Bacterial Pathogenesis: Methods and Protocols (Methods in Molecular Biology, 2674) 1071632426, 9781071632420

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Methods in Molecular Biology 2674

Pontus Nordenfelt · Mattias Collin  Editors

Bacterial Pathogenesis Methods and Protocols Second Edition

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-by step 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.

Bacterial Pathogenesis Methods and Protocols Second Edition

Edited by

Pontus Nordenfelt and Mattias Collin Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden

Editors Pontus Nordenfelt Infection Medicine, Department of Clinical Sciences Lund University Lund, Sweden

Mattias Collin Infection Medicine, Department of Clinical Sciences Lund University Lund, Sweden

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-3242-0 ISBN 978-1-0716-3243-7 (eBook) https://doi.org/10.1007/978-1-0716-3243-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023 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 With antibacterial agents becoming less and less effective, it is more important than ever to understand the nature of bacterial infections. Bacterial infections are still one of the leading causes to morbidity and mortality worldwide, and recent meta-analyses indicate that 4.95 million deaths in 2019 could be attributed to infections with antimicrobial resistant (AMR) bacteria [1]. We are most likely soon entering a post-antibiotic era where infections with both well-known bacterial pathogens and bacteria previously not considered threats will contribute to a substantial impact on humanity. While the terms pathogenicity and virulence have been used to describe the ability of bacteria to cause disease already since Henle and Koch formulated their postulates in 1884, the concepts have been under constant debate and development. The molecular update to Koch’s postulates made possible by the molecular genetics revolution was a great leap forward in our understanding of bacterial-host interactions at the molecular level [2]. However, both the original and molecular Koch’s postulates are in nature bacterio-centric with limitations in describing the whole process ranging all the way from commensalism to severe infections. A more recent very appealing and potentially unifying view on the pathogenesis of infection is the so-called damage response framework, where both bacterial properties and the host responses are taken into account as contributors to disease [3]. Theoretical frameworks are instrumental in putting forward new hypotheses in relation to bacterial infections, but they will in many instances remain unanswered until experimentally tested. Because of this, it is absolutely essential to develop methodologies that can assess many different aspects of bacterial infections in order to move our understanding forward for the sake of knowledge itself, and for developing novel means of controlling bacterial infections now and in the future. The development of these methodologies requires a wide range of skills and expertise, including, but not limited to, microbiology, immunology, structural biology, molecular biology, genetics, imaging, and computational methods. In this volume, Bacterial Pathogenesis: Methods and Protocols, Second Edition, we have been able to recruit expert authors that, even though they are presenting very different methodological approaches, all share a fundamental interest in understanding bacterial infections from molecules to organisms. The methods describe the investigation of a wide range of bacterial species, such as Yersinia pseudotuberculosis, Mycobacterium bovis, Streptococcus pneumoniae, Klebsiella pneumoniae, Salmonella typhimurium, Mycobacterium marinum, Streptococcus pyogenes, Actinomyces, Streptococcus mutans, Lactobacillus, Fusobacterium, Bacteriodetes thetaiotaomicron, Helicobacter pylori, Staphylococcus aureus, Salmonella typhimurium, and Peptoniphilus harei. However, many of the protocols can be modified and generalized to study any bacterial pathogen of choice. Part I contains four chapters dealing with multicellularity in the form of biofilms and the important subcellular compartments in the form of extracellular vesicles. Part II describes methodology for transcriptional analysis, methods for studying plasmid dynamics, and tools for phylogenetic analysis of bacterial genomes. Part III describes quite diverse and creative methods with the common theme of identifying and/or characterizing bacterial effector proteins interfering with host systems. Part IV contains a number of chapters describing

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ways to analyze and quantify cellular and humoral responses toward bacteria. Part V describes advanced analysis of vascular responses during infection in mice, barcoded infections with mutant libraries in cells (that can also be applied in vivo), a mycobacterial mouse model of infection to follow disease development and inflammatory processes, and finally an in vitro meningitis model to study interactions between pneumococci and neurons. We are indebted to John M. Walker, the series editor, for renewed confidence in us for the second time to edit this volume and for very helpful and prompt support during the whole process. However, above all, we are grateful to all the talented authors that have shared their expertise with us and the readers. It has been a very rewarding process interacting with all of you! We are both fostered within the Division of Infection Medicine, Department of Clinical Sciences, Lund University (formed and for many years headed by Lars Bjo¨rck), with important postdoctoral experiences in Tim Springer’s laboratory at Harvard University and Vince Fischetti’s laboratory at Rockefeller University, respectively. These environments have trained generations of outstanding researchers within infection biology, and we are truly standing on the shoulders of giants (no one mentioned, no one forgotten). Lund, Sweden

Pontus Nordenfelt Mattias Collin

References 1. Antimicrobial Resistance Collaborators (2022) Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 399:629–655 2. Falkow S (1988) Molecular Koch’s postulates applied to microbial pathogenicity. Rev Infect Dis 10 (Suppl 2):S274–S276 3. Casadevall A, Pirofski L (2003) The damage-response framework of microbial pathogenesis. Nat Rev Microbiol 1:17–24

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

PART I

BIOFILMS AND SUBCELLULAR COMPARTMENTS

1 Measuring Niche-Associated Metabolic Activity in Planktonic and Biofilm Bacteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Supradipta De and Anders P. Hakansson 2 Formation and Analysis of Mono-species and Polymicrobial Oral Biofilms in Flow-Cell Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jessica Neilands, Gunnel Svens€ a ter, Gabriella Boisen, Carolina Robertsson, Claes Wickstro¨m, and Julia R. Davies 3 Isolation and Purification of Mycobacterial Extracellular Vesicles (EVs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Komal Umashankar Rao and Gabriela Godaly 4 Strategies to Isolate Extracellular Vesicles from Gram-Negative and Gram-Positive Bacteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ana Rita Narciso and Marie-Ste´phanie Aschtgen

PART II

v xi

3

33

55

61

BACTERIAL GENETICS, GENOMICS, AND PHYLOGENETICS

5 Total Bacterial RNA Isolation and Northern Blotting Analysis. . . . . . . . . . . . . . . . 73 Jens Karlsson, Hannes Eichner, and Edmund Loh 6 Phylogenetic Analysis of Bacterial Pathogen Genomes . . . . . . . . . . . . . . . . . . . . . . . 87 Xavier Didelot 7 Determination of Growth Rate and Virulence Plasmid Copy Number During Yersinia pseudotuberculosis Infection Using Droplet Digital PCR. . . . . . . . . . . . . . 101 Tifaine Hechard and Helen Wang

PART III

IDENTIFICATION AND CHARACTERIZATION OF BACTERIAL EFFECTOR PROTEINS

8 Methods to Analyze the Contribution of Complement Evasion Factor (CEF) to Streptococcus pyogenes Virulence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Haniyeh Aghababa, Jacelyn M. S. Loh, and Thomas Proft 9 Expression of the Bacterial Enzyme IdeS Using a GFP Fusion in the Yeast Saccharomyces cerevisiae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Tova Lindh, Mattias Collin, Rolf Lood, and Magnus Carlquist

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10

Mass Spectrometry-Based Methods to Determine the Substrate Specificities and Kinetics of N-Linked Glycan Hydrolysis by Endo-β-N-Acetylglucosaminidases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jonathan J. Du, Diego Sastre, Beatriz Trastoy, Blaine Roberts, Daniel Deredge, Erik H. Klontz, Maria W. Flowers, Nazneen Sultana, Marcelo E. Guerin, and Eric J. Sundberg 11 Identification of Substrates of Secreted Bacterial Protease by APEX2-Based Proximity Labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanxuan Xie, Yang Mao, Zong-Wan Mao, and Wei Xia 12 Affinity-Purification Combined with Crosslinking Mass Spectrometry for Identification and Structural Modeling of Host–Pathogen Protein–Protein Complexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lotta J. Happonen 13 Elucidating the Stoichiometries of Host–Pathogen Protein Interactions with Mass Photometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emma-Jayne Proctor, Sandeep Satapathy, and Martina Sanderson-Smith

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201

PART IV ANALYSIS OF HOST RESPONSES TO BACTERIA 14

Analysis of Neutrophil and Monocyte Inflammation Markers in Response to Gram-Positive Anaerobic Cocci . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tobias Schmidt and Ariane Neumann 15 Quantification of Phagocytosis Using Flow Cytometry . . . . . . . . . . . . . . . . . . . . . . Therese de Neergaard and Pontus Nordenfelt 16 Antibacterial Neutrophil Effector Response: Ex Vivo Quantification of Regulated Cell Death Associated with Extracellular Trap Release . . . . . . . . . . . Tiziano A. Schweizer, Sanne Hertegonne, Cle´ment Vulin, Annelies S. Zinkernagel, and Srikanth Mairpady Shambat 17 Measurement of Antibody Binding Affinity on Bacterial Surfaces Using Flow Cytometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vibha Kumra Ahnlide and Pontus Nordenfelt 18 Detection of Inflammasome Activation in Murine Bone Marrow-Derived Macrophages Infected with Group A Streptococcus . . . . . . . . . . . . . . . . . . . . . . . . . . Christine Valfridsson, Elsa Westerlund, Dora Hancz, and Jenny J. Persson

PART V 19

20

211 221

235

251

261

IN VIVO AND IN VITRO INFECTION MODELS

In Vivo Profiling of the Vascular Cell Surface Proteome in Murine Models of Bacteremia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 Charlotte Spliid, Jeffrey D. Esko, Johan Malmstro¨m, and Alejandro Gomez Toledo Barcoded Consortium Infections: A Scalable, Internally Controlled Method to Study Host Cell Binding and Invasion by Pathogenic Bacteria . . . . . . . . . . . . . 295 Maria Letizia Di Martino and Mikael E. Sellin

Contents

21

22

ix

A Murine Mycobacterium marinum Infection Model for Longitudinal Analyses of Disease Development and the Inflammatory Response . . . . . . . . . . . . 313 Julia Lienard, Kristina Munke, and Fredric Carlsson In Vitro Approaches for the Study of Pneumococcal-Neuronal Interaction and Pathogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 ˜ o-Vian Kristine Farmen and Miguel Tofin

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

335

Contributors HANIYEH AGHABABA • Department of Molecular Medicine & Pathology, School of Medical Sciences and Maurice Wilkins Centre for Biomolecular Discovery, The University of Auckland, Auckland, New Zealand MARIE-STE´PHANIE ASCHTGEN • Karolinska Institute, Department of Microbiology, Tumor and Cell Biology, Stockholm, Sweden GABRIELLA BOISEN • Section for Oral Biology and Pathology, Faculty of Odontology, Malmo¨ University, Malmo¨, Sweden MAGNUS CARLQUIST • Applied Microbiology, Department of Chemistry, Lund University, Lund, Sweden FREDRIC CARLSSON • Department of Biology, Faculty of Science, Lund University, Lund, Sweden MATTIAS COLLIN • Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden JULIA R. DAVIES • Section for Oral Biology and Pathology, Faculty of Odontology, Malmo¨ University, Malmo¨, Sweden SUPRADIPTA DE • Department of Translational Medicine, Division of Experimental Infection Medicine, Wallenberg Laboratory, Lund University, Malmo¨, Sweden THERESE DE NEERGAARD • Department of Clinical Sciences Lund, Faculty of Medicine, Division of Infection Medicine, Lund University, Lund, Sweden DANIEL DEREDGE • Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD, USA MARIA LETIZIA DI MARTINO • Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden XAVIER DIDELOT • School of Life Sciences and Department of Statistics, University of Warwick, Coventry, UK JONATHAN J. DU • Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA HANNES EICHNER • Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solna, Sweden; Clinical Microbiology, Karolinska University Hospital, Solna, Sweden; Department of Microbiology, New York University, New York, NY, USA JEFFREY D. ESKO • Department of Cellular and Molecular Medicine, University of California, San Diego, CA, USA KRISTINE FARMEN • Department of Neuroscience, Karolinska Institutet, Solna, Sweden MARIA W. FLOWERS • Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA GABRIELA GODALY • Department of Microbiology, Immunology and Glycobiology, Institution of Laboratory Medicine, Lund University, Lund, Sweden MARCELO E. GUERIN • Structural Glycobiology Laboratory, Biocruces Bizkaia Health Research Institute, Cruces University Hospital, Barakaldo, Bizkaia, Spain; Ikerbasque, Basque Foundation for Science, Bilbao, Spain ANDERS P. HAKANSSON • Department of Translational Medicine, Division of Experimental Infection Medicine, Wallenberg Laboratory, Lund University, Malmo¨, Sweden

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Contributors

DO´RA HANCZ • Immunology Section, Department Experimental Medical Science, Lund University, Lund, Sweden; Novo Nordisk A/S, Bagsvaerd, Denmark LOTTA J. HAPPONEN • Department of Clinical Sciences Lund, Division of Infection Medicine, Lund University, Lund, Sweden TIFAINE HECHARD • Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden SANNE HERTEGONNE • Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland JENS KARLSSON • Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solna, Sweden; Clinical Microbiology, Karolinska University Hospital, Solna, Sweden ERIK H. KLONTZ • Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD, USA; Institute of Human Virology, University of Maryland School of Medicine, Baltimore, MD, USA VIBHA KUMRA AHNLIDE • Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden JULIA LIENARD • Department of Biology, Faculty of Science, Lund University, Lund, Sweden TOVA LINDH • Applied Microbiology, Department of Chemistry, Lund University, Lund, Sweden; Genovis AB, Lund, Sweden EDMUND LOH • Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solna, Sweden; Clinical Microbiology, Karolinska University Hospital, Solna, Sweden; Singapore Centre on Environmental Life Sciences Engineering, Nanyang Technological University, Singapore, Singapore JACELYN M. S. LOH • Department of Molecular Medicine & Pathology, School of Medical Sciences and Maurice Wilkins Centre for Biomolecular Discovery, The University of Auckland, Auckland, New Zealand ROLF LOOD • Genovis AB, Lund, Sweden; Infection Medicine, Department of Clinical Sciences, Lund University, Lund, Sweden SRIKANTH MAIRPADY SHAMBAT • Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland JOHAN MALMSTRO¨M • Department of Clinical Sciences, Division of Infection Medicine, BMC, Lund University, Lund, Sweden YANG MAO • School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, China ZONG-WAN MAO • MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, Sun Yat-Sen University, Guangzhou, China KRISTINA MUNKE • Department of Experimental Medical Science, Faculty of Medicine, Lund University, Lund, Sweden ANA RITA NARCISO • Karolinska Institute, Department of Microbiology, Tumor and Cell Biology, Stockholm, Sweden JESSICA NEILANDS • Section for Oral Biology and Pathology, Faculty of Odontology, Malmo¨ University, Malmo¨, Sweden ARIANE NEUMANN • Department of Clinical Sciences Lund, Division of Infection Medicine, Lund University, Lund, Sweden PONTUS NORDENFELT • Department of Clinical Sciences Lund, Faculty of Medicine, Division of Infection Medicine, Lund University, Lund, Sweden

Contributors

xiii

JENNY J. PERSSON • Immunology Section, Department Experimental Medical Science, Lund University, Lund, Sweden EMMA-JAYNE PROCTOR • Illawarra Health and Medical Research Institute (IHMRI), University of Wollongong, Wollongong, Australia; Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, Australia THOMAS PROFT • Department of Molecular Medicine & Pathology, School of Medical Sciences and Maurice Wilkins Centre for Biomolecular Discovery, The University of Auckland, Auckland, New Zealand KOMAL UMASHANKAR RAO • Department of Microbiology, Immunology and Glycobiology, Institution of Laboratory Medicine, Lund University, Lund, Sweden BLAINE ROBERTS • Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA CAROLINA ROBERTSSON • Section for Oral Biology and Pathology, Faculty of Odontology, Malmo¨ University, Malmo¨, Sweden MARTINA SANDERSON-SMITH • Illawarra Health and Medical Research Institute (IHMRI), University of Wollongong, Wollongong, Australia; Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, Australia DIEGO SASTRE • Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA SANDEEP SATAPATHY • Blavatnik Institute of Cell Biology, Harvard Medical School, Boston, MA, USA; The Broad Institute of MIT and Harvard, Cambridge, MA, USA TOBIAS SCHMIDT • Department of Clinical Sciences Lund, Division of Pediatrics, Lund University, Lund, Sweden; Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden TIZIANO A. SCHWEIZER • Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland MIKAEL E. SELLIN • Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden CHARLOTTE SPLIID • Department of Cellular and Molecular Medicine, University of California, San Diego, CA, USA NAZNEEN SULTANA • Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA ERIC J. SUNDBERG • Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA GUNNEL SVENSA€ TER • Section for Oral Biology and Pathology, Faculty of Odontology, Malmo¨ University, Malmo¨, Sweden MIGUEL TOFIN˜O-VIAN • Department of Neuroscience, Karolinska Institutet, Solna, Sweden ALEJANDRO GOMEZ TOLEDO • Department of Clinical Sciences, Division of Infection Medicine, BMC, Lund University, Lund, Sweden BEATRIZ TRASTOY • Structural Glycobiology Laboratory, Biocruces Bizkaia Health Research Institute, Cruces University Hospital, Barakaldo, Bizkaia, Spain CHRISTINE VALFRIDSSON • Immunology Section, Department Experimental Medical Science, Lund University, Lund, Sweden CLE´MENT VULIN • Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland HELEN WANG • Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden

xiv

Contributors

ELSA WESTERLUND • Immunology Section, Department Experimental Medical Science, Lund University, Lund, Sweden; Truly Labs, Medicon Village, Lund, Sweden CLAES WICKSTRO¨M • Section for Oral Biology and Pathology, Faculty of Odontology, Malmo¨ University, Malmo¨, Sweden WEI XIA • MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, Sun Yat-Sen University, Guangzhou, China YANXUAN XIE • MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, Sun Yat-Sen University, Guangzhou, China ANNELIES S. ZINKERNAGEL • Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland

Part I Biofilms and Subcellular Compartments

Chapter 1 Measuring Niche-Associated Metabolic Activity in Planktonic and Biofilm Bacteria Supradipta De and Anders P. Hakansson Abstract Most pathobionts of the respiratory tract form biofilms during asymptomatic colonization to survive and persist in this niche. Environmental changes of the host niche, often resulting from infection with respiratory viruses, changes of the microbiota composition, or other host assaults, can result in biofilm dispersion and spread of bacteria to other host niches, resulting in infections, such as otitis media, pneumonia, sepsis, and meningitis. The niches that these bacteria encounter during colonization and infection vary markedly in nutritional availability and contain different carbon sources and levels of other essential nutrients needed for bacterial growth and survival. As these niche-related nutritional variations regulate bacterial behavior and phenotype, a better understanding of bacterial niche-associated metabolic activity is likely to provide a broader understanding of bacterial pathogenesis. In this chapter, we use Streptococcus pneumoniae as a model respiratory pathobiont. We describe methods and models used to grow bacteria planktonically or to form biofilms in vitro by incorporating crucial host environmental factors, including the various carbon sources associated with specific niches, such as the nasopharynx or bloodstream. We then present methods describing how these models can be used to study bacterial phenotypes and their association with metabolic energy production and the generation of fermentation products. Key words Biofilm, Carbon source, Colonization, Epithelium, Fermentation, Metabolism, Mucosa, Nasopharynx, Respiratory tract, Sepsis, Streptococcus pneumoniae, Virulence

1

Introduction Respiratory tract infections (RTIs) are the fourth leading cause of morbidity and mortality worldwide and are caused by a number of viruses and bacteria acting alone or in concert to cause disease [1, 2]. This occurs primarily in children, the elderly, and immunocompromised individuals. Most bacterial respiratory pathobionts first colonize the upper respiratory tract without causing symptoms. To cause disease, changes in the local environment, such as changes in the local microbiome composition, either alone or in association with virus infection [3–8], or other host assaults [9–11], cause dissemination of the pathogen to distant sites [12, 13]. The

Pontus Nordenfelt and Mattias Collin (eds.), Bacterial Pathogenesis: Methods and Protocols, Methods in Molecular Biology, vol. 2674, https://doi.org/10.1007/978-1-0716-3243-7_1, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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Supradipta De and Anders P. Hakansson

various niches that the bacteria encounter during colonization and during transition to disease vary markedly in environmental or niche factors, including access to nutrients. Streptococcus pneumoniae (the pneumococcus), one of the major causes of infections and death, is a formidable host colonizer and is thought to be carried in the nasopharynx of between 1.9 and 5.8 billion people at any given time [14]. To successfully inhabit the host respiratory mucosa, pneumococci, like many other organisms, establish biofilms, i.e., complex organized communities of bacteria encased in an extracellular matrix, containing polymeric substances, such as carbohydrates, lipids, proteins, and DNA [15–19], that are produced both by the bacteria and the host [20–24]. Biofilm bacteria have distinct phenotypes compared with disease-causing bacteria due to specific gene expression profiles, of which genes involved in nutrient uptake and metabolism constitute a major portion [25]. The structure of the biofilm and the specialization of bacteria provide advantages for biofilm persistence in the host niche through increased resistance to environmental and host challenges [26–28]. In fact, colonization is harder to eradicate with antibiotic treatment than infection [22, 23, 29]. As colonization always precedes infection and the nasopharynx is the main niche where these bacteria live [30], studying their metabolic phenotype during colonization is important, which will be described in this chapter. Biofilm formation has, by far, primarily been studied in model systems using media containing glucose. Although such models have provided important information of relevance for pneumococcal colonization, the nasopharynx is generally devoid of glucose [31–33]. Instead, survival of the bacteria in this niche relies on their ability to obtain and metabolize other carbon sources and nutrients to grow and survive, which is evident from the large number of transporters present in the pneumococcal genome [34]. Studies have shown that, rather than glucose, the nasopharynx contains mucins with various sialylated glycoconjugates and pneumococci express glycosidases that can degrade glucans to release free carbohydrates [33, 35]. Carbohydrates, such as sialic acid, hyaluronic acid, N-acetyl glucosamine, and galactose, are all carbon sources available in the nasopharynx that broth-grown, planktonic pneumococci have been shown to import and metabolize [31–33]. These carbon sources are therefore potentially important for biofilm formation in vitro and in vivo [36]. Galactose, in particular, has been suggested to be a key sugar during colonization and infection [31, 37, 38]. However, upon leaving or dispersing from biofilms, the bacteria sometimes disseminate and cause infections in other niches, such as the middle ear, the lung, and the bloodstream, where nutrient conditions are different, which will affect their metabolism and behavior. In this chapter, we will present methods used to measure the metabolic activity and output of bacteria grown under different environmental and nutritional conditions. The methods described

Pneumococcal Niche-Associated Metabolism

5

here incorporate key features of the nasopharyngeal environment necessary to obtain biofilms in vitro that are structurally and functionally equivalent to colonizing biofilms in vivo [22, 39], as well as features associated with bloodstream infection. Analyses of the metabolism of strains of S. pneumoniae grown both planktonically and in biofilm format in glucose, galactose, and other carbon sources are useful strategies to assess phenotypes and metabolic activity of bacteria inhabiting various niches during their life cycle.

2

Materials

2.1 Growth of Bacterial Strains and Preparation of Bacterial Stocks

1. Although these protocols have been established with S. pneumoniae, they are applicable to other organisms as well. For S. pneumoniae, selection of strains should be done based on the research interest and nature of the experiments. There are over 100 serotypes of S. pneumoniae [40, 41]. The common pneumococcal strains used by us and other laboratories to study metabolism and pathogenesis are listed in Table 1. 2. Bacterial growth media: Two main media are used to perform these methods: (a) Prepare Todd-Hewitt medium (BD Biosciences) according to the manufacturer’s instructions and supplement with 0.5% yeast extract (BD Biosciences). Sterilize the medium either by autoclaving it or by sterile filtration using a 0.2 μm or 0.45 μm vacuum filter system (use vacuum filtration units with PES filter; available from various vendors). (b) Prepare chemically defined medium (CDM) from reagent stocks (see Note 1 and Table 2 for full details). Filtersterilize with a 0.22 μm or 0.45 μm vacuum filter system, as above. For experiments add carbon source of choice from concentrated stocks.

Table 1 Bacterial strains and cell line used in the study Strains: S. pneumoniae

Relevant genotype or phenotype

Source of reference

TIGR4

Wild-type, serotype 4 encapsulated strain

[34, 58]

D39

Wild-type serotype 2 encapsulated strain

[59, 60]

EF3030

Wild-type serotype 19F encapsulated strain

[61]

EF10175

Wild-type serotype 19F encapsulated strain

[61]

Human mucoepidermoid pulmonary carcinoma cells

CRL-1849; ATCC

Cell line NCI-H292

6

Supradipta De and Anders P. Hakansson

Table 2 Ingredients in CDM

Ingredients

Mass (mg) (5× stock)

Mass (mg) (1×) Stock C (in H2O)

General ingredients Sodium acetate, 3 H2O

4500

DL-alanine

500

Sodium phosphate, monobasic, monohydrate

3195

L-arginine, free base

500

Sodium phosphate, dibasic, anhydrous

7350

Glycine

500

Potassium phosphate, monobasic

1000

Hydroxy-L-proline

500

Potassium phosphate, dibasic

200

L-isoleucine

500

Sodium bicarbonate

5000

L-lysine

500

Choline chloride

1000

L-proline

500

L-cysteine hydrochloride

750

L-serine

500

L-threonine

1000

L-valine

500

Stocks A–F

Mass (mg) (5× stock) Stock D (in 1 M HCl)

Stock A (in H2O) Magnesium sulfate, heptahydrate

3500

Adenine, free base

100

Manganese sulfate, anhydrous

25

Ferrous sulfate, heptahydrate

25

Stock E (in 1 M NaOH)

Ferric nitrate, nonahydrate

5

Guanine hydrochloride

100

Calcium chloride, anhydrous

25.5

Uracil

100

Folic acid

4

Stock B (in 1 M HCl) L-aspartic acid

500

Stock F (in H2O)

L-cysteine, free base

2500

PABA

1

L-cystine dihydrochloride

250

Biotin

1

L-glutamic acid

500

Niacinamide

5

L-glutamine

1000

β-NAD

12.5

L-histidine, free base

500

D-calcium pantothenate

10

L-leucine

500

Pyridoxal hydrochloride

5

L-methionine

500

Pyridoxamine dihydrochloride

5 (continued)

Pneumococcal Niche-Associated Metabolism

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Table 2 (continued) Mass (mg) (5× stock)

Ingredients

Mass (mg) (1×)

L-phenylalanine

500

Riboflavin

10

L-tryptophan

500

Thiamine hydrochloride

5

L-tyrosine, free base

500

Cyanocobalamin

0.5

Ingredients for preparation of 1 liter CDM. For preparation see Note 1 Stocks A, B, C, E: Dissolve ingredients in 80 mL diluent as indicated above and adjust to 100 mL final volume. A volume of 20 mL is then added per liter of CDM Stock D: Dissolve in 10 mL diluent. A volume of 2 mL is then added per liter of CDM Stock F: Dissolved in 200 mL and split into 10 × 20 mL or in 100 × 2 mL tubes. A volume of 20 mL is then added to 1 liter or 1 mL is added per 50 mL batch of CDM directly before use Preparation: Finally, as the volume of stocks B and D (20 mL) prepared in 1 M HCl and the volume of stock E (20 mL) prepared in 1 M NaOH are similar, the medium should be buffered to a pH of approximately 7.0. The medium could be used at this pH, but for pneumococci that ferment and produce acid fermentation products, setting the pH to 7.2 by addition of 1 M NaOH is recommended

3. Carbohydrate stock solutions: Prepare carbohydrate stocks for use in the experiments. Dissolve each carbon source of interest at concentrations from 0.25 to 2.75 M in water, depending on experiment. High concentrations of carbon source may require heating of the solution to completely dissolve the sugar (see Note 2). 4. Spectrophotometer for measuring optical density at 600 nm (OD600) (see Note 3). 5. Bacterial freezing solution: Prepare 80% (v/v) glycerol in water and sterilize the solution by autoclaving it (see Note 4). 6. Blood agar plates: Blood agar plates can be purchased from a vendor but can also be made in the laboratory. For preparation of blood agar plates, prepare Tryptic Soy Agar (BD Biosciences) according to the manufacturer’s instructions and supplement with 5% sheep blood and pour into Petri dishes to gel. For step-by-step preparation of blood agar plates, see Note 5. 2.2 Preparation of the Epithelial Substratum

1. Cells: Use epithelial cell lines, such as human mucoepidermoid pulmonary carcinoma cells NCI-H292 (CRL-1849, ATCC) or human lung carcinoma cells A549 (CCL-185, ATCC). For information about additional cell types that can be used, see Note 6. 2. Cell culture medium: Supplement RPMI-1640 medium (containing 0.3 mg/mL of L-glutamine) with 10% fetal bovine serum, 1 mM sodium pyruvate, 100 U/mL penicillin, and 100 μg/mL streptomycin (all reagents are available from Thermo Fisher).

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3. Cell detachment: Prepare phosphate-buffered saline (PBS; 0.4 M NaCl, 0.0027 M KCl, 0.010 M phosphate buffer, pH 7.2; Medicago or other vendors) for washing the cell monolayer. Use trypsin (0.25%) in PBS (pH 7.2) for detachment of cells (can be purchased ready-made from various vendors). 4. Cell fixation: Prepare 4% (w/v) paraformaldehyde (PFA) solution in PBS. 2.3 Biofilm Formation In Vitro on Fixed Epithelial Cells

1. 24-well plates with a substratum of PFA-fixed epithelial cells (from Subheading 3.1.2). 2. Wash solution: Use PBS, pH 7.2 (see Subheadings 2.2 and 3). 3. Frozen stock(s) of pneumococci (from Subheading 3.1.1). 4. Chemically defined medium (CDM) with addition of appropriate carbon source (from Subheading 2.1, item 2). 5. Incubator system set to 34 °C and 5% CO2 for biofilm growth.

2.4 Biomass and Antibiotic Resistance of Planktonic and In Vitro Biofilm Bacteria

1. Gentamicin solution (50 mg/mL, available from Thermo Fisher or other vendors).

2.5 Scanning Electron Microscopy

1. Fixation solution: Mix 2.5% glutaraldehyde (Sigma G7651, EM grade) in 0.1 M sodium cacodylate (Sigma C0250) buffer, pH 7.2. Prepare solution fresh immediately prior to use. To retain extracellular matrix components and capsule, add 0.075% ruthenium red (from 1% stock in H2O, Sigma R2751) and 0.075 M lysine acetate (from 1 M stock in H2O, Sigma L1884) (see Note 7).

2. Blood agar plates (see Subheading 2.1, item 6).

2. Wash solution: Prepare 0.2 M sodium cacodylate buffer pH 7.2 with 0.075% ruthenium red (from 1% stock in H2O, Sigma R2751) and 0.075 M lysine acetate (from 1 M stock in H2O, Sigma L1884) to retain extracellular matrix components. 3. Ethanol (EtOH): Prepare 30%, 50%, 75%, 95%, and 100% EtOH solutions for fixation. 4. Coating solutions: (HMDS).

Prepare

100%

2.6 Biofilm Dispersal with Heat In Vitro

1. A separate incubator set at 38.5 °C.

2.7 Bacterial Energy Production

1. Bacterial suspension.

2.7.1 Oxidation Assay (Iodonitrotetrazolium, INT)

hexamethyldisilazane

2. Blood agar plates (see Subheading 2.1, item 6).

2. PBS (Subheading 2.2, item 3), with addition of appropriate carbon sources. 3. 96-well flat-bottomed microtiter plates for absorbance.

Pneumococcal Niche-Associated Metabolism

9

4. Iodonitrotetrazolium chloride (INT; Sigma I8377): Prepare a 6 mM stock solution in PBS. 5. Spectrophotometer for repeated measurements of optical density at 550 nm (OD550) (see Note 8). 2.7.2 Determination of Intracellular ATP

1. Bacterial suspension. 2. PBS (Subheading 2.2, item 3), with addition of appropriate carbon sources. 3. 96-well white microtiter plates for luminescence. 4. ATP determination kit (Thermo Fisher/Invitrogen Cat# A22066). 5. Triton-X100 solution: Prepare a 10% solution of Triton-X100 (v/v) in water. 6. Multiplate reader for repeated measurements of luminescence (see Note 8).

2.8 Bacterial Fermentation Products 2.8.1

Lactate

1. Bacterial suspension. 2. CDM (Subheading 2.1, item 2) or PBS (Subheading 2.2, item 3), with addition of appropriate carbon sources. 3. 96-well flat-bottomed microtiter plates for absorbance. 4. Potassium phosphate buffer: Prepare 1 M KH2PO4 (monobasic) and 1 M K2HPO4 (dibasic) and mix and dilute in water to obtain a 100 mM potassium phosphate buffer, pH 9. 5. Next add 461 mg of phenylhydrazine hydrochloride (Sigma 114715) directly into 10 mL of the 100 mM potassium phosphate buffer to prepare a 320 mM solution (see Note 9). 6. NAD+ stock: Add 164 mg of nicotinamide adenine dinucleotide (NAD) sodium salt (Sigma N0632) to 1 mL deionized water to prepare a 48 mM stock solution. 7. Lactate dehydrogenase (LDH) stock: Prepare a 200 U/mL stock of LDH (Sigma L7022) in deionized water. 8. Lactate standards: Prepare fresh L-lactate, sodium salt solutions ranging from 10 to 10,000 μM in 100 mM potassium phosphate buffer. 9. Spectrophotometer for repeated measurements of optical density at 340 nm (OD340) (see Note 8).

2.8.2

Hydrogen Peroxide

1. Bacterial suspension. 2. CDM (Subheading 2.1, item 2) or PBS (Subheading 2.2, item 3), with addition of appropriate carbon sources. 3. 96-well black microtiter plates for fluorescence.

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4. Amplex Red Hydrogen Peroxide/Peroxidase Assay kit (Thermo Fisher/Invitrogen Cat# A22188). 5. Multimode reader for repeated measurements of fluorescence (see Note 8). 2.8.3

Acetate

1. Bacterial suspension. 2. PBS (Subheading 2.2, item 3), with addition of appropriate carbon sources. 3. 96-well flat-bottomed microtiter plates for absorbance. 4. Acetate colorimetric assay kit (Sigma-Aldrich Cat# MAK086). 5. Spectrophotometer for repeated measurements of absorbance (see Note 1).

2.8.4

Ethanol

1. Bacterial suspension. 2. CDM (Subheading 2.1, item 2) or PBS (Subheading 2.2, item 3), with addition of appropriate carbon sources. 3. 96-well flat-bottomed microtiter plates for absorbance. 4. Ethanol assay kit (Megazyme Cat# K-ETOH). 5. Spectrophotometer for repeated measurements of absorbance (see Note 8).

2.8.5

Formate

1. Bacterial suspension. 2. CDM (Subheading 2.1, item 2) or PBS (Subheading 2.2, item 3), with addition of appropriate carbon sources. 3. 96-well flat-bottomed microtiter plates for absorbance. 4. Ethanol assay kit (Sigma MAK059). 5. Spectrophotometer for repeated measurements of absorbance (see Note 8).

3

Methods The methods below have been optimized using strains of S. pneumoniae. However, the methods are not restricted to pneumococci but are applicable also to other respiratory pathobionts, especially to facultative or aerotolerant anaerobic organisms, such as streptococci and staphylococci that primarily ferment carbohydrates.

3.1 Adaptation and Growth of Planktonic Bacteria 3.1.1 Preparation of Planktonic Bacterial Strains

Since bacterial strains are generally grown in glucose-based media, it is important to adapt the strains to the respective carbon sources other than glucose to allow study of their metabolism in these assays. In this chapter, the metabolism in the presence of glucose or galactose will be highlighted and compared; therefore, the growth and adaptation in these carbon sources are described

Pneumococcal Niche-Associated Metabolism

11

Fig. 1 Overview of general metabolic pathways in streptococci. The cartoon presents the three major types of transporter used to import carbon sources with ABC transporters and phosphotransferase systems (PTS) being the major transport modalities. Additional transporters, such as the Sia transporter for sialic acid import is also shown. Imported carbohydrates are then processed through various metabolic pathways to result in Glucose6-phosphate, the starting point of glycolysis (the Embden-Meyerhof-Parnas pathway) resulting in breakdown of hexoses to the triose pyruvate. As the Krebs cycle (tricarboxylic acid, TCA cycle) and electron transfer chain and oxidative phosphorylation are not intact in streptococci, pyruvate is fermented to various end products that can be measured

below. However, these methods are feasible with other carbon sources that pneumococci can utilize as well, some of which are presented in Fig. 1. Growth and adaptation of bacterial strains are done as follows:

1. Streak out the pneumococcal strain(s) of interest on blood agar plates and incubate overnight at 37 °C (see Note 10). 2. Using a sterile inoculation loop, transfer individual colonies from the blood agar plate into a glass tube (16 × 100 mm) or a 15 mL conical tube containing 10 mL of THY medium or CDM supplemented with carbon source of interest (55 mM final concentration for growth experiments). Tighten the cap and grow statically at 37 °C until the bacterial growth reaches the late logarithmic stage, indicated by an OD600 of approximately 0.6 (see Note 11). 3. Add 2 mL of 80% glycerol solution directly to the culture, mix well by pipetting, and then transfer the bacterial suspension to microcentrifuge cryotubes in 500 μL aliquots and store at 80 °C (see Note 12).

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4. Use the initial 500 μL stock culture for growth in CDM supplemented with respective carbon source in 1:10 dilution (500 μL stock culture directly added to 4.5 mL of respective media) and repeat growth at least three times before utilizing them for the metabolism studies. 3.1.2 Preparation of the Epithelial Substratum

Since pneumococci cause respiratory tract infection, the bronchial epithelial cell line H292 is used here to describe the preparation of an appropriate epithelial substratum. However, other cell lines like Detroit 562, Calu-3, or A549 can be used as well depending on the type of study. 1. Pre-warm cell culture media to 37 °C. 2. Quickly thaw frozen stocks of respective cell line (cell line stocks are either provided by vendors or can be prepared for long-term storage as described in Note 6). 3. Disinfect the cell vial with 70% EtOH. 4. Transfer the cells to a 50 mL polypropylene tube with at least 30 mL pre-warmed medium (see Note 13). 5. Centrifuge at 1200 rpm for 5 min, remove the supernatant, and resuspend cell pellet in fresh media. 6. Add resuspended cells to a cell culture flask and allow to grow. Once cell layer is at least 70% confluent, the cells can be passaged. 7. To passage the cells, remove the medium, wash the cell monolayer twice with PBS to remove residual medium, and detach the confluent cell monolayer from the flask by adding trypsin solution, noting that the detachment time will vary depending on the cell line. Detachment of cells can be visualized macroscopically as well as in a microscope as the cells round up and detach from the surface. Resuspend the detached cells from one 75 cm2 cell culture flask in 12 mL of fresh cell culture medium (the fetal bovine serum contains trypsin inhibitors that will inactivate trypsin to allow reattachment of the cells). Add 0.5 mL cell suspension per well in 24-well cell culture plates and incubate at 37 °C in 5% CO2 until near-confluent (80–90% confluency). If the cells are being used for scanning electron microscopy (see Subheading 3.4), grow the cells on round 13 mm glass cover slips placed in the bottom of the 24 well plates. 8. Aspirate the medium and wash the cell monolayers three times with approximately 1 mL of PBS per well. 9. The confluent epithelial substratum can now either be fixed or used immediately for biofilm formation on live epithelial substratum (see Subheading 3.2).

Pneumococcal Niche-Associated Metabolism

13

10. To fix the cells, add 0.5 mL of 4% PFA solution (in PBS) per well and incubate for 1 h at room temperature or overnight at 4 °C. Remove the PFA, wash three times with PBS, and store at 4 °C in PBS solution for up to a month. Keep the plates in a plastic storage box to prevent fluid evaporation. 3.2 Biofilm Formation In Vitro

Cells from the respiratory tract generally provide a better surface for pneumococcal biofilm formation than cells from other parts of the body (such as keratinocytes and sarcoma cells; Hakansson AP, unpublished observation). Growth on respiratory epithelial cells provides most pneumococcal strains with the capacity to form well-structured and functional biofilms, which is not observed on abiotic surfaces over the same time period [unpublished observations; 22]. In general, pneumococcal biofilms are able to form on either fixed or live epithelial cells in our models. However, to enable biofilm formation on live epithelial cells, biofilm bacteria first formed on fixed cells are transferred onto live cells, as these bacteria have a lower expression of cytotoxic factors in comparison to brothgrown, planktonic pneumococci that show considerable toxicity toward live epithelial cells [25, 42] (see Fig. 2 for a schematic of the methodologies presented here).

Fig. 2 A cartoon of the methodology presented in this protocol. Pneumococcal biofilms can be formed in vitro on epithelial cells (see Subheading 3.1). Mature biofilms formed on fixed epithelial cells are also used in biofilm dispersal induced by exposure to increased temperature (mimicking fever), where both the supernatant and biofilm fractions are collected for comparison (see Subheading 3.5). Ways to assess the biofilm phenotype and metabolism (see Subheading 3.4) are shown in the right part of the cartoon

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To form biofilms in vitro on fixed epithelial cells: 1. Thaw frozen stock(s) of pneumococcal strain(s) of interest (see Subheading 3.1.1). 2. Make a 1:100 dilution of the frozen stock in CDM to the desired volume needed for seeding the 24-well plate(s). For instance, take 120 μL frozen stock and dilute 100 times by adding 12 mL CDM for seeding one 24-well plate. 3. Add 0.5 mL of the diluted pneumococcal stock in each well of a 24-well plate containing fixed cells (see Subheading 3.1.2). After 12 h of incubation, add 0.5 mL of medium for a total volume of 1 mL/well. 4. Incubate the bacteria at the nasopharyngeal temperature of 34 °C in 5% CO2 for optimal biofilm formation (see Note 14), carefully exchanging 0.5 mL of the supernatant with 0.5 mL fresh CDM approximately every 12 h (see Note 15 for important details regarding the medium). Avoid disturbing the biofilm as much as possible by keeping the plate leveled, pipetting off the used medium with a 5 mL or 10 mL serological pipette, and adding 0.5 mL fresh CDM slowly down the side of each well (see Note 16). 5. Grow biofilms for appropriate times, but for at least 48 h to obtain mature biofilms (see Note 17). 6. Assess biofilm structure and function according to Subheadings 3.3 and 3.4. 3.3 Bacterial Density and Antibiotic Resistance of Planktonic and Biofilm Bacteria

The bacterial density (the number of viable bacteria per biofilm) and the tolerance to antibiotics are indicators of biofilm structure and function. Although a number of other methods are available to address the structure of biofilms (fluorescence microscopy or SEM; see Subheading 3.4) and function (gene transcriptional analysis or proteome analysis), our previous methodology papers on this topic [43, 44] have indicated that measurements of bacterial density and antibiotic tolerance successfully predict structure and function of the biofilms. To determine bacterial density and antibiotic resistance in vitro: 1. Prepare biofilms according to Subheading 3.2. 2. Remove the supernatant and add 0.5 mL PBS with or without the appropriate concentration, usually around 250–500 μg/ mL, of gentamicin and incubate for 3 h at 34 °C in 5% CO2. For determination of the appropriate antibiotic concentration, see Note 18. 3. After incubation, resuspend the bacteria by scraping the biofilm from the bottom of the well using the tip of a 20–200 μL pipette tip. Float the 24-well plate in a water bath sonicator

Pneumococcal Niche-Associated Metabolism

15

and sonicate for 2 s to loosen all biofilm cells and disperse aggregates, and further disrupt the biofilm bacteria by pipetting up and down and transfer the bacterial suspension to a microfuge tube and vortex the tube. For details regarding resuspension of the biofilm, see Note 19. 4. To determine the total viable colony forming units (CFUs), make tenfold serial dilutions (in PBS) for each sample. Plate 100 μL per dilution on blood agar plates and incubate at 37 °C overnight. Count colonies on the plates to determine the CFU per mL or CFU per biofilm. 3.4 Assessment of Biofilm Structure by Scanning Electron Microscopy (SEM)

To ensure an appropriate biofilm phenotype, biofilm structure provides important insight. Biofilms typically contain specific structures of organized chains or clusters of bacteria surrounded by extracellular matrix with pores that allow nutrient penetration into the biofilm and the disposal of waste products. This structural phenotype is most effectively observed by scanning electron microscopy [22] (exemplified in Fig. 3). An enrichment of bacteria with a lowered metabolic rate [45], together with the matrixencased structure, makes biofilms highly tolerant to antimicrobial agents and host protective mechanisms [46]. The development of a biofilm phenotype is strongly related to a concomitant and substantial modulation of expression of genes and proteins, as described elsewhere [9, 25, 47]. This section describes methods to evaluate these biofilm features by scanning electron microscopy. Sample preparation for scanning electron microscopy: 1. Form biofilms according to the methods in Subheading 3.2 on epithelial substratum grown on glass coverslips in 24-well plates (Subheading 3.1). 2. Fixation: Prepare fixation solution (according to Subheading 2.6) and incubate sample at room temperature for 1 h. Change fixation solution once during the fixation process. Samples can also be fixed at 4 °C overnight. If so, equilibrate samples to room temperature before proceeding with protocol. 3. Washes: Perform 3 × 10 min rinses of the samples using wash buffer (prepared according to Subheading 2.6) without agitation. 4. Dehydration: Subject sample(s) to grade ethanol (EtOH) incubations going from 30% to 50% to 75% to 95% to 100% EtOH for 10 min each (see Note 20). 5. Coating: Exchange sample buffer to 100% HMDS (hexamethyldisilazane) in a chemical hood to coat samples and evaporate off in the hood until completely dry. Repeat once.

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Fig. 3 Scanning electron microscopy images of colonizing strain EF3030 in glucose-based or galactose-based CDM media. Images for each growth condition (glucose in panel A and galactose in panel B) are from the same area in the sample. The right image for each growth condition shows the area at low magnification (4000×) with the scale bar representing 20 μm. The white square indicates the area shown in the left image at higher magnification (15,000×) with the scale bar representing 5 μm. Biofilm formation was structurally similar between biofilms grown in glucose and galactose. However, biofilms grown in glucose-based media showed somewhat more extracellular matrix formation than biofilms grown in galactose

6. Subject samples to SEM imaging using a Hitachi SU-70 SEM or similar instrument. If assessed by SEM the next day, keep dry and dust-free (if kept completely dry, samples can be stored and processed later). 3.5 Biofilm Heat Dispersal In Vitro

Colonization is thought to be the first step in pneumococcal pathogenesis. While colonizing bacteria primarily reside in the nasopharynx asymptomatically, changes in the nasopharyngeal environment associated with virus infection and virus-induced signals, such as increased temperature (fever), can induce dispersal of a population from the biofilm that is transcriptionally and phenotypically distinct [42]. For instance, IAV-dispersed or heat-dispersed bacteria have major alterations in both metabolic and virulence gene expression, resulting in a more inflammatory and more invasive bacterial population than the biofilms they originated from or

Pneumococcal Niche-Associated Metabolism

17

compared to broth-grown planktonic bacteria [25]. Understanding biofilm dispersal and the transition to disease offers opportunities to understand differences in metabolic pathways and end-products between more virulent phenotypes of the bacteria and those involved in colonization. The methods below describe biofilm dispersal in response to heat exposure (mimicking fever). Other dispersal signals have been identified [42] and can be used in the same system and evaluated in the same way. 1. Prepare biofilms in vitro for 48–72 h according to Subheading 3.2 in two 24-well plates. 2. Carefully remove the supernatant, wash twice with 0.5 mL CDM containing appropriate carbon source, and replace with 0.5 mL fresh CDM by slowly adding it down the side of the well. 3. For heat-induced dispersal, place one plate in an incubator with febrile-range temperature (38.5 °C) and keep a control plate at 34 °C during the incubation period. For biofilm dispersal with other reagents, add the reagents at appropriate concentrations together with untreated control biofilms and incubate for up to 4 h before assessing dispersal of the biofilm. 4. After 4 h of incubation, transfer the supernatant with a 5 mL or 10 mL pipette into pre-labeled microcentrifuge tubes and vortex the tubes. Resuspend the remaining biofilm in 0.5 mL PBS and then determine the total viable counts for all samples according to Subheading 3.3. 5. Biofilm dispersal can be determined either as the increased number of bacterial colonies observed in the supernatant of treated biofilms or as a ratio of supernatant over biofilm biomass for each sample. In the latter example, the ratio should increase with increased dispersal and release of biofilm bacteria in the supernatant. 3.6 Overall Energy Metabolism 3.6.1

Oxidation Assay

The iodonitrotetrazolium chloride (INT) assay measures energy production in cells based on overall redox activity associated with metabolic activity. The tetrazolium salt is an electron acceptor, which is reduced to furazan by the oxidoreductases in mitochondria or bacteria and produces a red insoluble dye, formazan, which is measured in a colorimetry assay at 530–550 nm and used to differentiate between metabolically active and inactive cells (Reaction 1) [48]. An example of data provided by this assay is shown in Fig. 4.

Reaction 1 Tetrazolium reduction by oxidoreductases to produce formazan. Black circle indicates the product measured in the assay

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Supradipta De and Anders P. Hakansson

Fig. 4 INT oxidation in S. pneumoniae D39 bacterial cells. Suspensions of planktonic or biofilm D39 pneumococci adapted to glucose or galactose were grown in respective media and resuspended in PBS with no carbon source or 50 mM glucose or galactose in the presence of 0.6 mM INT as an electron acceptor. Oxidation of INT resulted in violet coloration that was measured every 10 min over 18 h at an optical density of 550 nm. The OD550 nm at 12 h (a) was shown to produce reproducible results between experiments and is used to determine oxidation of the bacteria. OD550 nm per 108 CFUs is presented in graph (b) for the various samples. The data is based on three individual experiments performed in duplicate. The dashed line represents the time points chosen for measurement (a) or background oxidation of INT of bacterial suspensions in the absence of a carbon source (b)

Pneumococcal Niche-Associated Metabolism

19

To measure carbon source utilization of the pneumococcal strains, the oxidation assay using INT is performed as follows: 1. Add 10 μL of 6 mM INT and 10 μL each of 0.5 M carbon sources to each well of a microtiter plate in triplicates. 2. Grow bacteria planktonically or as biofilms in glucose- or galactose-based media, or using other appropriate carbon sources, as described above (Subheadings 3.1.1 and 3.2). 3. Mechanically disrupt the biofilm bacteria by pipetting and transferred to microfuge tubes. 4. Pellet biofilm and planktonic organisms and wash by centrifugation at 11,000× g for 2 min with PBS and resuspend in PBS to an OD600 of approximately 0.4. 5. Separately, remove an aliquot of bacteria to determine the concentration of the bacterial suspension. Bacterial concentration in colony-forming units (CFUs) per mL is determined from plate counts of overnight growth of serial dilutions on blood agar (Subheading 3.3). 6. Add bacterial suspension (80 μL) to each well of a 96-well microtiter plate, incubate the plate at 37 °C, and measure the optical density at 550 nm (OD550) every 10 min over 18 h in a multi-well plate reader. 7. All samples were compared with control wells without any carbon source added (Fig. 4). Carbon utilization generally peaks at 12 h for most experimental conditions. 8. Oxidation is displayed as the OD550 per 108 CFU of bacteria. 3.6.2

ATP Production

Besides bacterial intracellular oxidation activity, overall metabolic activity can also be measured by determining production of intracellular bacterial ATP as previously described [51]. The luminescent ATP assay detects the light emitted from the lysed cell sample which is measured at 560 nm. When the enzyme luciferase reacts with substrate luciferin in the presence of ATP from the sample, it forms oxyluciferin along with pyrophosphate and light emission (Reaction 2).

Reaction 2 ATP determination assay reaction. Black circle indicates the product (light) measured in the assay

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To measure ATP production by the pneumococcal strains, intracellular ATP determination was performed as follows: 1. Grow bacteria planktonically or as biofilms in glucose- or galactose-based media, or using other appropriate carbon sources, as described above (Subheadings 3.1.1 and 3.2). 2. Mechanically disrupt the biofilm bacteria by pipetting and transfer to microfuge tubes. 3. Pellet bacteria and wash twice in PBS by centrifugation at 11,000 rpm for 2 min in a microcentrifuge and resuspend in PBS to the original volume (unless concentration of bacterial suspension is needed). 4. Separately, remove an aliquot of bacteria to determine the concentration of the bacterial suspension. Bacterial concentration in colony-forming units (CFUs) per mL is determined from plate counts of overnight growth of serial dilutions on blood agar (Subheading 3.3). 5. Prepare stocks of carbon sources (0.25 M in PBS) and add 10 μL/well in a 96-well plate (25 mM final concentration). 6. Add 90 μL washed and “starved” bacterial suspension to wells with glucose or other carbon sources at various intervals to obtain samples that have been exposed to carbon sources for 0, 5, 10, 15, 20, 25, 30, and 60 min (or longer if needed). 7. After incubation determine intracellular ATP using the ATP determination kit (Thermo Fisher Scientific/Invitrogen Cat#: A22066) according to the manufacturer’s instructions with slight modifications. 8. Prepare reaction buffer for ATP determination (10 mL; from kit A22066): (a) 500 μL 20× reaction buffer (component E). (b) 500 μL 10 mM D-luciferin (component A). (c) 500 μL 10 mM Triton-X100 (see Note 21). (d) 100 μL DTT solution. (e) 8.4 mL H20. (f) 0.2 μL firefly luciferase. 9. Measure bacterial ATP production: Add 10 μL of bacterial suspension in duplicate to 90 μL of reaction buffer containing 0.5% Triton-X100 required to lyse the bacteria in white 96-well plate (see Note 21). 10. Measure luciferin conversion by chemiluminescence as a measure of ATP in the sample using a Synergy 2 plate reader (BioTek) with sensitivity set to 160–200 or comparable multi-well luminescence reader.

Pneumococcal Niche-Associated Metabolism

21

11. ATP concentration in each sample is determined from the ATP standard curve (according to the manufacturer’s instructions) and presented as ATP concentration in nM per 108 CFUs. 3.7 Measuring Fermentation Metabolites

3.7.1 Inducing Bacterial Metabolism and Fermentation

Most streptococci produce energy from carbon sources through glycolysis or other parallel pathways, which end with the generation and secretion of fermentation products. These organisms lack a complete tricarboxylic cyclic acid (Krebs) cycle and the enzymes required for oxidative phosphorylation [49]. Thus, in this paper, we will focus on the measurement of fermentation products. 1. Grow bacteria in appropriate media as biofilms (see Subheading 3.2) or planktonically to an OD600 nm of approximately 0.6 (see Subheading 3.1.1). 2. Mechanically disrupt the biofilm bacteria by pipetting and transfer to microfuge tubes. 3. Pellet the bacteria and wash twice in PBS by centrifugation at 11,000× g for 2 min and resuspend in PBS to the original volume. 4. Separately, remove an aliquot of bacteria to determine the concentration of bacteria (as described in Subheading 3.3). Bacterial concentration in colony-forming units (CFUs) per mL are determined from plate counts of overnight growth of serial dilutions on blood agar. 5. Energize the bacterial suspensions with the appropriate carbon source of interest. For general metabolic activity through glycolysis and fermentation pathways, add 25 mM carbon source (final concentration) to start the reaction. 6. Run the reactions for 30–60 min and remove 250 μL bacterial suspension every 5 min from each sample, pellet the bacteria by centrifugation at 11,000× g for 2 min, and save the supernatant containing fermentation products immediately by transferring to a sterile microfuge tubes to be saved at -20 °C until further analysis.

3.7.2 Lactate Determination

Lactate produced and secreted by the bacteria was determined essentially as described [50]. Determination of lactate is done indirectly where lactate in the presence of the enzyme lactate dehydrogenase (LDH) converts to pyruvate by reducing NAD+ to NADH, which can be measured colorimetrically by absorbance of the sample at 340 nm (Reaction 3). An example of data obtained by this assay is shown in Fig. 5. To measure lactate in the supernatants of stimulated bacterial cells, the following procedure was followed: 1. Stimulate biofilm and planktonic bacteria as described in Subheading 3.7.1 and use supernatant for analysis.

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Reaction 3 Measurement of lactate. Black circle indicates the product measured in the assay

Fig. 5 Lactate production by S. pneumoniae. Production of lactate by pneumococcal strain D39. Bacteria were grown planktonically or as biofilms in the presence of glucose or galactose. Supernatants from planktonic bacteria or biofilm bacteria were used to measure the concentrating of lactate. The data is presented as the mean concentration of lactate (in mM) or per 108 CFU with standard deviations. The number of biological replicates varied from 4 to 11. Statistical analysis was performed with one-way ANOVA with Dunnett’s multiple comparison test. **P < 0.01. Generally, bacteria grown in galactose produced significantly less lactate

2. Mix 100 μL of sample supernatant or lactate standard 10–10,000 μM in duplicate with 200 μL reaction buffer (Subheading 2.7.1) into a microtiter plate. 3. Measure the absorbance of formed NADH at 340 nm in a multi-well microplate spectrophotometer. The concentration of lactate was determined from the standard curve of known concentrations of sodium lactate determined on the same plate. 4. Lactate concentration is presented in mM per 108 CFUs. 3.7.3 Hydrogen Peroxide Determination

Hydrogen peroxide production is seen in S. pneumoniae fermentation through the enzyme SpxB during production of acetyl phosphate and acetate (Reaction 4). The measurement uses the Amplex Red Hydrogen Peroxide assay kit in which the reagent Amplex Red is mixed with H2O2 in a 1:1 ratio to produce the red fluorescent oxidation product resorufin in presence of horse radish peroxidase. 1. Stimulate biofilm and planktonic bacteria as described in Subheading 3.7.1 and use supernatant for analysis.

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Reaction 4 Hydrogen peroxide measurement through resorufin production (black circle)

2. Prepare reagents according to the manufacturer’s instructions followed by the working solutions containing 100 μM Amplex Red, 0.2 U/mL of HRP in a total of 5 mL reaction buffer. 3. Add 50 μL per well of H2O2 standards (0–10 μM) in duplicate wells of a black fluorescence microtiter plate as well as vehicle solution (PBS or medium) or supernatant from treated samples. 4. Add 50 μL working solution to each well containing standards, controls, or samples. 5. Incubate the plate in the dark for 30 min at room temperature before analysis of fluorescence. Alternatively, and optimally, continuously read the fluorescence in a microplate reader set for excitation in the range of 530–560 nm and emission detection at 590 nm. 6. The concentration of H2O2 was determined from the standard curve determined on the same plate and the final concentration was presented in μM per 108 CFUs. 3.7.4 Acetate Determination

Acetate concentration in the sample was measured by the acetate assay kit based on colorimetric approach using a coupled enzyme assay. 1. Stimulate biofilm and planktonic bacteria as described in Subheading 3.7.1 and use supernatant for analysis (only certain conditions can be used as indicated in Note 22). 2. Thaw the acetate colorimetric kit to room temperature. 3. Prepare acetate standards by diluting 10 μL of the 100 mM acetate stock solution with 990 μL of water to prepare a 1 mM standard solution and add 0, 2, 4, 6, 8, and 10 μL of the 1 mM standard solution into duplicate wells of a 96 well plate, generating 0–100 μM standard curve. Add acetate assay buffer to each well to bring the volume to 50 μL. 4. Add 50 μL of controls and sample supernatant in duplicate wells of a 96-well microtiter plate. Should the sample concentration of acetate be outside the linear range of the standard curve, a lower volume can be used. If so, add acetate assay buffer to bring volume of 50 μL (to control for certain background signals, please see Note 23).

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5. Prepare reaction mix (per well: 2 μL each of acetate enzyme mix, ATP, acetate substrate mix, and probe plus 42 μL acetate assay buffer). 6. Add 50 μL of the reaction mix to each of the wells containing standards, controls, or samples. Mix well using a horizontal shaker or by pipetting, incubate the reaction for 40 min at room temperature away from light, and measure the absorbance at 450 nm, Alternatively, and optimally, continuously read the absorbance in a microplate reader every 1–2 min, set to 450 nm. 7. The concentration of acetate is determined from the standard curve of known concentrations of acetate determined on the same plate. Acetate concentration is presented in μM per 108 CFUs. 3.7.5 Ethanol Determination

Ethanol is a potential fermentation product of carbohydrate metabolism in streptococci. Ethanol determination is performed in two-step enzyme reaction by absorbance measurement (Reaction 5). In the first reaction, ethanol is oxidized to acetaldehyde by NAD+ by alcohol dehydrogenase (ADH). As the equilibrium of this reaction lies in favor of ethanol and NAD+, a second reaction is performed to “trap” the products. This is achieved by the quantitative oxidation of acetaldehyde to acetic acid in the presence of aldehyde dehydrogenase (Al-DH) and NAD+. The amount of NADH formed in Reactions (1) and (2) is measured by an increase in absorbance at 340 nm. 1. Stimulate biofilm and planktonic bacteria as described in Subheading 3.7.1 and use supernatant for analysis. 2. Thaw the ethanol colorimetric kit to room temperature and use according to manufacturer’s instructions. 3. Buffer (Bottle 1), enzyme solutions (Bottles 3 and 4), and ethanol standard solution (5 mg/mL) are used directly as is. NAD+ (Bottle 2) is dissolved in 12.4 mL of distilled water before use. 4. Add 10 μL of ethanol controls (diluted to provide final concentration of 0.1–1.2 μg/mL) and sample supernatant in duplicate wells of a 96-well microtiter plate. 5. Then add mixture containing 200 μL distilled water, 20 μL of buffer (Bottle 1), 20 μL of NAD+ (Bottle 2), and 5 μL of Al-DH (Bottle 4). 6. Mix and read baseline absorbance at 340 nm in a spectrophotometer. 7. Start reaction by adding 2 μL/well of ADH (Bottle 3) and read the absorbance every minute for 10 min or until it has stabilized.

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Reaction 5 Ethanol determination assay with black circles indicating products that are being measured in the reaction

8. The concentration of ethanol is determined from the standard curve of known concentrations of ethanol determined on the same plate. Ethanol concentration is presented in μg/mL per 108 CFUs. 3.7.6 Formate Determination

The presence of formate in a sample could also be measured using enzymatic assay measuring colorimetric data at 450 nm. 1. Stimulate biofilm and planktonic bacteria as described in Subheading 3.7.1 and use supernatant for analysis. 2. Thaw the formate assay kit to room temperature. 3. Prepare acetate standards by diluting 10 μL of the 100 mM formate standard solution with 990 μL of water to prepare a 1 mM standard solution and add 0, 2, 4, 6, 8, and 10 μL of the 1 mM standard solution into duplicate wells of a 96-well plate, generating 0–100 μM standard curve. Add format assay buffer to each well to bring the volume to 50 μL. 4. Add 50 μL of controls and sample supernatant in duplicate wells of a 96-well microtiter plate. Should the sample concentration of acetate be outside the linear range of the standard curve, a lower volume can be used. If so, add formate assay buffer to bring volume to 50 μL (to control for certain background signals, please see Note 24). 5. Prepare reaction mix (per well: 2 μL each of formate enzyme mix and formate substrate mix plus 46 μL formate assay buffer). 6. Add 50 μL of the reaction mix to each of the wells containing standards, controls, or samples. Mix well using a horizontal shaker or by pipetting, incubate the reaction for 60 min at room temperature away from light, and measure the absorbance at 450 nm, Alternatively, and optimally, continuously read the absorbance in a microplate reader every 1–2 min, set to 450 nm. 7. The concentration of formate is determined from the standard curve of known concentrations of acetate determined on the same plate. Acetate concentration is presented in μM per 108 CFUs.

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Notes 1. To facilitate the preparation of CDM [51], the components have been divided into separate stocks (see Table 1 for detailed information about preparing each stock solution). Here, Stocks A–E are prepared in 5× solutions and aliquoted into five tubes (each for 1 liter of CDM) and stored at 4 °C. Stock F is also prepared in 5× concentration but is then stored at -20 °C either in 10 × 20 mL or 200 × 1 mL aliquots to be thawed and added fresh to 1 liter or 50 mL CDM containing stocks A– E, respectively, immediately before use, depending on the amount of media needed for the experiment. To make 1 liter of CDM, add the ingredients listed under “General ingredients per liter CDM” in Table 2 to approximately 750 mL of distilled water. Pour in one aliquot each of Stocks A–E (5×). Bring the volume up to 1 liter with distilled water. Check the pH of the medium and set with 1 M NaOH to a pH of 7.2. Filter-sterilize the medium with a 0.22 or 0.45 μm vacuum filter system. Depending on the amount of media needed, either use the whole bottle directly or aliquot the medium into 20 × 50 mL conical tubes and store at 4 °C. Bring the media to room temperature and add 1 mL of freshly thawed Stock F to one 50 mL conical tube or 20 mL of Stock F to 1 liter. Finally, add appropriate concentration of carbon source. For growth of bacteria, a final concentration of 55 mM (10 g/L) of hexoses is being used. For stimulation experiments to produce fermentation products, a final concentration of 25–50 mM is most often used. 2. For growth of bacteria in CDM, a concentration of 10 g/L (55 mM) is suggested. For this purpose, glucose stocks of 2.75 M can be made, where 1 mL is added into 50 mL of medium. However, for other carbon sources, this concentration may not be possible to obtain and the carbon source may not completely dissolve. Lower concentrations are then recommended. The majority of carbon sources used in our studies can be dissolved at 1 M concentration, and for some carbon sources heating the solution to 37 °C will facilitate dissolving the carbon source. In addition, various final concentrations of carbon source are suggested for the experiments in these methods. 3. The optical density (OD) of the culture can be measured in any regular spectrophotometer with a sterile cuvette (1 cm pathlength). However, we recommend using 16 × 100 mm glass tubes with screw caps together with a Spectronic™ Spectrophotometer that has an adapter for test tubes. This enables quick reading of the OD and also prevents contamination of the culture as the glass tubes do not need to be uncapped for each OD reading.

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4. We use 80% (v/v) glycerol solution in water based on its lower viscosity that facilitates pipetting as compared with 100% glycerol. 5. To make blood agar plates, mix and dissolve tryptic soy broth powder and Bacto-Agar (according to manufacturer’s instructions) in water (BD Biosciences), and autoclave. Cool to approximately 55 °C, and then add sheep blood to a final concentration of 5%. Pour approximately 10 mL of blood agar in each Petri dish (92 × 16 mm) and let gel at room temperature. Store plates at 4 °C, preferably in sealed plastic bags or boxes to prevent loss of moisture and to avoid contamination. 6. Primary bronchial epithelial cells commercially available from Lonza (NHBE) or ATCC (PCS-300-010) have been used with their recommended media and are useful for validation purposes as well as for producing biofilms on live cells. We have not used Detroit 562 (CLL-138, ATCC) nasopharyngeal carcinoma cells or Calu-3 (HTB-55; ATCC) lung adenocarcinoma cells in our studies, but based on their origin, it is likely that they would also be suitable for pneumococcal biofilm formation. 7. During SEM preparation, L-lysine and ruthenium red are added to the fixation and wash solutions to retain polysaccharide matrix as described by Hammerschmidt et al. [52] and observed in our biofilm studies [22, 44]. 8. We have used a Synergy II multimode, multi-well spectrophotometer (BioTek) for these studies. However, any multi-well spectrophotometer that can be set to measure absorbance, fluorescence, and luminescence will be suitable for these studies. Optimally, readers with repeated reading ability would be preferable for many of the assays in this protocol. 9. Phenyl hydrazine is a crucial ingredient in the assay as it covalently attaches to the pyruvate formed in the reaction and interferes with substrate inhibition by making it impossible for the LDH to run the reaction in the opposite and preferable direction [50]. 10. To distinguish pneumococci from other viridans streptococci, pneumococci are sensitive to optochin and will show a growth inhibition zone around an optochin disc (Sigma) placed on a blood agar plate after overnight incubation. In addition, please note that pneumococcal viability on plates decrease with prolonged incubation time. Therefore, it is advised to plate the bacteria late in the day and inoculate early in the morning with an incubation time of 12–14 h being optimal.

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11. Pneumococci are facultative (aerotolerant) anaerobes, and bacterial culture tubes should be capped and incubated without shaking. Avoid exceeding an OD600 of 0.6 (late-logarithmic phase corresponding to approximately 3 × 108 CFU/mL) as pneumococci undergo autolysis at higher ODs or prolonged incubation. 12. Preparation of frozen stocks is recommended as compared to liquid cultures seeded from pneumococci grown on blood agar plates. The latter method has a longer and more variable incubation time that is not as reproducible than using frozen aliquots from the same stock. Frozen stock aliquots can be made in different volumes depending on what they are needed for. 13. DMSO is toxic for the cells and should be removed as much as possible. However, some cells dislike being centrifuged immediately after thawing. In that case, dilution in plenty of medium generally works fine. 14. The temperature of 34 °C is chosen in order to mimic the measured temperature of the nasopharyngeal environment [53]. In addition, as compared with 37 °C, we have shown previously that biofilm formation improves at 34 °C for pneumococci as well as for other species residing in the nasopharynx [22, 42]. 15. The choice of medium plays an important part in biofilm formation. As shown previously, pneumococci do not form well-structured and functional biofilms in nutrient-rich media, such as THY or brain heart infusion [22, 54]. To facilitate the optimal growth of biofilms, we recommend seeding the bacteria as early as possible during the day to enable a first change of the medium later the same day. Changing medium regularly (at least every 12 h) is of great importance to prevent the pneumococci from undergoing autolysis and will ensure that the biofilm remains stable for up to a week. Although biofilm formation can be detected already after 24 h, pneumococci generally require 48–72 h to form robust biofilms. However, depending on the pneumococcal strain used, growth time and frequency of media change may vary. These parameters also depend on the carbon source used. 16. This is the most critical step of the procedure. During the first 24 h, the biofilms are delicate and must be handled gently when removing and replacing the media. Do not use a vacuum aspirator. A 5 mL or 10 mL serological pipette induces less shear force and works better than a 1 mL pipette tip in this regard, both for removing media and for adding fresh media. 17. Some strains and species form robust biofilms within 48 h, but some strains and in the presence of some carbon sources, longer times (72–96 h) may be warranted.

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18. The biomass of a mature biofilm should range between 5 × 107 and 1 × 109 CFU/mL. Reduced antibiotic sensitivity to gentamicin indicates a structured and functional biofilm, which can result from a developed physical barrier to the antibiotic or a slowed metabolism, which renders the antibiotic ineffective. A robust biofilm will tolerate gentamicin exposure well. Generally, approximately one log10 decrease is seen compared with an untreated biofilm. This is in comparison to at least 4 log10 killing of broth-grown bacteria with the same concentration of gentamicin. Importantly, the effective antibiotic concentration may vary between strains. It is necessary to determine each strain’s sensitivity to the antibiotic such that the chosen concentration will kill at least 4 log10 of brothgrown bacteria over the same period of time. Gentamicin is used as it penetrates biofilms poorly [55, 56], but penicillin G can also be used at a concentration of approximately 1 μg/mL for pneumococci [22]. 19. Use the pointy end of a 20–200 μL pipette tip to completely scrape the bottom of the 24-well plate, using a top-to-bottom and left-to-right pattern. Pipette the suspension up and down vigorously to further disrupt the biofilm bacteria. Avoid introducing bubbles. Sonication will loosen the biofilm aggregates. However, do not sonicate longer than 2 s as the bacteria may lyse. It is important to disrupt the biofilm bacteria into single cells for accurate viable plate counts. View in the microscope before plating. 20. When imaging the biofilms samples, charging may occur as a result of incomplete dehydration during the sample preparation. To reduce the charging, the samples can be sputter coated, e.g., coated with chromium or palladium/gold. 21. Triton-X100 was used to lyse bacteria as other anionic and cationic detergents, such as SDS, and deoxycholate inhibits the firefly luciferase in the assay, which has been described previously [57]. 22. Please note that many supernatants from bacterial growth in media, such as CDM, may not be used in this assay as these media contain high levels of sodium acetate. 23. ADP and NADH in the samples will generate a background signal. To remove the effect of ATP and NADH background, a sample blank may be set up for each sample by omitting the acetate enzyme mix. 24. NADPH in the samples will generate a background signal. To remove the effect of NADPH background, a sample blank may be set up for each sample by omitting the formate enzyme mix.

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43. Chao Y, Bergenfelz C, Ha˚kansson AP (2017) In vitro and in vivo biofilm formation by pathogenic streptococci. Methods Mol Biol 1535: 285–299 44. Chao Y, Bergenfelz C, Hakansson AP (2019) Growing and characterizing biofilms formed by Streptococcus pneumoniae. Methods Mol Biol 1968:147–171 45. Lewis K (2010) Persister cells. Annu Rev Microbiol 64:357–372 46. Fux CA, Stoodley P, Hall-Stoodley L, Costerton JW (2003) Bacterial biofilms: a diagnostic and therapeutic challenge. Expert Rev AntiInfect Ther 1:667–683 47. Vidal JE, Howery KE, Ludewick HP, Nava P, Klugman KP (2013) Quorum-sensing systems LuxS/autoinducer 2 and Com regulate Streptococcus pneumoniae biofilms in a bioreactor with living cultures of human respiratory cells. Infect Immun 81:1341–1353 48. Hongwei Y, Zhanpeng J, Shaoqi S, Tang WZ (2002) INT-dehydrogenase activity test for assessing anaerobic biodegradability of organic compounds. Ecotoxicol Environ Saf 53:416– 421 49. Willenborg J, Goethe R (2016) Metabolic traits of pathogenic streptococci. FEBS Lett 590:3905–3919 50. Lundholm L, Mohme-Lundholm E, Vamos N (1963) Lactic acid assay with L(plus)lactic acid dehydrogenase from rabbit muscle. Acta Physiol Scand 58:243–249 51. van de Rijn I, Kessler RE (1980) Growth characteristics of group A streptococci in a new chemically defined medium. Infect Immun 27:444–448 52. Hammerschmidt S, Wolff S, Hocke A, Rosseau S, Mu¨ller E, Rohde M (2005) Illustration of pneumococcal polysaccharide capsule during adherence and invasion of epithelial cells. Infect Immun 73:4653–4667 53. Keck T, Leiacker R, Riechelmann H, Rettinger G (2000) Temperature profile in the nasal cavity. Laryngoscope 110:651–654

54. Marks LR, Reddinger RM, Hakansson AP (2012) High levels of genetic recombination during nasopharyngeal carriage and biofilm formation in Streptococcus pneumoniae. MBio 3:e00200–e00212 55. Carmen JC, Nelson JL, Beckstead BL, Runyan CM, Robison RA, Schaalje GB, Pitt WG (2004) Ultrasonic-enhanced gentamicin transport through colony biofilms of Pseudomonas aeruginosa and Escherichia coli. J Infect Chemother 10:193–199 56. Abdi-Ali A, Mohammadi-Mehr M, Agha Alaei Y (2006) Bactericidal activity of various antibiotics against biofilm-producing Pseudomonas aeruginosa. Int J Antimicrob Agents 27: 196–200 57. Simpson WJ, Hammond JR (1991) The effect of detergents on firefly luciferase reactions. J Biolumin Chemilumin 6:97–106 58. Hava DL, Camilli A (2002) Large-scale identification of serotype 4 Streptococcus pneumoniae virulence factors. Mol Microbiol 45:1389– 1406 59. Avery OT, Macleod CM, McCarty M (1944) Studies on the chemical nature of the substance inducing transformation of pneumococcal types: induction of transformation by a deoxyribonucleic acid fraction isolated from pneumococcus type III. J Exp Med 79:137–158 60. Lanie JA, Ng WL, Kazmierczak KM, Andrzejewski TM, Davidsen TM, Wayne KJ, Tettelin H, Glass JI, Winkler ME (2007) Genome sequence of Avery’s virulent serotype 2 strain D39 of Streptococcus pneumoniae and comparison with that of unencapsulated laboratory strain R6. J Bacteriol 189:38–51 61. Andersson B, Dahmen J, Frejd T, Leffler H, Magnusson G, Noori G, Eden CS (1983) Identification of an active disaccharide unit of a glycoconjugate receptor for pneumococci attaching to human pharyngeal epithelial cells. J Exp Med 158:559–570

Chapter 2 Formation and Analysis of Mono-species and Polymicrobial Oral Biofilms in Flow-Cell Models Jessica Neilands, Gunnel Svens€ater, Gabriella Boisen, Carolina Robertsson, Claes Wickstro¨m, and Julia R. Davies Abstract The oral microbiota, which is known to include at least 600 different bacterial species, is found on the teeth and mucosal surfaces as multi-species communities or biofilms. The oral surfaces are covered with a pellicle of proteins absorbed from saliva, and biofilm formation is initiated when primary colonizers, which express surface adhesins that bind to specific salivary components, attach to the oral tissues. Further development then proceeds through co-aggregation of additional species. Over time, the composition of oral biofilms, which varies between different sites throughout the oral cavity, is determined by a combination of environmental factors such as the properties of the underlying surface, nutrient availability and oxygen levels, and bacterial interactions within the community. A complex equilibrium between biofilm communities and the host is responsible for the maintenance of a healthy biofilm phenotype (eubiosis). In the face of sustained environmental perturbation, however, biofilm homeostasis can break down giving rise to dysbiosis, which is associated with the development of oral diseases such as caries and periodontitis. In vitro models have an important part to play in increasing our understanding of the complex processes involved in biofilm development in oral health and disease, and the requirements for experimental system, microbial complexity, and analysis techniques will necessarily vary depending on the question posed. In this chapter we describe some current and well-established methods used in our laboratory for studying oral bacteria in biofilm models which can be adapted to suit the needs of individual users. Key words Biofilm, Mixed-species communities, Protein pellicle, Flow-cell models, Confocal scanning laser microscopy

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Introduction Bacteria are found in multi-species communities, or biofilms, throughout the oral cavity, attached to the hard tissue surfaces of the teeth and the soft mucosal surfaces of the gingiva, palate, cheeks, and tongue. The oral microbiome is known to contain at least 600 different taxa at the species level, with the dominant ones belonging to six major phyla: Firmicutes, Bacteroides, Proteobacteria, Actinobacteria, Spirochaetes, and Fusobacteria [1]. The oral

Pontus Nordenfelt and Mattias Collin (eds.), Bacterial Pathogenesis: Methods and Protocols, Methods in Molecular Biology, vol. 2674, https://doi.org/10.1007/978-1-0716-3243-7_2, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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microbiota plays an important role in maintaining health through the prevention of colonization by pathogenic species and is normally quite resilient to change [2]. In the face of sustained environmental perturbation, however, homeostasis in biofilm communities can break down giving rise to oral diseases, including dental caries and periodontitis. The oral surfaces, which are bathed in saliva, are covered with a pellicle of adsorbed salivary proteins. These include the mucins MUC5B and MUC7, salivary agglutinin (gp340), lysozyme, cystatin, and secretory IgA [3]. Biofilm formation is initiated when oral bacteria expressing surface adhesins recognizing protein and glycan motifs present within the pellicle bind and establish themselves on the teeth and mucosal surfaces [4]. Bacteria which contribute to this early biofilm formation are termed primary colonizers. Once attached to the surface, they themselves provide novel binding sites for co-aggregation of secondary colonizers, thus promoting further biofilm accumulation [5, 6]. Community development is dependent upon the ability of the bacteria to resist the shear forces generated by mastication and salivary flow, and surface attachment has been demonstrated to cause a range of changes making sessile bacteria phenotypically different from their planktonic counterparts [7]. During maturation of complex biofilms, bacteria are affected by both the presence of other species and environmental factors such as the properties of the surface, nutrient availability, pH, oxygen levels, and metabolic end products from neighboring cells. All these factors together conspire to influence the competitiveness of different species within the biofilm and ultimately lead to variations in biofilm composition at different sites in the oral cavity [8]. Supra-gingival biofilms of the tooth are characterized by high levels of streptococcal species including Streptococcus oralis, Streptococcus mitis, and Streptococcus gordonii as well as species from the genera Rothia, Corynebacterium, and Actinomyces [9], and healthy supragingival plaque consortia can cooperate to degrade and exploit the complex glycoproteins in saliva as a nutrient source [10]. At the gingival margins of the tooth, however, the microflora is exposed to nutrients present in the serum-rich transudate originating from the gingival sulcus, and a relative increase in the proportion of Fusobacterium, Veillonella, and Capnocytophaga has been observed at these sites [11]. In health, a complex equilibrium with the host maintains eubiosis and prevents the development of virulence properties in oral biofilms. However, under some conditions, the physiology and composition become altered giving rise to a dysbiotic biofilm phenotype [12]. In caries, frequent intake of fermentable carbohydrates resulting in lactic acid production by, for instance, oral streptococci causes repeated episodes of low pH in supragingival biofilms. Gradually, the shift toward a more acid-tolerant

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microbiota leads to increased acid production and, eventually, localized demineralization of the hard tissues of the tooth [13, 14]. In periodontitis, buildup of biofilms at the gingival margin of the tooth initiates an inflammatory response in the adjacent soft tissues which increases the flow of protein-rich exudate into the gingival sulcus. Bacteria able to exploit this abundance of nutrients increase in number giving rise to a proteolytic biofilm phenotype which can drive and reinforce the inflammatory process and eventually lead to breakdown of the bone and tissues supporting the tooth [13, 14]. In vitro models represent important tools for studying the complex processes involved in biofilm development in oral health and disease. The experimental system (properties of the surface and presence or absence of, e.g., flow, to model shear forces, or a protein coating to model the pellicle) as well as the microbial complexity (single- or multi-species biofilms) and duration (early or mature biofilm formation) will vary depending on the scientific rationale, as will the analysis techniques applied. In this chapter we describe some current and well-established methods used in our laboratory for studying oral bacteria in biofilm models which can be adapted to suit the needs of individual users.

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Materials

2.1 Coating of FlowCell Surfaces 2.1.1 Bacteria-Free Saliva or Salivary Fractions

For applications such as bacterial adhesion studies, initial salivary coatings need to be bacteria-free. Saliva is therefore prepared using isopycnic density-gradient centrifugation under non-denaturing conditions [15]. From the resulting fractions, an essentially bacteria-free “whole saliva” coating solution is prepared by pooling all fractions except the pellet containing the bacteria. Subpopulations of salivary proteins can also be prepared by, for instance, pooling the high-density fractions to enrich salivary gel-forming mucins (MUC5B) or low-density fractions to provide a mixture of salivary non-mucin proteins. The appropriate fractions are pooled and dialyzed against phosphate-buffered saline or artificial saliva buffer and stored in aliquots at -20 °C. Phosphate-buffered saline (PBS): 70 mM NaCl, 7 mM K2HPO4, 2.5 mM KH2PO4, pH 7.2. Artificial saliva buffer (ASB): 15.5 mM KCl, 2.6 mM KH2PO4, 0.2 mM MgCl2, 2.6 mM Na2HPO4, 10 mM NaCl, and 4.4 mM NH4Cl, pH 6.8.

2.1.2

Serum

To model gingival exudate, human or equine serum is diluted 1:1 or 1:5 with Milli-Q water or PBS and used immediately.

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2.2 Fluorescence in Situ Hybridization (FISH)

Phosphate-buffered saline (PBS): 70 mM NaCl, 7 mM K2HPO4, 2.5 mM KH2PO4, pH 7.2. Sterilize by autoclaving at 121 °C for 20 min. Fix solution: 4% formalin. Permeabilization solution: 10 mg/mL lysozyme, 0.167 M Tris-HCl, and 0.008 M EDTA. Dissolve 60 mg chicken egg white lysozyme in 4 mL Milli-Q water. Add 1 mL of 1 M TrisHCl buffer pH 7.5 and 1 mL of 50 mM EDTA solution, pH 8.0. The Tris-HCl and EDTA solutions can be stored at 4 °C, but the lysozyme should be added fresh prior to use. Hybridization buffer: 0.9 M NaCl, 25% v/v formamide, 20 mM Tris-HCl, and 0.01% w/v SDS. Dissolve 2.63 g NaCl in 31.5 mL Milli-Q water. Add 12.5 mL formamide, 1 mL of 1 M Tris-HCl buffer pH 7.5, and 5 mL of 0.1% w/v sodium dodecyl sulfate (SDS) solution. Washing buffer: 20 mM Tris-HCl, EDTA 5 mM, 159 mM NaCl, and 0.01% w/v SDS. Dissolve 0.465 g NaCl in 39 mL Milli-Q water. Add 1 mL of 1 M Tris-HCl buffer pH 7.5, 5 mL of 50 mM EDTA solution pH 8.0, and 5 mL 0.1% w/v SDS solution.

2.3 Glycosidase Activity

The following substrates, available commercially, can be used to detect glycosidase activity associated with biofilm bacteria. 4-Methylumbelliferyl-N-acetyl-β-D-glucosaminide (N-acetyl-β-Dglucosaminidase) 4-Methylumbelliferyl-N-acetyl-O-D-neuraminide (neuraminidase) 4-Methylumbelliferyl-β-D-fucoside (β-D-fucosidase) 4-Methylumbelliferyl-α-L-fucoside (α-L-fucosidase) 4-Methylumbelliferyl-α-L-arabinopyranoside arabinopyranosidase)

(α-L-

4-Methylumbelliferyl-α-D-glucoside (α-D-glucosidase) 4-Methylumbelliferyl-β-D-glucoside (β-D-glucosidase) Working solutions of 100 μg/mL are prepared in 50 mM TES (N-Tris[hydroxymethyl]methyl-2-aminoethane sulphonic acid) buffer, pH 7.5 and kept at -20 °C. 2.4 Analysis of Biofilm Acid Tolerance

Minimal defined medium (MM4): Dissolve 1.32 g (NH4)2SO4, 0.01 g NaCl, 0.01 g MnCl2.4H2O, 0.01 g FeSO4.7H2O, 0.03 g sodium acetate, 0.2 g MgSO4.H2O, 2 mg pyridoxine, 2 mg nicotinic acid, 2 mg pantothenic acid, 4 mg riboflavin, 4 mg thiamin, 0.2 mg biotin, 0.2 mg p-aminobenzoic acid, 0.2 mg folic acid, 0.5 g glutamic acid, 0.2 g cysteine, 0.2 g asparagine, 0.1 g valine, 0.1 g leucine, 0.1 g serine, and 3.6 g glucose in 40 mM phosphate/ citrate buffer. Adjust to pH 7.5 or pH 3.5 and make up to 1 L.

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2.5 Extraction of Intracellular Proteins

Extraction buffer: 10 mM Tris-HCl, pH 6.8, 1 mM EDTA, 5 mM MgSO4. Dissolve 60.5 mg Tris(hydroxymethyl)aminomethane (Tris base) in 50 mL Milli-Q water. Dissolve 0.158 g Tris-HCl in 100 mL Milli-Q water. Add Tris base to Tris-HCl solution until the pH reaches 6.8. Dissolve 29 mg EDTA and 0.123 g MgSO4 in 100 mL of the Tris-HCl buffer, pH 6.8. Aliquot and store at -20 °C. Lysis buffer: 8 M urea, 2% 3-((3-cholamidopropyl) dimethylammonio)-1-propanesulfonate (CHAPS), 1% DTT, 2% IPG buffer. Dissolve 0.48 g urea, 0.02 g 3-((3-cholamidopropyl) dimethylammonio)-1-propanesulfonate (CHAPS), and 0.01 g dithiothreitol (DTT) in 500 μL Milli-Q water. Add 20 μL IPG buffer, pH 4–7 and make up to 1 mL with Milli-Q water. Vortex and leave to rest for 15 min at room temperature until all components have dissolved. Readjust final volume to 1 mL with Milli-Q water if necessary. Aliquot and store at -20 °C. Use within 2 months.

2.6 TwoDimensional Polyacrylamide Gel Electrophoresis (2DE)

Rehydration buffer: 8 M urea, 2% CHAPS, 10 mM DTT, 2% IPG buffer. Dissolve 0.48 g urea; 0.02 g CHAPS and 1.6 mg DTT in 500 μL Milli-Q water. Add 20 μL IPG buffer, pH 4–7 and 20 μL 0.5% bromophenol blue solution. Vortex and leave to rest for 15 min at room temperature until all components have dissolved. Readjust final volume to 1 mL with Milli-Q water if necessary. Aliquot and store at -20 °C. Use within 2 months. Running buffer: 50 mM Tris-HCl, pH 8.7, 0.1% SDS, 0.384 mM glycine. Dissolve 6.14 g Tris HCl, 7.4 g Tris base, 2 g SDS, and 57.7 g glycine in 2 L Milli-Q water. Store at 4 °C until use. Equilibration buffer 1: 50 mM Tris-HCl buffer, pH 6.8 containing 2% SDS, 26% glycerol and 16 mM DTT. Equilibration buffer 2: 50 mM Tris-HCl buffer, pH 6.8 containing 2% SDS, 26% glycerol, 250 mM iodoacetamide (IAA), and 0.005% bromophenol blue. For two 14% polyacrylamide gels (185 × 200 × 1.0 mm): Gel buffer: 1.5 M Tris buffer, pH 8.8. Prepare 500 mL and store at 4 °C until use (see Note 1). Ammonium persulfate solution: 400 μL of a fresh solution of 10% w/v ammonium persulphate (APS) in Milli-Q water. 1. Working in a fume hood, mix 18.8 mL Tris buffer, 750 μL 10% w/v SDS solution, 4 mL 87% w/v glycerol solution, and 26.4 mL 40% bis-acrylamide solution with 24.6 mL Milli-Q water and place under vacuum for 10 min (see Note 2). 2. Add 375 μL 10% APS solution and 38 μL tetramethylethylenediamine (TEMED) solution, stirring gently to initiate the polymerization reaction.

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3. Pour into gel casting frames. Leave 1 cm of space at the top for the IPG strip. Drop isobutanol onto the top of the gels and leave under a fume hood for 3–4 h to allow complete polymerization. Agarose solution: 0.5% w/v agarose in Milli-Q water. Dissolve 0.05 g agarose in 10 mL Milli-Q water. Warm until dissolved completely and use immediately.

3

Methods

3.1 Preparation of Bacterial Cultures for Biofilm Experiments

The oral cavity contains a wide range of different bacterial species with widely differing requirements when grown outside their natural environment. A full description of these is beyond the remit of this chapter, so here we give a general description of the approach used in our laboratory. Mono-species biofilm experiments simply require the use of a nutrient medium and environmental conditions that support growth of the bacteria of interest. For instance, biofilms of oral streptococci can successfully be established in full strength or 25% Todd-Hewitt broth, brain-heart infusion (BHI), or tryptone yeast extract broth (TYE), maintained in 5% CO2 in air. For multi-species biofilms, however, the situation can be more complex, and care must be taken to ensure that the nutrient medium used is able to support growth of all the component species, thus maintaining the intended species diversity over time. If the bacteria requiring anaerobic growth conditions are included in a multi-species biofilm, the nutrient medium should be pre-reduced, and the whole biofilm should be maintained under oxygen-free conditions (we routinely use 10% H2, 5% CO2 in N2).

3.1.1 Preparation of Bacteria for Mono-species Experiments

1. Grow the bacteria on suitable agar plates in an appropriate environment (anaerobic or CO2 in air) until sufficient growth is obtained. 2. Take single colonies and suspend in an appropriate volume of nutrient medium (depending on the final volume of bacterial suspension needed for the experiment). 3. Grow overnight under appropriate environmental conditions. 4. Dilute the bacterial suspension 1:10 in fresh medium and grow until exponential growth phase is reached [optical density (OD) ≈ 0.4–0.6 at 600 nm].

3.1.2 Preparation of Bacteria for Multi-species Biofilm Experiments

Ideally, exponential growth phase cultures should be prepared as above and then mixed to obtain a starting culture for the multispecies biofilm experiments. However, due to their different growth rates, obtaining all the bacteria in the same growth phase simultaneously can be very challenging. To circumvent such

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problems, starting cultures can be prepared by mixing bacteria taken directly from colonies on agar plates (see Note 3). 1. Grow each of the bacteria required for the multi-species biofilm separately on suitable agar plates in an appropriate environment (anaerobic or CO2 in air) until sufficient growth is obtained. 2. For each bacterial species, suspend a sufficient number of colonies in a nutrient medium to give a suspension with OD600 ≈ 0.1. 3. Mix equal volumes of the different suspensions to give a final volume sufficient for the experiment. 3.2 Coating of FlowCell Surfaces

To model oral surfaces in vivo, it may be appropriate to condition the flow-cell surfaces with a relevant protein film such as whole saliva, a native mucin-rich or non-mucin-rich salivary fraction, or serum [16, 17]. 1. Use a volume of coating solution that covers the surface. For whole saliva and mucin-rich saliva, add CaCl2 solution to a final concentration of 10 mM just before coating to promote binding. 2. Incubate for 3 h at 37 °C in a humid chamber. 3. Remove access coating solution and gently rinse with PBS.

3.3

Biofilm Models

3.3.1 Static Biofilm Models

The models used to study biofilm formation and properties will vary depending on the research question. For some purposes, such as studying differences in ability to form biofilms, it may be sufficient to use simple static models. However, they lack the shear forces that would be present during biofilm formation in the oral cavity, and there may be issues such as nutrient depletion and/or a buildup of metabolic waste products over time which can affect bacterial physiology. Flow-cell models have the advantage of a continuous flow of medium that adjusts pH and nutrient levels, as well as removing metabolic waste products. However, drawbacks include the need for larger volumes of nutrient media, technical problems such as a greater risk of contamination and leakage, as well as the formation of air bubbles in the system. This protocol can be successfully applied to a variety of surfaces including microtiter plates, titanium discs/plates, hydroxyapatite discs, catheter surfaces, Ibidi microslides, and layers of, e.g., epithelial cells. Gentle shear forces can be generated by placing the model system on a rocking platform. 1. For mono-species biofilms, prepare cultures of bacteria in exponential growth phase in an appropriate nutrient broth as described in Subheading 3.1.1. Add nutrient medium to give an OD600 of 0.05–0.2 (see Note 4).

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2. For studies of interactions with eukaryotic cell layers, the bacterial suspension should be washed by centrifuging (5000 rpm, 5 °C, 5 min) and resuspending in an equal volume of cell culture medium three times. Finally, the bacterial suspension should be adjusted to give an OD600 of 0.05–0.2. bacteria prepared as above should be resuspended in cell culture medium. 3. For multi-species biofilms, prepare the bacterial suspension as described in Subheading 3.1.2. Dilute with appropriate nutrient medium to give an OD600 of 0.05–0.2. 4. If a pellicle is required, prepare as described in Subheading 3.2. 5. Place an aliquot of the bacterial solution on the surface and maintain for 30 mins to 2 h at 37 °C (see Note 5). For anaerobic bacteria and multi-species biofilms containing obligate anaerobes, test surfaces should be maintained in an anaerobic environment. 6. Remove non-adhered bacteria by gently washing three times with PBS (pre-reduced if necessary). For studies of bacterial interactions with cell layers, the cell surfaces should be rinsed with cell culture medium. 7. For investigation of bacterial adhesion and early biofilm formation, surfaces should be analyzed directly, whereas for studies on more mature biofilms, the surfaces should be incubated in nutrient medium over time to allow biofilm growth. 3.3.2

Flow-Cell Models

This protocol can be successfully carried out using specialized flowcell apparatus as well as various commercially available flow-cell systems. Custom-made apparatus allows collection of large amounts of biofilm material for investigations of protein expression in biofilms. The mini flow-cell microslide system from Ibidi™ GmbH, which is available in a range of different designs and with various surfaces (uncoated, tissue culture-treated, glass or coated with poly-L-lysine, collagen I or collagen IV), can also easily be used for culture of epithelial cell layers. We favor the μ-slide VI mini-flow cells with six channels (volume 30 μL and a total surface area of 1.2cm2/channel) which are compatible with inverted CSLM (Fig. 1). For mini flow cells, prepare a quantity of bacterial suspension in exponential phase sufficient to allow completion of the whole experiment at the flow rate used (see Subheading 3.1.1 or 3.1.2.). Coat the flow-cell surface as required under sterile conditions and rinse with an appropriate nutrient medium (pre-reduced if necessary). Add sufficient nutrient medium to ensure that the wells and channels are completely full.

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Fig. 1 (a) The mini flow-cell model showing the arrangement of the flow cell, nutrient medium, multi-channel pump, and waste containers in the incubator. (b) A closeup image showing the connection of the inflow and outflow tubing to the luer fittings of the mini-flow cell. (c) An image showing an early S. gordonii biofilm formed in the model, stained with LIVE/DEAD® BacLight™ viability stain. (d) A closeup image of the biofilm, stained with LIVE/DEAD® BacLight™ viability stain. The bar represents 5 μm

1. Place the flow cell on a flat surface and connect autoclaved, silicon tubing to a multichannel pump which can deliver to the required number of channels (see Note 6). 2. Place the bacterial suspension on a magnetic stirrer and stir gently. Pump the suspension so that the inflow tubing to each channel is completely filled. Connect one tubing to each luer fitting on the inflow side of the flow cell. It is essential to ensure that there are no bubbles in the connection on the inflow side as these will pass over the surface and may remove the coating. 3. Connect silicon tubing to the outflow side of each channel and place the ends in a flask of sufficient volume to accommodate the waste bacterial suspension from the whole experiment. 4. Start the pump and check that there are no bubbles or leaks in the system. 5. Flow nutrient medium through the channels at a rate of 3.6 mL/h for the required length of time.

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Fig. 2 The custom flow-cell system. A. The flow-cell setup in the incubator showing the pump used to create an even flow over the surfaces. B. A closeup image of the glass surfaces and spacer used in the system. C. Examples of different titanium surfaces as well as a holder for metal discs that can be used in the system. D. Measurements of the flow-cell holder. E. Measurements of the glass slides, spaces, cover (height 4.8 mm), and the holders that keep the flow cell sealed together. All materials should be autoclavable. The flow cells are sealed at the bottom and top with a 3 mm O-ring with diameter of 73 mm

The custom-made flow cells in our laboratory comprise two parallel glass slides separated by 1.6 mm rubber spacers and mounted in a holder, sealed with O-rings, and covered by a lid [modified from [18]] (Fig. 2). Flow is controlled by a peristaltic pump and is laminar as long as the rate is below 45 mL/h. The flow cells have a total volume of 2.1 mL and a surface area of 26 cm2. This system is suitable for shorter experiments. Thick biofilms create a nonlaminar flow which can cause clogging. The two glass slides allow different analyses of cells that have been exposed to the same conditions. Although we usually use glass coated with salivary

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proteins, it is also possible to use other surfaces such as titanium, plastic, or hydroxyapatite in this model. 1. Sterilize all components by autoclaving. Plastic three-way taps should be sterilized according to the manufacturer’s recommendations. 2. Assemble the flow cells under sterile conditions and autoclave the assembled flow cell. For multi-species biofilms containing bacteria which require anaerobic growth conditions, the whole flow-cell apparatus should be placed in an anaerobic environment. 3. Prepare at least 200 mL of bacterial suspension in exponential phase (see Subheading 3.1.1 or 3.1.2.). 4. Concentrate the cell suspension 10× by centrifuging (5000 rpm, 5 °C, 5 min) and resuspending in 20 mL of desired medium. 5. Place the flow cell vertically in the holder and attach the inflow and outflow tubing according to connection diagram (Fig. 1). 6. Place the flask with the bacterial suspension on a magnetic stirrer and put both the inflow and outflow tubing in the flask. 7. Start the peristaltic pump (flow rate 42 mL/h) and allow the bacterial suspension to recirculate for 2 h. 8. Transfer the inflow tubing to a flask filled with fresh medium and place the outflow tubing in a flask to collect the waste suspension. Rinse the flow cell at a flow rate of 200 mL/h for 1 h to remove nonadherent cells. 9. Transfer the inflow tubing to a new flask filled with fresh nutrient medium, sterilizing the outside of the tubing with 70% ethanol before placing in the medium. Reduce the flow rate to 42 mL/h and run the experiment for desired length of time (see Note 7). 10. At the end of the experiment, close the valves to the flow cell prior to removing the tubing, remove tubing and remove the valves over a test tube. Open the flow cell and remove the biofilm by scraping into the test tube using a sterile razor blade. Biofilm Analysis

Where possible, bacterial distribution and properties in biofilms are ideally studied in situ. However, while some assays are designed or adaptable for use with imaging techniques, others are not. Where such analyses are required, they can be undertaken on biofilm bacteria or supernatants which have been removed from static or flow-cell models.

3.4.1 Analysis of Biofilm Mass In Situ

For analysis of biofilm formation and growth, for example, in microtiter plates, cells can be stained with crystal violet and the amount of staining determined using spectroscopic methods after solubilization.

3.4

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1. Prepare static or flow-cell biofilms as required and remove nonadhered cells by gently washing three times with PBS. 2. Add 125 μL 0.1% solution (w/v) of crystal violet in Milli-Q water and stain for 10 min at room temperature. 3. Remove excess crystal violet solution and wash wells gently with PBS until no further stain is removed. Invert plate and allow to dry. 4. Solubilize crystal violet associated with the biofilm in 30% acetic acid or 95% ethanol. 5. Measure the OD at 590 nm. 3.4.2 Analysis of Surface Coverage and Bacterial Viability in Biofilms In Situ

For analysis of biofilm surface coverage and bacterial viability, cells can be stained with the LIVE/DEAD® BacLight™ viability stain. This methodology can be used to investigate the action of antimicrobial substances, by treating established biofilms with the agent and studying the effects on viability. 1. Prepare static or flow-cell biofilms as required and remove nonadhered cells by gently washing three times with PBS. 2. Mix 1 μL component A (Syto 9) and 1 μL component B (propidium iodide) with 1 mL PBS. 3. Add sufficient reagent to cover the biofilm and leave for 10 min in the dark. 4. View immediately in a fluorescence microscope (see Note 8). 5. Capture images for analysis (see Note 9).

3.4.3 Analysis of GramPositive and GramNegative Bacteria in Biofilms In Situ

For analysis of biofilm surface coverage and spatial distribution of Gram-positive and Gram-negative bacteria in biofilms, cells can be stained with the LIVE BacLight™ Bacterial Gram Stain Kit (Invitrogen). 1. Prepare biofilms in a static or flow-cell system compatible with microscopy. 2. Mix 1.5 μL component A (Syto 9) and 1.5 μL component B (hexidium iodide) with 1 mL PBS. 3. Add sufficient reagent to cover the biofilm and leave for 10 min in the dark. 4. View immediately in a fluorescence microscope (see Note 10). 5. Capture images for analysis (see Note 9).

3.4.4 Analysis of Biofilm Composition In Situ Using 16S r-RNA Fluorescent In Situ Hybridization (FISH)

Studying the composition of multi-species biofilms in real time is difficult in intact biofilms. However, it is possible to run parallel experimental replicates which can be stopped at different time points and the bacteria subjected to FISH analysis (Fig. 3).

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Fig. 3 A fluorescence in situ hybridization (FISH) image of a multi-species biofilm comprising Streptococci (yellow), Actinomyces (red), Lactobacilli (blue), and Fusobacterium (green). The bar represents 10 μm

1. Prepare biofilms in a static or flow-cell system compatible with microscopy and fix at room temperature for 30 min using fixing solution. 2. Rinse three times with sterile PBS. 3. Enhance permeability of the bacterial cells by treating with permeabilization buffer at 37 °C for 9 mins. 4. Rinse three times with Milli-Q water and dehydrate the bacterial cells stepwise by treating with 50%, 80%, and 99% ethanol for 3 min each without rinsing in between. 5. Allow the biofilm to dry at room temperature for 10 min. 6. Prepare the probe solution by diluting the 16S rRNA probe stock solution(s) (100 pmol/μL) 1:30 in hybridization buffer. If several probes are used, they are all added to the same probe solution (e.g., 3.3 μL probe A + 3.3 μL probe B + 93.4 μL hybridization buffer). 7. Add probe solution to the biofilm and incubate for 90 min on a rocking platform at a temperature optimized for hybridization with your specific probes (we often use 48 °C).

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8. Replace the probe solution with washing solution and incubate at your hybridization temperature for a further 15 min. 9. Replace the washing solution with Milli-Q water and store at 4 °C until imaging. 10. View in a fluorescence microscope equipped with light sources and filters appropriate for the fluorophores used to label the 16S rRNA probes. 11. Capture images for analysis (see Note 9). 3.4.5 Analysis of Metabolic Activity in Biofilms In Situ

For analysis of metabolic activity, cells can be stained with the BacLight™ RedoxSensor CTC Viability Kit (Invitrogen) where 5-cyano-2,3-ditolyl tetrazolium chloride (CTC) is reduced intracellularly by the electron transport system (ETS) or dehydrogenases to give a red formazan precipitate [19]. 1. Prepare biofilms in a static or flow-cell system compatible with microscopy. 2. Add CTC staining solution to the biofilms and incubate at 37 ° C under appropriate conditions for 30–60 min. 3. If required, counterstain with, e.g., DAPI (blue) or Syto 24 (green) for 15 min. 4. View immediately in a fluorescence microscope (see Note 11). 5. Capture images for analysis (see Note 9).

3.4.6 Analysis of Biofilm Proteolytic Activity In Situ

Expression of surface-associated proteolytic activity in biofilms can be analyzed using protein substrates such as casein or gelatin coupled to a fluorescent marker, often fluorescein isothiocyanate (FITC) (Fig. 4). These highly quenched substrates become strongly fluorescent upon cleavage by proteolytic enzymes. In our experience both FITC-casein and FITC-gelatin serve as relatively nonspecific substrates and appear to be compatible with microscopic studies [9]. 1. Prepare biofilms in a static or flow-cell system compatible with microscopy. 2. Rinse with sterile PBS and add sufficient FITC-labeled substrate (diluted to 10 μg/mL in PBS) to cover the biofilm. Incubate at 37 °C under appropriate conditions for 60 min. 3. If required, counterstain with, e.g., Syto 64 (red) for 15 min. 4. View immediately in a fluorescence microscope (see Note 12). 5. Capture images for analysis (see Note 9).

3.4.7 Analysis of Biofilm Acid Tolerance In Situ

We have developed a method for identifying levels of acid tolerance in biofilms using a low pH challenge followed by staining with LIVE/DEAD® BacLight™ viability stain to identify the proportion of surviving (acid tolerant) bacteria [20]. The method has been

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Fig. 4 An image showing proteolytic activity in a dental plaque biofilm as demonstrated with a FITC-conjugated protease substrate (FITC-casein)

Fig. 5 CSLM image of a 4-h S. mutans biofilm stained with LIVE/DEAD® BacLight™ viability stain. (a) Control biofilms kept at pH 7.5 show high viability as seen by the large proportion of green cells. (b) Biofilms allowed to adapt at pH 5.5 for 2 h prior to acid stress (exposure to pH 3.5 also showed high viability) (b), while non-adapted cells (c) did not survive the acid challenge at pH 3.5 as seen by the large proportion of red cells (dead)

successfully applied to in vivo oral biofilms (dental plaque) [21] as well as in vitro biofilms of saccharolytic oral bacteria [22] (Fig. 5). 1. Prepare biofilms in a static or flow-cell system compatible with microscopy by adding a bacterial suspension prepared as described in Subheading 3.1.1 or 3.1.2. and allow to adhere for 2 h at 37 °C in 5% CO2 in air.

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2. Rinse test biofilms twice with MM4 pH 3.5 and add sufficient MM4 pH 3.5 to cover the biofilm. Incubate in a humid chamber at 37 °C for 30 min in 5% CO2 in air. 3. Rinse control biofilms twice with MM4 pH 7.5 and add sufficient MM4 pH 7.5 to cover the biofilm. Incubate in a humid chamber at 37 °C for 30 min in 5% CO2 in air. 4. Prepare LIVE/DEAD® BacLight™ viability stain by mixing 1 μL component A (Syto 9) and 1 μL component B (propidium iodide) with 1 mL PBS. 5. Add sufficient reagent to cover the biofilms and leave for 10 min in the dark. 6. View immediately in a fluorescence microscope (see Note 8). 7. Capture images for analysis (see Note 9). 8. Assess proportion of acid tolerant cells as those staining green (i.e., viable after an acid challenge which kills nonadapted cells) as a proportion of the total cell number (green + red cells). 9. Compare with control biofilms maintained at pH 7.5 which should contain >90% viable cells. 3.4.8 Analysis of Composition of Bacteria Recovered from Biofilms

Studying the composition of multi-species biofilms in real time is difficult in intact biofilms. However, it is possible to run parallel experimental replicates which can be stopped at different time points and the bacteria removed for investigation. The relative proportions of bacteria can be investigated using techniques such as culturing (see Note 3), qPCR, or 16S Illumina sequencing.

3.4.9 Analysis of Glycosidase Activity in Bacteria Recovered from Biofilms

A range of commercially available fluorogenic substrates can be used to study the activity of a glycosidases in bacterial biofilms. In those presented here, methylumbelliferone released from the substrate by activity of the enzyme can be detected using a microplate reader equipped for measuring fluorescence. 1. Remove bacteria from the biofilm by scraping and disperse in an appropriate volume of 50 mM TES buffer, pH 7.5. 2. Add 20 μL of the substrate to a microtiter plate designed for use with fluorometry. 3. Add 50 μL of bacterial suspension. 4. Incubate for 3 h at 37 °C. 5. Measure fluorescence (see Note 13).

3.4.10 Analysis of Lactic Acid from Bacteria Recovered from Biofilms

The production of lactic acid by oral bacteria (particularly streptococci and lactobacilli) in biofilms in response to, for instance, a sucrose or glucose challenge, can be investigated using kits, e.g., Megazyme L-Lactic Acid (L-Lactate) Assay Kit based on the oxidation of lactate to pyruvate by NAD+ and subsequent regeneration

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of NADH through conversion of pyruvate to D-alanine and 2-oxoglutarate. NADH levels are measured as absorbance at 340 nm (see Note 14). 1. Prepare biofilms in a static or flow-cell system. 2. Add a solution of glucose or sucrose (20–100 mM) to the biofilm for 15–60 min. 3. Remove the biofilm supernatant and analyze according to the manufacturer’s instructions. 3.4.11 Analysis of Acetic Acid from Bacteria Recovered from Biofilms

The production of acetic acid by oral bacteria in biofilms can be investigated using kits, e.g., Megazyme Acetic Acid Assay Kit, based on a series of reactions that lead to oxidation of NADH to NAD+, which is inversely proportional to the acetic acid concentration. The absorbance at 340 nm measures the consumption of NADH to thereby determine the acetic acid concentration (see Note 15). 1. Prepare biofilms in a static or flow-cell system. 2. Treat biofilm as required. 3. Remove the biofilm supernatant and analyze according to the manufacturer’s instructions.

3.5 Extraction of Intracellular Proteins from Biofilm Bacteria

Bacteria in biofilms can show alterations in phenotype when compared to their planktonic counterparts, which are often manifested as changes in the intracellular protein profile. For any studies of the intracellular proteome, the initial extraction of the proteins is a key step. Here we present a tried and tested method used in our laboratory to extract intracellular proteins from oral streptococci. 1. Grow biofilms on coated or uncoated glass surfaces in flow cells for 18 h as described in Subheading 3.3.2., with 25% ToddHewitt broth containing 10 mM glucose. Open flow cells, scrape biofilm cells off the surfaces with a razor blade and suspend in 700 μL lysis buffer. The sample can be stored at 4 °C until the next step. 2. Sonicate the cells in the presence of glass beads (0.1 mm) for 4 × 5 min on iced water. The temperature in the sample should not exceed 30 °C. The sonicator tip should be mounted 2 mm from the bottom of the tube to reduce heat development. 3. Centrifuge the lysed bacteria at 17,000 g at 4 °C to remove cell wall fragments and intact bacterial cells. Collect the supernatant avoiding the glass beads. The supernatant can be stored at -20 °C before determination of the protein content [most easily performed with a commercially available kit (e.g., 2D Quant Kit, Amersham Biosciences)].

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3.6 Analyses of Intracellular Protein Profiles of Biofilm Bacteria

3.6.1 Isoelectric Focusing (IEF)

Two-dimensional electrophoresis (2DE) can be utilized for analyses of intracellular protein profiles, secreted proteins, membrane proteins, or cell wall proteins. Below we describe a standard protocol for investigation of intracellular protein profiles of biofilm cells grown in our customized flow cells. The analysis is conducted in two steps, isoelectric focusing followed by polyacrylamide gel electrophoresis. 1. Prepare the sample by adding it to the IPG-strip rehydration buffer. The largest sample volume that can be used is 165 μL diluted with the same volume of rehydration buffer. Pipette the samples (330 μL) into each lane of the reswelling tray, and add the liquid slowly to the center of the lane to avoid air bubbles (see Note 16). 2. Remove the protective cover from the IPG strip (18 cm, pH 4–7, GE Healthcare Life Sciences) and position it with the gel side down on top of the sample without trapping any air bubbles. Cover the strips completely with DryStrip cover oil (GE Healthcare Life Sciences) to avoid evaporation and urea crystallization. Close the lid to the tray and incubate at room temperature for 25–30 h. The rehydrated strips are approximately 0.5 mm thick. 3. Prepare for IEF before removing the IPG strips from the reswelling tray. Establish water cooling (15 °C) using a thermostatic circulator. Position the cooling plate on the IEF apparatus (Multiphor II, GE Healthcare Life Sciences) and ensure the surface is horizontal. Position the glass strip container avoiding large air bubbles to ensure good thermal contact between the cooling plate and the container. Pipette some oil into the container and place the plastic holder for the strips on the oil. 4. Remove the rehydrated IPG strips from the reswelling tray. Rinse carefully with Milli-Q water to remove excess rehydration buffer and prevent formation of urea crystals on the gel surface during IEF. Use a damp filter paper to drain excess moisture. Immediately, transfer the strips with the gel side up into the plastic holder. The acidic end (pH 4) of the strip should have turned yellow, due to the bromophenol blue pH indicator in the rehydration buffer, and should be placed toward the anode (the red electrode). 5. Place the moistened electrode strips across the anodic and cathodic side and position the electrodes. When the electrodes are properly aligned, press them down to contact the electrode strips. Place the lid over the apparatus and start IEF. We have successfully used the following program for separation of intracellular proteins: 150 V for 1 h, 300 V for 3 h, 600 V for 3 h,

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1200 V for 12 h, and 3500 V for 20 h. A low initial voltage minimizes sample aggregation, and a gradual increase in voltage is particularly important for higher protein loads (100 μg per IPG strip). 6. Turn off the power of the apparatus and remove the strips. These should be individually stored in saran wrap at -80 °C. 3.6.2 Equilibration of IPG Strips

IPG strip equilibration saturates the strip with the SDS buffer system required for the second dimension. Note that the SDS-PAGE gels must be ready for use before undertaking this step. 1. Place the IPG strips in screw-capped tubes with the plastic film toward the tube wall, and add 10 mL of equilibration working solution 1. Cap the tubes, place them in a rotary shaker, and equilibrate for 15 min. 2. Decant the working equilibration solution 1 and add 10 mL working equilibration solution 2. Equilibrate for 15 min on a shaker (see Note 17). 3. Prepare a 0.5% agarose solution for use in the following (SDS-PAGE) step.

3.6.3 Polyacrylamide Gel Electrophoresis (SDSPAGE)

We have successfully used homogenous 14% SDS-polyacrylamide gels for separation of intracellular proteins from oral streptococci where the desired range of separation was 10–75 kDa. 1. Blot excess moisture from the edge of the IPG strips using a piece of dry filter paper, and then place the strips on the edge of a moist filter paper for up to 10 min. 2. Dip the IPG strip in SDS electrophoresis buffer and position it between the glass plates on top of the polymerized gel. Ensure that no air bubbles are trapped between the strip and the gel. Place a Teflon piece at one end of the gel to make a well for the molecular weight markers. 3. Seal the IPG strip in place by imbedding it in the warm 10% agarose solution. 4. Connect the electrophoresis apparatus to a power pack and apply a constant current of 10 mA for 15 min followed by 20 mA for 90 min in accordance with the instructions from the manufacturer. 5. Run the gel for at 17 mA/gel for 16–17 h. Stop electrophoresis when the dye front is approximately 1 mm from the bottom of the gels (a total of 2300–2500 V/h per gel).

3.6.4 Visualization and Identification of Proteins Following 2DE

Staining gels with silver is the most sensitive nonradioactive method for visualization of proteins (Fig. 6). For silver staining, around 25 μg protein should be loaded onto the gels. Fluorescent stains such as SYPRO™ Ruby Protein Gel Stain also give good sensitivity.

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Fig. 6 A silver-stained 2DE gel showing intracellular proteins from Streptococcus mutans

Staining with Coomassie brilliant blue, although much less sensitive (at least 100 μg protein is required), is both simpler and more quantitative than silver staining. Coomassie staining is compatible with mass spectrometry, and protein spots can be excised from the gel and identified using techniques such as LC-MS/MS.

4

Notes 1. Larger volumes can be made and stored at room temperature until use. 2. Bis-acrylamide is toxic and appropriate safety routines should be adopted. 3. When planning multi-species biofilm experiments, it can be an advantage to consider using mixtures of bacteria with distinctly different colony morphologies on blood or Brucella agar. This allows culturing and colony counting to be used to follow changes in biofilm composition over time.

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4. For static biofilm experiments, a starting culture with a lower OD600 is required, since the proportion of bacteria attaching to the surface is usually greater than when flow systems are used. 5. All pipetting steps should be conducted carefully to avoid generating excessive shear forces that may remove the surface coating. 6. The mini-flow cell system should be maintained in an incubator at 37 °C. All solutions should be maintained at 37 °C. For experiments using obligate anaerobes, the biofilm system should be maintained under anaerobic conditions. 7. It is important that flow-cell experiments carried out over a long time period, i.e., more than 12 h, are conducted in a separate area and that all equipment is maintained a sterile as possible. We have encountered problems with contamination from Staphylococcus epidermidis and/or Staphylococcus aureus from skin or Bacillus subtilis from the environment in longerterm experiments. Contamination can be detected by culturing a small aliquot of the biofilm material harvested at the end of the experiment. 8. The excitation and emission wavelengths for Syto 9 and propidium iodide are 485/498 and 535/617, respectively. 9. The number of images required for analysis will vary depending on the question. However, we usually try to ensure that around 1000 bacterial cells are analyzed in each experiment. 10. The excitation and emission wavelengths for Syto 9 and hexidium iodide are 485/498 and 518/600 nm, respectively. 11. The excitation and emission wavelengths for CTC, Syto 24, and DAPI are 450/630, 490/515, and 344/450 nm respectively. 12. The excitation and emission wavelengths for FITC and Syto 64 are 488/519 and 599/619 nm, respectively. 13. The excitation and emission wavelengths for methylumbelliferone are 355 and 460 nm. 14. Alternatively, measurement of lactic acid can be undertaken using more advanced techniques such as NMR. The nutrient medium may need to be optimized for this technique. 15. Alternatively, measurement of acetic acid can be undertaken using more advanced techniques such as NMR. The nutrient medium may need to be optimized for this technique. 16. Addition of the sample to the rehydration buffer allows larger quantities of proteins and more dilute samples to be loaded and separated with 2DE. 17. DTT preserves the fully reduced state of denatured proteins, and iodoacetamide prevents reoxidation during electrophoresis, which can result in streaking and other artefacts in the gels.

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Acknowledgments The authors acknowledge funding from The Foresight Programme at Malmo¨ University, The Biofilms Research Center for Biointerfaces, and Odontologisk Forskning Region Ska˚ne. References 1. Dewhirst FE, Chen T, Izard J, Paster BJ, Tanner AC, Yu WH, Lakshmanan A, Wade WG (2010) The human oral microbiome. J Bacteriol 192:5002–5017 2. Wade W (2021) Resilience of the oral microbiome. Periodontol 2000 86:113–122 3. Siqueira WL, Zhang W, Helmerhorst EJ, Gygi SP, Oppenheimer FG (2007) Identification of protein components in in vivo human acquired enamel pellicle using LC-ESI-MS/MS. J Proteome Res 6:2152–2160 4. Nobbs AH, Lamont RJ, Jenkinson HF (2009) Streptococcus adherence and colonization. Microbiol Mol Biol Rev 73:407–450 5. Diaz PI, Chalmers NI, Rickard AH, Kong C, Milburn CL, Palmer RJ Jr, Kolenbrander PE (2006) Molecular characterization of subjectspecific oral microflora during initial colonization of enamel. Appl Environ Microbiol 72: 2837–2848 6. Kolenbrander PE, Andersen RN, Blehert DS, Egland PG, Foster JS, Palmer RJ Jr (2002) Communication among oral bacteria. Microbiol Mol Biol Rev 66:486–505 7. Stoodley P, Sauer K, Davies DG, Costerton JW (2002) Biofilms as complex differentiated communities. Annu Rev Microbiol 56:187–209 8. Xu X, He J, Xue J, Wang Y, Li K, Zhang K, Guo Q, Liu X, Zhou Y, Cheng L, Li M, Li Y, Li Y, Shi W, Zhou X (2015) Oral cavity contains distinct niches with dynamic microbial communities. Environ Microbiol 17:699–710 9. Costalonga M, Herzberg MC (2014) The oral microbiome and the immunology of periodontal disease and caries. Immunol Lett 162:22– 38 10. Wickstro¨m C, Herzberg MC, Beighton D, Svens€ater G (2009) Proteolytic degradation of human salivary MUC5B by dental biofilms. Microbiology 155:2866–2872 11. The-Human-Microbiome-Project-Consortium (2012) Structure, function and diversity of the healthy human microbiome. Nature 486:207–214 12. Killian M, Chapple IL, Hannig M, Marsh PD, Meuric V, Pedersen AM, Tonetti MS, Wade WG, Zaura E (2016) The oral microbiome -

an update for oral healthcare professionals. Br Dent J 221:657–666 13. Marsh PD (2003) Are dental diseases example of ecological catastrophes? Microbiology 149: 279–294 14. Lamont RJ, Koo H, Hajishengallis G (2018) The oral microbiota: dynamic communities and host interactions. Nat Rev Microbiol 16: 745–759 15. Davies JR, Wickstro¨m C, Thornton DJ (2012) Gel-forming and cell-associated mucins: preparation for structural and functional studies. Methods Mol Biol 842:27–47 16. Robertsson C, Svens€ater G, Blum Z, Jakobsson ME, Wickstro¨m C (2021) Proteomic response in Streptococcus gordonii DL1 biofilm cells during attachment to salivary MUC5B. J Oral Microbiol 13:1967636. https://doi.org/10. 1080/20002297.2021.1967636 17. Davies JR, Kad T, Neilands J, Kinnby B, Prgomet Z, Bengtsson T, Khalaf H, Svens€ater G (2021) Polymicrobial synergy stimulates Porphyromonas gingivalis survival and gingipain expression in a multi-species subgingival community. BMC Oral Health 21(1):639. https://doi.org/10.1186/s12903-02101971-9 18. De Palma VA (1976) Correlation of surface electrical properties with initial events in bioadhesion. Ph.D. thesis. State University of New York, Buffalo, NY 19. Dorkhan M, Cha´vez de Paz LE, Skepo¨ M, Svens€ater G, Davies JR (2012) Salivary pellicles on titanium and their effect on metabolic activity in Streptococcus oralis. Microbiol 158:390– 397 20. Welin-Neilands J, Svens€ater G (2007) Acid tolerance of biofilm cells of Streptococcus mutans. Appl Environ Microbiol 73:5633–5638 21. Senneby A, Davies JR, Svens€ater G, Neilands J (2017) Acid tolerance properties of dental biofilms in vivo. BMC Microbiol 17(1):165. https://doi.org/10.1186/s12866-0171074-7 22. Boisen G, Davies JR, Neilands J (2021) Acid tolerance in early colonizers of oral biofilms. BMC Microbiol 21(1):45. https://doi.org/ 10.1186/s12866-021-02089-2

Chapter 3 Isolation and Purification of Mycobacterial Extracellular Vesicles (EVs) Komal Umashankar Rao and Gabriela Godaly Abstract Bacterial extracellular vesicles (EVs) contain numerous active substances that mediate bacterial interactions with their host and with other microbes. Best defined are the EVs from Gram-negative bacteria that have been shown to deliver virulence factors, modulate the immune responses, mediate antibiotic resistance, and also inhibit competitive microbes. Due to the complex cell wall structures of Gram-positive bacteria and mycobacteria, EVs from these bacteria were only recently reported. This protocol describes the isolation of EVs from mycobacteria. Key words Mycobacterium tuberculosis, Extracellular vesicles, Vesicle isolation

1

Introduction Tuberculosis (TB) is found in every country and remains one of the leading infectious causes of death worldwide. Last year, 10 million fell ill with Mycobacterium tuberculosis (Mtb), the causative bacteria of TB, and 1.5 million died [1]. Current treatment problems include ineffective intracellular penetration, drug resistance, and lengthy clinical trials [2–4]. Efforts to control TB include development of new and more effective drug treatments and improved diagnostics. Most TB drugs target cell wall biosynthesis, protein synthesis, amino acid biosynthesis/metabolism, or ATP synthesis [5]. Novel treatment includes antimicrobial peptides, phenazines, piperidines, and quinolones [6–9]. Of these, the antimicrobial effect of a multivalent peptide has been well investigated in preclinical studies [9– 11]. The research on TB vaccine and diagnosis has been focusing on Mtb secreted protein antigens for the past century, but the recent discovery of mycobacterial EVs containing Mtb products has shifted the attention for their potential use in blood-based diagnostics [12].

Pontus Nordenfelt and Mattias Collin (eds.), Bacterial Pathogenesis: Methods and Protocols, Methods in Molecular Biology, vol. 2674, https://doi.org/10.1007/978-1-0716-3243-7_3, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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Fig. 1 An overview of mycobacterial EV isolation. The three categories in methods have been highlighted and each step has been explained in detail in the Methods section

EVs are membrane-bound structures ranging from 25 to 250 nm in diameter [13]. While specific contents are influenced by the cell of origin and its physiologic state, EVs carry proteins, metabolites, and toxins enclosed in a lipid bilayer. To date, the structural origin of mycobacterial EVs appears contradictory, with some studies suggesting the cytoplasmic membrane as their point of origin and other studies indicating a role for the bacilli’s cell wall. In addition, mycobacterial EVs are found as unilamellar or bilayered which suggests different structural origins and perhaps different biogenesis pathways [12, 14, 15]. Additional studies are needed to clarify the contradictory findings reported to date. In this chapter we have outlined a detailed protocol (see Fig. 1 for outline) on mycobacterial EV isolation using ultracentrifugation and density gradient. The protocol has been adapted from several other methods and optimized for Mycobacterium bovis bacillus Calmette-Guerin (BCG), a model mycobacterial strain used in TB studies [12, 16–21].

2 2.1

Materials Vesicle Induction

1. Mycobacterium bovis bacillus Calmette-Guerin Montreal frozen stock (BCG) (see Note 1). 2. Middlebrook 7H9 broth (7MH9) (see Note 1). 3. 250 mL sterile flask.

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4. Shaking incubator. 5. Minimal media (MM) (see Note 1). 6. 1 L sterile flasks. 7. Middlebrook 7H10 agar plates (M7H10). 2.2 Vesicle Harvesting

1. 0.45 μM filter papers. 2. Filtration unit. 3. Ultracentrifuge with angle rotor. 4. Amicon 100 kDa ultra centrifugal filter units.

2.3 Vesicle Purification

1. Ultracentrifuge with a swing bucket rotor. 2. Dulbecco’s phosphate buffered saline (DPBS). 3. OptiPrep density gradient medium. 4. BCA protein assay kit.

3 3.1

Methods Vesicle Induction

1. Start by growing frozen BCG stock in 7MH9 broth in a sterile 250 mL flask. This will be the inoculum culture. 2. Transfer 9 mL of media and add 1 mL of defrosted bacterial vial to the flask (see Note 2). 3. Place the flask in a shaking incubator at 37 °C covered with foil for 7 days till it reaches exponential phase (see Note 3). 4. Transfer BCG grown in 7MH9 into MM (see Note 4). 5. Grow bacteria in MM in 1 L flasks until it reaches late exponential phase (see Note 5). 6. Before harvesting the vesicles, transfer a small volume of sample onto 7MH10 plate and check for cell viability and contamination (see Note 6).

3.2 Vesicle Harvesting

1. Centrifuge at 4000× g for 15 min, separate the pellet from supernatant, and collect in a sterile container. 2. Vacuum filter the cell-free supernatant using 0.45 μM filter. 3. Save 5 mL of the filtered supernatant and set aside (see Note 7). Concentrate (30X) the remaining supernatant at 4000 rpm for 10–15 min using 100 kDa exclusion filter tubes. 4. The retentate (left over sample in each tube) is usually around 250–500 μL. Rinse and collect the retentate using the leftover supernatant into a sterile tube. 5. Pool all the retentate in one tube.

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3.3 Vesicle Purification

1. Centrifuge the retentate at 4000 rpm followed by 15,000 rpm for 15 min at 4 °C and collect the supernatant and eliminate any further cell debris from the sample. 2. Next centrifuge the collected supernatant at 100,000× g for 2 h. 3. Save the pellet and resuspend in 1 mL DPBS. 4. Prepare a density gradient of OptiPrep ranging from 35% to 10% using DPBS. Overlay the suspended pellet with 1 mL of each density gradient layer starting from 35% to 10% as the last layer. Centrifuged at 100,000× g for 20 h in a swing bucket rotor (see Note 8). Vesicles usually fraction just above 35% or 30% OptiPrep layer. The layer looks like a cloudy band (Fig. 1). Collect 1 mL of each fraction carefully and save the layer with band. 5. Quantify the band using BCA protein assay. 6. Aliquot the sample into tubes and store in -80 °C until further analysis.

4

Notes 1. You can use any strain of mycobacteria as per your choice. Please find the protocol for media preparation below: M7H9 preparation: Suspend 2.35 g of Middlebrook 7H9 broth base in a 450 mL of distilled water. Add 2 mL of glycerol and heat if necessary to dissolve the medium completely. Sterilize by autoclaving at pressure (121 °C) for 10 min. Cool to 45 °C or below and aseptically add 500 uL of sterile Tween 80 and 1 vial of Middlebrook ADC Growth Supplement. MM preparation: 1 g potassium dihydrogen phosphate, 2.5 g disodium phosphate, 0.5 g asparagine, 50 mg ferric ammonium citrate, 0.5 g magnesium sulfate heptahydrate, 0.5 mg calcium chloride, 0.1 mg zinc sulfate, 0.1% (v/v) glycerol in 1 L volume, pH 7.0. Sterilize by autoclaving at pressure (121 °C) for 10 min and aseptically add sterile 1 mL of Tween 80. 2. Always vortex your frozen bacterial stocks to avoid loss of sample, as mycobacteria tend to settle in the bottom. This will ensure a stable culture. 3. The number of days to grow an inoculum culture is dependent on your bacterial species. Make sure you have a growth curve prior to starting this to check when your culture reaches exponential phase. The culture volume is dependent on your MM culture volume.

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4. When you transfer your 7MH9 culture into MM, divide the inoculum based on your MM volume. Suppose you start a 1 L culture add 10 mL of inoculum, and if it is 2 L, add 20 mL. 5. It is good to have a growth curve for your strain in MM to estimate when the culture reaches late exponential phase. The yield is said to be larger in late exponential phase. 6. Growing it on a plate is to check if the culture is still viable as only metabolically active cells release vesicles. Another reason is to check if your culture is not contaminated since these cultures stand in the incubator for 3–4 weeks. This is the time taken for it to reach late exponential phase if the bacteria are a slowgrowing species. 7. The volume saved is for rinsing one concentration tube. The volume may vary based on how many concentration tubes used. We use four tubes and usually the 1 L flask contains a total volume of 600 mL MM including MH79 inoculum culture. This is divided into 4X 150 mL from which 5 mL is saved. Each 150 mL is concentrated in one Amicon filter tube, hence achieving the 30X concentrate. 8. Separation is better with density gradient systems to use a swing bucket rotor compared to an angle rotor. Please use only sterile chemical after this step. It is a good tip to sterile filter DPBS before preparing OptiPrep gradients. References 1. (WHO) WHO (2021) Global tuberculosis report 2021, p 25. ISBN: 978–92–4003702-1 ´ GJ, Mateos LM, Letek M (2020) 2. Mourenza A Novel treatments against Mycobacterium tuberculosis based on drug repurposing. Antibiotics 9(9):550 3. Walzl G, Ronacher K, Djoba Siawaya JF, Dockrell HM (2008) Biomarkers for TB treatment response: challenges and future strategies. J Infect 57(2):103–109 4. Ginsberg AM, Spigelman M (2007) Challenges in tuberculosis drug research and development. Nat Med 13(3):290–294 5. Fernandes GFS, Thompson AM, Castagnolo D, Denny WA, Dos Santos JL (2022) Tuberculosis drug discovery: challenges and new horizons. J Med Chem 65(11):7489–7531 6. Kumar M, Singh SK, Singh PP, Singh VK, Rai AC, Srivastava AK et al (2021) Potential antiMycobacterium tuberculosis activity of plant secondary metabolites: insight with molecular

docking interactions. Antioxidants (Basel) 10(12):1990 7. Maiolini M, Gause S, Taylor J, Steakin T, Shipp G, Lamichhane P et al (2020) The war against tuberculosis: a review of natural compounds and their derivatives. Molecules 25(13) 8. Quan D, Nagalingam G, Payne R, Triccas JA (2017) New tuberculosis drug leads from naturally occurring compounds. Int J Infect Dis 56:212–220 9. Tenland E, Krishnan N, Ronnholm A, Kalsum S, Puthia M, Morgelin M et al (2018) A novel derivative of the fungal antimicrobial peptide plectasin is active against Mycobacterium tuberculosis. Tuberculosis (Edinb) 113: 231–238 10. Tenland E, Pochert A, Krishnan N, Umashankar Rao K, Kalsum S, Braun K et al (2019) Effective delivery of the anti-mycobacterial peptide NZX in mesoporous silica nanoparticles. PLoS One 14(2):e0212858 11. Rao KU, Henderson DI, Krishnan N, Puthia M, Glegola-Madejska I, Brive L et al

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(2021) A broad spectrum anti-bacterial peptide with an adjunct potential for tuberculosis chemotherapy. Sci Rep 11(1):4201 12. Prados-Rosales R, Baena A, Martinez LR, Luque-Garcia J, Kalscheuer R, Veeraraghavan U et al (2011) Mycobacteria release active membrane vesicles that modulate immune responses in a TLR2-dependent manner in mice. J Clin Invest 121(4):1471–1483 13. Caruana JC, Walper SA (2020) Bacterial membrane vesicles as mediators of microbe – microbe and microbe – host community interactions. Front Microbiol 11:432 14. Prados-Rosales R, Weinrick BC, Pique DG, Jacobs WR Jr, Casadevall A, Rodriguez GM (2014) Role for Mycobacterium tuberculosis membrane vesicles in iron acquisition. J Bacteriol 196(6):1250–1256 15. Chiplunkar SS, Silva CA, Bermudez LE, Danelishvili L (2019) Characterization of membrane vesicles released by Mycobacterium avium in response to environment mimicking the macrophage phagosome. Future Microbiol 14: 293–313 16. Dauros Singorenko PCV, Whitcombe A, Simonov D, Hong J, Phillips A, Swift S, Blenkiron C (2017) Isolation of membrane vesicles from prokaryotes: a technical and biological

comparison reveals heterogeneity. J Extracell Vesicles 6(1):1324731 17. Prados-Rosales R, Brown L, Casadevall A, Montalvo-Quiros S, Luque-Garcia JL (2014) Isolation and identification of membrane vesicle-associated proteins in Gram-positive bacteria and mycobacteria. MethodsX 1:124– 129 18. Prados-Rosales R, Carreno LJ, BatistaGonzalez A, Baena A, Venkataswamy MM, Xu J et al (2014) Mycobacterial membrane vesicles administered systemically in mice induce a protective immune response to surface compartments of Mycobacterium tuberculosis. MBio 5(5):e01921–e01914 19. Yuan F, Li YM, Wang Z (2021) Preserving extracellular vesicles for biomedical applications: consideration of storage stability before and after isolation. Drug Deliv 28(1): 1501–1509 20. Bos J, Cisneros LH, Mazel D (2021) Real-time tracking of bacterial membrane vesicles reveals enhanced membrane traffic upon antibiotic exposure. Sci Adv 7(4):eabd1033 21. Klimentova J, Stulik J (2015) Methods of isolation and purification of outer membrane vesicles from gram-negative bacteria. Microbiol Res 170:1–9

Chapter 4 Strategies to Isolate Extracellular Vesicles from Gram-Negative and Gram-Positive Bacteria Ana Rita Narciso and Marie-Ste´phanie Aschtgen Abstract Membrane vesicles are produced by all Gram-negative and Gram-positive bacteria investigated so far. Membrane vesicles are spherical bilayers of phospholipids released by the bacteria to their surrounding environment and whose average size is comprised between 20 and 300 nm. The purification of these vesicles is often a challenge, as the yield and purity are often crucial for further analyses or use. In this chapter, we describe the most used method to isolate membrane vesicles from culture supernatant of Streptococcus pneumoniae and Klebsiella pneumoniae using ultracentrifugation followed by a density gradient method. Key words Bacteria, Membrane vesicles, Gram-negative, Gram-positive, Ultracentrifugation, Density gradient

1

Introduction Shed membrane vesicles (MV) have multiple roles in bacteria physiology and are used to deliver molecules to other bacteria as well to the host cells. Purified MV often carry virulence factors as cargo and have been studied for their immunomodulatory potential during infection as well as vaccine candidates [8, 9]. MV are spherical structures shed by bacteria to their extracellular environment which do not lead to bacteria lysis. Although the precise mechanism of vesicles bulging from the bacteria remains unclear, MV have been shown to be naturally produced by most bacteria species and in most growth conditions [10]. However, the amount of secreted MV as well as their content depends on the bacterial growth environment [11]. By analyzing the membrane vesicles composition, regulation, and functions, we can study their diverse capacities to modulate the host response, deliver virulence factors, impact bacteria-bacteria interaction and biofilm formation, and, overall,

Pontus Nordenfelt and Mattias Collin (eds.), Bacterial Pathogenesis: Methods and Protocols, Methods in Molecular Biology, vol. 2674, https://doi.org/10.1007/978-1-0716-3243-7_4, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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better understand the roles of secreted MV in bacteria physiology and pathogenesis. Gram-negative bacteria release outer membrane vesicles (OMV) which contain outer membrane proteins, periplasmic proteins, peptidoglycan fragments, liposaccharides, and also DNA and RNA [2]. Gram-positive bacteria release inner membrane vesicles (IMV) which contain inner membrane proteins, cytoplasmic proteins, DNA, and RNA [3]. Although the MV produced have different composition and density, similar methods can be used or adapted to isolate both vesicle types. To study MV, the yield and the purity of the vesicles are essential. Several methods and commercially available kits based on precipitation, membrane affinity, or size-exclusion chromatography have been developed to optimize MV purification and reduce the initial volume, number of steps, and time needed. These methods lead to isolation of different mix of vesicle subtypes but also different contaminants which can result in differences in the downstream analyses performed. Furthermore, it remains unclear how these different methods impact the MV physiological characteristics [12]. The advantages and disadvantages of some of these methods for OMV purification have been compared elsewhere [6]. In this chapter, we describe the most used protocol to efficiently purified membrane vesicles from both Gram-positive and Gram-negative bacteria using Klebsiella pneumoniae MGH78578 and Streptococcus pneumoniae TIGR4 as representative bacteria. Briefly, the protocol consists of sequential ultracentrifugation of bacterial culture supernatant followed by a density gradient. Filtered supernatant culture is first centrifuged at high speed to pellet MV. At this step, the heterogeneous crude MV preparation contains additional components from the supernatant, such as pili, flagella fragments, or protein aggregates which can interfere with the following analyses or in the case of vaccine preparation have undesired immunomodulatory effects [7]. Due to the lipid content, the density of MV is lower than that of soluble protein structures and thus can be further separated using a density gradient. During centrifugation MV migrate to lighter fractions, while other protein structures will be collected in the pellet (Fig. 1).

2

Materials All solutions should be prepared using ultrapure water (prepared by purifying deionized water, to attain a sensitivity of 18 MΩ-cm at 25 °C) and analytical-grade reagents. Prepare and store all reagents at room temperature (unless indicated otherwise). Follow all waste disposal regulations when disposing waste materials. Note that the following materials and methods described within this chapter might need to be adjusted and optimized for other bacteria species or other culture media used.

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Fig. 1 MV preparation workflow steps. (1) Bacteria culture in liquid media. (2) Removal of intact bacteria with low-speed centrifugation followed by sterile filtration. (3) Concentration of the culture supernatant using ultracentrifugation. (4) Purification of the crude MV preparation using a density gradient and ultracentrifugation. The MV containing fraction is collected and washed twice before the MV pellet can be resuspended

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2.1 Bacterial Culture Media

1. For K. pneumoniae, Luria-Bertani liquid medium is prepared by dissolving 10 g of tryptone, 5 g of yeast extract, and 10 g of sodium chloride in deionized water. Adjust the volume to 1 L and the pH to 7.0. Sterilize by autoclaving at 120 °C for 20 min (at 15 psi) on liquid cycle. For LB agar plate, add 15 g of agar to 1 L of LB before autoclaving. 2. For S. pneumoniae, C+Y, a complex defined liquid medium whose preparation involves several steps, is described elsewhere [1]. Shortly, to prepare 1 L of C+Y, 5 g of Bacto™ Casamino acids, 1.1 g of yeast extract, and 1.2 g of sodium acetate are dissolved in deionized water. Separately, 5 mg of L-tryptophan, 50 mg of L-cysteine, 25 mg of L-glutamine, and 250 mg of sodium pyruvate are dissolved in water and successively added to the first solution. Four additional solutions need to be prepared separately and successively added to the initial solution. The four solutions, respectively, contain (1) potassium phosphate, (2) sugars and nucleic acids, (3) vitamins such as niacin and thiamine hydrochloride, and (4) ions including iron (II) sulfate heptahydrate and copper sulfate pentahydrate. The volume is adjusted to 1 L at pH 7.9–8.0 using sodium hydroxide. C+Y is sterilized by autoclaving at 120 °C for 20 min (at 15 psi) on liquid cycle. To prepare blood agar plate, dissolve trypticase soy agar (TSA) according to the label. Add 15 g of agar for 1 L final and autoclave at 120 °C for 20 min. When the mixture has cooled down to 60 °C, add 5% sterile, defibrinated sheep blood before pouring the medium. 3. Spectrophotometer capable of reading absorbance at 600 nm for K. pneumoniae or 620 nm for S. pneumoniae. 4. Orbital 37 °C incubator and static 37 °C incubator with 5% CO2.

2.2 Supernatant Containing Membrane Vesicle Preparation

1. Filtration unit with a 0.22 μm PES membrane and vacuum pump.

2.3 Ultracentrifugation

1. Polypropylene (38 mL, 25 × 89 mm) ultracentrifugation tubes.

2.4 Discontinuous Iodixanol Gradient Separation

1. OptiPrep™ density gradient medium: iodixanol solution (density 1.320 ± 0.001 g/mL) (see Note 1).

2. Refrigerated benchtop centrifuge.

2. Precooled Beckman ultracentrifuge with a swinging bucket (SW 32 Ti).

2. OptiPrep diluent solutions. For Solution A, 0.25 M sucrose, 1 mM EDTA 10 mM Hepes-NaOH, pH 7.4. For Solution B, OptiPrep™ Diluent: 0.25 M sucrose, 6 mM EDTA, 60 mM Hepes-NaOH, pH 7.4. To prepare the 50% OptiPrep™ working solution, mix 7.5 mL of OptiPrep™ with 1.5 mL of Solution B.

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Table 1 Iodixanol gradient pipetting scheme for 10 mL of each density 50% OptiPrep™ (mL)

Solution A (mL)

45% OptiPrep™

9

1

40% OptiPrep™

8

2

35% OptiPrep™

7

3

30% OptiPrep™

6

4

25% OptiPrep™

5

5

20% OptiPrep™

4

6

5% OptiPrep™

1

9

3. OptiPrep solutions for each fraction of the gradient 45% to 5% according to the Table 1. 4. Precooled Beckman ultracentrifuge with swinging-bucket rotor (SW 40 Ti). 5. Polypropylene (14 mL, 14 × 95 mm) ultracentrifugation tubes (see Note 2). 2.5

SDS-PAGE

1. Electrophoresis tank and generator. 2. Bis-Tris 4–12% precast gels. 3. SDS-PAGE running buffer containing 50 mM MOPS, 50 mM Tris Base, 0.1% SDS, 1 mM EDTA, pH 7.7. To prepare 500 mL of 20× MOPS SDS running buffer, dissolve 104.6 g of MOPS, 60.6 g of Tris Base, 10 g of SDS, and 3.72 g of EDTA in 400 mL ultrapure water; mix well and adjust the volume to 500 mL with ultrapure water. 4. LDS sample buffer 4X. 5. BenchMark prestained molecular weight standards. 6. Coomassie G-250 stain.

3

Methods

3.1 Cultivation of S. pneumoniae and K. pneumoniae

1. For K. pneumoniae, using frozen stocks, inoculate one LB plate and incubate overnight at 37 °C. For S. pneumoniae, using frozen stocks, inoculate one blood agar plate and incubate overnight at 37 °C, 5% CO2. 2. The following day, inoculate 5 mL of C+Y at 0.05 (OD620nm) and incubate at 37 °C, 5% CO2 until the OD620nm reaches 0.7. Add 15% glycerol final and freeze until ready to use. For K. pneumoniae, inoculate 5 mL of LB with one colony and incubate overnight at 37 °C.

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3. On the third day, inoculate 500 mL of C + Y medium at OD620nm of 0.05 using the glycerol frozen S. pneumoniae stock and incubate at 37 °C, 5% CO2 until the density reaches 0.7. For K. pneumoniae 500 mL of LB broth is inoculated with 1/100 volume of the overnight culture and incubated at 200 rpm at 37 °C until an OD600nm of 1. 3.2 Isolation of Membrane Vesicles from Supernatant Cultures (see Note 3)

1. Separate intact bacteria from the membrane vesicles containing supernatant by centrifugation (5000 × g 20 min, 4 °C) (see Note 4). 2. Filter the supernatant through a 0.22 μm PES membrane filter unit attached to a receiver bottle using a vacuum pump (see Note 5). 3. Ultracentrifuge the filtered supernatant at 130,000 × g 4 °C for at least 6 hours (see Note 6). Two rounds of ultracentrifugation are needed for an initial culture volume of 500 mL. 4. The supernatant is removed and the pellet containing the membrane vesicles is resuspended in 100 μL of PBS (see Note 7).

3.3 Membrane Vesicle Purification Through OptiPrep Density Gradient

1. The membrane vesicle fraction is loaded at the bottom of a 13 mL tube. For S. pneumoniae, the crude membrane vesicle fraction is adjusted to 2 mL with 50% (wt/vol) OptiPrep and carefully overlaid with 9 mL of OptiPrep 30% and 3 mL of OptiPrep 5%. For K. pneumoniae, the crude membrane vesicle fraction is adjusted to 500 μL with 45% (wt/vol) OptiPrep and overlaid with six successive layers of 1.8 mL of 45, 40, 35, 30, 25, and 20% OptiPrep density gradient medium (Fig. 2) (see Note 8). 2. Gradients are centrifuged at 155,000 × g for at least 8 h at 4 °C using a swinging-bucket rotor. A 2 mL fraction containing the visible membrane vesicles is collected. 3. A ring containing the membrane vesicles is identified by visual examination (cream or white color for S. pneumoniae and brown for K. pneumoniae). The above layer is removed using a P1000 pipette (leaving about 1 cm above the membrane vesicle ring). A 2 mL fraction containing the visible membrane vesicles is collected (see Note 9). 4. To wash the membrane vesicles, the collected fraction volume is adjusted with PBS to 12 mL and ultracentrifuge at 155,000 × g for at least 6 h at 4 °C. The membrane vesicle pellet is resuspended in 100 μL of PBS and stored at -80 °C (see Note 10).

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Fig. 2 Illustration of bottom-loaded density gradient ultracentrifugation. Crude MV preparations are loaded at the bottom of the tubes. On top of the samples, and before ultracentrifugation, the different density layers can be observed. After ultracentrifugation, a visible ring containing the MV form while most of the other bacterial structures remain at the bottom of the tube

5. MV protein profile can be analyzed by SDS-PAGE. Respectively, 2 and 7.5 μg of proteins (see Note 11) for S. pneumoniae and K. pneumoniae as well as 5 μL of protein standards are loaded on a Bis-Tris 4–12% precast gel. The electrophoresis is run at 80 V until the samples have entered the gels and then continue at 150 V until the front dye from the loading buffer reaches the bottom of the gel. Following electrophoresis, the gel is washed in water then stained using Coomassie blue and imaged with the Coomassie program of an imager (Fig. 3). 6. MV preparations can be negatively stained and examined by transmission electron microscopy (TEM). A single drop (5–10 μL) of MV in PBS is placed on a glow discharged carbon-coated 400 mesh grids for 1 min. The excess liquid is removed using filter paper. The grid is then stained with 1% aqueous uranyl acetate for 1 min. The excess liquid is removed using filter paper, and the grid is set to air dry before analysis by TEM (Fig. 4).

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Fig. 3 SDS-PAGE profile of isolated MV from S. pneumoniae (Sp) and K. pneumoniae (Kp). Molecular weights are indicated on the left side

Fig. 4 Transmission electron microscopy images of isolated MVs from S. pneumoniae (a) and K. pneumoniae (b) (scale bar: 1 μm)

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Notes 1. Iodixanol (OptiPrep™) is the most frequently used density medium for MV purification as it is isosmotic and therefore prevents damage to membrane structure [4]. However, alternative density gradient media such as sucrose or dextran can also be used. 2. The yield of MV might be low. In this case, using ultraclear centrifuge tubes may help to visualize the MV ring formed after gradient centrifugation. 3. Alternatively, to the ultracentrifugation step, MV can be precipitated by slowly adding ammonium sulfate (39 g /100 mL supernatant), stirring slowly for 20 min, and then centrifuged (11,000× g, 30 min, 4 °C) to pellet the MV. The MV are then washed twice using PBS and ultracentrifuged (100,000× g, 30 min, 4 °C). 4. When mucoid strains are used, it might be necessary to centrifuge at higher velocity to obtain a clear supernatant prior to filtration. Specifically, for some mucoid S. pneumoniae strains, centrifugation at 11,000 × g for 30 min at 4 °C is necessary for pelleting. 5. The size range of MV depends on the strain and culture conditions, and the actual size of the produced MV may have larger size (above 200 nm); therefore, a typical 0.22 μm might selectively deplete the preparation in larger MV [5]. 6. After ultracentrifugation, the recovered pellet is enriched in MV but also contains other bacterial structures such as pili and flagella fragments of protein aggregates. However, depending on the final use for the MV, the purification protocol can be stopped after this step and the MV preparation is resuspended in PBS and kept at -80 °C. 7. If there is truly little MV released in the culture supernatant, the culture supernatant can be first concentrated. Ultrafiltration techniques allow to pass bacterial supernatant through a membrane with a specific molecular weight cutoff (usually 100 kDa) to remove most of the non-MV-associated proteins while concentrating the MV-containing retentate. 8. If the pellet obtained in the previous step is too dense, up to 500 μL of PBS might be necessary to fully resuspend the material. In that case, undiluted OptiPrep™ solution, which is at 60% (w/v), can be used to prepare the first layer of the gradient to achieve the desired concentration. 9. After gradient, the MV ring is not always visible. In this case, individual fractions of 500 μL to 1 mL can be collected and analyzed for MV components.

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10. Further washing steps might be necessary for S. pneumoniae MV as they are less dense and do not pellet easily in the presence of even small residues of OptiPrep™, making it difficult to wash them. A pellet might not be visible after the first wash, with only a cloudy residue floating at the bottom of the tube; if so, collect liquid fractions carefully until 1–2 mL of liquid is left at the bottom and repeat washes until the OptiPrep™ is fully cleaned and a visible pellet is formed (usually three washes are enough). 11. Due to the lipidic content of the samples, we suggest quantifying proteins using the bovine serum albumin assay or the Qubit fluorescent-based assay which are more accurate than the Nanodrop. References 1. Alloing G, Granadel C, Morrison DA, Claverys JP (1996) Competence pheromone, oligopeptide permease, and induction of competence in Streptococcus pneumoniae. Mol Microbiol 21: 471–478. https://doi.org/10.1111/j. 1365-2958.1996.tb02556.x 2. Avila-Caldero´n ED, Ruiz-Palma M del S, Aguilera-Arreola MaG, Vela´zquezGuadarrama N, Ruiz EA, Gomez-Lunar Z, Witonsky S, Contreras-Rodrı´guez A (2021) Outer membrane vesicles of gram-negative bacteria: an outlook on biogenesis. Front Microbiol 12:557902. https://doi.org/10. 3389/fmicb.2021.557902 3. Briaud P, Carroll RK (2020) Extracellular vesicle biogenesis and functions in gram-positive bacteria. Infect Immun 88:e00433–e00420. https://doi.org/10.1128/IAI.00433-20 4. Ford T, Graham J, Rickwood D (1994) Iodixanol: a nonionic iso-osmotic centrifugation medium for the formation of self-generated gradients. Anal Biochem 220:360–366. https://doi.org/10.1006/abio.1994.1350 5. Gorringe A, Halliwell D, Matheson M, Reddin K, Finney M, Hudson M (2005) The development of a meningococcal disease vaccine based on Neisseria lactamica outer membrane vesicles. Vaccine 23:2210–2213. https://doi.org/10.1016/j.vaccine.2005. 01.055 6. Klimentova´ J, Stulı´k J (2015) Methods of isolation and purification of outer membrane vesicles from gram-negative bacteria. Microbiol Res 170:1–9. https://doi.org/10.1016/j. micres.2014.09.006

7. Kulp A, Kuehn MJ (2010) Biological functions and biogenesis of secreted bacterial outer membrane vesicles. Annu Rev Microbiol 64: 163–184. https://doi.org/10.1146/annurev. micro.091208.073413 8. McMillan HM, Kuehn MJ (2021) The extracellular vesicle generation paradox: a bacterial point of view. EMBO J 40:e108174. https:// doi.org/10.15252/embj.2021108174 9. Narciso AR, Iovino F, Thorsdottir S, Mellroth P, Codemo M, Spoerry C, Righetti F, Muschiol S, Normark S, Nannapaneni P, Henriques-Normark B (2022) Membrane particles evoke a serotypeindependent cross-protection against pneumococcal infection that is dependent on the conserved lipoproteins MalX and PrsA. Proc Natl Acad Sci U S A 119:e2122386119. https:// doi.org/10.1073/pnas.2122386119 10. Schwechheimer C, Kuehn MJ (2015) Outermembrane vesicles from Gram-negative bacteria: biogenesis and functions. Nat Rev Microbiol 13:605–619. https://doi.org/10.1038/ nrmicro3525 11. Toyofuku M, Nomura N, Eberl L (2019) Types and origins of bacterial membrane vesicles. Nat Rev Microbiol 17:13–24. https://doi. org/10.1038/s41579-018-0112-2 12. Veerman RE, Teeuwen L, Czarnewski P, Gu¨clu¨ler Akpinar G, Sandberg A, Cao X, Pernemalm M, Orre LM, Gabrielsson S, Eldh M (2021) Molecular evaluation of five different isolation methods for extracellular vesicles reveals different clinical applicability and subcellular origin. J Extracell Vesicles 10:e12128. https://doi.org/10.1002/jev2.12128

Part II Bacterial Genetics, Genomics, and Phylogenetics

Chapter 5 Total Bacterial RNA Isolation and Northern Blotting Analysis Jens Karlsson, Hannes Eichner, and Edmund Loh Abstract The study of bacterial gene expression during infection provides vital information for researchers to understand bacterial pathogenesis and infection. The ability to obtain clean and undegraded RNA could be challenging and daunting and remains the most crucial experimental step prior to downstream analyses, such as Northern blotting, quantitative PCR (qPCR), and RNA-seq. This chapter describe two methods (acid guanidinium thiocyanate (TRIzol) phenol-chloroform and hot phenol) commonly used to isolate total bacterial RNA and are suitable for both Gram-positive and Gramnegative bacteria. Procedures such as RNA quantification and DNase treatment are also included to ensure amount and quality of the RNA samples. The second part of the chapter includes a method used to analyze bacterial gene expression (Northern blotting), two methods to generate radioactive probes, as well as target detection using a phosphorimager. Key words Gram-positive, Gram-negative, Bacteria, RNA isolation, Northern blot, Radioactive probing

1

Introduction RNA isolation and Northern blot analysis have been the bread and butter of molecular biology, especially in gene regulation research since the 1990s. The first successful extraction of nucleic acid was achieved by a Swiss physician, Friedrich Miescher, in 1869 (review by [1]). Today, there are many different methods to extract nucleic acid isolated from various sources, such as by phenol-chloroform [2], alkaline [3], cesium chloride gradient centrifugation [4], and cellulose chromatography [4]. Unlike DNA extraction, isolation of RNA often requires special care and precautions as the molecule is unstable and has a very short half-life once extracted due to the presence of RNases. Selecting which method to isolate bacterial total RNA extraction relies on several factors such as the presence of the thick peptidoglycan in Gram-positive bacteria, extracellular matrix (biofim), and production of polyuronide and alginate (mucoid). The first and arguably

Pontus Nordenfelt and Mattias Collin (eds.), Bacterial Pathogenesis: Methods and Protocols, Methods in Molecular Biology, vol. 2674, https://doi.org/10.1007/978-1-0716-3243-7_5, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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the most crucial step of bacterial RNA isolation is the disruption or lysis of the bacterial cell envelope. Time indicated and intensity in the protocol for lysis by bead beating may vary depending on the surface composition of the bacteria and may require further trial and error. The RNA isolation protocols presented here are acid guanidinium thiocyanate (TRIzol)-phenol-chloroform (suitable for Grampositive and Gram-negative bacteria) and hot phenol (suitable for Gram-negative bacteria). TRIzol-phenol-chloroform-based RNA extraction, developed in 1987 [2], is a common method in many laboratories. This single-step RNA extraction allows the RNA to be separated to remain in an aqueous phase, while DNA and proteins remain in an inter- or lower organic phase, respectively. Finally, RNA can ultimately be extracted by precipitation from the aqueous phase. For many downstream usages of the RNA such as Northern blot, qPCR, and RNA-seq, the purity of RNA is paramount especially for the study of gene expression. Upon successfully extracting bacterial total RNA, Northern blot analysis (developed by James Alwine, David Kemp, and George Stark [5]) allows the observation of specific target RNA expression as well as size identification. Northern blotting comprises multiple steps including RNA size separation by electrophoresis, the transfer of RNA onto a blotting membrane, and the detection of target by means of a hybridization probe on the membrane. A simplified workflow diagram is shown in Fig. 1.

Fig. 1 A workflow of Northern blotting analysis. Total RNA isolation (Subheading 3.1 or 3.2), followed by quantification of RNA samples (Subheading 3.3) and DNase treatment (Subheading 3.4). Gel electrophoresis (Subheading 3.5.1) and membrane transfer (Subheading 3.5.2). Probe incubation (Subheading 3.5.3) with probe preparation (Subheading 3.6) and target detection (Subheading 3.7)

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Materials

2.1 RNA Isolation using TRIzol-Acid Phenol

1. Acid phenol, pH 4.5. 2. Bead beater homogenizer. 3. Centrifuge. 4. Chloroform/isoamyl alcohol (C:IAA) 24:1. 5. EDTA, 0.5 M. 6. Glass beads, 0.1 mm. 7. Isopropanol, ice cold. 8. MQ Water, diethyl pyrocarbonate (DEPC) treated. 9. Sodium acetate (NaAc), 3 M. 10. TRIzol. 11. RNA loading dye 2×: 98% v/v formamide, 10 mM ethylenediaminetetraacetic acid (EDTA), 300 μg/mL bromophenol blue. 12. Resuspension solutions: 10% glucose, 12.5 mM Tris pH 7.6, 5 mM EDTA (see Note 1).

2.2 RNA Isolation Using Hot Phenol

1. Acid phenol, pH 4.5. 2. Centrifuge. 3. Isopropanol, ice cold. 4. KMT (resuspension buffer, pH 7.4): 10 mM potassium chloride (KCl), 5 mM magnesium chloride (MgCl2), 10 mM Tris, 2.5% sodium dodecyl sulfate (SDS), 1% β-mercaptoethanol. 5. MQ-water – DEPC treated. 6. NEMST (lysis buffer, pH 7.4): 0.4 M sodium chloride (NaCl), 40 mM EDTA, 20 mM Tris.

2.3 Determining the Quality and Quantity of RNA

1. Agarose. 2. MQ-water – DEPC treated. 3. Nucleic acid spectrophotometer. 4. Tris-acetate-EDTA (TAE). 5. Tris-borate-EDTA (TBE).

2.4

DNase Treatment

1. Acid phenol/chloroform/isoamyl alcohol (P:(C:IAA), 1:1(24: 1)). 2. Chloroform/isoamyl alcohol (C:IAA) 24:1. 3. Centrifugal evaporator.

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4. Centrifuge. 5. MQ water. 6. RNase-free DNase I. 2.5

Northern Blot

1. Centrifuge. 2. Denhardt’s solution 50×: 1% w/v (Ficoll 400, polyvinylpyrrolidone, bovine serum albumin). 3. HEPES 10×: 0.2 M Na-HEPES, 50 mM NaAc, 10 mM EDTA, 0.1% DEPC. 4. NaAc, 3 M. 5. Paper towels. 6. Positively charged nylon membranes. 7. Prehybridization and hybridization solution: 50% v/v formamide, 5× SSC, 5× Denhardt’s solution, 1% w/v SDS, (100 μg/ mL denatured salmon sperm DNA or herring testes DNA, just before use) (see Note 2). 8. RNA loading dye 6×: 0.1% w/v (xylene cyanol, bromophenol blue, orange G), 10 mM EDTA (dissolved in 95% formamide). 9. RNA sample buffer 1×: (1× HEPES, 4% formaldehyde, dissolved in 50% formamide). 10. Running buffer: 1× HEPES (diluted from 10× HEPES using DEPC-treated MQ water). 11. SSC 20×: 3 M NaCl, 0.35 M tri-sodium citrate (pH: 7.0). 12. Tank and glass for capillary transfer. 13. UV crosslinker. 14. 3 mm cellulose chromatography paper.

2.6 Probe Preparation

1. Ethanol. 2. Megaprime DNA labeling system. 3. Phosphorus-32 (32P) isotope γ-dATP. 4. Phosphorus-32 (32P) isotope α-dATP. 5. Sodium acetate (NaAc), 3 M. 6. T4 polynucleotide kinase.

2.7 Membrane Exposure and Development

1. Hybridization oven. 2. Phosphorimager. 3. Wash I buffer (2× SSC, 0.1% SDS, in MQ water). 4. Wash II buffer (0.2× SSC, 0.05% SDS, in MQ water).

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Methods

3.1 RNA Isolation Using TRIzol-Acid Phenol

Suitable for Gram-positive and Gram-negative bacteria. 1. Grow bacteria in growth media. Harvest at sought optical density. 2. Pour bacterial suspension into a pre-chilled tube (15 mL/ 50 mL) and centrifuge in a tabletop centrifuge (pre-chilled at 4 °C) at 2200× g (or 4000 revolutions per minute (rpm)) for 5 min. 3. Discard supernatant (continue to step 4 or possible for longer storage of pellet at -80 °C). 4. Resuspend bacterial pellet with 400 μL of ice-cold resuspension solution and 60 μL of 0.5 M EDTA (keeping each tube on ice). 5. Aliquot 0.4 g of glass beads (size: 0.1 mm) and 0.5 mL of acid phenol into homogenizing tubes. 6. Insert tubes into the bead beater. 7. Beat at maximum intensity setting for 1 min, remove tubes, and keep on ice for 1 min. Repeat 2 rounds (see Note 3). 8. Centrifuge tubes at 17,000× g (13,000 rpm) for 5 min at 4 °C. 9. Transfer the supernatant containing the RNA to a new microcentrifuge tube. 10. Add 1 mL of TRIzol to each tube and resuspend the mixture carefully using a 1 mL pipette tip. 11. Leave tubes on bench for 5 min at room temperature. 12. Add C:IAA in a 1:1 volume ratio and resuspend the mixture carefully using the 200 μL pipette tip (a pink cloud should be observed). 13. Vortex the mixture for 10 s. 14. Centrifuge tubes at 17,000× g (13,000 rpm) for 5 min at 4 °C. 15. Transfer the upper aqueous phase to a new microcentrifuge tube. Add C:IAA in a 1:1 volume ratio. Vortex the mixture for 10 s. Centrifuge the tubes at 17,000× g (13,000 rpm) for 5 min at 4 °C. 16. Transfer the upper, aqueous phase to a new microcentrifuge tube. Add 0.7 volume of ice-cold isopropanol. Resuspend the mixture carefully with a 1 mL pipette tip. 17. Put tubes into a -20 °C freezer for at least 30 min. 18. Centrifuge tubes at 17,000× g (13,000 rpm) for 20 min at 4 °C. 19. Carefully remove and discard all the supernatant from the tube using a 200 μL pipette tip (see Note 4).

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20. Dry the RNA pellet inside the tube in a centrifugal evaporator (see Note 5). 21. Resuspend the pellet with 200 μL of DEPC-treated MQ water (see Note 6). 3.2 RNA Isolation Using Hot Phenol

Suitable for Gram-negative bacteria. 1. Grow bacteria in growth media. Harvest at sought optical density. 2. Pour bacterial suspension into a tube (15 mL/50 mL) containing ice and centrifuge in an tabletop centrifuge (pre-chilled at 4 °C) at 2200× g (4000 rpm) for 5 min. 3. Discard supernatant and freeze the bacterial pellets at -80 °C (continue to step 4 or possible for longer storage). 4. Resuspend each bacterial pellet with 1 mL of ice-cold KMT buffer (keeping tubes in ice bucket). 5. Aliquot mixture (pellet dissolved with KMT buffer) into 2 microcentrifuge tubes (approximately 600 μL each). 6. Add 200 μL acid phenol and 400 μL NEMST buffer into each tube and vortex for 10 s. 7. Incubate at 90 °C for 5 min and directly put on ice for 5 min. 8. Centrifuge tubes at 17,000× g (13,000 rpm) for 2 min at room temperature. 9. Transfer the upper phase to a new microcentrifuge tube. 10. Add 200 μL of acid phenol into the tube. Vortex the mixture for 10 s. Centrifuge the tubes at 13,000× g (13,000 rpm) for 5 min at 4 °C (repeat steps 9 and 10 two times). 11. Add 400 μL of C:IAA. Vortex the mixture for 10 s. Centrifuge the tubes at 13,000× g (13,000 rpm) for 5 min at 4 °C (repeat steps 9 and 11 two times). 12. Precipitate the RNA by adding 0.7 volume isopropanol into each tube and incubate at -20 °C overnight. 13. Centrifuge tubes at 17,000× g (13,000 rpm) for at least 20 min at 4 °C. 14. Carefully remove and discard all the supernatant from the tube with a 200 μL pipette tip. 15. Dry briefly the tubes (not more than 2 min) in a centrifugal evaporator. 16. Resuspend the pellet with 200 μL of DEPC treated MQ water (see Note 6). 17. Proceed to quantify the RNA concentration (see Subheading 3.3).

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Fig. 2 Gel electrophoresis of RNA isolated from Neisseria meningitidis

3.3 Determining the Quality and Quantity of RNA

1. Remove 10 μL of RNA sample from (Subheading 3.1 and 3.2) into a new microcentrifuge tube.

3.3.1 Agarose Gel Method

3. Prepare an agarose gel (1.2%, TAE or TBE), with ribonucleic acid stain of choice.

2. Add 10 μL of 2× RNA loading dye.

4. Run samples on the 1.2% agarose gel (80 Volts for 45 min) and visualize with excitation wavelength appropriate for the chosen ribonucleic acid stain. 5. 23S 16S and 5S ribosomal RNAs should be clearly observed. Precursors of 23S and 16S can sometimes be observed (see Fig. 2). 6. DNA is present if large smears are observed within each lane on the agarose gel. Proceed to “Subheading 3.4 DNase treatment.” 3.3.2 Nucleic Acid Spectrophotometer Method

1. Add 1–2 μL of RNA sample onto a nucleic acid spectrophotometer (see Note 7). 2. Measure the total RNA concentration. 3. The ratio of A260 to A280 should be 1.8 or higher. Lower ratio indicates contamination with impurities (leftover solvents). If value is lower, proceed to “DNase treatment.” 4. Every A260 unit reflects approximately 40 μg/mL concentration of RNA.

3.4 DNase Treatment (see Note 8)

1. Remove 178 μL of total RNA sample and add into a new microcentrifuge tube. 2. Add 20 μL of 10× DNase buffer and 2 μL of DNase I (total of 20 U) into the tube (see Note 9). 3. Incubate at 37 °C for 1–2 h (see Note 10).

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4. Add 200 μL of acid phenol/chloroform/isoamyl alcohol (P: (C:IAA), 1:1(24:1)) into the tube. Vortex the mixture for 10 s. Centrifuge the tubes at 17,000× g (13,000 rpm) for 5 min at 4 °C. 5. Transfer the upper phase to a new microcentrifuge tube. Increase total volume sample to 300 μL with DEPC-MQ. 6. Add 300 μL of C:IAA. Vortex the mixture for 10 s. Centrifuge the tubes at 17,000× g (13,000 rpm) for 5 min at 4 °C. 7. Transfer the upper phase to a new microcentrifuge tube. Add 0.1 volume of 3 M NaAc (pH 4.5) and resuspend. 8. Add 2.5 volume of 99.5% ice-cold ethanol and invert tube at least five times. Place tubes into a -20 °C freezer for at least 30 min (possible for longer storage). 9. Centrifuge tubes at 17,000× g (13,000 rpm) for at least 20 min at 4 °C. 10. Carefully remove and discard all the supernatant from the tube with a 200 μL pipette tip. 11. Briefly dry the RNA pellet inside the tubes (not more than 2 min) in a centrifugal evaporator. 12. Resuspend the pellet with 200 μL of DEPC-treated MQ water (see Note 6). 13. Proceed to quantify the RNA concentration (see Subheading 3.3). 3.5

Northern Blot

3.5.1 RNA Sample Preparation and Formaldehyde Agarose Gel

1. Add 20 μg of total RNA into a microcentrifuge tube. 2. Add 0.1 volume of 3 M NaAc (pH 4.5) and resuspend. 3. Add 2.5 volume of 99.5% ice-cold ethanol and invert tube at least five times. Place tubes into a -20 °C freezer for at least 30 min (proceed to step 4 or possible for longer storage). 4. Centrifuge tubes at 17,000× g (13,000 rpm) for at least 20 min at 4 °C. 5. Carefully remove and discard all the supernatant from the tube with a 200 μL pipette tip. 6. Briefly dry the RNA pellet inside the tubes (not more than 2 min) in a centrifugal evaporator. 7. Resuspend the pellet with 15 μL of RNA sample buffer (see Note 6). 8. Incubate at 65 °C for 3 min and directly put on ice for 5 min. 9. Resuspend the mixture with 3 μL of 6× loading dye and briefly spin the condensation accumulated on the tube lid. 10. Proceed to loading samples into gel.

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Table 1 Recommended volume of reagents to generate a Northern blot agarose gel Northern blot agarose gel (1.5%) 150 mL 100 mL Agarose

2.2 g

1.5 g

MQ water (DEPC treated)

105 mL 70 mL

10× HEPES

15 mL

10 mL

37% formaldehyde (*to be added last after partial cooling of dissolved agarose solution. This prevents creation of toxic fumes)

30 mL

20 mL

11. To make a 1.5% RNA gel, mix and heat gel mixture (1.5% agarose, 1× HEPES) (see Table 1 for exact volume). 12. When agarose is dissolved, add formaldehyde to gel mixture at a final concentration of 7.4% (see Table 1 for recipe (see Note 11)). 3.5.2 Transfer of RNA from Gel onto Membrane

1. Cut nylon membrane to a desired size (same dimension of the gel) (medium gel 9 cm × 11 cm, large gel 12 cm × 14 cm). 2. Cut 12 pieces of 3 mm cellulose chromatography paper (same dimension of the gel). 3. Cut one long 3 mm cellulose chromatography paper (length of 28 cm × gel width). 4. Cut enough paper towels (Cellstoff) of the gel dimension and to a thickness of 10–12 cm when stacked on top of each other. 5. Assemble capillary transfer arrangement according to Fig. 3. 6. Secure weight on top of the assembled transfer arrangement at room temperature, overnight. 7. Remove the nylon membrane and air dry the membrane for 1 h at room temperature. 8. Crosslink RNA with membrane using a UV crosslinker at 1400 × 100 μJ/cm2.

3.5.3 Hybridization of Membrane and Probe Preparation

1. Insert crosslinked membrane into a hybridization tube (see Note 12). 2. Prehybridize the membrane with 10 mL of hybridization solution at 50 °C for 2 h (see Note 13). 3. Add DNA probe (preparations see below) and hybridize the membrane at 50 °C overnight (see Note 14).

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Fig. 3 Left, a Northern blot transfer assembly. Right, picture shows an example of Northern blot transfer performed in the lab 3.6 Probe Preparations 3.6.1 End-Labeling with Polynucleotide Kinase and Isotope γ-dATP

1. Label DNA probe with polynucleotide kinase (PNK). Add the following into a microcentrifuge tube: 1 μL 10 pmol DNA probe

2 μL 10× PNK-Buffer A 3 μL γ-dATP 1 μL PNK 13 μL MQ water = total volume 20 μL. 2. Incubate at 37 °C for 1 h. 3. Precipitate labeled probe. Add the following into a microcentrifuge tube: 80 μL MQ water 10 μL 3 M NaAc 300 μL 100% EtOH 20 μL PNK reaction mix = total volume 410 μL. 4. Precipitate at -20 °C for 1 h. 5. Centrifuge the precipitate at 17,000× g (13,000 rpm) at room temperature for 30 min. 6. Carefully transfer the supernatant into a new tube, using a 200 μL pipette tip and making sure to transfer to the last drop. 7. Dispose the tube in a designated radioactive waste bucket. 8. Carefully resuspend the labeled probe pellet with 100 μL MQ water.

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9. Aliquot 10–20 μL of the mixture into a new PCR tube (excess can be stored in -20 °C for later use). 10. Heat the aliquot at 95 °C for 5 min and immediately snap cool on ice. 11. Add everything to the hybridization tube. 3.6.2 Random Primer Labeling with Isotope αdATP (see Note 15)

1. Prepare DNA probe with random primers using Megaprime DNA labeling system. 2. Add 2 μL of DNA probe template into a tube (generated by PCR, binds to desired target sequence, ~200 bp). 3. Add 3 μL ddH2O + 5 μL “Primer” into the tube. 4. Boil mixture at 100 °C for 5 min and briefly spin down vapor after boiling. 5. Add 4 μL of each dNTP (exclude radioactive dNTP, here dATP), total 12 μL. 6. Add 5 μL “reaction buffer.” 7. Add 16 μL ddH2O. 8. Add 2 μL Klenow fragment. 9. Add 5 μL radioactive α-dNTP (here α-dATP). 10. Incubate mixture at 37 °C for 1 h. 11. Add 5 μL EDTA to stop reaction. 12. Boil mixture at 100 °C for 5 min and immediately snap cool on ice. 13. Add everything to the hybridization tube.

3.7 Membrane Exposure and Development

1. Carefully pour out the hybridization buffer containing dATP and dispose according to local radiation rules. 2. Pre-warm Wash II buffer in the same temperature as hybridization in the oven. 3. Wash membrane by adding 50 mL of Wash I buffer into the hybridization tube, rotating tube at room temperature for 15 min. 4. Discard Wash I buffer according to local radiation safety rules. 5. Wash membrane by adding pre-warmed Wash II buffer into the hybridization tube, rotating tube at same hybridizing temperature for 15 min. 6. Discard Wash II buffer according to local radiation safety rules. 7. Wrap membrane in plastic foil. 8. Expose prewrapped membrane on a phosphor screen and image the phosphor screen using a phosphorimager (see Note 16) (see Fig. 4). 9. After exposure, blank phosphor screen with white light for 15 min.

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Fig. 4 Northern blot of an RNA transcript from a monocistronic gene and RNA transcripts (single and co-transcribed) from a polycistronic operon, of Neisseria meningitidis using γ-dATP end-labeled probes

10. Probe hybridized on the membrane can be stripped by adding boiling 0.1% SDS and let cool to room temperature (see Note 17). 11. The stripped membrane is ready for next probe or wrapped with plastic foil and stored in -20 °C until needed.

4

Notes 1. Water and other solutions should be treated with DEPC to inhibit RNase activity. 2. Pre-mixed hybridization buffer could be purchased (Rapid-hyb buffer, and Church and Gilbert’s hybridization buffer (VWR)). 3. Gram-positive bacteria may require several rounds of step 7, while Gram-negative may only require one round of beating. It is also important not to over-beat the samples as the shear force could destroy the RNA sample. 4. Remove as much remaining supernatant as possible with an even finer tip. 5. Consider gentle heat settings. Alternatively, the pellet can be air dried in an airflow cabinet containing a HEPA filter. Air drying on the bench is also possible but not recommended. 6. RNA pellet could sometimes be transparent and hard to see. 7. Nucleic acid spectrophotometer (e.g., NanoDrop™, Qubit™, Bioanalyzer).

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8. The following protocol in Subheading 3.4 is for DNase treatment of RNA sample (total volume of 200 μL). If less volume is required, adjust reagents accordingly. 9. Total volume depends on the DNase buffer and DNase I stock concentration. 10. Time incubated with DNase depends on the level of DNA contamination. The higher the concentration of DNA contamination may require longer incubation time of max 2 h. 11. Do not heat gel mixture with formaldehyde. 12. RNA sample facing inside the tube. 13. The hybridization temperature varies depending on specific gene and probe. Higher temperature may result in higher specificity but may result in weaker signals. Lower temperature may result in stronger signals, but lower specificity and unspecific hybridization. 14. Prehybridization and hybridization temperature should be the same. 15. Adjust exposure time according to desired intensity. 16. Random primer labeling with isotope α-dATP is not suitable for gene target smaller than 100 bp. 17. May require several stripping steps for strong signals. Housekeeping genes should be probed last, as the signals are often strong and may not be possible to be fully stripped.

Acknowledgments Work in Edmund Loh’s laboratory is funded by the Swedish Foundation for Strategic Research (ICA14-0013); Knut and Alice Wallenberg Foundation (2014.0177, 2019.0324); and the Swedish Research Council (Dnr: 2014-2050, 2021-02945, 2021-06676). References 1. Dahm R (2005) Friedrich Miescher and the discovery of DNA. Dev Biol 278(2):274–288. https://doi.org/10.1016/j.ydbio.2004.11.028 2. Chomczynski P, Sacchi N (1987) Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal Biochem 162(1):156–159. https://doi.org/10. 1006/abio.1987.9999 3. Birnboim HC, Doly J (1979) A rapid alkaline extraction procedure for screening recombinant plasmid DNA. Nucleic Acids Res 7(6):

1513–1523. https://doi.org/10.1093/nar/7. 6.1513 4. Cseke LJ, Kaufman PB, Podila GK, Tsai C-J (eds) (2003) Handbook of molecular and cellular methods in biology and medicine, 2nd edn. CRC Press 5. Alwine JC, Kemp DJ, Stark GR (1977) Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes. Proc Natl Acad Sci U S A 74(12):5350–5354. https:// doi.org/10.1073/pnas.74.12.5350

Chapter 6 Phylogenetic Analysis of Bacterial Pathogen Genomes Xavier Didelot Abstract The development of high-throughput sequencing technology has led to a significant reduction in the time and cost of sequencing whole genomes of bacterial pathogens. Studies can sequence and compare hundreds or even thousands of genomes within a given bacterial population. A phylogenetic tree is the most frequently used method of depicting the relationships between these bacterial pathogen genomes. However, the presence of homologous recombination in most bacterial pathogen species can invalidate the application of standard phylogenetic tools. Here we describe a method to produce phylogenetic analyses that accounts for the disruptive effect of recombination. This allows users to investigate the recombination events that have occurred, as well as to produce more meaningful phylogenetic analyses which recover the clonal genealogy representing the clonal relationships between genomes. Key words Bacterial pathogens, Whole genome sequencing, Phylogenetics, Recombination, Clonal genealogy

1

Introduction The development of high-throughput sequencing technology over the past two decades has led to a significant reduction in the time and cost required to sequence whole genomes of bacterial pathogens [1–3]. Many studies have therefore emerged that compare hundreds or even thousands of genomes within a given bacterial species or population. Two of the first such studies were conducted over 10 years ago, in Staphylococcus aureus ST239 [4] and Streptococcus pneumoniae PMEN1 [5]. One of the most fundamental methods to represent the relationships between bacterial genomes is to build and draw a phylogenetic tree [6]. The vast majority of phylogenetic methods are based on a model in which evolution occurs only via point mutations. However, this is not applicable to most bacterial species, due to the occurrence of recombination events driven by transformation, transduction, and/or conjugation [7]. The relative effect of recombination vs mutation can be measured using the r/m value [8]. For instance, a value of

Pontus Nordenfelt and Mattias Collin (eds.), Bacterial Pathogenesis: Methods and Protocols, Methods in Molecular Biology, vol. 2674, https://doi.org/10.1007/978-1-0716-3243-7_6, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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A

C

B

D

Fig. 1 An example of ancestral recombination graph (a), with highlighted in bold the trees that correspond to the first genomic site (b), to the last genomic site (c), and to the clonal genealogy (d)

r/m ¼ 2 means that recombination introduces twice as many substitutions as mutation does (see Note 1). The effect of recombination can vary by several orders of magnitudes across bacterial species [9] but can also vary between lineages of a same species [10, 11] and even between infected hosts as a result of differing opportunities for recombination with other strains [12]. Ignoring the effect of recombination when performing a phylogenetic analysis can be misleading about the actual relationships between the genomes [13, 14]. Formally, the full ancestry of the bacterial genomes can be represented as an ancestral recombination graph [15] with gene conversion [16, 17]. An example of such a graph is shown in Fig. 1a: each recombination event is represented as a split going backward in time in the graph, with one ancestral line (the recipient) contributing most of the genetic material and the other ancestral line (the donor) contributing only a relatively small fragment. Embedded within this graph are the phylogenetic trees that are being followed by each genomic site, such as the first and the last

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sites as illustrated in Fig. 1b, c, respectively. Another important tree embedded within the ancestral recombination graph is the so-called clonal genealogy, which is obtained by following the path of the recipients (as opposed to the donors) of each recombination event, as illustrated in Fig. 1d. Note that in the example shown in Fig. 1, the clonal genealogy in Fig. 1d is the same as the tree for the first site Fig. 1b, which means that the first site is part of the so-called clonal frame, which is the set of sites that are not affected by recombination. On the other hand, the last site illustrated in Fig. 1c is affected by one of the recombination events and therefore not part of the clonal frame. Inferring the whole ancestral recombination graph is a very difficult statistical problem, even when making simplifying assumptions [18, 19], and therefore this approach does not scale to the analysis of large numbers of whole bacterial genomes. A much more tractable approach is to focus on the inference of the clonal genealogy. The ClonalFrame model [20] considers this clonal genealogy, as well as the sets of the mutation and recombination events that happened on each of its branches, but makes no attempt to trace the origin of the recombination events, so that it is a tree-based model rather than a graphical model. Other software that attempt to reconstruct the clonal genealogy include Gubbins [21] and recHMM [22]. The methodology described below shows how the ClonalFrameML software [23] can be used to fit the ClonalFrame model [20] to large numbers of whole bacterial genomes. This methodology has been applied in a large number of studies and a wide range of bacterial pathogens, including Clostridium difficile [24], Staphylococcus aureus [25], Staphylococcus epidermidis [26], Klebsiella pneumoniae [27], and Pseudomonas aeruginosa [28].

2 2.1

Materials Genomic Data

2.2 Computer Hardware

The first required material is whole genome sequencing data from a set of bacterial isolates. This may be new genomic data sequenced as part of a specific study or previously sequenced data such as the one available for a wide range of bacterial species in publicly accessible databases such as GenBank [29], EnteroBase [30], BIGSdb [31], or Pathogenwatch [32]. Typically, all the genomes would be from the same bacterial species and may even be selected to represent a single lineage within the species. The methodology described below can analyze hundreds or even thousands of genomes. The methodology presented here requires a computer which may be running Windows (32-bit or 64-bit), Mac OS X (32-bit or 64-bit), or Unix/Linux. Unless a very large number of genomes are going to be included in the analysis, a standard laptop computer should be all that is required.

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2.3 Computer Software

Several software tools are useful when performing a phylogenetic analysis of bacterial pathogen genomes: 1. Mauve [33]. 2. MUMmer [34]. 3. RAxML [35]. 4. FastTree [36]. 5. PhyML [37]. 6. The European Molecular Biology Open Software Suite (EMBOSS) [38]. 7. ClonalFrameML [23]. 8. Gubbins [21]. 9. recHMM [22]. 10. ape [39]. 11. Rcandy [40]. 12. BactDating [41]. 13. skygrowth [42]. 14. TransPhylo [43, 44]. All these software tools are freely available online and can be installed relatively easily on a Windows, Mac, or Unix/Linux machine.

3

Methods

3.1 Producing the Input Alignment

Before the phylogenetic method can be initiated, an alignment of the genomes needs to be produced. If the genomes have been assembled by mapping against a reference and calling variants [45], then the assembled genomes are already aligned against each other since they are all aligned against the reference genome. Alternatively, the genomes may have been assembled using a de novo approach [46], in which case they need to be aligned. Aligning de novo assemblies directly against each other can be done, for example, using Mauve [33], but this typically does not scale well to more than a few dozens of genomes. A more scalable approach is to align the de novo assemblies against a reference, for example, using MUMmer [34]. Another option is to find the genes shared between the genomes and align them one by one [47]. Depending on the method used to produce the alignment, it may be made of a single block (e.g., if the genomes were aligned against a complete reference) or several blocks (e.g., if genes were aligned separately). If the alignment is made of a single block, it can be stored, for example, in a FASTA file (see Note 2). If it is made of

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several blocks, it can be stored as a XMFA file, which is simply the concatenation of one FASTA file for each of the blocks, with “¼” symbols separating the blocks (see Note 3). It is important to make sure that the alignment contains all the aligned genomic positions, whether polymorphic or not, and does not only contain the single nucleotide polymorphisms (SNPs). If data is only given for SNPs relative to a reference, then a whole genome alignment needs to be generated by inserting the SNPs in their correct positions along the reference genome and where sites are not polymorphic “padding” with the sequence of the reference genome. 3.2 Building an Initial Phylogeny

Once a whole genome alignment has been prepared, the next step is to build an initial phylogeny, which means a phylogeny that ignores recombination (see Note 4). Several software tools can perform this task, including RAxML [35], FastTree [36] or PhyML [37]. If the alignment is in XMFA format because it is made of several blocks, the blocks need to first be concatenated into a FASTA file. In the examples of code below, we consider that this file is called ali. fasta.

RAxML [35] can be applied directly to this file using, for example, the command: raxml -m GTRGAMMAI -s ali.fasta -p 1 -n raxmlout

This command will generate a phylogeny stored in the Newick file (see Note 5) named RAxML_bestTree.raxmlout. FastTree [36] can also be applied directly to the FASTA file using a command like: fasttree -nt ali.fasta > fasttree.nwk

This command will generate a phylogeny stored in the Newick file (see Note 5) named fasttree.nwk. PhyML [37] requires to first convert the FASTA file into PHYLIP format, which can be done, for example, using seqret which is part of the European Molecular Biology Open Software Suite (EMBOSS) [38]. The following commands can be used to run seqret and PhyML: seqret -osformat phylip3 ali.fasta ali.phylip phyml -b 0 -v 0 -c 1 -s BEST -q -i ali.phylip

This command will generate a phylogeny stored in the Newick file (see Note 5) named ali.phylip_phyml_tree.txt.

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3.3 Recombination Analysis

The phylogeny constructed in the previous step ignored the effect of recombination but can be used by the software ClonalFrameML [23] as a starting point for the construction of a new phylogeny that will be corrected for the effect of recombination (see Note 6). Simultaneously with the correction of the phylogeny, ClonalFrameML attempts to infer the recombination events that occurred on each branch as well as three global parameters: R/theta is the ratio of rates at which recombination and mutation occurred, delta is the mean length of recombination tracts, and nu is the mean distance between donors and recipients in recombination events. An estimate of the relative effect of recombination vs mutation r/m can be obtained by multiplying together these three parameters R/theta, delta, and nu. Let us consider that the whole genome alignment is stored in the file ali.fasta and that the phylogeny constructed in the previous step is stored in the file initial.nwk. The basic command to run ClonalFrameML is then of the form: ClonalFrameML initial.nwk ali.fasta cfmlout

A number of options can be added at the end of this command. A full list can be obtained with the command ClonalFrameML -h but the most frequently useful options are as follows: •

-xmfa_file true specifies that the input file is in XMFA format instead of FASTA format.

•

-ignore_user_sites

•

-kappa

•

-prior_mean can be used to specify the prior means respectively for the parameters R/theta, 1/delta, and nu and the phylogenetic branch lengths.

•

-prior_sd

•

-emsim can be used to simulate the uncertainty in the parameter estimates using a bootstrapping approach.

•

-output_filtered true can be used to output a filtered alignment including only nonrecombinant sites.

•

-embranch true

can be used to exclude certain genomic sites from the analysis.

can be used to specify the relative rate of transitions versus transversions.

can be used to specify the prior standard deviation respectively for the same parameters as in the previous option.

can be used to perform a recombination analysis under an alternative model, in which each branch has separate parameters (see Note 7).

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File Outputs

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Running ClonalFrameML produces several output files, each of which starts with the prefix cfmlout specified in the command line. These output files are as follows: •

cfmlout.labelled_tree.newick.

•

cfmlout.em.txt. This file contains the estimated values of the parameters R/theta, nu, and 1/delta and the branch lengths. The file contains one line for each of these parameters and five columns corresponding to the parameter name, parameter posterior mean, parameter posterior variance, and finally the a_post and b_post values for each parameter, which are the shape and rate of the posterior Gamma distribution for each parameter. This is useful to know the full posterior distribution, but for most users the mean and variance given in the second and third columns are all that is needed.

•

cfmlout.importation_status.txt.

•

cfmlout.position_cross_reference.txt.

A vector of comma-separated values indicating for each location in the input sequence file the corresponding unique pattern index, or a zero if the site is not polymorphic.

•

cfmlout.ML_sequence.fasta.

This file contains the ancestral sequences reconstructed by maximum likelihood for all nodes of the phylogeny. It is a FASTA file alignment with one position for each unique pattern as defined in the cfmlout. position_cross_reference.txt file. For the leaves of the phylogeny, the sequence is the same as in the input file, with the exception of the sites where data was missing (represented as N or a dash symbol in the input file) for which the most likely sequence was imputed.

•

This file is generated only if the emsim option is used. It contains the bootstrapped values for the three parameters R/theta, delta, and nu. The file contains as many lines as was specified following the emsim option, and three columns containing the R/theta, delta, and nu parameters, respectively.

•

cfmlout.filtered.fasta.

This Newick file (see Note 5) contains the tree corrected for recombination. All internal nodes are labeled so that they can be referred to in other files.

This file contains the list of inferred recombination events. There is one line for each event, with the first column indicating the branch on which the event was found and the second and third columns indicating the first and last genomic positions affected by the recombination event.

cfmlout.emsim.txt.

This file is generated only if the output_filtered option is used. It contains a FASTA alignment of the sites unaffected by recombination (see Note 8).

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Fig. 2 An example of graphical output generated by ClonalFrameML 3.5 Graphical Representation

The phylogeny corrected for recombination generated by ClonalFrameML is stored as a Newick file which can be opened and displayed by many software. ClonalFrameML also includes a R script called cfml_results.R which can be used to generate a figure of the ClonalFrameML output. Using this script requires a working R environment [48] including the ape package [39]. An example of the graphical output is shown in Fig. 2. It includes the phylogenetic tree on the left-hand side and a matrix on the righthand side with a row for each node of the phylogenetic tree and a column for each genomic position. The horizontal dark blue bars represent recombination events. Sites that are nonpolymorphic for a given branch are shown in light blue. Polymorphisms are shown in a color indicating their level of homoplasy: white means no homoplasy and the range from yellow to red represents increasing degrees of homoplasy. Another method to represent graphically the results of a phylogenetic recombination analysis is implemented in Rcandy [40].

3.6 Downstream Analyses

The methodology described can produce recombination-corrected phylogenies for most bacterial pathogens (see Note 9) in a matter of hours (see Note 10). Several additional analyses may be performed subsequently, depending on the interests of the researchers. As noted above, the ClonalFrame model does not attempt to trace the source of the recombination events. However, this can be performed in a post-processing step, by taking each recombination event sequence and comparing it with a pool of likely donors (which may be the same genomes as the ones used in the phylogenetic analysis, or different ones). This strategy can be used to build a map of recombination flux between different lineages or species, as was previously applied, for example, in Bacillus cereus [49], Salmonella enterica [50], and Campylobacter [51].

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Alternatively, it may be useful to compare the clonal genealogy with phenotypic values for each of the isolates, such as resistance or virulence profiles. An ancestral character estimation (ACE) approach [52] can be used to determine branches of the tree on which the phenotypic values changed, or if the phenotype is only partially associated with the genomes it may be preferable to consider the distribution of phenotypes on clades on the phylogeny [53]. A genome-wide association study (GWAS) may also be performed to try and identify the likely genetic determinants for a phenotype of interest, with the clonal genealogy being useful to control for the confounding effect of population structure [54]. Finally, for many epidemiological applications, it is useful to build a dated phylogeny in which branch lengths are measured, for example, in years rather than numbers of substitutions. The recombination-corrected phylogenies produced by the methodology above represent a good starting point for this and can be converted into dated phylogenies, for example, using the BactDating software [41]. The resulting dated phylogeny can then be used for example to investigate pathogen population size dynamics [42] or networks of transmission links [43, 44].

4

Notes 1. The relative effect of recombination vs mutation, denoted r/m, should not be confused with the relative rates at which recombination and mutation events occur, sometimes denoted rho/theta or R/theta. For instance, it is possible to have recombination occurring less often than mutation, so that R/theta 1. This is because recombination events, unlike mutation events, are likely to substitute more than one site at a time. 2. Here is a toy example of a FASTA file for three genomes of length 12 bp: >1 ACGTACGTACGT >2 ACGTACGTTCGT >3 ACCTACGTACGT

3. Here is a toy example of XMFA file for three genomes and two blocks of length 12 bp and 10 bp, respectively:

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4. Note that since the alignment is made of all sites and not just the SNPs, there is no need for an ascertainment bias correction such as the Lewis correction [55]. The other reason why an alignment of all sites and not just SNPs is required is that the relative position of the SNPs along the genomes is important to determine which recombination events tool place. 5. A Newick file is a commonly used format to store a phylogenetic tree. It contains the topology of the tree as indicated by brackets as well as the length of the branches following colon symbols. Here is a toy example of a Newick file for three genomes labeled A, B, and C: ((A:0.01,B:0.02):0.01,C:0.04)

Many software can be used to open, draw, and manipulate Newick files, such as FigTree which is available from http:// tree.bio.ed.ac.uk/software/figtree/ 6. If dealing with a bacterial pathogen that does not recombine at all, such as Mycobacterium tuberculosis, there is no need to correct the initial phylogeny for the effect of recombination. 7. When the embranch parameter is used, an alternative evolutionary model is used in which each branch has a separate value for the three parameters R/theta, delta, and nu. Under this model, the extent to which different parameter values vary between branches is controlled by the embranch_dispersion parameter. A small dispersion means that branches are expected to have rather similar parameters, as is the case for the default value of 0.01. For most recombination analyses, it is recommended to start running ClonalFrameML with the default model (i.e., not using the embranch option) and using the alternative model with increasing values of the dispersion only if the results are not satisfactory.

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8. The filtered alignment could in principle be used to generate a phylogenetic tree based on only the non-recombination regions. However, this is not usually a good idea in practice, because there may only be few regions that were not affected by recombination on at least one of the branches. Indeed, the filtered alignment file can often be empty, meaning that there was not a single unaffected site. The best phylogenetic tree is therefore the one produced directly by ClonalFrameML rather than one based on the filtered alignment [56]. 9. The only situation where the methodology described here would not be appropriate is if recombination has occurred so often that it becomes impossible to reconstruct the clonal genealogy. For instance, recombination in Helicobacter pylori can replace more than 20% of the genome in just 3 years of within-host evolution [12, 57], so that reconstructing the clonal genealogy of the whole species over thousands of years is clearly impossible. In such cases, a population-based approach is preferable to a phylogenetic approach [58]. 10. The construction of the initial phylogeny usually takes one to a few hours, depending on the number of genomes, their diversity, and the software used (FastTree is usually a bit faster but may also be a bit less accurate than PhyML and RAxML). Correcting the initial phylogeny for the effect of recombination using ClonalFrameML usually takes a few hours to a few days, once again depending on the number and diversity of the genomes. References 1. Didelot X, Bowden R, Wilson DJ et al (2012) Transforming clinical microbiology with bacterial genome sequencing. Nat Rev Genet 13: 601–612 2. Ko¨ser CU, Ellington MJ, Cartwright EJP et al (2012) Routine use of microbial whole genome sequencing in diagnostic and public health microbiology. PLoS Pathog 8: e1002824 3. Loman NJ, Pallen MJ (2015) Twenty years of bacterial genome sequencing. Nat Rev Microbiol 13:787–794 4. Harris SRR, Feil EJ, Holden MT et al (2010) Evolution of MRSA during hospital transmission and intercontinental spread. Science 327: 469–474 5. Croucher NJ, Harris SR, Fraser C et al (2011) Rapid pneumococcal evolution in response to clinical interventions. Science 331:430–434

6. Yang Z, Rannala B (2012) Molecular phylogenetics: principles and practice. Nat Rev Genet 13:303–314 7. Ochman H, Lawrence JG, Groisman EA (2000) Lateral gene transfer and the nature of bacterial innovation. Nature 405:299–304 8. Didelot X, Maiden MCJ (2010) Impact of recombination on bacterial evolution. Trends Microbiol 18:315–322 9. Vos M, Didelot X (2009) A comparison of homologous recombination rates in bacteria and archaea. ISME J 3:199–208 10. Didelot X, Eyre DW, Cule M et al (2012) Microevolutionary analysis of Clostridium difficile genomes to investigate transmission. Genome Biol 13:R118 11. Mostowy RJ, Croucher NJ, De Maio N et al (2017) Pneumococcal capsule synthesis locus cps as evolutionary hotspot with potential to

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generate novel serotypes by recombination. Mol Biol Evol 34:2537–2554 12. Didelot X, Nell S, Yang I et al (2013) Genomic evolution and transmission of helicobacter pylori in two south African families. Proc Natl Acad Sci U S A 110:13880–13885 13. Schierup MH, Hein J (2000) Consequences of recombination on traditional phylogenetic analysis. Genetics 156:879–891 14. Hedge J, Wilson DJ (2014) Bacterial phylogenetic reconstruction from whole genomes is robust to recombination but demographic inference is not. MBio 5:e02158–e02114 15. Griffiths RC, Marjoram P (1997) An ancestral recombination graph. Prog Popul Genet Hum Evol (Minneapolis, MN, 1994) 87:257–270 16. Wiuf C, Hein J (2000) The coalescent with gene conversion. Genetics 155:451–462 17. Didelot X, Lawson DJ, Falush D (2009) SimMLST: simulation of multi-locus sequence typing data under a neutral model. Bioinformatics 25:1442–1444 18. Didelot X, Lawson DJ, Darling AE, Falush D (2010) Inference of homologous recombination in bacteria using whole-genome sequences. Genetics 186:1435–1449 19. Vaughan TG, Welch D, Drummond AJ et al (2017) Inferring ancestral recombination graphs from bacterial genomic data. Genetics 205:857–870 20. Didelot X, Falush D (2007) Inference of bacterial microevolution using multilocus sequence data. Genetics 175:1251–1266 21. Croucher NJ, Page AJ, Connor TR et al (2015) Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Res 43:e15 22. Zhou Z, McCann A, Weill F-X et al (2014) Transient Darwinian selection in Salmonella enterica serovar Paratyphi A during 450 years of global spread of enteric fever. Proc Natl Acad Sci 111:12199–12204 23. Didelot X, Wilson DJ (2015) ClonalFrameML: efficient inference of recombination in whole bacterial genomes. PLoS Comput Biol 11: e1004041 24. Dingle KE, Didelot X, Quan TP et al (2017) Effects of control interventions on Clostridium difficile infection in England: an observational study. Lancet Infect Dis 17:411–421 25. Ledda A, Price JR, Cole K et al (2017) Re-emergence of methicillin susceptibility in a resistant lineage of Staphylococcus aureus. J Antimicrob Chemother 72:1285–1288

26. Me´ric G, Mageiros L, Pensar J et al (2018) Disease-associated genotypes of the commensal skin bacterium Staphylococcus epidermidis. Nat Commun 9:5034 27. van Dorp L, Wang Q, Shaw LP et al (2019) Rapid phenotypic evolution in multidrugresistant Klebsiella pneumoniae hospital outbreak strains. Microb Genomics 5:e000263 28. Ozer EA, Nnah E, DIdelot X et al (2019) The population structure of Pseudomonas aeruginosa is characterized by genetic isolation of exoU+ and exoS+ lineages. Genome Biol, Evol 11:1780-1796 29. Benson DA, Cavanaugh M, Clark K et al (2017) GenBank. Nucleic Acids Res 45:D37– D42 30. Zhou Z, Alikhan NF, Mohamed K et al (2020) The EnteroBase user’s guide, with case studies on Salmonella transmissions, Yersinia pestis phylogeny, and Escherichia core genomic diversity. Genome Res 30:138–152 31. Jolley KAA, Maiden MCJ (2010) BIGSdb: scalable analysis of bacterial genome variation at the population level. BMC Bioinformatics 11:595 32. Argimo´n S, Yeats CA, Goater RJ et al (2021) A global resource for genomic predictions of antimicrobial resistance and surveillance of Salmonella Typhi at pathogenwatch. Nat, Commun, p 12 33. Darling AE, Mau B, Perna NT (2010) progressiveMauve: multiple genome alignment with gene gain, loss and rearrangement. PLoS One 5:e11147 34. Kurtz S, Phillippy A, Delcher AL et al (2004) Versatile and open software for comparing large genomes. Genome Biol 5:R12 35. Stamatakis A (2014) RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30:1312– 1313 36. Price MN, Dehal PS, Arkin AP (2010) FastTree 2--approximately maximum-likelihood trees for large alignments. PLoS One 5:e9490 37. Guindon S, Dufayard J-F, Lefort V et al (2010) New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol 59: 307–321 38. Rice P (2000) The European molecular biology open software suite EMBOSS : the European molecular biology open software suite. Trends Genet 16:276–277 39. Paradis E, Schliep K (2019) Ape 5.0: an environment for modern phylogenetics and

Phylogenetic Analysis of Bacteria evolutionary analyses in R. Bioinformatics 35: 526–528 40. Chaguza C, Tonkin-Hill G, Lo SW et al (2022) RCandy: an R package for visualizing homologous recombinations in bacterial genomes. Bioinformatics 38:1450–1451 41. Didelot X, Croucher NJ, Bentley SD et al (2018) Bayesian inference of ancestral dates on bacterial phylogenetic trees. Nucleic Acids Res 46:e134–e134 42. Volz EM, Didelot X (2018) Modeling the growth and decline of pathogen effective population size provides insight into epidemic dynamics and drivers of antimicrobial resistance. Syst Biol 67:719–728 43. Didelot X, Fraser C, Gardy J, Colijn C (2017) Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks. Mol Biol Evol 34:997–1007 44. Didelot X, Kendall M, Xu Y et al (2021) Genomic epidemiology analysis of infectious disease outbreaks using TransPhylo. Curr Protoc 1:1– 23 45. Nagarajan N, Pop M (2013) Sequence assembly demystified. Nat Rev Genet 14:157–167 46. Bankevich A, Nurk S, Antipov D et al (2012) SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19:455–477 47. Maiden MCJ, Jansen van Rensburg MJ, Bray JE et al (2013) MLST revisited: the gene-bygene approach to bacterial genomics. Nat Rev Microbiol 11:728–736 48. R Core Team R: A Language and Environment for Statistical Computing (2019) 49. Didelot X, Barker M, Falush D, Priest FG (2009) Evolution of pathogenicity in the

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Bacillus cereus group. Syst Appl Microbiol 32: 81–90 50. Didelot X, Bowden R, Street T et al (2011) Recombination and population structure in salmonella enterica. PLoS Genet 7:e1002191 51. Sheppard SK, Didelot X, Jolley KA et al (2013) Progressive genome-wide introgression in agricultural campylobacter coli. Mol Ecol 22: 1051–1064 52. Joy JB, Liang RH, Mccloskey RM et al (2016) Ancestral reconstruction. PLoS Comput Biol 12:e1004763 53. Ansari MA, Didelot X (2016) Bayesian inference of the evolution of a phenotype distribution on a phylogenetic tree. Genetics 204:89– 98 54. Collins C, Didelot X (2018) A phylogenetic method to perform genome-wide association studies in microbes that accounts for population structure and recombination. PLoS Comput Biol 14:e1005958 55. Lewis PO (2001) A likelihood approach to estimating phylogeny from discrete morphological character data. Syst Biol 50:913–925 56. Didelot X, Parkhill J (2022) A scalable analytical approach from bacterial genomes to epidemiology. Phil Trans Roy Soc B 377:20210246 57. Kennemann L, Didelot X, Aebischer T et al (2011) Helicobacter pylori genome evolution during human infection. Proc Natl Acad Sci U S A 108:5033–5038 58. Yahara K, Furuta Y, Oshima K et al (2013) Chromosome painting in Silico in a bacterial species reveals fine population structure. Mol Biol Evol 30:1454–1464

Chapter 7 Determination of Growth Rate and Virulence Plasmid Copy Number During Yersinia pseudotuberculosis Infection Using Droplet Digital PCR Tifaine Hechard and Helen Wang Abstract Pathogenic bacteria have evolved the ability to evade their host defenses and cause diseases. Virulence factors encompass a wide range of adaptations that allow pathogens to survive and proliferate in the hostile host environment during successful infection. In human pathogenic Yersinia species, the potent type III secretion system (T3SS) and other essential virulence factors are encoded on a virulence plasmid. Here, we investigated the bacterial growth rate and plasmid copy number following a Yersinia infection using droplet digital PCR (ddPCR). ddPCR is an exceptionally sensitive, highly precise, and cost-efficient method. It enables precise quantification even from very small amounts of target DNA. This method also enables analysis of complex samples with large amounts of interfering DNA, such as infected tissues or microbiome studies. Key words Yersinia, Growth rate, Replication, Plasmid copy number, Droplet digital PCR, Infection

1

Introduction Following bacterial growth rate in an in vivo model is essential to understand infection mechanisms [1–3]. Bacterial growth rates during infection depend not only on nutrient availability but also on the host immune response [4]. Growth rate in vivo determined using viable bacterial count (CFU/mL) plotted as a function of time has been regarded as the standard method so far. This traditional CFU counting method gives only an estimation of population size increase during a given time period; however, it cannot determine the growth rate per se [5]. Another method was developed based on the replication status using whole genome sequencing (WGS) approach [2, 6–9]. Yersinia, like most bacteria, has a circular chromosome, replicating bidirectionally from an origin of replication (oriC) to the termination site (terC), forming a replication “fork” (Fig. 1a) [10, 11]. The speed of replication correlates

Pontus Nordenfelt and Mattias Collin (eds.), Bacterial Pathogenesis: Methods and Protocols, Methods in Molecular Biology, vol. 2674, https://doi.org/10.1007/978-1-0716-3243-7_7, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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Fig. 1 Principle of growth rate determination in bacterial populations using ddPCR. (a) Bidirectional replication of circular chromosome results in higher DNA dosage at the region close to the origin of replication (oriC) in bacterial population with logarithmic growth. Orange lines indicate the position of oriC, black lines indicate the position of the replication terminus (ter), and blue lines are the middle (mid) between oriC and ter. (b) Relative target DNA copies/μL is calculated as the ratio of copies/μL of a primer pairs at a given position on the chromosome relative to the copies/μL for the primers at the ter position

with the speed of cell division to ensure that each daughter cell will possess one fully replicated chromosome [12–15]. If the conditions are very advantageous for bacterial growth, one chromosome can be replicated several times simultaneously. This means that during fast logarithmic growth, there will be more than one origin of replication per cell [16]. When analyzing the WGS read coverage in fast-growing bacterial populations, the region around the oriC has more coverage depth than the region around terC. When plotting the raw reads against a linear chromosome sequence, a “V-shaped” profile is observed for logarithmically growing bacteria, while a nongrowing population will have a flat profile [2, 6, 17] (Fig. 1b). The growth rate of the population can therefore be determined by the ratio between the peak and the trough of the V-shape. A peak-to-trough ratio (PTR) greater than 1 will indicate a growing bacterial population (Fig. 2a). Plasmids often encode important functions including bacterial virulence and antibiotic resistance. In Yersinia, increased gene dosage of plasmid-encoded genes is essential for virulence [18]. Plasmid copy number (PCN) can be estimated by indirect methods

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Fig. 2 ddPCR enables precise determination of peak-to-trough (PTR) and plasmid copy number (PCN). (a and b) Y. pseudotuberculosis cultures were grown at 37
C +Ca2+ (fast growth) or at 37
C Ca2+ (slow growth). In absence of calcium, the bacteria are growing poorly due to the metabolic burden from the expression of plasmid-encoded genes. (a) PTRs were calculated as the concentration of target DNA copies/μL for the primers close to oriC divided by the target DNA copies/μL for primers close to ter. PTRs calculated from ddPCR data correlate with the growth rate measured by OD600 (b) PCN changes determined by ddPCR from the same cultures than for PTR

such as measuring the activity of enzyme/proteins encoded on the plasmid (luciferase, fluorescent proteins, etc.). It can also be measured by direct quantification, for example, quantitative PCR (qPCR) or WGS [19]. The PCN is calculated as the ratio of the signal from the plasmid by the signal from the chromosome (Fig. 2b). The use of WGS for determination of growth rate and PCN is a robust method, able to determine the bacterial growth in environmental or clinical samples [6, 7]. However, genomic sequencing remains a relatively expensive and time-consuming method, poorly adapted to large-scale screening. qPCR can also be used in these settings, and it provides an inexpensive and widely available alternative [8]. However, the sensitivity and dynamic range of qPCR is relatively limited, especially when the target bacterial DNA is present in low concentrations and when large amounts of untargeted DNA are interfering the reaction. These limited conditions are often present in infection models; therefore, there is a need for more sensitive and precise methods for quantification of target DNA [2, 20]. Here, we apply an exceptionally sensitive, highly precise, and cost-efficient method to determine growth rate and PCN during Yersinia infection based on ddPCR. ddPCR is a method that allows the absolute quantification of a target DNA [21]. The method is based on the division of the PCR reaction into 20,000 droplets resulting in random distributions of sample DNA with some

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Fig. 3 Workflow of experimental steps for ddPCR. Each sample is partitioned into 20,000 droplets. Droplets are uniform in size allowing precise target quantification. Target DNA is amplified by PCR. The droplets containing at least one copy of target DNA will emit increased fluorescence signal. Droplets are read individually and can be separated into negative and positive droplets depending on signal amplitude. The QuantaSoft software will use Poisson statistics to calculate target DNA copies per μL

droplets containing target DNA. After the PCR reaction, the droplets are then qualitatively scored as “positive” (containing the target DNA) or “negative” (without target DNA) based on the fluorescent signals (Figs. 3 and 4). This method allows accurate detection of less abundant target DNA even in the presence of high concentration of background DNA (Fig. 4). Moreover, in contrast to traditional qPCR, ddPCR does not require the use of a standard curve and allows for precise determination of the absolute number of target DNA molecules in a sample based on the Poisson distribution [2, 20, 21]. ddPCR-based methods have been extensively used to detect eucaryotic DNA in cancer diagnosis and occasionally in clinical microbiology settings [22, 23]. Precise quantification of bacterial DNA target and determination of growth rate using ddPCR have been applied in a Yersinia infection model as well as in vitro-grown E. coli [2]. The method presented here can be adapted to any other bacteria with circular DNA without much modifications and applied to in vitro, in vivo, and environmental samples. This opens up possibilities to study bacteria reliably and accurately in many different environments where target DNA is less abundant and with large amounts of interfering DNA.

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Fig. 4 Signal detection limits for a dynamic range of target and background DNA using ddPCR. (a–b) Y. pseudotuberculosis DNA was isolated from a culture grown to stationary phase at 26
C. Fluorescence amplitude is measured for positive (blue) and negative (gray) droplets. (a) Fluorescence amplitude for increasing concentrations of Y. pseudotuberculosis DNA and 20 ng/mL mouse DNA as background. (b) Fluorescence amplitude for 0.5 pg/mL Y. pseudotuberculosis DNA, with increasing concentrations of mouse DNA as background

2

Materials

2.1 DNA Extraction and Quantification

1. GeneJET Genomic DNA purification kit (Thermo Fisher Scientific). 2. Qubit 3.0 fluorometer (Thermo Fisher Scientific).

2.2 ddPCR Supermix Components

1. QX200 ddPCR EvaGreen Supermix (Bio-Rad, 186–4034). 2. Primers. 3. Nuclease-free PCR grade water (Sigma). 4. DNA restriction enzyme compatible with assay (e.g., New England Biolabs). 5. Low binding 1.7 mL microcentrifuge tubes. 6. Microseal “B” Plate Sealing Film, adhesive (Bio-Rad, MSB1001) or equivalent. 7. ddPCR plates, 96 well, semi-skirted (Bio-Rad, 120–01925). 8. Centrifuge.

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2.3 Droplet Generation and Droplet Reader Components

1. QX200 AutoDG Droplet Digital PCR (ddPCR) System (Bio-Rad). (a) Automated Droplet Generator (186–41,101). (b) QX200 Droplet Reader (186–4003). 2. AutoDG Oil for EvaGreen (Bio-Rad, 186–4112). 3. DG32 AutoDG Cartridges (Bio-Rad, 186–4109). 4. Pipet Tips for AutoDG (Bio-Rad, 186–4120). 5. ddPCR 96-well plates, semi-skirted (Bio-Rad, 120–01925). 6. Pipet tip waste bins for the AutoDG System (Bio-Rad, 186–4125). 7. Cooling block (Bio-Rad, 120–02819). 8. Pierceable foil heat seal (Bio-Rad, 181–4040) or equivalent. 9. PX1 PCR Plate Sealer (Bio-Rad, 181–4000) or equivalent. 10. C1000 Touch™ Thermal Cycler (Bio-Rad, 185–1197) or equivalent. 11. Droplet Reader Oil (Bio-Rad, 186–3004). 12. QuantaSoft™ Software (Bio-Rad, 186–4011). 13. Microsoft Excel (Microsoft Office) or equivalent.

3

Methods All procedures are to be conducted at room temperature and reagents should thaw on ice and be vortexed to eliminate concentration gradients formed during storage. Restriction enzyme should be removed from 20
C immediately prior to use and returned to 20
C immediately after use. We recommend running samples in biological triplicates. Technical duplicates are not essential in well-optimized assays due to the nature of the method, but they can be included during method establishment (see Subheading 3.9).

3.1 Sample Acquisition

3.2

DNA Extraction

Sampling methodology is not described in this protocol as it will vary depending on the sample to be analyzed and is not the object of this chapter. Here, we used Y. pseudotuberculosis samples from murine tissues and in vitro cultures. In theory, this method could be easily adapted to samples from various origins. 1. Extract DNA from the samples using GeneJET Genomic DNA purification kit (Thermo Fisher Scientific), according to manufacturer’s instructions (see Note 1). 2. Measure DNA concentration Qubit 3.0 fluorometer (Thermo Fisher Scientific) according to manufacturer’s instruction.

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Primer Design

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The design of the ddPCR primers is similar to design of primers for qPCR. It should follow these general guidelines: 1. Primer’s melting temperature (Tm) should be 50–65
C with 5
C difference between both primers. 2. The primer length should be 18–24 bp. 3. The primer GC percentage should be between 50% and 60%. 4. The amplicon size should be 60–200 bp. 5. The amplicon should be in a non-amplified region. 6. These features should be avoided in primer design: (a) Stretches of 4 or more C or G. (b) G or C on 30 end of the primer. (c) Hairpin structures. (d) Primer dimers. (e) Nonspecific priming (BLAST the primers against all DNA types present in the sample). 7. For normal chromosome detection and PCN determination, primer-binding sites are chosen close to the terminus of replication to minimize variation in chromosome copy number due to different replication rates. 8. For PCN determination, the primer-binding sites should be located away from any resistance cassettes so that possible spontaneous amplification of the cassette is less likely to affect the results. 9. The primers/amplicons will have to be tested on increasing amounts of template to ensure correct detection efficiency. 10. Annealing temperature can be determined by testing a range of temperatures around the calculated Tm of the primers. The temperature allowing the largest fluorescence amplitude between the positive and the negative droplets will be the preferred annealing temperature. More details about each mentioned primer design requirement are available in the “Droplet Digital™ PCR Applications Guide” (see Note 2).

3.4 Preparation of Reaction Mixtures

ddPCR reactions must be set to a minimum final volume of 22 μL to avoid mechanical errors during automated droplet generation (see Note 3). To avoid DNA concentration discrepancies between reactions, it is preferable to prepare enough Supermix containing the target DNA. The mixture can be aliquoted and primer pairs to be tested added last (see Note 4).

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1. Start by calculating how much to dilute the DNA to get 1 pg/ mL DNA in reaction mixture (see Note 5). 2. Dilute DNA samples as needed in PCR-grade water. 3. Here, we prepared reaction mixture for a final volume of 25 μL per reaction: (a) 12.5 μL EvaGreen. (b) 2 units restriction enzyme (see Note 6). (c) 1 pg of template DNA (see Note 5). (d) Nuclease-free water to make total volume 24 μL (1 μL primers is added after aliquoting). 4. Vortex gently to mix, avoid creating bubbles. Spin down. 5. Aliquot 24 μL of reaction mixture into the wells of a ddPCR 96-well plate; avoid creating bubbles. A minimum of eight samples can be loaded in a column, do not leave empty well. Include a no template control (NTC) and positive and negative controls (see Note 7). 6. Add 0.5 μL of forward primer (5 μM) and 0.5 μL of reverse primer (5 μM) per well of the ddPCR 96-well plates. 7. Seal the plate with a plate sealing film, vortex, and then spin down in a plate centrifuge to get rid of any bubbles. Remove the seal before to place the plate in the droplet generator. 3.5 Droplet Generation

The following instructions are designed for using an Automated Droplet Generator; for manual droplet generation, refer to the Instruction Manual of the QX100 or QX200 Droplet Generator (see Note 2). 1. A minimum of eight samples dispensed in one single column is required for automated droplet generation (see Note 7). Place your sample plate in the front-left plate compartment of the Automated Droplet Generator; make sure there are no bubbles in the samples and no seal on the plate (see Note 8). 2. Place a ddPCR 96-well plate onto a cooling block in the frontright plate compartment (see Note 9). 3. Place the AutoDG Cartridges in the back of the machine; one set of cartridges can accommodate for 4  8 samples (see Note 7). A maximum of three sets of cartridges can be placed for a full 96-well assay. 4. Place the AutoDG Oil for EvaGreen in its place (front left); make sure the level of oil left is sufficient by checking it on the machine screen. 5. Place tip boxes in the middle compartments (see Note 10). 6. Make sure the bin is empty.

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7. Click on “Configure Sample Plate” button and select the columns of the plate containing samples. Confirm and initiate automated droplet generation (Thresh); %count all cells that are above threshold for green fluorescence intensity --> dead cells 77. new_row = {key, celli, Nb_cells_above_thresh}; %create a new row for table 78. donor3 = [donor3;new_row]; %add row to table 79. end 80. End 81. 82. % C/export data 83. writetable(donor3, 'donor3.csv', 'Delimiter',','); %save the table as csv

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Acknowledgments We thank Dr. Alejandro Go´mez-Mejia for his assistance and technical help with figure arrangements. This work was supported and funded by the Swiss National Science Foundation project grant 31003A_176252 (to A.S.Z.), the Uniscientia Foundation Grant (to A.S.Z. and S.M.S.), and the Swedish Society for Medical Research (SSMF) foundation grant P17-0179 (to S.M.S.). References 1. Kobayashi SD, Malachowa N, DeLeo FR (2018) Neutrophils and bacterial immune evasion. J Innate Immun 10:432–441. https:// doi.org/10.1159/000487756 2. Nasser A, Moradi M, Jazireian P et al (2019) Staphylococcus aureus versus neutrophil: scrutiny of ancient combat. Microb Pathog 131: 259–269. https://doi.org/10.1016/j. micpath.2019.04.026 3. von Ko¨ckritz-Blickwede M, Winstel V (2022) Molecular prerequisites for neutrophil extracellular trap formation and evasion mechanisms of Staphylococcus aureus. Front Immunol 13. https://doi.org/10.3389/fimmu.2022. 836278 4. Brinkmann V, Reichard U, Goosmann C et al (1979) (2004) neutrophil extracellular traps kill bacteria. Science 303:1532–1535. https://doi.org/10.1126/science.1092385 5. Missiakas D, Winstel V (2021) Selective host cell death by Staphylococcus aureus: a strategy for bacterial persistence. Front Immunol 11. https://doi.org/10.3389/fimmu.2020. 621733 6. Franc¸ois M, le Cabec V, Dupont M-A et al (2000) Induction of necrosis in human neutrophils by Shigella flexneri requires type III secretion, IpaB and IpaC invasins, and actin polymerization. Infect Immun 68:1289– 1296. https://doi.org/10.1128/IAI.68.3. 1289-1296.2000 7. Mori Y, Yamaguchi M, Terao Y et al (2012) α-Enolase of Streptococcus pneumoniae induces formation of neutrophil extracellular traps. J Biol Chem 287:10472–10481. https://doi.org/10.1074/jbc.M111.280321 8. Arai Y, Yamashita K, Mizugishi K et al (2013) Serum neutrophil extracellular trap levels

predict thrombotic microangiopathy after allogeneic stem cell transplantation. Biol Blood Marrow Transplant 19:1683–1689. https:// doi.org/10.1016/j.bbmt.2013.09.005 9. von Ko¨ckritz-Blickwede M, Chow O, Ghochani M, Nizet V (2010) Visualization and functional evaluation of phagocyte extracellular traps. Methods Microbiol 37:139–160 10. Fuchs TA, Abed U, Goosmann C et al (2007) Novel cell death program leads to neutrophil extracellular traps. J Cell Biol 176:231–241. https://doi.org/10.1083/jcb.200606027 11. Zhao W, Fogg DK, Kaplan MJ (2015) A novel image-based quantitative method for the characterization of NETosis. J Immunol Methods 423:104–110. https://doi.org/10.1016/j. jim.2015.04.027 12. Schweizer TA, Mairpady Shambat S, Vulin C et al (2021) Blunted sFasL signalling exacerbates TNF-driven neutrophil necroptosis in critically ill COVID-19 patients. Clin & Trans Immunol 10:e1357. https://doi.org/10. 1002/cti2.1357 13. Mairpady Shambat S, Go´mez-Mejia A, Schweizer TA et al (2022) Hyperinflammatory environment drives dysfunctional myeloid cell effector response to bacterial challenge in COVID-19. PLoS Pathog 18:e1010176. https://doi.org/10.1371/journal.ppat. 1010176 14. Desai J, Mulay SR, Nakazawa D, Anders H-J (2016) Matters of life and death. How neutrophils die or survive along NET release and is “NETosis” = necroptosis? Cell Mol Life Sci 73:2211–2219. https://doi.org/10.1007/ s00018-016-2195-0

Chapter 17 Measurement of Antibody Binding Affinity on Bacterial Surfaces Using Flow Cytometry Vibha Kumra Ahnlide and Pontus Nordenfelt Abstract Antibody binding to bacterial surfaces plays a crucial role in immunity, and a key characteristic of this protein–protein interaction is the binding affinity. Determining the affinity of an antibody binding to its antigen is the first step in predicting the function in a physiological environment where other competing protein interactions may be present. Antibody–antigen affinity is often evaluated with isolated proteins. It is informative to also be able to assess antibody binding to a bacterial surface where many antigens might be present, including multiple copies of the specific antigen the antibody recognizes, and in a context where the antigen might be in a more natural conformation. In this chapter, we present a flow cytometry-based assay to measure and calculate the cell surface binding affinity or avidity of any mono- or polyclonal antibody solution. Key words Antibodies, Affinity, Avidity, Bacteria, Flow cytometry

1

Introduction Protein–protein binding is, in essence, an interaction resulting in a stable complex with lower free energy than when the proteins are unbound. The binding is formed through cumulative attractive forces between the atoms in the proteins, and the affinity is a measure of the strength of the binding interaction. For antibody– antigen binding, the affinity is the cumulative strength of the interaction between the epitope and paratope at a single site [1]. Avidity is the measure of the overall strength of the binding at multiple different sites and can thus describe the added binding strength of a multivalent or polyclonal antibody solution. It can be difficult to know whether an antibody is engaged via one (i.e. affinity) or two Fab sites (i.e. avidity), and thus for simplicity, we use the term affinity throughout this chapter when we refer to binding strength between antibodies and bacteria unless otherwise specified. The dissociation constant KD is a commonly used

Pontus Nordenfelt and Mattias Collin (eds.), Bacterial Pathogenesis: Methods and Protocols, Methods in Molecular Biology, vol. 2674, https://doi.org/10.1007/978-1-0716-3243-7_17, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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measure of affinity [2] and can be expressed as the ratio between the concentration of bound complexes [EA] and the free concentration of the antibody [A] and epitope [E] as follows: KD =

½A ½E  ½EA

ð1Þ

Additional aspects that affect the total binding are the steric properties of the molecules and the affinity and concentration of other competing ligands and receptors [3]. In the case of antibody binding to bacteria, several bacteria express surface proteins that interfere with antibody binding through Fc-binding sites and steric blocking of functional epitopes [3–7]. From Eq. (1), it is clear that an assay to measure affinity would require quantitative consideration of the dependencies involved. Common approaches for affinity measurement include surface plasmon resonance [8, 9] and isothermal titration calorimetry [10, 11]. However, they require purified proteins and cannot measure affinity in complex scenarios such as on a bacterial surface. Many surface proteins are also difficult to maintain in a physiological conformation when not in their natural surroundings. Historically, radioligand assays have been used to assess the affinity of protein interactions with intact bacteria, using Scatchard plots for the estimates [12]. Today, with the high measurement precision attained using modern flow cytometers, a direct computational fitting to binding data yields more accurate results. There are important experimental factors to consider when doing affinity measurements [13]. There must be enough time for the binding to reach a state of equilibrium and this is controlled for by doing incubation time controls and seeing how long time is required for the binding to be constant. Another important factor to ensure correct affinity estimation is to make sure that the antigen epitopes are present at a concentration that is much lower than the KD. This concentration can be difficult to know beforehand. It can also be difficult to assess the number of antigens available on a bacterial surface. Moreover, as the KD is dependent on the free concentrations of both the antibody and epitope, it is important to keep these values stable for each measurement. This is especially important for lower affinities, where there is otherwise a risk of underestimating the affinity. For this reason, when measuring low-affinity (μM range) interactions, our assay involves no washing of primary antibodies to keep the total amount of free antibodies constant. The following protocol describes antibody binding to bacterial surface protein M1 on Streptococcus pyogenes. Our assay is based on binding measurements of a concentration titration and analysis of the resulting binding curve.

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Material In this chapter, we are describing the specific protocol for antibody binding to S. pyogenes strain SF370, so media and bacteria-specific reagents may need to be exchanged to match the bacteria being studied. To begin with, consider what you know about the binding you wish to study. Is the antibody binding specific or non-specific, monoclonal or polyclonal? This determines certain aspects of the experimental setup and what analysis method should be used for affinity estimation.Your maximum concentration should preferably be close to the saturation level for the binding curve. Try to estimate this by considering at what concentrations you have been able to detect binding through other assays. As the affinity is a geometrical value, the maximum concentration should, if possible, be at least twice the concentration of a rough EC50 estimate. Are there any IgG-binding proteins present on your bacteria? In the case of Fc-interacting surface proteins, it may be useful to perform enzymatic cleavage of the antibodies at the hinge region using IdeS.

2.1 Bacteria and Antibody Reagents

1. Bacteria, heat-killed or live, S. pyogenes strain SF370 and mutant SF370ΔM (see Notes 1 and 2). 2. IgG solution, monoclonal or polyclonal. 3. IdeS (Hansa Biopharma or Genovis) (see Note 3). 4. PBS solution. 5. Tube centrifuge with a swing-out rotor. 6. Microcentrifuge sonicator (VialTweeter, Hielscher). 7. Low-binding 96-well plates (see Note 4). 8. Flow cytometer CytoFLEX (Beckman-Coulter).

2.2 Fluorescent Reagents

3

1. Fluorescently conjugated IgG or secondary antibody. As secondary antibodies we use AlexaFluor 647 (Fab)2 fragments goat anti-human IgGFab (see Note 5).

Method The experimental assay can be divided into the following steps (see Fig. 1 for an overview): preparation of bacteria and antibodies, opsonization, and preparing the dilution series on the 96-well plate. The plate is then run in a flow cytometer. Thereafter the measurements are used to calculate an affinity estimate.

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Fig. 1 Descriptive schematic of the experimental method 3.1

Prepare Bacteria

1. Prepare an overnight culture: Bacteria are inoculated in 10 mL Todd-Hewitt supplemented with 0.2% yeast extract (THY) and incubated at 37 °C overnight at 5% CO2. 2. Let bacteria grow to the early exponential phase (about 3–4 h) by adding 400 μL of overnight culture in a new tube with 10 mL THY (see Note 6). 3. Centrifuge and wash with PBS 2 times and resuspend in 1 mL PBS (see Note 7). 4. Sonicate the tube of bacteria using 100% amplitude and 0.5 cycle setting for 2 min (see Note 8).

3.2 Prepare Antibodies

1. IgG samples were incubated overnight with 1 μg/mL of IdeS at 37 °C.

3.3

1. Add the maximum IgG concentration of each dilution series to one low-binding Eppendorf tube to a total volume of 100 μL of PBS.

Prepare Samples

2. Add 10 μL of bacteria to each tube.

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3. Incubate the tubes on shake at 400 rpm for 30 min (see Note 9). If you are using fluorescently conjugated primary antibodies, skip to step 6. 4. Label the bacteria by adding the fluorescent secondary antibody. 5. Incubate on shake at 400 rpm for 30 min. 6. Prepare a 96-well plate by adding a constant concentration of bacteria to each well, apart from the first one for the highest concentration, and keep it on ice. 5 μL of bacterial solution in 45 μL PBS (see Note 10). 7. Prepare a control well for each bacterial colony with 5 μL of bacteria in 45 μL PBS. 8. Sonicate tube with opsonized bacteria for 0.5 min using 100% amplitude and 0.5 cycle setting (see Note 11). 9. Make a serial dilution on the well plates using 50 μL of the tube volume as the first well. Take the remaining 50 μL in the tube, and add to the second well that has been prepared with bacteria and mix well by pipetting up and down a few times. Thereafter, add half the volume from the second well to the third well, and mix by pipetting up and down. This procedure is repeated for all the prepared wells. The excess 50 μL in the final well is discarded. 10. Incubate the well plate on shake at 400 rpm for 30 min before running the samples in a flow cytometer. 3.4

Flow Cytometry

1. Startup your flow cytometer as instructed (see Notes 12 and 13). 2. Calibrate the flow cytometer using standard beads. 3. Run the bacteria control well. 4. Create a gate for the bacteria using the forward and side scatter data. Create a gate for the background fluorescent signal using the control well data (see Notes 14, 15, and 16). 5. Run the rest of the wells on the plates with set stopping rules for your bacterial gate (see Notes 17, 18, and 19).

3.5 Affinity Calculation

Affinity is estimated by performing a theoretical fit to the measured binding curve using the dissociation constant as an unknown variable. There are several ways to do this, depending on your antibody samples (see Note 20). For a one-site one ligand binding, an ideal binding curve equation can be used. This can be implemented on your own using any programming language with a non-linear fitting function. We recommend that you perform the fitting with the maximum concentration as an additional unknown variable. This is especially

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helpful when you can see that you have not managed to reach saturation of your binding on your measured data. The following piece of code is an implementation in MATLAB (see Note 21).

%%Ideal binding curve function %A0=total antibody concentration, E0= total epitope concentration %interpolated value is normalised to 1, Kd= Dissociation constant. function EA = Idealfunc(A0,E0,Kd) EA = (A0+E0+Kd)./2 - (((A0+E0+Kd)./2).^2 -E0.*A0).^(1/2); end %%Find best fit using fminsearch %conc = total antibody concentration %meanexpbinding = mean binding value of replicates %stdexpbinding = standard deviation of replicates errornorm = @(x) sum(1./stdexpbinding .*(meanexpbinding Idealfunc(conc, x(2), x(1))).^2) fitvalues = fminsearch(errornorm,[0.1, 100]) %initial guesses for Kd and maximum binding level Kd = fitvalues(1) maxbind = fitvalues(2) %%Scaling of experimental data meanexpbinding = meanexpbinding./maxbind stdexpbinding = stdexpbinding./maxbind %%Plot data and fit hold on %C = logspace concentration arrray fit = Idealfunc(C, 1, Kd); plot(C,fit, 'Linewidth', 1) ax = gca; set(ax,'xscale','log') errorbar(conc,meanexpbinding,stdexpbinding, 'x', 'Linewidth', 1) title('Binding curve') xlabel('Total antibody concentration') ylabel('Bound antibody') It is also possible to use readily available software such as GraphPad to perform ideal binding curve fitting. GraphPad offers other common models, such as a one-ligand two-site model, as well as models that can handle unspecific binding. For calculating the avidity of a polyclonal IgG solution with Fc binding present, we have developed a competitive binding model that is readily available as a MATLAB script on GitHub [7]. Figure 2 shows binding of IVIG to protein M1 with competitive binding model fitting [7]. Assuming a broad distribution of affinities present in IVIG, we calculate the mean and range of this affinity distribution for the experimentally measured binding.

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Fig. 2 The plot shows example data of normalized binding as a function of total IgG concentration for measured binding of IVIG to M protein together with the calculated best fit to the model output. The mean and range of the affinity distribution for the measured IVIG binding is given in the plot

4

Notes 1. The advantage of working with live bacteria is that the binding affinity may be affected by molecular changes in the epitope caused by heat killing or fixation. However, working with heatkilled bacteria ensures that there is no cellular activity that may affect the studied binding, such as a change in epitope expression or antibody interaction through bacterial proteins. We do not recommend working with fixed bacteria for quantitative assays as the fixation process may lead to excess clump formation. 2. If your antibody solution is polyclonal with nonspecific antibodies, consider using a surface protein mutant as a control. In the above-described experimental setup, the binding of an unspecific antibody solution, such as IVIG or donor serum, to protein M1 on SF370 is calculated by subtracting the measured binding on an M1-mutant SF370.

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3. Consider using IdeS-cleaved antibodies if you have Fc binding sites on the bacterial surface proteins, such as protein M1. 4. Low-binding plates are necessary to use due to the sticky nature of S. pyogenes. 5. Use (Fab)2-specific secondaries if you are working with IdeScleaved antibodies to ensure that the data you are collecting is solely Fab-binding. 6. The expression of and cell wall localization of M protein is dependent on the growth phase, and the amount of cell wallanchored M protein is high in the early log phase. 7. For live bacteria, we use PBS with 0.2% glucose. 8. S. pyogenes strain SF370 aggregates strongly. Sonication is a necessary step for a more homogeneous distribution and even division into samples. 9. It can be useful to do a time-to-equilibrium analysis [13]. Similar binding affinities can have a vastly different time to reach a state of equilibrium. 30 min has been shown to be sufficient for most IgG solutions for protein M1 binding. 10. If you have binding-enhancing molecules that you wish to investigate, these can be added in this step. 11. Different IgG solutions can give different amounts of clump formation and chain elongation. 12. Live bacteria are kept on a cooling block. 13. We performed flow cytometry using a CytoFLEX (BeckmanCoulter) with a 638 nm laser and a 660/10 APC filter. 14. It is possible to use fluorescently labeled bacteria, but we find it is just as well to use a side and forward scatter gating to identify the bacteria. 15. APC-A gating was done using bacteria control. If using a secondary fluorescent antibody, perform APC-A gating on a control well with only bacteria and a secondary antibody. 16. The stopping rules used are as follows: 90 s, 200 μL, or 20,000 events in gating for bacteria. 17. The reading direction of the plate should preferably be replicate-wise. 18. Acquisition speed can be altered depending on the number of counts/second. Approximately 1000 counts/second is a reasonable speed. 19. Note the counts/μL in your bacterial gate to ensure you have more or less the same concentration of bacteria across the replicates and serial dilutions. There may be large variations in the bacterial concentrations due to bacterial aggregation and insufficient sonication.

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20. We typically collect three biological replicates for each data point, i.e. repeat the experiment on three different colonies. 21. The goodness of fit for a binding curve can, for instance, be calculated using the bootstrap method. References 1. Karush F (1978) The affinity of antibody: range, variability, and the role of multivalence. In: Litman GW, Good RA (eds) Immunoglobulins. Comprehensive immunology, vol 5. Springer, Boston, MA. https://doi. org/10.1007/978-1-4684-0805-8_3 2. Pollard TD (2010) A guide to simple and informative binding assays. Mol Biol Cell 21: 4061–4067. https://doi.org/10.1091/mbc. e10-08-0683 3. Bjo¨rck L, Kronvall G (1984) Purification and some properties of streptococcal protein G, a novel IgG-binding reagent. J Immunol Baltim Md 133:969–974 4. Forsgren A, Sjo¨quist J (1966) “Protein A” from S. aureus. I. Pseudo-immune reaction with human gamma-globulin. J Immunol Baltim Md 97:822–827 5. Nordenfelt P, Bjrck L (2013) IgG-binding bacterial proteins and pathogenesis. Future Microbiol 8:299–301. https://doi.org/10.2217/ fmb.13.6 6. Bahnan W, Happonen L, Khakzad H, Ahnlide VK, de Neergaard T, Wrighton S, Andre´ O, Bratanis E, Tang D, Hellmark T, Bjo¨rck L, Shannon O, Malmstro¨m L, Malmstro¨m J, Nordenfelt P (2022) A human monoclonal antibody bivalently binding two different epitopes in streptococcal M protein mediates immune function. Embo Mol Med e16208. https:// doi.org/10.15252/emmm.202216208 7. Ahnlide VK, de Neergaard T, Sundwall M, Ambjo¨rnsson T, Nordenfelt P (2021) A

Predictive model of antibody binding in the presence of IgG-interacting bacterial surface proteins. Front Immunol 12:629103. https://doi.org/10.3389/fimmu.2021. 629103 8. Pattnaik P (2005) Surface plasmon resonance. Appl Biochem Biotech 126:79–92. https:// doi.org/10.1385/abab:126:2:079 9. Malmqvist M (1993) Surface plasmon resonance for detection and measurement of antibody-antigen affinity and kinetics. Curr Opin Immunol 5:282–286. https://doi.org/ 10.1016/0952-7915(93)90019-o 10. Dam TK, Torres M, Brewer CF, Casadevall A (2008) Isothermal titration calorimetry reveals differential binding thermodynamics of variable region-identical antibodies differing in constant region for a univalent ligand*. J Biol Chem 283(31366–31):370. https://doi.org/ 10.1074/jbc.m806473200 11. Pierce MM, Raman CS, Nall BT (1999) isothermal titration calorimetry of protein–protein interactions. Methods 19:213–221. https://doi.org/10.1006/meth.1999.0852 12. Scatchard G (1949) The attractions of proteins for small molecules and ions. Ann Ny Acad Sci 51:660–672. https://doi.org/10.1111/j. 1749-6632.1949.tb27297.x 13. Jarmoskaite I, AlSadhan I, Vaidyanathan PP, Herschlag D (2020) How to measure and evaluate binding affinities. Elife 9:e57264. https:// doi.org/10.7554/elife.57264

Chapter 18 Detection of Inflammasome Activation in Murine Bone Marrow-Derived Macrophages Infected with Group A Streptococcus Christine Valfridsson, Elsa Westerlund, Do´ra Hancz, and Jenny J. Persson Abstract Inflammasomes are large multiprotein complexes that assemble mainly in innate immune cells after detection of microbial or sterile insults. Activation of inflammasomes is a key proinflammatory event during infection, and many pathogens have evolved specific evasion mechanisms to evade or inhibit inflammasome activation. One such pathogen is the common bacterium group A Streptococcus (GAS), which causes a wide range of diseases of varying severity. GAS secretes a multitude of virulence factors whereof the pore-forming protein streptolysin O (SLO) is the main inflammasome activation determinant. Here we provide a protocol for reliable evaluation of inflammasome activation in murine bone marrow-derived macrophages (BMDM) infected with GAS, including instructions for generating BMDMs and growing the bacterium. This protocol can easily be modified to other bacterial pathogens, or human macrophages. Key words Inflammasome, Group A Streptococcus, Murine bone marrow-derived macrophages

1

Introduction Two decades ago, Ju¨rg Tschopp and colleagues at the University of Lausanne [1] discovered that caspase-1-mediated maturation of the proinflammatory cytokine interleukin (IL)-1β depended on the formation of a large cytosolic protein complex—an inflammasome—allowing proximity-dependent autoactivation of the protease caspase-1. Active caspase-1 may then cleave pro-IL1β and pro-IL-18 into their active forms, as well as the poreforming protein Gasdermin D inducing pyroptotic cell death [2]. Inflammasome activation is mainly a feature of innate immune cells such as macrophages, neutrophils, and dendritic cells, but has also been shown in adaptive immune cells and different types of barrier cells [3–5]. A handful of inflammasome activating pattern recognition receptors have been identified to date, of which the most well

Pontus Nordenfelt and Mattias Collin (eds.), Bacterial Pathogenesis: Methods and Protocols, Methods in Molecular Biology, vol. 2674, https://doi.org/10.1007/978-1-0716-3243-7_18, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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studied are the NLRs Nlrp1, Nlrp3 (which is of particular importance here), and Nlrc4, and the ALR AIM2. Their activating ligands constitute an array of microbial and endogenous compounds, and once detected, sensor oligomerization seeds the formation of the inflammasome complex by providing a scaffold to which the bimodular adaptor protein ASC and caspase-1 are recruited [2]. These canonical inflammasomes are accompanied by the non-canonical activation pathway, where cytosolic LPS directly activates murine caspase-11 (human caspase-4 and caspase-5) [6], and a so-called “alternative” activation that, in contrast to canonical inflammasome activation, allows IL-1β secretion from live cells [7]. In macrophages, the Nlrp3 inflammasome differs from other inflammasomes in that it requires two signals for its activation. The first signal (“priming”) leads to transcriptional upregulation of Nlrp3 and Il1B along with post-transcriptional modifications required for inflammasome assembly. The second and activating signal initiates complex assembly and cleavage of cytokines and Gasdermin D and can be conveyed by a surprisingly wide array of stimuli including ATP, bacterial pore-forming toxins, mitochondrial DNA, and lysosomal destabilization to name a few [8]. It seems like most of these diverse signals converge on efflux of K+ across the plasma membrane, and it has been proposed that Nlrp3 acts as a de facto sensor of changes in homeostatic ion levels. Streptococcus pyogenes, or group A Streptococcus (GAS), is a major human bacterial pathogen causing a vast array of diseases of varying severity, ranging from mild superficial infections of the oropharyngeal mucosa and the skin to life-threatening, necrotic deep tissue infections. Mainly in children, GAS may also cause asymptomatic carriage and the severe post-infectious sequelae rheumatic fever and glomerulonephritis [9]. Among the many virulence factors expressed by GAS, whereof a handful have been implicated in inflammasome activation [10, 11], the major activating determinant is the pore-forming toxin streptolysin O (SLO) [12–14]. Our previous studies have also revealed that the SLO co-toxin NADase may limit secretion of inflammasome-dependent IL-1β from infected macrophages, possibly representing an immune evasion mechanism developed in GAS [13, 15]. IL-1β has been suggested to be central to protection against severe GAS infections, which is highlighted by the observation that rheumatoid arthritis patients treated with Anakinra (IL-1R antagonist) suffer a significantly increased risk of developing life-threatening GAS infections [16]. Potential physiological roles for IL-18 and Gasdermin D in GAS infections remain to be elucidated. Here we describe an in vitro protocol for analysis of inflammasome activation in bone marrow-derived murine macrophages infected with GAS. Of note, as several of the readouts we use for

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inflammasome activation can also be detected down-stream of other pathways, or due to unrelated technical or experimental issues, it is highly advisable to analyze more than one readout on more than one level within the activation chain, e.g., cytokine release and cell death along with caspase-1 activation and caspase1, ASC, or Nlrp3 dependency. This protocol can easily be adapted for different pathogens, distinct inflammasome sensors, and different cell types of both murine and human origin.

2

Materials Prepare all solutions in a LAF bench; make sure you work under sterile conditions.

2.1 Harvesting Bone Marrow and Generating Bone Marrow-Derived Macrophages (BMDMs) 2.1.1

Femur Excision

1. One 4–12-week-old mouse (see Note 1). 2. 1 tube with 5–10 mL cold Bone Marrow Media (BMM) supplemented with antibiotics (penicillin/streptomycin [p/s]). For 500 mL BMM combine: 20 ng/mL recombinant mouse M-CSF or 50 mL 3T3-M-CSF-cell conditioned media (see Note 2), 50 mL heat-inactivated fetal bovine serum (FBS), 5 mL 100 X glutamine, 5 mL 100X p/s (this can be left out if not needed), and 345 mL RPMI culture media. Filter sterilize and store media at 4 °C. 3. 70% ethanol in a spray bottle. 4. 50 mL tube with 70% ethanol to sterilize instruments. 5. Surgical instruments: surgical pad, scalpel, scissors, forceps, and needles to pin the mouse to the surgical pad. 6. Ice. 7. Paper towels.

2.1.2 Bone Marrow (BM) Harvest

1. Class II laminar flow (LAF) hood. 2. Surgical instruments: scalpel (see Note 3), forceps. 3. 50 mL tube with 70% ethanol to sterilize instruments. 4. 5 mL syringes (one per bone). 5. 25 gauge (G) needles (one per bone). 6. Two 92 × 16 mm Polystyrene petri dishes (see Note 4). 7. Ice-cold BMM, you need 5 mL/bone for flushing out the BM. 8. Two sterile 15 mL tubes. If you wish to derive macrophages directly, continue to point 2.1.3 Generation of BMDMs from bone marrow. If you prefer to freeze your BM now and derive macrophages at a later timepoint, instead continue on points 9–13. For freezing BM:

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9. Freezing media: 5% DMSO (see Note 5) in heat-inactivated FBS. Mix: 2.5 mL DMSO with 47.5 mL heat-inactivated FBS, and sterilize using a steriflip 0.22 μm PES membrane. Prepare freezing media and store at 2–8 °C until use if used the same day or freeze in -20 °C for long time storage. 10. Dulbecco’s Phosphate Buffered Saline (D-PBS) without Ca2+ and Mg2+ (Ca2+ and Mg2+ facilitate cellular adhesion to the surface, making harvesting cells more difficult). 11. Three sterile 50 mL tubes. 12. Sterile cryogenic storage vials (cryovials). 13. Cell freezing chamber. 2.1.3 Generation of BMDMs from Bone Marrow

1. Class II LAF hood. 2. 70% ethanol. 3. Fresh BM or cryovial(s) containing frozen BM. 4. Sterile stripettes individually wrapped of variable size (5, 10, and 25 mL). 5. Disposable, sterile centrifuge 50 mL tubes. 6. Seven 150 × 15 mm non-tissue-culture treated petri dishes (bacteriological grade). 7. BMM, you will need 30 mL of media per petri dish. 8. Centrifuge with cooling that can speed up to ~300 g, swingout rotor with adaptors for 50 mL tubes. 9. 37 °C, 5% CO2 incubator (see Note 6). If you want to freeze your BMDMs, you will also need: 10. Hemocytometer and Trypan Blue to determine viable and total cell counts. 11. Sterile cryovials. 12. Cell freezing chamber.

2.1.4 Thawing of Frozen BMDMs

1. Class II LAF hood. 2. 70% ethanol. 3. Cryovial(s) containing frozen BMDMs. 4. 37 °C water bath, or beaker with 37 °C water. 5. BMM. 6. Sterile 15 mL centrifuge tubes, one tube/frozen vial. 7. Centrifuge with cooling that can speed up to ~300 g, swingout rotor with adaptors for 15 mL tubes. 8. Sterile stripettes individually wrapped of variable size (5, 10, and 25 mL). 9. 150 × 15 mm non-tissue-culture treated petri dishes (bacteriological grade), 1 dish/frozen vial containing 107 BMDMs.

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Bacterial Culture

2.2.1 Streaking GAS on Blood Agar (Can be done up to a week before infection)

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1. Glycerol stock/swab of GAS. 2. Blood agar plates (see Note 7). 3. Sterile inoculation loop/swab. 4. 37 °C, 5% CO2 incubator. 5. Parafilm.

2.2.2 Inoculation of an Overnight Culture (The day before infection)

1. THY broth (see Note 8). 2. Sterile culture tube. 3. Sterile inoculation loop/swab. 4. Bacteria on blood agar plate. 5. 37 °C, 5% CO2 incubator.

2.3 Infection of BMDMs with GAS

1. Class II LAF hood.

2.3.1 Re-plate BMDMs onto Non-tissue CultureTreated Plates (The day before infection)

3. BMM media.

2. BMDMs on petri dish(es). 4. PBS. 5. Sterile cell scraper. 6. Hemocytometer and Trypan Blue to determine viable and total cell counts. 7. Light microscope (X40 magnification). 8. 15 mL tubes. 9. Centrifuge with cooling that can speed up to ~300 g, swingout rotor with adaptors for 15 mL tubes. 10. Non-tissue culture-treated plates, choose plates with number of wells according to the desired experimental setup; see Table 1. 11. 37 °C, 5% CO2 incubator.

Table 1 Recommendations for plates and cell densities to use per experimental setup Experimental setup

Plate

Cells/well

Volume (μL)

Measure secreted factors (2.4.1 and 3.4.1)

96-well

75 × 10

100

Detect mature proteins by western blot (2.4.2 and 3.4.2)

6-well 12-well

1-2 × 106 0.5-1 × 106

2000 500

Measure in-cell caspase-1 activation (2.4.3 and 3.4.3)

96-well

75 × 104

100

96-well

75 × 10

100

Measure cell death induction (2.4.4 and 3.4.4)

4

4

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2.3.2 Prime the BMDMs with LPS (4–15 h before infection)

1. BMM media. 2. E. coli LPS. 3. 37 °C, 5% CO2 incubator. 4. 1.5 mL tubes for diluting LPS.

2.3.3 Infect the BMDMs with GAS

1. OD meter/spectrophotometer. 2. Centrifuge that can speed up to ~2800 g. 3. Class II LAF hood. 4. BMM media. 5. PBS. 6. Vortex. 7. 37 °C, 5% CO2 incubator. 8. Gentamicin. 9. Culture plate/Eppendorf tube.

2.3.4 Serial Dilution for MOI Determination

1. PBS. 2. Sterile spreader. 3. Blood agar plates. 4. 37 °C, 5% CO2 incubator.

2.4 Inflammasome Activation Readouts (See Note 9) 2.4.1 Measure Release of Secreted Factors

Here, we have included the vendors we use for each respective material that have worked well for us. Similar material, of varying quality, may of course be available from other vendors. 1. IL-1β ELISA kit (R & D systems). 2. IL-18 ELISA kit (Thermo Scientific). 3. Caspase-1 ELISA kit (AdipoGen). 4. ELISA plate (if not included in the kit). 5. Stop solution (if not included in the kit). 6. Plate reader measuring absorbance. 7. ATP or Nigericin.

2.4.2 Detect Mature Cytokine and Active Caspase-1 by Western Blot

1. Serum-free media (BMM media without serum). 2. Lysis buffer: Nonidet P-40 buffer (150 mM sodium chloride, 1% NP-40, 50 mM Tris–HCl, 10x complete protease inhibitor cocktail (Roche)). 3. Sample buffer: Laemmli buffer 2-Mercaptoethanol (Bio-Rad). 4. Heating block. 5. Benchtop centrifuge.

(Bio-Rad)

containing

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6. Primary antibodies: anti-IL-1β (AF-401; R & D Systems), antiIL-18 (5180R; BioVision), anti-caspase-1 p20 (AG-20B-0042; AdipoGen). 7. Secondary antibodies: rabbit anti-goat IgG-HRP (61-1620; Thermo Scientific), donkey anti-mouse IgG-HRP (715-036151; Jackson Immuno Research), and goat anti-rabbit IgG-HRP (111-035-144; Jackson Immuno Research). 8. Trichloroacetic acid (Sigma). 9. Acetone. 10. Standard materials and equipment for protein gel electrophoresis (SDS-PAGE) and western blot (can be found elsewhere). 2.4.3 Measure In-Cell Caspase-1 Activation

1. Caspase-Glo1 (Promega). 2. White (see Note 10) 96-well plate with flat, clear bottom. 3. Plate reader measuring bioluminescence.

2.4.4 Observe and Measure Cell Death Induction

1. Light microscope (X40 magnification).

Observe Cell Death Induction Measure Cell Death by LDH Release

1. CytoTox 96 assay (Promega). 2. Triton-X. 3. 96-well plate with flat bottom. 4. Plate reader measuring absorbance.

Measure Cell Death by PI Uptake

1. Propidium iodide (Thermo Fisher). 2. Triton-X. 3. Black (see Note 11) 96-well plate with flat, clear bottom. 4. Plate reader with incubator function (37 °C, 5% CO2) measuring fluorescence.

3

Methods

3.1 Harvesting Bone Marrow and Generating BMDMs

1. Insert your instruments into 70% ethanol.

3.1.1

3. Euthanize the mouse (see Note 13); spray the mouse with 70% ethanol. Pin the mouse, abdomen up, through the paws to the surgical pad, so it’s fully stretched out.

Femur Excision

2. Put a paper towel on the surgical pad; spray it with ethanol (see Note 12).

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4. Lift the lower part of the abdominal skin with forceps, and make an incision into the skin with scissors. Keep the skin lifted and keep cutting all the way down to the paw on the hind leg. Repeat on the other hind leg (see Note 14). 5. Next step is to free up the femur and tibia from skin and tissue. Start by cutting away the big muscle on top of the femur (see Note 15): pull and stretch the skin and tissue with the forceps and cut as close to the bone as you can. 6. Using the scalpel, scrape off the remaining tissue from the bone. 7. Follow the femur with the scalpel blade until you reach the hip joint. Pull gently on the femur using the forceps, and find the end of the bone with the blade. Carefully cut the femur free from the joint. At the other end of the bone, similarly free the femur from the tibia (see Note 16). 8. Check that the cut-out bone is intact in both ends. If the bone is damaged and the BM has been exposed (see Note 17), the tissue might be contaminated and the bone should be discarded. 9. Spray ethanol on a paper towel, wrap the bone inside, and gently rub (see Note 18) the bone to get rid of any leftover tissue. 10. Put the clean tissue-free bone into BMM on ice. 3.1.2 Bone Marrow Harvest

For one mouse (2 femurs): 1. Pre-chill two 15 mL tubes.

2. Fill 2 syringes with 5 mL ice-cold BMM media and cap with 25 G needles. Procedure: 1. Leave your forceps and scalpel to soak in a 50 mL tube with 70% ethanol (see Note 19).

2. Take out two petri dishes; pour some media in one of them for holding the bones until flushing. 3. Pre-chill the centrifuge and appropriate buckets to 4 °C (see Note 20). Put your bones in the petri dish with media. 4. Move one bone using the forceps to the empty petri dish. 5. Cut both heads off the bone with a clean sterilized scalpel blade. Cut just enough of the head of the bone so that you can see the marrow (red) when looking down the end of the bone. 6. Hold the bone with sterilized forceps, and insert the needle of a 5 mL syringe (filled with cold BMM) into the marrow. Slowly flush out the marrow as you move the needle in and out of the

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medullary cavity and collect in a 15 mL tube. After 2.5 mL of BMM has flushed through, turn the bone and flush similarly from the other end into the same 15 mL tube. Keep the cells on ice until all dissections are done and all bones are flushed (see Note 20). 7. Centrifuge the collected BM at ~57 g for 1 min at 4 °C to remove debris. 8. Transfer the supernatant to a new 15 mL tube and discard the old tube. 9. Centrifuge the cells at ~250 g for 10 min at 4 °C to pellet the cells; carefully aspirate most of the supernatant leaving about 100 μL in which to resuspend the BM cells. 10. If you are deriving macrophages from your BM now: add 5 mL of BMM and gently invert to resuspend cells. 11. If you want to freeze your BM and derive macrophages at another time: add 1 mL of freezing media and transfer to a cryovial. Put the vial in a freezing chamber in -80 °C overnight, and the next day move the frozen BM (on dry ice) to -150 °C for storage (see Note 21). 3.1.3 Generation of BMDMs from Bone Marrow

1. Plate out cells as desired in non-tissue-culture-treated (see Note 22) bacteriological grade dishes. Usually, cells from two femurs are plated onto 7 (see Note 23) 150 × 15 mm petri dishes (see Note 24) in a final volume of 30 mL of BMM (see Note 25) without antibiotics (see Note 26). 2. On the fourth day of growth, feed your cells by adding 10 mL fresh BMM to each culture dish. 3. On the sixth or seventh day of growth, the BMDMs should be harvested and used for experiments (see Note 27) or frozen for later use. 4. Aspirate the media and add 7 mL cold PBS. Rest the plate on ice or in the fridge for a few minutes to allow the cells to loosen from the plastic. 5. Use a cell scraper to harvest cells. Pipette PBS up and down to wash cells off the plate. (If you rest your cells on ice for 7–10 min, you will be able to wash off the cells). Put the cell suspension in a chilled 15 mL tube. Wash the plate with an additional 7 mL of cold PBS and combine in the same tube. Carefully pipette suspension up and down to ensure you as much as possible end up with a single cell suspension. 6. Take out an aliquot (e.g., 10 μL) for cell count. 7. Centrifuge your BMDM suspension at ~250 g for 10 min (4 °C).

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8. During the centrifugation, use your aliquot to determine the total number of viable cells using a hemocytometer and Trypan Blue. 9. According to the desired final cell density (see Note 28) for freezing your macrophages, calculate the required total volume of freezing medium. 10. Aseptically decant (see Note 29) the supernatant without disturbing the cell pellet, and resuspend the cells in cold freezing media to a concentration of 107 cells/mL. 11. Dispense aliquots 0.5–1 mL of the cell suspension into cryogenic storage vials. As you aliquot, frequently and gently mix the cells in the original tube to maintain a homogeneous cell suspension. 12. Freeze the cells by placing the cryovials in a chilled freezing chamber and store them at -80 °C overnight. 13. Move the frozen BMDMs (see Note 30) (on dry ice) to -150 ° C for storage. 3.1.4 Thawing of Frozen BMDMs (See Note 31)

1. Prepare a tube with 9 mL BMM for every vial of cells you are about to thaw. 2. Get your cells from the -150 °C freezer, put the vials on ice, and immediately take them to the cell lab. 3. Thaw your frozen cells rapidly (24 h p. i.): Remove also spleen, mesenteric lymph nodes, and liver. To evaluate the severity of infection, homogenize these organs, dilute the homogenate, and plate on MacConkey agar plates containing 15 μg/mL chloramphenicol. Incubate ON at 37 °C and enumerate the colonies to assess systemic Salmonella spread. 18. It is important to not exceed 30 min of gentamicin treatment, to prevent cecum tissue deterioration. 19. It is critical to wash away gentamicin extensively and guarantee the cecum integrity and host cell viability before the tissue homogenization. We have experimentally determined that 9 washes of exactly 45 s ensure a good clearance of gentamicin. 20. Adding chloramphenicol to the enrichment cultures ensures to select for the tagged strains only, killing any potential contaminant coming from the mouse residual microbiota. 21. Luminal content represents the population that successfully colonized the mouse gut. Therefore, we can use it as the input sample reference for qPCR data. Also, the comparison of the abundance of each tagged wild-type or mutant strain in the luminal content vs the mixed inoculum provides an estimate for potential bottlenecks during the luminal colonization stage. 22. The stringent quantification of strain abundances in mixed consortia relies on direct comparisons between tagA-G. To ensure a reliable experimental setup, generate qPCR standard curves for detection of each tag, using a forward primer (one of the seven specific primers for each tag) and a common reverse primer (ydgA_R), combined with serial dilution of each of the strains included in the inoculum samples. Based on the standard curve, establish the detection limit. In our experimental setup, we established a conservative detection limit of 5 × 105 [16].

Acknowledgments We thank members of the Sellin laboratory for helpful discussion. References 1. Hume PJ, Singh V, Davidson AC, Koronakis V (2017) Swiss Army pathogen: the Salmonella entry toolkit. Front Cell Infect Microbiol 7: 348. https://doi.org/10.3389/fcimb.2017. 00348

2. Geddes K, Cruz F, Heffron F (2007) Analysis of cells targeted by Salmonella type III secretion in vivo. PLoS Pathog 3:2017–2028. https://doi.org/10.1371/journal.ppat. 0030196

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3. Loetscher Y, Wieser A, Lengefeld J et al (2012) Salmonella transiently reside in luminal neutrophils in the inflamed gut. PLoS One:7. https:// doi.org/10.1371/journal.pone.0034812 4. Hansmeier N, Miskiewicz K, Elpers L et al (2017) Functional expression of the entire adhesiome of Salmonella enterica serotype Typhimurium. Sci Rep 7:10326. https://doi. org/10.1038/s41598-017-10598-2 5. Fattinger SA, Sellin ME, Hardt WD (2021) Salmonella effector driven invasion of the gut epithelium: breaking in and setting the house on fire. Curr Opin Microbiol 64:9–18. https:// doi.org/10.1016/j.mib.2021.08.007 6. Lambert MA, Smith SGJ (2008) The PagN protein of Salmonella enterica serovar Typhimurium is an adhesin and invasin. BMC Microbiol 8:142. https://doi.org/10.1186/14712180-8-142 7. Rosselin M, Abed N, Virlogeux-Payant I et al (2011) Heterogeneity of type III secretion system (T3SS)-1-independent entry mechanisms used by Salmonella Enteritidis to invade different cell types. Microbiology 157:839–847. https://doi.org/10.1099/mic.0.044941-0 8. Steele-Mortimer O (2008) Infection of epithelial cells with Salmonella enterica. Methods Mol Biol 431:201–211. https://doi.org/10. 1007/978-1-60327-032-8_16 9. Grant AJ, Restif O, McKinley TJ et al (2008) Modelling within-host spatiotemporal dynamics of invasive bacterial disease. PLoS Biol 6: e74. https://doi.org/10.1371/journal.pbio. 0060074 10. Maier L, De´ M, Diard R et al (2014) Granulocytes impose a tight bottleneck upon the gut luminal pathogen population during Salmonella Typhimurium colitis. PLoS Pathog 10. https://doi.org/10.1371/journal.ppat. 1004557 11. Porwollik S, Genovese K, Chu W et al (2018) Neutral barcoding of genomes reveals the dynamics of Salmonella colonization in cattle and their peripheral lymph nodes. Vet Microbiol 220:97–106. https://doi.org/10.1016/j. vetmic.2018.05.007 12. Abel S, Abel zur Wiesch P, Chang H-H et al (2015) Sequence tag–based analysis of microbial population dynamics. Nat Methods 12: 223–226. https://doi.org/10.1038/nmeth. 3253 13. Melton-Witt JA, Rafelski SM, Portnoy DA, Bakardjiev AI (2012) Oral infection with signature-tagged Listeria monocytogenes reveals organ-specific growth and dissemination routes in Guinea pigs. Infect Immun 80:

720–732. https://doi.org/10.1128/iai. 05958-11 14. Martin CJ, Cadena AM, Leung VW et al (2017) Digitally barcoding Mycobacterium tuberculosis reveals in vivo infection dynamics in the macaque model of tuberculosis. MBio 8: e00312-17. https://doi.org/10.1128/mBio. 00312-17 15. Rego ROM, Bestor A, Sˇtefka J, Rosa PA (2014) Population bottlenecks during the infectious cycle of the Lyme disease spirochete Borrelia burgdorferi. PLoS One 9. https://doi. org/10.1371/journal.pone.0101009 16. Di Martino ML, Ek V, Hardt W-D et al (2019) Barcoded consortium infections resolve cell type-dependent Salmonella enterica serovar Typhimurium entry mechanisms. MBio 10. https://doi.org/10.1128/mBio.00603-19 17. Barthel M, Hapfelmeier S, Quintanilla-Martı´nez L et al (2003) Pretreatment of mice with streptomycin provides a Salmonella enterica serovar Typhimurium colitis model that allows analysis of both pathogen and host. Infect Immun 71:2839–2858. https://doi.org/10. 1128/iai.71.5.2839-2858.2003 18. Fattinger SA, Bo¨ck D, Di Martino ML et al (2020) Salmonella Typhimurium discreetinvasion of the murine gut absorptive epithelium. PLoS Pathog 16:e1008503. https://doi. org/10.1371/journal.ppat.1008503 19. Geiser P, Di Martino ML, Ventayol PS et al (2021) Salmonella enterica serovar Typhimurium exploits cycling through epithelial cells to colonize human and murine enteroids. MBio 12:1–18. https://doi.org/10.1128/mBio. 02684-20 20. Datsenko KA, Wanner BL (2000) One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc Natl Acad Sci U S A 97:6640–6645. https://doi. org/10.1073/pnas.120163297 21. Koskiniemi S, Pr€anting M, Gullberg E et al (2011) Activation of cryptic aminoglycoside resistance in Salmonella enterica. Mol Microbiol 80:1464–1478. https://doi.org/10. 1111/J.1365-2958.2011.07657.X 22. Mariathasan S, Hewton K, Monack DM et al (2004) Differential activation of the inflammasome by caspase-1 adaptors ASC and Ipaf. Nature 430:213–218. https://doi.org/10. 1038/nature02664 23. Kro¨ger C, Colgan A, Srikumar S et al (2013) An infection-relevant transcriptomic compendium for Salmonella enterica serovar Typhimurium. Cell Host Microbe 14:683–695. https://doi.org/10.1016/j.chom.2013.11.

Barcoded Consortium Infections 010/attachment/7593fa2f-686f-446f-a3b8fef706d6c1ad/mmc5.xlsx 24. Ek V, Fattinger SA, Florbrant A et al (2022) A motile doublet form of Salmonella Typhimurium diversifies target search behavior at the epithelial surface. Mol Microbiol 117:1156– 1172. https://doi.org/10.1111/mmi.14898 25. Starr T, Bauler TJ, Malik-Kale P, SteeleMortimer O (2018) The phorbol 12-myristate-13-acetate differentiation protocol is critical to the interaction of THP-1 macrophages with Salmonella typhimurium. PLoS One 13: e0193601. https://doi.org/10.1371/journal. pone.0193601

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26. Sellin ME, Mu¨ller AA, Felmy B et al (2014) Epithelium-intrinsic NAIP/NLRC4 inflammasome drives infected enterocyte expulsion to restrict Salmonella replication in the intestinal mucosa. Cell Host Microbe 16:237–248. https://doi.org/10.1016/j.chom.2014. 07.001 27. Fattinger SA, Geiser P, Samperio Ventayol P et al (2021) Epithelium-autonomous NAIP/ NLRC4 prevents TNF-driven inflammatory destruction of the gut epithelial barrier in Salmonella-infected mice. Mucosal Immunol 14:615–629. https://doi.org/10.1038/ S41385-021-00381-Y

Chapter 21 A Murine Mycobacterium marinum Infection Model for Longitudinal Analyses of Disease Development and the Inflammatory Response Julia Lienard, Kristina Munke, and Fredric Carlsson Abstract Mycobacterial infections, including tuberculosis, are a major health problem globally. Prevention and treatments of tuberculosis are challenging due to the poor efficacy of the current vaccine and the emergence of drug-resistant strains. Therefore, it is critical to increase our basic understanding of mycobacterial virulence strategies as well as the host immune response during infection in the complex in vivo setting. While existing infection models provide valuable tools for investigating mycobacterial pathogenesis, they also exhibit limitations that can be addressed by the development of complementary models. Here we describe recent advances to the murine Mycobacterium marinum infection model, in which the bacteria produce a local infection restricted to the tail tissue. The M. marinum model has the advantage of mimicking some of the key hallmarks of human tuberculosis not replicated in the conventional murine Mycobacterium tuberculosis model, such as the formation of granulomas with central caseating necrosis and the spontaneous development of a latency-like stage. Moreover, the model is non-lethal and enables longitudinal analysis of disease development in live animals. In this chapter, we report protocols to prepare infected tissue samples for detailed and quantitative analysis of the immune response by flow cytometry, immunofluorescence microscopy, RT-qPCR, ELISA, and Western blot, as well as for the analysis of bacterial load and localization. Key words Tuberculosis, Mycobacterium marinum, Mouse model, Granuloma formation, Caseating necrosis, Latency, Inflammatory response, Bacterial localization

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Introduction It is estimated that one fourth of the world’s population is infected with M. tuberculosis, a human pathogen responsible for ~1.5 million deaths annually [1]. To facilitate fundamental studies into M. tuberculosis pathogenesis and virulence mechanisms, safer and experimentally more amenable mycobacterial species may be used as models. Among these, the closely related M. marinum represents

Pontus Nordenfelt and Mattias Collin (eds.), Bacterial Pathogenesis: Methods and Protocols, Methods in Molecular Biology, vol. 2674, https://doi.org/10.1007/978-1-0716-3243-7_21, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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a particularly relevant system [2–5]. M. marinum is a natural pathogen of fish and amphibians where it causes tuberculous disease with many features of human tuberculosis [6]. It is also able to infect immunocompetent humans and induce the formation of dermal granulomas pathologically very similar to those formed in tuberculosis patients [6, 7]. However, M. marinum is generally unable to produce systemic infection in humans—likely due to its low optimal growth temperature (~32 °C)—and therefore poses a low risk for laboratory personnel, allowing work to be carried out in BSL-2 facilities. Thus, M. marinum is an experimentally tractable model organism that shares virulence factors and mechanisms of inducing and maintaining infection with M. tuberculosis [3, 5, 6]. This situation gives M. marinum an advantage to study mycobacterial pathogenesis compared to, for example, the attenuated Mycobacterium bovis bacille Calmette-Gue´rin (BCG) strain, which lacks critical virulence determinants [8–10]. The M. marinum mouse model is based on intravenous injection of bacteria via the tail vein [11], and basic analyses of the infection have previously been described in detail [11, 12]. Upon injection M. marinum is seeded systemically, but the bacteria are unable to colonize internal organs productively while successfully establishing and maintaining infection in the tail [11]. Tropism for the tail is likely due to the cooler environment provided in this tissue and is not merely a consequence of inoculation at the site of injection. This interpretation is supported by the finding that intracardiac injection of M. marinum similarly produces infection localized to the tail [11]. Visible granulomatous lesions appear in the tail ~1 week post infection, and over the course of the first 3–4 weeks of infection, the lesions increase in size and become more numerous [11, 13]. The lesions subsequently regress and eventually heal following the decreased bacterial load observed after 3 weeks of infection, suggesting the natural development of latency [11]. Determination of the accumulated length of all visible lesions in individual tails allows for quantitative and longitudinal studies of disease progression in live animals [11, 13]. Moreover, M. marinum infection causes erosion of tail vertebrae, a trait that can be quantitated by measuring bone volume using microcomputed tomography (micro-CT), and represents an indirect readout of inflammation [11]. Thus, two different quantitative traits, visible tail lesions and bone volume, may be used to measure disease and inflammation during infection. Here we describe the development of new protocols that enable more detailed and quantitative analysis of the inflammatory response to M. marinum infection.

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Materials

2.1 Preparation of M. marinum for Infection

1. Albumin-Dextrose-Catalase (ADC) enrichment: 5% bovine albumin fraction V, 2% dextrose, 0.003% catalase, distilled H2O. Filter-sterilize. 2. Oleic acid-Albumin-Dextrose-Catalase (OADC) enrichment: 0.06% oleic acid, 5% bovine albumin fraction V, 2% dextrose, 0.003% catalase, 0.85% sodium chloride, distilled H2O. Filtersterilize. 3. 7H9 composition per liter: 4.7 g Middlebrook 7H9 powder (2.5 g disodium phosphate, 1.0 g monopotassium phosphate, 0.5 g L-glutamic acid, 0.5 g ammonium sulfate, 0.1 g sodium citrate, 0.05 g magnesium sulfate, 0.04 g ferric ammonium citrate, 2.0 mg Mycobactin J, 1.0 mg copper sulfate, 1.0 mg pyridoxine, 1.0 mg zinc sulfate, 0.5 mg biotin, 0.5 mg calcium chloride), 2 mL glycerol, 0.5 g tween 80, 100 mL ADC, distilled H2O (to 1 L). Filter-sterilize. 4. 7H10 agar composition per liter: 19.47 g Middlebrook 7H10 powder (15 g agar, 1.5 g disodium phosphate, 1.5 g monopotassium phosphate, 0.5 g ammonium sulfate, 0.5 g L-glutamic acid, 0.4 g sodium citrate, 0.04 g ferric ammonium citrate, 0.025 g magnesium sulfate, 1.0 mg zinc sulfate, 1.0 mg copper sulfate, 1.0 mg pyridoxine hydrochloride, 0.5 mg biotin, 0.5 mg calcium chloride, 0.25 mg malachite green), 5 mL glycerol, distilled H2O (to 900 mL). Sterilize by autoclaving 20 min. 100 mL pre-warmed OADC is added after autoclavation when the solution is around 50 °C. 5. 26G1/2 needle. 6. Hemacytometer. 7. Inverted microscope. 8. Centrifuge for conical tubes (2500 g). 9. Phosphate buffer saline (PBS). 10. Squared petri dishes (100 × 15 mm) with grid (6 by 6 squares). 11. 30 °C incubator.

2.2 Intravenous Injection Via the Tail Vein

1. Mouse restrainer.

2.3 CFU Enumeration from the Infected Tail Tissue

1. Homogenizer PT 1200E (Polytron) with 10 × 105 mm saw tooth adaptors.

2. Heating lamp. 3. 29G 0.5 mL insulin syringe.

2. Triton X 100. 3. Dissection tools (forceps, bone scissors, scalpel).

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4. 50 mL conical tubes. 5. Phosphate buffer saline (PBS). 6. 7H10 agar (see Subheading 2.1). 7. Squared petri dishes (see Subheading 2.1). 2.4 Preparation of Tail Tissue for Flow Cytometry Analyses

1. 60 mL straight sample container with screw cap. 2. 50 mL conical tubes. 3. 37 °C incubator. 4. Dissection tools (forceps, bone scissors, scalpel). 5. Cylindrical stirrer bars (15 x 4.5 mm). 6. Magnetic stirrer with multiple positions. 7. DMEM 5% FCS: Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 5% heat-inactivated fetal calf serum (FCS). 8. Liberase TM (Roche). 9. DNAse I (Sigma). 10. Nylon cell strainer (70 μm and 40 μm). 11. 5 mL syringe plungers. 12. Phosphate buffer saline (PBS). 13. FACS buffer (PBS supplemented with 3% heat-inactivated fetal calf serum (FCS) and 2 mM EDTA). 14. 96-well plate with V-bottom. 15. Plasmid pTEC15 (#30174, Addgene) for Wasabi fluorescence expression. 16. Paraformaldehyde.

2.5 Preparation of Tail Tissue for Immunofluorescence Microscopy

1. AntigenFix (Diapath). 2. Sucrose. 3. Iso-pentane. 4. Phosphate buffer saline (PBS). 5. 2 mL Eppendorf tubes. 6. Vertical jar for glass slides. 7. Glass beaker. 8. Glass slides. 9. Cryostat. 10. Hydrophobic barrier pen. 11. Plastic cryomold (Tissue Tek). 12. Permeabilization buffer: PBS containing 1% saponin, 2% BSA, 1% FCS, 1% donkey or goat serum.

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13. Rat serum. 14. Primary, secondary, and tertiary antibodies. 15. ProLong Gold Antifade Mountant. 16. Slide scanning fluorescence microscope. 2.6 Preparation of Tail Skin Tissue for Protein Content Analyses or RNA Extraction

1. 1.5 mL Eppendorf tubes. 2. 15 mL conical tubes. 3. 5 mL round bottom tubes. 4. Liquid nitrogen. 5. Biopulverizer. 6. Metal hammer. 7. Homogenizer (see Subheading 2.3). 8. Phosphate buffer saline (PBS). 9. cOmplete, EDTA-free Protease Inhibitor Tablets. 10. Centrifuge (20,000 g centrifugations).

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3.1 Mouse Infection Model 3.1.1 Preparation of M. marinum for Infection

1. M. marinum is grown in 25 mL Middlebrook 7H9 medium at 30 °C, under agitation (100 rpm) and in the dark, until logarithmic growth phase (OD600nm ~ 0.7). 2. In a conical tube, collect bacterial cultures by centrifugation at 2500 g for 10 min at room temperature, and wash twice with 10 mL sterile PBS. Resuspend the final pellet in 9 mL PBS to obtain an appropriate bacterial concentration. 3. As bacteria may clump together, pass the bacterial suspension three times through a 26G1/2 needle, by projecting the suspension against the inner wall of a conical tube, to help disrupting aggregates. 4. To remove any residual bacterial aggregates, the bacterial suspension is centrifuged twice at 500 g for 1 min where supernatants containing enriched single cell bacteria are collected each time in a new tube. 5. Evaluate the concentration of the bacterial suspension by counting the bacteria loaded in an hemacytometer. Counting the bacteria is done under the microscope. See Note 1. 6. Dilute the bacteria in sterile PBS at a final concentration of 35 × 107/mL. Keep the bacteria at room temperature until intravenous injection. 7. Determine the precise inoculum prepared by serially diluting (7 times ten-fold dilutions) and plating the bacteria

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(corresponding to dilutions 1/102 to 1/107) on Middlebrook 7H10 agar. The use of squared petri dishes (100 × 15 mm) with grid (6 by 6 squares) is a practical solution to plate the diluted bacteria, where a 10 μL drop of each dilution is deposed in each square. When the agar is dry, place the agar plates in a sealed plastic bag and incubate at 30 °C. Enumeration of the bacteria can be done after 6–7 days of incubation. 3.1.2 Intravenous Injection Via the Tail Vein

1. Place the mouse under a heating lamp, or alternatively, heat the tail with paper tissue wet with warm water, to dilate the tail veins. 2. Gently place the mouse in an opaque restrainer, and inject 200 μL of bacteria into the tail vein using a 29G 0.5 mL insulin syringe (corresponding to 5 × 107 bacteria per mouse). A similar location of injection sites between animals (~3 cm from the tail base) is preferable.

3.1.3 Monitoring of Disease Development: Quantification of Visible Tail Lesions

1. Using an inoculum of 5 × 107 bacteria per mouse, the first skin tail lesions start to be visible around 7 days post infection. At regular intervals (e.g., two to three times per week), measure the length of visible lesions using a ruler, after placing the infected mouse in a restrainer. 2. The lesions are typically represented as the accumulated length of all individual lesions for each infected mouse (see Fig. 1).

3.1.4 Analysis of Bacterial Growth in the Infected Tail Tissue by CFU Enumeration

1. Sacrifice the infected mouse and sever the tail at the base using a bone scissor. Place the tail in a pre-weighted 50 mL conical tube and keep on ice. 2. Weight the conical tube containing the tail, and subtract the weight of the empty tube to obtain the weight of the tail.

Fig. 1 Representative tail lesions of two C57Bl/6 mice at 21 days post infection with M. marinum. Visible granulomatous lesions are indicated with yellow arrow heads

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3. Cut the tail into 2–5 mm pieces using a bone scissor or a scalpel. Transfer tail pieces into a 50 mL conical tube containing 3 mL PBS containing 0.1% Triton X 100 to lyse the cells. Keep on ice. 4. Homogenize the tail pieces using a homogenizer PT 1200E (Polytron) with 10 × 105 mm saw tooth adaptors. See Notes 2 and 3. 5. Centrifuge the tail suspension at 500 g for 5 min, and collect the supernatant to perform ten-fold serial dilutions that are plated on Middlebrook 7H10 agar, as described above in Subheading 3.1.1 (see step 7). Place the agar plates at 30 °C. 6. Count the colony-forming units (CFU) after about 7 days, and calculate the number of bacteria per animal or gram tail tissue. 3.1.5 Preparation of Tail Tissue for Downstream Flow Cytometry Analyses

1. Sacrifice the mouse and sever the tail at the base. Using a scalpel, make a longitudinal incision from the base to the tip of the tail. Using two forceps, open the incision at the location of the base of the tail by pulling the skin, and completely separate the skin from the bones (see Fig. 2). Cut the skin in as small pieces as possible (1–2 mm) with the scalpel (see Note 4), and place the pieces in a 60 mL straight sample container with screw cap, containing 5 mL DMEM 5% FCS (see Note 5). For clarity, an illustration of this protocol is provided in Fig. 3. 2. Add 30 μg/mL Liberase TM (Roche) and 52 μg/mL DNAse I (Sigma), and agitate at 37 °C using magnetic stirring for 1 h.

Fig. 2 Illustration of the procedure to separate the tail skin tissue from the bone, as described in the main text

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Fig. 3 Schematic representation of the protocol used to prepare tail tissue for downstream flow cytometry analyses, as described in the main text. (Created with BioRender.com)

3. Put samples on ice to stop the enzymatic digestion. 4. Mash digested samples through a 70 μm nylon cell strainer into a 50 mL conical tube, using the plunger of a 5 mL syringe. Collect residual cells on the filter into the conical tube by washing the filter with 10 mL DMEM 5% FCS. 5. Centrifuge the cell suspension at 500 g for 5 min at 4 °C, and resuspend the pelleted cells in 10 mL ice-cold FACS buffer (PBS supplemented with 3% FCS and 2 mM EDTA). 6. Filter the cell suspension using a 40 μm nylon cell strainer, and centrifuge at 500 g for 5 min at 4 °C. Discard the supernatant and resuspend the cell pellet in the residual liquid.

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Fig. 4 Flow cytometry analysis of tail tissues from C57Bl/6 mice infected with M. marinum (carrying the pTEC15 plasmid expressing wasabi) 21 days post infection, or uninfected (UI), as indicated. (a) Gating strategy defining neutrophils (Ly6G+CD11b+), monocytes-derived cells and macrophages (CD64+), conventional dendritic cells (cDC; CD64-MHCII+CD11c+), B cells (CD19+) and T cells (TCRβ+CD4+ or CD8+) in WT infected or UI mouse, as indicated. (b) FACS plot defining infected hematopoietic cells (Live CD45+ cells) in a WT infected mouse

7. Transfer each single cell sample to a 96-well plate with V-bottom. 8. Proceed with antibody staining for flow cytometry analyses (see Fig. 4 as an example). See Note 6 for the identification of infected cells by flow cytometry. See Note 7 for fixation prior flow cytometry sample acquisition. 3.1.6 Preparation of Tails for Immunofluorescence Microscopy

1. Cut off the tail ~1 cm from the base, and collect 2–3 conjunctive pieces of ~0.5–1 cm each from the severed end (usually where the tail lesions start to form). 2. Fix the tail pieces in 3% AntigenFix (Diapath) for 2 h on ice. Make sure that the tissue is completely covered.

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3. Briefly dry the tail pieces on paper, and transfer them into an Eppendorf tube (2 mL) with PBS containing 35% Sucrose. Make sure the tissue is completely covered. 4. Incubate overnight at 4 °C on rotation rack. Dehydration is complete when the tissue has sunk to the bottom of the tube. 5. To freeze the samples, place a glass beaker with iso-pentane in a container filled with dry ice. Make sure the beaker is surrounded by dry ice to keep it cold (the solution should go from clear to cloudy in 10–20 min). 6. Put O.C.T (optimal cutting temperature) compound in a plastic cryomold (Tissue-Tek). 7. Briefly dry two tail pieces on paper, and place them in the cryomold using a tweezer. Press the tail pieces to the bottom and avoid making bubbles in the O.C.T. 8. Freeze the samples by carefully dropping the cryomold into the beaker with iso-pentane for at least 2 min. For storage, remove cryomold plastic and keep at -20 or -80 °C prior to cutting. 9. Cut frozen tissue samples into 15 μm cross sections using a cryostat and mount on a glass slide. Mount up to six sections per glass slide. Glass slides can be stored at -20 or -80 °C prior to staining. 10. For staining, circle the tissue sections with a hydrophobic barrier pen. Circle two to three sections into one area. 11. Wash sections with large volumes of PBS using a vertical staining jar, in the dark for 5–10 min. Dry the slides using paper; avoid touching the tissue/area within the hydrophobic barrier. 12. All staining steps should be performed in a staining chamber (closed box with wet absorbing paper). Incubate sections in permeabilization buffer (PBS containing 1% saponin, 2% BSA, 1% FCS, 1% donkey, or goat serum depending on the antibody species) for 30 min at RT. Cover the tissue by filling up the areas defined by the hydrophobic barrier. 13. Incubate sections in permeabilization buffer with the desired primary antibodies overnight at 4 °C. Cover the tissue by filling up the areas defined by the hydrophobic barrier. 14. Wash sections with large volumes of PBS using a vertical glass jar, in the dark for 5–10 min. Dry the slides using paper; avoid touching the tissue/area within the hydrophobic barrier. 15. Incubate sections in permeabilization buffer with the appropriate secondary antibodies for 1 h at RT. Cover the tissue by filling up the areas defined by the hydrophobic barrier. 16. Wash sections with large volumes of PBS using a vertical staining jar, in the dark for 5–10 min. Dry the slides using paper; avoid touching the tissue/area within the hydrophobic barrier.

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17. Block sections in permeabilization buffer supplemented with 2% serum from the same animal species as the tertiary fluorochrome-conjugated antibody used in step 18 below (most often rat) for 30–60 min at RT. Cover the tissue by filling up the areas defined by the hydrophobic barrier. 18. Stain sections in permeabilization buffer supplemented with 2% serum as described in step 17 above. Add the appropriate amount of tertiary fluorochrome-conjugated antibody, as described by the manufacturer. Incubate for 1 h at RT. Cover the tissue by filling up the areas defined by the hydrophobic barrier. 19. Wash sections with large volumes of PBS using a vertical staining jar, in the dark for 5–10 min. Dry the slides using paper; avoid touching the tissue/area within the hydrophobic barrier. 20. Mount the slides in ProLong Gold Antifade Mountant (Invitrogen). Cover the tissue with one to two drops per area defined by the hydrophobic barrier, and mount the upper slide carefully to avoid bubbles. 21. Acquire images using a fluorescence slide scanning microscope (see Fig. 5 as an example).

Fig. 5 Microscopy analysis of a tail cross section from C57Bl/6 mice infected with M. marinum (carrying the pTEC15 plasmid expressing wasabi) 21 days post infection. The image shows immunofluorescence staining of CD64+ myeloid cells (blue), GR1+ neutrophils (red), myeloperoxidase/MPO+ (white), and wasabi+ bacteria (green). The image was acquired using Z-stack acquisition on an OLYMPUS VS-120 virtual slide microscope. Scale bar; 200 μm in the left panel. Boxes, R1 and R2, show highlighted regions in the right panels. R1 Region 1, R2 Region 2, B bone, BM bone marrow

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3.1.7 Preparation of Tail Skin Tissue for Cytokines and Protein Content Analyses

1. Collect tail skin tissue as described in Subheading 3.1.5 step 1 and place in a pre-weighed 15 mL conical tube. Put tube on dry ice. 2. Weigh tube containing the tail skin to calculate the weight of the skin tissue. At this point, tissues can be stored at -80 °C for future preparation of lysates. 3. Deep freeze the tail skin by immersion in liquid nitrogen for 30–60 s, and transfer in a similarly pre-chilled biopulverizer. See Note 8. 4. Insert the frozen tail skin in the biopulverizer and pulverize by banging 2–3 times with a metal hammer. 5. Using forceps, immediately scrape off the resulting powder from the biopulverizer into a 5 mL round bottom tube containing 0.5 mL PBS supplemented with the appropriate amount of proteases inhibitor (following the manufacturer’s instructions). 6. Homogenize the tail pieces using a homogenizer as described in Subheading 3.1.4 step 4. See Note 3. 7. Transfer to a 1.5 mL tube and incubate 90 min on ice. 8. Remove debris by centrifugation at 20,000 g during 20 min at 4 °C. Collect supernatant to a new tube, and use directly for cytokines/protein analyses or store it at -80 °C. Avoid repeated freeze-thawing cycles.

3.1.8 Preparation of Tail Skin Tissue for RNA Extraction

1. Proceed as described in Subheading 3.1.7 steps 1–6 but omit the use of proteases inhibitor. 2. Transfer to a 1.5 mL tube and collect tissue by centrifugation at >17,000 g for 10 min at 4 °C. 3. Discard supernatant. 4. Perform RNA extraction immediately, or alternatively, deep freeze the sample and store it at -80 °C for future extraction.

4

Notes 1. Wait a few minutes to let the bacteria sediment onto the glass in order to minimize the number of “floating” bacterial cells. Count the cells using a 40× or 60× objective. 2. The tube containing the infected tissues is placed in a small glass cylinder containing ice during homogenization, to avoid exposing the tail tissues to a damaging temperature. 3. The tooth is disinfected in between samples by running the homogenizer first in sterile water, then in disinfection solution (e.g., chlorine), and finally in a third container with sterile water.

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4. Quickly wetting the skin tissue (into the medium used for tissue collection) greatly helps the cutting process into small pieces as the tissue will adhere to the cutting mat. 5. This volume of DMEM medium is optimized for the size of the container indicated, allowing a good agitation of the sample during enzymatic digestion. 6. To enable identification of infected cells in the tissue by flow cytometry, M. marinum was transformed with the pTEC15 plasmid allowing expression of wasabi green fluorescence that exhibits strong brightness, as our initial tests using M. marinum expressing GFP from the chromosome were unsuccessful. 7. All antibody-stained samples were fixed in 2% paraformaldehyde (methanol-free) during 20 min at room temperature, prior to their analysis on a flow cytometer. 8. To limit the use of liquid nitrogen when cooling the biopulverizer, store it first a few hours at -80 °C.

Acknowledgments Work in the Carlsson laboratory is supported by the Swedish Research Council, the Knut and Alice Wallenberg Foundation, ¨ sterlund. Work in the Lienard and the foundation of Alfred O laboratory is supported by the Swedish Research Council and the Royal Physiographic Society in Lund, as well as the foundations of Magnus Bergvall, and Per-Erik and Ulla Schyberg. References 1. WHO (2021) Global tuberculosis report. A v a i l a b l e v i a h t t p s : // w w w. w h o . i n t / publications/i/item/9789240037021 2. Shiloh MU, Champion PA (2010) To catch a killer. What can mycobacterial models teach us about Mycobacterium tuberculosis pathogenesis? Curr Opin Microbiol 13:86–92 3. Cambier CJ, Falkow S, Ramakrishnan L (2014) Host evasion and exploitation schemes of Mycobacterium tuberculosis. Cell 159: 1497–1509 4. Stinear TP, Seemann T, Harrison PF, Jenkin GA, Davies JK, Johnson PD, Abdellah Z, Arrowsmith C, Chillingworth T, Churcher C, Clarke K, Cronin A, Davis P, Goodhead I, Holroyd N, Jagels K, Lord A, Moule S, Mungall K, Norbertczak H, Quail MA, Rabbinowitsch E, Walker D, White B, Whitehead S, Small PL, Brosch R, Ramakrishnan L, Fischbach MA, Parkhill J,

Cole ST (2008) Insights from the complete genome sequence of Mycobacterium marinum on the evolution of Mycobacterium tuberculosis. Genome Res 18:729–741 5. Tobin DM, Ramakrishnan L (2008) Comparative pathogenesis of Mycobacterium marinum and Mycobacterium tuberculosis. Cell Microbiol 10:1027–1039 6. Cosma CL, Sherman DR, Ramakrishnan L (2003) The secret lives of the pathogenic mycobacteria. Annu Rev Microbiol 57:641– 676 7. Travis WD, Travis LB, Roberts GD, Su DW, Weiland LW (1985) The histopathologic spectrum in Mycobacterium marinum infection. Arch Pathol Lab Med 109:1109–1113 8. Pym AS, Brodin P, Brosch R, Huerre M, Cole ST (2002) Loss of RD1 contributed to the attenuation of the live tuberculosis vaccines

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Mycobacterium bovis BCG and Mycobacterium microti. Mol Microbiol 46:709–717 9. Hsu T, Hingley-Wilson SM, Chen B, Chen M, Dai AZ, Morin PM, Marks CB, Padiyar J, Goulding C, Gingery M, Eisenberg D, Russell RG, Derrick SC, Collins FM, Morris SL, King CH, Jacobs WR Jr (2003) The primary mechanism of attenuation of bacillus CalmetteGuerin is a loss of secreted lytic function required for invasion of lung interstitial tissue. Proc Natl Acad Sci U S A 100:12420–12425 10. Lewis KN, Liao R, Guinn KM, Hickey MJ, Smith S, Behr MA, Sherman DR (2003) Deletion of RD1 from mycobacterium tuberculosis mimics bacille Calmette-Guerin attenuation. J Infect Dis 187:117–123

11. Carlsson F, Kim J, Dumitru C, Barck KH, Carano RA, Sun M, Diehl L, Brown EJ (2010) Host-detrimental role of Esx-1mediated inflammasome activation in mycobacterial infection. PLoS Pathog 6:e1000895 12. Lienard J, Carlsson F (2017) Murine Mycobacterium marinum infection as a model for tuberculosis. Methods Mol Biol 1535:301–315 13. Watkins BY, Joshi SA, Ball DA, Leggett H, Park S, Kim J, Austin CD, Paler-Martinez A, Xu M, Downing KH, Brown EJ (2012) Mycobacterium marinum SecA2 promotes stable granulomas and induces tumor necrosis factor alpha in vivo. Infect Immun 80:3512–3520

Chapter 22 In Vitro Approaches for the Study of Pneumococcal-Neuronal Interaction and Pathogenesis Kristine Farmen and Miguel Tofin˜o-Vian Abstract CFU- and confocal microscopy-based in vitro methods to assess pneumococcal adhesion and invasion of relevant human cells, such as neurons, remain a powerful tool to understand the basis of host–pathogen interactions. In recent years, there has been a continuous refinement of confocal detection of human and bacterial cells through the use of specific, fluorochrome-labelled antibodies. Used in combination, these assays provide both the means for quantification and enough flexibility to accommodate specific experimental needs. Key words Streptococcus pneumoniae, SH-SY5Y cell line, Neuronal differentiation, Adhesion assay, Gentamicin protection assay, Immunofluorescence, Colony forming units

1

Introduction Despite advances in vaccine and antibiotic development, and better access to healthcare systems, the Gram-positive bacterium Streptococcus pneumoniae remains a health threat as the main cause of bacterial meningitis worldwide. After breaching the blood–brain barrier and invading the central nervous system, the bacteria cause an acute immune response that leads to meningitis, brain injury, and often permanent neurological damage [5, 6]. Moreover, infections of the brain and the direct damage to neurons they cause have been linked to neurological diseases and dementia [3]. It is, therefore, imperative to develop empirical strategies to better understand the ways by which pathogens such as S. pneumoniae interact, adhere, invade, and destroy critical human cells including neurons, which are highly differentiated and with limited regenerative potential once adulthood is reached. Several mechanisms by which the pneumococcus is able to produce this neurological damage have been proposed, from direct toxic effects on neurons and other CNS cell types, to indirect acute neuroinflammation

Pontus Nordenfelt and Mattias Collin (eds.), Bacterial Pathogenesis: Methods and Protocols, Methods in Molecular Biology, vol. 2674, https://doi.org/10.1007/978-1-0716-3243-7_22, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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triggered by the activation of brain immune sentinels. In order to accurately assess these complex processes of host–pathogen interaction, a wide range of techniques must be employed. First, a simple, powerful, and reproducible in vitro model for neurons or neuron-like culturing must be employed. In neurobiology, both human and non-human models have been used, from human cell lines to rodent primary neuronal cultures. However, these models also have relevant limitations. Human cell lines lack many properties of adult differentiated neurons, whereas rodent primary cells have limited passage capacity, require the continuous use of rodents, and show important differences to human neurons. Differentiated neurons from human-induced pluripotent or neuroepithelial stem cells have also been used, but they have shown high variability in their differentiation efficiency [4]. On the other hand, differentiated SH-SY5Y neuronal-like cells represent a consistent and reproducible human neuronal cell model. SH-SY5Y cells are a subclone of the parental neuroblastoma cell line SK-NSH, developed in 1970 from a bone marrow biopsy [1]. Importantly, the neuroblastoma cell line can be differentiated into mature neuron-like cells, by treating them with extracellular stimuli including retinoic acid, neurotrophins such as the brain-derived neurotrophic factor (BDNF), and phorbol esters. When undifferentiated, SH-SY5Y cells proliferate quickly, cluster together in clumps, morphologically show short and few neurite-like elongations, and express surface markers of immature neurons. After the differentiation process, however, their proliferation decreases, and their neurites extend and branch in longer processes. Importantly, they begin to express a variety of mature neuron markers, such as microtubule associated protein (MAP) 2, neuronal nuclei (NeuN), neuronspecific enolase (NSE), and synaptophysin (SYN). Therefore, this cell line represents a reliable method to perform translational experiments that model the CNS in vitro and is extensively used by the neuroscience research community [7]. As in most pathogenic bacteria, the physical interaction of the bacterium with host cells is commonly the first aspect of the pneumococcal pathogenesis to be studied. Adherence is the first step of colonization, usually a highly specialized interaction between defined sets of host and pathogen agents like surface-exposed proteins [2]. Despite their conceptual simplicity, assays that study pneumococcal adhesion and invasion remain useful tools to examine the molecular basis of host–pathogen interactions. These methods are flexible, as they can be easily modified to accommodate treatments of both bacteria and cells with different kinds of drugs, inhibitors, antibodies, or peptides to assess different aspects of pathogenesis, host signalling pathways, or intracellular trafficking processes involved and affected during pneumococcal infections of the CNS. In neurons, for example, this has allowed the identification of β-actin as a relevant mediator of pneumococcal interaction

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with the neuron [8]. Moreover, different pneumococcal serotypes employ highly different strategies and proteins to adhere and invade, many of which are still unknown. Thus, insights obtained from the study of pneumococcal adhesion and invasion are fundamental to understand its pathogenesis. In highly specialized and morphologically complex cells such as neurons or differentiated SH-SY5Y cells, imaging techniques such as immunocytochemistry staining with labelled antibodies remain valuable. In recent years, important advances in microscopy equipment, techniques, and image processing have allowed the development of high- and super-resolution microscopy. Even though other techniques such as STED imaging or electron microscopy provide much more augmentation, high-resolution confocal microscopy in both fixed and live imaging setups remain the gold standard to image pneumococcal–host interactions (see Fig. 1).

2 2.1

Materials Cell Culture

1. The neuroblastoma cell line SH-SY5Y. 2. Neuronal differentiation media, 1:1 EMEM and F12 Ham medium, 1% P/S, 10% HI-FBS, 10 μM retinoic acid. 3. Neuronal seeding media, 1:1 EMEM and F12 Ham medium, 5% HI-FBS. 4. Coverslips pre-coated with poly-D-Lysine.

2.2

Infection

1. Phosphate-Buffered Saline (PBS). 2. Gentamicin. 3. Penicillin. 4. Saponin 0.1%. 5. Trypsin. 6. Blood agar plates. 7. Cell incubator.

2.3 Immunocytochemistry

1. Cells cultured on coverslips. 2. Phosphate-Buffered Saline (PBS). 3. Bovine serum albumin (BSA). 4. Primary and secondary antibodies. 5. Paraformaldehyde (PFA) 4%, methanol-free. 6. Triton X-100. 7. Glycine. 8. Tween 20.

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Fig. 1 Summary of methodology

9. Mounting medium with or without DAPI. 10. High-resolution glass slides.

3 Methods 3.1 Differentiation of Neurons

1. The neuroblastoma cell line SH-SY5Y are differentiated into neurons by exposing them to retinoic acid (10 μM) for a minimum of 9 days, with the neuronal differentiation media changed every second day (see Note 1). Cells are seeded in

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6-well plates at the density 300,000 and 500,000 cells/well for adhesion and invasion assays, respectively. For immunocytochemistry, poly-D-lysine coated coverslips are used (see Note 2). 3.2

Infection

1. Replace the neuronal differentiation media with neuronal seeding media. Incubate the cells overnight at 37 °C with 5% CO2. 2. Discard media from the cells and wash neurons with pre-heated PBS. Add 1 mL per well of warm neuronal seeding media, and incubate the cells for 1 h at 37 °C with 5% CO2. 3. Resuspend S. pneumoniae aliquots in neuronal seeding media, and infect the neurons at a multiplicity of infection (MOI) of 10 (adhesion) or 20 (invasion) (see Notes 3 and 4). Gently spin down the plates at 50 × g for 5 min. 4. Incubate the cells at 37 °C with 5% CO2 for 2 h (see Note 5).

3.3

Adhesion

1. Collect the supernatant, which represents the non-adhered bacteria. 2. Gently wash the cells twice in pre-warmed PBS, in order to eliminate unbound bacteria. 3. Add 1 mL of pre-warmed trypsin to each well, and incubate for 15 min at 37 °C and 5% CO2. 4. Collect the cell suspension; this fraction represents the adhered bacteria. 5. Plate serial dilution of the non-adhered and adhered bacterial suspension on blood agar plates. Incubate at 37 °C with 5% CO2 overnight. 6. The next day, count the colony forming units (CFU). 7. The percentage of adhered bacteria is calculated by the following formula: Adhesion% =

3.4

Invasion

ðAdhered bacteriaÞ CFU mL ðTotal number of bacteriaÞ

CFU mL

× 100%

1. Collect the supernatants and plate the serial dilutions on blood agar plates. This represents the non-adhered bacteria. 2. Gently wash the cells twice in pre-warmed PBS, in order to eliminate unbound bacteria. 3. To remove bound extracellular bacteria, add 1 mL fresh pre-warmed neuronal seeding media containing 200 μg/mL gentamicin and 10 μg/mL penicillin. Incubate for 1 h at 37 °C and 5% CO2.

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4. Collect the supernatant, and plate 50 μL on blood agar plate. This is the control for complete removal of extracellular bacteria. 5. Gently wash the cells twice in pre-warmed PBS. 8. Add 500 μL of pre-warmed saponin 0.1% in PBS and 500 μL trypsin, and incubate for 15 min at 37 °C with 5% CO2. 6. Collect the cell suspension in Eppendorf tubes; spin down at 10000 × g for 3 min. Discard the supernatant. 7. Resuspend the pellet in 50 μL PBS; mix thoroughly. 8. Plate the full volume (100 μL) on a blood agar plate, and use a loop to spread the liquid. Avoid plating the viscous cell component, as this will lyse the bacteria on plate. This represents the invaded bacteria. 9. Incubate the blood agar plates overnight at 37 °C with 5% CO2. 10. Count the CFU and calculate CFU/ml. 11. Calculate the percentage of invasion based on the following formula: Invasion% =

3.5 Immunocytochemistry

ðInvaded bacteriaÞ CFU mL ðTotal number of bacteriaÞ

CFU mL

× 100%

1. Wash cells 1 time with warm PBS. 2. Fix cells with 4% (w/v) PFA in PBS (pH 7.4), 15 min at RT (see Note 6). Check fixation periodically under light microscope. 3. Wash 3 times in PBS and store at 4 °C, or immediately continue: 4. (Optional) Permeabilize cells with 0.1% Triton X-100 (in PBS), 10 min at RT (see Note 7). 5. (If permeabilized) Wash cells 3 times in PBS. 6. Block cells with 1–5% BSA + 22.52 mg/mL glycine (see Note 8) in PBST (PBS + 0.1% Tween 20) for 1 h at RT. BSA percentage and blocking time depends on specificity of antibodies. 7. Incubate cells with the primary antibody (or antibodies) diluted in 1% BSA (in PBS) overnight at 4 °C. Usual primary antibody dilution for immunocytochemistry is 1:100 (see Note 9). 8. Wash 3 times with PBS, 5 min per wash. 9. Incubate cells with the secondary antibody (or antibodies) diluted in 1% BSA (in PBS), 1 h at RT in the dark. Usual secondary antibody dilution for immunocytochemistry is 1: 500.

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10. (During secondary antibody dilution) Take out mounting medium from the freezer/fridge, and let it warm to RT. 11. Wash 3 times with PBS, 5 min per wash, in the dark. 12. Mount coverslips with a drop of mounting medium (with or without DAPI; see Note 10), on glass slides. Always protect slides from the light. 13. (Optional) Seal coverslips with nail polish to prevent drying and movement under microscope. 14. Store in the dark at 4 °C.

4

Notes 1. Multiple differentiation protocols have been developed for different needs, with variable culture conditions. A frequent alternative is neurobasal medium supplemented with B-27 and retinoic acid. If kept longer than 9 days, media should include BDNF in addition to retinoic acid, as the latter inhibits proliferation, and some cells will detach during the differentiation process. 2. Coating coverslips with Poly-D-Lysine enhances the attachment and differentiation of the cells. Sterile coverslips are placed onto the culture wells and incubated with 50 μg/mL poly-D-lysine solution in PBDS -/- (no calcium, no magnesium) for 2 h at room temperature. As free poly-D-lysine is cytotoxic, wells and coverslips must be thoroughly washed with sterile deionized water and left to dry for 2 h or overnight. 3. The MOI must be optimized based on the pneumococcal strain used. The number of invading bacteria is reduced compared to the amount of adhered; therefore the MOI needed to obtain higher output is increased in an invasion assay. 4. Depending on the invasion capacity of the S. pneumoniae strain, either the total number of bacteria (as described in this protocol) or a serial dilution must be plated. 5. For invasion assay the capsule of S. pneumoniae reduces its capacity; therefore an unencapsulated strain will yield higher numbers of invaded bacteria. In such cases the length of the infection time might be reduced. 6. Fixation can also be achieved with cold 100% methanol for 5 min at RT, depending on requirements. Methanol fixation makes proteins insoluble by removing the hydrate cover, causing proteins to re-fold and collapse. Thus, it preserves antigenicity better and results in highly specific, densely labelled signal, with low background fluorescence, at the cost of structural degeneration. Aldehyde fixation crosslinks between

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proteins, chemically altering them, which might affect antigenicity and signal intensity. But it is more stable, and the fixed structure is better preserved, so it’s usually the preferred method. 7. Permeabilization should be considered not only in terms of antigen to be studied, but also the host–pathogen process studied. For adhesion-related experiments, permeabilization will interfere with the downstream image analysis, whereas invading bacteria will be better visualized after permeabilization. 8. Glycine is only required if fixing with PFA. It’s used to block unreacted aldehydes after fixation, which can cause an increase in background fluorescence. 9. Working concentration of both primary and secondary antibodies may have to be titrated if not provided by the manufacturer. Dilution of primary antibodies is usually around 1:100. Dilution of secondary antibodies range from 1:500 to 1:1000, but the optimized signal will be highly dependent on the experimental conditions. 10. Use of DAPI should be carefully considered, as it will stain the bacteria as well as the cell nuclei. References 1. Biedler JL, Helson L, Spengler BA (1973) Morphology and growth, tumorigenicity, and cytogenetics of human neuroblastoma cells in continuous culture. Cancer Res 33:2643–2652 2. Brissac T, Orihuela CJ (2019) In vitro adhesion, invasion, and transcytosis of Streptococcus pneumoniae with host cells. Methods Mol Biol 1968:137–146 ˜ o-Vian M, Iovino F (2021) 3. Farmen K, Tofin Neuronal damage and neuroinflammation, a bridge between bacterial meningitis and neurodegenerative diseases. Front Cell Neurosci 15: 680858 4. Hu BY, Weick JP, Yu J, Ma LX, Zhang XQ, Thomson JA, Zhang SC (2010) Neural differentiation of human induced pluripotent stem cells follows developmental principles but with variable potency. Proc Natl Acad Sci U S A 107: 4335–4340

5. Iovino F, Orihuela CJ, Moorlag HE, Molema G, Bijlsma JJE (2013) Interactions between bloodborne Streptococcus pneumoniae and the blood-brain barrier preceding meningitis. PLoS One 8:e68408 6. Iovino F, Seinen J, Henriques-Normark B, van Dijl JM (2016) How does Streptococcus pneumoniae invade the brain? Trends Microbiol 24: 307–315 7. Shipley MM, Mangold CA, Szpara ML (2016) Differentiation of the sh-sy5y human neuroblastoma cell line. J Vis Exp 53193 8. Tabusi M, Thorsdottir S, Lysandrou M, Narciso AR, Minoia M, Srambickal CV, Widengren J, Henriques-Normark B, Iovino F (2021) Neuronal death in pneumococcal meningitis is triggered by pneumolysin and RrgA interactions with β-actin. PLoS Pathog 17:e1009432

INDEX A

D

Adhesion assay...................................................... 328, 331 Affinity .......................................... 62, 126, 251–259, 286 Affinity-purification.................... 181–183, 186, 198, 290 Anaerobic bacteria..........................................40, 212, 218 Antibodies ..........................................120–123, 126, 127, 131, 138, 140, 145, 149–151, 164, 214, 216, 219, 222, 223, 238, 242, 243, 251–259, 267, 274, 275, 281, 317, 321–323, 328, 329, 332–334 Avidity................................................................... 251, 256

Density gradient ................ 35, 56–59, 62–64, 66, 67, 69 Droplet digital PCR (ddPCR) ............................ 101–114

B

F

Bacteria .................................... v, vi, 3–29, 33–36, 38–41, 43–45, 47, 49, 52, 53, 55, 57, 59, 61–70, 73, 74, 77, 78, 84, 101–104, 121, 125–127, 132, 149, 169, 173, 212–216, 218, 226–233, 235, 240– 244, 246, 251–255, 257, 258, 265, 270–273, 276, 279, 280, 289, 295–309, 314, 317–319, 323, 324, 327, 328, 331–334 Bacterial infection ........................................... v, 235, 236, 242, 244 Bacterial pathogens ..............................v, 87–97, 262, 306 Bacterial proteases ......................................................... 133 Binding .......................................... 34, 39, 105, 120–122, 126, 145, 149, 150, 170, 201, 203, 206, 207, 237, 241, 251–259, 295–309 Binding stoichiometry ......................................... 204, 207 Biofilm ..................................................v, 3–29, 33–53, 61

Fermentation ........................................... 7, 20, 22, 24, 26 Flow-cell models .......................................................33–53 Flow cytometry ................................. 125, 126, 132, 138, 143, 219, 221–234, 237, 238, 242–244, 246, 251–259, 320, 321, 325 Fused proteins ...................................................... 135, 145

C Carbon source ...................................... 4, 5, 7–12, 17–20, 26, 28 Clonal genealogy......................................... 88, 89, 95, 97 Clusterin ..............................................190, 193, 195, 197 Colonization................................................. 4, 16, 17, 34, 309, 328 Colony forming units (CFUs) ............ 15, 18–25, 28, 29, 101, 125, 241, 272, 273, 280, 296, 304, 308, 315–316, 318, 319, 331, 332 Complement evasion ........................................... 119–127 Complement proteins .......................................... 121–127 Covalent crosslinking........................................... 182, 183

E Endo-β-N-acetylglucosamines (ENGases) .................149, 151, 157–159, 162, 163, 165 Epithelium ................................... 7–8, 12–15, 27, 39, 40, 169, 170, 295–297, 301–303, 307, 308 Extracellular vesicles (EVs)................................... v, 55–59

G Galleria mellonella infection model ............................. 121 Genetically tagged strains ............................................. 296 Gentamicin protection assay................................ 296, 297 Glycocalyx............................................................. 285, 286 Gram-negative ......................................44, 62, 74, 77, 78, 84, 212, 232 Gram-positive .......................................44, 62, 73, 74, 77, 84, 232, 327 Gram-positive anaerobic cocci (GPAC).............. 211–219 Granuloma formation ................................................... 314 Green fluorescent protein (GFP) ....................... 131–145, 182, 186, 188, 190, 197, 239, 325 Group A Streptococcus (GAS).................. 119, 121, 202, 261–281 Growth rate ...................................................38, 101–114, 132, 280

H Host-pathogen interactions....................... 147–148, 181, 201–207, 295–297, 328 Human blood plasma ................................................... 183

Pontus Nordenfelt and Mattias Collin (eds.), Bacterial Pathogenesis: Methods and Protocols, Methods in Molecular Biology, vol. 2674, https://doi.org/10.1007/978-1-0716-3243-7, © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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336 Index I

O

Immunofluorescence .........................170, 172, 174–175, 316, 321–323 Immunogenic secreted protein (ISP) ................ 182, 183, 186, 188, 190, 193–195, 197, 198 Immunoglobulin-degrading enzyme (IdeS) ..............120, 131–145, 253, 254 Induction ...................................132, 138, 140, 141, 143, 144, 265, 298, 307 Infection ...........................................v, vi, 3–5, 11, 16, 61, 101–114, 119–121, 169, 181, 211, 212, 215, 231, 236, 241, 242, 244, 246, 262, 265–266, 272–274, 276, 277, 279–281, 289, 295–309, 313–325, 327–329, 331, 333 Inflammasome ..............................................261–281, 308 Inflammation markers.......................................... 211–219 Inflammatory response ..................................35, 313–325 Invasion ..............................................295–309, 328, 329, 331–333

Oligomerization ............................................................ 262 Opsonization ..................................... 222, 223, 233, 237, 241, 253

K Kinetics ....................................................... 147–163, 236, 241–243, 276

L Latency .......................................................................... 314 LC-MS ..................................52, 154–156, 162–164, 170

M Mass photometry (MP) ....................................... 201–207 Mass spectrometry (MS)...................... 52, 147–163, 170, 173, 177, 181–198 Membrane vesicles ............................................. 61, 62, 66 Metabolism........................................ 4, 5, 10, 12, 13, 24, 29, 55 Mouse model.......................................... vi, 296, 304, 314 M proteins ................................................... 202, 257, 258 Mucosa..............................................................4, 262, 308 Murine bone marrow-derived macrophages ...... 261–281 Mycobacterium marinum ..................................v, 313–325 Mycobacterium tuberculosis ..................... 55, 96, 313, 314

N Nasopharynx......................................................... 4, 16, 28 Neuronal differentiation ...................................... 329–331 Neutrophil extracellular traps (NETs) ............... 212, 222, 235–246 Neutrophils....................... 120, 211–219, 222, 235–247, 261, 321, 323 N-glycans ..................................................... 148, 151, 159 Northern blot............................................................73–85

P Phagocytosis .................................................120, 221–234 Phylogenetics...........................................v, 87, 88, 90, 92, 94, 96, 97 Plasmid copy number (PCN) .............................. 101–114 Plasminogen ......................................................... 202, 207 Plasminogen-binding Group A Streptococcal M-like proteins (PAM)......................................... 202, 204 Polydispersity................................................................. 206 Protein pellicle...........................................................34, 35 Protein–protein interaction .......................................... 132 Protein–protein ............................................181–198, 201 Proteomics.................................131, 162, 181, 183, 189, 201, 286, 287, 292 Proximity labeling (PL) ...............................169–178, 181

R Recombinant expression............................................... 131 Recombination ............................................87–89, 91–97, 144, 298, 301 Refeyn ................................................................... 202, 205 Regulated cell death (RCD)................................ 235–247 Replication...........................................101, 102, 107, 298 Respiratory tract................................................... 3, 11, 13 RNA isolation............................................................73–85

S Salmonella Typhimurium ......................................... v, 298 Sepsis.................................................................... 212, 218, 286, 287 Secreted bacterial protease................................... 169–178 SH-SY5Y cell line ................................................. 329, 330 Staphylococcus aureus ............................ v, 53, 87, 89, 120, 235, 237, 242, 244–246, 287 Streptococcus pneumoniae ............................ v, 4, 5, 10, 18, 22, 62, 64–70, 87, 327, 331, 333 Streptococcus pyogenes...............................v, 119–127, 132, 149, 182, 223, 225, 226, 231, 232, 252, 253, 258, 262 Structural modeling ............................................. 181–198 Substrate ................................. 18, 24, 25, 27, 36, 45, 47, 121, 123, 135, 145, 147–165, 169–178, 215, 274, 281

T Tuberculosis (TB) ............................................55, 56, 314

BACTERIAL PATHOGENESIS: METHODS

AND

PROTOCOLS Index 337

U

Virulence mechanisms .................................................. 313

Ultracentrifugation ......................................56, 62–67, 69

W

V

Whole genome sequencing (WGS)...............89, 101–103

Vascular dysfunction ..................................................... 286 Vesicle isolation ............................................55–59, 62, 66 Virulence.......................................v, 16, 34, 95, 101–114, 119–127, 212, 292, 296, 314 Virulence factor .................................... 61, 119–121, 169, 170, 183, 262, 295, 296, 314

Y Yeast ........................................... 5, 38, 64, 131–145, 170, 225, 254, 278, 299 Yersinia ..............................................................v, 101–114