Bacterial Secretion Systems: Methods and Protocols (Methods in Molecular Biology, 2715) 1071634445, 9781071634448

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Methods in Molecular Biology 2715

Laure Journet · Eric Cascales  Editors

Bacterial Secretion Systems 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 Secretion Systems Methods and Protocols Second Edition

Edited by

Laure Journet and Eric Cascales Laboratoire d'Ingénierie des Systèmes Macromoléculaires, Institut de Microbiologie de la Méditerranée, Aix-Marseille Univ, CNRS, Marseille, France

Editors Laure Journet Laboratoire d’Inge´nierie des Syste`mes Macromole´culaires, Institut de Microbiologie de la Me´diterrane´e Aix-Marseille Univ, CNRS Marseille, France

Eric Cascales Laboratoire d’Inge´nierie des Syste`mes Macromole´culaires, Institut de Microbiologie de la Me´diterrane´e Aix-Marseille Univ, CNRS Marseille, France

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-3444-8 ISBN 978-1-0716-3445-5 (eBook) https://doi.org/10.1007/978-1-0716-3445-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024 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. Paper in this product is recyclable.

Preface In their ecological niches, bacteria are in contact with other prokaryotic and eukaryotic cells. Bacteria therefore evolved mechanisms to communicate and collaborate with these cells. They also developed antagonistic behaviors to eliminate competitors and to infect eukaryotic host cells. These aggressive actions are mediated by effector toxins with specific activities which will ultimately cause target cell lysis or re-routing of metabolic or trafficking pathways in the host. The proper delivery of bacterial effectors into the milieu or directly into target cells is assured by dedicated machines called secretion systems. Up to now, 11 secretion systems have been described in bacteria, and additional systems allow the exposition of toxins or adhesins at the cell surface or at the extremity of a pilus structure. These multiprotein transenvelope complexes differ in composition, mechanism of assembly, and mode of recruitment and transport of the toxins. However, studying these macromolecular complexes requires common techniques, ranging from the bioinformatic identification of machine components and effectors to methods to define the interaction between the different subunits and the development of reporters to follow effector translocation in vivo. Finally, state-of-the-art techniques have made significant progresses to analyze these large complexes from purified samples. The purpose of this book, Bacterial Secretion Systems, is to provide protocols that cover the broad arsenal of techniques used to study a secretion system from A to Z: identifying and localizing the different subunits, defining interactions within subunits, purifying and imaging large complexes, defining the assembly pathway by fluorescence microscopy and the role of energy for secretion, identifying secreted effectors as well as reporters to follow effector transport. Most of these techniques are not restricted to the study of secretion systems but are also of specific interest for any researcher interested on multi-protein complexes of the bacterial cell envelope. The book starts with a chapter describing a computer program recently developed to identify gene clusters encoding secretion systems within bacterial genomes. Then six chapters describe methods to define the sub-cellular localization of the different subunits of the multi-protein system: prediction programs, fractionation, cell surface exposition, and isopycnic density gradients to partition inner and outer membranes. Three techniques allowing to determine the topology of membrane proteins (substituted cysteine accessibility, protease accessibility, and reporter fusions) are then detailed. Then, eleven chapters cover the genetic, cytologic, biochemical, and biophysical methods used to study protein-protein interactions. One chapter is dedicated to the use of fluorescence microscopy to provide information on the biogenesis of secretion systems. Six chapters describe biochemical and structural techniques to purify and image protein and large complexes. Computer and in vivo methods to identify effectors are detailed as well as the reporter fusions used to validate effector secretion or translocation. Finally, functional assays to measure toxic activities of effectors into bacterial cells or in the Galleria model are described. Laure Journet Eric Cascales

Marseille, France

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

1 Identification of Protein Secretion Systems in Bacterial Genomes Using MacSyFinder Version 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sophie S. Abby, Re´mi Denise, and Eduardo P. C. Rocha 2 Protein Sorting Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Henrik Nielsen 3 Cell Fractionation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Melissa Petiti, Laetitia Houot, and Denis Duche´ 4 Components Subcellular Localization: Identification of Lipoproteins Using Globomycin and Radioactive Palmitate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nienke Buddelmeijer 5 Components Subcellular Localization: Identification of Lipoproteins Using Alkyne Fatty Acids and Click Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Karine Nozeret and Nienke Buddelmeijer 6 Defining Membrane Protein Localization by Isopycnic Density Gradients . . . . . Rhys A. Dunstan, Iain D. Hay, and Trevor Lithgow 7 Components Subcellular Localization: Cell Surface Exposure . . . . . . . . . . . . . . . . Anna Konovalova 8 Probing Protein Topology and Conformation by Limited Proteolysis . . . . . . . . . Maı¨ale`ne Chabalier, Thierry Doan, and Eric Cascales 9 Exploring Uniform, Dual, and Dynamic Topologies of Membrane Proteins by Substituted Cysteine Accessibility Method (SCAM™) . . . . . . . . . . . . Mikhail Bogdanov 10 Preparation of Uniformly Oriented Inverted Inner (Cytoplasmic) Membrane Vesicles from Gram-Negative Bacterial Cells . . . . . . . . . . . . . . . . . . . . . Mikhail Bogdanov 11 Defining Membrane Protein Topology Using pho-lac Reporter Fusions . . . . . . . Gouzel Karimova and Daniel Ladant 12 Measure of Peptidoglycan Degradation Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yoann G. Santin and Eric Cascales 13 Protein–Protein Interaction: Bacterial Two Hybrid . . . . . . . . . . . . . . . . . . . . . . . . . Gouzel Karimova, Emilie Gauliard, Marilyne Davi, Scot P. Ouellette, and Daniel Ladant 14 Protein–Protein Interactions: Oxidative Bacterial Two Hybrid . . . . . . . . . . . . . . . Callypso Pellegri, Emmanuelle Bouveret, and Laetitia Houot 15 Protein–Protein Interactions: Yeast Two Hybrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jer-Sheng Lin and Erh-Min Lai

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Protein–Protein Interactions: Bimolecular Fluorescence Complementation and Cytology Two Hybrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dyuti Purkait, Mohd Ilyas, and Krishnamohan Atmakuri Bacterial One- and Two-Hybrid Assays to Monitor Transmembrane Helix Interactions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abdelrahim Zoued, Jean-Pierre Duneau, and Eric Cascales Protein–Protein Interactions: Co-immunoprecipitation. . . . . . . . . . . . . . . . . . . . . . Jer-Sheng Lin, Jemal Ali, and Erh-Min Lai Protein–Protein Interaction: Tandem Affinity Purification in Bacteria . . . . . . . . . Julie P. M. Viala and Emmanuelle Bouveret In Vivo Site-Directed and Time-Resolved Photocrosslinking of Envelope Proteins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yassin A. Abuta’a, Anne Caumont-Sarcos, Ce´cile Albenne, and Raffaele Ieva Identification of Protein Partners by APEX2 Proximity Labeling . . . . . . . . . . . . . Ophe´lie Remy and Yoann G. Santin Blue Native PAGE Analysis of Bacterial Secretion Complexes . . . . . . . . . . . . . . . . Susann Zilkenat, Eunjin Kim, Tobias Dietsche, Julia V. Monjara´s Feria, Claudia E. Torres-Vargas, Mehari Tesfazgi Mebrhatu, and Samuel Wagner Surface Plasmon Resonance: A Sensitive Tool to Study Protein–Protein Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Badreddine Douzi Defining Assembly Pathways by Fluorescence Microscopy . . . . . . . . . . . . . . . . . . . Andreas Diepold Large Complexes: Cloning Strategy, Production, and Purification . . . . . . . . . . . . Samira Zouhir, Wiem Abidi, and Petya V. Krasteva Starting with an Integral Membrane Protein Project for Structural Biology: Production, Purification, Detergent Quantification, and Buffer Optimization—Case Study of the Exporter CntI from Pseudomonas aeruginosa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maxime Me´gret-Cavalier, Alexandre Pozza, Quentin Cece, Franc¸oise Bonnete´, Isabelle Broutin, and Gilles Phan Structural Analysis of Protein Complexes by Cryo-Electron Microscopy . . . . . . . Athanasios Ignatiou, Ke´vin Mace´, Adam Redzej, Tiago R. D. Costa, Gabriel Waksman, and Elena V. Orlova CryoEM Data Analysis of Membrane Proteins. Practical Considerations on Amphipathic Belts, Ligands, and Variability Analysis . . . . . . . Alexia Gobet, Loı¨ck Moissonnier, and Vincent Chaptal Structural Analyses of Bacterial Effectors by X-Ray Crystallography . . . . . . . . . . . Chloe´ Dugelay, Virginie Gueguen-Chaignon, and Laurent Terradot Structural Analysis of Proteins from Bacterial Secretion Systems and Their Assemblies by NMR Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gisele Cardoso de Amorim, Benjamin Bardiaux, and Nadia Izadi-Pruneyre

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Use of Bastion for the Identification of Secreted Substrates . . . . . . . . . . . . . . . . . . Jiawei Wang, Jiahui Li, and Christopher J. Stubenrauch Identification of Effectors: Precipitation of Supernatant Material . . . . . . . . . . . . . Nicolas Flaugnatti and Laure Journet Metabolic Labeling: Snapshot of the Effect of Toxins on the Key Cellular Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dukas Jure˙nas Effector Translocation Assay: Differential Solubilization . . . . . . . . . . . . . . . . . . . . . Irina S. Franco, Sara V. Pais, Nuno Charro, and Luı´s Jaime Mota Monitoring Effector Translocation with the TEM-1 Beta-Lactamase Reporter System: From Endpoint to Time Course Analysis . . . . . . . . . . . . . . . . . . Julie Allombert, Anne Vianney, and Xavier Charpentier Quantifying Substrate Protein Secretion via the Type III Secretion System of the Bacterial Flagellum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rosa Einenkel, Manuel Halte, and Marc Erhardt Quantitative Determination of Antibacterial Activity During Bacterial Coculture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Juliana Alcoforado Diniz, Christopher Earl, Ruth E. Hernandez, Birte Hollmann, and Sarah J. Coulthurst Investigating Secretion Systems and Effectors on Galleria mellonella . . . . . . . . . . Antonia Habich and Daniel Unterweger

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

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Contributors SOPHIE S. ABBY • Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, Grenoble, France WIEM ABIDI • Universite´ de Bordeaux, CNRS, Bordeaux INP, CBMN, UMR 5248, Pessac, France; ‘Structural Biology of Biofilms’ Group, European Institute of Chemistry and Biology (IECB), Pessac, France; Department of Plant Molecular Biology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland YASSIN A. ABUTA’A • Laboratoire de Microbiologie et Ge´ne´tique Mole´culaires (LMGM), Centre de Biologie Inte´grative (CBI), Universite´ de Toulouse, CNRS, UPS, Toulouse, France CE´CILE ALBENNE • Laboratoire de Microbiologie et Ge´ne´tique Mole´culaires (LMGM), Centre de Biologie Inte´grative (CBI), Universite´ de Toulouse, CNRS, UPS, Toulouse, France JULIANA ALCOFORADO DINIZ • Division of Molecular Microbiology, School of Life Sciences, University of Dundee, Dundee, UK JEMAL ALI • Institute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan; Molecular and Biological Agricultural Sciences Program, Taiwan International Graduate Program, National Chung-Hsing University and Academia Sinica, Taipei, Taiwan; Graduate Institute of Biotechnology, National Chung-Hsing University, Taichung, Taiwan JULIE ALLOMBERT • CIRI, Centre International de Recherche en Infectiologie, Inserm, U1111, Universite´ Claude Bernard Lyon 1, CNRS, UMR5308, E´cole Normale Supe´rieure de Lyon, Univ Lyon, Lyon, France KRISHNAMOHAN ATMAKURI • Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India BENJAMIN BARDIAUX • Institut Pasteur, Universite´ Paris Cite´, CNRS UMR 3528, Bacterial Transmembrane Systems Unit, Paris, France MIKHAIL BOGDANOV • Department of Biochemistry & Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX, USA FRANC¸OISE BONNETE´ • Universite´ Paris Cite´, CNRS, UMR 7099, Laboratoire de Biologie Physico-Chimique des Prote´ines Membranaires, Institut de Biologie Physico-Chimique, Paris, France EMMANUELLE BOUVERET • Institut Pasteur, Department of Microbiology, Unit Stress, Adaptation and Metabolism in enterobacteria, Universite´ Paris Cite´, UMR CNRS 6047, Paris, France ISABELLE BROUTIN • Universite´ Paris Cite´, CNRS, UMR 8038, Laboratoire CiTCoM (Cibles The´rapeutiques et Conception de Me´dicaments), Faculte´ de Pharmacie de Paris, Paris, France NIENKE BUDDELMEIJER • Institut Pasteur, Universite´ Paris Cite´, CNRS UMR6047, INSERM U1306, Biology and genetics of the bacterial cell wall Unit, 25-28 rue du docteur Roux, Paris cedex 15, France ERIC CASCALES • Laboratoire d’Inge´nierie des Syste`mes Macromole´culaires, Institut de Microbiologie de la Me´diterrane´e, Aix-Marseille Univ, CNRS, Marseille, France

xi

xii

Contributors

ANNE CAUMONT-SARCOS • Laboratoire de Microbiologie et Ge´ne´tique Mole´culaires (LMGM), Centre de Biologie Inte´grative (CBI), Universite´ de Toulouse, CNRS, UPS, Toulouse, France QUENTIN CECE • Universite´ Paris Cite´, CNRS, UMR 8038, Laboratoire CiTCoM (Cibles The´rapeutiques et Conception de Me´dicaments), Faculte´ de Pharmacie de Paris, Paris, France MAI¨ALE`NE CHABALIER • Laboratoire d’Inge´nierie des Syste`mes Macromole´culaires, UMR7255, Institut de Microbiologie de la Me´diterrane´e, Aix-Marseille Univ – CNRS, Marseille, France VINCENT CHAPTAL • Molecular Microbiology and Structural Biochemistry, UMR5086 CNRS University Lyon 1, Lyon, France XAVIER CHARPENTIER • CIRI, Centre International de Recherche en Infectiologie, Inserm, U1111, Universite´ Claude Bernard Lyon 1, CNRS, UMR5308, E´cole Normale Supe´rieure de Lyon, Univ Lyon, Lyon, France NUNO CHARRO • UCIBIO – Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, NOVA University Lisbon, Caparica, Portugal TIAGO R. D. COSTA • Centre for Bacterial Resistance Biology, Department of Life Sciences, Imperial College, London, UK SARAH J. COULTHURST • Division of Molecular Microbiology, School of Life Sciences, University of Dundee, Dundee, UK MARILYNE DAVI • Unite´ de Biochimie des Interactions Macromole´culaires, De´partement de Biologie Structurale et Chimie, Institut Pasteur, CNRS, UMR 3528, Paris, France GISELE CARDOSO DE AMORIM • Nu´cleo Multidisciplinar de Pesquisa em Biologia, Campus Duque de Caxias, Universidade Federal do Rio de Janeiro, Duque de Caxias, RJ, Brazil RE´MI DENISE • APC Microbiome Ireland & School of Microbiology, University College Cork, Cork, Ireland ANDREAS DIEPOLD • Department of Ecophysiology, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany TOBIAS DIETSCHE • Section of Cellular and Molecular Microbiology, Interfaculty Institute of Microbiology and Infection Medicine (IMIT), University of Tu¨bingen, Tu¨bingen, Germany THIERRY DOAN • Laboratoire d’Inge´nierie des Syste`mes Macromole´culaires, UMR7255, Institut de Microbiologie de la Me´diterrane´e, Aix-Marseille Univ – CNRS, Marseille, France BADREDDINE DOUZI • Universite´ de Lorraine, INRAE, DynAMic, Nancy, France DENIS DUCHE´ • Laboratoire d’Inge´nierie des Syste`mes Macromole´culaires, UMR7255, Institut de Microbiologie de la Me´diterrane´e, Aix-Marseille Univ – CNRS, Marseille, France CHLOE´ DUGELAY • Microbiologie Mole´culaire et Biochimie Structurale (MMSB), Universite´ Lyon 1, CNRS, UMR5086, Lyon, France JEAN-PIERRE DUNEAU • Laboratoire d’Inge´nierie des Syste`mes Macromole´culaires, UMR7255, Institut de Microbiologie de la Me´diterrane´e, Aix-Marseille Univ, CNRS, Marseille, France RHYS A. DUNSTAN • Centre to Impact AMR, Monash University, Melbourne, VIC, Australia; Infection Program and Department of Microbiology, Biomedicine Discovery Institute, Monash University, Melbourne, VIC, Australia CHRISTOPHER EARL • Division of Molecular Microbiology, School of Life Sciences, University of Dundee, Dundee, UK

Contributors

xiii

ROSA EINENKEL • Humboldt Universit€ at zu Berlin, Berlin, Germany MARC ERHARDT • Humboldt Universit€ a t zu Berlin, Berlin, Germany; Max Planck Unit for the Science of Pathogens, Berlin, Germany NICOLAS FLAUGNATTI • Laboratoire d’Inge´nierie des Syste`mes Macromole´culaires (LISM, UMR7255), Institut de Microbiologie de la Me´diterrane´e, Aix Marseille Univ, CNRS, Marseille, France; Laboratory of Molecular Microbiology, School of Life Sciences, Ecole Polytechnique Fe´de´rale de Lausanne (EPFL), Lausanne, Switzerland IRINA S. FRANCO • Associate Laboratory i4HB – Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, Caparica, Portugal; UCIBIO – Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, NOVA University Lisbon, Caparica, Portugal EMILIE GAULIARD • Unite´ de Biochimie des Interactions Macromole´culaires, De´partement de Biologie Structurale et Chimie, Institut Pasteur, CNRS, UMR 3528, Paris, France; Universite´ Paris Cite´, Cellule Pasteur, Paris, France ALEXIA GOBET • Molecular Microbiology and Structural Biochemistry, UMR5086 CNRS University Lyon 1, Lyon, France VIRGINIE GUEGUEN-CHAIGNON • Protein Science Facility, SFR Biosciences, Centre National de la Recherche Scientifique UAR3444, Universite´ de Lyon, Lyon, France ANTONIA HABICH • Institute for Experimental Medicine, Kiel University, Kiel, Germany; Max Planck Institute for Evolutionary Biology, Plo¨n, Germany MANUEL HALTE • Humboldt Universit€ at zu Berlin, Berlin, Germany IAIN D. HAY • School of Biological Sciences, The University of Auckland, Auckland, New Zealand RUTH E. HERNANDEZ • Division of Molecular Microbiology, School of Life Sciences, University of Dundee, Dundee, UK BIRTE HOLLMANN • Division of Molecular Microbiology, School of Life Sciences, University of Dundee, Dundee, UK LAETITIA HOUOT • Laboratoire d’Inge´nierie des Syste`mes Macromole´culaires, UMR7255, Institut de Microbiologie de la Me´diterrane´e, Aix-Marseille Univ – CNRS, Marseille, France RAFFAELE IEVA • Laboratoire de Microbiologie et Ge´ne´tique Mole´culaires (LMGM), Centre de Biologie Inte´grative (CBI), Universite´ de Toulouse, CNRS, UPS, Toulouse, France ATHANASIOS IGNATIOU • Institute for Structural and Molecular Biology, School of Biological Sciences, Birkbeck College, London, UK MOHD ILYAS • Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India NADIA IZADI-PRUNEYRE • Institut Pasteur, Universite´ Paris Cite´, CNRS UMR 3528, Bacterial Transmembrane Systems Unit, Paris, France LAURE JOURNET • Laboratoire d’Inge´nierie des Syste`mes Macromole´culaires, Institut de Microbiologie de la Me´diterrane´e, Aix-Marseille Univ, CNRS, Marseille, France DUKAS JURE˙NAS • Laboratoire de Ge´ne´tique et Physiologie Bacte´rienne, De´partement de Biologie Mole´culaire, Faculte´ des Sciences, Universite´ Libre de Bruxelles (ULB), Gosselies, Belgium GOUZEL KARIMOVA • Unite´ de Biochimie des Interactions Macromole´culaires, De´partement de Biologie Structurale et Chimie, Institut Pasteur, CNRS, UMR 3528, Universite´ Paris Cite´, Paris, France

xiv

Contributors

EUNJIN KIM • Section of Cellular and Molecular Microbiology, Interfaculty Institute of Microbiology and Infection Medicine (IMIT), University of Tu¨bingen, Tu¨bingen, Germany ANNA KONOVALOVA • Department of Microbiology and Molecular Genetics, McGovern Medical School, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA PETYA V. KRASTEVA • Universite´ de Bordeaux, CNRS, Bordeaux INP, CBMN, UMR 5248, Pessac, France; ‘Structural Biology of Biofilms’ Group, European Institute of Chemistry and Biology (IECB), Pessac, France DANIEL LADANT • Unite´ de Biochimie des Interactions Macromole´culaires, De´partement de Biologie Structurale et Chimie, Institut Pasteur, CNRS, UMR 3528, Universite´ Paris Cite´, Paris, France ERH-MIN LAI • Institute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan; Molecular and Biological Agricultural Sciences Program, Taiwan International Graduate Program, National Chung-Hsing University and Academia Sinica, Taipei, Taiwan; Biotechnology Center, National Chung-Hsing University, Taichung, Taiwan JIAHUI LI • Infection Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia; Centre to Impact AMR, Monash University, Melbourne, VIC, Australia JER-SHENG LIN • Institute of Plant and Microbial Biology, Academia Sinica, Taipei, Taiwan TREVOR LITHGOW • Centre to Impact AMR, Monash University, Melbourne, VIC, Australia; Infection Program and Department of Microbiology, Biomedicine Discovery Institute, Monash University, Melbourne, VIC, Australia KE´VIN MACE´ • Institute for Structural and Molecular Biology, School of Biological Sciences, Birkbeck College, London, UK MEHARI TESFAZGI MEBRHATU • Section of Cellular and Molecular Microbiology, Interfaculty Institute of Microbiology and Infection Medicine (IMIT), University of Tu¨bingen, Tu¨bingen, Germany MAXIME ME´GRET-CAVALIER • Universite´ Paris Cite´, CNRS, UMR 8038, Laboratoire CiTCoM (Cibles The´rapeutiques et Conception de Me´dicaments), Faculte´ de Pharmacie de Paris, Paris, France LOI¨CK MOISSONNIER • Molecular Microbiology and Structural Biochemistry, UMR5086 CNRS University Lyon 1, Lyon, France JULIA V. MONJARA´S FERIA • Section of Cellular and Molecular Microbiology, Interfaculty Institute of Microbiology and Infection Medicine (IMIT), University of Tu¨bingen, Tu¨bingen, Germany LUI´S JAIME MOTA • Associate Laboratory i4HB – Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, Caparica, Portugal; UCIBIO – Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, NOVA University Lisbon, Caparica, Portugal HENRIK NIELSEN • Department of Health Technology, Technical University of Denmark, Lyngby, Denmark KARINE NOZERET • Institut Pasteur, Universite´ Paris Cite´, CNRS UMR6047, Helicobacter Pathogenicity Unit, 25-28 rue du docteur Roux, Paris cedex 15, France ELENA V. ORLOVA • Institute for Structural and Molecular Biology, School of Biological Sciences, Birkbeck College, London, UK SCOT P. OUELLETTE • Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA

Contributors

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SARA V. PAIS • UCIBIO – Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, NOVA University Lisbon, Caparica, Portugal CALLYPSO PELLEGRI • Laboratoire d’Inge´nierie des Syste`mes Macromole´culaires, UMR7255, Institut de Microbiologie de la Me´diterrane´e, Aix-Marseille Univ – CNRS, Marseille, France MELISSA PETITI • Laboratoire d’Inge´nierie des Syste`mes Macromole´culaires, UMR7255, Institut de Microbiologie de la Me´diterrane´e, Aix-Marseille Univ – CNRS, Marseille, France GILLES PHAN • Universite´ Paris Cite´, CNRS, UMR 8038, Laboratoire CiTCoM (Cibles The´rapeutiques et Conception de Me´dicaments), Faculte´ de Pharmacie de Paris, Paris, France ALEXANDRE POZZA • Universite´ Paris Cite´, CNRS, UMR 7099, Laboratoire de Biologie Physico-Chimique des Prote´ines Membranaires, Institut de Biologie Physico-Chimique, Paris, France DYUTI PURKAIT • Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India ADAM REDZEJ • Institute for Structural and Molecular Biology, School of Biological Sciences, Birkbeck College, London, UK OPHE´LIE REMY • de Duve Institute, UCLouvain, Brussels, Belgium EDUARDO P. C. ROCHA • Institut Pasteur, Universite´ Paris Cite´, CNRS UMR3525, Microbial Evolutionary Genomics, Paris, France YOANN G. SANTIN • Laboratoire d’Inge´nierie des Syste`mes Macromole´culaires, UMR7255, Institut de Microbiologie de la Me´diterrane´e, Aix-Marseille Univ, CNRS, Marseille, France; de Duve Institute, UCLouvain, Brussels, Belgium CHRISTOPHER J. STUBENRAUCH • Infection Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia; Centre to Impact AMR, Monash University, Melbourne, VIC, Australia LAURENT TERRADOT • Microbiologie Mole´culaire et Biochimie Structurale (MMSB), Universite´ Lyon 1, CNRS, UMR5086, Lyon, France CLAUDIA E. TORRES-VARGAS • Section of Cellular and Molecular Microbiology, Interfaculty Institute of Microbiology and Infection Medicine (IMIT), University of Tu¨bingen, Tu¨bingen, Germany DANIEL UNTERWEGER • Institute for Experimental Medicine, Kiel University, Kiel, Germany; Max Planck Institute for Evolutionary Biology, Plo¨n, Germany JULIE P. M. VIALA • Laboratoire d’Inge´nierie des Syste`mes Macromole´culaires (UMR 7255), Institut de Microbiologie de la Me´diterrane´e, Aix-Marseille Univ., CNRS, Marseille, France ANNE VIANNEY • CIRI, Centre International de Recherche en Infectiologie, Inserm, U1111, Universite´ Claude Bernard Lyon 1, CNRS, UMR5308, E´cole Normale Supe´rieure de Lyon, Univ Lyon, Lyon, France SAMUEL WAGNER • Section of Cellular and Molecular Microbiology, Interfaculty Institute of Microbiology and Infection Medicine (IMIT), University of Tu¨bingen, Tu¨bingen, Germany; German Center for Infection Research (DZIF), Partner-site Tu¨bingen, Tu¨bingen, Germany; Excellence Cluster “Controlling Microbes to Fight Infections” (CMFI), Tu¨bingen, Germany GABRIEL WAKSMAN • Institute for Structural and Molecular Biology, School of Biological Sciences, Birkbeck College, London, UK

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JIAWEI WANG • European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK; Infection Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia; Centre to Impact AMR, Monash University, Melbourne, VIC, Australia SUSANN ZILKENAT • Section of Cellular and Molecular Microbiology, Interfaculty Institute of Microbiology and Infection Medicine (IMIT), University of Tu¨bingen, Tu¨bingen, Germany ABDELRAHIM ZOUED • Laboratoire d’Inge´nierie des Syste`mes Macromole´culaires, UMR7255, Institut de Microbiologie de la Me´diterrane´e, Aix-Marseille Univ, CNRS, Marseille, France; Centre International de Recherche en Infectiologie, UMR5308, Universite´ Claude Bernard Lyon 1 – INSERM – CNRS, Lyon, France SAMIRA ZOUHIR • Universite´ de Bordeaux, CNRS, Bordeaux INP, CBMN, UMR 5248, Pessac, France; ‘Structural Biology of Biofilms’ Group, European Institute of Chemistry and Biology (IECB), Pessac, France; CNRS, LBPA, Ecole Normale Supe´rieure Paris-Saclay and Universite´ Paris-Saclay, Gif-sur-Yvette, France

Chapter 1 Identification of Protein Secretion Systems in Bacterial Genomes Using MacSyFinder Version 2 Sophie S. Abby, Re´mi Denise, and Eduardo P. C. Rocha Abstract Protein secretion systems are complex molecular machineries that translocate proteins through the outer membrane and sometimes through multiple other barriers. They have evolved by co-option of components from other envelope-associated cellular machineries, making them sometimes difficult to identify and discriminate. Here, we describe how to identify protein secretion systems in bacterial genomes using the MacSyFinder program. This flexible computational tool uses the knowledge gathered from experimental studies to identify homologous systems in genome data. It can be used with a set of predefined MacSyFinder models, “TXSScan,” to identify all major secretion systems of diderm bacteria (i.e., with inner and LPS-containing outer membranes) as well as evolutionarily related cell appendages (pili and flagella). For this, it identifies and clusters co-localized genes encoding proteins of secretion systems using sequence similarity search with Hidden Markov Model (HMM) protein profiles. Finally, it checks if the clusters’ genetic content and genomic organization satisfy the constraints of the model. TXSScan models can be altered in the command line or customized to search for variants of known secretion systems. Models can also be built from scratch to identify novel systems. In this chapter, we describe a complete pipeline of analysis, starting from (i) the integration of information from a reference set of experimentally studied systems, (ii) the identification of conserved proteins and the construction of their HMM protein profiles, (iii) the definition and optimization of “macsy-models,” and (iv) their use and online distribution as tools to search genomic data for secretion systems of interest. MacSyFinder is available here: https://github.com/ gem-pasteur/macsyfinder, and MacSyFinder models here: https://github.com/macsy-models. Key words Comparative genomics, Genome annotation, Bioinformatic detection, Macromolecular systems, Bioinformatic modeling

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Introduction Bacteria produce proteins to interact with other individuals, prokaryotes or eukaryotes, to operate changes in their local environment, or to uptake resources. Many of the proteins involved in these processes need to be secreted to the outside of the cell. Bacteria with an LPS-containing outer membrane (henceforth

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_1, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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called diderms) face a formidable challenge to secrete these proteins because they must transport them through the inner membrane, the cell wall, the outer membrane, and sometimes additional extra barriers such as the bacterial capsule and membranes of other cells. The complexity of these molecular processes and the key roles of protein secretion systems in bacterial ecology and virulence have spurred much interest in their study (for recent reviews, see [1–9]). There are at least eight well-known protein secretion systems in diderms, and now even up to ten described (numbered from T1SS to T11SS, excluding the T7SS from Mycobacteria), but others probably remain to be uncovered [10]. There are few computational tools to identify and characterize protein secretion systems in bacterial genomes (for a list see, Table 1 of [10]). Their development becomes urgent in light of the availability of many thousands of genomes and the ease with which new ones can be sequenced. These tools should be able to identify components of the protein secretion systems and assess if they are sufficient to define an instance of a given system. When the components are highly conserved proteins, they can be identified with high sensitivity by sequence similarity search. The identification of fast-evolving components might be more complicated because of poor sequence conservation. Additionally, some components may not be strictly necessary for a functional system, and it may be difficult to know which of the two factors explains their absence from an instance of the system. Under these conditions, it is useful to split the components of secretion systems into those that should be present in the instance (“mandatory”) and those that may be absent (“accessory”). The former correspond to highly conserved, easily identifiable components, while the latter correspond to components that may be lacking in systems because they are missing or not detected. This nomenclature does not presume anything about the biological role of the accessory components: they may be biologically essential but unidentifiable by sequence similarity search. This classification aims at describing the system in a way that facilitates its identification in genomes. We will use it throughout this text. The evolution of secretion systems has involved the co-option of many components from other molecular machineries [11]. These components have sometimes been co-opted in turn for other cellular machineries [12]. As a result, many components of protein secretion systems have homologs in other systems [13, 14]. This increases the risk of misidentifications. For example, the T3SS and the flagellum are evolutionarily related, and several of their core components belong to homologous families [13]. In this specific case, the discrimination between the two systems is facilitated by the existence of flagellum-associated mandatory proteins that are always absent from T3SS (e.g., FlgB) and vice versa (e.g., the secretin). These components can be qualified as “forbidden” in the other system to prevent misidentifications.

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The analysis of the genetic context can also improve the discrimination between protein secretion systems and other molecular systems. For example, the components of the T3SS are usually encoded in one single locus, which facilitates their discrimination from those of the flagellum [15]. Another example is provided by the three mandatory components of the T1SS, which all have homologs in other systems [16], even if none of the other systems includes all three [17]. Notably, the abc (ABC transporter) and the mfp (membrane fusion protein) components are systematically encoded in the same locus in T1SS (and only in this system). Hence, three types of information facilitate the unambiguous detection of secretion systems: identifying pertinent and forbidden components, the completeness of the set of components, and their genetic organization. This information can be put together in a model of the system that can be used by a computer program that we developed and called MacSyFinder for “Macromolecular System Finder” [18]. This program, now in its second version (“v2”), searches genomes for instances satisfying the characteristics described in the model in terms of genetic content (or “quorum”) and organization [18, 19]. Sometimes good computational models of protein secretion systems are not available. Creating novel (or better) models requires to identify the relevant components and their genetic organization. Most secretion systems were studied on a small number of bacteria, where they were sometimes remarkably well characterized in terms of their components, their genetic regulation, their structure, and sometimes their assembly pathways. In contrast, the other instances of the systems are usually very poorly characterized. The challenge posed to the researcher interested in identifying novel instances of a given type of system is thus to produce models with relevant descriptions of the current knowledge of the system. This is difficult because the number of components and their organization may vary widely. For example, the type IV secretion system (T4SS) locus of Legionella pneumoniae is encoded by more than twice the number of genes of the vir T4SS of Agrobacterium tumefaciens [20, 21]. In addition, most T4SS are encoded in a single locus, but there are intracellular pathogens where they are encoded in several distant loci [22]. The key point in the production of novel models is thus the identification of the traits that are conserved and can be most useful to identify a certain type of systems. The production of models involves generalizing knowledge that was obtained from specific examples. These models are quantitative representations of the composition and organization of the known systems. When they work, they vastly facilitate the identification of homologous systems. When they fail, they highlight gaps in our understanding of the system, which often raises interesting biological questions.

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Genetic description

Model Type I secretion system

abc

mfp

d



With this model, MacSyFinder will identify a T3SS secretin “T3SS_sctC” when it finds a hit for any of these three protein profiles. We show another example with the case of SprA of the T9SS in Subheading 3.2.2. 7. The procedure to optimize the co-localization criterion starts by setting it to a value higher than expected (but not too high, otherwise, several occurrences of the system could be agglomerated). This produces large clusters that are expected to contain all relevant co-localized genes. This parameter may be subsequently refined by plotting the distribution of the maximal distance found between two consecutive components in the clusters of the reference dataset. To exemplify, we searched for “single-locus” T6SS in a set of 1528 bacterial genomes using a minimal co-localization distance of 20 genes [10]: macsyfinder

--db-type

gembase

--sequence-

db bacterial_genomes_proteins.fasta -o macsyfinder_opt_coloc_T6SS --inter-gene-max-space bacteria/diderm/T6SSi

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--models

TXSScan

bacteria/

diderm/T6SSi

The distribution of distances observed in genomes suggests that a smaller value (e.g., 14) is enough to identify all relevant clusters (Fig. 5b). 8. The quorum criterion can be optimized in multiple ways, depending on the distribution of the secretion systems across genomes. Suppose the system is encoded at a single locus (but may be found in several copies per replicon). In that case, the quorum can be optimized by studying the distribution of the number of different components detected in each cluster. For this purpose, one can use a model with a very relaxed quorum criterion (e.g., set to “1”) and draw the distribution of the number of components found in each cluster (with at least one component) (Fig. 5a). This can be done directly in the command line, for example, using the T6SSi model available in TXSScan:

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Fig. 5 Optimization of the quorum and co-localization parameters for the T6SSi. (a) Distribution of the number of different components of T6SSi co-localized (d ≤ 20). (b) Distribution of the maximal distance between consecutive components in the T6SSi detected with d ≤ 20. The minimal number of components required for a T6SSi was set to 11 in the T6SSi model, which corresponds to the start of the second peak in the distribution represented in panel (a) T6SSi detected as full systems are colored in blue. Figures and legends are freely reproduced with modification from [10] (as specified by the Creative Commons Attribution (CC BY) license version 4.0) macsyfinder

--db-type

gembase

--sequence-

db bacterial_genomes_proteins.fasta –o macsyfinder_opt_quorum_T6SS --min-genes-required bacteria/diderm/T6SSi 1 --min-mandatory-genes-required bacteria/diderm/T6SSi 1 --models TXSScan bacteria/ diderm/T6SSi

The number of (accessory and mandatory) components in each cluster can be computed from the easy-to-parse (tabulation-separated) “best_solution.tsv” output file in the “macsyfinder_opt_quorum_T6SS” folder, e.g., using a Python script based on the pandas library (https://pandas.pydata.org) which computes the number of different genes (distinct value in the “hit_gene_ref” column) detected in each different “system/cluster” (distinct value in the “sys_id” column). This specific example shows many clusters with more than 11 components (Fig. 5a). This is in line with the presence of more than 13 core components in most T6SSi [39]. A final model with a quorum set to 11 would thus accurately identify novel instances of the T6SSi [10]. When the system is typically encoded in a single copy per genome scattered in several loci (“multi_loci”), the analysis of the clusters is less informative, especially if there are other systems in the genome with homologs to these components.

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Nevertheless, it can be complemented with information on the number of components per replicon. This analysis should use low stringency co-localization and quorum parameters, e.g., all genes can be set to “loner,” and the quorum set to a small value. We exemplify this by searching to optimize the T9SS model (see Fig. 1 and Subheading 3.2.2): (a) Copy the “T9SS.xml” file as “T9SS_loner.xml” in the “./ local-TXSScan/TXSScan-refine/definitions” folder, and all the T9SS profiles (“T9SS_*.hmm”) in the “./localTXSScan/TXSScan-refine/profiles” folder. (b) Add “loner = ‘1’” to the definition line of each gene in the new file “T9SS_loner.xml.” (c) Alter the values of the parameters “min_genes_required” and “min_mandatory_genes_required,” either by using the command line (as in the command line below) or by modifying the XML file in the system definition line (“min_mandatory_genes_required = ‘1’ min_genes_required = ‘1’”). (d) Run MacSyFinder on the modified version of the model: macsyfinder --db-type gembase --sequence-db bacterial_genomes_proteins.fasta –o macsyfinder_opt_quorum_T9SS --min-genes-required T9SS_loner 1 -min-mandatory-genes-required T9SS_loner 1 --models-dir ./local-TXSScan --models TXSScan-refine T9SS_loner

The distribution of “accessory” and “mandatory” components shows many replicons with more than seven components. The model using a quorum of seven is able to identify novel instances of the T9SS accurately [10]. References 1. Spitz O, Erenburg IN, Beer T, Kanonenberg K, Holland IB, Schmitt L (2019) Type I secretion systems-one mechanism for all? Microbiol Spectr 7. https://doi. org/10.1128/microbiolspec.PSIB0003-2018 2. Korotkov KV, Sandkvist M (2019) Architecture, function, and substrates of the Type II secretion system. EcoSal Plus 8. https://doi. org/10.1128/ecosalplus.ESP-0034-2018 3. Gala´n JE, Lara-Tejero M, Marlovits TC, Wagner S (2014) Bacterial type III secretion systems: specialized nanomachines for protein

delivery into target cells. Annu Rev Microbiol 68:415–438. https://doi.org/10.1146/ annurev-micro-092412-155725 4. Costa TRD, Harb L, Khara P, Zeng L, Hu B, Christie PJ (2021) Type IV secretion systems: Advances in structure, function, and activation. Mol Microbiol 115:436–452. https://doi. org/10.1111/mmi.14670 5. Fan E, Chauhan N, Udatha DBRKG, Leo JC, Linke D (2016) Type V secretion systems in bacteria. Microbiol Spectr 4. https://doi.org/ 10.1128/microbiolspec.VMBF-0009-2015

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In silico Identification of Secretion Systems interface for sequence alignment and phylogenetic tree building. Mol Biol Evol 27:221–224. https://doi.org/10.1093/molbev/msp259 28. Waterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ (2009) Jalview Version 2--a multiple sequence alignment editor and analysis workbench. Bioinformatics 25:1189– 1191. https://doi.org/10.1093/bioinformat ics/btp033 29. Buchfink B, Reuter K, Drost H-G (2021) Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods 18:366– 368. https://doi.org/10.1038/s41592-02101101-x 30. Miele V, Penel S, Duret L (2011) Ultra-fast sequence clustering from similarity networks with SiLiX. BMC Bioinform 12:116. https:// doi.org/10.1186/1471-2105-12-116 31. Enright AJ, Van Dongen S, Ouzounis CA (2002) An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res 30:1575–1584. https://doi.org/10. 1093/nar/30.7.1575 32. Blum M, Chang H-Y, Chuguransky S, Grego T, Kandasaamy S, Mitchell A, Nuka G, Paysan-Lafosse T, Qureshi M, Raj S, Richardson L, Salazar GA, Williams L, Bork P, Bridge A, Gough J, Haft DH, Letunic I, Marchler-Bauer A, Mi H, Natale DA, Necci M, Orengo CA, Pandurangan AP, Rivoire C, Sigrist CJA, Sillitoe I, Thanki N, Thomas PD, Tosatto SCE, Wu CH, Bateman A, Finn RD (2021) The InterPro protein families and domains database: 20 years on. Nucleic Acids Res 49:D344– D354. https://doi.org/10.1093/nar/ gkaa977 33. Shrivastava A, Johnston JJ, van Baaren JM, McBride MJ (2013) Flavobacterium johnsoniae GldK, GldL, GldM, and SprA are required for secretion of the cell surface gliding motility adhesins SprB and RemA. J Bacteriol 195: 3201–3212. https://doi.org/10.1128/JB. 00333-13 34. McBride MJ, Zhu Y (2013) Gliding motility and Por secretion system genes are widespread among members of the phylum bacteroidetes. J Bacteriol 195:270–278. https://doi.org/10. 1128/JB.01962-12 35. Zhu Y, McBride MJ (2014) Deletion of the Cytophaga hutchinsonii type IX secretion system gene sprP results in defects in gliding

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Chapter 2 Protein Sorting Prediction Henrik Nielsen Abstract Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global property-based, and homology-based prediction. In this chapter, the strengths and drawbacks of each of these approaches are described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory. Key words Protein sorting, Subcellular location, Secretion, Transmembrane proteins, Prediction, Machine learning

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Introduction Protein sorting prediction—in other words, inferring the subcellular location (SCL) of proteins from their amino acid sequences— has a long history in bioinformatics [1]. The first attempts at predicting the best known sorting signals, the transmembrane α-helix (TMH) and the secretory signal peptide (SP), were published in 1982–1983, long before bioinformatics was even established as a field [2, 3]. Since then, a plethora of methods for predicting sorting signals and SCL have been published, and it can be a daunting task to select the most relevant and reliable methods for analyzing a set of sequences. Of course, the development of algorithms and the growth in available training data has led to an increase in the predictive performance of the available methods. In 2005, some of the authors of the PSORTb method for predicting SCL in bacteria [4] even concluded that “on average, recent high-precision computational methods such as PSORTb now have a lower error rate than laboratory methods” [5]. This conclusion should be taken with a grain of salt; firstly, it applies only to high-throughput laboratory

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_2, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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methods; secondly, it should be remembered that computational methods will never be better than the data used to train them. Nevertheless, the authors had a point regarding the experimental sources of error which can easily render a high-throughput experiment less reliable than a well-trained computational method. It can be difficult, however, to decide what to believe when the authors of every computational method tend to describe their performance as being superior to all others. There are different ways of defining the problem, different ways of measuring the performance, and different prerequisites used for prediction. The aim of this chapter is not to provide a definite answer to which method is best for which problem—such a checklist would quickly become outdated—but instead to install in the reader a toolbox for critically evaluating bioinformatic algorithms. This will involve a number of examples of computational methods selected for their relevance for bacteria. In general, prediction methods will only be mentioned if they either provide publicly available web servers or have strong historical relevance.

2

Three Approaches to Prediction It is crucial to understand that there are basically three different approaches to protein sorting prediction. The first approach is recognition of the actual sorting signals. The abovementioned early methods for TMH and SP recognition [2, 3] were examples of this. Several more recent examples will be given in Subheadings 5 and 7. The second approach is prediction based on global properties of the proteins, e.g., their amino acid composition. This approach was first used to discriminate between intracellular and extracellular proteins in both prokaryotic end eukaryotic proteins in 1994 [6]. It has been shown that the main part of the differences in amino acid composition between intracellular and extracellular proteins resides in the surface-exposed amino acids, which makes sense since the surfaces should be adapted to the varying physicochemical environments of the different SCLs [7]. This analysis was done for eukaryotic proteins only, but it would be fair to assume that the observation holds true also for bacterial proteins. Two early SCL prediction methods which used only the amino acid composition were NNPSL in 1998 [8] and SubLoc in 2001 [9], based on artificial neural networks (ANN) and support vector machines (SVM), respectively (see Subheading 3). They were limited in their applicability, because their data set did not include any membrane proteins, and they did not distinguish between Grampositive and Gram-negative bacteria.

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Using only the amino acid composition for prediction of course throws away all sequence information, including possible signatures of actual sorting signals. One way to retain some of this information while still keeping a fixed number of parameters is to count the occurrences of amino acid pairs, either adjacent or separated by a small distance. Nakashima and Nishikawa in 1994 [6] thus found that including composition of amino acid pairs with a separation distance of up to five positions improved predictive performance. The third approach is prediction by sequence homology. When trying to predict functional aspects of an unknown protein, the standard procedure is to do a BLAST search [10] and then infer such aspects from the functional annotations of the found homologues. Therefore, the intuitive expectation is that such a procedure will also work for SCL—in other words, that a protein tends to stay in the same compartment in the course of evolution. Indeed, a significant part of the “subcellular location” annotations of bacteria in Swiss-Prot (the manually annotated part of UniProt [11]) is found with “sequence similarity” as the evidence (more than three times as many as the corresponding annotations with experimental evidence). However, it is not trivial to determine how similar a pair of proteins has to be in order to perform an inference about SCL. Nair and Rost [12], working with eukaryotic proteins only, concluded that more than 70% identical residues in a pairwise BLAST search are needed to correctly infer SCL for 90% of the query proteins. On the other hand, the authors of the CELLO method for both eukaryotes and bacteria [13] found that SCL prediction by a simple BLAST search was better than a machine learning method above a pairwise identity cutoff as low as 30%. The simplest possible homology-based prediction is the direct transfer of annotation from the best BLAST hit, i.e., the query protein is used to search a database of proteins with experimentally known SCLs, and then the SCL of the best hit is assigned to the query. However, more advanced approaches to homology-based prediction are also possible, using indirect means to infer SCL from the annotation of homologues which do not necessarily have experimentally known SCLs. This annotation could be derived from keywords or functional descriptions [14, 15], titles and/or abstracts of literature references [16, 17], or Gene Ontology terms [18–24]. In this context, it should be mentioned that many signal-based and global property-based methods use a BLAST search to build a profile of related sequences in order to enhance the prediction by using the profile as an encoding for the amino acids in the query sequence. This does not make these methods homology-based, since they do not use the annotations of the found hits.

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In addition to the three approaches described here, it is of course possible to construct hybrids of them. Most of the multicategory methods described in Subheading 9 are of the hybrid type. When comparing methods based on sorting signals, global properties, or homology, it is important to realize that each approach has its strengths and weaknesses. Homology-based methods, or hybrid methods containing homology-based components, often present the best measured performances, but the performance depends critically on the source of the query protein. Organisms that have been subject to intense research will naturally tend to have more high-quality annotations, so proteins from those and their close relatives will find more close homologues with richer annotations from which to make predictions, while predictions for less well-studied organisms will suffer from lack of annotations of close homologues. This is typically not taken into consideration when reporting the predictive performances of such methods. Signal-based and global property methods should be expected to be less sensitive to the source of the query protein, unless the signals in the training data are very organism specific. There are two advantages to using global property or homology-based methods. First, they can be used also for those compartments where the actual sorting signals are not known or are too poorly characterized to support a prediction method. Second, they may work for sequences that are fragments from which the actual sorting signal may be missing or for amino acid sequences derived from genomic or metagenomic sequence where the start codon of the protein has not been correctly predicted, thus obscuring any N-terminal sorting signals. On the downside, global property or homology-based methods do not provide the same degree of insight into the information processing in the cell, since they ignore which parts of the sequence are actually important for sorting. Another drawback is that such methods will not be able to distinguish between very closely related proteins that differ in the presence or absence of a sorting signal, and they will not be able to predict the effects of small mutations that destroy or create a sorting signal.

3

Algorithms for Prediction A rich variety of computational algorithms have been used in the prediction of SCL from amino acid sequence. Common to all of them is that they take a number of sequence-derived inputs and produce an output which can be the presence or absence of a sorting signal (for signal-based predictors), or an assignment of the protein into one of a number of possible SCL classes (for multi-category predictors). For Gram-negative bacteria, the number of SCL classes is most often defined as five (cytoplasm, inner

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membrane, periplasm, outer membrane, and extracellular), while for Gram-positive bacteria, the corresponding number is four (cytoplasm, membrane, cell wall, and extracellular). Obviously, this is only true for “classical” Gram-positives and Gram-negatives, ignoring the “problematic” bacteria which either stain Gram-positive, although they have an outer membrane (such as genus Deinococcus) or stain Gram-negative, although they have no outer membrane (phylum Tenericutes). Some algorithms (e.g., sequence alignment and Hidden Markov Models, HMMs) are naturally designed to work with sequences, while others (e.g., traditional Artificial Neural Networks, ANNs, and Support Vector Machines, SVMs) take a fixed number of input values. When working with the latter category, one can either input the sequence as a series of overlapping windows of fixed length (typical for signal-based predictors) or extract a fixed number of features from the sequence (typical for global property predictors). However, newer types of ANNs used within the field of Deep Learning can work with sequences of varying length directly; this is true for Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) (see below). Numerical prediction algorithms can roughly be divided into two groups: statistical and machine learning, although it can sometimes be a matter of definition where to draw the distinction. Both classes of methods have a number of free parameters that must be estimated from the data, but while the parameters in statistical methods can be calculated directly, machine learning methods depend on an iterative optimization process (“training”) where parameters are gradually changed until the classification error has reached a minimum. The simplest sequence pattern recognition method is the consensus sequence or regular expression, e.g., “RR.[FGAVML] [LITMVF]” for SPs following the Tat (twin-arginine translocation) pathway. The way to read it is as follows: there should be two consecutive arginines, followed by any amino acid, then one amino acid from the group “FGAVML” and then one from the group “LITMVF.” It is easy to check whether such a pattern is present in a sequence, but it is also a very crude method, because it defines absolute requirements for certain amino acids at certain positions and only provides “yes” or “no” answers. The pattern above, for example, ignores the fact that not all amino acids from the groups “FGAVML” and “LITMVF” are equally probable at positions 4 and 5 (compare to the heights of the individual letters in positions 17 and 18 in Fig. 1). An alternative to the consensus sequence or regular expression is the position-weight matrix (PWM) [25], a statistical windowbased method which is very useful for characterizing and predicting short sequence motifs. The procedure when constructing a PWM is to use a set of examples of the motif of interest (the training set) to

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Fig. 1 Sequence logo of Tat (twin-arginine translocation) signal peptides from both Gram-positive and Gramnegative bacteria, aligned to the PROSITE profile PS51318/TAT. The height of each stack of letters corresponds to the information (conservation) at that position, while the height of each individual letter is proportional to the fraction of that amino acid at that position. Note that individual sequences may be shorter or longer than 40 amino acids; in the logo, they have been stretched or shortened to fit the model. (Picture from PROSITE [28] made with WebLogo [173])

estimate the probability of each amino acid at each position and then convert those probabilities into weights. The score for a new sequence window can then be calculated by looking up the weights for each amino acid in each position in the window and adding them up. In this way, the weight matrix can give a quantitative answer to how well a sequence window fits the pattern. A graphical counterpart to the PWM is the sequence logo [26], where each position is summarized by a stack of letters. The height of each stack is equivalent to the information content—a measure of the conservation—of that position, while the height of each letter is proportional to the probability of the corresponding amino acid at that position. Figure 1 is an example of a sequence logo. A straightforward extension of the position-weight matrix is the sequence profile, which allows for insertions and deletions in the sequence and therefore can model motifs of variable length. It is possible to formulate a profile in probabilistic terms—then it becomes a profile Hidden Markov Model (HMM) [27]. An HMM is a machine learning algorithm, since the probabilities it contains are found by an iterative optimization process from a set of training data. After training, new sequences can be evaluated in terms of the probability that the sequence was generated by the model (a process known as decoding the HMM). It should be emphasized that not all HMMs are profile HMMs—any grammar that can be described as a diagram of connected states can be modeled as an HMM. For example, a cyclic HMM can describe a repeating pattern, and a branched HMM can describe a choice between alternative patterns. Examples of cyclic HMMs are given in Subheading 7. Several publicly available databases specialize in creating and storing profiles for protein families or domains. Among these are PROSITE [28] (see Table 1), which contains both regular expression patterns and PWM-like profiles, and Pfam [29] and TIGRFAMs [30], which are databases of profile HMMs. InterPro [31] (see Table 1) is a special case, since it does not create its own profiles

Table 1 Web addresses of the servers reviewed in this chapter Name

Website address

References

PROSITE

https://prosite.expasy.org/prosite.html

[28]

InterPro (and Pfam and TIGRFAMs)

https://www.ebi.ac.uk/interpro/

[31]

SignalP

https://services.healthtech.dtu.dk/services/SignalP-6.0

[54–59]

DeepSig

https://deepsig.biocomp.unibo.it/

[64]

PrediSi

http://www.predisi.de/

[65]

Signal-BLAST

http://sigpep.services.came.sbg.ac.at/signalblast.html

[66]

LipoP

https://services.healthtech.dtu.dk/services/LipoP-1.0

[69]

SPEPlip

http://gpcr.biocomp.unibo.it/cgi/predictors/spep/pred_ spepcgi.cgi

[71]

PRED-LIPO

http://bioinformatics.biol.uoa.gr/PRED-LIPO/

[70]

TatFind

http://signalfind.org/tatfind.html

[73]

TatP

https://services.healthtech.dtu.dk/services/TatP-1.0

[74]

PRED-TAT

http://www.compgen.org/tools/PRED-TAT/

[75]

SecretomeP 2.0

https://services.healthtech.dtu.dk/services/SecretomeP-2.0

[78]

PREFFECTOR

http://korkinlab.org/preffector (L)

[82]

T1SEstacker

http://www.szu-bioinf.org/T1SEstacker/

[85]

Bastion6

https://bastion6.erc.monash.edu/

[86]

T4SEpre

http://www.szu-bioinf.org/T4SEpre/ (D)

[90]

Bastion4

https://bastion4.erc.monash.edu/

[91]

T4SE-XGB

https://github.com/CT001002/T4SE-XGB (D)

[92]

DeepT3_4

https://github.com/jingry/autoBioSeqpy/tree/2.0/examples/ T3T4 (D)

[93]

EffectiveT3

https://www.effectors.org/method/effectivet3 (D)

[98]

pEffect

https://rostlab.org/services/peffect/

[104]

Bastion3

http://bastion3.erc.monash.edu/

[105]

DeepT3

https://github.com/lje00006/DeepT3 (D)

[106]

WEDeepT3

https://bcmi.sjtu.edu.cn/~yangyang/WEDeepT3.html (T)

[107]

TMHMM

https://services.healthtech.dtu.dk/services/TMHMM-2.0

[114]

HMMTOP

http://www.enzim.hu/hmmtop/

[115]

Phobius & PolyPhobius

https://phobius.sbc.su.se/

[120, 127]

Philius

https://www.yeastrc.org/philius/

[121]

MEMSAT-SVM

Available through the PSIPRED protein sequence analysis workbench, http://bioinf.cs.ucl.ac.uk/psipred/

[123] (continued)

Table 1 (continued) Name

Website address

References

SCAMPI

http://scampi.cbr.su.se/

[128]

TOPCONS

https://topcons.net/

[131, 132]

TOPCONS-single

https://single.topcons.net/

[133]

CCTOP

http://cctop.ttk.hu/

[134]

TMbed

https://github.com/BernhoferM/TMbed (D)

[135]

DeepTMHMM

https://dtu.biolib.com/DeepTMHMM

[136]

PRED-TMBB

http://biophysics.biol.uoa.gr/PRED-TMBB/

[139, 140]

B2TMPRED

http://gpcr.biocomp.unibo.it/cgi/predictors/outer/pred_ outercgi.cgi

[144]

TBBpred

http://crdd.osdd.net/raghava/tbbpred/

[145]

ConBBPRED

http://bioinformatics.biol.uoa.gr/ConBBPRED/

[143]

BOCTOPUS

https://b2.topcons.net/

[146, 147]

BOMP

http://services.cbu.uib.no/tools/bomp

[148]

HHomp

https://toolkit.tuebingen.mpg.de/tools/hhomp

[149]

BetAware

https://betaware.biocomp.unibo.it/BetAware/

[150, 151]

CW-PRED

http://bioinformatics.biol.uoa.gr/CW-PRED/

[153, 154]

PSORTb

https://www.psort.org/psortb/

[4, 48, 161]

PSORTm

https://www.psort.org/psortm/ (D)

[163]

Proteome analyst

http://pa.wishartlab.com/pa/pa/ (L)

[15, 34]

Gneg-PLoc GposPLoc

http://www.csbio.sjtu.edu.cn/bioinf/Cell-PLoc/

[19, 20]

Gneg-mPLoc GposmPLoc

http://www.csbio.sjtu.edu.cn/bioinf/Cell-PLoc-2/

[21, 22]

PSLpred

http://crdd.osdd.net/raghava/pslpred/

[165]

LocTree3

https://rostlab.org/services/loctree3/

[166]

CELLO

http://cello.life.nctu.edu.tw/

[13]

SOSUI-GramN

https://harrier.nagahama-i-bio.ac.jp/sosui/sosuigramn/ sosuigramn_submit.html

[168]

BUSCA

http://busca.biocomp.unibo.it/

[170]

DeepLoc

https://services.healthtech.dtu.dk/services/DeepLoc-2.0

[171, 172]

Notes (D): Program available for download only; no web server provided (L): The website requires login, but registration is free (T): The URL generated a time-out error at the time of writing

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35

but collects profiles from a number of contributing databases, including PROSITE, Pfam, and TIGRFAMs. Most profiles in these databases are evolutionarily related families and/or domains, but there are also instances of functional motifs that are similar due to common selection pressure rather than common descent. Among these are a few protein sorting motifs, which can be used as prediction tools—examples will be given in Subheading 5. To predict the presence of a specific PROSITE motif in your own sequences, click “ScanProsite” at the PROSITE home page [32], choose “Option 3,” and then enter your sequences and the identifier of the PROSITE entry you wish to scan for. Alternatively, you can use the sequence search function at the InterPro home page which will provide hits to several family and domain databases besides PROSITE, including Pfam. Among the methods that use a fixed set of numbers as input, the simplest possible is the Naı¨ve Bayes classifier, which assumes that all the input variables are independent. It can have surprisingly good performance also in cases where the assumption of independence is known to be violated [33], and it is sometimes preferred over more advanced machine learning methods because it offers the opportunity to explain exactly which input variables were important for each prediction [34, 35]. Two very popular machine learning algorithms which are widely used in biological sequence analysis are the Artificial Neural Network (ANN) [36] and the Support Vector Machine (SVM) [37]. This is not the place to go into technical details about the workings of ANNs and SVMs, but they both have large numbers of parameters that must be estimated via a set of training data, and they are both potentially able to model situations where there are correlations between input features. In later years, deep learning has become increasingly popular in bioinformatics [38, 39]. The term “deep learning” is commonly used to describe ANNs with more than one hidden layer, but most often, certain specific architectures are used. Convolutional Neural Networks (CNNs) use a constant set of weights to scan the entire input field and can therefore recognize sequence motifs regardless of their position in the input sequence. In Recurrent Neural Networks (RNNs), information does not only flow from input to output but also between the hidden units of the network, which gives the model a kind of memory, taking into account what it has “seen” before while processing the current input. Even more recently, protein language models have been applied to various protein prediction tasks [40–42]. The idea behind these is that a large deep ANN is trained on a large set of proteins to guess masked amino acids from their sequence context. The trained language model thus gains a general understanding of how proteins look, and this can be exploited in “downstream tasks” such as prediction of secondary structure or subcellular location. The standard approach is to take the internal high-dimensional

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representations from the pretrained language model and use those for encoding each amino acid when solving the downstream task. The point is that the data for pretraining are not necessarily labeled, so it is possible to use, e.g., all of UniProt for pretraining.

4

Performance of Prediction Methods After having trained a statistical or machine learning method, it is crucial to test its predictive performance on another dataset. This is a very important point: It is not enough that a trained method can reproduce its input examples exactly—in fact, it is not even interesting, since a database can do the same. What is interesting is whether a model can generalize from the examples in the training set and produce useful output for sequences it has not “seen” before. There is often a certain degree of trade-off between training set and test set performance. If a model reproduces its training examples in too much detail, it uses its parameters to fit not only the common pattern in the data but also the individual noise in each data point. When this happens, the performance on the test set goes down, and the model is said to be overfitted—colloquially speaking, it cannot see the forest for the trees. Avoiding overfitting can be tricky; it may involve limiting the number of free parameters in the model, adding some regularizing terms to the parameters, or—especially in the case of ANNs— stopping the training early. In some cases, this is done using the test set performance as a criterion for choosing the optimal number of free parameters or the best point to stop the training; but in fact this is cheating, since the test set in such a procedure has been part of the training process. Instead, three data sets should be used: a training set, a validation set for optimizing the model architecture and training process, and a true test set (also known as evaluation set) for measuring the performance. Instead of using a fixed part of the data as test set, performance evaluation is often done by cross-validation, where the data set is divided into a number of folds, and each fold is in turn used as a test set, while the others are used as the training set. The final performance is then calculated as an average of the test set performances. The number of folds can vary; most often, five- or tenfold crossvalidation is used, but some authors prefer N-fold cross-validation, where N is the number of data points—in other words, just one example at a time is held out, while the training is performed on all other examples. This is also known as leave-one-out crossvalidation or jackknife test.

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The necessity for splitting the data into training and test is not special for bioinformatics; it applies to all prediction tasks. However, bioinformatics has an added complication: Sequences are related by descent. If there are sequences in the test set that are closely related to sequences in the training set, the measured performance is arguably not a true test performance. This can be taken into account by reducing homology in the data set before splitting it into folds (homology reduction) or by ensuring that no too closely related pair of sequences end up in different folds (homology partitioning). Two widely used algorithms for homology reduction were published early in the history of bioinformatics [43]. There are diverging views concerning exactly how closely related two sequences should be allowed to be in order to be separated into different folds. Some authors arbitrarily set a rather high cutoff, e.g., 80% or 90% identity in a pairwise alignment [8, 44]. One approach to a nonarbitrary definition is to identify a cutoff above which the problem could be better solved by alignment than by machine learning [45, 46]. Another approach is to use a cutoff in alignment score above which there is statistical significance of homology [47]. These approaches tend to result in much lower cutoff values, typically corresponding to ≈25% identity in long alignments [45]. When comparing reported performances of different methods, it is important to consider which type of homology reduction or partitioning was used (if any). However, it is debatable whether homology reduction or partitioning makes sense when constructing homology-based methods. The whole point of such methods is to use the annotations of homologues, the closer the better, and by reducing homology in the data set, one would be reducing away the very data that the method needs. When reporting performances of prediction methods, a variety of measures may be used, potentially confusing the untrained reader. The conceptually simplest performance measure, the fraction or percentage of correct answers (also known as accuracy), can be misleading if the classes are imbalanced (i.e., not the same size). As an example, consider a method for predicting cell wall-binding proteins and a dataset which has 99 negative examples (non-cell wall-binding proteins) for each positive example. If the method consistently answers “non-cell wall binding,” it will be correct 99% of the time, even though the “prediction” is completely non-informative. Instead, several alternative measures are often used. When discriminating between two classes, the most important performance measures can be defined in terms of the numbers of true positives (TP), true negatives (TN), false positives (type I errors or overpredictions, FP) and false negatives (type II errors or misses, FN):

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• Sensitivity (also known as recall or true positive rate—how many of the positive examples are found?): – Sn =

TP TPþFN

• Specificity (also known as true negative rate—how many of the negative examples are found?): – Sp =

TN TNþFP

• Precision (also known as positive predictive value—how many of the positive predictions are true?): – Pr =

TP TPþFP

• Matthews correlation coefficient—a measure which takes values between -1 and 1, where 1 is a perfect prediction, 0 is a random guess or non-informative prediction, and - 1 is a prediction that is consistently wrong: TP × TN - FP × FN – MCC = p ðTPþFPÞðTPþFNÞðTNþFPÞðTNþFNÞ

It should be mentioned that the term “specificity” is not unequivocal; it has sometimes been used to denote what is here referred to as precision (e.g., in [48]). Whenever a prediction method gives a quantitative output, there is a trade-off between sensitivity and specificity, controlled by the threshold (also known as cutoff) above which a prediction is considered positive. Lowering the threshold reduces the number of false negatives, thereby increasing the sensitivity, but it also increases the number of false positives, thereby reducing the specificity (and the precision). It is possible to plot the sensitivity as a function of the false positive rate (1 minus specificity) for varying threshold values—such a plot is known as a ROC (receiver operating characteristic) curve. The area under the ROC curve (usually referred to as AUC or AROC) can be used as a threshold-independent performance measure; it will be 1 for a perfect prediction, 0.5 for random guesses, and 0 for a consistently wrong prediction. The AUC can also be interpreted as the probability that a randomly selected positive example will have a higher score than a randomly selected negative example. When predicting more than two classes—e.g., a number of SCLs—the maximal information about the prediction is provided by the so-called confusion matrix: a table showing, for each observed class, how many examples were predicted to belong to each class. This can be used to see not only how well each class was predicted but also which classes were particularly difficult to distinguish. From the confusion matrix, sensitivity, specificity, precision, and MCC can be calculated for each class. There are also measures that summarize a whole confusion matrix in one number, such as the Gorodkin correlation coefficient, which is a generalization of the MCC to more than two classes, or the normalized mutual information coefficient [49, 50]. In practice, these are rarely calculated, and the percentage of correct answers is often used instead, despite the shortcomings of this measure.

Protein Sorting Prediction

5

39

Recognition of Signal Peptides The secretory signal peptide (SP) is among the earliest prediction targets for bioinformatic algorithms. The oldest SP prediction methods used a simple PWM for the SP cleavage site, first with a reduced alphabet [3] and later with weights for all amino acids [51]. Another very early SP prediction method used two simple sequence-derived features, peak hydrophobicity and length of the uncharged region, to discriminate SPs, but did not predict the cleavage site [52]. SPs are present in all domains of life, but it was discovered early that there are differences between broadly defined systematic groups [53]. SPs of Gram-positive bacteria are longer than those of Gram-negative bacteria, which in turn are longer than those of eukaryotes. A sequence logo of SPs from Gram-negative bacteria is shown in Fig. 2. In 1997, the SP predictor SignalP (see Table 1) was among the first to use ANNs for sorting signal prediction [54]. Later, in versions 2 and 3, an HMM was added to the method [55, 56], while version 4 was again purely ANN-based [57]. Version 5 from 2019 [58] was the first SignalP to be based on deep learning (both CNN and RNN), while version 6 from 2022 [59] is based on protein language models. SignalP is among the most cited prediction servers in bioinformatics, and it has performed favorably in comparative studies [60–63].

Fig. 2 Sequence logo of signal peptides from Gram-negative bacteria, aligned after their cleavage site (between positions -1 and 0). The visible features are the cleavage site specifying residues in -3 and 1 (strong preference for alanine), the hydrophobic region that approximately stretches from -16 to -7, and a preference for the positively charged lysine in the N-terminal region. Note that no stretching or shortening of the sequences has been performed, they have simply been aligned by the cleavage site; this is why no completely conserved methionine is seen in the left-hand side. (Picture made with WebLogo [173])

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Henrik Nielsen

SignalP 5 was not, however, the first deep learning-based SP prediction method; that honor goes to the CNN-based DeepSig [64] from 2018 (see Table 1). Another SP prediction method worth mentioning, although it is as old as 2004, is the PWM-based PrediSi [65] (see Table 1). There is also Signal-BLAST [66] (see Table 1) which, quite unusually for a sorting signal prediction method, is homology-based. It uses BLAST [10] with some customized settings to search a reference set of SPs and non-SPs and returns the class of the best hit as its prediction. In addition, some TMH prediction programs also offer SP prediction; see Subheading 7 for details. The performance of SP prediction in bacteria is generally high, with SignalP 6.0 reporting MCC values of 0.81–0.88 in distinguishing between SPs and non-SPs and a cleavage site sensitivity of 64%–80%, with the highest values for Gram-positive bacteria. Note that these performances are cross-validation performances on a strictly homology-partitioned dataset, so they reflect the performance you would expect if you submitted sequences that were completely unrelated to any in the SignalP 6.0 dataset. It should be stressed that the presence of an SP does not necessarily mean that the protein is secreted. First, it may be periplasmic or integrated into the outer membrane in Gram-negative bacteria; second, it may be bound to the cell wall in Gram-positive bacteria (see Subheading 8); third, there may be downstream TMHs keeping the protein integrated in the cytoplasmic membrane. It was reported in 1994 that cleavable SPs are rarely found in bacterial cytoplasmic membrane proteins [67]; but a quick search in UniProt [68] reveals that they are not that rare after all, so a prediction of SPs should always be combined with a search for TMHs (see Subheading 7) before drawing conclusions about the SCL. SignalP up to version 4 and the other SP predictors mentioned so far only predicted classical SPs, translocated by the Sec system and cleaved by type I signal peptidases. For lipoproteins cleaved by lipoprotein signal peptidase (SPase II), other prediction methods have been developed, such as the HMM-based LipoP [69] (see Table 1) and PRED-LIPO [70] (see Table 1), and the ANN-based SPEPlip [71] (see Table 1). In addition to these methods, there is also a profile in PROSITE [28] dedicated to lipoproteins from both Gram-negative and Gram-positive bacteria, named PROKAR_LIPOPROTEIN (PS51257). A sequence logo of lipoprotein SPs aligned to this model is shown in Fig. 3. For SPs translocated by the twin-arginine protein translocation (Tat) pathway, there are also a few dedicated prediction methods available. In addition to the twin-arginine motif in the N-terminal region that gave them their name, they also differ from Sec SPs by

Protein Sorting Prediction

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Fig. 3 Sequence logo of lipoprotein signal peptides from both Gram-positive and Gram-negative bacteria, aligned to the PROSITE profile PS51257/PROKAR_LIPOPROTEIN. Lipid attachment occurs at the completely conserved cysteine in position 35. Note that individual sequences may be shorter or longer than 35 amino acids; in the logo, they have been stretched or shortened to fit the model. (Picture from PROSITE [28] made with WebLogo [173])

being on average longer and less hydrophobic [72]. The available servers are TatFind [73] (see Table 1), which is based on a regular expression combined with a set of simple rules concerning hydrophobicity and charge, TatP [74] (see Table 1), which is based on a regular expression combined with two ANNs, and the newer HMM-based PRED-TAT [75] (see Table 1). In addition, there are three motifs available in InterPro: the PROSITE profile TAT (PS51318), the Pfam profile TAT_signal (PF10518), and the TIGRFAMs profile TAT_signal_seq (TIGR01409). A logo of sequences aligned to the PROSITE profile is shown in Fig. 1. Having to consult several methods in order to predict the types of your SPs is clearly not an optimal situation. SignalP 5 aimed to change this by integrating prediction of classical SPs (Sec/SPI) with prediction of lipoprotein and Tat SPs (Sec/SPII and Tat/SPI). However, SignalP 5 was still not complete, since it failed to predict the rare classes of Tat lipoproteins (Tat/SPII) and the special SPs of type IV pilins, cleaved by signal peptidase III (Sec/SPIII). This was made possible by the protein language models in SignalP 6, where pretraining on a very large corpus of unannotated sequences could be supplemented by fine-tuning on very small sets of annotated proteins. SignalP 6 now predicts all five types of SPs in bacteria and archaea. One additional feature of SignalP 6 is that it does not “care” about the organismal origins of the sequences. It was trained with the traditional labels of Gram-positives, Gram-negatives, and archaea, but when testing it with randomized organism labels, it was found that performance was not affected. This means that it is applicable to sequences of unknown origin and to sequences of bacteria that do not fit neatly within the Gram-positive/Gramnegative division, such as Mycoplasma, Thermotoga, or Negativicutes.

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Prediction of Secretion Without Signal Peptides In Gram-negative bacteria, secretion without an N-terminal, cleaved SP happens to proteins belonging to secretion systems of types I, III, IV, and VI [76, 77]. For Gram-positive bacteria, the phenomenon appears to be less important, but there are some examples of proteins exported via, e.g., the Wss, holin, and SecA2 pathways [77, 78]. This has sometimes been referred to as “nonclassical secretion” [78–80], but at least in Gram-negative bacteria, this term should properly be reserved for secretion that happens independently of any of the numbered secretion systems, e.g., by membrane vesicles [81]. SecretomeP 2.0 [78] (see Table 1) is a general secretion predictor from 2005 designed to handle secretion without SPs. At the time, it was not easy to locate experimentally confirmed examples of this, so the SecretomeP authors took a different approach, based on the idea that secreted proteins must be expected to share certain features independent of the pathway used to secrete them. Thus, the positive training dataset simply consisted of classically secreted proteins with the SP removed. A large number of structural and functional features calculated from the amino acid sequence were then tested for their predictive power for SCL, and the most promising features were used to train an ANN. Unfortunately, the ability of SecretomeP to predict types I, III, IV, and VI secretion has never been tested. It is possible that effectors of the types III, IV, and VI secretion systems, which get injected directly into the host cytoplasm, do not share the extracellular characteristics that SecretomeP is trained to recognize. The competing method SecretP [79] is no longer available, which is a shame, since the version SecretP 2.1 [80] had an interesting property: Given a secreted protein from a Gram-negative bacterium, it gave a prediction about which secretion system had been responsible. To my knowledge, no currently available prediction method can do this. A more recent general secretion predictor is PREFFECTOR [82] (see Table 1). It claims to be an effector predictor, but its positive training data consisted of proteins secreted by types I–VI secretion systems, while the term “effector” should probably be limited to substrates of the types III, IV, and VI secretion systems. When it comes to secretion system-specific predictors, there are many methods published, especially concerning types III and IV secretion. In this chapter, I can only present a selection; a more complete account is given in the 2021 review by Hui et al. [83]. In contrast, very little attention has been directed toward types I and VI secretion. One method for machine learning-based prediction of type I secretion has been published [84], but it is not available, and furthermore, the prediction was limited to those type

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I secreted proteins that contain RTX repeats. A more recent method, T1SEstacker, is available as a web server (see Table 1) [85]. It claims to be able to predict non-RTX as well as RTX type I secretion substrates. One server specifically offers prediction of type VI secretion: Bastion6 [86] (see Table 1 and Chap. 32). Type IV secretion is better investigated, with several bioinformatic analyses published specifically concerning the phenomenon in two species, Legionella pneumophila and Coxiella burnetii [87– 89]. One SVM-based machine learning method with a broader scope, T4SEpre [90] (see Table 1), is available for download. T4SEpre uses the C-terminal 100 amino acids (shorter window lengths were also tested but did not perform equally well). Within this window, T4SEpre uses a PWM-like method for encoding the sequence, together with predicted secondary structure and solvent accessibility. The authors reported the performance on type IVa and type IVb effectors separately but additionally found that their method trained on IVa and tested on IVb or vice versa did work to some degree, showing that the signals for the two pathways are not completely different. Newer methods for prediction of type IV effectors include Bastion4 [91] (see Table 1) and T4SE-XGB [92] (see Table 1). DeepT3_4 [93] (see Table 1) is a deep learning method that distinguishes between type III and type IV effectors. The prediction of type III effectors has received a lot of attention in later years, and the rest of this section will be dedicated to type III secretion. Several experimental results point to the signal for secretion being within the N-terminal part of the protein [94], but it is not clear whether the signal is read at the mRNA level or the protein level. The mRNA hypothesis is predominantly based on the observation that some proteins retained their ability to be secreted after two balanced frameshift mutations completely changed the amino acid sequence of the N-terminal part of the protein [95], but it remains controversial [96]. Two methods published in the same journal issue in 2009 introduced the use of machine learning for type III effector prediction: the SVM-based SIEVE [97] and the Naı¨ve Bayes-based EffectiveT3 [98] (see Table 1). The EffectiveT3 authors tested several machine learning methods including SVM but found that Naı¨ve Bayes gave the best performance. In both methods, a feature selection procedure was carried out; but the feature sets were quite different—SIEVE used evolutionary conservation, phylogenetic profiles, G + C content of the encoding gene, and sequence of the N-terminal part of the protein, while EffectiveT3 used amino acid composition and occurrence of short degenerate motifs such as “Polar–hydrophobic–polar.” Later the same year, an unnamed method was published by Lo¨wer and Schneider [99] using a moving window input, which was processed by an ANN or an SVM.

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Newer methods include BPBAac [100] which uses a PWM-like approach, T3_MM [101], which is based on first-order Markov models, and the hybrid methods BEAN [102, 103] and pEffect [104] (see Table 1), which both use homology to known effectors to enhance prediction. Additionally, there is the sister method to Bastion6 and Bastion4, namely, Bastion3 [105] (see Table 1). Recently, deep learning has also come to the type III effector prediction field with methods like DeepT3 [106] (see Table 1), WEDeepT3 [107] (see Table 1), and the previously mentioned DeepT3_4 [93] (see Table 1). Performances are difficult to compare, especially since the authors disagree on which negative set to use. SIEVE used all proteins in each represented organism which were not in the positive set, yielding a ratio of positive to negative examples of approximately 1:120, while the authors of EffectiveT3, BPBAac, T3_MM, and BEAN constructed balanced training sets where the ratio was 1:2 by sampling annotated proteins without annotation about type III secretion. Lo¨wer and Schneider used an even more balanced set where the ratio was approximately 1:1. While the SIEVE approach will tend to underestimate performance because there could be undiscovered positive examples in the negative data, the balanced dataset approach measures performance in an unrealistic setting. Furthermore, as the SIEVE authors correctly remark, “any method of filtering negative examples to provide a more confidently nonsecreted set might introduce significant biases in the data set that could render classification trivial” [94]. The pEffect authors took a radically different approach and included eukaryotic as well as bacterial data in their negative set, which makes sense if you imagine the method used in a metagenomics setting. The resulting ratio of positive to negative examples was 1:30 after homology reduction. This illustrates a common dilemma in many bioinformatic problems: The predictive performance is dependent on the sampling of the negative dataset. Sidorczuk et al. recently carried out a thorough investigation of this phenomenon in a similar problem, that of predicting antimicrobial peptides [108]. After training 12 machine learning methods using 11 negative data sampling methods, they concluded that each method performed best when using the negative set definition it was originally trained on. Another caveat about measuring performance is illustrated by the methods BPBAac and T3_MM which originate from the same group. Although they both reported reasonably high performances (sensitivity 91% and 90%, specificity 97% and 91%), they disagreed wildly about predictions in genomes of Salmonella strains that had not been part of the training set: Only around 25% of the predictions by the more conservative method, BPBAac, were shared by T3_MM [101].

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An interesting aspect of these methods—perhaps more interesting than their predictive performances—is what they can tell us about the actual signal for type III secretion. The first lesson is that the signal is apparently universal across different systematic groups within the Gram-negative bacteria. Several of the method authors tested this by a phylogenetically informed cross-validation, i.e., testing the performance on one systematic group of bacteria with a method trained on the others [97, 98, 100, 101]. The fact that it is possible to predict type III secretion to some degree from amino acids speaks against the mRNA hypothesis, although nobody actually tested the hypothesis by training a nucleotide sequence-based predictor and comparing its performance to an amino acid sequence-based predictor. As mentioned, the first version of SIEVE used G + C content as an input feature, but the authors later found that this feature could be removed without major impact on the performance, and the final version of SIEVE used only amino acid sequence as input [94]. An alternative explanation for the observations that led to the mRNA hypothesis could be that the signal is so unspecific that a spurious reading frame has a relatively high chance of producing a type III secretion signal. This is investigated by the authors of EffectiveT3 and BPBAac, who tested their predictors on artificially created frameshifted sequences and found that 10–14% of frameshifted positive examples were predicted positive [98, 100]. Since the signal is so weakly defined, it is reasonable to ask whether sequence order of the amino acids plays any role or whether it is just the composition of the N-terminal region. SIEVE and BPBAac both use the actual sequence as input, while EffectiveT3, T3_MM, and BEAN use the composition of amino acids and/or amino acid pairs. The better performance of BPBAac over T3_MM using the same dataset could be taken as a sign that positions of amino acids do have a role to play. Another question is whether the signal is actually N-terminal, as many of the methods assume. The EffectiveT3 authors tested this by training on the 15 C-terminal residues instead of N-terminal and found no performance above the random expectation [98]. The EffectiveT3 and SIEVE authors and Lo¨wer and Schneider all investigated how many N-terminal residues were required for prediction and found no improvement by moving beyond 30 (except in the case of plant pathogens predicted by EffectiveT3, where the limit seemed to be 50) [97–99]. In contrast, the BPBAac authors used 100 N-terminal positions and found that to be superior to 50 [100]. BEAN version 1 used 51 N-terminal positions, but in BEAN 2.0, two additional windows were added comprising positions 52–121 from the N-terminus and 1–50 from the C-terminus [102, 103]. The authors state that their results show that C-terminal signals sometimes play a role, but since the two windows were added simultaneously, it might as well be the 52–121

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window that made the difference (which would be in accordance with the BPBAac result). The pEffect method, by contrast, uses the entire sequence as input, and the authors write: “We establish that “signals” for the recognition of type III effectors are distributed over the entire protein sequence instead of being confined to the N-terminus” [104]. However, the evidence for this conclusion is not very strong, since it is no surprise that homology between known effectors is distributed across the entire sequence. In order to draw conclusions on the placement of the signal, it is necessary to consider the non-homology part of pEffect, the so-called de novo prediction, and that decreased when using fragments instead of the whole sequence (which the homology-based performance did not). To get a real answer to the question, it would be necessary to train an N-terminal version of the de novo part of pEffect to see whether it performed equally well.

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Prediction of Transmembrane Topology In Gram-negative bacteria, transmembrane proteins come in two flavors: α-helix proteins, almost exclusively in the cytoplasmic membrane, and β-barrel proteins, exclusively in the outer membrane. Prediction of transmembrane α-helices (TMHs) has a long history in bioinformatics. Initially, the basis for the prediction was simply a plot of the hydrophobicity, averaged in a sliding window over the sequence [2, 109]. A slightly more advanced approach was represented by TOP-PRED in 1992 [110], which combined hydrophobicity analysis with counting the number of positively charged residues in each loop in order to choose the topological model which best conformed to the “positive-inside rule” [111]. Prediction of transmembrane β-strands is more complicated, not only due to the lower number of known examples but also due to their shorter length and lower hydrophobicity. Typically, only those amino acid side chains facing the lipid phase are hydrophobic, while those facing the pore of the β-barrel tend to be polar. The first attempt at a transmembrane β-barrel (TMBB) topology prediction method, published in 1985 [112], did not focus on hydrophobicity patterns but instead on predicting the turns separating the strands. However, the next year, a method for structural prediction that accounted for amphipathic β-strands was published [113]. Later, machine learning methods have been used to predict membrane protein topology, i.e., which parts of the sequence are inside, transmembrane, and outside. In particular, the HMM technology has been popular in this area, because it provides the ability to model the “grammar” of the problem: if a TMH or a strand follows an inside loop, it must be followed by an outside loop, and vice versa. This is typically modeled by a cyclic HMM, having

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submodels for helices/strands, inside loops, and outside loops. In the rest of this section, TMH prediction will be described first, followed by TMBB prediction. The best known HMM for TMH prediction is TMHMM [114] (see Table 1), but also HMMTOP [115] (see Table 1) has found a wide usage. A comparative analysis in 2001 found TMHMM to be the best performing TMH predictor [116], and accordingly, it has become one of the most cited prediction methods in bioinformatics (>13,000 citations according to Google Scholar). Newer surveys covering more recently published predictors unfortunately do not provide quantitative performance comparisons [117–119]. Since hydrophobicity is a feature of both SPs and TMHs, these two are easily confused by prediction methods. TMHMM often falsely predicts an SP as a TMH, and versions 1–3 of SignalP would often predict a TMH close to the N-terminus as an SP. Newer topology prediction methods such as the HMM-based Phobius [120], Philius which is based on Dynamic Bayesian Networks [121], the ANN-based MEMSAT3 [122], the SVM-based MEMSAT-SVM [123], and the ANN + HMM-based SPOCTOPUS [124] deal with this problem by modeling both these signals (see Table 1). Another complicating factor is the fact that multi-spanning membrane proteins sometimes have so-called reentrant loops— segments of the sequence that dip into the membrane but do not span it, leaving the membrane on the same side from which they entered. Reentrant loops are apparently not very frequent in bacteria; only 161 examples from this domain are currently reported in UniProt (where the feature is called “intramembrane”), just four of them with experimental evidence. OCTOPUS [125], SPOCTOPUS, MEMSAT-SVM, and CCTOP (see below) make an attempt at predicting reentrant loops. The use of profiles of homologous sequences generated by BLAST or PSI-BLAST (see Subheadings 2 and 3) in the training and prediction of TMH recognition methods has been shown to enhance predictive performance by approximately 10 percentage points [126]. Methods that use profiles include PRODIVTMHMM [126], PolyPhobius [127], MEMSAT3, MEMSATSVM, OCTOPUS, and SPOCTOPUS. An interesting alternative method is SCAMPI [128] (see Table 1) which does not use machine learning nor statistics on a training set to calculate its parameters, instead they are based on a series of experiments where all 20 possible amino acids have been inserted at various positions into a model TMH [129]. These experiments have been used to calculate an apparent free energy contribution, ΔGapp, which is used as an analogue to a hydrophobicity scale. The overall ΔGapp for each sequence window is calculated and used as input to an HMM-like model with only two free

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parameters to be estimated from the training data. The SCAMPI authors reported a performance comparable to the best machine learning methods. Consensus methods for TMH prediction have in some cases been shown to perform better than any of the constituent methods. An early effort in this direction was BPROMPT from 2003 [130]. The newer server TOPCONS [131, 132] (see Table 1) offers a consensus prediction of both TMHs and SPs based on OCTOPUS, SPOCTOPUS, PolyPhobius, Philius, and SCAMPI. TOPCONS reports 83% correctly predicted topologies on a benchmark set. The downside of TOPCONS is the running time, increased by the fact that four of the five predictors are based on profiles which first have to be constructed from a database search. An alternative consensus server, only based on methods that do not require profiles, is TOPCONS single [133] (see Table 1), which does approximately six percentage points worse than TOPCONS, but 70 times faster. Another consensus server is CCTOP [134] (see Table 1) which uses ten constituent methods plus searches against databases of known TM protein structures. More recently, two membrane topology prediction methods based on protein language models have appeared: TMbed [135] (see Table 1) and DeepTMHMM [136] (see Table 1). The latter, a successor to the popular TMHMM, is so far only published as a preprint. They both use pretrained language models instead of sequence profiles for encoding the input sequences and therefore run much faster than profile-based methods such as TOPCONS and CCTOP while reaching an even better performance. In TMBB topology prediction, the first machine learning method was an ANN published in 1998 [137], but the early 2000s saw a surge in the publication of HMMs, including HMM-B2TMR [138], PRED-TMBB [139, 140] (see Table 1), and ProfTMB [141, 142]. A comparative study in 2005 [143] found HMM-based predictors to perform better than ANN- and SVM-based predictors but achieved the best performance by constructing a consensus of HMM-B2TMR, PRED-TMBB, and ProfTMB together with the ANN-based methods B2TMPRED [144] (see Table 1) and TBBpred [145] (see Table 1). The resulting method, ConBBPRED (see Table 1), is available as a server but is very impractical to use since it does not launch the constituent methods but requires the user to obtain the various predictions and input those to the server manually. BOCTOPUS [146, 147] (see Table 1) is a newer hybrid method, where four window-based SVMs calculate scores for each residue being in inner loop, outer loop, pore-facing transmembrane, and lipid-facing transmembrane, respectively. These scores are fed to a filter that delineates the barrel domain (if any) and then to an HMM that calculates the final topology.

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Some other methods have focused on the task of discriminating between outer membrane TMBB proteins and other proteins rather than predicting the correct topology, and here, the cyclic HMM is not necessarily the best approach. BOMP [148] (see Table 1) from 2004 is one example which does not use machine learning, but simple statistical measures including a C-terminal pattern (regular expression) that is found in many outer membrane proteins. HHomp [149] (see Table 1) uses a collection of profile HMMs, built from outer membrane proteins of known structure, to search for matches to the query sequence. It is thus based on the idea that most TMBB proteins are evolutionarily related, and for predicted TMBB proteins, it additionally provides a classification into a number of functional subgroups. BetAware [150, 151] (see Table 1) uses an ANN to scan the entire sequence and predict whether it is a TMBB protein. The second version of BetAware includes a probabilistic model, a so-called Grammatical-Restrained Hidden Conditional Random Field, for predicting the topology, but it is only invoked when the ANN output indicates that the query is a TMBB protein. The methods that are best one task at are not necessarily the same that are best at the other task. In the paper about BOCTOPUS version 2 [147], the authors compare their performance to a number of other methods and find that BOCTOPUS 2 is better at predicting the correct topology than a number of other recent methods (PRED-TMBB, ProfTMB, and BetAware), but HHomp and BetAware yield better discrimination. Importantly, the language model-based TMbed and DeepTMHMM also predict TMBB proteins and claim state-ofthe-art performance in this task as well. Note that TMBB prediction is still more difficult than TMH prediction; DeepTMHMM reported 92% and 83% correct TMH topologies (depending on whether or not the proteins have SPs) and 80% correct TMBB topologies.

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Prediction of Cell Wall-Binding Motifs There are two ways proteins can be associated with the Grampositive cell wall: by covalent attachment and non-covalent binding. The first group is characterized by a special sortase-cleaved motif with the consensus sequence “LPXTG” followed by what looks like a reversed SP: a stretch of hydrophobic amino acids followed by a region with positively charged residues [152]. There are two available methods for prediction of this signal: the HMM-based CW-PRED server [153, 154] (see Table 1) and the PROSITE profile GRAM_POS_ANCHORING (PS50847). A sequence logo of LPXTG signals aligned to the PROSITE profile is seen in Fig. 4. CW-PRED seems to be less restrictive than the

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Fig. 4 Sequence logo of cell-wall-attached proteins from Gram-positive bacteria, aligned to the PROSITE profile PS50847/GRAM_POS_ANCHORING. Cleavage occurs between position 4 and 5. (Picture from PROSITE [28] made with WebLogo [173])

PROSITE profile; while the latter has an absolute requirement for the proline in the second position, CW-PRED can also detect the variant “LAXTG.” One type of non-covalent binding is described by the approximately 20 aa long PROSITE profile CW (PS51170) and the corresponding Pfam profile CW_binding_1 (PF01473). It occurs mainly in two Gram-positive bacterial protein families: choline binding proteins and glucosyltransferases [155, 156]. The motif occurs as a repeat, typically many times per protein. The PROSITE profile appears to be more sensitive, with typically more hits per sequence reported in UniProt, than the Pfam profile. In the glucosyltransferases, most of the CW motifs occur in glucan-binding domains [157], and TIGRFAMs has an approximately 60 aa long profile associated with glucan binding, glucan_65_rpt (TIGR04035), which roughly corresponds to three tandem copies of the CW motif. The fact that a cell wall-binding motif forms part of a glucan-binding motif diminishes its value for predicting cell wall localization somewhat, since glucan is not part of the peptidoglycan cell wall, and glucan-binding proteins therefore should be classified as secreted. Enzymes involved in bacterial cell wall degradation often have a peptidoglycan-binding domain of approximately 60 aa [158], which is totally unrelated to the repeats mentioned above. A Pfam profile, PG_binding_1 (PF01471), is available. Another example with a Pfam profile is the WxL domain (PF13731) [159].

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Multi-category Predictors The first software to attempt a classification of proteins into multiple SCLs was PSORT [160]. It was basically a signal-based method, incorporating the previously mentioned early methods for prediction of SPs [51, 52] and TMHs [109], but it also used amino acid composition, especially for recognizing outer membrane proteins.

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For bacteria, PSORT has been superseded by PSORTb [4, 48, 161] (see Table 1), which is now in version 3. Version 1 was for Gram-negative bacteria only, but in version 2, Gram-positive bacteria were included. Version 3 additionally offers predictions for Archaea and the “problematic” bacteria mentioned above. PSORTb is a hybrid method, incorporating both signal-based, global property-based, and homology-based predictions. The signal-based component comprises recognition of SPs and TMHs and a database of motifs (regular expressions) derived from PROSITE, which are found to be exclusive to specific SCLs. The global properties component is SVM-based; in version 1, its input consisted of amino acid composition only, but in versions 2 and 3, a collection of overrepresented subsequences is used. The homologybased component is a simple BLAST with direct annotation transfer. Finally, a Bayesian network is used to integrate the outputs from the components and arrive at a final prediction. The final prediction, however, may be “unknown.” PSORTb values precision over recall, so it prefers to deliver no prediction rather than a prediction with weak evidence. It may also arrive at two SCLs, signifying that the protein is predicted to function in both compartments, or belong to the interface between the compartments. The SCLs predicted by PSORTb 3 in some cases extend beyond the standard five categories for Gram-negative bacteria; there are new subcategory SCLs such as “Fimbrial,” “Flagellar,” “Spore,” and “Host-associated.” The reported precision of PSORTb 3 on Gram-negative bacteria (the five main categories only) is 97%, with a recall of 94%. This is tested by fivefold crossvalidation with a dataset that was homology reduced, but only down to 80% identity. Note that this high recall only applies to bacterial species that are well represented in the data set or closely related to such species; a benchmark paper reported that in Campylobacter jejuni, PSORTb 3 returned “unknown” for as many as 47% of the protein sequences [162]. More recently, the PSORTb group released PSORTm [163], a modified version of PSORTb 3 designed for metagenomic datasets. It can deduce Gram-stain and membrane organization from taxonomic information, and its homology search module has been optimized for working with fragments of limited length. Otherwise, it is computationally the same as PSORTb 3, except that the SP prediction module has been removed, since metagenomic reads do not necessarily include the SP region. Another predictor that owes its high performance to homology is the SCL predictor built into the prediction workbench Proteome Analyst [15, 34] (see Table 1). It uses a combination of direct and indirect annotation transfer by retrieving up to three hits from the Swiss-Prot part of UniProt by BLAST and then parsing the “subcellular location” field, the keywords, and the cross-referenced InterPro entries. The retrieved words are then processed by a

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Naı¨ve Bayes classifier. Other machine learning methods (ANN and SVM) were also tried, and although they could enhance performance by a few percent, the authors decided to stick with Naı¨ve Bayes in order to be able to provide explanations for the individual predictions. The PSORTb 3 paper [161] reports that PSORTb 3.0 and Proteome Analyst 3.0 have comparable precisions but make complementary predictions, so that a combined analysis with both methods has the highest coverage overall. Kuo-Chen Chou’s group has published a long series of predictors for protein SCL (see, e.g., [18]). They prefer to publish one website per organism group instead of providing one website with an option for selecting organism group, and furthermore they tend to change the name for each new version instead of adding a version number. For bacteria, the relevant predictors are named GnegPLoc [19], Gpos-PLoc [20], Gneg-mPLoc [21], and GposmPLoc [22] (see Table 1). Two additional servers, iLoc-Gneg [23] and iLoc-Gpos [24], are no longer online. The PLoc/ mPLoc servers are hybrid methods, mostly relying on indirect homology annotation through the Gene Ontology (GO) terms of database hits. GO [164] is an ordered system (a directed acyclic graph) of controlled terms, which describe the biological process, molecular function, and cellular component of proteins. In the prediction servers, GO terms are extracted from all database hits with a pairwise identity above a certain cutoff, and then a k-nearest neighbor classifier is applied to the high-dimensional vectors of occurrences of GO terms. If no hits are found, or if the found hits have no GO annotation, a profile-based global property approach is used. However, the corresponding papers contain no information about how often the global property approach is needed, and the performance of this approach has never been reported separately. Note that the PLoc and mPLoc servers only accept one sequence per submission. The new feature of the mPLoc servers relative to the PLoc servers is the ability to predict multiple SCLs, i.e., predict whether a protein can exist in more than one cellular compartment. This can be of importance in eukaryotes, where many proteins, for example, may shuttle between the cytoplasm and the nucleus, but it is of somewhat minor importance in bacteria. In the dataset for Gneg-mPLoc, there were only 64 such examples out of 1392 homology-reduced proteins, while the corresponding number for Gpos-mPloc was only 4 out of 519 proteins. PSLpred [165] (see Table 1) is a hybrid method which is specific to Gram-negative bacteria. It uses amino acid composition, dipeptide composition, physicochemical parameters, and PSI-BLAST against a database of proteins with known SCL and integrates the features by SVM. Interestingly, the authors found that the PSI-BLAST module on its own performed worse than the other modules. The final reported overall accuracy is 91% on the PSORTb 2 data.

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Another hybrid approach is LocTree3 [166] (see Table 1), which has two components: a PSI-BLAST [10] homology search with direct transfer of SCL annotation and a global property approach corresponding to LocTree2 [167]. If no homology hits are found with an E-value better than a specified cutoff, the LocTree2 method is applied. It consists of a decision tree of SVMs trained with a “profile kernel,” basically using the occurrence of short substrings in profiles made by PSI-BLAST as input. When delivering a prediction, LocTree3 reports whether the evidence is based on homology or on LocTree2. For bacteria, LocTree2 has a reported performance (overall accuracy) of 86%, and LocTree3 has 90%. Interestingly, in the LocTree3 paper, the measured accuracy for PSORTb 3.0 is only 57%. This huge difference to PSORTb’s own reported performance is hard to explain but may reflect different views on the exactly correct way to parse UniProt’s SCL annotations. LocTree2/3 claims to be able to predict SCLs for all domains of life; but it seems less well suited for Gram-positive bacteria, since it offers no opportunity to choose between Gram-positives and Gram-negatives. Thus, it may predict categories like periplasm and outer membrane for Gram-positive bacteria, while it totally fails to predict cell wall. The methods described so far in this section have all been wholly or partly homology-based. However, there is also the global property-based CELLO [13] (see Table 1), an SVM-based predictor for both eukaryotes, Gram-negative bacteria, and Grampositive bacteria. The SVMs are organized in a two-level system, where the first level contains a number of SVMs trained on various sequence encodings, and the second layer is a “jury SVM,” which decides on the prediction based on the outputs of the first-layer SVMs. The sequences are encoded by total amino acid composition, dipeptide composition, and amino acid composition (in some cases with a reduced alphabet) in a number of partitions of each sequence. The performance for Gram-negative bacteria is reported to be 95% overall accuracy without homology reduction and 83% with homology reduction (down to 30% identity). Another predictor that is not homology-based is SOSUIGramN [168] (see Table 1) which, as the name suggests, is specific to Gram-negative bacteria. It is not based on machine learning but on various physicochemical properties measured over the entire sequence and in the N- and C-terminal region. Tests for these measured parameters were then arranged in a complex decision tree that takes into account that, e.g., secretion can happen through various pathways. The authors of the consensus method MetaLocGramN [162] (no longer online) benchmarked four methods: PSORTb 3, CELLO, SOSUI-GramN, and PSLpred, and found PSORTb 3 to be best in overall performance. However, PSORTb 3 had a

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very low sensitivity for the extracellular category, where PSLpred was found to be better. The consensus method MetaLocGramN was not only based on these four predictors but also on a number of signal-based predictors focusing on SPs, TMHs, TMBBs, and type III secretion signals. To integrate all the predictors, an ANN was tried but did not work consistently better than the constituent methods. Instead, the finished method was based on statistical feature selection and logistic regression, and the result was reported to be better than PSORTb 3, especially for the extracellular category, measured on a new dataset. Another meta-predictor, specific to Gram-positive bacteria, was GP4 [169] (no longer online). For SP prediction, it included two different SignalP versions as well as LipoP, TatP, Phobius, and PrediSi, while it used TMHMM and Phobius for TMH prediction. In addition, it used a large number of InterPro profiles. The features were integrated by a scoring system combined with simple decision rules. In contrast to these, the meta-predictor BUSCA [170] (see Table 1) is still functional. The reason for its persistence may be that it is based exclusively on tools developed by the same group (e.g., DeepSig and BetAware), which could make it easier to maintain. However, the resolution for bacteria is limited with only four categories for Gram-negatives and three for Gram-positives (with periplasm and cell wall missing, respectively).

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Discussion As is apparent from this chapter, the many possible ways of approaching the SCL prediction problem have resulted in a large number of available prediction servers (and an even larger number of unavailable ones). Comparing their performances can be complicated, and all their authors tend to claim superior performance for their particular method. Add to this that the usability is sometimes limited (some web servers allow only one or a few sequences in each submission), that response times vary a lot, and that there are almost as many different output formats as there are servers, and you get a rather frustrating situation. Even the definitions of SCLs may vary from server to server—as an example, a peripheral membrane protein may be defined as belonging to the membrane, or to the compartment it protrudes into. This situation is clearly not ideal for the user, who might prefer a “one-stop shop” to go to for all sequence-based prediction needs, an equivalent of UniProt or InterPro. But this kind of confusion is probably inevitable in a field that is evolving so fast. Scientific competition is basically beneficial, and competing groups should certainly not be discouraged from publishing their predictors

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independently. That being said, prediction servers ought to follow certain standards concerning usability, reproducibility, definitions, and formats. The multi-category prediction methods report quite impressive performances, and as described in Subheading 1, the error rates for prediction may now be lower than the error rates for highthroughput experiments. However, it is important to keep in mind that these performances are achieved by analyzing the annotations of homologues found by sequence similarity searches. I see three problems with this. First, predictions for novel organisms and metagenomics samples with few known homologues will necessarily be harder than for the organisms the training and test sets were built from, so coverage and precision for such organisms will be considerably lower than the reported performances. Second, the annotations used for prediction are themselves error-prone and not necessarily derived from experiments. Especially when relying on keywords and GO terms, there is a real danger of circular reasoning, where annotations based on predictions are used as a basis for new predictions, which then may enter the databases as annotations. Third, homology-based predictions do not reflect a real biological knowledge about the protein sorting process in the way a successful signal-based predictor does. In the field of eukaryotic SCL prediction, recent progress has been made through versions 1 and 2 of the method DeepLoc [171, 172] (see Table 1). DeepLoc 1.0 was a deep ANN consisting of a convolutional layer followed by a recurrent layer and then a so-called attention layer that showed, for each individual prediction, which parts of the sequence were important for that prediction. In this way, the method becomes a hybrid between a signal-based and a global property-based method: It is up to the network, in each case, to choose whether to focus the attention on specific signals or spread the attention more globally. The present version, DeepLoc 2.0, is based on a pretrained language model and features multi-label prediction (i.e., it can predict proteins that exist in more than one SCL). The performance of DeepLoc is better than competing methods, even homology-based methods, although it does not use homology information of any kind. Work is in progress on a prokaryotic version of DeepLoc, so it will be interesting to see how that will compare to methods such as PSORTb, and whether it will be able to capture the many different forms of secretion. References 1. Nielsen H, Tsirigos KD, Brunak S, von Heijne G (2019) A brief history of protein sorting prediction. Protein J 38:200–216. https:// doi.org/10.1007/s10930-019-09838-3

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Chapter 3 Cell Fractionation Melissa Petiti, Laetitia Houot, and Denis Duche´ Abstract Protein function is generally dependent on its subcellular localization. In gram-negative bacteria such as Escherichia coli, a protein can be targeted to five different compartments: the cytoplasm, the inner membrane, the periplasm, the outer membrane, and the extracellular medium. Different approaches can be used to determine the protein localization within cell such as in silico identification of protein signal sequences and motifs, electron microscopy and immunogold labeling, optical fluorescence microscopy, and biochemical technics. In this chapter, we describe a simple and efficient method to isolate the different compartments of Escherichia coli by a fractionation method and to determine the presence of the protein of interest. For inner membrane proteins, we propose a method to discriminate between integral and peripheral membrane proteins. Key words Spheroplast, Peptidoglycan, Osmotic shock, Freeze and thaw, Protein solubilization, Membrane, Subcellular localization

1

Introduction Many gram-negative bacteria secrete extracellular proteins such as hydrolytic enzymes or toxins. Secretion can occur through specific macrocomplex systems composed of a more or less large number of proteins located in the cell envelope. Identifying the localization of these proteins is therefore an important task to address the assembly and the molecular mechanism of these secretion systems. Four subcellular compartments compose Gram-negative bacteria, even five if we consider the extracellular medium in which effectors are delivered. These different compartments are the cytoplasm, the inner membrane (IM), the periplasm, in which the peptidoglycan layer extends, and the outer membrane (OM) [1, 2]. Isolation and characterization of effectors from the extracellular medium will be described in Chap. 33 of this book. Many different cell fractionation protocols are available [3–8]. A recent article compares the different protocols and proposes a robust and standardized method to avoid cross-complementation

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_3, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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[9]. In this chapter, we propose a simple and commonly used protocol to unambiguously determine protein localization in E. coli. We will first describe a simple and efficient method to recover proteins from the periplasm and to generate spheroplasts from Escherichia coli cells. Then we will present a method to recover the cytoplasmic and the membrane fractions from the spheroplasts by several cycles of freezing and thawing. Finally, we present how treatments with specific buffers can give insight into proteinmembrane associations.

2

Materials

2.1 Cell Fractionation

1. TES buffer: 200 mM Tris–HCl, pH 8.0, 0.5 mM EDTA (ethylenediaminetetraacetic acid), 0.5 M Sucrose. 2. Lysosyme 10 mg/mL (freshly prepared solution). 3. DNase 1 10 mg/mL. 4. MgCl2 1 M (stock solution). 5. 100× Phe´nylme´thylsulfonyl (PMSF) 0.1 M in absolute ethanol. Store at -20 °C. 6. N-Ethylmaleimide (NEM) 0.4 M in absolute ethanol. Store at -20 °C sheltered from the light. 7. Beckman coulter Optima TLX ultracentrifuge with TLA 55 K rotor or equivalent.

2.2 Proteins Solubilization

1. Urea 2 M. 2. NaCl 0.5 M. 3. Triton X-100 1% (v/v) 4. Sodium carbonate 100 mM pH 11.5, ice cold. 5. Trichloroacetic acid (TCA) 10% (v/v). The stock solution [TCA 100% (w/v)] is stored at 4 °C in a brown bottle. 6. Acetone 90% (v/v) in ultrapure water stored at -20 °C in a brown bottle.

3

Methods

3.1 Cell Fractionation/ Spheroplasts Formation

In this section, we detail step by step spheroplasts preparation from Escherichia coli cells (see Note 1) using a method based on lysozyme/EDTA treatment [3–4] and a mild osmotic shock [5–8] (see Note 2). 1. Grow a 3 mL starter culture overnight in LB medium at 37 °C with required antibiotics.

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2. Inoculate a 20 mL culture at OD600 = 0.05, and incubate at 37 °C until the optical density of the culture is around 0.8. If necessary, induce protein production under the required conditions (see Note 3) 3. Take 1 mL of culture, and centrifuge for 5 min at 5000× g to pellet the cells. Discard the supernatant. Resuspend the pellet in an appropriate volume of SDS-PAGE (sodium dodecyl sulfate polyacrylamide gel electrophoresis) loading buffer. This fraction will be referred as the total cell fraction (T). The next steps will be performed at 4 °C, and all the buffers must previously be cooled on ice before use. 4. Centrifuge the remaining culture for 5 min at 5000× g at 4 °C (see Note 4). Discard the supernatant. 5. Gently resuspend the cell pellet in 200 μL TES buffer (described in materials section, see Note 5). Do not vortex and do not pipette; only resuspend the cell pellet by inverting the tube. 6. Add 8 μL of a freshly prepared solution of lysozyme (10 mg/ mL in TES buffer), and mix gently by shaking the tube. 7. Add 720 μL of TES buffer diluted 2× in water (v/v) containing 5 mM of NEM if necessary, and incubate for 30 min on ice. NEM is added to prevent nonspecific disulfide bond formation. Gently mix the suspension to perform the osmotic shock by gently inverting and rolling the tube (see Note 6). 8. Centrifuge at 5000× g for 5 min at 4 °C. Keep the pellet as the spheroplast fraction (IM + cytoplasm (+OM)) and the supernatant as the periplasmic fraction (P). 9. Resuspend the spheroplast fraction in 1 mL TES buffer diluted 2× in water (v/v) containing 2 mM of PMSF, 2 mM of MgCl2, 5 mM of NEM, and 10 μg/mL of DNase 1 (see Notes 7 and 8). 10. Lyse the spheroplasts by performing four cycles of freezing and thawing, from -273 °C (liquid azote) to 37 °C (see Note 9). 11. Remove unbroken cells and cell debris by centrifugation at 2000× g for 5 min. Keep the supernatant as cytoplasmic and membrane fraction. 12. Centrifuge the supernatant at 120,000× g, 4 °C for 45 min. Use a Beckman coulter Optima TLX ultracentrifuge and a TLA55 fixed-angle rotor for small volume ultracentrifugation or similar. The pellet is kept as the membrane fraction, and the supernatant is conserved as the cytoplasmic fraction. 13. The membrane fraction is suspended in 1 mL TES buffer diluted 2× in water (v/v) containing or not 5 mM of NEM or in the desired buffer (see Note 10). Separation of inner

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membrane and outer membrane is described in Chap. 6 of this book. 14. At this step, fractions (OD600 = 0.2–0.4) could be tested for the presence of the protein of interest by SDS-PAGE and Western blot analysis with required antibodies. As a control, the same fractions can be tested for the presence of specific IM, OM, cytoplasm, or periplasm markers. 3.2 Protein Solubilization (See Note 11)

1. Prepare 1 mL of membrane fractions as previously described. 2. Aliquot the membrane fractions into five samples, 200 μL each. 3. Centrifuge at 120,000× g for 45 min at 4 °C to pellet the membranes as described previously (see Subheading 3.1, step 12). 4. Resuspend each pellet in either 200 μL of 0.5 M NaCl, 2 M urea, 100 mM sodium carbonate, pH 11.5 ice cold or 1% (v/v) Triton X-100 in order to compare the five extraction conditions. 5. Incubate at least 1 h at 4 °C with agitation. 6. Centrifuge the suspensions at 120,000× g for 45 min at 4 °C. Carefully collect the different supernatants, and transfer into new tubes. 7. Resuspend each pellet in SDS-PAGE loading buffer and kept as the membrane-associated protein fractions. 8. Add 10% TCA (final concentration) to supernatant samples, and incubate at least for 1 h at 4 °C to allow protein precipitation (see Notes 12 and 13). 9. Centrifuge at 18,000× g for 30 min at 4 °C. 10. Wash the pellets with 200 μL acetone 90% (prechilled solution). 11. Centrifuge at 18,000× g for 10 min at 4 °C. 12. Discard carefully the supernatant and air-dry the pellet containing the extracted membrane proteins at RT for 5–10 min (see Note 14). 13. Resuspend pellets in SDS-PAGE loading buffer and keep as extracted membrane protein fractions. 14. Perform a Western blot analysis to identify the extraction condition that is suitable to the protein of interest.

4

Notes 1. Spheroplasts are cells resulting from a loss of the bacterial cell wall. Outer membrane has been altered, but the cytoplasm remains delimited by the inner membrane [10].

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2. Escherichia coli cells are first incubated in a concentrated sucrose solution containing EDTA. Sucrose makes the medium hypertonic, while EDTA chelates divalent cations and destabilizes the outer membrane. Then, lysozyme is added to cleave the periplasmic peptidoglycan layer. However, peptidoglycan hydrolysis is not total, and a mild osmotic shock is required to maximize the procedure. Then the periplasmic content of the cell is separated from the spheroplasts by centrifugation. 3. The volume of the cell culture can be adapted according to the downstream application. 4. Precool centrifuge before use. 5. The TES buffer is responsible for outer membrane destabilization. 0.5 M sucrose makes the medium hypertonic, 0.5 mM EDTA and 200 mM Tris–HCl, pH 8 affect the membrane structure by removing the lipopolysaccharide coat from the cells [11]. 6. This mild osmotic shock provokes a sudden influx of water in the periplasmic space and increases the distance between polysaccharide chains of the peptidoglycan. This facilitates the lysozyme binding and the degradation of the peptidoglycan [11]. 7. Phenylmethylsulfonyl (PMSF) is a serine protease inhibitor. However, it has been shown that addition of trypsin inhibitor after proteolysis is not required to prevent further digestion, as trypsin digestion is very specific. 8. During spheroplasts lysis, DNA is released in the medium, adheres to membranes, and makes the preparation difficult to handle. To circumvent this issue, DNase 1 is added to lysates. As DNase 1 activity requires magnesium, Mg2+ is added in excess in order to overtake chelation by the EDTA present in the TES buffer. 9. Three to five cycles of freezing and thawing are an efficient and a simple method to disrupt spheroplasts [12]. However, spheroplasts can also be disrupted by sonication. In this case, sonicate spheroplast suspension twice for 30 s. Keep the suspension cold during sonication. A Branson Microtip Sonifier 450 can be used with a microtip probe. 10. Membrane fraction resuspension can be difficult. Passing the sample through the needle of a syringe several times can optimize this step. 11. When studying a poorly characterized protein, it is important to compare different extraction conditions to optimize protein solubilization. The use of appropriate solubilization buffer can provide information about protein localization into the cell and even to differentiate between integral and peripheral membrane proteins. Thus, a high salt buffer allows the extraction

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of peripheral proteins associated with membrane by electrostatic interactions. Urea 2 M is commonly used to extract peripheral proteins that associate with the membrane by hydrophobic bonds [13]. Triton-X100 is the most commonly used detergent for the solubilization of integral inner membrane proteins [14]. It is worthy of note that, during preparation of membrane fractions, membranes tend to reanneal by an unknown mechanism, resulting in the formation of closed membrane vesicles that might trap some of the proteins of interest. In this case, alkaline carbonate buffer can be added to convert membrane vesicles to open membrane sheets and therefore to release trapped proteins into the supernatant [15]. Membranes can subsequently be cleared from the sample by centrifugation. 12. This step can be done overnight in a cold room under rotary agitation. 13. Be careful, after centrifugation pellets are not always visible. Carefully place the tubes in the centrifuge to locate the location of the future pellet. 14. Never let the pellet air-dry completely, as this will dramatically impede resuspension.

Acknowledgments This work was supported by the “Centre National de la Recherche Scientifique” and the “Agence National de la Recherche” (MEMOX ANR-18-CE11). References 1. Kaback HR (1972) Transport across isolated bacterial cytoplasmic membranes. Biochim Biophys Acta 265:367–416 2. Kellenberger E, Ryther A (1958) Cell wall and cytoplasmic membrane of Escherichia coli. J Biophys Biochem Cytol 25:323–326 3. Neu HC, Heppel LA (1964) The release of ribonuclease into the medium when Escherichia coli cells are converted to spheroplasts. J Biol Chem 239:3893–3900 4. Pierce JJ, Turner C, Keshavarz-Moore E, Dunnill P (1997) Factors determining more efficient large-scale release of a periplasmic enzyme from E. coli using lysozyme. J Biotechnol 58:1–11 5. French C, Keshavarz-Moore E, Ward JM (1996) Development of a simple method for the recovery of recombinant proteins from the

Escherichia coli periplasm. Enzym Microb Technol 19:332–338 6. Skerra A, Plu¨ckthun A (1991) Secretion and in vivo folding of the Fab fragment of the antibody McPC603 in Escherichia coli: influence of disulphides and cis-prolines. Protein Eng 4: 971–979 7. Manoil C, Beckwith J (1986) A genetic approach to analyzing membrane protein topology. Science 233:1403–1408 8. Nossal NG, Heppel LA (1966) The release of enzymes by osmotic shock from Escherichia coli in exponential phase. J Biol Chem 241:3055– 3062 9. Malherbe G, Humphreys DP, Dave´ E (2019) A robust fractionation method for protein subcellular localization studies in Escherichia coli. BioTechniques 66:171–178

Cell Fractionation 10. Kaback HR (1971) Bacterial membranes. In: Jakoby WB (ed) Methods in enzymology, vol 22. Academic, New York/London, pp 99–120 11. Witholt B, Heerikhuizen HV, De Leij L (1976) How does lysozyme penetrate through the bacterial outer membrane. Biochim Biophys Acta 443:534–544 12. Mowbray J, Moses V (1976) The tentative identification in Escherichia coli of a multienzyme complex with glycolytic activity. Eur J Biochem 66:25–36 13. Schook W, Puszkin P, Bloom W, Ores C, Kochwa S (1979) Mechanochemical properties

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of brain clathrin: interactions with actin and alpha-actinin and polymerization into basketlike structures or filaments. Proc Natl Acad Sci U S A 76:116–120 14. Schnaitman CA (1971) Solubilization of the cytoplasmic Membrane of Escherichia coli by Triton X-100. J Bacteriol 108:545–552 15. Fujiki Y, Fowler S, Shio H, Hubbard AL, Lazarow PB (1982) Polypeptide and phospholipid composition of the membrane of rat liver peroxisomes: comparison with endoplasmic reticulum and mitochondrial membranes. J Cell Biol 93:103–110

Chapter 4 Components Subcellular Localization: Identification of Lipoproteins Using Globomycin and Radioactive Palmitate Nienke Buddelmeijer Abstract Bacterial lipoproteins are characterized by fatty acids, derived from membrane phospholipids, which are covalently attached to their amino terminus via posttranslational modification in the cytoplasmic membrane. Here, I describe the detection of one of the intermediate forms of lipoprotein, diacylglycerylprolipoprotein, using 3H-palmitate labeling and inhibition of signal peptidase II (Lsp) by globomycin and detection by fluorography. Key words Tris-Tricine SDS gel electrophoresis, Globomycin, 3H-palmitate labeling, Fluorography

1

Introduction Lipoproteins are abundant proteins of the bacterial cell envelope that fulfill a variety of physiological functions [1]. They are modified at their amino terminus by fatty acids that are derived from membrane phospholipids [2]. In proteobacteria and actinobacteria, the modification pathway is composed of three integral membrane enzymes, i.e., a diacyl glyceryltransferase (Lgt), a signal peptidase (Lsp), and an acyltransferase (Lnt) [3]. Lipoproteins are synthesized in the cytoplasm as pre-prolipoproteins, containing a so-called lipobox sequence for recognition and binding by the enzymes, and fatty acid modification, which are inserted into the cytoplasmic membrane via the Sec or Tat secretion machineries. The first enzyme pre-prolipoprotein phosphatidylglycerol diacylglyceryl transferase (Lgt) adds a diacylglyceryl moiety from phosphatidylglycerol onto the invariant cysteine of the lipobox, resulting in the formation of diacylglyceryl-prolipoprotein. In the second step, lipoprotein signal peptidase II (Lsp) cleaves the signal peptide from diacylglyceryl-prolipoprotein resulting in a free α-amino

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_4, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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group on diacylglyceryl-cysteine of the apolipoprotein. The third and last step in the lipoprotein modification process is N-acylation catalyzed by apolipoprotein N-acyltransferase (Lnt). The acyl donor is phosphatidylethanolamine of which the sn-1 acyl group is transferred onto the α-amino group of apolipoproteins resulting in mature triacylated proteins. The Lgt and Lsp enzymes are conserved in all bacterial species and essential in proteobacteria, while Lnt is only described in proteobacteria and actinobacteria. Lnt is not essential for viability in several proteobacteria. The intermediate forms of small lipoproteins or peptides can be analyzed using gel electrophoresis techniques in combination with specific inhibitors or mutant strains. The natural antibiotic globomycin and its derivatives specifically inhibit Lsp leading to an accumulation of diacylglyceryl-prolipoprotein in the cytoplasmic membrane [4]. This intermediate carries a diacylglyceryl moiety that in case of E. coli is composed of C16:0 and C18:cis-11 fatty acids and still has the signal peptide attached [5, 6]. The migration of this form of lipoprotein on a high-resolution SDS-polyacrylamide gel is slower than prolipoprotein, apolipoprotein, and mature lipoprotein. The use of labeled fatty acids and globomycin thus allows for the identification of lipoproteins and Lsp inhibition. The advantage of radioactive fatty acids is that they are identical in chemical structure to endogenous fatty acids, and the lipid moiety of phospholipids acts therefore similar as substrates in the lipoprotein modification pathway. In the following chapter (Chap. 5), we will describe a protocol using alkyne-labeled fatty acids and click chemistry to identify lipoproteins and to study enzyme activity.

2

Materials

2.1 3H-Palmitate (C16:0) Labeling of E. coli Bacterial Cultures

1. M63 minimal medium: 7 g K2HPO4, 3 g KH2PO4, 2 g (NH4)2SO4 per liter in ultrapure water. Sterilize by autoclaving for 15 min at 121 °C. Then add 0.5 mg FeSO4 and 0.2% glucose (v/v), 0.5‰ sterilized vitamin B1 (v/v) and 1 mM MgSO4 (see Note 1). 2. Escherichia coli strains (see Note 2). 3. 5 mCi/mL of 9,10-3H(N)-palmitate in ethanol. Specific activity: 30–60 Ci (1.11–2.22 TBq)/mmol. 4. Incubator.

2.2 Inhibition of Lsp by Globomycin

1. M63 minimal medium: 7 g K2HPO4, 3 g KH2PO4, 2 g (NH4)2SO4 per liter in ultrapure water. Sterilize by autoclaving for 15 min at 121 °C. Then add 0.5 mg FeSO4 and 0.2% glucose (v/v), 0.5‰ sterilized vitamin B1 (v/v) and 1 mM MgSO4.

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2. Escherichia coli strains (see Note 2). 3. Globomycin: Use final concentration of 160 μg/mL for E. coli K12 strains and 5 μg/mL for B strains (see Note 3). 4. Incubator. 2.3 Immune Precipitation of Lipoproteins

1. Antibodies specific to lipoprotein of interest (see Note 4). 2. 100% Tricloroacetic acid (TCA). 3. Acetone. 4. Solubilization buffer: 25 mM Tris-HCl, pH 8.0, 1% SDS, 1 mM EDTA. 5. Immune precipitation buffer: 50 mM Tris-HCl, pH 8.0, 150 mM NaCl, 1 mM EDTA, and 2% Triton X-100. 6. Protein G-Sepharose. 7. Wash buffer I: 50 mM Tris-HCl, pH 8.0, 1 M NaCl, 1% Triton-X-100. 8. Wash buffer II: 10 mM Tris-HCl pH 8.0. 9. Tabletop microcentrifuge.

2.4 Tris-Tricine SDS Gel Electrophoresis

1. Mini-gel caster system and SDS-PAGE migration apparatus. 2. Cathode buffer (Top, 10×): 1 M Tris, 1 M Tricine, 1% SDS pH 8.25. Do not adjust pH. 3. Anode buffer (bottom, 10×): 1 M Tris–HCl, pH 8.9. 4. Gel buffer (3×): 3 M Tris–HCl, 0.3% SDS, pH 8.45. 5. Acrylamide 40% acrylamide/bis 19:1 (5% crosslinker). Store at 4 °C. 6. Ammonium persulfate (APS): 10% solution in water. Store at 20 °C. 7. N, N, N, N′-tetramethyl-ethylenediamine (TEMED). Store at 4 °C. 8. SDS loading buffer (3×): 150 mM Tris-HCl, pH 6.8, 6% SDS, 0.3% bromophenol blue, 30% glycerol. 9. Water bath at 100 °C. 10. i-Butanol. 11. Vacuum gel-drying system. 12. Amplify solution. 13. X-ray film.

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Methods

3.1 3H-Palmitate Labeling of E. coli Cultures

1. Grow cultures of E. coli in minimal medium at 37 °C. 2. Add 100 μCi/mL 3H-palmitate to the cell culture at early exponential phase (OD600 of 0.2), and let cultures grow for 2 h (see Note 5).

3.2 Inhibition of Lsp by Globomycin

Add globomycin to the cell culture after 1 h of growth in the presence of 3H-palmitate, and let cultures grow for an additional 1 h (see Note 6).

3.3 Immune Precipitation of Lipoproteins

This protocol is based on reference (7). 1. Add a final concentration of 10% TCA to 1 mL of cell culture to precipitate all proteins. Incubate on ice for 30 min. 2. Centrifuge precipitated proteins for 1 min at 14,000× g in a tabletop centrifuge at 4 °C. 3. Wash the pellets twice with 1 mL ice-cold (-20 °C) acetone. Carefully remove all supernatant. 4. Briefly dry protein pellets at room temperature. 5. Resuspend pellets in 50 μL of solubilization buffer, and boil samples for 2 min. Let cool down. 6. Add 450 μL immune precipitation buffer (see Note 7). 7. Centrifuge samples for 10 min at 14,000× g in a tabletop centrifuge. 8. Take 200 μL from the top and add 300 μL immune precipitation buffer. 9. Add antibodies and incubate on ice overnight. 10. Add 100 μL Protein G-Sepharose slush, and incubate on ice for 20 min. 11. Centrifuge 1 min at 8000× g at 4 °C in tabletop centrifuge, and wash pellet twice with wash buffer I (see Note 8). 12. Wash once with wash buffer II. 13. Resuspend slush in 100 μL SDS loading buffer, and boil for 2 min to release proteins from Protein G-Sepharose. 14. Centrifuge samples for 5 min in tabletop centrifuge, and use supernatant for gel electrophoresis (see Note 9).

3.4 Tris-Tricine SDS Gel Electrophoresis

Tris-Tricine SDS gels are especially useful to separate small proteins and peptides (less than 30 kDa) [8]. 1. Prepare a mini-gel format separating gel (16%) by mixing 5 mL of gel buffer, 6 mL acrylamide solution, and 4 mL water (total volume 15 mL). Add 5 μL TEMED and 50 μL APS and cast gel

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Fig. 1 Accumulation of diacylglyceryl-prolipoprotein (DAG-PLP) in the envelope of E. coli B cells labeled with 3H palmitate upon treatment with globomycin (5 μg/ mL). Without globomycin (lanes 1 and 3), with globomycin (lanes 2 and 4), antiLpp immunoprecipitates in lane 3 (sample from lane 1), and lane 4 (sample from lane 2). (Figure adapted with permission from Fig. 5 of [10] corresponding to lanes 7 through 10)

in an 8.6 cm × 6.8 cm × 0.75 cm gel holder. Allow space for stacking gel, and overlay with water or i-butanol. 2. Prepare stacking gel (4%) by mixing 3.3 mL gel buffer, 1 mL acrylamide solution, and 5.7 mL water (total volume 10 mL). Add 7.5 μL TEMED and 75 μL APS and cast gel and insert comb immediately. 3. Load samples on gel along with protein molecular weight standard. Migrate proteins by electrophoresis at 30 V until the samples have entered the stacking gel, and continue at 200 V till the dye front reached the bottom of the gel (see Note 10). 4. After migration of samples, pry gel plates open and briefly wash gel in water. 5. Transfer to amplify solution and let soak for 10 min under agitation. 6. Dry gel in gel dryer under vacuum for 60 min at 80 °C (see Note 11). 7. Transfer the gel into a cassette and expose to an X-ray film or imaging plate for 3H detection. Expose the film for 10 days at -80 °C. Let the cassette warm up to room temperature before developing the X-ray film (Fig. 1).

4 Notes 1. Efficient labeling of phospholipids and lipoproteins is best obtained with bacterial cultures grown in minimal medium. Rich medium Luria Broth Miller (LB) (5 g of yeast extract, 10 g of peptone, 10 g of NaCl per liter in ultrapure water) can also be used but contains fatty acids from yeast extract. An alternative is to add casamino acid to the minimal medium. 2. Various strains of E. coli can be used. Labeling of lipoproteins in other bacterial species is possible if palmitate is taken up and incorporated into phospholipids. Capacity for fatty acids uptake and susceptibility to globomycin needs to be addressed empirically. Sensitivity to globomycin is variable among strains.

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3. Globomycin is commercially available (Sigma). Derivatives of globomycin have been described, some of which are efficient in inhibiting cell growth of bacterial species other than E. coli [9]. The concentration of these derivatives needs to be determined empirically. 4. Lpp or Braun’s lipoprotein of E. coli (78 amino acids) has been the reference lipoprotein to study modification and cellular localization. Antibodies against other bacterial lipoproteins have been used. It is recommended to use proteins of small size or peptide fragments (up to 10 kDa) to facilitate identification of intermediate forms of lipoprotein modification. 5. For E. coli K12 strains such as MC4100 or MG1655, a final OD600 of 0.6–0.8 is obtained. 6. Globomycin is used at 5 ug/mL for E. coli B strains and at 160 ug/mL for K12 strains. Extensive exposure to globomycin leads to cell lysis due to inhibition of essential enzyme Lsp and as a result accumulation of Lpp in the cytoplasmic membrane while still being cross-linked to the peptidoglycan. 7. Triton X-100 solubilizes lipoproteins from the membrane. 8. Pellet is antigen-antibody-Sepharose slush. 9. The amount of sample to charge needs to be determined empirically and depends on the antigen and antibody used. 10. Electrophoresis of 16% Tris-Tricine gels take longer than regular SDS-PAGE gels. Calculate up to 4 h for a mini-gel format. 11. Remove vacuum from gel before switching off the pump to avoid cracking of the gel. References 1. Kovacs-Simon A, Titball RW, Michell SL (2010) Lipoproteins of bacterial pathogens. Infect Immun 79:548–561 2. Lai J-S, Philbrick WM, Wu HC (1980) Acyl moieties in phospholipids are the precursors for the fatty acids in murein lipoprotein in Escherichia coli. J Biol Chem 255:5384–5387 3. Buddelmeijer N (2015) The molecular mechanism of bacterial lipoprotein modification– how, when and why? FEMS Microbiol Rev 39:246–261 4. Inukai M, Takeuchi K, Shimizu K, Arai M (1978) Mechanism of action of globomycin. J Antibiot 31:1203–1205 5. Cronan JE Jr, Rock CO (1996) Biosynthesis of membrane lipids. In: Neidhardt FC (ed) Escherichia coli and Salmonella: molecular and cellular biology. ASM, Washington, DC 6. Hantke K, Braun V (1973) Covalent binding of lipid to protein. Diglyceride and amide-

linked fatty acid at the N-terminal end of the murein-lipoprotein of the Escherichia coli outer membrane. Eur J Biochem 34:284–296 7. Kumamoto CA, Gannon PM (1988) Effects of Escherichia coli secB mutations on pre-maltose binding protein conformation and export kinetics. J Biol Chem 263:11554–11558 8. Schagger H (2006) Tricine-SDS-PAGE. Nat Protoc 1:16–22 9. Kiho T et al (2004) Structure-activity relationships of globomycin analogues as antibiotics. Bioorg Med Chem 12:337–361 10. Hussain M, Ichihara S, Mizushima S (1980) Accumulation of glyceride-containing precursor of the outer membrane lipoprotein in the cytoplasmic membrane of Escherichia coli treated with globomycin. J Biol Chem 255:3707– 3712

Chapter 5 Components Subcellular Localization: Identification of Lipoproteins Using Alkyne Fatty Acids and Click Chemistry Karine Nozeret and Nienke Buddelmeijer Abstract Lipoproteins contain fatty acids at their amino termini through which they are anchored in lipid membranes. Here, we describe the use of alkyne fatty acids and click chemistry to label lipoproteins in bacterial cells. Exogenous fatty acids containing an alkyne group are incorporated in phospholipids and lipoproteins during bacterial growth. Protein precipitation followed by click chemistry renders lipoproteins fluorescent that can be visualized by in-gel fluorography. Alkyne phospholipids that are the acyl donor in the maturation process of lipoproteins can also be isolated from bacterial membranes for further enzymatic and kinetic studies of acyltransferase that use phospholipids as substrate. Key words Alkyne fatty acids, Click chemistry, Fluorescence detection

1

Introduction The posttranslational modification of bacterial lipoproteins occurs in the cytoplasmic membrane by three sequential enzymatic reactions catalyzed by integral membrane proteins [1]. The membrane phospholipids are the acyl donor for the pre-prolipoprotein phosphatidylglycerol diacylglyceryl transferase (Lgt) and apolipoprotein N-acyltransferase (Lnt) enzymes of this pathway. The use of radioactive chemicals, including radiolabeled fatty acids, has several drawbacks. It requires a designated workspace, is costly due to waste handling, and necessitates long exposure times to visualize results. As an alternative, fluorescently labeled fatty acids [2] and phospholipids are used to study lipoprotein biogenesis. Here we describe the use of alkyne fatty acids and click chemistry to label lipoproteins [3, 4] and phospholipids [5]. Modified (alkyne or fluorescent) phospholipids can serve as substrate for enzymatic in vitro studies of acyltransferases (Fig. 1). As an example, we include the acyl transfer reaction catalyzed by Lnt of E. coli

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_5, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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Fig. 1 Incorporation of rhodamine upon click chemistry reaction with precipitated proteins from E. coli cells grown in the presence of alkyne-palmitate. Total cell extracts from a wild type strain (MG1655) and a lpp::Tn10 derivative were used for analysis. Proteins were stained with coomassie brilliant blue (CBB) and in-gel fluorescence was recorded at 596 nm. Asterisk indicates Lpp protein

[5]. The alkyne fatty acid on sn-1 of phosphatidylethanolamine is transferred onto a biotinylated peptide and analyzed using highresolution Tris-Tricine-SDS gel electrophoresis (Fig. 2). The alkyne-modified lipoproteins allow for further downstream identification of lipoproteins by mass spectrometry.

2

Materials

2.1 Labeling of E. coli Cells with Alkyne Fatty Acids

For lipoprotein and phospholipid labeling, bacterial cells are grown following the same procedure. 1. M63 minimal medium: 7 g K2HPO4, 3 g KH2PO4, 2 g (NH4)2SO4 per liter in ultrapure water. Sterilize by autoclaving for 15 min at 121 °C. Then add 0.5 mg FeSO4 (see Note 1). 2. Just before use, add 0.2% glucose (v/v), 0.5‰ sterilized vitamin B1 (v/v), and 1 mM MgSO4 (see Note 2). 3. Escherichia coli strains (see Note 3). 4. Alkyne fatty acids (alk-FA): 13-tetradecynoic acid (alk-14), 15-hexadecynoic acid (alk-16), or 17-octadecynoic acid (alk-18) as 50 mM stock solution in dimethyl sulfoxide (DMSO) (see Note 4). 5. Incubator.

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Fig. 2 In vitro activity of apolipoprotein N-acyltransferase with alkyne PE as substrate from E. coli lipid extract. In-gel detection of Cy5 fluorescence at 670 nm (Adapted from Fig. 5 in [5]) 2.2 Protein Preparation for Fluorescent Labeling of Lipoproteins

1. Phosphate-buffered saline (PBS). 2. 10 mg/mL lysozyme stock solution in H2O. 3. Lysis buffer: 50 mM triethanolamine (TEA), 150 mM NaCl, 0.1% SDS, EDTA-free protease inhibitor cocktail, 5 mM phenylmethylsulfonyl fluoride (PMSF, from 250 mM stock in ethanol), and 0.1 μL benzonase. 4. 12% SDS buffer: 50 mM TEA, 150 mM NaCl, and 12% SDS. 5. Tabletop centrifuge. 6. Sonicator.

2.3 Phospholipid Preparation for Fluorescent Labeling

1. Phosphate-buffered saline (PBS).

2.3.1 Total Phospholipid Extraction from Bacteria

4. 0.1% (v/v) aqueous acetic acid.

2. Methanol (MeOH). 3. Chloroform (CHCl3). 5. Tabletop centrifuge. 6. Speedvac.

2.3.2 Phospholipid Isolation by Thin Layer Chromatography (TLC)

1. Acetone. 2. 10 mM POPE (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine) stock solution in CHCl3. 3. Total PL fraction from bacterial cell extract after isolation by TLC (see Subheading 2.3.1). 4. Eluent solution: Dichloromethane (DCM):MeOH:H2O (65: 28:4). 5. Chemical fume hood. 6. Ninhydrin spray (see Note 5). 7. Heat gun. 8. Silica gel 60, nonfluorescent, 2 mm thick, glass plate for preparative TLC.

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2.3.3 PL Extraction from TLC Plate

1. Eluent solution: DCM:MeOH:H2O (65:28:4). 2. MeOH. 3. MeOH:acetic acid:H2O (94:1:5). 4. Scalpel blade. 5. Sonicator. 6. Tabletop centrifuge. 7. Pasteur glass pipette with cotton plug. 8. Speedvac.

2.3.4 Estimation Quantity of PE in PL Extract

1. Silica gel 60, nonfluorescent, 0.25 mm thick plate for analytical TLC. 2. Acetone. 3. 135 μg POPE standard dissolved in CHCl3. 4. A small fraction of PL extract solubilized in CHCl3. 5. Eluent solution: DCM:MeOH:H2O (65:28:4). 6. Chemical fume hood. 7. Ninhydrin spray. 8. Heat gun.

2.4 Click Chemistry Reaction for Protein Labeling and Phospholipid Labeling (See Note 6)

1. 10 mM azido-rhodamine in DMSO.

2.4.1 For Protein Labeling

4. 50 mM CuSO4.5H2O freshly prepared in ddH2O.

2. 50 mM Tris(2-carboxyethyl) phosphine hydrochloride (TCEP) freshly prepared in ddH2O. 3. 2 mM Tris[(1-benzyl-1H-1,2,3,-triazol-4-yl)methyl] amine (TBTA) in 4:1 tert-butanol:DMSO. 5. Vortex. 6. PBS. 7. 4% SDS buffer: 50 mM TEA, 150 mM NaCl, 4% SDS. 8. Methanol (MeOH). 9. Chloroform (CHCl3). 10. Tabletop centrifuge.

2.4.2 For Phospholipid Labeling

1. 10 mM azido-Cy5 in DMSO. 2. 50 mM TCEP freshly prepared in ddH2O. 3. 2 mM TBTA in 4:1 tert-butanol:DMSO. 4. 50 mM CuSO4.5H2O freshly prepared in ddH2O. 5. Vortex.

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2.5 Enzymatic Activity Test

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1. Active or inactivated acyltransferase enzyme in compatible reaction buffer: 50 mM Tris–HCl pH 7.2, 150 mM NaCl, and 0.1% Triton X-100 (v/v) (see Note 7). 2. Acyl donor: 100 μM POPE or membrane extracted PE-Alk-C16 or PE-Alk-C18 (ffi100 μM) (see Note 8). 3. Peptide substrate: 100 uM synthetic peptide conjugated with biotin solubilized in water. 4. Enzyme reaction buffer containing 0.1% Triton X-100 (see Note 9). 5. Sonicator. 6. Vortex. 7. Thermomixer with heated lid.

2.6 Protein Gel Electrophoresis 2.6.1 Common Material for SDS-PAGE

1. Mini-gel caster system and SDS-PAGE migration apparatus. 2. Ammonium persulfate (APS): 10% solution in water. Store at 20 °C. 3. N, N, N, N′-tetramethyl-ethylenediamine (TEMED). Store at 4 °C. 4. Laemmli SDS loading buffer (4×): 250 mM Tris–HCl, pH 6.8, 8% SDS, 0.4% bromophenol blue, 40% glycerol, and 10 mM DTT. 5. Heat block or water bath at 100 °C.

2.6.2 Tris-Tricine SDSPAGE

1. Cathode buffer (Top, 10×): 1 M Tris, 1 M Tricine, 1% SDS pH 8.25. Do not adjust pH. 2. Anode buffer (bottom, 10×): 1 M Tris–HCl, pH 8.9. 3. Gel buffer (3×): 3 M Tris–HCl, 0.3% SDS, pH 8.45. 4. Urea. 5. Acrylamide: 40% acrylamide/bis 19:1 (5% crosslinker). Store at 4 °C. 6. Low molecular weight marker.

2.6.3 Tris-Glycine SDSPAGE

1. Separating buffer (4×): 1 M Tris–HCl, pH 8.8. 2. Stacking buffer (4×): 0.5 M Tris–HCl, pH 6.8. 3. Acrylamide: 40% acrylamide/bis 37.5:1 (2.6% crosslinker). 4. Migration buffer: 25 mM Tris–HCl, pH 8.3, 192 mM glycine, and 0.1% SDS. 5. Molecular weight marker.

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2.7 In-Gel Fluorescent Detection of Lipoproteins or Lipopeptides

3

1. Methanol, acetic acid (10%). 2. Imager Bio-Rad (Red Epi illumination, Emission 700/50 Filter) or STORM (excitation wavelength 532 nm, 580 filter, 30 nm band-pass).

Methods

3.1 Alkyne Fatty Acid Labeling of E. coli

1. Grow 5 mL cultures of Escherichia coli in M63 medium at 37 ° C (see Note 10).

3.1.1 For Protein Labeling

2. At OD600 1.7–2, add 20–50 μM alk-FA from 50 mM stock solution in DMSO (see Note 4). As negative control, add equivalent volume of DMSO. 3. Incubate cells at 37 °C for 30 min.

3.1.2 For Phospholipid Labeling

1. Grow 500 mL cultures of Escherichia coli in M63 minimal medium at 37 °C. 2. At OD600 1.7–2, add 20 μM alk-FA from 50 mM stock solution in DMSO (see Note 4). For negative control, add equivalent volume of DMSO. 3. Incubate cells at 37 °C for 3 h.

3.2 Protein Preparation for Fluorescent Labeling of Lipoproteins

1. Centrifuge 4 mL cells (OD600 4.0) in Tabletop centrifuge at 14,000× g for 5 min (see Note 10). 2. Wash the cells twice with PBS. 3. Resuspend pellet in 300 μL lysis buffer. 4. Sonicate for 10 sec and incubate on ice for 10 min. 5. Add 5 μL of 10 mg/mL lysozyme and incubate on ice for 30 min. 6. Add 150 μL of 12% SDS buffer to reach a final concentration of 4% SDS (450 μL total). 7. Sonicate 5 sec. 8. Centrifuge cell lysate at 8000× g for 5 min to remove cell debris. 9. Determine protein concentration in lysate by BCA method (about 3–5 mg/mL).

3.3 Phospholipid Preparation for Fluorescent Labeling In Vitro

1. Centrifuge 500 mL cells at 4000× g for 30 min at 4 °C.

3.3.1 Total Phospholipid Extraction from Bacteria

4. Add 2 mL of MeOH to each tube.

2. Wash the cells twice with 1 volume PBS. 3. Resuspend the pellet in 3 mL PBS, divide in 3× 1 mL in 5 mL tube, and agitate for 20 min. 5. Add 500 μL CHCl3 to each tube.

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6. Vortex to obtain a single liquid phase with most of the cellular protein forming a precipitate (see Note 11). 7. Agitate for 1 h at room temperature and centrifuge at 20,000× g for 2 min at RT. 8. Transfer the supernatant to new 5 mL tubes and discard the pellet. 9. Add 500 μL CHCl3. 10. Add 1 mL aqueous acetic acid 0.1% (v/v). 11. Vortex and centrifuge at 20,000× g for 5 min. 12. Discard the upper aqueous phase and transfer the lower organic phase to a new tube. 13. Dry in a speed-vac and store the dry lipid pellet at -20 °C for future analysis. 3.3.2 Phospholipid Isolation by Thin-Layer Chromatography (TLC)

1. Equilibrate the TLC plate in acetone under a chemical fume hood. 2. Saturate the migration tank with mixture DCM:MeOH:H2O (65:28:4).

vapor

of

eluent

3. Apply the total lipid extract and the control POPE onto the TLC plate (see Note 12). 4. Develop in the eluent mixture DCM:MeOH:H2O (65:28:4) until the solvent front is 2–4 cm from the top. 5. Dry the plate for 2 min under a fume hood to evaporate excess solvent. 6. Mask the spot of lipid extracts with aluminum and stain only the reference spot of POPE with a ninhydrin spray (see Note 5). 7. Heat the plate at 100 °C for 5 min with a heat gun. 8. Scrape the areas of the silica gel containing PE with a scalpel blade. 9. Transfer the silica gel powder to a 15 mL centrifuge tube. 10. Resuspend the silica gel powder in 6 mL of eluent solvent DCM:MeOH:H2O (65:28:4), vortex, sonicate (30 s), and centrifuge for 5 min at 4000× g (see reference (6)). 11. Transfer the supernatant to a collection tube. 12. Resuspend the pellet of powder silica gel twice in 4 mL of eluent solvent DCM:MeOH:H2O (65:28:4), vortex, sonicate (30 s), and centrifuge for 5 min at 4000× g. Collect supernatants in a collection tube. 13. Resuspend the pellet of powder silica gel in 4 mL of MeOH: aqueous acetic acid:H2O (94:1:5), vortex, sonicate (30 s), and centrifuge for 5 min at 4000× g. 14. Transfer the supernatant to a collection tube.

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15. Pool the supernatants, and filter twice on a Pasteur pipette with a cotton plug to eliminate silica particles. 16. Evaporate the solution in a speedvac, and store dried phospholipids at -20 °C. 17. Perform an analytical TLC by loading 1/15 of the total volume of isolated PL next to 135 μg POPE solubilized in CHCl3 as control (see Note 12). Perform TLC and ninhydrin staining as described above. 3.4 Enzyme Activity Test with Alkyne PE from PL Extract

As an example, activity of apolipoprotein N-acyltransferase (Lnt) of E. coli is described using alkyne PE as acyl donor (see references (5, 7)). 1. Mix the two substrates (PL and peptide) in reaction buffer containing a low amount of detergent in 1.5 mL tubes. For the Lnt reaction: PE-Alk-C16 or PE-Alk-C18 (ffi100 μM) and FSL1-biotin (mass 2.247 kDa) (100 μM) were mixed in reaction buffer (50 mM Tris–HCl pH 7.2, 150 mM NaCl and 0.1% Triton X100 (v/v)) (see Note 13). 2. Sonicate for 3 min and incubate at 37 °C for 5 min. 3. Add active or inactive purified enzyme, in case of Lnt 17.2 nM (see references (5, 7)). 4. Mix with the reaction (final volume 20 uL) by carefully pipetting up and down (see Note 14). 5. Incubate the reaction for 16 h at 37 °C in a thermomixer with heated lid.

3.5 Click Chemistry Reaction 3.5.1 For Protein Labeling

1. Add 4% SDS buffer to 50 μg of protein (about 10–16 μL) to a total volume of 45 μL. 2. Add in the following order the click reagents: • 0.5 μL azido-rhodamine. • 1 μL TCEP. • 0.5 μL TBTA. Vortex high 5 s. • 1 μL CuSO4. Vortex high 5 s. 3. Incubate at room temperature in the dark for 60 min. 4. Precipitate protein by MeOH/CHCl3: • Add 4 volumes MeOH (180 μL). • Add 1.5 volume CHCl3 (67.5 μL). • Add 3 volumes ddH2O (135 μL). Vortex. Centrifuge at 20,000× g for 1 min.

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Remove aqueous layer. Wash protein samples twice with 4 volumes MeOH (180 μL). Centrifuge at 20,000× g for 1 min. Air-dry protein pellet. 5. Resuspend protein pellets in 35 μL PBS and 15 μL 4× SDS sample buffer. 6. Denature proteins at 100 °C for 5 min. 7. Load 20 μg, corresponding to 20 μL, per lane on Tris-Glycine SDS-PAGE (corresponds to 0.1 OD600 units). 3.5.2 For Phospholipid Labeling

Upon completion of the enzymatic reaction (Subheading 3.4), the product and residual alkyne-phospholipid substrate are fluorescently labeled through a click chemistry reaction. 1. Add to the 20 μL reaction in the following order the click reagents: • 0.2 μL azido-Cy5. • 0.4 μL TCEP. • 0.2 μL TBTA. Vortex high 5 s. • 0.4 μL CuSO4. Vortex high 5 s. 2. Incubate at room temperature in the dark for 60 min. 3. Dilute 1 μL of the reaction in 375 μL water. 4. Take 1 μL and dilute in 1× SDS sample buffer. 5. Denature proteins/peptides at 100 °C for 5 min. 6. Load 10 μL, per lane on Tris-Tricine SDS-PAGE.

3.6 Gel Electrophoresis 3.6.1 For Protein Labeling

1. Migrate samples on a Tris-Glycine SDS-PAGE at 30 V until color front is at resolving gel, then increase to 100 V. 2. Fix gel in 40% MeOH, 10% acetic acid for 5 min (shaking). 3. Wash the gel in water for 5 min and scan directly. 4. Read fluorescence using STORM or Bio-Rad Imager.

3.6.2 For Lipopeptide In Vitro Labeling Using Alkyne Phospholipid

1. Prepare a high-resolution Tris-Tricine-Urea SDS-PAGE gel consisting of three sequentially polymerized layers: (i) resolving gel 20% containing 6 M urea; (ii) spacer gel 11% with 4 M urea; and (iii) stacking gel 4% (see reference (8)). 2. Migrate samples at room temperature at 30 V until color front is at resolving gel, then increase to 50 V and finish at 75 V (see Note 15). 3. Directly analyze the gel in the Bio-Rad Imager (Red Epi illumination, Emission 700/50 Filter) (see Note 16).

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Notes 1. Add FeSO4 after autoclaving of the M63 medium. 2. Add carbon source (0.2% D-glucose) just before use. 3. Various E. coli K12 strains can be used. Lipoproteins can be alkyne labeled in other bacterial species if they take up alkyne fatty acids from the medium. 4. Alkyne fatty acids are available at Tocris Bioscience and Cayman Chemical Company; some alkyne phospholipids are currently available at Avanti Polar Lipids. Standard phospholipids were purchased from Avanti Polar Lipids. 5. Lipids containing amine groups such as POPE are stained with ninhydrin and appear as pink/purple. Phospholipids can also be stained with iodine vapor or sulfuric acid [9]. 6. Alternative click chemistry-based methods can be used to label proteins that are compatible with downstream application such as mass spectrometry [10]. 7. In vitro activity has been described using apolipoprotein N-acyltransferase of E. coli [5]. The method is applicable for other acyltransferases that depend on phospholipids as acyl substrate. 8. The isolated and extracted alkyne PE fraction also contains endogenous non-alkyne PE. 9. Triton X-100 is added to contain PL in a mixed micelle suspension. 10. Volume of bacterial growth can be scaled up for downstream analysis including proteomic studies. 11. In case a two-phase mixture formed, add MeOH dropwise until a single phase is observed. 12. Let the spots dry by exposure to air. 13. Reaction conditions need to be optimized empirically. 14. Do not vortex the reaction; the suspension needs to be gently mixed. 15. Depending on the length of the gel, migration may take up to 4 h. 16. To detect the product FSL-1-biotin-Cy5 resulting from the click chemistry reaction. Western blotting and detection with streptavidin-HRP probe can be performed in parallel to verify the shift in migration upon complete acyl transfer of the reaction.

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References 1. Legood S, Boneca IG, Buddelmeijer N (2021) Mode of action of lipoprotein modification enzymes-Novel antibacterial targets. Mol Microbiol 115:356–365 2. Thiele C, Papan C, Ho¨lper D et al (2012) Tracing fatty acid metabolism by clickchemistry. ACS Chem Biol 7:2004–2011 3. Hannoush RN (2012) Profiling cellular myristoylation and palmitoylation using omegaalkynyl fatty acids. Methods Mol Biol 800:85– 94 4. Rangan KJ, Yang YY, Charron G, Hang HC (2010) Rapid visualization and large-scale profiling of bacterial lipoproteins with chemical reporters. J Am Chem Soc 132:10628–10629 5. Nozeret K, Boucharlat A, Agou F, Buddelmeijer N (2019) A sensitive fluorescence-based assay to monitor enzymatic activity of the essential integral membrane protein

apolipoprotein N-acyltransferase (Lnt). Sci Rep 9:15978 6. Skipski VP, Peterson RF, Barclay M (1964) Quantitative analysis of phospholipids by thinlayer chromatography. Biochem J 159:e61146 7. Nozeret K, Pernin A, Buddelmeijer N (2020) Click-chemistry based Fluorometric assay for apolipoprotein N-acyltransferase from enzyme characterization to high-throughput screening. J Vis Exp. https://doi.org/10.3791/61146 8. Schagger H (2006) Tricine-SDS-PAGE. Nat Protoc 1:16–22 9. Holzl G, Dormann P (2021) Thin-layer chromatography. Methods Mol Biol 2295:29–41 10. Hannoush RN, Sun J (2010) The chemical toolbox for monitoring protein fatty acylation and prenylation. Nat Chem Biol 6:498–506

Chapter 6 Defining Membrane Protein Localization by Isopycnic Density Gradients Rhys A. Dunstan, Iain D. Hay, and Trevor Lithgow Abstract Bacterial membrane proteins account for around one-third of the proteome in many species and can represent much more than half of the mass of the membranes. Classic techniques in cell biology can be applied to characterize bacterial membranes and their membrane protein constituents, and here we describe a protocol for the purification of outer membranes and inner membranes from Escherichia coli. This allows for compositional analysis of the membranes as well as functional analyses. The procedure can be applied with minor modifications to other bacterial species including those carrying capsular polysaccharide attached to the outer membrane. Key words Sucrose density gradient, Membrane biogenesis, Beta-barrel proteins, Cytoplasmic membrane, Lipoproteins

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Introduction Gram-negative bacteria are characterized by two membranes. The inner (cytoplasmic) membrane is a phospholipid bilayer into which alpha-helical transmembrane proteins are integrated and onto which peripheral membrane proteins are attached by lipidmediated or protein-protein interactions [1, 2]. The outer membrane of Escherichia coli has a complex topography with vast arrays of β-barrel proteins containing relatively little lipid interspersed with a lipid phase with an outer leaflet predominantly composed of lipopolysaccharides and an inner leaflet of phospholipids [3–6]. This, together with a massively high ratio of protein:lipid [7–10], gives the outer membrane a greater buoyant density than the inner membrane. For example, density measurements made on membranes purified from Borrelia burgdorferi show that the outer membrane density (1.19 g/cm3) is sufficiently different to the inner membrane density (1.12 g/cm3) to allow for the membrane fractions to be separated by density-based

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_6, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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centrifugation methods [11]. The buoyant density of the inner membrane appears to be consistent in various bacterial lineages, ranging from 1.12 to 1.14 g/cm3 in species from B. burgdorferi to Caulobacter crescentus to Rhodopseudomonas capsulata [11–13]. In 1975, Yamato, Anraku, and Hirosawa reported a reproducible procedure to disrupt E. coli cells in a French press, recover a relatively pure membrane fraction, and segregate it into inner and outer membrane fractions by sucrose isopycnic gradient ultracentrifugation [14]. This methodology was validated using marker enzyme assays: measuring partial reactions of oxidative phosphorylation for the inner membrane and finding that phospholipase A activity segregated into a higher density fraction. These fractions were therefore deemed to represent the inner and outer membranes, and electron microscopy was used to assess their relative morphology and the homogeneity of the membrane preparations [14]. We now have a mature knowledge of the protein and lipid composition of the inner and outer membranes for a range of bacterial species. By fractionating sucrose gradients and analyzing the fractions by SDS-PAGE and immunoblotting with antibodies (Fig. 1), it is possible to follow the co-purification of specific proteins of interest with either membrane [15–18]. Proteomics has been applied to assess and validate the purity of membrane fractions of numerous genera including Campylobacter [19], Caulobacter [20], Citrobacter [16], Corynebacterium [21], Neisseria [22], Proteus [23], Pseudomonas [24], and Salmonella [25]. Here we detail an optimized protocol for the purification of outer and inner membrane fractions from E. coli. This protocol was developed for studies tracking the assembly of the type 2 secretion system in E. coli [17, 18] and has been applied to demonstrate effects on the outer membrane protein composition when the betabarrel assembly machinery is diminished [26], when LPS biosynthesis is diminished [27], or to dissect the assembly pathways for beta-barrel and alpha-helical membrane proteins into the outer membrane [18]. We have also applied the method to successfully fractionate membranes from Acinetobacter baumannii [28], Klebsiella spp. [29], and C. crescentus [15] and anticipate that it will work with other and diverse bacterial species.

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Materials

2.1 Membrane Purification

1. LB medium: 10 g tryptone, 5 g yeast extract, and 5 g NaCl, make up to 1 L with distilled water. Autoclave and store at room temperature. 2. 1 M Tris stock solution: Add 121.1 g of Tris to 800 mL of distilled water. Adjust the pH to 7.5 with HCl, and then dilute

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Fig. 1 Schematic of the method for separation of outer membranes from inner membranes. (a–e) Methodological details as described in the text. (f) Sample of analysis, with individual fractions from the sucrose gradient submitted into wells on two replicate polyacrylamide gels for SDS-PAGE, visualized by Coomassie blue staining (upper panel) and immunoblotting (lower panel)

to 1 L with distilled water. Autoclave and store at room temperature. 3. 500 mM EDTA stock solution: Add 186.1 g of EDTA (disodium ethylenediamine tetraacetate.2H2O) to 800 mL distilled water. Adjust the pH to 8.0 with NaOH , and then dilute to 1 L with distilled water. Autoclave and store at room temperature (see Note 1). 4. Ultrapure sucrose. 5. Tris buffer: 10 mM Tris–HCl, pH 7.5 (dilute 10 mL of 1 M Tris stock solution to 1 L with distilled water). 6. 100 mg/mL Lysozyme: Add 1 g of lysozyme to 10 mL of distilled water, prepare 500 μL aliquots, and store at -20 °C. 7. 100 mM PMSF: Dissolve 174 mg of PMSF in 10 mL of isopropanol. Prepare 500 μL aliquots, and store at -20 °C (see Note 2) 8. EDTA buffer: 1.65 mM EDTA, pH 7.5 (dilute 660 μL of EDTA stock solution to 200 mL of distilled water). Store at room temperature. 9. TS buffer: 10 mM Tris-HCl, pH 7.5, 0.75 M sucrose (dissolve 51.3 g of sucrose in 200 mL of Tris buffer). Store at 4 °C.

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10. TES buffer: 3.3 mM Tris-HCl, pH 7.5, 1.1 mM EDTA, 0.25 M sucrose (add 1 volume of TS buffer to 2 volumes of 1.65 mM EDTA buffer). Approximately 40 mL for each sample will be required. Store at 4 °C. 11. 5 mM EDTA, pH 7.5 (dilute 5 mL of EDTA stock solution to 500 mL with distilled water). 12. 25% sucrose solution: 25% (w/w) sucrose, 5 mM EDTA, pH 7.5 (add 7.5 g of sucrose to 22.5 mL of EDTA). Pass through a 0.45 μM filter, and store at 4 °C. 13. Avestin Emulsiflex or other cell disruptor or similar. 14. Centrifuge with Sorvall SS34 tubes and rotor (or similar with ability to spin up to approximately 50 mL at up to 15,000 g). 15. Ultracentrifuge with Beckman 70.1 Ti tubes (open-top thick wall polycarbonate tubes) and rotor (or similar with ability to spin approximately 100,000 g). 16. Teflon tissue grinder/Dounce homogenizer (Wheaton). 2.2 Sucrose Density Fractionation

1. 5 mM EDTA, pH 7.5 (see above). Store at room temperature. 2. Ultrapure sucrose. 3. Sucrose EDTA fractions: 35–60% (w/w) sucrose, 5 mM EDTA, and pH 7.5 solutions. For example, to make 50% (w/w), add 15 g of sucrose to 15 mL of 5 mM EDTA. Pass through a 0.45 μM filter and store at 4 °C. 4. Displacing sucrose solution: 70% (w/w) sucrose, 5 mM EDTA, pH 7.5 (add 140 g of sucrose to 60 mL of EDTA) (see Note 3). 5. Beckman Coulter SW 40 Ti tubes (disposable plastic tubes) and rotor. 6. ISCO fractionator.

2.3 Isolation of Membranes After Density Fractionation

1. TES buffer (see above). 2. 25% (w/w) sucrose 5 mM EDTA and pH 7.5 solution (see above). 3. Beckman 70.1 Ti tubes (open-top thick wall polycarbonate tubes) and rotor.

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Methods

3.1 Membrane Purification

1. Grow a 5 mL O/N starter culture in LB at 37 °C from a single colony. 2. Dilute culture 1:100 into 400 mL of LB with antibiotics as required, and grow until OD600 = ~1.0 (see Note 4).

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3. Pellet cells by centrifugation for 5 min at 5000× g 4 °C (see Note 5). 4. Resuspend cells in 10 mM Tris-HCl, pH 7.5 (~200 mL). 5. Repeat centrifugation, and resuspend the pellet in 10 mL of TS. 6. Add 50 μg/mL lysozyme (5 μL of stock) and 2 mM PMSF (200 μL of stock) to break down the peptidoglycan layer and inhibit host serine proteases, respectively. 7. Slowly add 2 volumes (20 mL) of 1.65 mM EDTA, pH 7.5 to destabilize the outer membrane for lysis. 8. Incubate 10 min on ice. 9. Lyse cells using an Avestin Emulsiflex 2–3 passes at ~15,000 psi will be required to fully lyse cells. 10. Centrifuge cell lysate at 15,000× g, 20 min at 4 °C to remove the cell debris. 11. Collect the supernatant and pellet total membranes by ultracentrifugation at 38,000 rpm, 45 min, 4 °C (70.1 Ti rotor – use ~8 mL in each tube). 12. Resuspend membrane pellets in 1 mL of TES using a Dounce homogenizer. 13. Pool membranes and make up to ~8 mL with TES, and centrifuge 38,000 rpm, 45 min, 4 °C. 14. Resuspend membrane pellet in a small volume (~400 μL) of 25% sucrose in 5 mM EDTA, pH 7.5 using a Dounce homogenizer, and either snap-freeze in liquid nitrogen for storage at -80 °C or continue to sucrose density fractionation. 3.2 Sucrose Density Fractionation

1. Immediately before use, carefully prepare a six-step sucrose gradient from 60% to 35% w/w (1.9 mL of 60%, 55%, 50%, 45%, 40%, 35%) in SW40 tubes (see Note 6). A sharp interphase between the layers should be clearly visible (see Note 7). 2. Layer 400 μL of total membranes on top of a 60–35% w/w gradient. 3. Spin in an ultracentrifuge using the SW40 rotor for 17 hr. at 34,000 rpm, 4 °C (see Note 8). 4. Isolate 1 mL fractions (use an ISCO fractionator with 70% sucrose, 5 mM EDTA, pH 7.5 as displacing fluid, or carefully pipette 1 mL fractions off the top of the gradient). Store each fraction at -80 °C until use (see Note 9). 5. To visualize on SDS-PAGE with Coomassie brilliant blue staining (Fig. 1), load 30 μL of each fraction, or ~ 15 μL for analysis by western immunoblotting with antibodies against welldocumented inner and outer membrane proteins (e.g., F1β is

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a subunit of the ATP synthase complex in the inner membrane; BamA is a subunit of the BAM complex in the outer membrane). 3.3 Isolation of Membranes After Density Fractionation

1. To isolate membranes from specific fractions, add TES buffer (to a final volume of ~8 mL) to each fraction of interest or to pooled fractions and pellet by ultracentrifugation, 1.5 h, 4 °C, and 38,000 rpm (70 Ti .1 rotor). 2. Resuspend each fraction in ~100 μL 25% (w/w) sucrose, 5 mM EDTA, pH 7.5 with a Dounce homogenizer, and store membranes at -80 °C (see Note 10).

4

Notes 1. EDTA will not be soluble until pH reaches 8.0. Use vigorous stirring and heat (if needed). 2. PMSF may crystalize in solution; vortex thoroughly before use. 3. Vigorous stirring and heat may be required to fully dissolve sucrose. 4. Protocol can be adjusted for optimal conditions for protein expression or conditional shutdowns, etc., if needed. 5. Everything (buffers, tubes, etc.) should be at 4 °C from now on. 6. For some species of bacteria, the difference in density between the outer membrane and inner membrane is relatively small, and some protocols need to be adjusted accordingly. For example, a recent study on A. baumannii [30] showed the need to alter the composition of the middle step of their three-step gradient to better separate the two membranes. Our protocol uses six steps (35:40:45:50:55:60 w/w sucrose), and this gradient readily separates the inner and outer membranes of A. baumannii [28]. 7. Carefully pipette the sucrose gradient solutions to the edge of the tube as close to the meniscus layer as possible. This will prevent the disruption between the sucrose fractions interface. 8. Due to the different nature of the outer membrane between bacterial strains (capsulated, non-capsulated, etc.), the duration and speed of centrifugation may need to be adjusted. For example, when performing sucrose gradients on total membranes from the capsulated Klebsiella pneumoniae, we routinely centrifuge for 40 h at 33,300 rpm. 9. When collecting by drops using the fractionator, approximately 20 drops are equivalent to 1 mL. Alternatively, fractions can be isolated by carefully piercing the bottom of the tube and

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allowing the fraction to drip out the bottom; or if visible, individual layers can be directly isolated from the tube by piercing the side of the tube with a syringe and sucking the respective layers out. Depending on the amount of protein added to the gradient, the denser OM should predominantly be present in the bottom section of the gradient and may be present as a white band; the lighter IM will be present in the top section of the gradient and will generally be more diffuse and may have a reddish appearance. 10. The volume of 25% (w/w) sucrose, 5 mM EDTA, pH 7.5 solution used will vary with the amount of membranes isolated or desired membrane concentration for later experiments. References 1. Dalbey RE, Wang P, Kuhn A (2011) Assembly of bacterial inner membrane proteins. Annu Rev Biochem 80:161–187. https://doi.org/ 10.1146/annurev-biochem-060409-092524 2. Okuda S, Tokuda H (2011) Lipoprotein sorting in bacteria. Ann Rev Microbiol 65:239– 259. https://doi.org/10.1146/annurevmicro-090110-102859 3. Gunasinghe SD, Shiota T, Stubenrauch CJ et al (2018) The WD40 protein BamB mediates coupling of BAM complexes into assembly precincts in the bacterial outer membrane. Cell Rep 23:2782–2794. https://doi.org/10. 1016/j.celrep.2018.04.093 4. Kamio Y, Nikaido H (1976) Outer membrane of Salmonella Typhimurium: accessibility of phospholipid head groups to phospholipase c and cyanogen bromide activated dextran in the external medium. Biochemistry 15:2561– 2 5 7 0 . h t t p s : // d o i . o r g / 1 0 . 1 0 2 1 / bi00657a012 5. Rassam P, Copeland NA, Birkholz O et al (2015) Supramolecular assemblies underpin turnover of outer membrane proteins in bacteria. Nature 523:333–336. https://doi.org/10. 1038/nature14461 6. Smit J, Kamio Y, Nikaido H (1975) Outer membrane of Salmonella Typhimurium: chemical analysis and freeze-fracture studies with lipopolysaccharide mutants. J Bacteriol 124: 942–958. https://doi.org/10.1128/jb.124. 2.942-958.1975 7. Schnaitman CA (1970) Protein composition of the cell wall and cytoplasmic membrane of Escherichia coli. J Bacteriol 104:890–901. https://doi.org/10.1128/jb.104.2.890-901. 1970 8. Osborn MJ, Gander JE, Parisi E, Carson J (1972) Mechanism of assembly of the outer

membrane of Salmonella Typhimurium. Isolation and characterization of cytoplasmic and outer membrane. J Biol Chem 247:3962– 3972 9. Horne JE, Brockwell DJ, Radford SE (2020) Role of the lipid bilayer in outer membrane protein folding in Gram-negative bacteria. J Biol Chem 295:10340–10367. https://doi. org/10.1074/jbc.REV120.011473 10. Sun J, Rutherford ST, Silhavy TJ, Huang KC (2022) Physical properties of the bacterial outer membrane. Nat Rev Microbiol 20:236– 248. https://doi.org/10.1038/s41579-02100638-0 11. Bledsoe HA, Carroll JA, Whelchel TR et al (1994) Isolation and partial characterization of Borrelia burgdorferi inner and outer membranes by using isopycnic centrifugation. J Bacteriol 176:7447–7455. https://doi.org/10. 1128/jb.176.24.7447-7455.1994 12. Clancy MJ, Newton A (1982) Localization of proteins in the inner and outer membranes of Caulobacter crescentus. Biochim Biophys Acta 686:160–169. https://doi.org/10.1016/ 0005-2736(82)90108-0 13. Flammann HT, Weckesser J (1984) Characterization of the cell wall and outer membrane of Rhodopseudomonas capsulata. J Bacteriol 159: 191–198. https://doi.org/10.1128/jb.159.1. 191-198.1984 14. Yamato I, Anraku Y, Hirosawa K (1975) Cytoplasmic membrane vesicles of Escherichia coli. A simple method for preparing the cytoplasmic and outer membranes. J Biochem 77:705–718 15. Clements A, Bursac D, Gatsos X et al (2009) The reducible complexity of a mitochondrial molecular machine. Proc Natl Acad Sci U S A 106:15791–15795. https://doi.org/10. 1073/pnas.0908264106

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16. Selkrig J, Mosbahi K, Webb CT et al (2012) Discovery of an archetypal protein transport system in bacterial outer membranes. Nat Struct Mol Biol 19:506–510, S501. https:// doi.org/10.1038/nsmb.2261 17. Dunstan RA, Heinz E, Wijeyewickrema LC et al (2013) Assembly of the type II secretion system such as found in Vibrio cholerae depends on the novel pilotin AspS. PLoS Pathog 9: e1003117. https://doi.org/10.1371/journal. ppat.1003117 18. Dunstan RA, Hay ID, Wilksch JJ et al (2015) Assembly of the secretion pores GspD, Wza and CsgG into bacterial outer membranes does not require the Omp85 proteins BamA or TamA. Mol Microbiol 97:616–629. https://doi.org/10.1111/mmi.13055 19. Hobb RI, Fields JA, Burns CM, Thompson SA (2009) Evaluation of procedures for outer membrane isolation from campylobacter jejuni. Microbiology 155:979–988. https://doi.org/ 10.1099/mic.0.024539-0 20. Anwari K, Webb CT, Poggio S et al (2012) The evolution of new lipoprotein subunits of the bacterial outer membrane BAM complex. Mol Microbiol 84:832–844. https://doi.org/10. 1111/j.1365-2958.2012.08059.x 21. Marchand CH, Salmeron C, Bou Raad R et al (2012) Biochemical disclosure of the mycolate outer membrane of Corynebacterium glutamicum. J Bacteriol 194:587–597. https://doi. org/10.1128/JB.06138-11 22. Masson L, Holbein BE (1983) Physiology of sialic acid capsular polysaccharide synthesis in serogroup B Neisseria meningitidis. J Bacteriol 154:728–736 23. Siegmund-Schultze N, Kroll HP, Martin HH, Nixdorff K (1991) Composition of the outer membrane of Proteus mirabilis in relation to serum sensitivity in progressive stages of cell form defectiveness. J Gen Microbiol 137: 2753–2759

24. Jagannadham MV, Abou-Eladab EF, Kulkarni HM (2011) Identification of outer membrane proteins from an Antarctic bacterium Pseudomonas syringae Lz4W. Mol Cell Proteomics 10 (M110):004549. https://doi.org/10.1074/ mcp.M110.004549 25. Bishop RE, Gibbons HS, Guina T et al (2000) Transfer of palmitate from phospholipids to lipid A in outer membranes of gram-negative bacteria. EMBO J 19:5071–5080. https://doi. org/10.1093/emboj/19.19.5071 26. Charlson ES, Werner JN, Misra R (2006) Differential effects of yfgL mutation on Escherichia coli outer membrane proteins and lipopolysaccharide. J Bacteriol 188:7186–7194. https:// doi.org/10.1128/JB.00571-06 27. Steeghs L, de Cock H, Evers E et al (2001) Outer membrane composition of a lipopolysaccharide-deficient Neisseria meningitidis mutant. EMBO J 20:6937–6945. https://doi.org/10.1093/emboj/20.24. 6937 28. Grinter R, Morris FC, Dunstan RA et al (2021) BonA from Acinetobacter baumannii forms a divisome-localized decamer that supports outer envelope function. mBio 12:e0148021. https://doi.org/10.1128/mBio.01480-21 29. Bi W, Liu H, Dunstan RA et al (2017) Extensively drug-resistant Klebsiella pneumoniae causing nosocomial bloodstream infections in China: molecular investigation of antibiotic resistance determinants, informing therapy, and clinical outcomes. Front Microbiol 8: 1230. https://doi.org/10.3389/fmicb.2017. 01230 30. Cian MB, Giordano NP, Mettlach JA, Minor KE, Dalebroux ZD (2020) Separation of the cell envelope for Gram-negative bacteria into inner and outer membrane fractions with technical adjustments for Acinetobacter baumannii. J Vis Exp 158. https://doi.org/10.3791/ 60517

Chapter 7 Components Subcellular Localization: Cell Surface Exposure Anna Konovalova Abstract Surface-exposed proteins of Gram-negative bacteria are represented by integral outer membrane β-barrel proteins and lipoproteins. There are no computational methods to predict surface-exposed lipoproteins, and therefore lipoprotein topology must be experimentally tested. This chapter describes several distinct but complementary methods for detection of surface-exposed proteins: cell surface protein labeling, accessibility to extracellular protease or antibodies, and SpyTag/SpyCatcher system. Key words Biotinylation, PEGylation, Surface proteolysis, Whole cell dot blot, Protein topology

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Introduction Bacteria interact with the environment, including the eukaryotic host, by displaying proteins and appendages on the cell surface. Cells of Gram-negative bacteria are surrounded by an additional membrane known as the outer membrane (OM), and its outer leaflet forms the cell surface [1]. The outer leaflet of the OM is composed of lipopolysaccharide (LPS) and decorated with proteins. Integral β-barrel proteins (referred to as outer membrane proteins or OMPs) are extremely abundant and cover around 80% of the surface area in the model Gram-negative Escherichia coli [2]. OMPs often display long extracellular loops and, in some cases, extracellular domains. In the case of OMPs, topology and extracellular loops can be easily predicted computationally due to presence of β-strands that have alternating hydrophobic and polar amino acids [3, 4]. The second class of proteins that can be found in the OM are so-called lipoproteins. These are peripheral proteins that are tethered to the OM by N-terminal lipids. For a long time, lipoproteins were thought to be exclusively found in the inner leaflet of OM facing the aqueous periplasm [5]. However, in recent years, a

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_7, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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number of surface-exposed lipoproteins, which either complete face cell exterior or exist in transmembrane complexes that feature surface-exposed domains, have been identified (for reviews, see [6– 8]). In contrast to OMPs, lipoproteins are a very diverse group of proteins; they do not share sequence or structure similarity. Many surface-exposed lipoproteins have no obvious transmembrane domains and are assembled on the cell surface by novel mechanisms [6–8]. Therefore, lipoprotein surface exposure and/or topology in the OM cannot be predicted and must be experimentally tested. Several methods have been developed for the detection of surface-exposed proteins. All of them are based either on the availability of a function group for protein modification/labeling or protein accessibility to extracellular proteases or antibodies. Cell Surface Protein Labeling Protein labeling approaches utilize reagents that are able to react efficiently with certain functional groups and form a covalent bond [9]. Reagents with N-hydroxysuccinimide (NHS) esters are often the first choice. They target primary amines (available in side chains of lysines residues or N-terminus of the protein if not modified). Lysines are relatively abundant within protein sequences and, because of the charge, usually solvent accessible in protein structures. The other commonly used reagents are based on maleimides, which react with the cysteine sulfhydryl groups. Cysteines are not commonly found in protein sequences, a fact that can be taken advantage of using genetically introduced cysteine codons to study detailed protein topology (see Chap. 9 for more information). Protein labeling reagents vary greatly in their properties, such as size, hydrophobicity, and detection methods (see Table 1 for examples). Reagents for selective surface labeling should be cell impermeable. When working with Gram-negative bacteria, the unique permeability properties of the OM must be taken in consideration [10]. The OM is an asymmetric membrane with phospholipids in the inner leaflet and the LPS in the outer leaflet. LPS is negatively charged and bridged with divalent cations, such as Mg2+ and Ca2+ (see Note 1). These lateral interactions seal the OM and make it impermeable to hydrophobic compounds. On the other hand, the OM also contains protein channels with allowing diffusion of nutrients and small molecules. Therefore, small-size hydrophilic reagents can enter the periplasm and label proteins on both sides of the membrane. Because of these properties of the OM, the selection of the reagents for cell surface labeling is often the opposite of the product instruction manuals that were most often developed for labeling eukaryotic cells. In our lab, we have used hydrophobic NHS-LC-LC-biotin to selectively label surface-exposed lipoproteins in E. coli [11, 12]. In

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Table 1 Protein labeling reagents for selective surface labeling Functional group to be labeled

Name

Polar

Molecular weight, Da

Primary amine

NHS-LC-LC-Biotin NHS-PEG(n)-Biotin

+

567.70 Available in 1–10 kDa range

Sulfhydryl

Mal-PEG(n)-Biotin

+

Available in 1–10 kDa range

addition, hydrophilic reagents with sizes significantly larger than the diffusion limit (600 Da for E. coli [13]) will also preferentially label the cell surface. The OM in other bacteria may have different properties, so reagents should be validated every time for their cell surface selectivity. For example, not all Gram-negative bacteria may have such a highly asymmetric OM and hence are not as resistant to hydrophobic compounds as E. coli. The sensitivity to detergents serves as a good indication of the presence of phospholipids in the outer leaflet. If this is the case, using hydrophobic reagents is not recommended. This rule also applies to E. coli mutants with OM biogenesis defects. In addition, when the permeability properties of the OM are unknown, it is better to use high molecular weight reagents to avoid the generation of false-positive results. Protein labeling reagents allow the detection of modified protein either by adding a biotin group, a long-chain PEG linker, or a combination of these. Protein biotinylation allows immunoblot detection using anti-biotin antibodies or streptavidin conjugates. However, because all cell surface proteins are labeled, the specific protein of interest often has to be purified (or at least enriched) before the detection. Working with high molecular weight PEG linkers provides an advantage for direct detection of the labeled protein in a cell lysate based on the size shift during immunoblot analysis using protein-specific antibodies. Biotinylated proteins can be affinity purified using streptavidin or streptactin resins and then probed either with protein-specific antibodies or analyzed by mass spectrometry. This approach became popular for initial proteomic-based characterization of surface proteome, although its utility has been proven limited by a large number of false-positive hits. These false-positive hits often arise due to co-purifying interaction partners of biotinylated proteins, as traditional streptavidin or streptactin resins are incompatible with denaturing agents. The recent development of StrepTactin®XT resin, which is compatible with up to 6 M urea, holds promise to increase the stringency of biotinylated protein purification and minimize false-positive hits. Protein Accessibility to Extracellular Proteases Protein accessibility to extracellular proteases, also known as surface proteolysis, is

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another common method to study protein surface exposure [14– 16]. It is based on the addition of proteases with broad specificity, such as trypsin or proteinase K to intact cells. Because proteases cannot enter the cell, only surface-accessible proteins or domains of proteins will be cleaved. Antibodies, which recognize the protein of interest, are then used to detect cleavage using immunoblotting. Proteins can be inherently protease resistant because of their tight folding, lack of protease cleavage sites, or because they are protected by interactions with other proteins. Therefore, the negative results of the protease shaving experiments are hard to interpret. One of the way to address this problem is to test whether the protein of interest is protease sensitive in the cell lysate done under non-denaturing conditions, for example, by using a mild detergent lysis solution (see Note 2). It is crucial to use proper controls to ensure that proteases do not target periplasmic proteins for the following reasons. First, proteins are essential structural components of the OM and contribute significantly to its stability. Therefore, complete proteolysis of surface domains can destabilize the OM, which is apparent by the degradation of the periplasmic proteins. For this reason, titration experiments are advisable to find the optimal concentration of the protease. Second, both trypsin and proteinase K retain their activity in SDS [17, 18], and, therefore, it can digest proteins in cell lysates during the preparation of the samples for the immunoblotting. This also leads to the generation of false-positive results. To avoid this, protease inhibitors should be added, and excess protease should be removed prior to the cell lysis in SDS loading buffer. Protein Accessibility to Extracellular Antibodies A number of assays that utilize extracellularly added antibodies to the whole cells are used to study protein surface exposure [19–21]. These include dot blots, whole-cell ELISA, immunofluorescence, and flow cytometry. Just like proteases, antibodies cannot enter intact cells and hence will bind to only surface-accessible epitopes. Antibodies to be used in these assays should satisfy two requirements. First, they should be able to recognize the native protein. Many antibodies, which bind to denatured proteins during immunoblot procedures, cannot bind to native proteins because binding epitopes are hidden in the protein structure. This is especially important when antibodies were raised against denatured protein or a peptide. Second, antibodies should be polyclonal. For example, if a transmembrane protein contains a surface-exposed and periplasmic domain, then using the monoclonal antibodies, which recognize the periplasmic domain, will lead to false-negative results. However, experiments with polyclonal and monoclonal (or epitope-specific) antibodies can provide valuable insights into

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protein topology [22–24]. Epitope tagging approach, where a known epitope (see Table 2 for examples) is fused to a protein of interest, followed by detection with commercially available highaffinity antibodies, can be used in protein topologies studies. It is generally recommended to choose epitope insertion sites in the unstructured loops and turns of protein to minimize interference with overall protein folding. This epitope fusion approach is only useful when the functionality of the constructs can be established. A protocol for a dot blot assay is described below. This assay is easy, inexpensive, and does not require dedicated equipment. SpyTag/SpyCatcher Labeling SpyTag/SpyCatcher approach is one of the latest additions to the toolbox and involves covalent labeling of a specific epitope (SpyTag) by extracellularly added protein (SpyCatcher) [25]. SpyCatcher is a small protein that rapidly and spontaneously forms an irreversible isopeptide bond with SpyTag-containing protein. Optimized SpyTag2/SpyCatcher2 pair [26] is fast reacting (within minutes), can be carried out under physiological conditions, and is compatible with detergents. Thus far, SpyTag/SpyCatcher was successfully used to study autotransporter adhesins that contain large extracellular domains [25, 27, 28]. However, it can also be used to study protein topology. There are two advantages of this system over antibody-based detection. First, its rapid and covalent nature of the interaction is more specific and likely more sensitive, allowing the detection of low abundant proteins. Second, SpyCatcher can be genetically encoded for in vivo labeling [29] and can be adopted for detection of not only surfaceexposed but also periplasmically located SpyTag, aiding with topology analysis and providing periplasmic controls. Important Considerations Each of the above methods has its own advantages and disadvantages; ideally, a combination of methods should be used. One of the common limitations is that the ability to detect the protein on the cell surface depends not only on protein localization but also on its sequence and structure, as these features determine the presence and accessibility of groups for labeling, protease sites, or epitopes for antibody binding. In addition, surface proteins can by physically occluded from detection by interactions with other proteins, hidden within long sugar chains of LPS, an S layer, or an extracellular matrix. Even in E. coli K-12 laboratory strains that do not produce O-antigen, LPS often occludes protein extracellular loops from interactions with antibodies and nanobodies [30, 31]. In some cases, using mutant bacterial strains that produce a truncated, also known as deep rough LPS, can aid with detection [30, 31]. However, we do not recommend using these strains for chemical labeling described above (such as

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Table 2 Epitope tags that can be used for topology studies Tag name

Tag amino acid sequence

Hemagglutinin (HA) tag

YPYDVPDYA

FLAG-tag

DYKDDDDK

Strep II tag

WSHPQFEK

c-Myc

EQKLISEEDL

V5

GKPIPNPLLGLDST

His6

HHHHHH

SpyTag2

VPTIVMVDAYK

biotinylation), as their OM is extremely permeable to a high range of chemicals regardless of size and hydrophobicity [10]. On the other hand, these methods can also generate falsepositive results. For example, surface labeling and proteolysis can destabilize the OM, allowing the reagent and proteases to get access to the periplasm. In addition, many immunodetection techniques require cell fixation, which can also lead to a number of artifacts [32]. Therefore, it is very important to incorporate careful controls for OM integrity when planning for these experiments. As a general recommendation, the protein(s) with known periplasmic topology should be used as negative controls. These include either soluble periplasmic proteins or lipoproteins with experimentally verified periplasmic localization. Ideally, such proteins should not be a part of a bigger protein complex and should be readily detectable with specific antibodies. If such proteinspecific antibodies are not available, it is possible to use heterologously expressed proteins as controls, for example, periplasmically localized fluorescent proteins (such as mCherry or super folding GPF [33]), maltose binding protein, GST, or other proteins for which antibodies are commercially available. However, it is important to remember that protein overexpression can also lead to aberrant results. Therefore, when using such heterologous proteins, one should verify that they don’t have a negative impact on cell growth and OM permeability. In addition, making a variant of a protein of interest, which would localize it to different compartments (e.g., by swapping signal sequences), can also serve as a negative control.

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Materials

2.1 Cell Surface Labeling Based on a Modification of Primary Amines

1. NHS reagents (see Table 1 for product information). NHS reagents are moisture sensitive. Store desiccated at 4 °C, and equilibrate to room temperature (RT) before opening. Prepare 25 mM stock solution according to product instructions immediately before use. 2. Labeling buffer containing no primary amines, such as PBS: 10 mM Na2HPO4, 1.8 mM KH2PO4, 2.7 mM KCl, 137 mM NaCl, and pH 8.0. 3. Quenching solution: 1 M glycine or 1 M Tris–HCl, pH 8.0.

2.2 Cell Surface Labeling Based on a Modification of Sulfhydryls

1. Maleimide reagents (see Table 1 for product information). Maleimide reagents are moisture sensitive. Store desiccated at 4 °C, and equilibrate to RT before opening. Prepare 25 mM stock solution according to product instructions immediately before use. 2. Labeling buffer containing no sulfhydryls, such as TBS: 50 mM Tris–HCl, 150 mM NaCl, pH 7.0 or PBS: 10 mM Na2HPO4, 1.8 mM KH2PO4, 2.7 mM KCl, 137 mM NaCl, and pH 7.0. 3. TCEP (Tris(2-carboxyethyl)phosphine) solution: 500 mM (optional).

2.3 Cell Surface Proteolysis

1. Proteinase K solution: 20 mg/mL 2. Reaction buffer, TBS: 50 mM Tris–HCl, 150 mM NaCl, 5 mM CaCl2, pH 8.0. 3. Phenylmethylsulfonyl fluoride (PMSF): 500 mM in ethanol. 4. SDS loading buffer 1×: 50 mM Tris–HCl, pH 6.8, 2% SDS, 10% glycerol, and 0.002% bromophenol blue.

2.4 Whole-Cell Dot Blot Assay

1. Nitrocellulose membrane. 2. PBS: 10 mM Na2HPO4, 1.8 mM KH2PO4, 2.7 mM KCl, 137 mM NaCl, pH 7.0. 3. EDTA: 0.5 M. 4. Blocking buffer: PBS with 2% nonfat dried milk. 5. Antibodies for detection of the protein of interest as well as a negative control. 6. Appropriate horseradish peroxidase (HRP)-conjugated secondary antibodies. 7. Chemiluminescent substrate for HRP detection.

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2.5 SpyTag/ SpyCatcher Labeling

1. SpyCatcher2 (BioRad #TZC001). SpyCatcher2 reacts specifically with SpyTag2 (Table 2). 2. PBS: 10 mM Na2HPO4, 1.8 mM KH2PO4, 2.7 mM KCl, 137 mM NaCl, pH 7.0. Other buffers in the pH range 5–8 can be used. 3. BugBuster 10X Protein Extraction Reagent (Millipore Sigma # 70921-3). 4. SDS loading buffer 1X: 50 mM Tris–HCl, pH 6.8, 2% SDS, 10% glycerol, 0.002% bromophenol blue.

3

Methods

3.1 Cell Surface Labeling Based on a Modification of Primary Amines

This protocol is valid for any NHS-based reagent. 1. Collect exponentially growing cells by centrifugation (see Note 1). 2. Wash cells three times with ice-cold PBS to remove aminecontaining culture media. 3. Resuspend cells to 1010 cells/mL. 4. Add NHS reagent to a final concentration of 2.5 mM. 5. Incubate at RT for 30 min. 6. Add 1/10 of the volume of the quenching solution. 7. Collect the cells by centrifugation. 8. Wash cells twice with PBS supplemented with 100 mM glycine or directly in 100 mM Tris–HCl to quench and remove excess of the reagent. 9. Analyze by immunoblotting or follow with protein purification if needed (see Note 2).

3.2 Cell Surface Labeling Based on a Modification of Sulfhydryls

This protocol is valid for any maleimide-based reagent. 1. Collect exponentially growing cells by centrifugation (see Note 1). 2. Wash cells three times with ice-cold TBS or PBS. 3. Resuspend cells to 1010 cells/mL. 4. (Optional) If the protein contains oxidized (disulfide bonded) cysteines, treat cells with 5 mM TCEP in TBS or PBS pH 7.0 for 30 min at RT. Wash cells twice with TBS or PBS to remove excess of TCEP (see Note 3). 5. Add the reagent to a final concentration of 2.5 mM. 6. Incubate at RT for 30 min. 7. Collect the cells by centrifugation.

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8. Wash cells twice with PBS or TBS to remove excess of the reagent. 9. Analyze by immunoblotting or follow with protein purification if needed (see Note 2). 3.3 Cell Surface Proteolysis

1. Prepare 2× dilutions of proteinase K in the range of 20–1.25 mg/mL in the reaction buffer. 2. Collect exponentially growing cells by centrifugation (see Note 1). 3. Resuspend cells to 1010 cells/mL in the reaction buffer. Use 90 μL of cell suspension for the each reaction. 4. Add 10 μL of corresponding proteinase K solution or 10 μL of reaction buffer (untreated control). Incubate at RT for 30 min. 5. Preheat SDS-loading buffer in a 96 ° C thermoblock or a boiling water bath. 6. Add 1 μL of PMSF stock solution to inactivate proteinase K. 7. Collect cells by centrifugation, and wash twice with the reaction buffer supplemented with 5 mM of PMSF to remove the excess of the proteinase K. 8. Resuspend cells in 100 μL of preheated SDS loading buffer. Boil immediately for at least 10 min. 9. Analyze by immunoblotting (see Note 2).

3.4 Whole-Cell Dot Blot Assay

1. Collect exponentially growing cells by centrifugation (see Note 1). 2. Resuspend cells to 109 cells/mL in PBS. Split into two tubes. 3. Add EDTA to the final concentration of 10 mM to one of the tubes, and sonicate on ice four times for 30 s to prepare cell lysate (see Note 4). 4. Spot 2 μL of cell suspension or cell lysate on a nitrocellulose membrane and air-dry (approx. 5 min). 5. Place the membrane in the blocking solution. Incubate with gentle shaking for 30 min at RT. 6. Add an appropriate amount of primary antibodies (see Note 5). Incubate with gentle shaking for 1 h at RT. 7. Wash the membrane five times for 3 min with PBS. 8. Add a blocking buffer containing secondary antibodies. Incubate with gentle shaking for 1 h at RT. 9. Wash the membrane five times for 3 min with PBS. 10. Use chemiluminescent substrate and develop according to standard immunoblot procedure.

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3.5 SpyTag/ SpyCatcher Labeling

1. Collect exponentially growing cells by centrifugation (see Note 1). 2. Resuspend cells to 109 cells/mL in PBS. Prepare three tubes with 45 μL of cell suspension each (for unlabeled negative control, whole-cell labeling, cell lysate labeling). 3. Add 5 μL of BugBuster 10X reagent to one of the tubes to prepare cell lysate (see Note 5). Add 5 μL of PBS to the remaining tubes. 4. Add 5 μL of undiluted SpyCatcher2 protein to two tubes omitting negative control. Incubate reactions at RT for 5–15 min. 5. Place the membrane in the blocking solution. Incubate with gentle shaking for 30 min at RT. 6. Collect cells by centrifugation and resuspend cells in 50 μL of SDS loading buffer, boil for 5–10 min. 7. Analyze samples by immunoblotting using protein-specific antibodies (recommended) or epitope-based antibodies. You should observe a size upshift corresponding to 15.7 kDa if labeling occurred.

4

Notes 1. Using culture media supplemented with cations helps to reinforce the OM and prevent permeability. If using media with low cation concentration (e.g., LB), add 10 mM MgSO4 and 5 mM CaCl2. In addition, cations can be added to the reaction buffers of any procedures described without interference. 2. If negative results are obtained, analyzing the protein accessibility for labeling or protease cleavage might be necessary. To prepare gentle cell lysate, add BugBuster 10X Protein Extraction Reagent to the cell suspension. Unlike the original BugBuster, this reagent does not add salts or buffer components and can be used with labeling/protease assays without interference. 3. β-mercaptoethanol and dithiothreitol (DTT) contain sulfhydryl groups and are incompatible with maleimide labeling. TCEP does not contain sulfhydryl groups and, therefore, can be used to reduce disulfide bonds prior to labeling. 4. Using detergents for cell lysis (such as BugBuster reagent) will interfere with protein binding to the nitrocellulose membrane. Prepare lysate by sonication. Adding EDTA helps to disperse LPS and form membrane vesicles with mixed orientation. Sometimes it is necessary to readjust the concentration of primary antibodies for a dot blot assay. As a general

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recommendation, start using a concentration three times higher than a concentration used for an immunoblot procedure. 5. Preparation of gentle cell lysate (e.g., by using BugBuster 10X reagent) is necessary to ensure epitope accessibility for labeling under native conditions. SpyCatcher is compatible with a wide range of mild detergents. If no labeling is observed in the cell lysate, it may indicate that the epitope is hidden in the protein structure, and therefore negative results of whole-cell labeling cannot be interpreted. References 1. Saha S, Lach SR, Konovalova A (2021) Homeostasis of the Gram-negative cell envelope. Curr Opin Microbiol 61:99–106. https://doi.org/10.1016/j.mib.2021.03.008 2. Benn G et al (2021) Phase separation in the outer membrane of Escherichia coli. Proc Natl Acad Sci U S A 118:e2112237118. https:// doi.org/10.1073/pnas.2112237118 3. Singh NK, Goodman A, Walter P, Helms V, Hayat S (2011) TMBHMM: a frequency profile based HMM for predicting the topology of transmembrane beta barrel proteins and the exposure status of transmembrane residues. Biochim Biophys Acta 1814:664–670. https://doi.org/10.1016/j.bbapap.2011. 03.004 4. Hayat S, Elofsson A (2012) BOCTOPUS: improved topology prediction of transmembrane beta barrel proteins. Bioinformatics 28: 516–522. https://doi.org/10.1093/bioinfor matics/btr710 5. Narita SI, Tokuda H (2017) Bacterial lipoproteins; biogenesis, sorting and quality control. Biochim Biophys Acta Mol Cell Biol Lipids 1862:1414–1423. https://doi.org/10.1016/ j.bbalip.2016.11.009 6. Hooda Y, Moraes TF (2018) Translocation of lipoproteins to the surface of gram negative bacteria. Curr Opin Struct Biol 51:73–79. https://doi.org/10.1016/j.sbi.2018.03.006 7. Wilson MM, Bernstein HD (2016) Surfaceexposed lipoproteins: an emerging secretion phenomenon in Gram-negative bacteria. Trends Microbiol 24:198–208. https://doi. org/10.1016/j.tim.2015.11.006 8. Konovalova A, Silhavy TJ (2015) Outer membrane lipoprotein biogenesis: lol is not the end. Philos Trans R Soc Lond Ser B Biol Sci 370: e2015.0030. https://doi.org/10.1098/rstb. 2015.0030

9. Hermanson GT (2013) Bioconjugate techniques, 3rd edition. 1:1146. https://doi.org/ 10.1016/B978-0-12-382239-0.00005-4 10. Nikaido H (2003) Molecular basis of bacterial outer membrane permeability revisited. Microbiol Mol Biol Rev 67:593–656 11. Cowles CE, Li Y, Semmelhack MF, Cristea IM, Silhavy TJ (2011) The free and bound forms of Lpp occupy distinct subcellular locations in Escherichia coli. Mol Microbiol 79:1168– 1 1 8 1 . h t t p s : // d o i . o r g / 1 0 . 1 1 1 1 / j . 1365-2958.2011.07539.x 12. Konovalova A, Perlman DH, Cowles CE, Silhavy TJ (2014) Transmembrane domain of surface-exposed outer membrane lipoprotein RcsF is threaded through the lumen of betabarrel proteins. Proc Natl Acad Sci U S A 111: E4350–E4358. https://doi.org/10.1073/ pnas.1417138111 13. Rosenbusch JP (1990) Structural and functional properties of porin channels in E. coli outer membranes. Experientia 46:167–173 14. Wilson MM, Anderson DE, Bernstein HD (2015) Analysis of the outer membrane proteome and secretome of Bacteroides fragilis reveals a multiplicity of secretion mechanisms. PLoS One 10:e0117732. https://doi.org/10. 1371/journal.pone.0117732 15. Pinne M, Haake DA (2009) A comprehensive approach to identification of surface-exposed, outer membrane-spanning proteins of Leptospira interrogans. PLoS One 4:e6071. https:// doi.org/10.1371/journal.pone.0006071 16. Pugsley AP, Kornacker MG, Ryter A (1990) Analysis of the subcellular location of pullulanase produced by Escherichia coli carrying the pulA gene from Klebsiella pneumoniae strain UNF5023. Mol Microbiol 4:59–72 17. Hilz H, Wiegers U, Adamietz P (1975) Stimulation of proteinase K action by denaturing agents: application to the isolation of nucleic

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acids and the degradation of ‘masked’ proteins. Eur J Biochem 56:103–108 18. Porter WH, Preston JL (1975) Retention of trypsin and chymotrypsin proteolytic activity in sodium dodecyl sulfate solutions. Anal Biochem 66:69–77 19. Blom K, Lundin BS, Bolin I, Svennerholm A (2001) Flow cytometric analysis of the localization of Helicobacter pylori antigens during different growth phases. FEMS Immunol Med Microbiol 30:173–179 20. Matsunaga J, Werneid K, Zuerner RL, Frank A, Haake DA (2006) LipL46 is a novel surfaceexposed lipoprotein expressed during leptospiral dissemination in the mammalian host. Microbiology 152:3777–3786. https://doi. org/10.1099/mic.0.29162-0 21. Pinne M, Haake D (2011) Immunofluorescence assay of leptospiral surfaceexposed proteins. J Vis Exp. https://doi.org/ 10.3791/2805 22. Moeck GS et al (1994) Genetic insertion and exposure of a reporter epitope in the ferrichrome-iron receptor of Escherichia coli K-12. J Bacteriol 176:4250–4259 23. Newton SM, Klebba PE, Michel V, Hofnung M, Charbit A (1996) Topology of the membrane protein LamB by epitope tagging and a comparison with the X-ray model. J Bacteriol 178:3447–3456 24. Konovalova A, Perlman DH, Cowles CE, Silhavy TJ (2014) Transmembrane domain of surface-exposed outer membrane lipoprotein RcsF is threaded through the lumen of β-barrel proteins. Proc Natl Acad Sci U S A 111:E4350–E4358. https://doi.org/10. 1073/pnas.1417138111 25. Hatlem D, Trunk T, Linke D, Leo JC (2019) Catching a SPY: using the SpyCatcher-SpyTag and related systems for labeling and localizing bacterial proteins. Int J Mol Sci 20:20092129. https://doi.org/10.3390/ijms20092129

26. Keeble AH et al (2017) Evolving accelerated amidation by SpyTag/SpyCatcher to analyze membrane dynamics. Angew Chem Int Ed Engl 56:16521–16525. https://doi.org/10. 1002/anie.201707623 27. Sikdar R, Bernstein HD (2019) Sequential translocation of polypeptides across the bacterial outer membrane through the trimeric autotransporter pathway. mBio 10: e01973-19. https://doi.org/10.1128/mBio. 01973-19 28. Chauhan N et al (2019) Insights into the autotransport process of a trimeric autotransporter, Yersinia Adhesin A (YadA). Mol Microbiol 111:844–862. https://doi.org/10.1111/ mmi.14195 29. Bedbrook CN et al (2015) Genetically encoded Spy peptide fusion system to detect plasma membrane-localized proteins in vivo. Chem Biol 22:1108–1121. https://doi.org/10. 1016/j.chembiol.2015.06.020 30. Storek KM et al (2018) Monoclonal antibody targeting the beta-barrel assembly machine of Escherichia coli is bactericidal. Proc Natl Acad Sci U S A 115:3692–3697. https://doi.org/ 10.1073/pnas.1800043115 31. Storek KM et al (2019) Massive antibody discovery used to probe structure-function relationships of the essential outer membrane protein LptD. elife 8:e46258. https://doi. org/10.7554/eLife.46258 32. Schnell U, Dijk F, Sjollema KA, Giepmans BN (2012) Immunolabeling artifacts and the need for live-cell imaging. Nat Methods 9:152–158. https://doi.org/10.1038/nmeth.1855 33. Dinh T, Bernhardt TG (2011) Using superfolder green fluorescent protein for periplasmic protein localization studies. J Bacteriol 193: 4984–4987. https://doi.org/10.1128/JB. 00315-11

Chapter 8 Probing Protein Topology and Conformation by Limited Proteolysis Maı¨ale`ne Chabalier, Thierry Doan, and Eric Cascales Abstract Proteases are enzymes that catalyze the hydrolytic degradation of other proteins into peptides or amino acids through the digestion of the peptide bond. Promiscuous proteases that target a wide range of proteins are distinguished from specific proteases that have a narrow range of substrates. In terms of activity, endoproteases cleave their substrates at specific residues within the target proteins, whereas exoproteases cleave from one extremity and may have processive activities. Proteases are therefore very useful tools to study proteins, notably their structure or conformation. In addition, proteases can be used to probe the topology of bacterial membrane proteins. Here, we describe limited protease accessibility assays to define inner membrane protein topology and conformational changes based on digestion profiles. Key words Secretion system, Membrane protein, Inner membrane, Insertion, Topology, Transmembrane segment, Bitopic, Polytopic, Conformation, Proteolysis, Protease, Proteinase K, Carboxypeptidase Y

1

Introduction Bacterial secretion systems are multiprotein machines that catalyze the traffic of protein substrates across the cell envelope [1]. Most secretion systems described so far assemble large channels that are comprised of inner and outer membrane proteins [1]. In secretion systems, inner membrane proteins are crucial to assemble platforms for pilus polymerization, substrate recruitment, and selection or for energetic purposes [1]. Defining inner membrane protein topology, a term referring to the number, position and orientation of transmembrane helices (TMH), is therefore an important step to characterize these proteins. Depending on the number and position of these TMH, inner membrane proteins are categorized into bitopic and polytopic proteins (Fig. 1). Bitopic membrane proteins consist of a single membrane-embedded domain connecting two soluble domains located in two different compartments. The TMH

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_8, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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Fig. 1 Nomenclature of inner membrane proteins with selected topologies. Are shown the topologies of a bitopic protein with a N-terminal TMH (a), a bitopic protein with a C-terminal TMH (b), and polytopic proteins with different numbers of TMH (c). Representative examples of inner membrane (IM) proteins with these topologies associated with bacterial secretion systems are listed below. For polytopic proteins, the number of transmembrane segments are indicated in brackets

of bitopic proteins could be located at the N- or C-terminus. By contrast, polytopic membrane proteins consist to multiple TMH that are connected by extramembrane domains, called loops. Bitopic proteins with N-terminal TMH are relatively common, and this category includes GcpC, YscD, VirB10, and PorM subunits associated with the type II (T2SS), type III (T3SS), type IV (T4SS), and type IX (T9SS) secretion systems, respectively [2–5]. Bitopic proteins with C-terminal TMH, also called C-tail proteins, are scarce. In secretion systems, only the type VI secretion system (T6SS) TssL protein has been demonstrated to adopt this topology [6, 7]. Polytopic membrane proteins are also commonly associated with secretion system, and this category includes the HlyB, YscU, VirB6, TssM, and PorL proteins that are, respectively, associated with T1-, T3-, T4-, T6-, and T9SS [5, 8–12]. Inner membrane protein TMH position and orientation could be predicted using computational methods based on hydrophobicity patterns and the “positive-inside” rule (see Chap. 2 by Nielsen). Several approaches have also been developed to experimentally define protein topology [13, 14], including the pho-lac dualreporter system (see Chap. 11 by Karimova) and the substituted cysteine accessibility method (SCAM, see Chap. 9 by Bogdanov). In this chapter, we will describe a third approach based on the accessibility of extramembrane, soluble domains to exogenous proteases. In addition to assessing inner membrane topology, protease accessibility assays are also of interest to test in vitro translocation of proteins [7, 15]. Finally, this method can be used to test whether

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Fig. 2 Schematic representation of an inner membrane protein that undergoes energy-dependent conformational change. A bitopic protein with a single TMH (purple) is represented in two different conformations depending on the energy status, the transition being controlled by ATP hydrolysis, or proton-motive force (PMF). The cleavage site, accessible in one of the two conformations (red arrow), is shown by the red sphere. Representative examples of inner membrane (IM) proteins associated with bacterial secretion systems or macromolecular systems of the cell envelope and subjected to energy-dependent conformational changes are listed below

proteins are subjected to conformational changes in vivo. Indeed, inner membrane proteins associated with secretion systems or multiprotein systems of the cell envelope such as the TonB or Tol-Pal systems can also be involved in the energization of the apparatus, notably by harvesting the proton-motive force (PMF) or the energy from ATP, and transducing it to the outer membrane [16–20]. This is the case of the T4SS VirB10, T9SS PorM, TonB, and TolA proteins [16–20]. Energy sensing and transduction can be associated with conformational changes (Fig. 2) that can be probed by limited proteolysis. The protocol described here can also be adapted to identify energy-dependent conformational transitions.

2

Material

2.1 Cell Growth and Spheroplast Preparation

1. Lysogeny broth (LB) or the recommended medium to grow the strain of interest. 2. TNS buffer: 20 mM Tris–HCl, pH 8.0, 100 mM NaCl, 30% sucrose: Dissolve 0.243 g of Tris(hydroxymethyl) aminomethane, 0.684 g of NaCl and 30 g of sucrose in sterile distilled water (final volume of 100 mL). Adjust the pH to 8.0 with 1 M HCl.

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3. TN buffer: 20 mM Tris–HCl, pH 8.0, 100 mM NaCl: Dissolve 0.243 g of Tris(hydroxymethyl) aminomethane and 0.684 g of NaCl in 100 mL of sterile distilled water. Adjust the pH to 8.0 with 1 M HCl. 4. 0.5 M Ethylene diamine tetraacetic acid (EDTA), pH 8.0: Dissolve 1.86 g of EDTA (disodium salt) in 10 mL of sterile distilled water. Adjust the pH to 8.0 with 10 M NaOH. 5. Lysozyme stock solution (100×). 10 mg/mL lysozyme: Dissolve 10 mg of goose egg lysozyme in 1 mL of sterile distilled water. Store at -20 °C. 6. Incubator. 7. Spectrophotometer to measure bacterial density. 8. Labtop centrifuge. 2.2 Protease Accessibility Assay

1. Triton X-100 stock solution. 10% Triton X-100: Mix 1 mL of 100% Triton X-100 with 9 mL of sterile distilled water (see Note 1). Store at room temperature. 2. Carboxypeptidase Y stock solution (100×). 10 mg/mL Carboxypeptidase Y: Dissolve 10 mg of purified carboxypeptidase Y in 1 mL of sterile distilled water. Store at -20 °C. 3. Proteinase K stock solution (100×). 10 mg/mL proteinase K: Dissolve 10 mg of purified proteinase K in 1 mL of sterile distilled water. Store at -20 °C. 4. Cocktail of protease inhibitors (complete, Roche, or equivalent). 5. Phenylmethylsulfonyl fluoride (PMSF) stock solution (100×). 100 mM PMSF: Dissolve 17.4 mg of PMSF in 1 mL of absolute ethanol (see Note 2). Store at -20 °C. 6. 50% trichloroacetic acid (TCA) solution: Dissolve 50 g of TCA in 30 mL of distilled water. Complete to 100 mL with distilled water (see Note 3). 7. Acetone. 8. Vortex. 9. For conformational change assays: 2.5 M sodium arsenate (Na3AsO4), dissolve 519 mg in 1 mL of distilled water; 2 mM carbonyl cyanide-m-chlorophenylhydrazone (CCCP), dissolve 0.4 mg in 1 mL of dimethylsulfoxide; 2.5 M potassium chloride (KCl), dissolve 3.72 g in 10 mL of distilled water; 8 mM valinomycin, dissolve 8.9 mg in 1 mL of distilled water; 2 mM nigericin, and dissolve 1.45 mg in 1 mL of distilled water.

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1. SDS-PAGE loading buffer: 60 mM Tris–HCl, pH 6.8, 2% SDS, 10% glycerol, 5% β-mercaptoethanol, and 0.01% bromophenol blue. 2. Water bath at 96 °C. 3. Mini-gel caster system and SDS-PAGE apparatus. 4. Protein blotting apparatus 5. Antibodies for protein immunodetection.

3

Method

3.1 Cell Growth and Spheroplast Preparation (See Note 4)

1. Grow a 30-mL culture in the appropriate medium to allow cell growth and the production of the protein of interest (see Notes 5 and 6). 2. Collect cells by centrifugation at 5000 × g for 10 min at 4 °C. 3. Discard the supernatant, and gently resuspend the cell pellet into optical density at 600 nm (OD600) of 12 in ice-cold TNS buffer. Incubate on ice for 10 min. 4. Add EDTA at 1 mM final concentration (see Note 7). Incubate on ice for 5 min. 5. Add lysozyme at the final concentration of 100 μg/mL, and incubate on ice for 15–40 min (see Note 8). 6. Dilute the sample twice with ice-cold TN buffer, mix by gently inverting the tube, and keep on ice for 10 min. 7. Collect spheroplasts by centrifugation at 10,000× g for 5 min at 4 °C. 8. Gently resuspend spheroplasts to an OD600 of 6 in ice-cold TN buffer.

3.2 Protease Accessibility (See Notes 9 and 10)

1. Divide the cell suspension in 5 samples, numbered 1–5. Sample 1 will remain untreated. 2. Add 1% (final concentration) of Triton X-100 in samples 3 and 5 to lyse spheroplasts (see Note 10). Mix by vortexing and incubation 10 min on ice. 3. Add carboxypeptidase Y (100 μg/mL final concentration from the 10 mg/mL stock solution) in tubes 2 and 3. Incubate for 30 min on ice. 4. Add proteinase K (100 μg/mL final concentration from the 10 mg/mL stock solution) in tubes 4 and 5. Incubate for 30 min on ice. 5. Quench the proteolysis reaction by adding PMSF and inhibitor cocktail in tubes 1–5. Incubate for 5 min on ice.

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6. Add 0.5 volume of 50% TCA in tubes 1–5. Mix by vortexing, and incubate for 20 min on ice. 7. Collect precipitated material by centrifugation at 20,000× g for 20 min at 4 °C. 8. Discard supernatant and add 500 μL of acetone. Vortex. 9. Collect precipitated proteins by centrifugation at 20,000× g for 20 min at 4 °C. 10. Discard supernatant, and keep tubes open until the pellet is dry (see Note 11). 3.3 Sample Analysis by SDS-PAGE and Immunodetection

1. Resuspend the pellet in SDS-PAGE loading buffer by throughout vortexing. 2. Boil the samples in a water bath for 5–10 min (see Note 12). 3. Proceed to SDS-PAGE and immunoblotting using your favorite protocol (see Note 13). A schematical example of expected results for topology mapping using proteolysis is shown in Fig. 3. A schematical example of expected results for energy-dependent conformational change assay using proteolysis is shown in Fig. 4.

Fig. 3 Schematic representation of expected results for probing membrane topology by limited proteolysis. The expected immunoblot results for inner membrane proteins with the topology shown below are schematically represented. Samples 1–5 are shown (1, untreated sample; 2, carboxypeptidase Y; 3, carboxypeptidase Y on Triton X-100-lysed spheroplasts; 4, proteinase K; 5, proteinase K on Triton X-100-lysed spheroplasts). The representation of the protein degradation products corresponding to the immuno-detected bands is shown on right of each “blot”

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Fig. 4 Schematic representation of expected results for probing energydependent conformational change by limited proteolysis. The expected immunoblot results for proteins subjected to energy-dependent conformational change are schematically represented. Samples subjected or not to a treatment with an energy dissipator were treated (+) or not (-) with an endoprotease. The representation of the protein degradation products corresponding to the immuno-detected bands is shown on right of each “blot”

4 Notes 1. Triton X-100 is a detergent used to lyse cells and solubilize a subset of membrane proteins. It is a viscous solution and therefore should be pipetted slowly and with care. 2. PMSF is a serine protease inhibitor with a short half-life. Due to its instability in solution, it is recommended to prepare fresh solution extemporarily. 3. Trichloroacetic acid is highly irritating. It should be therefore manipulated with care (gloves, laboratory suit, and glasses). 4. For Gram-negative bacteria, spheroplasts should be prepared to provide access of the protease to the periplasmic side of the inner membrane. For Gram-positive bacteria, grow, harvest, and resuspend the cells as specified as in steps 1–2 of Subheading 3.1 and then proceed to step 8 of Subheading 3.1. 5. Use the appropriate medium to grow the cells. In case the expression of the gene coding the protein of interest needs to be induced, add the inducer at the appropriate concentration. 6. For assessing energy-dependent conformational changes, drugs that collapse the ATP pool (sodium arsenate, final concentration 25 mM), the pmf (CCCP, final concentration

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10–20 μM), the Δψ (valinomycin, final concentration 40 μM supplemented with KCl, final concentration 50 mM), and the ΔpH (nigericin, final concentration 10 μM) components of the pmf should be added in the cell culture and incubated for 30 min before harvesting the cells. 7. This concentration of EDTA is commonly used for disturbing the lipopolysaccharide layer of the outer membrane in E. coli cells. Other bacterial strains may need higher concentrations of EDTA. 8. Lysozyme concentration and incubation time should be adapted to the bacterial strain used in the assay. Efficient spheroplast preparation of most Gram-negative bacteria requires incubation on ice for 15–40 min. 9. For inner membrane protein topology, protease accessibility should be tested with two proteases: one processive exopeptidase hydrolyzing from the C-terminus of the protein (e.g., carboxypeptidase Y) and one endopeptidase with low or broad specificity (e.g., trypsin, papain, proteinase K). When using the calcium-dependent proteinase K, add 0.1 mM CaCl2 in the TN buffer. For simplification purposes, this protocol describes an assay with carboxypeptidase Y and proteinase K. For conformational change assays, it is recommended to use a broad range of endoproteases. 10. Appropriate controls include protease accessibility assays with lysed spheroplasts. Spheroplasts are lysed by the addition of 1% Triton X-100. The presence of Triton X-100 in the assay buffer does not interfere with most proteases. 11. If available, the pellet could be dried using a vacuum SpeedVac concentrator (or equivalent). 12. A number of highly hydrophobic polytopic inner membrane proteins precipitate in SDS-PAGE loading buffer when boiled. For the first assay, keep the concentrating gel during the immunoblot to check that the protein is not retained in the well. 13. If the position of the cleavage site(s) for conformational changes needs to be identified, the cleaved fragments should be purified and subjected to mass spectrometry analyses [19].

Acknowledgments Work in EC laboratory is supported by the Centre National de la Recherche Scientifique, the Aix-Marseille Universite´, and grants from the Agence Nationale de la Recherche (ANR-20-CE110011 and ANR-20-CE11-0017) and the Fondation BettencourtSchueller.

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References 1. Costa TR, Felisberto-Rodrigues C, Meir A, Prevost MS, Redzej A, Trokter M, Waksman G (2015) Secretion systems in Gram-negative bacteria: structural and mechanistic insights. Nat Rev Microbiol 13:343–359 2. Bleves S, Lazdunski A, Filloux A (1996) Membrane topology of three Xcp proteins involved in exoprotein transport by Pseudomonas aeruginosa. J Bacteriol 178:4297–4300 3. Ross JA, Plano GV (2011) A C-terminal region of Yersinia pestis YscD binds the outer membrane secretin YscC. J Bacteriol 193:2276– 2289 4. Das A, Xie YH (1998) Construction of transposon Tn3phoA: its application in defining the membrane topology of the Agrobacterium tumefaciens DNA transfer proteins. Mol Microbiol 27:405–414 5. Vincent MS, Canestrari MJ, Leone P, Stathopulos J, Ize B, Zoued A, Cambillau C, Kellenberger C, Roussel A, Cascales E (2017) Characterization of the Porphyromonas gingivalis type IX secretion trans-envelope PorKLMNP core complex. J Biol Chem 292: 3252–3261 6. Aschtgen MS, Zoued A, Lloube`s R, Journet L, Cascales E (2012) The C-tail anchored TssL subunit, an essential protein of the enteroaggregative Escherichia coli Sci-1 Type VI secretion system, is inserted by YidC. Microbiology 1:71–82 7. Pross E, Soussoula L, Seitl I, Lupo D, Kuhn A (2016) Membrane targeting and insertion of the C-tail protein SciP. J Mol Biol 428:4218– 4227 8. Gentschev I, Goebel W (1992) Topological and functional studies on HlyB of Escherichia coli. Mol Gen Genet 232:40–48 9. Allaoui A, Woestyn S, Sluiters C, Cornelis GR (1994) YscU, a Yersinia enterocolitica inner membrane protein involved in Yop secretion. J Bacteriol 176:4534–4542 10. Jakubowski SJ, Krishnamoorthy V, Cascales E, Christie PJ (2004) Agrobacterium tumefaciens VirB6 domains direct the ordered export of a DNA substrate through a type IV secretion system. J Mol Biol 341:961–977 11. Ma LS, Lin JS, Lai EM (2009) An IcmF family protein, ImpLM, is an integral inner

membrane protein interacting with ImpKL, and its walker a motif is required for type VI secretion system-mediated Hcp secretion in Agrobacterium tumefaciens. J Bacteriol 191: 4316–4329 12. Logger L, Aschtgen MS, Gue´rin M, Cascales E, Durand E (2016) Molecular dissection of the interface between the Type VI secretion TssM cytoplasmic domain and the TssG baseplate component. J Mol Biol 428:4424–4437 13. Traxler B, Boyd D, Beckwith J (1993) The topological analysis of integral cytoplasmic membrane proteins. J Membr Biol 132:1–11 14. van Geest M, Lolkema JS (2000) Membrane topology and insertion of membrane proteins: search for topogenic signals. Microbiol Mol Biol Rev 64:13–33 15. Cunningham K, Lill R, Crooke E, Rice M, Moore K, Wickner W, Oliver D (1989) SecA protein, a peripheral protein of the Escherichia coli plasma membrane, is essential for the functional binding and translocation of proOmpA. EMBO J 8:955–959 16. Larsen RA, Thomas MG, Postle K (1999) Protonmotive force, ExbB and ligand-bound FepA drive conformational changes in TonB. Mol Microbiol 31:1809–1824 17. Germon P, Ray MC, Vianney A, Lazzaroni JC (2001) Energy-dependent conformational change in the TolA protein of Escherichia coli involves its N-terminal domain, TolQ, and TolR. J Bacteriol 183:4110–4114 18. Cascales E, Christie PJ (2004) Agrobacterium VirB10, an ATP energy sensor required for type IV secretion. Proc Natl Acad Sci U S A 101:17228–17233 19. Song L, Perpich JD, Wu C, Doan T, Nowakowska Z, Potempa J, Christie PJ, Cascales E, Lamont RJ, Hu B (2022) A unique bacterial secretion machinery with multiple secretion centers. Proc Natl Acad Sci U S A 119:e2119907119 20. Vincent MS, Comas Hervada C, SebbanKreuzer C, Le Guenno H, Chabalier M, Kosta A, Guerlesquin F, Mignot T, McBride MJ, Cascales E, Doan T (2022) Dynamic proton-dependent motors power type IX secretion and gliding motility in Flavobacterium. PLoS Biol 20:e3001443

Chapter 9 Exploring Uniform, Dual, and Dynamic Topologies of Membrane Proteins by Substituted Cysteine Accessibility Method (SCAM™) Mikhail Bogdanov Abstract A described simple and advanced protocol for Substituted Cysteine Accessibility Method as applied to transmembrane (TM) orientation (SCAM™) permits a topology analysis of proteins in their native state and can be universally adapted to any membrane system to either systematically map an uniform or identify and quantify the degree of mixed topology or establish transmembrane assembly dynamics from relatively static experimental data such as endpoint topologies of membrane proteins. In this approach, noncritical individual amino acids that are thought to reside in the putative extracellular or intracellular loops of a membrane protein are replaced one at the time by cysteine residue, and the orientation with respect to the membrane is evaluated by using a pair of membrane-impermeable non-detectable and detectable thiolreactive labeling reagents. For the most water-exposed cysteine residues in proteins, the thiol pKa lies in the range of 8–9, and formation of cysteinyl thiolate ions is optimum in aqueous rather in a nonpolar environment. These features and the ease of specific chemical modification with thiol reagents are central to SCAM™. Membrane side-specific sulfhydryl labeling allows to discriminate “exposed, protected or dynamic” cysteines strategically “implanted” at desired positions throughout cysteine less target protein template. The strategy described is widely used to map the topology of membrane protein and establish its transmembrane dynamics in intact cells of both diderm (two-membraned) Gram-negative and monoderm (one-membraned) Gram-positive bacteria, cell-derived oriented membrane vesicles, and proteoliposomes. Key words Membrane protein, Topology, Cysteine, Maleimides, SCAM™

1

Introduction

1.1 Membrane Protein Topology and Topogenesis

The vast majority of membrane proteins are estimated to adopt an α-helical bundle (Fig. 1a) structure, which contains transmembrane domains (TMDs) that span the membrane in zigzag but not in “one-by-one” fashion (Fig. 1b). A fundamental aspect and primary structural element of the structure of integral membrane proteins is membrane protein topology. Membrane protein topology refers to the two-dimensional structural information of a membrane protein and describes the way a polypeptide chain is arranged in the

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_9, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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Fig. 1 Three-dimensional view on membrane protein topology of Escherichia coli lactose permease (LacY) probed by SCAM™. (a) A highly resolved α-helical bundle of lactose permease reproduced from Ref. [57] with permission from American Association for the Advancement of Science. (b) X-marks diagnostic single cysteines which were introduced strategically one at the time to probe sidedness of this protein by SCAM™

membrane, i.e., the number of TMDs and their orientation in the membrane that indicates the sidedness of extramembrane domains (EMDs)[1–4]. Although topology of membrane proteins provides low-resolution structural information, it can be a starting point for different biochemical experiments or modeling of threedimensional structures. Membrane protein topology and assembly are governed by structural principles and topological rules and directed by topogenic signals and sequences in the nascent polypeptide chain that are recognized and decoded not only by the protein insertion and translocation machineries (translocon) but also by the given lipid profile [4, 5]. Thus each membrane protein may contain different combinations of topogenic signals (positively and negatively charged residues) and sequences (the number of charged flanking residues, the hydrophobicity and length of the TMDs, EMDs with potential phosphorylation, and glycosylation sites) that cooperate sequentially or differently with the translocon components, protein itself, and given lipid profile to finalize its membrane topology [4, 5]. Although the lipid bilayer is widely considered as a non-flipping zone for most proteins, some integral membrane proteins possess the capacity to reversibly reorient themselves

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during or after insertion if membrane phospholipid composition is changed [3, 4, 6–9], the membrane is depolarized [10], or components of the translocon interact with each other during ATP-driven protein substrate translocation [11]. Membrane proteins can be also engineered to flip after assembly if a strong topological retention signal is introduced at the very end of the polypeptide and then removed post-insertionally [12]. Phosphorylation of an extramembrane domain, which alters its charge nature, could also induce post-insertional topological changes [13]. A structural approach for dynamic membrane protein organization is not achievable by X-ray crystallography. Membrane protein site- and membrane sidespecific sulfhydryl labeling was considered as a powerful in vivo and in vitro tool to investigate the dynamic topology of membrane proteins or topology of membrane proteins with anomalous biogenesis as was used to establish a detailed mechanistic understanding for how lipid-protein interactions govern dynamic membrane protein structure and function by execution of novel Charge Balance Rule [4, 6–9, 14]. 1.2

Method of Choice

Topology studies offer guidance to membrane protein structure and function. Given the enormous number of sequences that are available in genome-sequencing projects, it is not realistic to assume that the structures of all encoded proteins will be generated by crystallographic approaches, especially for membrane proteins. Moreover, purification, crystallization, and structure determination of hydrophobic membrane proteins still remain a challenge. Also, the exact boundaries between TMD ends and EMDs remain largely unknown. For most of the highly resolved membrane proteins, hydrophobic thicknesses of TMDs do not seem to match the lipid bilayer thickness expected or experimentally determined from the acyl chain length of the surrounding lipids. Fortunately, SCAM™ can be used to define the boundaries between membraneembedded regions and the loop regions exposed to the aqueous phase of membrane proteins in their native environment thus supplementing high-resolution structural information [15]. Although X-ray crystallography produces highly detailed structural information of membrane proteins, crystal structures may be distorted due to purification and crystallization constraints. Information on interactions with other proteins and the lipid environment are also lost during purification, since these crucial interactions are substituted by protein-detergent ones. Heterologous expression in a host strain with a different lipid composition than the native host can also result in loss of proper lipid-protein interactions, which can affect topological organization and function. Crystal structures itself are also static, and a structural basis for dynamic TM events is not approachable either by crystallography or even NMR [16, 17]. Therefore, the biochemical topological analyses with low resolution can be invaluable for characterizing membrane

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proteins for which high-resolution structures are not yet available and building a reliable mechanistic model even in the absence of high resolved protein structures [15, 18]. Due to all these facts and limitations, the non-crystallographic approaches have been developed and employed to determine lower-resolution topological arrangement of TMDs in full-length membrane proteins [6, 15, 19–24] and understand its relationship to mechanism of membrane protein insertion process or biological function [2–4, 24]. To verify predicted membrane protein topology models, the existence of all the putative TMDs and EMDs must be verified, and the hydrophilic loops must be localized to one or the other sides of the membrane. These strategies are quite varied but utilize the impermeability of the membrane bilayer to hydrophilic molecules, the difference in properties between the compartments separated by the membrane, and incorporation into proteins of a large variety of reporter groups whose orientation is presumed to reflect the topology of the protein. Experimental topology mapping techniques are but not limited to cysteine scanning, glycosylation mapping, insertion of proteolytic sites, foreign antigenic epitopes, glycosylation motifs, or as complex as fusions of C-terminally truncated proteins to enzymatic and fluorescent topology reporters [15, 22, 25]. Together, these tools document EMD residue or inserted tag accessibility, and therefore topology of a membrane protein. Reporter domains should ideally lack intrinsic topogenic information, be readily identified, and passively and efficiently follow topogenic information presented by the nascent target protein fragment. Nevertheless, translational gene fusion approaches assume that the folding of the C-terminally truncated proteins does not depend on C-terminal sequence and therefore could not always faithfully assign the predicted TM topology for many polytopic membrane proteins [26]. Thus SCAM™ has emerged as the method of choice due to its relative simplicity, reliability, and feasibility [15]. In this approach, SCAM [27] was adapted to map and assign TM topology of polytopic membrane proteins (designed SCAM™) [7]. SCAM™ is still relatively labor intensive but yet most informative and least invasive topology mapping method and the most useful technique thus far developed for topology studies. This method demonstrates that reporter groups can be as simple as a single amino acid substitution. Aside from simplicity, the advantage of this approach is that topology is documented in the context of the full-length membrane protein molecule and the chemical modification can be done using whole cells, thereby avoiding problems related to the conversion of cells into membrane vesicles with a uniform orientation. This contrasts with genetic methods that infer topology from the disposition of reporter molecules fused to fragments of the target protein and therefore fully ignore the longrange inter-helical and inter-loop interactions. SCAM™ was also further developed to map either uniform, dual, mixed, unusual, or

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dynamic membrane protein topologies in either intact cells, isolated membranes vesicles, or liposomes by using a two-step labeling protocol [6, 7, 9, 28–31]. 1.3 Justifying SCAM™ Legacy and Advantages

The reasons for broad application of SCAM™ are both conceptual and practical. The strategic use SCAM™ for mapping membrane topology has circumvented many limitations of alternative methods used to map a topology of integral membrane proteins: 1. Since only single amino acid changes are made, cysteine chemistry has the highest resolution in that water accessibility of individual cysteine residues can be determined. 2. One of the advantages of this approach over previously reported procedures is extremely high sensitivity. Sulfhydryl reagents could be used to detect protein SH groups with sensitivities in the femtomole range. 3. Cysteine has no or little preference for a particular secondary structure. The cysteine residue is a relatively hydrophobic, nonbulky residue, and its introduction at most positions in a membrane protein is likely to be tolerated usually leaving target proteins active. The modifications of the protein are minimal and restricted to a single amino acid residue change, and so they are not likely to disturb the membrane topology. Due to only minimal changes in the primary sequence, structural perturbation of introduced cysteine mutations is essentially absent or much milder than in other methods commonly used to determine topology. 4. Since the individual cysteine replacements can be made over a long stretch of consecutive residues within putative EMD preferably along its entire length, the loss of inaccessibility of one or two residues should not undermine the validity of the information obtained from the rest. 5. The protein does not require complicated purification procedure. 6. Detection of engineered cysteine modifications is simple, and analysis is done by chemical modification by a broad range of commercially available reagents that differ in charge, size, mass, and hydrophilicity. 7. Chemical modification can be done using intact cells thereby avoiding problems related to the conversion of cells into membrane vesicles with a uniform orientation (see Note 1). This procedure does not require the preparation of spheroplasts or the use of chemicals to permeabilize the membrane. 8. Analysis can be done on the full-length protein. 9. The actual analysis is done by chemical modification after insertion of the protein into the membrane.

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10. This method is capable of distinguishing accessibility of residues separated by only three or four residues and is useful in precise fine-structure mapping the ends of TMDs in polytopic membrane proteins. 11. Sonication is a rapid technique for cell disruption that is not labor intensive and can be used to label water-exposed cysteines on both sides of the membrane without regard for their normal disposition. 12. This method is generally applicable to any cell, cell-derived oriented membrane vesicles, cellular organelle, or liposome, e.g., any membrane system surrounded by a single membrane. 13. Can be adapted to any membrane protein and expression system. 1.4 Application of SCAM™

In most cases, SCAM™ provides topological information after orientation of protein within membrane was established [7, 15]. Application of this approach allowed either detailed mapping or significant refinement of the topology of a variety of integral membrane proteins including a more accurate mapping of the ends of TMDs of protein topology of which has been already established by other methods [21–23]. However, SCAM™ is not only an alternative approach to low-resolution determination of membrane protein structure but also constitutes an attractive independent approach to dynamic studies of membrane proteins. Dynamic aspects of protein structure as a function of the physiological state of the cell are best probed in whole cells or membranes. SCAM™ was used to monitor dynamic conformational and topological changes accompanied with substrate binding and release during enzyme turnover and function [32, 33]. Labeling of single cysteine replacements of a major component (SecG) of the SecYEG translocon with 4-acetamido-4′-maleimidylstilbene-2,2′-disulfonic acid (AMS) either at rest state or during ATP-dependent preprotein translocation clearly demonstrated that a cytoplasmic region of SecG undergoes topology inversion [34]. Treatment of spheroplasts with AMS revealed that Cys39 in the cytoplasmic region of SecG could be labeled from the periplasmic side only in the presence of ATP and during protein translocation, whereas a cytoplasmic protein elongation factor Tu remained unlabeled. Accordingly, treatment of inverted inner membrane vesicles with AMS also revealed that Cys88 and Cys111 residues in the periplasmic region were labeled exclusively from the cytoplasmic side of membranes only during protein translocation as demonstrated by the amount of SecG that is gel-shifted after alkylation (Fig. 2a). The extent of cytoplasmically exposed Cys39 derivatized by AMS appeared to decrease in the presence of ATP, while Cys60, located within membrane-spanning region, was not labeled by AMS irrespective of the presence or absence of ATP (Fig. 2a). Therefore, SecG

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Fig. 2 Visualization of SecG topology inversion cycle by SCAM™. (a) Topological inversion of SecG was detected by labeling with membrane-impermeable AMS of a periplasmic Cys from the cytoplasmic side of membranes. Inside-out oriented inner membrane vesicles containing the specified single-cysteine SecG derivative were subjected to proOmpA translocation at 37 °C in the presence and absence of ATP. (b) A model for the dynamic reorientation of SecG coupled with insertion–deinsertion cycle of SecA. SecG, a subunit of the SecYEG channel, undergoes topology inversion coupled with SecA-ATPase-dependent translocation of protein precursors across membrane. (Figure is kindly provided by Drs. Ken-ichi Nishiyama and Hajime Tokuda and partially reproduced from Ref. [34] with permission Oxford University Press on behalf of the Japanese Biochemical Society)

displays an unusual property of inverting its orientation in the membrane, which is tightly coupled to its function and linked with the insertion-deinsertion cycle of SecA (Fig. 2b). Although the labeling patterns derived from SCAM™ assay usually reflect steady-state topology of a membrane protein, semiquantitative analysis of the surface accessibility of individual cysteines introduced into extramembrane loops can be carried out at various stages of protein assembly. In this case, SCAM™ can be used to provide topological information during membrane insertion, folding, and assembly of proteins. Cysteine accessibility during bacteriorhodopsin translation was monitored by pulse-chase radiolabeling and modification by AMS to determine the order and timing of insertion of TM segments into the membrane of Halobacterium salinarum [35]. In this in vivo assay, the rate of insertion of TMDs into the H. salinarum cytoplasmic membrane was monitored by rapid modification of unique cysteines in extracellular EMDs of the protein with AMS resulting in a shift in mobility of the protein in SDS-PAGE. SCAM™ was also utilized to establish a packing geometry of pilin VirB2 subunit and its

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ATP-dependent in- and out-membrane dynamics and reorganization within the T-pilus and T4SS secretion channel [36]. The applicability of SCAM™ was successfully extended to the study of TM topology of membrane protein assembled in different lipid environments [7, 28, 37]. By combining SCAM™ with mutants of E. coli in which membrane phospholipid composition can be systematically controlled, the role of phospholipids as determinants of membrane protein topological organization was established [3, 4, 6]. This approach was essential to test TM protein conformation sensitive to the lipid composition [29] and directly monitor any conformational changes that are associated with change of phospholipid composition either in vivo [6–9] or in vitro [30, 38]. The use of cysteine-specific probes in combination with “lipid” mutants makes this approach a powerful means of deriving of a molecular understanding of highly dynamic topogenesis process from relatively static experimental data such as endpoint topologies of membrane proteins [2–4]. The ability to change lipid composition post-assembly of a membrane protein (by either resupplying or diluting a desired lipid) demonstrated the potential for polytopic membrane proteins to change their topological organization after insertion and assembly in the membrane [6–9]. Thus, SCAM™ became a unique technique to establish a detailed mechanistic understanding for how lipid-protein interactions [6] and interactions with protein itself [29] contribute to overall TM topogenesis [4] (Fig. 4). The use of cysteine-specific probes in combination with “lipid” mutants makes this approach a powerful means of achieving a molecular understanding of highly dynamic topogenesis using static endpoint experiments. By taking advantage of Escherichia coli strains in which lipid composition can be controlled temporarily during membrane protein synthesis and assembly as well as postassembly, it was possible for the first time to observe dynamic changes in protein topology as a function of membrane lipid composition in vivo [6–9] or in vitro [30, 38]. When assembled in membranes of phosphatidylserine synthase gene (pssA) null mutants of Escherichia coli lacking phosphatidylethanolamine (PE), the major phospholipid of this organism, the N-terminal six-TMD helical bundle of lactose permease (LacY) adopts an inverted topology where periplasmic domains become cytoplasmic and vice versa. TMD VII with low hydrophobicity exits the membrane, and the remaining C-terminal five TMDs remain in their native topology [6–9]. The pssA gene (Fig. 3a) was initially placed under control of the araB promoter [7, 28, 37] which was later replaced by the more tightly regulated tet promoter [6–9, 28–31] The latter is regulated by the positive inducer anhydrotetracycline (aTC) to turn on or turn off the synthesis PE. In these experiments, cells were grown first in the presence of isopropyl β-D-1-thiogalactopyranoside

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Fig. 3 Building a dynamic picture of transmembrane large-scale rearrangement of LacY from endpoint topology assays. (a) Experimental rationale. A plasmid copy of OPtac-lacY controlled expression of monocysteine LacY (IPTG initiates expression of LacY) and a chromosomal copy of OPtet-pssA controlled expression of phosphatidylserine synthase (aTc initiates biosynthesis of PE) were combined in the same lacY pssA null cells (strain AT2033). Phospholipid composition of E. coli cells as a function of dose-dependent homogenous pssA gene induction by various amount of aTC is shown (panel A). (b) SCAM™ was performed on intact cells either after initial assembly of LacY in PE-deficient cells, with IPTG induction but before addition of aTc or after complete removal of IPTG and induction of PE synthesis (+PE) by aTc for 3 h (maximum PE level) during logarithmic growth. The monocysteines residing within normally (+PE) cytoplasmic domains (NT-C6) were examined by SCAM™ in AT2033 grown in the absence of aTc (< 3% PE) or examined after induction of PE

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(IPTG) without aTc to allow membrane insertion and misfolding of LacY in the absence of PE. Then cells were switched to growth without IPTG in the presence of aTc to permit biosynthesis of PE in the absence of newly synthesized LacY. The orientation of EMDs of LacY relative to the membrane bilayer was determined before (-PE) or after (+PE) growth in the absence or presence, respectively, of aTc using SCAM™. As shown in Fig. 3b, diagnostic cysteines strategically engineered residing within normally (+PE) cytoplasmic domains (NT-C6), when analyzed in cells grown in the absence of aTc (< 3% PE), were labeled whether or not cells were disrupted, indicating periplasmic exposure. Diagnostic cysteines residing in normally periplasmic domain P1, P3, or P5 were labeled only after cell disruption, indicating cytoplasmic exposure. Then topology of LacY initially assembled in -PE cells was examined after induction of PE synthesis. Cysteines residing in EMDs C2, C4, or C6 initially exposed to the periplasm in -PE cells were only biotinylated after cell disruption consistent with a return to normal topology (Fig. 3b). The same strain has been employed to determine the effects of reversible changes in lipid composition on preexisting TMD protein structure and function in a bidirectional manner [9]. In this case, cells were grown first in the presence of IPTG and aTc to allow membrane assembly of LacY containing a diagnostic L14C (NT) or H205C (C6) replacement in EMDs flanking the N-terminal six-TMD bundle. It appears that at intermediate PE levels (30%), LacY displays a dual topology. More than 50% of cysteines for LacY L14C (NT) or H205C (C6) were protected from biotinylation during sonication by pretreatment of intact cells with AMS indicating that more than half of the LacY molecules inserted in the wildtype orientation, while the remainder adopted an inverted topology. Then cells were switched to growth without IPTG in the absence of aTc to stop biosynthesis of PE in the absence of newly synthesized LacY. Reduction of PE levels to 5% due to continued cell growth resulted in misorientation of nearly all of the LacY. Therefore, a mixture of topologically oriented forms is dependent on both protein sequence and membrane lipid composition, and topological inversions of TMDs are reversible in both directions. The ability to change lipid composition in vivo post-assembly of a membrane protein (by either resupplying or diluting a desired lipid metabolite) demonstrated the potential for polytopic membrane proteins to change their topological organization after insertion and assembly in the membrane. Therefore, SCAM™ can be used in vivo to observe how lipid-protein interactions [6–9] and ä Fig. 3 (continued) synthesis (PE is reaching physiologically normal level, 75% as shown on panel A) in the absence of new LacY synthesis (Figure is reproduced from Ref. [6] with permission from Rockefeller Press and American Society of Cell Biology)

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interactions within a protein itself [29] contribute to overall TMD topogenesis [4]. Thus, SCAM™ is a powerful, well-tested technique for examining static and dynamic membrane protein topologies. By taking advantage of SCAM™, the simple yet wellcharacterized bacterial system and in vitro membrane protein reconstitution techniques, we can easily manipulate the lipid composition in proteoliposomes (tentatively designated as fliposomes), both in a dose-dependent manner and temporally, to identify the function of individual lipids involved in complex and dynamic processes like topogenesis of membrane proteins. If two oppositely oriented topological conformers are fully interconvertible by postassembly synthesis or dilution of PE, in vivo TMD switching may require no cellular factors and therefore is determined solely by lipid-protein interactions. To investigate the molecular determinants required for postassembly topological reorganization, LacY was analyzed in an in vitro proteoliposome system in which lipid composition can be systematically controlled before (liposomes) and after (fliposomes) membrane protein reconstitution using a methyl-β-cyclodextrin (MβCD)-mediated lipid exchange technique [30, 31]. MβCD is able to facilitate the exchange of phospholipids between the outer monolayers of donor multilamellar lipid vesicles (MLVs) and recipient small unilamellar vesicles (liposomes) without bilayer fusion or disruption of bilayer integrity [39]. This new “fliposome” technology provides a means to determine the minimum and sufficient requirements for a protein to flip between topologically distinct states and determine whether this interconversion is thermodynamically determined by the properties of the protein interacting with its lipid environment independent of other cellular factors. Purified LacY was first reconstituted in vitro in the absence or presence of PE followed by a post-reconstitution supply of PE to almost wild-type levels (up to 60%) (Fig. 4a) or a reduction of PE levels from 70% to 106 cfu/mg, is adequate for most needs. 13. Statistically, only one-third of the recombinant plasmids are expected to encode in-frame fusions between the target protein truncations and the PhoA–LacZα reporter. The cells bearing plasmids with out-of-frame fusions can be easily detected on dual-indicator agar plates since they should stay colorless. 14. It is important to note that the PhoA–LacZα reporter might influence the folding of the target protein when inserted internally. Often, C-terminal PhoA–LacZα fusions have higher enzymatic activities than the corresponding sandwich fusions, indicating lower expression levels and/or more steric problems in the latter [19]. 15. Bacteria expressing high levels of phosphatase activity, i.e., when a PhoA–LacZα reporter is localized in the periplasm, will turn blue because of the conversion of the X-Pho substrate into a blue-colored precipitated product. Cells that express high levels of β-galactosidase activity, i.e., when a PhoA– LacZα reporter is localized in the cytoplasm, will turn red due to the conversion of the Red-Gal substrate into an insoluble red compound. Fusions of the PhoA–LacZα reporter within TMSs usually result in a purple pigmentation of the colonies (i.e., a combination of both red and blue colorations as a mixture of cytoplasmic and periplasmic fusions). In addition, for ExoIII-generated phoA–lacZα fusion library constructions, utilization of such dual-indicator agar plates allows for easy detection (and elimination) of colorless colonies that harbor noninformative, out-of-frame fusions.

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16. It should be noted that, whereas true in-frame fusions to cytoplasmic domains develop red coloration in 12–16 h with E. coli TG1, out-of-frame fusions are also able to develop red coloration after a longer incubation period (> in 30–48 h). The precise timing is likely to be different for different E. coli strains and would depend on the growth rate [19]. 17. It is possible to grow subcultures from overnight inoculations to a mid-exponential phase in the presence of IPTG, i.e., increasing the time of induction (3.5–4 h instead of 1 h). 18. Adjust the aliquot volume for the OD measurements depending on the equipment used. The volume of cells used in a reaction may depend on the level of expected enzymatic activity.

Acknowledgment This work was supported by the Institut Pasteur and the Centre National de la Recherche Scientifique (CNRS UMR 3528, Biologie Structurale et Agents Infectieux). References 1. von Heijne G (2006) Membrane-protein topology. Nat Rev Mol Cell Biol 7:909–918. https://doi.org/10.1038/nrm2063 2. Islam ST, Lam JS (2013) Topological mapping methods for α-helical bacterial membrane proteins - an update and a guide. MicrobiologyOpen 2:350–364. https://doi.org/10.1002/ mbo3.72 3. Dobson L, Remenyi I, Tusnady GE (2015) The human transmembrane proteome. Biol Direct 10:1–18. https://doi.org/10.1186/ s13062-015-0061-x 4. Chen CP, Rost B (2002) State-of-the-art in membrane protein prediction. Appl Bioinforma 1:21–35 5. Tusnady GE, Simon I (2010) Topology prediction of helical transmembrane proteins: how far have we reached? Curr Protein Pept Sci 11: 5 5 0 – 5 6 1 . h t t p s : // d o i . o r g / 1 0 . 2 1 7 4 / 138920310794109184 6. Dobson L, Remenyi I, Tusnady GE (2015) CCTOP: a consensus constrained TOPology prediction web server. Nucleic Acids Res 43: W408–W412 7. Peters C, Konstantinos D, Shu N et al (2016) Improved topology prediction using the terminal hydrophobic helices rule. Bioinformatics 32:1158–1162. https://doi.org/10.1093/bio informatics/btv709

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Bacteriol 191:33–46. https://doi.org/10. 1128/JB.00331-08 25. Islam ST, Taylor VL, Qi M et al (2010) Membrane topology mapping of the O-antigen flippase (Wzx), polymerase (Wzy), and ligase (WaaL) from Pseudomonas aeruginosa PAO1 reveals novel domain architectures. mBio 1: e00189-10. https://doi.org/10.1128/mBio. 00189-10 26. Korres H, Verma NK (2004) Topological analysis of glucosyltransferase GtrV of Shigella flexneri by a dual reporter system and identification of a unique reentrant loop. J Biol Chem 279: 22469–22476. https://doi.org/10.1074/jbc. M401316200 27. Nair AH, Korres H, Verma NK (2011) Topological characterisation and identification of critical domains within glucosyltransferase IV (GtrIV) of Shigella flexneri. BMC Biochem 12: 1–14. https://doi.org/10.1186/1471-209112-67 28. Karimova G, Davi M, Ladant D (2012) The beta-lactam resistance protein Blr, a small membrane polypeptide, is a component of the Escherichia coli cell division machinery. J Bacteriol 194:5576–5588. https://doi.org/10. 1128/JB.00774-12 29. Falord M, Karimova G, Hiron A et al (2012) GraXSR proteins interact with the VraFG ABC transporter to form a five-component system required for cationic antimicrobial peptide sensing and resistance in Staphylococcus aureus. Antimicrob Agents Chemother 56: 1047–1058. https://doi.org/10.1128/AAC. 05054-11 30. Georgiadou M, Castagnini M, Karimova G et al (2012) Large-scale study of the interactions between proteins involved in type IV pilus biology in Neisseria meningitidis: characterization of a subcomplex involved in pilus assembly. Mol Microbiol 84:857–873. https://doi. org/10.1111/j.1365-2958.2012.08062.x 31. Dong G, Tian XL, Cyr K et al (2016) Membrane topology and structural insights into the peptide pheromone receptor ComD, a quorum-sensing histidine protein kinase of Streptococcus mutans. Sci Rep 6:26502– 26513. https://doi.org/10.1038/srep26502 32. Poupel O, Moyat M, Groizeleau J et al (2016) Transcriptional analysis and subcellular protein localization reveal specific features of the essential WalKR system in Staphylococcus aureus. PLoS One 11:e0151449. https://doi.org/10. 1371/journal.pone.0151449 33. Bojer MS, Wacnik K, Kjelgaard P et al (2019) SosA inhibits cell division in Staphylococcus aureus in response to DNA damage. Mol

pho-lac Reporter Fusions Microbiol 112:1116–1130. https://doi.org/ 10.1111/mmi.14350 34. Bernard PE, Duarte A, Bogdanov M, Musser JM, Olsen RJ (2020) Single amino acid replacements in RocA disrupt protein-protein interactions to alter the molecular pathogenesis of group A Streptococcus. Infect Immun 88: e00386–e00320. https://doi.org/10.1128/ IAI.00386-20 35. Britton AP, van der Ende SR, van Belkum MJ et al (2020) The membrane topology of immunity proteins for the two-peptide bacteriocins carnobacteriocin XY, lactococcin G, and lactococcin MN shows structural diversity. MicrobiologyOpen 9:e00957. https://doi.org/10. 1002/mbo3.957 36. Rajagopalan K, Dworkin J (2020) Escherichia coli YegI is a novel Ser/Thr kinase lacking conserved motifs that localizes to the inner membrane. FEBS Lett 594:3530–3541. https:// doi.org/10.1002/1873-3468.13920 ˜ a A, Altuna J, Queralt-Martı´n 37. Gonza´lez-Magan M et al (2022) The P. aeruginosa effector Tse5 forms membrane pores disrupting the membrane potential of intoxicated bacteria. Commun Biol 5:1189–1204. https://doi.org/10. 1038/s42003-2-004140-y 38. Sobe RC, Gilbert C, Vo L et al (2022) FliL and its paralog MotF have distinct roles in the stator activity of the Sinorhizobium meliloti flagellar motor. Mol Microbiol 118:223–243. https://doi.org/10.1111/mmi.14964 39. Green MR, Sambrook J (2012) Molecular cloning: a laboratory manual, 4th edn. Cold Spring Harbor Laboratory Press, Cold Spring Harbor/New York 40. Gibson D, Young L, Chuang RY et al (2009) Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat Methods 6: 343–345. https://doi.org/10.1038/nmeth. 1318 41. Nilsson J, Persson B, von Heijne G (2000) Consensus predictions of membrane protein topology. FEBS Lett 486:267–269. https:// doi.org/10.1016/S0014-5793(00)02321-8 42. Boyd D, Traxler B, Beckwith J (1993) Analysis of the topology of a membrane protein by using a minimum number of alkaline phosphatase fusions. J Bacteriol 175:553–556. https:// doi.org/10.1128/jb.175.2.553-556.1993 43. Cassel M, Seppala S, von Heijne G (2008) Confronting fusion protein-based membrane protein topology mapping with reality: the Escherichia coli ClcA H+/Cl- exchange

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Chapter 12 Measure of Peptidoglycan Degradation Activity Yoann G. Santin and Eric Cascales Abstract Most bacterial secretion systems are large machines that cross the cell envelope to deliver effectors outside the cell or directly into target cells. The peptidoglycan layer can therefore represent a physical barrier for the assembly of these large machines. Secretion systems and their counterparts such as type IV pili, flagella, and conjugation machines have therefore evolved or hijacked enzymes with peptidoglycan degradation activity. These enzymes are usually glycoside hydrolases that cleave the glycan chains of the peptidoglycan. Their activities are spatially controlled to avoid cell lysis and to create local rearrangement of the cell wall. In addition, peptidoglycan hydrolases may not be only required for the proper assembly of the secretion systems but may directly participate to the release of the effectors. Finally, several antibacterial effectors possess peptidoglycan degradation activity that damage the cell wall once delivered in the target cell. Here, we describe protocols to test the peptidoglycan degradation activity of these proteins in vitro and in solution. Key words Cell wall, Peptidoglycan, Secretion system, Lytic transglycosylase, Remazol blue

1

Introduction The peptidoglycan is a mesh-like structure that provides the shape and protection against external pressure to bacterial cells. It is composed of glycan chains resulting from the polymerization of N-acetylmuramic acid (MurNAc)-N-acetylglucosamine (GlcNAc) disaccharides. These chains are linked by peptide stems that differ from one species to the other. With pores of ~ 2 nm, the cell wall constitutes a physical barrier for the passage of macromolecules and for the assembly of cell envelope spanning complexes [1–3]. Most trans-envelope multi-protein machineries have therefore evolved dedicated enzymes that locally degrade the cell wall to provide sufficient space for their assembly and insertion without compromising the bacterial shape and survival [3–5]. These enzymes usually cleave the β-1,4 bond between the N-acetylmuramic acid and the N-acetylglucosamine of the glycan chains and form nonreducing 1,6-anhydromuropeptides characteristics of

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_12, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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lytic transglycosylases (LTGs) [4–8]. Genes encoding these enzymes are found associated in type III (T3SS), type IV (T4SS), type VI (T6SS), type VII (T7SS), or type X (T10SS) secretion or flagellum gene clusters [3, 4, 7, 9–14]. The best-studied specialized LTGs are FlgJ and SltF that are associated with flagellar assembly [15–17] and EtgA and VirB1 that are necessary for the biogenesis of the T3SS and T4SS secretion systems, respectively [6, 18– 23]. However, these enzymes could be deleterious for the bacterial cell, and therefore, their activity needs to be restricted to the site of assembly in order to avoid breaches in the cell wall. Studies have provided evidence that the LTGs are recruited via specific interactions to subunits of the machine [24–27] and, in a few cases, that these interactions stimulate the activity of the LTG [10, 26–28]. In addition, the recently described T10SS is comprised of two components: a holin and a peptidoglycan hydrolase that is not required for the assembly of the secretion system but for effector release [13, 14]. A peptidoglycan hydrolase is also required for secretion of the typhoid toxin by Salmonella typhi [29]. Finally, a large number of antibacterial effectors targeting the peptidoglycan, such as amidases, glycosyl hydrolases, and transpeptidases, are delivered by secretion systems [30–37]. Methods have been developed and used to test whether putative LTGs have peptidoglycan hydrolase activities. An indirect approach is to clone the gene encoding the putative LTG to a signal sequence in order to address the protein to the periplasm of Escherichia coli and follow the cell growth after induction as overproduction of the LTG causes cell lysis [38, 39]. More direct protocols have been developed using purified LTGs, including zymogram [40, 41]. However, this technique, which consists to subject the purified LTGs to SDS–PAGE in a gel supplemented with purified peptidoglycan, has limits such as the refolding of the protein after migration. Additional approaches, performed in solution, do not need the denaturation and refolding steps. These turbidimetric assays, detailed below, are methods to follow the activity of the purified LTG on peptidoglycan or peptidoglycan labeled with the Remazol Brilliant Blue (RBB) dye [42, 43]. The peptidoglycan assay relies on the decrease of absorbance of the peptidoglycan solution [42], whereas the RBB assay relies on the release on the dye captured into the peptidoglycan net [43] in the presence of the LTG. In addition, more precise approaches, such as the analysis of peptidoglycan degradation products released after incubation of the peptidoglycan with the purified protein by reverse-phase highperformance liquid chromatography coupled to mass spectrometry [44, 45], allow to define the site of cleavage of the enzyme.

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Material

2.1 Peptidoglycan Purification

1. 8% sodium dodecyl sulfate (SDS) solution: Resuspend 8 g of SDS in 100 mL sterile distilled water. 2. 20 mM Tris–HCl, pH8.0, 100 mM NaCl: Dissolve 2.43 g of Tris(hydroxymethyl) aminomethane and 5.84 g of NaCl in 1 L of sterile distilled water. Adjust the pH at 8.0 with 1 M HCl. 3. 20 mM Tris–HCl, pH7.2, 50 mM NaCl: Dissolve 2.43 g of Tris(hydroxymethyl) aminomethane and 2.92 g of NaCl in 1 L of sterile distilled water. Adjust the pH at 7.2 with 1 M HCl. 4. 0.5 M NaCl: Dissolve 29.22 g of NaCl in 1 L of sterile distilled water. 5. α-amylase stock solution (100×): 20 mg/mL α-amylase in 20 mM Tris–HCl, pH7.2. Store at -20 °C. 6. Pronase stock solution (100×): 20 mg/mL pronase in 20 mM Tris–HCl, pH7.2. Incubate the pronase stock solution for 1 h at 56 °C. Store at -20 °C. 7. French press, Emulsiflex apparatus, or any apparatus to disrupt bacterial cells. 8. Vortex. 9. Water bath at 96 °C. 10. Incubator at 37 °C. 11. Ultracentrifuge (Beckman with TLA100.3 and TLA100.4 rotors or equivalent).

2.2 Turbidimetric Analyses of Peptidoglycan Degradation

1. 60 mM MES, pH6.0, 180 mM NaCl buffer: Dissolve 11.71 g of 2-(N-morpholino)ethanesulfonic acid and 10.52 g of NaCl in 1 L of sterile distilled water. 2. Purified protein to be tested. 3. Lysozyme stock solution: 10 mg/mL egg-white lysozyme in sterile distilled water. 4. Incubator at 37 °C. 5. Spectrophotometer.

2.3 Peptidoglycan Labeling with Remazol Brilliant Blue

1. 400 mM NaOH: Dissolve 16 g of NaOH in 1 L of sterile distilled water. 2. Remazol Brilliant Blue stock solution (10×): Dissolve 1.566 g of Remazol Brilliant Blue R (Sigma–Aldrich) in 10 mL of sterile distilled water. 3. 1 M HCl: Dilute 10 mL of HCl 37% solution (10 M) with 90 mL of distilled water.

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4. Phosphate buffer saline (PBS) buffer: Dissolve 1.44 g of Na2HPO4, 0.24 g of KH2PO4, 0.2 g of KCl, and 8 g of NaCl in 1 L of sterile distilled water. Adjust pH to 7.4 with 1 M HCl. 5. Incubator at 37 °C. 6. Vortex. 7. Ultracentrifuge equivalent). 2.4 RBB-Labeled Peptidoglycan Degradation Assay

(Beckman

with

TLA100.3

rotor

or

1. PBS buffer: Dissolve 1.44 g of Na2HPO4, 0.24 g of KH2PO4, 0.2 g of KCl, and 8 g of NaCl in 1 L of sterile distilled water. Adjust pH to 7.4 with 1 M HCl. 2. Purified protein to be tested. 3. Ethanol 96° or absolute. 4. Lysozyme stock solution: 10 mg/mL egg-white lysozyme in sterile distilled water. 5. Incubator at 37 °C. 6. Ultracentrifuge equivalent).

(Beckman

with

TLA100.3

rotor

or

7. Spectrophotometer.

3

Methods

3.1 Peptidoglycan Purification

The peptidoglycan purification protocol is adapted from [46, 47]: 1. Grow the cells in 400 mL of the appropriate medium until the culture reaches an A600 ~ 1–1.2. 2. Harvest cells by centrifugation at 10,000× g for 20 min at 4 °C. Resuspend cells in 20 mL of 20 mM Tris–HCl, pH8.0, 100 mM NaCl. Break the cells by three passages at the French press or using an Emulsiflex apparatus. 3. Pellet cell envelopes by centrifugation at 400,000× g (90,000 rpm in a Beckman TLA-100.4 rotor) for 45 min at 4 °C. Resuspend cells in 10 mL of 0.5 M NaCl. 4. Add 10 mL of 8% SDS and incubate for 1 h at 96 °C. 5. Leave the solution at room temperature overnight. 6. Pellet the peptidoglycan by ultracentrifugation at 400,000× g at 25 °C for 45 min (see Note 1). 7. Resuspend the peptidoglycan fraction in 10 mL of 0.5 M NaCl and add 10 mL of 8% SDS. Incubate for 30 min at 96 °C. 8. Pellet the peptidoglycan by ultracentrifugation at 400,000× g at 25 °C for 30 min and resuspend the peptidoglycan in 10 mL of water.

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9. Repeat step 8 two times. 10. Resuspend peptidoglycan in 10 mL of 20 mM Tris–HCl, pH7.2, 50 mM NaCl supplemented with 200 μg/mL of α-amylase and 200 μg/mL of pronase. Incubate overnight at 37 °C. 11. Add 10 mL of 8% SDS and incubate for 1 h at 96 °C. 12. Pellet the peptidoglycan by ultracentrifugation at 400,000× g at 25 °C for 30 min and resuspend the peptidoglycan in 10 mL of water. 13. Repeat step 12 two times. 14. Resuspend the peptidoglycan pellet in 1 mL of water. Store at 4 °C. 3.2 Turbidimetric Analyses of Peptidoglycan Degradation

1. Dilute 125 μL of the purified peptidoglycan suspension obtained at step 14 in Subheading 3.1. with 875 μL of 60 mM MES, pH6.0, 180 mM NaCl, and incubate at 37 °C for 30 min. Use three tubes for each reaction to measure peptidoglycan hydrolysis in triplicate. 2. Measure the A600 for each tube (see Note 2). 3. Add 2–5 nmoles of the protein to be tested to each tube and incubate at 37 °C (see Note 3). 4. Measure the A600 every 10 min and plot the difference of absorbance (absorbance at time t subtracted from the initial absorbance) against time (see Note 4). A typical example of the turbidimetric peptidoglycan assay is shown in Fig. 1.

3.3 Peptidoglycan Labeling with Remazol Brilliant Blue

The peptidoglycan labelling protocol is adapted from [43]: 1. Mix 250 μL of the purified peptidoglycan fraction obtained at step 14 in Subheading 3.1. with 250 μL of 400 mM NaOH and incubate for 30 min at 37 °C. 2. Add the Remazol Brilliant Blue dye to the mixture at the final concentration of 25 mM. Vortex and incubate the mixture overnight at 37 °C. 3. Add 500 μL of 1 M HCl and mix by vortexing. 4. Pellet the peptidoglycan by ultracentrifugation at 400,000× g at 25 °C for 30 min and resuspend the peptidoglycan in 2 mL of water. 5. Repeat step 3 two times. 6. Resuspend the peptidoglycan pellet in 250 μL of PBS buffer. Store at 4 °C.

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Fig. 1 LTG activity measured by the peptidoglycan hydrolysis assay. A representative example of peptidoglycan degradation is shown. Purified peptidoglycan was incubated with buffer (open square) or purified LTG (closed circles), and the absorbance at 600 nm (A600) was measured every 20 min. The difference of absorbance at time t minus the absorbance at time zero (ΔA600) was plotted against time (in min) 3.4 RBB-Labeled Peptidoglycan Degradation Assay

1. Dilute 10 μL of RBB-labeled peptidoglycan obtained at step 5 in Subheading 3.3. with 90 μL of PBS buffer and incubate at 37 °C for 30 min. Use nine tubes for each reaction to measure peptidoglycan hydrolysis in triplicate, at three different times. 2. Add 0.2–0.5 nmol of the protein to be tested to the mixture and incubate at 37 °C (see Note 3). This step corresponds to time zero. 3. Add 100 μL of ethanol in three tubes 30 min after time zero, to quench the reaction. 4. Pellet the peptidoglycan by ultracentrifugation at 400,000× g at 25 °C for 30 min. 5. Measure the A595 of the supernatant. 6. Repeat steps 3 and 4 1 h and 4 h after time zero. A typical example of the dye release assay is shown in Fig. 2.

4

Notes 1. Do not incubate at 4 °C to avoid SDS precipitation. 2. Typically, an A600 ~ 0.4–0.7 is measured from peptidoglycan purified from Escherichia coli. 3. Control assays include incubation of the peptidoglycan suspension with (i) buffer and (ii) purified lysozyme. Ideally, additional controls include incubation of the peptidoglycan with (i) the protein to be tested but bearing amino acid substitutions

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Fig. 2 LTG activity measured by the RBB release assay. A representative example of peptidoglycan degradation is shown. (a) RBB-labeled peptidoglycan was incubated with buffer (open bars) or purified LTG (+ LTG, blue bars), and the absorbance at 595 nm (A595) of the supernatant was measured after 0.5, 1, and 4 h of incubation. (b) Photographs of supernatant fractions of RBB-labeled peptidoglycan incubated with buffer (left tube) or purified LTG (+ LTG, right tube) after 4 h of incubation

in the catalytic site (if known or predicted) and (ii) the wildtype protein in the presence of 100 μM of bulgecin A, an inhibitor of lytic transglycosylases [27, 48]. 4. The initial rate of the hydrolysis reaction (in AU/min/mol) can be calculated from the slope of the initial linear curve.

Acknowledgments Work in EC laboratory is supported by the Centre National de la Recherche Scientifique, the Aix-Marseille Universite´, the Agence Nationale de la Recherche (ANR-20-CE11-0017), and the Fondation Bettencourt-Schueller. References 1. Demchick P, Koch AL (1996) The permeability of the wall fabric of Escherichia coli and Bacillus subtilis. J Bacteriol 178:768–773 2. Scheurwater E, Reid CW, Clarke AJ (2008) Lytic transglycosylases: bacterial space-making autolysins. Int J Biochem Cell Biol 40:586– 591 3. Scheurwater EM, Burrows LL (2011) Maintaining network security: how macromolecular structures cross the peptidoglycan layer. FEMS Microbiol Lett 318:1–9 4. Koraimann G (2003) Lytic transglycosylases in macromolecular transport systems of Gramnegative bacteria. Cell Mol Life Sci 60:2371– 2388 5. Dik DA, Marous DR, Fisher JF, Mobashery S (2017) Lytic transglycosylases: concinnity in

concision of the bacterial cell wall. Crit Rev Biochem Mol Biol 52:503–542 6. Ho¨ltje JV (1996) Lytic transglycosylases. EXS 75:425–429 7. Zahrl D, Wagner M, Bischof K, Bayer M, Zavecz B, Beranek A, Ruckenstuhl C, Zarfel GE, Koraimann G (2005) Peptidoglycan degradation by specialized lytic transglycosylases associated with type III and type IV secretion systems. Microbiology 151:3455–3467 8. van Heijenoort J (2011) Peptidoglycan hydrolases of Escherichia coli. Microbiol Mol Biol Rev 75:636–663 9. Kohler V, Probst I, Aufschnaiter A, Bu¨ttner S, Schaden L, Rechberger GN, Koraimann G, Grohmann E, Keller W (2017) Conjugative type IV secretion in Gram-positive pathogens:

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TraG, a lytic transglycosylase and endopeptidase, interacts with translocation channel protein TraM. Plasmid 91:9–18 10. Hausner J, Hartmann N, Jordan M, Bu¨ttner D (2017) The predicted lytic transglycosylase HpaH from Xanthomonas campestris pv. vesicatoria associates with the type III secretion system and promotes effector protein translocation. Infect Immun 85:e00788-16 11. Weber BS, Hennon SW, Wright MS, Scott NE, de Berardinis V, Foster LJ, Ayala JA, Adams MD, Feldman MF (2016) Genetic dissection of the type VI secretion system in Acinetobacter and identification of a novel peptidoglycan hydrolase, TagX, required for its biogenesis. MBio 7:e01253-16 12. Bobrovskyy M, Willing SE, Schneewind O, Missiakas D (2018) EssH peptidoglycan hydrolase enables Staphylococcus aureus type VII secretion across the bacterial cell wall envelope. J Bacteriol 200:e00268–e00218 13. Palmer T, Finney AJ, Saha CK, Atkinson GC, Sargent F (2021) A holin/peptidoglycan hydrolase-dependent protein secretion system. Mol Microbiol 115:345–355 14. Krone L, Faass L, Hauke M, Josenhans C, Geiger T (2023) Chitinase A, a tightly regulated virulence factor of Salmonella enterica serovar Typhimurium, is actively secreted by a type 10 secretion system. PLoS Pathog 19: e1011306 15. de la Mora J, Ballado T, Gonza´lez-Pedrajo B, Camarena L, Dreyfus G (2007) The flagellar muramidase from the photosynthetic bacterium Rhodobacter sphaeroides. J Bacteriol 189: 7998–8004 16. de la Mora J, Osorio-Valeriano M, Gonza´lezPedrajo B, Ballado T, Camarena L, Dreyfus G (2012) The C terminus of the flagellar muramidase SltF modulates the interaction with FlgJ in Rhodobacter sphaeroides. J Bacteriol 194:4513–4520 17. Nambu T, Minamino T, Macnab RM, Kutsukake K (1999) Peptidoglycan-hydrolyzing activity of the FlgJ protein, essential for flagellar rod formation in Salmonella typhimurium. J Bacteriol 181:1555–1561 18. Mushegian AR, Fullner KJ, Koonin EV, Nester EW (1996) A family of lysozyme-like virulence factors in bacterial pathogens of plants and animals. Proc Natl Acad Sci U S A 93:7321– 7326 19. Kohler PL, Hamilton HL, Cloud-Hansen K, Dillard JP (2007) AtlA functions as a peptidoglycan lytic transglycosylase in the Neisseria gonorrhoeae type IV secretion system. J Bacteriol 189:5421–5428

20. Zhong Q, Shao S, Mu R, Wang H, Huang S, Han J, Huang H, Tian S (2011) Characterization of peptidoglycan hydrolase in Cag pathogenicity island of Helicobacter pylori. Mol Biol Rep 38:503–509 21. Garcı´a-Go´mez E, Espinosa N, de la Mora J, Dreyfus G, Gonza´lez-Pedrajo B (2011) The muramidase EtgA from enteropathogenic Escherichia coli is required for efficient type III secretion. Microbiology 157:1145–1160 22. Arends K, Celik EK, Probst I, GoessweinerMohr N, Fercher C, Grumet L, Soellue C, Abajy MY, Sakinc T, Broszat M, Schiwon K, Koraimann G, Keller W, Grohmann E (2013) TraG encoded by the pIP501 type IV secretion system is a two-domain peptidoglycan-degrading enzyme essential for conjugative transfer. J Bacteriol 195:4436–4444 23. Laverde Gomez JA, Bhatty M, Christie PJ (2014) PrgK, a multidomain peptidoglycan hydrolase, is essential for conjugative transfer of the pheromone-responsive plasmid pCF10. J Bacteriol 196:527–539 24. Ho¨ppner C, Carle A, Sivanesan D, Hoeppner S, Baron C (2005) The putative lytic transglycosylase VirB1 from Brucella suis interacts with the type IV secretion system core components VirB8, VirB9 and VirB11. Microbiology 151:3469–3482 25. Creasey EA, Delahay RM, Daniell SJ, Frankel G (2003) Yeast two-hybrid system survey of interactions between LEE-encoded proteins of enteropathogenic Escherichia coli. Microbiology 149:2093–2106 26. Burkinshaw BJ, Deng W, Lameigne`re E, Wasney GA, Zhu H, Worrall LJ, Finlay BB, Strynadka NC (2015) Structural analysis of a specialized type III secretion system peptidoglycan-cleaving enzyme. J Biol Chem 290:10406–10417 27. Santin YG, Cascales E (2017) Domestication of a housekeeping transglycosylase for assembly of a Type VI secretion system. EMBO Rep 18: 138–149 28. Herlihey FA, Osorio-Valeriano M, Dreyfus G, Clarke AJ (2016) Modulation of the lytic activity of the dedicated autolysin for flagellum formation SltF by flagellar rod proteins FlgB and FlgF. J Bacteriol 198:1847–1856 29. Hodak H, Gala´n JE (2013) A Salmonella Typhi homologue of bacteriophage muramidases controls typhoid toxin secretion. EMBO Rep 14:95–102 30. Russell AB, Hood RD, Bui NK, LeRoux M, Vollmer W, Mougous JD (2011) Type VI secretion delivers bacteriolytic effectors to target cells. Nature 475:343–347

Peptidoglycan Remodelling Enzymes 31. Russell AB, Singh P, Brittnacher M, Bui NK, Hood RD, Carl MA, Agnello DM, Schwarz S, Goodlett DR, Vollmer W, Mougous JD (2012) A widespread bacterial type VI secretion effector superfamily identified using a heuristic approach. Cell Host Microbe 11:538–549 32. Brooks TM, Unterweger D, Bachmann V, Kostiuk B, Pukatzki S (2013) Lytic activity of the Vibrio cholerae type VI secretion toxin VgrG-3 is inhibited by the antitoxin TsaB. J Biol Chem 288:7618–7625 33. Whitney JC, Chou S, Russell AB, Biboy J, Gardiner TE, Ferrin MA, Brittnacher M, Vollmer W, Mougous JD (2013) Identification, structure, and function of a novel type VI secretion peptidoglycan glycoside hydrolase effector-immunity pair. J Biol Chem 288: 26616–26624 34. Sibinelli-Sousa S, Hespanhol JT, Nicastro GG, Matsuyama BY, Mesnage S, Patel A, de Souza RF, Guzzo CR, Bayer-Santos E (2020) A family of T6SS antibacterial effectors related to l, d-transpeptidases targets the peptidoglycan. Cell Rep 31:107813 35. Le NH, Pinedo V, Lopez J, Cava F, Feldman MF (2021) Killing of Gram-negative and Gram-positive bacteria by a bifunctional cell wall-targeting T6SS effector. Proc Natl Acad Sci U S A 118:e2106555118 36. Carobbi A, Di Nepi S, Fridman CM, Dar Y, Ben-Yaakov R, Barash I, Salomon D, Sessa G (2022) An antibacterial T6SS in Pantoea agglomerans pv. betae delivers a lysozyme-like effector to antagonize competitors. Environ Microbiol 24:4787–4802 37. Souza DP, Oka GU, Alvarez-Martinez CE, Bisson-Filho AW, Dunger G, Hobeika L, Cavalcante NS, Alegria MC, Barbosa LR, Salinas RK, Guzzo CR, Farah CS (2015) Bacterial killing via a type IV secretion system. Nat Commun 6:6453 38. Engel H, Kazemier B, Keck W (1991) Mureinmetabolizing enzymes from Escherichia coli: sequence analysis and controlled overexpression of the slt gene, which encodes the soluble lytic transglycosylase. J Bacteriol 173:6773– 6782

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39. Lommatzsch J, Templin MF, Kraft AR, Vollmer W, Ho¨ltje JV (1997) Outer membrane localization of murein hydrolases: MltA, a third lipoprotein lytic transglycosylase in Escherichia coli. J Bacteriol 179:5465–5470 40. Leclerc D, Asselin A (1989) Detection of bacterial cell wall hydrolases after denaturing polyacrylamide gel electrophoresis. Can J Microbiol 35:749–753 41. Bernadsky G, Beveridge TJ, Clarke AJ (1994) Analysis of the sodium dodecyl sulfate-stable peptidoglycan autolysins of select gramnegative pathogens by using renaturing polyacrylamide gel electrophoresis. J Bacteriol 176: 5225–5232 42. Fibriansah G, Gliubich FI, Thunnissen AM (2012) On the mechanism of peptidoglycan binding and cleavage by the endo-specific lytic transglycosylase MltE from Escherichia coli. Biochemistry 51:9164–9177 43. Uehara T, Parzych KR, Dinh T, Bernhardt TG (2010) Daughter cell separation is controlled by cytokinetic ring-activated cell wall hydrolysis. EMBO J 29:1412–1422 44. Scheurwater EM, Clarke AJ (2008) The C-terminal domain of Escherichia coli YfhD functions as a lytic transglycosylase. J Biol Chem 283:8363–8373 45. Clarke AJ (1993) Compositional analysis of peptidoglycan by high-performance anionexchange chromatography. Anal Biochem 212:344–350 46. Leduc M, Joseleau-Petit D, Rothfield LI (1989) Interactions of membrane lipoproteins with the murein sacculus of Escherichia coli as shown by chemical crosslinking studies of intact cells. FEMS Microbiol Lett 51:11–14 47. Cascales E, Lloube`s R (2004) Deletion analyses of the peptidoglycan-associated lipoprotein Pal reveals three independent binding sequences including a TolA box. Mol Microbiol 51:873– 885 48. Imada A, Kintaka K, Nakao M, Shinagawa S (1982) Bulgecin, a bacterial metabolite which in concert with beta-lactam antibiotics causes bulge formation. J Antibiot (Tokyo) 35:1400– 1403

Chapter 13 Protein–Protein Interaction: Bacterial Two Hybrid Gouzel Karimova, Emilie Gauliard, Marilyne Davi, Scot P. Ouellette, and Daniel Ladant Abstract The bacterial two-hybrid (BACTH, for “Bacterial Adenylate Cyclase-based Two-Hybrid”) system is a simple and fast genetic approach to detect and characterize protein–protein interactions in vivo. This system is based on the interaction-mediated reconstitution of a cAMP signaling cascade in Escherichia coli. As BACTH uses a diffusible cAMP messenger molecule, the physical association between the two interacting chimeric proteins can be spatially separated from the transcription activation readout, and therefore, it is possible to analyze protein–protein interactions that occur either in the cytosol or at the inner membrane level as well as those that involve DNA-binding proteins. Moreover, proteins from bacterial origin can be studied in an environment similar (or identical) to their native one. The BACTH system may thus permit a simultaneous functional analysis of the proteins of interest—provided the hybrid proteins retain their activity—and their association state. This chapter describes the principle of the BACTH genetic system and the general procedures to study protein–protein interactions in vivo in E. coli. Key words Two-hybrid system, Protein interaction assay, Membrane protein, cAMP signaling, Chimeric proteins

1

Introduction Two-hybrid systems are genetic assays that permit the detection and characterization of protein–protein interactions in vivo. This approach was pioneered by Fields and Song, who described the original yeast two-hybrid system [1]. All following two-hybrid techniques are based on the co-expression, in the same cell, of two hybrid proteins that, upon interaction, produce a phenotypic and/or selective trait [2]. In the bacterial two-hybrid system (BACTH, for “Bacterial Adenylate Cyclase-based Two-Hybrid”), the readout of the interactions relies on the complementation between two fragments from the adenylate cyclase of Bordetella pertussis to reconstitute a cAMP signaling cascade in E. coli [3]. As it exploits a cAMP signaling cascade, the BACTH system can be easily applied to study interactions between membrane

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_13, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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proteins [4], and it has indeed been widely used to characterize the assembly of bacterial secretion systems and cell division components. These specialized nanomachines, which bacteria use to secrete a wide variety of compounds (small molecules, sugars, proteins, DNA, etc.), consist of up to tens of proteins that assemble in the bacterial membranes in multimolecular complexes. The BACTH system has been instrumental to characterize the molecular interactions between these different components of various (secretion) systems [5–9]. This chapter describes the principle of this genetic system and outlines the main procedures to study protein–protein interactions in vivo in E. coli. Principle of the Bacterial Adenylate Cyclase-Based TwoHybrid (BACTH) System The BACTH system is a simple and fast approach to detect and characterize protein–protein interactions in vivo. It offers all the advantages of working with E. coli and is readily accessible to many researchers having basic knowledge in standard microbiological and molecular biology techniques (plasmid preparations, transformation, PCR, etc.). The BACTH system is based on the interaction-mediated reconstitution of an adenylate cyclase enzyme activity in an E. coli cya mutant, defective in its endogenous adenylate cyclase [3, 10]. It exploits the fact that the catalytic domain of adenylate cyclase (CyaA) from B. pertussis [11] consists of two complementary fragments, T25 and T18, which are not active when physically separated (Fig. 1a). When these two fragments are fused to interacting polypeptides, X and Y, heterodimerization of the hybrid proteins results in functional complementation between the T25 and T18 fragments and, therefore, in cAMP synthesis (Fig. 1b). Cyclic AMP produced by the reconstituted chimeric enzyme binds to the catabolite activator protein, CAP. The cAMP/CAP complex is a pleiotropic regulator of gene transcription in E. coli [12]. It turns on the expression of several endogenous genes, including genes of the lac and mal operons involved in lactose and maltose catabolism (Fig. 1c). Consequently, bacteria become able to utilize lactose or maltose as the unique carbon source and can be easily distinguished on indicator and/or selective media [3, 10]. General Procedure Detection of in vivo interactions between two proteins of interest with the BACTH system requires the co-expression of these proteins as fusions with the T25 and T18 fragments in bacteria that are lacking its endogenous adenylate cyclase activity (E. coli cya). This is achieved by using two compatible vectors, one expressing the T25 fusion (pKT25 or pKNT25) and the other one expressing the T18 fusion (pUT18 or pUT18C) [10, 13]. The bacteria are

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Fig. 1 Principle of BACTH system. (a) When the two fragments of B. pertussis adenylate cyclase, T25 and T18, are co-expressed as separate polypeptides, they cannot assemble, and there is no enzyme activity. (b) When the T25 and T18 fragments are co-expressed as fusions with polypeptides X and Y that can interact, the association of the hybrid proteins T25-X and T18-Y reconstitutes the adenylate cyclase activity. (c) Cyclic AMP synthesized by the reconstituted enzyme binds to the catabolite activator protein (CAP) and the cAMP/CAP complex can associate with specific promoter DNA and activates transcription of catabolite operons (such as lac operon or mal regulon)

co-transformed with the two recombinant plasmids and plated on either indicator or selective media to reveal the resulting Cya+ phenotype (Fig. 2). The efficiency of complementation between the two hybrid proteins can be further quantified by measuring cAMP levels (a direct measure of the reconstituted adenylate cyclase enzymatic activity) or by assaying the β-galactosidase enzymatic activities in bacterial extracts [3, 10], an easy and robust assay that is directly correlated with the cAMP produced in the cells since the expression of β-galactosidase is positively regulated by cAMP/CAP. The hybrid proteins expressed in E. coli can also be characterized by using diverse biochemical approaches, such as immunodetection, immunoprecipitation, copurification, etc. The BACTH system has been used by many laboratories to detect and characterize interactions between a wide variety of bacterial, eukaryotic, or viral proteins [3, 13–16]. An attractive aspect of this genetic assay is that, because it uses a cAMP signaling cascade, the interaction between the hybrid proteins does not need to take place near the transcription machinery as is the case with the yeast two-hybrid system or many other bacterial two hybrid systems [1, 2]. For this reason, the BACTH system is particularly appropriate to study interactions between membrane proteins as these interactions cannot be easily tested with transcription-based two-hybrid systems [4, 14, 17].

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Fig. 2 Analysis of protein–protein interactions with the bacterial two-hybrid system. See text for detailed explanations

2 2.1

Materials Equipment

1. Equipment for DNA cloning and bacterial transformation. 2. Incubator for plates and shaking liquid cultures. 3. 2.2 mL 96-well storage plate or deep-well, sterile. 4. 1.2 mL polypropylene 96-well storage block or glass tubes, sterile. 5. Microporous tape sheet (e.g., AirPore, Qiagen). 6. Multichannel pipettor. 7. Shaker (for shaking deep-well 96-well blocks). 8. Microplate reader (Tecan or equivalent plate reader). 9. Equipment and reagents for Western blotting (optional).

2.2

Bacterial Media

1. LB broth: 10 g of NaCl, 10 g of tryptone, and 10 g of yeast extract, adjust pH to 7.0 with NaOH, add deionized H2O to a final volume of 1 L, and autoclave. 2. LB plates: Add 15 g of agar per liter of LB broth and autoclave. Allow the medium to cool down to less than 45 °C, then add the antibiotics, and pour the plates. 3. LB/X-Gal plates: To prepare LB/X-Gal plates, the LB/agar medium (above) is autoclaved, allowed to cool down to less than 45 °C and supplemented, just before pouring plates, with

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40 μg/mL of the X-Gal (5-bromo-4-chloro-3-indolyl-β-Dgalactopyranoside) chromogenic substrate and appropriate antibiotics. IPTG (isopropyl-β-D-thiogalactopyranoside, final concentration of 0.5 mM) is usually also added to the medium in order to induce full expression of the hybrid proteins as well as that of the β-galactosidase reporter enzyme. 4. MacConkey/maltose medium: 40 g of MacConkey agar is dissolved in 1 liter of distilled water and autoclaved (see Note 1). A stock solution of glucose-free maltose (20% in water) is sterilized by filtration. Maltose (1% final concentration) and antibiotics (ampicillin at 100 μg/mL and kanamycin at 50 μg/mL) are added to the autoclaved MacConkey medium just before pouring plates. IPTG (final concentration of 0.5 mM) is usually added to the medium in order to induce full expression of the hybrid proteins. 5. 5× M63/maltose minimal medium: 10 g (NH4)2SO4, 68 g KH2PO4, 2.5 mg FeSO4.7H2O. Add deionized H2O to a final volume of 1 L, adjust pH to 7.0 with KOH, and autoclave. When necessary, vitamin B1 is added to a final concentration of 1 μg/mL and casamino acids at 50 μg/mL. 6. M63/maltose plates: Autoclave 15 g of agar in 800 mL H2O. Then, add 200 mL sterile 5× M63 medium, 0.2–0.4 % of maltose, and the appropriate antibiotics at half the usual concentrations (i.e., 50 μg/mL of ampicillin, 25 μg/mL of kanamycin) just before pouring plates. 2.3 Solutions for β-Galactosidase Assays

1. β-galactosidase assay medium (PM2): 70 mM Na2HPO4, 30 mM NaH2PO4, 1 mM MgSO4, 0.2 mM MnSO4, pH 7.0. Add 100 mM of β-mercaptoethanol just before use (see Note 2). 2. Substrate solution: ONPG, o-nitrophenol-β-galactoside, solution of 4 mg/mL in PM2 medium without β-mercaptoethanol (store at – 20 °C). 3. Stop solution: 1 M Na2CO3. 4. Chloroform. 5. SDS 0.1%: Dissolve 0.1 g of sodium dodecyl sulfate into 100 mL of H2O.

2.4 BACTH Reporter Strains, Plasmids, and Antibodies

1. E. coli reporter strain carrying a deletion of the cya gene (see Note 3). 2. Set of compatible vectors allowing genetic fusions of the proteins of interest at either the N or the C-termini of the T25 fragment (pKT25 and pKNT25) or of the T18 fragment (pUT18 and pUT18C) (see Note 4).

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3. Anti-CyaA monoclonal antibody (3D1, sc-13582; Santa Cruz Biotechnology) for T18 fragment detection. 4. Rabbit polyclonal antiserum directed against the purified B. pertussis CyaA protein (serum L24023, DL unpublished) for T25 fragment detection.

3

Methods

3.1 General Methodology

The general methodology to analyze interactions between two proteins of interest with the BACTH system is diagrammed in Fig. 2: – In a first step, clone the genes encoding the two proteins of interest (e.g., X and Y) into the two sets of BACTH vectors (pKT25 or pKNT25 and pUT18C or pUT18) by using standard molecular biology techniques [20] or with the Gateway® recombineering technique [19]. – In a second step, co-transform the recombinant plasmids encoding the T25-X (or X-T25) and T18-Y (or Y-T18) hybrid proteins into competent BACTH cells (DHM1, DHT1, or BTH101), and plate the transformed cells on indicator plates (i.e., LB–X-Gal or MacConkey media supplemented with maltose) or on selective plates (synthetic medium supplemented with maltose as unique carbon source) [3, 4, 10, 21, 22]] (see Note 1). Complementation is usually detected within 1–3 days of incubation at 30 °C (or 37 °C, although it is generally less efficient at this temperature). If no interaction occurs, then colonies will remain colorless on indicator plates or will not grow on selective plates.

3.2 Construction of BACTH Plasmids Encoding the Hybrid Proteins 3.2.1 Standard Cloning of Genes Encoding Proteins of Interest into BACTH Vectors

This section assumes that the reader has background knowledge in basic molecular biology techniques. Additional protocols for molecular cloning, PCR, DNA analysis, and transformation can be found in many textbooks (e.g., [20]) or on the Internet. 1. Design specific primers to amplify the genes encoding the proteins of interest. The primers should include restriction sites (e.g., BamHI on 5′ primer and KpnI on 3′ primer) for allowing oriented cloning of the amplified genes into the BACTH vectors. Be careful to correctly position these restriction sites so that the genes of interest will be in frame with the T25 and T18 open reading frames. 2. PCR amplify the genes encoding the proteins of interest by using a standard protocol [20]. 3. Purify the PCR-amplified DNA fragments by using a standard PCR purification kit (available from various companies), and

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digest them with the appropriate restriction enzymes (e.g., BamHI and KpnI or others depending upon the restriction sites introduced into the primers). Digest the BACTH vectors with the same restriction enzymes. 4. Ligate the digested fragments and vectors with T4 DNA ligase [20]. Transform the ligation mixtures into competent XL1-Blue cells (Stratagene), and plate transformants on LB plates supplemented with appropriate antibiotics. Incubate plates at 30 °C, for 24–36 h. 5. Pick 6 to 12 colonies for each cloning experiment and grow them overnight at 30 °C in 4 mL LB plus antibiotics (see Note 5). Purify the plasmid DNAs using a standard protocol or a commercial kit (e.g., QIAprep Spin Miniprep Kit from Qiagen). Check the recombinant plasmids by restriction analysis and DNA sequencing to verify that no mutation has been introduced during the PCR amplification. 3.2.2 GatewayTM Cloning of the Genes Encoding the Proteins of Interest into BACTHGW Vectors

The Gateway® cloning technology (Life Technologies, Thermo Fisher Scientific) is used to transfer genes of interest into the BACTH–Gateway destination vectors, pST25-DEST, pSNT25DEST, and pUT18C-DEST [17]. For detailed descriptions of the Gateway® cloning techniques, the reader should refer to the manufacturer’s guidelines: 1. PCR amplify the genes of interest (from genomic DNA or from other appropriate sources) using appropriate primers that also contain specific attB sites (see Note 6), and purify the PCR products as above. 2. Mix the purified PCR products with the pDONR™221 plasmid [19], add the BP Clonase™ II enzyme, and incubate 2 h at room temperature to allow the BP recombination reaction. Add 2 μg of proteinase K to terminate the recombination reaction, and transform the mixture into E. coli XL1 competent cells. Select the transformants on LB plates supplemented with 50 μg/mL of kanamycin [17]. 3. Purify plasmid DNA from 3 to 4 independent clones from each cloning as above, and check the recombinant plasmids by restriction analysis and DNA sequencing. In the resulting plasmids (pDONR™-gene X), the genes of interest are flanked by attL recombination sites and can be easily transferred into Gateway® destination vectors, by an “LR” reaction [19]. 4. Mix the entry pDONR™-gene X plasmid, with the appropriate BACTHGW destination vectors, pST25-DEST, pSNT25DEST, or pUT18C-DEST. Add the LR Clonase™ enzyme mix (see manufacturer’s guidelines) and incubate 1 h at 25 °

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C. Longer incubation times may be necessary for larger inserts. Add proteinase K as above to terminate the reaction. 5. Transform the mixture in E. coli XL1 competent cells. Select the transformants on LB plates supplemented with the appropriate antibiotic (spectinomycin or ampicillin). 6. Purify plasmid DNA from 2 to 3 independent clones from each cloning as above, and check the recombinant plasmids by restriction analysis and/or DNA sequencing. The resulting plasmids encode the T25 or T18 fragments fused in frame to the gene of interest (gene X). 3.3 Analysis of Interactions by Screening Procedure on Indicator Plates

1. Prepare chemically competent or electro-competent DHT1, DHM1, or BTH101 cells by using standard procedures [ [20]; see Note 7]. 2. Co-transform the BACTH competent cells with one of the recombinant plasmids encoding the T25 fusions (pKT25, pKNT25, pST25-DEST, or pSNT25-DEST derivatives) and one of the recombinant plasmids encoding the T18 fusions (pUT18, pUT18C, or pUT18C-DEST derivatives). 3. In parallel, co-transform a separate aliquot of cells with plasmids pKT25 and pUT18C (coding for the unfused T25 and T18 fragments) to serve as a negative control. For a positive control, co-transform another aliquot of cells with plasmids pKT25-zip and pUT18C-zip (encoding the T25 and T18 fragments fused to a leucine-zipper dimerization motif). 4. Plate different amounts of the transformation mixture (in order to have no more than 2–500 colonies per plate) on LB–X-Gal or MacConkey–maltose indicator plates (plus antibiotics) and incubate at 30 °C for 24–48 h. Results of typical phenotypic assays on LB–X-Gal or MacConkey–maltose plates are shown in Fig. 2. DHM1 (or other BACTH strains) transformants, expressing the T25-zip and T18-zip hybrid proteins that can heterodimerize through their leucine zipper motif, form blue colonies on LB– X-Gal medium and red colonies on MacConkey/maltose, while cells expressing the unfused T25 and T18 remain colorless.

3.4 BACTH Screening of Interacting Partners: Selection Procedure on Minimal Medium

The BACTH system can be used to screen libraries in order to isolate partners of a protein of interest (e.g., protein X, classically designated as “bait”) as follows: 1. Construct a library of genomic DNA (or cDNA) fragments in one of the BACTH vectors, e.g., pKT25, by using standard procedures [20]. Obviously, the quality (i.e., the complexity) of the library is critical for the success in isolating putative partners. A brief summary of a procedure used in our laboratory to

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construct a library of genomic E. coli chromosomal DNA fragments in the pKT25 vector is provided in Note 8 (further experimental details can be found in [21, 22]). Clone the gene encoding protein X into one of the BACTH vectors (e.g., pUT18C) to generate “bait” plasmid pUT18C-X that codes for the T18-X hybrid protein. 2. Transform pUT18C-X into a BACTH reporter strain (e.g., DHM1). 3. Prepare electro-competent cells from the resulting transformants DHM1/pUT18C-X (see Note 9). 4. Transform electro-competent DHM1/pUT18C-X cells with 50–100 ng of DNA from the BACTH DNA library constructed in plasmid pKT25. Add 1 mL of LB (or Super Optimal Broth, SOB, [20]) medium and incubate 90 min at 30 °C. Collect the cells by centrifugation, wash them 4–5 times with M63 medium, and plate them (approximately 1 × 106 transformants/plate) on M63 minimal medium agar supplemented with maltose (0.2%), as sole carbon source, kanamycin, ampicillin, IPTG, and X-Gal (to facilitate the detection of Cya+ clones that are Mal+ and also Lac+). 5. Incubate plates at 30 °C for 4–8 days until appearance of blue Cya+ colonies. Re-isolate these colonies on fresh plates, purify their pKT25 plasmids, and further characterize the DNA inserts by sequencing. This procedure (and related ones) has been used in our laboratory to isolate several novel components of the E. coli cell division machinery [21, 22]. 3.5 Quantification of Functional Complementation Between Hybrid Proteins by βgalactosidase Assays

Quantification of the functional complementation mediated by interaction between the different hybrid proteins is performed by measuring β-galactosidase activities in bacterial liquid cultures [3, 10]. These β-galactosidase activity assays are conveniently carried out in 96-well microtiter plate format as it allows performing many assays in parallel [17, 22]. Other methods for β-galactosidase assays can be found elsewhere [16, 20, 25]: 1. Pick eight independent colonies from each set of transformation (i.e., expressing a given couple of T25 and T18 hybrid proteins), and use them to inoculate 300–400 μL of sterile LB broth supplemented with 0.5 mM IPTG and appropriate antibiotics and distributed into individual wells of a 96-well microtiter plate (2.2 mL 96-well storage plate or deep-well). Seal the plate with a microporous tape sheet to allow gas exchange and incubate overnight at 30 °C on a rotary shaker. 2. Dilute the cultures fivefold by adding appropriate volume of M63 medium to the same microplate.

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3. Transfer 175 μL of the diluted cultures into a flat bottom microtiter plate and record the OD595nm absorbance data with a microplate reader. 4. Transfer 200 μL of the diluted bacterial suspensions into a new microtiter plate (1.2 mL polypropylene 96-well storage block), and add 7 μL of 0.05% SDS (sodium dodecyl sulfate) and 10 μL of chloroform to permeabilize the cells. Mix vigorously, and then, leave the plate under a fume hood at room temperature for 30–40 min to allow chloroform evaporation. 5. In a new microtiter plate, distribute 105 μL/well of PM2 reaction buffer containing 100 mM β-mercaptoethanol and 0.1% o-nitrophenol-β-galactoside (ONPG). Start the enzymatic reactions by adding 20 μL aliquots of the permeabilized cells and incubate the plate at room temperature for 20–30 min or until sufficient yellow color has developed. In parallel, perform control assays with 20 μL of M63 medium instead of cells. 6. Stop the reaction by the adding 50 μL of 1M Na2CO3 and record the OD405 absorbance data with a microplate reader. 7. Analyze data with an appropriate software (e.g., Microsoft Excel or other spreadsheet programs). For each well, calculate the enzymatic activity, A (in relative units) according to: A = 1000 × (OD405 – OD405 in control wells) / (OD595 – OD595 in control wells) / t(min) of incubation. Results are given in relative units (RU) of β-galactosidase activity. It is important to include in the assay negative and positive controls, i.e., bacteria that express noninteracting (e.g., T25 and T18 only or fused to proteins that do not interact or should not interact with the protein of interest) and interacting (e.g., T25-zip and T18-zip) hybrid proteins, respectively. Under routine conditions, the β-galactosidase activities measured with the positive controls (T25-zip/T18-zip) are defined as 100 % activity, while the β-galactosidase activities of the negative controls (T25/T18) should be below 2–3 % of positive control activity. The β-galactosidase activities in cells expressing the hybrid proteins of interest should be at least 4–5 times higher than the background level to demonstrate a positive interaction in BACTH assay [17, 21, 22, 24]. 3.6 Characterization of Hybrid Proteins by Western Blots

In many cases, it is important to characterize immunologically and/or biochemically the hybrid proteins and eventually to quantify their level of expression in the complementing cells. For this, Western blot analysis of the hybrid proteins can be carried out using standard procedures [20]. The T25 fragment can be detected with a rabbit polyclonal antiserum directed against the purified B. pertussis CyaA protein (serum L24023, DL unpublished), while the T18 fragment is revealed by an anti-CyaA monoclonal

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antibody (3D1, sc-13582) that reacts specifically with the C-terminal region of T18 [21, 26]. Alternatively, it is also possible to append to the T25 and/or T18 fragments different epitope tags (e.g., myc, HA, or T7 tags) that can be detected with specific monoclonal antibodies or a 6×-histidine tag that permits purification of the complex of hybrid proteins by chromatography on NiNTA-agarose resin [27]. These modified fragments can be used to perform immunoprecipitation experiments or pull-down assays in order to demonstrate by direct biochemical means the physical association of the hybrid proteins [20].

4

Notes 1. Two types of indicator plates are commonly used to reveal protein interaction with the BACTH assay: LB–X-Gal plates— In E. coli, expression of the lacZ gene encoding β-galactosidase is positively controlled by cAMP/CAP. Hence, bacteria expressing interacting hybrid proteins form blue colonies on rich LB medium, in the presence of the chromogenic substrate X-Gal (see Fig. 2), while cells expressing noninteracting proteins remain white (pale blue). MacConkey medium—E. coli cya bacteria are unable to ferment lactose or maltose [15, 20]—they form white (or pale pink) colonies on MacConkey indicator media containing maltose (see Fig. 2), while Cya+ bacteria form red colonies on the same media (fermentation of the sugar results in the acidification of the medium and induces a color change of the phenol red dye). Note that all MacConkey agar base media are not of equal quality. MacConkey from Difco Laboratories (cat # 216830) is strongly recommended. Selection of cells expressing interacting proteins can be done by plating transformants on a selective medium consisting of a synthetic minimal medium supplemented with maltose as a unique carbon source [4, 21, 22]: As the mal regulon (involved in maltose catabolism) expression is under a strict cAMP/CAP dependency, only Cya+ bacteria can utilize maltose as a carbon source. Hence, only the cells that express interacting hybrid proteins will be able to grow on this minimal medium (Fig. 2). X-Gal and IPTG are also commonly added to the selective medium to facilitate the early visualization of the Cya+ colonies (these cells should also be Lac+ and therefore exhibit a blue phenotype on X-Gal). Note that, when using the DHT1 as a reporter strain [23, 24], casamino acids should be added to the minimal medium/maltose plates to allow growth, as this strain is ilv- (i.e., unable to synthetize isoleucine and valine).

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2. β-mercaptoethanol is considered toxic, causing irritation to the skin and respiratory tract upon inhalation and should be manipulated under a fume hood. In fact, it can be easily omitted from the PM2 buffer: The β-galactosidase activities will be reduced by a factor of 2 which is not problematic as only relative enzymatic activities will be considered. 3. Several adenylate cyclase deficient (cya) E. coli reporter strains, DHT1, DHM1, and BTH101 (see genotypes below) can be used as host organisms for detection of protein–protein interactions in the BACTH assay [4, 13, 18]. Other E. coli cya strains (see E. coli strain collection at http://cgsc.biology.yale. edu) may also be used as well. The different genetic backgrounds of these strains provide different complementation efficiencies and different reporter gene stringencies. DHT1 [F-, cya-854, ilv 691::Tn10, recA1, endA1, gyrA96 (Nal r), thi1, hsdR17, spoT1, rfbD1, glnV44(AS)] is a recA strain that displays a high BACTH complementation efficiency and fast growth, but it requires casamino acid supplementation for growth on minimal medium as it carries an ilv mutation. DHM1 [F-, cya-854, recA1, endA1, gyrA96 (Nal r), thi1, hsdR17, spoT1, rfbD1, glnV44(AS)] is a ilv+ DHT1 derivative able to grow on minimal media plus sugars, but it displays a lower complementation efficiency and slower growth than the parental DHT1. BTH101 [F-, cya-99, araD139, galE15, galK16, rpsL1 (Str r), hsdR2, mcrA1, mcrB1] also displays a good BACTH efficiency and fast growth, but some instability of plasmids may be observed due to the Rec+ character of the strain. The frequencies of spontaneous Lac+ revertants (due to cAMP/CAP independent promoter mutations) of these different strains range from 10-7 to 10-8, while frequencies of spontaneous Mal+ revertants are below the detection threshold (i.e., 106 cfu/μg) sufficient for most routine transformations. Briefly, freshly re-isolated cells are grown in 1 liter of LB at 37 °C to OD 0.25–0.3, cooled on ice and pelleted by centrifugation. Cells are washed twice in 100 mL

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ice-cold 0.1 M CaCl2 solution. Cells are finally resuspended in 30–40 mL ice-cold 0.1 M CaCl2 and incubated overnight at 4 °C (it is critical to maintain cells, buffers, and vessels well chilled at all stages of the process). For transformation, 50 μL of chemically competent DHM1 cells are mixed in a chilled microcentrifuge tube with 5–10 ng of each plasmid, incubated 30 min at 4 °C, and then heat-shocked at 42 °C for 2 min. One mL of LB is then added and the cell suspension is further incubated at 30 °C for 60–90 min before being plated. Different volumes of the transformation mixture should be plated in order to obtain about 100–200 colonies per plate. It is important that the number of colonies does not exceed 500, otherwise the detection of positive clones might be difficult. It should be noted that after prolonged incubation (4–5 days), negative colonies (i.e. cya-) will show a weak red (on MacConkey–maltose) or blue spot (on LB-X-Gal) in the center, but will remain colorless at the periphery. It may be worth testing also the complementation at 37 °C, although in many cases, complementation is less efficient than at 30 °C. 8. The genomic DNA (≈ 50 μg) from a Δcya derivative of the E. coli strain MG1655 was randomly fragmented by sonication (size range of 500 to 1500 bp). The fragments were end-repaired by Mung Bean nuclease and treated with a mixture of T4 DNA polymerase and Klenow fragment (with dNTP). In parallel, the pKT25 vector (10 μg) was digested with SmaI and dephosphorylated with shrimp alkaline phosphatase, and the linearized vector was gel purified. The bluntended DNA fragments were then ligated with the SmaI-digested pKT25 vector and transformed into electrocompetent ElectroMAX DH10B cells (Thermo Fisher Scientific). About 5 × 105 independent clones were thus obtained. All these colonies were pooled and their plasmid DNA was purified and used as a stock for the BACTH DNA library [22]. 9. Efficient (>108 cfu/μg) electro-competent DHM1/pUT18CX cells can be prepared as follows [20]: Freshly reisolated cells are grown at 37 °C in 1 liter of LB containing 100 μg/mL ampicillin until OD600 of 0.5–0.7. Cells are chilled on ice and pelleted by centrifugation at 4 °C. Cells are washed at least three times with ice-cold water and resuspended in 10 mL of 10% glycerol (in water). For transformation, 50 μL are transferred into an electroporation cuvette (1 mm wide) previously equilibrated on ice, and 50–100 ng of DNA from the BACTH plasmid DNA library are added. After mixing and a few min of incubation at 4 °C, the cuvette is placed in an electroporator (e.g., BioRad) set at 2.5 Kvolts, 100 Ohms capacitance, and electroporation is carried out. One mL of LB media is

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immediately added to the cuvette, and cells are further incubated at 30 °C for 60–90 min. Cells are then collected by centrifugation (5 min at 6000 rpm) and washed several times with M63 medium (to remove all nutrients from the rich medium) before being plated on M63 minimal medium agar (approximately 1 × 106 transformants/plate).

Acknowledgment This work was supported by Institut Pasteur and the Centre National de la Recherche Scientifique (CNRS UMR 3528, Biologie Structurale et Agents Infectieux). EG was supported by PhD funding from the Universite´ Paris Diderot, Sorbonne Paris Cite´, Cellule Pasteur, Paris, France. References 1. Fields S, Song O (1989) A novel genetic system to detect protein-protein interactions. Nature 340:245–246 2. Stynen B, Tournu H, Tavernier J, Van Dijck P (2012) Diversity in genetic in vivo methods for protein-protein interaction studies: from the yeast two-hybrid system to the mammalian split-luciferase system. Microbiol Mol Biol Rev 76:331–382 3. Karimova G, Pidoux J, Ullmann A, Ladant D (1998) A bacterial two-hybrid system based on a reconstituted signal transduction pathway. Proc Natl Acad Sci U S A 95:5752–5756 4. Karimova G, Dautin N, Ladant D (2005) Interaction network among Escherichia coli membrane proteins involved in cell division as revealed by bacterial two-hybrid analysis. J Bacteriol 187:2233–2243 5. Jack RL, Buchanan G, Dubini A, Hatzixanthis K, Palmer T, Sargent F (2004) Coordinating assembly and export of complex bacterial proteins. EMBO J 23:3962–3972 6. Paschos A, den Hartigh A, Smith MA, Atluri VL, Sivanesan D, Tsolis RM, Baron C (2011) An in vivo high-throughput screening approach targeting the type IV secretion system component VirB8 identified inhibitors of Brucella abortus 2308 proliferation. Infect Immun 79:1033–1043 7. Cisneros DA, Bond PJ, Pugsley AP, Campos M, Francetic O (2012) Minor pseudopilin self-assembly primes type II secretion pseudopilus elongation. EMBO J 31:1041– 1053

8. Georgiadou M, Castagnini M, Karimova G, Ladant D, Pelicic V (2012) Large-scale study of the interactions between proteins involved in type IV pilus biology in Neisseria meningitidis: characterization of a subcomplex involved in pilus assembly. Mol Microbiol 84:857–873 9. Zoued A, Durand E, Brunet YR et al (2016) Priming and polymerization of a bacterial contractile tail structure. Nature 531:59–63 10. Karimova G, Ullmann A, Ladant D (2000) A bacterial two-hybrid system that exploits a cAMP signaling cascade in Escherichia coli. Methods Enzymol 328:59–73 11. Ladant D, Ullmann A (1999) Bordatella pertussis adenylate cyclase: a toxin with multiple talents. Trends Microbiol 7:172–176 12. Lawson CL, Swigon D, Murakami KS et al (2004) Catabolite activator protein: DNA binding and transcription activation. Curr Opin Struct Biol 14:10–20 13. Karimova G, Ullmann A, Ladant D (2001) Protein-protein interaction between Bacillus stearothermophilus tyrosyl-tRNA synthetase subdomains revealed by a bacterial two-hybrid system. J Mol Microbiol Biotechnol 3:73–82 14. Fransen M, Brees C, Ghys K et al (2002) Analysis of mammalian peroxin interactions using a non-transcription-based bacterial two-hybrid assay. Mol Cell Proteomics 1:243–252 15. Dautin N, Karimova G, Ladant D (2003) Human immunodeficiency virus (HIV) type 1 transframe protein can restore activity to a dimerization-deficient HIV protease variant. J Virol 77:8216–8226

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16. Battesti A, Bouveret E (2012) The bacterial two-hybrid system based on adenylate cyclase reconstitution in Escherichia coli. Methods 58: 325–334 17. Ouellette SP, Gauliard E, Antosova Z, Ladant D (2014) A gateway®-compatible bacterial adenylate cyclase-based two-hybrid system. Environ Microbiol Rep 6:259–267 18. Dautin N, Karimova G, Ullmann A, Ladant D (2000) Sensitive genetic screen for protease activity based on a cyclic AMP signaling cascade in Escherichia coli. J Bacteriol 182:7060–7066 19. Hartley JL, Temple GF, Brasch MA (2000) DNA cloning using in vitro site-specific recombination. Genome Res 10:1788–1795 20. Sambrook J, Russell DW, Sambrook J (2006) The condensed protocols from molecular cloning: a laboratory manual. Cold Spring Harbor Laboratory Press, Cold Spring Harbor 21. Karimova G, Robichon C, Ladant D (2009) Characterization of YmgF, a 72-residue inner membrane protein that associates with the Escherichia coli cell division machinery. J Bacteriol 191:333–346 22. Karimova G, Davi M, Ladant D (2012) The beta-lactam resistance protein Blr, a small membrane polypeptide, is a component of the Escherichia coli cell division machinery. J Bacteriol 194:5576–5588 23. Ouellette SP, Karimova G, Subtil A, Ladant D (2012) Chlamydia co-opts the rod shapedetermining proteins MreB and Pbp2 for cell division. Mol Microbiol 85:164–178

24. Ouellette SP, Rueden KJ, Gauliard E et al (2014) Analysis of MreB interactors in Chlamydia reveals a RodZ homolog but fails to detect an interaction with MraY. Front Microbiol 5:279 25. Griffith KL, Wolf REJ (2002) Measuring betagalactosidase activity in bacteria: cell growth, permeabilization, and enzyme assays in 96-well arrays. Biochem Biophys Res Commun 290:397–402 26. Robichon C, Karimova G, Beckwith J, Ladant D (2011) Role of leucine zipper motifs in association of the Escherichia coli cell division proteins FtsL and FtsB. J Bacteriol 193:4988– 4992 27. Battesti A, Bouveret E (2008) Improvement of bacterial two-hybrid vectors for detection of fusion proteins and transfer to pBAD-tandem affinity purification, calmodulin binding peptide, or 6-histidine tag vectors. Proteomics 8: 4768–4771 28. Houot L, Navarro R, Nouailler M et al (2017) Electrostatic interactions between the CTX phage minor coat protein and the bacterial host receptor TolA drive the pathogenic conversion of vibrio cholerae. J Biol Chem 292: 13584–13598 29. Volkwein W, Krafczyk R, Jagtap PKA et al (2019) Switching the post-translational modification of translation elongation factor EF-P. Front Microbiol 10:1148

Chapter 14 Protein–Protein Interactions: Oxidative Bacterial Two Hybrid Callypso Pellegri, Emmanuelle Bouveret, and Laetitia Houot Abstract Protein–protein interaction studies are essential to understand how proteins organize themselves into interaction networks and thus influence cellular processes. Protein binding specificity depends on the correct three-dimensional folding of the polypeptide sequences. One of the forces involved in the structuring and stability of proteins is the formation of disulfide bonds. These covalent bonds are formed posttranscriptionally by the oxidation of a pair of cysteine residues and can serve structural, catalytic, or signaling roles. Here, we describe an engineered E. coli adenylate cyclase mutant strain with an oxidative cytoplasm that promotes correct folding of proteins with disulfide bonds. This genetic background expands the set of host strains suitable for studying protein–protein interactions in vivo by the adenylate cyclase two-hybrid approach. Key words Bacterial two hybrid, Oxidative cytoplasm, Protein–protein interactions

1

Introduction The formation of disulfide bonds participates in the folding and stability of many extra-cytoplasmic and secreted proteins in Grampositive and Gram-negative bacteria. These bonds result from the oxidation of thiol groups between two cysteine residues in a polypeptide sequence. Disulfide bonds have multiple biological functions among proteins. They play structural roles or serve as redox sensors. Indeed, cysteines can be reversibly oxidized or reduced, which induces conformational switches of the protein in response to the redox state of the environment [1]. Structural disulfide bonds also tend to decrease the number of possible conformations for a given protein, a feature that is particularly important for periplasmic or secreted proteins in a harsh extracellular environment where they are subjected to multiple physicochemical stresses. Thus, proteins functioning in these oxidative environments often rely on multiple disulfide bonds to increase their stability. This is the case for many membrane proteins with signal transduction function

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_14, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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[2], secreted toxins (cholera, diphtheria toxin), bacterial virulence factors like those of the T6SS [3], and pilins [4, 5]. For some proteins, disulfide bonds are essential to their functionality, as reduction of the cysteines can lead to protein denaturation. However, for other proteins, SS-bonds participate in protein stability but are dispensable. Correct pairing of cysteines during polypeptide folding can be critical for higher-order protein structuration and functionality. Indeed, mispairing of cysteines can result in misfolded and nonfunctional proteins. While disulfide bonds can form spontaneously in the presence of oxygen, the reaction is slow. In the bacterial periplasm, a dedicated range of thiol oxidoreductases, Dsb (for disulfide bond), catalyze the formation of SS-bonds (DsbA, DsbB) and the correction of mispaired cysteines (DsbC) [1]. The bacterial two-hybrid system (BACTH) in Escherichia coli is a popular technique for the study of protein–protein interactions in vivo. The method, originally developed by Karimova et al., is based on the reconstitution of a signaling cyclic adenosine monophosphate (cAMP) transduction cascade in the cytoplasm of an E. coli adenylate cyclase-deficient strain [6]. Briefly, the two catalytic domains of Bordetella pertussis adenylate cyclase, named T18 and T25, are fused separately to proteins of interest. Interaction between the two tested proteins will bring the T18 and T25 domains in proximity in the cytoplasm, enabling the reconstitution of an active enzyme and leading to cAMP synthesis. This signal positively regulates the expression of the lac or mal operons, which can be assessed by growth on reporter medium (for review, see [7]). In Chap. 13 of this book, Ladant and colleagues describe the BACTH methodology in detail, based on the use of classical reporter strains such as BTH101 and DHM1. However, the cell cytoplasm is not permissive to the formation of disulfide bonds due to the presence of numerous reductases and reducing agents such as glutathione. This feature limits the use of BACTH to proteins whose folding is not dependent on disulfide bonds. This limitation was first addressed by the development of modified plasmid backbones in a BACTH-TM system [8]. The insertion of transmembrane helices between the proteins of interest and the T18 and T25 fragments of the adenylate cyclase allows protein–protein interactions in the periplasm to be monitored by cAMP production in the cytoplasm. Here, we present an evolution of the method that is now suitable to study both SS-bond folded and SS-bond independent proteins directly in the cell cytoplasm without the need of cloning in new vectors. The E. coli Oxi-BTH strain (oxidative bacterial two hybrid) is an adenylate cyclase-deficient strain engineered from the commercial SHuffle T7 Express strain (New England Biolabs) [9]. It is deleted for glutaredoxin reductase (gor) and thioredoxin reductase (trxB) genes and expresses a cytoplasmic version of the

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disulfide bond isomerase DsbC, promoting correct disulfide bond formation in the cell cytoplasm and proper oxidative folding of proteins containing multiple cysteines (Fig. 1a). The strain was validated in an interaction study between phage adhesion proteins and their corresponding bacterial receptor [9]. Indeed, the OxiBTH strain was able to detect binding between the two tested partners, whereas the BTH101 strain did not, demonstrating that SS-bond folding of the phage adhesion proteins is essential for the interaction. However, the Oxi-BTH strain being lacZ-, interactions can only be detected by monitoring the expression of the maltose operon, which requires either MacConkey plates or plates of minimal medium supplemented with maltose. In order to follow the interactions on X-Gal plates and also to permit quantification by beta-galactosidase assays, we reintroduced the lacZ gene in the OxiBTH genome to obtain the Oxi-Blue strain, now suitable to the use of the common X-Gal reporter medium (Fig. 1b, [10]). In conclusion, the Oxi-strains (Oxi-BTH and Oxi-Blue) are powerful tools to extend the scope of the two-hybrid approach to proteins susceptible to fold via SS-bonds, such as envelope proteins or secreted proteins. As the method relies on the use of the standard pUT18 and pKT25 vectors, the protein fusion of interest can be tested in parallel in both the regular BTH101 or DHM1 strain (reduction of the SS-bonds) and the Oxi-strains (SS-bond formation).

2 2.1

Materials Equipment

1. Standard material for DNA cloning. 2. Water bath or dry bath heater (37 °C and 42 °C). 3. Benchtop centrifuge. 4. Wood applicators, such as Merck, ref.#Z406430. 5. 96 deep-well plates or culture tubes, sterile such as 96-well 2.2 mL deep-well plate (e.g., ABGENE, ref.#24704). 6. Microporous tape sheet, such as porous film AeraSeal™ Clearline®. 7. Sterile square petri dishes 120 × 15 mm. 8. Multichannel pipette 2–10 μL (see Note 1). 9. Incubator (30 °C and 37 °C).

2.2 Bacterial Media and Solutions

1. Ampicillin stock solution (25 mg/mL), filter sterilized. Store the solution at 4 °C. 2. Kanamycin stock solution (25 mg/mL), filter sterilized. Store the solution at 4 °C.

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Fig. 1 Bacterial adenylate cyclase two-hybrid assay in oxidative reporter strains. (a) Schematic representation of an interaction dependent on the correct folding of a protein by a disulfide bond. The interaction is not possible in the reducing cytoplasm of the BTH101 or DHM1 strains. In contrast, the Oxi-BTH and Oxi-Blue strains are engineered to have an oxidizing cytoplasm and to produce a cytoplasmic form of the disulfide bond isomerase DsbC. This environment is favorable to correct folding of proteins with thiol bonds. (b) Example of an interaction assay performed between the CTX filamentous phage minor coat protein pIII-N1 and its receptor, the TolA3 domain of Vibrio cholerae. The proteins of interest are fused to the T18 and the T25 domains of B. pertussis adenylate cyclase, as described in [9], and coproduced in either the BTH101 or the Oxi-Blue reporter strains spotted on LB/X-Gal/IPTG medium. The blue color indicates an interaction between the partners. As pIII-N1 folds via four disulfide bonds, the interaction with its partner can only be visualized in the Oxi-Blue strain. The interaction between TolB and Pal serves as a positive control in both strains, as it is not dependent on the redox state of the proteins

3. IPTG (isopropyl 1-thio-β-D-galactopyranoside) stock solution, 100 mM in distilled water, filter-sterilized and stored at 4 °C. 4. 2YT medium. Dissolve 16 g of tryptone, 10 g of yeast extract, and 5 g of NaCl in distilled water to reach a final volume of 1 L. Sterilize by autoclaving for 15 min at 121 °C. 5. Lysogeny broth (LB) medium and LB agar plates. Dissolve 10 g NaCl, 10 g tryptone, and 5 g yeast extract in 1 L distilled water. Sterilize the medium by autoclaving. Add 15 g of agar/L before autoclaving to make LB agar plates. Supplement with

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ampicillin at 100 μg/mL and kanamycin at 50 μg/mL just before pouring the plates. 6. Maltose stock solution, 20%. Dissolve glucose-free maltose in distilled water (20 g in 100 mL) and filter-sterilize. 2.3 BACTH Reporter Media

There are three possible reporter media that are commonly used in the BACTH method. The M63/maltose and MacConkey/maltose media rely on the consumption of maltose by the bacteria and thus are adapted to the study of interactions in the Oxi-BTH and the Oxi-Blue strain backgrounds. The X-Gal/IPTG medium requires the use of a lacZ+ strain and is therefore suitable for the Oxi-Blue strain only (see Note 2): 1. M63 medium supplemented with maltose plate: Prepare a 5X M63 stock medium (dissolve 10 g (NH4) 2SO4, 68 g KH2PO4, 2.5 mg FeSO4.7H2O) in 1 L distilled water, adjust to pH = 7.0, and sterilize by autoclaving. To prepare the reporter medium, dissolve 15 g agar in 800 mL distilled water and sterilize by autoclaving. Add 200 mL of the 5X M63 solution, 1 mL of MgSO4.7H2O 1 M, 10 mL of 20% maltose, 2 mL of 0.05% vitamin B1 (thiamin), antibiotics (50 μg/mL ampicillin; 25 μg/mL kanamycin), and IPTG 0.5 mM to induce full production of the protein fusions of interest. Mal+ cells will form white colonies on the reporter medium after 2–7 days of incubation at 30 °C (see Notes 3 and 4). 2. MacConkey maltose plate: Dissolve 40 g MacConkey agar base in 1 L H2O, mix well until complete dissolution, and autoclave (see Notes 5 and 6). Just before pouring the plates, add 50 mL 20% maltose solution (1% final concentration), ampicillin 100 μg/mL, and kanamycin 50 μg/mL. IPTG 0.5 mM can be included to ensure full expression of the constructs. Mal+ cells will form red colonies on the MacConkey medium after 24–72 h of incubation at 30 ° C (see Note 4). 3. LB–X-Gal plate: LB agar medium is supplemented with 87.5 mg of X-Gal (5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside) dissolved in 500 μL of DMF (N, N-dimethylformamide), ampicillin 100 μg/mL, kanamycin 50 μg/mL, and IPTG 0.5 mM just before pouring the plates (see Note 7). Functional complementation of the adenylate cyclase can be detected by the observation of blue colonies after 24–72 h at 30 °C (see Note 4).

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2.4 Two-Hybrid Reporter Strains and Plasmids

1. E. coli reporter strain engineered to provide an oxidative cytoplasm and deleted of the adenylate cyclase gene cya. We recommend the Oxi-BTH strain [9] or the Oxi-Blue strain (Pellegri et al. unpublished) (see Note 2). 2. BACTH vectors producing the proteins of interest fused to the B. pertussis T18 adenylate cyclase domain (such as pUT18 or pUT18C) (see Note 8). 3. BACTH vectors producing the proteins of interest fused to the B. pertussis T25 adenylate cyclase domain (such as pKT25 or pKNT25) (see Note 8). 4. Control vectors to test negative and positive signal of interaction between disulfide-bond folded proteins (see Note 9).

3

Methods 1. Prepare competent cells by standard protocol using CaCl2 [11] (see Note 10). 2. Construct the BACTH plasmids to produce the hybrid proteins (see Notes 8 and 9). 3. Introduce the couple of plasmids into the competent cells of choice by co-transformation: Add 1 μL of pKT25 vector and 0.8 μL of pUT18 vector (i.e., 5 to 10 ng of each plasmid) in 40 μL competent reporter cells Oxi-BTH or Oxi-Blue cell. Mix contents by gently flicking the tubes and incubate on ice for 30 min. Transfer tubes to a dry bath heater that has been preheated to 42 °C. Leave the tubes in the rack for exactly 45 s. Rapidly transfer the tubes to an ice bath. Allow the cells to chill for 2 min. Add 600 μL 2YT to each tube. Incubate cultures for 60 min in a dry bath heater set at 37 °C with agitation to allow expression of the antibiotic resistance genes encoded by the plasmids. Spread the transformed competent cells onto LB agar plate supplemented with ampicillin and kanamycin using sterile glass beads. Use the appropriate volume of cell suspension so as to obtain isolated clones on the plates (see Note 4). 4. Invert the plates and incubate at 37 °C for 24–30 h or at 30 °C for 48–72 h (see Note 11). 5. Prepare the sterile 96-deep-well plate: for each co-transformation to test, fill a well with 600 μL of LB medium supplemented with ampicillin 100 μg/mL, kanamycin 50 μg/ mL, and IPTG 0.5 mM. 6. For each co-transformant to test, collect 4–6 colonies with a wooden stick and disperse the cells in one growth mediumfilled well (see Note 1).

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7. Cover the deep-well plate with a porous adhesive film. 8. Incubate the deep-well plate at 30 °C with shaking at 180 rpm for 24–30 h. 9. Spot 3.5–4 μL of each culture with a multichannel pipette on the reporter medium square plate (M63 minimal medium plate, MacConkey plate, or LB–X-Gal plate for Oxi-Blue) (see Note 1). 10. Incubate at 30 °C for 24–48 h (LB/X-Gal and MacConkey medium) or longer (M63/maltose medium) and monitor the interaction signal (see Notes 4 and 12).

4

Notes 1. We recommend the use of multichannel pipettes (2–10 μL) for the regular spotting of the cultures on the reporter plates, especially when testing large number of protein pairs, which is often the case in BATCH assays. To facilitate the spotting, we recommend planning the culture in the deep-well plate as ready for the spotting. Beware not to pipet the bottom of the well, as dead cells tend to sediment. 2. Strain genotypes: Oxi-BTH: Shuffle T7 Express ( fhuA2 lacZ::T7 gene1 [lon] ompT ahpC gal λatt::pNEB3-r1-cDsbC (SpecR, lacIq) ΔtrxB sulA11 R(mcr-73::miniTn10--TetS)2 [dcm] R(zgb-210::Tn10 --TetS) endA1 Δgor Δ(mcrC-mrr)114::IS10), Δcya. Oxi-Blue: Oxi-BTH ΔphoA lacZ+. DHM1: F-, cya-854, recA1, endA1, gyrA96 (Nalr), thi1, hsdR17, spoT1, rfbD1, glnV44(AS). BTH101: F-, cya-99, araD139, galE15, galK16, rpsL1 (Strr), hsdR2, mcrA1, mcrB1. 3. X-Gal can be added to the M63/maltose reporter medium to facilitate the observation of the colonies. 4. The Oxi-BTH and Oxi-Blue strains have a reduced growth rate compared to the BTH101 and DHM1 strains. We recommend increasing the time of recovery to 1 h after transformation of the Oxi-strains. Growth on the reporter plate media and development of the signal will also be longer than for the regular BACTH strains, from 24 to 72 h (LB/X-Gal and MacConkey/ maltose media) and 2–7 days (M63/maltose medium). 5. MacConkey medium can be simply boiled in a microwave for 5 min to avoid autoclaving. This method is suitable for immediate utilization of the reporter medium. It is best not to store the poured plates for later use, as sterilization is less effective than when autoclaving.

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6. Not all the MacConkey agar media have equivalent quality. Difco # 216830 is recommended. 7. X-Gal solution in DMF must be prepared extemporaneously. 8. The T18 and T25 domains can be fused to either the C-terminus or the N-terminus of the proteins of interest. However, fusions can generate steric hindrance incompatible with an interaction between the partners. Similarly, the position of the T18 or T25 domain may be incompatible with the reconstitution of adenylate cyclase activity if the reporter domains are too far apart in the complex between the partners. It may therefore be interesting to test both N-terminal and C-terminal fusions of the proteins of interest to overcome these potential limitations. 9. Several options of proteins devoid of disulfide bonds and suitable as controls are available (pUT18-zip, pKT25-ZIP, pUT18-Pal, and pKT25-TolB [6, 9]). For positive control of disulfide-bond-dependent interaction in the Oxi-strains, we recommend to use the filamentous phage minor coat protein pIII and its bacterial receptor TolA (either pIII-N1CTX/ TolA3V. cholerae or pIII-N1M13/TolA3E. coli) [9] and Fig. 1b. 10. Lac+ or Mal + revertants may spontaneously occur. Before preparing the competent cells, the cya- reporter strains from the frozen stock should be reisolated on the reporter medium of choice (MacConkey/maltose or LB/X-Gal/IPTG plates) and grown overnight at 37 °C to ensure the selection of a white colony to start the culture. 11. To run more sustainable experiments with less disposable plastic plates and to obtain isolated clones, it is possible to spread two (or more) co-transformations on a single LB agar plate supplemented with ampicillin and kanamycin. After the recovery step, centrifuge the transformed cells for 5 min at 8000 rpm, discard the supernatant so that only about 50 μL remains, and resuspend the pellet in this volume by pipetting. Divide the plate in half and carefully drop the suspension at one end of the half plate. With a wooden stick, streak the cells from the drop to isolate colonies on the plate. The other half of the plate can be used to spread another transformation. 12. Following the initial recommendations on the BACTH method, Oxi-BTH and Oxi-Blue transformant cells spotted on the reporter media should be incubated at 30 °C to obtain efficient complementation activity. However, due to the slow growth rate of the Oxi-strains, it may be worth testing the incubation at 37 °C.

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Acknowledgments This work was supported by the Centre National de la Recherche Scientifique (CNRS) and by the Aix-Marseille University. CP is supported by a PhD fellowship from Aix-Marseille Univ. References 1. Landeta C, Boyd D, Beckwith J (2018) Disulfide bond formation in prokaryotes. Nat Microbiol 3:270–280. https://doi.org/10. 1038/s41564-017-0106-2 2. Mashruwala AA, Bassler BL (2020) The Vibrio cholerae quorum-sensing protein VqmA integrates cell density, environmental, and hostderived cues into the control of virulence. MBio 11:e01572-20. https://doi.org/10. 1128/mBio.01572-20 3. Mariano G, Monlezun L, Coulthurst SJ (2018) Dual role for DsbA in attacking and targeted bacterial cells during type VI secretion systemmediated competition. Cell Rep 22:774–785. https://doi.org/10.1016/j.celrep.2017. 12.075 4. Reardon-Robinson ME, Osipiuk J, Chang C et al (2015) A disulfide bond-forming machine is linked to the sortase-mediated pilus assembly pathway in the Gram-positive bacterium Actinomyces oris. J Biol Chem 290:21393–21405. https://doi.org/10.1074/jbc.M115.672253 5. Baker JL, Dahlberg T, Bullitt E, Andersson M (2021) Impact of an alpha helix and a cysteine– cysteine disulfide bond on the resistance of bacterial adhesion pili to stress. Proc Natl Acad Sci U S A 118:e2023595118. https:// doi.org/10.1073/pnas.2023595118 6. Karimova G, Pidoux J, Ullmann A, Ladant D (1998) A bacterial two-hybrid system based on

a reconstituted signal transduction pathway. Proc Natl Acad Sci U S A 95:5752–5756. https://doi.org/10.1073/pnas.95.10.5752 7. Battesti A, Bouveret E (2012) The bacterial two-hybrid system based on adenylate cyclase reconstitution in Escherichia coli. Methods 58: 325–334. https://doi.org/10.1016/j.ymeth. 2012.07.018 8. Ouellette SP, Gauliard E, Antosova´ Z, Ladant D (2014) A Gateway®-compatible bacterial adenylate cyclase-based two-hybrid system. Environ Microbiol Rep 6:259–267. https:// doi.org/10.1111/1758-2229.12123 9. Houot L, Navarro R, Nouailler M et al (2017) Electrostatic interactions between the CTX phage minor coat protein and the bacterial host receptor TolA drive the pathogenic conversion of Vibrio cholerae. J Biol Chem 292: 13584–13598. https://doi.org/10.1074/jbc. M117.786061 10. Pellegri C, Moreau A, Duche´ D et al (2023) Direct interaction between fd phage pilot protein pIII and the TolQ-TolR protondependent motor provides new insights into the import of filamentous phages. J Biol Chem 299 (8):105048 11. Sambrook J, Fritsch EF, Maniatis T (1989) Molecular cloning: a laboratory manual. Cold Spring Harbor Laboratory, New York

Chapter 15 Protein–Protein Interactions: Yeast Two Hybrid Jer-Sheng Lin and Erh-Min Lai Abstract The yeast two-hybrid system is a powerful and commonly used genetic tool to investigate the interaction between artificial fusion proteins inside the nucleus of yeast. Here, we describe how to use the Matchmaker GAL4-based yeast two-hybrid system to detect the interaction of the Agrobacterium type VI secretion system (T6SS) sheath components TssB and TssC41. The bait and prey gene are expressed as a fusion to the GAL4 DNA-binding domain (DNA-BD) and GAL4 activation domain (AD, prey/library fusion protein), respectively. When bait and prey fusion proteins interact in yeast nucleus, the DNA-BD and AD are brought into proximity, thus activating transcription of reporter genes. This technology can be widely used to identify interacting partners, confirm suspected interactions, and define interacting domains. Key words Protein–protein interaction, Yeast two hybrid, Gal4 transcriptional activation domain (AD), Gal4 DNA-binding domain (BD), Saccharomyces cerevisiae AH109, Type VI secretion system, TssB, TssC

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Introduction The yeast two-hybrid system (Y2H) was first developed in 1989, revolutionized the process of searching and identifying the interacting proteins [1]. To date, Y2H system provides a useful and sensitive method for detecting not only stable interacting proteins but also weak and transient protein interactions [2]. Because Y2H is performed in vivo, the great advantage of this system is that the testing proteins are more likely to be in their native conformations, which may lead to increased sensitivity and accuracy of detection [1, 3, 4]. Importantly, Y2H system is complementary to biochemical methods such as co-immunoprecipitation/pull-down followed by western blotting or mass spectrometry analysis to increase the accuracy and dynamics for a more complete and reliable map of interactions [2]. Notably, the yeast two-hybrid method has been modified and improved greatly in the past years, such as applications in protein–DNA interaction, yeast three-hybrid, and amenable for interaction studies of membrane proteins, DNA-binding

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_15, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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proteins and RNA-binding proteins [5–8]. More recent advancements in Y2H systems focused on high-throughput protein–protein interaction studies by combining next-generation sequencing (NGS) with Y2H for obtaining genome-wide interactome networks [9–11]. Using the Matchmaker GAL4-based yeast twohybrid system (Clontech, Mountain View, CA) as an example, the principle of Y2H system is illustrated in Fig. 1a, which is based on the properties of the yeast GAL4 transcription factor that consists of separable domains responsible for DNA-binding and transcriptional activation [3]. The bait proteins are expressed as a fusion to the GAL4 DNA-binding domain (DNA-BD), while the prey proteins are expressed as fusions to the GAL4 activation domain (AD). When bait and prey fusion proteins interact in yeast nucleus, the DNA-BD and AD are brought into proximity to restore into a functional GAL4 transcriptional activator, which binds onto an upstream activating sequence (UAS) of reporter genes (such as ADE2 and HIS3) for transcriptional activation. The Y2H has been widely used to detect interactions of wide range of proteins from yeast, bacteria, animal, and plant systems. The Y2H has been successfully used to study the interaction of proteins involved in bacterial protein secretion from type IV [12–15] and type VI secretion systems (T4SS, T6SS) in Agrobacterium tumefaciens [16, 17]. Here, the Y2H protocol describes the use of Matchmaker yeast two-hybrid system to detect the interaction of the Agrobacterium T6SS sheath components TssB and TssC41 according to the instructions of the user manual (Clontech, Mountain View, CA) with minor modifications. TssB and TssC interact to form a cogwheel-like tubular structure, which is analogous to outer sheath structure of a contractile phage and wraps around the T6SS tail tube to propel the tail tube toward the target cell interior upon infection [18, 19]. In A. tumefaciens, we showed the interaction of the T6SS sheath components TssB and TssC41 by Y2H assay, co-purification in E. coli, and co-IP in A. tumefaciens [17]. For Y2H assay, each bait and prey plasmid pair was co-transformed into Saccharomyces cerevisiae strain AH109. The transformants were selected by their growth on synthetic dextrose (SD) minimal medium lacking tryptophan (Trp) and leucine (Leu) (SD-WL medium), which are the nutritional selection marker for pGBKT7 and pGADT7, respectively. The positive interaction of expressed fusion proteins was then determined by their growth on SD lacking Trp, Leu, adenine (Ade), and histidine (His) (SD-WLHA medium) at 30 °C for at least 3 days (Fig. 1b). The positive interactions are only observed for plasmid pairs expressing TssB and TssC41 but not when each of them co-expressed with vector only, suggesting the specific interactions of TssB and TssC41 (Fig. 2) [17].

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Fig. 1 The principle and experimental flowchart of yeast two-hybrid system. (a) Schematic diagram of the principle of yeast two-hybrid system. Two testing proteins are each fused with two different Gal4 domains, with the bait protein fused to the Gal4 DNA-binding domain (DNA-BD, 1–147 a.a.), and the prey protein fused to the Gal4 transcriptional activation domain (AD, 768–881 a.a.). In yeast strain AH109, transcriptional activation of the reporters (ADE2, HIS3, and MEL1) only occurs in a cell that bait protein interact with prey protein to restore functional Gal4 transcription factor binding to the Gal4-responsive promoter GAL UAS [3]. (b) Experimental flowchart of yeast two-hybrid system. Co-transformation was performed by using PEG/LiAcmediated transformation method. SD-WL medium is representative of synthetic dextrose (SD) minimal medium lacking tryptophan (Trp) and leucine (Leu). SD-WLHA medium is representative of synthetic dextrose minimal medium lacking Trp, Leu, adenine (Ade), and histidine (His)

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Materials All growth media and solutions are prepared using Milli-Q purified water and analytical or molecular biology grade reagents.

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Fig. 2 TssB and TssC41 interact with each other in yeast strain AH109. SD-WL medium (SD minimal medium lacking Trp and Leu) was used for the selection of plasmids. SD-WLHA medium (SD minimal medium lacking Trp, Leu, His, and Ade) was used for the auxotrophic selection of bait and prey protein interactions. The positive interaction was determined by the growth on SD-WLHA medium at 30 °C for at least 2 days. The positive control (+) showing interactions of SV40 large T-antigen and murine p53 and negative control (vector) is indicated. (Reproduced from [17] with permission from Public Library of Science, PLOS) 2.1 Yeast Strain and Vectors: (Information Below Is According to [3])

1. The yeast Saccharomyces cerevisiae strain AH109: The complete genotype of AH109 is provided below: MATa, trp1-901, leu2-3, 112, ura3-52, his3-200, gal4Δ, gal80Δ, LYS2:: GAL1UAS-GAL1TATA-HIS3, GAL2UAS-GAL2TATA-ADE2, URA3:: MEL1UAS-MEL1TATA-lacZ. AH109 strain is gal4- and gal80-; this prevents interference of native regulatory proteins with the regulatory elements in the two-hybrid system. AH109 features three reporters, which are ADE2, HIS3, and MEL1 (or lacZ) under the control of distinct GAL4 upstream activating sequences (UASs) and TATA boxes. 2. The pGBKT7 vector: The pGBKT7 vector contains a multiple cloning site (MCS) for cloning to express proteins with N-terminal fusion to amino acids 1–147 of the GAL4 DNA-binding domain (DNA-BD). In yeast, fusion proteins are expressed at high levels from the constitutive ADH1 promoter (PADH1). Transcription is terminated by the T7 and ADH1 transcription termination signals (TADH1). The pGBKT7 vector can replicate autonomously in both E. coli and S. cerevisiae from the pUC and 2 μ ori, respectively. The vector carries the kanamycin-resistant gene for selection in E. coli and the TRP1 nutritional marker for selection in yeast. In addition, pGBKT7 also contains the T7 promoter and a c-Myc epitope tag for in vitro transcription and translation of the c-Myc-tagged fusion protein without GAL4 DNA-BD.

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3. The pGADT7 vector: The pGADT7 vector contains MCS for cloning to express protein with N-terminal fusion to amino acids 768–881 of the GAL4 activation domain (AD). In yeast, fusion proteins are expressed at high levels from the constitutive ADH1 promoter (PADH1). Transcription is terminated at the ADH1 transcription termination signal (TADH1). The fusion protein is targeted to the yeast nucleus by the SV40 nuclear localization sequences that have been added to the activation domain sequence. pGADT7 also contains the T7 promoter and an HA epitope tag, for in vitro transcription and translation of the HA-tagged fusion protein without GAL4 AD. The pGADT7 vector can replicate autonomously in both E. coli and S. cerevisiae from the pUC and 2 μ ori, respectively. The vector carries ampicillin-resistant gene for selection in E. coli and the LEU2 nutritional marker for selection in yeast. 2.2 Yeast Cultures and Yeast Transformation [20]

1. YPDA medium: 20 g Bacto peptone, 10 g yeast extract, 20 g glucose, 40 mg adenine, 15 g agar (for plate use only); add water to 1 L and autoclave. 2. Minimal synthetic defined (SD) plate: 1.675 g yeast nitrogen base without amino acid, 5 g glucose, 3.75 g agar; add water to 250 mL and autoclave. Dropout (DO) supplements (such as -Trp-Leu or -Trp-Leu-Ade-His) can be added to the minimal SD base to make a synthetic, defined medium lacking the specified nutrients (see Note 1). 3. Carrier DNA: 10 mg/mL salmon sperm DNA (ssDNA) (UltraPure™ Salmon Sperm DNA Solution, ThermoFisher); store at -20 °C (see Note 2). 4. 10 X LiAc: 1 M lithium acetate, pH 7.5 (see Note 3), autoclave and store at room temperature (RT). 5. 40% PEG solution: 22 g polyethylene glycol (molecular weight is 6000 or 3350 Da); add 31 mL of water, autoclave, and store at room temperature. 6. Plasmid DNA: ~200 ng per plasmid for co-transformation (see Note 4). 7. Laminar flow.

2.3

Selective Media

1. Selective medium for transformants: Minimal synthetic defined (SD) plate with -Leu/-Trp dropout (DO) supplement (containing every essential amino acids except for leucine and tryptophan) (see Note 5). 2. Selective medium for protein–protein interactions: Minimal synthetic defined (SD) plate with -Leu/-Trp/-His/-Ade dropout (DO) supplement (containing every essential amino acids except for leucine, tryptophan, histidine, and adenine).

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2.4 Preparation of Yeast Cultures for Protein Extraction and Western Blot

1. YPDA medium: See step 1 in Subheading 2.2.

2.5 Preparation of Yeast Protein Extracts

1. 1.5 mL microtube.

2. 2X minimal synthetic defined (SD) medium: 40 g Bacto peptone, 20 g yeast extract, 20 g glucose, 80 mg adenine, with 2X -Leu/-Trp dropout (DO) supplement (containing every essential amino acids except for leucine and tryptophan); add water to 1 L and autoclave.

2. Acid-washed glass beads (425–600 μm). 3. Protease inhibitor cocktails solution. 4. Phenylmethylsulfonyl fluoride (PMSF) stock solution: 0.1 M. 5. Yeast protein extraction buffer (see Note 6): 0.1% NP-40, 250 mM NaCl, 50 mM Tris–HCl, pH 7.5, 5 mM EDTA (from a 0.5 M, pH 8.0 stock solution); mix well and place on ice. Before use, add 1 mM DTT, 2X protease inhibitor cocktails (stock, 50X), 4 mM PMSF, mix well, and then, ready for use.

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Methods

3.1 Gene Construction in pGBKT7 and pGADT7 Vectors

The constructs used for yeast two-hybrid analysis are generated based on the information of vector map and multiple cloning site (MCS) provided from the user manual (Clontech) [3]. Briefly, bait and prey coding sequence (without stop codon) are PCR amplified with appropriate primers, digested with appropriate enzymes, and cloned into the same sites of pGBKT7 or pGADT7 [17].

3.2 Preparation of Yeast Cultures for Yeast Transformation [20]

1. Inoculate 3 mL of YPAD with a colony of AH109 (see Note 7) and incubate at 30 °C overnight (>16 h) with shaking (250 rpm) to the stationary phase (see Note 8). 2. Subculture by adding 1 mL of AH109 overnight culture into 50 mL of fresh YPDA medium. 3. Incubate at 30 °C for 4 h with shaking (250 rpm) (see Note 9). 4. Pour the cells into 50 mL tubes and pellet the cells at 450× g for 3 min at 4 °C or RT (see Note 10). 5. Discard the supernatant and resuspend the cell pellet with 10 mL of sterile water by vortexing, and re-pellet the cells at 450× g for 3 min at 4 °C or RT (see Note 11). 6. Resuspend the cell pellet in 100 μL 10X LiAc and 900 μL of sterile water (final concentration is 1X LiAc) (see Note 12). Incubate cell suspension at 30 °C for 1 h with gentle shaking (150 rpm) (see Note 13). 7. The suspended yeast competent cells are ready to use for transformation (see Note 14).

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3.3 PEG/LiAcMediated Transformation of Yeast (Small-Scale Transformation of Bait and Prey Plasmids) (See Note 15)

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1. Pretreat ssDNA (salmon sperm DNA) by heating at 100 °C for 10 min and then, put on ice for 5–10 min before use (see Note 16). 2. Add 80 μL of heat-treated ssDNA (10 μg/μL) into 1 mL yeast competent cell (final concentration ~ 0.8 mg/mL), and mix well (see Note 17). 3. Aliquot 100 μL of cell mixture into 1.5 mL microtube, and add an approximate amount of plasmid DNA (about 3–5 μL) (see Note 18). Mix well by vortexing and incubate at 30 °C for 30 min (see Note 19). 4. Freshly prepare LiAc–PEG solution (10X LiAc, 40% PEG = 1: 10, mix 1 mL of 10X LiAc with 10 mL of 40% PEG), and add 700 μL of LiAc–PEG solution to the cell mixture after 30-min incubation (see Note 20). Resuspend cell mixture immediately by vortexing (see Note 21) before incubation at 30 °C for 1 h. 5. Heat shock at 42 °C for 5 min (see Note 22). 6. Pellet the cells by centrifugation at 14,500× g for 1 min at RT (see Note 23). 7. Discard the supernatant as much as possible to remove PEG. Resuspend the cells in 300 μL of sterile water (see Note 24).

3.4 Selection of Transformants

1. Streak cells on SD/-Trp-Leu selective plate and incubate at 30 °C for 2–3 days (see Note 25). 2. Patch single colony on SD/-Trp-Leu selection plate and incubate at 30 °C for 2 days (see Note 26).

3.5 Testing for Protein–Protein Interactions

1. Patch cells on both SD/-Trp-Leu (control) and SD/-TrpLeu-His-Ade selection plates for 3–6 days (see Note 27).

3.6 Preparation of Yeast Cultures for Protein Extraction (See Note 28)

1. Grow the culture from a single colony (see Note 29) in 3 mL YPAD or 2X SD selection medium (see Note 30) overnight at 30 °C.

2. Photograph the plate to record the final protein–protein interaction results (e.g., TssB and TssC41 can interact strongly in yeast, Fig. 2) [17].

2. Add 100 μL of overnight culture (OD600 should reach to 1.5) in fresh 5 mL 2X SD selection medium. Incubate at 30 °C with shaking (about 250 rpm) until the OD600 reaches to 0.4–0.6. Depending on the tested proteins, it may take 4–5 h to reach desired cell number (see Note 31). 3. Place cells in 15 mL tubes and pellet the cells at 1000× g for 5 min at 4 °C. 4. Discard the supernatant and resuspend the cell pellet with 10 mL of sterile water by vortexing (see Note 32).

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5. Re-pellet the cells at 1000× g for 5 min at 4 °C. 6. Repeat steps 4–5. 7. Discard the supernatant. You can continue to extract yeast protein or immediately freeze the cell pellet by placing the tube in liquid nitrogen, and then, store cells at -80 °C until western blot analysis. 3.7 Preparation of Yeast Protein Extracts and Western Blot Analysis

1. Keep protein samples on ice; add 100 μL of freshly prepared yeast protein extraction buffer to the tube followed by the addition of 50 μL of acid-washed glass beads (see Note 33). 2. Vortex the tubes at maximum speed for 30 s, and then, place tubes on ice for 30 s (see Note 34). Repeat this step for six times. 3. Transfer the supernatant above the settled glass beads to a new 1.5 mL microtube using P200 pipetman, and place tubes on ice. The supernatant is the first cell extract. 4. Add 50 μL of yeast protein extraction buffer to the tube containing glass beads and vortex the tubes at highest speed for 30 s, and then, transfer the supernatant (the second cell extract) above the settled glass beads to the 1.5 mL microtube containing the first cell extract. 5. Centrifuge the combined cell extract at 14,500× g for 5 min at 4 °C (see Note 35). 6. Transfer the supernatant to a new 1.5 mL microtube and measure protein concentration (see Note 36), and prepare the protein samples for western blot analysis using appropriate antibodies.

4

Notes 1. Yeast nitrogen base without amino acid and dropout (DO) supplements are very hygroscopic to be curdled. Both of them need to be stored in a humidity cabinet. 2. Please aliquot the stock carrier DNA into 100 μL working stock aliquots in order to avoid the quality changes by repeated heating. 3. The pH of 1 M lithium acetate must be adjusted to pH 7.5 by acetic acid. 4. In general, we routinely obtain 100–200 colonies for a successful co-transformation, by using 200 ng per plasmid DNA. 5. It is not necessary to prepare the stock solution for dropout (DO) supplement. Please add the dropout (DO) supplement

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directly to the minimal synthetic defined (SD) medium before autoclaving. 6. Yeast protein extraction buffer must be freshly prepared prior to use. 7. To grow the overnight culture of the yeast strain AH109, please use the colonies freshly streaked out (less than 2 months old). We also recommend to refresh the yeast AH109 strain on YPAD plate every 2 months. 8. Please grow the yeast AH109 strain to the stationary phase, which corresponds to an OD600 higher than 1.5. 9. After 4 h incubation, the value of OD600 is about 0.3–0.4. Please note that yeast cells may be precipitated. It is recommended to take out the flask and shake the culture a few times during incubation. 10. Centrifugation at 4 °C or RT does not significantly affect the transformation efficiency. 11. All steps are carried out in a laminar flow under aseptic conditions. 12. Discard the supernatant as much as possible. Add 900 μL of sterile water, and then add 100 μL of 10X LiAc. 13. This is a very critical step as the excessive speed may cause yeast cell breakage and reduce the transformation efficiency. 14. Yeast competent cells must be freshly prepared to maintain high transformation efficiency. 15. All steps of PEG/LiAc-mediated transformation of yeast should be carried out in a laminar flow under aseptic conditions. 16. It should be noted that we recommend to pretreat ssDNA (salmon sperm DNA) at 100 °C for only 10–15 min. Prolonged heating may cause instability of ssDNA. 17. This step should be carried out on ice to maintain low temperature. 18. We recommend to use 200 ng/per plasmid for PEG/LiAcmediated co-transformation of yeast. 19. We recommend to vortex the mixture for only 1 s before incubation in incubator at 30 °C for 30 min. LiAc–PEG solution can be prepared during the incubation time. 20. LiAc–PEG solution is very sticky; therefore, it is better to use blunt end tip by cutting the end of regular pipet tip to draw the solution. It should be noted that this step is very critical and must be done within 2 min. Otherwise, the following resuspending step will be difficult to perform. Therefore, please avoid handling more than ten samples at the same time.

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21. Resuspend cell mixture immediately by vortexing using the maximum speed for 2–3 s. 22. Before heat shock, we recommend to gently shake the microtube several times to mix the cell mixture well. 23. Please pellet the cells directly by centrifugation at 14,500× g for 1 min. It is not necessary to incubate cells on ice before centrifugation. 24. Final cell suspension can be stored at 4 °C for overnight before use. 25. In general, we would recommend to pick 6–8 single colonies for further analysis. If possible, please try to choose the relatively large colonies, which usually correlate with high protein expression levels. 26. We highly recommend to use a flat toothpick (750 Flat Toothpicks, Diamond Brands) to patch single colony on selection plate. The use of sharp toothpick often causes the breakage of the agar surface. The yeast cells are ready for further protein– protein interactions test when the yeast cells have nicely grown after 2–3 days of incubation. 27. Please use a flat toothpick to patch cells on selection plate. In general, it is recommended to patch three individual colonies for protein–protein interaction analysis in each tested interacting pairs. In many cases, the growth rate of the colonies on selective plate is correlated with the binding strength of the two tested proteins. 28. It is highly recommended to perform western blot analysis to confirm the proper expression of tested proteins using commercially available antibody for tagged epitope of fusion proteins. 29. To prepare the yeast protein extraction, please use the yeast cells freshly grown on plates within a week. 30. It’s recommended to use the appropriate SD minimal medium with selection to maintain the extrachromosomal plasmid(s) of transformants. The use of 2X SD minimal medium with more nutrients can facilitate faster and better growth of transformants. 31. During late log phase, the ADH1 promoter shuts down and the expression level of endogenous yeast proteases is increased. Therefore, please do not grow the yeast cells over saturation. 32. Resuspend and wash the cell pellet by vortexing using the maximum speed for 2–3 s after adding 10 mL of sterile water. 33. Because it is very difficult to take accurate amount of acidwashed glass beads by using pipette, we recommend to use a small spatula instead.

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34. We use the Vortex-Genie 2 mixer (Scientific Industries, Inc.) to vortex the tubes at maximum speed for 30 s. Please wear thick gloves to protect your hands from being temporarily paralyzed during vortex. 35. The purpose of this step is to remove cell debris and glass beads by centrifugation. 36. To minimize the amount of protein extract used to determine protein concentration, it is recommended to use the NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific Inc.) for the measurement.

Acknowledgments We thank Jemal Ali for updating the references. This work was supported by research grant from the Taiwan National Science and Technology Council (104-2311-B-001-025-MY3) to EM Lai. JS Lin was the recipient of postdoctoral fellowships from Academia Sinica. References 1. Fields S, Song O (1989) A novel genetic system to detect protein-protein interactions. Nature 340:245–246 2. Stasi M, De Luca M, Bucci C (2015) Twohybrid-based systems: powerful tools for investigation of membrane traffic machineries. J Biotechnol 202:105–117. https://doi.org/ 10.1016/j.jbiotec.2014.12.007 3. Clontech (2007) Matchmaker™ GAL4 two-hybrid system 3 & libraries user manual. https://www.takarabio.com/documents/ User%20Manual/PT3247/PT3247-1.pdf 4. Chien CT, Bartel PL, Sternglanz R, Fields S (1991) The two-hybrid system: a method to identify and clone genes for proteins that interact with a protein of interest. Proc Natl Acad Sci U S A 88:9578–9582 5. Causier B, Davies B (2002) Analysing proteinprotein interactions with the yeast two-hybrid system. Plant Mol Biol 50:855–870 6. Petschnigg J, Groisman B, Kotlyar M et al (2014) The mammalian-membrane two-hybrid assay (MaMTH) for probing membraneprotein interactions in human cells. Nat Methods 11:585–592. https://doi.org/10.1038/ nmeth.2895 7. Reece-Hoyes JS, Barutcu AR, McCord RP et al (2011) Yeast one-hybrid assays for genecentered human gene regulatory network mapping. Nat Methods 8:1050–1052. https://doi.org/10.1038/nmeth.1764

8. Reece-Hoyes JS, Marian Walhout AJ (2012) Yeast one-hybrid assays: a historical and technical perspective. Methods 57:441–447. https:// doi.org/10.1016/j.ymeth.2012.07.027 9. Yachie N, Petsalaki E, Mellor JC et al (2016) Pooled-matrix protein interaction screens using barcode fusion genetics. Mol Syst Biol 12:863. https://doi.org/10.15252/msb. 20156660 10. Weimann M, Grossmann A, Woodsmith J et al (2013) A Y2H-seq approach defines the human protein methyltransferase interactome. Nat Methods 10:339–342. https://doi.org/10. 1038/nmeth.2397 11. Trigg SA, Garza RM, MacWilliams A et al (2017) CrY2H-seq: a massively multiplexed assay for deep-coverage interactome mapping. Nat Methods 14:819–825. https://doi.org/ 10.1038/nmeth.4343 12. Tsai YL, Chiang YR, Narberhaus F, Baron C, Lai EM (2010) The small heat-shock protein HspL is a VirB8 chaperone promoting type IV secretion-mediated DNA transfer. J Biol Chem 285:19757–19766. https://doi.org/10. 1074/jbc.M110.110296 13. Baron C, Thorstenson YR, Zambryski PC (1997) The lipoprotein VirB7 interacts with VirB9 in the membranes of Agrobacterium tumefaciens. J Bacteriol 179:1211–1218 14. Das A, Anderson LB, Xie YH (1997) Delineation of the interaction domains of

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Agrobacterium tumefaciens VirB7 and VirB9 by use of the yeast two-hybrid assay. J Bacteriol 179:3404–3409 15. Das A, Xie YH (2000) The Agrobacterium T-DNA transport pore proteins VirB8, VirB9, and VirB10 interact with one another. J Bacteriol 182:758–763 16. Ma LS, Lin JS, Lai EM (2009) An IcmF family protein, ImpLM, is an integral inner membrane protein interacting with ImpKL, and its walker a motif is required for type VI secretion system-mediated Hcp secretion in Agrobacterium tumefaciens. J Bacteriol 191:4316–4329. https://doi.org/10.1128/JB.00029-09 17. Lin JS, Ma LS, Lai EM (2013) Systematic dissection of the Agrobacterium type VI secretion system reveals machinery and secreted components for subcomplex formation. PLoS One 8:

e67647. https://doi.org/10.1371/journal. pone.0067647PONE-D-12-38170 18. Bonemann G, Pietrosiuk A, Diemand A, Zentgraf H, Mogk A (2009) Remodelling of VipA/VipB tubules by ClpV-mediated threading is crucial for type VI protein secretion. EMBO J 28:315–325. https://doi.org/10. 1038/emboj.2008.269 19. Lossi NS, Manoli E, Forster A et al (2013) The HsiB1C1 (TssB-TssC) complex of the Pseudomonas aeruginosa type VI secretion system forms a bacteriophage tail sheathlike structure. J Biol Chem 288:7536–7548. https://doi. org/10.1074/jbc.M112.439273 20. Ito H, Fukuda Y, Murata K, Kimura A (1983) Transformation of intact yeast cells treated with alkali cations. J Bacteriol 153:163–168

Chapter 16 Protein–Protein Interactions: Bimolecular Fluorescence Complementation and Cytology Two Hybrid Dyuti Purkait, Mohd Ilyas, and Krishnamohan Atmakuri Abstract Identifying protein–protein interactions between machine components of bacterial secretion systems and their cognate substrates is central to delineating how the machines operate to translocate their substrates. Further, establishing which among the machine components and their substrates interact with each other facilitates (i) advancement in our understanding of the architecture and assembly of the machines, (ii) understanding the substrates’ translocation routes and mechanisms, and (iii) how the machines and the substrates talk to each other. Currently, though diverse biochemical methods exist in identifying direct and indirect protein–protein interactions, they primarily remain in vitro and can be quite labor intensive. They also may capture/exhibit false-positive interactions because of barrier breakdowns as part of methodology. Thus, adopting novel genetic approaches to help visualize the same in vivo can yield quick, advantageous, reliable, and informative protein–protein interactions data. Here, we describe the easily adoptable bimolecular fluorescence complementation and cytology-based two-hybrid assays to understand the bacterial secretions systems. Key words Bimolecular fluorescence complementation (BiFC), Cytology-based two hybrid (C2H), Nonfluorescing halves, Protein–protein interactions, Functional reconstitution

1 Introduction Most cellular processes carried out by all living systems operate and perform through unique protein–protein interactions (PPIs). Except for few types of simple secretory machines, most complex secretory machines of bacteria not only assemble through complex PPIs but also do so to operate and deliver their cognate substrates to pre-dictated niches. In turn, the transport-competent substrates, effectors, and secretory proteins traverse through their cognate secretory pathways/machinery by performing several PPIs that are often spatially and temporally regulated [1, 2]. Therefore,

Authors Dyuti Purkait and Mohd Ilyas have equally contributed to this chapter. Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_16, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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identifying the key protein–protein interactions and the players that execute such interactions is vital. Rather than merely employing complex and elaborate biochemical experimentations to identify the PPIs and the underlying players, direct visualizations of such protein–protein interactions in bacterial cells through bimolecular fluorescence complementation (BiFC) and cytology-based twohybrid (C2H) assays have gained significant momentum and found to be very informative [3–6]. 1.1 Bimolecular Fluorescence Complementation (BiFC)

In the last few decades, “protein complementation” studies have mined diverse range of proteins to shortlist few that retain the unique capability to functionally reconstitute their two nonfunctional halves when separately fused with an interacting protein pair [7–9]. Johnsson and Varshavsky (1994) evaluated ubiquitin halves in yeast and found them to functionally reconstitute only when each half was fused to one member of a known interacting protein pair [10]. On similar lines, upon employing synthetic interacting peptides, Ghosh et al. (2000) detected complementation of two halves of green fluorescent protein (GFP) variants in E. coli [11]. The Miyawaki’s group (2004) observed similar results in mammalian cells with the yellow fluorescent protein (YFP) halves [12]. Since BiFC relies on the interacting protein partners for fluorescence reconstruction of the two nonfluorescent halves of a fluorescent protein, it is an extremely sensitive technique [13]. It also prevents its necessity on cell lysis, requirement of external reagents for quantitating interactions and is largely immune to influence by cellular conditions. Consequently, it supersedes fluorescence resonance energy transfer (FRET)-based in vivo visualization of protein–protein interactions [14] which is hypersensitive to interference from the same cellular conditions [13]. Further, BiFC assay for a said protein partner complex is not hampered with other interacting proteins [8, 13]. Additionally, BiFC prevents possible issues the biochemical assay ligands or enzyme substrates bring with them during chromogenic or fluorogenic reactions. Further, because of minimal experimental tweaks, BiFC allows long-term real-time, spatial, and temporal studies possible. Given the possibilities of simultaneous employment of different fluorescent protein halves within a single cell, BiFC can provide insight into PPIs of several machine components and substrates at the same time. In the recent past, several studies have been performed to understand PPIs behind the functioning of the secretion systems and their effector proteins [15–17]. In recent times, improved versions of BiFC have evolved to help identify protein–protein interactions in varied environments and diverse systems [8, 9, 16, 18–30]. However, though several reporter proteins such as ubiquitin, β-galactosidase, and dihydrofolate reductase have also be fragmented and then assembled back

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through interacting partners, reconstitution of fluorescence with fluorescent fragments eliminates requirement of additional stains and reagents, stoichiometric protein level constraints to visualize protein complexes, and limitations on real-time monitoring of the PPIs. Despite several advantages, under some conditions and in a few experimental model systems, without being fused to interacting partners, the nonfluorescent halves per se may intrinsically come together and fluoresce. Therefore, before setting up several BiFC assays, we suggest investigators to always test initial experiments in their bacterial environment of their choice, with a couple of fluorescent proteins’ halves for identifying the specificity of fluorescence complementation and then move forward. Given the introduction of various novel microscopes and their technology in the last decade, we believe that designing a well-thought-of BiFC experiment to understand PPIs is the only limitation. 1.1.1 Cytology-Based Two Hybrid

2

In contrast to BiFC, C2H primarily involves targeting of interacting protein partners by cell division proteins such as DivIVA (from Bacillus subtilis) or FtsZ (from Escherichia coli) to their native localizing sites, i.e., poles and midcell, respectively. Thus, when either of the cell division proteins fused to a bait protein target the interacting GFP/YFP-fused prey protein to midcell/poles, the protein partners in question are said to interact [4]. While BiFC efficiently establishes interactions between soluble machine components [3, 6], C2H helps explore interactions between soluble and membrane-associated machine components of any secretion system [3, 4, 7, 31]. Recent advances in BiFC also can help detect such interactions as well determine membrane proteins topology [13]. Since C2H involves targeting of partner proteins to midcell and/or poles, this assay works best when prey and bait proteins per se do not exhibit similar localization patterns. FtsZ fused to a bait protein can sometimes lead to cell filamentation especially when the fusion protein dominates over native FtsZ during cell division. Given the broad conservation of FtsZ and DivIVA across bacteria, this C2H-based screens should ideally work even in phylogenetically diverse species including extremophilic bacteria and to some extent in Archaea [4, 32].

Materials

2.1 Bimolecular Fluorescence Complementation

1. Plasmid vectors (either regular or Gateway based) for expression of potential interacting partners as fusions to nonfluorescing/nonfunctional halves. The plasmids must be of (i) different incompatibility groups, (ii) different antibiotic markers, (iii) similar copy numbers, and (iv) preferably harbor

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identical/similar promoter(s) for comparative expression levels. 2. DNA encoding either full length or N- and C-terminal nonfluorescing halves of the fluorescent protein under consideration. [Also refer to Table 2 of [33] for fragment lengths of each half of fluorescent protein(s) under consideration.] 3. DNA encoding proteins of interest, i.e., the known or to be tested potential interacting partners. 4. Appropriate cloning primers (with required restriction sites designed into them) for PCR-based amplification followed by directed cloning of the required halves and interacting proteins. Alternatively, Gateway-based pDONR vectors could be used (commercially available with Thermo Fisher Scientific, USA) for cloning and then moving the potential interacting proteins into appropriate pDEStination vectors harboring the two nonfluorescing/nonfunctional halves. 5. DNA encoding mutated proteins of interest or site-directed mutagenesis kit for generating mutant proteins—to test that the interaction of protein partners (of interest/under study) is indeed driving nonfluorescent halves to interact. 6. Competent cells of E. coli and other bacterial systems under consideration (if any). If using Gateway technology, use E. coli DH5α (or equivalent) for selection and E. coli DB3.1 (or superior equivalents such as One Shot® ccdB Survival™ 2 T1R—refer to Thermo Fisher Scientific Gateway cloning kit) for cloning and maintaining pDONR and pDEStination vectors. 7. Appropriate growth media for in vitro growing of bacteria under consideration. 8. Additional reagents to confirm fusions by immunoblotting. 9. Electroporator (for electroporating constructs into competent cells) or a water bath or dry heat block (for heat shock—for transformation of constructs into chemical-based competent cells). 10. Ten shaking incubator. 11. Fluorescence microscope equipped with 20× to 100× objectives, a 100× oil immersion phase-contrast objective, a CCD camera, appropriate filters to help visualize fluorescent proteins [34], and accompanying software for image captures, image analysis, and instrument control. 2.2 Cytology-Based Two Hybrid

1. Plasmid vectors (either regular or Gateway based) for expression of potential interacting partners as fusions to either of the cell division protein or to the fluorescent reporter. The plasmids must be again of (i) different incompatibility groups,

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(ii) different antibiotic markers, (iii) similar copy numbers, and (iv) harbor an identical/similar promoter(s) for comparative expression levels. 2. DNA encoding fluorescent protein and cell division proteins. 3. DNA encoding proteins of interest, i.e., the interacting partners under study. 4. Appropriate cloning primers (with required restriction sites designed into them) for PCR-based amplification followed by directed cloning of the interacting proteins, cell division proteins, and fluorescent reporters. Alternatively, Gateway-based pDONR vectors could be used for cloning and then moving the potential interacting proteins into appropriate pDEStination vectors harboring the fluorescing reporter or the cell division proteins. 5. DNA encoding mutated proteins of interest or site-directed mutagenesis kit for generating mutant proteins—to test that indeed the interaction of protein partners is driving the fluorescent reporter to midcell/poles in the cells. 6. Competent cells of E. coli and other bacterial systems under consideration (if any). If using Gateway technology, use E. coli DH5α for selection and E. coli DB3.1 (or superior equivalents such as One Shot® ccdB Survival™ 2 T1R—refer to Thermo Fisher Scientific Gateway cloning kit) for cloning and maintaining pDONR and pDEStination vectors. 7. Appropriate growth media for in vitro growing of bacteria under consideration. 8. Additional reagents to confirm fusions by immunoblotting. 9. Electroporator (for electroporating constructs into competent cells) or a water bath or dry heat block (for heat shock—for transformation of constructs into chemical-based competent cells). 10. Shaking incubator. 11. Fluorescence microscope equipped with 20X to 100X objectives, a 100X oil immersion phase-contrast objective, a CCD camera, appropriate filters to help visualize fluorescent proteins [13], and accompanying software for image captures, image analysis, and instrument control.

3

Methods Until mentioned, all steps could be performed at room temperature.

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3.1 Bimolecular Fluorescence Complementation

1. Select fluorescent protein of interest, it’s halves, and appropriate fusion sites (see Notes 1 and 2). 2. Select appropriate controls (see Note 3). 3. Amplify the required DNA fragment by PCR (see Note 4). 4. Use standard restriction–ligation procedures to clone the PCR amplicons into appropriate expression vectors (see Note 5). 5. Transform the ligation mixture in electro- or chemically competent bacteria (see Notes 6 and 7). 6. Inoculate four to five colonies from freshly transformed bacteria separately into required growth media and grown at required conditions to approximately an OD (optical density at A600nm) of 0.1. 7. Induce the expression of the proteins under study with appropriate inducers for required duration (see Note 8). 8. Wash few hundred cells with fresh media (to stop induction). 9. Observe cells under fluorescent microscope (see Notes 9–11). 10. Perform image analyses using ImageJ or commercially available software that comes along with fluorescent microscopes of most companies (see Note 12).

3.2 Cytology-Based Two Hybrid

1. Select fluorescent protein, it’s halves, and appropriate fusion sites (see Notes 13 and 14). 2. Select appropriate controls (see Note 15). 3. Amplify the required DNA fragment by PCR (see Note 4). 4. Use standard restriction–ligation procedures to clone the PCR amplicons into appropriate expression vectors (see Note 5). 5. Transform the ligation mixture in electro- or chemically competent bacteria (see Notes 6 and 7). 6. Inoculate four to five colonies from freshly transformed (important for superior fluorescent intensity) bacteria separately into required growth media and grown at required conditions to approximately an OD (optical density at A600nm) of 0.1. 7. Induce the expression of the proteins under study with appropriate inducers for required duration (see Note 8). 8. Wash few thousand cells with fresh media (to stop induction). 9. Observe at least 300 cells under fluorescent microscope (see Notes 9–11). 10. Perform image analyses using ImageJ or commercially available software that comes along with fluorescent microscopes of most companies (see Note 16).

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Notes 1. For interaction studies in Agrobacterium, we have used GFP halves, N’GFP (1–154 amino acid residues) and GFP’C (153– end) [3, 6]. YFP and CFP halves can also be exploited [33]. While generating fusion proteins, no linkers/spacers were used while connecting protein partners to nonfluorescing halves. However, in the recent past, linkers/spacers of 5–17 amino acid residues have been used for better success in eukaryotic model systems [33]. So, it is worth trying with and without spacers as few protein partners may totally alter the secondary structure of the nonfluorescent halves making the assay incompatible. 2. The success of BiFC also largely relies on to which end (N- or C-terminus) of the protein partners the nonfluorescent fragments get fused. In our studies, we have primarily fused the N-terminus end of N’GFP half to the C-terminal end of a protein partner and the C-terminus end of the C’GFP half to the N-terminal end of another protein partner. However, it is important to evaluate other fusion ends (eight combinations in total—either ends of the partner proteins and nonfluorescing halves) to narrow down on suitable fusion ends. It is also important to evaluate that the fused protein partners do not show altered localization patterns (e.g., cytosolic to membrane bound and vice versa). All fused protein partners need to be evaluated for their expression kinetics and accumulation by western analysis. 3. To make sure that BiFC works in model system(s) under study, appropriate controls are to be first evaluated: (i) Clone and express nonfluorescing halves alone (under the same/similar promoter used for experimental study)—to confirm that the halves per se do not interact. If they do, then switch to test several other fluorescent protein halves [33]. (ii) Clone and express noninteracting protein partners (from established studies) fused to same nonfluorescing halves to make sure that the noninteracting partners do not bring the nonfluorescing halves together to exhibit fluorescence. (iii) Clone and express nonfluorescing halves fused to protein partners (under study) mutants (point mutants, at their site of interaction)—this also helps confirm/evaluate various residues at interaction site(s). However, if the interacting protein pair is fairly uninvestigated or their site of interaction undetermined, this negative control could well be skipped. Alternatively, BiFC could be performed with these constructs to determine the site of interaction(s).

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(iv) Clone and express either protein partners fused to a nonfluorescing half together with another construct expressing the other nonfluorescing half alone—eliminates spurious interactions driving fluorescence. (v) Clone and express positively interacting protein partners (from established studies) fused to same nonfluorescing halves to make sure that the assay works under the conditions being used. 4. Any high-fidelity proofreading polymerase could be used for PCR-based amplification of the required DNA fragments. 5. Standard cloning techniques could be applied to move fragments of interest into expression vectors chosen for the study. For Gateway-based cloning, the required kits are available with Life Technologies (now associated with Thermo Fisher Scientific). 6. Commercially available electro- or chemically competent bacteria or those generated by standard methods can be employed for transformations. We generally transform with 10–25 ng of plasmid DNA for obtaining several hundred colonies. 7. Better fluorescence levels are usually obtained with freshly transformed cells than when using cells stored away at 4 °C or stocked at -80 °C. 8. Proper gene induction needs standardization. It depends upon the model system under study, the type of inducer, the copy number of plasmids, as well as toxicity issues. 9. Generally, good images can be obtained with observing cells under 100× oil immersion phase-contrast objective. 10. As a standard practice, it is recommended to perform immunoblotting to check the level of expression and monitor the fusion proteins in the bacterial cultures under study. 11. If nonfluorescing halves of either YFP or CFP (BiFC) or YFP and CFP (C2H) are used, the cells might need to be briefly incubated at 30 °C for the fluorescent proteins to mature and generate fluorescence of high intensity. 12. If per se the two nonfluorescing halves interact in the model system under results study, alternative fluorescent protein halves have to be evaluated. If point mutations in interaction sites of protein partners do not abolish fluorescence, then complementation of the nonfluorescing halves could be nonspecific. At this juncture, it might be important to evaluate if higher protein levels affect the outcome. If so, the concentration and/or time of induction could be modified. If not, an alternate promoter could be evaluated. Otherwise, alternates to BiFC have to be explored (e.g., C2H). However, after

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evaluating all controls (see Note 3), fluorescence is observed only when both protein partners’ fusions are available, and then, the two proteins under study are said to interact. 13. For C2H-based protein–protein interaction studies in Agrobacterium and E. coli, for retargeting, we have used two cell division proteins DivIVA (from B. subtilis) and FtsZ (from E. coli) and for fluorescence, GFP [3, 4, 6]. YFP and CFP can also be explored as alternate fluorescent proteins. While generating fusion proteins, no linkers/spacers were used and seem unnecessary. However, for success of C2H, it is important to evaluate all possible fusion sites, i.e., fusion of cell division proteins and fluorescent protein to either ends of protein partners under study. 14. We have fused C-terminal end of FtsZ or DivIVA to the N-terminal end of the protein partners [4, 6]. As a standard practice, it is recommended to routinely perform immunoblotting to evaluate levels of expression and monitor protein fusion stability in the bacterial cultures under study. 15. To make sure C2H works in model system(s) under study, appropriate controls are to be first evaluated: (i) Clone and express interacting partners alone to confirm that neither of them localizes to midcell and poles. If one of them does localize to these locations, while it must be fused to either of the cell division proteins, the other partner in question must be fused to the fluorescent protein under consideration. If, however, both potential interacting partners localize to the midcell/poles, C2H cannot be utilized as the method of study for interactions. (ii) Clone and express GFP/YFP/CFP fused protein partners (under study) to confirm that neither fusions localize to midcell/poles. If any fusions localize to poles/midcell, it is important to evaluate if the localization is natural or merely an artifact of inclusion bodies. Thus, expression and localization could be evaluated with alternate promoter(s), altering concentrations of inducer, and modifying inducing time or temperature. (iii) Clone and express fluorescent protein/cell division proteins fused to protein partners (under study) mutants (point mutants, at their site of interaction)—this also helps confirm/evaluate various residues at interaction site(s). However, if the interacting protein pair is fairly uninvestigated or their site of interaction undetermined, this negative control could well be skipped. (iv) Clone and express either protein partners fused to a fluorescent protein together with another construct expressing the either cell division proteins alone—eliminates spurious interactions driving retargeting.

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(v) Clone and express positively interacting protein partners (from established studies) fused to retargeting cell division proteins/fluorescent protein to make sure that the assay works under the conditions being used. 16. If per se, in the model system under study, the two protein partners localize to the midcell/poles, C2H cannot be employed. If point mutations in interaction sites of protein partners do not abolish retargeting, then localization could be nonspecific or a consequence of inclusion bodies. At this juncture, it might be important to evaluate if higher protein levels affect the outcome. If so, the concentration and/or time of induction could be modified. If not, an alternate promoter could be evaluated. Otherwise, alternates to C2H have be explored (e.g., BiFC). However, after evaluating all controls (see Note 15), fluorescence is observed only at midcell/poles, and then, the two proteins under study are said to interact.

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Chapter 17 Bacterial One- and Two-Hybrid Assays to Monitor Transmembrane Helix Interactions Abdelrahim Zoued, Jean-Pierre Duneau, and Eric Cascales Abstract In transenvelope multiprotein machines such as bacterial secretion systems, protein–protein interactions not only occur between soluble domains but might also be mediated by helix–helix contacts in the inner membrane. Several assays have been therefore developed to test homotypic and heterotypic interactions between transmembrane α-helices in their native membrane environment. Here, we provide detailed protocols for two genetic assays, TOXCAT and GALLEX, which are based on the reconstitution of dimeric regulators allowing the control of expression of reporter genes. Key words Secretion system, Membrane protein, Protein–protein interaction, Transmembrane segment, Helix–helix interaction, One hybrid, Two hybrid, cI repressor, TOXCAT, GALLEX

1

Introduction The proper assembly of multiprotein complexes such as bacterial secretion systems requires specific interaction between the different subunits. While most of the interactions involve contacts between soluble domains of these subunits, the transmembrane helices (TMH) of inner membrane proteins are also key players in membrane protein complex formation. For example, the type II secretion (T2SS) associated GspC, GspL, and GspM proteins interact with each other via their TMH [1]. A similar situation has been evidenced for the type VI secretion system (T6SS) TssLM–TagL complex [2–6]. The TMH could be involved in homotypic interaction, i.e., participate to the formation of dimers such as the type IV secretion (T4SS)- and T6SS-associated VirB10 and TssL inner membrane proteins [4, 5, 7] or in heterotypic interactions with other subunits [1–3]. Monitoring interactions within TMH is not an easy task, as mutations within, deletion of, or swapping of the TMH could interfere with the conformation or position of the soluble domains and therefore may indirectly affect protein–protein

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_17, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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interactions. Genetic one- or two-hybrid approaches based on fusions to transcriptional reporters, such as the λcI repressor, TOXCAT, GALLEX, and BACTH assays, have been thus developed. While cI-repressor and TOXCAT can only be used for testing homotypic interactions, the GALLEX and BACTH approaches can also be used to monitor interactions between different TMH. This chapter provides protocols to monitor homotypic and heterotypic transmembrane helix–helix interactions using TOXCAT and GALLEX. We refer the reader to excellent reviews summarizing the forces exerted to catalyze transmembrane helix folding and insertion as well as the different methods to analyze transmembrane helix interactions in bacteria [8, 9]. 1.1 Monitoring TMH Homotypic Interactions

Methods to test homodimerization of transmembrane helices, such as the λ cI repressor and TOXCAT assays, are based on one-hybrid reporter fusion. The cI transcriptional regulator represses the expression of early promoters of the bacteriophage λ genome. Repression only occurs when cI dimerizes, a behavior conferred by the C-terminal domain. The λ cI repressor assay is therefore based on the reconstitution of a dimeric λ cI repressor by two interacting fragments [10– 12]. The construct consists to a fusion between the monomeric N-terminal DNA-binding domain of λ cI (called cI’) with the TMH (Fig. 1a). TMH-mediated cI’ dimerization induces binding of cI to its operator sequence allowing repression of phage λ early genes, hence conferring protection against superinfection by phage λ (Fig. 1a). The cI repressor assay has been successfully used to demonstrate that the T2SS XcpR and T4SS VirB4 and VirB11 proteins oligomerize [13–15]. The TOXCAT assay is based on the characteristics of the Vibrio cholerae ToxR regulator: A strict dimerization-dependent transcriptional activator constituted of an N-terminal helix-turn-helix DNA binding domain and a C-terminal dimerization domain. The construct consists of a fusion in which the TMH is inserted between the monomeric ToxR DNA-binding domain and the MalE periplasmic protein (Fig. 1b). By supporting growth on maltoseminimal media, MalE allows to check that the TMH is properly inserted and oriented. TMH-mediated ToxR dimerization induces binding of ToxR on its operator sequence allowing transcription of a reporter gene. In the initial ToxR system, the reporter gene is lacZ [16], while the TOXCAT and TOXGREEN assays use the cat and gfp genes, respectively [16, 18] (Fig. 1b). Hence, dimerization of the TMH could be then assessed by measuring β-galactosidase activity (ToxR), chloramphenicol acetyltransferase (resistance to chloramphenicol) activity, and GFP fluorescence, respectively [16–19]. The TOXCAT assay has been successfully used to provide evidence that the TMH of the T4SS VirB10 and T6SS TssL

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Fig. 1 Schematic representation of the assays for monitoring TMH homotypic interactions. (a) cI repressor assay. The TMH of interest (orange) is fused to the cI DNA-binding domain (green). Reconstitution of the cI dimer results in the repression of early phage λ genes (blue). Repression of phage λ genes confers protection against phage λ infection. (b) ToxR, TOXCAT, and TOXGREEN assays. The TMH of interest (orange) is fused between the Vibrio cholerae ToxR DNA-binding domain (red) and the MalE protein (blue). Reconstitution of the ToxR dimer results in the expression of the reporter genes (blue, lacZ, cat, and gfp for the ToxR, TOXCAT, and TOXGREEN assays, respectively)

subunits oligomerizes [5, 7]. Further improvements of the ToxR and TOXCAT assays have been published [20–22]. 1.2 Monitoring TMH Heterotypic Interactions

Methods to test heterodimerization of transmembrane helices, such as the GALLEX and BACTH assays, are based on two-hybrid reporter fusion. The GALLEX assay is based on the reconstitution of a dimeric LexA transcriptional repressor by two interacting TMH. The construct consists of a fusion in which each TMH is inserted between the monomeric LexA N-terminal DNA-binding domain and the MalE periplasmic protein. The elegant improvement is that one of the two TMHs is fused to the wild-type LexA N-terminal domain, whereas the second TMH is fused to a LexA N-terminal domain variant bearing a mutation in the DNA-binding motif (LexA408), allowing recognition of a different operator sequence (op408). Formation of helix heterodimers induces binding of LexA on a dual operator sequence (opWT/op408), allowing repression of a reporter gene [23–25] (Fig. 2a). The GALLEX assay has been successfully used to provide information on TMHs organization within the type IX secretion system GldLM complex [26].

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Fig. 2 Schematic representation of the assays for monitoring TMH heterotypic interactions. (a) GALLEX assay. The first TMH of interest (orange) is fused between the wild-type LexA DNA-binding domain (WT LexA) and MalE, whereas the second TMH (blue) is fused between the LexA408 variant (LexA 408) and MalE. Reconstitution of the LexAWT/LexA408 dimer results in the repression of the reporter gene (blue). (b) BACTH assays. The first TMH of interest (orange) is fused to the T18 domain of the B. pertussis adenylate cyclase, whereas the second TMH (blue) is fused to the T25 of adenylate cyclase. Reconstitution of the T18/T25 adenylate cyclase results in the production of cAMP. Binding of cAMP to the catabolite activator protein (CAP) induces the expression of the reporter gene (blue)

The bacterial two-hybrid assay (BACTH) is based on the reconstitution of the adenylate cyclase activity conferred by the T18 and T25 domains of the Bordetella pertussis Cya protein [27– 29] (Fig. 2b). Widely used for testing interactions between soluble domains or proteins in multiprotein complexes such as the divisome or secretion systems [30–38], it has only been scarcely used for the study of transmembrane helix–helix interactions [39– 41]. More recently, a BACTH assay has been specifically developed for the study of membrane proteins and has been used to probe interactions between Chlamydia trachomatis inclusion membrane proteins [42]. A detailed protocol for the bacterial two-hybrid assay by Ladant is described in Chap. 13. In this chapter, we therefore provide protocols for the TOXCAT and GALLEX assays. Additional assays, such as BlaTM, are also powerful methodologies to monitoring homo- and heterotypic transmembrane interactions, including antiparallel interactions [43, 44].

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Material

2.1 Monitoring TMH Homotypic Interactions: The TOXCAT Assay

1. pcckan vector [17] (see Note 1). 2. Escherichia coli NT326 or MM39 bacterial strains [17] (see Note 2). 3. Lysogeny broth (LB) medium: Dissolve 10 g of tryptone, 5 g of yeast extracts, and 10 g of NaCl in 1 L of distilled water. Autoclave for 15 min at 121 °C. For LB agar plates, add 15 g of bacto-agar prior to autoclave. 4. M9–maltose medium: Dissolve 0.6 g of Na2HPO4•12H20, 0.3 g of KH2PO4•H20, 50 mg of NaCl, 100 mg of NH4Cl, and 1.5 g of bacto-agar in 90 mL of distilled water. Autoclave. Add 100 mg of casamino acids, 400 mg of maltose, 25 mg of MgSO4•7H20, and 1 mg of CaCl2. 5. Ampicillin stock solution (250×): 25 mg/mL ampicillin. Dissolve 250 mg of ampicillin in 10 mL of distilled water. Filter to sterilize. Store at 4 °C. 6. Chloramphenicol stock solution: Dissolve 90 mg of chloramphenicol in 1 mL of absolute ethanol. Store at -20 °C. 7. 2.5 mM chloramphenicol solution: Dissolve 8.1 mg of chloramphenicol in 10 mL of ethanol. 8. Sodium dodecyl sulfate (SDS)–polyacrylamide gel electrophoresis (PAGE) loading buffer: 60 mM Tris–HCl, pH 6.8, 2% SDS, 10% glycerol, 5% β-mercaptoethanol, 0.01% bromophenol blue. 9. Lysis buffer: 25 mM Tris–HCl, 2 mM EDTA, pH 8.0. Dissolve 303 mg of Tris(hydroxymethyl) aminomethane and 58 mg of ethylene diamine tetra acetic acid (EDTA, disodium salt) in 100 mL of sterile distilled water. Adjust pH to 8.0. 10. Reaction buffer: 100 mM Tris–HCl, pH 7.8, 0.1 mM acetylCoA, 0.4 mg/mL 5,5′-dithiobis-(2 nitrobenzoic acid) (dTNB). Dissolve 121 mg of Tris, 0.81 mg of acetyl-CoA, and 4 mg of dTNB in 10 mL of sterile distilled water. Adjust the pH to 7.8 with HCl. 11. 10-mm filter paper disk. 12. 96-well microplates. 13. Anti-maltose binding protein (MBP) antibodies for MalE immunodetection. 14. Incubator. 15. Spectrophotometer. 16. Benchtop centrifuge. 17. Water bath at 96 °C.

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18. Mini-gel caster system and SDS–PAGE apparatus. 19. Protein blotting apparatus. 20. Sonifier. 21. Microplate reader. 2.2 Monitoring TMH Heterotypic Interactions: The GALLEX Assay

1. pAML100, pAML108, vectors [23].

pBML100,

and

pBML108

2. Escherichia coli NT326 or MM39 bacterial cells [17] (see Note 2). 3. Escherichia coli SU202 bacterial strain [23, 45] (see Note 3). 4. LB medium: see Subheading 2.1. 5. M9–maltose medium: see Subheading 2.1. 6. Ampicillin stock solution (250×): see Subheading 2.1. 7. Chloramphenicol stock solution (1000×): 40 mg/mL chloramphenicol. Dissolve 400 mg of chloramphenicol in 10 mL of ethanol. Filter to sterilize. Store at 4 °C. 8. Isopropyl-β-D-thiogalactopyranoside (IPTG) stock solution (500×). 0.1 M IPTG. Dissolve 238 mg of IPTG in 10 mL of sterile distilled water. Filter to sterilize. Store at 4 °C. 9. X-Gal stock solution (1000×): 40 mg/mL X-Gal. Dissolve 40 mg of 5-Bromo-4-chloro-3-indolyl β-D-galactopyranoside (X-Gal) in 1 mL of dimethylformamide. Prepare fresh and do not store. 10. 0.1% SDS: Dissolve 50 mg of sodium dodecyl sulfate (SDS) in 50 mL of distilled water. 11. Chloroform. 12. Ortho-nitrophenyl-β-D-galactopyranoside (ONPG) stock solution. 4 mg/mL ONPG: Dissolve 20 mg of ONPG in 5 mL of buffer Z. 13. SDS–PAGE loading buffer: 60 mM Tris–HCl, pH 6.8, 2% SDS, 10% glycerol, 5% β-mercaptoethanol, 0.01% bromophenol blue. 14. Buffer Z: Dissolve 2.15 g of Na2HPO4•12 H2O, 0.29 g of Na2HPO4•H20, 75 mg of KCl and 25 mg of MgSO4•7H20 in 100 mL of distilled water. Adjust the pH to 7.0. Add 270 μL of β-mercaptoethanol. Prepare fresh and do not store. 15. Anti-MBP antibodies for MalE immunodetection. 16. 96-well microplates. 17. Incubator. 18. Spectrophotometer. 19. Benchtop centrifuge.

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20. Water bath at 96 °C. 21. Mini-gel caster system and SDS–PAGE apparatus. 22. Protein blotting apparatus. 23. Microplate reader.

3

Methods

3.1 Monitoring TMH Homotypic Interactions: The TOXCAT Assay

1. Clone the DNA fragment corresponding to the TMH to be studied into the pcckan vector [17] to yield a plasmid producing the ToxR’–TMH–MalE fusion protein. Before testing the homodimerization of the TMH, check that your fusion protein is properly produced (Steps 3–8) and inserted in the inner membrane (Steps 9–10). The dimerization of the TMH is then assessed by the disk diffusion assay (Steps 11–16) and quantified by measuring the chloramphenicol acetyltransferase activity (Steps 17–26). 2. Transform the empty pcckan vector and your pcckan construct into NT326 or MM39 E. coli [17] competent cells. Select on LB–ampicillin plates (see Note 1). 3. Pick a single colony of each transformation and grow cells in 20 mL of LB medium supplemented with ampicillin (100 μg/ mL) until an optical density at 600 nm (OD600) of 0.8. 4. Harvest 2 mL of cells by centrifugation at 4000× g for 5 min. 5. Discard the supernatants and resuspend the cell pellets into 20 μL of SDS–PAGE loading buffer. 6. Boil the samples for 10 min at 96 °C. 7. Separate proteins by SDS–PAGE and transfer onto nitrocellulose membrane using your favorite protocol. 8. Use western blotting to immunodetect your fusion protein using commercial anti-MalE (anti-MBP) antibodies. 9. Streak 20 μL of the bacterial culture obtained after Step 3 in Subheading 3.1 onto M9–maltose medium. 10. After incubation for 48 h at 37 °C, verify that your strain grew on M9–maltose medium. 11. Drop a 10-mm filter paper disk at the center of a LB–ampicillin plate (see Note 4). 12. Add 60 μL of the chloramphenicol stock solution (90 mg/mL) on the filter paper disk. 13. Incubate LB plates with chloramphenicol disks for 6 h at 37 °C. 14. Remove the disk.

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15. Spread 2 mL of the culture obtained at Step 3 in Subheading 3.1 on the LB–ampicillin plate to make a lawn. Eliminate the excess of culture. 16. After incubation for 16 h at 37 °C, measure the halo of chloramphenicol sensitivity (see Note 5). 17. Centrifuge 3 mL of the culture obtained at Step 3 in Subheading 3.1 at 4000× g for 5 min (in triplicate). 18. Discard the supernatant and resuspend the cell pellet in 500 μL of lysis buffer. Vortex. 19. Lyse the cells by sonication using a sonifier. 20. Clear the lysate by centrifugation at 10,000× g for 15 min. 21. In a 96-well microplate, mix 15 μL of the cleared lysate with 220 μL of reaction buffer. 22. Measure the absorbance at 412 nm (A412; see Note 6) and at 550 nm (A550; cell debris) every 20 s for 4 min using a microplate reader. 23. Inject 15 μL of 2.5 mM chloramphenicol in each well. 24. Measure the absorbance at 412 nm (see Note 6) and at 550 nm (cell debris) every 20 s for 10 min using a microplate reader. 25. Divide each A412 value by the corresponding A550 value and plot these values against time. 26. Calculate the chloramphenicol acetyltransferase activity based on the slope in the linear part of the curve (initial rate). 3.2 Monitoring TMH Heterotypic Interactions: The GALLEX Assay

1. Clone the DNA fragment corresponding to the first TMH to be studied (TMH1) into the pAML100 vector [23] to yield a pACYC184 derivative plasmid producing the LexAWT’– TMH1–MalE fusion protein. Clone the DNA fragment corresponding to the second TMH to be studied (TMH2) into the pBML108 vector [23] to yield a pBR322 derivative plasmid producing the LexA408’–TMH2–MalE fusion protein. Before testing the heterodimerization of the TMH, check that your fusion protein is properly produced (Steps 3–8) and inserted in the inner membrane (Steps 9–10). The dimerization of the TMH is then assessed on LB–X-Gal plates (Steps 11–14) and quantitated by measuring the β-galactosidase activity (Steps 15–22). 2. Transform the empty pAML100 and pBLM108 vectors as well as the pAML100–TMH1 and pBLM108–TMH2 constructs into NT326 or MM39 E. coli [17] competent cells. Select on LB plates supplemented with ampicillin (pBML100 derivatives) or chloramphenicol (pAML100 derivatives).

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3. Pick a single colony of each transformation and grow cells in 3 mL of LB medium supplemented with IPTG and ampicillin or chloramphenicol until OD600 of 0.8. 4. Harvest 2 mL of cells by centrifugation at 4000× g for 5 min. 5. Discard the supernatants and resuspend the cell pellets into 20 μL of SDS–PAGE loading buffer. 6. Boil the samples for 10 min at 96 °C. 7. Separate proteins by SDS–PAGE and transfer onto nitrocellulose membrane using your favorite protocol. 8. Use western blotting to immunodetect your fusion protein using commercial anti-MalE (anti-MBP) antibodies. 9. Streak 20 μL of the bacterial culture obtained after Step 3 in Subheading 3.2 onto M9–maltose medium. 10. After incubation for 48 h at 37 °C, verify that your strain grew on M9–maltose medium. 11. Co-transform the pAML100 and pAML100–TMH1 vectors in combination with the pBLM108 and pBLM108–TMH2 vectors into SU202 E. coli [23, 45] competent cells (see Note 7). Select on LB plates supplemented with ampicillin and chloramphenicol. 12. Pick a single colony of each transformation and grow cells in 3 mL of LB medium supplemented with IPTG, ampicillin, and chloramphenicol until an OD600 of 0.8. 13. Drop 15 μL of the bacterial culture obtained after Step 12 in Subheading 3.2 onto LB plates supplemented with IPTG, ampicillin, chloramphenicol, and X-Gal. 14. After 6, 14, and 24 h of incubation at 37 °C, observe the coloration of the spots. White spots correspond to strains with no β-galactosidase activity (i.e., interaction between the two TMHs), whereas blue spots correspond to strains with β-galactosidase activity (i.e., no interaction between the two TMH) (see Note 8). 15. Mix 200 μL of the bacterial culture obtained after Step 12 in Subheading 3.2 with 800 μL of buffer Z into a 1.5-mL Eppendorf tube. Vortex. 16. Add one drop of 0.1% SDS and two drops of chloroform to lyse cells. Vortex for 10 sec. 17. In a 96-well microplate, mix 50 μL of the cleared lysate with 150 μL of buffer Z. 18. Measure the absorbance at 420 nm (absorption wavelength of ortho-nitrophenol, the product of degradation of ONPG) and at 550 nm (A550; cell debris) every 30 s for 2 min using a microplate reader.

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19. Inject 40 μL of the ONPG solution in each well. 20. Measure the absorbance at 420 nm and at 550 nm every 30 s for 20 min using a microplate reader. 21. Divide each A420 value by the corresponding A550 value and plot these values against time. 22. Calculate the β-galactosidase activity based on the slope in the linear part of the curve (initial rate).

4

Notes 1. pcckan is a vector comprising the sequence corresponding to the ToxR N-terminal domain and that corresponding to MalE separated by a multiple cloning site, allowing insertion of the sequence corresponding to the TMH of interest. Positive and negative controls have been developed by Russ and Engelman corresponding to the wild-type and mutated TMH of the glycophorin A, respectively [17]. 2. NT326 and MM39 strains [17] do not produce the maltosebinding protein (MBP) and therefore could be used as reporters to verify the proper insertion of the ToxR’–TMH–MalE and LexA–TMH–MalE fusions. 3. Strain SU202 is a reporter for the GALLEX assay [23, 45]. It has a chromosomally integrated fragment corresponding to a hybrid operator sequence (opWT/op408) controlling the expression of the lacZ reporter gene. 4. Use three LB–ampicillin plates per strain to be tested. 5. The diameter of the halo reflects the ability of the strain to resist chloramphenicol and therefore is directly and inversely linked to the expression of the cat gene that is induced by the TMH dimerization. If the TMH dimerizes, the expression level of cat is high, and hence, the diameter of the halo is small. 6. The reaction catalyzed by the chloramphenicol acetyltransferase consists to the acetylation of the chloramphenicol and the release of free co-enzyme A. Co-enzyme A then reacts with the 5,5′-dithiobis-(2-nitrobenzoic acid), resulting in an increase of the absorbance at 412 nm. 7. You should obtain the combinations pAML100 + pBLM108, pAML100 + pBLM108–TMH2, pAML100– TMH1 + pBLM108, and pAML100–TMH1 + pBLM108– TMH2. 8. MacConkey/maltose could be used as reporter medium instead of LB–X-Gal plates. In case of use of MacConkey/ maltose plates, the coloration of the spots differs: Yellow spots correspond to strains with no β-galactosidase activity

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(i.e., interaction between the two TMHs), whereas red spots correspond to strains with β-galactosidase activity (i.e., no interaction between the two TMHs).

Acknowledgments Work in EC laboratory is supported by the Centre National de la Recherche Scientifique, the Aix-Marseille Universite´, and grants from the Agence Nationale de la Recherche (ANR-20-CE110011 and ANR-20-CE11-0017) and the Fondation BettencourtSchueller. References 1. Lallemand M, Login FH, Guschinskaya N, Pineau C, Effantin G, Robert X, Shevchik VE (2013) Dynamic interplay between the periplasmic and transmembrane domains of GspL and GspM in the type II secretion system. PLoS One 8:e79562 2. Ma LS, Lin JS, Lai EM (2009) An IcmF family protein, ImpLM, is an integral inner membrane protein interacting with ImpKL, and its walker a motif is required for type VI secretion system-mediated Hcp secretion in Agrobacterium tumefaciens. J Bacteriol 191:4316–4329 3. Aschtgen MS, Gavioli M, Dessen A, Lloube`s R, Cascales E (2010) The SciZ protein anchors the enteroaggregative Escherichia coli type VI secretion system to the cell wall. Mol Microbiol 75:886–899 4. Durand E, Zoued A, Spinelli S, Watson PJ, Aschtgen MS, Journet L, Cambillau C, Cascales E (2012) Structural characterization and oligomerization of the TssL protein, a component shared by bacterial type VI and type IVb secretion systems. J Biol Chem 287:14157– 14168 ˜ a AP, 5. Zoued A, Duneau JP, Durand E, Espan Journet L, Guerlesquin F, Cascales E (2018) Tryptophan-mediated dimerization of the TssL transmembrane anchor is required for type VI secretion system activity. J Mol Biol 430:987– 1003 6. Santin YG, Camy CE, Zoued A, Doan T, Aschtgen MS, Cascales E (2019) Role and recruitment of the TagL peptidoglycanbinding protein during type VI secretion system biogenesis. J Bacteriol 201:e00173– e00119 7. Garza I, Christie PJ (2013) A putative transmembrane leucine zipper of agrobacterium VirB10 is essential for T-pilus biogenesis but

not type IV secretion. J Bacteriol 195:3022– 3034 8. Schneider D, Finger C, Prodo¨hl A, Volkmer T (2007) From interactions of single transmembrane helices to folding of alpha-helical membrane proteins: analyzing transmembrane helix-helix interactions in bacteria. Curr Protein Pept Sci 8:45–61 9. Fink A, Sal-Man N, Gerber D, Shai Y (2012) Transmembrane domains interactions within the membrane milieu: principles, advances and challenges. Biochim Biophys Acta 1818: 974–983 10. Hu JC (1995) Repressor fusions as a tool to study protein-protein interactions. Structure 3: 431–433 11. Leeds JA, Beckwith J (1998) Lambda repressor N-terminal DNA-binding domain as an assay for protein transmembrane segment interactions in vivo. J Mol Biol 280:799–810 12. Leeds JA, Beckwith J (2000) A gene fusion method for assaying interactions of protein transmembrane segments in vivo. Methods Enzymol 2327:165–175 13. Turner LR, Olson JW, Lory S (1997) The XcpR protein of Pseudomonas aeruginosa dimerizes via its N-terminus. Mol Microbiol 26:877–887 14. Dang TA, Zhou XR, Graf B, Christie PJ (1999) Dimerization of the Agrobacterium tumefaciens VirB4 ATPase and the effect of ATP-binding cassette mutations on the assembly and function of the T-DNA transporter. Mol Microbiol 32:1239–1253 15. Rashkova S, Zhou XR, Chen J, Christie PJ (2000) Self-assembly of the Agrobacterium tumefaciens VirB11 traffic ATPase. J Bacteriol 182:4137–4145

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Chapter 18 Protein–Protein Interactions: Co-immunoprecipitation Jer-Sheng Lin, Jemal Ali, and Erh-Min Lai Abstract Proteins often do not function as single substances but rather as team players in a dynamic network. Growing evidences show that protein–protein interactions are crucial in many biological processes in living cells. Genetic (such as yeast two hybrid, Y2H) and biochemical (such as co-immunoprecipitation, co-IP) methods are the commonly used methods to identify the interacting proteins. Immunoprecipitation (IP), a method using a target protein-specific antibody in conjunction with Protein A/G affinity beads, is a powerful tool to identify the molecules interacting with specific proteins. Therefore, co-IP is considered to be one of the standard methods to identify and/or confirm the occurrence of the protein–protein interaction events in vivo. The co-IP experiments can identify proteins via direct or indirect interactions or in a protein complex. Here, we use two different co-Ip protocols as an example to describe the principle, procedure, and experimental problems of co-IP. First, we show the interaction of two Agrobacterium type VI secretion system (T6SS) sheath components TssB and TssC41, and secondly, we show the protocol we used for determining the interaction of an epitope-tagged T6SS effector, Tde1 expressed in Agrobacterium with endogenously expressing adaptor/chaperone protein Tap1. Key words Protein–protein interaction, Immunoprecipitation (IP), Co-immunoprecipitation (co-IP), Immobilization, Protein A/G Sepharose, Physical interaction

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Introduction The earliest concept of immunoprecipitation (IP) was used to trace protein turnover by pulse labeling of total proteins during translation using radioactive amino acids added in the cell culture [1, 2]. The use of antibodies for immunoprecipitation can cause the spontaneous precipitation of antigen–antibody complexes formed by interaction of certain polyclonal antibodies with their antigens. As a consequence, the antigen was purified from the protein mixture using a specific antibody immobilized on beads directly or precipitated by affinity beads conjugated by Protein A/G that binds the conserved region of antibody. Purified antigens (proteins) were then visualized by SDS–PAGE followed by autoradiography [3]. Co-IP adapts the concept of IP to identify

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_18, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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interacting partners and becomes one of the most popular methods for protein–protein interaction studies in recent years. In a typical experiment, co-IP consists of several steps including preparation of protein extract (usually a cell lysate), coupling a specific antibody to beads, purification of specific protein complexes, and analysis of the co-IP complexes (Fig. 1) [4]. The unbound proteins are washed away while the antibody, bait protein, and proteins associated to the bait are eluted. Purified protein complexes can then be identified by mass spectrometry or western blot analysis. Depending on the specificity and quality of antibody and experimental conditions, co-IP experiments may generate significant background noise due to nonspecific binding to the antibody or beads. Thus, negative controls of samples without the bait protein or antibody run in parallel are critical in identifying specific interacting proteins. The increased sensitivity of mass spectrometry instrumentation has also greatly reduced the quality and quantity of starting protein sample

Fig. 1 Schematic diagram of the principle of co-immunoprecipitation. Antigen-containing protein sample (usually a cell lysate), specific antibody, and affinity beads (usually Protein A/G, which can specifically bind to conserved region of antibody) are added sequentially for binding reaction. The affinity beads with bound proteins are collected by centrifugation. The supernatant containing unbound proteins is discarded and further washed away during washing steps. Antibody and antigen are eluted with a buffer that dissociates proteins from affinity beads. Purified protein complexes can be further used for immunoblot or other biochemical analysis

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Fig. 2 Co-immunoprecipitation analysis of TssB and TssC41 in A. tumefaciens. Total protein extracts isolated from A. tumefaciens wild-type strain C58 treated with DTBP cross-linker were solubilized by buffer containing 1% SDS and then diluted into Triton X-100-containing solution for IP. Coprecipitated proteins were identified by western blotting. Co-IP was also performed with antibody against RNA polymerase alpha subunit (RpoA) as a negative control. The proteins analyzed and sizes of molecular weight standards are indicated on the left and right, respectively, and with arrows when necessary. (I, input; IP, immunoprecipitation) (reproduced from [10], no permission is required for reuse of the content published from the Public Library of Science, PLOS)

required for successful protein identifications, which allows for a more complete and reliable map of interactions [5, 6]. Co-IP has been successfully used to study the interaction of proteins involved in bacterial type IV and type VI secretion systems (T4SS and T6SS) in Agrobacterium tumefaciens [7–13]. An example is presented in Fig. 2; we present a protocol for co-IP experiment adapted from “immunoprecipitation (IP) technical guide and protocols” [3] and “detection of protein–protein interactions by coprecipitation” [14] with minor modifications. Depending on the binding activity of partner proteins, a cross-linker protein can be added. Cross-linkers stabilize weaker and transient interactions, while strong interacting proteins do not require a cross-linking reagent. Here, in the first protocol, we used the cleavable and membrane permeable cross-linker dimethyl 3,3′-dithiobispropionimidate (DTBP) to cross-link interacting proteins before cell lysis in order to ensure the identification of both stable and weak interacting proteins of type VI secretion components, including the T6SS sheath components TssB and TssC41 in A. tumefaciens [10]. Coprecipitated proteins are further identified by western blotting. Using this protocol, we found that TssB and TssC41 were, respectively, coprecipitated with each other in A. tumefaciens (Fig. 2). In contrast, TssC41 and TssB were not precipitated by control antibody (Fig. 2). Together with the interacting data obtained by yeast two-hybrid, co-purification in E. coli, we concluded that TssB and TssC41 can interact strongly to form a protein complex [10]. In the second protocol, we detected the interaction of Tde1, a T6SS effector protein with its adapter/chaperon protein Tap1 [13, 15, 16]. Due to the tight interaction between Tde1 and Tap1,

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cross-linker was not added for co-IP. In addition, the Tde1 bait protein is tagged with hemagglutinin (HA) epitope. Thus, the protocol used here is based on the technical bulletin of Sigma-Aldrich using commercial anti-HA antibody with modifications [17].

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Materials

2.1 Materials for CoIP by Cross-Linking 2.1.1 Cross-Linking of the Bacterial Cells (See Note 1)

1. Bacterial cells (see Note 2). 2. Dimethyl 3,3′-dithiobispropionimidate (DTBP): 0.5 M (see Note 3). 3. Phosphate buffer: 20 mM sodium phosphate, pH 7.6, 20 mM sodium chloride. 4. 1 M Tris–HCl buffer, pH 7.6.

2.1.2 Preparation of Bacterial Cell Extracts (See Note 4)

1. TES buffer: 50 mM Tris–HCl, pH 6.8, 2 mM EDTA, 1% SDS. 2. NP1 buffer: 150 mM Tris–HCl, pH 8.0, 0.5 M sucrose, 10 mM EDTA. 3. Lysozyme (see Note 5). 4. Triton X-100. 5. Rotating wheel. 6. Protease inhibitor cocktail.

2.1.3 Protein Sample Preclearing

1. Protein A Sepharose™ CL4B (GE Healthcare Life Sciences) (see Note 6). 2. 2 mL microtube. 3. Rotating wheel.

2.1.4 Coupling of Antibodies to Protein A Sepharose Beads

1. Specific antibody and control antibody.

2.1.5 Purification and Isolation of Protein Complexes

1. NP1 buffer supplemented with 1% Triton X-100 (see Note 7).

2. Protein A Sepharose™ CL4B. 3. Rotating wheel.

2. NP1 buffer supplemented with 0.1% Triton X-100. 3. Elution buffer: 0.1 M glycine–HCl, pH 2.5. 4. 2× SDS sample buffer: 100 mM Tris–HCl, pH 6.8, 4% SDS, 20% glycerol, 5% 2-mercaptoethanol, 2 mM EDTA, 0.1 mg/ mL bromophenol blue (see Note 8).

2.1.6 TrueBlot for Protein Detection of Co-IP Complexes

1. Minigel caster system and SDS–PAGE apparatus. 2. Transblot apparatus for western blot transfer.

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3. Rabbit TrueBlot®: Anti-Rabbit IgG HRP (horseradish peroxidase) conjugated secondary antibody, which enables unhindered detection of molecules (eBioscience, Inc.). 2.2 Materials for CoIP Without CrossLinking

1. PBS buffer (10x stock): 1.37 M NaCl, 27 mM KCl, 100 mM Na2HPO4, 18 mM KH2PO4, pH 7.4. 2. Lysozyme (10 mg/mL stock) (see Note 2). 3. Protease inhibitor cocktail (10x stock) (cOmplete tablets, EDTA-free, EASYpack ReF. No. 4693132001) (see Note 3). 4. EZview™ Red Anti-HA Cat#E6779) (see Note 4).

Affinity

Gel

(Sigma–Aldrich,

5. Sonicator. 6. Rotating wheel. 7. Vortex mixer.

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Methods

3.1 Methods for CoIP by Cross-Linking

3.1.1 Cross-Linking of the Sample

All procedures are performed in a cold room or on ice unless indicated. For example, we perform the cross-linking and preclearing steps at room temperature. 1. Grow bacterial cells (e.g., A. tumefaciens) under appropriate culture conditions. 2. Centrifuge the bacterial cell culture at 6000 × g for 10 min at 4 °C; wash the cells by resuspending the cell pellet with 12 mL phosphate buffer followed by centrifugation at 6000 × g for 10 min at 4 °C. Repeat this washing step two times, then the cell pellet in the same buffer is adjusted to OD600 4 (see Note 9). 3. Add cross-linker DTBP in the cell suspension to a final concentration of 5 mM (see Note 10). 4. Incubate the mixture at room temperature for 45 min (see Note 11). 5. Quench the cross-linking reaction by adding Tris–HCl, pH 7.6 to a final concentration of 20 mM for 15 min (see Note 12). Collect the cells by centrifugation at 6000 × g for 10 min at 4 °C, and wash the cells twice by resuspending the cell pellet with 12 mL of 50 mM Tris–HCl, pH 7.6, followed by centrifugation at 6000 × g for 10 min at 4 °C before cell lysis (see Note 13).

3.1.2 Preparation of Bacterial Cell Extracts

1. Resuspend. 2. Incubate the cell resuspension for 30 min at 37 °C with shaking at 200 rpm.

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3. Add 18 mL of NP1 buffer supplemented with 1.5 mg/mL lysozyme (see Note 15), and incubate for 2 h on ice. 4. Incubate the mixture for 30 min at 37 °C with shaking at 200 rpm. 5. Add Triton X-100 to a final concentration of 4% and incubate for 20 min at room temperature with rotation (see Note 16). 6. Add protease inhibitor cocktail to the working concentration (1×) (see Note 17), and incubate for 15 min at 37 °C with shaking at 200 rpm. 7. Place the sample mixture for at least 3 h at 4 °C with rotation (see Note 18). 8. Add 64 mL of NP1 buffer to the mixture (see Note 19); the final concentration of SDS and Triton X-100 is about 0.05% and 1%, respectively. The insoluble material is removed by centrifugating twice 15 min at 14,000 × g. The resulting supernatant is the detergent-solubilized solution (see Note 20). 9. The choice of detergents and the appropriate concentration used for co-IP is according to the user’s manual of Protein A Sepharose. 3.1.3 Protein Sample Preclearing and Coupling of Antibody to Protein A/G Beads

The preclearing step will reduce the background noise caused by the adhesion of some protein components to the Protein A Sepharose: 1. For each 2 mL of the detergent-solubilized solution, add a 60-μL bed volume of Protein A Sepharose, and incubate for 60 min at room temperature with rotation (see Note 21). 2. Remove Protein A Sepharose with nonspecifically bound proteins by centrifugation for 5 min at 5000 × g at 4 °C (see Note 22). 3. After the preclearing step, the supernatant (protein sample) will serve as the starting material for co-IP. The supernatant (about 1.5 mL) is directly incubated with antibody with optimized titer (see Note 23) and Protein A Sepharose (about 60 μL) overnight at 4 °C with slow rotation (see Note 24).

3.1.4 Purification and Isolation of Protein Complexes

1. After overnight incubation, pellet the beads by centrifugation at 5000 × g at 4 °C. The supernatant is designated as the “flow through” for co-IP. 2. Wash the beads twice with 1 mL NP1 buffer supplemented with 1% Triton X-100 and once with 1 mL NP1 buffer supplemented with 0.1% Triton X-100 by centrifugation at 5000 × g at 4 °C. Discard each wash solution (supernatant) or collect them for SDS–PAGE analysis to examine the washing efficiency. Typically, repeat this washing step 4–8 times to remove

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nonspecific binding proteins. In general, the washing steps are carried out until no signal can be detected in the negative control. Then proceed to step 3 or step 4 to recover the co-IP complexes. 3. Sample-buffer elution: Add 100 μL of 2× SDS sample buffer to the microtube containing beads and place the tube at 96 °C for 20 min. After centrifugation at 10,000 × g at room temperature for 5 min, the supernatant is ready to use for western blot analysis (see Note 25). 4. The low-pH elution: Add 100 μL of elution buffer to the microtube containing beads and place the tube at room temperature with low-speed rotation for 20 min to elute the proteins. After centrifugation at 10,000 × g at room temperature for 5 min, the co-IP proteins should be eluted in supernatant (see Note 26). 3.1.5 TrueBlot for Protein Detection of Co-IP Complexes

3.2 Methods for CoIP Without CrossLinking 3.2.1 Bacterial Culture Collection

1. Analyze the samples by western blot. 2. When the antibody used for co-IP was generated from rabbit, we recommend to use the Rabbit TrueBlot® as secondary antibody with 5000 × dilution to minimize interfering signals caused by immunoglobulin heavy and light chains (see Note 27). All procedures are performed in a cold room or on ice.

1. Grow bacterial cells expressing a bait protein tagged with hemagglutinin (HA) (see Note 33) under appropriate culture conditions. 2. Centrifuge the bacterial cell culture at 10,000 × g for 5 min at 4 °C, wash by resuspending the pellet with 10 mL of 1× PBS buffer (see Note 28), and continue with the cell lysis (see Note 32).

3.2.2 Cell Lysis and Preparation of Bacterial Cell Extracts

1. Normalize the OD600 to 5 in 1 mL ice cold 1× PBS buffer supplemented with 1x proteinase inhibitor cocktail (see Note 30) and 1 mg/mL of lysozyme (see Note 29). 2. Swirl gently and incubate the cell resuspension for 20 min on ice. 3. Sonicate the bacterial suspension on ice at amplitude of 30 μ for 5 s. Invert and repeat the sonication four times while keeping the samples on ice for 15–20 s between two sonication steps to avoid overheating of the sample (see Note 32).

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4. Centrifuge at 16,000 × g for 10 min at 4 °C, and carefully transfer the soluble proteins from the supernatant to a new 1.5 mL tube. 5. Keep 100 μL of the sample supernatant for the input fraction and the remaining 900 μL to be used for the IP in the following steps. 3.2.3 Immunoprecipitation

1. Take 25 μL of EZview Red Anti-HA Agarose slurry (see Note 31) to a new 1.5 mL microcentrifuge tubes using a wide orifice pipette tip. 2. Equilibrate the beads in 1 mL of 1× PBS and mix gently by vortexing with the lowest possible speed. 3. Centrifuge at 8200 × g for 30 s and carefully discard the supernatant and resuspend it in 100 μL of PBS. 4. Transfer 900 μL of the soluble fraction to 100 μL equilibrated EZview Red Anti-HA Agarose. 5. Vortex briefly and incubate the mixture for 1 h on a slow-speed rotating wheel at 4 °C to allow binding of HA-tagged proteins with the anti-HA agarose. 6. After 1 h incubation, centrifuge resin-bound immune complexes for 30 s at 8200 × g at 4 °C and discard the supernatant. 7. Resuspend the bead pellet by gentle vortexing in 1 mL of ice cold 1× PBS. 8. Repeat the washing step two more times and remove the supernatant carefully. 9. Resuspend the resin-bound immune complexes and the input fraction in 100 μL of 2× SDS loading buffer by gentle vortexing, boil for 5 min, and analyze by SDS–PAGE/immunoblot analysis.

4

Notes 1. The purpose of cross-linking of the bacterial cells is to fix the protein interactions before cell lysis and detergent treatment, especially for those weak and dynamic interactions. 2. You must use fresh, live bacterial cells (without freezing) for cross-linking reaction. 3. Always freshly prepare DTBP before use. It is not feasible to dissolve DTBP directly in the chemical bottle by pipetting. Thus, it is easier to handle by weighing DTBP powder on weight paper and transfer the powder to a microtube, followed by adding metered volume of buffer to slowly dissolve the powder by vortex.

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4. Several methods can be used to prepare bacterial cell extracts (such as sonication or French press). Here, we use lysozyme/ detergent-solubilized method for our co-IP sample preparation [18]. 5. We recommend freshly preparing the lysozyme stock solution in NP1 buffer with the concentration of 1 M before use. 6. Both Protein A and Protein G can associate to rabbit serum with highly affinity [4]. Here, we chose Protein A Sepharose for our co-IP experiment. 7. Due to the viscous property of Triton X-100, please make sure to stir and mix the NP1 buffer with Triton X-100 well before use. We recommend preparing the 10% Triton X-100 stock solution prior to use. 8. 2-mercaptoethanol should be added freshly to 2 × sample buffer prior to use. 9. The excess concentration of bacterial cells will cause poor crosslink efficiency. 10. DTBP should be freshly prepared as 0.5 M stock with buffer, in which we use phosphate buffer as to be consistent with the cell suspension buffer. 11. Please gently mix the mixture once every 10 min. 12. Please directly add the 1 M Tris–HCl (pH 7.6) stock solution to the mixture until the final concentration of 20 mM, and then, mix well to stop the cross-linking reaction. 13. The cross-linked cells can be frozen at -80 °C until use. However, we recommend the experiments to be conducted within 2 weeks after storage. 14. The concentration of cross-linked cells can be reduced. However, if the concentration of bacterial cells is higher than OD 20, the efficiency of cell lysis will become poor. 15. Use NP1 buffer to dissolve lysozyme first before use. 16. Use rotating wheel for this step. 17. The protease inhibitor cocktail is 100 × stock. 18. Place the sample mixture on the rotating wheel in cold room. 19. After adding NP1 buffer to the mixture, carefully mix the mixture well by vortex. 20. The detergent-solubilized solution can be directly used for the co-IP experiment. 21. Generally, the preclearing efficiency is better when the incubation is performed under room temperature than in a cold room.

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22. Transfer the supernatant to a new 2 mL microtube. The treated protein sample can be directly used for the subsequent co-IP experiment. 23. The titer of antibody for co-IP is based on the reference of antibody titer used for western blotting. In general, we use five- to ten-fold higher concentration than the titer used for western blotting. 24. Place the sample mixture on the rotating wheel in cold room with gentle rotation (about 10–12 rpm). This is a critical step as excessive rotation speed will affect the binding efficiency of antibody and Protein A Sepharose. 25. Sample-buffer elution method is ideal for western blot analysis. 26. The low-pH elution is ideal for enzymatic or functional assays after the low pH is neutralized. In general, the efficiency of elution is lower by the low-pH elution method as compared with sample-buffer elution method. 27. Rabbit IgG TrueBlot® is a unique anti-rabbit IgG immunoblotting reagent (used as the secondary antibody). Rabbit IgG TrueBlot enables detection of immunoblotted target protein bands, with reduced interfering immunoprecipitating immunoglobulin heavy (55 kDa) and light (23 kDa) chains. The Rabbit TrueBlot®: Anti-Rabbit IgG HRP can be reused at least 3–5 times. 28. Prepare a 1× PBS solution from the 10× stock. 29. Freshly prepare 10 mg/mL of lysozyme stock solution in 1× PBS buffer and keep it on ice until use. 30. To make 10× stock solution of protease inhibitor cocktail, dissolve 1 tablet of the cOmplete protease inhibitor cocktail in a 5 mL of 1× PBS. 31. EZview™ Red Anti-HA Affinity Gel is a red-colored anti-HA agarose affinity gel where 2.0–2.4 mg anti-HA antibody is cross-linked per a 1 mL of packed gel. Mix the EZview Red Anti-HA Agarose before use by gentle vortexing. 32. Different lysis buffers (CelLytic™ M, CelLytic™ MT, or RIPA Buffer) [17] or cell lysis methods (such as sonication or French press) can be used to prepare bacterial cell extracts. Here, we use lysozyme/detergent-solubilization followed by sonication method for our co-IP sample preparation. 33. Depending on the expression level of the bait protein, the prey protein, and the antibody quality for the detection, the OD600 of starting cells or anti-HA bead amount can be varied. Accordingly, the method of preparation of bacterial cell extracts can be selected for efficient cell lysis.

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Acknowledgments This work was supported by research grant from the Taiwan National Science and Technology Council (104-2311-B-001-025 -MY3, 110-2311-B-001-032-MY3) to EM Lai. JS Lin was the recipient of postdoctoral fellowships from Academia Sinica. J Ali received the PhD student fellowship from Molecular and Biological Agricultural Sciences Program, Taiwan International Graduate Program, National Chung-Hsing University and Academia Sinica. References 1. Kessler SW (1975) Rapid isolation of antigens from cells with a staphylococcal protein A-antibody adsorbent: parameters of the interaction of antibody-antigen complexes with protein a. J Immunol 115:1617–1624 2. Kessler SW (1976) Cell membrane antigen isolation with the staphylococcal protein A-antibody adsorbent. J Immunol 117:1482– 1490 3. ThermoFisher (2009) Immunoprecipitation (IP) technical guide and protocols. https:// t o o l s . t h e r m o fi s h e r. c o m / c o n t e n t / s f s / brochures/TR0064-Immunoprecipitationguide.pdf 4. Lee C (2007) Coimmunoprecipitation assay. Methods Mol Biol 362:401–406. https://doi. org/10.1007/978-1-59745-257-1_31 5. Aebersold R, Mann M (2003) Mass spectrometry-based proteomics. Nature 422: 1 9 8 – 2 0 7 . h t t p s : // d o i . o r g / 1 0 . 1 0 3 8 / nature01511 6. Gevaert K, Vandekerckhove J (2000) Protein identification methods in proteomics. Electrophoresis 21:1145–1154 7. Atmakuri K, Cascales E, Christie PJ (2004) Energetic components VirD4, VirB11 and VirB4 mediate early DNA transfer reactions required for bacterial type IV secretion. Mol Microbiol 54:1199–1211 8. Atmakuri K, Cascales E, Burton OT, Banta LM, Christie PJ (2007) Agrobacterium ParA/ MinD-like VirC1 spatially coordinates early conjugative DNA transfer reactions. EMBO J 26:2540–2551. https://doi.org/10.1038/sj. emboj.7601696 9. Anderson LB, Hertzel AV, Das A (1996) Agrobacterium tumefaciens VirB7 and VirB9 form a disulfide-linked protein complex. Proc Natl Acad Sci U S A 93:8889–8894 10. Lin JS, Ma LS, Lai EM (2013) Systematic dissection of the Agrobacterium type VI secretion system reveals machinery and secreted

components for subcomplex formation. PLoS One 8:e67647 11. Ma LS, Narberhaus F, Lai EM (2012) IcmF family protein TssM exhibits ATPase activity and energizes type VI secretion. J Biol Chem 287:15610–15621. https://doi.org/10. 1074/jbc.M111.301630 12. Wang L, Lacroix B, Guo J, Citovsky V (2018) The Agrobacterium VirE2 effector interacts with multiple members of the Arabidopsis VIP1 protein family. Mol Plant Pathol 19: 1172–1183. https://doi.org/10.1111/mpp. 12595 13. Bondage DD, Lin JS, Ma LS, Kuo CH, Lai EM (2016) VgrG C terminus confers the type VI effector transport specificity and is required for binding with PAAR and adaptor-effector complex. Proc Natl Acad Sci U S A 113:E3931– E3940. https://doi.org/10.1073/pnas. 1600428113 14. Elion EA (2007) Detection of protein-protein interactions by coprecipitation. Curr Protoc Immunol. John E Coligan Editor, chapter 8. https://doi.org/10.1002/0471142727. im0807s76 15. Ali J, Yu M, Sung L-K, Cheung YW, Lai E-M (2023) A glycine zipper motif governs translocation of type VI secretion toxic effectors across the cytoplasmic membrane of target cells. EMBO Rep 24:e56849. https://doi.org/10. 15252/embr.202356849 16. Ma LS, Hachani A, Lin JS, Filloux A, Lai EM (2014) Agrobacterium tumefaciens deploys a superfamily of type VI secretion DNase effectors as weapons for interbacterial competition in planta. Cell Host Microbe 16:94–104. https://doi.org/10.1016/j.chom.2014. 06.002 17. Sigma-Aldrich (2022) EZview™ Red Anti-HA Affinity Gel technical bulletin.pdf. 18. Cascales E, Christie PJ (2004) Definition of a bacterial type IV secretion pathway for a DNA substrate. Science 304:1170–1173

Chapter 19 Protein–Protein Interaction: Tandem Affinity Purification in Bacteria Julie P. M. Viala and Emmanuelle Bouveret Abstract The discovery of protein–protein interaction networks can lead to the unveiling of protein complex (es) forming cellular machinerie(s) or reveal component proteins of a specific cellular pathway. Deciphering protein–protein interaction networks therefore contributes to a deeper understanding of how cells function. Here we describe the protocol to perform tandem affinity purification (TAP) in bacteria, which enables the identification of the partners of a bait protein under native conditions. This method consists in two sequential steps of affinity purification using two different tags. For that purpose, the bait protein is translationally fused to the TAP tag, which consists of a calmodulin-binding peptide (CBP) and two immunoglobulin G (IgG)-binding domains of Staphylococcus aureus protein A (ProtA) that are separated by the tobacco etch virus (TEV) protease cleavage site. After the first round of purification based on the binding of ProtA to IgG-coated beads, TEV protease cleavage releases CBP-tagged bait protein along with its partners for a second round of purification on calmodulin affinity resin and leaves behind protein contaminants bound to IgG. Creating the TAP-tag translational fusion at the chromosomal locus allows detection of protein interactions occurring in physiological conditions. Key words Protein–protein interaction, Protein complex, Affinity purification, Tandem affinity purification (TAP), Calmodulin-binding peptide (CBP), Protein A (ProtA), Tobacco etch virus (TEV), Escherichia coli, Salmonella

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Introduction At the end of the 1990s, mass spectrometry combined with genome sequencing rendered possible the rapid and systematic identification of all the proteins present in a purified sample. However, a protocol amenable to standardized and systematic purification of protein complexes without any prior knowledge was missing. In 1999, the laboratory of B. Se´raphin at the European Molecular Biology Laboratory in Heidelberg, Germany, proposed such a generic procedure for the identification of protein complexes in yeast [1]. This permitted the subsequent description of the full interactome of yeast [2, 3]. This method has since been used in a

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variety of organisms. We first described its use in bacteria [4], and soon after this, it was used to obtain the first interactome of Escherichia coli [5]. One general principle of the tandem affinity purification (TAP) method involves the use of two successive steps of affinity purification to lower as much as possible the amount of contaminants, together with an elution preserving the interactions (without significantly changing the buffer’s chemical properties) between these two steps. Specifically, the original TAP tag consists of two repeats of the immunoglobulin G (IgG)-binding domain of Protein A (Prot A) from Staphylococcus aureus and a calmodulin-binding peptide (CBP), separated by a TEV protease cleavage site (Fig. 1). However, it must be noted that any combination of affinity tags is potentially usable. Published examples are the GS-TAP (Protein G and Strep tag), the sequential peptide affinity (SPA) tag (CBP and 3Flag), the SF-TAP (Strep-tag II and Flag tag), and the HB tag (6Histidine and Biotin) (see [6] for specific references). The second general principle of the TAP procedure is to use physiological expression of the recombinant tagged protein. This needs to be adapted to each organism of interest. For E. coli and closely related bacteria, lambda Red-based recombination [7] combined with specific dedicated SPA and TAP cassettes [8] makes it very easy to introduce the tag at the 3′ extremity of the gene on the chromosome to obtain the physiological production of a recombinant protein tagged at its C-terminus (Fig. 2). If more convenient, however, TAP-tag translational fusion can also be expressed from a plasmid (Fig. 3). We present here the TAP protocol that has been successfully used in our institute to purify protein complexes of E. coli, Salmonella, and Bacillus subtilis [4, 9–11]. A detailed protocol for the SPA purification has been published before [12]. To isolate a protein complex by TAP, a strain producing a recombinant bait protein tagged with the TAP must be constructed first (Fig. 1, step 1). Then, a soluble extract is prepared from a sufficient volume of bacteria (about 500 mL). The complex is enriched by a first step of affinity chromatography on IgG beads (Fig. 1, step 5). After washes, TEV protease is added, which cleaves the specific site located between the CBP and ProtA domains, resulting in the elution of the specifically bound material (Fig. 1, step 6). This material is purified a second time by affinity of the CBP tag with calmodulin beads (Fig. 1, step 7). After washes, the purified complex is eluted by adding EGTA that chelates the calcium required for the CBP/calmodulin interaction (Fig. 1, step 8). The totality of the purified material is analyzed on sodium dodecyl sulfate (SDS)– polyacrylamide gel electrophoresis (PAGE). Bands detected by Coomassie blue or silver staining are cut from the gel and analyzed by mass spectrometry.

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Fig. 1 Guidelines for overall TAP procedure

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Fig. 2 Engineering of a C-terminal TAP-tag translational fusion at chromosomal locus. Scheme of the TAP-tag translational fusion at the chromosomal locus (a) and corresponding nucleotidic and protein TAP-tag sequences (b). CBP sequence is in clear purple; uppercases at the beginning of the nucleotidic CBP sequence correspond to the primer sequence mentioned in Subheading 3.1, TEV protease cleavage site is in yellow, and Protein A sequence is in dark purple with stop codon in red

Fig. 3 Engineering of a plasmidic N-terminal TAP-tag translational fusion. Map of plasmid pEB587 used to create a N-terminal TAP-tag translational fusion under the control of the PBAD arabinose-inducible promoter (a) and the corresponding nucleotidic and protein TAP-tag sequences (b). Protein A sequence is in dark purple, TEV protease cleavage site is in yellow, CBP sequence is in clear purple, the multicloning site is underlined, and restriction enzyme sites are indicated

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This is the basic TAP procedure. Note that the procedure can be amenable to adaptation or improvements depending on the specific needs. For example, the extensive washes and the duration of the procedure only allow for the recovery of relatively stable complexes. For the detection of more transient or unstable interactions, a cross-linking procedure can be applied before purification [13–15]. This might be helpful also for the purification of membrane complexes, where modifications have to be made in the protocol for solubilization of the membranes [3]. Finally, it is possible to play with the two tags to gain information on the organization of the complexes. Indeed, in some cases, one bait protein might participate in the formation of several types of complexes. To purify one specific type of complex, it is therefore possible to put the two tags on two distinct proteins that are both members of the desired type of complex (split tag method [9, 16]). Alternatively, it is possible to perform the subtraction method that consists in eliminating the unwanted complex (es) during the first purification step by leaving it, for example, on IgG beads, thanks to a partner protein of the bait that belongs to the unwanted complex and bears a noncleavable ProtA tag. The desired complex, made of untagged partner proteins, will elute with the bait after TEV protease cleavage [16, 17]. To our knowledge, the TAP procedure has not been used very much for the characterization of secretion systems in bacteria, certainly owing to the difficulty of working with integral envelope components. However, we used it successfully to reveal a protein– protein interaction involved in the posttranslational maturation of the translocon of Salmonella T3SS [10]. And the TAP procedure has proved to be powerful in identifying the target of effectors of Yersinia T3SS [18, 19], Legionella T4SS [14, 20] or Pseudomonas T6SS in eukaryotic host cells [21]. In addition, as mentioned earlier, it is amenable to several improvements that might make it possible to identify unsuspected partners of the secretion machineries in the bacterium.

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Materials

2.1 Engineering of a TAP-Tag Translational Fusion and Verification of Production of Hybrid Protein by Western Blot

1. Bacterial strain with translational fusion between protein of interest and TAP tag at chromosomal locus (see Note 1). 2. Alternatively, translational fusion between protein of interest and TAP tag on an appropriate plasmid (see Note 2). 3. Yeast extract and tryptone media (2YT): 16 g of yeast extract, 10 g of tryptone, 10 g of NaCl, make up to 1 L with distilled water. Autoclave and store at room temperature.

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4. Lysogeny broth (LB): 5 g of yeast extract, 10 g of Bacto Tryptone, 10 g of NaCl, make up to 1 L with distilled water. Autoclave and store at room temperature. 5. Minigel caster system and SDS-PAGE apparatus. 6. Transblot apparatus for western blot. 7. Peroxidase anti-peroxidase antibody (PAP) (Sigma). 2.2 Preparation of the Protein Extract

1. PBS: 8 g of NaCl, 0.2 g of KCl, 0.2 g of KH2PO4, 2.9 g of Na2HPO4, make up to 1 L with distilled water. Autoclave and store at room temperature. 2. 10% of Nonidet P-40 (NP-40 or Igepal): Mix 10 mL of NP-40 with 90 mL of distilled water, pass through a 0.2 μm filter, and store at room temperature (see Note 3). 3. Protein A binding buffer: 10 mM of Tris–HCl, pH 8, 150 mM of NaCl, 0.1% of NP-40. Approximately 50 mL will be required per sample per experiment. Prepare 500 mL containing 5 mL of 1 M Tris–HCl, pH 8, 15 mL of 5 M NaCl, 5 mL of 10% NP-40, and 475 mL of distilled water. Store at 4 °C. 4. 0.1 M phenylmethylsulfonyl fluoride (PMSF): Dissolve 87.1 mg of PMSF in 5 mL isopropanol. Prepare 1-mL aliquots and store at -20 °C (see Note 4). 5. Liquid nitrogen. 6. Sonicator, French press, or cell disruptor. 7. Centrifuge tubes and rotor compatible with spinning volumes of 250 mL and 50 mL, at approximately 5000× g and 10 ml at approximately 25,000× g.

2.3 Tandem Affinity Purification

1. IgG Sepharose 6 fast flow (GE Healthcare). 2. Protein A binding buffer: See Subheading 2.2. 3. 0.5 M EDTA (C10H14N2Na2O8. 2H2O): Dissolve 18.6 g of EDTA in 80 mL of distilled water, make up to 100 mL with distilled water once the pH has been adjusted to 8 with 10 N NaOH (see Note 5). Autoclave and store at room temperature. 4. TEV cleavage buffer: 10 mM of Tris–HCl, pH 8, 150 mM of NaCl, 0.1% of NP-40, 0.5 mM of EDTA, 1 mM of dithiothreitol (DTT) (see Note 6). 5. AcTEV™ protease (Invitrogen). 6. Calmodulin binding buffer: 10 mM of Tris–HCl, pH 8, 150 mM of NaCl, 0.1% of NP-40, 1 mM of magnesium acetate, 1 mM of imidazole, 2 mM of CaCl2, 10 mM of β-mercaptoethanol. Approximately 40 mL will be required per sample per experiment. Prepare 500 mL containing 5 mL of 1 M Tris–HCl, pH 8, 15 mL of 5 M NaCl, 5 mL of 10% NP-40, 500 μL of 1 M magnesium acetate, 500 μL of 1 M

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imidazole, 1 mL of 1 M CaCl2, 348.5 μL of 14.3 M β-mercaptoethanol (see Note 7), and 473 mL of distilled water. Store at 4 °C. 7. 1 M CaCl2: Dissolve 11.1 g into 100 mL of distilled water. Autoclave and store at room temperature. 8. Calmodulin affinity resin (Agilent). 9. 1 M EGTA: Dissolve 19 g in 40 mL of distilled water, make up to 50 mL with distilled water once the pH has been adjusted to 8 with 10 N NaOH (see Note 5), 0.2 μm filter, and store at 4 °C. 10. Calmodulin elution buffer: 10 mM of Tris–HCl, pH 8, 150 mM of NaCl, 0.1% of NP-40, 1 mM of magnesium acetate, 1 mM of imidazole, 2 mM of EGTA, 10 mM of β-mercaptoethanol. Approximately 1 mL will be required per sample per experiment. Prepare 100 mL containing 1 mL of 1 M Tris–HCl, pH 8, 3 mL of 5 M NaCl, 1 mL of 10% NP-40, 100 μL of 1 M magnesium acetate, 100 μL of 1 M imidazole, 200 μL of 1 M EGTA, 69.7 μL of 14.3 M β-mercaptoethanol (see Note 7), and 94.5 mL of distilled water. Store at 4 °C. 11. Disposable chromatography columns of 10 mL with narrow bottom, for example, Poly-Prep chromatography column from Biorad. 12. Rotating wheel. 13. Centrifuge tubes and rotor compatible with spinning volumes of 10 mL at approximately 25,000× g. 2.4 Trichloroacetic Acid Precipitation

1. 16 mg/mL sodium deoxycholate: Dissolve 160 mg of sodium deoxycholate in 10 mL of water. Pass through a 0.2 μm filter and store at room temperature. 2. Liquid trichloroacetic acid (TCA) (stock is 100%). 3. TCA washing buffer: Mix 70 mL of acetone, 20 mL of ethanol, 5 mL of 1 M Tris–HCl, pH 8, and 5 mL of distilled water. Store at 4 °C. 4. SDS–PAGE loading buffer.

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Methods A translational fusion between the protein of interest and TAP tag, either at the chromosomal locus or on an appropriate plasmid, must be constructed (see Notes 1 and 2). Translational fusion at the chromosomal locus will allow a physiological expression, while constructing the translational fusion on a plasmid may be more amenable.

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3.1 Verification of Expression of TAP-Tag Translational Fusion by Western Blot

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1. Prepare a cytoplasmic or a crude protein extract (see Note 8). 2. Load 10 μg of protein extract (or proteins corresponding to a bacterial sample of 0.3 OD600 unit) on a SDS–PAGE and proceed to transfer and western blot to verify production of hybrid protein (see Note 9). 3. Perform a one-step western blot using PAP antibody (see Note 10) and using an appropriate substrate to detect horseradish peroxidase activity (see Note 11).

3.2 Preparation of the Protein Extract

1. Day 1: Inoculate 10 mL of 2YT media with a bacterial colony and grow overnight at 37 °C with shaking (see Note 8). 2. Day 2: Dilute culture 100-fold in 500 mL LB and grow 5 h 30 at 37 °C with shaking until OD600 ≈ 2–3. 3. Pellet bacteria by centrifugation 20 min 5000× g at 4 °C. 4. Wash once with cold PBS, transfer to 50 mL centrifuge tubes, centrifuge again for 10 min 5000× g at 4 °C, discard supernatant, and freeze bacterial pellets with liquid nitrogen. 5. Maintain frozen bacterial pellet at -80 °C until you are ready to prepare cytosolic protein extract and proceed to tandem affinity purification. 6. Day 3: Resuspend frozen bacterial pellets with 10 mL of Protein A binding buffer containing 0.5 mM PMSF (see Note 4). 7. Use sonication, French Press, or cell disruptor to break bacterial cells (see Note 12). 8. Centrifuge for 30 min at 25,000× g at 4 °C and save supernatant, which is the cytoplasmic protein extract.

3.3 Tandem Affinity Purification

From here, carry out all procedures with gloves to avoid contamination of your sample(s) with keratin: 1. Put 200 μL of IgG Sepharose beads in a disposable chromatography column and wash by gravity with 5 mL of Protein A binding buffer. 2. Binding of Protein A tag to IgG Sepharose beads. After washing the beads, close the bottom of the chromatography column, and, using a pipette, transfer 9 mL of the cytoplasmic protein extract. Close the top of the column and put on a wheel for 2 h at 4 °C. 3. Remove first the top plug of the column and then the bottom one. Leave the unbound material flow by gravity and discard. 4. Wash three times the IgG beads with 10 mL of Protein A binding buffer.

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5. TEV protease cleavage. Close bottom of column; fill it with 1 mL of TEV cleavage buffer and 100 units of AcTEV™ protease. Close top of column and put on wheel at room temperature for 1 h. 6. Remove top and bottom plugs and recover elution fraction by gravity. Add additional 200 μL of TEV cleavage buffer in column to recover as much material as possible from the sides of column. 7. Add 3 mL calmodulin binding buffer and 3 μL of 1 M CaCl2 (see Note 13) to elution fraction. 8. Binding by the CBP tag part on calmodulin affinity resin. Put 200 μL of calmodulin affinity resin in a new disposable chromatography column; wash it with 5 mL of calmodulin binding buffer. Then, close bottom of column. 9. Add the 4.2 mL of the elution fraction (obtained at Steps 6 and 7). Close top of column and put on wheel for 1 h at 4 °C. 10. Remove first the top plug of the column and then the bottom one. Leave unbound material to flow by gravity and discard. 11. Wash three times calmodulin affinity resin with 10 mL of calmodulin binding buffer. 12. Elution. Elute with five times 200 μL of calmodulin elution buffer. 13. Pool fraction 2, 3, and 4 and proceed to TCA precipitation of elution fraction 1; pooled fractions 2, 3, and 4; and fraction 5. 3.4 Trichloroacetic Acid Precipitation

1. To each of the eluted protein samples, add 1/100th of 16 mg/ mL of sodium deoxycholate. Vortex and leave on ice 30 min. 2. Add TCA to 10% final. Vortex and leave on ice 30 min. 3. Centrifuge 15 min 15,000× g at 4 °C. 4. Wash twice with TCA washing buffer. 5. Leave pellets to dry on bench and resuspend in 20 μL protein loading buffer 1×.

3.5 Analysis by SDS– PAGE and Mass Spectrometry

1. Load totality of samples on SDS–PAGE (see Note 14) and stain with Coomassie blue. 2. Unstain and then rinse with distilled water. 3. Cuts bands to identify partner proteins by mass spectrometry (see Note 15).

4

Notes 1. A C-terminal TAP-tag translational fusion can be introduced at the chromosomal locus using the λred recombination system [7]. To prepare the appropriate polymerase chain reaction

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(PCR) product, use pJL72 plasmid as template (this latter harbors a cassette made of the TAP tag and the kanamycin resistance gene, Fig. 2a) [8], design a forward primer that contains, in the 5′-end, the 45 nucleotides that are immediately upstream the stop codon of the gene of interest, followed by the sequence 5′-TCCATGGAAAAGAGAAG-3′ (this sequence will hybrid to the CBP tag, Fig. 2b), and design a reverse primer that contains at its 5′-end the reverse complement 45 nucleotides that are immediately downstream the stop codon of the gene of interest, followed by the sequence 5′-CATATGAATATCCTCCTTAG-3′ (Fig. 2a). 2. Alternatively, the sequence corresponding to the open reading frame of the gene of interest can be cloned in the plasmid pEB587 [22] (Fig. 3a), which allows a N-terminal TAP-tag translational fusion (Fig. 3b) under the control of the arabinose-inducible promoter PBAD. 3. Gently agitate the solution for complete dissolution of NP-40 if necessary. 4. PMSF crystallizes at -20 °C, so heat the PMSF aliquot to 37 ° C to redissolve the PMSF before use. We use PMSF as generic protease inhibitor, but protease inhibitor cocktail can be used as well. 5. EDTA and EGTA may not be soluble until pH had been adjusted to 8 with 10 N NaOH. 6. Add DTT to the volume of buffer you will need when starting the experiment. DTT is necessary for TEV activity. 7. Add β-mercaptoethanol to the volume of buffer you will need when starting the experiment. 8. Also, plan to prepare a protein extract of an untagged strain as negative control of the experiment. Or use a strain that produce a TAP tag but uncoupled from the bait protein as negative control. 9. Translational TAP tag fusion adds 20 kDa to the mass of the protein of interest; 3 kDa corresponds to the CBP tag and 15 kDa to the Protein A tag. 10. Immunoglobulins will bind the protein A fragment of TAP tag. 11. In our experience, the detection of the tagged protein in crude extracts using this PAP antibody is mandatory for a successful TAP purification. 12. French press or cell disruptor might be more gentle to preserve protein complexes. 13. Addition of extra CaCl2 is required to quench EDTA that was previously necessary for TEV protease activity.

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14. Usually, a 12% SDS–PAGE allows visualization of low- and high-molecular-weight proteins. Alternatively, proteins can just be sandwiched in the stacking gel and all together analyzed by mass spectrometry. Plan a biological triplicate for the sample of interest and its negative control. 15. Use one blade per band if proteins have been separated through the running gel. References 1. Rigaut G, Shevchenko A, Rutz B et al (1999) A generic protein purification method for protein complex characterization and proteome exploration. Nat Biotechnol 17:1030–1032 2. Gavin AC, Bosche M, Krause R, Grandi P, Marzioch M et al (2002) Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415:141–147 3. Gavin AC, Aloy P, Grandi P, Krause R, Boesche M et al (2006) Proteome survey reveals modularity of the yeast cell machinery. Nature 440: 631–636 4. Gully D, Moinier D, Loiseau L, Bouveret E (2003) New partners of acyl carrier protein detected in Escherichia coli by tandem affinity purification. FEBS Lett 548:90–96 5. Butland G, Peregrin-Alvarez JM, Li J, Yang W, Yang X et al (2005) Interaction network containing conserved and essential protein complexes in Escherichia coli. Nature 433:531–537 6. Collins MO, Choudhary JS (2008) Mapping multiprotein complexes by affinity purification and mass spectrometry. Curr Opin Biotechnol 19:324–330 7. 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 8. Zeghouf M, Li J, Butland G, Borkowska A, Canadien V et al (2004) Sequential Peptide Affinity (SPA) system for the identification of mammalian and bacterial protein complexes. J Proteome Res 3:463–468 9. Gully D, Bouveret E (2006) A protein network for phospholipid synthesis uncovered by a variant of the tandem affinity purification method in Escherichia coli. Proteomics 6:282–293 10. Viala JP, Prima V, Puppo R, Agrebi R, Canestrari MJ, Lignon S et al (2017) Acylation of the type 3 secretion system translocon using a dedicated acyl carrier protein. PLoS Genet 13: e1006556

11. Pompeo F, Luciano J, Galinier A (2007) Interaction of GapA with HPr and its homologue, Crh: novel levels of regulation of a key step of glycolysis in Bacillus subtilis? J Bacteriol 189:1154–1157 12. Babu M, Butl G, Pogoutse O, Li J, Greenblatt JF et al (2009) Sequential peptide affinity purification system for the systematic isolation and identification of protein complexes from Escherichia coli. Methods Mol Biol 564:373– 400 13. Stingl K, Schauer K, Ecobichon C, Labigne A, Lenormand P et al (2008) In vivo interactome of Helicobacter pylori urease revealed by tandem affinity purification. Mol Cell Proteomics 7:2429–2441 14. Mousnier A, Schroeder GN, Stoneham CA, So EC, Garnett JA et al (2014) A new method to determine in vivo interactomes reveals binding of the Legionella pneumophila effector PieE to multiple rab GTPases. MBio 5:e01148– e01114 15. So EC, Mousnier A, Frankel G, Schroeder GN (2019) Determination of in vivo interactomes of Dot/Icm type IV secretion system effectors by Tandem Affinity Purification. Methods Mol Biol 1921:289–303 16. Puig O, Caspary F, Rigaut G et al (2001) The tandem affinity purification (TAP) method: a general procedure of protein complex purification. Methods 24:218–229 17. Bouveret E, Rigaut G, Shevchenko A, Wilm M, Seraphin B (2000) A Sm-like protein complex that participates in mRNA degradation. EMBO J 19:1661–1671 18. Berneking L, Schnapp M, Rumm A et al (2016) Immunosuppressive Yersinia effector YopM binds DEAD box helicase DDX3 to control ribosomal S6 kinase in the nucleus of host cells. PLoS Pathog 12(6):e1005660 19. Berneking L, Schnapp M, Nauth T, Hentschke M (2019) Tandem Affinity Purification of SBP-

TAP in Bacteria CBP-tagged type three secretion system effectors. Bio Protoc 9:e3277 20. So EC, Schroeder GN, Carson D, Mattheis C, Mousnier A et al (2016) The Rab-binding profiles of bacterial virulence factors during infection. J Biol Chem 291:5832–5843 21. Sana TG, Baumann C, Merdes A, Soscia C, Rattei T et al (2015) Internalization of Pseudomonas aeruginosa strain PAO1 into epithelial

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cells is promoted by interaction of a T6SS effector with the microtubule network. MBio 6: e00712 22. Battesti A, Bouveret E (2008) Improvement of bacterial two-hybrid vectors for detection of fusion proteins and transfer to pBAD-tandem affinity purification, calmodulin binding peptide, or 6-histidine tag vectors. Proteomics 8: 4768–4771

Chapter 20 In Vivo Site-Directed and Time-Resolved Photocrosslinking of Envelope Proteins Yassin A. Abuta’a, Anne Caumont-Sarcos, Ce´cile Albenne, and Raffaele Ieva Abstract In vivo site-directed photocrosslinking provides a means to probe the vicinity of proteins in their native cellular environment. Because this method relies on the incorporation of unnatural amino acid analogs that are similar in size to natural amino acids, crosslink products are indicative of direct protein–protein interactions. Here, we present the use of this approach to monitor both transient and stable interactions of two proteins of the envelope of Escherichia coli. First, we describe a protocol to characterize the interactions of a secretory protein as it transverses the bacterial envelope with temporal and spatial resolution. We combine site-directed photocrosslinking with radiolabeling of proteins and lipids. Second, we describe a method to purify a photocrosslinked partner protein and to analyze it by mass spectrometry. We use in-gel protein digestion and peptide fragmentation by MALDI-TOF/TOF tandem mass spectrometry to determine the site of interaction on the photocrosslinked partner. Key words Site-directed photocrosslinking, Mass spectrometry, Protein–protein interaction, Protein–lipid interaction, Protein secretion, Escherichia coli, Bacterial envelope

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Introduction Efficient and correct sorting of proteins, from the site of synthesis to the compartment where they function, is vital for the cell. This task is particularly challenging for proteins that have to cross one or more cellular membranes before reaching their destination, such as proteins secreted across the bacterial envelope. Protein secretion is aided by specialized molecular machineries (translocases), which insert or transport their client proteins into and across lipid bilayers [1]. Capturing interactions between secretory proteins and their translocases “at work” is a key approach to elucidate the molecular events that govern these sophisticated reactions. Several biochemical methods have been developed to describe the interactions of proteins within multi-subunit complexes in their

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_20, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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cellular native environment, including native complex isolation by affinity purification or immunoprecipitation, blue native polyacrylamide gel electrophoresis of assembly intermediates, and chemical crosslinking. Interactions between secretory proteins and their transport machineries, however, are often too short-lived to be detected by these methods. Different strategies can be designed to “trap” client proteins in complex with their translocases. This can be achieved, for instance, by stabilizing structural motifs within the client protein or by fusing it to a bulkier protein moiety, generating an obstruction that prevents completion of the transport reaction. Although the “trapping” strategy may help to capture a transient intermediate complex that forms at a specific stage of the transport reaction, it may not provide information on the temporal sequence of molecular events that precede and follow formation of the captured intermediate. A further limitation of methods based on native complex isolation or chemical crosslinking concerns the mapping of protein– protein interactions. To this end, a method for the incorporation of unnatural photoactivatable amino acids into newly synthesized proteins has been originally developed using an amber suppression approach in cell free translation systems [2]. A subsequent modification of the method has allowed the expression of proteins that contain photoprobes at specific sites and their photocrosslinking in vivo. To this end, cells are transformed with a plasmid encoding an engineered orthogonal aminoacyl tRNA synthetase that loads a cognate amber suppressor tRNA with a photoreactive amino acid analog, such as p-benzoyl-L-phenylalanine (Bpa) [3]. The photoprobe can be introduced at amber codons engineered at specific positions in the open reading frame (ORF) of a gene of interest. Upon irradiation with ultraviolet (UV) light (350–365 nm), Bpa forms a highly reactive radical that can crosslink to C–H bonds in the vicinity [4]. As Bpa has a relatively small diameter (approximately 4 Å), it crosslinks proteins that are in close proximity, allowing relatively accurate mapping of protein–protein interaction sites. In this chapter, we describe two applications of the in vivo sitedirected photocrosslinking method. One application is optimized to analyze transient and consecutive interactions of a client protein with its transport machinery. To understand the temporal sequence of these interactions, in vivo site-directed photocrosslinking is combined with cell radiolabeling using a pulse-chase methodology. Upon immunoprecipitation of the protein of interest, the formation of crosslinked products can be followed over time. The molecular size estimation of the crosslink products suggests potential partner candidates that are subsequently verified by performing control immunoprecipitations. The second application of the method helps to detect and characterize rather stable interactions. After performing photocrosslinking, the protein of interest and its

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crosslink products are purified. Identification of the crosslinked partner proteins is performed by in-gel proteolytic digestion and MALDI-TOF mass spectrometry. The crosslinked site on the partner protein is then mapped by fragmentation using tandem mass spectrometry analysis. The first example protocol described here analyzes the biogenesis of EspP, an autotransporter secreted by Escherichia coli O157: H7, by performing cell labeling with radioactive amino acids (see Subheadings 2.1 and 3.1). Autotransporters are a class of virulence factors produced by Gram-negative bacteria. After transport across the inner membrane, the autotransporter carboxy-terminal “β domain” integrates into the outer membrane by folding into a β-barrel structure, while its amino terminal “passenger domain” is ultimately translocated into the extracellular space. The mechanism by which the passenger domain is transported across the outer membrane has been debated for long time [5, 6]. This approach was used to reveal that passenger domain secretion and outer membrane integration of the β domain are both mediated by the β-barrel assembly machinery (BAM complex) in a coordinated reaction. BAM is a multi-subunit complex consisting of BamA, an integral outer membrane protein, and four lipoproteins, BamBCDE, which associate to the inner leaflet of the outer membrane lipid bilayer [7, 8]. Consecutive interactions of distinct EspP domains, first with periplasmic chaperones and then with subunits of the BAM machinery, were identified leading to a detailed model of autotransporter biogenesis [9–11]. In addition, the first protocol illustrates how site-directed photocrosslinking can be combined with radiolabeling of cells using radioactive inorganic phosphate [12] to reveal the interaction of the EspP β domain with lipids of the bacterial outer membrane [10, 11]. The second example protocol focuses on the detection of a stable interaction and its characterization by tandem mass spectrometry (see Subheadings 2.2 and 3.2). To this end, we illustrate site-directed photocrosslinking of the outer membrane lipoprotein DolP, a factor crucial to preserve the integrity of the envelope protecting E. coli from some antibiotics and detergents [13]. DolP interacts with the central subunit of the BAM complex, BamA, and with the major integral outer membrane protein OmpA [14]. The interactions of DolP were revealed by employing sitespecific photocrosslinking, followed by affinity chromatography and sodium dodecyl sulfate (SDS)–polyacrylamide gel electrophoresis (PAGE). Western blotting analysis using a BamA-specific antiserum identified the DolP–BamA crosslink product. Another interaction was characterized by performing in-gel proteolytic digestion of the crosslink product stained with Coomassie Blue, followed by MALDI-TOF mass spectrometry. The obtained peptide fingerprint led to the identification of OmpA. Further peptide fragmentation spectra led to the identification of a peptide in the

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C-terminal periplasmic domain of OmpA as a site of interaction with DolP.

2

Materials

2.1 Biogenesis of the Autotransporter EspP: Site-Directed and Time-Resolved Photocrosslinking in Cells Metabolically Labeled with Radioisotopes 2.1.1 Plasmid Construction and Transformation of E. coli Cells 2.1.2 Expression of a EspP Variant Containing Bpa and Pulse-Chase Radiolabeling of Cells Using 35S-Labeled Amino Acids

1. Site-directed mutagenesis kit or a similar set of reagents for high-fidelity polymerase chain reaction (PCR) 2. Chemical and electro-competent E. coli cells 3. Plasmid miniprep kit 4. Cuvettes for electroporation 5. Electroporator 6. Lysogenic broth (LB) agar plates containing selected antibiotics

1. 10× M9 salts (67.8 g/L Na2HPO4, 30 g/L KH2PO4, 5 g/L NaCl, 10 g/L NH4Cl) 2. M9 complete medium containing 1× M9 salts, 1 mM MgSO4, 0.1 mM CaCl2, 0.2% w/v glycerol, 40 μg/mL L-amino acids (except methionine and cysteine) 3. Disposable 125 mL Erlenmeyer flasks 4. A high-specific activity mixture of 35S-cysteine and 35S-methionine (1075–1175 Ci/mmol) 5. A stock solution of non-radiolabeled methionine and cysteine (100 mM methionine, 100 mM cysteine) 6. 100 mM solution

isopropyl-β-D-thio-galactoside

(IPTG)

stock

7. Bpa (Bachem) 8. Water bath shaker 2.1.3 Radiolabeling with 32 P-Labeled Inorganic Phosphate and Expression of an EspP Variant Containing Bpa

1. Disposable 125 mL Erlenmeyer flasks 2. Modified G56 medium (45 mM MES, pH 7.0, 10 mM KCl, 10 mM MgCl2, 15 mM (NH4)2SO4, 5 μg/L thiamine, 0.2% glycerol, 40 μg/mL L-amino acids) 3. A stock solution of 0.5 M KH2PO4 4. Radioactive KH232PO4 (900–1100 mCi/mmol) 5. 100 mM IPTG stock solution 6. Bpa (Bachem)

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1. High-intensity UV lamp such as Tritan 365 UV-A (365 nm) lamp (Spectroline) 2. 20 mL petri dishes 3. Six-well culture plates 4. Disposable pipettes

2.1.5 Immunoprecipitation and SDS–PAGE

1. Antisera specific for EspP, BamA, and BamB or other proteins of interest 2. Protein solubilization buffer: 15% glycerol, 200 mM Tris base, 15 mM EDTA, 4% SDS, 2 mM phenylmethylsulfonyl fluoride (PMSF) 3. Radioimmunoprecipitation assay (RIPA) buffer: 50 mM Tris– HCl, pH 8, 150 mM NaCl, 1% Igepal CA-630, 0.5% sodium deoxycholate, 0.1% SDS 4. Staphylococcus aureus Protein A Sepharose beads 5. SDS–polyacrylamide gels 6. 4× SDS–PAGE sample buffer: 8% SDS, 40% glycerol, 240 mM Tris–HCl, pH 6.8, 1.6% β-mercaptoethanol, 0.04% bromophenol blue 7. Gel drying system 8. Storage phosphor screen

2.2 Site-Directed Photocrosslinking of the Outer Membrane Lipoprotein DolP: Purification of a Photocrosslinked Partner Protein and Characterization by Tandem MS

1. 10× M9 salts (67.8 g/L Na2HPO4, 30 g/L KH2PO4, 5 g/L NaCl, 10 g/L NH4Cl) 2. M9-CSA complete medium containing 1× M9 salts, 1 mM MgSO4, 0.1 mM CaCl2, 0.2% w/v glycerol, 0.2% w/v casamino acids 3. 200 mL Erlenmeyer flasks 4. 100 mM IPTG stock solution 5. Bpa (Bachem)

2.2.1 Expression of a DolPHis Variant Containing Bpa 2.2.2 Envelope Isolation, Solubilization, DolPHis Affinity Chromatography, and SDS–PAGE

1. 20 mM Tris–HCl, pH 8 2. Cell Disruptor (Constant Systems LTD or similar) 3. 3.5 mL polypropylene bell-top quick-seal centrifuge tubes (Beckman Coulter) 4. Optima MAX 130 K Ultracentrifuge (Beckman Coulter) 5. Solubilization buffer: 12% glycerol, 200 mM Tris–HCl pH 8, 15 mM EDTA, 4% SDS, 2 mM PMSF

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6. RIPA buffer supplemented with 20 mM, 50 mM, or 500 mM imidazole 7. Spin column for affinity chromatography with 35 μm pore size filters 8. Ni-NTA agarose beads (e.g., Protino® Ni-NTA agarose) 9. SDS–polyacrylamide gels 10. 4× SDS–PAGE sample buffer: 8% SDS, 40% glycerol, 240 mM Tris–HCl, pH 6.8, 1.6% β-mercaptoethanol, 0.04% bromophenol blue 2.2.3 MALDI-TOF Mass Spectrometry Analysis of Crosslinked Proteins

1. α-cyano-4-hydroxycinnamic acid (HCCA) 2. Acetonitrile (ACN, HPLC grade) 3. Trifluoroacetic acid (TFA) 4. HCCA matrix solution (saturated), 6 μg/μL in ACN-0.1% TFA, 50:50 (v/v) 5. Trypsin (V5111, Promega): 10 ng/μL in 25 mM NH4HCO3, pH 7.8 6. Washing buffer: 25 mM NH4HCO3, pH 7.8-ACN, 50:50, [v/v] 7. Ultrasonic bath (digital ultrasonic cleaner RS Pro) 8. SpeedVac vacuum concentrator 9. MALDI-Plate Opti-TOF™ Cal Mix 5 (SCIEX) 10. MALDI-TOF/TOF SCIEX 5800 instrument 11. Peptide mass standards kit for calibration of MALDI-TOF instrument (SCIEX) 12. Data Explorer Software (SCIEX)

3

Method

3.1 Biogenesis of the Autotransporter EspP: Site-Directed and Time-Resolved Photocrosslinking in Cells Metabolically Labeled with Radioisotopes 3.1.1 Strategy Design and Plasmid Constructions to Overproduce Photoprobed EspP

1. Clone a construct encoding the selected protein of interest under the control of an inducible promoter in an expression plasmid that can be propagated in the desired bacterial model organism. In this example, pRI23, an RB11-derived plasmid, harbors a construct encoding EspP(586TEV) (see Note 1) under the control of the IPTG-inducible lac promoter and an ampicillin-resistance cassette. The selected model organism for EspP expression is the laboratory E. coli strain AD202, a MC4100-derived strain that lacks the gene encoding the outer membrane protease OmpT [15]. 2. Use a PCR-based site-specific mutagenesis approach to incorporate an amber codon at a specific position of the ORF of interest. In this example, the mutagenesis approach is

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conducted on pRI23 to replace the Trp 1149 codon of EspP with an amber codon, generating pRI23-1149Bpa (see Note 2). 3. Digest the parental DNA used as template in the PCR reaction with the restriction enzyme DpnI. 4. Transform ultracompetent E. coli cells using a small aliquot of the amplified DNA product. 5. Select single colonies growing on LB containing 100 μg/mL of ampicillin, extract plasmidic DNA, and verify the correct incorporation of the amber codon by plasmid sequencing. 6. Co-transform by electroporation the E. coli strain AD202 with pRI23-1149Bpa and pEVOL-pBpF. The latter plasmid, harboring a p15A origin and a chloramphenicol resistance gene, codes for an engineered amino acyl tRNA synthetase and a cognate amber suppressor tRNA of Methanocaldococcus jannaschii (see Note 3). Plate cells on LB agar containing 100 μg/ mL of ampicillin and 30 μg/mL of chloramphenicol. 7. Select a single colony of transformed cells. 3.1.2 Preparation of Cell Cultures for EspP Expression and Radiolabeling

Two methodologies of radiolabeling are used (Fig. 1). In one culture, 35S protein labeling is conducted following a “pulsechase” procedure, in which all newly synthesized proteins become radiolabeled by exposing cells for a short time period to 35S-methionine and 35S-cysteine (“pulse” phase). Subsequently, addition of an excess of non-radiolabeled (“cold”) methionine and cysteine prevents further incorporation of radiolabeled amino acids into proteins, thus ending the pulse phase and starting the “chase” phase. By combining 35S-labeling with site-specific photocrosslinking and protein immunoprecipitation, the interactions of EspP at sequential steps of its biogenesis can be monitored over time. In a parallel culture, 32P-labeled inorganic phosphate is incorporated into phospholipids and lipopolysaccharide (LPS), allowing for the identification of interactions between the EspP β domain and lipids of the outer membrane. 1. Start two 5 mL overnight cultures of AD202 cells transformed with pRI23-1149Bpa and pEVOL-pBpF, one in M9 medium and one in G56 medium containing 0.13 mM KH2PO4. Supplement both cultures with 100 μg/mL of ampicillin and 30 μg/mL of chloramphenicol. 2. The following day, centrifuge cells from both cultures and wash them once with fresh medium (see Note 4). Finally, resuspend cells, respectively, in 2 mL of fresh M9 and 2 mL of fresh G56 media, respectively, and measure the optical density of the culture at a wavelength of 550 nm (OD550) in a spectrophotometer.

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Fig. 1 Flow diagram of the illustrated experimental procedure. Two cultures are conducted in parallel. One culture is subjected to pulse-chase labeling using 35S-methionine and 35S-cysteine in order to label all newly synthesized proteins (left). The other culture is subjected to labeling with 32P-inorganic phosphate, in order to label cellular lipids (right)

3. Use M9 medium-resuspended cells to inoculate a 50 mL culture in M9 medium. 4. Use G56-resuspended cells to inoculate a 12 mL culture in G56 media w/o phosphate in a disposable flask. The starting OD550 of both cultures is set to 0.03. 3.1.3 Expression of Photoprobed EspP and Preparation of Cells for 35SPulse-Chase Labeling

When the 50 mL culture in M9 medium reaches an OD550 of 0.2, induce EspP expression by adding 200 μM of IPTG (see Note 5). Immediately after, add 1 mM of Bpa (see Notes 6 and 7). Incubate the culture for additional 30 min at 37 °C: 1. During this incubation time, label six 15 mL tubes as follows (this step is preparatory for the next experimental phase of radiolabeling). –

35

–

35

–

35

–

35

S Time 1 min -UV S Time 7 min -UV S Time 15 min -UV S Time 1 min +UV

In Vivo Site-Directed Photocrosslinking

–

35

–

35

307

S Time 7min +UV S Time 15 min +UV

2. Label three wells of a 6-well culture plate as follows: –

35

–

35

–

35

S Time 1 min +UV S Time 7 min +UV S Time 15 min +UV

3. Fill the tubes with ice chips, approximately to the 5 mL volume mark line. Place the ice collected in the “+UV” tubes into the corresponding labeled wells of the 6-well culture plate. Keep the “-UV” tubes and the 6-well culture plate in two separate ice buckets. 4. After 30 min of IPTG induction, perform pulse-chase labeling with 35S-methionine and 35S-cysteine, as described in Subheading 3.1.5. 3.1.4 Cell Labeling with 32 P-Inorganic Phosphate, Expression of Photoprobed EspP and Photocrosslinking

1. Immediately after inoculation, supplement the 12 mL culture in G56 medium with 133 μCi/mL KH232PO4 (see Note 8). 2. When the culture reaches an OD550 of 0.2, induce expression of EspP by adding 200 μM of IPTG (see Note 5). Immediately add 1 mM of Bpa (see Notes 6 and 7). Incubate the culture for additional 45 min at 37 °C. 3. During this incubation time, label two 15 mL tubes as follows: –

32

–

32

P -UV P +UV

4. Fill both tubes with ice chips, approximately to the 5 mL volume mark line. Place the ice collected in the “32P +UV” tube into a well of a six-well culture plate. Label the well accordingly. Keep the “32P -UV” tube and the six-well culture plate in two separate ice buckets. 5. After 45 min of IPTG induction, place 5 mL of the culture in the “32P -UV”-labeled tube and 5 mL in the “32P +UV”-labeled plate containing ice chips. 6. Immediately move the six-well plate under the lamp to expose sample “32P +UV” to UV light for 5 min (see Notes 9 and 10). After this time, place the sample into the corresponding 15 mL tube previously labeled “32P +UV” (see Note 11). 7. Collect cells in both 15 mL tubes by centrifugation at 2000× g for 5 min at 4 °C. 8. Resuspend the cells pelleted in each tube in 1 mL of M9 salts; place each sample in 1.5 mL Eppendorf tubes. Precipitate the whole protein content with trichloroacetic acid.

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9. Collect precipitated proteins by centrifugation at 16,000× g for 15 min at 4 °C. 10. Wash samples with ice-cold acetone. Dry protein pellets. 3.1.5 35S-Pulse-Chase Labeling of Cells and Photocrosslinking

1. Move 35 mL of the M9 culture into a disposable 125 mL flask. 2. Place the flask in the water bath shaker. 3. Start a timer counting up (see Note 12). 4. Time 30″: Add 11 μCi/mL of 35S-Met/35S-Cys protein labeling mix. Close the flask and swirl it rapidly by hand. Place the flask in the water bath to prevent excessive cooling of the culturing medium. 5. Time 1′00″: Add 350 μL of 100 mM cold methionine and cysteine. Close the flask; swirl it rapidly by hand. Place the flask in the water bath. 6. Time 2′00″: Remove 10 mL using a disposable pipette. Place 5 mL into the well-labeled “35S Time 1 min +UV” containing ice chips and 5 mL into the 15 mL tube “35S Time 1 min -UV” containing ice chips. Close the flask and place it in the water bath. 7. Immediately move the six-well culture plate under the lamp to expose sample “35S Time 1 min +UV” to UV light for 5 min (see Notes 9 and 10). After this time, place the sample into the corresponding 15 mL tube labeled “35S Time 1 min +UV” (see Note 11). 8. Time 8′00″: Repeat Steps 6 and 7 using the well-labeled “35S Time 7 min +UV” and the tubes labeled “35S Time 7 min -UV” and “35S Time 7 min +UV.” 9. Time 16′00″: Repeat Steps 6 and 7 using the well-labeled “35S Time 15 min +UV” and the tubes labeled “35S Time 15 min -UV” and “35S Time 15 min +UV.” 10. Place all 15 mL tubes in a centrifuge and collect cells at 2000× g for 5 min at 4 °C. 11. Resuspend the cells pelleted in each tube using 1 mL M9 salts; place each sample in 1.5 mL Eppendorf tubes. Precipitate the whole protein content in each sample by adding trichloroacetic acid. 12. Collect precipitated proteins by centrifugation at 16,000× g for 15 min at 4 °C. 13. Wash samples with ice-cold acetone. Dry protein pellets.

3.1.6 Immunoprecipitation of EspP and Analysis of Photocrosslinking Products

1. Solubilize precipitated proteins by adding 50 μL of protein solubilization buffer to each sample. 2. Place tubes in a thermo-block at 95 °C with agitation set to 1000 rpm.

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3. Add 1 mL of RIPA buffer. Subject samples to a clarifying spin at 16,000× g for 10 min at 4 °C to pellet non-solubilized proteins. 4. Move three 300 μL aliquots of the supernatant from 35 S-labeled samples into new Eppendorf tubes and supplement them with 1 μL of anti-EspP, BamA, or BamB antisera, respectively. Move a single 300 μL aliquot from each 32P-labeled sample into a new tube and supplement it with 1 μL of antiEspP antiserum. 5. Mix and incubate samples on ice for 2 h. 6. Add S. aureus Protein A Sepharose beads to sediment antibodies. 7. Wash beads with high-salt RIPA buffer (containing 500 mM NaCl) at least twice (see Note 13). 8. Elute proteins using SDS–PAGE sample buffer. 9. Analyze eluted proteins by SDS–PAGE (see Note 14). Dry gels and expose them for autoradiography using storage phosphor screens. Phosphorimager-acquired autoradiography images are shown in Fig. 2 (see Notes 15 and 16 concerning how the identified crosslinks provide information on the consecutive steps of EspP biogenesis). 3.2 Site-Directed Photocrosslinking of the Outer Membrane Lipoprotein DolP: Purification of a Photocrosslinked Partner Protein and Characterization by Tandem MS

1. The plasmid pDolPHis-52Bpa was built by site-directed mutagenesis as described in Subheading 3.1. In this plasmid, the Val 52 codon of the dolP ORF is replaced with an amber codon (see Note 2). Start a 5 mL overnight culture of E. coli cells (a BW25113 derivative strain deleted of endogenous dolP) transformed with the plasmids pDolPHis-52Bpa and pEVOLpBpF using M9-CSA complete medium supplemented with 100 μg/mL of ampicillin and 30 μg/mL of chloramphenicol at 37 °C.

3.2.1 Preparation of Cell Cultures Expressing the Photoprobed DolP Variant

2. The next day, inoculate 200 mL of fresh M9-CSA complete medium supplemented with 100 μg/mL of ampicillin and 30 μg/mL of chloramphenicol to OD550 of 0.05 using an aliquot of the overnight culture. Incubate until OD550 reaches 0.2–0.3. 3. Induce DolPV52BpaHis expression by adding 200 μM of IPTG (see Note 5). Immediately add 1 mM of Bpa (see Notes 6 and 7). Incubate the culture for additional 1.5 h at 37 °C. 4. Collect cells by centrifugation and discard the supernatant. Resuspend the cell pellet in 40 mL cold M9 salts on ice. 5. Equally divide the cell resuspension in two 20 mL aliquots labeled as “-UV” or “+UV.”

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Fig. 2 Transient and stable interactions detected at a specific position of the EspP β domain. AD202 cells transformed with plasmids pRI23-1149Bpa and pEVOL-pBpF are subjected either to pulse-chase labeling with radioactive amino acids (lanes 1–18) or to labeling with radioactive phosphate (lanes 19, 20). Each sample is divided into two equal aliquots, one of which is subjected to UV irradiation (lanes 10–18 and 20). All samples are subjected to immunoprecipitation using the indicated antisera. The crystal structure of EspP (PDB: 2QOM) is shown to highlight the position of Trp 1149

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1. Place an open 20 mL petri dish on ice. Transfer the cells resuspension from the “+UV” tube in the dish. 2. Place the petri dish under the UV lamp and irradiate cells with UV light for 10 min. Place the cells resuspension back in the “+UV” tube. 3. Collect the cells in both “-UV” and “+UV” tubes by centrifugation and resuspend the pellets in 3 mL Tris–HCl 20 mM pH 8. 4. Lyse the cells using the cell disruptor system. 5. Centrifuge the samples at 5000× g for 15 min at 4 °C to pellet the cell debris, and transfer the supernatants to 3.5 mL polypropylene bell-top quick-seal centrifuge tubes. 6. Perform ultracentrifugation at 100,000× g for 30 min at 4 °C to isolate cell membranes. Discard the supernatant and solubilize the pelleted membranes by adding 80 μL of protein solubilization buffer. 7. Heat the samples at 95 °C for 5 min under agitation at 1000 rpm. Then, add 1.6 mL of RIPA buffer. Mix again the samples for 5 min at 1000 rpm at room temperature. 8. Subject samples to a clarifying spin at 11,000× g for 15 min at 4 °C. Transfer the supernatants to fresh 2 mL Eppendorf tubes and keep 50 μL as total fractions in separate tubes. 9. Prepare a 50% slurry of Ni-NTA resin in a fresh 1.5 mL Eppendorf tube and equilibrate it in RIPA buffer supplemented with 20 mM imidazole. 10. Transfer 60 μL of equilibrated Ni-NTA resin to each tube containing solubilized proteins. Incubate for 1.5 h under mild agitation at 4 °C. 11. Transfer the samples to an empty column containing a 35 μm filter. Centrifuge at 80× g for 30 s at 4 °C. 12. Wash seven times the beads in the column by adding 500 μL of RIPA buffer supplemented with 50 mM imidazole and centrifuging at 80× g for 30 s at 4 °C. 13. Elute bound proteins from the column by adding 35 μL of RIPA buffer supplemented with 500 mM imidazole. Repeat this step three times to obtain a total elution volume of 105 μL. 14. Analyze the total fractions and eluted proteins by SDS–PAGE (see Note 14).

3.2.3 Identification of Crosslinked Proteins and Mapping of the Interactions by MALDI-TOF Mass Spectrometry

1. Wear gloves to minimize keratin contamination in all steps of protein sample preparation. 2. Using a laboratory scalpel, excise the gel bands corresponding to the non-crosslinked bait protein (Fig. 3a) and the crosslinked product (Fig. 3b) in the lanes containing the -UV and +UV elution fractions, respectively (see Note 17).

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4700 Reflector Spec #1=>BC=>NR(2.00)[BP = 1553.7, 67686]

100

100

55

Relative intensity (%)

40SVGTQVDDGTLEBpaR53

80

36

70

28

1894.90

20

**

DolP 17

1910.95

30

1115.49

993.54 1027.58 1103.63

40

1878.90

1627.76

50

*

2016.00

% Intensity

60

10

0

960

1268

1576

2192

1409,67

1533.86

55

DolP- OmpA

80

36

2227,09

2383.6687

*

2225.7769 2226.7625

2210.7581

XL-peptide 2016.7125

1894.6646

17

1910.95

1831.7842

1847.7932

1878.90

1654.6139

**

1878.6339

1894.90

28

1910.6354

1654.83

*

1680.6210

*

1450.5759

1378.5966

1378,77 1307.4956

1233.4476

*

1475.5588

10

1066.3553

20

1179.4480

*

30

1115.3396

40

1083.3906

50

1083.55 1115.49

1027.58

60

1045.4359

70

993.54

% Intensity

Relative intensity (%)

+

72

1553.6681

1409.4933

1280.4952

100

–

UV kDa

4700 Reflector Spec #1=>BC=>NR(2.00)[BP = 842.4, 9506]

90

0

960

1268

1576

1884

2192

2500

Mass (m/z)

960

2500

mass (m/z)

c

y13

y6*

100

2500

2500

mass (m/z)

1280,65

100

1884

Mass (m/z)

960

b

0

+

72

90

0

–

UV kDa

1533.86

a

y7*y6*

y 2*

S V G T Q V D D G T L E Bpa R

100

**

80

GIPADK b5*

60

50

40

**

b5*

y7* / y13

70

y2*

% Intensity

Relative intensity (%)

90

30

20

10

0

0

400

0

780

1160

1540

Mass (m/z)

mass (m/z)

1920

2300

2300

Fig. 3 Mass spectrometry analyses of DolP containing Bpa at position Val 52. BW25113 derivative cells deleted of endogenous dolP carrying pEVOL-pBpF and a pDolPHis with an amber codon engineered at position Val 52 of the dolP ORF were subjected to UV crosslinking. After Ni-affinity purifications, eluates were subjected to SDS–PAGE and Coomassie Blue staining. Bands corresponding to DolPHis and its major UV-specific crosslink products (in frame) were trypsin digested and subjected to MALDI-TOF MS analyses. DolP and DolP together with OmpA were identified by peptide mass fingerprinting (blue arrows for DolP and red arrows for OmpA) in the samples obtained from the bait DolP protein (a) and the crosslink product (b), respectively. (a)

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3. Further cut the gel slices into 1x1 mm pieces (approximatively 3–4 pieces) and place them into 1.5 mL clean Eppendorf tubes. In addition, excise a protein-free gel slice as a negative control (see Note 18). At this stage, samples can be stored at -20 °C. 4. Wash the gel slices with 100 μL of washing buffer, vortex for 10 min at 450 rpm, and centrifuge 30 s at 12,000× g at room temperature. Pipet off the supernatant. Repeat this step at least twice (see Note 19). 5. Dry the gel slices for 15–20 min in a SpeedVac vacuum concentrator at 45 °C (see Note 20). 6. Add 20 μL of trypsin (10 ng/μL in 25 mM NH4HCO3, pH 7.8) and incubate overnight at 37 °C (see Note 21). 7. Add 10% final ACN to the digest, then vortex for 5 min at 450 rpm at room temperature, and sonicate 300 s at 70 W under pulse mode. The peptide solution can be stored at -20 ° C for further analyses. 8. Deposit 1 μL of the saturated HCCA matrix solution onto the MALDI plate and add 1 μL of the peptide solution. Let the mixture co-crystallize exposed to ambient air (see Note 22). 9. Perform MS or tandem MS using the MALDI-TOF/TOF instrument in reflector positive mode, using the following parameters: MS mode: Mass range: 800–4000 Da Accelerating voltage: 20 kV Grid voltage: 68% Laser intensity: 2000–3500 Number of shoots per spectrum: 5000 MS/MS mode: Accelerating voltage: 8 kV (Source 1) and 15 kV (Source 2) Grid voltage: 86% ä Fig. 3 (continued) The mass of a tryptic peptide containing Bpa in place of V52 (m/z = 1627.76) was identified in the band containing only DolPHis (green arrow). (b) A new mass (m/z = 2227.09) predicted to correspond to a crosslinked peptide (XL-peptide) between DolP (40SVGTQVDDGTLEBpaR53) and OmpA (289GIPADK294) was detected, whereas the peptide at m/z = 1627.76 was not observed. (c) The peptide at m/z = 2227.09 predicted to correspond to the crosslinked peptide identified by MALDI-TOF (see b) was selected for further fragmentation by MALDI-TOF/TOF tandem mass spectrometry using CID-off mode. MS/MS fragmentation confirmed the crosslink between peptide 40SVGTQVDDGTLEBpaR53 of DolP and peptide 289GIPADK294 of OmpA. Keratin tryptic peptides or trypsin autodigestion peptides are labeled with asterisks. Indicated masses correspond to [M + H]+ ions. (This figure is adapted from (Ranava et al. 2021, eLife) which was published under the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) (https:// creativecommons.org/licenses/by/4.0/))

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Laser intensity: 2000–3500 Number of shoots per spectrum: 5000 Mass selection of the precursor ion: +/- 0.5 Da 10. Calibrate MALDI-TOF mass spectrometer using external peptide mass standards or using the masses of the peptides expected for the non-crosslinked bait protein. 11. Analyze data using Data Explorer software to generate mass lists for each peptide mixture. 12. Identify the proteins of interest by peptide mass fingerprinting using MS-Fit program of Protein Prospector tool (https:// prospector.ucsf.edu/). 13. Manually calculate the mass of the peptide containing Bpa by adding the molecular weight difference between Bpa and the amino acid that it has replaced. In this example, an increase of 152.15 Da is expected between the tryptic peptide containing Bpa and the one containing Val at position 52. 14. To identify the peptide of the partner protein that has been crosslinked, predict the theoretical molecular weight of all putative crosslinked tryptic peptides (determined using MS-Digest program of Protein Prospector). Take into account the molecular weight difference between Bpa and the amino acid that it has replaced and the loss of two hydrogen atoms due to the formation of a covalent bond between Bpa and the C–H group on the partner protein. 15. Assign putative crosslinked peptides by comparing experimental and predicted molecular weights. In this example, a crosslinked peptide with a m/z = 2227.09 is predicted (Fig. 3). 16. Confirm the sequence of the crosslinked peptides by tandem mass spectrometry using MALDI-TOF/TOF (see parameters in point 9). The putative crosslinked peptide is selected and then fragmented using collision-induced dissociation (CID)off mode (see Note 23). 17. Analyze the fragmentation pattern using the MS-Pattern program of Protein Prospector for “b” and “y” ion assignment. Manual assignment can be needed for crosslinked peptides. 18. Map the interaction using fragmentation data. In this example, one crosslinked peptide was identified. Bpa at position 52 of DolP crosslinked the OmpA amino acid segment 289GIPADK294, which maps in the periplasmic domain of OmpA (Fig. 3 and see Notes 24 and 25 for data interpretation).

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Notes 1. In order to slow down EspP biogenesis and increase the temporal resolution of sequential EspP interactions, a modified EspP variant is used. EspP(586TEV) harbors a short linker insertion in the EspP passenger domain that delays (but does not permanently block) both passenger domain secretion and β domain insertion into the lipid bilayer [9, 10]. 2. When available, use structural data to guide the strategy for site-specific incorporation of photoprobes. In the case of EspP, Trp 1149 is situated in a β-strand of the β domain with its side chain projecting into the outer leaflet of the outer membrane lipid bilayer [16]. Thus, a photoprobe at position 1149 would be predicted to be in close proximity to the outer membrane LPS upon completion of EspP biogenesis. In the case of DolP, Val 52 is situated on the first BON (Bacterial OsmY Nodulation) domain (BON1) and is oriented to bind partner proteins in the periplasm. 3. A series of plasmids for efficient incorporation of unnatural amino acids into proteins of interest using amber suppression have been developed by Schultz and coworkers [17] and have been deposited into the Addgene plasmid repository. 4. This washing step helps to remove β-lactamase that might have been released in the medium of overnight cultures. Thus, the concentration of ampicillin in the new culture remains optimal for plasmid maintenance throughout the entire time of cell growth. 5. The level of induction of a given protein of interest has to be determined empirically. Excessive protein overexpression may lead to formation of aggregates, which can generate undesired photocrosslinking reactions. 6. Bpa is highly insoluble at a neutral pH; thus, a 1000× stock solution of 1 M Bpa is prepared in 1 M NaOH to facilitate rapid dilution of Bpa. When added to the culture, slowly dispense the Bpa stock solution in the culture medium using a micropipette while gently swirling the culture flask. 7. The pEVOL-pBpF plasmid carries two copies of the engineered amino acyl tRNA synthetase. One copy is under control of the constitutive glnS promoter, while the other is under control of the arabinose-inducible araBAD promoter. Thus, incorporation of Bpa can be optimized by supplementing cell cultures with arabinose, and the use of the pEVOL system can be adapted to a wide variety of protein expression protocol [17]. 8. It is recommended to start a parallel identical culture in G56 containing 0.13 mM KH2PO4 (instead of 32P-labeled

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inorganic phosphate) to monitor cell growth. This will limit the risk of contaminating equipment with 32P while measuring the culture optical density to assess bacterial growth. 9. Wear UV-protective goggles. If using a mercury lamp (instead of a LED lamp), turn on the lamp 10 min before sample irradiation. After this warming-up time, the lamp irradiates with maximal power. 10. The positioning of the UV lamp with respect to the samples will depend of its wattage power. The described protocol is conducted using a LED lamp. The lamp is positioned approximately 4 cm from the samples. If using a traditional (non-LED) lamp, such as Spectroline SB-100P (Spectronics Corporation), which tends to heat up when power is on, it is advisable to ensure the presence of ice chips in the sample to prevent its excessive heating. 11. If using a traditional (non-LED) lamp, such as Spectroline SB-100P, do not switch off the UV lamp in between irradiations of different samples, unless the waiting time is longer than 20 min. Once switched off, it takes about 15 min before it can be reactivated. 12. To successfully conduct the pulse-chase technique, it is critical that the several described steps are conducted in parallel and coordinated in a timely fashion. Thus, the labeling and preparation of tubes where different samples will be collected (described in the previous section) have to be conducted before proceeding with the pulse-chase labeling. In addition, UV irradiation of samples is performed in the interval times of the chase phase. To minimize the time of handling and preparation of the samples, make sure that the following reagents have been previously thawed and ready to be used on the bench: an aliquot of the 35S-Met/35S-Cys protein labeling mix, cold methionine and cysteine, an automatic pipettor and disposable 10 mL pipettes, a P100 micropipette set to 35 μL, and a P1000 micropipette set to 350 μL. 13. In the case of sample labeled with 32P-inorganic phosphate, four washing steps with high-salt RIPA buffer should be conducted to reduce the background of radioactivity detected after SDS–PAGE and autoradiography. 14. The type of gel and the concentration of polyacrylamide to choose will depend on the size of the proteins that contains the photocrosslinker and the generated crosslinking products. In an initial experiment, it might be necessary to analyze samples with different gels of varying polyacrylamide concentrations to increase the chance of resolving crosslinking products. 15. Analysis of pulse-chase labeled samples not irradiated with UV light reveals that a ~135 kDa band, corresponding to EspP, is

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progressively converted to a ~30 kDa band (Fig. 2, lanes 1–3). This conversion is the result of EspP passenger domain cleavage by an autoproteolytic reaction occurring in the membraneintegrated β domain [18]. Upon exposure to UV irradiation, two high-molecular-weight products are immunoprecipitated with EspP antibodies (Fig. 2, lanes 10–12, products labeled “1” and “2”). In parallel reactions, an antiserum against BamA precipitates product 1 (Fig. 2, lane 16), while an antiserum against BamB precipitates product 2 (Fig. 2, lane 13). The signal intensities of EspP-BamA and EspP-BamB crosslinking products are maximal after 1 min chase and decrease over time (lanes 10–18), indicating that the EspP position 1149 interacts with BamA and BamB at an early stage of biogenesis prior to cleavage of the secreted passenger domain. Another crosslink product detected in samples exposed to UV irradiation migrates 2–4 kDa higher than EspP β domain (Fig. 2, lanes 10–12, product labeled “3”). This crosslinking product is generated with high efficiency, and its amount is proportional to the amount of mature EspP β domain that forms over time (lanes 10–12). Thus, the crosslinking product 3 results from a stable interaction of the mature EspP β domain with a lowmolecular-weight factor. In a parallel experiment, cellular lipids are labeled with 32P. Upon UV irradiation, EspP-specific antibodies precipitate a 32P-labeled product that runs with the same apparent molecular weight of crosslinking product 3 (Fig. 2, lane 20). Furthermore, this crosslinking product can be detected using antibodies specific for E. coli LPS [10, 11]. Thus, product 3 reveals an interaction of EspP with lipids of the outer leaflet of the outer membrane. 16. This protocol detects two types of interactions for EspP amino acid position 1149: (i) transient interactions with BamA and BamB at an early stage of EspP biogenesis, prior to passenger domain cleavage; and (ii) an interaction of the mature EspP β domain with the LPS of the outer membrane, following secretion and cleavage of the passenger domain, that remains stable over time. Together with analyses of the interactions occurring at other amino acids of EspP, the site-directed photocrosslinking approach in radiolabeled cells has helped to describe consecutive intermediate steps of the autotransporter assembly reaction in the bacterial outer membrane [9–11]. In brief, distinct autotransporter segments interact with periplasmic chaperones such as Skp and SurA at an early stage of its transport through the cellular envelope. At a later time, transient interactions with one of three subunits of the BAM complex (BamA, BamB, and BamD) are mapped at amino acid positions which are located approximately 120 degrees away from each other at the periplasmic side of the folded EspP β-barrel,

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suggesting the formation of an intermediate where the assembling EspP β domain is at the center of the BAM complex. On the circumference of the folded barrel, amino acid 1149 is positioned between the BamA and the BamB interacting sites. At a subsequent stage, a stable interaction of position 1149 with the outer membrane LPS indicates the release of the EspP β domain by the BAM complex into the lipid bilayer. 17. Carefully clean the scalpel using 20% ethanol between each excision to avoid contamination between samples, and then, wipe using a precision paper (KimTech). 18. Proteins or their crosslink products need to be present in Coomassie Blue-stainable amounts to allow in-gel trypsin digestion and peptide detection by MALDI-TOF MS analysis. 19. For highly stained bands, increase the number of washes. 20. Handle with care the tubes after the drying step to avoid to lose the dehydrated pieces of gels which are very light. 21. If trypsin solution is completely absorbed by the gel pieces, add more trypsin solution in order to cover them. 22. HCCA matrix enables highly sensitive MALDI-TOF-MS measurement of peptides from 0.7 to 4 kDa. 23. If the fragmentation of crosslinked peptide is not efficient enough using CID-off mode, MALDI-TOF/TOF MS/MS analyses should be done using CID-on mode to perform high-energy fragmentation and increase the frequency of “b” and “y” ions. LC-ESI-MS/MS approach can also be carried out to improve fragmentation pattern. 24. This crosslinking approach enable to map protein–protein interaction but can also provide insight into protein folding (if intramolecular crosslink products are identified) or protein homooligomerization. 25. In E. coli, DolP interacts with the BAM complex by associating with its central subunit BamA [14]. Notably, DolP is upregulated by the σE-mediated envelope stress transcriptional response, which is triggered when unfolded outer membrane proteins (OMPs) accumulate in the periplasm. σE upregulates OMP biogenesis factors, including the BAM complex and downregulates OMPs, helping bacteria to cope with stress. We provided evidence that DolP is important for proper folding of BamA that overaccumulates in the outer membrane, thus supporting OMP biogenesis and envelope integrity. By using photocrosslinking, we have demonstrated that DolP interacts not only with BamA but also with OmpA, one of the most abundant OMPs. By using tandem mass spectrometry, we could map the site of interaction in the C-terminal periplasmic domain of OmpA (Fig. 3). By mimicking a feature

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of the σE envelope stress response (i.e., OmpA depletion), we found that OmpA competes with BamA for an interaction with DolP. We proposed that, during envelope stress, the stressinduced depletion of OmpA contributes to enhance the association of DolP with BamA [14].

Acknowledgments This work is supported by the Centre National de la Recherche Scientifique and the University Paul Sabatier of Toulouse. YA Abuta’a is supported by a PhD fellowship from the Fondation pour la Recherche Me´dicale. The protocol on EspP radiolabeling was originally developed in the laboratory of Dr. Harris Bernstein (National Institutes of Health, Maryland, USA). References 1. Holland IB (2010) The extraordinary diversity of bacterial protein secretion mechanisms. In: Economou A (ed) Protein secretion. Humana Press, Totowa, pp 1–20 2. Ellman J, Mendel D, Anthony-Cahill S et al (1991) [15] Biosynthetic method for introducing unnatural amino acids site-specifically into proteins. In: Methods in enzymol, vol 202. Elsevier, pp 301–336 3. Chin JW, Martin AB, King DS et al (2002) Addition of a photocrosslinking amino acid to the genetic code of Escherichia coli. Proc Natl Acad Sci U S A 99:11020–11024 4. Dorman G, Prestwich GD (1994) Benzophenone photophores in biochemistry. Biochemistry 33:5661–5673 5. Dautin N, Bernstein HD (2007) Protein secretion in gram-negative bacteria via the autotransporter pathway. Annu Rev Microbiol 61:89– 112 6. Leyton DL, Rossiter AE, Henderson IR (2012) From self sufficiency to dependence: mechanisms and factors important for autotransporter biogenesis. Nat Rev Microbiol 10: 213–225 7. Hagan CL, Silhavy TJ, Kahne D (2011) β-barrel membrane protein assembly by the bam complex. Annu Rev Biochem 80:189–210 8. Noinaj N, Rollauer SE, Buchanan SK (2015) The β-barrel membrane protein insertase machinery from Gram-negative bacteria. Curr Opin Struct Biol 31:35–42

9. Ieva R, Bernstein HD (2009) Interaction of an autotransporter passenger domain with BamA during its translocation across the bacterial outer membrane. Proc Natl Acad Sci U S A 106:19120–19125 10. Ieva R, Tian P, Peterson JH, Bernstein HD (2011) Sequential and spatially restricted interactions of assembly factors with an autotransporter β domain. Proc Natl Acad Sci U S A 108:E383-91 11. Pavlova O, Peterson JH, Ieva R, Bernstein HD (2013) Mechanistic link between β barrel assembly and the initiation of autotransporter secretion. Proc Natl Acad Sci U S A 110:E938– E947 12. Ganong BR, Leonard JM, Raetz CR (1980) Phosphatidic acid accumulation in the membranes of Escherichia coli mutants defective in CDP-diglyceride synthetase. J Biol Chem 255: 1623–1629 13. Onufryk C, Crouch M-L, Fang FC, Gross CA (2005) Characterization of six lipoproteins in the σE regulon. J Bacteriol 187:4552–4561 14. Ranava D, Yang Y, Orenday-Tapia L et al (2021) Lipoprotein DolP supports proper folding of BamA in the bacterial outer membrane promoting fitness upon envelope stress. elife 10:e67817 15. Akiyama Y, Ito K (1990) Secy protein, a membrane-embedded secretion factor of E. coli, is cleaved by the ompT protease

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in vitro. Biochem Biophys Res Commun 167: 711–715 16. Barnard TJ, Dautin N, Lukacik P et al (2007) Autotransporter structure reveals intra-barrel cleavage followed by conformational changes. Nat Struct Mol Biol 14:1214–1220 17. Young TS, Ahmad I, Yin JA, Schultz PG (2010) An enhanced system for unnatural

amino acid mutagenesis in E. coli. J Mol Biol 395:361–374 18. Dautin N, Barnard TJ, Anderson DE, Bernstein HD (2007) Cleavage of a bacterial autotransporter by an evolutionarily convergent autocatalytic mechanism. EMBO J 26:1942– 1952

Chapter 21 Identification of Protein Partners by APEX2 Proximity Labeling Ophe´lie Remy and Yoann G. Santin Abstract Proximity labeling methods enable the identification of proteins in the vicinity of a protein of interest in living cells. Among them, APEX2 proximity is a powerful method to spatiotemporally define in vivo “proxisomes” in dynamic bacterial protein systems. Here we describe a standardized APEX2 proximity labeling protocol and possible adaptations to capture protein partners in native conditions. Key words Proximity labeling, APEX2, Biotinylation, Proxisome, Interactome, Bacterial labeling

1

Introduction Identification of proteins involved in a specific biological process is a crucial step to understand the physiology of living cells. For that purpose, methods for investigating in vivo protein–protein interactions have been largely used such as co-purification-based methods (e.g., pull-down, co-immunoprecipitation, or tandem affinity purification). However, these methods notoriously fail to detect weak or transient protein interactions, which often leads to an underestimated list of identified protein partners [1]. To overcome such limitations, proximity labeling methods, including BioID [2] and APEX2 proximity labeling [3], have been developed and allow systematic and unbiased identification of protein partners in native conditions. APEX2 proximity labeling relies on the activity of APEX2, an engineered 27 kDa ascorbate peroxidase that catalyzes the oxidation of small biotinylated derivative substrates into highly reactive biotinylated molecules upon the addition of hydrogen peroxide [4]. Once generated, these reactive molecules covalently tag electron-rich amino acids (mostly tyrosine residues) of proteins in the vicinity (~20 nm) of the APEX2 active site. When fused to a protein of interest (POI, acting as a bait protein) in the cell, APEX2

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_21, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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irreversibly tags direct interacting partners as well as proteins in proximity of the POI. Biotinylated proteins are then identified by mass spectrometry upon cell lysis and specific enrichment with streptavidin beads [5], thus defining the POI “proxisome” [6]. More details are provided in this review [6]. Recently, APEX2 proximity labeling has been used to tag the periplasmic content of mycobacteria [7], to reveal the type VI secretion system size-control mechanism in enteroaggregative E. coli [8] and to identify subdomains of the Trypanosoma brucei flagellum [9]. Here, we describe a standardized APEX2 proximity labeling protocol to identify protein partners in bacterial cells. This protocol serves as a basis for in vivo proximity labeling studies but should require some adaptations due to the broad diversity of bacterial lifestyles. A few possible adaptations are therefore provided in the Notes section.

2

Materials

2.1 General Equipment

1. Incubator 2. Vortex 3. Thermal mixer (Thermomixer C Eppendorf or equivalent) 4. Cell density meter 5. Centrifuge for equivalent)

small

volumes

(Eppendorf

5425R

or

6. Centrifuge for big volumes (Eppendorf 5810R or equivalent) 7. Sonicator or any apparatus to disrupt bacterial cells 8. Centrifugal filter or any device for small compounds removal (23,000 U/mg) of egg-white lysozyme in 1 mL of sterile distilled water. Divide the stock into 100 μL aliquots and store them at -20 °C for several months.

2.6 Visualization and Enrichment of Biotinylated Proteins

1. 3% BSA–PBS: Dissolve 1.5 g of bovine serum albumin in 50 mL of 1X PBS buffer. Can be stored at 4 °C for several days. 2. Streptavidin–HRP: Streptavidin–horseradish peroxidase conjugate (Invitrogen or equivalent). 3. Protein loading buffer (Laemmli sample buffer or equivalent). 4. Streptavidin beads: Dynabeads M-280 Streptavidin (10 mg/ mL) or equivalent. 4. 200 mM biotin: Dissolve 48.9 mg of biotin in 1 mL of sterile DMSO. Divide the stock into 100 μL aliquots and store them at -20 °C for several months. 5. 1 M DTT: Dissolve 1.54 g of dithiothreitol in 10 mL of distilled water. Divide the stock into 2 mL aliquots and store them at -20 °C for several months.

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Methods

3.1 Biotin-Phenol Incorporation

1. This is the most critical step, because we noticed that biotinphenol (BP) does not cross easily the bacterial inner membrane in certain conditions. For labeling in the cytoplasm, some adaptations might be required according to the bacterial specie and/or the study (see Note 2). 2. From a typical overnight culture, dilute 1:100 in 15–50 mL of the selected medium (e.g., M9 or LB) until the culture reaches the conditions in which proximity labeling needs to be performed (e.g., specific optical density, time upon the addition of a drug) (see Note 3). 3. Harvest cells by centrifugation at 4000 × g for 5 min at room temperature (RT). 4. Wash cells by centrifugation in 1 mL of the selected medium at 4000 × g for 5 min at RT. 5. Repeat step 4 once and resuspend the final pellet in 2 mL of growth medium. Measure the optical density (see Note 4). 6. Collect 1 mL in a 2 mL single-use tube. This tube will be the labeling tube. 7. Collect the remaining 1 mL in another 2 mL single-use tube; this tube will be the negative control tube. 8. Incubate each sample with 1 mM final of BP (5 μL of the 200 mM BP stock) for 30 min in a thermal mixer at 800 rpm. Adjust the temperature according to your bacterial species.

3.2 APEX2 Proximity Labeling

1. At the end of the BP incubation, prepare quenchers by adding 1 mL of 1X PBS buffer in tubes containing the sodium L-ascorbate and the sodium azide powder (1 M final concentration) (see Note 5). 2. Prepare just before use the hydrogen peroxide solution by diluting 11.2 μL of the 30% H2O2 stock solution in 1 mL of 1X PBS (100 mM final concentration) (see Note 6). 3. To start the proximity labeling, add 10 μL of the diluted H2O2 solution prepared in step 2 to the labeling tube (1 mM final concentration). Do not add H2O2 in the negative control tube. 4. Quickly vortex and leave tubes in the thermal mixer at 800 rpm for 1 min (see Note 7). 5. Quench the reaction by adding 10 μL of each quencher in both samples (10 mM final concentration) and mix by vortexing.

3.3

Cell Lysis

1. Pellet cells by centrifugation at 4000 × g for 5 min at RT. 2. From this step, keep the samples and buffers on ice.

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3. Resuspend cells in 750 μL of cold lysis buffer A supplemented with 7.5 μL of 10 mg/mL lysozyme (10 μg/mL final concentration), and mix by brief vortexing (see Note 8). 4. Incubate for 40 min on ice (see Note 9). 5. Add 750 μL of the cold lysis buffer B and mix by vortexing. Keep the samples on ice. 6. At this step, you can use any way to disrupt bacterial cells such as sonication and/or freeze–thaw cycles. 7. Centrifuge the samples at 20000 × g for 10 min at 4 °C to discard cellular debris and obtain a cleared cell lysate. 8. Transfer each cleared lysate in a new single-use tube. Keep the samples on ice (see Note 10). 3.4 Biotinylation “Fingerprint”

APEX2 proximity labeling should be confirmed by western blotting before proceeding, as follows: 1. For each sample, combine 75 μL of the cleared lysate with 25 μL of 4X protein loading buffer, and then boil at 96 °C for 10 min. 2. Load the samples on a polyacrylamide gel to separate proteins by SDS–PAGE, and then transfer the samples to a nitrocellulose membrane using the standard equipment (see Note 11). 3. Block the membrane with 10 mL of 3% BSA–PBS buffer for 1 h at RT (see Note 12). 4. Discard the blocking solution. 5. Incubate the membrane in 10 mL of 0.5 μg/mL streptavidin– HRP in 3% BSA–PBS buffer for 1 h at RT, and then wash the membrane with 1% PBS three times for 5 min each time. 6. Detect biotinylated proteins using the standard HRP chemiluminescent reaction. 7. A typical example of APEX2 proximity labeling results is shown in Fig. 1.

3.5 Enrichment of Biotinylated Partners

Note that this step is usually performed by the proteomics core facility: 1. This is a critical step, because residual BP remains in the cell lysate and may compete for binding to streptavidin beads. We thus recommend using centrifugal filter devices (e.g., 3 kDa Amicon centrifugal filter unit) or dialysis methods to eliminate residual BP before starting protein enrichment (see Note 13). 2. Collect 300 μL of streptavidin beads (3 mg) and wash them with 1 mL of lysis buffer B three times for 5 min each time (see Note 14). 3. Resuspend streptavidin beads in 300 μL of lysis buffer B.

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Fig. 1 Example of APEX2-dependent biotinylation profiles. In the negative control (lane 1), only endogenous biotinylated proteins should be visible. For example, in E. coli, only one band corresponding to the biotin carboxyl carrier protein BCCP (accB gene) is observed at 22.5 kDa [7, 8]. Note that in bacteria that exhibit too much endogenous biotinylated proteins, biotin probes should be replaced as done in this study [7]. In the labeling samples, several bands should be observed, corresponding to APEX2-tagged proteins in the vicinity of your bait protein (i.e., its “proxisome”). Since bait proteins likely exhibit different interactions, this labeling profile should be unique for each protein of interest (comparison lane 2 and 3)

4. Incubate 700 μL of the labeling sample with the 300 μL of streptavidin beads for 1 h at RT on a rotator. 5. Pellet beads using a magnetic rack and collect the supernatant. Save the supernatant in a single-use tube for subsequent controls (see Note 15). 6. Wash bead sample with 1 mL of lysis buffer B for 5–10 min at RT on a rotator (see Note 16). Pellet beads using a magnetic rack and discard the wash solution. 7. Repeat step 6 twice. 8. Elute biotinylated proteins from the streptavidin beads by boiling the sample in 30 μL of 2X protein loading buffer supplemented with 2 mM biotin and 20 mM DTT for 10 min at 96 ° C. 9. Briefly vortex the beads and spin down samples to bring down condensation. 10. Pellet beads using a magnetic rack and collect the eluate. 11. Put the eluate on ice (see Note 17). 12. Use 5 μL of the eluate to control protein enrichment by western blotting as shown in Subheading 3.4.

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1. This part highly relies on the proteomics core facility you will choose and cannot be described in this typical protocol. However, important guidance is provided in [6]. 2. We recommend the use of quantitative proteomics analyses to statistically identify in vivo proxisomes. A schematic pipeline of the different approaches, including the stable isotope labeling by amino acids in cell culture (SILAC), the tandem mass tagging (TMT), and the label-free quantification (LFQ), is shown in [6]. 3. Conventional MS/MS analysis is then usually performed to identified biotinylated partners upon on bead trypsin digestion.

4

Notes 1. Sodium azide is toxic; wear a mask and gloves when weighing it. 2. We noticed that a high cellular density is often required for proper BP incorporation in liquid conditions. Furthermore, BP incorporation can also be performed on solid medium when compatible with your study. Briefly, grow cells to reach log phase; concentrate them to reach ~10 A600. Then drop 0.5–1 mL of the concentrated cells on the solid medium supplemented with BP (from 1 to 10 mM). Incubate for hours (optimization required), and scrap the drop using a loop in 1 mL of the selected medium. Continue at Subheading 3.1, step 3. This adaptation was used in [8]. Another way is to perform the APEX2 proximity labeling on cellular extracts (i.e., upon cell lysis). In that case, incubate cleared cellular extracts with 0.5–1 mM of BP for 30 min at 37 °C prior the H2O2 pulse. Continue at Subheading 3.4, step 1. 3. Empirical optimizations must be conducted to adjust the density of cells required for proper BP incorporation and proximity labeling. 4. Because of the high cell density, dilute 50–100 times before measuring A600. 5. Wear gloves when handling sodium azide. Strongly vortex to dissolve quencher powder. Quencher solutions should not be used again and should be disposed as chemical waste. 6. Due to the small volume collected from the stock solution, we recommend to strongly vortex the mix to ensure a complete homogeneity. This step is crucial for a proper labeling. H2O2 solution should not be used again (loss of activity) and should be disposed as hazardous waste.

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7. We noticed that 1 min is sufficient for a good labeling in bacteria. Longer labeling time only increases the amount of a specific biotinylation and impede both biotinylated protein enrichment and identification. 8. This step may differ according to the organism. If the lysis is not complete, try to increase lysozyme concentration and/or the incubation duration. 9. Solubilization of the membrane fraction is required if your protein of interest likely interacts with membrane partners. Add ionic or nonionic detergents (e.g., 10% SDS or 0.2% Igepal CA-630, respectively) in lysis buffers A and B. 10. Cleared lysates can be stored at -20 °C for several weeks prior proceeding. 11. Depending on the sample, we recommend loading 10–15 μL of A600 ~0.5–0.8. 12. Do not use milk for the blocking step. Milk contains a huge amount of free biotin molecules that prevent streptavidin to bind biotinylated proteins. Note that blocking can be performed overnight at 4 °C. 13. Depending on the sample, we recommend diluting the sample 200–500 times before proceeding. 14. Empirical optimizations must be conducted to adjust the amount of beads required for a proper protein enrichment. 15. Supernatant is considered as the “flow-through” and should be analyzed by western blotting as shown in Subheading 3.4. to evaluate the quantity of biotinylated proteins. If too much biotinylated proteins are observed on the blot, you should dilute more your sample to eliminate residual BP and/or increase the amount of streptavidin beads. 16. Spin the sample to collect beads which are trapped in the tube cap. 17. The eluate can be stored at -20 °C for several weeks prior mass spectrometry analysis.

Acknowledgment The authors would like to thank Charles de Pierpont for critical review of the book chapter and Geraldine Laloux for support and language editing. YGS is supported by a postdoctoral fellowship from the EMBO (ALTF 911-2021).

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References 1. Branon T, Han S, Ting A (2017) Beyond immunoprecipitation: exploring new interaction spaces with proximity biotinylation. Biochemistry 10:1021 2. Roux KJ, Kim DI, Raida M, Burke B (2012) A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells. J Cell Biol 196:801–810 3. Rhee H-W, Zou P, Udeshi ND, Martell JD, Mootha VK, Carr SA et al (2013) Proteomic mapping of mitochondria in living cells via spatially restricted enzymatic tagging. Science 339: 1328–1331 4. Lam SS, Martell JD, Kamer KJ, Deerinck TJ, Ellisman MH, Mootha VK et al (2014) Directed evolution of APEX2 for electron microscopy and proximity labeling. Nat Methods 12:51–54 5. Hung V, Udeshi ND, Lam SS, Loh KH, Cox KJ, Pedram K et al (2016) Spatially resolved proteomic mapping in living cells with the engineered peroxidase APEX2. Nat Protoc 11:456–475

6. Santin YG (2019) Uncovering the in vivo proxisome using proximity-tagging methods. BioEssays 41:1900131 7. Ganapathy US, Bai L, Wei L, Eckartt KA, Lett CM, Previti ML et al (2018) Compartmentspecific labeling of bacterial periplasmic proteins by peroxidase-mediated biotinylation. ACS Infect Dis 4:918–925 8. Santin YG, Doan T, Lebrun R, Espinosa L, Journet L, Cascales E (2018) In vivo TssA proximity labelling during type VI secretion biogenesis reveals TagA as a protein that stops and holds the sheath. Nat Microbiol 3:1304–1313 9. Ve´lez-Ramı´rez DE, Shimogawa MM, Ray SS, Lopez A, Rayatpisheh S, Langousis G et al (2021) APEX2 proximity proteomics resolves flagellum subdomains and identifies flagellum tip-specific proteins in Trypanosoma brucei. mSphere 6:e01090-20

Chapter 22 Blue Native PAGE Analysis of Bacterial Secretion Complexes Susann Zilkenat, Eunjin Kim, Tobias Dietsche, Julia V. Monjara´s Feria, Claudia E. Torres-Vargas, Mehari Tesfazgi Mebrhatu, and Samuel Wagner Abstract Bacterial protein secretion systems serve to translocate substrate proteins across up to three biological membranes, a task accomplished by hydrophobic, membrane-spanning macromolecular complexes. The overexpression, purification, and biochemical characterization of these complexes is often difficult, thus impeding progress in understading structure and function of these systems. Blue native (BN) polyacrylamide gel electrophoresis (PAGE) allows for the investigation of these transmembrane complexes right from their originating membranes, without the need of long preparative steps, and is amenable to the parallel characterization of a number of samples under near-native conditions. Here, we present protocols for sample preparation, one-dimensional BN PAGE and two-dimensional BN/SDS PAGE, as well as for downstream analysis by staining, immunoblotting, and mass spectrometry on the example of the type III secretion system encoded on Salmonella pathogenicity island 1. Key words Bacterial secretion systems, Membrane proteins, Blue native polyacrylamide gel electrophoresis, Two-dimensional polyacrylamide gel electrophoresis, Sucrose gradients, Bacterial cell fractionation, Type III secretion systems, Salmonella typhimurium, Escherichia coli

1

Introduction Blue native (BN) polyacrylamide gel electrophoresis (PAGE) is an electrophoretic method that was originally developed by Sch€agger and von Jagow to investigate the composition of protein complexes of the respiratory chain from isolated mitochondrial membranes [1]. Since its introduction, it has been widely adopted to characterize individual membrane protein complexes like the bacterial Sec and Tat translocons [2, 3] or mitochondrial and chloroplast import complexes [4, 5], as well as to assess compositions of global complexomes in wild-type conditions and upon perturbation [6– 10]. Lately, it has also been recognized as a suitable tool for the elucidation of composition and assembly of bacterial protein secretion systems: T4SS [11, 12], T3SS [13–17], and T7SS [18].

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_22, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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BN PAGE relies on the mild extraction of membrane proteins and membrane protein complexes by nonionic detergents, which preserve the native protein conformation [19]. Charging and electrophoretic migration of the extracted proteins are facilitated by adsorption of the anionic, water-soluble blue dye Coomassie G to the hydrophobic regions of the extracted membrane proteins, which are then separated based on their complex size in gradient gels of a Tricine-based PAGE. Here, we explain the use of BN PAGE for the characterization of bacterial secretion systems on the example of the needle complex of the Salmonella typhimurium type III secretion system encoded on Salmonella pathogenicity island 1, a > 180 component complex of 4.5 MDa [17, 20, 21]. We provide protocols for preparation of samples from whole bacterial cells, crude and purified membranes, and immunoprecipitated material, for BN PAGE-based separation of complexes using one-dimensional BN PAGE or two-dimensional BN/SDS PAGE and for detection and analysis of separated complexes by Coomassie and silver staining, immunoblotting, and mass spectrometry. The individual protocols are provided as modules that can be combined freely according to individual needs.

2

Materials

2.1 Sample Preparation 2.1.1 Sample Preparation General Materials

1. Buffer K: 50 mM triethanolamin (TEA), 250 mM sucrose, 1 mM ethylenediaminetetraaceticacid (EDTA) (see Note 1), pH 7.5 (adjusted with acetic acid (HAc). Store at 4 °C. 2. Lysozyme solution: 10 mg/mL in ddH2O. Store aliquots of 100 μL at -20 °C. 3. Phosphate-buffered saline (PBS): 137 mM NaCl, 2.7 mM KCl, 4.3 mM Na2HPO4, 1.47 mM KH2PO4, pH 7.4, adjusted with 1 M NaOH. 4. DNase solution: 10 mg/mL in 1x PBS. Store aliquots of 100 μL at -20 °C. 5. 1 M MgSO4 in ddH2O. Store at room temperature. 6. Complete protease inhibitor cocktail (without EDTA). 7. ACA750: 750 mM aminocaproic acid, 50 mM Bis–Tris in ddH2O. 8. Liquid nitrogen. 9. 10% (w/v) n-dodecyl-β-D-maltoside (DDM). 10. BN loading buffer: 5% Serva Blue G (see Notes 2 and 3), 250 mM aminocaproic acid, 25% glycerol in ddH2O.

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1. Common material from Subheading 2.1. 2. Glass beads, acid washed, 150–212 μm. 3. Homogenizer, e.g., SpeedMill Plus (Analytik Jena) or FastPrep-24 (MP Biomedicals). 4. Ultracentrifuge.

2.1.3 Membrane Fractionation by Sucrose Density Gradient Centrifugation

1. Common material from Subheading 2.1. 2. Buffer 2x M: 100 mM TEA, 2 mM EDTA (see Note 1), pH 7.5 adjusted with acetic acid (HAc). 3. Sucrose solutions (see Notes 4 and 5) for two gradients in 14 mm × 89 mm tubes: See Table 1. 4. Buffer L: 50 mM TEA, 250 mM sucrose (see Note 1), pH 7.5 (adjusted with HAc). Store at 4 °C. 5. French press. 6. SW 41 Ti rotor (Beckman) for ultracentifuge. 7. Dounce homogenizer. 8. Seton 14 mm × 89 mm open top polyclear ultracentrifuge tubes for SW 41 Ti rotor (see Notes 6–8) (optional, see Subheading 3.1.4). 9. Syringes and hypodermic Subheading 3.1.4).

needles

(optional,

see

10. Gradient Station (Biocomp Instruments), including accessories (optional, see Subheading 3.1.4). 11. Bicinchoninic acid (BCA) protein assay. 2.1.4 Immunoprecipitation of Membrane Protein Complexes

1. Anti-FLAG M2 Affinity Gel (see Note 9).

2.2

1. Precast native polyacrylamide gradient gels, e.g., Invitrogen NativePAGE™ Bis–Tris, or SERVAGel™ N gel systems.

Blue Native PAGE

2.2.1 One-Dimensional BN PAGE Using Precast Mini Gels

2. 3x FLAG peptide (see Note 9). 3. Rotating wheel.

2. 10x BN anode buffer: 500 mM Bis–Tris–HCl, pH 7.0. Store at 4 °C. 3. 10x BN cathode buffer A: 500 mM Tricine, 150 mM Bis–Tris, 0.2% (w/v) Serva Blue G. Do not adjust pH and store at 4 °C. 4. 10x BN cathode buffer B: 500 mM Tricine, 150 mM Bis–Tris. Do not adjust pH and store at 4 °C. 5. NativeMark™ Unstained Protein Standard (Thermo Fisher).

2.2.2 Two-Dimensional BN/SDS PAGE

1. Hoefer SE600 or SE660 vertical electrophoresis unit. 2. GelBond PAG film (Lonza).

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Table 1 Sucrose solutions for two gradients Percentage of sucrose (w/w)

Sucrose

Buffer 2x M

57% sucrose solution

Buffer 1x M

57%

22.8 g

17.2 mL 12.8 mL

10.0 mL

55% sucrose solution

Buffer 1x M

50%

4.55 mL

0.45 mL

45%

4.1 mL

0.91 mL

40%

3.6 mL

1.36 mL

35%

3.2 mL

1.8 mL

30%

2.7 mL

2.3 mL

32% Percentage of sucrose (w/w)

Sucrose

Buffer 2x M

55%

19.25 g

13.5 mL

3. Double-sided Scotch tape (Tesa Photo® Film) (see Note 10). 4. Gel Seal. 5. 3x BN gel buffer: 1.5 M aminocaproic acid, 150 mM Bis–Tris– HCl, pH 7.0. Store at 4 °C. 6. Acrylamide 30% T, 3% C. 7. Amonium persulfate (APS): 10% (w/v) in ddH2O. Store at 20 °C. 8. N, N, N′, N′-tetramethyl-ethylenediamine (TEMED). Store at 4 °C. 9. 80% (v/v) glycerol in ddH2O. Autoclave and store at room temperature. 10. Gradient maker, e.g., Hoefer SG30 or SG50. 11. Peristaltic pump including tubing, e.g., Cytiva P-1. 12. 10x BN anode buffer: 500 mM Bis–Tris–HCl, pH 7.0. Store at 4 °C. 13. 10x BN cathode buffer A: 500 mM Tricine, 150 mM Bis–Tris, 0.2% (w/v) Serva Blue G. Do not adjust pH and store at 4 °C. 14. 10x BN cathode buffer B: 500 mM Tricine, 150 mM Bis–Tris. Do not adjust pH and store at 4 °C. 15. NativeMark™ Unstained Protein Standard (Thermo Fisher). 16. Acrylamide 30% T, 2.6% C. 17. 10% (w/v) sodium dodecylsulfate (SDS) solution in ddH2O, store at room temperature. 18. SDS stacking gel buffer: 0.5 M Tris–HCl, pH 6.8. 19. SDS resolving gel buffer: 1.5 M Tris–HCl, pH 8.8.

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20. 10x SDS PAGE running buffer: 0.25 M Tris, 1.92 M glycine, 1% (w/v) SDS. 21. SDS equilibration buffer: 2% (w/v) SDS, 1% (v/v) β-mercaptoethanol, 50 mM Tris–HCl, pH 6.8. 22. Low melting agarose solution: 1% (w/v) low melting agarose, 0.5% (w/v) SDS, a few grains of bromphenol blue, 50 mM Tris–HCl, pH 6.8. 23. Razor blades, scissors. 24. Filter paper. 25. Protein standard. 2.3 Protein Detection and Analysis 2.3.1 Colloidal Coomassie Staining

1. Fixing solution: 50% (v/v) ethanol, 3% (w/v) phosphoric acid in ddH2O. 2. Serva Blue G (see Note 2). 3. ddH2O. 4. Neuhoff’s solution: 16% (w/v) ammonium sulfate, 25% (v/v) methanol, 5% (v/v) phosphoric acid; fill up to 100% with ddH2O. 5. Storage solution: 5% (v/v) glacial acetic acid in ddH2O.

2.3.2

Silver Staining

Because silver staining is extremely sensitive to trace impurities in water, it is strongly recommended to use high-quality ddH2O in all recipes and protocol steps. 1. Fixing solution: 45% (v/v) methanol and 5% (v/v) glacial acetic acid in ddH2O. 2. Sensitizing solution: 0.02% (w/v) sodium thiosulfate in ddH2O. Always prepare fresh. 3. Silver nitrate solution: 0.1% ddH2O. Always prepare fresh.

(w/v)

silver

nitrate

in

4. Developing solution: 2% (w/v) sodium carbonate and 0.04% (v/v) formalin in ddH2O. Prepare this solution the day of use and add formalin to the developer at most 1 h before use. 5. Stop solution: 1% (v/v) glacial acetic acid in ddH2O. 6. Plastic containers: Polyethylene trays are recommended (see Note 11). 7. Rocking table. 8. Special waste container for silver nitrate waste. 2.3.3 Immunoblotting Using Dual-Color Detection

1. 10x SDSPAGE running buffer: 0.25 M Tris base, 1.92 M glycine, 1% (w/v) SDS. 2. 10x Transfer buffer: 0.25 M Tris base, 1.92 M glycine, 0.05% SDS.

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3. Polyvinylidene fluoride (PVDF) membrane. 4. Wet blot unit. 5. 100% methanol. 6. 10x Tris-buffered saline (TBS): 200 mM Tris–HCl, 1.5 M NaCl, pH 8.0. 7. TBST: TBS/0.05% Tween 20. 8. 0.1% Ponceau S, 5% acetic acid in ddH2O. 9. Primary antibodies. 10. Secondary DyLight antibodies (Thermo Fisher). 11. LiCor Odyssey infrared scanner. 12. Image Studio Software (LiCor) or Quantity One Software (Bio-Rad). 2.3.4 Preparation of BN PAGE-Separated Complexes for Analysis by Mass Spectrometry

1. 5% HAc (v/v) 2. 100% ethanol 3. Powder-free gloves (newly opened box) 4. Light table 5. Razor blades 6. Tweezers

3

Methods Methods are divided in sample preparation (Subheading 3.1), BN PAGE (Subheading 3.2), and protein detection and analysis (Subheading 3.3). Modules within these divisions can be combined as needed to yield a complete protocol. Each module contains a suggestion for suitable methodological combinations.

3.1 Sample Preparation 3.1.1 Extraction of Membrane Proteins from Whole Bacterial Cells

This protocol serves to extract membrane protein complexes from small amounts of whole bacterial cells. The extracted proteins are best run on BN mini gels and analyzed by immunoblotting as described previously [13]: 1. All steps are to be performed at 4 °C or on ice unless otherwise stated. 2. Harvest 0.4–0.6 optical density units (ODU, see Note 12) of bacterial cells by centrifugation for 2 min at 5000× g and 4 °C. 3. Aspirate supernatant. 4. Optional: Snap freeze cell pellet in liquid nitrogen and store cell pellet at -80 °C (see Note 13). Thaw pellet on ice to continue. 5. Resuspend bacterial cell pellet in 10 μL freshly prepared buffer K, supplemented with protease inhibitor cocktail, 10 μg/

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mL lysozyme, 10 μg/mL DNase, and 1 mM MgSO4 (see Note 14). 6. Incubate for 30 min on ice to allow for cell wall digestion. 7. Add 70 μL buffer ACA750 and mix (see Note 15). 8. Freeze–thaw three times in liquid nitrogen 1 min/ 20 °C water 1 min. 9. Add 10 μL freshly prepared 10% DDM and mix to allow for extraction of membrane proteins (see Note 16). 10. Incubate for 1 h on ice with occasional mixing. Alternatively, place into shaker for 1.5 mL reaction tubes, cooled down to 4 ° C, and mix at 1000 rpm. 11. Spin sample for 20 min at 20,000× g and 4 °C to pellet unsolubilized material (see Note 17). 12. Transfer 45 μL of supernatant to new 1.5 mL tube containing 5 μL BN loading buffer and mix. 13. Load 25 μL of the suspension per well of a BN mini gel (see Subheading 3.2.1). 3.1.2 Extraction of Membrane Proteins from Crude Bacterial Membrane Preparations

This protocol describes the preparation of small amounts of crude bacterial membranes and extraction of membrane protein complexes thereof (see Note 18). The extracted proteins are best run on BN mini gels and analyzed by immunoblotting as described previously [16]; however, we have also made good experience with the analysis of these preparations by two-dimensional BN/ SDS-PAGE: 1. All steps are to be performed at 4 °C or on ice unless otherwise stated. 2. Harvest 2–10 ODU of bacterial cells by centrifugation for 2 min at 5000× g and 4 °C (see Notes 19 and 20). 3. Aspirate supernatant. 4. Optional: Snap freeze cell pellet in liquid nitrogen and store cell pellet at -80 °C (see Note 13). Thaw pellet on ice to continue. 5. Resuspend bacterial cell pellet in 750 μL freshly prepared buffer K supplemented with protease inhibitor cocktail, 1 mM EDTA, 10 μg/mL lysozyme,10 μg/mL DNase (see Note 14). 6. Incubate for 30 min on ice to allow for cell wall digestion. 7. Meanwhile, prepare 2 mL screw cap tubes with 500 μL glass beads. Label the tubes on the lid and on the side. Place the tubes on ice for cooling (see Notes 21 and 22). 8. Add 0.8 μL 1 M MgSO4 to each sample (final concentration). 9. Transfer the cell suspension to a bead-containing tube and close properly.

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10. Break up cells by bead milling (2 min continuous mode using SpeedMill Plus, 2 × 20 s at 4 m/s using FastPrep-24). For cooling of samples, cool down the SpeedMill Plus sample holder to -20 °C or place the samples on ice for 5 min after first 20 s cycle when using the FastPrep-24. 11. Pellet glass beads by centrifugation for 1 min at 1000× g and 4 °C. 12. Transfer supernatant to a fresh 1.5 mL tube. Take care to pipet carefully and transfer as few glass beads as possible in this step. 13. Add 1 mL buffer K to the glass beads and mix vigorously to wash off the remaining material. 14. Pellet glass beads again by centrifugation for 1 min at 1000× g and 4 °C. 15. Transfer supernatant to the 1.5 mL tube containing the supernatant of step 12. Take care to pipet carefully and transfer as few glass beads as possible in this step. 16. Pellet glass beads and cell debris by centrifugation for 10 min at 10,000× g and 4 °C. 17. Transfer 1.3 mL of supernatant to 1.5 mL ultracentrifugation tubes (see Note 23). Take care to avoid the transfer of glass beads or cell debris. 18. Balance tubes to the same weight using buffer K for subsequent ultracentrifugation. 19. Pellet crude membranes by ultracentrifugation for 45 min at 55,000 rpm (135,520× g) and 4 °C (Beckman TLA-55 rotor). 20. Discard supernatant. 21. Optional: Store membrane pellet at -80 °C. Thaw pellet on ice to continue. 22. Resuspend membrane pellet in ACA750 (see Note 15) by carefully pipetting up and down 40 times using 100–200 μL pipette tips. 8 μL buffer per ODU harvested bacterial cells are recommended, e.g., 24 μL for three ODU bacteria. 23. Add 1 μL freshly prepared 10% DDM per ODU harvested bacteria (3 μL for 3 ODU) to allow for extraction of membrane proteins (see Note 16). 24. Incubate for 1 h on ice with occasional mixing. Alternatively, place into shaker for 1.5 mL reaction tubes, cooled down to 4 ° C, and mix at 1000 rpm. 25. Spin sample for 20 min at 20,000× g and 4 °C to pellet unsolubilized material (see Note 17). 26. Transfer 18 μL of supernatant to new 1.5 mL tube containing 2 μL BN loading buffer and mix.

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27. Load 10–20 μL of the suspension per well of a BN mini gel (see Note 24). Larger amounts of up to 50 μL have to be used for the analysis of membrane protein complexes by two-dimensional BN/SDS PAGE (see dedicated protocol in Subheading 3.2.2). 3.1.3 Preparation of Crude Membranes for Sucrose Density Gradient Centrifugation

This protocol describes the preparation of crude membranes in large scale that are used for further membrane fractionation using sucrose density gradient centrifugation described in Subheadings 3.1.4 and 3.1.5 (see Note 25): 1. All steps are to be performed at 4 °C or on ice unless otherwise stated. 2. Harvest 500–2000 ODU of bacterial cells by centrifugation for 15 min at 6000× g and 4 °C, e.g., in Beckman JLA-8.1000 (see Note 26). 3. Pour off supernatant. 4. Resuspend bacterial pellet in 35 mL cold PBS. 5. Transfer suspension to 50 mL Falcon tube. 6. Pellet bacteria by centrifugation for 10 min at 6000× g and 4 ° C. 7. Resuspend bacterial pellet in 10–15 mL buffer K supplemented with protease inhibitor cocktail, 1 mM EDTA, 10 μg/mL DNase, and 10 μg/mL lysozyme (all final concentrations). 8. Pass the cell suspension two times through a French press at 18,000 psi (high position, 1000–1100 units). 9. Add MgSO4 to a final concentration of 1 mM to activate the DNase. Mix. 10. Pellet cell debris by centrifugation for 20 min at 24,000× g and 4 °C (see Notes 27 and 28). 11. Transfer supernatant to suitable ultracentrifugation tubes or bottles, e.g., for Beckman Type 45 Ti. Fill up the bottles with buffer K. 12. Pellet crude membranes by centrifugation for 45 min at 234,000× g and 4 °C (45,000 rpm in Beckman Type 45 Ti). 13. Discard supernatant. Wipe off residual supernatant with lintfree tissue. 14. Resuspend crude membranes in 500 μL buffer 1x M. Use a cut 1 mL pipette tip to detach membrane from tube wall. Transfer crude suspension to 1 mL dounce homogenizer. Homogenize with loose piston 15 times. 15. Store crude membrane suspension on ice until further use and proceed to alternatives (see Subheadings 3.1.4 or 3.1.5).

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3.1.4 Membrane Fractionation by Sucrose Density Gradient Centrifugation Using a Biocomp Gradient Station

This protocol describes the preparation of separated inner and outer membranes of Gram-negative bacteria using sucrose density gradient centrifugation and extraction of membrane protein complexes thereof. Sucrose gradient formation and fractionation are described in two ways: either supported by a Biocomp Gradient Station (this section, see Note 29) or manually without the need for specialized equipment (see Subheading 3.1.5). The extracted proteins of inner or outer membrane fractions, respectively, are suitable for any of the described downstream analyses. We previously described the use of purified inner membranes for the analysis of complete membrane complexomes by two-dimensional BN/SDS PAGE [6–10], one-dimensional BN PAGE followed by quantitative immunoblotting [13], and immunoprecipitation followed by one-dimensional BN PAGE or two-dimensional BN/SDS PAGE, respectively [13–15]: 1. Prepare Seton 14 × 89 mm open top polyclear centrifuge tubes for SW41 Ti rotor. Mark at “half full, long cap,” as described in the Gradient Station manual. 2. Place tubes in the tube holder of the Gradient Station. Fill up to 2–3 mm above half full mark with the 32% sucrose solution using a syringe and the needle provided with the Gradient Station (Fig. 1a). 3. Carefully underlay with 57% sucrose solution using a syringe and the needle provided with the Gradient Station (see Note 30). Bring up 57% sucrose solution exactly to the half full mark. While underlaying, keep the tip of the syringe just below the phase boundary. Strictly avoid air bubbles coming out of the needle (Fig. 1b). 4. When removing the syringe needle, do so swiftly and make sure to fix the piston to prevent further leakage of the 57% solution during the movement. 5. Close the tubes with the long caps provided by the Gradient Station. Avoid entrapment of air bubbles by pushing the cap down at a slight angle so that air can leave through the ventilation whole in the cap (Fig. 1c). 6. Level gradient platform according to the Gradient Station manual. 7. Place tube holder at the center of the gradient platform. 8. Run the gradient protocol SW41 LONG-SUCR-32-57%-w/ w-2ST (step 1, 4:00 min, 70°, 30 rpm; step 2, 0:25 min, 85°, 25 rpm, check Gradient Station manual for programming instructions). 9. Remove excess sucrose solution from cap (Fig. 1d).

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Fig. 1 Preparation of sucrose gradients (a–e) using the Biocomp Instruments Gradient Station or (f–g) by hand. For details, see Subheadings 3.1.4 and 3.1.5

10. Carefully place gradients on ice until further use. Proceed to step 11 for the ultracentrifugation of sucrose gradients. 11. Carefully layer the membrane suspension from step 15 in Subheading 3.1.3. on top of the continuous 32–57% (see Note 31, Fig. 1e, g). 12. Tare two opposing tubes with buffer 1x M to Ri = 180 RUs The experimental Rmax is always lower than the theoretical calculated Rmax. In fact, it is difficult to obtain 100% active ligand on the surface (having the same orientation, immobilized at the same interface, where all binding sites are available and all molecules are correctly folded). In order to avoid this heterogeneity, it is recommended to immobilize between 20% and 50% of additional ligand. In general, kinetic measurements need a small amount of ligand immobilization (Rmax between 50 and 150 RUs). For affinity studies, the binding capacity can vary from low to moderate levels (Rmax between 100 and 800 RUs). The important factor in the last case is the capacity of the analyte to saturate the surface during contact time.

Surface Plasmon Resonance

3.4 Ligand and Analyte Preparation

369

1. After purification, it is important to characterize the multimerization state of your proteins. This can be done using size exclusion chromatography (SEC), dynamic light scattering (DLS) or SEC coupled to multiple angle light scattering (SEC-MALS). If your protein forms multimers, it is recommended to use it as ligand. 2. The purified proteins must be dialyzed over night at 4 °C against the running buffer to be used on the SPR experiment (see Note 4). 3. Check the purity and the stability of the proteins at least 1 day before doing the experiment. This can be done by SDS–PAGE and Coomassie blue staining. 4. If the proteins are already purified in the running buffer and conserved at -80 °C, check their quality after thawing by concentration quantification. Compare the concentration of the protein sample before freezing and after thawing 1 day before the experiment to be sure that thawing the protein did not lead to precipitation. 5. Thaw proteins by hand or on ice for 30 min. The thawing method depends on the protein folding and buffer composition, and the choice of the thawing method should be optimized before starting the protein–protein interaction investigation. 6. Before using, the proteins must be centrifuged at 16,000 g for 20 min at 4 °C. 7. Collect the supernatant and keep it on ice on a new 1.5 mL Eppendorf tube. 8. Measure the concentration using NanoDrop or an alternative spectroscopic method (Bradford or Bicinchoninic acid assays). Try to use the same method during the SPR experiment.

3.5 Prepare the Material and the Buffers

1. Turn on the BIAcore T200 system and prime the system with filtered ultrapure water (see Note 5). 2. Take the sensor chip CM5 from 4 °C and keep it at room temperature (RT) for at least 1 h before the experiment. 3. Prepare 1 L of the running buffer by diluting the stock solution ten times. 4. Filter the buffer using 0.22 μM membrane filter. 5. Prime the system using the running buffer at least three times to be sure that all system tubing are properly flushed with the running buffer. 6. Insert the CM5 sensor chip on the sensor chip port and close the sensor port. 7. Prime the system three times.

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8. When priming is finished, the system will be automatically shifted to a continuous standby flow. 9. Start a manual run with setting the flow path 2 and the flow rate of 30 μL min-1. 10. Prepare two vials: One containing 400 μL of running buffer and the other with 400 μL of 50 mM NaOH regeneration solution. 11. Eject the rack tray and place your vials in the rack. 12. The rack will be inserted after 1 min. 13. Inject the running buffer for 1 min for three times. 14. Inject the regeneration solution for 30 s after each running buffer injection until the baseline becomes stable. 3.6

pH Scouting

The aim of the pH scouting experiment is to test the pre-concentration of the ligand on the sensor surface at different pHs. The goal is to determine the suitable pH at which the ligand can be adsorbed at highest concentration on the dextran matrix by electrostatic interaction. Typically, the optimal pH for pre-concentration should be at 1 pH unit below the isoelectric point (pI) of your protein. At low pH (3.5 < pH < 5.5), the carboxylated dextran is negatively charged, and the ligand is positively charged (pI > 6). The choice of the pH depends on the “real” pI of your protein. A low pH may be not suitable for many proteins. In order to avoid protein precipitation or denaturation, perform the protein dilution before doing the experiment. pH scouting is performed only in one flow cell. It is recommended to use the flow cell planned for immobilization. The BIAcore T200 control software contains an immobilization wizard method to help users to find the optimum pH for ligand immobilizing. Nevertheless, you can easily set up a manual run in which you inject the ligand diluted at different pHs: 1. Open the BIAcore T200 control software and go to file/open/ New wizard template/immobilization scouting wizard/new. 2. The next steps consist in setting the different parameters: – Specify the different solutions used for your experiments and the corresponding pH. – Set the protein injection and dissociation times. The injection time can be fixed at 2 min and the dissociation time at 1 min. – Specify the regeneration solution, which is usually 50 mM NaOH (see Note 6). – Set the flow rate used for the experiment at 10 μL.min-1.

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Fig. 3 pH scouting of TssE from type VI secretion system over CM5 sensor surface. The pH of each solution tested is indicated on the top of the corresponding sensogram

3. Dilute the protein at different pHs (4, 4.5, 5, and 5.5) using the immobilization buffers (see Step in Subheading 4) (see Note 7). 4. The final protein concentration after dilution on the regeneration solutions should be in the range of 5–200 μg mL-1 (see Note 8). 5. Once performed, the choice of the optimal immobilization pH is based on the pattern of sensograms. The ligand should bind rapidly to the sensor surface and completely dissociate after the end of the injection. If this condition is satisfied by all pHs used, use the highest pH because it is the less offensive for your protein. 6. For example, in the case of the immobilization of TssE from type VI secretion system from enteroaggregative Escherichia coli and based on the pH scouting sensograms (Fig. 3), the optimum pH use for its immobilization is pH 5. 3.7 Immobilization of the Ligand Using Amine Coupling 3.7.1 Wizard Template Method

Here, we will describe the automated and manual methods separately.

1. In the BIAcore T200 control software dialogue window, go to file/open/New wizard template/immobilization/new.

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2. Specify the sensor surface (CM5), the flow path (flow cell 2), and the amine coupling method. 3. The wizard template offers the possibility to choose between specifying the aim of your immobilization level on RUs or the contact time of your ligand and the flow rate: – If you choose to specify the aim for immobilization level, you have to fix a target level. The system will inject 10 μL of the ligand at 5 μL min-1. This short injection aims to the estimation of the rate of the pre-concentration based on the reached level and the slope of the sensogram. This step is followed by the injection of the regeneration solution (50 mM of NaOH to regenerate the surface before activation). – If you choose to specify the contact time and the flow rate, the concentration of your protein and the contact time should be estimated to obtain the desired level of Ri. It is recommended to use a low flow rate (5 μL min-1) to maximize the ligand contact time to the surface (see Note 8). 4. Thaw the EDC, NHS, and the ethanolamine solutions from 20 °C at RT for 10 min (the solutions are provided in the amine coupling kit). See Item 3 in Subheading 2 (see Note 9). 5. Thaw the ligand and dilute it in the corresponding pH solution at a concentration between 10 and 50 μg mL-1 (the concentration depends on the desired Ri and on the level of RUs obtained on the pH scouting). 6. All solutions should be prepared on the appropriate vials. 7. Eject the rack tray. 8. Place the vials in the right positions specified when setting up the experiment. 9. Insert the rack. 10. Run the immobilization program. 3.7.2

Manual Method

1. In the BIAcore T200 control software, open manual run. 2. Set the flow rate at 10 μL min-1. 3. Select the flow path 2 (for the flow cell 2). 4. Select start. 5. Dilute your protein in 100 μL of 10 mM sodium acetate with the suitable pH and at the desired concentration (10–50 μg mL-1). 6. Mix 120 μL of EDC (0.4 M in water) and 120 μL of NHS (0.1 M in water).

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7. Prepare 120 μL of ethanolamine (1 M of ethanolamine HCl at pH 8.5). 8. Inject the EDC/NHS mixture between 6 and 10 min over the surface. 9. After the activation step, set the flow rate at 5 μL.min-1. 10. Inject your ligand (see Note 10). 11. Inject the ethanolamine for 5 min. 12. To calculate your binding capacity, you have to subtract the RUs obtained after activation from the RUs obtained after ethanolamine deactivation. 13. Wait until the signal is stabilized to perform the analyte-binding analysis (see Note 11). 3.8 Immobilization of a Control Ligand

Nonspecific binding of the analyte to the sensor surface is a common problem faced by SPR users. In some cases, the activation/ deactivation of the reference cell is sufficient to eliminate nonspecific binding. However, frequently, the activation/deactivation does not help, and it is recommended to immobilize an irrelevant protein on the sensor surface. A large number of proteins can be used to this end: for example, thioredoxin, maltose-binding protein (MBP), or a homemade protein, not related to the ligand: 1. Dissolve 1 mg of the thioredoxin powder in 10 mM sodium acetate at pH 4 in 1.5 mL Eppendorf tube. 2. Centrifuge the protein at 16,000 g for 20 min at 4 °C. 3. Make aliquots of 80 μL and freeze using nitrogen liquid and storage tubes at -80 °C. 4. Open manual run in the BIAcore T200 control software. 5. Set the flow rate at 10 μL min-1. 6. Select the flow cell number 1 (reference flow cell). 7. Perform Steps 4–13 from Subheading 3.7.2. The target level of the thioredoxin immobilized on the reference flow cell should be the same as the Ri of the ligand.

3.9 Analyte-Binding Analysis

This step is performed after the immobilization of ligand and the stability of the baseline. The choice of the analyte concentration depends on the strength of the interaction. If you are testing a new interaction, it is recommended to start with μM concentration (10–50 μM). If this leads to a high signal with a very slow dissociation, you have to decrease your analyte concentration to nM range. In the case of an antibody–antigen, it is known that the interaction is tight and you can start with nM concentration (100 nM). The function of your proteins can help you to estimate the strength of the interaction. For example, in the case of hemolysin-coregulated protein Hcp and the tail sheath component TssB, in the T6SS, Hcp

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hexamers are able to pack and form a tail tube wrapped by the tail sheath composed by two proteins TssB and TssC [21]. The contraction of the tail sheath leads to the expulsion of the Hcp tail tube to the extracellular milieu. Based on the dynamic of such complex, you can estimate a weak interaction between Hcp and TssB. Consequently, we started with the injection of TssB as analyte with concentration at μM range. Despite the availability of wizard methods in the BIAcore T200 control software (wizard template), it is recommended to perform also a manual run (see Note 12): 1. In the BIAcore T200 control software, open manual run. 2. Set the flow rate at 30 μL.min-1. 3. Select the flow path 2–1 (the analyte will be injected over the flow cells 1 and 2). 4. Select start. 5. Thaw your analyte on ice for 30 min (see Subheading 3.4). 6. Measure your analyte concentration (see Subheading 3.4). 7. Make dilution of your analyte on the running buffer. If you estimate that the affinity is in μM range, prepare 50 μM of the analyte. 8. Eject the rack tray. 9. Place your sample in the selected position. 10. Insert the rack. 11. Inject the analyte for 1 min. When studying the binding of the analyte to the ligand, the subtraction of the signal of the reference flow cell from that of the flow cell containing the ligand should give a typical sensogram of protein–protein interaction (Fig. 1). If the interaction is weak (μM range) or transient, low RU values are obtained after the injection of the analyte (e.g., less than 20 RUs), and the sensograms have fast association and dissociation phases. In this case, it is recommended to perform a binding analysis with increasing concentration of the analyte. On the contrary, if the interaction is strong (nM range), higher RU values are obtained, and the association and dissociation phases are slow. In this case, after the regeneration step, try less analyte concentrations. If no change in the RU value is observed, either the analyte did not bind to the ligand, or the ligand immobilized to the sensor surface is inactivated. In this case, the immobilization of the ligand by non-covalent method (see Subheading 3.2) may help solving the problem. An alternative possibility may be to immobilize the analyte (if the protein is suitable for immobilization) and test the opposite interaction by injecting the ligand.

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Once the binding of the analyte on the ligand is confirmed, the regeneration step can be started. The goal of this step is to totally dissociate the analyte from the ligand-binding sites without affecting the activity of the immobilized ligand. To this end, many regeneration solutions can be tested depending on the type of the interaction and the nature of the ligand (see Item 6 in Subheading 2). If the interaction is reversible and fast dissociation is observed, washing with low ionic strength solutions may accelerate the dissociation (1 M NaCl, 2 M MgCl2) or ethylene glycol (from 10% to 100%) for hydrophobic interactions. In the case of high-affinity interactions, stronger solutions may be necessary (low or high pH solutions or highly hydrophobic solutions). If the ligand is an antibody, it is recommended to use a strong acid solution (10 mM phosphoric acid). If the analyte-binding analysis is performed using manual run, it is recommended to test the regeneration solutions in the same cycle: 1. After analyte injection (Step 11 in Subheading 3.9), estimate the strength of the interaction. 2. Prepare 100 μL of NaCl 1 M, MgCl2 2 M, 10 mM glycine HCl pH 3, 10 mM HCl (pH 2), and 10 mM HEPES NaOH (pH 9). 3. Inject 30 μL of 1 M NaCl. If no dissociation is observed, go to next step. 4. Inject 30 μL of 2 M MgCL2. If no dissociation is observed, go to the next step. 5. Inject 30 μL of 10 mM glycine HCl. If no dissociation is observed, go to the next step. 6. Inject 30 μL of 10 mM HCl. If no dissociation is observed, go to the next step. 7. Inject 30 μL of 10 mM HEPES NaOH. If no dissociation is observed, go to step 9. After each injection step, measure the amount of the analyte remaining bound. If the decrease of the signal is below 30%, move to the next regeneration solution. 8. If the amount of the decrease is more than 30% but below 90%, repeat the injection of the suitable solution. 9. If this procedure is not sufficient to remove more than 90% of the analyte, try higher concentration of the regeneration solution. 10. If the decrease on the signal is important (more than 100%), the regeneration solution is not suitable and may affect the ligand activity. If so, try to inject the analyte at the same concentration used in step 3 (see Subheading 3.9).

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11. If the same binding level is obtained, use the same regeneration solution at lower concentration. 12. If the binding level is lower compared to the first analyte injection, try another type of regeneration buffer. 13. If the regeneration fails, try to combine two solutions among those giving more than 30% of surface regeneration. 14. If the residual activity of the ligand is less than 90% after the regeneration, the flow cell is not suitable any more for binding analysis. It is recommended to immobilize the ligand by amine coupling in a different flow cell or to use a non-covalent immobilization method (see Note 13). 3.11 Affinity and Kinetic Measurements

The pattern of the sensogram resulting from ligand immobilization and the analyte binding gives you information about the strength of the interaction. If the association and the dissociation are fast, it is difficult to estimate the rates of the interaction. This suggests that your experiment will allow only the estimation of the binding affinity of the complex. On the other hand, if the sensogram shows slow association/dissociation phases (5 min for the association and 10–60 min or more for the dissociation), it will be possible to estimate the kinetics of the interaction.

3.11.1

The affinity of molecule A to molecule B is described by the dissociation constant KD (Fig. 4). KD is expressed in molar (M). The KD can be calculated using SPR data by the equilibrium binding analysis. The steady-state binding level is related to the concentration of the analyte (Fig. 5).

Affinity: Theory

3.11.2 Affinity: The Experiment

1. To determine the dissociation constant, the analyte concentrations must be varied from 0.1 × KD to 10–100 × KD. 2. Go the T200 BIAcore software control dialog interface and open file/open/new wizard template/kinetics-affinity. 3. Specify your flow path (2–1) if your ligand is immobilized.

Fig. 4 Representation of the different binding parameters of an interaction between two molecules A and B

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Fig. 5 Determination of the KD using the response units at the equilibrium and the concentrations of the analyte

4. Specify your chip type (CM5). 5. Select the regeneration if needed. 6. Select three start-up cycles with the buffer to stabilize the baseline signal before starting the experiment. 7. Specify the contact time, the flow rate, and the dissociation time (see Note 14). 8. Specify the contact time and the flow rate of the regeneration solution. 9. Specify a stabilization period (100 s). 10. Fill your sample identity and specify the serial twofold dilution of the analyte used for the experiment. 11. Prepare your analyte (see Subheading 3.4). 12. Prepare 10 twofold dilutions of your analyte starting from 10× KD as specified on the wizard template (see Note 15). The dilutions must be prepared on the suitable vials (see Note 16). 13. Place the vials on the corresponding positions. 14. Start the experiment. 3.11.3 Affinity: Data Analysis

1. For the evaluation of your data, open the BIAcore evaluation software. 2. Open surface-bound kinetics/affinity from kinetics/affinity bottom on the toolbar. 3. Select the data to analyze. All sensograms are shown in different colors except for the blanks shown in light gray (see Note 17). 4. If you select next, the blank curves will be subtracted from the others sensograms automatically. 5. Select affinity for steady-state evaluation. The top panel shows a plot of the response at the equilibrium (Req) against analyte concentrations (CA) based on the average response of the selected region on the plateau of each sensogram (see Note 18). 6. Select next. 7. Select the binding model (1:1 binding). 8. Select fit.

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Fig. 6 Determination of the rate of the association kon of a complex AB

Fig. 7 Determination of the rate of the dissociation koff of a complex AB

The calculated KD is represented by a vertical line in the curve plot. The KD corresponds to the concentration of the analyte at half of the Rmax. 3.11.4

Kinetics: Theory

3.11.5 Kinetics: The Experiment

In addition to the KD, SPR allows the determination of the association and the dissociation rate constants. The first phase of the sensogram which corresponds to the injection of the analyte over the ligand and its binding allows the calculation of the rate of the formation of the complex (kon) according to the equation described in Fig. 6. The unit of kon is M-1 s-1. The dissociation phase which corresponds to removal of the analyte allows to calculate the rate of the dissociation (koff) by using the equation presented in Fig. 7. The unit of koff is s-1. Mass transport is one of the most known limitations for determining the association rate constant. Mass transport takes place when the rate of analyte binding to the flow-cell surface is higher than the rate of diffusion of the analyte. By contrast, a limitation for the determination of the dissociation rate constant comes from the rebinding of the analyte to the ligand due to the inefficient effusion of the free analyte from the ligand surface. These issues can be avoided by the immobilization of a low amount of the ligand (50–150 RUs) and performing the binding analysis at high flow rate (30–100 μL.min-1): 1. To perform the experiment, the analyte concentrations must be varied from 0.1× KD to 10× KD. 2. Perform Steps 2–13 as described in Subheading 3.11.2.

3.11.6 Kinetics: Data Analysis

The best way to analyze the kinetic data is to use the BIAcore T200 evaluation software. Kinetic data are interpreted in terms of an interaction model, and the kinetic constants obtained from the SPR data analysis are apparent constants, which are valid in the context of the binding model adopted. The simpler model used for

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the determination of the rates of an interaction is the Langmuir model in which it is assumed that the molecule A binds B with 1:1 stoichiometry and the binding events are independents and equivalents: 1. To perform kinetic analysis, follows steps 1–4 as described in Subheading 3.11.3. 2. Select kinetics. 3. Select 1:1 binding model and then select fit. 4. The results are displayed as fitted curves in black over the original sensograms. 5. The quality control interface gives you the statistic quantifying the quality of your fitting. 6. For the access to the calculated values, go to report window.

4

Notes 1. Xantec manufactures (www.Xantec.com) offer a large number of different types of sensor surfaces. These chips are less expensive and give the same results as the BIAcore chips. 2. To achieve good data, having pure and stable protein samples is critical. The purity of the ligand is very important to ensure binding specificity and capacity. Impurities can be immobilized with the ligand and therefore lead to a nonspecific binding or alter the accurate determination of the affinity or kinetic measurements. The purity of the analyte is important for the determination of the affinity and the kinetic parameters. In fact, injections of impurities with the analyte over the ligand can give false constants or rates due to an incorrect estimation of analyte concentrations. Analyte purity must be more than 95% and has to be analyzed by SDS–PAGE. It is important to check the quality of the ligand and the analyte at least 1 day before starting the amine coupling procedure. It is essential to apply rigorous purification and conservation protocols especially of protein samples. 3. Thioredoxin from Escherichia coli (Sigma) could be used as negative control for soluble proteins. Once immobilized on the control flow cell, it reduces the nonspecific binding of analyzed ligands. 4. If your proteins are not stable in the running buffer (HBS–EP), it is recommended to use an operating buffer with a different composition, such as phosphate-buffered saline (PBS). 5. Be sure that the maintenance procedures are properly performed before starting the experiments.

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6. The use of 50 mM NaOH solution for regeneration is sometimes sufficient. Nevertheless, the injection time may not be sufficient to dissociate the ligand from the sensor surface. In this case, it is recommended to use multiple injections with short contact times (e.g., three injections with 30 s of contact time). 7. If the theoretical pI of the ligand is around 5, start with pH range between 3.5 and 4.5. If your ligand is an acidic protein (pI < 4), you can use the covalent immobilization using thiol groups or a non-covalent immobilization method. 8. If you are using the immobilization wizard template from BIAcore control software, do not mix the EDC and the NHS solution. The software dialog box will ask you to prepare an empty vial to mix the two solutions. 9. The solutions should be prepared just before performing the experiments. 10. The injection of the ligand on the sensor surface is an irreversible step. Be careful when you inject the ligand; do not immobilize over the desired level. To minimize this risk, perform a short injection (5–10 μL of your ligand to estimate the immobilizing level). Once the relationship between the injection time and the reached RU values is established, perform a second injection to reach the final Ri. Ligand contact should be completed within 15 min after activation of the surface with EDC/NHS to ensure coupling. 11. The baseline stability is an indication of the “good” quality of the ligand after immobilization. It is important to wait until the baseline becomes stable and no decrease on RUs is observed. 12. The use of the wizard template method is recommended if the regeneration buffer is known or described in the literature. If not, manual runs with different regeneration buffers should be carried out to optimize the regeneration step after analyte binding. 13. In case you do not find an efficient regeneration buffer, it is recommended to immobilize the ligand in a new flow cell. The kinetic measurement could be undertaken using the singlecycle kinetics (SCK) with no regeneration between injections. In the SCK experiment, increased concentrations of the analyte are injected sequentially in the same cycle. 14. The association and the dissociation times are determined based on the sensogram obtained after the analyte-binding analysis. The dissociation time should be sufficient to go back at the original level of the baseline. If it is time-consuming, a regeneration step can be added with soft regeneration solution to avoid the ligand inactivation.

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15. The volume of the analyte dilution solution depends on the contact times and the flow rate. These parameters are estimated during the analyte-binding analysis test. 16. When making the twofold dilutions, avoid forming bubbles when mixing your analyte on the buffer. If bubbles are present, centrifuge your sample and put it in a new vial. 17. The sensograms are adjusted to zero at the start of the analyte injection on both the response and the time axis. 18. You can adjust the region used for the calculation of the Req by selecting it using the setting bottom.

Acknowledgments This work was funded by the National Research Institute for Agriculture, Food, and Environment INRAE and the Universite´ de Lorraine. References 1. Barison N, Lambers J, Hurwitz et al (2012) Interaction of MxiG with the cytosolic complex of the type III secretion system controls Shigella virulence. FASEB J 26:1717–1726 2. Benabdelhak H, Kiontke S, Horn C (2003) A specific interaction between the NBD of the ABC-transporter HlyB and a C-terminal fragment of its transport substrate haemolysin A. J Mol Biol 327:1169–1179 3. Girard V, Coˆte´ JP, Charbonneau ME et al (2010) Conformation change in a selfrecognizing autotransporter modulates bacterial cell-cell interaction. J Biol Chem 285: 10616–10626 4. Schroder G, Lanka (2003) TraG-like proteins of type IV secretion systems: functional dissection of the multiple activities of TraG (RP4) and TrwB (R388). J Bacteriol 185:4371–4381 5. Swietnicki W, O’Brien S, Holman K et al (2004) Novel protein-protein interactions of the Yersinia pestis type III secretion system elucidated with a matrix analysis by surface plasmon resonance and mass spectrometry. J Biol Chem 279:38693–38700 6. Zoued A, Durand E, Brunet YR et al et al (2016) Priming and polymerization of a bacterial contractile tail structure. Nature 531:59– 63 7. Douzi B, Ball G, Cambillau C et al (2011) Deciphering the Xcp Pseudomonas aeruginosa type II secretion machinery through multiple

interactions with substrates. J Biol Chem 286: 40792–40801 8. Douzi B, Durand E, Bernard C et al (2009) The XcpV/GspI pseudopilin has a central role in the assembly of a quaternary complex within the T2SS pseudopilus. J Biol Chem 284: 34580–34589 9. Douzi B, Spinelli S, Blangy S (2014) Crystal structure and self-interaction of the type VI secretion tail-tube protein from enteroaggregative Escherichia coli. PLoS One 9:e86918 10. Felisberto-Rodrigues C, Durand E, Aschtgen MS et al (2011) Towards a structural comprehension of bacterial type VI secretion systems: characterization of the TssJ-TssM complex of an Escherichia coli pathovar. PLoS Pathog 7: e1002386 11. Zoued A, Durand E, Bebeacua C et al (2013) TssK is a trimeric cytoplasmic protein interacting with components of both phage-like and membrane anchoring complexes of the type VI secretion system. J Biol Chem 288:27031– 27041 12. Pineau C, Guschinskaya N, Robert X et al (2014) Substrate recognition by the bacterial type II secretion system: more than a simple interaction. Mol Microbiol 94:126–140 13. Halder PK, Roy C, Datta S (2019) Structural and functional characterization of type three secretion system ATPase PscN and its regulator PscL from Pseudomonas aeruginosa. Proteins 87:276–288

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14. Casu B, Smart J, Hancock MA (2016) Structural analysis and inhibition of TraE from the pKM101 type IV secretion system. J Biol Chem 291:23817–23829 15. Peess C, von Proff L, Goller S et al (2015) Deciphering the stepwise binding mode of HRG1beta to HER3 by surface plasmon resonance and interaction map. PLoS One 10: e0116870 16. Ohlson S, Strandh M, Nilshans H (1997) Detection and characterization of weak affinity antibody antigen recognition with biomolecular interaction analysis. J Mol Recognit 1:135– 138 17. Fischer MJ (2010) Amine coupling through EDC/NHS: a practical approach. Methods Mol Biol 627:55–73

18. Hutsell SQ, Kimple RJ, Siderovski DP et al (2010) High-affinity immobilization of proteins using biotin- and GST-based coupling strategies. Methods Mol Biol 627:75–90 19. Khan F, He M, Taussig MJ (2006) Doublehexahistidine tag with high-affinity binding for protein immobilization, purification, and detection on ni-nitrilotriacetic acid surfaces. Anal Chem 78:3072–3079 20. Della Pia EA, Martinez KL (2015) Single domain antibodies as a powerful tool for high quality surface plasmon resonance studies. PLoS One 10:e0124303 21. Cianfanelli FR, Monlezun L, Coulthurst SJ (2016) Aim, load, fire: the type VI secretion system, a bacterial nanoweapon. Trends Microbiol 24:51–62

Chapter 24 Defining Assembly Pathways by Fluorescence Microscopy Andreas Diepold Abstract Bacterial secretion systems are among the largest protein complexes in prokaryotes and display remarkably complex architectures. Their assembly often follows clearly defined pathways. Deciphering these pathways not only reveals how bacteria accomplish to build these large functional complexes but can provide crucial information on the interactions and subcomplexes within secretion systems, their distribution within the bacterium, and even functional insights. Fluorescence microscopy provides a powerful tool for biological imaging, which presents an interesting method to accurately define the biogenesis of macromolecular complexes using fluorescently labeled components. Here, I describe the use of this method to decipher the assembly pathway of bacterial secretion systems. Key words Fluorescence microscopy, Biogenesis, Secretion systems, Fluorescently labeled proteins, Macromolecular complexes, Epistasis experiments, Subcellular localization

1

Introduction Bacterial secretion systems are macromolecular machines that mediate the transport of proteins between bacteria or from bacteria to eukaryotic cells [1, 2]. These complexes incorporate one or multiple copies of a large number of different proteins that are recruited in a hierarchical order. Important insights into the assembly of secretion systems were obtained by the purification and visualization of assembly intermediates, either in strains lacking certain components of the system or upon overexpression of defined components [3–5]. However, the often low number of stable intermediates and the difficulties in obtaining and visualizing these have limited the utilization of this approach. Increasingly, the assembly of secretion systems is therefore deciphered based on the localization of fluorescently labeled subunits. Variations of this approach have been applied for a variety of secretion systems including the Tat system [6–9], the type II secretion system (T2SS) [10, 11], the T3SS [12–14], the T4SS [15–17], the T6SS [18–21], or more recently the T9SS [22]. The rationale is that a

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_24, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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fluorescently labeled component forms distinctive fluorescent foci where the secretion system resides, in wild-type cells and in strains lacking components that are not required for its assembly but will have a diffuse fluorescent pattern when components responsible for its recruitment are missing. Prerequisites for this approach are the genetic amenability of the bacterium to create fluorescent fusion proteins and a specific distribution of the secretion system(s) within the bacterium (see Note 1). This method can be used to obtain a detailed description of the assembly pathway in live bacteria by visualizing the labeled subunits in strains lacking other components of the secretion system. Beyond this, it can also give insights into the kinetics and adaptivity of secretion system assembly [23]. In this chapter, I describe a generally applicable approach to decipher the assembly pathway of secretion systems using fluorescently labeled proteins.

2 2.1

Materials Strains

1. Strain(s) expressing a stable fusion of a fluorescent protein to the target protein of interest, e.g., superfolder green fluorescent protein (sfGFP) fused either to the N- or C- terminus of the target protein, where possible, from the native genetic background (see Notes 2 and 3). 2. Additional deletions of other components of the secretion system or assembly factors in the strain background mentioned above, to allow the investigation of the order of assembly. 3. Recommended: Untagged autofluorescence.

strain

as

a

control

for

4. Recommended: Strain expressing the fluorescent protein from plasmid at a comparable rate and in the same bacterial cell compartment as the target protein (see Note 4). 2.2 Sample Preparation

1. Incubator shaker. 2. Spectrophotometer. 3. Culture medium: Standard culture medium and appropriate antibiotics for overnight incubation and growth of bacteria, e.g., Luria Broth (LB) or M9 minimal medium (see Note 5). 4. Microscopy buffer: Nonfluorescent minimal medium or imaging buffer, e.g., phosphate-buffered saline (PBS) (see Note 6).

2.3 Microscope Slide Preparation

1. Low-melting agarose (or agar), commercially available. 2. Microscopy slides and cover slips compatible with the used microscope. Standard sizes include 75*25*1 mm glass slides and 22*22 mm cover slips, with No.1 (0.13–0.16 mm) being

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the most commonly used thickness. Alternatively, depression slides or flow cells can be used. 3. Microwave oven to prepare agarose solution. 2.4 Image Acquisition

1. Automated inverted epifluorescence microscope with 60× or 100× objective (see Note 7). 2. Appropriate optical filters for visualizing fluorescence. 3. Dichroic mirrors compatible with the used fluorophores and filter sets. 4. Incubation chamber for microscope stage, where required.

2.5 Software for Image Processing

3

1. Proprietary software often preinstalled on microscopy controller, commercial software like Adobe Photoshop, or opensource solutions like ImageJ, a widely used and adaptable open-source image processing program (http://rsb.info.nih. gov/ij/) [24]. For automated detection of fluorescent foci corresponding to assembled secretion systems or assembly intermediates, freely available software packages such as Oufti [25], MicrobeJ [26], or a variety of other programs (reviewed in [27]) are available.

Methods As the protocols for propagation of bacteria and induction of secretion systems greatly vary, we aim to provide a general protocol, which can be adapted to the specificities of the studied secretion system. All buffers and solutions should be prepared using ultrapure water at room temperature.

3.1 Preparation of Bacteria and Setup of Microscopy Equipment

1. Streak bacteria from long-term storage stock onto LB agar plates containing the required additives and antibiotics and incubate at the required temperature (e.g., 37 °C), until single colonies are visible (usually 12–36 h). 2. Inoculate overnight cultures of bacteria with single colonies from agar plates and grow in shaking incubator at the required temperature and agitation. 3. On the next day, determine the optical density at 600 nm wavelength (OD600) of the overnight culture and inoculate a main culture to OD600 that is suitable for the expression of the analyzed secretion system (a culture volume of 5 mL is sufficient, and an OD600 of 0.1 is a good starting point for many systems). 4. Incubate bacteria in a shaking incubator until they reach early stationary phase (usually 1–2 h).

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Fig. 1 Schematic representation of different ways to image bacteria for the determination of assembly pathways (a) Simple agarose patch. Approximately 50 μL of agarose solution (beige) are transferred onto a microscope slide and quickly covered with a cover slip (gray), which is then gently and evenly pressed onto the agarose to form an evenly distributed patch. This simple method is sufficient for quick imaging but often leads to uneven or sloped agarose patches, which reduce image quality (b) Supported agarose patch. To ensure a more level surface of the agarose patch, the covering cover slip can be supported by two flanking cover slips (c) Sealed agarose patch. Double-sided tape or commercially available adhesives (e.g., “Gene Frame,” Thermo Fisher) (rippled pattern) can be used to permanently adhere the cover slip to the sample. This prevents evaporation during imaging, which is very useful for longer experiments. However, the decreasing oxygenation of the sample should be kept in mind in this case (d) Depression slide. Commercially available microscopy slides with a concave depression are used for the agarose support matrix. 80–120 μl of agarose are applied to the center of the depression and covered with a cover slip (e) Flow cell. Bacteria are applied to the flow cell and allowed to attach to the top of the slide (which may be a cover slip). This setup allows to image cells while flowing medium to support growth and oxygenation and also to change the condition, e.g., to induce the assembly or activation of the secretion system at a given time

5. Unless a flow cell is used, in the meantime, prepare a 1.5% solution of low-fluorescence agarose in microscopy buffer (see Note 8). While less than 100 μL of agarose solution is required per strain, higher volumes are easier to prepare, and the agarose concentration is less influenced by evaporation. Add the agarose to the buffer, which is then carefully brought to boiling in a microwave oven. Be aware of the possibility of delays in boiling and use precaution. Check that the agarose is completely dissolved and allow to cool to approximately 55 ° C. After cooling, add any required additives (antibiotics, inducers) (see Note 9). Prepare a thin pad of 1.5% agarose on a microscopy slide (Fig. 1) (see Note 10).

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6. Induce secretion system according to standard conditions (e.g., temperature shift, addition of inducer). 7. Harvest exponentially growing cells (OD600 ~ 0.8–1) by centrifugation (2400× g, 4 min; these values depend on the bacterium). Carefully resuspend bacterial pellet in imaging buffer and recentrifuge once, using the same settings, before resuspending in imaging buffer to an OD600 ~ 2 (see Note 11). 8. Remove cover slip from agarose patch prior to spotting the bacteria, and wait until no more liquid areas are visible on the surface of the agarose patch (usually 1–5 min). 9. Spot bacteria by either of the following three methods: (a) Pipette 1–2 μL of resuspended bacteria into the center of the agarose or agar patch without damaging the patch itself, let dry for about 1–2 min (see Note 12), and carefully cover with a cover slip. (b) Spot 1–2 μL of resuspended bacteria onto a cover slip and carefully cover with an agarose or agar pad. (c) When using a flow cell, first equilibrate the flow cell with the imaging buffer, then load bacterial resuspension into flow cell, and allow for attachment to the imaging interface (which may be a cover slip). Bacteria can then either be imaged directly, under a constant flow, or at a specific time point, e.g., after the induction of assembly of the secretion system by application of a different buffer. 3.2

Microscopy

1. Place a drop of immersion oil onto the center of the cover slip and in the case of an inverted microscope flip the slide, so that the cover slip faces the objective. Carefully insert the slide into the microscope and bring the lens into contact with the immersion oil. 2. In phase-contrast or differential interference contrast (DIC) mode, slowly decrease the distance between the lens and the cover slip, until bacteria are visible (see Note 12 in case large numbers of detached or swimming bacteria are visible). 3. Adjust Koehler illumination of microscope for optimal phasecontrast or DIC images [28]. 4. To determine which phase-contrast/DIC plane corresponds to the best fluorescence plane, run an automated z stack of phase contrast/DIC and fluorescence images (see Note 13). For bacteria with a diameter of about 1 μm, z stacks containing 6–20 planes with Δz = 100–150 nm yield sufficient coverage.

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5. Capture phase-contrast and fluorescence micrographs: (a) For kinetic studies: Every 30 s (depending on the kinetics of the event of interest) with a minimal exposure time to minimize bleaching and phototoxicity effects (see Note 14). (b) For the determination of the assembly pathway: Single micrograph or z stack image (see Note 15) in WT and mutant strains. 3.3 Image Processing

1. Phase-contrast and fluorescence images can be adjusted and merged using ImageJ or equivalent software (see Note 16). 2. Slight movements of the whole field during the time of the experiment can be corrected by registering individual frames using StackReg plug-in in ImageJ (http://fiji.sc/StackReg). 3. Blurring of the image can be reduced by deconvolution, a mathematical post-imaging process which removes or reassigns the fraction of detected photons caused by out-of-focus structures. This is especially useful when three-dimensional information from z stacks is available. Care should be taken not to mistake deconvolution artifacts for clustering, and controls, such as the expression of the free fluorophore at a comparable level in the same bacterial compartment, are mandatory when applying deconvolution. 4. Detection and quantification of bacteria and fluorescent foci can be performed in ImageJ or using more specialized analysis software package, as mentioned earlier [25–27].

3.4 Determination of the Assembly Pathway

1. To build an assembly pathway from the aforementioned data, determine the presence and number of foci per cell in wild-type and each mutant strains. Nucleating component(s) of the system are correctly localized in foci in strains lacking all other components of the secretion system. Subsequently, assembled proteins correctly localize in foci in strains expressing all earlier assembled proteins. Finally, protein(s) that require the presence of all the other components of the system are recruited at the end of the assembly. Proteins that are recruited at the same time or interact together before being recruited to the system will show the same localization in mutant strains and usually require each other to form foci. Proteins that assemble independently but are anchored by other substructures of the secretion system display mobile fluorescent foci in the absence of the anchor protein(s) or other components needed for assembly of the anchor structure [29]. Where required, this can be tested by time-course microscopy.

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Notes Notes 1–4 correspond to the prerequisites for the applicability of this approach: 1. Studying components present in multiple copies in the secretion system greatly facilitates the analysis (see Note 7 for details). Most bacterial secretion systems have a sufficiently distinct localization within the bacterium that differs from the distribution of the free component. If the spacing of individual secretion systems is close to the resolution limit of fluorescence microscopy (d = λ / 2NA ≈ 200 nm, with λ = the wavelength of light used and NA the numerical aperture of the objective), structured illumination microscopy (SIM [30]), which is compatible with the approach described here, can be used. In case no distinct localization of the secretion system exists or can be observed (e.g., for soluble subcomplexes or assembly intermediates), standard fluorescence microscopy cannot distinguish between the free and assembled state of the labeled component, and more sophisticated methods such as diffusion-based fluorescence correlation spectroscopy (FCS), super-resolution microscopy, or interaction-based Fo¨rster resonance energy transfer microscopy (FRET) have to be applied. 2. While most fluorescent proteins are relatively inert to interactions, their size of 25–30 kDa [31] can lead to cleavage or degradation of the fusion protein and impede assembly or functionality of the tagged protein. Smaller alternatives, such as tetracysteine tags [32, 33], require additional manipulation [34] and may also disturb the function of the protein (own unpublished observations). Antibody epitopes, such as FLAG or ALFA-Tag [35, 36], can be used but require additional steps for the immunofluorescence not described here and can lead to method-based artifacts. In any case, it is therefore essential to test expression level and stability of the fusion protein (by immunoblot), as well as the functionality of secretion system in the corresponding strains (by a functional assay). While fusions that influence the function may be perfectly fine tools to decipher assembly, this has to be corroborated by independent experiments. To maximize the chances of obtaining a functional fusion protein, both termini of the protein and internal flexible loops should be considered. Flexible linkers between the fluorophore and the secretion system component (e.g., a stretch of 6–15 amino acids with a high glycine content) have been shown to preserve functionality of the fusion protein. Chromosomal fusion proteins are preferred to avoid mislocalization due to overproduction of the protein or wrong timing and order of the expression of subunits. Moreover,

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chromosomal fusions allow to analyze the secretion system under close-to-wild-type conditions. However, especially for C-terminal fusions, care has to be taken not to disturb the expression of downstream genes in the same operon, and it has proven helpful to repeat the genetic region upstream of the following gene. 3. Concerning the choice of fluorescent protein, many variants of GFP have been produced which vary in spectral properties, degrees of multimerization, folding rates, and functionality in oxidizing environments, so it can be helpful to try different fusion proteins [31]. Due to their fast folding, low multimerization tendency, and proper folding in the periplasm, sfGFP mNeonGreen, mCherry, or mScarlet are good starting points. The listed fluorescent proteins were additionally observed to retain functionality of the T3SS in cases where GFP fusions were nonfunctional [37, 38]. Most bacteria also display considerably less autofluorescence in the red spectrum; however, red fluorescent proteins are often less photostable than green ones, which might make them less suitable for time-course studies. 4. While these control strains are not absolutely required, especially in the presence of good controls for the protein of choice itself (i.e., deletion of a protein required for its localization), they are immensely valuable to set up and test the microscopy pipeline. 5. M9 and similar buffers have the advantage that due to their low autofluorescence, they can be directly used in microscopy. This ensures constant external conditions for the bacteria and may eliminate the need for the washing step described in step 8. 6. The choice of imaging buffer is crucial to obtain reproducible results, as it will influence the bacterial metabolism and possibly the state of the secretion system to be analyzed. While PBS is a popular and easy-to-obtain imaging buffer, some bacteria show visible alterations in cell morphology in PBS within less than an hour. Preliminary experiments can reveal whether cell morphology and the distribution of secretion systems are affected in different imaging buffers. 7. The microscope must have a sufficient resolution and, most importantly, a high sensitivity to visualize and resolve the assembled proteins. A 100× objective is required for most distributions of secretion systems, although the formation of a polar spot or the distribution of few membrane-bound foci in large bacteria can be detected with a 60× objective. Sensitivity of the microscope is crucial, especially for low-stoichiometry components. The labeled protein has to be present in multiple copies within the complex to be detectable. In our experience,

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sensitive widefield microscopes can detect about 5–10 molecules within a diffraction-limited spot over low background. For single-molecule detection, more sensitive methods, such as total internal reflection microscopy (TIRF) [39] or photoactivated localization microscopy/stochastic optical reconstruction microscopy (PALM / STORM) [40], have to be applied. 8. The pad can also be done with agar (instead of agarose). This can be especially useful for longer time-course experiments, where bacteria can be incubated for 1 h at optimal growth temperature prior to microscopy acquisition to allow cell division on a plane surface. 9. The agarose solution should be prepared or redissolved freshly before the experiment. The solution will stay liquid in a 55 °C water bath; small aliquots can be kept in a tabletop incubator shaker in 1.7 mL reaction tubes (higher temperature and shaking may be required to prevent solidification of the agarose in this case). 10. The depth of the pad can vary; however, the surface should remain as smooth as possible. The pad can be prepared using a microscopy slide and a cover slip, with spacers, using commercially available systems (e.g., Gene Frame) (Fig. 1), alternatively using two microscope slides or prewarmed protein gel chambers for larger patches. Depression slides are a simple alternative that works well in many cases. The pad should be bubble-free to facilitate the observation. Let the agarose solidify and dry at room temperature (>1 min, longer storage times are possible if the pad remains covered). 11. An OD of 2 leads to about 5% of the area being covered with bacteria (for E. coli; this obviously depends on the size of the bacterium). Increasing the OD will increase confluence leading to more cell–cell contact. Keep in mind that the assembly of some secretion systems may be influenced by the local cell density. 12. Depending on their surface and the properties of the agarose patch, bacteria may take some time to settle at this point. If a large part of the bacteria are still moving after some minutes, the volume of bacterial resuspension should be reduced and drying times increased. Don’t dry the agarose pad at 4 °C to avoid drifts during the observation. 13. To avoid saturation, strong photobleaching, or phototoxicity effects, all fluorescence images should be acquired with the minimal exposure time required to reach a sufficient signal/ noise ratio. The optimal exposure time has to be determined for each protein; depending on the sensitivity of the microscope, exposure times of 5–50 ms for phase contrast or DIC

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and 50 ms–1 s for fluorophores are good starting points. Narrow band microscopy excitation filters can also reduce photobleaching. 14. For time-lapse experiments, many microscopy systems allow to define fields of view (x, y, z, focus offset) that are stored and then automatically accessed by a motorized stage. The fields of view should be sufficiently apart to avoid cross-photobleaching (run preliminary experiment with long exposures to determine the area of bleaching, if required). Ten fields of view at an OD of about 2 usually yield a sufficient number of bacteria for further analysis. 15. Z stacks allow a more complete coverage of the bacterium, ensuring images that comprise the region of interest (often the center of the bacterium). In addition, the threedimensional data yields information about the spatial distribution of the secretion systems within the bacterium and allows better deconvolution of the images. However, imaging z stacks leads to stronger photobleaching and is therefore generally avoided in kinetic experiments. For kinetic experiments, keeping the focus is of particular importance, and hardwarebased focusing systems or sealed plates can be of advantage. 16. To avoid the loss of raw data, the original images files should be preserved. Keep the low and high boundary below the background value and above the highest measured intensity, respectively, to prevent misinterpretation of the data. Within an experiment, these values should be kept constant after background correction. References 1. Costa TRD, Felisberto-Rodrigues C, Meir A et al (2015) Secretion systems in Gramnegative bacteria: structural and mechanistic insights. Nat Rev Microbiol 13:343–359 2. Green ER, Mecsas J (2016) Bacterial secretion systems: an overview. Microbiol Spectr 4 3. Kimbrough TG, Miller SI (2000) Contribution of Salmonella typhimurium type III secretion components to needle complex formation. Proc Natl Acad Sci U S A 97:11008–11013 4. Fronzes R, Sch€afer E, Wang L et al (2009) Structure of a type IV secretion system core complex. Science 323:266–268 5. Chandran Darbari V, Waksman G (2015) Structural biology of bacterial type IV secretion systems. Annu Rev Biochem 84:603–629 6. Rose P, Fro¨bel J, Graumann PL, Mu¨ller M (2013) Substrate-dependent assembly of the

Tat translocase as observed in live Escherichia coli cells. PLoS One 8:e69488 7. Alcock F, Baker MAB, Greene NP et al (2013) Live cell imaging shows reversible assembly of the TatA component of the twin-arginine protein transport system. Proc Natl Acad Sci U S A 110:E3650–E3659 8. Eimer E, Fro¨bel J, Blu¨mmel A-S, Mu¨ller M (2015) TatE as a regular constituent of bacterial twin-arginine protein translocases. J Biol Chem 290:29281–29289 9. Alcock F, Stansfeld PJ, Basit H et al (2016) Assembling the Tat protein translocase. Elife 5:e20718 10. Lybarger S, Johnson TL, Gray M et al (2009) Docking and assembly of the type II secretion complex of Vibrio cholerae. J Bacteriol 191: 3149–3161

Assembly Determination by Fluorescence Microscopy 11. Johnson TL, Sikora AE, Zielke RA, Sandkvist M (2013) Fluorescence microscopy and proteomics to investigate subcellular localization, assembly, and function of the type II secretion system. Methods Mol Biol 966:157–172 12. Diepold A, Amstutz M, Abel S et al (2010) Deciphering the assembly of the Yersinia type III secretion injectisome. EMBO J 29:1928– 1940 13. Diepold A, Sezgin E, Huseyin M et al (2017) A dynamic and adaptive network of cytosolic interactions governs protein export by the T3SS injectisome. Nat Commun 8:15940 14. Burgess JL, Case HB, Burgess RA, Dickenson NE (2020) Dominant negative effects by inactive Spa47 mutants inhibit T3SS function and Shigella virulence. PLoS One 15:e0228227 15. Aguilar J, Zupan J, Cameron TA, Zambryski PC (2010) Agrobacterium type IV secretion system and its substrates form helical arrays around the circumference of virulence-induced cells. Proc Natl Acad Sci 107:3758–3763 16. Chetrit D, Hu B, Christie PJ et al (2018) A unique cytoplasmic ATPase complex defines the Legionella pneumophila type IV secretion channel. Nat Microbiol 3:678–686 17. Ghosal D, Jeong KC, Chang YW et al (2019) Molecular architecture, polar targeting and biogenesis of the Legionella Dot/Icm T4SS. Nat Microbiol 4:1173–1182 18. Durand E, Nguyen VS, Zoued A et al (2015) Biogenesis and structure of a type VI secretion membrane core complex. Nature 523:555– 560 19. Gerc AJ, Diepold A, Trunk K et al (2015) Visualization of the Serratia type VI secretion system reveals unprovoked attacks and dynamic assembly. Cell Rep 12:2131–2142 20. Corbitt J, Yeo JS, Davis CI et al (2018) Type VI secretion system dynamics reveals a novel secretion mechanism in Pseudomonas aeruginosa. J Bacteriol 200:e00744–e00717 21. Stietz MS, Liang X, Li H, Zhang X, Dong TG (2020) TssA–TssM–TagA interaction modulates type VI secretion system sheath-tube assembly in Vibrio cholerae. Nat Commun 11:1–11 22. Shrivastava A, Berg HC (2020) A molecular rack and pinion actuates a cell-surface adhesin and enables bacterial gliding motility. Sci Adv 6:1–7 23. Milne-Davies B, Wimmi S, Diepold A (2021) Adaptivity and dynamics in type III secretion systems. Mol Microbiol 115:395–411

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24. Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675 25. Paintdakhi A, Parry B, Campos M et al (2016) Oufti: an integrated software package for highaccuracy, high-throughput quantitative microscopy analysis. Mol Microbiol 99:767– 777 26. Ducret A, Quardokus EM, Brun YV (2016) MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis. Nat Microbiol 1:16077 27. Jeckel H, Drescher K (2021) Advances and opportunities in image analysis of bacterial cells and communities. FEMS Microbiol Rev 45:fuaa062 28. Ko¨hler A (1893) Ein neues Beleuchtungsverfahren fu¨r mikrophotographische Zwecke. Zeitschrift fu¨r wissenschaftliche Mikroskopie 10:433–440 29. Diepold A, Wiesand U, Cornelis GR (2011) The assembly of the export apparatus (YscR,S, T,U,V) of the Yersinia type III secretion apparatus occurs independently of other structural components and involves the formation of an YscV oligomer. Mol Microbiol 82:502–514 30. Karadaglic´ D, Wilson T (2008) Image formation in structured illumination wide-field fluorescence microscopy. Micron 39:808–818 31. Shaner NC, Steinbach PA, Tsien RY (2005) A guide to choosing fluorescent proteins. Nat Methods 2:905–909 32. Adams S, Campbell R, Gross L et al (2002) New biarsenical ligands and tetracysteine motifs for protein labeling in vitro and in vivo: synthesis and biological applications. J Am Chem Soc 124:6063–6076 33. Andresen M, Schmitz-Salue R, Jakobs S (2004) Short tetracysteine tags to beta-tubulin demonstrate the significance of small labels for live cell imaging. Mol Biol Cell 15:5616–5622 34. Enninga J, Mounier J, Sansonetti PJ et al (2005) Secretion of type III effectors into host cells in real time. Nat Methods 2:959–965 35. Hopp TP, Prickett KS, Price VL et al (1988) A short polypeptide marker sequence useful for recombinant protein identification and purification. Bio/Technology 6:1204–1210 36. Go¨tzke H, Kilisch M, Martı´nez-Carranza M et al (2019) The ALFA-tag is a highly versatile tool for nanobody-based bioscience applications. Nat Commun 10:1–12 37. Diepold A, Kudryashev M, Delalez NJ et al (2015) Composition, formation, and regulation of the cytosolic C-ring, a dynamic

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component of the type III secretion injectisome. PLoS Biol 13:e1002039 38. Meiresonne N, Consoli E, Mertens LMY et al (2019) Superfolder mTurquoise2 ox optimized for the bacterial periplasm allows high efficiency in vivo FRET of cell division antibiotic targets. Mol Microbiol 111:1025–1038

39. Poulter NS, Pitkeathly WTE, Smith PJ, Rappoport JZ (2015) Advanced fluorescence microscopy. Springer, New York 40. MacDonald L, Baldini G, Storrie B (2015) Does super-resolution fluorescence microscopy obsolete previous microscopic approaches to protein co-localization? Methods Mol Biol 1270:255–275

Chapter 25 Large Complexes: Cloning Strategy, Production, and Purification Samira Zouhir, Wiem Abidi, and Petya V. Krasteva Abstract With few exceptions—such as myxobacteria, filamentous cyanobacteria, and actinomycetes (Rokas, Annu Rev Genet 42:235–251, 2008)—bacteria are defined as unicellular prokaryotes or single, self-sufficient cells containing all the genetic material necessary for their physiology and reproduction, while maintaining none or a minimum of intracellular organelles for pathway compartmentalization. The latter is therefore primarily achieved through the assembly of macromolecular complexes that can secure spatiotemporal control of a plethora of physiological processes, such as precise midcell division, assembly of diverse motility organelles and chemotaxis sensory arrays, metabolic channeling of substrates and toxic intermediates, localized signal transduction via soluble intracellular second messengers or the secretion of signaling molecules, competition effectors, and extracellular matrix components (Cornejo et al., Curr Opin Cell Biol 26:132–138, 2014; de Lorenzo et al., FEMS Microbiol Rev 39:96–119, 2015; Krasteva and Sondermann, Nat Chem Biol 13: 350–359, 2017; Abidi et al., FEMS Microbiol Rev 46(2):fuab051, 2022; Altinoglu et al., PLoS Genet 18: e1009991, 2022). Oftentimes, pathway-specific components are encoded by clusters of co-regulated genes (Lawrence, Annu Rev Microbiol 57:419–440, 2003), which not only allows for facilitated macrocomplex assembly and rapid physiological adaptation in cellulo but can also be harnessed for the recombinant coexpression and purification of intact multicomponent nanomachines for structure-function studies of medical or biotechnological relevance. Important examples are synthase-dependent exopolysaccharide secretion systems that provide key biofilm matrix components in a vast variety of free-living or pathogenic species and at the molecular level secure the physical conduit, protection, chemical modifications and energetics for the processive extrusion of hydrophilic biopolymers through the complex bacterial envelope (Abidi et al., FEMS Microbiol Rev 46(2):fuab051, 2022). Here, we present cloning, expression, and purification strategies for the structure-function studies of macromolecular assemblies involved in bacterial cellulose secretion (Bcs) (Krasteva et al. Nat Commun 8:2065, 2017; Abidi et al. Sci Adv 7:eabd8049, 2021) that can be adapted to a variety of multicomponent cytosolic or membrane-embedded assemblies. Key words Protein complex, Protein production, Protein purification, Heterologous expression, Macromolecular complexes

Samira Zouhir and Wiem Abidi contributed equally. Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_25, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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Introduction Bacterial cellulose secretion systems include a variety of multicomponent secretory assemblies that reside in the bacterial envelope and secure both the reiterative catalytic reactions for processive glucose polymerization and the physical conduit for polymer extrusion to the extracellular space [1]. In Gram-negative bacteria, the catalytic platform is typically provided by the cellulose synthase, BcsA, and its inner membrane (IM) partner co-polymerase, BcsB. Whereas the former incorporates a catalytic glycosyl transferase domain, a transmembrane domain for IM polymer extrusion and a C-terminal PilZ domain sensing the active site-remodeling second messenger c-di-GMP, the BcsB subunit is a tail-anchored protein with a large donut-shaped periplasmic module incorporating alternating carbohydrate-binding and flavodoxin-like domains and a C-terminal anchor indispensable for glucose polymerization [1]. In addition to the BcsAB tandem, the core Bcs components typically include a periplasmic hydrolase, BcsZ, that can tackle abortive secretion in the periplasm or enable polysaccharide release from the cell surface and an outer membrane porin BcsC, which features multiple tetratricopeptide (TPR) scaffolding repeats in the periplasm [1]. Depending on the microorganism and the type of secreted polymer, the Bcs systems can include a variety of additional accessory subunits, such as a cytosolic BcsRQE vestibule-forming complex for synthase-proximal c-di-GMP enrichment; a phosphoethanolamine transferase, BcsG, or a WssFGHI celluloseacetylation complex for polymer modifications; or a system-specific cytoskeletal scaffold, BcsHD, for secretion of parallel-packed, crystalline cellulose [1]. Typically, Bcs subunits are encoded by one or few clustered bcs operons, such as bcsEFG and bcsRQABZC in E. coli [1, 2]. Preliminary studies on protein–protein interactions among secretion system components can be done rapidly by cell-based reporter assays such as the bacterial two-hybrid functional complementation approach using split adenylate cyclase (AC) fragments, each fused to potential binding partners as an N- or C-terminal fusion and co-expressed in a cya-deficient strain on X-gal-supplemented medium for blue-white colony screening (see Chapter 13) [3]. We strongly recommend testing both C-terminal and N-terminal fusions to both AC fragments for each protein of interest, and including co-transformations with the complementary AC-fragment only-encoding (or “empty”) vector in addition to the standard negative and positive controls. The putative subcellular localization, membrane protein topology, and putative hydrolytic processing (e.g., removal of signal peptides [4]) should be taken into account when designing the AC fusions and interpreting the results, in order to ensure cytosolic localization and preserved AC-fused state for all tested proteins or protein modules of interest.

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Importantly, this and other two-hybrid approaches based on functional complementation between fused reporter fragments can not only provide an initial idea of protein–protein interactions within larger macromolecular complexes but can also indicate suitable sites for the introduction of non-disruptive epitope tags for macrocomplex purification. Purification of stable macrocomplexes for structure-function studies can be done by the addition of affinity tag-coding sequences to key subunits of the studied system in the endogenous locus on the bacterial genome. This approach can be especially useful for multicomponent complexes, the minimal structural components or regulatory elements of which are unknown and/or which could be challenging to express recombinantly in a heterologous host. For example, in our founding studies on the enterobacterial Bcs secretion system [5], we resorted to the use of a dual HA-FLAG affinity tag-coding sequence inserted without polar effects at the end of the bcsA gene in the native bcsRQABZC operon of the E. coli 1094 commensal strain known to secrete cellulose as its main biofilm matrix component. In order to increase the copy number of the assembled secretory macroassemblies per cell, the genome was additionally modified by the insertion of a two-directional constitutive-promoter cassette at the bcs inter-operon region in the 1094 bcsAHA-FLAG genetic background [5]. Using mild detergent extraction and α-FLAG affinity purification (see below) on cells grown in cellulose secretion-promoting medium (M63B1), we discovered the formation of a stable, multicomponent Bcs macrocomplex that encompasses most of the inner membrane and cytosolic subunits (BcsRQABEF) [5]. Similar initial characterization of stably associating components of a given system of interest can be especially useful for the design of inducible overexpression strategies for increased protein yield, as often required for high-resolution structural studies. In addition, a heterologous plasmid-based expression background can be used for rapid mutation-based testing of the roles of individual subunits, protein domains, or conserved residues in the assembly and function of the studied macrocomplexes. Multiple strategies for recombinant protein expression exist with regard to both the choice of a host bacterial strain for protein overproduction and the choice of compatible inducible vectors. As a standard workhorse for protein overproduction, we typically resort to the E. coli BL21*(DE3) strain, which allows protein (co-)expression under the control of both E. coli and T7 RNA polymerase-dependent promoters due to the engineered λDE3 lysogen for the expression of an IPTGinducible T7 RNA polymerase in the genome [6]. Conversely, protein overexpression can be induced from a diverse set of promoters under the control of either the E. coli (e.g., the IPTGinducible lac, lacUV5, tac, trc, or T5-lac operator promoters, the arabinose-inducible araBAD promoters, or the tetracyclininducible tetA promoter) or T7 RNA polymerase (e.g., the IPTG-

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inducible T7-lac operator promoter) [7, 8]. Basal expression due to “leaky” promoters can be controlled either by the choice of expression vectors per se (e.g., the pBAD vectors allow tight control of arabinose-inducible genes under the araBAD promoter via expression of AraC that acts as strong transcription repressor in the absence of arabinose [9]) or by the use of strains featuring increased tolerance to toxic protein production (e.g., the C41/C43 (DE3) strains that have been empirically selected to this effect [10, 11] or strains carrying the pLysS chloramphenicol-resistant plasmid, which produce small amounts of T7 lysozyme to inhibit basally expressed T7 RNA polymerase prior to induction [12]). A particular advantage of functional bacterial assemblies is that they are often encoded by one or few operons transcribed as polycistronic messenger RNAs [13, 14], which in turn allows the cloning of sequential genes of interest under a single inducible promoter of a plasmid vector for protein overexpression. Moreover, the cloning of gene clusters as they have evolved in their natural operon context can preserve subtle effects of sequential protein expression on gene expression regulation. Examples include transcription antitermination and potential polar effects on downstream genes, translational arrest release or neutralization of toxic factors by already expressed binding partners, or translational coupling between neighboring genes where the translating ribosome or 30S subunit completing an upstream gene can restart translation onto the next open reading frame with the two interacting proteins emerging in close proximity for intersubunit stabilization and/or optimal complex assembly [15–17]. In addition to cloning gene clusters together, co-expression of an even greater number of protein subunits can be achieved by the combination of different expression vectors. It is important to note that co-expressed vectors need not only to encode different antibiotics resistance genes but also to depend on different replicons and/or plasmid partitioning systems in the host cells [18]. In our hands, we have seen particular success with combinations of the dual T7 promoter-based Duet vectors from Novagen (e.g., pRSFDuet-1 carrying a kanamycinresistance gene in combination with pCDFDuet-1 coding for streptomycin-resistance), as well as with the combination of pRSF-Duet1 with the trc promoter-based pProEx-Htb vector [5, 15, 19, 20]. 1.1 Cloning, Expression, and Purification of the E. coli Bcs Macrocomplex

Here, we first discuss the cloning, expression, and purification of the E. coli Bcs macrocomplex—a multicomponent, membraneembedded secretory assembly that encompasses most of the inner membrane (BcsA, BcsB, and BcsF) and cytosolic (BcsR, BcsQ, and BcsE) components [5, 19] (Fig. 1).

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Fig. 1 The E. coli Bcs secretion system. (a) Thumbnail representation of the Bcs subunits’ topology. OM outer membrane, PG peptidoglycan, IM inner membrane, pEtN phosphoethanolamine modified. (b) Genetic engineering of the E. coli 1094 2 K7 bcsAHA-FLAG strain for Bcs complex expression and purification from the endogenous bcs chromosomal locus [5]. A genetic cassette comprising a kanamycin resistance gene flanked by FRT (FLP recognition target) sites was PCR-amplified with primers designed to insert an in-frame HA-FLAG tandem tag-coding sequence at the 3′ end of the bcsA gene based on short homologous regions and a plasmid-encoded λ Red recombinase. The resistance gene was subsequently excised from the chromosome with the help of a pCP20 plasmid-encoded FLP recombinase. This procedure allows rapid modification of target regions using linear PCR fragments, and the λ Red and FLP helper plasmids can be easily cured by growth at 37 °C due to temperature-sensitive replicons [29]. In addition, a two-directional promoter cassette was introduced in the bcs inter-operon region, where the bcsRQABZC operon is constitutively expressed under a derepressed Ptet promoter and the bcsEFG operon is expressed under a constitutive λ promoter (PcL) [5]. (c) Plasmid-based recombinant overexpression based on pCDFDuet-1 and pRSFDuet-1 plasmid compatibility. bcsRQAB and bcsEFG were cloned under the first T7 promoter of the respective vectors for IPTG-inducible overexpression in BL21*(DE3) cells [5, 19]. (d) Primers used for PCR amplification of the target vectors and corresponding inserts. The inverse PCR amplification of the vectors allows redesign of the MCS (multiple cloning site) region for minimal target protein sequence modification and/or removal or insertion of additional epitope tags

Second, we discuss the cloning, expression, purification, and stabilization of the co-polymerase BcsB. These particular experiments were initially designed to validate the propensity of BcsB to selfpolymerize in a superhelical fashion, as observed in the periplasmic

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1.2 Cloning Expression, Purification, and Stabilization of Multimeric BcsB

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crown of the Bcs macrocomplex. The on-column cross-linking approach presented here was to our knowledge originally reported by Shukla et al. [21] and was used as an orthogonal assay to detect BcsB oligomers in solution. It can be harnessed, however, for the size exclusion chromatography (SEC)-coupled cross-linking of any other macromolecular complex of interest, allowing for simultaneous complex stabilization, buffer exchange, and removal of aggregate species and partial assemblies.

Materials

2.1 Cloning, Expression, and Purification of the Bcs Macrocomplex 2.1.1 Cloning of the BcsRQABEF Macrocomplex

1. Desalted synthetic oligonucleotides carrying the appropriate restriction sites and additional 6-nucleotide overhangs 2. Phusion DNA Polymerase (HF buffer and DMSO solution included; New England Biolabs) or equivalent high fidelity DNA Polymerase 3. 25 mM dNTP stock solution in water (dGTP, dATP, dTTP, and dCTP mixed in equal amounts from 100 mM stock solutions for each) 4. Restriction enzymes: PstI-HF, NotI-HF, BamHI-HF (highfidelity versions with reduced star activity from New England Biolabs), and DpnI 5. Calf Intestinal Phosphatase enzyme (Quick CIP, New England Biolabs) or equivalent 6. T4 polynucleotide kinase (PNK) (New England Biolabs) or equivalent 7. T4 DNA ligase enzyme 8. pRSFDuet-1 and pCDFDuet-1 plasmid templates (Novagen) 9. E. coli 1094 genomic DNA (purified from a 5-mL overnight culture in LB using the GenElute Bacterial Genomic DNA kit (Sigma-Adrich) or equivalent) 10. PCR thermocycler (Bio-Rad S1000 or similar) 11. Agarose gel electrophoresis equipment (Bio-Rad PowerPac power supply and the Mini Sub-Cell GT system or similar) 12. Heating/cooling thermoblock (Eppendorf ThermoMixer C or similar) 13. Blue Light table for gel excision (Invitrogen Safe Imager 2 or similar) 14. Gel Imager (Bio-Rad Gel Doc EZ or similar) 15. E. coli DH5α chemically competent cells (genotype: F- λΦ80’ lacZΔM15 Δ(argF-lac) U169 phoA supE44 recA1 relA1 endA1 thi-1 hsdR17(rk-, mk+) gyrA96)

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16. LB-agar plates supplemented with appropriate antibiotics (40 μg/mL kanamycin or 100 μg/mL streptomycin) 17. Heating/cooling microbiological shaker and static incubator 18. DNA extraction kits for gel and PCR cleanup (e.g., “NucleoSpin Gel and PCR Clean-Up”) and alkaline lysis-based kit for plasmid purification such as “NucleoSpin Plasmid” (MachereyNagel) or equivalent. 2.1.2 Expression and Purification of the Bcs Macrocomplex

1. Purified plasmid DNA. 2. Chemically competent E. coli BL21*(DE3) cells (genotype: FompT hsdSB (rB-, mB-) gal dcm rne131 (DE3)). 3. Streptomycin (1000X stock solution: 100 mg/mL stock solution) and kanamycin (1000X stock solution: 40 mg/mL stock solution). 4. Phosphate-buffered Terrific Broth (TB) medium. 5. 1 M IPTG stock solution. 6. Chicken egg lysozyme powder (Sigma-Aldrich) or equivalent. 7. Cellobiose powder (Sigma-Aldrich) or equivalent. 8. cOmplete Mini EDTA-free protease inhibitor cocktail (Roche) or equivalent. 9. AppCp stock solution (Jena Bioscience, 50 mM stock solution). 10. c-di-GMP stock solution (Jena Bioscience, 1 mM stock solution). 11. 1 M HEPES, 5 M NaCl, 70% glycerol, and 1 M MgCl2 stock solutions. 12. Detergents: digitonin (Sigma-Aldrich, powder stored at room temperature and solubilized immediately before use); anagrade n-dodecyl-β-D-maltopyranoside (β-DDM) (Anatrace): 10% stock solution stored at -20 °C; decyl maltose neopentyl glycol (DM-NPG) (Anatrace): 10% stock solution stored at 20 °C; lauryl maltose neopentyl glycol (LM-NPG) (Anatrace): 4% stock solution stored at -20 °C. 13. Lysis buffer: 20 mM HEPES pH 8.0, 120 mM NaCl, 10% Glycerol, 5 mM MgCl2, 10 μM AppCp, 2 μM c-di-GMP, 250 μM of Cellobiose, 100 μg/mL chicken egg lysozyme and 1 tablet/50 mL cOmplete Mini EDTA-free protease inhibitor cocktail. 14. Solubilization buffer: 20 mM HEPES pH 8.0, 120 mM NaCl, 10% glycerol, 5 mM MgCl2, 2 μM AppCp, 2 μM c-di-GMP, 0.4% digitonin, 0.4% DDM, 0.4% DM-NPG, and 0.2% LM-NPG.

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15. Washing buffer: 20 mM HEPES pH 8.0, 120 mM NaCl, 10% glycerol, 5 mM MgCl2, 2 μM AppCp, 2 μM c-di-GMP, and 0.008% LM-NPG. A final wash step without glycerol is carried out prior to elution. 16. Washing buffer without glycerol: 20 mM HEPES pH 8.0, 120 mM NaCl, 5 mM MgCl2, 2 μM AppCp, 2 μM c-di-GMP, and 0.008% LM-NPG. 17. Elution buffer: 20 mM HEPES pH 8.0, 120 mM NaCl, 5 mM MgCl2, 2 μM AppCp, 2 μM c-di-GMP, 0.008% LM-NPG, and 3X FLAG peptide (at 100 μg/mL final concentration). 18. Heating/cooling microbiological shaker (Eppendorf Innova S44i or similar). 19. Spectrophotometer for optical density measurements at 600 nm wavelength (Ultrospec 10, Biochrom Spectrophotometers or similar). 20. Large- and mid-capacity high-speed centrifuge (Beckman Coulter Avanti J-E, Avanti J-26XP or similar) and rotors (Beckman Coulter JLA 9.1000, JLA 8.100 or similar for cell pelleting and JA 25.50 for lysate clearing). 21. Preparative ultracentrifuge with a SW 28 Ti Swinging-Bucket rotor (Beckman Coulter). 22. Avestin Emulsiflex C5 high-pressure homogenizer for cell lysis (operated at 4 °C). 23. Potter-Elvehjem PTFE pestle and glass tube for membrane fraction resuspension (Sigma-Aldrich) or similar. 24. ANTI-FLAG M2 Affinity gel (A2220, Sigma-Aldrich) and 3X FLAG peptide (F4799, Sigma-Aldrich; solubilized at 10 mg/ mL in 120 mM HEPES pH 8.0, 120 mM NaCl (100X stock solution). 25. A Poly-Prep empty polypropylene chromatography column (Bio-Rad). 26. 100 kDa molecular weight cut-off (MWCO) concentrator (e.g., Amicon Ultra-0.5, Millipore). 27. A NanoDrop microvolume spectrophotomer. 2.2 Cloning, Expression, Purification, and Stabilization of the Copolymerase BcsB 2.2.1 Cloning of Multimeric BcsBFL

1. Desalted synthetic oligonucleotides carrying the appropriate restriction sites (in bold) and additional 6-nucleotide overhangs: BcsB_1_Bam_s: 5′- GGA TCC CAT ATG AAA AGA AAA CTA TTC TGG ATT TGT GCA GTG GCT ATG, and BcsB_779_Not_nostop_as: 5′- CTA TAG GCG GCC GCC TCG TTA TCC GGG TTA AGA CGA CGA CGA C. 2. Expression vector: IPTG-inducible pET21b. Adds a C-terminal hexahistidine tag to the expressed protein for IMAC purification, therefore no stop codon is included in the

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reverse oligonucleotide for bcsBFL amplification. Confers resistance to ampicillin. 3. Restriction enzymes (NotI-HF and NdeI), phosphatase enzyme Quick CIP, T4 DNA ligase and Phusion DNA Polymerase, and dNTP substrate mix. 4. Thermocycler, thermoblock, and agarose gel electrophoresis equipment as above. 5. DH5α chemically competent cells and ampicillinsupplemented LB medium and LB-agar plates (final ampicillin concentration: 100 μL/mL). 2.2.2 Expression and Purification of Multimeric BcsBFL

1. Purified plasmid DNA, chemically competent E. coli BL21* (DE3) cells, antibiotic-supplemented LB medium, LB-agar plates and TB medium, and IPTG. 2. Ampicillin (1000X stock solution: 100 mg/mL ampicillin sodium salt solution in water). 3. Detergents: synthetic drop-in substitute for digitonin (GDN101, Anatrace, 10% stock solution stored at -20 °C), digitonin, β-DDM, DM-NPG, and LM-NPG (see item 12 in Subheading 2.1.2). 4. Stock solutions: 1 M HEPES pH 8.0, 5 M NaCl, 70% glycerol, 1 M imidazole pH 8.0, and 1 M MgCl2 stock solutions. 5. Lysis buffer: 20 mM HEPES pH 8.0, 500 mM NaCl, 19 mM imidazole, 10% glycerol, and 1 tablet/50 mL cOmplete Mini EDTA-free protease inhibitor cocktail. 6. IMAC Buffer A: 20 mM HEPES pH 8.0, 500 mM NaCl, 0.008% LM-NPG, and 19 mM imidazole. 7. IMAC Buffer B: 20 mM HEPES pH 8.0, 500 mM NaCl, 0.008% LM-NPG, and 200 mM imidazole. 8. Gel filtration buffer: 20 mM HEPES, pH 8.0, 500 mM NaCl, 0.006% (w/v) LM-NPG, and 0.006% (w/v) GDN101. 9. Heating/cooling microbiological shaker and static incubator. Spectrophotometer for OD600 measurements. 10. Centrifuges for cell pelleting and lysate clearing as above. A sonicator for cell lysis. 11. A Poly-Prep empty polypropylene chromatography column (Bio-Rad) and Talon Superflow cobalt-based IMAC medium (Cytiva). 12. A PD-10 desalting column (Cytiva).

2.2.3 SEC-Coupled OnColumn Cross-Linking

1. Gel filtration buffer: 20 mM HEPES, pH 8.0, 500 mM NaCl, 0.006% (w/v) LM-NPG, and 0.006% (w/v) GDN101. 2. A medium-pressure liquid chromatography system (Bio-Rad NGC or similar).

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3. A Superose 6 Increase 10/300 GL column (Cytiva). 4. Glutaraldehyde solution (25% stock solution, Sigma-Aldrich, or equivalent). 5. Denaturing polyacrylamide gel electrophoresis (SDS-PAGE) equipment (e.g., Mini-Protean Tetra Cell and 4–20% gradient mini-gels, Bio-Rad; Expedion InstantBlue Coomassie Stain).

3

Methods

3.1 Cloning, Expression, and Purification of the BcsRQABEF Macrocomplex 3.1.1 Cloning of the BcsRQABEF Macrocomplex

1. PCR-amplify the bcsRQAHA-FLAGB and the bcsEFG regions (primers in Fig. 1; see Notes 1 and 2). Verify fragment amplification by agarose gel electrophoresis and purify the DNA product using a PCR clean-up kit. 2. Inverse PCR-amplify the target pCDFDuet-1 and pRSFDuet-1 vectors (primers in Fig. 1) and verify fragment amplification by agarose gel electrophoresis. Add 60 U DpnI directly to the PCR reaction, mix, and incubate for 3 h at 37 °C to digest the template plasmid. Purify the DNA product using a PCR cleanup kit. 3. Digest the inserts with the respective pairs of restriction enzymes (PstI-HF/ NotI-HF for bcsRQAHA-FLAGB and BamHI-HF/NotI-HF for bcsEFG). We typically use 60 U of eаch enzyme in a reaction volume of 30 μL for 3 h at 37 °C. Digest the vectors with the same enzymes and add 5 U of Quick CIP during the last hour of digestion. Gel-purify the DNA-digested DNA products. 4. Mix purified insert and target vector in a 3:1 to 10:1 ratio and add 400 U T4 DNA ligase and 1X T4 ligase buffer in a total volume of 10 μL. The buffer should be thawed gently and kept ice-cold to avoid hydrolysis of the included ATP. Incubate the ligation reaction at 16 °C for at least 1 h or overnight. 5. Transform chemically competent DH5α cells with a third of the ligation reaction and plate on LB-agar plates supplemented with 1X of the respective antibiotic (40 μg/mL for kanamycin and 100 μg/mL for streptomycin). Incubate at 37 °C overnight. 6. Pick single colonies and inoculate each in 5 mL liquid LB medium supplemented with 1X antibiotic. Grow overnight at 37 °C. Extract plasmid DNA. Positive clones can be identified by colony PCR prior to colony growth for plasmid amplification and purification, or by test-digestion after plasmid extraction. Rule out mutations by DNA sequencing. 7. For the addition of separate tags, point mutations, or deletions, we typically resort to inverse PCR. For example, we inserted an

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N-terminal STREP tag at the N-terminus of BcsE in the pRSFDuet-1* vector using primers 5’-CCG CAG TTC GAA AAA GGA TCC ATG AGG GAC ATT GTG GAC CCT G and 5′-GTG GCT CCA GCT AGC CAT GGT ATA TCT CCT TAT TA AAG, where the STREP tag sequence is shown in bold. Following completion, the PCR reaction is digested with DpnI as described in step 2 in Subheading 3.1.1 and gel-purified. The eluted DNA is then subjected to 5′-phosphorylation using the T4 PNK enzyme for 40 min at 37 °C and then ligation using the T4 DNA ligase at 16 °C as above (see Note 3). 3.1.2 Expression and Purification of the Bcs Macrocomplex (See Fig. 2)

1. Co-transform chemically competent E. coli BL21*(DE3) cells for recombinant expression with expression vectors pCDFDuet1-bcsHisRQAHA-FLAGB and pRSFDuet1*-bcsSTREP EFG (see Note 4) and grow cells on a LB agar plate with appropriate antibiotics at about 0.7X final concentration each (70 μg/mL streptomycin and 30 μg/mL kanamycin). 2. Inoculate a starter culture of 100 mL antibioticssupplemented, phosphate-buffered TB (see Note 5) with multiple colonies, and let it grow a few hours until saturation

Fig. 2 Workflow for the expression and purification of the E. coli Bcs macrocomplex

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(typically 3–4 h at 37 °C with good co-transformation efficiency). If only a few co-transformant colonies are available, inoculate them together in 1 mL and grow overnight before diluting in the starter culture the next day. 3. Inoculate 2 L of TB with the small culture, and grow cells at 37 °C until the optical density at 600 nm (OD600) reaches 0.8–1.2, at which point move the culture to 17 °C, allow 10–15 min to cool, and induce recombinant protein expression overnight by the addition of IPTG at 0.7 mM final concentration. 4. Following 16 h of protein expression, collect the cells by centrifugation at 5000 g for 20 min at 4 °C. 5. Resuspend the pelleted cells on ice in 40–50 mL of lysis buffer and lyse using an Emulsiflex C3 or C5 high-pressure homogenizer (Avestin), operated at 4 °C (see Note 6). 6. Remove cell debris from the lysate via centrifugation at 12000 g for 15 min at 4 °C. 7. Pellet the membrane fraction from the supernatant via ultracentrifugation at 26500 rpm and 4 °C using the Beckman SW28 Ti swinging bucket rotor (59,000 (min)–126,000 g (max)). 8. Remove the supernatant and resuspend gently the pelleted membranes in 20–30 mL of solubilization buffer using a Potter-Elvehjem homogenizer (see Notes 7 and 8). Let incubate for 1 h at 17 °C under gentle agitation (50 rpm). Avoid sample foaming during resuspension and agitation. Submit the sample to a second ultracentrifugation identical to step 7. 9. Incubate the supernatant with 100 μL of anti-FLAG M2 affinity gel (pre-equilibrated in the washing buffer). Pass the sample through the resin two–three times at 4 °C under gravity flow. 10. Wash the resin with up to 15–20 mL of washing buffer and do a last wash with 3 mL of glycerol-free washing buffer (see Note 9). 11. Elute with ~400 μL of elution buffer. 12. Concentrate the sample to 0.8–1 mg/mL using a 100-kDa cutoff Amicon Ultra-0.5 concentrator and use immediately for the preparation of cryo-EM grids (see Note 10). 3.2 Cloning, Expression, Purification, and Stabilization of the Multimeric BcsBFL 3.2.1 Cloning of Multimeric BcsBFL

1. PCR amplify the bcsB coding region using genomic DNA as a template. Verify the correct amplification by agarose gel electrophoresis (5 μL PCR in 1% agarose gel pre-stained with 0.2X GelGreen). Clean-up the rest of the PCR product, digest with NdeI and NotI-HF enzymes, and gel-purify the insert DNA for ligation.

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2. Digest the pET21B vector with the NdeI and NotI-HF enzymes (3 h at 37 °C), and add 5 U of Quick CIP during the last hour of restriction digestion. Gel-purify the vector DNA for ligation. 3. Mix the insert and vector DNA in 3:1 to 10:1 ratio, and incubate with 400 U T4 DNA ligase in the supplied 1X buffer. Incubate for 1 h to overnight at 16 °C as above, then transform chemically competent DH5α cells with the ligated expression construct. Plate on ampicillin-supplemented LB-agar plates and incubate at 37 °C. 4. Inoculate single colonies in 5 mL ampicillin-supplemented LB, and grow overnight for plasmid amplification. 5. Extract the plasmid DNA, and test for correct bcsB insertion and lack of mutations by test-digestion and DNA sequencing. 3.2.2 Expression and Purification of Multimeric BcsBFL

1. Transform chemically competent E. coli BL21*(DE3) cells for recombinant expression with expression vectors pET21bbcsBFL, and grow cells on an ampicillin-supplemented (100 μg/mL) LB agar plate. 2. Inoculate a starter culture of 40 mL antibiotics-supplemented, phosphate-buffered TB with multiple colonies, and let it grow a few hours until saturation (typically 3–4 h at 37 °C with good co-transformation efficiency). 3. Inoculate 2 L of TB with the small culture, and grow cells at 37 °C until the optical density at 600 nm (OD600) reaches 0.8–1.2, at which point move the culture to 17 °C, allow 10–15 min to cool and induce recombinant protein expression overnight by the addition of IPTG at 0.7 mM final concentration. 4. Following 16 h of induced protein expression, collect the cells by centrifugation at 5000 g for 20 min at 4 °C. 5. Resuspend the pelleted cells on ice in 40–50 mL of lysis buffer, and lyse on ice by sonication. We recommend pulsed sonication with longer pauses to avoid sample overheating. 6. Pellet non-lysed cells by 15-min centrifugation at 12000 g and 4 °C. 7. Collect the supernatant and add solubilized detergents to final concentrations as follows: 0.4% β-DDM, 0.4% digitonin, 0.4% DM-NPG, 0.2% GDN101, and 0.2% LM-NPG. Incubate at 17 °C under gentle agitation for 1 h. 8. Clarify the extract by centrifugation at 60000 g for 1 h. Collect the supernatant. 9. Apply the supernatant onto the Talon Superflow resin, mix, incubate for 15 min, and let pass by gravity (see Note 11).

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10. Wash extensively with IMAC Buffer A (up to 100 column bed volumes), and elute with 4-column bed volumes of IMAC Buffer B. 11. Buffer-exchange the protein in gel filtration buffer using a disposable PD-10 column. 3.2.3 SEC-Coupled OnColumn Cross-Linking

1. Equilibrate the Superose 6 Increase 10/300 GL column with at least 1.5 column volumes of gel filtration buffer (40 mL). 2. Inject 200 μL of 0.25% glutaraldehyde on the column, and let run for 5 mL (see Notes 12–15). 3. Stop the run, flush-clean the sample loop and the injection port with gel filtration buffer, and load the concentrated protein sample. 4. Inject the protein on the column, and elute with 40 mL gel filtration buffer. Collect the eluting protein in fractions. Aggregates elute typically around elution volume of 8 mL from the protein injection start. 5. The mild crosslinking of the eluted protein (complexes) relative to the input sample can be visualized by SDS-PAGE using standard electrophoresis equipment.

4

Notes 1. Due to the very large size of the inserts, we recommend using a high-fidelity DNA polymerase (e.g., Phusion, New England Biolabs). Gentle pipetting and the use of a blue-light, and not UV, transilluminator are crucial for preserving DNA integrity and for successful cloning. We recommend extracted genomic DNA as template, but colony PCR can also be used. For primer design, we typically design oligonucleotides whose geneoverlapping region features melting temperatures of ~62 °C as determined by the IDTDNA OligoAnalyzer tool. 2. A typical PCR reaction contains 33.6 μL of ultrapure water, 10 μL HF buffer, 3.2 μL of DMSO, 0.7 μL dNTP mix (25 mM stock concentration for each dNTP), 0.5 μL template DNA, 0.5 μL of each primer (7 μM stock concentration), and 1 μL (2 U) of Phusion DNA Polymerase. We typically use a total of 30 cycles for target DNA amplification. 3. A typical phosphorylation/ligation protocol includes the incubation of 8 μL gel-purified, DpnI-digested inverse PCR product with 1 μL T4 DNA ligase buffer, and 1 μL (10 U) T4 PNK (New England Biolabs). Following 40-min incubation at 37 ° C, we typically add additional 0.5 μL T4 DNA ligase buffer to provide additional ATP, 3.5 μL of ultrapure water and 1 μL (400 U) of T4 DNA ligase.

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4. The immunoaffinity purification is more sensitive than the more standard immobilized metal affinity chromatography (IMAC) purification via polyhistidine tags and features a dissociation constant (Kd) in the low nanomolar range for the immobilized M2 anti-FLAG antibody. It considerably limits non-specific interactions and contaminating proteins, and allows elution via the addition of the 1X or 3X FLAG peptide in otherwise preserved buffer composition (as opposed to elevated imidazole or salt concentrations in IMAC or ion exchange-based purifications). To further limit the co-purification of non-specific proteins, the lysates can be pre-cleared with Mouse IgG-Agarose (A0919, Sigma-Aldrich). The presence of an additional tag on BcsA and the introduction of separate tags on additional Bcs components were used to determine the Bcs macrocomplex composition and can be harnessed for tandem affinity purification strategies [22–25]. 5. For 1 L of phosphate-buffered terrific broth (TB), mix 12 g of tryptone, 24 g of yeast extract, and 8 mL of glycerol, and add ultrapure water up to 900 mL. Autoclave, cool, and add 100 mL of 10X TB salts (for 100 mL or per liter TB: 2.2 g KH2PO4 and 9.4 g K2HPO4 in water, autoclaved separately). 6. Bacterial cells can be lysed commonly by sonication or highpressure homogenization. The former method uses the energy of ultrasound to generate air bubbles (cavitation) in the bacterial suspension that subsequently implode to damage the cell membrane and shear nucleic acids locally around the metal probe. It important to note that the sonication process generates significant heat due to localized pressure and velocity release and can be damaging to multicomponent membraneembedded protein complexes, even when carried out on ice. We have observed better preservation of membrane complexes when using a high-pressure homogenizer such as Emulsiflex C3 or C5, where high pressure is applied to the sample upon passage through the narrow orifice of the homogenizing valve. The equipment requires supply of compressed air and we strongly recommend use at 4 °C to limit denaturing heat generation. 7. The choice of detergent should be optimized for each target membrane protein or protein complex, where the homogenized membrane fraction is split in aliquots and each is incubated with a detergent or a mix of detergents (usually at 1–2% final detergent concentration for 1–3 h at 4 °C). The fractions are subsequently subjected to a second ultracentrifugation step, and the distribution of protein in the extract vs. insoluble fraction is determined by SDS-PAGE or Western blot. Generally, non-ionic detergents provide for milder extraction conditions and target primarily protein–lipid interactions while preserving

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protein–protein interfaces within the micelles [26, 27]. It is also important to consider the downstream use of the purified sample. Whereas digitonin has proven very useful for the initial extraction of multicomponent assemblies, the addition of maltoside and neopentyl glycol detergents increases digitonin’s own solubility [8]. Our choice of LM-NPG as a final purification detergent was driven by its very low critical micelle concentration (CMC) that can improve the overall sample quality for downstream electron microscopy data acquisition and analysis. Importantly, although the CMC for LM-NPG is presumably about 0.001% in water, we observed much better sample homogeneity at 0.008% in the elution buffer. 8. In order to ease the expression and purification procedure, the pelleted membrane fraction can be submerged in 5 mL of solubilization buffer without detergents, flash frozen in liquid nitrogen, and stored at -80 °C. In general, for membrane proteins and protein complexes, we recommend freezing after cell pelleting, lysis, and membrane fractionation and before detergent extraction of the protein target. For cytosolic assemblies, the protocol can be interrupted after cell pelleting and resuspension in buffer and before the actual lysis. 9. Glycerol was omitted in the final wash step and from the elution buffer in order to limit background scattering and potential effects on vitrification during cryo-EM sample preparation and data collection. 10. The sample was used immediately for the preparation of Gold Quantifoil R1.2/1.3 grids for cryo-EM data collection. We recommend initial screening concentrations of about 1 mg/ mL for cryo-EM and about 20-50X lower concentration for negative-stain EM (see Fig. 3). 11. We recommend the use of ~0.5 mL packed resin per liter of expression culture in order to minimize non-specific interactions. We have also consistently observed that the Talon Superflow resin leads to the co-purification of significantly less non-specific proteins in comparison to Ni-NTA resin in batch (Qiagen) or as prepacked columns (HisTrap, Cytiva, or similar). 12. The concentration of glutaraldehyde can be adjusted dependent on the sample of interest. The 0.25% (~25 mM) concentration allows for very mild, partial crosslinking that can stabilize more fragile assemblies while minimizing non-specific protein aggregation. 13. As the higher hydrodynamic radius of the protein sample incurs faster mobility through the chromatography column, protein assemblies passing through the injected glutaraldehyde bolus would be briefly exposed to the crosslinking agent and any

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Fig. 3 Structural studies of the E. coli Bcs secretion system. (a) SDS-PAGE of the purified Bcs macrocomplex. (b) Negative-stain electron microscopy of the purified Bcs macrocomplex. From left to right, a representative micrograph, 2D class averages, and 3D electron density reconstruction. (Data from Krasteva et al. [5], reproduced under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). (c) Cryo-EM of the purified Bcs macrocomplex. From left to right, 2D class averages and 3D electron density reconstruction with assigned densities and fitted atomic models. (Data from Abidi et al. [19]; reproduced under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). (d) SDS-PAGE of the purified BcsBFL protein. (e) CryoEM of the purified BcsBFL protein. From left to right: 2D class averages; 3D electron density reconstruction and refined atomic model of the BcsB periplasmic regions; and 3D atomic model of BcsBFL. (f) Principle of the SEC-coupled glutaraldehyde (GA) crosslinking. (g) SEC-coupled on-column cross-linking. Top, SEC elution profile; bottom, SDS-PAGE analysis of the input, IMAC-purified BcsBFL, and the partially cross-linked SEC elution fractions (Data from Abidi et al. [19]; reproduced under the CC BY 4.0 license (https://creativecommons. org/licenses/by/4.0/))

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non-specific aggregates would be simultaneously separated by the size-exclusion matrix. The latter would also separate excess glutaraldehyde thus abolishing the need of crosslinker quenching. 14. Glutaraldehyde acts as a homo-bifunctional crosslinker that interacts with several functional groups such as amine, thiol, imidazole, or phenol and can therefore crosslink a variety of amino acids. Nevertheless, it is known that the cross-linking reactions occur primarily between the ε-amino groups of lysine residues due to their higher nucleophilicity. Depending on the protein target or downstream experiments, a different crosslinker can be used. 15. The amine-reactivity of glutaraldehyde requires the use of amine-free buffering agents (e.g., HEPES and not Tris-HCl), and imidazole from previous purification steps should also be removed by buffer exchange prior to the SEC-coupled crosslinking. Albeit less efficiently, glutaraldehyde can also introduce crosslinks between proteins and nucleic acids [28], which is to be taken into account if nucleotide-containing ligands are used or expected.

Acknowledgments We would like to thank the editors for the invitation to contribute this book chapter and for their contributions towards the final version of the manuscript. The experimental works described here received funding from the ERC Executive Agency under grant agreement 757507—BioMatrix-ERC-2017-StG (to P.V.K.) and was also supported by the I2BC, IECB, the CNRS, an ATIPAvenir starting grant (to P.V.K.), and a Universite´ de Bordeaux IDEX Junior Chair grant (to P.V.K.). We are grateful to all current and former members of the SBB group for insightful discussions and/or technical assistance. References 1. Abidi W, Torres-Sa´nchez L, Siroy A, Krasteva PV (2022) Weaving of bacterial cellulose by the Bcs secretion systems. FEMS Microbiol Rev 46(2):fuab051. https://doi.org/10.1093/ femsre/fuab051 2. Ro¨mling U, Galperin MY (2015) Bacterial cellulose biosynthesis: diversity of operons, subunits, products, and functions. Trends Microbiol 23:545–557. https://doi.org/10. 1016/j.tim.2015.05.005 3. Karimova G, Pidoux J, Ullmann A, Ladant D (1998) A bacterial two-hybrid system based on

a reconstituted signal transduction pathway. Proc Natl Acad Sci U S A 95:5752–5756 4. Teufel F, Almagro Armenteros JJ, Johansen AR et al (2022) SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat Biotechnol 40:1023–1025. https://doi. org/10.1038/s41587-021-01156-3 5. Krasteva PV, Bernal-Bayard J, Travier L et al (2017) Insights into the structure and assembly of a bacterial cellulose secretion system. Nat Commun 8:2065. https://doi.org/10.1038/ s41467-017-01523-2

Membrane Protein Complex Purification 6. Jeong H, Kim HJ, Lee SJ (2015) Complete genome sequence of Escherichia coli strain BL21. Genome Announc 3:e00134–e00115. h t t p s : // d o i . o r g / 1 0 . 1 1 2 8 / g e n o m e A . 00134-15 7. Pawel J, Tuck Seng W (2015) From genetic circuits to industrial-scale biomanufacturing: bacterial promoters as a cornerstone of biotechnology. AIMS Bioengineering 2:277–296. https://doi.org/10.3934/bioeng.2015.3.277 8. Durand E, Lloubes R (2017) Large complexes: cloning strategy, production, and purification. Methods Mol Biol 1615:299–309. https:// doi.org/10.1007/978-1-4939-7033-9_24 9. Guzman LM, Belin D, Carson MJ, Beckwith J (1995) Tight regulation, modulation, and high-level expression by vectors containing the arabinose PBAD promoter. J Bacteriol 177:4121–4130. https://doi.org/10.1128/ jb.177.14.4121-4130.1995 10. Miroux B, Walker JE (1996) Over-production of proteins in Escherichia coli: mutant hosts that allow synthesis of some membrane proteins and globular proteins at high levels. J Mol Biol 260:289–298. https://doi.org/10. 1006/jmbi.1996.0399 11. Dumon-Seignovert L, Cariot G, Vuillard L (2004) The toxicity of recombinant proteins in Escherichia coli: a comparison of overexpression in BL21(DE3), C41(DE3), and C43 (DE3). Protein Expr Purif 37:203–206. https://doi.org/10.1016/j.pep.2004.04.025 12. Studier FW (1991) Use of bacteriophage T7 lysozyme to improve an inducible T7 expression system. J Mol Biol 219:37–44. https:// doi.org/10.1016/0022-2836(91)90855-z 13. Jacob F, Perrin D, Sanchez C, Monod J (1960) Operon: a group of genes with the expression coordinated by an operator. C R Hebd Seances Acad Sci 250:1727–1729 14. Jacob F (2011) The birth of the operon. Science 332:767. https://doi.org/10.1126/sci ence.1207943 15. Zouhir S, Abidi W, Caleechurn M, Krasteva PV (2020) Structure and multitasking of the c-di-GMP-sensing cellulose secretion regulator BcsE. mBio 11:e01303–e01320. https://doi. org/10.1128/mBio.01303-20 16. Huber M, Faure G, Laass S et al (2019) Translational coupling via termination-reinitiation in archaea and bacteria. Nat Commun 10:4006. https://doi.org/10.1038/s41467-01911999-9 17. Yamaguchi Y, Park JH, Inouye M (2011) Toxin-antitoxin systems in bacteria and archaea. Annu Rev Genet 45:61–79. https:// doi.org/10.1146/annurev-genet110410-132412

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18. Novick RP (1987) Plasmid incompatibility. Microbiol Rev 51:381–395. https://doi.org/ 10.1128/mr.51.4.381-395.1987 19. Abidi W, Zouhir S, Caleechurn M, Roche S, Krasteva PV (2021) Architecture and regulation of an enterobacterial cellulose secretion system. Sci Adv 7:eabd8049. https://doi.org/ 10.1126/sciadv.abd8049 20. Abidi W, Decossas M, Torres-Sa´nchez L et al (2022) Bacterial crystalline cellulose secretion via a supramolecular BcsHD scaffold. Sci Adv 8:eadd1170. https://doi.org/10.1126/sciadv. add1170 21. Shukla AK, Westfield GH, Xiao K et al (2014) Visualization of arrestin recruitment by a G-protein-coupled receptor. Nature 512: 2 1 8 – 2 2 2 . h t t p s : // d o i . o r g / 1 0 . 1 0 3 8 / nature13430 22. Hopp TP, Prickett KS, Price VL et al (1988) A short polypeptide marker sequence useful for recombinant protein identification and purification. Bio/technology (Nature Publishing Company) 6:1204–1210. https://doi.org/ 10.1038/nbt1088-1204 23. Wegner GJ, Lee HJ, Corn RM (2002) Characterization and optimization of peptide arrays for the study of epitope-antibody interactions using surface plasmon resonance imaging. Anal Chem 74:5161–5168. https://doi.org/10. 1021/ac025922u 24. Chen GI, Gingras AC (2007) Affinitypurification mass spectrometry (AP-MS) of serine/threonine phosphatases. Methods 42: 298–305. https://doi.org/10.1016/j.ymeth. 2007.02.018 25. Li Y (2011) The tandem affinity purification technology: an overview. Biotechnol Lett 33: 1487–1499. https://doi.org/10.1007/ s10529-011-0592-x 26. Seddon AM, Curnow P, Booth PJ (2004) Membrane proteins, lipids and detergents: not just a soap opera. Biochim Biophys Acta 1666(1–2):105–117. https://doi.org/10. 1016/j.bbamem.2004.04.011 27. Anandan A, Vrielink A (2016) Detergents in membrane protein purification and crystallisation. Adv Exp Med Biol 922:13–28. https:// doi.org/10.1007/978-3-319-35072-1_2 28. Kuykendall JR, Bogdanffy MS (1992) Efficiency of DNA-histone crosslinking induced by saturated and unsaturated aldehydes in vitro. Mutat Res 283(2):131–136. https:// doi.org/10.1016/0165-7992(92)90145-8 29. 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

Chapter 26 Starting with an Integral Membrane Protein Project for Structural Biology: Production, Purification, Detergent Quantification, and Buffer Optimization—Case Study of the Exporter CntI from Pseudomonas aeruginosa Maxime Me´gret-Cavalier, Alexandre Pozza, Quentin Cece, Franc¸oise Bonnete´, Isabelle Broutin, and Gilles Phan Abstract Production, extraction, purification, and stabilization of integral membrane proteins are key steps for successful structural biology studies, in particular for X-ray crystallography or single particle microscopy. Here, we present the purification protocol of CntI from Pseudomonas aeruginosa, a new metallophore exporter of the Drug Metabolite Transporter (DMT) family involved in pseudopaline secretion. Subsequent to CntI purification, we optimized the buffer pH, salts, and additives by differential scanning fluorimetry (DSF), also known as Thermofluor Assay (TFA) or fluorescent thermal stability assay (FTSA), with the use of dye 1-AnilinoNaphthalene-8-Sulfonic acid (ANS), a fluorescent molecule compatible with detergents. After the buffer optimization, the purified CntI was analyzed by Size Exclusion Chromatography coupled with Multi-Angle Laser Light Scattering (SEC-MALLS), UV absorbance, and Refractive Index detectors, in order to determine the absolute molar mass of the protein–detergent complex, the detergent amount bound to the protein and the amount of protein-free detergent micelles. Altogether, these biophysical techniques give preliminary and mandatory information about the suitability of the purified membrane protein for further biophysical or structural investigations. Key words Membrane protein, Detergent, Purification, Ultrafiltration, Size exclusion chromatography, Multi-angle light scattering, Differential scanning fluorimetry, Melting temperature, Refractive index

1

Introduction Membrane proteins are essential in many cellular functions, such as secretion, metabolite transport, ion diffusion, cellular communication, and signaling. In pharmaceutical industry, membrane proteins represent more than 65% of the drug targets [1, 2]. Nevertheless, the number of membrane protein structures in the Protein Data Bank (PDB) is still low (3.8% according to the PDB statistics,

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_26, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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https://www.rcsb.org/stats) and grows slowly because of technical difficulties in producing and purifying milligrams of functional and stable membrane proteins for structural studies. Typical reasons are protein overexpression into already crowded biological membrane of the host-cell and stability of the protein extracted from physiological membrane with the help of non-biological surfactants (mainly detergents). Stabilization of the membrane protein in detergent for structural biology is highly empirical [3, 4]. Fortunately, the hindsight of many decades of membrane protein purification and crystallization [5] teaches us to carefully look at the biochemical and structural characterization of the closest protein homologue to start with, in particular for buffer and detergent choices during the first purification attempt. In this chapter, we present the production and purification protocol of the integral membrane protein CntI, the exporter of pseudopaline, a largespectrum metallophore discovered recently in the pathogen Pseudomonas aeruginosa [6]. CntI is a polytopic integral membrane protein of the Drug Metabolite Transporter (DMT) family, made of 284 amino acids (MW = 30.696 Da, calculated pI = 9.45) and predicted with ten transmembrane α-helices. The closest homologue of known 3D-structure is YddG from Starkeya novella with 26.7% amino acids identity (MW = 29.452 Da, calculated pI = 9.59), which was successfully extracted and purified in Dodecyl β-Maltoside (DDM) detergent [7]. Because of protein similarities in terms of size, amino acid composition, and calculated charge repartition with YddG (calculated charges at physiological pH are +5.9 and + 5.2 for YddG and CntI, respectively, https:// www.protpi.ch/Calculator/ProteinTool), we first decided to purify CntI in a similar way. Subsequently, buffer, salts, and additives were optimized by Differential Scanning Fluorimetry (DSF), also known as Thermofluor Assay (TFA) or fluorescent thermal stability assay (FTSA), in order to identify the best buffer compositions that would promote thermal stability of CntI, and likely crystallization. In our case, we used the hydrophobic fluorescent dye 1-anilinonaphthalene-8-sulfonic acid (ANS), which seems to favor interaction with denatured over folded membrane protein– detergent complexes [8]. Usually, excess of ANS is added to the protein at μM concentration, with protein–dye molar ratio of 1:50 to 1:400. The fluorescence signal of the dye is then monitored (excitation wavelength at 350 nm/emission wavelength at 480 nm) while a temperature gradient is applied to the mixture, usually from 20 °C to 90 °C. With increasing temperature, the protein unfolds and provides hydrophobic patches for dye interaction, which increases the fluorescence signal. The interaction of the dye with the denaturing protein leads to a sigmoidal curve over temperature increase, and the inflexion point of the slope is defined as an apparent melting temperature of the protein (Tm), quantifying the relative thermal stability of the protein.

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DSF approach allows us to screen stability effect of different buffer variations such as pH, concentration of salt, type of detergent, or other additives, like glycerol or imidazole used as eluant in the specific case of metal affinity column chromatography purification. Biochemical analysis of membrane protein molecular weight (MW) could be tricky. Indeed, typical Size Exclusion Chromatography (SEC) analysis, once calibrated with appropriate MW standards, gives an estimation of the protein–detergent complex MW, which varies depending on the class and the amount of associated detergent molecules. Besides, analysis of MW from Sodium Dodecyl Sulfate–Poly Acrylamide Gel Electrophoresis (SDS-PAGE) often shows unexpected migration distance compared to the MW standards, with around 10–30% differences from expected weight [9]. This is generally due to partial unfolding of the membrane protein with the Laemmli sample buffer. Consequently, determining the absolute molecular weight of the protein by triple-detection SEC-Multi Angle Laser Light Scattering (MALLS) analysis is a suitable biophysical approach since it does not rely on the shape of the protein, or the amount of associated detergent. SECMALLS analysis consists in separation of protein–detergent complex and protein-free detergent micelles by SEC coupled to triple detection by simultaneous measurements of ultraviolet absorbance at 280 nm (UV280) mainly of the protein, static light scattering at 658 nm at three different angles Δ(LS(θ)), and refractive index (ΔRI) of both protein and detergent [10]. The light scattering difference between the sample and the buffer is converted into Rayleigh ratio by the Astra software (from Wyatt Technology) using the following equation: RðθÞ = ΔðLSðθÞÞ 

n0 nt

2



Rt It

in which n0 and nt are the refractive index of the buffer and the toluene, respectively, Rt and It the Rayleigh ratio and the scattering light intensity of toluene at θ = 90°. The Astra software allows then to determine the detergent/ protein mass ratio (δ) in the protein–detergent complex using the values of protein and detergent refractive index increments, ((dn/dc)protein; (dn/dc)detergent), together with the extinction coefficient of protein and detergent (A280 protein; A280 detergent) with the following formula: 1 dn  1þδ dc =

ΔRI  UV 280

protein

þ

δ dn  1þδ dc

detergent

1 δ  A 280 protein þ  A 280 detergent 1þδ 1þδ

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The refractive index increment for the protein can be obtained from the SedFit software and for the detergent directly from batch measurements using the refractometer. In this formula, the left-hand side term corresponds to the protein–detergent complex refractive index increment: dn dc

complex

1 dn  1þδ dc

=

protein

þ

δ dn  1þδ dc

detergent

while the right-hand side term corresponds to the protein–detergent complex refractive index increment, using the experimental values of UV280, ΔRI, and both protein and detergent extinction coefficients: dn dc =

complex

ΔRI  UV 280

=

ΔRI c complex

1 δ  A 280 protein þ  A 280 detergent 1þδ 1þδ

The calculated Rayleigh ratio R(θ) and the other parameters ((dn/dc)complex and ccomplex) are therefore used to determine the absolute molar mass of the protein–detergent complex following the formula: RðθÞ

MW complex = k

dn dc

2 complex

:c complex

where ccomplex is the concentration of the protein–detergent complex and k an apparatus constant. Finally, the absolute protein molar mass is determined with the formula: MW protein =

MW complex ð1 þ δÞ

Finally, protein concentration, usually around 10–20 mg/mL, is necessary for structural biology, in particular for crystallization. Ultrafiltration by medium speed centrifugation is commonly used to concentrate the protein. Nevertheless, depending on the type of membrane filter materials (cellulose triacetate or polyethersulfone), molecular weight cut-off, geometry (dead-end versus cross flow filtration) and centrifugation speed (500–5000 g), protein-free detergent micelles could dramatically accumulate and possibly influence the crystallization process, or also destabilize and aggregate the protein [11, 12]. An accurate quantification of detergent micelles is essential for further structural studies, but depends on specific state-of-the-art technologies, such as mass spectrometry [13]. Here, we took advantage of SEC-MALLS analysis to quantify protein-free detergent micelles after linear correlation of the integrated refractive index (ΔRI) and the quantity of DDM [14].

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Materials

2.1 Bacterial Culture and CntI Production

1. E. coli strain C43 (DE3) deleted from the acrb gene (Δacrb) transformed with pET-TEV expression plasmid, kanamycin resistant, with T7 inducible promoter of recombinant CntI (with eight His N-terminal tag followed by a TEV protease cleavage site) (see Note 1). 2. Six flasks of 2.5 L volume size containing 1 L of sterilized Terrific Broth (TB) media: 12 g/L tryptone, 24 g/L yeast extract, 5 g/L glycerol, and 89 mM phosphate buffer (17 mM KH2PO4 and 72 mM K2HPO4). 3. 1 M isopropyl β-D-1-thiogalactopyranoside (IPTG) and 50 mg/mL kanamycin. 4. Orbital incubator shaker with capacity for at least six flasks of 1 L.

2.2 Bacterial Cell Lysis and Membrane Isolation

1. 100 mL of bacterial lysis buffer: 20 mM HEPES, pH 7.5, 200 mM NaCl, EDTA-free protease inhibitor cocktail tablet, and 250 U/μL benzonase. 2. Centrifuge with rotor suitable for 50 mL conical plastic tubes. 3. Ultracentrifuge with rotor Ti 45 (Beckman). 4. Two polycarbonate bottles of 70 mL (Beckman). 5. Four 50 mL conical bottom tubes. 6. Cell disruptor (CellD, Constant Systems Ltd. or equivalent). 7. Protein assay BiCinchoninic Acid (BCA) kit for total protein quantification (e.g., QuantiPro™ BCA Assay Kit, SigmaAldrich).

2.3 Solubilization of CntI with Detergent

1. 1 L of solubilization buffer: 20 mM HEPES, pH 8, 200 mM NaCl, 1% (w/v) n-Dodecyl-β-D-Maltopyranoside (DDM), 10 mM imidazole, pH 8, EDTA-free protease inhibitor cocktail (to be added extemporaneously), 10% glycerol (see Note 2). 2. Cell homogenizer Potter type of 50 mL. 3. Ultracentrifuge with rotor Ti 45 (Beckman). 4. Six polycarbonate bottles of 70 mL (Beckman). 5. Stirrer and 4 cm magnetic bar.

2.4 Purification of CntI Solubilized in Detergent 2.4.1 Immobilized Affinity Chromatography (IMAC)

¨ KTA Pure or Purifier, 1. Automated protein purification system (A Cytiva, or equivalent) equipped with 96 deep well fraction collector and a loading sample pump. 2. HisTrap Ni-NTA pre-packed column of 5 mL (Cytiva) (or equivalent).

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3. 500 mL of equilibration buffer A: 20 mM HEPES, pH 8, 200 mM NaCl, 10 mM imidazole, 0.03% (w/v) DDM 4. 250 mL of elution buffer B: 20 mM HEPES, pH 8, 200 mM NaCl, 500 mM Imidazole, 0.03% (w/v) DDM. 2.4.2 Size Exclusion Chromatography (SEC)

¨ KTA Pure or Purifier, 1. Automated protein purification system (A Cytiva, or equivalent) equipped with 96 deep well fraction collector. 2. 1 L SEC running buffer: 20 mM MES, pH 6, 150 mM NaCl, 0.03% (w/v) DDM (see Note 3) 3. Superdex 200 SEC column with a total volume of 124 mL (e.g., HiLoad 16/600, Cytiva) (see Note 4). 4. Vivaspin® Ultra centrifugal filtration device for protein concentration, with membrane molecular weight cut-off (MWCO) of 50,000 Da (Sartorius, or equivalent) (see Note 5). 5. 96 deep wells plate adapted to AKTA pure fraction collector.

2.5 Thermofluor Assay (or Differential Scanning Fluorescence)

1. qPCR thermocycler instrument (e.g., CFX384 Touch RealTime PCR System, Biorad). 2. qPCR microplate (e.g., Hard-Shell-PCR plates, 384 wells, thin wall, white well, Biorad) (see Note 6). 3. Plastic film for plate sealing (e.g., MicroAmp™ optical adhesive film, ThermoFischer). 4. Fluorescent dye 1-Anilinonaphthalene-8-Sulfonic (1,8-ANS), >98% purity (Invitrogen).

Acid

5. 2× ready to use screening buffer of different conditions. 6. Dimethylsulfoxide (DMSO). 7. n-Dodecyl-β-D-Maltopyranoside (DDM). 8. Centrifuge with rotor adapted to 96 wells plate. 2.6 SEC-MALLS (Size Exclusion Chromatography— Multi Angle Laser Light Scattering)

1. High Pressure Liquid Chromatography (HPLC) pump with low-pressure pulsation (e.g., Solvent-delivery system LC-20 AD, Shimadzu Technology). 2. Vacuum degasser (e.g., DGU-20A, Shimadzu Technology). 3. On-line filter (e.g., 0.1 μm pore size). 4. Hamilton syringe for microvolume injection (e.g., 25 μL). 5. A manual injection valve with injection loop of adapted volume (e.g., 20 μL). 6. 1 M NaOH and/or 1% SDS; Washing solution A: 0.5 M NaCl, 0.5 M NaOH; washing solution B: 1% (w/v) SDS, 10 mM EDTA, 100 mM NaCl 7. Deionized and degassed water for washing or equilibrating HPLC system.

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8. SEC mobile phase: 20 mM MES, pH 6.0, 150 mM NaCl, 0.03% (w/v) DDM. 9. An analytical SEC column (e.g., Superdex 200 increase 10/300, Cytiva). 10. UV-Vis Absorbance detector (e.g., SPD-20A, Shimadzu Technology). 11. Multi-Angle Laser Light Scattering (MALLS) and Refractive Index (RI) detectors (miniDAWN TREOS and Optilab T-rEX respectively, from Wyatt Technology). 12. Astra software (Wyatt Technology). 13. Bovine Serum Albumine (BSA) at 5 mg/mL in the mobile phase (filtered and centrifuged). 14. Amicon® and Vivaspin® commercial ultra-centrifugal filters of different types, shape, and molecular weight cut-off (MWCO) (e.g., polyether sulfone or cellulose; conic or flat; classical or inversed gravity flow; 30, 50, or 100 kDa MWCO) for sample preparation.

3

Methods

3.1 Cell Culture and CntI Expression

Here, we describe an optimized protocol to produce CntI using 2 L flask culture. 1. Inoculate 100 mL of freshly prepared TB media with E. coli C43 (DE3) transformed with the expression plasmid pETTEV-CntI from LB agar plate (or frozen glycerol stock). Grow the cell primary culture overnight at 37 °C, with shaking at 180 rpm (rotation per minute). 2. The day after, inoculate 6 × 1 L of TB media supplemented with kanamycin at 50 μg/mL final (plasmid resistance), with around 10 mL per flask to start with OD600nm of 0.05. 3. Grow the cells at 37 °C, 180 rpm until OD600nm of 0.6–0.9 (see Note 7). 4. Induce the T7 promoter expression plasmid using 1 mM IPTG and grow overnight (~16 h) at 18 °C, with shaking at 180 rpm. 5. Harvest the cells by centrifugation for 20 min at 6000 g. 6. Discard supernatant and resuspend the cell pellet in lysis buffer. 7. Homogenize the cells with the Potter. Refrigerate the pellet on ice for immediate lysis or store at -80 °C for several weeks.

3.2 Bacterial Cell Lysis and Membrane Preparation

1. Thaw pellet and add lysozyme at 1 mg/mL final, benzonase at 50 U/100 mL, and EDTA-free protease inhibitors cocktail. 2. Lyse cells using a cell disruptor refrigerated at 4 °C, by three passages at 2.4 kbar.

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3. Pellet unbroken cell and insoluble debris by centrifugation (12,500 g, 25 min, 4 °C) (see Note 8). 4. Discard the pellet and pour the supernatant into chilled ultracentrifuge tubes and centrifuge at 150,000 g for 1 h (see Note 9). 5. Resuspend the membrane pellet in lysis buffer with the Potter homogenizer. The sample will be called “crude extract” of CntI. 6. Measure the total concentration of protein by colorimetric assay compatible with detergent such as BCA assay, using Bovine Serum Albumin (BSA) as standard protein, following commercial recommendations. 7. Freeze with liquid nitrogen and store for several weeks at -80 °C, or pursue to the Subheading 3.3. 3.3 Extraction and Solubilization of CntI

1. Prepare 500 mL of crude extract diluted at 1–2 mg/mL of total protein with the solubilization buffer supplemented with 1% (w/v) DDM (see Note 10). Incubate at 4 °C under agitation overnight. 2. Centrifuge at 150,000 g during 1 h, at 4 °C to pellet the insoluble material.

3.4 Purification of Solubilized CntI 3.4.1 Immobilized Affinity Chromatography (IMAC)

1. Load supernatant of solubilized protein extract onto 5 mL HisTrap Ni-NTA column equilibrated beforehand in buffer A with peristaltic pump of the AKTA system, at 1 mL/min (see Note 11). 2. Wash with 10 Column Volume (CV) of 50 mM then 100 mM of imidazole until UV absorbance at 280 nm falls back to the baseline. Make sure to keep all the proteins eluted form the washing steps, in case of binding issue with the protein of interest CntI. 3. Eluate CntI with buffer B at a flow rate of 2 mL/min, collect 10 mL per fraction.

3.4.2 Size Exclusion Chromatography (SEC)

1. Equilibrate the preparative Superdex 200 SEC column with two CV (250 mL approximately) of SEC buffer. 2. Concentrate the IMAC elution fractions, around 40 mL, with 50 kDa MWCO ultra-centrifugal filter at 3500 g, 4 °C, by steps of 5–10 min in order to avoid protein aggregation by overconcentrating, to obtain a final volume of 1–2 mL. 3. Estimate the protein concentration by UV absorbance and theoretical extinction coefficient 50,880 M-1.cm-1 (https:// web.expasy.org/protparam/).

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4. Introduce the concentrated IMAC elution in the injection loop of the AKTA system. Make sure the injection loop size is two times higher than the sample volume to inject (e.g., for 2 mL loop, inject 1 mL of sample). 5. Perform purification with SEC buffer at a flow rate of 0.35 mL/min. Collect 1 mL fractions in 96 deep wells plate. 6. SEC elution fractions are analyzed by SDS-PAGE. The sample is denaturated in Laemmli buffer without boiling. Indeed, CntI is an integral α-helical membrane protein that aggregated at high temperature, i.e., 95 °C. 7. Either pursue with Subheading 3.6.2 or concentrate with 50 kDa MWCO ultra-centrifugal filter of adapted volume by 3500 g, 5 min cycle of centrifugation. 3.5 Stability Condition Screening by Fluorescent Thermal Stability Assay (FTSA)

FTSA can readily be used to compare relative protein stability under different conditions, therefore facilitating the optimization of protein preparations for further studies. The fluorophore must be protected from light when stored and moved from plate to qPCR thermocycler to avoid photobleaching.

3.5.1 Determination of Appropriate Concentration of Fluorophore and Protein

1. Dissolve and aliquot ANS stock solution at 10 mM in DMSO from the manufacture powder bottle. 2. In a qPCR plate (96- to 384-wells format), screen varying concentrations of ANS from 0.05 to 1.5 mM (e.g., 0.05, 0.10, 0.25, 0.50, 0.75, 1.0, and 1.5 mM) and protein from 0.5 to 10 μM (e.g., 0.5, 1.0, 2.0, 4.0, and 10 μM) in 20 μL of total volume per well: in 10 μL final volume, first, add water; second, purified protein CntI; third, fluorophore ANS; and finally, add 10 μL of protein buffer with detergent at 2× CMC. In our specific case, 10 μM of CntI and 50–100 μM of ANS gave the best results (see Note 12) (Fig. 1). 3. Prepare in the same way the controls without protein, i.e., with ANS, DDM, or ANS + DDM. 4. Centrifuge the plate at 5000 g (see Note 13). 5. Selection of the wavelength filters compatible with ANS fluorescence: in our case, we used the same parameters as SYBR Safe dye, i.e., λexcitation 450–490 nm and λemission 515–530 nm. 6. Apply a temperature range from 20 to 90 °C (increasing speed of 0.75 °C/min) with 0.5 °C reading step.

3.5.2 Screen of Buffer Solutions and Additives

1. Prepare screening buffer solutions 2× with commercial or home-made kit, including various pH (4.2–10.2), buffering agents (phosphate, cacodylate, acetate, citrate, HEPES, MES, TRIS, BIS-TRIS, and bicine), salt concentration (NaCl), and additives (e.g., imidazole or glycerol in our specific case).

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Fig. 1 Thermofluor assay of CntI (membrane protein) with the fluorescent probe 8-anilino-1-naphthalenesulfonic acid (ANS). The melting curve of CntI is tracked thanks to its interaction with ANS. There are four phases during temperature increase: (1) binding of ANS with CntI at room temperature shows initial fluorescence; (2) from 20 °C to 55 °C, fluorescence quenching is probably due to ANS dissociation; (3) from 55 °C to 65 °C, the interaction of ANS with denatured CntI enhances the fluorescence. We observe a sigmoidal curve where the inflection point corresponds to the melting temperature (Tm) of the protein; (4) from 65 °C to 90 °C, ANS dissociates from aggregated CntI, resulting in fluorescence quenching

2. Prepare pre-mixed solution of protein, fluorophore and detergent optimized previously (Subheading 3.5.1), concentrated 2×. 3. Repeat step 2 without CntI protein as control. 4. In each well, deposit 10 μL of screen buffer solution 2×, then 10 μL of pre-mixed protein 2× or control solution 2×. 5. Proceed to steps 4–6 of Subheading 3.5.1. 6. Identification of stabilizing buffers from primary screening should be refined by additional screening, as long as the protein quantity is enough. For instance, exchange CntI buffer with the best one identified from the first screen, then go back to step 1 for another round of screening in order to improve the protein stability condition. 3.5.3

Data Analysis

1. Export data of Relative Fluorescence Unit (RFU) and first derivative of RFU (dF/dT) versus temperature in spreadsheet file format with the qPCR software.

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Fig. 2 Effect of ionic strength on CntI stabilization by thermofluor assay. CntI is initially in the buffer 20 mM MES, pH 6.0, 150 mM NaCl, DDM 0.12 mM. We tested the increasing concentration of additional NaCl (0.05–1 M) on CntI stability. 2A, melting curves of CntI (RFU, Relative Fluorescence Unit) with ANS dye and different concentration of additional NaCl (0.05–1 M). 2B, first derivative of melting curves (dF/dT) highlights the Tm (melting temperature) as the maximum dF/dT. Tm of the starting buffer is 56.5 °C. Increment of NaCl tends to increase Tm, i.e., adding 1 M of NaCl results in Tm of 71 °C. The ionic strength clearly stabilizes CntI

2. Determine the melting temperature (Tm) with first derivative numerical analysis of the melt curves using data visualization software. Protein stability with highest Tm is desired (see Note 14) (Fig. 2). 3.6 Analysis of Protein Detergent Complexes (PDC) by SEC-MALLS 3.6.1 Preparation of the SEC-MALLS System

Separated peaks of protein detergent complex (PDC) and detergent micelles are required to determine molar masses unambiguously.

1. Proceed to an optimal wash of the SEC column with two CV of 1 M NaOH and/or 1% SDS (see Note 15). Finish by abundant washing with deionized water (at least three CV). 2. Equilibrate the system and the column with appropriate solvent (or buffer) for enough time until the light scattering and refractometer baselines are stable with minimum background noise (e.g., overnight) and signal drift. 3. For a membrane protein, select the “Protein Conjugate” mode that includes the analysis with the three detectors (laser light scattering, UV absorbance, and refractive index). This mode

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allows to obtain the absolute molecular weight of the membrane protein–detergent complex, and both protein and detergent mass fractions. It also determines the peak molecular composition. 4. In order to establish the experiment template, a run with a monodisperse protein standard (e.g., BSA) dissolved in the running buffer is done. This step is mandatory to calibrate the SEC-MALLS system. The calibration of the SEC-MALLS system is done in three steps: (a) The normalization of each light scattering (LS) detectors with the 90° LS detector. (b) The alignment of UV, LS, and RI peaks of the BSA monomer. This step allows to determine the interdetector volumes. (c) The band broadening correction to take the sample diffusion through the detectors into account. 3.6.2 Preparation and Analysis of PDC (Protein– Detergent Complex)

1. Pool the monodisperse elution fractions of the protein from the last purification step (Subheading 3.4.2). The total volume is 15–20 mL. 2. Estimate the concentration of the purified protein in the total homogenized elution by UV absorbance at 280 nm, using the extinction coefficient. 3. Split the total volume of elution into the appropriate number of identical fractions, i.e., 1.5–2 mL, for a desired number of protein concentration tests to be analyzed by SEC-MALLS. 4. Concentrate by ultrafiltration the fractions under the different conditions to be compared, e.g., speed (500 or 3500 g), MWCO of the filter (10, 30, 50, or 100 KDa), type, and shape of centrifuge filter unit (Vivaspin®, Amicon® or Centriprep®). 5. Flash cool the samples in liquid nitrogen if the SEC-MALLS experiments cannot be performed the same day. 6. Determine the % of protein recovery after ultrafiltration by measuring the UV absorbance at 280 nm and using the extinction coefficient of 50,880 M-1.cm-1 (see Note 16). 7. Concentrate under the same condition the buffer without protein for control analysis. 8. For each sample, inject into the SEC-MALLS system 20 μL of CntI concentrated at 5–6 mg/mL (see Note 17). 9. Fractionate and eluate at 0.3 mL/min, at 25 °C (see Note 18). 10. Save the results and analyze the chromatograms with Astra software (Wyatt Technology) (Fig. 3).

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Fig. 3 Effect of ultrafiltration unit type on detergent micelle concentration analyzed by SEC-MALLS. After concentration of CntI with Vivaspin® or Amicon® ultrafiltration unit with a membrane MWCO of 30 kDa, CntI is analyzed by SECMALLS (SEC column S200 increase 10/300, Cytiva). CntI and detergent micelle elution peaks are observed by refractive index (RI) detection. The chromatography plot shows CntI elution as red and pink lines, after concentration with Vivaspin® and Amicon® respectively. The expected elution volume of DDM micelles is shown as blue and green lines after concentration of the buffer (20 mM MES, pH 6; 150 mM NaCl, 0.03% DDM) with Vivaspin® and Amicon® respectively. Calculated molecular weights (MWs) are indicated for CntI-DDM complex, DDM, and CntI. In the same concentration condition, we observed that Amicon® unit concentrates DDM micelle, unlike Vivaspin®

3.6.3 Evaluation of Detergent Micelles Accumulated During Protein Concentration

1. Inject into the SEC-MALLS system, for calibration, accurate amounts of DDM from 10 to 300 μg prepared in the same condition as protein analysis in Subheading 3.6.2. 2. Calculate with any suitable software (Origin, Exel, etc.) the peak area of refractive index signal subtracted from the baseline (ΔRI). 3. Plot integrated ΔRI against the corresponding detergent quantity. If the curve is linear, use the linear fit equation as a standard to calculate the quantity of detergent micelles of the concentrated samples (Fig. 4). 4. Establish the optimal filter and centrifugation speed for protein concentration step by choosing the condition where protein recovery is the highest and detergent micelle concentration is the lowest (see Note 19).

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Fig. 4 Quantification of DDM micelles by SEC-MALLS. (a) different quantities of DDM (from 10 to 300 μg) are injected on a SEC column (S200 increase 10/300, Cytiva). (b) linear correlation of the integrated area under the peak of RI (Refractive Index) with the quantity of DDM is used as standard

4

Notes 1. It is usual to include a tag removal strategy for crystallization of the recombinant protein. The TEV cleavage site is made of eight amino acids, and the specific proteolysis takes place at carboxy side of the sixth residue. That is why the His tag with TEV site must be added at the N-terminus to remove most of the extra amino acids from the cleavage site. The plasmid we used was designed and made by the team of Dr. Pascal Arnoux (CEA, Cite´ des Energies, France). 2. Glycerol is a common, but not mandatory, protein stabilizer additive in particular for membrane protein. 3. If the protein-free detergent micelle concentration is too high at the end of the purification, reduce the detergent concentration of the SEC running buffer down to 1.1× CMC. 4. The choice of the SEC column is based on the hypothetical size of the molecules to fractionate, i.e., CntI as protein detergent complex (PDC) and protein-free detergent micelle. Often, PDC molar mass is higher than the membrane protein by itself, and detergent micelles could unexpectedly show the same observed size as PDC. 5. In order to reduce the quantity of protein-free micelles concentration, the choice of the membrane molecular weight cut-off (MWCO) should be the closest to the PDC size.

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6. Beware: The qPCR plate we used shows background fluorescence in our testing conditions. 7. Bacterial culture reaches OD600nm of 8 with baffled flask (oxygenation enhancer) and OD600nm of 3.2 with standard flasks. 8. A supplemented centrifugation step can be made after lysis if there are excessive foam and turbidity (e.g., 5 min at 12,500 g). 9. It is possible to check by Western-blot with an anti-His tag antibody if CntI remains in the supernatant. In that case, increase centrifugation duration by two. 10. From 6 L of cell culture, we usually obtain 100 mL of membrane crude extract at 20–50 mg/mL of total membrane protein. Solubilizing the whole crude extract at once would end up with many liters to deal with. Solubilizing 500 mL volume of crude extract is easier to manipulate during the next steps of ultracentrifugation and affinity chromatography purification. The remains of the non-solubilized crude extract could be saved in -80 °C for later preparation. 11. Supernatant loading at low flow increases the protein fixation on the affinity column. It is also possible to load the supernatant in a closed circuit overnight at 4 °C. 12. Since ANS causes background fluorescence via detergentfluorophore interaction, we recommend to use minimum of detergent concentration, i.e., at 1× CMC, or even 0.5× CMC since an excess of detergent is already in the buffer of the protein. 13. Foam formation can easily happen when pipetting microvolume of detergent and protein. When there are bubbles, try to centrifuge the plate at 5000 g for 15–30 min. Note that centrifugation might not be enough to remove all the foam. 14. Compare data with and without protein (control) to spot the fluorescence artifact. The expected melting temperatures (Tm) are usually around 60–80 °C. 15. The washing step of the column is crucial because of the high sensibility of MALLS detector. Especially with SEC columns, washing with only NaOH might not be enough. Add a supplementary wash with high concentration of SDS to solubilize hydrophobic contaminant that could cause strong background noise to the light scattering detection. Wash with 1% SDS at room temperature to avoid SDS crystallization at low temperature. 16. A gradient of protein concentration is formed during ultrafiltration. Make sure to mix the concentrated sample before measuring the absorbance. 17. In order to compare the quantity of free detergent micelle retained by the filter membrane, it is important to inject the same amount of protein after concentration.

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18. For better resolution of SEC peak separation, the flow can be reduced at 0.2–0.1 mL/min. Nevertheless, the same speed has to be used for all the samples for proper comparison. 19. During protein purification steps, concentration by ultrafiltration is already applied before the SEC. Note that free micelle could already accumulate at this step. Besides, in our case, SEC elution volume of CntI and free detergent micelle tend to overlap. It is thus difficult to separate completely free detergent micelle.

Acknowledgments We thank Dr. Pascal Arnoux for cloning and providing the expression plasmid of CntI in pET-TEV. We thank Dr. Daniel Picot for his help in peak integration of refractive index curves. This work was supported by “Agence Nationale de la Recherche,” grant number ANR-20-CE11-0018). References 1. Santos R, Ursu O, Gaulton A et al (2017) A comprehensive map of molecular drug targets. Nat Rev Drug Discov 16:19–34 2. Overington JP, Al-Lazikani B, Hopkins AL (2006) How many drug targets are there? Nat Rev Drug Discov 5:993–996 3. Stetsenko A, Guskov A (2017) An overview of the top ten detergents used for membrane protein crystallization. Crystals 7:197 4. Birch J, Axford D, Foadi J et al (2018) The fine art of integral membrane protein crystallisation. Methods 147:150–162 5. Errasti-Murugarren E, Bartoccioni P, Palacı´n M (2021) Membrane protein stabilization strategies for structural and functional studies. Membranes 11:155 6. Lhospice S, Gomez NO, Ouerdane L et al (2017) Pseudomonas aeruginosa zinc uptake in chelating environment is primarily mediated by the metallophore pseudopaline. Sci Rep 7: 17132 7. Tsuchiya H, Doki S, Takemoto M et al (2016) Structural basis for amino acid export by DMT superfamily transporter YddG. Nature 534: 417–420 8. Kohlstaedt M, von der Hocht I, Hilbers F et al (2015) Development of a Thermofluor assay for stability determination of membrane

proteins using the Na(+)/H(+) antiporter NhaA and cytochrome c oxidase. Acta Crystallogr D Biol Crystallogr 71:1112–1122 9. Rath A, Glibowicka M, Nadeau VG et al (2009) Detergent binding explains anomalous SDS-PAGE migration of membrane proteins. Proc Natl Acad Sci 106:1760–1765 10. Slotboom DJ, Duurkens RH, Olieman K, Erkens GB (2008) Static light scattering to characterize membrane proteins in detergent solution. Methods 46:73–82 11. Feroz H, Kwon H, Peng J et al (2018) Improving extraction and post-purification concentration of membrane proteins. Analyst 143:1378– 1386 12. Gobet A, Zampieri V, Magnard S et al (2023) The non-Newtonian behavior of detergents during concentration is increased by macromolecules, in trans, and results in their overconcentration. Biochimie 205:53–60 13. Chaptal V, Delolme F, Kilburg A et al (2017) Quantification of detergents complexed with membrane proteins. Sci Rep 7:41751 14. Strop P, Brunger AT (2005) Refractive indexbased determination of detergent concentration and its application to the study of membrane proteins. Protein Sci Publ Protein Soc 14:2207–2211

Chapter 27 Structural Analysis of Protein Complexes by Cryo-Electron Microscopy Athanasios Ignatiou, Ke´vin Mace´, Adam Redzej, Tiago R. D. Costa, Gabriel Waksman, and Elena V. Orlova Abstract Structural studies of bio-complexes using single particle cryo-Electron Microscopy (cryo-EM) is nowadays a well-established technique in structural biology and has become competitive with X-ray crystallography. Development of digital registration systems for electron microscopy images and algorithms for the fast and efficient processing of the recorded images and their following analysis has facilitated the determination of structures at near-atomic resolution. The latest advances in EM have enabled the determination of protein complex structures at 1.4–3 Å resolution for an extremely broad range of sizes (from ~100 kDa up to hundreds of MDa (Bartesaghi et al., Science 348(6239):1147–1151, 2015; Herzik et al., Nat Commun 10:1032, 2019; Wu et al., J Struct Biol X 4:100020, 2020; Zhang et al., Nat Commun 10:5511, 2019; Zhang et al., Cell Res 30(12):1136–1139, 2020; Yip et al., Nature 587(7832):157–161, 2020; https://www.ebi.ac.uk/emdb/ statistics/emdb_resolution_year)). In 2022, nearly 1200 structures deposited to the EMDB database were at a resolution of better than 3 Å (https://www.ebi.ac.uk/emdb/statistics/emdb_resolution_year). To date, the highest resolutions have been achieved for apoferritin, which comprises a homo-oligomer of high point group symmetry (O432) and has rigid organization together with high stability (Zhang et al., Cell Res 30(12):1136–1139, 2020; Yip et al., Nature 587(7832):157–161, 2020). It has been used as a test object for the assessments of modern cryo-microscopes and processing methods during the last 5 years. In contrast to apoferritin bacterial secretion systems are typical examples of multi protein complexes exhibiting high flexibility owing to their functions relating to the transportation of small molecules, proteins, and DNA into the extracellular space or target cells. This makes their structural characterization extremely challenging (Barlow, Methods Mol Biol 532:397–411, 2009; Costa et al., Nat Rev Microbiol 13:343–359, 2015). The most feasible approach to reveal their spatial organization and functional modification is cryoelectron microscopy (EM). During the last decade, structural cryo-EM has become broadly used for the analysis of the bio-complexes that comprise multiple components and are not amenable to crystallization (Lyumkis, J Biol Chem 294:5181–5197, 2019; Orlova and Saibil, Methods Enzymol 482:321–341, 2010; Orlova and Saibil, Chem Rev 111(12):7710–7748, 2011). In this review, we will describe the basics of sample preparation for cryo-EM, the principles of digital data collection, and the logistics of image analysis focusing on the common steps required for reconstructions of both small and large biological complexes together with refinement of their structures to nearly atomic resolution. The workflow of processing will be illustrated by examples of EM analysis of Type IV Secretion System. Key words Cryo-electron microscopy, Sample preparation, Single particle analysis, Image processing, Type IV secretion system Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_27, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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1 Structural Studies by Cryo-EM of Macro-complexes as Illustrated by Studies of Type IV Secretion Systems Different approaches have been used to decipher conformational changes linked to the functional activity of complexes. X-ray crystallography, NMR, and electron microscopy (EM) combined with biochemical and biophysical methods allow deeper understanding of the mechanisms that underlie macromolecular complex functions. This was clearly demonstrated by studies of the ribosome [1–3]. Recent advances in EM such as the invention of direct electron detection cameras, development of automated data collection software, as well as progress in image processing algorithms have dramatically expanded the range of biological macromolecules amenable to studies by structural cryo-electron microscopy (cryoEM). The main advantage of EM is that it does not require the crystallization of samples and can be used to determine the structures of bio-complexes within a large range of sizes: from ~100 kDa to many MDa. It was found that current cryo-EM methods are the most efficient approaches for analysis of membrane proteins/complexes, and, as for example, for Type IV Secretion Systems (T4SSs). During the last decade, image processing methods have been extensively improved allowing the analysis of image quality more consistently and the assessment of distortions caused by the microscope that prevent the determination of high-resolution structures. New approaches have been developed to reveal sample quality more consistently such as its homogeneity and stability, and to differentiate distinct conformations by enabling determination of the distributions of particles within these different states leading to their eventual characterization [4–8]. A few different packages with state-of-the-art image processing algorithms are now routinely used for the analysis of macromolecular complexes exhibiting a range of symmetries, or asymmetry such as EMAN2, RELION, CryoSPARC, Ximdisp, CryoDRGN, and others [5–13]. Another important factor that has contributed to modern achievements is the increase in computing power making it possible to collect tens of thousands of micrographs and analyze millions of particle images from heterogeneous samples [5–13]. Nonetheless, the basic workflow (Fig. 1) of sample imaging and image processing remains almost unchanged while computational tools derived from algorithms implemented in new software packages have been radically improved. The remarkable success in improving the resolution of EM structures during the last decade would not be possible without advances in sample preparation and data collection at cryogenic temperatures in what we now refer to as “cryo-EM”. This approach for sample preparation and the amalgamation of methods used in

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Fig. 1 Workflow of EM structural analysis. The biochemical components are highlighted in green, while the computational elements are shown in light, dark blue, and purple. The initial steps of processing are shown in light blue, which include image frame alignment, CTF estimation and correction, normalization, and filtering of images. The subsequent steps of alignment, statistical analysis, determination of particle orientations, and three-dimensional reconstructions (3D), are shown in dark blue. The final steps (in purple) are related to the interpretation and validation of the reconstructions obtained

X-ray crystallography and EM image processing have made it possible to achieve details at near atomic resolution for many protein complexes [14–22]. This has led to the determination of structures in which the main components of Gram-negative bacterial secretion systems have been resolved at the levels approaching the atomic resolution [23–27]. The most recent structures of bacterial secretion systems have provided important information regarding the mechanistic details of how bacteria assemble these highly specialized nano-machines to secrete proteins and DNA to the bacterial extracellular space, and to the eukaryotic or bacterial target cells. Among the Gram-negative bacterial secretion systems, Type IV Secretion Systems (T4SSs) possess the unique ability to secrete proteins, DNA, or protein–DNA complexes in an ATP-dependent process. The ability of T4SSs to secrete a variety of substrates involved in pathogenesis and the spreading of conjugative plasmids encoding antibiotic resistance genes made this secretion system an important target for structural biology studies. Recently, a new structure of the T4SS has been published in Nature at a resolution of 3.5–6 angstrom revealing new features of the nearly complete complex [26].

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Fig. 2 Cryo-EM structures of different T4SS complexes. (a) Cryo-EM structure of the outer-membrane core complex (OMCC) at a resolution of 15 Å [EMD-5031, 25]. (b) Cryo-EM structure of the core complex at a resolution of 12.4 Å [EMD-2232, 26]. (c) Structure of the near complete T4SS complex (in negative stain) at overall resolution of ~23 Å. (EMD-2567, figure adapted from Ref. [25] with permission from Nature). (d) Composite electron density map of the R388 T4S system. This map is composed of the assembly of two not symmetrized maps of the OMCC and the IMC at a resolution of 3.3 Å and 4 Å, respectively, arches and stalk at a resolution of 6.2 Å [26]. The OMCC, the stalk, the arches, and the IMC are shown in green, red-brown, yellow, and dark blue, respectively. (EMD-13765, EMD-13767, Figure adapted from Ref. 26 with permission from Nature)

Among all Gram-negative T4SSs, the ones encoded by the Ti plasmid of Agrobacterium tumefaciens together with the conjugative pR388, pKM101, and F-plasmids from E.coli are the best characterized. These macromolecular complexes comprise at least 11 proteins: from VirB2 to VirB11, and VirD4 [27]. The first main advance in the understanding of the general architecture of a T4SS took place when the cryo-EM structure of the so-called outer membrane core complex (OMCC), encoded by the conjugative pKM101 plasmid, was solved to a resolution of 15 Å. This 1.1 MDa structure anchored to the outer membrane is composed of 14 copies of VirB7, VirB9, and VirB10 (Fig. 2a) [25]. The OMCC has been obtained at the higher resolution of 12.4 Å later and has provided further details of the structural organization of the proteins that form this complex (Fig. 2b) [24]. A more complete assembly of the T4SS (VirB3-VirB10) encoded by the conjugative R388 plasmid has been solved by NS (negative staining) EM [25]. This remarkable structure provided the first view of both the OMCC and the bipartite inner-membrane complex (IMC) and how these are linked by a structure referred to as the stalk (Fig. 2c) [25]. Recently, a new structure of the T4SS has been determined at a resolution of 3.5–6 angstrom revealing new features of the nearly complete T4SS complex (Fig. 2d) [26]. From the point of view of a specialist in cryo-EM single particle analysis (SPA), this review may not be considered complete and

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does not provide sufficient mathematical background for the experienced reader. However, the aim is to provide a general overview of imaging using an electron microscope and the current basic steps of SPA. It will therefore include a brief outline of sample preparation for cryo-EM, the effects of radiation damages, and advances in the procedure for data collection. We describe steps considered as pre-processing and methods used to obtain structures and methods for their validation. More information can be found in other reviews and book chapters [28–31], should the reader require more details on the subjects described here.

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Sample Preparation in Cryo-EM While EM provides superior resolution to light microscopy, a more complex procedure for sample preparation has to be employed since samples need to be imaged in a vacuum. This is because images are created by an accelerated beam of electrons within the electron microscope. Under normal circumstances, electrons collide with the atoms that constitute air losing both their energy and scattering directions. To obtain a high-quality image of the sample, it is therefore necessary to keep the electron path free of air molecules (i.e., under vacuum) to eliminate unwanted scattering thus enabling them to move directly to the sample. Biological objects (macromolecules and cells) in their native conditions are in aqueous solutions; they therefore have to be made rigid and stable in the microscope column under vacuum to avoid evaporation or changes during the time they are exposed to the electron beam. This requires the use of specialized preparation methods where the dehydration of the sample and staining will minimize molecular distortions or alternatively, stabilization using solidified (frozen) hydrated biological samples [29, 32]. Cryo-EM methods of sample preparation allow preservation of the structural integrity of bio-complexes keeping them in a nearly native hydrated state in the microscope vacuum. The method proposed by Dubochet, Glaeser, and co-workers [32–35] is now used as a well-established technique for freezing aqueous solutions of samples on cryo-EM grids. The EM grid constitutes a round metallic plate (~3 mm in diameter and usually made from copper (they can be made from gold or tungsten) with a fine mesh (typically are used the grids with 200, or 300, or 400 mesh/inch square grids), although the patterns of the mesh can vary [36–39]. More often, the grids containing 400 squares per inch are used in SPA (Fig. 3). Depending on the sample, a continuous thin layer of carbon film or perforated with irregular, termed Lacey grids, or regular holes (such as QUANTIFOIL) should be placed on the top of the copper grid (Fig. 3) [36–39]. The type of grid selected in terms of the size/ shape of the holes and distances between them is dependent on the

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Fig. 3 Cryo-EM sample preparation. (a) 3 μL of sample is applied to a grid covered with a holey carbon film. (b) Zoomed in view of a small grid area showing the microscopic holes in carbon film. (c) Cross-section of a hole with sample particles embedded in vitreous water. (d) The grid is transferred into a microscope at liquid nitrogen temperature

sample. All these types can be used for automated and manual data collection. The conventional procedure for freezing is the following [34, 37, 39]: (a) Drop of the sample (~3 μL) is applied onto a grid, which was glow-discharged to make its surface more hydrophilic, or hydrophobic, depending on the properties of the molecular complex under the study. (b) Sample is rested for a short time on the grid (0.5–2 min, depending on the sample). (c) Grid is maintained on the plunger. (d) Excess of the sample is blotted to make a thin layer of sample solution on the grid. (e) Grid is rapidly plunged into liquid ethane (or propane) that has temperature of -182 °C. Ethane has to be cooled by liquid nitrogen in advance. Plungefreezing in liquid ethane takes place in ~10-5 s trapping the

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biological molecules in their native, hydrated state embedded in amorphous ice that has the same structure as solid water. The amorphous ice does not have a crystalline structure. Cooling by plunging into liquid ethane is much faster than plunging directly into liquid nitrogen because liquid ethane is used close to its freezing point, so it does not evaporate and produces an insulating gas layer. This fast freezing prevents the formation of ice crystals and keeps samples at nearly native hydrated state, and the process is known as vitrification of samples [32, 34–37, 40]. For the following cryo-EM procedures, the grids have to be kept at temperatures no greater than -170 °C, for transfer into the microscope using a cryo-transfer holder, and during the imaging, otherwise, the amorphous ice will change its conformation and become crystalline, destroying the sample and contaminating the grid. Another important advantage of maintaining the sample at cryogenic temperatures is that the radiation damages induced by the electron beam will be significantly reduced when electrons pass through the sample [29]. More recently, automated and controlled devices have been developed (Vitrobot (https://www.thermofisher. com/uk/en/) or Leica EM GP (https://www.leica-microsystems. com/products) [35]), thus allowing higher reproducibility in grid preparation [37–41]. It is recommended that the first estimation of sample quality is made using the negative stain technique, which is fast, robust, and reliable [29]. It allows rapid assessment of sample quality, concentration, and suitability for the following cryopreparations.

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Data Acquisition

3.1 Direct Electron Detectors

During the last decade, digital detectors have enabled the direct detection of electrons without the intermediate step of transforming electrons into photons and then into an electrical signal (Fig. 4) replacing photographic films and CCD cameras. Direct detector devices (DDD) use an array of radiation hardened active pixel sensors (a pixel circuit), which are integrated into a silicon complementary metal oxide semiconductor (CMOS) chip [43, 44]. In this case, the electron energy is transferred directly into electrical signal. Another advance of this technology is that an amplifier is built into each pixel and allows fast signal readout from each single sensor (or pixel) nearly simultaneously (Fig. 4) providing very high frame readout speeds [43–45]. This feature allows the possibility of recording an image during the overall exposure as a set of smaller sub-exposures. In cryo-EM, it provides a valuable option for electron dose fractionation that is important in studies of radiationsensitive biological samples. Usage of the dose-fractionation is now provided by global direct detector technology. Depending on the detector type (Gatan, DE-Direct Electron, and Thermo Fisher

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Fig. 4 Digital cameras. In direct detectors, active pixel sensor detectors, mostly based on CMOS technology, can capture the incident electrons [42]

Scientific) and available software, it is possible to record from just a few to hundreds of sub-frames per exposure [42]. The removal of the electron–photon transformation step and the fibre optics improves the Signal to Noise Ratio (SNR) on images registered by DDD. This can be best described in terms of the Detective Quantum Efficiency (DQE) [43–45]. The DQE is a measure of the efficiency of signal transfer by the camera and defined as a ratio of the SNR in the output image registered by the camera sensors to the SNR at the input image. DQE = ðSNR out Þ2 =ðSNR in Þ2 The ratio depends on the spatial frequency (sizes of the details) of the image. A perfect detector would not distort the input signal, and in an ideal system, the output should be the same as the input. Therefore, the DQE of the ideal system would be equal to one for all frequencies. However, cameras distort the fine details in images, and this is reflected by significant decline in the DQE at high frequencies [42, 44–47]. Direct detectors allow registering electrons within high range of energies and are now used in 300 keV microscopes. The high sensitivity of these systems in sensing electrons has enabled scientists to create sensors of smaller size and coupled with improvements in software has offered a new mode in image recording, which is known now as a counting mode. In this mode, the system can record single electrons (this mode is implemented in K2, K3 (Gatan), Falcon 4 (Thermo Scientific™ Falcon™ 4), and DE cameras [42, 46–49]. 3.2 Micrograph SubFrame Alignment

Cryo-EM images recorded with DDD cameras (Falcon4 (Thermo Scientific™ Falcon™ 4), DE (Direct Electron), or K3 (Gatan)) represent sets of sub-frame images (movies) [50–54]. High rates of image recording can reveal the distortion of images induced by

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drift of the grid (sample) within the EM, which can be corrected by specifically designed procedures such as motion-correction [50–54]. Typically, alignment of frames starts from frame N-1 that is aligned to the last frame. When these two images are aligned, the corresponding pixels are summed, and frame N-2 will be aligned to this new sum. Then frames N-1 and N-2 will be summed and the frame N-3 is aligned to this new sum. The process is repeated in the same way towards the first frame. There are variations in algorithms when summation is done not for two frames for the following alignment, but for four or five frames or all frames. There are several algorithms for the motion correction of frame shifts that currently use alignments not of the entire image, but of regular lattice of smaller regions (for example 5 × 5 frame subareas). This approach helps to determine not only the translational shifts but also rotations of the frames under the beam [53, 54]. Typically, the alignment is done starting from the last frames to the first ones since the characteristic features needed for the alignment are visible better on the last frames. Alignment is refined iteratively when, on the next round of alignment, the total sum obtained during the previous round is used as a reference. The entire procedure improves firstly the signal-noise-ratio of the reference, and the iterative approach improves quality of the alignment. There are now a few software packages that can be used for the frame alignment. The image shown in Fig. 5a represents the sum of the original frames without correction. The trajectory of the sub-area shifts during the successive exposures indicates that the movement of initial shifts of the sample is large at the beginning but then reduces, and different subareas move slightly differently (Fig. 5b). A power spectrum from the sum of the frames without motion correction reveals that the Thon rings are fading rapidly due to the shifts in different direction indicated in Fig. 5b. When the movie frames are aligned (after motion correction), the Thon rings became better defined and extend to nearly 3.5 Å (Fig. 5c, right panel). This signifies on the presence of high-resolution details in the images. Summation of the motion corrected sub-frames generates the final sharper image (Fig. 5d). 3.3 Radiation Damage

Images in EM are generated by the electron beam that illuminates the sample and then electromagnetic lenses form the image in the plane of the camera. While short wavelength electron beams significantly improve the image resolution of biological molecules, it has been demonstrated that bio-samples are very sensitive to highenergy electron irradiation that takes place during imaging [55– 57]. Electrons are accelerated in the electron gun of the microscope, and while the majority are only deflected by the electric field of the sample atoms (elastic scattering), some electrons collide and transfer their energy to the sample atoms (inelastic scattering).

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Fig. 5 Cryo-EM images with motion correction. (a) Representative cryo-EM image of vitrified T4SS particles (without motion correction). (b) Motion of patches in X and Y directions in successive frames. (c) Left: the power spectrum of movie frames summed without motion correction. Right: the spectrum after motion correction. (d) Image after correcting the frame shifts. Protein is black in the cryo-EM images

These atoms became ionized and therefore can cause X-ray emission, and form radicals resulting in rearrangements of chemical bonds [55–62]. High-energy electrons of the beam in EM may induce displacements, bond breakage, and mass loss of low atomic number elements such as carbon, nitrogen, and oxygen [60, 61]. These changes in biological complexes depend on the time of the overall exposure time to electron irradiation (cumulative dose) and were estimated using spot fading diffraction experiments on two-dimensional (2D) crystals and analysis of singleparticle images [58, 60–62].

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The resultant sub-frames of the image recorded with DDD cameras can then be used for specimen drift correction and usage of first part of the frame set to see the images of the object at the reduced electron dose. Typically, the images on the first two or three frames demonstrate large shifts of the sample, which reduces as movement slows. However, the last frames often indicate that the sample is damaged by the beam. A 3D structure derived from experiments where the sample has been overexposed can differ markedly from the structure of the native molecule. It has been shown using crystallography that exposure of crystals to X-ray induces decarboxylation of glutamate and aspartate residues, breakage of disulphide bonds, and loss of hydroxyl-groups from tyrosine and methylthio group of methionine [60]. Bartesaghi and co-authors compared the density maps reconstructed from different fractions of the total exposure (10, 20, or 30 e-/Å2) [62]. Analysis of the high-resolution cryo-EM structures shows that densities for residues with positively charged and neutral sidechains are well resolved, while the residues with negatively charged sidechains were less resolved having weaker densities. Negatively charged glutamate and aspartate exhibit on average 30% less density than the similarly sized neutral glutamine and asparagines, which is consistent with observations of X-ray analysis [58, 60–62]. Therefore, the user can use all frames for the quality assessment of images and samples and only the first half or first two thirds of sub-frames (depending on the type of the DDD used in experiments) for the reconstruction of the native complex. Cryo-EM imaging has a major benefit in reducing radiation damages by keeping samples at cryo-temperatures during imaging. Vitrified samples at liquid nitrogen temperatures preserve their native structure and should be imaged at these low temperatures, which increases tolerance to ionizing radiation damage, since the free radicals generated from inelastic scattering events are unable to diffuse through the sample and cause secondary damage [56, 57, 60]. In addition, cryogenic temperatures (liquid nitrogen -195.8 °C) also restrict movements and the degrees of freedom of the molecular atoms after a bond are broken therefore limiting structural rearrangements induced by irradiation [59, 60]. As a result, keeping and imaging samples at such low temperatures improves radiation tolerance from two to six times compared to room temperature imaging [59, 61, 62]. Other additional approaches that help to reduce the level of radiation damage include usage of low-dose mode during data collection [63]. Low-dose imaging is based on reducing the exposure time of the sample to electrons by focusing on an adjacent area that is sufficiently close to the area of interest but does not overlap it. “Search” mode requires a low-magnification overview image that is used to identify areas of interest, while the “imaging” (or “photo”) mode is used for actual data collection at high-

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magnification. The “focus” mode is set typically at the same magnification as imaging mode, but the beam is shifted to an adjacent area [64]. Such interchange between these modes is implemented in the systems for the automated data collection and significantly allows reduction of radiation damages of the samples. Currently, all electron microscopes used for biological studies come with pre-installed low-dose hard- and soft-ware allowing the efficient exchange between imaging modes.

4

Processing of 2D Images

4.1 Contrast Transfer Function

The aim of single particle reconstruction is to provide an accurate representation of the three-dimensional structure of a molecule using a set of 2D image projection data. The frozen vitrified samples of macromolecular complexes are considered to be very thin objects so their images can be described as linear projections of the Coulomb potential of the molecular complex [29–31]. This approximation is used for the subsequent reconstruction procedure. However, images produced by the electron microscopes do not represent projections of the molecules under study. Deviations from the real densities of projections are induced by aberrations of the optical system of the EM microscope [64, 65]. The effect of these aberrations on the registered image of the object is described by the function termed as the Contrast Transfer Function (CTF) of a microscope [29–31, 64, 65]. The CTF is defined by the type of electron source (beam coherence), the acceleration voltage that regulates the electron wavelength, and aberrations of the objective lens (Cs, Cc, and astigmatism). The major factors affecting the CTF are the degree of spherical aberration (Cs) of the objective lens and the level of defocus (Δf ). As a result, the CTF modulates the amplitudes and phases of the registered image. The effect of the microscope CTF can be clearly observed on a power spectrum (Fourier space) of the image registered by the camera, which demonstrates variations in the magnitude of the various frequency components of the image (Fig. 6). The image contrast in EM micrographs depends on the operating conditions of the microscope such as the level of focus and aberrations. The overall effect of these factors on the image can be described in Fourier (diffraction) space (Fig. 6a, b) by the following equation: F fψobs ½r g = F fψsam ½r g∙CTFðq Þ∙E ðq Þ; where F{Ψobs} is the Fourier transform of the observed image; r are the distances in the images; q are spatial frequencies (Fourier space coordinate); F{Ψsam} is the Fourier transform of the specimen; CTF(q ) is the contrast transfer function of the microscope; and E(q ) is an envelope function, which describes frequency-dependent attenuation of the contrast transfer

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Fig. 6 Contrast transfer function (CTF) and corrections. (a) The CTF in an experimental spectrum (upper half of the panel). It is compared with a theoretically calculated CTF (bottom half of the panel). Image was taken relatively far from focus. For the accurate CTF determination, the Thon rings from both parts of the images should match. (b) Comparison of the theoretical and experimental spectra for the image taken closer to focus. Astigmatism assessment is done by measuring ellipticity of Thon rings (Defocusmax and Defocusmin). (c) The CTF oscillates changing the image contrast from negative to positive depending on frequency. Information is lost only where the CTF is zero. Dashed line shows effect of the envelope function on the signal transfer suppression at high spatial frequencies. (d) CTF correction by phase flipping: negative lobes of the CTF are flipped over to positive. (e) Noise suppression close to CTF zeroes by calculation of CTF2

function. E(q ) is related to the effects of various instabilities in the microscope, local spread in defocus, variation in the sample thickness, and specimen decay under the beam irradiation that lead to the loss of high frequency information (Fig. 6c) [31, 32, 66].

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The major effects of spherical aberration of the objective lens on images of biological samples are phase changes and, therefore, alterations in the representation of densities. The CTF for the biological samples can be described by the formula. Phase CTF = - 2 sin π Δf λq 2 - C s λ3 q 4 =2 where Cs is the spherical aberration constant; Δf the defocus; q the spatial frequency; and λ the electron wavelength. The spherical aberration coefficient and the electron wavelength are the only constants, and these values remain fixed for each electron microscope [29–31]. The effect of the CTF on the image appears as oscillations of the power spectrum that take the form of concentric bright and dark rings (named as Thon rings [64, 65]). Dark regions show the positions of all zero crossings of the CTF, and bright regions correspond to areas where the CTF has large amplitudes that are either positive or negative (Fig. 6c). The CTF limits the amount of information that can be obtained from electron images. At zero crossings of the CTF, no information is transmitted, and specimen features corresponding to such spatial frequencies will not be visible in the final image. Biological samples viewed in ice under close to focus conditions display very low amplitude contrast since the difference between their densities and that of water is very small [29, 30]. Consequently, the images are typically taken far from focus (in underfocus mode) to increase the weight of low frequencies and thus improve the visibility of particles [29, 30]. It is useful to remember that low frequencies are responsible for the overall shape and appearance of particles in images, while the high frequencies are related to the fine details. High defocusing (images taken far from focus) induces changes in the distribution of density information related to fine details, which could be lost due to attenuation of amplitudes at high frequencies. The level of defocus used for imaging depends on the size of the bio-complex. Images of small particles (~60–200 kDa) are often collected with a large defocus, sometimes up to 4–6 μm while viruses with diameters of 50 nm and larger can be imaged at 0.5–1.0 μm. 4.2 Defocus Determination and Correction for the CTF Effects

An EM projection image is only considered as a faithful representation of the registered projection of the object of study if it has been corrected for the CTF modulation effects of the microscope. To correct an image for the CTF effects and obtain the image that corresponds to the projection, it is necessary to determine the defocus at which the image was taken and to assess it for astigmatism and drift. A nominal value of the defocus set on the microscope does not usually represent the actual defocus at which the image was recorded. Although the acceleration voltage and the spherical

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aberration of the lens in the microscope are constant, the typical deviations in sample thickness and variations in the height of the supporting film in the holder will cause local deviations in defocus. As a result, the CTF that depends on the actual defocus should be determined for each image. Finding the exact defocus and level of astigmatism in cryo-EM images is of crucial importance when working towards producing a high-resolution structure. The CTF determination is performed by calculation of power spectra (or amplitudes) of small patches (256 × 256 or bigger) from the average of all aligned sub-frames. The spectrum is then correlated with a set of theoretically calculated CTFs in a range of possible defocus values. The highest correlation between the observed and one of the theoretical CTFs indicates the actual defocus of the image and defines frequencies where the phases have to be flipped (Fig. 6b, c). Different software packages for automated defocus determination are currently used, which include: CTFIND4 (implemented in RELION and CryoSPARC), EMAN2, IMAGIC5, and CrYOLO [9, 11, 66–70]. Astigmatic images have power spectra that are not rotationally symmetric, and this can complicate and reduce the accuracy of the CTF determination. Generally, cryo-EM images, which have greater than 5% astigmatism, are not used for further processing. The level of astigmatism can be calculated as follows: Astigmatism = ðDefocusmax - Defocusmin Þ=Defocusavg Correction of the recorded images for the CTF modulation effects of the microscope is carried out in Fourier space after CTF determination of the image by multiplying the alternating bands of phases of the CTF (in a simplified option) by a square wave function, which has the value of -1 at positions where the contrast transfer is negative and + 1 where the contrast transfer is positive. This has the effect of reversing or “flipping” the negative lobes of the CTF into positive contrast thus restoring the correct image phases (Fig. 6d, e). The missing information where the CTF crosses zeroes is restored by combining images at different defocuses so that where some images lack spatial information at a particular frequency others will provide the complementary missing information. High spatial frequencies are suppressed by the envelope decay, so amplitude correction is also important for maximizing high-resolution details. This operation usually involves applying a Wiener filter to remove noise from the CTF [70–72]. 4.3

Particle Selection

The structural analysis process in EM begins with selecting images of individual particles from micrographs. This involves defining their unique locations (x, y) within the image field and saving these coordinates in a data file that is used in the following steps

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of processing. This can be done using packages like RELION, CryoSPARC, EMAN2, Ximdisp, and some others [9–11, 68, 73– 79]. The initial approach often used is for the user to select single particle images by clicking on the image area that looks like a particle using a mouse. The coordinates of these points will be stored and then used to extract individual particles within a square box of designated dimension. The cut-out area must be large enough to retain some field around the object with some background. It is recommended that it should be twice the maximum size of the molecule under the study. Images of the selected particles must not be overlapping, squashed, distorted, fragmented, or in direct contact with other particles. Particles can also be selected automatically with particle identification/picking programs such as Gautomatch, Autopicker, BShow, FindEM, and blob picking [75– 79]. These programs utilize the local correlation between a template (an initial reference) and a small image area (of the size corresponding to the size of the reference) to measure the degree of similarity between them. Areas that show maximum correlation with the references are boxed out. The poor contrast in ice images and the presence of artifacts, which could resemble target particles, generally decrease the accuracy of automatic selection. New approaches for particle picking based on deep learning have been implemented in crYOLO, TOPAZ, and some other packages [70, 80–82]. An example of a micrograph of vitrified sample of the T4SS core-outer membrane complex is shown in Fig. 7.

Fig. 7 Particle picking. (a) Cryo-EM micrograph of the R388 T4SS sample. (b) Red circles designate examples of particles selected for the processing. The images were collected with a post-GIF K2 Summit direct electron detector (Gatan) operating in counting mode, at a magnification of 130,000 corresponding to a pixel size of 1.048 Å. The image has been taken far from defocus to increase the contrast and allowing visualization of the lipidic bi-layer belt around T4SS transmembrane domains. (Figure adapted from Ref. [25] with permission from Nature)

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Normalization of all images is an essential pre-processing step. The contrast and intensity can vary from image to image during data acquisition even when all the EM settings are the same. This effect arises due to a few factors, which include differences in the thickness of the carbon support film or ice, particle orientations, uneven staining, or merging images from different data collection sessions. Normalization of particle images is done by setting the mean pixel grey value of each image to the same level, commonly zero and rescaling the standard deviation to an equal value for every particle image. Without normalization, the density variations such as very bright or very dark regions within an image can bias the crosscorrelation procedures, which are later used for alignment and calculation of particle classes. Typically, normalization is based on the following formula: ρnorm = ððρi,j - ρavg Þ=σ old Þ σ new σ old and σ new are the standard i,j deviations of the original and target images respectively, ρi,j is the density of a pixel in the image array coordinates.

4.5 Classification of Particle Images

Radiation damages, a low signal/noise ratio in images in addition to sample heterogeneity and variations in conformations of bio complexes induce some ambiguities in analysis of their structural organisation. That can be resolved by statistical analysis of millions of molecular particle images. The images extracted from micrographs should be examined for their quality, consistency of the molecular assemblies, and their structures represented in images. Manual inspection of this vast number of individual images is neither practical nor realistic since it is time consuming, and users are prone to errors and some subjectivity. Routinely, automated particle picking is followed by the 2D classification of selected particle datasets. The essence of this procedure is the comparison of particle images within a dataset and cluster similar particle images into groups. Typically, 2D classification consists of several rounds. The initial step of classification aims to establish if images correspond to the object of interest or they represent fragments of micrographs mistakenly selected, disintegrated particles, unspecified blobs, bright spots, or edges of the carbon films. This step is often referred to as data cleaning. This comparison is initially performed on a small subset of the data (just a few thousand images) and then gradually expanded to the entire dataset eventually producing a few tens or 200 classes. Visual inspection of classes is feasible and consequently allows the use images comprising selected classes in the next steps of processing. Images composing classes that are inconsistent in appearance (i.e., junk images not related to the sample) should be removed from subsequent steps of processing. The next step is typically used to assess characteristic views of bio-complexes and initial distribution of their orientations. High

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percentage of classes having the similar appearance indicates strong preferences in the particle orientations. If the 2D classes do not demonstrate well-resolved features, it may indicate that a sample is unstable, or it overconcentrated and images contain adjacent particles that are overlapping with the central particle image, or the particles have strong specific interactions with the carbon support film, or with the water/air surface that produces a strong preference in the orientation of particles. Images comprising classes that have well-defined features and show different views have to be chosen for the 3D reconstruction analysis. 4.5.1 Principal Component Analysis

Principal component analysis (PCA) reduces the number of variables in order to find the most significant variations in the measurements [69, 83]. The essence of the procedure is a transformation of a set of observations of possibly correlated variables (in our case images or reconstructed volumes) into a set of values of uncorrelated variables called principal components. In theory, the number of the principal components should be equal to the number of original variables. However, since images contain a high level of noise, the number of the meaningful components became much smaller. The principal components are described by the eigenvectors of the data matrix. PCA is the simplest of the true eigenvectorbased multivariate analyses [69, 83]. It is now used in the 3D classification (24).

4.5.2 Maximum Likelihood Estimation

Maximum likelihood estimation (MLH) is a method that assesses parameters that would correspond to a statistical model [84]. When applied to a dataset (such as the image dataset) and given a statistical model (the initial 3D model), MLH provides estimates of how another new reconstruction would correspond to the suggested model and what sort of deviations could be observed. This concept can be rephrased: once a model is specified in relation to a certain set of parameters (e.g., orientations, positions of the mass center, distribution of domains, and overall sizes the MLH evaluate how well the suggested model fits to the data collected (EM images). The quality of this correlation is assessed by finding parameter values of a model that best fits the data—a procedure called parameter estimation. In the EM case, that would be a 3D model that maximally corresponds to the dataset, otherwise, the model has to be modified. If there are several models, then the dataset will be divided during processing into groups corresponding to the models. MLH has many properties that should be taken in account during estimation; these include sufficiency (complete information about the parameter reflecting features of interests), consistency (numbers of images relating to this or other 3D models), efficiency (the lowest-possible variance of parameter estimates), and could be other practical parameters [84–87]. This MLH method is used in the analysis of 3D reconstructions and implemented in RELION [9].

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K-Means

Cluster (or classification) analysis is a tool to identify groups of similar objects. This type of analysis is used for grouping similar images (particles in the same orientations). The aim of the K-means approach is to create groups (classes) of data subsets that are as similar as possible while also keeping the classes as far (different) as possible [88, 89]. It ascribes data objects to a class in such way that the sum of the squared distances between the objects and the centroid of the class (arithmetic mean of all the sub data in this group) are at the minimum, and the distances between members of the class are minimal. The lower variations within the classes, the more similar are members of this group of the dataset. The starting points are selected at random and named as the seeds. The seeds should be placed as far from each other as possible. The next step is to take each point belonging to a given dataset and associate it with the nearest centroid. Averages are calculated for each class (initially using a subset of the data), and the distance between each image and the obtained averages are calculated. A new class will be formed by the images that were the closest to one of the averages. Then the class averages are recalculated. This process is done iteratively with increasing numbers of images used for calculations of averages until the images cease to move between classes [88–90]. In this method (used in RELION, XMIPP, and EMAN [9, 10, 12, 68]), the user defines a number of classes as K; it is often not bigger than ten, but with improved algorithms and GPU usage, up to 200 classes can be defined (see the last versions of RELION). Here, the algorithm randomly assigns at the beginning a few images as initial classes [9], the next step increases the number of images, compares them with the initially selected images and calculates the averages. The iterations include gradually increasing the number of images and reassessments of distances between images comprising the averages and distances between the averages. The K-means method is reasonably fast, and it works better in low dimensional space since dimensionality increases the time and a local minima problem may occur. If we are using several millions of images, the last iterations may take significant time. However, the multiprocessor computers can help to make this task sufficiently rapid.

4.5.4 Modification and New Developments in Classification

Processing cryo-EM image data to reveal heterogeneity in the protein structure and to refine 3D maps to high resolution requires analysis of large datasets. The stochastic gradient descent (SGD) and branch-and-bound maximum likelihood optimization algorithms permit the major steps in cryo-EM structure determination to be performed in significantly shorter time. Furthermore, SGD with Bayesian marginalization allows ab initio 3D classification, enabling fast-automated analysis and identification of different conformations of protein complexes without bias from a reference map. These algorithms are implemented in CryoSPARC [11, 90].

4.5.3

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Recently, new approaches have been suggested for the fast classification of images that are based on usage of denoised images. The essence of these methods is based mainly on three techniques: spatial filtering, temporal accumulation, and deep learning. Deep learning uses a neural network to reconstruct the signal. The neural network is trained using various noisy and reference signals such as data from known test objects that can be applied to the new data [11, 90–94]. 4.6 Determination of Particle Orientation

In order to obtain the 3D structure of a biocomplex from EM images, the orientation of each of the individual particle image must be determined. The location of an individual molecule can be identified by X, Y, and Z coordinates, and the shifts of different particles with respect to each other can be described in the same way as shifts in X,Y, and Z. The particles can also be rotated by α, β, and γ angles that are defined as Euler angles. This means that a molecule has six degrees of freedom in space. In the microscope projection, images are taken along the Z-axis of the translational system of coordinates; therefore, the shift in Z direction is not significant (we assume that the electron beam is parallel) but the shifts in the X and Y directions need to be determined. During translational alignment, the center of the molecule is set to X = 0 and Y = 0. In order to calculate the 3D map from individual images or class-sums, it is necessary to determine the orientations of the characteristic views (classes) relative to each other. The angular distribution of different images should evenly cover the Euler sphere or the asymmetric triangle (the independent asymmetrical part of Euler sphere for particles with symmetry). This is essential to achieve an even representation of all details in the structure, or at least a set of images chosen for the reconstruction should have such a distribution of angles that cover a big circle of the Euler sphere [95]. If the distribution of angles is patchy, this leads to the appearance of stripes in the 3D electron density map. The size of the trustworthy details that might be resolved can be assessed using a formula derived by Crowther [96] assuming that projections are evenly distributed: R = D=N N—number of views, D—diameter of particle, R—target resolution. The N number can be significantly decreased for the same resolution if the complex has high order of the point group symmetry.

4.6.1 Projection Matching

Projection matching requires an initial model, and it is based on a simple principle of comparison of the images with projections of the model (Fig. 8) [29]. As a 3D template (an initial map), one can use the low-resolution negative-stain EM 3D reconstruction, the low-pass filtered X-ray model, or the EM map of a homolog. This template is projected in all possible directions covering the entire

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Fig. 8 Projection matching procedure. (a) Projection matching in real space. A set of images is compared to a set of references calculated from an initial model (low resolution). Once the best match is found between the image and one of the model projections, based on the height of the correlation peak, the shift relative to the matching reference and angles of that reference are assigned to the image. Images 1 and 6 have the best correlation with model projection a (red arrows), while images 2 and 5 match image e (blue arrows). Image 3 corresponds to the tilted view c (yellow arrow). A new 3D map is calculated using images with the assigned angles. The refined 3D reconstruction is then reprojected with a smaller angular increment to generate new references for the next iteration of refinement. (b) Projection matching in Fourier space. Fourier transforms are calculated from a set of images and compared successively to a set of central sections of a 3D Fourier transform from an initial model. Once the best match is found between the FT of the image and one of the references, based on the height of the correlation peak, the shift relative to the matching reference and angles of that reference are assigned to the image. A new 3D map is calculated using the FT of images with the assigned angles. The 3D FT is calculated from the refined 3D reconstruction, and the new central sections are used as references for the next iteration of refinement [28]

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Euler sphere with a certain angular increment. Then the images or class averages of the dataset are compared with these references, and the angles corresponding to the reference with the best crosscorrelation will be assigned to the image [28]. Projection matching helps to determine the out-of-plane rotations of the object. During the angular refinement, the angular increment between projections of the model is reduced, or more projections with a small increment can be calculated around the initial angle, so the orientation angle will be more accurately defined. This method is easy to use, however, it is extremely time-consuming due to the long computation during which it is necessary to try all possible in-plane alignments and to compare each image to a set of references. Nonetheless, the usage of multiprocessor computers helps to speed up the process. Once the Euler angles are assigned to all images or class averages, a new 3D reconstruction will be calculated, and a new set of refined higher resolution model projections will be computed for the next round of projection matching. Projection matching can be done both in real space (space of projection images) and in Fourier space (Fig. 8) [29, 96, 97]. Software packages currently favor Fourier space due to the advent of fast algorithms to calculate correlations between images and model projections. Most current programs, such as RELION, CryoSPARC, and EMAN2, are based on projection matching procedures, which are performed in Fourier space [9–11, 68]. 4.6.2 Angular Reconstitution

The EM images represent projections of embedded molecules in random orientations. If there is no initial model, the angular reconstitution technique is the method of choice to determine the relative orientation of images. The common line projection theorem postulates that every pair of 2D projections of the same 3D object has, at least, one mutual 1D line projection that is called a common line projection [69, 98]. Thus, matching of 1D line projections for different images allows to establish the relationship between the 2D projections and determine angles between the common lines and, therefore the relative Euler angles of the images. It is also possible to determine orientations using common lines in Fourier space [96, 97, 99]. The theorem of the central section states that the 2D Fourier transform of a 2D projection represents a 2D central section through the 3D Fourier transform of the 3D density. In Fourier space, a common line corresponds to the crosssection of Fourier transforms of images. It means that two Fourier transforms from two different 2D projections of the same 3D object have at least one common central line [96, 97, 99]. Here, a comparison of the radial lines of the Fourier transforms of one image with all possible radial lines of the Fourier transform of the other image is performed. Again, similar to the real space, the angle between common lines of the two images with respect to the third one gives the angle between these two views.

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3D Analysis of EM Structures EM images are considered as 2D projections of the 3D object [29, 100–103]. This is due to the large depth of focus in TEMs. The depth of defocus is related to the acceleration voltage: the higher voltage, the greater the depth of focus, which can be up to 200 nm. Therefore, the image should represent a projection (the total sum of electron densities along the beam rays) in the image plane produced. However, for the images to be considered as projections, they have to be corrected for the CTF effects (see above). Once the CTF correction is done, and the Euler angles have been assigned to each projection image, then the 3D electron density for the particle can be determined. Several approaches are used to calculate 3D densities of the molecules from their projections. Since the current trend is towards complete automation of the image processing, two approaches are now commonly used in EM due to the efficiency of their implementations. One is where the reconstruction is calculated in Fourier space; the other performs reconstructions in real space and based on the filtered back-projection algorithms [28, 29, 100–103].

5.1 3D Reconstruction in Fourier Space

This approach is based on the theorem that states that the Fourier transform of a 2D projection of a 3D object constitutes a central section of the 3D Fourier transform of the object [96, 97]. This means that the Fourier transform of projections from different angular views can be merged to fill up the Fourier space with different 2D sections that are calculated from the images. Recovery of the 3D structure of the object in real space is done by reverse transformation of its 3D Fourier transform (Fig. 9) [99, 100, 103, 104]. In Fourier space, the large number of central sections used causes them to overlap at and close to the point of the origin. This leads to overweighting of the low frequency components in the Fourier transform and to the effect of blurring similar to the simple back projection in real space. So currently used algorithms based on Fourier methods employ down weighting of low or high frequencies by applying a weighting filter to the resultant map.

5.2 3D Reconstruction in Real Space

These methods calculate the 3D distribution of densities in the space of the object (Fig. 10). The exact filtered back projection algorithm is implemented in many software packages related to EM tomography [101, 102]. In this method, each image of the dataset (or classes) is stretched along a direction defined by the determined orientations of images. Electron densities in 3D are obtained by summation of rays from stretched projections. The electron density generated by these summations produces densities for each voxel. As more projections are included in the 3D reconstruction, the

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Fig. 9 Reconstruction using Fourier transform of images. (a) T4SS outer-membrane core complex (14-fold symmetry) is observed in different orientations on the supporting film. (b) Projections of these particles that are equivalent to EM images in the direction of the electron beam. (c) Fourier transforms (2D FT) of the projections shown in b. s—is the FT of the side view and e—the FT of the end view. (d) 2D transforms that share at least one common line in reciprocal space. Common lines between the side view projection(s) and the end view projection (e) are shown in orange lines. Green lines indicate common lines between symmetry related side projections. The angles between pairs of common lines determine the relative Euler angle orientations. (e) An inverse Fourier transformation of the combined 2D transforms generates an improved real space structure seen as electron density

voxels become better defined (Fig. 10f). To avoid additional low frequency background induced by the procedure of projection stretching, images are filtered in advance (although in some packages the filter is applied to the resulting 3D reconstruction). A high pass filtering of the input 2D projections corrects the overweighting of the low frequency components thus restoring amplitude balance and so blurring is minimized. 5.3 Structure Refinement

All single-particle EM packages use nearly the same procedure for the refinement of structures after the first 3D model has been obtained (Fig. 1). It is done by realignment of single-particle images with new references obtained from the new model. It is often combined with determination of angles: projection matching using a smaller angular increment and/or a local refinement of the

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Fig. 10 3D reconstruction in real space. Different images of the core OMC are shown and situated around the Euler sphere. Each particle image is backprojected along its assigned Euler angle in real space. As the back-projected density from each class average intersects the 3D electron density of the entire object, it is captured as a 3D map

angles [28, 97, 98]. The new 3D model is used to calculate a fresh set of re-projections, the number of which is increased according to the reduced angular increment. This enlarged set is used in projection matching to refine the orientation of the particle images. As soon as new angles are determined, they are assigned to the images that have the best correlation with the model projections. While all implementations share the same principles of refinement, the details of the algorithms and the degree to which the user can control the process vary significantly. 5.4 3D Classification for Analysis of Heterogeneity Molecular Complexes

The dynamics of structural changes can be assessed quantitatively using an iterative 3D classification procedure, which is based on one or a combination of 3D PCA, SDG, K-mean, and MLH approaches [11–13, 69, 84–87, 90, 105, 106] (Fig. 1). The structures obtained can be ordered according to the degree of their conformational changes according to the values of the principal components or extent of displacements between corresponding domains. This aids the visualization of motions involving flexible domains

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enabling determination of their phases and links to other domains within the complex. Evaluation of the particle numbers that correspond to each conformation allows quantitative assessment of the probability of the intermediate states. The best-resolved regions of the structures typically correspond to more stable elements where more flexible or peripheral components have significantly lower resolution. There are several new packages such as cryoDRGN (Deep Reconstructing Generative Networks), which uses a method for heterogeneous cryo-EM reconstruction based on deep neural networks. The neural networks, which are known for their ability to model complex, can learn to identify heterogeneous ensembles within cryo-EM maps. It has been shown that a representation of structure by this approach can model density maps at high resolution as precursors, before revealing the full cryoDRGN framework usage for unsupervised heterogeneous reconstruction analysis [7, 8, 11, 87, 93].

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Evaluation of the Structure Quality When the map of a new structure is obtained, the molecular mass and its oligomeric state should be estimated. The map needs to be inspected at a 1 σ threshold of density level (that may vary depending on the software used) that corresponds typically to the molecular weight of the complex in the study. If the complex consists of several interacting proteins, the map should not have disconnected fragments of densities, they should be continuous as well as above the background noise. The concept of resolution is based on assessment of the minimal distance between two points in the image at which they can still be distinguished from one another. That criterion was formulated as the Rayleigh criterion: when the center of a peak of a one-point image falls exactly on the first zero of the second point [107]. In electron crystallography, the signal-related Fourier components of the image are linked to the reflections on a regular lattice, the reciprocal lattice, and a resolution is defined by the frequencies of the reflections that are above background noise and therefore available for the Fourier synthesis [107]. This crystallographic resolution Rc and Raleigh’s point-to-point resolution distance d are related by d = 0.61/Rc. How can this be done in an objective manner in single particle analysis? For several decades, we have used the concept of Fourier shell correlation (see below). However, during the last few years, due to the tremendous achievements of single particle cryo-EM, several modified approaches have been suggested, and these methods of the resolution assessment became closer to the criteria used in X-ray crystallography.

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In single-particle analysis, there is no well-defined periodical pattern in the Fourier spectra. Common practice is to look for data consistency by splitting the dataset randomly in half and comparing the two resulting averages (3D reconstructions). The resolution of a 3D map (a size of the reliable details) can be assessed by Fourier shell correlation (FSC). Two Fourier transforms are calculated, and the corresponding spherical shells are compared using normalized cross-correlation as a function of spatial frequency (R) (radius in the Fourier space). The value of the cross-correlation is used to assess at which frequencies (or level of detail size) these two maps begin to differ. It is important to note that the resolution implied by Fourier shell correlation (FSC) resolution depends on how the data were divided and which threshold of FSC has been used for the assessment. Previously, it was considered if the correlation falls below the 0.5 threshold, then the details are different [108]. Currently, there are several criteria to determine the threshold used in FSC, and nowadays, the threshold of 0.1432 has become increasingly popular, since it is closer to the measurements used in X-ray crystallography [109, 110]. Scheres and Chen proposed a “gold standard” method for evaluation of structure quality [110]. According to this approach, the initial dataset is divided into two halves from the very beginning, and two models are refined independently. This approach helps in the refinement of structures to avoid biasness towards the same model used for the alignment and determination of angles. Once the structures have been obtained, the FSC curves can be determined as usual. If one would like to compare the structure obtained with some other known structure, it is critical that both structures should be aligned for a correct FSC estimation (Fig. 11). The main disadvantage of FSC is that for the resolution assessment, it is required to divide data into two halves and that significantly reduces the dataset used for the final 3D reconstruction. An alternative approach for the evaluation of the reliability of details in 3D reconstructions (using the complete dataset) is the randomization of phases (or the randomization of both randomization of phases and amplitudes) above the frequencies critical for the assessment of details. The main concept of the approach is to modify an original dataset of particle images in such a way that the amplitudes and phases beyond a certain chosen frequency are substituted by random values. This randomization of phases (and sometimes amplitudes) at high frequencies is equivalent to the replacement of high frequency structural details by noise. The modified dataset is subsequently subjected to the same image processing procedure used for the original experimental data. The FSC between these two structures usually demonstrates a sharp drop at the same frequency where the substitution of phases and amplitudes has occurred [111]. Any non-zero FSC values beyond the resolution where the noise was introduced reflects a level of bias during image

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Fig. 11 Examples of Fourier shell correlation (FSC) and resolution assessments. (a) FSC curves for two independent 3D structures that have been aligned and masked with a loose soft mask shown as a halo around the particle images. A resolution assessed at the 0.143 level is 3.4 Å. (b) The second structure is rotated by 10° around the rotational axis. While the overall shape still coincides well, small details are no longer in the register. This is reflected in the FSC curve, which falls rapidly, and the resolution is only 26.5 Å. This corresponds to the size of major domains within the structure. (c) The structures are not aligned. FSC falls even more rapidly and indicates only the consistency in the overall sizes ~53 Å. (d) FSC between two aligned 3D structures with the same tight mask. Good correlation at high frequencies indicates on the correspondence between masks imposed on the structures and therefore, a resolution is overestimated

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processing. It is important to note that datasets with high frequency noise contain little information about the real structure compared to the original dataset. This may mean that particle orientations will be less accurately defined and may affect the value of FSC at the frequencies close to the selected threshold chosen for the phase substitution. 3D masking may also affect the behavior of the FSC curves at high frequencies. The FSC can be improved if the featureless regions are masked out. A tight mask with a very sharp boundary can produce strange artifacts in FSC such as increasing up to the highest frequency defined by the sampling of images (Nyquist frequency, https://www.gatan.com/nyquist-frequency) indicating that details in the structure are related to the mask but not to the sample structure (Fig. 11). 6.2 Local Estimation of Resolution

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Another approach that has become popular during the last years is resolution analysis of the 3D density maps obtained from the whole datasets through computing the cross-correlation between neighboring voxels in the Fourier domain. The ResMap algorithm [112] is based on assessing a local-sinusoidal model at r = 2d (where d is the voxel spacing in Å) that has the best fit within a small window of the image. As the small window moves through the map, the Fourier neighbor correlation computes the correlation between neighboring voxels in the Fourier domain and the sinusoidal model [112]. The likelihood ratio tests are conducted for all voxels in the volume. At a fixed wavelength equal to d, the standard likelihood-ratio test can detect whether a local-sinusoid is a meaningful part of the model approximation. The test requires an estimate of the noise variance, which can be evaluated from the region surrounding the structure. The smallest r at which the likelihoodratio test passes at a given p-value (probability) defines the resolution. The p-value is the measure of whether the outcome of the test is due to an actual effect or mere a random chance. Voxels that satisfy the test are assigned a resolution r, while those that fail will be examined at a larger r (bigger wavelength of the sinusoidal model). The algorithm produces a local resolution map with a resolution assigned to every voxel in the density map (Fig. 12).

Interpretation and Fitting of Atomic Models Final verification of the quality of the obtained EM map is done by analysis of the quality of fitting of known atomic structures, homologous atomic models, or results obtained by de novo tracing of the polypeptide chain into subunits or protein component domains. Previously, the resolution of most density maps produced by SPA was between 20 Å and 30 Å. At such a low resolution, large domains can be recognized according to their overall shape. This level of detail does not provide sufficient information relating to the

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Fig. 12 Local resolution of IMC-Arches-Stalk domain of T4SS [EMD-13767, 28]. (a) The overall resolution derived from Fourier Shell Correlation (FSC). FSC curves show the correlation between two independently refined half-maps as a function of frequency; the various curves are color-coded as follows: without masking (blue), spherical mask (green), loose mask (red), tight mask (cyan), and corrected (purple). A cut-off 0.143 (blue line) was used for resolution estimation. (b) The angular distribution of images used during refinement of the IMC-Arches-Stalk structure. (c) The density map is colored by local resolution (σ = 0.06). Local resolution was calculated using CryoSPARC and colored as indicated in the table below the map. The values indicated are in Angstroms. (Figure adapted from Ref. [26] with permission from Nature)

interaction between proteins and possible active sites for example or the locations of specific domains in multi-domain assemblies. The usage of antibodies and different methods of labeling specific domains, or construction from reasonable pseudo atomic models based on the EM maps themselves, however, has allowed their identification and positioning. Nonetheless, additional (often substantial) biochemical research is required to verify interpretations. In sub-nanometre resolution maps (6–9 Å range), densities corresponding to α-helices are characteristically cylindrical with an obvious “twist” that aids accurate fitting and the determination of overall structural handedness. At 4.5 Å, β-strands in β sheets begin to resolve while at 4 Å, densities corresponding to large amino acid side chains become visible. At resolutions of ~3.7 Å or higher, the de novo tracing of polypeptide chains can be performed using methods developed in X-ray crystallography [113–115]. The highest resolution achieved using cryo-EM has currently been reported as approaching 1.5 Å where the atoms of individual amino-acid are visible [14–20].

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An efficient approach is when pseudo atomic models obtained by homology modeling are fitted into cryo-EM density maps to build atomic models of the individual proteins. The basic principle of the fitting procedure is assessment of the correlation between a density map and the model. The optimal cross correlation between the EM map and modeled densities of the atomic structure localizes the best fit of the model. Such fitting is performed using the maps at a resolution greater than 3.7 Å. Depending on the software used, the search can be accomplished in reciprocal or real space. The initial stage of the fitting procedure is carried out manually or automatically as a “rigid-fit” if a homologous model exists. These can be constructed using the programs Phyre, I-Tasser, RoseTTAfold, and AlphaFOLD2 [116–119]. Regions of the protein that are naturally flexible and do not immediately fit into the electron density can be adjusted manually in Chimera [120] or Coot [121] or in a more automated manner using FlexEM [114], IMODFIT [122], or other available packages. The flexibly fitted structure can be optimized and checked for clashes using Phenix [123]. This last step allows fixing the positions of secondary elements while simultaneously applying geometrical restraints. There are specific strategies that directly relate to the fitting of homology models [124] into EM density. Electron microscopy– iterative modular optimization (EM-IMO) methods allow the refinement of protein models based on homology modeling [125]. These methods endeavors to build, modify, and refine the local structures of protein models using cryo-EM maps as constraints. A multi-parameter refinement strategy that combines EM-IMO and molecular dynamics with the fine-tuning of parameters permits building backbone models for different conformations of proteins at the near-atomic-resolution. The use of EM-IMO demonstrates that homology modeling and multiparametric refinement protocols offer a practical strategy for building atomic models based on medium- to high-resolution cryo-EM density maps [124]. Recent developments in single-particle electron cryo-electron microscopy now allow structures to be routinely solved at a resolution close to 3 Å. To facilitate the interpretation of EM reconstructions, X-ray packages such as REFMAC have been modified for the optimal fitting of atomic models into EM maps [126]. External structural information such as interaction between domains and links to their function can also enhance the reliability of the derived atomic models, stabilize refinement, and reduce overfitting [115]. Atomic models obtained as a result of flexible fitting should also be evaluated for their correctness and consistency. A Ramachandran plot [127] is routinely used to visualize the distribution of dihedral torsion angles. These angles of a polypeptide backbone are the most dominant local structural parameters that dictate protein folding. Geometrical constraints and steric clashes between atoms

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of the main chain and sidechains of each residue make some angles disallowed. The assessment of atomic model quality generated using MolProbity [128] and their correlation with the EM density maps with which they are associated need to be provided in publications.

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Application of the 3D Analysis to the T4SS Here, the focus will be on the analysis of a component of the T4SS structure that will be used as an example of 3D analysis. The structural model of the T4SS was constructed using four electron density maps obtained at resolutions ranging from 2.5 to 6.7 Å [26]. Heterogeneity analysis through classification of 1,300,000 particle images led to a selection of 566,815 particles that were subjected to 3D homogeneous refinement in the absence of symmetry constraints. The largest component of the T4SS that consisted of the IMC-Arches-Stalk complex was first tackled at medium resolution. As a result, the overall structure was obtained at a resolution of 6.18 Å (Fig. 13, upper part of the workflow outlined in grey). From this map, two flexible, multi-domain sub-complexes were selected for the further focus refinement involving one of the three “large bulks” of densities (the IMC protomers) located around the central part of the complex, and the central cone-shaped structure termed the Stalk (Fig. 2d). The first task was the analysis of IMC density within the T4SS system based on the identification of a single IMC protomer (shown as a light-yellow oval on the left-hand side of Fig. 13). This was achieved by computationally subtracting the remainder of the T4SS (highlighted in blue) from the experimental images resulting in the generation of a 3.7 Å map. When computed at lower contour levels (2 sigma), substantial density for a further two IMC regions was revealed. A total of five IMC domains were ultimately identified that all appeared to be related by sixfold symmetry although a sixth subunit was absent. The route for analysis of the Stalk is shown on the right side of Fig. 13 (outlined in reddish-brown color). Here, the Stalk domain is highlighted by a light-yellow oval where the region highlighted in blue within the complex was “subtracted” to enable its analysis. This resulted in a structure at a resolution of 3.71 Å. Usage of the fivefold rotational symmetry aided refinement of the Stalk structure to 3.28 Å. The combination of these refined elements of T4SS and other elements such as the O and I layer of the OMCC provided important information on the structural organization of the complete complex (Fig. 2d) [26].

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Fig. 13 Workflow used to generate the inner-membrane complex (IMC)-Arches-Stalk map and the localrefined maps of the single IMC-protomer and Stalk. C1 symmetry was used to obtain a 6.18 Å resolution structure of the complex (EMD-13767). The initial steps used in processing are shown in the grey box. A model was built and used for symmetry analysis of the IMC-Arches, and Stalk. Parts of the structure (outlined with the yellow ovals) were used for refinements after signal subtraction indicated by blue shading. The IMC protomer (EMD-12933) was refined by signal subtraction to 3.75 Å resolution (outlined in the blue box on the left site). The Stalk was refined to 3.71 Å in the absence of symmetry constraints (EMD-12709) and subsequently refined with C5 symmetry to a resolution of 3.28 Å (outlined in reddish-brown on the right of the figure, EMD-12968). The data processing steps (described in the blue rectangles) were done using CryoSPARC, while the grey rectangle indicates processing performed using RELION. (Figure adapted from Ref. [26] with permission from Nature)

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Conclusions Despite the impressive recent achievements within the EM field, there are still many challenges in areas such as the structural studies of large multi-protein complexes with low or no symmetry. Such complexes are typically flexible or can be unstable resulting in a need for better approaches to deal with sample heterogeneity, which invariably means that greater computer power will be required. Cryo-EM and methods of image analysis have become important and powerful tools in the analysis of these forms of bio-complexes. Recent advances in the EM field have greatly contributed to unraveling the so far elusive structural and mechanistic details of Gram-negative bacterial secretion systems. This level of details has not only led to a better understanding of the mechanics underlining the process of substrate secretion through the T4SS apparatus but has also provided a unique opportunity to visualize how this bacterial nano-machine is structurally organized within both the outer and the inner membranes. New methods of data and structure classification, remarkable achievements in the prediction of protein atomic models combined with accomplishments in cryo-EM, now provide great opportunities for accumulating valuable structural and mechanistic knowledge. These are required for the design and assembly of new bio-complexes of medical relevance. Fitting of the predicted models into biologically active EM structures such as previously uncharacterized bacterial complexes will aid in the understanding of hostpathogen interactions and how they can be modulated to curb the spread of pathogenicity and antibiotic resistance.

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Chapter 28 CryoEM Data Analysis of Membrane Proteins. Practical Considerations on Amphipathic Belts, Ligands, and Variability Analysis Alexia Gobet, Loı¨ck Moissonnier, and Vincent Chaptal Abstract Membrane proteins data analysis by cryoEM shows some specificities, as can be found in other typical investigations such as biochemistry, biophysics, or X-ray crystallography. Membrane proteins are typically surrounded by an amphipathic belt that will have some degree of influence on the 3D reconstruction and analysis. In this chapter, we review our experience with the ABC transporter BmrA, as well as our statistical analysis of amphipathic belts around membrane proteins, to bring awareness on some particular features of membrane protein investigations by cryoEM. Key words Membrane Protein, Membrane complex, Structure, Electron microscopy, Detergents, Amphipols

1 Introduction Electron microscopy under cryogenic conditions (cryoEM) has been a major game changer for structural investigation of membrane proteins, especially to obtain high resolution structures using single particle analysis. This was granted by technological advances on the microscopes and the detectors, and yielded a true resolution revolution [1] that permitted investigators to reach routinely the same resolutions as the one given by X-ray crystallography. Besides, to reach high resolution, cryoEM is able to capture protein undergoing turnover transitions, and is thus able to decipher many transition states in the conformational spectra of proteins. All these elements make that cryoEM has become a major route for membrane proteins structural investigations.

Alexia Gobet and Loı¨ck Moissonnier contributed equally with all other contributors. Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_28, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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Like for X-ray crystallography, cryoEM analysis of membrane proteins comes with specificities distinct from soluble proteins studies, and investigators willing to embark in membrane protein structural investigations should be aware of them. This chapter aims at addressing these specificities, using the ABC transporter BmrA as an archetypical example of membrane proteins of moderate size. All specificities found on this protein will also apply on other proteins of similar size. This chapter has been built on four main parts: (1) practical implication on data-processing and experimental handling of membrane proteins, (2) specificities of the processed maps, (3) ligands in cryoEM maps, and (4) visualizing movements captured by cryoEM.

2 2.1

Typical Workflow of Membrane Protein Structure Determination Typical Workflow

2.2 Membrane Proteins and Special Orientations: GridType

A typical data-processing workflow for membrane proteins starts at sample preparation. It is of outmost importance to have a “good” sample that will distribute nicely within holes, which shows no aggregation or special orientation (discussed below), etc. If the membrane protein sample behaves well, advances in software have been so large in the past years that no special difficulty is envisioned. It is our experience that difficulties arising in data processing almost always stem from a sample not being ideally distributed within the holes. Thus, we would like to stress that for well-behaved samples, almost automatic data processing using a blob-picker to detect particles and classical 2D classification followed by initial model generation and refinement can yield a near high-resolution structure. If the procedure fails, it is often worth going back to the sample preparation stage as many difficulties in data processing will be solved by having particles well distributed in the ice (Fig. 1). A problem sometimes encountered in cryoEM data processing in general is preferred orientation, caused by particles mostly oriented by a clustering at the air–water interface, and thus lacking all the views needed for high-resolution reconstruction [2, 3]. It results in lower resolution, and the appearance of artifacts in the map. This phenomenon is also encountered for membrane proteins, albeit it must be said that the use of amphipathic molecules is this time an advantage to prevent this phenomenon, and these molecules can be used to modulate it. Indeed, detergents or other polymers are surfactants, naturally distributing at the air–water interface. They thus saturate this surface and tend to protect membrane proteins from distributing at this interface. Alternatively, they can be supplemented to the purified membrane protein to first saturate this interface before the protein does, allowing particles to distribute

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Fig. 1 Standard workflow of membrane protein data processing. All data processing problems will originate from sample preparation, so emphasis is placed on this crucial step; membrane protein environment to obtain particles well inserted and dispersed in the holes with good ice quality. Particularities of membrane protein maps that can be found and that are discussed in this chapter are listed

within holes. The use of fluorinated detergents has been reported for this purpose as well as CHAPS, CHAPSO, digitonin, or octylglucoside [3–5]. It must be noted that other detergents also have this capability as well as polymers such as amphipols [6], where addition of tensio-active molecules tend to reinforce the liquid film formed in the holes. All detergents used for this purpose tend to have high critical micellar concentration, synonym of a fast exchange between monomers and micelles and a significant number of free monomers to be able to saturate the air–water interface. Another important parameter to take into account to prepare cryoEM grids of membrane proteins is the type of grid to be used. Indeed, most grids have a holey carbon layer on top in which holes are present and where the liquid distributes. Carbon, being naturally hydrophobic, tends to adsorb non-specifically a large amount of sample before it has time to distribute within holes. Thus, for membrane proteins that are naturally hydrophobic, it is necessary to increase sample concentration to saturate the carbon and then to introduce proteins in holes. Typically, for the ABC transporter BmrA in detergents, concentration of 3–4 mg/mL is needed for a good sample distribution [7]. This has also been seen for GPCRs where it is not unusual to see protein concentration as high as 6–10 mg/mL [8]. Replacing detergents by peptidiscs somewhat reduces this effect, and much lower protein concentration (0.6 mg/mL) is needed to apply the ABC transporter Pdr5 on a grid [9]. Investigators are thus directed to critically assess the concentration at which they apply the sample on the grid according to the amphipathic environment they use. Alternatively, new types of grids are available, using a gold thin sheet to replace the carbon

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layer, which reduces a bit the non-specific adsorption of membrane protein. It is also noted that the use of graphene-oxide layers is a good alternative to preferred-orientation, allowing more random particle distribution and orientation and yielding high-resolution reconstruction [10]. 2.3 How to Distinguish Real Membrane Protein Particles from Micelles

One difficulty encountered more and more frequently is to solve high resolution structures of detergent micelles. Indeed, detectors have improved so much that it is fairly easy to see detergent micelles on micrographs and it is difficult to differentiate a micelle from a subdomain or a side view in particle picking. Therefore, blobpicking often selects detergent micelles on micrographs, resulting in large particle datasets after extraction with mostly micelles. It is possible for users to be more stringent during particle picking in order to reduce too much low-level picking on micrographs. However, and this is especially true for small membrane proteins or proteins with defined domains, this will be at the expense of selecting particles that could be useful for high resolution reconstruction. We therefore tend to pick as many particles as possible and sort them through 2D and 3D classification. Detergent micelles can be distinguished in 2D classification by an absence of secondary structure on which the particle alignment can anchor itself (Fig. 2). Detergent micelles can also be observed in ab-initio model generation via a model with no shape, smaller size compared to the protein, and won’t refine to high-resolution with defined secondary structures. If too much detergent is included in the final sample concentration before applying it on the grid (see methods on how to control detergent amount here: [11]), many detergent micelles can be seen and can sometimes replace membrane proteins in the holes resulting in absence of good particles during picking, and an entire dataset wasted. Figure 2 shows an example of 2D classes of the DDM/Cholate detergent mixture micelles (left) and BmrA 2D classes (right).

2.4 Types of Amphipathic Solvents Available for Membrane Protein Structural Investigations

As invoked above, many types of amphipathic solvents are available to handle membrane proteins [12]. Detergents are still a very useful tool to extract and handle membrane proteins, and result in many instances in behavior similar to native membranes, as seen on the highly debated wide inward-open structure of the ABC transporter MsbA [13]. Membrane proteins in detergents are easy to handle for cryoEM grids. Detergents can then be replaced by multiple environments. A very popular environment is the polymer amphipol [12], very useful to stabilize membrane proteins and that has been successfully used to solve membrane protein structures. Membrane proteins can also be placed in peptidiscs [14] or saposins [15], which are both amphipathic peptides that wrap around the hydrophobic region of the membrane protein with success to solve

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Fig. 2 2D classification of detergent micelles or membrane protein. (a) 2D classes of detergent micelles from the DDM/Cholate mixture in the 1:1 ratio. Absence of secondary structure is noticeable along with a small size of the particles (around 50 Å diameter). (b) 2D classes of the ABC transporter BmrA. Typical protein shape can be observed, as well as transmembrane helices and the detergent belt. Data processing has been conducted in cryosparc v3.3 with a box size of 200 Å

their structure. They can also be placed in lipidic nanodiscs [16], which come in multiple flavors. As mentioned above, the membrane protein concentration used for these different environments is to be adapted, with higher concentration required for proteins in detergents, and a smaller one for membrane proteins in other types of environments. Finally, it has been recently reported the structure of membrane proteins in liposomes [17] to mimic even more the natural environment. It should be noted that this later method requires extensive optimization to include liposomes within holes, in good and homogeneous amount for data collection. 2.5 The Case of Small Membrane Proteins

The limit at which proteins can be solved by cryoEM keeps on being pushed back to smaller and smaller proteins, so no prognostic will be made here on what would be the limit. But certainly, smaller proteins have less contrast and are thus harder to spot on micrographs, for example for screening purposes. Also, smaller proteins tend to be more spherical, which makes the alignment procedures more difficult to perform and to reach higher resolution. Membrane protein can benefit from their insertion into lipidic nanodiscs, which will both increase the size and guide the alignment by bringing the membrane plane in which the membrane protein is inserted. Additionally, the use of antibody or nanobody is a good alternative, both increasing the size of the protein and bringing asymmetry that will be useful for orientation and 3D reconstruction.

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Fig. 3 Scatter plot of membrane protein resolution, solved by cryoEM or X-ray crystallography. (Data have been extracted in April 2022, resolution extracted from rcsb.org for each entry, and membrane proteins were extracted using Membrane proteins of known structures’ database (http://blanco.biomol.uci. edu/mpstruc/) and the PDBTM [21] database as described in [22, 23]) 2.6 Membrane Protein and Low Resolution: Model Building

As shown on Fig. 3, structures of membrane proteins solved by cryoEM still stay at moderate resolution. Two main issues stem from this limitation: (i) Building an accurate model and placing side chains correctly is a major challenge at low resolution. This is always the case at moderate resolution, as some parts of the structure are mobile and local resolution will be probably worse than at the protein core. This problem is central to model building and not specific to cryoEM, so care is emphasized in tracing the chain. Thankfully, the new improvement in de novo structure modeling using deep learning [18, 19] serves as an excellent guide to protein modeling. Also, the model generated can be used to create a new template using Alphafold2 in refinement-like procedures [20], which helps with correcting mistakes during model building. (ii) 3D reconstruction can result in maps with inverted hands, as a particle extracted has no information of being seen from the top or from the bottom. If the map is of sufficient resolution,

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then a careful inspection can allow for the distinction of the correctness of the hand, but this is definitely harder at lower resolution. Here again, the use of models originating from deep-learning methods is encouraged as it will be impossible to fit them in maps with inverted hands, thus leading to the correct choice for the hand.

3

Visualization of Amphipathic Belts

3.1 Visualization of Amphipathic Belts in cryoEM Maps

Membrane proteins are always in couple with their amphipathic belt, and cryoEM allows for the first time to visualize it. Before, the only way to study structurally this belt was through molecular dynamics simulations, as the belt was not observable by X-ray crystallography. We have studied this belt using statistical analysis to identify features or trends that could potentially be useful for investigators. An important point raised is that, contrary to X-ray crystallography, cryoEM maps are not normalized, and it is thus difficult to talk about map levels to refer to particular features in the map. By looking at hundreds of maps, we were able to identify a feature in map density histograms that reveals the position of the amphipathic belt, and somewhat normalizes its use. We identified four levels [24] (Fig. 4): (i) level 0 corresponds to the visualization of high-resolution features; (ii) level 1 corresponds to the location where the map density curve inflexes and corresponds to the appearance of the high contour signal for the amphipathic belt; (iii) this belt increases in size until level 2, where the curve stops to increase logarithmically and signals the maximum observed size for the belt; (iv) level 3 signifies the appearance of large amount of low-level map features everywhere throughout the box, corresponding to noise. One word of caution is raised as the rise in map density histograms between levels 1 and 2 can also be observed for proteins displaying flexible domains, and for which the appearance of low-resolution features will also give rise to such behavior on the map density curve. Thus, user input is advised to ensure the correspondence between cryoEM maps and location of hydrophobic belts. We compared membrane protein structures that have been solved in multiple environments to explore whether this amphipathic environment would have an influence on 3D reconstructions. Using statistical methods, we could show that for a single protein, the size of the amphipathic belt is similar between nanodisc, amphipols, and detergents, contrary to general belief. By comparing with detergent quantification, we could also reveal that the size of the belt observed in cryoEM is smaller than the volume occupied by the total amount of solvent covering the hydrophobic patch of membrane proteins. Comparison can be

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Fig. 4 Visualization of amphipathic belts in cryoEM reconstructions. (a) 3D volume of the ABC transporter BmrA (EMD-12170) seen at different contour levels, which are displayed on top (blue number). These contour levels correspond to features in the map density histogram displayed in (b). (c) Correspondence with the quantified amount of detergent for this transporter, displayed using the detbelt server

made using the detbelt server [25] (www.detbelt.ibcp.fr) to estimate the volume occupied by detergents or amphipols around any membrane protein. The points raised provide critical thinking to investigators when they observe cryoEM maps. 3.2 Influence of Averaging and Symmetry on the Visualization of Amphipathic Belts in cryoEM

CryoEM relies on averaging to increase the signal resulting from protein features while decreasing the noise. There is also the notion behind it that proteins are in the same orientation, thus allowing to increase the signal. However, amphipathic belts are known to be very fluid, and are thus by definition never in the same place. The consequence is that averaging only keeps the common minimum layer in all the particles used for 3D reconstruction and it is the reason why amphipathic belts are nice and round in 3D reconstruction. Another feature revealing this underlying principle is the fact that the belt increases in size from level 1 to level 2 on the map, denoting different levels of common minimum during the reconstruction, or an increase in flexibility. Besides, symmetry is often used to improve resolution in 3D reconstructions, and this can have impacts on the shape of amphipathic belts that can be sometimes seen as following closely the protein shape, and in some extreme cases to create some clear

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artifacts on the shape of the amphipathic belts (note that these artifacts are observed on the amphipathic belt but symmetry is key to improve resolution on the protein, and does not trigger the same artifacts on the protein map). 3.3 Lipids or Detergents in CryoEM Maps

4

In addition to observing nearly the whole of amphipathic belts, cryoEM maps are also rich in map features in the outside of proteins, facing the hydrophobic environment, which could be interpreted as lipids or other amphipathic molecules. A nice review of this topic has been written by V. Biou [26], and the reader is referred to this article for further details. This fact also raises the question of how to interpret map features in the amphipathic belt region, how to distinguish a real lipid/detergent, etc. from noise. Often, only small portions of the lipid are observed, making it difficult to model it with certainty. Investigators are encouraged to back up their modeling claims with biochemistry and mutant data to ascertain positioning of such ligands, flexible in nature. Improvement in resolution will on one hand help to distinguish between all these options, and new technological improvements (microscopes, detectors, and software) continuously improve resolution, leading to a bright future in modeling of hydrophobic elements around membrane proteins. On the other hand, it should be kept in mind that these ligands are naturally flexible, and often display different orientation from protein to protein (or particle to particle). Therefore, averaging will cancel out their individual position and it might never be possible to model them in one place, while they should be inspected with molecular dynamics simulations instead.

Visualization of Ligands

4.1 Ligand Visualization and Resolution

Visualizing ligands in cryoEM maps is of major interest, but it could be difficult to ascertain their presence. Many factors play a role in this difficulty, the first one being resolution. Like for X-ray crystallography, lower resolution makes it harder to visualize the ligand, with sometimes only partial densities present for some ligand moieties. But unlike crystallography where the phases could be refined leading an improvement in the maps, the cryoEM maps are final and investigators need to judge the presence of their ligands on those. And the use of heavy atom for their anomalous signal cannot be used in cryoEM. Another difficulty results from partial occupancy and the use of symmetry in the calculation of the maps (discussed below), which can result in a loss of signal for the ligand, and a low contour level. Depending on the location of the ligand, it could be at similar contour level as the amphipathic belt, adding to the difficulty in judging if a particular density originates from the ligand of interest or from amphipathic molecules or even

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noise in the reconstruction. For the ABC transporter BmrA, we were faced with this difficulty. We were able to ascertain the presence of Rhodamine 6G by multiple methods. (1) First, the presence of the density corresponded to the ligand binding pocket. (2) We were able to improve the ligand density by using a mask removing the detergent belt and using non-uniform refinement without symmetry [7]. It should be noted that this procedure worked with cryosparc v2, but further improvements of the algorithms make that the influence of the belt in reconstruction appears to be less and less as software evolve. We encourage to try several refinement procedures and judge the output. (3) The resolution of BmrA in the same condition but without ligand shows a disappearance of the density. (4) Biochemistry showed that Rhodamine 6G binds more strongly than possible competitors for this binding site. (5) Molecular dynamics simulation with Rhodamine 6G in the site defined by cryoEM confirmed a plausible binding site. The use of mutants to probe a binding-site is also a tool to be used for this purpose. This example shows that the use of many methods to ascertain the presence of a ligand is a good idea to back up a claim on ligand position. 4.2 Effect of Symmetry on Ligand Visualization: Case of Flexible Ligands or Plastic Binding-Sites

CryoEM is an averaging method to reach high resolution, as discussed above on the contribution to visualize amphipathic solvents. Therefore, if a ligand is partially present in the binding-site of an oligomeric protein, the quality of the map for the ligand will be decreased if a symmetry is enforced. Similarly, if a ligand is fully present in all binding site but is mobile, averaging will keep the common parts of all sites and cancel out the diverging parts. It should be noted that these effects are not restricted to ligands and have also been observed on protein subdomains for example [27– 30]. There isn’t a magic way to deal with this problem that was also present in X-ray crystallography. In cryoEM, the generation of both symmetrized and non-symmetrized maps and their comparison can give clues on the effect of symmetry on the presence of a particular feature. If the ligand is part of a clearly identifiable subdomain, it could be worthwhile to perform local refinement of this domain, perhaps in combination with particle subtraction to attempt to increase the signal. Another avenue is the use of symmetry expansion followed by local refinement in the absence of symmetry, which could give rise to maps that will differ around the ligandbinding site. Here again, investigators are encouraged to perform a multiple investigation of the ligand binding site as described in Subheading 3.1 to ascertain the position of their ligand.

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Variability Analysis of cryoEM Structures

5.1 Membrane Proteins are Flexible Objects

Membrane proteins are flexible objects, with different parts of the structure undergoing a whole range of deformations. It could be rotations and deformations of the trans-membrane part, or movements of domains on either side of the membrane. These movements can be small or large depending on the type of protein or complex observed, and vary if a ligand is bound to the protein. The ability to observe these movements is definitely a hallmark of cryoEM, able to capture protein flexibility during the freezing process, and is able to render this variability by cutting edge computation of particle variability compared to a consensus map. It results in a series of maps showing the protein movement.

5.2 Interpretation of Membrane Protein Movements

While seeing a protein move is an amazing sight, very quickly, we want to dig deeper and decipher the parts of the protein moving, understand how they are moving, what part is the driver of the movement, which part is reacting to a signal, what type of rotation/ translation is occurring, etc. It is very hard to draw such conclusion by looking at a map, and even for a specialist of a protein, it is impossible to pinpoint to specific residues. This is even more true if the protein is large, or the movement calculated at low resolution. We thus created a software that refines a structure in each of the maps originating from variability. It results in an ensemble of structures that describe the protein movement. This structure bundle can be used to dissect out the flexible or mobile parts of the structure, measure deformation angles and distances, identify pivot points, etc. It allows to decipher protein flexibility deeper, and is a tool to explore experimental flexibility [31].

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Conclusion Like other aspects of their studies, membrane proteins have specificities. For example, investigators need to have knowledge on detergents or lipids to analyze their purification experiments or binding assays. While the amphipathic environment has influence on membrane protein behavior in solution, it also influences cryoEM reconstructions, and create specific features that can be observed and analyzed. We have presented here some of the specificities linked to membrane protein studies by cryoEM, to bring awareness on these specificities and provide a framework for critical thinking.

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Acknowledgments This work was supported by the CNRS, the French National Research Agency grant number ANR-19-CE11-0023-01 to VC and AG, and the Doctoral School fellowship to LM. The authors wish to thank Xavier Robert for his help in extracting the data to create Fig. 3, the distribution of resolution between methods. References 1. Ku¨hlbrandt W (2014) The resolution revolution. Science 343:1443–1444 2. Noble AJ, Dandey VP, Wei H et al (2018) Routine single particle CryoEM sample and grid characterization by tomography. elife 7: e34257 3. Armstrong M et al (2020) Microscale fluid behavior during Cryo-EM sample blotting. Biophys J 118:708–719 4. Chen J, Noble AJ, Kang JY, Darst SA (2019) Eliminating effects of particle adsorption to the air/water interface in single-particle cryo-electron microscopy: bacterial RNA polymerase and CHAPSO. J Struct Biol X 1:100005 5. Baker MR, Fan F, Serysheva II (2015) Singleparticle Cryo-EM of the ryanodine receptor channel in an aqueous environment. Eur J Transl Myol 25:4803 6. Michon B et al (2023) Role of surfactants in electron cryo-microscopy film preparation. Submitted 7. Chaptal V, Zampieri V, Wiseman B et al (2022) Substrate-bound and substrate-free outwardfacing structures of a multidrug ABC exporter. Sci Adv 8:eabg9215 8. Danev R et al (2021) Routine sub-2.5 Å cryoEM structure determination of GPCRs. Nat Commun 12:1–10 9. Harris A et al (2021) Structure and efflux mechanism of the yeast pleiotropic drug resistance transporter Pdr5. Nat Commun 12:5254 10. Glaeser RM (2018) Proteins, interfaces, and cryo-EM grids. Curr Opin Coll Interf Sci 34: 1–8 11. Gobet A, Zampieri V, Magnard S et al (2022) The non-Newtonian behavior of detergents during concentration is increased by macromolecules, in trans, and results in their overconcentration. Biochimie 205:53–60 12. Le Bon C, Michon B, Popot JL, Zoonens M (2021) Amphipathic environments for determining the structure of membrane proteins by single-particle electron cryo-microscopy. Q Rev Biophys 54:e6

13. Galazzo L et al (2022) The ABC transporter MsbA adopts the wide inward-open conformation in E. coli cells. Sci Adv 8:eabn6845 14. Carlson ML et al (2018) The Peptidisc, a simple method for stabilizing membrane proteins in detergent-free solution. elife 7:e34085 15. Flayhan A et al (2018) Saposin lipid nanoparticles: a highly versatile and modular tool for membrane protein research. Structure 26:345– 355.e5 16. Denisov IG, Sligar SG (2017) Nanodiscs in membrane biochemistry and biophysics. Chem Rev 117:4669–4713 17. Yang X et al (2022) Structure deformation and curvature sensing of PIEZO1 in lipid membranes. Nature 604:377–383 18. Jumper J et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583–589 19. Baek M, Baker D (2022) Deep learning and protein structure modeling. Nat Methods 19: 13–14 20. Terwilliger TC et al (2022) Improved AlphaFold modeling with implicit experimental information. Nat Methods 19:1376–1382 21. Kozma D, Simon I, Tusna´dy GE (2013) PDBTM: Protein Data Bank of transmembrane proteins after 8 years. Nucleic Acids Res 41:D524–D529 22. Martin J et al (1865) Specific Xray diffraction patterns of membrane proteins caused by secondary structure collinearity. Biochim Biophys Acta Biomembr 2022:184065 23. Robert X et al (2017) X-ray diffraction reveals the intrinsic difference in the physical properties of membrane and soluble proteins. Sci Rep 7:17013 24. Zampieri V, Gobet A, Robert X, Falson P, Chaptal V (1863) CryoEM reconstructions of membrane proteins solved in several amphipathic solvents, nanodisc, amphipol and detergents, yield amphipathic belts of similar sizes corresponding to a common ordered solvent

Cryo-electron Microscopy of Membrane Proteins layer. Biochim Biophys Acta Biomembr 2021: 183693 25. Zampieri V, Hilpert C, Garnier M et al (2021) The Det.Belt Server: a tool to visualize and estimate amphipathic solvent belts around membrane proteins. Membranes 11:459 26. Biou V (1865) Lipid-membrane protein interaction visualised by cryo-EM: a review. Biochim Biophys Acta Biomembr 2023:184068 27. Jones R et al (2022) Structural basis and dynamics of Chikungunya alphavirus RNA capping by the nsP1 capping pores. BioRxiv 2022.08.13.503841

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28. Bai X-C et al (2015) Sampling the conformational space of the catalytic subunit of human γ-secretase. elife 4:e11182 29. Ilca SL et al (2015) Localized reconstruction of subunits from electron cryomicroscopy images of macromolecular complexes. Nat Commun 6:1–8 30. Roh S-H et al (2017) Subunit conformational variation within individual GroEL oligomers resolved by Cryo-EM. Proc Natl Acad Sci U S A 114:8259–8264 31. Afonine PV et al (2022) Conformational space exploration of cryo-EM structures by variability refinement. BioRxiv 2022.12.23.521827

Chapter 29 Structural Analyses of Bacterial Effectors by X-Ray Crystallography Chloe´ Dugelay, Virginie Gueguen-Chaignon, and Laurent Terradot Abstract X-ray crystallography is a method of choice to determine and analyze protein structures. Although large complexes are challenging to crystallize and cryo-electron microscopy is thus better suited for these, crystallography can still be efficient in solving structures of single components of secretion systems or effectors. Many of the different steps leading to structure determination by X-ray crystallography have been automatized. Here, we describe a generic approach to obtain crystals, solve the structure of a given protein, and perform a preliminary analysis, highlighting novel and efficient possibilities offered by automatization and contribution of Alpha Fold 2 structure prediction. Key words X-ray crystallography, Bacterial effector, Secretion system, X-ray diffraction, Protein data bank, Alpha Fold

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Introduction Bacterial secretion systems are of considerable interest from a number of perspectives. They are involved in many biological processes such as conjugation, transformation, biofilm formation, or infection. Some of them deliver protein effectors that modulate host defenses and metabolism, thereby promoting infection. Structural biology, in particular X-ray crystallography, has played a crucial role in deciphering the function of these molecules. The development of cryo-electron microscopy has enabled the study of large, multicomponents flexible assembly that were particularly difficult (or impossible) to crystallize [1]. However, for single protein components (or low complexity complexes) such as bacterial effectors, X-ray crystallography is still a very efficient method to determine the structure of a protein. The past decade has seen spectacular advances in automation for several steps of the X-ray crystallography process, including crystallization conditions screening, data collection and processing,

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_29, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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structure determination, model building, and refinement [2–7]. As a consequence, determining a structure by X-ray crystallography can be very rapid, even as fast as a couple of weeks, and the recent COVID pandemic has also demonstrated that Drug discovery using automated X-ray crystallography structure determination can be remarkably efficient [8]. The process of solving a crystal structure is entirely dependent on the availability of diffracting crystals for a given protein, and this remains the major bottleneck of the method. But once the diffraction data is available and at resolutions higher than ~3.5 Å, structure determination can be, in most cases, rather straightforward. To solve the phase problem of X-ray diffraction, protein crystallographers rely on two main methods: (i) molecular replacement (MR) or (ii) experimental phases determination using derivatives or intrinsic sulfuric atoms. On the one hand, producing derivative crystals can be achieved by incorporating heavy metals in the crystals or producing Selenomethionine protein to generate new crystals. In that case, solving the structure requires careful data collection and wavelengthtunable beamlines to optimize the diffraction data collection. On the other hand, MR uses an available 3D model of a homologous protein to place it in the asymmetric unit to obtain preliminary phases. With Alpha fold de novo prediction from a single aminoacid sequence, structural biology enters a new era, where these prediction models can be used in MR to solve the phase problem of virtually any diffracting crystal, even if their diffraction is at lower resolution. In previous chapter(s), protein purification and characterization protocols are described and are thus not discussed here, although these are important steps for protein crystallization. Assuming that you have pure, monodispersed protein, we describe the different steps (Fig. 1) to solve the structure of a given protein or domain by X-ray crystallography at a high brilliance synchrotron. This chapter assumes that the reader has a good knowledge of the principles of X-ray diffraction. We incorporate the use of Alpha fold 2 [9] to generate models for structure determination by MR and a list of a number of procedures for preliminary structural analysis of the model obtained that can identify key properties and guide future functional experiments.

2

Materials

2.1 Drop Setup Using Mosquito Robot

1. Mosquito Robot (STP Labtech https://www.sptlabtech.com/ products/mosquito). 2. RockImager 182 (Formulatrix https://formulatrix.com/pro tein-crystallization-systems/rock-imager-crystallizationimagers/).

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Fig. 1 Pipeline for protein structure determination by X-ray crystallography. Arrows in green and orange indicate user manipulation or automated procedure, respectively. Sizes of the arrows represent relative importance of automation versus human intervention

3. MRC plates 2-lens 96-well crystallization plate (Swissci). 4. Nunc 96-well plates, conical. 5. Pipet 1–10 μL, 20–200 μL, and 100–1000 μL and corresponding pipet tips. 6. Eight-channel pipet and corresponding pipet tips. 7. Ultrafree MC GV Centrifugal filter (Millipore). 8. Sealing films: visualization film and covering film before experiment (Greiner). 9. 96 well crystallization screens (Table 1). 2.2 Manual Optimization of Crystallization Conditions

1. 24-well pre-greased plates. 2. MRC 48-well plates (Swissci). 3. Siliconized coverslides 18 mm for 24-well plates (Hampton Research). 4. Pipet 1–10 μL and 100–1000 μL and corresponding pipet tips. 5. Stock solutions of precipitant, salts, and buffers. These can be home-made or purchased directly at Hampton Research or Calibre Scientific: https://hamptonresearch.com/, https:// calibrescientific.com/). 6. Binocular (Olympus SZX10).

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Table 1 High-throughput crystallization screens routinely used for screening or optimization Company

Reference

Kit name

Hampton Research

HR2-116/HR2-117

Natrix 1 & 2

Calibre Scientific

MDL-MD1-39

MemGold eco

Calibre Scientific

MDL-MD1-63

MemGold2 eco

Hampton Research

HR2-082

PEGRx I

Hampton Research

HR2-084

PEGRx II

Calibre Scientific

MDL-MD1-37 (eco)

JCSG+

Hampton Research

HR2-110/HR2-112

Crystal screen 1&2

Calibre Scientific

MDL-MD1-106

MIDAS plus

Calibre Scientific

MDL-MD1-104

BCS

Calibre Scientific

MDL-MD1-29

PACT1er

Calibre Scientific

NXT-130705

AmSO4

Calibre Scientific

NXT-130706

MPD

Hampton Research

HR2-126/HR2-098

PEG/Ion

Calibre Scientific

MDL-MD1-46

Morpheus

Calibre Scientific

MDL-MD1-38

Proplex 1 & 2

Hampton Research

HR2-138

Additives screen

Hampton Research

HR2-406

Detergent screen

Hampton Research

HR2-132

CryoPro

Calibre Scientific

MDL-MD1-100

Angstrom additives

Calibre Scientific

MDL-MD1-93

Morpheus additives

Calibre Scientific

MDL-MD1-120

Durham additives

Screening

Optimization

2.3 Crystal Harvesting Freezing

1. Freezing loop: 18 mm Mounted CryoLoop-20 micron (Hampton Research, 30 pack: 5 of each diameter size 0.1–1.0 mm). 2. Freezing bucket: Research).

Cryogenic

Foam

Dewar

(Hampton

3. Storage dewar: CX100 Taylor-Wharton cryogenic dry shipper.

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4. Pucks and accessories (Calibre Scientific): Uni-puck, magnetic wand, Puck-Shelved Shipping Cane, Angled Cryo-Tongs, Cane Lifting Tool (Hook), and Puck Dewar Loading Tools set. 5. Liquid nitrogen. 2.4 Data Collection, Processing, Model Building, and Analysis

1. Access to synchrotron facility X-ray crystallography beamline. 2. Computer with installed ccp4 [10] and Phenix [11]. 3. Protein analysis and visualization software: Coot [12], Pymol (Schrodinger), and Chimera [13]. 4. Storage place 500 Mo minimum.

3

Methods

3.1 Crystal Screening

Six crystallization screens are routinely used in the first screening approach: Crystal screen or JCSG+, PEGIon, MbClassII, Natrix, Morpheus, BCS, or Pact1er (Table 1). Crystal screening is performed on MRC 2-lens 96-well plates using a Mosquito robot. 1. First, manually fill the reservoirs of the MRC plates with 70 μL of the screens using the eight-channel pipet. 2. Temporarily cover the plates with sealing film while preparing the protein. 3. Concentrate the pure and homogeneous protein (see Note 1) to approximatively 10 mg/mL and filter on a 0.22 μm centrifugal filter. 4. As MRC plate contains two drops per well, one can test two concentrations of 5 and 10 mg/mL for example or, if not enough protein is available, test only one concentration, 10 mg/mL, and the purification buffer in the second drop as a control. This allows to check whether the crystals obtained are protein crystals or salt crystals from the purification buffer. 5. Deposit the protein in a Nunc plate on an 8-well column, which will be used by the robot for dispensing in crystallization plates. To make sure that enough protein will be transferred to the six crystallization plates, 18 μL is deposited in each well of this column. 6. Program the Mosquito robot to set up drops of 200 nL of protein + 200 nL of crystallization solution. It first transfers 200 nL of protein from a column of a Nunc plate to the first well, then 200 nL of crystallization solution from the reservoir is added to the protein drop. In the software program, specify to run half of the plate, then the other half to avoid evaporation of the drops. Figure 2 illustrates the flowchart of this screening step. 7. Run the Mosquito robot. Once the two drops are made, immediately cover the plate with the visualization sealing film.

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Fig. 2 Crystallization screening. (a) Manually fill MRC plates with the crystallization screens using multichannel pipette. (b) Dispense nanodrops using Mosquito robot (STP Labtech). (c) Place your plates in the RockImager system (Formulatrix). (d) Follow crystal growth according to the defined schedule. (Photos are from @CNRS/Virginie Gueguen-Chaignon and @CNRS/Vanessa Cusimano)

Fig. 3 Example of screening and optimization of crystals. Pictures of crystal optimization obtained in four steps. (a) Screening was performed in MRC 96-well plates with 300 μL drops (150 μL protein solution + 150 μL reservoir). Needle crystals were obtained in PACT premier™ screen condition E9 (20% PEG3350, 0.2 M Na K Tartrate) (maximum length 50 μm). (b) Crystals were improved by optimization in MRC 96-well plate with the screen PEGion F3 condition (12% PEG3350, 4% Tacsimate). (c) Then crystals were optimized manually in 24-well plates in 3 μL hanging drops containing 11–14% PEG3350 and 3–7% Tacsimate. (d) Finally, larger crystals were obtained by using microseeding in 24-well plate. (Photos are from @CNRS/Virginie GueguenChaignon)

8. On the RockImager imaging robot, design a barcode corresponding to the plate to identify the owner, the project, the date, the screen, and the schedule of imaging. 9. Incubate the plate in the robot at 19 °C. Images are taken regularly. Using a Fibonacci sequence to set the imaging schedule allows for good tracking of crystal growth. 3.2

Optimization

Once conditions of crystallization have been identified, crystals often need to be upgraded in size and quality. Several approaches can be considered according to the quantity of pure protein available, the shape of crystals, and the parameters that have to be optimized. You may need to increase crystal size, slow crystal growth, and improve monodispersity and shape. For all approaches, optimization can be performed manually or by using automated systems. An example of possible optimization steps is given in Fig. 3.

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The first optimization step can be to use a screen dedicated to the main precipitant that led to the first crystals. For example, PEGsI and PEGsII can be used if PEG is the main precipitant or AmSO4 or MPD screens can be used to optimize AmSO4 or MPD conditions, respectively. Then the crystallization conditions are optimized by varying several parameters: the percentage of precipitating agent, the pH of the buffer, and the concentration of salt additive. The protein concentration can also be optimized by reducing it if too many precipitates are visible or by increasing it if the crystals are small and/or appear late. The crystallization temperature is also an optimization factor; if the crystals appeared very quickly, then redoing the plate at 4 °C can slow down the growth and increase the crystal size. 1. In 24-well plates, vary the concentration and pH around the initial crystallization condition. Fill the reservoir with 500 μL of these crystallization solutions. 2. Deposit 1 μL of protein solution on silicone glass cover slide placed on a plastic support (or a paper). 3. Deposit gently 1 μL of the corresponding reservoir solution on the protein drop. 4. Take the cover slide, turn them upside down, and place them on the corresponding well in the crystallization tray. Press slightly on the cover slide to generate vacuum with the grease. 5. Process like this for the remaining wells, close the tray, and leave it in the temperature control chamber or room. 6. The days after, inspect the plates at regular period using the binocular microscope and look for single crystals. Other methods can be applied to increase the quality, regularity, and size of crystals (see Note 2). 3.3 Crystal Harvesting and Freezing

1. Prepare the material, and cool the puck into liquid nitrogen (Fig. 4). 2. Look for the crystals that meet quality criteria, single crystals, with a size above 30 μm. 3. Prepare your cryo-protectant solution according to the reservoir solution (see Note 3). 4. Open the crystallization tray and deposit your cover slide with the drop up on the transparent lid of the tray. Deposit 1 μL of reservoir solution near your drop as to make a second drop on the cover slide. Fish and transfer a single crystal using the mounted loop and magnetic wand. Make sure you transfer a single crystal at this stage. Once the crystal is in the drop, add 0.5 μL of cryo-protectant solution using the pipet. Let it equilibrate for 1 min.

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Fig. 4 Crystal cryo-protection and freezing. (a) Prepare the material that you will need. From top to bottom: loops mounted on a magnetic base, and magnetic wand, the plate containing crystals, Uni-puck and cover, trays for liquid nitrogen to accommodate pucks. Transport device for pucks. (b) Incubate your crystals with cryo-protectant solution. Use the loop and magnetic wand to manipulate crystals. Transfer your crystals from the cryo-protectant solution to the liquid nitrogen. Be as fast as you can. Once all the crystals are frozen, you can place the puck filled and covered into the transport device and place it in the dry shipper or storage dewar. (Photos are from @CNRS/Virginie Gueguen-Chaignon and @CNRS/Vanessa Cusimano)

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5. Repeat this step 3 times, increasing the percentage of cryoprotectant solution. Transfer the crystal equilibrated in the nearby drop of cryo-protectant with the loop (see Note 4). 6. Fish a single crystal, and plunge it rapidly into the liquid nitrogen up to covering the loop and base (Fig. 4). Once frozen, place the loop in the puck and write down the position for future data collection. 3.4 X-Ray Diffraction Screening and Data Collection

We perform X-ray diffraction screening at the high brilliance European Synchrotron Radiation Facility (ESRF) or at the synchrotron SOLEIL. 1. Pucks with harvested crystals are placed in the robot storage (for instance Automatic sample changer FlexHCD) by the local contacts of the synchrotron. Here, we use ESRF set-up as an example for data collection. 2. Mount the frozen crystal using beamline robot from the control cabin or remote access using Mxcube [14]. Test the crystal for diffraction individually by collecting 1° rotation images at position 0, 90°, 180°, and 270°. Crystal characterization is performed automatically using the program BEST [15], which determines the space group of the crystal, the best orientation for the goniometer, and number of images and oscillation angle. BEST also optimizes data collection strategy to consider radiation damage. If diffraction is good (individual spots, lattice identified) move to the next step. If not, try another crystal. 3. If you suspect a metal bound to your protein, it is a good practice to perform an Extended X-Ray Absorption Fine Structure (EXAFS) scan of your crystal to identify or confirm the metal present. If present, adjust the wavelength of data collection so that you can measure the anomalous signal (see Note 5). 4. Collect data in a separate folder with the crystal name. Adjust the diffraction resolution (position of the detector) and the wavelength. If you intend to use anomalous signal for phasing, optimize your data collection strategy accordingly (highly redundant, shorter exposition).

3.5 Data Processing and Analysis

1. At the ESRF, using the data server IspyB [16], collected data will be automatically analyzed, reflection-integrated using XDS [17], scaled, and merged using AIMLESS [18]. Diffraction anisotropy will be also assessed and included in the Star aniso file. You will obtain a .mtz file with your intensities in the most probable space group. This file contains your experimental data truncated at the appropriate resolution. Verify in the .log file that the dataset parameters are satisfactory (intensities, Rpim, CC1/2, completeness, etc.) (see Note 6).

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2. Alternatively, you can use the XDS output XDS_ASCII.hkl. Use this file in ccp4i with the program AIMLESS to determine the space group and also generate the free set of reflection (5% of total) that will be kept aside with the label FreeR_flag. A good practice is to save all log files from program for future pdb deposition. 3.6 Structure Determination and Model Building

1. Determine the number of molecules to search for in your asymmetric unit (AU) using your .mtz file in the program MATTHEWS in ccp4. This will also give you the solvent content of your crystal (in percentage). 2. If you have found a suitable homologous protein structure, download its .pdb file coordinates as search model (for instance, name it MRsearchmodel.pdb). 3. Open a web browser Alpha Fold window and input your sequence (https://colab.research.google.com/github/ sokrypton/ColabFold/blob/main/AlphaFold2.ipynb). Execute all. Wait until modeling is done and download your model (.pdb file). In Pymol, open the .pdb, display as cartoon, and color it by spectrum/b-factors. Here, the model will be colored in rainbow according to the prediction quality (pLLD) from very good (red) to poor (blue). 4. Examine the model and cut parts that are protruding out of the core and/or are badly predicted (with low pLLD). Save your model as the MRsearchmodel.pdb. 5. Open ccp4 > Molecular replacement>Phaser. Load your structure factor (mtz) file. The space group and resolution should load automatically. Use the MR search parameter to upload your MRsearchmodel.pdb from the homologous protein structure or Alpha fold modified model. Indicate the solvent content of your crystals (obtained in step 1) and the number of molecules to search for. You can do the MR search in the current space group or in eniantomers (recommended). Run the program. 6. If successful, you will have as output a .pdb file containing all the models (if you asked for several) placed in the AU and a . mtz file with the refined structure factors (read the manual here https://www.phaser.cimr.cam.ac.uk/index.php/Phaser-2.7.1 7:_Manual for details). If unsuccessful, check that you have indicated the right number of molecules to search for or try different space groups (see Note 7). 7. Open the map (the function autopen.mtz will display both the 2Fo-Fc in blue and difference map Fo-Fc in green (positive difference) and red (negative difference)). Open the .pdb file in COOT and examine the quality of the density map (contoured at around 1.3 σ) and the fitting of your model in the density.

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Modify your model according to the electron density by manually deleting and rebuilding the model. Alternatively, use automatic model building. For this, you can use ccp4 software Bucaneer [19], ARP/wARP, or phenix Autobuild with the original .mtz file, from phaser and the .pdb model output from phaser. 3.7 Refinement/ Validation/Deposition

1. Once you have a complete model, refine your model against your X-ray data using Phenix or Refmac. This step is iterative and should be done carefully with proper refinement restrains so that Rwork/Rfree diminish together. Many refinement parameters can be adjusted manually (restrains, non-crystallography symmetry, and Rs difference values). Using Phenix for refinement will provide you with many validation parameters that you can monitor/check to make sure that your model is being ameliorated to fit the X-ray experimental data. Geometric parameters should all be valid and monitored carefully. 2. Check your model/data refinement with PDB-redo automated refinement [20]. 3. Once your model is complete, all waters and ligands are built and refined, validate and deposit your structure using the Protein Data Bank One Dep server (https://deposit-pdbe.wwpdb. org/deposition).

3.8 Structure Analysis 3.8.1 PBDSUM: General Properties of Your Protein

A good start for protein structure analysis is to use the server PDBSum from EMBL-EBI [21]. This will provide you with a number of structural properties. 1. Log into the PDBsum and go to the “generate” on the left menu or go directly here (http://www.ebi.ac.uk/thorntonsrv/databases/pdbsum/Generate.html). 2. Upload your coordinate file and start. Indicate the name of your structure and your e-mail. You will receive a link after 1 h or less. 3. Go to your results page and you will find all the results. 4. You can analyze the different elements: secondary structure elements, topology diagram, and putative clefts/binding sites.

3.8.2 PISA Analysis, Interface and Multimeric Assemblies

1. Log into the PISA server (https://www.ebi.ac.uk/pdbe/pisa/ ) and launch PISA [22]. 2. Use “coordinates” and click on “upload” to input your coordinate file. PISA will find the number and names of chains in your structure. 3. Click on “interfaces”. Wait for the job to be finished. The output is a list of interfaces analyzed by PISA and provide you

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with characteristics (e.g., surface buried, solvation free energy gain upon formation of the interface). 4. Select an individual interface and click on “details”. This will provide you with all the information about the given interface, notably the interacting residues and the characteristics of the interactions between them. PISA will also list each hydrogen bond, salt bridge, etc. 5. PISA will also give a “significance” score for the interface (between 0 and 1). Interfaces with score of 1 are generally genuine. 3.8.3 Comparing Structures Using DALI

1. Log into the DALI server (http://ekhidna2.biocenter.helsinki. fi/dali) [23] and select the PDB search tab to compare the tri-dimensional structure of interest with all structures available in the Protein Data Bank. Upload the PDB file into DALI server and submit your request. 2. Once the job is done, the DALI server will send you an email with a link to access at the results page. Click on the link and click on “Matches against full PDB”. 3. The results are presented as a list of structures ranked by the Z-score defined as the sum of equivalent residue-wise Cα–Cα distances among two proteins [24]. The higher the Z-score, the more the structure of interest shares similarities with the uploaded structure. To analyze all results, DALI server also provides you with additional information: the Root-meanSquare-Deviation, the number of residues of the structure of interest aligned with the proposed structure “lali,” and the number of total residues of the proposed structure “nres” (see Note 8). Finally, the last column of the summary results is a “description” corresponding to the function of the structure. 4. To compare the structures, you can download the text file generated by DALI. Alternatively, download the PDB code of the best match(es) from the PDB database. Open Chimera 1.16. Click on the File tab and select open. Select your structure of interest and the structure you downloaded and that you want to compare. Use the Matchmaker tab to structurally align the models (Fig. 5a). This will align your model and generate the corresponding sequence alignment. 5. To compare the conservation of residue, visualize the sequence alignment based on the structure alignment by Chimera. Click on tools structure comparison and Match align (Fig. 5c, d). 6. Save the Chimera sequence alignment as ALN file. Log the ALN file into the Endscript 3.0 (https://espript.ibcp.fr/ ESPript/cgi-bin/ESPript.cgi) [25]. Add the PDB file of the structure of interest into the “secondary structure depiction” section and click on Submit.

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Fig. 5 Example of exploitation of DALI results. A search for homologue of the protein FutA1 [31] (PDB accession number 2PT1) using DALI gives the structure of the FbpA (PDB accession number 1Q35) as a significant hit. (a) The two structures coordinates are uploaded and displayed as cartoon into UCSF Chimera. The FutA1 and the FbpA are colored in cyan and magenta, respectively. Using Tools > Structure comparison >Matchmaker, the structures are superimposed. (b) The two structures are superimposed with a RMSD of 0.940 Å indicated in the dialog window. (c) Using tools > Structure comparison > Match Align, you will generate a structure-based sequence alignment of the two models. (d) Once the job is done, a window opens with the corresponding sequences aligned. (e) Using this sequence alignment, you can generate publication quality pictures using ESPript 3.0 server. The picture will depict the alignment with secondary structures and conserved residues colored in red. Here, the FbpA residues implicated in metal binding were identified by Shouldice et al. [32] and are Tyr 178, Tyr 198, and Tyr 199 (indicated by black dots). The alignment shows that the residues are conserved in FutA1. (f) The two superimposed structures have been uploaded in Pymol and displayed as cartoon structures of FbpA and FutA1 are colored in magenta and cyan, respectively. The side chains of Tyr 178, 198, and 199 (FbpA) and the side chains of Tyr 143, 199, and 200 (FutA1) are shown as ball-and-stick. The iron atom bound to FutA1 is shown as a grey sphere

7. The color coding of ESPript server 3.0 highlights the level of residue conservation (Fig. 5e). The combined use of Chimera and ESPript allows to visualize the conservation of residue and the conservation of folding. 8. To visualize details of the structures (metal coordination, side chain positions, and conservation), you can also use Pymol as shown in the example (Fig. 5f). 3.8.4 Analysis of Surface Properties Charge and Hydrophobicity

1. Open the PDB file in UCSF Chimera X software [26], and select the molecule display tab and click on “hydrophobicity.” The surface of your model will be displayed and colored according to hydrophobicity ranging from dark cyan (most hydrophilic) to white to dark goldenrod (most lipophilic).

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Fig. 6 Example of the FutA1 CheckMyMetal server output. The holoprotein FutA1 (PDB accession number: 2PT2) is uploaded into the CheckMyMetal server. The left panel shows a ball-and-stick representation of FutA1 metal binding site (iron in orange, carbon in grey, nitrogen in blue, and oxygen in red). The distances between the residues involved in the interaction and the metal are represented by dotted white line. Distances are specified in Angstrom. Residues are surrounded by the experimental 2fo–fc Map (blue wire). The right panel summarizes the characteristics of the protein—metal binding site evaluated by the CMM server. Each parameter is colored according to the accuracy of the value from « not applicable » to « acceptable »

2. To visualize the electrostatic potential of the surface of a protein, log the PDB file into UCSF Chimera X software. Select the molecule display tab, and click on electrostatic. Negatively charged surface is colored in red while positively charged surface is colored in blue. Metal Ligand Properties

1. Upload the PDB file into the “CheckMyMetal” server (https://cmm.minorlab.org/metal-sites/showsite) [27]. 2. The server characterizes the metal ligand properties as the geometry of metal coordination, the metal ligand distances, and the atomic contact (Fig. 6). All properties are defined by a color depending on the physicochemical characteristics of the metal-ligand interactions.

3.8.5 Conserved Surface Residues

1. Upload the PDB file of the structure into the Consurf server (https://consurf.tau.ac.il/consurf_index.php) [28] and submit your request. 2. Once the job is done, the Consurf server will send you an email with a link to access at the results page. Click on the link and go to the Results page.

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3. Consurf server colored the residues according to their conservation. The conservation is split into nine levels of colors from Variable to Conserved. 4. Download the high-resolution Figures and open these with Chimera or Pymol software. Visualize conserved residues at the surface via the color code of Consurf. 5. Consurf generates Multiple Sequence Alignment in FASTA format or on line with the color code. It allows to access at the list of sequence homologues of the protein of interest.

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Notes 1. Before any crystallization experiment, the quality of protein should be checked with several biophysical approaches: absence of aggregation and monodispersity with dynamic light scattering (DLS) experiment, oligomerization state, and monodispersity using a multiangle light scattering (MALS) coupled to a size exclusion chromatography, folding of the protein with circular dichroı¨sm. Stability of the protein of interest can also be checked in several buffers using differential scanning fluorimetry (NanoDSF) or thermal shift assay (TSA). 2. If the crystals are small and numerous, they can be used to seed new drops. Two techniques can be implemented: you can slide a cat’s or horse’s whisker through the drop containing the microcrystals, which will cling to the whisker. Then the whisker is passed through a new drop and the microcrystals will serve as seeds for larger crystals. This approach is used in 24-well plates. For the second technique, microcrystals are crushed with a small glass pestle and serial dilutions in crystallization solution are prepared up to 1/10,000th. Then, a new drop is set up containing both the crystallization solution, the protein, and the dilution with crushed microcrystals. This microseeding technique can also be performed in 96-well plates, using the Mosquito, to multiply conditions. 3. Crystallization conditions should be carefully considered and cryoprotection solutions prepared using 15–25% glycerol or ethylene glycol (final concentrations). Other cryoprotectants might be used such as sugars, high concentration salts, or solvents. To have a good idea of glycerol concentrations to be used as efficient cryoprotectant, these have been assessed for 50 different crystallization solutions [29]. 4. Manipulate crystals very gently and avoid touching them with the loop. Try to place them in the middle of the loop without breaking them. If they are fragile, add the cryoprotectant solution directly in the crystallization drop as to limit the manipulation steps.

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5. By collecting data at the correct wavelength, you optimize the anomalous signal in the diffraction data and thus directly use this data to obtain experimental phases and solve the crystal structure. This was used recently in our group to solve the crystal structure of the effector domain TreTu of the Salmonella enterica Typhimurium rearrangement hot spot (Rhs) toxin using Zn as metal present in the crystallization conditions [30]. 6. Expertise in X-ray crystallography is likely to be required at some point if MR does not work automatically. First, data processing is generally better performed by automation using the beamline on-the-fly server. If you are to do it manually, then you should have a strong knowledge of how to interpret the results of your integration/scaling/space group attribution. Moreover, even if you find the correct space group and dataset at good resolution, MR is not always straightforward. It depends on the protein structure, the crystal system, the solvent content, the protein structure (multidomain, secondary structure content), and many other parameters. 7. At this point, it is not always clear which space group the crystal belongs to. The final/definitive space group will be obtained with structure determination and sometimes also during refinement. 8. With the number of residues of the structure of interest aligned with the proposed structure “lali” and the number of total residues of the proposed structure “nres,” it is possible to calculate the percentage of total residues aligned between the protein of interest and the DALI match.

Acknowledgments This work was supported by the French National Research Agency (grant number ANR-19-CE11-0012). The authors declared no competing interests. References 1. Bai XC, McMullan G, Scheres SH (2015) How cryo-EM is revolutionizing structural biology. Trends Biochem Sci 40:49–57 2. Nanao M, Basu S, Zander U, Giraud T, Surr J, Guijarro M, Lentini M, Felisaz F, Sinoir J, Morawe C et al (2022) ID23-2: an automated and high-performance microfocus beamline for macromolecular crystallography at the ESRF. J Synchrotron Radiat 29:581–590

3. Nurizzo D, Bowler MW, Caserotto H, Dobias F, Giraud T, Surr J, Guichard N, Papp G, Guijarro M, Mueller-Dieckmann C et al (2016) RoboDiff: combining a sample changer and goniometer for highly automated macromolecular crystallography experiments. Acta Crystallogr D Struct Biol 72:966–975 4. de Sanctis D, Beteva A, Caserotto H, Dobias F, Gabadinho J, Giraud T, Gobbo A, Guijarro M, Lentini M, Lavault B et al (2012) ID29: a highintensity highly automated ESRF beamline for

X-Ray Crystallography of Bacterial Effector Proteins macromolecular crystallography experiments exploiting anomalous scattering. J Synchrotron Radiat 19:455–461 5. Afonine PV, Grosse-Kunstleve RW, Echols N, Headd JJ, Moriarty NW, Mustyakimov M, Terwilliger TC, Urzhumtsev A, Zwart PH, Adams PD (2012) Towards automated crystallographic structure refinement with phenix. refine. Acta Crystallogr D Biol Crystallogr 68: 352–367 6. Adams PD, Afonine PV, Bunkoczi G, Chen VB, Echols N, Headd JJ, Hung LW, Jain S, Kapral GJ, Grosse Kunstleve RW et al (2011) The Phenix software for automated determination of macromolecular structures. Methods 55:94–106 7. Zwart PH, Afonine PV, Grosse-Kunstleve RW, Hung LW, Ioerger TR, McCoy AJ, McKee E, Moriarty NW, Read RJ, Sacchettini JC et al (2008) Automated structure solution with the PHENIX suite. Methods Mol Biol 426:419– 435 8. Douangamath A, Fearon D, Gehrtz P, Krojer T, Lukacik P, Owen CD, Resnick E, Strain-Damerell C, Aimon A, Abranyi-Balogh P et al (2020) Crystallographic and electrophilic fragment screening of the SARS-CoV2 main protease. Nat Commun 11:5047 9. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Zidek A, Potapenko A et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583–589 10. Hough MA, Wilson KS (2018) From crystal to structure with CCP4. Acta Crystallogr D Struct Biol 74:67 11. Adams PD, Afonine PV, Bunkoczi G, Chen VB, Davis IW, Echols N, Headd JJ, Hung LW, Kapral GJ, Grosse-Kunstleve RW et al (2010) PHENIX: a comprehensive Pythonbased system for macromolecular structure solution. Acta Crystallogr D Biol Crystallogr 66:213–221 12. Emsley P, Cowtan K (2004) Coot: modelbuilding tools for molecular graphics. Acta Crystallogr D Biol Crystallogr 60:2126– 2132; Epub 2004 Nov 2126 13. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera--a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612 14. Gabadinho J, Beteva A, Guijarro M, Rey-Bakaikoa V, Spruce D, Bowler MW, Brockhauser S, Flot D, Gordon EJ, Hall DR et al (2010) MxCuBE: a synchrotron beamline

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control environment customized for macromolecular crystallography experiments. J Synchrotron Radiat 17:700–707 15. Bourenkov GP, Popov AN (2006) A quantitative approach to data-collection strategies. Acta Crystallogr D Biol Crystallogr 62:58–64 16. Delageniere S, Brenchereau P, Launer L, Ashton AW, Leal R, Veyrier S, Gabadinho J, Gordon EJ, Jones SD, Levik KE et al (2011) ISPyB: an information management system for synchrotron macromolecular crystallography. Bioinformatics 27:3186–3192 17. Kabsch W (1993) Automatic processing of rotation diffraction data from crystals of initially unknown symmetry and cell constants. J Appl Crystallogr 26:795–800 18. Evans PR, Murshudov GN (2013) How good are my data and what is the resolution? Acta Crystallogr D Biol Crystallogr 69:1204–1214 19. Bond PS, Cowtan KD (2022) ModelCraft: an advanced automated model-building pipeline using Buccaneer. Acta Crystallogr D Struct Biol 78:1090–1098 20. Joosten RP, Long F, Murshudov GN, Perrakis A (2014) The PDB_REDO server for macromolecular structure model optimization. IUCrJ 1:213–220 21. Laskowski RA, Jablonska J, Pravda L, Varekova RS, Thornton JM (2018) PDBsum: structural summaries of PDB entries. Protein Sci 27:129– 134 22. Krissinel E, Henrick K (2007) Inference of macromolecular assemblies from crystalline state. J Mol Biol 372:774–797 23. Holm L, Laiho A, Toronen P, Salgado M (2022) DALI shines a light on remote homologs: one hundred discoveries. Protein Sci: e4519 24. Holm L, Rosenstrom P (2010) Dali server: conservation mapping in 3D. Nucleic Acids Res 38:W545–W549 25. Robert X, Gouet P (2014) Deciphering key features in protein structures with the new ENDscript server. Nucleic Acids Res 42: W320–W324 26. Pettersen EF, Goddard TD, Huang CC, Meng EC, Couch GS, Croll TI, Morris JH, Ferrin TE (2021) UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci 30:70–82 27. Zheng H, Cooper DR, Porebski PJ, Shabalin IG, Handing KB, Minor W (2017) CheckMyMetal: a macromolecular metal-binding validation tool. Acta Crystallogr D Struct Biol 73: 223–233 28. Ashkenazy H, Abadi S, Martz E, Chay O, Mayrose I, Pupko T, Ben-Tal N (2016) ConSurf

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2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res 44: W344–W350 29. Garman EF, Mitchell EP (1996) Glycerol concentrations required for cryoprotection of 50 typical protein crystallization solutions. J Appl Crystallogr 29:584–587 30. Jurenas D, Rey M, Byrne D, Chamot-Rooke J, Terradot L, Cascales E (2022) Salmonella antibacterial Rhs polymorphic toxin inhibits translation through ADP-ribosylation of EF-Tu P-loop. Nucleic Acids Res

31. Koropatkin N, Randich AM, BhattacharyyaPakrasi M, Pakrasi HB, Smith TJ (2007) The structure of the iron-binding protein, FutA1, from Synechocystis 6803. J Biol Chem 282: 27468–27477 32. Shouldice SR, Dougan DR, Williams PA, Skene RJ, Snell G, Scheibe D, Kirby S, Hosfield DJ, McRee DE, Schryvers AB et al (2003) Crystal structure of Pasteurella haemolytica ferric ion-binding protein A reveals a novel class of bacterial iron-binding proteins. J Biol Chem 278:41093–41098

Chapter 30 Structural Analysis of Proteins from Bacterial Secretion Systems and Their Assemblies by NMR Spectroscopy Gisele Cardoso de Amorim, Benjamin Bardiaux, and Nadia Izadi-Pruneyre Abstract Bacterial secretion systems are built up from proteins with different physicochemical characteristics, such as highly hydrophobic transmembrane polypeptides, and soluble periplasmic or intracellular domains. A single complex can be composed of more than ten proteins with distinct features, spreading through different cellular compartments. The membrane and multicompartment nature of the proteins, and their large molecular weight make their study challenging. However, information on their structure and assemblies is required to understand their mechanisms and interfere with them. An alternative strategy is to work with soluble domains and peptides corresponding to the regions of interest of the proteins. Here, we describe a simple and fast protocol to evaluate the stability, folding, and interaction of protein sub-complexes by using solution-state Nuclear Magnetic Resonance (NMR) spectroscopy. This technique is widely used for protein structure and protein–ligand interaction analysis in solution. Key words Protein structure, NMR spectroscopy, Periplasmic domain, Protein–protein interaction, Protein–ligand interaction

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Introduction Gram-negative bacteria have developed an arsenal of secretion machineries composed of membrane and periplasmic protein assemblies [1]. Information on their structure and interactions is highly useful to understand their mechanism of assembly and their function. The secretion machineries span the envelope and are thus composed of proteins with several domains in different cellular compartments. The study of such membrane and multicomponent systems, although very informative, is not straightforward and requires particular expertise, as it is difficult to isolate the entire complexes using standard overexpression and purification strategies. A widely used alternative strategy that eases their overexpression, purification, as well as structural and interactions analysis is to express and study their soluble parts (either periplasmic or cytoplasmic) as isolated domains. Domains can be infered by using different

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_30, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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prediction tools and websites [2], or from predicted structure using methods such as AlphaFold [3]. As a general rule, the first step of structural and interaction studies of any domain or protein is to ensure that they are well-folded. Therefore, to increase the chance of folding, it is better to express the domains of interest in their natural cellular compartment. This is particularly important for proteins with disulfide bridges or containing metals or ions as cofactors. All proteins are expressed in the cytoplasm and they can be translocated to the periplasm by different strategies of fusion with an N-terminal signal peptide (e.g., PelB, DsbA, and MalE) or a periplasmic protein (reviewed in [4]). Here we describe a fast and accurate method to check the folding, and analyze the stability and interaction of domains and proteins by using solution-state Nuclear Magnetic Resonance (NMR) spectroscopy. NMR is the technique of choice for studying and analyzing the structure of proteins and protein–ligand complexes in solution and under physiological conditions. Ligands can be macromolecules (e.g., proteins and nucleic acids) or small molecules such as cofactors, metals, drugs, and sugars. NMR provides the possibility to map molecular interactions, determine the binding surfaces, as well as obtaining information on conformational/ dynamics changes, protein aggregation, denaturation, and stability upon binding, in a qualitative and quantitative way. All this information can be obtained by the widely used two-dimensional (2D) 1H-15N HSQC (Heteronuclear Single Quantum Coherence) spectrum [5]. This experiment is highly sensitive and gives interpretable signals in only a few minutes. It is also very informative, allowing to check the overall folding and the stability of a protein, and is a valuable tool for molecular interaction study. 1.1 Overall Folding, Interaction, and Structure/Dynamics Changes Upon Complex Formation

The 1H-15N HSQC spectrum (Fig. 1a, right panel) shows signals from N-H atom pairs. Therefore, in a protein, each amino acid residue (except Prolines) is represented by a signal in this spectrum, including the amide groups from the peptide backbone and the side chain N-H groups from asparagine, glutamine, and tryptophan. Each signal is defined by its 1H and 15N chemical shifts, a parameter related to the corresponding atom chemical environment, i.e., the amino acid nature, its structure, and neighboring atoms. Because of this, the HSQC spectrum serves as a sensitive “fingerprint” of a protein and can be used to gain valuable information on protein folding, complex formation in the presence of a partner, and conformational/dynamics changes upon binding. The chemical shift dispersion, i.e., the distribution of the signals throughout the 1H and 15N frequencies, is very informative about protein structure. A high chemical shift dispersion indicates that the protein atoms are experiencing different chemical environments, meaning that the protein is well-folded. A representative example of a poor signal dispersion in the 1H-15N HSQC spectrum

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Fig. 1 General overview of structural analysis and interactions of soluble protein domains by NMR. (a) The purified 15N-labeled protein in buffer with 5–15% D2O is transferred into an NMR tube before inserting the sample in the NMR spectrometer. One dimensional 1H (1D 1H) or two dimensional (2D 1H-15N HSQC spectra, black) are recorded. The folding and stability of the protein are checked by looking at signal dispersion in both experiments. (b) Unlabeled ligand solution (same buffer) is added to the labeled protein solution (in a saturating condition), and new 1D 1H and 2D 1H-15N HSQC spectra are recorded for the mixture (red). Superposition of the 1H-15N HSQC spectra recorded in the absence and presence of the ligand reveals changes in cross-peaks positions due to the addition of the ligand. (c) Chemical shift perturbations (CSP) occurring upon binding of the unlabeled ligand with the labeled protein are computed for all assigned amide resonances in the protein sequence. Residues with significant CSP are mapped on the protein structure to highlight potential binding areas with the ligand

of a partially unfolded periplasmic domain, the anti-sigma factor HasS C-terminal domain from Serratia marcescens, is shown in Fig. 2b [6]. In addition, the shape of the NMR signal provides valuable information on the structure, dynamics, and molecular interaction. The signal intensity, shift, and/or linewidth are related to the protein molecular weight and dynamics. For example, the formation of a complex results in broader signals compared to those of the protein alone. Furthermore, the chemical shift of an atom is extremely sensitive to changes in its chemical environment. These

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Fig. 2 NMR analysis of the periplasmic domain (HasSCTD) of the anti-sigma factor HasS from Serratia marcescens. (a) Domain organization of HasS (top) and construct used for NMR experiments (bottom). (b) 1 15 H- N-HSQC spectrum of 15N-labeled HasSCTD at 250 μM in 50 mM sodium phosphate buffer, pH 7, 50 mM NaCl, and 0.05% zwittergent. The signals are not well dispersed. The spectrum is highly crowded between 7.5 and 8.6 ppm showing that the protein is not well folded. Some assignments are indicated in one letter amino acid code with their number corresponding to their position in the primary sequence. (c) AlphaFold model of full-length HasS. The HasSCTD domain is colored in blue and the predicted transmembrane region in yellow. (Figure adapted from [6])

changes can be easily observed in the HSQC spectrum, and their magnitude can be determined by mapping their chemical shift perturbation (CSP) (Fig. 1b; see below Subheading 3.2.3). The modifications of the chemical shifts upon the addition of a ligand can also be used to calculate the affinity constant for the complex [7]. The CSP undergone by residues during titration with a protein ligand, for example, is an important parameter for studying protein interaction. In this analysis, a threshold value is defined, above which the chemical shift changes are considered significant. CSP can be monitored by a series of 2D 1H-15N HSQC spectra, recorded after the addition of increasing concentrations of the ligand. From this information, it is possible to know which residues might be directly involved in the interaction, and this data can be used to calculate a structural model of the complex by using, for instance, molecular docking softwares such as HADDOCK [8, 9]. This strategy was used to study the interaction of a bacterial

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Fig. 3 Analysis of the interaction of the C-terminal periplasmic domain of HasB (HasBCTD) with a 21-mer peptide corresponding to the TonB-box of the membrane transporter HasR of the Has system (Heme acquisition system) from Serratia marcescens. (a) HasB domain organization (top) and construct used for NMR experiments (bottom). (b) 1H-15N-HSQC spectra of 15N-labeled HasBCTD in the absence (black) or in the presence (blue) of the TonB-box peptide (unlabeled). Numbers indicate residue position in the primary sequence. The spectral variation from black to blue indicates that there is a binding. In addition, the binding of the peptide does not modify the overall folding of HasBCTD, since the spectral fingerprint of the protein is conserved upon binding. (c) Chemical shift perturbation (CSP) values plotted along the HasBCTD sequence. (d) Residues with high CSP values (>0.2 ppm) highlighted in orange on the HasBCTD structure in cartoon (left) and surface (right) representations. (e) Docking model of the HasBCTD (black) in complex with the TonB-box peptide (orange). (Figure adapted from [10])

outer membrane transporter, HasR with its protein partner in the periplasm, a TonB-like protein, HasB. Using the C-terminal periplasmic domain of HasB (131 residues) and a 21-mer peptide corresponding to the TonB box of HasR, we were able to determine the residues involved in the interaction through CSP mapping (Fig. 3). This information based on the CSP values was used to build a structural model of the protein complex by using the HADDOCK software. The model was further validated by

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in vitro and in vivo mutations and functional assays [10]. Chemical shift mapping upon binding to partner proteins has been extensively used to study various components of bacterial secretion machineries, such as type 2, 3, 4, and 6 secretion systems [11– 18], type 4 pili [19–21], fimbriae [22], chaperon-usher pili [23], or curli [24]. The use of CSP to map the binding surface is not limited to protein–protein interactions. For instance, metal ions often act as cofactors and their interaction with proteins can also be monitored by NMR. Lopez-Castilla and co-workers showed by NMR that the main protein of the type 2 secretion system endopilus from Klebsiella oxytoca was in a calcium-bound state when extracted from the bacteria, and that its folding and stability depended on the calcium (Fig. 4). The combination of the NMR study with in vivo assays highlighted and explained the regulatory role of calcium in the secretion process [25]. NMR was also used to probe the interaction of calcium with the protein subunit of the type 4 pilus from Enterohemorrhagic Escherichia coli, and to identify the residues involved in the binding. This conserved set of calcium binding residues is key for the assembly and stability of the pilus [26]. Analysis of CSP by NMR is also applicable to protein–DNA complexes, as exemplified by the study of the type 4 pilins ComP and FimT involved in DNA transformation in Neisseria meningitidis and Legionella pneumophila, respectively [27, 28].

2

Materials Protein samples for NMR experiments should be obtained through heterologous expression using 15N isotopic labeling [29]. Different expression platforms could be used, paying attention to particular labeling schemes [30, 31]. Here, we focus on uniformly labeled proteins and domains. However, the same protocol can be applied for proteins that are labeled specifically on certain amino acids or groups.

2.1 Sample Preparation

1.

15

N-labeled purified protein sample in the appropriate buffer— 50 μM minimal protein concentration in 600 μL. The buffer pH should be at least one point above or below the target protein isoelectric point (pI). If possible, choose a more acidic pH to decrease the chemical exchange with water protons and improve the amide signal detection. The salt concentration should not be high, typically below 200 mM.

2. Deuterium oxide (D2O). 3. Unlabeled ligand solution at a higher concentration, at least ten times higher than the protein sample and in the exact same buffer. It can be lyophilized and added as a powder, in order to avoid protein sample dilution. In this case, it is important to take into account the final salt concentration.

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Fig. 4 Analysis of the interaction of the C-terminal periplasmic domain of PulG (PulGp) from the Klebsiella oxytoca type 2 secretion system with calcium ions. (a) PulG domain organization (top) and construct used for NMR experiments (bottom). A PelB signal sequence, followed by a His-tag, and TEV cleavage site are inserted in place of the transmembrane segment upstream of the periplasmic domain. (b) 1H-15N-HSQC spectra of 15 N-labeled PulGp in buffer (blue), with 1 mM of calcium (blue) or with 20 mM of EGTA added to the buffer (red). Numbers indicate residue position in the primary sequence. The high signal dispersion denotes a wellstructured protein. In the presence of EGTA, the signal dispersion is reduced, and additional peaks appear between 7.7 and 8.7 ppm, indicating the presence of disordered regions. (c) Chemical shift perturbation (CSP) values plotted along the PulGp sequence in the presence of calcium and EGTA. Proline residues and unassigned amide resonances in the calcium bound state are highlighted with blue arrows. Resonances affected by the presence of EGTA with unambiguous assignment are indicated with red dots. Dashed lines at 0.2 ppm and 0.04 ppm correspond to thresholds for labeling residues with significant CSP values. (d) PulGp

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2.2 NMR Experiments

1. NMR tube—3 or 5 mm diameter. 2. NMR spectrometer (500 MHz or higher field). 3. TopSpin software (Bruker). 4. 1D 1H pulse sequence. 5. 1H-15N HSQC pulse sequence. 6. Automatic pipettes and tips.

3

Methods The 1H and 15N chemical shift assignments for the backbone of the studied protein should be obtained through a set of specific NMR experiments [32] or be downloaded from the Biological Magnetic Resonance Bank (https://bmrb.io/). However, without assignments, the experiments can still be performed to obtain information on the folding, the stability, and interaction of the system, although the nature of the amino acid residues involved in the molecular interactions cannot be identified. All the NMR experiments must be performed on an isotopically 15 N-labeled protein. The ligand must not be 15N-labeled. All the commands used here are specific to Bruker NMR spectrometers, even though all the experiments can be run with any NMR instrument.

3.1 Sample Preparation

1. Prepare at least 600 μL of your protein sample in the appropriate buffer (see Note 1). Protein concentration must be between 50 and 100 μM (see Note 2). Add 5–15% (v/v) of D2O (see Note 3). 2. Prepare the ligand solution (see Note 4). Both protein and ligand must be in the exact same buffer to avoid changes in buffer conditions that could also cause chemical shift changes.

3.2 NMR Experiments

1. Add 600 μL of your free protein sample (with 5–15% v/v D2O) into the NMR tube, taking care to avoid air bubbles (see Note 5). 2. Before inserting the tube into the NMR spectrometer (see Note 6), check the probe temperature and adjust it as necessary (see Note 7). After inserting the tube, wait until the temperature is stable. Typical temperatures for protein analysis by NMR are between 20 and 30 °C (close to the physiological temperature).

ä Fig. 4 (continued) structure where residues exhibiting significant CSP values are colored in dark blue (CSP > 0.2 ppm) and light blue (CSP > 0.04 ppm). The calcium ion is shown as a yellow sphere and side chains of Ca2+ coordinating residues are displayed as sticks. (Figure adapted from [25])

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1. First, you need to acquire a 1D 1H spectrum for an initial assessment of protein folding and stability. 2. Create a new experiment by typing EDC in the TopSpin command line (see Note 8). Choose the appropriate name for the experiment and add notes, such as sample conditions, in the window. For the solvent, choose “H2O + D2O” and for the experiment, you can use zgesgp or any 1D 1H pulse sequence you may prefer. 3. Lock the magnetic field using the command LOCK, and choose the “H2O + D2O” option in the pop-up window. 4. Tune and match the 15N and 1H frequencies in an automatic mode typing ATMA. 5. Shim using the automatic Topshim tool. Type TOPSHIM GUI in the command line, click on the START button and wait until it finishes. 6. Measure the 90° proton pulse by typing PULSECAL. Note down the pulse length and power. 7. Click on the AcquPars tab to check and modify the parameters accordingly. The recommended parameters are: NS = 2, DS = 16, SW = 14 ppm, and O1P = 4.7 ppm. Usually, you do not need to change any other parameters. If needed, you can increase NS and decrease SW (see Note 9). When you measure the 90° pulse in the experiment window, it will be set automatically. 8. Adjust the gain by typing RGA. 9. Start the acquisition by typing ZG. 10. At the end of the acquisition, use the appropriate script to process the spectrum. For processing 1D experiments on a Bruker spectrometer using a pre-set script, type PRO or EFP. 11. Adjust the peak phase. Use the command apk for automatic phasing. You can also do it manually by clicking on Process/ Adjust Phase, a tab containing phasing parameters will open. Phase the peaks with the zero- and first-order phase corrections and save. 12. Make a first inspection of the proton spectrum, observing chemical shift dispersion and linewidth. If you observe peaks with chemical shifts lower than 0.0 ppm and higher than 9.0 ppm, it is indicative that your protein is well structured (see Note 10). Broadened linewidths indicate protein aggregation or oligomerization. In this case, you should screen solvent conditions (see Note 11).

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3.2.2 Mapping Ligand– Protein Binding and Interfaces

1. Create a new experiment (see step 2 in Subheading 3.2.1) for the 1H-15N-HSQC acquisition. You can use hsqcetf3gpsi or any HSQC pulse sequence that you prefer. 2. No need to lock, shim, tune and match, or measure the 90° pulse again if you performed the 1D experiment immediately before. 3. Click on the AcquPars tab to check and modify the parameters, including the 90° pulse obtained previously (see step 6 in Subheading 3.2.1). The recommended parameters are NS = 4, DS = 16, and SW = 14 ppm (1H dimension) and 40 ppm (15N dimension), O1P = 4.7 ppm, and O2P = 118 ppm. Usually, you do not need to change any other parameters. However, to improve the peak intensity, you can increase NS and decrease SW (see Note 9). 4. Adjust the gain by typing RGA. 5. Start the acquisition by typing ZG. 6. When acquisition finishes, use the appropriate script to process the spectrum. For processing 2D experiments on Bruker equipment, type XFB. 7. Adjust peak phase. Use the command apk2d for automatic phasing. You can also do it manually by clicking on Process/ Adjust Phase, a tab containing phasing parameters will open. Right-click on a peak and select Add, then repeat for another peak at a distant region of the spectrum. Select the R icon to extract the rows. Phase the peaks with the zero- and first-order phase corrections, as for 1D phasing, and save. 8. First, inspect the HSQC spectrum (see Note 12), observing the dispersion of the chemical shifts and the linewidth (see Note 10). 9. Acquire three 1H-15N HSQC spectra for the free protein and use them to determine the experimental error. 10. The ligand can be titrated into the protein or you can add a molar excess of the ligand with respect to the protein (at least a 2:1 ratio) (see Notes 15 and 16). 11. Acquire a new 1D 1H and 2D 1H-15N-HSQC spectra for each ligand concentration added to the protein sample. You may have as many titration points as you want and until saturation (see Note 16).

3.2.3

CSP Measurement

1. Determine the chemical shift perturbations (CSP) that occur upon interaction of the ligand in the HSQC spectra of the 15 N-labeled protein (Fig. 1b). It is calculated by comparing the chemical shifts in the 1H-15N-HSQC spectrum of the free protein to those in the spectrum collected at the highest ligand/protein molar ratio. It is recommended to work in a saturating condition for most of the residues (see Note 16).

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2. Use CSP =

the 1 2

2

following

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equation:

2

Δ∂HN þ ð0:15 × Δ∂N Þ , where ΔδHN and ΔδN

are the chemical shift differences of 1H and 15N, respectively, recorded in the absence and presence of the ligand. CSP values may be represented as shown in Figs. 1c, 3c, and 4c. 3. The standard deviation (SD) of the average weighted Δδ (i.e., CSP) values for all assigned amides can be used as a cutoff to identify the protein residues involved in ligand binding. Residues displaying statistically significant CSP values (higher than the average plus one standard deviation) are considered directly affected by ligand binding and might be part of the binding surface (see Note 17). The highest CSPs are usually observed for residues in direct interaction with the ligand. Other CSPs can reflect conformational/dynamic changes of the protein upon ligand binding.

4

Notes 1. Optimizing buffer conditions might be necessary to ensure that all protein signals are observed in the HSQC spectrum. Using lower pHs may help to increase the number of signals observed in the spectrum. This is explained by the fact that solvent-accessible amide hydrogens are exchanged with the solvent protons, therefore, their corresponding signals become invisible. This phenomenon is decreased at acidic pH (lowest exchange at pH 3, in H2O and at 25 °C) [33]. 2. High-concentration protein samples will require more concentrated ligand solutions. This could be a problem if the ligand has low solubility. Working around 50–100 μM might be a good compromise between protein NMR signal intensity, experiment time, and ligand concentration. 3. Whether you choose a 3 mm NMR tube, use 15% D2O for better shimming. 4. The ligand concentration should be as high as possible, to avoid significant changes in protein concentration caused by dilution during titration. If the ligand solution is not stable at a high concentration, it can be lyophilized and added as a powder to the NMR tube solution. 5. If there are air bubbles in your sample inside the NMR tube, try to remove them by gently tapping the bottom of the tube or by sonication. 6. Before inserting the tube, you must check if its position in the spinner is adjusted correctly by using the appropriate tool. To insert the tube, type ej (eject) in the TopSpin command line,

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then place the tube with the spinner in the center of the air exit and type ij (inject). 7. To check or modify the temperature in Bruker equipment, type EDTE in the command line. 8. To create a new experiment by typing EDC, you must have another experiment opened in the TopSpin window. After creating the new experiment, if it is not a 1D 1H, you must choose the correct pulse sequence by clicking on the three points button on the right of the PULPROG parameter (pulse program for acquisition) and choose the zgesgp or any 1D 1H pulse sequence you want. 9. If the peak intensity is too low, you can increase the number of scans (NS) to improve signal intensity. The sweep width (SW) is the length of the frequency window and must be adjusted to each protein. Start by using SW = 14 ppm (1H dimension); 40 ppm (15N dimension, in the HSQC experiment), check the chemical shifts of the signals that lie in the extremities of the spectrum and adjust SW so the window starts and finishes just after these signals. If no or very few signals are detected in the HSQC spectrum, either the sample is not 15N-labeled or the protein is aggregated. 10. Chemical shift dispersion can vary from protein to protein related to the secondary structure content. Proteins that are rich in alpha-helical secondary structures are prone to low chemical shift dispersion. 11. If a screening of solvent conditions is needed, you may use an unlabeled protein sample to screen using 1D 1H experiments. Always check for chemical shift dispersion and linewidths. 12. It is important to first inspect the HSQC spectrum to evaluate protein behavior. Pay attention to the chemical shift dispersion, signal linewidth, and the number of peaks. In the HSQC spectrum, a signal for each NH pair of the protein should be observed. To determine the total number of peaks, you might see in this spectrum: (a) Consider the number of residues in your protein or domain. (b) Add two for each glutamine and asparagine (for the side chain amine groups). (c) Add one per tryptophan (for the indole group). (d) Subtract the number of prolines and the N-terminal residue. 13. The HSQC spectrum of the protein alone can be used to check the stability of the sample over time and as a control for the binding experiment. The reproducibility (superimposition of

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peaks with comparable intensities) of the HSQC experiment of the protein sample (without the ligand) at different time shows the stability of the sample in the experimental conditions. It also shows that the spectral variation upon the addition of the ligand is not due to a degradation or a modification of the protein. 14. Check the pH after the addition of the ligand and adjust it if needed. A pH modification, even a very small (>0.2 pH unit), can modify the spectrum and bias the chemical shift perturbation analysis. This is due to protonation changes of charged residues and their environment. Typically, around the physiological pH (6.5–7.5), histidine residues can display different chemical shifts. 15. If you plan to use NMR titration to calculate binding affinity, use at least six titration points [34]. 16. You can verify whether a saturating condition for a specific residue is achieved by observing chemical shift changes upon ligand addition. If the NH peak position does not change in a given ligand concentration, the corresponding residue is considered to be saturated. 17. If an experimental or predicted structure of the protein is available, CSP measurements can be mapped on the structure using standard molecular graphics software, such as Pymol (http://www.pymol.org) or UCSF Chimera (https://www. cgl.ucsf.edu/chimera/). Either highlight the most perturbed residues or put the CSP values in the B-factor column of a PDB file to color the molecular surface with an increasing color-scale (as shown in Fig. 3d).

Acknowledgements This work was supported by the French Agence Nationale de la Recherche (ANR) under grants Synergy-T2SS ANR-19-CE110020-01 and Energir ANR-21-CE11-0039. References 1. Rapisarda C, Fronzes R (2018) Secretion systems used by bacteria to subvert host functions. Curr Issues Mol Biol 25:1–42. https:// doi.org/10.21775/cimb.025.001 2. Wang Y, Zhang H, Zhong H, Xue Z (2021) Protein domain identification methods and online resources. Comput Struct Biotechnol J 19:1145–1153. https://doi.org/10.1016/j. csbj.2021.01.041

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Cell Factories 19:85. https://doi.org/10. 1186/s12934-020-01339-8 5. Bodenhausen G, Ruben DJ (1980) Natural abundance nitrogen-15 NMR by enhanced heteronuclear spectroscopy. Chem Phys Lett 69:185–189. https://doi.org/10.1016/ 0009-2614(80)80041-8 6. Malki I, Simenel C, Wojtowicz H et al (2014) Interaction of a partially disordered antisigma factor with its partner, the signaling domain of the TonB-dependent transporter HasR. PLoS One 9:e89502. https://doi.org/10.1371/ journal.pone.0089502 7. Williamson MP (2013) Using chemical shift perturbation to characterise ligand binding. Prog Nucl Magn Reson Spectrosc 73:1–16. https://doi.org/10.1016/j.pnmrs.2013. 02.001 8. de Vries SJ, van Dijk M, Bonvin AM (2010) The HADDOCK web server for data-driven biomolecular docking. Nat Protoc 5:883– 897. https://doi.org/10.1038/nprot. 2010.32 9. van Dijk AD, Boelens R, Bonvin AM (2005) Data-driven docking for the study of biomolecular complexes. FEBS J 272:293–312. https://doi.org/10.1111/j.1742-4658.2004. 04473.x 10. de Amorim GC, Prochnicka-Chalufour A, Delepelaire P et al (2013) The structure of HasB reveals a new class of TonB protein fold. PLoS One 8:e58964. https://doi.org/10. 1371/journal.pone.0058964 11. Escobar CA, Douzi B, Ball G et al (2021) Structural interactions define assembly adapter function of a type II secretion system pseudopilin. Structure 29(1116–1127):e1118. https://doi.org/10.1016/j.str.2021.05.015 12. Berry JL, Phelan MM, Collins RF et al (2012) Structure and assembly of a trans-periplasmic channel for type IV pili in Neisseria meningitidis. PLoS Pathog 8:e1002923. https://doi. org/10.1371/journal.ppat.1002923 13. Sharifahmadian M, Nlend IU, Lecoq L, Omichinski JG, Baron C (2017) The type IV secretion system core component VirB8 interacts via the beta1-strand with VirB10. FEBS Lett 591: 2491–2500. https://doi.org/10.1002/ 1873-3468.12770 14. Oka GU, Souza DP, Cenens W et al (2022) Structural basis for effector recognition by an antibacterial type IV secretion system. Proc Natl Acad Sci U S A 119:e2112529119. https://doi.org/10.1073/pnas.2112529119 15. Dazzoni R, Li Y, Lopez-Castilla A et al (2023) Structure and dynamic association of an assembly platform subcomplex of the bacterial type II

secretion system. Structure 31:152–165.e7. https://doi.org/10.1016/j.str.2022.12.003 16. Hu W, Anand G, Sivaraman J, Leung KY, Mok YK (2014) A disordered region in the EvpP protein from the type VI secretion system of Edwardsiella tarda is essential for EvpC binding. PLoS One 9:e110810. https://doi.org/ 10.1371/journal.pone.0110810 17. Jacobsen T, Dazzoni R, Renault MG et al (2022) Secondary structure and (1)H, (15) N & (13)C resonance assignments of the periplasmic domain of OutG, major pseudopilin from Dickeya dadantii type II secretion system. Biomol NMR Assign 16:231–236. https://doi. org/10.1007/s12104-022-10085-4 18. Kato J, Dey S, Soto JE et al (2018) A protein secreted by the salmonella type III secretion system controls needle filament assembly. elife 7:e35886. https://doi.org/10.7554/eLife. 35886 19. Bardiaux B, de Amorim GC, Luna Rico A et al (2019) Structure and assembly of the enterohemorrhagic Escherichia coli type 4 pilus. Structure 27(1082–1093):e1085. https://doi. org/10.1016/j.str.2019.03.021 20. Keizer DW, Slupsky CM, Kalisiak M et al (2001) Structure of a pilin monomer from Pseudomonas aeruginosa: implications for the assembly of pili. J Biol Chem 276:24186– 24193. https://doi.org/10.1074/jbc. M100659200 21. Shahin M, Sheppard D, Raynaud C et al (2023) Characterization of a glycan-binding complex of minor pilins completes the analysis of Streptococcus sanguinis type 4 pili subunits. Proc Natl Acad Sci U S A 120:e2216237120. https://doi.org/10.1073/pnas.2216237120 22. Ramboarina S, Garnett JA, Zhou M et al (2010) Structural insights into serine-rich fimbriae from Gram-positive bacteria. J Biol Chem 285:32446–32457. https://doi.org/10. 1074/jbc.M110.128165 23. Garnett JA, Muhl D, Douse CH et al (2015) Structure-function analysis reveals that the Pseudomonas aeruginosa Tps4 two-partner secretion system is involved in CupB5 translocation. Protein Sci 24:670–687. https://doi. org/10.1002/pro.2640 24. Sewell L, Stylianou F, Xu Y et al (2020) NMR insights into the pre-amyloid ensemble and secretion targeting of the curli subunit CsgA. Sci Rep 10:7896. https://doi.org/10.1038/ s41598-020-64135-9 25. Lopez-Castilla A, Thomassin JL, Bardiaux B et al (2017) Structure of the calciumdependent type 2 secretion pseudopilus. Nat

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Chapter 31 Use of Bastion for the Identification of Secreted Substrates Jiawei Wang, Jiahui Li, and Christopher J. Stubenrauch Abstract Bacteria use secretion systems to translocate numerous proteins into and across cell membranes, but have evolved more specialized secretion systems that can disrupt the normal cellular processes of host cells and compete bacteria or protect the bacteria from host defenses. Among them, Gram-negative bacteria utilize a variety of different proteins secreted by Type 1 to Type 6 secretion systems to transfer substrates into target cells or the surrounding environment, which play key roles in disease and survival. Therefore, these secreted proteins have attracted the attention of a wealth of researchers. The first step to characterizing new substrates of secretion systems is typically identifying candidates bioinformatically, and the Bastion series of substrate predictors provide biologists machine learning tools that can accurately predict these substrates. This chapter will explain how to use the Bastion series for identifying and analyzing secreted substrates in Gram-negative bacteria. Key words Bastion, Effector prediction, Secretion system, Bioinformatics, Machine learning

1

Introduction Within the bacterial armory are a number of weapons, called secretion systems, that bacteria deploy for a range of tasks: from invading host cells and attaching to surfaces to releasing toxins that damage host cells to release vital nutrients [1]. To date, there are ten known different types of secretion systems, where the first six (Type 1 to Type 6 secretion systems; abbreviated as T1SS to T6SS, respectively) are among the best studied nanomachines in bacteria. Given the important role secretion systems play in bacteria, the identification of secreted proteins can offer useful insights into their biological processes. Many of the substrates of secreted systems lack a canonical signal that targets them to a particular secretion system [2], so wet-lab experimental studies that aim to identify novel secreted proteins are difficult and time-consuming. Fortunately, machine learning techniques have become one of the most

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_31, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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effective ways to uncover novel substrates with a relatively quick turn-around, which can significantly accelerate experimental validation. For the above reasons, a series of predictors to identify substrates of each secretion system have been developed [3, 4]. Among these, the Bastion series (Bacterial secreted effectors of secretion systems) are widely used [5–7] and still well maintained. We previously developed the predictors Bastion3 [8], Bastion4 [9], and Bastion6 [10] to identify novel secreted proteins from type 3, type 4, and type 6 secretion systems in Gram-negative bacteria, respectively. Subsequently, BastionX (https://bastionx.erc.monash. edu/) was developed to predict multi-type secreted substances (types I, II, III, IV, and VI secreted proteins; type V was excluded because they are typically not released from the bacterial cell surface). BastionX is also available through BastionHub [11], a universal platform to provide an all-in-one service for users to analyze known substrates, predict novel proteins, and provide the services to compare the relationship between the known and unknown proteins within the type I, II, III, IV or VI secretion systems (Fig. 1). This chapter will therefore explore these three features of BastionHub.

Fig. 1 The framework of BastionHub (Reproduced from Ref. [11] with permission from Oxford University Press)

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Fig. 2 (a) the navigation bar and options of three major parts in BastionHub, and (b) results for the Browse function

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Method BastionHub is an online integrative platform (https://bastionhub. erc.monash.edu/) that is organized into three major parts (Fig. 2a): the Substrate investigation module (Browse, Search, Statistics, Download, and Detailed information functions), the Prediction module (HMM-based prediction and BastionX prediction), and the Relationship analysis module (Similarity analysis, Phylogenetic analysis, and Homology network analysis).

2.1 Known Substrate’s Analysis

The Substrate investigation module is organized into five parts: Browse, Search, Statistics, Download, and Detailed information functions (Fig. 2a). Step 1.1: Click Substrate investigation module and choose the patterns (Fig. 2a). 2.1.1 If users want to check the basic information of known substrates collected, please click Browse (Fig. 2a, b). This presents lists of known type I, II, III, IV, VI secreted substrates and is summarized with a BastionHub ID, Gene Name, Brief

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Description, Species, UniProt ID, NCBI Protein ID, and PubMed ID. Click the embedded links within the gene name or any of the four IDs, and users will be redirected to that substrate’s Detailed information page for comprehensive annotations and analyses hosted on BastionHub or to the relevant section within Uniprot, NCBI, or Pubmed depending on which link was selected. Users can also sort or search within the known proteins using the control strip (Fig. 2b, red frame), which comprises five parts (Search box, Refresh button, Toggle button, Columns button, and Download button). 2.1.2 If users want to find their interested sequences through keywords, please click Search. This provides users with more advanced search options than those available within the Browse page (Fig. 3). The search function allows exact queries in ID Search, or broader queries (that don’t require exact matches) using keywords. And there is the drop-down filter option to further refine results according to features (Fig. 3a, red frame). 2.1.3 If users want to check their statistics data, please click Statistics: this provides multiple options to visualize various types of known substrate proteins (Fig. 4). – Secretion type distribution: shows specific numbers of proteins and proportions (an information box will appear, when the mouse pointer moves over the pie chart) of each secretion system (Fig. 4a). – Species distribution: uses ring diagrams to show the distribution of the species of substrates (an information box will appear, when the mouse pointer moves over each part) according to bacterial species. Clicking the drop-down box could switch different secretion systems. And clicking any part of the ring diagram to be redirected to the summary list of relevant parts. Further, users can customize the ring diagram they want to display by clicking on the species color block (Fig. 4b, red frame). – Phylogenetic tree and Homology network: shows the genetic relationships and homologues of each known substrate within each selection system. And the link of each branch or dot will redirect to their corresponding Detailed information pages (Fig. 4c, d). 2.1.4 If users want to download data for further investigation and research, they could use Download. This provides multiple options for users to download files, including the whole database (in SQL format), sequences (in FASTA format), disorder files, and multiple sequence alignments. 2.1.5 If users want to view comprehensive information of interested protein(s), they could enter into Detailed

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Fig. 3 The page of Search in BastionHub: (a) Search and (b) Search results

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Fig. 4 The page of statistics in BastionHub: (a) Secretion type distribution, (b) Species distribution, (c) Phylogenetic tree, and (d) Homology network

information (Fig. 5). This provides detailed annotations for each substrate comprising their basic information, advanced annotations, and relationship analyses among their associated type of known substrates. Basic information consists of their UniProt ID, NCBI ID, gene name, brief description, secretion system type, species, gene ontology terms, function, sequence, length, and PubMed ID. For advanced annotations, we incorporated conserved domains, interactive 3D protein structures, predicted disorder area, molecule processing and post-translational modification information, metabolic pathway summaries, enzymatic and metabolic pathway details, mutagenesis results, pathogen-host interactions, protein-protein interactions, and protein families. Finally, we included five pre-calculated relationship analyses for each substrate: lists of 100% identical proteins, and similar proteins within

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Fig. 5 The page of Detailed information in BastionHub

BastionHub (if available), multiple sequence alignments, a phylogenetic tree, and a homology network. 2.2 Novel Substrate Prediction

The Prediction module can be divided into two parts, which are the HMM-based prediction to identify substrates that are similar to known substrates or BastionX prediction to identify novel substrates. Step 2.1: Click Prediction module and choose the prediction pattern (Fig. 6): HMM-based prediction. This uses HMMER to predict potential substrates for preliminary control screening. These HMM-based predictors are lightweight, rapid, and are ideal for even genome-scale lists of protein sequences, but will only retrieve the homologues of known substrates. BastionX prediction. This aims to achieve accurate prediction of various types of secreted substrates, especially those with relatively distant relationships from known substrates. (BastionX has an independent site which offers the same service at https://bastionx.erc.monash.edu/.) Step 2.2: Submit the interested protein sequence(s). There are two options for users to submit the sequence(s), which are to copy the sequence(s) directly to submit box or submit the FASTA file through the “Choose File” button (see Note 1).

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Fig. 6 The input page of BastionX and usage guidance

Step 2.3: Choose the interested pattern of prediction. Both HMM and BastionX prediction methods have different options for users to select: Usage, Version (BastionX only), Type (BastionX only), and Mode (BastionX V1.0 only). Usage: (1) Select for common use if users want to identify new substrates. This filters the query proteins prior to model prediction by using a built-in list of experimentally validated secreted substrates, or (2) Select for benchmarking test if users want to compare our predictor in benchmarking tests to other predictors. This disables the built-in list of experimentally validated substrate proteins, in order to retrieve the prediction stores for all query proteins. Version (BastionX only): (1) V1.0: This is the old vision of BastionX for a historical record. (2) V2.0: The latest vision of BastionX (to be published). Type (BastionX only): Considering the ease of use, the BastionX module provides different choices to users for predictions of secreted proteins. Researchers could choose the types of prediction from All (five types) to either type (T1SS, T2SS, T3SS, T4SS, and/or T6SS). Mode (BastionX V1.0 only): When predicting substrates, accuracy comes at the cost of time. We therefore have three modes to choose from that ranges in the time it takes to output the results. (1) Fast mode for rapid prediction, which is suitable for a preliminary screen of substrates amongst a large number of protein sequences, (2) Accurate mode for accurate prediction, which is suitable for accurate identification of target proteins, but is the slowest option, and (3) Balanced mode for a balance between prediction accuracy and speed.

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Fig. 7 The prediction results of BastionX in BastionHub

Step 2.4 (optional): Submit E-mall address and organization for the completion of task. Step 2.5: Successful job submission and status prompt. The page of Job submitted means the prediction of inquiry sequence submits successfully and shows user the information of the Job (Job ID, Job Name, Usage, Mode, Email, Organization, Sequence Number, and Job Status). This page will refresh every second. Step 2.6: Prediction results (Fig. 7). Protein info: provides Number (No.) and input name extracted from headline of inquiry sequence(s). Prediction Results based on Single Type Predictors: shows the prediction scores of each single type predictor. Prediction Results based on Final Ensemble Model: shows the final results of inquiry sequence(s), the possible secretion system, and type (distinguishes known substrates with Exp. and the computationally predicted ones with Pred.). And the protein(s) marked with Exp. has an embedded link to its Detailed Information page (see Note 2). 2.3 The Relationship Analysis Between the Known and the Novel Substrates

BastionHub provides options for users to transfer between different modules, including from prediction to prediction modules (HMM to BastionX only), from prediction to Relationship analysis module, and from any computational modules to detailed pages of homologous known substrates (see Note 3).

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Fig. 8 The results of HMM based prediction in BastionHub

Step 3.1 Choose further analysis (Fig. 8). From prediction to prediction: At the HMM-based prediction results page, users could easily select a set of target proteins (Fig. 8, red frame), and redirect them into a BastionX prediction module for more accurate prediction. From prediction to relationship: At the HMM or BastionX prediction results page, users could easily select a set of target proteins (Fig. 8, orange frame), and redirect them into the Relationship analysis module (Fig. 9). Similarity analysis (Fig. 9a): for potential substrates, their similar sequences can be searched against a user-selected BastionHub substrate dataset (i.e., type I, II, III, IV, or VI substrates) to check if they are homologous to any known substrate. All hits to known substrates for each potential substrate will be listed and sorted according to their similarity significance. Clicking any of the known substrates will jump to its pair-wise alignments against the query protein, where the corresponding BastionHub ID link can redirect users to the Detailed information page for the known substrate. Phylogenetic analysis (Fig. 9b): recognizes the closest relationships of inquiry proteins against a user-selected substrate dataset and visualizes by a phylogenetic tree where the query proteins are highlighted in orange, and links to the known substrates (identified using its BastionHub ID)

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Fig. 9 The results of functional analysis in BastionX: (a) similarity analysis, (b) phylogenetic analysis, and (c) homology network analysis

will redirect users to their corresponding Detailed information pages. Homology network analysis (Fig. 9c): maps potential secreted substrates onto a user-selected substrate dataset to provide a landscape of their locations amongst known substrates and interactive network can be used to identify the closest homologues of each potential secreted substrate indicated by red diamonds. Clicking any edge in the network will show the pairwise sequence alignments (Fig. 9c, dotted boxes) between the two linked known substrates, while links to the known substrates will redirect users to their corresponding Detailed information pages.

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Notes 1. Input sequence limits: (1) The length of each submitted sequence should be in the range of 30 and 5000; (2) Considering that substrate prediction is a little bit time-consuming, the maximum number of sequences allowed for each submission by the BastionX server should be no more than 50,000. And these limitations don’t apply to the BastionX Standalone Software. 2. The resulting interface of HMM-based prediction is the same as that of the BastionX prediction, except that users can select proteins from the HMM prediction results page and redirect them to the BastionX predictor. Otherwise, both predictors can redirect into the Relationship analysis module (see step 3.1 for details). 3. Relationship analysis module could do individual use by users and the way of submission is the same as in the Prediction module.

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Conclusion In this chapter, we show how to use Bastion series for the identification and analysis of secreted proteins from T1SS to T6SS (T5SS excluded) in Gram-negative bacteria. We hope this will facilitate researchers better opt their strategies and workflows to identify novel secreted substrates and validate their functions.

Acknowledgments J.W. is a recipient of Marie Skłodowska-Curie Postdoctoral Fellowship, EMBL Interdisciplinary Postdoctoral (EIPOD) Fellowship and EMBO Non-Stipendiary Fellowship (EMBO ALTF 400-2022), and a Junior Research Fellow at Wolfson College, the University of Cambridge, UK. C.J.S. is an Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) Fellow (DE230100700). References 1. Costa TR, Felisberto-Rodrigues C, Meir A et al (2015) Secretion systems in Gram-negative bacteria: structural and mechanistic insights. Nat Rev Microbiol 13:343–359. https://doi. org/10.1038/nrmicro3456 2. Lee YW, Wang J, Newton HJ, Lithgow T (2020) Mapping bacterial effector arsenals: in vivo and in silico approaches to defining the protein features dictating effector secretion

by bacteria. Curr Opin Microbiol 57:13–21. https://doi.org/10.1016/j.mib.2020.04.002 3. An Y, Wang J, Li C et al (2018) Comprehensive assessment and performance improvement of effector protein predictors for bacterial secretion systems III, IV and VI. Brief Bioinform 19: 148–161. https://doi.org/10.1093/bib/ bbw100

Bioinformatic Identification of Secreted Effectors 4. Zeng C, Zou L (2019) An account of in silico identification tools of secreted effector proteins in bacteria and future challenges. Brief Bioinform 20:110–129. https://doi.org/10.1093/ bib/bbx078 5. Sibinelli-Sousa S, Hespanhol JT, Nicastro GG et al (2020) A family of T6SS antibacterial effectors related to l,d-transpeptidases targets the peptidoglycan. Cell Rep 31:107813. https://doi.org/10.1016/j.celrep.2020. 107813 6. Hespanhol JT, Sanchez-Limache DE, Nicastro GG et al (2022) Antibacterial T6SS effectors with a VRR-Nuc domain are structure-specific nucleases. elife 11:e82437. https://doi.org/ 10.7554/eLife.82437 7. Serapio-Palacios A, Woodward SE, Vogt SL et al (2022) Type VI secretion systems of pathogenic and commensal bacteria mediate niche occupancy in the gut. Cell Rep 39:110731. https://doi.org/10.1016/j.celrep.2022. 110731

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8. Wang J, Li J, Yang B et al (2019) Bastion3: a two-layer ensemble predictor of type III secreted effectors. Bioinformatics 35:2017– 2028. https://doi.org/10.1093/bioinformat ics/bty914 9. Wang J, Yang B, An Y et al (2019) Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches. Brief Bioinform 20:931–951. https://doi.org/10.1093/bib/bbx164 10. Wang J, Yang B, Leier A et al (2018) Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors. Bioinformatics 34:2546–2555. https://doi.org/10.1093/ bioinformatics/bty155 11. Wang J, Li J, Hou Y et al (2021) BastionHub: a universal platform for integrating and analyzing substrates secreted by gram-negative bacteria. Nucleic Acids Res 49:D651–D6D9. https://doi.org/10.1093/nar/gkaa899

Chapter 32 Identification of Effectors: Precipitation of Supernatant Material Nicolas Flaugnatti and Laure Journet Abstract Bacterial secretion systems allow the transport of proteins, called effectors, as well as external machine components in the extracellular medium or directly into target cells. Comparison of the secretome, i.e., the proteins released in the culture medium, of wild-type and mutant cells provides information on the secretion profile. In addition, mass spectrometry analyses of the culture supernatant of bacteria grown in liquid culture under secreting conditions allow the identification of secretion systems substrates. Upon identification of the substrates, the secretion profile serves as a tool to test the functionality of secretion systems. Here, we present a classical method used to concentrate the culture supernatant, based on TCA precipitation. Key words Supernatant, TCA precipitation, Secretome

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Introduction Bacterial secretion systems are macromolecular machines dedicated to the transport of proteins across the cell envelope. These secretion systems deliver effectors outside the cell, either in the medium (T1SS, T2SS, T5SS, T6SS, T7SS, and T9SS) or directly into target cells (T3SS, T4SS, and T6SS) [1]. Secretion of effector proteins into the milieu can be observed in these systems, and the analysis of secretion supernatant has been widely used either to identify new secreted effectors or to probe the functionality of secretion systems. For contact-dependent system such as the T3SS, in vitro secretion in the medium can be observed upon certain conditions (e.g., Ca2+ depletion or acidic pH) [2, 3]. If effectors can be predicted by bioinformatics approaches for several of these secretion systems (see Chaps. 2 and 31), it is not always possible, and analysis of the content of the culture media, the so-called secretome, using global proteomic approaches has been widely used to identify secretion

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system substrates in T2SS [4–7], T6SS [8–10], T3SS [11], T9SS [12] and T7SS [13]. Upon identification of the substrates, the secretion profile is used to test the functionality of the secretion system using SDS-PAGE of supernatant fraction followed by Coomassie blue staining or immuno-staining by Western blot detection of specific effectors or components of the machinery. In some secretion system, such as T3SS or T6SS, structural external components are released in the milieu upon secretion and can also be used to test the proper assembly of the system. For example, the Hcp release assay is widely used to probe the functionality of T6SS [8]. Such analyses of secretomes require to concentrate the dilute solutions that are the culture supernatant or the biological fluids. This can be achieved using trichloroacetic acid (TCA) precipitation and acetone-based protocols [14, 15]. Alternative protocols have been proposed, using acetone alone, methanol/chloroform [16], ethanol [17], or a combination of pyrogallol red, molybdate, and methanol [18]. Here, we detail the most classical assay used to precipitate proteins of bacterial culture supernatant based on TCA precipitation that is used thoroughly in secretion systems studies. First, cells and supernatant are separated by centrifugation. Cell-free culture supernatant fraction samples are then obtained by further centrifugation and filtration and subjected to TCA precipitation before analysis by mass spectrometry or Western blot.

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Materials 1. Lysogeny Broth (LB) or the recommended medium to grow the strain of interest in secreting conditions. 2. TCA (CCl3COOH, MW: 163.39, TCA): 100% (w/v). Add 227 mL of ultrapure water to previously unopened bottle containing 500 g of TCA (see Note 1). Wear personal protective equipment and work under a fume hood. 3. Sodium Deoxycholate (DOC): 16 mg/mL (optional, see Note 2). Store at room temperature. 4. Bovine Serum Albumine (BSA): 100 μg/mL (optional, see Note 3). 5. Acetone. Pre-chill before use. 6. Refrigerated centrifuge capable of 21,460× g or table top centrifuge (see Note 4). 7. 0.22-μm-pore-size syringe filters (see Note 5). 8. 2-mL syringe. 9. 3 M Tris–HCl, pH 8.8

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10. SDS-PAGE loading buffer: 60 mM Tris–HCl, pH 6.8, 2% SDS, 10% glycerol, 5% β-mercaptoethanol, 0.01% bromophenol blue. 11. Boiling water bath or thermomixer. 12. Vortexer. 13. 2-mL microtubes (safe-lock) (see Note 4) 14. Fume hood and personal protective equipment for TCA handling. 15. Spectrophotometer to measure absorbance at λ = 600 nm. 16. SDS-PAGE and protein transfer apparatus.

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Methods 1. Grow a 10 mL bacterial strain culture in the appropriate medium and conditions allowing secretion (see Notes 6 and 7). Measure the optical density at λ = 600 nm (OD600). 2. Dispose the culture in 2 mL microtubes (see Note 8) and pellet cells by centrifugation at 6000× g for 5 min. 3. Carefully remove 1.8 mL of supernatant and transfer it in a new microtube and keep it on ice before performing step 5. 4. Carefully discard the remaining 200 μL of supernatant from the cell pellet obtained in step 3. (Centrifuge again at 6000× g for 5 min if the cells from the cell pellet started to resuspend). Keep this total cell fraction pellet on ice before resuspending the pellet in an appropriate volume of SDS-PAGE loading buffer (the equivalent of 0.2–0.5 OD600 units (ODU)/10 μL). Store on ice (or at -20 °C). 5. Centrifuge the 1.8 mL supernatant fraction obtained in step 3 at 16,000× g at 4 °C for 5 min. Carefully recover the supernatant and transfer it in a new microtube. Avoid recovering the remaining cells from the pellet, if any. 6. Filter-sterilize the supernatant using a syringe 0.22-μm filter and transfer the filtered supernatant directly in a new microtube. Check the volume (around 1.5 mL). This fraction constitutes the cell-free fraction (see Note 9). 7. Add TCA to a final concentration of 20% (add 375 μL of TCA to 1.5 mL of filtered supernatant). Invert four times to mix, vortex and keep on ice for 1 h to overnight. 8. Centrifuge at 21,000× g for 30 min at 4 °C. 9. Discard the supernatant as much as possible (see Note 10). 10. Resuspend the pellet in 400–500 μL cold acetone. Vortex.

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11. Centrifuge at 21,000× g for 15 min at 4 °C. Discard the supernatant with a pipette and further on a paper towel. Leave the tube open at room temperature to dry the pellet (see Note 11). 12. Resuspend the pellet in appropriate buffer for further analysis (such as mass spectrometry) or go to step 13 for SDS-PAGE analysis. 13. Resuspend TCA-precipitated pellets of supernatant fractions in an appropriate volume of SDS-PAGE loading buffer (1 ODU/ 10 μL). If the TCA-precipitated sample turns yellow, add 1 μL (or more) of Tris–HCl, pH 8.8. 14. Vortex. Heat the samples from step 4 (whole-cell fraction) and step 13 (cell-free supernatant precipitated fraction) at 95 °C for 10 min. (see Note 12). 15. Analyze whole cell samples and cell free supernatants by SDS-PAGE, followed by coomassie blue staining or immunoblot. If performing western blot, include a control for cell lysis, using antibodies detecting an internal protein. Alternatively, check the coomassie or silver staining profile.

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Notes 1. For a safe and easy preparation, avoid weighting out the TCA crystalline powder as it becomes easily syrupy upon contact with air moisture. The TCA solution must be kept in a dark glass bottle. It is very corrosive and should be handled with care with suitable protection. Do not use plastic containers. 2. DOC may be used as a carrier to assist protein precipitation. If using DOC, add the DOC stock solution at the final concentration of 0.16 mg/mL to the cell-free fraction obtained in step 6, vortex, and leave on ice for 30 min, then proceed to TCA precipitation as described in step 7. DOC should be washed out with further acetone washing steps (repeat steps 10 and 11 three times). However, it could be a problem with further mass spectrometry analysis. 3. Optional. If you want to have a precipitation efficiency control (to compare different sample conditions): add a protein with a known concentration such as BSA at the final concentration of 100 μg/mL to the cell-free fraction obtained in step 6, vortex, and leave on ice for 30 min, then proceed to TCA precipitation as described in step 7. Check the precipitation of your reference protein in your different conditions by western blot using anti-BSA antibodies [19].

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4. Tabletop centrifuge at maximum speed may be sufficient; however, we generally use a higher speed. TCA-resistant tubes should be used, such as Eppendorf tubes (check with your manufacturer for tube compatibility). 5. In principle, any 0.22 μm filter may be used. However, we had experience with a secreted protein that was retained on polyvinylidene fluoride filters, so we moved to Polyether sulfone (PES) filters. Be aware that material of the filter may be of importance. Take into account that the filtration of the supernatant will reduce the final volume of the sample. 6. A “non-secreting strain” should be used as a control, such as a mutant in a core component, the ATPase energizing the assembly of the secretion machinery or the substrate transport. 7. You must find conditions where secretion can be detected in vitro. Because effectors can be secreted at low levels, high sensitivity mass spectrometry methods may be required [10]. If the secretion system is not produced in laboratory conditions, native endogenous promoter(s) may be swapped for an inducible promoter (e.g., Ptac, Plac, and PBAD) to artificially induce the expression of the secretion system [20]. 8. A 5–10 mL of culture is generally sufficient. We usually transfer 2 mL of supernatant in 2-mL microtubes, leading to the recovery of 1.5 mL of cell free supernatant. An equivalent of 1 OD600 unit will be loaded on the gel for supernatant fractions analysis. To scale up experiments, you will have to use tubes with larger volumes compatible with high-speed spin and that are resistant to TCA. Appropriate 50 mL tubes (polyether) may be used; check first with your manufacturer for TCA compatibility. 9. At this stage, for bacteria producing high levels of vesicles (e.g., for T9SS in Bacteroidetes), an additional ultracentrifugation (30,000× g for 4 h at 4 °C) will allow separation of vesicles from vesicle-free supernatant [12]. 10. Check the orientation of the microtube before the centrifugation step since the pellet is not always visible at this stage. 11. You may use a vacuum concentrator (SpeedVac or equivalent) for 10 min to evaporate the acetone. However, pellets may be more difficult to resuspend if too dry, and this step may decrease recovery of the samples. 12. In some cases, we have observed that an additional freezing at -20 °C in SDS-PAGE loading buffer helps resuspension of TCA precipitates.

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Acknowledgments Work of L.J. was supported by the Centre National de la Recherche Scientifique, the Aix-Marseille Universite´, and grants from the Agence Nationale de la Recherche (ANR-14-CE14-0006 and ANR-18-CE15-0013). PhD studies of N.F. were supported by the ANR-14-CE14-0006 grant. References 1. Costa TR, Felisberto-Rodrigues C, Meir A et al (2015) Secretion systems in Gram-negative bacteria: structural and mechanistic insights. Nat Rev Microbiol 13:343–359 2. Cornelis GR, Biot T, Lambert de Rouvroit C et al (1989) The Yersinia yop regulon. Mol Microbiol 3:1455–1459 3. Beuzon CR, Banks G, Deiwick J, Hensel M, Holden DW (1999) pH-dependent secretion of SseB, a product of the SPI-2 type III secretion system of Salmonella typhimurium. Mol Microbiol 33:806–816 4. Coulthurst SJ, Lilley KS, Hedley PE, Liu H, Toth IK, Salmond GP (2008) DsbA plays a critical and multifaceted role in the production of secreted virulence factors by the phytopathogen Erwinia carotovora subsp. atroseptica. J Biol Chem 283:23739–23753 5. Kazemi-Pour N, Condemine G, HugouvieuxCotte-Pattat N (2004) The secretome of the plant pathogenic bacterium Erwinia chrysanthemi. Proteomics 4:3177–3186 6. Sikora AE, Zielke RA, Lawrence DA, Andrews PC, Sandkvist M (2011) Proteomic analysis of the Vibrio cholerae type II secretome reveals new proteins, including three related serine proteases. J Biol Chem 286:16555–16566 7. Burtnick MN, Brett PJ, DeShazer D (2014) Proteomic analysis of the Burkholderia pseudomallei type II secretome reveals hydrolytic enzymes, novel proteins, and the deubiquitinase TssM. Infect Immun 82:3214–3226 8. Hood RD, Singh P, Hsu F, Gu¨vener T et al (2010) A type VI secretion system of Pseudomonas aeruginosa targets a toxin to bacteria. Cell Host Microbe 7:25–37 9. Russell AB, Singh P, Brittnacher M et al (2012) A widespread bacterial type VI secretion effector superfamily identified using a heuristic approach. Cell Host Microbe 11:538–549 10. Fritsch MJ, Trunk K, Diniz JA, Guo M, Trost M, Coulthurst SJ (2013) Proteomic identification of novel secreted antibacterial toxins of the Serratia marcescens type VI secretion system. Mol Cell Proteomics 12:2735– 2749

11. Deng W, de Hoog CL, Yu HB et al (2010) A comprehensive proteomic analysis of the type III secretome of Citrobacter rodentium. J Biol Chem 285:6790–6800 12. Veith PD, Chen YY, Gorasia DG et al (2014) Porphyromonas gingivalis outer membrane vesicles exclusively contain outer membrane and periplasmic proteins and carry a cargo enriched with virulence factors. J Proteome Res 13:2420–2432 13. Ulhuq FR, Gomes MC, Duggan GM et al (2020) A membrane-depolarizing toxin substrate of the Staphylococcus aureus type VII secretion system mediates intraspecies competition. Proc Natl Acad Sci U S A 117:20836– 20847 14. Hwang BJ, Chu G (1996) Trichloroacetic acid precipitation by ultracentrifugation to concentrate dilute protein in viscous solution. BioTechniques 20:982–984 15. Ozols J (1990) Amino acid analysis. Methods Enzymol 182:587–601 16. Wessel D, Flu¨gge UI (1984) A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids. Anal Biochem 138:141–143 17. Pumirat P, Saetun P, Sinchaikul S, Chen ST, Korbsrisate S, Thongboonkerd V (2009) Altered secretome of Burkholderia pseudomallei induced by salt stress. Biochim Biophys Acta 1794:898–904 18. Caldwell RB, Lattemann CT (2004) Simple and reliable method to precipitate proteins from bacterial culture supernatant. Appl Environ Microbiol 70:610–612 19. Cheng AT, Ottemann KM, Yildiz FH (2015) Vibrio cholerae response regulator VxrB controls colonization and regulates the type VI secretion system. PLoS Pathog 11:e1004933 20. Gueguen E, Cascales E (2013) Promoter swapping unveils the role of the Citrobacter rodentium CTS1 type VI secretion system in interbacterial competition. Appl Environ Microbiol 79:32–38

Chapter 33 Metabolic Labeling: Snapshot of the Effect of Toxins on the Key Cellular Processes Dukas Jure˙nas Abstract Competing bacteria secrete vast variety of toxic effectors via secretion systems. Phospholipase, peptidoglycan-hydrolase, or pore forming toxins often manifest in the bursting of the prey cell. Other toxins reach cytoplasm of the prey where they affect cell division machinery, metabolism, nucleic acid integrity, or protein synthesis. Inhibition of cell division or DNA integrity, which summons SOS response, will often lead to bacterial cell filamentation readily observable under the microscope. However, other toxic activities will not manifest in interpretable phenotypic changes that would readily suggest their mechanism of toxicity. Activity measurements of the three fundamental cellular processes—replication, transcription and translation can pave the way for further understanding of the toxin’s activity. Method commonly known as metabolic labeling makes use of radioactive precursors for DNA, RNA and protein synthesis. This method provides highly sensitive snapshot of the activity of key cellular processes. Key words Replication, Transcription, Translation, Metabolic labeling, Bacterial toxins

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Introduction Synthesis of nucleic acids and proteins are at the essence of the cellular activity, they are highly conserved, and are thus among the most common targets of antibiotics and toxins [1–4]. The most direct way to estimate global changes in replication, transcription, and translation is to follow the rate of newly synthesized DNA, RNA, and proteins in vivo. Metabolic labeling is a method that uses chemical analogs of molecular building blocks (nucleotides, amino acids, or sugars) supplied in the cell culturing media. Isotopelabeled building blocks are primary choice since they adopt native chemical structure, are readily incorporated into macromolecules, and allow highly sensitive measurement of metabolic rates. The use of [3H]-thymidine or [3H]-uridine allows distinguishing between replication and transcription rates, while the [35S]-L-methionine or a mix of [35S]-L-methionine and [35S]-L-cysteine are commonly

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_33, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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Fig. 1 General scheme of metabolic labeling experiment. Overnight cultures are diluted to start fresh cultures and their optical densities are followed. At early exponential phase, aliquots of culture are incubated with radioactive precursor of DNA, RNA, or protein synthesis. The reaction is quenched, and macromolecules are precipitated in cold 10% TCA. Precipitated macromolecules are trapped on filters, while unincorporated label is washed out with excess of 10% TCA. Filters are immersed in scintillation liquid and signal is registered in scintillation counter. Results are corrected for optical density and plotted in the counts per minute (cpm) over time graph. (Figure was prepared using pictures from Servier Medical Art, licensed under a Creative Common Attribution 3.0 Generic License)

used to follow the rates of protein synthesis. While the 35S isotope loses signal over time due to fast radioactive decay (with half-life of 87 days) and thus requires fresh stocks delivered from the vendor, alternatively, translation rate can also be followed using [3H]labeled amino acids or [14C]-labeled amino acids [5–8]. Metabolic labeling experiments typically compare the rates of DNA, RNA, and protein synthesis before and after the induction of the toxins. For this reason, the toxin gene or a toxic domain of the effector is cloned under inducible promoter for controlled heterologous expression. Control and toxin-bearing cells are then cultivated in defined chemical medium until the exponential growth when the toxin expression is induced. Aliquots of cell cultures are sampled prior and at different times post expression of the toxin. Cells are incubated with radio-labeled precursors for a short pulse-time allowing their incorporation into newly synthesized macromolecules. Macromolecules are then precipitated with cold trichloroacetic acid and trapped on nitrocellulose filters. Filters are washed to remove excess of free label and the radioactivity is quantified by liquid scintillation counting (Fig. 1). In the exponentially growing cells, the effect of a toxin that directly affects one of the three essential processes is usually imminent and is visible as fast as 10–20 min after the induction of the toxin.

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While examples of secreted replication inhibitors have not yet been described, DNA polymerase or topoisomerase inhibitor toxins are common in bacterial toxin–antitoxin systems [9–11]. Replication inhibiting antibiotics such as ciprofloxacin or nalidixic acid are typically used as controls for [3H]-thymidine labeling. Direct transcription inhibitors have also not been described among secreted toxins so far, but RNA polymerase is a common target of antibiotics produced by bacteria and fungi [2]. Most commonly, antibiotic rifampicin is used as a control in [3H]-uridine incorporation assays. First direct translation inhibitors secreted by the type VI secretion system (T6SS) [8, 12, 13] and by the type V secretion system (T5SS) [14] have been recently described. Typical controls for protein synthesis inhibition in [35S]-L-methionine incorporation assays are antibiotics such as chloramphenicol, gentamicin, or tetracycline. Importantly, toxins that exhaust nucleotide or NAD (P) pools will affect all three processes. For example, the ppApp synthase toxin Tas1 drains the ATP pool and quickly affects replication, transcription, and translation [15], while a NADase toxin was shown to first inhibit transcription, then replication and later translation [16]. It is important to note that inhibition of one or another crucial process will eventually to some extent affect the other cellular processes [17, 18]. Surprisingly, in some cases, it was noticed that translation inhibition leads to increased transcription, which is explained by abrogation of nucleotide consumption upon translation arrest [15, 19]. As a result, in some cases, it might be necessary to follow kinetics of incorporation rates in order to gain full picture of the effect of a toxin.

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Materials Metabolic labeling experiments require special premises and training to handle radioactive materials. Experimenters should wear protective gloves and coat, and manipulate behind the protective plexiglass screens. All generated waste must follow strict regulations for radioactive waste disposal. Careful planning of the experiments should favor generation of minimal waste. Experimenter and premises should be regularly checked for contamination using Geiger counter. 1. Minimal M9 Media (48 mM Na2HPO4, 22 mM KH2PO4, 9 mM NaCl, 19 mM NH4Cl, 0.5% glucose, 1% casamino acids, and 4 μM vitamin B1), supplied with appropriate antibiotics, and at chosen moment supplied with inducers (typically 0.02–0.2% arabinose, 0.1–1 mM IPTG, or other). 2. Stock solutions for control inhibitors of replication, transcription, and translation, e.g., ciprofloxacin (final concentration 10 μg/mL), rifampicin (100 μg/mL), and chloramphenicol (30 μg/mL).

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3. Radio-labeled precursors e.g., [3H]-thymidine, [3H]-uridine, and [35S]-methionine (Perkin Elmer), typically 1–5 μCi per mL of sample; and corresponding unlabeled thymidine, uridine, and methionine for chasing (see Note 1). 4. Prechilled 10% TCA solution, aliquoted by 5 mL in 15 mL plastic falcons and stored on ice. 5. Nitrocellulose filters or GF/C grade glass microfiber discs (Whatman, Pall, Millipore, Fisher Scientific or similar). 6. Glass vacuum filtration device with glass funnel corresponding to the filter diameter. 7. Liquid scintillation cocktail, such as Ecolite(+)™ (MP Biochemicals), OptiPhase HiSafe (PerkinElmer), or similar, 5–10 mL per vial. 8. Scintillation counter (Beckmann, PerkinElmer, Hidex or similar).

3

Methods 1. Starting from overnight bacterial cultures, inoculate fresh cultures to obtain starting A600 of 0.02 in liquid M9 medium supplemented with appropriate antibiotics. If you are using catabolite repression sensitive systems, add 0.5% glucose to repress toxin induction in the pre-growth phase. You might need to remove glucose by washing the cells before the induction. 2. Prepare enough 15 mL falcon tubes with 5 mL of 10% TCA for each measured sample and store them in the ice bucket. Also prepare enough of cold 10% TCA for washing the samples (about 20 mL per each sample). 3. Prepare Eppendorf tubes with aliquots of radioactivity—typically 1–2 μCi of [3H]-thymidine and [3H]-uridine, and 3–5 μCi [35S]-methionine is used per 1 mL of culture sample. Dilute the labels to an easy-to-handle volume of the stock solution that can be easily aliquoted to Eppendorf for mixing with cultures. 4. At the OD of 0.2–0.4 aliquot, the first timepoint, pipet 1 mL of each culture into tubes containing [35S]-methionine for translation or [3H]-uridine or [3H]-thymidine for transcription or replication. Incubate your cells for a pulse of 5 minutes with radioactivity and then transfer the sample into a tube with 5 mL of 10% TCA. Precipitate the samples in TCA for at least 30 min at 4 °C. Samples can stay on ice for longer time; it should not affect the measurements.

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5. Meanwhile, induce the toxin synthesis with arabinose (0.02–0.2%) or IPTG (0.1–1 mM) (see Note 2). For positive control, use antibiotics that inhibit replication, transcription, or translation. At chosen times after the induction aliquot 1 mL of cultures to the Eppendorf tubes with label, incubate for 5 min and follow with precipitation step (see Note 3). 6. For the background measurement, use 1 mL of media incubated with the same amounts of labels as per measurements. Treat the controls in the same way as samples—precipitate in TCA and follow the next steps. 7. Once all the samples at all timepoints have been taken and all samples were precipitated for at least 30 min, capture the precipitated macromolecules on nitrocellulose or glass microfiber filters. Trap a filter between the flask and the glass funnel and clasp it with securing clip so that the liquid passes only through the filter. Attach the system to the vacuum pump. First, pass the 5 mL sample, then wash two times with 10 mL of 10% TCA. 8. Air dry the filters and immerse each filter in separate scintillation tubes prefilled with 5–10 mL of scintillation liquid. Be sure that the filter is completely immersed. 9. For positive control, to measure the maximum intensity of the label, inject the same amount of radioactivity that you used per sample, directly into a tube of scintillation liquid. This measurement should yield much higher counts than any of the samples, if it is not the case—increase the amount of label (see Note 4). 10. Arrange the tubes in the racks of scintillation counter machine and measure the counts per minute on the appropriate channel (3H, 35S, or 14C depending on the label used) (see Note 5). 11. Plot the measurements as counts per minute or express the rate of each process in percentage as compared to the control without the toxin, i.e., a strain carrying empty cloning vector.

4

Notes 1. It is preferable to chase the radioactive precursor pulse with cold (non-radioactive) homologue—nucleotide or amino acid. Several methods are described in literature—at the end of incubation, excess of cold precursor is added to the sample and incubated for five additional minutes [6, 20, 21], the cold homologue is added in washing TCA solution [22, 23] or filters are pre-soaked in cold homologue [24]. Nevertheless, the chase is optional and generally, method works without the chase. 2. To measure the effect of the toxin, choose gentle induction system, favor low copy vector, and moderate induction system, since high level of prolonged expression of many proteins can

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result in undesired effects on cell physiology, although it might not be biologically relevant. 3. Although generally considered as inoffensive method, it has been demonstrated that radiolabeling can be cytotoxic, affecting DNA integrity and cell cycle [25–27]. Therefore, it should only be used for short pulses prior measurement, rather than prolonged incubations. 4. If signal is too low, consider (i) increasing counting time from your already prepared samples, (iii) pro-longing the incorporation time of your sample with radioactivity if your protocol allows it, and (ii) adding higher quantity of radioactive precursorRadioactive precursors. 5. In cases when scintillation counter is not available, incorporation can be quantified by pelleting the precipitated cells, spotting the samples onto Whatman paper, exposing to a photostimulable phosphor screen, scanning with phosphor imager, and quantifying the spot intensity. In cases of measurements of protein synthesis rate, pelleted samples can also be subjected to SDS-PAGE and measured by autoradiography [22, 28].

Acknowledgments Work of D.J. was supported by a Research Grant 2022 from the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) and FNRS-MIS grant (F.4526.23) from Fonds de la Recherche Scientifique (FNRS). References 1. Santos JA, Lamers MH (2020) Novel antibiotics targeting bacterial replicative DNA polymerases. Antibiotics (Basel) 9:776. https:// doi.org/10.3390/antibiotics9110776 2. Kirsch SH, Haeckl FPJ, Mu¨ller R (2022) Beyond the approved: target sites and inhibitors of bacterial RNA polymerase from bacteria and fungi. Nat Prod Rep 39:1226–1263. https://doi.org/10.1039/d1np00067e 3. Jure˙nas D, Van Melderen L (2020) The variety in the common theme of translation inhibition by type II toxin-antitoxin systems. Front Genet 11:262. https://doi.org/10.3389/fgene. 2020.00262 4. Polikanov YS, Aleksashin NA, Beckert B, Wilson DN (2018) The mechanisms of action of ribosome-targeting peptide antibiotics. Front Mol Biosci 5:48. https://doi.org/10.3389/ fmolb.2018.00048

5. Halvorsen EM, Williams JJ, Bhimani AJ et al (2011) Txe, an endoribonuclease of the enterococcal Axe-Txe toxin-antitoxin system, cleaves mRNA and inhibits protein synthesis. Microbiology (Reading) 157:387–397. https://doi.org/10.1099/mic.0.045492-0 6. Lioy VS, Martı´n MT, Camacho AG et al (2006) pSM19035-encoded zeta toxin induces stasis followed by death in a subpopulation of cells. Microbiology (Reading) 152:2365– 2379. https://doi.org/10.1099/mic.0. 28950-0 7. Garcia-Pino A, Christensen-Dalsgaard M, Wyns L et al (2008) Doc of prophage P1 is inhibited by its antitoxin partner Phd through fold complementation. J Biol Chem 283: 30821–30827. https://doi.org/10.1074/jbc. M805654200

Metabolic Labeling Using Radioactive Precursors 8. Bullen NP, Sychantha D, Thang SS et al (2022) An ADP-ribosyltransferase toxin kills bacterial cells by modifying structured non-coding RNAs. Mol Cell 82:3484–3498.e11. https:// doi.org/10.1016/j.molcel.2022.08.015 9. Jaffe´ A, Ogura T, Hiraga S (1985) Effects of the ccd function of the F plasmid on bacterial growth. J Bacteriol 163:841–849. https://doi. org/10.1128/jb.163.3.841-849.1985 10. Dao-Thi M-H, Van Melderen L, De Genst E et al (2005) Molecular basis of gyrase poisoning by the addiction toxin CcdB. J Mol Biol 348:1091–1102. https://doi.org/10.1016/j. jmb.2005.03.049 11. Aakre CD, Phung TN, Huang D, Laub MT (2013) A bacterial toxin inhibits DNA replication elongation through a direct interaction with the β sliding clamp. Mol Cell 52:617– 628. https://doi.org/10.1016/j.molcel. 2013.10.014 12. Jure˙nas D, Payelleville A, Roghanian M et al (2021) Photorhabdus antibacterial Rhs polymorphic toxin inhibits translation through ADP-ribosylation of 23S ribosomal RNA. Nucleic Acids Res 49(14):8384–8395. https://doi.org/10.1093/nar/gkab608 13. Jure˙nas D, Rey M, Byrne D et al (2022) Salmonella antibacterial Rhs polymorphic toxin inhibits translation through ADP-ribosylation of EF-Tu P-loop. Nucleic Acids Res 50: 13114–13127. https://doi.org/10.1093/ nar/gkac1162 14. Michalska K, Gucinski GC, Garza-Sa´nchez F et al (2017) Structure of a novel antibacterial toxin that exploits elongation factor Tu to cleave specific transfer RNAs. Nucleic Acids Res 45:10306–10320. https://doi.org/10. 1093/nar/gkx700 15. Kurata T, Brodiazhenko T, Alves Oliveira SR et al (2021) RelA-SpoT Homolog toxins pyrophosphorylate the CCA end of tRNA to inhibit protein synthesis. Mol Cell 81:3160– 3170.e9. https://doi.org/10.1016/j.molcel. 2021.06.005 16. Skjerning RB, Senissar M, Winther KS et al (2019) The RES domain toxins of RES-Xre toxin-antitoxin modules induce cell stasis by degrading NAD+. Mol Microbiol 111:221– 236. https://doi.org/10.1111/mmi.14150 17. Bhattacharya S, Sarkar N (1981) Inhibition of deoxyribonucleic acid replication in Bacillus brevis by ribonucleic acid polymerase inhibitors. J Bacteriol 145:1442–1444. https://doi. org/10.1128/jb.145.3.1442-1444.1981 18. Sharp JD, Cruz JW, Raman S et al (2012) Growth and translation inhibition through

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sequence-specific RNA binding by Mycobacterium tuberculosis VapC toxin. J Biol Chem 287:12835–12847. https://doi.org/10. 1074/jbc.M112.340109 19. Jimmy S, Saha CK, Kurata T et al (2020) A widespread toxin-antitoxin system exploiting growth control via alarmone signaling. Proc Natl Acad Sci U S A 117:10500–10510. https://doi.org/10.1073/pnas.1916617117 20. Robson J, McKenzie JL, Cursons R et al (2009) The vapBC operon from Mycobacterium smegmatis is an autoregulated toxinantitoxin module that controls growth via inhibition of translation. J Mol Biol 390:353–367. https://doi.org/10.1016/j.jmb.2009.05.006 21. Winther KS, Brodersen DE, Brown AK, Gerdes K (2013) VapC20 of Mycobacterium tuberculosis cleaves the sarcin-ricin loop of 23S rRNA. Nat Commun 4:2796. https://doi. org/10.1038/ncomms3796 22. Zhang J, Zhang Y, Zhu L et al (2004) Interference of mRNA function by sequence-specific endoribonuclease PemK. J Biol Chem 279: 20678–20684. https://doi.org/10.1074/jbc. M314284200 23. Itsko M, Schaaper RM (2014) dGTP starvation in Escherichia coli provides new insights into the thymineless-death phenomenon. PLoS Genet 10:e1004310. https://doi.org/ 10.1371/journal.pgen.1004310 24. Christensen SK, Maenhaut-Michel G, Mine N et al (2004) Overproduction of the Lon protease triggers inhibition of translation in Escherichia coli: involvement of the yefMyoeB toxin-antitoxin system. Mol Microbiol 51:1705–1717. https://doi.org/10.1046/j. 1365-2958.2003.03941.x 25. Minor RR (1982) Cytotoxic effects of low levels of 3H-, 14C-, and 35S-labeled amino acids. J Biol Chem 257:10400–10413 26. Hu VW, Heikka DS (2000) Radiolabeling revisited: metabolic labeling with (35)Smethionine inhibits cell cycle progression, proliferation, and survival. FASEB J 14:448–454. https://doi.org/10.1096/fasebj.14.3.448 27. Hu VW, Black GE, Torres-Duarte A, Abramson FP (2002) 3H-thymidine is a defective tool with which to measure rates of DNA synthesis. FASEB J 16:1456–1457. https://doi.org/10. 1096/fj.02-0142fje 28. Cheverton AM, Gollan B, Przydacz M et al (2016) A salmonella toxin promotes persister formation through acetylation of tRNA. Mol Cell 63:86–96. https://doi.org/10.1016/j. molcel.2016.05.002

Chapter 34 Effector Translocation Assay: Differential Solubilization Irina S. Franco, Sara V. Pais, Nuno Charro, and Luı´s Jaime Mota Abstract The identification of effector proteins delivered into mammalian host cells by bacterial pathogens possessing syringe-like nanomachines is an important step towards an understanding of the mechanisms underlying virulence of these pathogens. In this chapter, we describe a method based on mammalian tissue culture infection models where incubation with a non-ionic detergent (Triton X-100) enables solubilization of host cell membranes but not of bacterial membranes. This allows the isolation of a Triton-soluble fraction lacking bacteria but enriched in proteins present in the host cell cytoplasm, nucleus, and plasma membrane. Using appropriate controls, this fraction can be probed by immunoblotting for the presence of bacterial effector proteins delivered into host cells. Key words Bacterial protein secretion system, Type III secretion, Effector; translocation, Detergent solubilization, SDS-PAGE, Immunoblotting

1

Introduction Gram-negative bacteria possess different macromolecular structures, known as type III, type IV, or type VI secretion systems, for delivery of effector proteins directly from the bacterial cytoplasm into eukaryotic or prokaryotic host cells [1–3]. This protein delivery or injection process is normally described as “effector translocation.” Demonstration that a particular bacterial effector protein is injected into mammalian host cells during infection is not a trivial task, as effectors are often delivered in minute amounts and can be short-lived within the host cell. Classical assays to monitor bulk effector translocation include the use of Bordetella pertussis calmodulin-dependent adenylate cyclase [4] or mature TEM-1 β-lactamase [5] reporter assays (described in Chap. 35). Throughout the years, many other methods have been developed not only to monitor bulk effector translocation but also to track real-time delivery of effector proteins into host cells [6]. Here, we describe a method for assessing effector translocation into mammalian cells by differential solubilization. It consists in the infection of tissue culture cells by

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_34, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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a bacterial pathogen, followed by lysis of the infected mammalian cells using a detergent that does not affect the integrity of bacterial membranes. The non-ionic detergents Triton X-100 [7] and digitonin [8] have been widely used for this purpose, based on the inability of Triton X-100 to solubilize outer membranes of Gramnegative bacteria (although it can solubilize inner membranes) [9– 11], and on the specificity of digitonin for cholesterol-rich membranes [12]. Subsequent high-speed centrifugation allows the separation of detergent-soluble (supernatant) and insoluble components (pellet) of the lysate, where the supernatant comprises cytoplasmic and plasma membrane components (including delivered effector proteins) and the pellet retains unbroken bacteria and nuclei that remained intact. Analysis of these fractions by immunoblotting allows confirming the presence of an effector protein of interest in the supernatant fraction, which is taken as evidence of effector translocation. A critical control is also probing for a bacterial protein that is not delivered into host cells. This ensures that during experimental manipulation, there was no contamination of the supernatant fraction with cytosolic bacterial proteins. Differential solubilization can be applied to monitor effector translocation by different Gram-negative bacteria and types of host cells. Furthermore, if effector-specific antibodies are available, it can be used to monitor translocation of endogenously expressed and non-modified effector proteins. This is in contrast with other effector translocation assays that require the modification of the gene encoding the effector to produce a protein with an epitope tag or fused to a reporter protein, usually expressed from a plasmid and often from an exogenous promoter. The differential solubilization procedure using Triton X-100 is illustrated by the two protocols detailed below, consisting in monitoring type III secretion (T3SS)mediated translocation (1) of the Yersinia enterocolitica effector YopE, expressed from its own promoter using a non-modified wild-type strain to infect RAW 264.7 murine macrophage-like cells (see Fig. 1), and (2) of the Salmonella enterica serovar Typhimurium (S. Typhimurium) effector SteA with a C-terminal double hemagglutinin epitope tag (SteA-2HA), expressed from its own promoter but encoded in an exogenous low-copy plasmid, during infection of HeLa cells (see Fig. 2). While YopE is translocated by extracellular Yersinia into host cells [4], SteA can also be translocated into the host cell cytoplasm from intracellular Salmonella residing within a membrane-bound vacuole [13], which further illustrates the versatility of the procedure.

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Fig. 1 Effector translocation by Yersinia enterocolitica during infection of RAW 264.7 macrophage-like cells. RAW 264.7 cells were infected by wild-type (wt) or T3SS-defective (yscN mutant) Y. enterocolitica bacterial strains. Triton-soluble and Triton-insoluble fractions from uninfected cells (UI) and from cells infected by wt or yscN mutant (ΔYscN) bacteria were prepared as described in Subheadings 2.1 and 3.1. The sample fractions were analyzed by SDS-PAGE and immunoblotting as described in Subheadings 2.2, 2.3, and 3.3. YopE is a Y. enterocolitica effector protein; SycO is a Y. enterocolitica T3S chaperone [18], used to control for possible significant bacterial cross-contamination of the Triton-soluble fraction (and as a loading control of the Triton-insoluble fraction); tubulin is a host cell protein, used as a loading control of the Tritonsoluble fraction

Fig. 2 Effector translocation by Salmonella enterica serovar Typhimurium (S. typhimurium) during infection of HeLa cells. HeLa cells were infected by S. Typhimurium steA mutant bearing a plasmid encoding C-terminal 2×HA epitopetagged wild-type SteA (SteAWT-2HA) or mutant SteA with lysine residue 36 replaced by alanine (SteAK36A-2HA). Triton-soluble and Triton-insoluble fractions from cells infected by the two strains were prepared as described in Subheadings 2.1 and 3.2 (see Note 29). The samples were analyzed by SDSPAGE and immunoblotting as described in Subheadings 2.2, 2.3, and 3.3. SteA is a Salmonella effector protein [13, 17], DnaK is a bacterial molecular chaperone, used to control for possible significant bacterial cross-contamination of the Triton-soluble fraction (and as a loading control of the Triton-insoluble fraction), and tubulin is a host cell protein used as a loading control of the Triton-soluble fraction. Note the detection of residual levels of tubulin in the Triton-insoluble fraction

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Materials (See Note 1)

2.1 Cell Culture, Infection, and Preparation of Cell Extracts

1. Cell lines: HeLa (clone HtTA-1) and RAW 264.7 cells (European Collection of Authenticated Cell Cultures, ECACC). 2. Bacterial strains and plasmids: Y. enterocolitica E40 (pYV40) (wild-type) and Y. enterocolitica E40 (pMSL41) (yscNΔ169–177; deficient in the YscN ATPase that is essential for the activity of the Yersinia T3SS) [14], S. Typhimurium steA mutant (an isogenic derivative of S. Typhimurium strain NCTC 12023 [identical to ATCC 14208s]) [15], carrying low-copy pWSK129-derived plasmids (six to eight copies per cell; [16] expressing C-terminal 2xHA epitope-tagged wild-type SteA (SteAWT-2HA) or mutant SteA with lysine residue 36 replaced by alanine (SteAK36A-2HA) under the control of the steA promoter [13, 17]. 3. Dulbecco’s Modified Eagle’s Media (DMEM) supplemented with 10% (v/v) heat-inactivated fetal bovine serum (FBS) (DMEM + FBS), stored at 4 °C. Commercial 500 mL bottles of heat-inactivated FBS are stored at -20 °C. The FBS is thawed by incubation at 4 °C during 48 h, followed by preparation of aliquots in 50 mL tubes stored at -20 °C. To prepare DMEM + FBS, the aliquots are thawed in a 37 °C water bath and added to a commercial 500 mL bottle of DMEM. Do not add antibiotics to cell culture medium. 4. Earle’s Buffered Salt Solution pH 7.4 (EBSS). Store at room temperature. 5. TrypLE™ Express (ThermoFischer Scientific). Store at room temperature. 6. Phosphate-buffered saline (PBS 1×): 137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 1.8 mM KH2PO4, pH 7.4. Store at room temperature. Prepared by diluting a stock of commercial PBS 10× in double-distilled water (ddH2O), followed by sterilization by autoclaving. 7. Nalidixic acid 3.5 mg/mL: dissolve appropriate amount in 0.1 M NaOH and filter (0.22 μm) sterilize. Store at -20 °C. Keep working aliquots at 4 °C. 8. Kanamycin 50 mg/mL: dissolve appropriate amount in ddH2O and filter (0.22 μm) sterilize. Store at -20 °C. Keep working aliquots at 4 °C. 9. Lysogeny broth (LB) medium: dissolve appropriate amount of LB powder in ddH2O and sterilize by autoclaving. Store at room temperature. Freshly supplemented with kanamycin (to 50 μg/mL) to grow S. typhimurium strains.

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10. LB agar: Dissolve appropriate amount of LB powder in ddH2O, add agar to 1.6% (w/v) and sterilize by autoclaving (store at room temperature). Allow to cool to 55 °C, and add adequate amounts of nalidixic acid (to 35 μg/mL) or kanamycin (to 50 μg/mL) to grow Y. enterocolitica or S. typhimurium strains, respectively. The plates can be stored at 4 °C for up to 2 months. 11. Brain heart infusion (BHI) medium: Dissolve appropriate amount of BHI powder in ddH2O and sterilize by autoclaving. Store at room temperature. Freshly supplemented with nalidixic acid (to 35 μg/mL) to grow Y. enterocolitica strains. 12. Triton X-100, stock solution at 10% (v/v) in PBS 1× (stored at 4 °C): Incubate Triton X-100 at 37 °C for 30 min, within the biological safety cabinet measure an adequate volume of Triton X-100 and add it to appropriate volume of sterile PBS 1× (e.g., 5 mL of Triton X-100 to 45 mL of PBS 1× in a 50 mL tube), mix well, and incubate 30 min at 37 °C. 13. Gentamicin 10 mg/mL. Store at 4 °C. 14. Protease inhibitor cocktail. Store at -20 °C. 15. CO2 incubator, microbiology incubators, class II biological safety cabinet, water bath, shaking water bath with adjustable temperature, mini-centrifuges. 2.2 Sodium Dodecyl Sulfate Polyacrylamide Gel (SDS-PAGE)

1. 1.5 M Tris-HCl, pH 8.8: Dissolve an appropriate amount of Tris base in ddH2O, adjust pH to 8.8 with HCl, adjust to desired volume with ddH2O, and sterilize by autoclaving. Store at room temperature. 2. 1.0 M Tris-HCl, pH 6.8: Dissolve an appropriate amount of Tris base in ddH2O, adjust pH to 8.8 with HCl, adjust to desired volume with ddH2O, and sterilize by autoclaving. Store at room temperature. 3. Acrylamide/bis-acrylamide (37.5:1 solution). Store at 4 °C. 4. SDS 20% (w/v): Dissolve an appropriate amount of SDS in ddH2O. It is not required to sterilize the solution. Store at room temperature. 5. Ammonium persulfate (APS) 10% (w/v). Store at 4 °C (see Note 2). 6. N, N, N, N′-tetramethyl-ethylenediamine (TEMED). Store at 4 °C. 7. 12% SDS polyacrylamide gel electrophoresis (PAGE) gels (for 2 mini-gels): Prepare resolving gel: 6.5 mL of H2O, 4.5 mL of acrylamide/bis-acrylamide (37.5:1 solution), 3.8 mL of 1.5 M Tris-HCl, pH 8.8, 75 μL of SDS 20% (w/v), 150 μL 10% (w/v) APS, 6 μL of TEMED (see Note 3). After polymerization,

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prepare stacking gel: 7.34 mL H2O, 1.25 mL of acrylamide/ bis-acrylamide (37.5:1 solution), 1.25 mL 1 M Tris-HCl, pH 6.8, 50 μL SDS 20% (w/v), 100 μL of APS 10% (w/v), 10 μL TEMED (see Note 4). 8. Protein Molecular Weight Marker. Store at -20 °C (see Note 5). 9. Tris-glycine buffer: 0.025 M Tris, 192 mM glycine, 0.1% (w/v) SDS. Prepare a 10× Tris-glycine stock solution without SDS (0.25 M Tris and 1.92 M glycine, using adequate amount of Tris base, glycine, and ddH2O). Store at room temperature. This stock solution is used to prepare the Tris-glycine running buffer, using adequate amounts of ddH2O and 20% (w/v) SDS. Store at room temperature. 10. SDS-PAGE loading buffer 5×: 0.25 M Tris-HCl, pH 6.8, 10% (w/v) SDS, 50% (v/v) glycerol, 0.5 M β-mercaptoethanol, 0.5% (w/v) bromophenol blue. Store at -20 °C. 11. SDS-PAGE mini-gel caster and migration apparatus. 2.3

Immunoblotting

1. Transfer buffer: 0.025 M Tris, 192 mM glycine, and 20% (v/v) methanol. Store at room temperature. 2. PBS 10×: 1.37 M NaCl, 0.027 M KCl, 0.1 M Na2HPO4, 0.02 M KH2PO4. Weigh the adequate amounts of each of the reagents, dissolve in ddH2O, adjust to final volume, and sterilize by autoclaving. Store at room temperature. 3. Washing solution (PBS-T): PBS 1× containing 0.2% (v/v) Tween-20. Store at room temperature. 4. Blocking Solution: PBST containing 4% (w/v) skim milk powder: Dissolve the appropriate amount of skim milk powder in PBST (see Note 6). Prepare fresh and store at 4 °C for up to 2 days. 5. Stripping buffer: 25 mM glycine, pH 2, 1% (w/v) SDS: Dissolve an adequate amount of glycine in ddH2O, adjust pH to 2 with HCl, add 20% (w/v) SDS to a final concentration of 1% (w/v), and adjust to desired volume using ddH2O. Store at room temperature. 6. Nitrocellulose membranes, 0.2 μm pore-size (see Note 7). 7. Ponceau solution: 0.1% (w/v) Ponceau solution in 0.5% (v/v) acetic acid: Dissolve Ponceau S in H2O and glacial acetic acid. 8. Whatman paper. 9. Autoradiography films. 10. Primary Antibodies (all stored at -20 °C): mouse monoclonal anti-DnaK (clone 8E2/2; Millipore; used at 1:5000); rat monoclonal anti-HA (clone 3F10; Roche; used at 1:1000), mouse monoclonal anti-TEM-1 (QED Bioscience; used at 1:

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500), mouse monoclonal anti-α-tubulin (clone B-5-1-2; Sigma-Aldrich; used at 1:1000); rabbit polyclonal anti-SycO ([18]; used at 1:500); rabbit polyclonal anti-YopE [19]; used at 1:1000). 11. Secondary antibodies: mouse and rabbit horseradish peroxidase (HRP)-conjugated secondary antibodies (used at 1: 10,000). Store working aliquots at 4 °C and stocks at -20 °C. 12. Immuno-detection kit such as Western Lightning Plus-ECL (Perkin Elmer) or similar reagent. 13. Gel Transfer apparatus. 14. Gel imaging apparatus.

3

Methods

3.1 Infection of RAW 264.7 Cells by Y. enterocolitica and Preparation of TritonSoluble and TritonInsoluble Fractions

1. RAW 264.7 cells are maintained in DMEM + FBS (with no antibiotics) at 37 °C in a humidified atmosphere with 5% (v/v) CO2. The cells are used for up to 15–20 passages. The cells are routinely tested for mycoplasma contamination, using Venor®GeM Advance (Minerva Biolabs GmbH). 2. The day before the infection, prepare RAW 264.7 cells and grow Y. enterocolitica strains: (1) seed RAW 264.7 cells at a density of 1 × 106 cells per well in six-well tissue culture plates; and (2) grow Y. enterocolitica in 5 mL of BHI, overnight at 26 ° C with continuous shaking (130 rpm). 3. Dilute the bacterial cultures grown overnight to an optical density at 600 nm (OD600) of 0.2 in fresh BHI and resume growth at 26 °C with continuous shaking (130 rpm) for 2 h (see Note 8). 4. To induce expression of the Yersinia T3SS genes, quickly shift the bacterial cultures to a shaking water bath (130 rpm) at 37 ° C and incubate for additional 30 min (see Note 9). 5. Centrifuge 1.5 mL of the bacterial culture (17,000× g, 1 min; see Note 10) and resuspend the bacterial pellet in 1 mL DMEM + FBS and measure the OD600. 6. Calculate the volume of the bacterial suspension that needs to be added to the RAW 264.7 cells to have a multiplicity of infection (MOI) of 50, i.e., 5 × 107 bacteria per well (see Note 11). 7. Add the calculated volume to the seeded RAW 264.7 cells. Carefully swirl the plates to obtain an even infection. 8. Incubate the infected cells for 3 h at 37 °C in a humidified atmosphere of 5% (v/v) CO2.

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9. After 3 h of infection, replace the medium of the infected cells by DMEM + FBS (previously warmed at 37 °C) containing 50 μg/mL gentamicin to kill extracellular bacteria (50 μg/mL) and incubate for an additional 2 h at 37 °C in a humidified atmosphere of 5% (v/v) CO2. 10. From this point, all manipulation should be done on ice and using ice-cold solutions. 11. Wash infected cells twice with ice-cold 1× PBS. 12. Add 250 μL of 1× PBS containing 0.1% (v/v) Triton X-100 and a protease inhibitor cocktail (see Notes 12 and 13). 13. Incubate cells for 10 min on ice. 14. To remove cells from the wells, pipet up and down several times (about 15–20 times) and transfer cells to a 1.5 mL tube. 15. Centrifuge samples at 17,000× g for 15 min (see Note 10) at 4 °C. Remove the top 200 μL of supernatant and repeat this centrifugation step (see Note 14). Recover the top 100 μL of this second centrifugation step and add 25 μL of 5× SDSPAGE loading buffer (this is the Triton-soluble fraction). 16. Remove all supernatant from the pellet of the first centrifugation and resuspend it in 200 μL of 1× SDS-PAGE loading buffer (this is the Triton-insoluble fraction). 17. Incubate samples for 10 min at 95–100 °C. 18. Use immediately 30 μL of the Triton-soluble fraction and 20 μL of the Triton-insoluble fraction for immunoblotting (see subsequent discussion) or keep samples at -20 °C or 80 °C until use. 3.2 Infection of HeLa Cells by S. typhimurium and Preparation of TritonSoluble and TritonInsoluble Fractions

1. HeLa cells are maintained in DMEM + FBS (with no antibiotics) at 37 °C in a humidified atmosphere with 5% (v/v) CO2. The cells are used for up to 15–20 passages. The cells are routinely tested for mycoplasma contamination, using Venor®GeM Advance (Minerva Biolabs GmbH). 2. The day before the infection, prepare HeLa cells and grow S. Typhimurium strains: (1) seed HeLa cells at a density of 2.5 × 105 cells per well in 6-well tissue culture plates; and (2) grow S. Typhimurium in 5 mL LB overnight at 37 °C, with continuous shaking (130 rpm) (see Note 15). 3. Dilute 1:33 the bacterial cultures grown overnight in 5 mL of fresh LB medium and grow the bacterial culture for 3 h 30 min at 37 °C with continuous shaking (130 rpm) (see Note 16). 4. 5–10 min before the bacterial incubation has ended (step 3), wash once the seeded HeLa cells with previously warmed EBSS and incubate for 15–20 min at 37 °C in a humidified atmosphere of 5% (v/v) CO2.

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5. Measure OD600 of the bacterial culture. 6. Dilute the bacterial culture in 5 mL of EBSS (previously warmed at 37 °C) to have a MOI of 100 (i.e., to 1.25 × 106 bacteria/mL), when adding 2 mL of this suspension to the seeded HeLa cells (see Note 17). 7. Remove EBSS and add 2 mL of the bacterial suspension to the monolayer of HeLa cells. This corresponds to the beginning of the infection (time zero). 8. Incubate the infected cells for 15 min at 37 °C in a humidified atmosphere of 5% (v/v) CO2. 9. Wash the infected cells three times with DMEM + FBS (previously warmed at 37 °C) containing 100 μg/mL of gentamicin (added fresh from the gentamicin 10 mg/mL stock solution just before the washing steps). 10. Incubate the infected cells in DMEM + FBS containing 100 μg/mL of gentamicin for 1 h at 37 °C in a humidified atmosphere of 5% (v/v) CO2. 11. Replace the medium of the infected cells by DMEM + FBS (previously warmed at 37 °C) containing 16 μg/mL of gentamicin (added fresh from the gentamicin 10 mg/mL stock solution just before the washing steps). 12. Incubate the infected cells for a total of 14 h of infection, using as reference the time zero of infection (see step 7 of Subheading 3.2) (see Note 18). 13. Wash HeLa cells with 1× PBS. 14. Add 250 μL of TrypLE Express to the HeLa cell monolayer and incubate the cells for 5 min at 37 °C. 15. Add 1 mL of DMEM + FBS and collect the cells into a 1.5 mL tube by extensively pipetting up and down (15–20 times). 16. Centrifuge at 17,000× g for 1 min (see Note 10), discard supernatant and wash cells in 1 mL of ice-cold 1× PBS. 17. Repeat centrifugation and washing step (step 16) (see Note 19). 18. From this point, all manipulation should be done on ice and using ice-cold solutions. 19. Resuspend the pellet in 100 μL of ice-cold 1× PBS containing 0.1% (v/v) Triton X-100 and a protease inhibitor cocktail. 20. Incubate for 10 min on ice with occasional homogenization. 21. Centrifuge cell lysates at 18,620× g for 15 min at 4 °C (see Note 10) to separate the Triton-soluble from Triton-insoluble fraction as described in steps 15–17 of Subheading 3.1. 22. Centrifuge samples at 18,620× g for 15 min (see Note 10) at 4 °C. Remove the top 80 μL of supernatant and repeat this

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centrifugation step (see Note 14). Recover the top 40 μL of this second centrifugation step and add 10 μL of 5× SDSPAGE loading buffer (this is the Triton-soluble fraction). 23. Remove all supernatant from the pellet of the first centrifugation and resuspend it in 100 μL of 1× SDS-PAGE loading buffer (this is the Triton-insoluble fraction). 24. Incubate samples for 10 min at 95–100 °C. 25. Use immediately 30 μL of the Triton-soluble fraction and 20 μL of the Triton-insoluble fraction for immunoblotting or keep samples at -20 °C or -80 °C until use. 3.3 SDS-PAGE and Immunoblotting

1. Load samples on separate wells of a 12% SDS-PAGE (see Notes 3, 20, and 21). 2. Run the SDS-PAGE for 70 min at 150 V (see Note 22). 3. Process the SDS-PAGE for transfer into nitrocellulose membranes (see Note 23). 4. After transfer, evaluate the efficiency of protein transfer by staining the membrane(s) with a 0.1% (w/v) Ponceau solution: sink the membrane in a few milliliters of the Ponceau solution and incubate with gentle shaking for 1–5 min, destain with distilled H2O until protein bands are visible, and use a pen or a pencil to label the bands of the molecular weight marks and the position of lanes. If appropriate, cut the blotting membrane in strips to be detected against specific primary antibodies. 5. Using a flat-bottom incubation vessel (e.g., a Petri dish), incubate the membrane(s) in blocking Solution for at least 1 h at room temperature, with gentle rocking (see Note 24). 6. Dilute primary antibody in the Blocking Solution and incubate for at least 1 h at room temperature, with gentle rocking (see Note 25). 7. Remove the primary antibody solution and store it at -20 °C (see Note 26). 8. Add an excess volume of PBS-T and rinse the membrane(s) by gentle swirling of the immunoblotting incubation vessel. Discard the PBST solution. 9. Add an excess volume of PBST and incubate the membrane (s) for 10 min at room temperature, with gentle rocking. Repeat twice (see Note 27). 10. Discard the PBST solution and incubate the membrane(s) for 1 h with appropriate HRP-conjugated secondary antibodies diluted in blocking Solution. 11. Discard the secondary antibody solution and wash the membrane as indicated in steps 8 and 9.

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12. Perform immunoblot detection using ECL detection system and acquire the final image using an imaging system or by exposure to ECL autoradiography films followed by processing in a darkroom using photography developer and fixer solutions (see Note 28). 13. If a membrane must be reprobed with other primary antibodies, wash the membrane in PBST (as described earlier in steps 8 and 9) and incubate it in an excess volume of stripping solution for 20 min at room temperature, with gentle rocking. 14. Wash with PBST (as described earlier in steps 8 and 9). 15. Proceed with the immunoblotting procedure restarting from step 5.

4

Notes 1. Prepare all solutions using ddH2O, unless otherwise indicated, and analytical grade reagents. Prepare all reagents at room temperature and store them at the indicated temperatures. Follow regulations and guidelines for manipulation and disposal of chemicals, mammalian cell cultures, and biosafety level class II organisms. All solutions and materials used for manipulation of mammalian cell cultures must be sterile and manipulated only within a biological safety cabinet, and those used for bacterial cultures must also be sterile and manipulated by aseptic techniques. 2. The recommended procedure in classical molecular biology laboratory textbooks (e.g., Sambrook et al., Molecular Cloning: A Laboratory Manual) is that the 10% (w/v) APS solution should be prepared fresh. We normally prepare a 10 mL stock solution of 10% (w/v) APS that we store at 4 °C and use within several weeks with no noticeable effect in the performance of the SDS-PAGE. 3. The description is for 12% SDS-PAGE, but the concentration of the resolving gel can be adjusted according to the molecular mass of the proteins being analyzed by recalculating the volumes of acrylamide/bis-acrylamide and ddH2O. 4. To facilitate the visualization of the wells, we normally add ~50 μL of a 2% (w/v) solution of Orange G (Sigma-Aldrich) to the stacking gel. 5. It is convenient to follow running of the SDS-PAGE and to label the bands in the nitrocellulose membrane with colored protein markers (see step 4 in Subheading 3.3), but other types of markers can be used.

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6. Other blocking agents (e.g., bovine serum albumin (BSA) or fish gelatin) can be used, but skim milk powder works very well. 7. Polyvinylidene difluoride (PVDF) membranes can also be used, but they need to be soaked in methanol for 15–30 s prior to use in immunoblotting. 8. The bacterial growth and infection conditions described are for Y. enterocolitica and need to be adapted to each particular bacterial species. 9. It is critical that the shift is done quickly and that a water bath is used to incubate the bacterial cultures at 37 °C. 10. Basically top speed in a microcentrifuge for 1 min; this is the g force at maximum rotations per minute in the microcentrifuge we normally use for this. 11. To accurately calculate the MOI for infection, it is necessary to establish the relation between the colony forming units (CFU)/mL of a culture grown in liquid medium and its corresponding OD600. For Y. enterocolitica cultures, we consider that an OD600 of 1 corresponds to 5 × 108 CFU/ml. 12. Optimal conditions to detect effector translocation for each particular experiment might have to be determined empirically, such as the duration of the infection or the solubilization agent used. In our hands, 0.1% (w/v) Triton-X100 works well for monitoring effector translocation by Y. enterocolitica or S. Typhimurium into tissue culture cells. Other commonly used detergents in this type of assay include 0.2% (w/v) saponin [20] and 0.02% (w/v) digitonin [8, 21]. 13. The infected cells can also be recovered as described for the S. Typhimurium infection of HeLa cells (steps 13–20 in Subheading 3.2). 14. This is a critical step, and disturbing the pellet when recovering the supernatant must be avoided. The second centrifugation step described is designed to circumvent this problem. 15. The bacterial growth and infection conditions described are for S. Typhimurium and need to be adapted to each particular bacterial species. 16. These are incubation conditions that induce expression of the genes encoding the Salmonella pathogenicity island-1encoded T3SS (SPI-1 T3SS) whose effectors promote invasion of HeLa cells. If infecting macrophages, the overnight culture of S. Typhimurium can be opsonized (or not) and used directly in macrophage infection [19]. 17. See Note 11. For S. Typhimurium cultures, we consider that an OD600 of 1 corresponds to 1 × 109 CFU/mL. Make sure to

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mix extremely well (vortex and invert the tube 10–15 times) the bacterial suspension. 18. These infection conditions are to monitor translocation of S. Typhimurium effector proteins by the SPI-2 T3SS, as SteA in this protocol [13, 17], which is induced within the vacuole where Salmonella resides within host cells. Translocation of SPI-2 effector proteins normally can be detected 6–8 h after bacterial inoculation of the tissue culture cells. Incubating the HeLa cells with S. Typhimurium for 14 h is convenient because the infection can be done late in the afternoon and the samples collected early in the morning. 19. This procedure makes it possible obtaining a pellet of infected cells and to concentrate the protein extract by adjusting the volumes of 1× PBS containing 0.1% (w/v) Triton X-100 used to lyse the infected cells. 20. Several controls should be used to discard possible cross-contamination of the obtained fractions. To discard the possibility of bacterial lysis and consequent release of bacterial components into the Triton-soluble fraction, the blots should be probed with an antibody against a bacterial non-translocated protein (see Figs. 1 and 2). Additionally, the presence of a host cell cytosolic protein in the Triton-soluble fraction (e.g., tubulin) can be confirmed (see Figs. 1 and 2). Demonstration of translocation of an effector by a particular secretion system is normally shown by using a secretiondefective strain (see Fig. 1). 21. If the proteins to be detected have significantly different molecular weights, the blotting membrane can be cut in strips in order to incubate each one separately with the appropriate primary antibody. If the bands of the proteins are expected to be too proximal, the membrane must be stripped and reprobed. 22. The running conditions indicated can be adjusted accordingly, depending on the molecular mass of the proteins that need to be analyzed, and the running time of the SDS-PAGE can be controlled visually based on the migration of the bromophenol blue dye or of the pre-stained protein markers. 23. Semi-dry or wet electroblotting apparatuses can be used. Wet electroblotting transfer is known to facilitate transfer of proteins with a molecular mass >100 kDa. 24. The blocking step can also be done overnight at 4 °C or after blocking for 1 h at room temperature; the membranes can be kept at 4 °C overnight or for 2 or 3 days. Use an excess volume of blocking to ensure that the membranes are fully covered at all times.

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25. The incubation with the primary antibody can also be done overnight at 4 °C. Exact conditions should be optimized for each specific antibody. The volume of the antibody solution used must ensure that the membranes are fully covered at all times, but the volume should be minimized because antibodies usually are expensive or scarce. For a small membrane, 5 mL of antibody solution in a 90 mm Petri dish is usually enough. 26. The antibodies diluted in blocking solution can normally be reused at least five or six times. If a decrease in performance is noticed, the procedure can be repeated with a fresh dilution of the antibody. 27. If appropriate, the membranes can be left for longer in PBST (at least 1–2 h). 28. Other detection reagents or image acquisition systems can be used. 29. The use of an uninfected control is recommended (see Fig. 1) but can be dispensable if the primary antibody used is known not to originate a background signal in immunoblotting (as was the case with the anti-HA antibody used in the experiment illustrated in Fig. 2).

Acknowledgments This work was supported by Fundac¸˜ao para a Cieˆncia e Tecnologia (FCT) in the scope of the projects UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences—UCIBIO, and LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy—i4HB., and by FCT research grants PTDC/BIA-MIC/2821/2012 and PTDC/ BIA-MIC/116780/2010. Irina Franco and Sara V. Pais were recipients of post-doctoral (SFRH/BPD/102378/2014) and PhD fellowships (SFRH/BD/52210/2013) from FCT. References 1. Costa TR, Felisberto-Rodrigues C, Meir A et al (2015) Secretion systems in Gram-negative bacteria: structural and mechanistic insights. Nat Rev Microbiol 13:343–359. https://doi. org/10.1038/nrmicro3456 2. Galan JE, Waksman G (2018) Proteininjection machines in bacteria. Cell 172: 1306–1318. https://doi.org/10.1016/j.cell. 2018.01.034 3. Green ER, Mecsas J (2016) Bacterial secretion systems: an overview. Microbiol Spectr

4. https://doi.org/10.1128/microbiolspec. VMBF-0012-2015 4. Sory MP, Cornelis GR (1994) Translocation of a hybrid YopE-adenylate cyclase from Yersinia enterocolitica into HeLa cells. Mol Microbiol 14:583–594 5. Charpentier X, Oswald E (2004) Identification of the secretion and translocation domain of the enteropathogenic and enterohemorrhagic Escherichia coli effector Cif, using TEM-1 beta-lactamase as a new fluorescence-based reporter. J Bacteriol 186:5486–5495. https://

Assessing Effector Translocation by Differential Solubilization doi.org/10.1128/JB.186.16.5486-5495. 2004 6. O’Boyle N, Connolly JPR, Roe AJ (2018) Tracking elusive cargo: illuminating spatiotemporal Type 3 effector protein dynamics using reporters. Cell Microbiol 20. https:// doi.org/10.1111/cmi.12797 7. Collazo CM, Galan JE (1997) The invasionassociated type III system of Salmonella typhimurium directs the translocation of Sip proteins into the host cell. Mol Microbiol 24: 747–756 8. Lee VT, Anderson DM, Schneewind O (1998) Targeting of Yersinia Yop proteins into the cytosol of HeLa cells: one-step translocation of YopE across bacterial and eukaryotic membranes is dependent on SycE chaperone. Mol Microbiol 28:593–601 9. Schnaitman CA (1971) Effect of ethylenediaminetetraacetic acid, Triton X-100, and lysozyme on the morphology and chemical composition of isolate cell walls of Escherichia coli. J Bacteriol 108:553–563 10. Schnaitman CA (1971) Solubilization of the cytoplasmic membrane of Escherichia coli by Triton X-100. J Bacteriol 108:545–552 11. Birdsell DC, Cota-Robles EH (1968) Lysis of spheroplasts of Escherichia coli by a non-ionic detergent. Biochem Biophys Res Commun 31: 438–446 12. Esparis-Ogando A, Zurzolo C, RodriguezBoulan E (1994) Permeabilization of MDCK cells with cholesterol binding agents: dependence on substratum and confluency. Am J Phys 267:C166–C176 13. Domingues L, Holden DW, Mota LJ (2014) The salmonella effector SteA contributes to the control of membrane dynamics of salmonellacontaining vacuoles. Infect Immun 82:2923– 2 9 3 4 . h t t p s : // d o i . o r g / 1 0 . 1 1 2 8 / I A I . 01385-13 14. Sory MP, Boland A, Lambermont I, Cornelis GR (1995) Identification of the YopE and

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YopH domains required for secretion and internalization into the cytosol of macrophages, using the cyaA gene fusion approach. Proc Natl Acad Sci U S A 92:11998–12002 15. Figueira R, Watson KG, Holden DW, Helaine S (2013) Identification of salmonella pathogenicity island-2 type III secretion system effectors involved in intramacrophage replication of S. enterica serovar typhimurium: implications for rational vaccine design. MBio 4:e00065. https://doi.org/10.1128/mBio.00065-13 16. Wang RF, Kushner SR (1991) Construction of versatile low-copy-number vectors for cloning, sequencing and gene expression in Escherichia coli. Gene 100:195–199 17. Domingues L, Ismail A, Charro N, RodriguezEscudero I, Holden DW, Molina M, Cid VJ, Mota LJ (2016) The Salmonella effector SteA binds phosphatidylinositol 4-phosphate for subcellular targeting within host cells. Cell Microbiol 18:949–969. https://doi.org/10. 1111/cmi.12558 18. Letzelter M, Sorg I, Mota LJ et al (2006) The discovery of SycO highlights a new function for type III secretion effector chaperones. EMBO J 25:3223–3233. https://doi.org/10.1038/sj. emboj.7601202 19. Diepold A, Amstutz M, Abel S et al (2010) Deciphering the assembly of the Yersinia type III secretion injectisome. EMBO J 29:1928– 1940. https://doi.org/10.1038/emboj. 2010.84 20. VanRheenen SM, Luo ZQ, O’Connor T, Isberg RR (2006) Members of a Legionella pneumophila family of proteins with ExoU (phospholipase A) active sites are translocated to target cells. Infect Immun 74:3597–3606. https://doi.org/10.1128/IAI.02060-05 21. Denecker G, Totemeyer S, Mota LJ et al (2002) Effect of low- and high-virulence Yersinia enterocolitica strains on the inflammatory response of human umbilical vein endothelial cells. Infect Immun 70:3510–3520

Chapter 35 Monitoring Effector Translocation with the TEM-1 Beta-Lactamase Reporter System: From Endpoint to Time Course Analysis Julie Allombert, Anne Vianney, and Xavier Charpentier Abstract Among the bacterial secretion systems, the Type III, IV, and VI secretion systems enable bacteria to secrete proteins directly into a target cell. This specific form of secretion, referred to as “translocation”, is essential for a number of pathogens to alter and/or kill the targeted cell. The translocated proteins, called effector proteins, can directly interfere with the normal processes of the targeted cell, preventing elimination of the pathogen and promoting its multiplication. The function of the effector proteins varies greatly depending on the considered pathogen and the targeted cell. In addition, there is often no magic bullet and the number of effector proteins can range from a handful to hundreds, with, for instance, over 300 effector proteins substrate of the Icm/Dot Type IV secretion system in the human pathogen Legionella pneumophila. Identifying, detecting, and monitoring the translocation of each of the effector proteins represent an active field or research and are key to understanding the bacterial molecular weaponry. Translational fusion of the effector with a reporter protein of known activity remains the best method to monitor effector translocation. The development of a fluorescent substrate for the TEM-1 beta-lactamase has turned this antibiotic-resistance protein into a highly versatile reporter system to investigate protein transfer events associated with microbial infection of host cells. We here described a simple protocol to assay translocation of an effector protein by the Icm/Dot system of the human pathogen Legionella pneumophila. Taking advantage that the protonophore CCCP inhibits the secretion activity, this simple protocol can be derived into a time course analysis to follow the kinetic of effector translocation into target cells. Key words Effector protein, Type IV secretion system, ß-lactamase fusion, CCF4, fluorescence, Legionella pneumophila

1

Introduction Protein delivery from the pathogen to the host represents a major and unifying theme in microbial pathogenesis. From the pathogen’s perspective, sending in the target host cell its own proteins represents an efficient strategy to interfere with the host cellular functions, preventing exposure to the host defense mechanism or even subverting the cells for its benefit. There are, however, barriers

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_35, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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preventing the diffusion of proteins out of the pathogenic cell and blocking the import of proteins in the host cell side. For instance, in Gram-negative bacteria, a protein would have to get across three membranes: the inner and outer membranes of the bacteria and the host cytoplasmic membrane. Remarkably, bacteria have evolved several multisubunit molecular machines to achieve the feat of “translocating” a protein from the pathogen cytoplasm directly to the cytoplasm or the target cell. The process is often referred to as “translocation,” and the translocated proteins, whose assumed function is to have an effect on the cell functions, are called “effectors”. The multisubunit molecular machines capable of translocating effectors include the Type III, IV, and VI systems of Gram-negative bacteria and the Type VII system of Mycobacteria [1]. The set of translocated effector molecules tends to be unique to each pathogen and reflects the unique needs and specific niches of each bacterial species. A major challenge is to identify the substrates of these systems and track their translocation in the host cell. Several methods have been reported to specifically detect this fraction of a bacterial protein that found its way to the host cell. The first of which is the popular CyaA system involving translational fusions of the effectors with the calmodulin-dependent catalytic domain of the Bordetella pertusis toxin CyaA [2]. This enzyme converts cellular ATP in cAMP in the presence of the eukaryotic protein calmodulin. Levels of cAMP production can be subsequently quantified. A less popular but clever method involves a translational fusion with the phosphorylable Elk peptide fused to the nuclear localization signal (NLS) from the large T antigen of SV40 [3]. The NLS sequence directs the fusion protein to the cell nucleus where the Elk tag is phosphorylated and can be detected with phosphospecific Elk-peptide antibodies. Another method is based on fractionation with digitonin that solubilize the eukaryotic plasma membrane but not the prokaryotic membranes [4]. As for the Cya and Elk-tag systems, these assays require disrupting the eukaryotic cell and performing subsequent analysis. The beta-lactamase translocation assay was developed to overcome these limitations and analyze translocation in living cells [5]. The beta-lactamase translocation assay (see Fig. 1) takes advantage of the fluorescent substrate CCF2-AM (or CCF4-AM) initially developed for the detection of TEM-1 beta-lactamase activity within eukaryotic cells [6]. The substrate consists of a coumarin and a fluorescein fluorophore connected by a beta-lactam ring. Because of the fluorophores’ spatial proximity, the fluorescence energy coming out the excitation of the coumarin moiety is entirely transferred to the fluorescein moiety, resulting in the emission of a green fluorescence. Enzymatic cleavage of the beta-lactam ring by the TEM-1 beta-lactamase frees the coumarin moiety, which, under excitation, now emits a blue fluorescence. This shift in fluorescence can be directly observed in the infected

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Fig. 1 Schematic representation of the TEM-1 reporter system to assess effector protein translocation in live eukaryotic cells. Upon passive entry into the eukaryotic cell, the non-fluorescent esterified CCF2/AM (or CCF4/AM) substrate is rapidly converted by cellular esterases into a charged and fluorescent CCF2. Excitation of the coumarin moiety (represented by a circle) at 409 nm results in fluorescence energy transfer (FRET) to the fluorescein moiety (represented by a hexagon), which emits green fluorescence signal at 520 nm. Injection of an effector fused to TEM-1 into a CCF2-loaded cell induces catalytic cleavage of CCF2 beta-lactam ring (represented by a square), disrupting FRET. This produces an easily detectable and measurable change in fluorescence from green to blue emission

host cell with an epifluorescence microscope or quantified with a spectrofluorometer. Translocation in the eukaryotic host cytoplasm of an effector-TEM-1 fusion protein triggers a change in fluorescence of the host cell, and this makes analysis of effector translocation rapid, easy, and reliable. Because fewer than 100 molecules of TEM-1 can be readily detected within a cell [6], the system was sensitive enough to detect translocation of a weakly produced fusion. Of note, the TEM-1 enzyme is naturally secreted in the periplasmic space by the Sec pathway. So, in order to use TEM-1 as a reporter of the ability of a fused protein to drive it to another secretion system, it is necessary to use a version deleted of its N-terminal secretion signal. Owing to its properties of a secreted protein, TEM-1 can efficiently unfold and refold and is highly permissive to protein fusion. This likely made it compatible with secretion by most secretion systems, and numerous studies have used it to demonstrate the translocation of effector proteins by the Type III, IV, and VI systems (for a review, see [7]). The system has

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also been successfully used to detect proteins secreted by the protozoan parasite Toxoplasma gondii in its host [8]. More than just a convenient way to demonstrate translocation of an effector, the beta-lactamase translocation reporter system can be used to monitor the kinetic parameters of the translocation process either continuously over short period of time [9, 10] or in an extended time course analysis, consisting of multiple data point [9]. The latter approach takes advantage of the potent inhibitory activity of the protonophore carbonyl cyanide 3-chlorophenylhydrazone (CCCP), which blocks the proton motive force and quickly depletes ATP levels. CCCP is added to a sample to “freeze” translocation at different time points (for instance, every 15 min). A single measurement of all samples at the end of the experiment allows to obtain a time course analysis. Since CCCP also inhibits Type III and VI systems, and potentially any ATP-dependent secretion systems [10–13], this approach could be implemented to follow the kinetic of effector translocation in any system. In addition, the assay can be miniaturized to a 384-well format and is compatible with high-throughput screens to identify small molecules that could inhibit translocation and prevent infection [14, 15]. Translocated effector proteins can also reveal the cells targeted by a pathogen in an infected host [16, 17]. We here provide a protocol for testing translocation of effector proteins that are the substrate of the Icm/Dot Type IV secretion system of the human pathogen Legionella pneumophila. L. pneumophila infects alveolar macrophages in the human lungs, and this cellular infection can be recapitulated in vitro using monocyte-derived macrophages (THP-1, U937 cells). Upon phagocytosis by the macrophages, L. pneumophila delivers numerous effector proteins in the host through its Icm/Dot Type IV secretion system. Ectopically expressed as fusions with the mature form of TEM-1 beta-lactamase, their translocation is detectable in less than an hour following infection. The beta-lactamase translocation assay is particularly easy, straightforward, and quick. It requires only a few pipetting steps and no sample processing. Typically, the results of the assay are obtained about 3 h postinfection. The protocol provided here could easily be adapted to other pathogens, secretion systems, and cellular infection models.

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Materials

2.1 Bacterial Strains, Beta-Lactamase Constructs, and Host Cells

1. Legionella pneumophila strain (Paris, Lens, Philadelphia-1). 2. Plasmids for expression of Beta-lactamase fusion proteins: The plasmid pXDC61 and its derivative used in this protocol are available from the non-profit plasmid repository addgene

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Fig. 2 Map of the plasmid pXDC61 to express beta-lactamase TEM-effector fusion proteins. A. The pXDC61 plasmid is a mobilizable (oriT) plasmid derived from the broad host range plasmid RSF1010. The plasmid confers chloramphenicol resistance. The ‘blaM gene encodes the mature form of the TEM-1 betalactamase deleted of its N-terminal secretion signal and is controlled by an IPTGinducible promoter. A polylinker (KpnI, SmaI, BamHI, and XbaI) is placed at the 3′ end of the ‘blaM gene. B. Detail of the polylinker for in-frame cloning of effector genes

(addgene.org, plasmids #21841, #21842, #21843, #21844) (see Note 1 and Fig. 2). 3. U937 cell line ATCC number: CRL-1593.2™. 2.2 Legionella pneumophila Media and Bacterial Growth

1. AYE medium: For 1 liter, dissolve 12 g yeast extract and 10 g ACES, adjust pH to 6.9 with 1 M KOH. Add 10 mL of cysteine 40 g/L and 10 mL of iron pyrophosphate 30 g/L. Fill volume to 1 L with distilled water and filter sterilize.

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2. CYE plates: For 1 liter, dissolve 10 g yeast extract and 10 g ACES, adjust pH to 6.9 with 1 M KOH, add 15 g of agar, 2 g of activated charcoal and autoclave. Add 10 mL of filter sterilized cysteine 40 g/L and 10 mL of filter sterilized ferric nitrate 25 g/L. When appropriate, add 5 μg/mL of chloramphenicol and 1 mM IPTG (see Note 2). 3. Disposable 13 mL polypropylene snap-cap tubes, sterile. 4. 1.5 mL microcentrifuge tubes, sterile. 5. 30 °C incubator. 6. Orbital shaker, 30 °C. 7. Spectrophotometer and cuvettes. 2.3 Cell Culture and Differentiation

1. RPMI medium supplemented with 10% Fetal Bovine Serum (FBS) and glutamine (i.e., RPMI 1640 GlutaMAXTM, Gibco). When appropriate, add 5 μg/mL of chloramphenicol and 1 mM IPTG. 2. Phorbol 12-myristate 13-acetate (PMA): 0.1 M. 3. Culture flask, 25 cm2, sterile. 4. Disposable 15 mL polypropylene snap-cap tubes, sterile. 5. 96-well black polystyrene microplates with clear bottom, sterile. 6. CO2 incubator, 37 °C. 7. Malassez counting chamber.

2.4 Translocation Assays

1. LiveBLAzer-FRET B/G loading kit (Invitrogen). This kit includes the CCF4/AM substrate (see Note 3). 2. Probenecid stock solution: 0.1 M. Dissolve 1.25 g of probenecid (Sigma) in 22 mL of 0.4 M NaOH by vigorous agitation. Add 22 mL of 100 mM phosphate buffer, pH 8.0, and stir to dissolve the precipitate that could form. Check pH, and if necessary adjust it to 8.0 with 1 M NaOH (if pH < 8) or HCl (if pH > 8). Distribute in 1 mL aliquot and store at 20 °C. 3. RPMI medium. 4. CCCP solutions. CCCP stock solution: 12 mM. Dissolve 4.91 mg CCCP in 2 mL of DMSO. Distribute in 0.2 mL aliquot and store at -20 °C. CCCP working solution: 120 μM. Dilute 10 μL CCCP stock solution in 0.990 mL DMSO. Distribute in 0.2 mL aliquot and store at -20 °C. 5. Fluorescence microplate reader equipped with a dual monochromator (e.g., Tecan Infinite M200) or equipped with an excitation filter at 405 nm and emission filters at 460 nm (blue

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fluorescence) and 530 nm (green fluorescence). Determine whether the plate reader reads the plate from top of bottom. 6. Inverted fluorescence microscope equipped with a betalactamase filter set (Chroma Set # 41031; Excitation filter: HQ405/20x (405 ± 10); Dichroic mirror: 425 DCXR; Emission filter: HQ435LP (435 long-pass)). Alternatively a 4′,6′-diamidino-2-phenylindole (DAPI) filter set (340- to 380-nm excitation and 425-nm long-pass emission) may be used to observe the blue fluorescence, while the green fluorescence may be observed with a GFP/fluorescein filter set.

3

Methods

3.1 Growth of the Infecting Legionella pneumophila Strains

The infecting strain should be grown under the conditions that have been previously determined to result in a successful infection. These conditions may vary depending on the strain and species used but should include chloramphenicol to maintain the plasmid and IPTG to induce expression of the tested effector fusion proteins. 1. Streak L. pneumophila strains carrying pXDC61-derived plasmids from a frozen stock to CYE plates supplemented with chloramphenicol and then incubated at 30 °C for 5 days. 2. With a sterile loop, scrape off a few colonies and transfer to a 1.5 mL microcentrifuge tube containing 1 mL of sterile ultrapure water. Resuspend bacteria by repeated pipetting. 3. Measure the optical density at 600 nm (OD600) of a tenfold diluted bacterial suspension. In a sterile 13 mL tube, inoculate 2 mL of AYE supplemented with chloramphenicol and IPTG with the appropriate volume of the previous bacterial suspension to reach a starting OD600 = 0.3. Incubate at 30 °C in orbital shaker for 3 days (see Note 4). 4. Validate the beta-lactamase fusions production by Western blot (see Note 5)

3.2 Maintenance and Differentiation of U937 Target Cells

U937 cells are monocytes that grow as a suspension and should be maintained at a cell density between 1.105 and 2.106 viable cells/ mL. 1. Seed a 25 cm2 culture flask with U937 cells from a frozen stock or from a previous culture flask in 10 mL of RPMI medium supplemented with Glutamine and FBS. The culture flask is incubated at 37 °C in a CO2 incubator for 5 days. 2. Determine the cellular concentration of the U937 cell culture with a Malassez counting chamber.

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3. Transfer 1.107 cells to a sterile 15 mL conical tube and centrifuge 5 min at 880 g. 4. Discard the supernatant and gently resuspend the pellet of cells in 10 mL of RPMI supplemented with Glutamine and FBS (pre-warmed at 37 °C) by slow repeated pipetting. This gives a cell suspension at 1.106 cells/mL. Add 0.5 μl of PMA. 5. Distribute 100 μL of cell suspension per well of a 96-well microplate (105 cells/well). Leave 3 wells without U937 cells (medium alone). They will be used for the blank fluorescence measurement. 6. Incubate at 37 °C in the CO2 incubator for 3 days. Following this incubation, the previously spherical and non-adherent cells should now be differentiated into macrophages-like cells that adhere to the bottom of the well and display a spread-out morphology. 3.3 Detection of Effector Translocation Using a Fluorescence Plate Reader

1. Grow L. pneumophila strains as described in Subheading 3.1. 2. Measure OD600 of liquid cultures of L. pneumophila strains. Adjust to OD600 = 1 with sterile ultrapure water. This gives a bacterial suspension at 109 bacteria/mL. 3. Add 200 μL of the resulting suspension to 800 μL of RPMI supplemented with Glutamine, FBS, chloramphenicol, and IPTG. Incubate these bacterial suspensions (2·108 bacteria/ mL) for 2 h at 37 °C in the CO2 incubator. 4. During the incubation time, prepare the CCF4-AM loading solution of the LiveBLAzer-FRET B/G loading kit. Determine the number of tested wells n (read step 5). Mix n*0.12 μL of CCF4-AM 6X solution with n*1.08 μL of solution B. Vortex for 10 s. Add n*15.8 μL of solution C and n*3 μL of probenecid 0.1 M (see Note 6). Vortex for 10 s. This loading solution should be kept away from strong light and is stable for 4 h at room temperature. 5. Add 10 μL of the bacterial suspension in wells of the 96-well microplate containing differentiated U937 cells (Subheading 3.2). This gives a multiplicity of infection (ratio bacteria to differentiated cell) of 20 (see Note 7). The bacterial suspension of each tested L. pneumophila strains is added to three different wells (triplicate). Centrifuge the microplate for 10 min at 2000 rpm (600 g) (see Note 8). If performing an endpoint translocation assay (typically 1 h of infection), incubate at 37 ° C in a CO2 incubator for 1 h and go to 7. If conducting a time course analysis, prepare as many infected wells as the number of time points to be acquired. Incubated for the desired time (for instance, 0, 15 min, 30 min, 45 min and so on for 3 h, see an example in Fig. 3).

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dotA/LepA

Fig. 3 Typical results of a translocation assay. A. Raw data, blank-subtracted fluorescence signals (Relative Fluorescence Unit, RFU) and fluorescence ratio (Emission 460 nm/530 nm) obtained for the secretion by the wild-type (WT) or ΔdotA L. pneumophila strain Lens of the known Dot/Icm effector LepA and the non-secreted cytoplasmic protein FabI. Secretion assays were done in triplicates for each strain. Fluorescence measurements were done with a Tecan M200 Infinite microplate reader equipped with a monochromator and a fluorescence top reading module. The measurement program includes fluorescence readings with an excitation wavelength of 405 nm, emission wavelengths of 460 and 530 nm and a gain set at 135. B. Graphical representation of the mean emission ratio for LepA and FabI and the corresponding standard deviations. Depending on the gain set for each of the two fluorescence measurements, this ratio can significantly change. Data acquired under different gain or in different plate reader can be normalized by setting the 460/530 ratio of the uninfected cells to 1. C. Fluorescence images of a typical translocation assay. Upon excitation at 405 nm, two images were captured at 460 and 530 nm and merged. D. Example of a time course analysis of TEM-LepA translocation. Graphical representation of the mean emission ratio for LepA and FabI from U937 cells infected with wild-type (WT), ΔdotA, and Δlpl0780 Legionella pneumophila Lens strains harboring a TEM-LepA or a TEM-FabI expression plasmid (MOI 20). Note that the kinetic of LepA translocation is delayed in the Δlpl0780 mutant. Results are obtained from three independent experiments made in triplicates and are presented as means ± SD [9]

6. In a time course analysis, add 10 μL of CCCP working solution at each tested time points (e.g., each 15 min during 3 h) (10 μM CCCP/well).

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7. At the end of the time course analysis (or at the end of the endpoint assays), add 20 μL of the loading solution to each of the tested wells of the 96-well microplate, including to 3 wells that contain media alone (see step 5 in Subheading 3.2). Incubate for 2 h in the dark at room temperature. 8. If you have access to a fluorescence plate reader with bottom read capabilities go directly to step 9. If the microplate reader is only equipped with a fluorescence top reading module, an extra step is needed because the fluorescence signals are quenched by the red solution C used in the loading CCF4 solution. 9. Following the 2 h CCF4 loading in the dark (step 6), discard delicately the liquid contained in each well of the 96-well microplate, including the 3 wells without cells. Replace with 50 μL of RPMI at room temperature and without FBS (dispensing medium with FBS tends to create bubbles that may interfere with fluorescence measurements). 10. Put the plate (lid on) in a fluorescence plate reader to start the measurements. Measure successively the blue fluorescence (Ex. 405 nm, Em. 460 nm) and green fluorescence (Ex. 405 nm, Em. 530 nm). Both measurements should be performed on blank wells (medium alone) in addition to the tested wells. 11. After collecting the raw data, perform a blank subtraction on each fluorescence reads. To evaluate secretion efficiency of the beta-lactamase fusions, divide the blank-subtracted blue fluorescence signal by the blank-subtracted green fluorescence signal (see Note 9). An example of expected results is shown in Fig. 3. 3.4 Visualization of Effector Translocation Using a Fluorescence Microscope

Following fluorescence quantifications, the infected cells may also be observed under a microscope to assess the percentage of translocation-positive (blue) and translocation-negative cells (green). 1. Follow the protocol of Subheading 3.3 until step 8. 2. Place the plate on an inverted microscope equipped with a 40× or 60× objective. 3. Observe cells with the beta-lactamase filter set (Ex: 405 ± 10; Dichroic mirror: 425; Em: 435 long-pass). Using this filter set, both green and blue cells can be visualized simultaneously. 4. Alternatively, blue cells can be visualized with a (DAPI) filter set (340- to 380-nm excitation and 425-nm long-pass emission). Green cells can be visualized with the filter set commonly used for the visualization of Green Fluorescent Protein (GFP). Overexposure of the cells with this filter set may bleach the fluorescein moiety of CCF2 (or CCF4), and that can result in

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the observation of blue fluorescence even in the absence of effector translocation. 5. If the image of the blue and green cells were acquired separately, the two images should be merged. Typical images are shown in Fig. 3.

4

Notes 1. Beta-lactamase fusion plasmids are derived from the pXDC61 plasmid (Fig. 2) [18]. They are introduced into the Legionella pneumophila by electroporation. These plasmids are constructed by cloning the coding sequence of the candidate effector gene in frame with the ‘blaM gene encoding the mature form of the TEM-1 beta-lactamase deleted of its N-terminal secretion signal. The candidate gene is cloned downstream of the ‘blaM gene in order to leave intact the potential C-terminal secretion signal of the candidate protein. The appropriate polylinker is shown fig. 2B. If the nature of the secretion signal is unknown, it is advisable to generate and test both fusion proteins at the C- or the N-terminus of the TEM-1 beta-lactamase. A NdeI site is available at the start codon of ‘blaM. The expression of these gene fusions is controlled by an IPTG-inducible promoter. 2. Culture conditions have to be optimized depending on the used Legionella species and strains in order to obtain bacteria in a virulent state (stationary phase). Here, we use CYE agar plates and AYE liquid medium but bacteria grown on BCYE agar plates and in LGM liquid medium are equally infective. 3. According to the supplier, “CCF2-AM and CCF4-AM differ by two carbons in the bridge linking the coumarin moiety to the lactam ring. Both are in the membrane-permeable, esterified forms, and can be used for assays in intact cells. CCF4-AM has better solubility properties (soluble for >24 h) than CCF2AM and is thus best suited for screening applications. In addition, CCF4-AM has slightly better FRET and thus slightly lower background than CCF2-AM”. In our hands, CCF2/ AM and CCF4/AM perform equally well. We have found no significant differences between the two compounds. 4. L. pneumophila grown on CYE agar plates may exhibit a heterogeneous population with a large part of filamentous bacteria. Therefore, bacteria are grown in liquid cultures before host cell infection in order to work with a homogeneous and more infective population. 5. It is advisable to assess the correct production and stability of the beta-lactamase fusions. This can be done using

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conventional Western-blot techniques that will not be described here. We recommend using the beta-lactamase monoclonal antibody clone 8A5.A10, which is available from a variety of suppliers. 6. The addition of probenecid in the CCF4 loading solution is required to inhibit organic anion transporter [19] and facilitate the loading of CCF4 by inhibiting the efflux from the cells. 7. The response of the beta-lactamase reporter should be determined as a function of multiplicity of infection (MOI). For L. pneumophila and phagocytic cells, between an MOI of 1–10, the beta-lactamase system seems to behave linearly and above 25 bacteria/cell, the system appears to saturate [14]. Therefore, we use an MOI of 20 as a compromise between sensitivity and linearity. Care should be taken to ensure that the MOI is not too high, for instance the TEM-FabI negative control should not produce blue fluorescence. This situation occurs in L. pneumophila when the MOI is over 50. 8. The motile L. pneumophila can make contact with the host cells without centrifugation. However, this experiment is based on the fluorescence signals of a host cell monolayer at a specific time point. Thus, the infection has to be synchronized by centrifugation in order to work with a host cell monolayer that is homogeneously infected. 9. Expected results are shown in Fig. 3. For Dot/Icm effector proteins, you should see an increase of the blue fluorescence and a decrease of the green fluorescence in comparison to the non-secreted protein (FabI) or in comparison with the TEM-effector fusion in the ΔdotA mutant impaired for its Icm/Dot Type IV secretion system. References 1. Costa TRD, Felisberto-Rodrigues C, Meir A et al (2015) Secretion systems in Gramnegative bacteria: structural and mechanistic insights. Nat Rev Microbiol 13:343–359 2. Sory MP, Cornelis GR (1994) Translocation of a hybrid YopE-adenylate cyclase from Yersinia enterocolitica into HeLa cells. Mol Microbiol 14:583–594 3. Day JB, Ferracci F, Plano GV (2003) Translocation of YopE and YopN into eukaryotic cells by Yersinia pestis yopN, tyeA, sycN, yscB and lcrG deletion mutants measured using a phosphorylatable peptide tag and phosphospecific antibodies. Mol Microbiol 47:807–823 4. Lee VT, Anderson DM, Schneewind O (1998) Targeting of Yersinia Yop proteins into the

cytosol of HeLa cells: one-step translocation of YopE across bacterial and eukaryotic membranes is dependent on SycE chaperone. Mol Microbiol 28:593–601 5. Charpentier X, Oswald E (2004) Identification of the secretion and translocation domain of the enteropathogenic and enterohemorrhagic Escherichia coli effector Cif, using TEM-1 beta-lactamase as a new fluorescence-based reporter. J Bacteriol 186:5486–5495 6. Zlokarnik G, Negulescu PA, Knapp TE et al (1998) Quantitation of transcription and clonal selection of single living cells with betalactamase as reporter. Science 279:84–88

TEM-1 Beta-Lactamase Reporter System 7. Pechous RD, Goldman WE (2015) Illuminating targets of bacterial secretion. PLoS Pathog 11:e1004981 8. Lodoen MB, Gerke C, Boothroyd JC (2010) A highly sensitive FRET-based approach reveals secretion of the actin-binding protein toxofilin during Toxoplasma gondii infection. Cell Microbiol 12:55–66 9. Allombert J, Jaboulay C, Michard C et al (2021) Deciphering Legionella effector delivery by Icm/Dot secretion system reveals a new role for c-di-GMP signaling. J Mol Biol 433: 166985 10. Bro¨ms JE, Meyer L, Sun K, Lavander M, Sjo¨stedt A (2012) Unique substrates secreted by the type VI secretion system of Francisella tularensis during intramacrophage infection. PLoS One 7:e50473 11. Minamino T, Namba K (2008) Distinct roles of the FliI ATPase and proton motive force in bacterial flagellar protein export. Nature 451: 485–488 12. Paul K, Erhardt M, Hirano T, Blair DF, Hughes KT (2008) Energy source of flagellar type III secretion. Nature 451:489–492 13. Wilharm G, Lehmann V, Krauss K et al (2004) Yersinia enterocolitica type III secretion depends on the proton motive force but not

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on the flagellar motor components MotA and MotB. Infect Immun 72:4004–4009 14. Charpentier X, Gabay JE, Reyes M et al (2009) Chemical genetics reveals bacterial and host cell functions critical for type IV effector translocation by Legionella pneumophila. PLoS Pathog 5:e1000501 15. Harmon DE, Davis AJ, Castillo C, Mecsas J (2010) Identification and characterization of small-molecule inhibitors of Yop translocation in Yersinia pseudotuberculosis. Antimicrob Agents Chemother 54:3241–3254 16. Geddes K, Cruz F, Heffron F (2007) Analysis of cells targeted by Salmonella type III secretion in vivo. PLoS Pathog 3:e196 17. Marketon MM, DePaolo RW, DeBord KL, Jabri B, Schneewind O (2005) Plague bacteria target immune cells during infection. Science 309:1739–1741 18. de Felipe KS, Glover RT, Charpentier X et al (2008) Legionella eukaryotic-like type IV substrates interfere with organelle trafficking. PLoS Pathog 4:e1000117 19. Steinberg TH, Newman AS, Swanson JA, Silverstein SC (1987) Macrophages possess probenecid-inhibitable organic anion transporters that remove fluorescent dyes from the cytoplasmic matrix. J Cell Biol 105:2695–2702

Chapter 36 Quantifying Substrate Protein Secretion via the Type III Secretion System of the Bacterial Flagellum Rosa Einenkel , Manuel Halte , and Marc Erhardt Abstract Protein transport across the cytoplasmic membrane is coupled to energy derived from ATP hydrolysis or the proton motive force. A sophisticated, multi-component type III secretion system (T3SS) exports substrate proteins of both the bacterial flagellum and virulence-associated injectisome system of many Gram-negative pathogens. The T3SS is primarily a proton motive force-driven protein exporter. Here, we describe a method to investigate the export of substrate proteins of the flagellar T3SS into the culture supernatant under conditions that manipulate the proton motive force. Further, we describe methods to precisely quantify flagellar protein export into the culture supernatant using a split NanoLuc luciferase, and how fluorescence labeling of the extracellular flagellar filament can bring insights into the protein export rate of individual flagellar T3SS. Key words Type III secretion system, T3SS, Bacterial flagellum, Protein export, Proton motive force, ΔpH gradient, ΔΨ gradient, Ionophore, Carbonyl cyanide m-chlorophenylhydrazone (CCCP), Valinomycin, Split NanoLuc luciferase, Fluorescence microscopy

1

Introduction Bacterial protein transportation systems utilize energy derived from the proton motive force (pmf) or ATP hydrolysis for the translocation of substrate proteins across biological membranes [1]. Protein substrates of the bacterial flagellum and the evolutionary related virulence-associated injectisome (or needle complex) are secreted by a homologous protein transportation system, termed type III secretion system (T3SS). Secretion of substrate proteins via the flagellar-specific type III secretion system (f-T3SS) or virulenceassociated type III secretion system (v-T3SS) of the injectisome complex is essential for the assembly of the respective nanomachine, as well as secretion of effector proteins in case of the

Authors Rosa Einenkel and Manuel Halte have equally contributed to this chapter. Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_36, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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injectisome system [reviewed in detail in [2–5]]. The T3SS is intrinsically a pmf-driven protein exporter that exploits the activity of an associated ATPase to facilitate substrate secretion [6–9]. The core T3SS consist of eight proteins that directly participate in the process of substrate protein translocation across the inner membrane [2]. Five integral membrane proteins (f-T3SS: FlhA FlhB FliP FliQ FliR; v-T3SS: SctV SctU SctR SctS SctT) form the export gate and are involved in the primary substrate recognition, substrate unfolding, export channel gating, energy transduction, and protein transport across the cytoplasmic membrane [10– 15]. An associated ATPase complex (f-T3SS: FliH FliI FliJ; v-T3SS: SctL SctN SctO) has a facilitating role in substrate recognition, substrate unfolding, and energy transduction, but is not strictly required for protein export [16]. A scaffold structure formed by an integral membrane ring (MS-ring; f-T3SS: FliF; v-T3SS: SctD SctJ) is essential for assembly of a functional core export apparatus [17]. In addition, accessory proteins form a cytoplasmic ring that facilitates substrate recognition and binding of ATPase complex components (f-T3SS: FliG FliM FliN; v-T3SS: SctQ) [18–20]. Upon completion of the scaffold structures, the periplasmic rod and extracellular hook assemble, followed by the secretion and assembly of the flagellin subunits FliC/FljB forming the filament [21]. The contribution of the pmf and ATP hydrolysis to the protein export process via T3SS has been examined using T3SS-dependent substrate protein secretion of mutant strains and after treatment with compounds that modulate the pmf. 1.1 Investigating fT3SS Substrate Protein Export in the Presence or Absence of Compounds Interfering with the pmf

Here, we describe the methodology to analyze the protein export of a f-T3SS specific substrate (FlgM) into the culture supernatant in the presence or absence of compounds interfering with the pmf (see Subheading 3.1).

1.2 Quantifying Protein Secretion of Individual f-T3SS Using Fluorescent Labeling of Flagellar Filaments

Then we present the methodology to analyze how fluorescence labeling of the extracellular flagellar filament can bring insights into the protein secretion rate of individual f-T3SS (see Subheading 3.2).

1.3 Quantifying Protein Secretion Via the f-T3SS Using Split NanoLuc Luciferase Fusion Proteins

Finally, we describe how a split NanoLuc luciferase can be used to precisely quantify protein export via the f-T3SS (see Subheading 3.3).

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Materials Standard chemicals are purchased in analytical quality from established commercial suppliers. Prepare all solutions using ultrapure water unless indicated otherwise.

2.1 Investigating fT3SS Substrate Protein Export in the Presence or Absence of Compounds Interfering with the pmf

1. Salmonella enterica serovar Typhimurium strains: TH3730 PflhDC5451::Tn10dTc[del-25], TH10874 ΔflgM5628::FRT ΔaraBAD923::flgM-FKF ParaBAD934 (see Note 1).

2.1.1 FlgM-Secretion Assay

3. Shaking incubator (see Note 2).

2. Lysogeny broth (LB) Lennox: 10 g tryptone, 5 g yeast extract, 5 g NaCl. Add 12 g agar for LB agar plates. Add water to a volume of 1 liter and autoclave. 4. Spectrophotometer for OD600 determination. 5. Anhydrotetracycline: 0.2 mg/mL stock solution in 50% H2O 50% ethanol (see Note 3). 6. L-arabinose: 20% stock in H2O (see Note 3). 7. Table-top centrifuge, refrigerated.

2.1.2 Protein Fractionation

1. Spectrophotometer for OD600 determination.

2.1.3 Protein Extraction by Filtration over a Nitrocellulose Filter

1. Nitrocellulose filter, 0.45 μm pore size.

2. Table-top centrifuge, refrigerated.

2. 2× SDS sample buffer: 100 mM Tris–HCl, pH 6.8, 4% SDS, 20% glycerol, 1% β-mercaptoethanol, 25 mM EDTA, and 0.04% bromophenol blue (see Note 4). 3. Heating block for 1.5 mL and 2.0 mL centrifugation tubes.

2.1.4 Protein Precipitation Using Trichloroacetic Acid

1. Trichloroacetic acid. Store at 4 °C. 2. Acetone. Store at -20 °C. 3. Vortexer. 4. 2× SDS sample buffer: 100 mM Tris–HCl, pH 6.8, 4% SDS, 20% glycerol, 1% β-mercaptoethanol, 25 mM EDTA, and 0.04% bromophenol blue (see Note 4). 5. Heating block for 1.5 mL and 2.0 mL centrifugation tubes.

2.1.5

Immunoblotting

1. 4–20% Precast gels. Store at 4 °C. 2. Mini-gel caster system and SDS-PAGE apparatus. 3. SDS running buffer: 25 mM Tris–HCl, 192 mM glycine, 0.1% SDS, and pH 8.3. 4. Western blot transfer membranes: 0.2 μm pore size Hybond-P polyvinylidene fluoride (PVDF) or 0.45 μm pore size nitrocellulose (see Note 5). 5. Western blot transfer buffer: 25 mM Tris–HCl, 192 mM glycine, 20% methanol, and pH 8.3.

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6. Trans-Blot apparatus for Western blot transfer. 7. Serum-purified anti-FlgM rabbit polyclonal antibodies [14], dilution 1:10,000. 8. Horseradish-peroxidase conjugated anti-rabbit polyclonal antibodies, dilution 1:10,000–1:20,000. 2.1.6 Assays to Inhibit the Proton Motive Force

1. Ionophore carbonyl cyanide m-chlorophenylhydrazone (CCCP): 20 mM stock solution: Dissolve 4.1 mg of CCCP in 1 mL of dimethyl sulfoxide (DMSO) (see Note 6). 2. Valinomycin: 20 mM stock solution: Dissolve 22.2 mg of valinomycin in 1 mL of distilled H2O. 3. Potassium chloride: 1 M stock solution: Dissolve 7.45 g of KCl in 100 mL of distilled H2O. 4. Potassium acetate: 1 M stock solution: Dissolve 9.81 g of CH3COOK in 100 mL of distilled H2O (see Note 7).

2.2 Quantifying Protein Secretion of Individual f-T3SS Using Fluorescent Labeling of Flagellar Filaments 2.2.1 Flagellin MultiLabeling

1. Salmonella enterica serovar Typhimurium strains: EM2046 Δhin-5717::FRT fliC6500 (T237C) PflhDC5451::Tn10dTc [del-25], EM11143 Δhin-5718::FRT fljB22993 (T310C) PflhDC5451::Tn10dTc[del-25] (see Note 8). 2. LB Lennox: 10 g tryptone, 5 g yeast extract, and 5 g NaCl. Add 12 g agar for LB agar plates. Add water to a volume of 1 liter and autoclave. 3. Phosphate Buffered Saline (PBS) 10×: 80 g NaCl, 26.80 g Na2HPO4 × 7H2O, 5 g NaCl, 2 g KCl, 2.70 g KH2PO4, and pH 7.4. Add water to a volume of 1 liter and autoclave. Dilute 1:10 in H2Odd to obtain a working solution PBS 1×. 4. Shaking incubator (see Note 2). 5. Spectrophotometer for OD600 determination. 6. Anhydrotetracycline (AnTc): 0.2 mg/mL stock solution in 50% H2O 50% ethanol (see Note 3). 7. Table-top centrifuge. 8. Heating block for 1.5 mL and 2.0 mL centrifugation tubes. 9. Maleimide dyes (DyLight™488/DyLight™550/ DyLight™633, ThermoFischer).

2.2.2 Preparation of Custom-Made Flow Cell

1. Microscopy slides (SuperFrost® Plus, VWR). 2. Coverslip (1.5 H, 18 × 18 mm) (Roth) (see Note 9). 3. Parafilm. 4. Poly-L-lysin 0.1% (w/v) in H2O (Sigma Aldrich). 5. Heating stirrers.

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6. Paraformaldehyde 4%. 7. Fluoroshield + DAPI (Merck). 2.2.3 Microscopy Imaging and Analysis Software

1. Epifluorescence inverted microscope. 2. Filters (see Note 10). 3. Objective 60× or 100× 4. Camera. 5. Fiji [22].

2.3 Protein Secretion via the f-T3SS Using Split NanoLuc Luciferase Fusion Proteins (See Note 11 and Fig. 3)

1. Salmonella enterica serovar Typhimurium LT2 strains: EM9905 Δhin-5717::FRT fliC23299::HiBiT (Δaa201-213 3×SAGASA-HiBiT-3×SAGASA), EM10744 Δhin-5717::FRT fliC23299::HiBiT (Δaa201-213::3×SAGASA-HiBiT-3×SAGA SA) ΔflgKL5739::FKF, EM10797 Δhin-5717::FRT fliC 23299::HiBiT (Δaa201-213::3×SAGASA-HiBiT-3×SAGASA) fliI23209 (E211D) (see Notes 12–14). 2. LB-Lennox: 10 g tryptone, 5 g yeast extract, and 5 g NaCl. Add 12 g agar for LB agar plates. Add water to a volume of 1 L and autoclave. 3. Shaking incubator. 4. Spectrophotometer for OD600 determination. 5. Table-top centrifuge. 6. Nano-Glo® HiBiT Extracellular Detection System (Promega). 7. White 384-well plate (Greiner). 8. Microplate reader for detection of bioluminescence (e.g BioTek Synergy H1).

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Methods Perform experiments at room temperature unless indicated otherwise.

3.1 Investigating fT3SS Substrate Protein Export in the Presence or Absence of Compounds Interfering with the pmf

1. Streak S. TYPHIMURIUM strains TH3730 (see Note 15) or TH10874 (see Note 16) for single colonies on fresh LB plates (see Note 1). Incubate overnight at 37 °C.

3.1.1 FlgM-Secretion Assay

3. Dilute overnight culture of S. Typhimurium strains TH3730 or TH10874 1:100 in 3 mL of LB and incubate at 37 °C in a water bath incubator, shaking at 180 rpm. Grow approximately 2 h until optical density (OD600) of 0.5. Induce flagellar gene expression by addition of 100 ng/mL of anhydrotetracycline

2. Inoculate a single colony of S. Typhimurium strains TH3730 or TH10874 into 1 mL of LB and incubate overnight at 37 °C in a water bath incubator, shaking at 180 rpm.

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and resume incubation at 37 °C for 60 min. Grow TH10874 cultures in LB supplemented with 0.2% L-arabinose to induce expression of FlgM for 60 min at 37 °C. 4. Pellet cells by 5 min centrifugation at 10,000× g and perform permeabilization and washing steps as detailed in Subheading 3.1.6. 5. Resuspend in 3 mL of LB medium containing appropriate dilutions of pmf-inhibitors and 100 ng/mL of anhydrotetracycline or 0.2% of L-arabinose for TH3730 or TH10874, respectively, and resume incubation at 37 °C for 30 min in a water bath incubator, shaking at 200 rpm as outlined in Subheading 3.1.6. 6. Store bacterial culture on ice until further treatment. 3.1.2 Protein Fractionation

1. Remove 0.5 mL of bacterial culture and record OD600 for normalization purposes (see step 4 in Subheading 3.1.3 and step 7 in Subheading 3.1.4). 2. Centrifuge 2 mL of bacterial culture at 10,000× g for 5 min in a tabletop centrifuge and transfer 1.8 mL of the supernatant to a new centrifugation tube. Discard remaining supernatant and store pellet one ice until further treatment (label with “cellular fraction”). 3. Centrifuge supernatant at 10,000× g for 5 min in a tabletop centrifuge and transfer 1.6 mL of the supernatant to a new centrifugation tube (label with “supernatant fraction”) (see Note 17). Two alternative methods can be used for collecting proteins from the culture supernatant: filtration on nitrocellulose filters (see Subheading 3.1.3) and TCA precipitation (see Subheading 3.1.4).

3.1.3 Protein Extraction by Filtration over a Nitrocellulose Filter (See Note 18)

1. Filter supernatant from step 3 in Subheading 3.1.2 through a prewetted 0.45 μm pore-size nitrocellulose filter for protein binding. 2. Elute proteins by addition of 40 μL of 2× SDS sample buffer and heat treatment for 30 min at 65 °C. 3. Resuspend bacterial pellet from step 2 in Subheading 3.1.2 and 3.2 in 50 μL of 2× SDS sample buffer and heat for 10 min at 95 °C. 4. Adjust cellular and supernatant fractions to 20 OD600 equivalents per μL by addition of 2× SDS sample buffer and keep on ice or store at -20 °C until further use.

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1. Add trichloroacetic acid to a final concentration of 10% to the supernatant from step 3 in Subheading 3.1.2 and incubate on ice for 30 min. 2. Centrifuge at 20,000× g for 30 min in a refrigerated tabletop centrifuge at 4 °C and discard supernatant. 3. Resuspend pellet in 1 mL of ice-cold acetone by vortexing. 4. Centrifuge at 20,000× g for 30 min in a refrigerated tabletop centrifuge at 4 °C and discard supernatant. 5. Dry pellet overnight or for 30 min in a laminar flow bench. 6. Resuspend supernatant pellet in 40 μL of 2× SDS sample buffer and heat for 10 min 95 °C. 7. Resuspend bacterial pellet from step 2 in Subheading 3.1.2 in 50 μL of 2× SDS sample buffer and heat for 10 min at 95 °C. 8. Adjust cellular and supernatant fractions to 20 OD600 equivalents per μL by addition of 2× SDS sample buffer and keep on ice or store at -20 °C until further use.

3.1.5

Immunoblotting

1. Load 200 OD600 equivalents of cellular and supernatant fractions onto 4–20% precast gels and perform protein separation by SDS-PAGE in standard Tris-glycine buffer. 2. Following separation, electrotransfer proteins to a 0.2 μm pore size Hybond-P PVDF transfer membrane (see Note 5) or a 0.45 μm pore size nitrocellulose membrane using a Trans-Blot transfer apparatus. 3. Perform immunodetection using appropriate concentrations of primary and secondary antibodies. In case of TH3730 or TH10874, secreted and cellular FlgM protein is detected using serum-purified anti-FlgM rabbit polyclonal antibodies [14]) and horseradish-peroxidase conjugated anti-rabbit polyclonal antibodies.

3.1.6 Assays to Inhibit the Proton Motive Force: Disruption of the pmf Using Carbonyl Cyanide MChlorophenylhydrazone (See Note 19 and Fig. 1a)

1. Grow bacterial cultures as described in Subheading 3.1.1, steps 1–3. 2. Pellet bacterial culture by 5 min centrifugation at 10,000× g, and resuspend in 3 mL of LB medium containing 0–20 μM carbonyl cyanide m-chlorophenylhydrazone and inducer of flagellar genes transcription as required for strains TH3730 and TH10874 (see Notes 20 and 21) as in step 3, Subheading 3.1.1. 3. Resume incubation at 37 °C for 30 min in a water bath shaker at 200 rpm and continue with step 6, Subheading 3.1.1.

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Fig. 1 (a) Effect of inhibition of the proton motive force on FlgM export. FlgM secretion was inhibited by addition of 10 μM uncoupling agent carbonyl cyanide m-chlorophenyl hydrazone (CCCP) and completely abolished by treatment with 20 μM CCCP in strain TH10874 (arabinose-inducible flgM). Cytoplasmic FlgM levels remained constant. (b) Effect of inhibition of the ΔΨ component of the proton motive force on FlgM export. FlgM secretion was inhibited by addition of valinomycin in the presence of K+. Cells were pretreated with 120 mM Tris to permeabilize the outer membrane to valinomycin where indicated. (c) Effect of inhibition of ΔpH on FlgM export. Secretion of FlgM for cultures grown in pH 5 was inhibited by addition of 34 mM potassium acetate. (Adapted by permission from Macmillan Publishers Ltd.: Nature [7], copyright 2008)

3.1.7 Assays to Inhibit the Proton Motive Force: Disruption of the ΔΨ Component of the pmf by K+/Valinomycin (See Note 22 and Fig. 1b)

1. Grow bacterial cultures as described in Subheading 3.1.1, steps 1–3. 2. Pellet bacterial culture by 5 min centrifugation at 10,000× g, and resuspend in 3 mL of LB medium containing 120 mM Tris–HCl, pH 7.3. Incubate for 2 min (see Note 23). 3. Pellet bacterial culture by 5 min centrifugation at 10,000 × g, discard supernatant, and resuspend in 3 mL of LB medium containing 120 mM Tris–HCl, pH 7.3, and 0–40 μM valinomycin in the presence or absence of 150 mM KCl. Add inducer of flagellar genes transcription as required for strains TH3730 and TH10874 (see Note 21). 4. Resume incubation at 37 °C for 30 min in a water bath shaker at 200 rpm and continue with step 6 in Subheading 3.1.1.

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1. Grow bacterial cultures as described in Subheading 3.1.1, steps 1–3. 2. Pellet bacterial culture by 5 min centrifugation at 10,000× g and resuspend in 3 mL of LB medium. 3. Wash bacterial cells by centrifugation at 10,000× g for 5 min and discard supernatant. 4. Resuspend pellet in 3 mL of LB medium at pH 7 or pH 5 in the presence or absence of 34 mM potassium acetate. Add inducer of flagellar genes transcription as required for strains TH3730 and TH10874 (see Note 21). 5. Resume incubation at 37 °C for 30 min in a water bath shaker at 200 rpm and continue with step 6 in Subheading 3.1.1.

3.2 Quantifying Protein Secretion of Individual f-T3SS Using Fluorescent Labeling of Flagellar Filaments 3.2.1 Fluorescent Labeling of Flagellar Filaments

1. Streak S. Typhimurium strains EM2046 or EM11143 for single colonies on fresh LB plates. Incubate overnight at 30 °C. 2. Inoculate a single colony of S. Typhimurium strains into 2 mL of LB and incubate overnight at 30 °C in dry incubator, shaking at 180 rpm. 3. Dilute overnight culture of S. Typhimurium strains 1:100 in 10 mL of LB and incubate at 30 °C in the incubator shaking at 180 rpm. Grow 2 h to reach early exponential phase. Induce flagellar gene expression by addition of 100 ng/mL of AnTc and resume incubation at 30 °C for 30 min. 4. Pellet cells by 3 min centrifugation at 4,000× g at room temperature. 5. Remove supernatant and resuspend cells in 10 mL of LB medium AnTc-free. 6. Resume incubation at 30 °C for an additional 30 min. 7. Aliquot 500 μL of culture in a 1.5 mL Eppendorf. 8. Add maleimide dye 1 at a final concentration of 10 μM (see Fig. 2a). Incubate in a thermocycler with low agitation (300 rpm) at 30 °C for 30 min. 9. Pellet the cells by centrifugation at 2,500× g for 2 min. Remove the supernatant and unbound maleimide dyes, resuspend the cells in 500 μL fresh LB supplemented with 10 μM of maleimide dye 2. 10. Resume incubation for 30 min at 30 °C with low agitation (300 rpm). 11. Repeat steps 8–10 up to six fragments labeled (see Fig. 2a). 12. After incubation with the last maleimide, wash once 2,500× g for 2 min at room temperature. Remove the supernatant and resuspend the cells in 500 μL PBS 1× (see Note 25). 13. Proceed with the preparation of the flow cell for microscopic observation.

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Fig. 2 Flagellar filament multi-labeling setup. (a) Experimental design for filament multi-labeling as described previously [27] and in Subheading 3.2.1. (b) Left: epifluorescence microscopy picture of flagellar filament stained as described above. Right: representation of the flagellar filament and measurement of the length of the successive fragments. The length of the fragments decreases over time. Scale bar = 2 μm. (c) Analysis of the length of the successive fragment plotted relative to the length of the basal fragment. As the basal fragment’s length increases, the apical fragment’s length decreases, indicating a decrease in the growth rate of the flagellar filament relative to the length 3.2.2 Preparation of Microscope Flow Cell

1. Add 50 μL drop of 0.1% poly-L-lysin on a microscopic slide: put the coverslip on the drop. Incubate coverslips in 0.1% polyL-lysin for at least 10 min. Recover the coverslip and let dry at room temperature or under a chemical hood (~10 min) with the poly-L-lysin coated side facing up. 2. Place 1–2 layers of parafilm onto a new microscope slide. Heat the parafilm at 60–70 °C on heating stirrer and press the parafilm to fuse it to the slide. Then press coverslip onto tape, with the poly-L-Lysin coated side facing down, forming a well. Repeat the heating step to fuse the coverslip with the parafilm. The poly-L-lysin coated face of the coverslip is now facing the inside of the slide. 3. Pipet gently to prevent filament breaking about 100 μL of the cells prepared above into the well and blot the excess off. Let the slide sit, coverslip-side down (1–2 min) to bind the cells to the poly-L-lysin. 4. Rinse slowly with 50 μL PBS 1×.

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Fig. 3 Experimental design to quantify substrate secretion using NanoBit®. (a) The substrate of interest is tagged with HiBiT, the small subunit of a luciferase complex. The cells express the tagged substrate. In this case, HiBiT is translationally fused to the flagellin FliC (FliC::HiBiT). FliC::HiBiT is recognized and secreted by flagellar type III secretion systems into the culture supernatant. (b) The supernatant is separated from cells by centrifugation and is transferred to a microplate. After adding the reaction mix, which contains the large subunit of the luciferase complex (LargeBiT) and the substrate, luminescence can be detected, which correlates to the quantity of secreted FliC::HiBiT. (c) Secretion Assay of the late substrate FliC::HiBiT using the Nano-Glo HiBiT extracellular detection system in a Δhin background. Shown are the mean relative light units (RLU) relative to the WT with standard deviation error bars and individual data points of three biological replicates measured in technical triplicates

5. Fix cells with 4% formaldehyde and incubate for 5 min at room temperature. 6. Rinse slowly with 50 μL PBS 1× two times. 7. Add 50 μL of mounting solution (Fluoroshield+DAPI) (see Note 26). 8. Directly image at the microscope or store the samples at 4 °C overnight (see Note 27). 3.2.3 Microscopy of Multi-Labeled Flagellar Filaments

1. Image the slide on an inverted microscope. 2. Settings will depend on the efficiency of the labeling. 3. Z-stacks are required to capture the whole filament during imaging. Parameters of the Z-stack will depend on the sample. Initial optimization pictures should be taken to get the optimal Z-stack range (see Fig. 2b, c).

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3.3 Quantifying Protein Secretion Via the f-T3SS Using Split NanoLuc Luciferase Fusion Proteins 3.3.1 Split NanoLuc Secretion Assay

1. Streak S. Typhimurium strains EM9905, EM10744, and EM10797 for single colonies on fresh LB plates. Incubate overnight at 37 °C. 2. Inoculate a single colony of the S. Typhimurium strains into 2 mL of LB and incubate overnight at 37 °C in dry incubator, shaking at 180 rpm. 3. Dilute overnight cultures of the S. Typhimurium strains 1:100 in 10 mL of LB and incubate at 37 °C in the incubator, shaking at 180 rpm. Grow ~2.5 h to reach late exponential phase (OD600 ~ 0.8–1.0). Measure and record OD600 for normalization purposes. 4. Transfer 1 mL of each culture into a 1.5 mL Eppendorf tube on ice. 5. Pellet the cells by centrifugation at 13,000× g at 4 °C for 3 min. 6. Transfer 500 μL of the supernatant into a fresh 1.5 mL Eppendorf tube, store at 4 °C until further use (see Note 28).

3.3.2 Detecting Luminescence Using the Nano-Glo® HiBiT Extracellular Detection System

All steps should be performed on ice and prepared buffers and samples stored at 4 °C. Thaw kit components at 4 °C or on ice (see Note 29). 1. Transfer 25 μL of each supernatant in technical triplicates into a white 384-well plate (see Note 30). Include 3 LB blanks. Store the 384-plate with the prepared supernatant samples at 4 °C. 2. Prepare the working solution as instructed by the manufacturer (Buffer:Substrate 1:50 and Buffer:LgBiT 1:100, e.g., for eight samples: 8 [+2 excess] × 25 μL = 250 μL buffer + 2.5 μL LgBiT + 5 μL substrate). Supernatant samples and working solution will be mixed 1:1. 3. Add 25 μL of the prepared working solution to each supernatant sample and LB blanks. Mix gently without bubble formation. 4. Measure luminescence using a microplate reader. Perform a measurement at t = 0 min and t = 10 min.

3.3.3 Analysis of NanoLuc Secretion Assay

Calculate the relative light units (RLU) of each strain using following equation: RLU = ðt 10 - t 0 Þ - ðmean of blankÞ Normalize the calculated values to the measured OD600 and to a control strain (e.g., wildtype EM9905, see Fig. 3c).

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Notes 1. Alternatively, use a mutant strain where ATP synthesis is uncoupled from the pmf: TH11802 ΔatpA::tetRA ΔflgM5628::FRT ΔaraBAD923::flgM-FKF ParaBAD934, which is deficient in a major subunit of the FOF1 ATP synthase. The absence of the FOF1 ATP synthase results in a growth defect, which can be partially rescued by growth in media containing 0.2% of glucose. 2. For best growth, use a shaking water bath incubator. 3. Filter sterilize using 0.2 μm mixed cellulose ester or polyethersulfone filters. 4. Add freshly prepared β-mercaptoethanol. 5. Transfer to a 0.2 μm pore size Hybond-P PVDF membrane is recommended for efficient electrotransfer of FlgM. 6. Dissolve freshly in dimethyl sulfoxide (DMSO). 7. Adjust pH to the desired final pH by addition of HCl or NaOH. 8. This protocol describes the labeling of S. Typhimurium flagellins FliC/FljB, but can be adapted to any flagellins/bacterial species with a known functional cysteine in their sequence. 9. Thickness of the coverslips should be verified on the objectives. For most objectives with magnification ≥60×, coverslip thickness of 0.17 mm (=1.5H) is optimal. 10. Any filters fitting the wavelength of fluorescent molecules Alexa488/Alexa555/Alexa647 and DAPI are suitable, e.g., MXR00255 (CFP/YFP/mCherry–Full Multiband Triple) (Semrock) and MXR00256–LED-DA/FI/TR/Cy5/Cy7-A (DAPI/FITC/TRITC/Cy5/Cy7–Full Multiband Penta) (Semrock). 11. NanoLuc (NLuc) luciferase is an engineered 19 kDa glow-type luciferase from the deep-sea shrimp Oplophorus gracilirostris that converts the substrate furimazine to emit blue light [23]. The system used here is based on the split NLuc consisting of two subunits: HiBiT (1.3 kDa) and LargeBit (LgBiT, 17.8 kDa), which are able to produce luminescence in the presence of furimazine when interacting [24]. 12. These strains carry a deletion of hin (recombinase), which locks the strains in the production of the flagellin FliC. Furthermore, the flagellin FliC is HiBiT-tagged. 13. This protocol can be used for quantification of various T3SS substrates of interest. However, additional genetic modifications may be necessary to ensure accurate quantification (e.g., for detection of HiBiT-tagged flagellin FliC, a deletion of hin

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(recombinase) is required to lock the cells in the production of FliC. Furthermore, a deletion of the genes flgKL encoding proteins of the hook filament junction assures that the flagellin cannot assemble into a filament and therefore is secreted fully into the supernatant). 14. The functionality and secretion ability of the translational substrate-HiBiT fusion needs to be tested prior to performing this assay, e.g., using motility assay and Western blotting. Possibly, it might be necessary to test different insertion sites for HiBiT and/or multiple protein linkers to obtain optimal functionality. 15. This strain harbors flagellar master regulatory operon flhDC under control of an anhydrotetracycline-inducible promoter and allows for constant expression of flagellar genes in the presence of inducer. 16. This strain harbors flgM as a non-structural reporter substrate of the flagellar T3SS under control of an arabinose-inducible promoter. The Para promoter harbors a mutation that results in flagellar class 3 transcription activity comparable with the wildtype (PMID 16912280). 17. This step minimizes potential contamination of the supernatant fraction by residual bacterial cells. 18. Protein binding to nitrocellulose filters is recommended for efficient recovery of secreted FlgM from the culture supernatant. 19. The pmf consists of two components, a proton concentration gradient (ΔpH) and a charge difference between the periplasmic and cytoplasmic faces of the membrane (ΔΨ). The ionophore carbonyl cyanide m-chlorophenylhydrazone (CCCP) disrupts both the proton gradient ΔpH and the membrane potential ΔΨ by causing an influx of H+ into the cytoplasm [6]. 20. To control for DMSO-induced effects, add 0.5% DMSO to samples not treated with CCCP. 21. Add inducer 100 ng/mL anhydrotetracycline or 0.2% L-arabinose for continuous expression of flagellar genes (TH3730) or flgM (TH10874), respectively. 22. Valinomycin renders membranes permeable to potassium, which dissipates the ΔΨ component of the pmf by balancing the charge difference [7, 25]. 23. Appropriate controls include samples not treated with 120 mM Tris–HCl, pH 7.3. 24. Weak acids such as acetate or benzoate cross the cytoplasmic membrane in neutral form and release a proton in the cytoplasm. The resulting decrease in the cytoplasmic pH essentially collapses the proton gradient ΔpH at an external pH of 5 [7, 26].

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25. This final washing step with PBS helps to improve the overall signal, fluorescence caused by unbound maleimide injected into the flow cell and artifact formed by the PFA fixation on LB medium. 26. If DNA staining of the cells is not required, or performed with other DNA staining directly on the living cells or in the flowcell after fixation, Fluoroshield can be replaced by other glycerol mounting solution such as mowiol. 27. In the case of storage overnight or for several day at 4 °C, seal the slides using nail polish to prevent drying. 28. Samples should not be stored for long time periods before adding the working solution, rather proceed immediately with the protocol after sample acquisition. 29. If the Nano-Glo® HiBiT Extracellular Buffer is used on multiple days within a week, do not re-freeze buffer, but rather keep it at 4 °C. Always prepare the working solution fresh and after sample acquisition. 30. Regarding the white 384-well plate, it is recommended to leave the wells at the edges empty and leave a well empty in between samples.

Acknowledgments This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant to M.E.; agreement no. 864971). References 1. Wickner W, Schekman R (2005) Protein translocation across biological membranes. Science 310:1452–1456 2. Halte M, Erhardt M (2021) Protein export via the type III secretion system of the bacterial flagellum. Biomol Ther 11:186 3. Minamino T (2014) Protein export through the bacterial flagellar type III export pathway. Biochim Biophys Acta 1843:1642–1648 4. Diepold A, Wagner S (2014) Assembly of the bacterial type III secretion machinery. FEMS Microbiol Rev 38:802–822 5. Erhardt M, Namba K, Hughes KT (2010) Bacterial nanomachines: the flagellum and type III injectisome. Cold Spring Harb Perspect Biol 2: a000299 6. Lee PC, Zmina SE, Stopford CM, Toska J, Rietsch A (2014) Control of type III secretion activity and substrate specificity by the

cytoplasmic regulator PcrG. Proc Natl Acad Sci U S A 111:E2027–E2036 7. Paul K, Erhardt M, Hirano T, Blair DF, Hughes KT (2008) Energy source of flagellar type III secretion. Nature 451:489–492 8. Minamino T, Namba K (2008) Distinct roles of the FliI ATPase and proton motive force in bacterial flagellar protein export. Nature 451: 485–488 9. Wilharm G, Lehmann V, Krauss K, Lehnert B, Richter S, Ruckdeschel K, Heesemann J, Tru¨lzsch K (2004) Yersinia enterocolitica type III secretion depends on the proton motive force but not on the flagellar motor components MotA and MotB. Infect Immun 72: 4004–4009 10. Hu¨sing S, Halte M, van Look U, Guse A, Ga´lvez EJC, Charpentier E, Blair DF, Erhardt M, Renault TT (2021) Control of membrane

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barrier during bacterial type-III protein secretion. Nat Commun 12:3999 11. Ward E, Renault TT, Kim EA, Erhardt M, Hughes KT, Blair DF (2018) Type-III secretion pore formed by flagellar protein FliP. Mol Microbiol 107:94–103 12. Fabiani FD, Renault TT, Peters B, Dietsche T, Ga´lvez EJC, Guse A, Freier K, Charpentier E, Strowig T, Franz-Wachtel M, Macek B, Wagner S, Hensel M, Erhardt M (2017) A flagellum-specific chaperone facilitates assembly of the core type III export apparatus of the bacterial flagellum. PLoS Biol 15: e2002267 13. Fukumura T, Makino F, Dietsche T, Kinoshita M, Kato T, Wagner S, Namba K, Imada K, Minamino T (2017) Assembly and stoichiometry of the core structure of the bacterial flagellar type III export gate complex. PLoS Biol 15:e2002281 14. Erhardt M, Wheatley P, Kim EA, Hirano T, Zhang Y, Sarkar MK, Hughes KT, Blair DF (2017) Mechanism of type-III protein secretion: regulation of FlhA conformation by a functionally critical charged-residue cluster. Mol Microbiol 104:234–249 15. Terahara N, Inoue Y, Kodera N, Morimoto YV, Uchihashi T, Imada K, Ando T, Namba K, Minamino T (2018) Insight into structural remodeling of the FlhA ring responsible for bacterial flagellar type III protein export. Science. Advances 4:eaao7054 16. Erhardt M, Mertens ME, Fabiani FD, Hughes KT (2014) ATPase-independent type-III protein secretion in Salmonella enterica. PLoS Genet 10:e1004800 17. Morimoto YV, Ito M, Hiraoka KD, Che YS, Bai F, Kami-Ike N, Namba K, Minamino T (2014) Assembly and stoichiometry of FliF and FlhA in Salmonella flagellar basal body. Mol Microbiol 91:1214–1226 18. Diepold A, Kudryashev M, Delalez NJ, Berry RM, Armitage JP (2015) Composition, formation, and regulation of the cytosolic c-ring, a dynamic component of the type III secretion injectisome. PLoS Biol 13:e1002039 19. Erhardt M, Hughes KT (2010) C-ring requirement in flagellar type III secretion is bypassed

by FlhDC upregulation. Mol Microbiol 75: 376–393 20. McMurry JL, Murphy JW, Gonza´lez-Pedrajo B (2006) The FliN-FliH interaction mediates localization of flagellar export ATPase FliI to the C ring complex. Biochemistry 45:11790– 11798 21. Aldridge P, Hughes KT (2002) Regulation of flagellar assembly. Curr Opin Microbiol 5:160– 165 22. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez J-Y, White DJ, Hartenstein V, Eliceiri K, Tomancak P, Cardona A (2012) Fiji: an opensource platform for biological-image analysis. Nat Methods 9:676–682 23. Hall MP, Unch J, Binkowski BF, Valley MP, Butler BL, Wood MG, Otto P, Zimmerman K, Vidugiris G, Machleidt T, Robers MB, Benink HA, Eggers CT, Slater MR, Meisenheimer PL, Klaubert DH, Fan F, Encell LP, Wood KV (2012) Engineered luciferase reporter from a deep sea shrimp utilizing a novel imidazopyrazinone substrate. ACS Chem Biol 7:1848–1857 24. Dixon AS, Schwinn MK, Hall MP, Zimmerman K, Otto P, Lubben TH, Butler BL, Binkowski BF, Machleidt T, Kirkland TA, Wood MG, Eggers CT, Encell LP, Wood KV (2016) NanoLuc complementation reporter optimized for accurate measurement of protein interactions in cells. ACS Chem Biol 11:400– 408 25. Minamino T, Morimoto YV, Hara N, Namba K (2011) An energy transduction mechanism used in bacterial flagellar type III protein export. Nat Commun 2:475 26. Minamino T, Imae Y, Oosawa F, Kobayashi Y, Oosawa K (2003) Effect of intracellular pH on rotational speed of bacterial flagellar motors. J Bacteriol 185:1190–1194 27. Renault TT, Abraham AO, Bergmiller T, Paradis G, Rainville S, Charpentier E, Guet CC, Tu Y, Namba K, Keener JP, Minamino T, Erhardt M (2017) Bacterial flagella grow through an injection-diffusion mechanism. elife 6:e23136

Chapter 37 Quantitative Determination of Antibacterial Activity During Bacterial Coculture Juliana Alcoforado Diniz, Christopher Earl, Ruth E. Hernandez, Birte Hollmann, and Sarah J. Coulthurst Abstract Antibacterial activity assays are an important tool in the assessment of the ability of one bacterium to kill or inhibit the growth of another, for example, during the study of the Type VI secretion system (T6SS) and the antibacterial toxins it secretes. The method we describe here can detect the ability of a bacterial strain to kill or inhibit other bacterial cells in a contact-dependent manner when cocultured on an agar surface. It is particularly useful since it enumerates the recovery of viable target cells and thus enables quantification of the antibacterial activity. We provide a detailed description of how to measure the T6SS-dependent antibacterial activity of a bacterium such as Serratia marcescens against a competitor prokaryotic organism, Escherichia coli, and describe possible variations in the method to allow adaptation to other attacker and target organisms. Key words Gram-negative bacteria, Protein secretion system, Type VI Secretion System, Coculture assay, Antibacterial activity, Bacterial competitive fitness, Toxin/Immunity pair

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Introduction To gain a fitness advantage in mixed microbial communities, many bacteria have developed the ability to kill competitor prokaryotic cells. Protein secretion systems are an important weapon in this war, particularly the Type VI Secretion System (T6SS), which can be utilized to kill both closely and distantly related competitors efficiently [1]. This versatile nanomachinery [2], which in some cases can also be used against eukaryotic targets [3], is widespread in Gram-negative bacteria. Work by different groups has resulted in the identification of many new antibacterial toxins, also called effectors, delivered directly into target bacterial cells by the T6SS.

Authors Juliana Alcoforado Diniz, Christopher Earl, and Ruth E. Hernandez contributed equally to the updated version and are listed alphabetically. Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_37, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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These include a variety of enzymes and pore forming proteins able to disrupt conserved and essential features of bacterial cells, including the cell wall, inner membrane, nucleic acids, and cellular cofactors. Together with these toxins, the secreting organism also possesses immunity proteins that are responsible for specific neutralization of each effector, providing self-resistance and resistance to effectors delivered by genetically-identical cells [3–5]. Recently, immunity protein-independent mechanisms of T6SS resistance have been reported, including, for example, modification of the cell envelope or activation of stress responses by the targeted cell [5]. Described here is a coculture-based assay for contactdependent antibacterial activity. Such assays represent an important tool to monitor the level and impact of the T6SS-dependent antibacterial activity and also confirm the identity of new effectorimmunity pairs. This assay is performed on the solid surface of an agar plate, since T6SS-mediated antibacterial activity requires intimate cell-cell contact to permit puncturing by the T6SS machinery [6–8]. In brief, the accessible method described here involves coculture of an attacker and a target strain of bacteria on the surface of an agar plate for a defined time, followed by the use of antibiotic selection to kill the attacker strain and allow recovery and enumeration of viable target cells. A schematic depiction of this method is given in Fig. 1. To determine the number of surviving target cells following exposure to the T6SS-wielding attacker, a standard serial dilution-based viable count of target cells is performed. This method is quantitative and reliable, with an extended dynamic range (target cell recovery from 101 to >1010 colony forming units (cfu) can be quantified due to the use of a serial dilution approach). Alternative assays exist, which use colorimetric or fluorescent reporters to distinguish target cells within a coculture [8– 10]. These approaches have the advantage of convenience, but are limited by a reduction in the dynamic range of the output and/or potential issues in separating or distinguishing attacker and target cells during quantification. In Serratia marcescens, the technique described here has been successfully implemented to demonstrate the existence and impact of T6SS-mediated antibacterial activity and to identify new T6SSdependent antibacterial toxins [11–15]. This technique, or minor variations of it, has also been used to measure T6SS-mediated antibacterial activity in other organisms, including Vibrio cholerae, Pseudomonas aeruginosa, Agrobacterium tumefaciens, and Chromobacterium violaceum [16–19]. One such adaptation of this assay allowed for the discovery and quantification of antifungal activity by the S. marcescens T6SS [20]. Overall, this assay can be an important tool to demonstrate T6SS-dependent activity against competitor organisms, to characterize the functionality of mutants in the T6SS and to confirm the identification of new toxin/

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Fig. 1 Schematic overview of the antibacterial activity assay. Following overnight growth in a “patch,” the target strain (Ta) and different attacker strains (wild type, WT; T6SS inactive mutant, ΔT6; a mutant lacking a toxin of interest, ΔTX; and a no-attacker, medium-only control, LB) are mixed in a ratio of 1 attacker to 1 target and spotted onto an agar solid surface. After a defined incubation period at the required temperature, the coculture spots (LB + target, green; wild type + target, blue; ΔT6SS + target, red; and Δtoxin + target, grey) are scraped off, the cells resuspended, and serial dilutions prepared. For an initial trial, these dilutions from neat (N) to 10-6 (-6) are spotted on an agar plate supplemented with antibiotic selective for growth of the target only. After incubation, estimation of target recovery from the trial plate is used to determine the dilution, which will provide a few tens of single colonies per plate in the proper experiment. For the proper experiment, an appropriate volume of the correct dilution of the coculture is spread on a selective plate with a glass spreader and the colonies are counted following overnight incubation; replicate experiments provide fully quantitative data. See text for full details

immunity pairs. It could also be applied to other inter-bacterial competitive mechanisms beyond the T6SS. Here, we describe in detail how to perform an antibacterial activity assay of S. marcescens (attacker) against Escherichia coli (target) as an example, along with ways to adapt the assay to other systems of interest.

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Materials 1. Liquid LB medium: 10 g tryptone, 10 g NaCl, 5 g yeast extract, 1000 mL deionized water. Mix, adjust pH to 7.5 and autoclave at 121 °C for 20 min.

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2. LB agar: 10 g tryptone, 10 g NaCl, 5 g yeast extract, 1000 mL deionized water, 16 g select agar. Mix, adjust pH to 7.5 and autoclave at 121 °C for 20 min. 3. Antibiotic: Dissolve 10 mg of streptomycin sulfate in 1 mL of deionized water and pass through a syringe filter with pore size 0.2 μm to sterilize. Aliquot and store at -20 °C. 4. LB agar plates: Following autoclaving, bring the molten agar to 55 °C. Under sterile conditions, dispense 20 mL into each 90 mm single vented plastic petri dish then allow to cool and set. 5. LB agar plates plus antibiotic: Prepare LB agar plates as above, except this time, add streptomycin to a final concentration of 100 μg/mL (1/100 dilution) while the molten agar is at 55 °C and mix well before pouring the plates. 6. Sterile disposable inoculation loops 10 μL. 7. Bent glass rod and ethanol for spreading cell suspensions on agar plates. 8. Optional: tally counter or pen-style colony counter for counting colonies. 9. 30 and 37 °C static laboratory incubator. 10. Laminar flow cabinet (if unavailable, alternative plate-drying methods can be used).

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Methods Carry out all procedures at room temperature and under sterile conditions. 1. Streak out the required target and attacker strains from freezer stocks and grow to single colonies according to their normal requirements. In this example, the target strain is a streptomycin resistant strain of Escherichia coli K12, such as strain MC4100 [21] (see Note 1). The attacker strains are selected according to the experiment; they should include, at a minimum, the wild type (e.g., S. marcescens Db10) and a T6SS inactive mutant strain (lacking one of the core components), plus as many other mutants as required. 2. Using a sterile inoculating loop, spread a single colony from each of the strains onto LB agar plate as a “patch” (Fig. 1) and incubate overnight at the optimal growth temperature for each strain. Up to five strains can be patched together on the same plate, taking care to keep them apart. 3. Dry the LB plates that will be used for the coculture spots the next day; for this, keep them open for 2 h in a laminar flow

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cabinet and then store at room temperature overnight (see Note 2). 4. Using a sterile disposable inoculating loop, scrape off a stripe of cells from each of the overnight patches described in step 2, and resuspend in 0.5 mL of sterile LB in a sterile 1.5 mL microcentrifuge tube (agitate the loop, remove, and then vortex the tube). 5. Measure the optical density of the resuspended cells for each of the target and attacker strains and normalize to an OD600 of 0.5 in a final volume of 100 μL of sterile LB media (e.g., if OD600 is 2.5, then add 20 μL of the culture to 80 μL of media). 6. Mix together normalized attacker and target cells at a ratio of 1 attacker: 1 target (e.g., 25 μL attacker +25 μL target) (see Note 3). Do this for each attacker and also include 1 LB: 1 target as a no-attacker control. 7. Spot 25 μL of each mixture onto an LB agar plate (all the spots from one replicate, i.e., one coculture spot for each attacker, go on the same plate). When the coculture is being performed at 37 °C (see Note 4), pre-warm this plate. 8. Wait for 5 min to allow the spots to dry and put the plates immediately in the incubator at 37 °C for 4 h. (see Note 5). 9. Following the incubation period, scrape off each spot using a sterile disposable loop and resuspend the cells in 1 mL sterile LB. Mix thoroughly on a vortex for approximately 30 s or until the pellet is completely dissolved. 10. Prepare serial tenfold dilutions of each resuspension from neat to 10-6 using sterile LB (e.g., 90 μL LB plus 10 μL preceding dilution). Make sure to change pipette tips and to vortex 5 s between each dilution step. Then put diluted samples onto antibiotic-containing selective media to enumerate the surviving target cells, in one of two formats described in steps 11 and 12: 11. Trial: When testing a particular attacker/target combination for the first time, it is advisable to perform a trial experiment to determine which is the correct dilution to spread on a selective plate in order give a few tens of single colonies. For this, perform a full serial tenfold dilution of the resuspended cells, from neat to 10-6, and spot 10 μL of each dilution onto an LB + streptomycin plate, as shown in Fig. 1. Incubate at 37 °C (or the target organism’s preferred growth temperature) overnight or until single colonies have grown. 12. Full-scale experiment: Prepare a suitable dilution of the resuspended cells for each attacker/target pair (based on the trial) and spread 50 μL or 100 μL onto LB + streptomycin plates. We recommend using a bent glass rod, sterilized by dipping in

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ethanol and burnt off using a flame, to spread the cell suspension evenly over the agar surface. Incubate at 37 °C (or the target organism’s preferred growth temperature) overnight or until single colonies have grown. It is important that wellspaced single colonies are achieved; if this is not the case, adjust the volume or dilution spread on the plate. 13. Count the colonies on the enumeration plates, using a tally or pen-style colony counter if preferred. Calculate the number of viable target cells recovered, expressed as cfu per coculture spot. See also Notes 6 and 7. Example (Fig. 2): Target with LB only: 30 colonies in 50 μL of 10-6 dilution → 30 × (1000/50) × 106 = 6 × 108 cells/spot. Target with wild type S. marcescens: 11 colonies in 100 μL of 10-2 dilution → 11 × (1000/100) ×102 = 1.1 × 104 cells/ spot. Target with T6SS mutant: 45 colonies in 100 μL of 10-6 dilution → 45 × (1000/100) × 106 = 4.5 × 108 cells/spot.

4

Notes 1. Streptomycin resistant strains: Target selection does not have to utilize streptomycin; however, an antibiotic to which the attacker is fully sensitive and the target is fully resistant is required. This can be achieved using an intrinsic resistance of the target strain, or alternatively a chromosomally encoded, stable resistance determinant can be introduced by standard genetic methods. 2. Agar drying time: In our experience, drying the plates sufficiently is critical to ensure consistent data. Drying time may differ depending on environment for LB agar plate preparation. 3. Coculture ratios: The initial ratio of attacker:target in the coculture spot can be varied. In our experience, 1:1 or 5:1 normally give the best outcome. However, especially if the two organisms are mis-matched in terms of growth rate or the killing effect is very small, more extreme ratios can work better. 4. Incubation temperatures: The temperature at which the coculture spots are incubated can be varied and optimized to suit the attacker/target combination. The attacker strain S. marcescens can grow at 37 °C or 30 °C; in this case, the choice is based on the target strain.

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Fig. 2 Graphical representation of data generated by the antibacterial activity assay. Top: Table showing the number of cfu per coculture spot resulting from a typical assay, from the example in the main text. Here, just the first replicate (R1) is represented so the mean corresponds to a single replicate and there is no standard error of the mean (SEM). However, in the proper competition experiment, at least four replicates should be performed and the mean ± SEM presented, typically with individual data points shown. Bottom: Graph generated using Microsoft Excel with the data provided in the table. The y-axis shows the number of recovered target cells, presented as number of cfu per coculture spot, and the x-axis shows the attacker strains: no attacker control (LB), wild type (WT) and an inactive T6SS mutant (ΔT6SS)

5. Incubation times: the length of the incubation time for the coculture of attacker and target can also be varied. 6. Number of replicates: In order to obtain quantitative data, four (or more) replicates are required. For convenience, two replicates per day, spaced out by an hour is ideal. Start the replicates from fresh patches of cells. 7. If desired, the recovery of the attacker following coculture can also be determined simultaneously, for example if the final ratio of attacker:target is of interest. In this case, the cells recovered from the coculture are enumerated in parallel on plates containing two antibiotics separately, one to which the target is sensitive and the attacker is resistant.

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Acknowledgments This work was supported by Coordenac¸˜ao de Aperfeic¸oamento de Pessoal de Nı´vel Superior (CAPES, PhD studentship to J.A.D.) and Wellcome (Senior Fellowship to S.J.C., PhD studentship to REH). References 1. Gallegos-Monterrosa R, Coulthurst SJ (2021) The ecological impact of a bacterial weapon: microbial interactions and the Type VI secretion system. FEMS Microbiol Rev 45:1–18 2. Coulthurst S (2019) The type VI secretion system: a versatile bacterial weapon. Microbiology 165:503–515 3. Hernandez RE, Gallegos-Monterrosa R, Coulthurst SJ (2020) Type VI secretion system effector proteins: effective weapons for bacterial competitiveness. Cell Microbiol 22:1–9 4. Jure˙nas D, Journet L (2021) Activity, delivery, and diversity of type VI secretion effectors. Mol Microbiol 115:383–394 5. Monjara´s Feria J, Valvano MA (2020) An overview of anti-eukaryotic T6SS effectors. Front Cell Infect Microbiol 10 6. Shneider MM, Buth SA, Ho BT et al (2013) PAAR-repeat proteins sharpen and diversify the type VI secretion system spike. Nature 500: 350–353 7. Russell AB, Hood RD, Bui NK et al (2011) Type VI secretion delivers bacteriolytic effectors to target cells. Nature 475:343–349 8. Schwarz S, West TE, Boyer F et al (2010) Burkholderia type VI secretion systems have distinct roles in eukaryotic and bacterial cell interactions. PLoS Pathog 6:e1001068 9. Gueguen E, Cascales E (2013) Promoter swapping unveils the role of the Citrobacter rodentium CTS1 type VI secretion system in interbacterial competition. Appl Environ Microbiol 79:32–38 10. Hachani A, Lossi NS, Filloux A (2013) A visual assay to monitor T6SS-mediated bacterial competition. J Vis Exp e50103 11. Alcoforado Diniz J, Coulthurst SJ (2015) Intraspecies competition in Serratia marcescens is mediated by type VI-secreted Rhs effectors and a conserved effector-associated accessory protein. J Bacteriol 197:2350–2360 12. English G, Trunk K, Rao VA et al (2012) New secreted toxins and immunity proteins encoded within the type VI secretion system gene

cluster of Serratia marcescens. Mol Microbiol 86:921–936 13. Fritsch MJ, Trunk K, Diniz JA et al (2013) Proteomic identification of novel secreted antibacterial toxins of the Serratia marcescens type VI secretion system. Mol Cell Proteomics 12: 2735–2749 14. Murdoch SL, Trunk K, English G et al (2011) The opportunistic pathogen Serratia marcescens utilizes type VI secretion to target bacterial competitors. J Bacteriol 193:6057–6069 15. Mariano G, Trunk K, Williams DJ et al (2019) A family of type VI secretion system effector proteins that form ion-selective pores. Nat Commun 10:5484 16. Hood RD, Singh P, Hsu FS et al (2010) A type VI secretion system of Pseudomonas aeruginosa targets a toxin to bacteria. Cell Host Microbe 7:25–37 17. Ma LS, Hachani A, Lin JS et al (2014) Agrobacterium tumefaciens deploys a superfamily of type VI secretion DNase effectors as weapons for interbacterial competition in planta. Cell Host Microbe 16:94–104 18. Alves JA, Leal FC, Previato-Mello M et al (2022) A quorum sensing-regulated type VI secretion system containing multiple nonredundant VgrG proteins is required for interbacterial competition in Chromobacterium violaceum. Microbiol Spectr 10:e01576– e01522 19. MacIntyre DL, Miyata ST, Kitaoka M et al (2010) The Vibrio cholerae type VI secretion system displays antimicrobial properties. Proc Natl Acad Sci 107:19520–19524 20. Trunk K, Peltier J, Liu YC et al (2018) The type VI secretion system deploys antifungal effectors against microbial competitors. Nat Microbiol 3:920–931 21. Casadaban M, Cohen S (1979) Lactose genes fused to exogenous promoters in one step using a Mu-lac bacteriophage: in vivo probe for transcriptional control sequences. Proc Natl Acad Sci U S A 76:4530–4533

Chapter 38 Investigating Secretion Systems and Effectors on Galleria mellonella Antonia Habich and Daniel Unterweger Abstract Infection experiments with Galleria mellonella enable the measurement of virulence that is mediated by secretion systems and their effector proteins in vivo. G. mellonella has an innate immune system and shares similarities with the complex host environment of mammals. Unlike other invertebrate model systems, experiments can be performed at mammalian body temperature. Here, we describe the systemic infection of G. mellonella with Pseudomonas aeruginosa with and without functional secretion systems. A Kaplan–Meier curve is constructed showing the percent survival of animals over time. Key words Galleria mellonella, Bacterial secretion systems, Pseudomonas aeruginosa, Bacterial toxins, Type III secretion system, Type V secretion system, Contact-dependent growth inhibition, Type VI secretion system

1

Introduction Galleria mellonella is an insect of the order lepidoptera that is used to test bacterial virulence by secretion systems and their effector proteins in vivo. Upon systemic infection, bacteria encounter host cells and face the insect’s innate immune system. The latter is composed of a humoral and cellular response (recently reviewed in detail by Me´nard et al. [1]). Upon oral infection, bacteria additionally encounter the insect’s intestinal microbiota. Secretion system-mediated host damage, immunogenicity, and immune evasion can shorten the insect’s life span. As such, host death is a powerful read-out of virulence. Multiple studies have demonstrated that findings on G. mellonella infections were generalizable to those on other animal models [2–4] and were reflective of pathogenicity in patients [5]. At least seven bacterial secretion systems in a range of species have been studied using infection experiments with G. mellonella: A functional type II secretion system was shown to contribute to the

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5_38, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

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virulence of Acinetobacter nosocomialis [6]. The type III secretion system (T3SS) enhanced the virulence of P. aeruginosa [7]. Without a functional type IV secretion system, Legionella pneumophila lost its virulence [8]. The type V secretion system (T5SS) of P. aeruginosa enhanced host colonization [9]. A mutant Acinetobacter baumanii with a dysfunctional type VI secretion system (T6SS) showed reduced virulence [10]. Virulent Staphylococcus lugdunensis strains harbor a putative type VII secretion system, although its role in virulence of these strains remains unconfirmed [11]. Similarly, the type IX secretion system is associated with increased virulence in G. mellonella, and causality remains to be tested [12]. These examples are just a glimpse of the existing literature, as demonstrated below by the example of the multiple studies on T6SS-mediated virulence. Infection studies using bacterial mutants that lack a functional T6SS apparatus have revealed T6SS-mediated virulence in A. baumanii [10], Burkholderia thailandensis [13, 14], Campylobacter jejuni [15], Francisella tularensis subsp. novicida [16], Klebsiella pneumoniae [17], and P. aeruginosa [18, 19]. Further, infection experiments with bacterial mutants that lack individual effectors have been used to demonstrate effectorspecific virulence as a result of particular effector activities [13, 14, 16, 17, 20]. In a few cases, the T6SS was found to have no effect on virulence in G. mellonella [21, 22]. Experiments with G. mellonella can be customized to address a variety of research questions on the secretion system of interest. Although the insects are most often infected systemically, oral infections [23] and infections of burn wounds [24] have also been described. Beyond the recording of host survival and measurements of bacterial load, various possibilities exist to explore changes in the host’s immune response and secretion systemmediated phenotypes more specifically by (i) measuring the expression of larval immune genes and bacterial virulence genes [25, 26], (ii) collecting material for histopathology [27], (iii) quantifying immune cells [28], and (iv) extracting phagocytic immune cells for characterization and infection experiments ex vivo [29]. Here, we describe systemic infections of G. mellonella with wild-type P. aeruginosa and P. aeruginosa mutants that lack a functional T3SS, T5SS, or T6SS. Each of these secretion systems and their effectors has been previously shown to contribute to virulence of P. aeruginosa [7, 9, 18–20, 30] and was tested for the purpose of this chapter side by side. Larval survival was recorded over time and plotted as a Kaplan–Meier curve. Prolonged survival was interpreted as a reduction in bacterial virulence due to dysfunctional secretion systems. In summary, we were able to measure the contribution of three secretion systems to the virulence of P. aeruginosa using the G. mellonella infection model.

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Materials 1. Galleria mellonella larvae: Wax moth larvae can be purchased from most live food vendors, purchased at research grade level from a few companies (e.g., TruLarv™ from BioSystems Technology Ltd), or bred in-house (see Note 1). After delivery, insects can be kept at 10 °C in the dark for up to 3 weeks. 2. Fine weighing scale. 3. Tweezers. 4. Lysogeny Broth (LB) growth medium: 10 g/L tryptone, 5 g/ L yeast extract, 10 g/L NaCl, dissolve in distilled water, autoclave to sterilize. 5. LB agar: 10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl, 15 g/L agar, dissolve in distilled water, autoclave to sterilize. 6. Pseudomonas isolation agar: 50 g/L Pseudomonas isolation agar (Sigma-Aldrich), dissolve in distilled water, add 20 mL glycerol, autoclave to sterilize. 7. Cooling microcentrifuge. 8. Petri dishes, 90 mm. 9. Falcon tubes, 50 mL. 10. Eppendorf tubes, 1.5 mL and 2 mL. 11. Q-tips. 12. Ethanol (70%). 13. Phosphate buffered saline (PBS). 14. Hamilton syringe. 15. Needle-cleaning kit including cleaning wires and cleaning solution (Hamilton Company). 16. Deep-well plate.

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Methods

3.1 Preparation of Insects

1. The day before the infection, select insects based on weight (see Note 2) and health. Larvae that are active and show no or minimal signs of melanization are considered healthy (Fig. 1a). 2. Keep insects at room temperature in the dark until use.

3.2 Preparation of Bacteria

1. Prepare liquid cultures of P. aeruginosa strains from glycerol stocks in LB broth and incubate at 37 °C, 180 RPM, overnight. 2. Dilute overnight cultures (1:50 in 2 mL liquid LB broth) and grow at 37 °C, 180 RPM to an OD600 of approximately 0.6.

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Fig. 1 G. mellonella larvae before and after infection. (a) Healthy larva. (b) The bacterial suspension is injected into the last right proleg. (c) Dead, heavily melanized larva

3. Measure OD600 and calculate the volume corresponding to the desired number of bacteria for infection (see Note 3). 4. Wash bacteria by centrifugation (6000× g, 5 min, 4 °C). Resuspend pellets in cold sterile PBS. 5. Dilute the bacterial suspension in cold PBS based on the above calculation (step 3). 6. Keep bacteria on ice until further use. 3.3

Infection

1. Examine the health of the selected larvae (see Subheading 3.1) and remove dead insects. 2. Prepare one 1.5 mL tube with 70% ethanol, two 1.5 mL tubes with PBS, and one empty 1.5 mL tube for liquid waste. 3. Fill the Hamilton syringe with 70% ethanol to capacity and let it sit for 20 min. Expel liquid and wash twice with sterile PBS. Washing is performed by filling the syringe to capacity and expelling the content as liquid waste. 4. Infect groups of ten insects with each bacterial strain and with PBS as a mock control: Clean the last right proleg and surrounding tissue of the larvae with 70% ethanol using a Q-tip. Inject 10 μL of the suspension with the bacteria of interest or PBS as a mock control into the last right proleg using the Hamilton syringe (Fig. 1b). To minimize the effect of time difference between individual infections, keep infected larvae at 4 °C until all insects are infected (see Note 4). 5. The syringe should be washed between infections with different bacterial strains (see Notes 5 and 6). 6. Incubate the infected larvae at 37 °C. 7. Clean syringes using the cleaning kit (see Note 7).

3.4

Death Monitoring

1. Examine the health of all larvae regularly and record the number of dead insects (see Note 8). A larva is considered dead when it does not respond to poking with tweezers. Dead insects are often heavily melanized (Fig. 1c). 2. Move dead larvae from petri dish into 2 mL tubes for investigations (see Subheading 3.5) or discard them. 3. Analysis.

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Fig. 2 Bacterial secretion systems shorten larval survival and increase virulence of P. aeruginosa. (a) Kaplan– Meier curves from one representative experiment. Results of the log-rank test are indicated (*P < 0.05; ***P < 0.001). The mutant ΔpscC lacks the gene with the locus tag PA1716, ΔCDI1 lacks PA0040, PA0041, and PA0041a, and ΔtssB2 lacks PA1657. (b) Ratio of bacterial load at the time of death to inoculum in CFUs/ mL. Each dot represents one insect. Mean and standard deviation are indicated. Data of both panels is derived from the same experiment

(i) Plot survival data as a Kaplan-Meier curve (Fig. 2a). (ii) Perform log-rank test to test for significant differences between survival curves. 3.5 Assess Bacterial Burden at the Time of Death

1. To assess the bacterial load of inoculum, plate bacterial suspensions and, if applicable, dilutions thereof on LB plates. 2. Incubate plates at 37 °C overnight. 3. Count colony forming units (CFUs) and determine the bacterial load of each inoculum. 4. To assess the bacterial load at the time of death, transfer a dead larva into a 2 mL tube containing 0.5 mL PBS using tweezers. 5. Smash larva using a 200 μL pipette tip. 6. Transfer liquid (with fat) into a deep well plate and make serial dilutions (up to 10-6) in PBS. 7. Plate four dilutions (10-3 to 10-6) on Pseudomonas isolation agar. 8. Incubate plates overnight at 37 °C. 9. Count CFUs and determine the bacterial burden of each larva. 10. For analysis, normalize the bacterial load per larva to inoculum and plot the resulting ratio to inoculum at the time of death for each bacterial strain (Fig. 2b, see Note 9).

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Notes 1. For Galleria mellonella breeding, supply larvae with food (mix 5 g honey, 5 g glycerol, 2 g yeast, 2 g milk powder, and 5–10 g wheat) and keep them at 30 °C in a well-sealed ventilated plastic box (pores below 0.4 mm) until they metamorphose into moths. Transfer moths into an insect cage at room temperature. After a few days, eggs can be found in the cage, commonly in corners and underneath the lid. Transfer eggs into a box with fresh food using a brush and incubate at 30 °C. Check on insects at least every other day. Once eggs become 6th-instar larvae and have reached the desired weight, larvae can be used for experiments. They can be stored at 10 °C without food for up to 2 weeks. In-house larvae were photographed for Fig. 1 and infected for Fig. 2. 2. Larvae bread in-house tend to be smaller than those purchased. We use in-house larvae weighing 150–300 mg and purchased larvae weighing 250–400 mg for experiments. 3. The infectious dose varies between strains and species. For P. aeruginosa PAO1, we aim for 60 bacteria per insect. 4. To minimize bias due to the order of strains with which the larvae are infected, randomly assign the order for each biological replicate. 5. Wash syringe by first incubating it with 70% ethanol for 10 min, followed by two short washing steps with 70% ethanol and two washing steps with sterile PBS. 6. For a better workflow, use two syringes in one experiment. While one syringe is being cleaned, the other syringe can be used to proceed with the injections. 7. To avoid build-up of debris, clean needle after roughly 30 injections and after every experiment. Fill syringe with cleaning solution to capacity and let sit for 20 min. Aspirate cleaning solution and wash twice with distilled water. Remove plunger. Insert cleaning wire into the needle and clean to remove debris. Place plunger back into the syringe and wash twice with distilled water. 8. Larvae should be monitored at least on an hourly basis. To gain survival data at a higher temporal resolution, larvae can also be checked every 30 or 15 min. 9. For additional reading, we refer to other instructions of G. mellonella infections [31–34].

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Acknowledgments We thank Alibek Galeev for establishing larvae experiments in our laboratory, Yasmin Claussen for taking care of the insects, Rahul Unni for comments on the manuscript, and Olga Vogler and Mandy Renner for technical assistance. This work was funded by the German Federal Ministry of Education and Research (grant 01KI2020) (to DU). AH receives support from the International Max Planck Research School for Evolutionary Biology. References 1. Me´nard G, Rouillon A, Cattoir V, Donnio P-Y (2021) Galleria mellonella as a suitable model of bacterial infection: past, present and future. Front Cell Infect Microbiol 11:782733 2. Wand ME, Mu¨ller CM, Titball RW, Michell SL (2011) Macrophage and Galleria mellonella infection models reflect the virulence of naturally occurring isolates of B. pseudomallei, B. thailandensis and B. oklahomensis. BMC Microbiol 11:11 3. Jander G, Rahme LG, Ausubel FM (2000) Positive correlation between virulence of Pseudomonas aeruginosa mutants in mice and insects. J Bacteriol 182:3843–3845 4. Harding CR, Stoneham CA, Schuelein R et al (2013) The Dot/Icm effector SdhA is necessary for virulence of Legionella pneumophila in Galleria mellonella and A/J mice. Infect Immun 81:2598–2605 5. Six A, Krajangwong S, Crumlish M et al (2019) Galleria mellonella as an infection model for the multi-host pathogen Streptococcus agalactiae reflects hypervirulence of strains associated with human invasive disease. Virulence 10: 600–609 6. Harding CM, Kinsella RL, Palmer LD, Skaar EP, Feldman MF (2016) Medically relevant Acinetobacter species require a type II secretion system and specific membrane-associated chaperones for the export of multiple substrates and full virulence. PLoS Pathog 12:e1005391 7. Miyata S, Casey M, Frank DW, Ausubel FM, Drenkard E (2003) Use of the Galleria mellonella caterpillar as a model host to study the role of the type III secretion system in Pseudomonas aeruginosa pathogenesis. Infect Immun 71:2404–2413 8. Harding CR, Schroeder GN, Reynolds S et al (2012) Legionella pneumophila pathogenesis in the Galleria mellonella infection model. Infect Immun 80:2780–2790 9. Melvin JA, Gaston JR, Phillips SN et al (2017) Pseudomonas aeruginosa contact-dependent

growth inhibition plays dual role in hostpathogen interactions. mSphere 2:e00336-17 10. Repizo GD, Gagne´ S, Foucault-Grunenwald ML et al (2015) Differential role of the T6SS in Acinetobacter baumannii virulence. PLoS One 10:e0138265 11. Lebeurre J, Dahyot S, Diene S et al (2019) Comparative genome analysis of Staphylococcus lugdunensis shows clonal complex-dependent diversity of the putative virulence factor, ess/Type VII locus. Front Microbiol 10:2479 12. Kim H-M, Davey ME (2020) Synthesis of ppGpp impacts type IX secretion and biofilm matrix formation in Porphyromonas gingivalis. NPJ Biofilms Microbiomes 6:5 13. Si M, Wang Y, Zhang B et al (2017) The type VI secretion system engages a redox-regulated dual-functional heme transporter for zinc acquisition. Cell Rep 20:949–959 14. Si M, Zhao C, Burkinshaw B et al (2017) Manganese scavenging and oxidative stress response mediated by type VI secretion system in Burkholderia thailandensis. Proc Natl Acad Sci U S A 114:E2233–E2242 15. Liaw J, Hong G, Davies C et al (2019) The Campylobacter jejuni type VI secretion system enhances the oxidative stress response and host colonization. Front Microbiol 10:2864 16. Brodmann M, Schnider ST, Basler M (2021) Type VI secretion system and its effectors PdpC, PdpD, and OpiA contribute to Francisella virulence in Galleria mellonella larvae. Infect Immun 89:e0057920 17. Storey D, McNally A, Åstrand M et al (2020) Klebsiella pneumoniae type VI secretion system-mediated microbial competition is PhoPQ controlled and reactive oxygen species dependent. PLoS Pathog 16:e1007969 18. Pissaridou P, Allsopp LP, Wettstadt S et al (2018) The Pseudomonas aeruginosa T6SSVgrG1b spike is topped by a PAAR protein eliciting DNA damage to bacterial

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Antonia Habich and Daniel Unterweger

competitors. Proc Natl Acad Sci U S A 115: 12519–12524 19. Li Y, Chen L, Zhang P, Bhagirath AY, Duan K (2020) ClpV3 of the H3-type VI secretion system (H3-T6SS) affects multiple virulence factors in Pseudomonas aeruginosa. Front Microbiol 11:1096 20. Habich A et al (2022) Core and accessory effectors of type VI secretion systems contribute differently to the intraspecific diversity of Pseudomonas aeruginosa. bioRxiv 21. Murdoch SL, Trunk K, English G et al (2011) The opportunistic pathogen Serratia marcescens utilizes type VI secretion to target bacterial competitors. J Bacteriol 193:6057–6069 22. Spiewak HL, Shastri S, Zhang L et al (2019) Burkholderia cenocepacia utilizes a type VI secretion system for bacterial competition. Microbiology 8:e00774 23. Lange A, Sch€afer A, Frick J-S (2019) A Galleria mellonella oral administration model to study commensal-induced innate immune responses. J Vis Exp 24. Maslova E, Shi Y, Sjo¨berg F et al (2020) An invertebrate burn wound model that recapitulates the hallmarks of burn trauma and infection seen in mammalian models. Front Microbiol 11:998 25. Mukherjee K, Altincicek B, Hain T et al (2010) Galleria mellonella as a model system for studying Listeria pathogenesis. Appl Environ Microbiol 76:310–317 26. Moya-Ande´rico L, Admella J, Fernandes R, Torrents E (2020) Monitoring gene expression during a Galleria mellonella bacterial infection. Microorganisms 8:1798

27. Emery H, Johnston R, Rowley AF, Coates CJ (2019) Indomethacin-induced gut damage in a surrogate insect model, Galleria mellonella. Arch Toxicol 93:2347–2360 28. Sheehan G, Kavanagh K (2018) Analysis of the early cellular and humoral responses of Galleria mellonella larvae to infection by Candida albicans. Virulence 9:163–172 29. Senior N, Titball RW (2020) Isolation and primary culture of Galleria mellonella hemocytes for infection studies. F1000Research 9: 1392 30. Sana TG, Hachani A, Bucior I et al (2012) The second type VI secretion system of Pseudomonas aeruginosa strain PAO1 is regulated by quorum sensing and fur and modulates internalization in epithelial cells. J Biol Chem 287: 27095–27105 31. McCarthy RR, Valentini M, Filloux A (2017) Contribution of cyclic di-GMP in the control of type III and type VI secretion in Pseudomonas aeruginosa. Methods Mol Biol 1657:213– 224 32. Koch G, Nadal-Jimenez P, Cool R. H, Quax WJ (2014) Assessing Pseudomonas virulence with nonmammalian host: Galleria mellonella. Methods Mol Biol in 681–688 33. Ramarao N, Nielsen-Leroux C, Lereclus D (2012) The insect Galleria mellonella as a powerful infection model to investigate bacterial pathogenesis. J Vis Exp 70:e4392 34. Andrea A, Krogfelt K, Jenssen H (2019) Methods and challenges of using the greater wax moth (Galleria mellonella) as a model organism in antimicrobial compound discovery. Microorganisms 7:85

INDEX A Adenylate cyclase (Cya) ...............................................135, 207–209, 218, 226, 228, 230, 232, 262, 396, 547 Affinity chromatography..................................... 286, 301, 303, 304, 311, 409, 419, 422, 429 Affinity purification ............................................. 154, 175, 286, 300, 397, 409 Agarose pad ................................................................... 391 Agar pad ........................................................................ 387 Agrobacterium tumefaciens..................................... 3, 236, 275, 277, 434, 594 Alkaline phosphatase ............................................ 182, 222 Alkyne fatty acids.......................................................79–88 Alkyne phospholipid .................................................87, 88 Alpha fold 2 ................................................................... 486 Amber suppressor tRNA ..................................... 300, 305 Amphipols ................................................... 473, 477, 478 Analyte ................................................. 364–369, 373–381 Antibacterial activity............................................. 593–599 APEX2 .................................................................. 321–328 Artificial neural network ......................................... 28, 31, 35, 39, 41, 42, 47 Ascorbate peroxidase .................................................... 321 Assembly pathways.................................... 3, 92, 383–392

B Bacterial two-hybrid............................................ 207–223, 225–232, 262, 364, 396 Bait ............................................. 214, 215, 236–238, 240, 241, 249, 274, 279, 282, 286, 290, 295, 311, 312, 314, 321, 326 Bastion .................................................................. 519–530 Bayesian network.......................................................47, 51 β-barrel protein .................................................. 46, 91, 99 β-galactosidase ............................................ 182, 183, 185, 187, 189–192, 215–219, 227, 248, 260, 266–269 BIAcore....................................... 365, 369–374, 376–380 Bimolecular fluorescence complementation (BiFC) ............................. 248–250, 252–254, 256 Biogenesis ....................................79, 101, 123, 133, 134, 160, 183, 198, 301–309, 315, 317, 318 Bioinformatics ............................................ 27, 28, 35, 37, 39, 43, 44, 46, 47, 127, 351, 533

Biotinylation ............................................... 101, 103, 130, 132, 133, 141, 143–145, 152, 166–168, 325, 326, 328 Bitopic................................................................... 111–113 BLAST ...................................................29, 40, 47, 51, 53 Blue native polyacrylamide gel electrophoresis (Blue native PAGE) ....................................300, 331–360 Bordetella pertussis...................................... 207–209, 212, 216, 226, 228, 230, 262, 547 Bradford assay ...................................................... 357, 369 Buffer optimization.............................................. 415–430

C Calmodulin.................................286, 291, 294, 547, 564 Calmodulin binding peptide (CBP) ...........................286, 288, 289, 294, 295 cAMP signaling .................................................... 207, 209 Carboxypeptidase Y .....................................114–116, 118 Cell envelope ...................................................65, 73, 111, 113, 197, 200, 533, 594 Cell lysate................................................84, 95, 101, 102, 107–109, 143, 274, 325, 555 Cell surface exposure ..............................................99–109 Cell wall ........................................... 2, 31, 40, 49, 50, 53, 54, 67, 176, 197, 198, 337, 355, 594 Cell wall-binding.......................................................49–50 cI repressor ........................................................... 260, 261 Click chemistry................................................... 74, 79–88 Co-culture ............................................................ 593–599 Co-immunoprecipitation (co-IP)................................235, 273–282, 321, 364 Conformational changes..................................... 113, 114, 116–118, 128, 432, 455 Consensus sequence..................................................31, 49 Coomassie staining ..................................... 312, 334, 347 Cross-linking .............................................. 275–281, 290, 364, 400, 403–404, 408, 411, 412 Cross-validation............................................................... 36 Cryo-electron microscopy (Cryo-EM) .............. 432–464, 471–481, 485 Cryogenic ...................................432, 437, 441, 471, 488 Crystallography .......................................... 122, 123, 137, 432, 433, 441, 456, 457, 460, 471, 472, 476, 477, 479, 480, 485–500

Laure Journet and Eric Cascales (eds.), Bacterial Secretion Systems: Methods and Protocols, Methods in Molecular Biology, vol. 2715, https://doi.org/10.1007/978-1-0716-3445-5, © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

609

BACTERIAL SECRETION SYSTEMS: METHODS AND PROTOCOLS

610 Index

Crystals ....................................................... 122, 310, 355, 437, 440, 441, 486, 488–494, 499, 500 Cysteine ......................................... 41, 73, 100, 106, 112, 122, 124, 126–144, 147, 149–154, 166–168, 171, 172, 225–227, 302, 305, 308, 316, 567, 568, 589 Cytology-based two-hybrid (C2H) ............................. 248 Cytoplasmic membrane (inner membrane)........... 40, 46, 73, 74, 78, 127, 139, 140, 143, 164, 564

D Detergent................................................ 70, 86, 101–103, 108, 109, 117, 146, 148, 154, 162, 167, 169, 171, 175, 278, 280, 301, 328, 332, 344, 355, 356, 358, 397, 401, 403, 407, 409, 410, 415– 430, 472, 473, 475, 477–481, 488, 548, 558 Detergent belt ............................................................... 480 Dialysis ........................................................................... 325 Differential solubilization .................................... 547–560 Digitonin ............................................................. 401, 403, 407, 410, 473, 548, 558, 564 Disulfide bonds ............................................ 67, 108, 137, 218, 225–228, 230, 232, 349 Dose fractionation......................................................... 437 Dual-color immunoblotting................................ 350–352

E Effector .............................................42–44, 46, 198, 275, 290, 363, 485, 500, 520, 533–537, 540, 547– 560, 563–574, 577, 593, 594, 601–606 Effector translocation ......................... 547–560, 563–574 Electron microscopy (EM) ................................... 92, 245, 283, 410, 411, 432–437, 439, 440, 443, 445, 447, 448, 450, 452–456, 459–462, 464, 471 EM grid ................................................................ 435, 439 Enzyme-linked immunosorbent assay (ELISA) .......... 102 Escherichia coli ............................................ 66, 69, 74–81, 84, 86, 88, 91, 92, 99–101, 103, 118, 122, 128, 129, 133, 134, 140, 141, 144, 150, 152, 160– 163, 165–167, 170, 173–175, 182, 183, 185, 187–189, 191–193, 198, 202, 207–209, 211, 213–215, 217, 218, 220–222, 226, 230, 236, 238, 239, 248–251, 255, 263–267, 286, 301, 302, 304, 305, 309, 317, 318, 344, 355, 357, 366, 371, 379, 391, 396–401, 403, 405, 407, 411, 419, 421, 508, 595, 596

F Fluorescence microscopy ..................................... 383–392 Fluorescent reporter ................................... 250, 251, 594

Fractionation ........................................ 65–70, 93, 95–96, 162, 190, 333, 338, 340–344, 357, 410, 564, 579, 582 Freeze and thaw ................................................... 325, 337

G GAL4 DNA binding domain (DNA-BD) .......... 236–238 Gal4 transcriptional activation domain........................ 237 Galleria mellonella ............................................... 601–606 GALLEX.............................................. 260–262, 264–268 Genetic assay ........................................................ 207, 209 Globomycin ...............................................................74–78 Glutaredoxin reductase ........................................ 218, 226

H HeLa cells .......................... 548, 549, 554–556, 558, 559 Helix–helix interactions ....................................... 260, 262 Hidden Markov model (HMM) .......................... 5, 7–10, 14, 16, 18–20, 31, 32, 39–41, 46–49, 127, 526, 528, 530 Homology reduction ...................................................... 37 Host cell ............................................ 142, 152, 290, 416, 519, 547–549, 559, 563–566, 573, 574, 601 HSQC spectrum ................................. 504–506, 512–514 Hybrid protein ...................................183, 188, 207–209, 211–217, 219, 221, 230, 290–291, 293

I Identification of components ............................... v, 73–88 Identification of effectors .................................... 533–537 Identification of the secretion systems.....................14–15 Image processing................................................. 385, 388, 432, 433, 453, 457 Immunoblotting ...................................... 92, 93, 95, 102, 106–108, 116, 250, 251, 254, 255, 282, 332, 334, 336, 337, 340, 345, 346, 350–352, 356, 358, 360, 548, 549, 552–558, 560, 579, 583 Immunodetection ............................................... 104, 115, 116, 182, 209, 263, 264, 360, 583 Immunoprecipitation (IP) ...........................................135, 141, 143, 146, 152, 154, 169, 175, 209, 217, 273, 275, 280, 300, 303, 305, 308–310, 333, 340, 344–345, 358 Infection ..................................................... 236, 261, 485, 547–551, 553–556, 558, 559, 566, 569, 570, 573, 574, 601–606 Insertion ............................................... 32, 103, 122–124, 127, 128, 130, 132, 137, 154, 160, 186–188, 197, 219, 220, 226, 260, 267, 315, 397, 399, 407, 475, 590

BACTERIAL SECRETION SYSTEMS: METHODS Inside-out .......................... 127, 149, 159–162, 166–168 Interactome ................................................. 236, 285, 286 Inverted membrane vesicles ............................... 160, 162, 164, 170, 171, 174

L Lambda Red-based recombination .............................. 286 Large complexes................................................... 396–412 Larvae .......................................................... 603, 604, 606 Legionella pneumophila ................................ 43, 508, 566, 567, 569–571, 573, 574, 602 Ligands ...............................................160, 248, 364–376, 378–380, 412, 471–481, 495, 498, 504–506, 510, 512, 513, 515 Lipobox ........................................................................... 73 Lipoprotein........................................... 40, 41, 73–88, 99, 100, 104, 160, 301, 303–304, 309–313 Lipoprotein labeling ....................................................... 80 Lysozyme....................................... 66, 67, 69, 81, 84, 93, 95, 114, 115, 118, 163, 199, 200, 202, 276–279, 281, 282, 323, 325, 328, 332, 337, 338, 398, 401, 421 Lytic transglycosylases (LTGs) ................... 197, 198, 203

M MacConkey/maltose medium ..................................... 211 Machine learning...............................................29, 31, 32, 35–37, 43, 44, 46–49, 52, 53, 127, 519 MacSyFinder................................................................ 1–23 Maleimide .......................................... 105, 108, 134, 137, 139–141, 147, 151–154, 163, 171, 580, 585, 591 Mass spectrometry ..........................................80, 88, 101, 118, 198, 235, 274, 285, 286, 293, 296, 301, 304, 311–314, 318, 322, 328, 332, 336, 345, 350, 351, 353, 359, 360, 418, 534, 536, 537 mCherry ...................................................... 104, 390, 589 Melting temperature ....................................................408, 416, 424, 425, 429 Membrane fraction..................................... 66, 67, 69, 70, 91, 92, 152, 190, 328, 340, 342, 343, 402, 406, 409, 410 Membrane preparations ........................................ 92, 150, 162, 164, 333, 337, 421–422 Membrane protein complexes .....................................259, 331–333, 336, 337, 339, 340, 344–345, 396–412 Membrane vesicles .................................42, 70, 108, 124, 126, 145, 150, 159, 160, 162–165, 168, 173, 174, 176 Metabolic labeling................................................ 539–544 Molecular replacement ........................................ 486, 494 Multi-angle laser light scattering (MALLS) ....... 415–430

AND

PROTOCOLS Index 611

N Naı¨ve Bayes classifier .................................................35, 51 Native conditions ........................................ 109, 321, 435 n-dodecyl-β-D-maltopyranoside (DDM) ...................401, 419, 420 n-dodecyl-β-D-maltoside (DDM) ..................... 332, 337, 338, 344, 345, 351, 355 Negative staining (NS) .......................434, 511, 512, 514 N-hydroxysuccinimide (NHS)-based reagent ............. 106 Ni NTA agarose beads .................................................. 304 Non-classical secretion.................................................... 42 Nuclear Magnetic Resonance spectroscopy (NMR) ............................ 122, 137, 432, 503–515

O One-hybrid .................................................................... 260 Orientation ........................................ 108, 111, 112, 122, 124, 126, 127, 130–132, 134, 137, 140, 141, 145, 149–151, 160–168, 173, 174, 181, 368, 433, 447–455, 459, 472–475, 478, 479, 492, 537 Oriented inner membrane vesicles ............................... 127 Ortho-nitrophenyl-β-D-galactoside (ONPG)............183, 185, 189, 191, 211, 216, 264, 267, 268 Osmotic shock........................................... 66, 67, 69, 162 Outer membrane protein (OMP) ........92, 301, 318, 353 Overfitting ....................................................................... 36 Oxidative bacterial two-hybrid (oxi-BTH).................218, 219, 226–228, 230–232

P Palmitate labeling......................................................74, 76 p-benzoyl-l-phenylalanine (Bpa) .................................. 300 Peptidoglycan (PG)...........................................65, 69, 78, 95, 131, 163, 197–203 Peptidoglycan-binding domain ...................................... 50 Performance measures .................................................... 38 Peripheral proteins ....................................................70, 99 Pho-lac dual reporter system (pho-lac reporter fusions) ........................ 181–193 Phosphatidylethanolamine...................... 74, 80, 128, 163 Plunge freezing ............................................................. 436 p-nitrophenyl phosphate (pNPP)................................183, 185, 190, 191 Polytopic............................................ 111, 112, 118, 124, 126, 128, 130, 137, 145, 161, 183, 192, 416 Position-weight matrix ...................................... 31, 39, 40 Positive-inside rule .......................................................... 46 Prediction ............................................... 27–55, 127, 137, 182, 186, 187, 464, 486, 494, 503, 521–528, 530 Prey .................................... 236–238, 240, 241, 249, 282

BACTERIAL SECRETION SYSTEMS: METHODS AND PROTOCOLS

612 Index

Pronase ................................................................. 199, 201 ProtA..................................................................... 286, 290 Protease .........................................................95, 100–104, 108, 112, 117, 118, 132, 133, 175, 244, 286, 288–291, 294, 295, 304, 419 Protease accessibility ............................................ 111–118 Protease inhibitor............................................69, 81, 102, 114, 117, 170, 240, 276–278, 281, 282, 295, 332, 336–338, 355, 401, 403, 419, 421, 551, 554, 555 Protein A/G-agarose affinity resin..............................146, 148, 169, 171 Protein A/G Sepharose ................................................ 143 Proteinase K ............................................... 102, 105, 107, 114–116, 118, 164, 213, 214 Protein complex .................................104, 175, 274–276, 278, 285, 286, 295, 331, 345, 346, 349–351, 355, 409, 410, 432–464, 503 Protein Data Bank (PDB) .................................. 310, 415, 495–498, 515 Protein G-Sepharose .................................................75, 76 Protein–lipid interaction ............................................... 409 Protein overproduction ................................................ 397 Protein partners................................................... 248–251, 253–256, 321–328, 507 Protein-protein interaction.................................... 91, 136 Protein sorting ..........................................................27–55 Protein topology .......................................... 46, 100, 103, 111–118, 121–144, 161, 181–193, 396 Proteolysis....................................69, 101, 102, 104, 105, 107, 111–118, 161, 162, 165, 428 Proton motive force ............................................ 113, 163, 566, 577, 580, 583–585 Protonophore ................................................................ 566 Proximity labeling ................................................ 321–328 Proxisome .................................................... 322, 326, 327 Pseudomonas aeruginosa ..............................................368, 415–430, 594, 602, 603, 605, 606 Pull-down .................................................... 217, 235, 321 Pulse-chase ................................................. 127, 134, 300, 302, 305–307, 310, 316 Purified peptidoglycan ................................ 198, 201, 202

R Radioactive precursors ......................................... 539–544 Reconstruction ........................................... 160, 248, 391, 411, 433, 440, 441, 448, 450, 453, 454, 456, 457, 461, 472–474, 477, 478, 480, 481 Red-Gal (6-chloro-3-indolyl-β-D-galactoside) ..........183, 184, 187, 189, 191, 192 Refractive index .......................................... 364, 417, 418, 421, 425, 427, 428 Regular expression ................................31, 32, 41, 49, 51 Remazol blue............................................... 198, 199, 201

Reporter gene............................................. 182, 184, 186, 188, 218, 236, 260–262, 267 Restriction site/ligation free cloning methods ..........186, 188, 212, 213, 219, 400, 402 ROC curve ...................................................................... 38

S Saccharomyces cerevisiae............................... 236, 238, 239 Salmonella enterica serovar Typhimurium ........ 548, 549, 579–581, 585, 588, 589 Scanning fluorimetry ........................................... 416, 499 SDS-boiling ................................................................... 107 Secretomes............................................................ 533, 534 Sec system ........................................................................ 40 Segmentation .......................................47, 112, 127, 151, 220, 314, 317, 509 Sequence logo ....................................... 32, 39–41, 49, 50 Sequence similarity search ....................2, 5, 8, 17, 18, 55 Serratia marcescens..................... 505–507, 594, 595, 598 Serva Blue G........................................332–334, 347, 354 Signal peptide (signal sequence) ............................ 27, 32, 39–46, 73, 74, 104, 198, 504, 509 Signal prediction .......................................................42–46 Silver staining .................... 286, 332, 334, 350, 355, 536 Single-particle analysis ................................ 434, 456, 457 Site-directed mutagenesis ................................... 144, 188, 250, 251, 302, 309 Site-directed photocrosslinking........................... 299–319 Size exclusion chromatography (SEC) .......................369, 400, 411, 415–430, 499 Size exclusion chromatography coupled to multi-angle laser light scattering (SEC-MALLS) ....... 415–430 Sodium carbonate ............................................66, 67, 334 Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE)...................... 67, 68, 75, 78, 83, 87, 92, 93, 95, 115, 116, 118, 127, 132, 141, 146, 148–149, 165, 166, 168, 170, 172, 291, 404, 408, 409, 411, 417, 423, 544, 549, 551–552, 554–557, 559, 579, 583 Solubilization ............................................. 66, 68–70, 75, 76, 132, 133, 146, 148, 154, 165–167, 169, 171–173, 175, 282, 290, 303, 308, 328, 401, 406, 410, 419, 422, 558 Sorting signals ..............................................27–30, 39, 40 Spheroplast .......................................66, 67, 69, 113–118, 124, 126, 162–164, 173 Streptavidin.................................101, 323, 325, 328, 367 Streptavidin beads ......................322, 323, 325, 326, 328 Structure determination ..................................... 122, 449, 472–477, 485–487, 494–495, 500 Subcellular localization ........................................ 187, 396 Substituted cysteine accessibility method (SCAM)................................... 121–144, 148–154, 165–167, 172

BACTERIAL SECRETION SYSTEMS: METHODS Sucrose gradient ..............................................91–97, 166, 168, 340, 341, 343–344, 354 Superfolder green fluorescent protein (sfGFP)...................................................... 384, 390 Supernatant ..........................................67, 70, 76, 85, 86, 95, 115, 116, 148, 168, 171, 202, 203, 232, 240–243, 265–267, 274, 278–280, 282, 293, 309, 311, 313, 326, 328, 336–338, 343–345, 369, 406, 407, 421, 422, 429, 533–537, 548, 554–556, 558, 570, 578, 582–585, 587, 588, 590 Support vector machine................. 28, 31, 35, 47, 51, 53 Surface exposed lipoproteins ................................. 99, 100 Surface plasmon resonance (SPR)....................... 363–381 Survival .............................. 197, 250, 251, 602, 605, 606

T Tandem affinity purification (TAP).............285–296, 321 TEM-1 beta-lactamase reporter .......................... 563–574 Thioredoxin reductase ......................................... 218, 226 3D reconstruction ...................................... 448, 450–455, 457, 475–478 Tobacco etch virus (TEV) .................................. 132, 133, 286, 288–291, 294, 295, 419, 428, 509 T7 overexpression ......................................................... 151 TOXCAT .............................................................. 260–266 Toxin/immunity pairs .................................................. 594 Toxins ................................. 65, 198, 220, 221, 226, 500, 519, 539–544, 564, 593–595 Transient protein interactions ............................. 235, 321 Transmembrane domains (TMDs) .................... 100, 121, 122, 124, 126–128, 130–135, 137, 140, 150, 154, 165, 167, 396, 446 Transmembrane protein ................................................. 46 Tris-Tricine gel electrophoresis (Tricine-SDS-PAGE)................. 75–77, 80, 83, 87 Trypsin .......................................................... 69, 102, 118, 175, 304, 312, 313, 318, 327 Twin-arginine protein translocation .............................. 40 Two-dimensional BN/SDS PAGE .................... 332, 333, 337, 339, 340, 344, 346–350 Two-hybrid system .............................207, 209, 235–238

AND

PROTOCOLS Index 613

Type III secretion (T3SS)....................................... 2, 3, 5, 7, 9, 17, 20, 21, 43–45, 54, 112, 160, 198, 290, 331, 332, 346, 347, 350–353, 355, 356, 383, 390, 526, 548, 550, 553, 558, 559, 577, 578, 587, 589, 590, 602 Type IV secretion (T4SS) ............................................3, 7, 43, 112, 113, 128, 198, 236, 259, 260, 290, 383, 432–435, 440, 446, 454, 460, 462, 464, 526, 566, 574, 602 Type VI secretion system (T6SS)....................... 7, 21, 22, 112, 198, 226, 236, 259, 260, 290, 371, 383, 519, 526, 530, 541, 593–596, 598, 599, 602

U Ultracentrifugation .................................... 67, 92, 95, 96, 200–202, 311, 338, 341, 355, 356, 406, 409, 429, 537 Ultrafiltration .............................418, 426, 427, 429, 430 Urea ............................ 66, 67, 70, 83, 87, 101, 153, 161

V Variability analysis ................................................ 471–481

W Wax moth ...................................................................... 603 Western blot ....................................................67, 68, 137, 142, 143, 148–149, 153, 172, 216–217, 240, 242, 244, 274, 276, 279, 282, 290–291, 293, 352, 409, 429, 534, 536, 569, 574, 579, 580

X X-Gal...................................................210–212, 214, 215, 217, 221, 227, 228, 231, 232, 264, 266, 267 X-Pho (5-bromo-4-chloro-3-indolyl-phosphate (BCIP)) .................. 183, 185, 187, 189, 191, 192 X-ray diffraction ................................................... 486, 492

Y Yeast transformation ............................................ 239, 240 Yeast two-hybrid (Y2H) ............ 207, 209, 235–237, 240