructose, lactate, and taurine with decreased glucose, glutamate, formate, and acetate in infected compared with convalescent patients and non-infected controls. These findings supported the case that SARS-CoV-2 infection alters both CD8+ T cytotoxicity and metabolic functions of immune cells. In line with the above studies, mitochondrial dysfunction and energy deficits have been identified in PBMCs in COVID-19 cases. Codo et al. described that monocytes and macrophages are enriched in the lungs of COVID-19 patients, and these cells adapt their metabolism toward increased glycolysis, which triggers mitochondrial ROS production, reduced epithelial cell survival, and facilitated SARS-CoV-2 replication [23]. Another study showed that PBMCs from COVID-19 patients had altered bioenergetics, depolarized mitochondria, and abnormal mitochondrial ultrastructure [24]. Furthermore, Ajaz et al. found mitochondrial dysfunction compensated for by increased glycolysis in PBMCs from patients with COVID-19 [25]. Cryopreservation of clinical samples such as PBMCs is essential for repeat analyses from a single blood draw in studies of infectious diseases such as COVID-19. In addition, cryopreservation of clinical samples is useful when the sampling and analysis locations are different and/or the samples should be kept until experiment is ready to be started. After blood collection, PBMCs can be isolated, cryopreserved, and thawed when used for experiments. However, this process can be affected by two major problems that render translational of the associated findings to the clinic difficult [26– 28]. One of these is the high number of cells that die during the thawing process. The second is that it is difficult to normalize values with protein concentrations in samples in which dead and viable cell are mixed. Here, we describe a method with a culture medium containing 15% FBS to improve PBMC viability which allows normalization of values to the number of living cells. Using this method, we found no significant differences between relevant values of cryopreserved cells and fresh cells. Therefore, we applied this method to evaluate mitochondrial respiration of frozen PBMCs from control and disease groups in the same manner as when fresh PBMCs were used. This approach can be applied not only to PBMC samples derived from COVID-19 patients but also to those from patients with other diseases marked by an altered inflammatory response.

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Materials Equipment

1. CO2 incubator at 37
C (5% CO2). 2. Non-CO2 37
C incubator. 3. Laminar flow cabinet. 4. Inverted microscope. 5. Seahorse XFp Analyzer (Agilent Technologies; Santa Clara, CA, USA) (see Note 1). 6. Water bath. 7. Fluorescence microscope and CCD camera. 8. Image J software. 9. Cryogenic tubes. 10. Hemacytometer.

2.2

Reagents

1. XF assay media: Seahorse XF RPMI (Agilent Technologies) supplemented with 10 mM glucose, 2 mM glutamine, and 1 mM pyruvate (see Note 2). 2. Seahorse XFp Cell Mito Stress Test Kit containing oligomycin, carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP), and rotenone/antimycin A (see Note 1). 3. Seahorse XFp FluxPak containing XFp sensor cartridges, Seahorse XFp cell culture miniplates, and Seahorse XF calibrant solution (Agilent Technologies) (see Note 1). 4. Culture media: RPMI 1640 media supplemented with 15% (v/v) fetal bovine serum (FBS), 100 U/mL penicillin, and 100 μg/mL streptomycin (see Note 3). 5. Phosphate-buffered saline (PBS) (pH 7.4). 6. Sterile water. 7. Poly-D-lysine. 8. Lymphocyte separation solution (d ¼ 1.077) (Nacalai Tesque; Kyoto, Japan) (see Note 4). 9. CELLBANKER (Nippon Zenyaku Kogyo; Tokyo, Japan) (see Note 5). 10. 0.02 mg/mL propidium iodide (PI) in PBS.

3

Methods (See Note 6)

3.1 PBMC Isolation and Cryopreservation

1. Isolate PBMCs from whole bloods using the lymphocyte separation solution in accordance with the manufacturer’s instructions.

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2. Suspend PBMCs in PBS and count the cell number. 3. Centrifuge cells at 400  g for 5 min and discard the medium. 4. Resuspend PBMCs in the CELLBANKER. 5. Freeze cells in cryogenic tubes (at least 106 cells/tube) and keep them at 80
C until they are used for experiments. 3.2 XFp Cell Mito Stress Test Preparation

1. On the day prior to the assay, add 20 uL poly-D-lysine (50 ug/ mL) in each well of Seahorse XFp cell culture miniplates. 2. Incubate 30 min at 37
C. 3. Remove solution by aspiration and wash surface with sterile water. 4. Allow to dry at least 2 h in the laminar flow cabinet. 5. Take out the cryogenic tube from 80
C storage. 6. Thaw in a 37
C water bath for less than 1 min. 7. In the laminar flow cabinet, pipet the content of the tube into a falcon tube containing 10 mL culture medium (containing 15% FBS). 8. Centrifuge at 400  g for 5 min. 9. Discard the supernatant by aspiration and resuspend the pellet in 250 μL culture medium. 10. Count the cell number using a hemacytometer and adjust to 2  106 cells/mL. 11. Add 75 uL of cell suspension to wells of the poly-D-lysine coated miniplates (1.5  105 cells/well) (see Note 7). 12. Fill the moats around the outside of the wells with 400 μL sterile water per chamber. 13. Allow plate to rest at room temperature in laminar flow cabinet for 1 h (see Note 8). 14. Monitor adherence using a microscope. 15. Incubate overnight in a 5% CO2 incubator at 37
C. 16. To prepare the assay, aliquot 2 mL/assay of XF calibrant in a 15 mL conical tube. 17. Place the tube in a non-CO2 incubator at 37
C overnight. 18. Take out the XFp sensor cartridge and separate the utility plate from the cartridge. 19. Place the sensor cartridge upside down on the bench. 20. Fill each well of the utility plate with 200 μL of sterile water. 21. Fill the moats around the outside of the wells with 400 μL sterile water per chamber.

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22. Return the sensor cartridge to the utility plate with sterile water (see Note 9). 23. Place the sensor cartridge/utility plate assembly in a non-CO2 incubator at 37
C overnight (see Note 10). 3.3 XFp Cell Mito Stress Test

1. On the day of the assay, turn on Seahorse XFp analyzer, and check that the temperature reaches 37
C. 2. Prepare the Seahorse XF RPMI medium on the day as in Subheading 2.2, step 1 above. 3. Warm the assay medium to 37
C in a water bath. 4. Remove the conical tube of XF calibrant and sensor cartridge/ utility plate assembly from the incubator. 5. Place the sensor cartridge upside down on the bench. 6. Discard water from the wells of utility plate. 7. Fill each well of the utility plate with 200 μL of pre-warmed XF calibrant. 8. Return the sensor cartridge to the utility plate with XF calibrant (see Note 9). 9. Place the sensor cartridge/utility plate assembly in the non-CO2 incubator at 37
C for 45–60 min. 10. Prepare the separate stock solutions of oligomycin (45 μM), FCCP (50 μM), and rotenone/antimycin A (25 μM) in assay medium (see Note 11). 11. Prepare the separate working solutions of oligomycin (15 μM), FCCP (25 μM), and rotenone/antimycin A (5 μM) in assay medium. 12. Load working solutions into the sensor cartridge as follows (see Note 12): (a) Port A: 20 μL oligomycin (final concentration in a well, 1.5 μM). (b) Port B: 22 μL FCCP (Final concentration in a well, 2.5 μM). (c) Port C: 25 μl rotenone/antimycin A (final concentration in a well, 0.5 μM). 13. Take out XFp cell culture miniplates from the 37
C CO2 incubator and examine the cells under a microscope to confirm confluence. 14. Remove the assay medium from the water bath. 15. Change the culture medium in the cell culture microplate to warmed assay medium using a pipette as follows (see Note 13):

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(a) Add 200 μL assay medium and remove 200 μL, 4–5 times. (b) Add 105 μL medium (180 μL medium in each well in total). 16. Place the cell culture microplate into a 37
C non-CO2 incubator for 45 min to 1 h prior to the assay. 17. Select the Agilent Seahorse XFp Cell Mito Stress Test and run the assay according to the manufacturer’s instructions. 18. After the test, save the data. 19. Immediately after the assay, stain the PBMCs with PI (see Note 14). 20. Wash PBMCs with PBS gently as described in Subheading 3.3, step 14. 21. Add PBS up to 100 μL. 22. Add 100 μL PI solution to give a final concentration of 0.01 mg/mL. 23. Incubate at 37
C for 15 min. 24. Observe PI-positive cells using a fluorescence microscope. 25. Wash cells with PBS very gently as described Subheading 3.3, step 14. 26. Take photographs of the bright-field image and the fluorescent PI image of the same field with CCD camera (see Fig. 1a). 27. Use ImageJ (or a suitable alternative) to define three (or more) regions of interest (ROI) (see Fig. 1a) (see Note 15). 28. Count total cell number in the bright-field image and PI-positive cell number in the fluorescence image (see Fig. 1b) (see Note 16). 29. Calculate viable cell count in a well as follows (see Fig. 1c for details) (see Note 17): (a) Calculate viable cell count in a ROI. (b) Take average of viable cell count of each ROI. (c) Calculate viable cell count in a well. 30. Analyze the data using Agilent Seahorse Analytics [30]. 31. Normalize the oxygen consumption rate (OCR) values as follows (see Fig. 1d) (see Note 18): (a) Go to the Normalization page. (b) Set the Normalization unit to Cells. (c) Set the Scale Factor to 100,000. (d) Input viable cell count in each well.

Fig. 1 Normalization of the data by living cell number counting. (a) bright-field and fluorescent PI image of the same field. (b) Total cell count in bright-field image and PI-positive cell number in the fluorescence image. (c) Determination of viable cell count per well in each ROI. (d) Normalization of oxygen consumption rate (OCR) values

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Notes 1. The kits and amounts of reagents are suitable for XFp Analyzer. When the Seahorse XFe24 or Analyzer Seahorse XFe96 Analyzer is used, the kits and amounts of reagents should be optimized for these. 2. If recommended medium and supplements are not used, pH-adjustment might be needed. 3. The 15% (v/v) FBS is important to increase viability of the PBCMs. 4. Similar reagents can be used here but should be optimized in line with experimental needs. 5. This is a series of cryopreservation media for stable long-term storage of cells. Other similar systems can be used from other suppliers. 6. EDTA blood samples were obtained by venepuncture from healthy volunteers. All subjects were non-smokers with no known recent exposure to genotoxic chemicals or radiation. The study was performed in accordance with the approval by the ethical committee of the University of Fukui. 7. When fresh PBMCs are used, the optimal concentration is 1.0  105 cells/well. 8. This can promote even cell distribution and reduce edge effects for some cell types [29]. 9. Make sure the filter is submerged in sterile water and there are no bubbles at the filter. 10. To prevent evaporation of the water, the incubator should be humid or the sensor cartridge/utility plate assembly placed into a humid container. 11. These solutions should be prepared fresh on the day of the assay. 12. The optimal final compound concentration for achieving maximal effect is cell line dependent and may be affected by assay medium types. Therefore, it is recommended that, for each new cell line or assay medium, a titration experiment for the compounds is performed. This is especially important with FCCP, as the titration curve tends to be sharp and too much FCCP can diminish responses in the O2 consumption rate. We performed the titration experiment for FCCP, and 2.5 μM FCCP was the optimal concentration for PBMCs. 13. This process should be performed gently to avoid cells detaching from the plate surface.

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Fig. 2 Comparison of the viabilities of PBMCs in the culture medium containing 10% and 15% FBS

14. PI staining should be done as soon as possible to avoid cell death due to long-term exposure to the compounds in the assay. We confirmed that there is no difference of cell viability between cells with and without assay, when cells are stained with PI immediately after the assay. 15. Each ROI area should be the same. 16. The viability will be 60–70% with this method (with medium containing 15% FBS), while the viability will be 40–50% when the cells will be cultured with the medium containing 10% FBS normally used for PBMCs culture [31] (see Fig. 2). 17. Viable cell count/well will be approximately 1.0  105 with this method. 18. The OCR values from cryopreserved PBCMs are not significantly different from the values of fresh PBCMs (see Fig. 3).

Acknowledgments This work was supported in part by JSPS KAKENHI (grant no. JP19K08041), the Naito Foundation, and the Ichiro Kanehara Foundation for the Promotion of Medical Science & Medical Care to K.I. We are grateful to Fumiho Yamamoto and Natsuki Miyakoshi for technical assistance and Tomoko Taniguchi for secretarial assistance. We also thank Yasuhiro Horie, Primetec Co., for technical advice.

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Fig. 3 Mitochondrial respiration function parameters of human fresh and cryopreserved (CS) PBCMs using the Seahorse XFp Analyzer. (a) The oxygen consumption rate (OCR) of human fresh and CS PBCMs after normalization. (b) Basal respiration, ATP production, maximal respiration, and spare respiratory capacity of human fresh and CS PBCMs after normalization. Two-tailed unpaired t-test was used after no violation of the equal variance assumption was confirmed by F test (n ¼ 3). There is no difference in OCR between fresh and CS PBCMs after normalization References 1. https://www.worldometers.info/coronavi rus/. Accessed 6 Oct 2021 2. https://coronavirus.jhu.edu/map.html. Accessed 6 Oct 2021 3. https://www.cdc.gov/flu/pandemic-res ources/1918-pandemic-h1n1.html. Accessed 6 Oct 2021 4. Guest PC (2021) Clinical, biological and molecular aspects of COVID-19. Adv Exp Med Biol 1321. Springer, New York. ISBN13:978-3030592608 5. Guest PC (2021) Identification of biomarkers, new treatments, and vaccines for COVID-19. Adv Exp Med Biol 1327. Springer, New York. ISBN-13:978-3030716967

6 . h t t p s : // o u r w o r l d i n d a t a . o r g / c o v i d vaccinations. Accessed 6 Oct 2021 7. Azar MM, Shin JJ, Kang I et al (2020) Diagnosis of SARS-CoV-2 infection in the setting of the cytokine release syndrome. Expert Rev Mol Diagn 20(11):1087–1097 8. Morris G, Bortolasci CC, Puri BK et al (2021) The cytokine storms of COVID-19, H1N1 influenza, CRS and MAS compared. Can one sized treatment fit all? Cytokine 144(155593). https://doi.org/10.1016/j.cyto.2021. 155593 9. Silvagno F, Vernone A, Pescarmona GP (2020) The role of glutathione in protecting against the severe inflammatory response triggered by

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COVID-19. Antioxidants (Basel) 9(7):624. https://doi.org/10.3390/antiox9070624 10. Saleh J, Peyssonnaux C, Singh KK et al (2020) Mitochondria and microbiota dysfunction in COVID-19 pathogenesis. Mitochondrion 54: 1–7 11. Lippi A, Domingues R, Setz C et al (2020) SARS-CoV-2: at the crossroad between aging and neurodegeneration. Mov Disord 35(5): 716–720 12. Polonikov A (2020) Endogenous deficiency of glutathione as the most likely cause of serious manifestations and death in COVID-19 patients. ACS Infect Dis 6(7):1558–1562 13. Muhammad Y, Kani YA, Iliya S et al (2021) Deficiency of antioxidants and increased oxidative stress in COVID-19 patients: a crosssectional comparative study in Jigawa, northwestern Nigeria. SAGE Open Med 9: 2050312121991246. https://doi.org/10. 1177/2050312121991246 14. Singh SP, Amar S, Gehlot P et al (2021) Mitochondrial modulations, autophagy pathways shifts in viral infections: consequences of COVID-19. Int J Mol Sci 22(15):8180. https://doi.org/10.3390/ijms22158180 15. Codo AC, Davanzo GG, Monteiro LB et al (2020) Elevated glucose levels favor SARSCoV-2 infection and monocyte response through a HIF-1alpha/glycolysis-dependent axis. Cell Metab 32(3):437–446.e5. https:// doi.org/10.1016/j.cmet.2020.07.007 16. Thompson EA, Cascino K, Ordonez AA et al (2021) Metabolic programs define dysfunctional immune responses in severe COVID-19 patients. Cell Rep 34(11):108863. https:// doi.org/10.1016/j.celrep.2021.108863 17. Vigo´n L, Fuertes D, Garcı´a-Pe´rez J et al (2021) Impaired cytotoxic response in PBMCs from patients with COVID-19 admitted to the ICU: biomarkers to predict disease severity. Front Immunol 12:665329. https://doi.org/ 10.3389/fimmu.2021.665329 18. Pontelli MC, Castro IA, Martins RB et al (2020) Infection of human lymphomononuclear cells by SARS-CoV-2. bioRxiv:2020.07.28.225912. https://doi.org/10. 1101/2020.07.28.225912 19. Manunta MDI, Lamorte G, Ferrari F et al (2021) Impact of SARS-CoV-2 infection on the recovery of peripheral blood mononuclear cells by density gradient. Sci Rep 11(1):4904. https://doi.org/10.1038/s41598-02183950-2 20. Matyushenko V, Isakova-Sivak I, Kudryavtsev I et al (2021) Detection of IFNgamma-secreting CD4(+) and CD8(+) memory T cells in COVID-19 convalescents after stimulation of

peripheral blood mononuclear cells with live SARS-CoV-2. Viruses 13(8):1490. https:// doi.org/10.3390/v13081490 21. Kang CK, Kim M, Lee S et al (2021) Longitudinal analysis of human memory T-cell response according to the severity of illness up to 8 months after severe acute respiratory syndrome coronavirus 2 infection. J Infect Dis 224(1):39–48 22. Singh Y, Trautwein C, Fendel R et al (2021) SARS-CoV-2 infection paralyzes cytotoxic and metabolic functions of the immune cells. Heliyon 7(6):e07147. https://doi.org/10.1016/j. heliyon.2021.e07147 23. Codo AC, Davanzo GG, Monteiro LB et al (2020) Elevated glucose levels favor SARSCoV-2 infection and monocyte response through a HIF-1alpha/glycolysis-dependent axis. Cell Metab 32(3):437–446.e5. https:// doi.org/10.1016/j.cmet.2020.07.007 24. Gibellini L, De Biasi S, Paolini A et al (2020) Altered bioenergetics and mitochondrial dysfunction of monocytes in patients with COVID-19 pneumonia. EMBO Mol Med 12(12):e13001. https://doi.org/10.15252/ emmm.202013001 25. Ajaz S, McPhail MJ, Singh KK et al (2021) Mitochondrial metabolic manipulation by SARS-CoV-2 in peripheral blood mononuclear cells of patients with COVID-19. Am J Physiol Cell Physiol 320(1):C57–C65 26. Marino M, Gigliotti L, Møller P et al (2021) Impact of 12-month cryopreservation on endogenous DNA damage in whole blood and isolated mononuclear cells evaluated by the comet assay. Sci Rep 11(1):363. https:// doi.org/10.1038/s41598-020-79670-8 27. Xu Y, Zou Q, Gao F et al (2021) Effect of warming process on the survival of cryopreserved human peripheral blood mononuclear cells. Biopreserv Biobank 19(4):318–323 28. Capelle CM, Cire´ S, Ammerlaan W et al (2021) Standard peripheral blood mononuclear cell cryopreservation selectively decreases detection of nine clinically relevant T cell markers. Immunohorizons 5(8):711–720 29. Lundholt BK, Scudder KM, Pagliaro L (2003) A simple technique for reducing edge effect in cell-based assays. J Biomol Screen 8(5): 566–567 30. https://seahorseanalytics.agilent.com 31. Barnabe M (2017) Peripheral blood mononuclear cells: PBMC isolation, preservation, and culture. Quartzy; Science News. https://blog. quartzy.com/2017/05/30/peripheral-bloodmononuclear-cells-pbmc-isolation-preserva tion-culture. Accessed 25 Oct 2021

Chapter 25 Multiplex Testing of Oxidative-Reductive Pathway in Patients with COVID-19 Paul C. Guest, Mitra Abbasifard, Tannaz Jamialahmadi, Muhammed Majeed, Prashant Kesharwani, and Amirhossein Sahebkar Abstract Infection with SARS-CoV-2, the causative agent of COVID-19, causes numerous cellular dysfunctions. The virus enters the host cells and hijacks the cell machinery for its replication, resulting in disturbances of the oxidative, reductive balance, increased production of damaging reactive oxygen species (ROS), and mitochondrial dysfunction. This damaging cycle can make cells less resistant to infection and make the host more likely to experience a severe disease course. Treatment with antioxidants has been tested as a potential approach to reduce the effects of this disorder. Here, we present a protocol to assess the impact of treatment with a mixture of curcuminoids on physiological and molecular biomarkers, focusing on determining total antioxidant capacity. We used a cohort of diabetes patients with an imbalance in redox mechanisms as such patients are more likely to become severely ill from COVID-19 than healthy persons. Key words SARS-CoV-2, COVID-19, Oxidative stress, ROS, Total antioxidant capacity, Diabetes, Antioxidants, Curcuminoids

1

Introduction SARS-CoV-2 is the causative agent of COVID-19. As of May 1, 2021, the ongoing COVID-19 pandemic has topped 150 million cases and 3.2 million deaths worldwide [1, 2]. The situation is highly dynamic, with the USA being the worst affected country, followed by India and Brazil (see Fig. 1). The risk of death after contracting the virus is approximately 2%, with the highest numbers appearing in the USA (590 K), followed by Brazil (406 K), Mexico (217 K), India (212 K), and the UK (127 K) (as of May 1, 2021) [1, 2]. It is now widely accepted that a cytokine storm response can significantly contribute to disease progression, which correlates with increased severity and is the most likely cause of mortality in COVID-19 cases [3, 4]. Septic shock, multi-organ failure, and respiratory failure appear to be the most common immediate

Paul C. Guest (ed.), Multiplex Biomarker Techniques: Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification, Methods in Molecular Biology, vol. 2511, https://doi.org/10.1007/978-1-0716-2395-4_25, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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cause of death [5–7]. In addition, the greatest risk factors for increased mortality in COVID-19 patients are older age or being male or the presence of comorbidities such as diabetes, hypertension, ischemic heart disease, and obesity [8, 9]. Besides the increased cytokine release and elevations in the classical biomarkers of acute inflammation, the evidence suggests that these factors trigger an impairment of the oxidative, reductive system in severe cases of COVID-19 [10–13]. Impairments in the antioxidant-pro-oxidant balance can lead to the generation of reactive oxygen species (ROS), caused by diminished antioxidant defenses in viral infections [14, 15] (see Fig. 2). Depletion of endogenous antioxidants occurs due to the increased demands on this system in counterbalancing the damaging effects of ROS on cellular membranes and macromolecules. Furthermore, the risk of a severe outcome of COVID-19 cases is likely to be higher if the patient is comorbid for a condition such as type 2 diabetes mellitus. This may be confounded by the occurrence of increased oxidative stress, along with reduced antioxidant capacity, in diabetes patients [16–18]. ROS consist of molecules such as hydrogen peroxide (H2O2), superoxide (O2∙
), single oxygen (1/2O2), and hydroxyl radical (∙OH) [19]. Excessive production of ROS in an organism can cause oxidative stress, which can have damaging effects on cells and tissues. In the healthy state, cells are equipped with endogenous antioxidants, which counteract any over-produced ROS [20]. Antioxidants can be classified as enzymatic and non-enzymatic molecules (see Table 1). Severe oxidative stress is the state in which these

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ORGAN AND TISSUE DAMAGE Fig. 2 Effects of SARS-CoV-2 infection on mitochondrial dysfunction, elevated ROS production, and organrelated pathologies. The SARS-CoV-2 spike proteins are cleaved by TMPRSS2, allowing binding to ACE2 and internalization of the virus via endocytosis. This leads to oxidative damage to the ETC, which, in turn, leads to an elevation of ROS. The increased ROS causes further mitochondrial damage, increased production of inflammatory cytokines, and activation of REDOX-sensitive pathways. Ultimately, this leads to the organ and tissue damage which are hallmarks of SARS-CoV-2 infection. ROS ¼ reactive oxygen species, REDOX ¼ oxidative-reductive, TMPRSS2 ¼ transmembrane protease serine 2, Mt. ¼ mitochondria, ETC ¼ electron transport chain

antioxidants cannot effectively counterbalance any increase in oxidative stress levels (see Fig. 3). For these reasons, identifying new compounds as a treatment for the damaging inflammatory and oxidative effects in COVID-19 cases has been considered a potential approach to reducing disease severity and improving patient outcomes.

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Table 1 Endogenous antioxidant defense system Antioxidant Enzymatic

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Fig. 3 A significant increase in ROS formation can overtax the endogenous antioxidant defense system, resulting in a shift to increased oxidative stress

Curcumin is a natural compound derived from turmeric rhizomes (Curcuma longa L.), with a recorded history of medicinal use. In addition to curcumin, these rhizomes contain other major curcuminoids known as demethoxycurcumin and bisdemethoxycurcumin (see Fig. 4), which possess a number of health-promoting benefits, including anti-inflammatory and antioxidant properties [21–23]. Curcumin has already been suggested as a potential therapeutic in COVID-19 through its immune-boosting, antiviral, antioxidant, and anti-inflammatory properties [24–27]. In addition, computer modeling studies have predicted an interference of curcumin with the binding of SARS-CoV-2 spike protein with host angiotensin-converting enzyme 2 (ACE2) receptors, thereby blocking the entry of the virus into cells [28]. Similar studies have indicated that curcumin may also block the activity of the SARSCoV-2 main protease and interfere with viral replication [29]. Here we present a clinical study of type 2 diabetes patients to demonstrate the potential of curcuminoids as a potential COVID19 treatment. The primary endpoint was an assay to determine the

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Fig. 4 Turmeric rhizome, powder, and major curcuminoids

Fig. 5 The total antioxidant capacity assay is based on the reduction of Cu2+ to Cu+ by endogenous antioxidants. The Cu+ reacts with an added chromogen which produces a color with a maximum absorbance at 570 nm

total antioxidant capacity (TAC) in serum taken from the patients treated with curcuminoids compared to those treated with placebo, as described [30]. The TAC assay measures the total antioxidant capacity of molecules based on the reduction of copper (II) to copper (I) (see Fig. 5). The reduced form of copper then reacts with a chromogenic reagent that produces a color with a maximum absorbance at 570 nm. In this way, the TAC assay acts as a multiplex monitoring tool of the enzymatic and non-enzymatic antioxidant systems in biological samples.

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Materials

2.1 Participants, Samples, and Reagents

1. Subjects aged 18–65 years with type 2 diabetes (n ¼ 118) (see Note 1). 2. Curcumin C3 Complex(R) capsules: 500 mg curcuminoids (curcumin, demethoxycurcumin, and bisdemethoxycurcumin), 5 mg piperine (Sabinsa Group Limited, Bangalore, India) (see Note 2). 3. Matching placebo capsules for shape, size, color, and texture (Sabinsa Group Limited). 4. Phlebotomy kit. 5. Blood serum collection tubes. 6. Glucose test strips and monitor. 7. Insulin immunoassay kit.

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1. TAC kit components (see Note 3): (a) 1 mM Trolox standard dissolved in 2% dimethyl sulfoxide (DMSO) (b) Assay diluent. (c) 2% Cu2+ reagent (prepare fresh in assay diluent) (d) 10 stop solution (e) Protein mask (prevents Cu2+ reduction by protein). 2. Phosphate-buffered saline (PBS, pH 7.4): 10 mM disodium phosphate, 1.8 mM monopotassium phosphate, 2.7 mM potassium chloride, 137 mM sodium chloride. 3. Microplate reader. 4. Double-deionized water. 5. Triton X-100.

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1. Ion exchange chromatography apparatus. 2. Microtiter plates. 3. Spectrophotometer capable or reading 570 nm. 4. 96-well plate with clear flat bottom. 5. Homogenizer. 6. Microcentrifuge. 7. SPSS Statistics for Windows v20.0 (IBM Corp., Armonk, NY, USA) or similar.

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Methods Treatment

1. Recruit subjects with type 2 diabetes according to the following criteria: Inclusion criteria. (a) Fasting plasma glucose (FPG) 7 mM (see Note 4). (b) Glycated hemoglobin (HbA1C) 6.5% (see Note 5). (c) Current use of antidiabetic medications. Exclusion criteria. (a) Females who are pregnant or breastfeeding. (b) No informed consent or lack of study compliance. (c) Presence of cancer, chronic liver disease, biliary or cholestatic diseases, renal failure, chronic inflammatory diseases, and non-diabetes endocrine diseases. (d) Hyperglycemia due to secondary causes. (e) Currently receiving hormone or herbal medicine remedies. 2. Initiate study in a randomized, double-blind placebo-controlled format. 3. Allocate subjects using a randomized, blinded system to: Group 1 – Curcuminoids treatment for 8 weeks (see Note 6). Group 2 – Placebo treatment for 8 weeks (see Note 6). 4. Administer capsules to subjects under participant- and researcher-blinded conditions. 5. Terminate the study after 8 weeks.

3.2 Blood Sampling and Laboratory Analyses

1. Collect blood samples from subjects fasted overnight at baseline (just before curcuminoids administration) and at the end of the study into serum tubes using standard phlebotomy techniques. 2. Leave the blood to clot 60 min at room temperature. 3. Centrifuge 10 min at 750  g and collect sera, taking care to avoid disturbing the pellet. 2. Aliquot the sera and store at -80  C until ready to begin the assay (see Note 7). 3. Determine glucose levels with strips and monitor, following the manufacturer’s instructions. 4. Determine the levels of insulin using the immunoassay kit. 5. Calculate insulin resistance using the homeostatic model assessment for insulin resistance (HOMA-IR) formula (see Note 8):

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fasting insulinðlIU=mLÞ  fasting glucoseðmmol=LÞ=22:5Þ

6. Determine weight and height, using standard procedures, and calculate BMI as weight (in kg) divided by height in meters squared (m2). 3.3

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1. Prepare 100 μL containing 0, 4, 8, 12, 16, and 20 nmoles Trolox in assay diluent in each designated well of the 96-well microplate. 2. Add 10 μL serum samples to each well of the microplate (see Note 9). 3. For the assay, add 100 μL freshly prepared 2% Cu2+ solution to all wells of the plate. 4. Incubate the plate 90 min at room temperature with gentle shaking in the dark. 5. Measure the absorbance in each well on the microplate reader at 570 nm. 6. Subtract the absorbance of the 0 nmoles Trolox blank from all readings and plot the corrected values for each of the standards. 7. Interpolate the concentrations of Trolox in each sample from the standard curve (see Fig. 6).

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1. Perform statistical analyses for all measures using the SPSS statistics package or similar. 2. Express data as mean  standard deviation as appropriate. 3. Carryout within-group comparisons using paired sample t-tests or Wilcoxon signed-ranks tests for data that is normally or non-normally distributed, respectively. 4. Perform between-group comparisons using independent samples t-test or Mann-Whitney U test for normally and non-normally distributed data, respectively. 5. Perform bivariate correlations between serum levels of TAC changes using Pearson’s and Spearman’s correlation coefficients for normally and non-normally distributed data, respectively (see Note 10). 6. Perform univariate analysis of covariance (ANCOVA) using a general linear model to adjust for effects of potential confounding factors on associations between curcumin supplementation and changes in TAC (see Note 11).

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Notes 1. Ensure that all approvals are in place before commencing the study. In this case, the protocol was approved by the Ethics Committee at the Baqiyatallah University of Medical Sciences, registered in the Iranian Registry of Clinical Trials, and written informed consent was received from all participants. 2. Clinical use of curcuminoids is limited by low oral bioavailability due to rapid intestinal and hepatic metabolism. In this study, curcuminoids were co-administered with piperine. This enhances absorption by blocking the intestinal and hepatic glucuronidation pathways. 3. This kit measures small molecule and protein antioxidants in the presence of a proprietary protein mask. The assay works by conversion of Cu2+ to Cu+ by the presence of antioxidants in the sample. The Protein Mask stops the reduction of Cu2+ reduction by protein-based antioxidants, leaving the analysis of only the small molecules (although we did not use that here as total antioxidants were measured). Chelation of the produced Cu+ with a colorimetric probe allows reading of the absorbance at 570 nm in proportion to the total antioxidant capacity. Trolox is a water-soluble analogue of vitamin E used as a standard antioxidant in the assay. 4. Normal levels for normal fasting blood glucose concentrations are between 3.9 and 5.6 mM. 5. The normal range for glycated hemoglobin levels is between 4% and 5.6%.

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6. This study was designed to investigate the effects of curcuminoids in type 2 diabetes patients. Depending on whether acute or chronic effects are under investigation, a different time course would likely be required for studies involving COVID19 patients. 7. Aliquoting should always be performed to minimize the loss or cryoprecipitation of some serum analytes that may occur with repeated freeze-thaw cycles. 8. Not only is diabetes a risk factor for more severe COVID-19 outcomes, but infection with the SARS-CoV-2 virus may also induce an insulin-resistant state in subjects who did not previously have a type 2 diabetes diagnosis [31]. At baseline, the blood glucose levels ranged from 7.8 to 11 mM, insulin levels average higher than 20 mIU/L and HOMO-IR values averaged greater than 8 in both placebo and control groups (a HOMO-IR greater than 3.8 is considered a sign of insulin resistance). 9. The sample volume may differ in modified versions of the assay as well as in other kits. Generally, 1–10 μL is sufficient. 10. This revealed that TAC levels were significantly increased in the treatment group compared with the control group, as described previously [30]. This finding supported the effect of curcuminoids supplementation to increase antioxidant capacity in patients with type 2 diabetes. This calls for future studies to assess the impact of curcuminoids in COVID-19 patients with or without diabetic comorbidities. 11. This revealed that the increase in TAC levels in the curcuminoid supplementation group remained significant after adjusting for baseline differences in BMI and insulin levels in both males and females [30].

Conflict of Interest Muhammed Majeed is the CEO of Sabinsa Corporation and Sabinsa Group Limited. References 1. https://www.worldometers.info/coronavirus/ 2. https://coronavirus.jhu.edu/map.html 3. Samprathi M, Jayashree M (2021) Biomarkers in COVID-19: an up-to-date review. Front Pediatr 8:607647. https://doi.org/10.3389/ fped.2020.607647 4. Rowaiye AB, Okpalefe OA, Onuh Adejoke O et al (2021) Attenuating the effects of novel

COVID-19 (SARS-CoV-2) infection-induced cytokine storm and the implications. J Inflamm Res 14:1487–1510 5. Xiao YJ, Dong X, Yang HZ et al (2020) Clinical features of 141 fatal cases of coronavirus disease in Jinyintan Hospital in Wuhan, China. Zhonghua Jie He He Hu Xi Za Zhi 44(4):354–359

Oxidative Stress in COVID-19 ˜ o J et al 6. Berenguer J, Ryan P, Rodrı´guez-Ban (2020) Characteristics and predictors of death among 4035 consecutively hospitalized patients with COVID-19 in Spain. Clin Microbiol Infect 26(11):1525–1536 7. Elezkurtaj S, Greuel S, Ihlow J et al (2021) Causes of death and comorbidities in hospitalized patients with COVID-19. Sci Rep 11(1): 4263. https://doi.org/10.1038/s41598021-82862-5 ´ lvarez-Bueno 8. Mesas AE, Cavero-Redondo I, A C et al (2020) Predictors of in-hospital COVID-19 mortality: a comprehensive systematic review and meta-analysis exploring differences by age, sex and health conditions. PLoS One 15(11):e0241742. https://doi. org/10.1371/journal.pone.0241742 9. Xiang G, Xie L, Chen Z, Hao S et al (2021) Clinical risk factors for mortality of hospitalized patients with COVID-19: systematic review and meta-analysis. Ann Palliat Med 10(3):2723–2735 10. Liu Z, Ying Y (2020) The inhibitory effect of curcumin on virus-induced cytokine storm and its potential use in the associated severe pneumonia. Front Cell Dev Biol 8:479. https://doi. org/10.3389/fcell.2020.00479 11. Silvagno F, Vernone A, Pescarmona GP (2020) The role of glutathione in protecting against the severe inflammatory response triggered by COVID-19. Antioxidants (Basel) 9(7):624. https://doi.org/10.3390/antiox9070624 12. Saleh J, Peyssonnaux C, Singh KK et al (2020) Mitochondria and microbiota dysfunction in COVID-19 pathogenesis. Mitochondrion 54: 1–7 13. Lippi A, Domingues R, Setz C et al (2020) SARS-CoV-2: at the crossroad between aging and neurodegeneration. Mov Disord 35(5): 716–720 14. Polonikov A (2020) Endogenous deficiency of glutathione as the most likely cause of serious manifestations and death in COVID-19 patients. ACS Infect Dis 6(7):1558–1562 15. Muhammad Y, Kani YA, Iliya S et al (2021) Deficiency of antioxidants and increased oxidative stress in COVID-19 patients: a crosssectional comparative study in Jigawa, northwestern Nigeria. SAGE Open Med 9: 2050312121991246. https://doi.org/10. 1177/2050312121991246 16. Luc K, Schramm-Luc A, Guzik TJ, Mikolajczyk TP (2019) Oxidative stress and inflammatory markers in prediabetes and diabetes. J Physiol Pharmacol 70(6). https://doi.org/10. 26402/jpp.2019.6.01

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17. Yaribeygi H, Atkin SL, Sahebkar A (2019) A review of the molecular mechanisms of hyperglycemia-induced free radical generation leading to oxidative stress. J Cell Physiol 234(2):1300–1312 18. Yaribeygi H, Butler AE, Barreto GE, Sahebkar A (2019) Antioxidative potential of antidiabetic agents: a possible protective mechanism against vascular complications in diabetic patients. J Cell Physiol 234(3):2436–2446 19. Poljsak B, Sˇuput D, Milisav I (2013) Achieving the balance between ROS and antioxidants: when to use the synthetic antioxidants. Oxidative Med Cell Longev 2013:956792. https:// doi.org/10.1155/2013/956792 20. Birben E, Sahiner UM, Sackesen C et al (2012) Oxidative stress and antioxidant defense. World Allergy Organ J 5(1):9–19 21. Borra SK, Mahendra J, Gurumurthy P et al (2014) Effect of curcumin against oxidation of biomolecules by hydroxyl radicals. J Clin Diagn Res 8(10):CC01-5. https://doi.org/ 10.7860/JCDR/2014/8517.4967 22. Abrahams S, Haylett WL, Johnson G et al (2019) Antioxidant effects of curcumin in models of neurodegeneration, aging, oxidative and nitrosative stress: a review. Neuroscience 406:1–21 23. Sahebkar A, Serban MC, Ursoniu S et al (2015) Effect of curcuminoids on oxidative stress: a systematic review and meta-analysis of randomized controlled trials. J Funct Foods 18:898–909 24. Mrityunjaya M, Pavithra V, Neelam R et al (2020) Immune-boosting, antioxidant and anti-inflammatory food supplements targeting pathogenesis of COVID-19. Front Immunol 11:570122. https://doi.org/10.3389/ fimmu.2020.570122 25. Babaei F, Nassiri-Asl M, Hosseinzadeh H (2020) Curcumin (a constituent of turmeric): new treatment option against COVID-19. Food Sci Nutr 8(10):5215–5227 26. Heidari Z, Mohammadi M, Sahebkar A (2021) Possible mechanisms and special clinical considerations of curcumin supplementation in patients with COVID-19. Adv Exp Med Biol 1308:127–136 27. Peter AE, Sandeep BV, Rao BG et al (2021) Calming the storm: natural immunosuppressants as adjuvants to target the cytokine storm in COVID-19. Front Pharmacol 11:583777. h ttps://do i.o rg/1 0.3 38 9/fph a r.20 20. 583777 28. Jena AB, Kanungo N, Nayak V et al (2021) Catechin and curcumin interact with S protein of SARS-CoV2 and ACE2 of human cell

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membrane: insights from computational studies. Sci Rep 11(1):2043. https://doi.org/10. 1038/s41598-021-81462-7 29. Teli DM, Shah MB, Chhabria MT (2021) In silico screening of natural compounds as potential inhibitors of SARS-CoV-2 main protease and spike RBD: targets for COVID-19. Front Mol Biosci 7:599079. https://doi.org/ 10.3389/fmolb.2020.599079 30. Panahi Y, Khalili N, Sahebi E et al (2017) Antioxidant effects of curcuminoids in patients

with type 2 diabetes mellitus: a randomized controlled trial. Inflammopharmacology 25(1):25–31 31. Chen M, Zhu B, Chen D et al (2021) COVID19 may increase the risk of insulin resistance in adult patients without diabetes: a 6-month prospective study. Endocr Pract:S1530-891X (21)00161-0. https://doi.org/10.1016/j. eprac.2021.04.004

Chapter 26 Point-of-Care Device for Assessment of Blood Coagulation Status in COVID-19 Patients Paul C. Guest and Hassan Rahmoune Abstract The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent driving the current COVID-19 pandemic. The acute respiratory distress that occurs in some severe COVID-19 cases has been linked with hypercoagulation or thrombotic events as well as a worse prognosis and increased risk of death. Thus, point-of-care devices that can be used for early detection of coagulation abnormalities would assist in COVID-19 management. This chapter describes the use of the Roche Diagnostics CoaguChek® XS test kit for potential use in COVID-19 personalized medicine approaches. Key words SARS-CoV-2, COVID-19, Coagulopathy, Clotting cascade, Point-of-care, CoaguChek

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Introduction Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has caused a global pandemic of disastrous proportions [1, 2]. Although the majority of COVID-19 patients recover from the disease, approximately 4.4 million individuals have died (as of August 23, 2021), and this number is likely to increase further [3]. It has now been established that the most severe cases of COVID-19 are associated with a heightened inflammatory response known as a cytokine storm (see Fig. 1) [4]. In turn, this can lead to increased production of damaging reactive oxygen species, endothelial damage, platelet aggregation, activation of the coagulation cascade, and clot formation, causing occlusion of the blood vessels in question [5]. The consequences of this can be drastic and include thrombosis, coagulopathy, damage to multiple organ systems, stroke, and death [6].

Paul C. Guest (ed.), Multiplex Biomarker Techniques: Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification, Methods in Molecular Biology, vol. 2511, https://doi.org/10.1007/978-1-0716-2395-4_26, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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Fig. 1 Schematic diagram showing the link between COVID-19 and potential coagulopathies. SARS-CoV2 infections leading to injury of alveolar epithelium can result in the cytokine storm effect. This can have multiple damaging impacts including hyper-activation of the clotting cascade via both the intrinsic and extrinsic pathways

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The term coagulation describes the process involving the transformation of blood from a fluid to a gel-like state. The mechanism occurs as a cascade of reactions in which inactive enzyme precursors are activated to drive a subsequent reaction, and this culminates in the cross-linking of fibrin polymers and formation of a clot (see Fig. 1) [7]. This can occur via two distinct starting points known as the intrinsic and extrinsic pathways [7, 8]. Although other factors are involved, the intrinsic pathway begins with endothelial damage resulting in activation of factor XII to factor XIIa (XII ! XIIa), which converts factor XI to factor XIa (XI ! XIa), which activates factor IX to factor IXa (IX ! IXa), which converts factor X into factor Xa (X ! Xa). The production of factor Xa converts prothrombin to thrombin, which coverts fibrinogen to fibrin. Finally, the fibrin polymerizes to produce fibrin strands, which are converted by factor XIII to form a fibrin mesh. In the extrinsic pathway, damaged blood vessels cause the release of tissue factor which goes on to activate factor VII to factor VIIa (VII ! VIIa), which converts factor X into factor Xa (X ! Xa). This is the point where the two pathways unite. The prothrombin time (PT) blood test is often used in the clinic to measure the coagulation state of patients [9]. This test measures the clotting time following addition of tissue factor, calcium, and phospholipids (thromboplastin) to a blood sample. For standardization purposes, the World Health Organization (WHO) established the international normalized ratio (INR) as the accepted reporting format for these tests [10]: INR ¼ patient PT=standard PTðusing a reference thromboplastin reagentÞ A number of studies have established that an increased INR is associated with increased severity and death outcomes in COVID19 patients [11]. This increase is thought to occur as a result of increased consumption of coagulation factors during disease progression [12]. The critical role of coagulopathies in COVID-19 outcomes calls for the application of point-of-care devices that can offer an accurate, user-friendly, and timely readout of the INR. The CoaguChek® XS system was originally designed as a point-of-care device for monitoring phenprocoumon, acenocoumarin, or warfarin therapies [13] but can also be used for measurement of clotting time in a number of coagulopathies, such as those seen in COVID19. The CoaguChek device determines the INR score using a drop of capillary blood from the subject [14]. The CoaguChek system consists of a microfluidic strip containing a thromboplastin reagent and iron filings [15]. This strip is placed in a slot in the CoaguChek meter, and application of the blood drop to the strip results in mixing of the thromboplastin and iron filings and diffusion of the blood by capillary action. The iron filings are stimulated to move in an electromagnetic field in the meter, and this movement is

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Detector

Apply sample

Fig. 2 Schematic diagram showing the mechanism of the CoaguChek device for determining INR. The system consists of a microfluidic strip containing thromboplastin and iron filings, which is inserted into a meter. Application of a blood drop to the strip causes mixing of the thromboplastin and iron filings and initial diffusion of the blood. The movement of the iron filings in the electromagnetic field of the meter is halted when the blood clots and this time is registered and converted to the INR value

detected optically. The time registered for cessation of movement due to coagulation is taken as the clotting time (PT), which is converted into an INR value (see Fig. 2). Coagulation tests have been used to monitor and manage the thrombotic risk associated with COVID-19 infection [16, 17]. This chapter describes the use of the CoaguChek system for determination of clotting time. This can be used in many circumstances such as monitoring the effects of anticoagulant treatments or as an assessment of diseases marked by coagulopathies such as COVID-19. We also describe the use of a user-friendly app that can be combined with the CoaguChek assessment of INR [18– 21]. This can be used in-hospital by physicians and by recovering outpatients as an aid for post-COVID-19 self-monitoring.

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Materials 1. Personnel protective equipment (PPE): FFP2 (N95) mask, disposable cap, goggles, gown, apron, latex gloves, and shoe covers (see Note 1). 2. Disinfectant: 1% sodium hypochlorite. 3. CoaguChek XS kit (Roche Diagnostics; Indianapolis, IN, USA) (see Note 2). (a) Meter. (b) 6 test strips (see Note 3) (c) Matching code chip for test strips (see Note 4). (d) 6 lances. 4. Additional lances (see Note 3). 5. Alcohol wipes. 6. Smartphone or tablet with the Coagu-App [22] (see Note 5).

3

Methods

3.1 Determination of INR

1. Perform the procedure and sampling in a dedicated sterilized room with the test personal wearing full PPE and the test subject wearing a mask [23] (see Note 1). 1. For the first use, set the date and time on the CoaguChek meter using the M and SET buttons (see Fig. 3). 2. With the meter power off, insert the code chip into the slot until it snaps into place, ensuring that the code matches that on the test strips (see Note 4). 3. Place the meter on a flat surface or hold in your hand, insert the test strip into the test strip guide, and use within 10 min of opening the strip container (see Fig. 4a) (see Note 6). 4. When the meter powers on, ensure that the code number that appears on the display screen matches the number on the strip container. 5. Once the flashing test strip appears on the screen, ensure that the blood drop is applied to the test strip within 3 min. 6. While the meter is warming up, ask the subject to warm their hand by holding it under their armpit or placing in warm water for approximately 30 s. 7. Ask the subject to hold their arm down so the hand is to the side and below the waist.

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Meter

SET buon Code chip

Code chip slot M (memory) buon

Power buon

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Fig. 3 The CoaguChek meter, test strip and matching code chip

8. Massage their finger from the base and then prick the tip of this finger with the lancet provided in the CoaguChek kit or with appropriate sterile substitute (see Fig. 4b) (see Note 7). 9. After the meter has warmed (3 min), apply the resulting blood drop to the test strip and hold in place until the meter beeps and a blood drop symbol appears (see Fig. 4c). 10. After 1 min, record the result that appears on the display (see Fig. 4d) (see Note 8). 11. Dispose of the lancet and test strip in an appropriate biohazardous waste container and wipe the area clean with the disinfectant and clean the test strip guide with a wet cotton swab. 12. Dry the meter and test strip guide. 3.2 Optional: Use of the Coagu-App

1. Open the Coagu-App on the smartphone or tablet. 2. For first use, the patient should configure the app for their specific needs including target INR range and potential anticoagulant drugs and dosages should these be required (see Note 9).

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Fig. 4 (a) Insert the test strip into the test strip guide. (b) Prick the patient’s finger with the lancet to draw a drop of blood. (c) Apply the blood drop by touching the fingertip to the test strip, and hold in place until the meter beeps and a blood drop symbol appears. (d) Record the INR value that appears after 1 min

3. Input the measured INR value which is added to the calendar and histogram of the app (see Fig. 5a, b) (see Note 10). 4. Determine the action that should be taken if any based on the current INR reading (see Fig. 5c). 5. During recovery or long COVID symptoms, visualize the entered values to see if a trend is emerging (see Fig. 5d) (see Note 11). 6. Set the app to send notifications as required (e.g., for taking medication or if other actions are required) (see Note 12). 7. Place comments daily as needed (see Note 13).

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Fig. 5 Incorporation of the Coagu-App. (a) The patient enters the INR value determined from the CoaguChek device, using a number picker, and stores it. The measured and saved value appears with the actual date on the start page of the Coagu-App, and the app gives the anticoagulant dose, if any, that should be taken that day. (b) The patient confirms the actions taken with a tap on the display. This is then registered in the histogram and in the calendar (red values in the calendar indicate a high reading). (c) If needed, the patient chooses a medication from the list and a confirmation that it has been taken is registered automatically. (d) The graph shows the measured INR values and medication use and relates these over a 6-month period

4

Notes 1. This procedure is described for studies of patients with active COVID-19 and long COVID and recovering patients. Full PPE is required for cases of the active disease, and safety should be considered as a priority. All bio-samples and materials should be used with the assumption that they can transmit infection. In addition, assessments should be put in place to anticipate and minimize any risks. Lastly, all sample-related materials require disposal with appropriate precautions in accordance with local regulations and in a biosafety level (BSL) 2 or 3 facility. 2. This procedure describes the use of the Coaguchek XS system as this was selected by the Medical Technologies Advisory Committee (MTAC) as part of a program testing point-ofcare systems by the National Health Service (NHS) in the UK [24]. Other similar systems are also available [INRatio2 PT/INR monitor (Alere), ProTime Microcoagulation system (International Technidyne Corporation (ITC)]. 3. Additional test strips come in packs of 24 and will require additional lances.

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4. Each container of test strips comes with a matching code chip. This gives the meter information on lot numbers and expiration dates of the test strips. 5. The Coagu-App can be used as optional extra for monitoring INR over time and to inform about the potential need for anticoagulant medications or changing dosages of these medications. This app was developed originally by Appamedix UG in Berlin, Germany, and is based on a universal design, making it user friendly for all ages. The evidence shows that selfmonitoring allows both clinical and patient benefits and may lead to reductions in heart attacks and strokes caused by blood clots [25]. For the smartphones, iOS (Apple) and Android (Google Play) units can be used. 6. The strip should be inserted into the guide prior to application of the blood drop to ensure an accurate start time for the clotting process. The meter should not be used at an altitude higher than 4300 m and should not be placed or operated near a strong magnetic field (such as a microwave oven). 7. If needed, gently squeeze the finger to encourage a drop of blood to form on the fingertip. 8. During the 1 min, the meter also performs a quality control check. 9. The patients should receive their target INR range from their doctor. 10. To accommodate the elderly, the app comes with relatively large touch-sensitive surfaces to compensate for possible motor inaccuracies, and larger font sizes are used at crucial points to compensate for potential visual impairments. 11. Here the user can see if the INR values are changing as a trend, which could inform on medication needs. 12. For example, if the medication is suddenly required or can be reduced, this can be listed in the app. 13. Comments can be stored in the calendar and are available for 6 months. Through the app, the patient can also contact their physician to share their information. References 1. Guest PC (ed) (2021) Clinical, biological and molecular aspects of COVID-19: 1321 (Advances in experimental medicine and biology). Springer, New York. ISBN-13:9783030592608 2. Guest PC (ed) (2021) Identification of biomarkers, new treatments, and vaccines for COVID19: 1327 (Advances in experimental medicine

and biology). Springer, New York. ISBN-13: 978-3030716967 3. https://www.worldometers.info/coronavi rus/#countries. Accessed 23 Aug 2021 4. Wang J, Jiang M, Chen X, Montaner LJ (2020) Cytokine storm and leukocyte changes in mild versus severe SARS-CoV-2 infection: review of 3939 COVID-19 patients in China and

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emerging pathogenesis and therapy concepts. J Leukoc Biol 108(1):17–41 5. Janardhan V, Janardhan V, Kalousek V (2020) COVID-19 as a blood clotting disorder masquerading as a respiratory illness: a cerebrovascular perspective and therapeutic implications for stroke thrombectomy. J Neuroimaging 30(5):555–561 6. Vinayagam S, Sattu K (2020) SARS-CoV2 and coagulation disorders in different organs. Life Sci 260:118431. https://doi.org/10. 1016/j.lfs.2020.118431 7. Luchtman-Jones L, Broze GJ Jr (1995) The current status of coagulation. Ann Med 27: 47–52 8. Chaudhry R, Usama SM, Babiker HM (2021) Physiology, coagulation pathways. In: StatPearls [Internet]. Bookshelf ID: NBK482253, Treasure Island. https://www.ncbi.nlm.nih. gov/books/NBK482253/ 9. Poller L (1980) Standardization of the APTT test. Curr Status Scand J Haematol Suppl 37: 49–63 10. Riley RS, Rowe D, Fisher LM (2000) Clinical utilization of the international normalized ratio (INR). J Clin Lab Anal 14(3):101–114 11. Zinellu A, Paliogiannis P, Carru C et al (2021) INR and COVID-19 severity and mortality: a systematic review with meta-analysis and metaregression. Adv Med Sci 66(2):372–380 12. Long H, Nie L, Xiang X et al (2020) D-dimer and prothrombin time are the significant indicators of severe COVID-19 and poor prognosis. Biomed Res Int 2020:6159720. https:// doi.org/10.1155/2020/6159720 13. van den Besselaar AM, Breddin K, Lutze G et al (1995) Multicenter evaluation of a new capillary blood prothrombin time monitoring system. Blood Coagul Fibrinolysis 6(8):726–732

14. https://diagnostics.roche.com/gb/en/ products/instruments/coaguchek-xs.html 15. Vacas M, Lafuente PJ, Unanue I et al (2004) Comparative study of two portable systems for oral anticoagulant monitoring. Hematol J 5(1):35–38 16. Litvinov RI, Evtugina NG, Peshkova AD et al (2021) Altered platelet and coagulation function in moderate-to-severe COVID-19. Sci Rep 11(1):16290. https://doi.org/10.1038/ s41598-021-95397-6 17. Hardy M, Lecompte T, Douxfils J et al (2021) Management of the thrombotic risk associated with COVID-19: guidance for the hemostasis laboratory. Thromb J 18:17. https://doi.org/ 10.1186/s12959-020-00230-1 18. Vegt J (2017) Development of a user-friendly app for assisting anticoagulation treatment. Methods Mol Biol 1546:303–308 19. Vegt J, Guest PC (2018) A user-friendly app for blood coagulation disorders. Methods Mol Biol 1735:499–504 20. http://www.coagu.com/en/ 21. http://www.inr-austria.at/index.php?article_ id¼6 22. https://apps.apple.com/gb/app/coagu/ id578252744 23. https://www.euro.who.int/__data/assets/ pdf_file/0005/268790/WHO-guidelineson-drawing-blood-best-practices-in-phlebot omy-Eng.pdf 24. https://www.nice.org.uk/guidance/dg14/ documents/pointofcare-coagulometers-thecoaguchek-xs-system-and-the-inratio2-ptinrmonitor-overview-2. Accessed 25 Aug 2021 25. Tideman PA, Tirimacco R, St John A et al (2015) How to manage warfarin therapy. Aust Prescr 38(2):44–48

Chapter 27 COVID-19 and the Assessment of Coenzyme Q10 Nadia Turton, Robert A. Heaton, and Iain P. Hargreaves Abstract Coenzyme Q10 (CoQ10) plays an essential electron carrier role in the mitochondrial electron transfer chain (ETC) as well as being a potent antioxidant and influencing inflammatory mediators. In view of these functions, the reason why certain individuals may be more susceptible to the severe disease or long-term complications (long COVID) of COVID-19 infection may be associated with an underlying deficit in cellular CoQ10 status. Thus, our group has outlined an analytical method for the determination of cellular CoQ10 status using HPLC linked UV detection at 275 nm. This method has been utilized in patient tissue samples to investigate evidence of a CoQ10 deficiency and thus may have potential in determining the possible susceptibility of individuals to severe disease associated with COVID-19 infection or to long COVID. Key words Coenzyme Q10, CoQ10, COVID-19, Virus, Mitochondrial electron transport chain, Antioxidant, Disease, Long COVID

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Introduction Mitochondria are crucial in the immune response, and viruses have been known to modulate mitochondrial functioning [1]. Thus, it is judicious to assume that at least some of the various clinical presentations associated with COVID-19 infection could be linked to mitochondrial dysfunction [2]. Assessment of surrogates of mitochondrial function in tissue samples from individuals infected with COVID-19 may provide insights into the pathogenesis of the viral disease. Moreover, the reason why certain individuals are more susceptible to the severe disease manifestations associated with COVID-19 infection or to long COVID may be linked to an impaired mitochondrial function [3]. This mitochondrial dysfunction may result from a decrease in the levels of the lipid-soluble antioxidant and electron carrier coenzyme Q10 (CoQ10). CoQ10 is a lipophilic molecule consisting of a benzoquinone ring and a side chain containing ten isoprenoid units (see Fig. 1) and exists in either the reduced, ubiquinol form (CoQ10H2) or the oxidized,

Paul C. Guest (ed.), Multiplex Biomarker Techniques: Methods and Applications for COVID-19 Disease Diagnosis and Risk Stratification, Methods in Molecular Biology, vol. 2511, https://doi.org/10.1007/978-1-0716-2395-4_27, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2022

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Fig. 1 Skeletal formula of coenzyme Q10 (CoQ10). Created using biorender.com

ubiquinone form (CoQ10) [4]. Its pivotal role is in the mitochondrial electron transport chain (ETC), where it accepts electrons derived from complex I (NADH ubiquinone reductase; EC 1.6.5.3) and complex II (succinate ubiquinone reductase; EC 1.3.5.1) and then transfers them to complex III (ubiquinol cytochrome c reductase; EC 1.10.2.2) allowing a continual passage of electrons through the chain and thus the process of oxidative phosphorylation (OP) to occur [5]. The synthesis of ATP by OP relies on both functioning mitochondrial ETC enzyme complexes together with an adequate supply of CoQ10. Although primary CoQ10 deficiencies are rare, it is becoming increasingly apparent that certain groups of patients, for example, individuals with cerebral ataxia [6] or those who are prescribed high-dose statins [7], may have or can develop a CoQ10 deficiency. Furthermore, there appears to be a correlation between increasing age and a lowering of CoQ10 status [8]. Interestingly, severe illness following COVID-19 infection generally affects individuals of an older age [9], as patients aged
60 years show more severe clinical manifestations and longer disease courses in comparison to those aged varImp(model_list_rf[[3]]) rf variable importance only 20 most important variables shown (out of 49) Overall Freq.5737.47400516494 100.000 Freq.5574.07557869908 60.698 Freq.3900.11582509389 52.995 Freq.5853.57897003433 50.953 Freq.5764.07787557415 46.734 Freq.5905.64372826057 34.610 Freq.11681.7761571967 26.185 Freq.2432.39025464168 24.120 Freq.3843.84542939388 23.211 Freq.4302.87808861727 23.125 Freq.8565.61296466527 17.977 Freq.8766.07321277154 16.405 Freq.8602.408003098 15.885 Freq.3161.12295451617 14.485 Freq.4284.49764556708 12.814 Freq.4627.41853472176 12.054 Freq.2471.53402914682 10.870 Freq.3825.73172264663 10.110 Freq.4073.3156717722 9.901 Freq.4939.19218039689 9.837

> varImp(model_list_rf[[4]]) rf variable importance only 20 most important variables shown (out of 49)

Freq.5853.57897003433 Freq.5574.07557869908 Freq.5764.07787557415 Freq.5737.47400516494 Freq.8766.07321277154 Freq.3843.84542939388 Freq.4384.63945656182 Freq.11681.7761571967 Freq.4419.76626118945 Freq.3900.11582509389 Freq.3161.12295451617 Freq.11076.7800481216 Freq.5905.64372826057 Freq.2471.53402914682 Freq.3218.80423111111 Freq.2676.79579519485 Freq.4302.87808861727 Freq.6958.84786359667 Freq.6647.45679934814 Freq.2432.39025464168

Overall 100.00 79.12 75.40 74.31 69.07 68.32 58.31 54.15 53.50 51.91 45.45 40.78 40.51 32.94 32.59 32.39 31.84 31.40 31.02 27.12

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3.2 Data Preprocessing and Machine Learning with Feature Selection

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1. Following step 3.1, 19 above, perform feature selection using the Information Gain method from the FSelector package (see Note 19). 2. Perform the feature selection step in the inner loop of the cross-validation (see Note 20).

parms_list_rf