Toll-Like Receptors: Methods and Protocols (Methods in Molecular Biology, 2700) [1st ed. 2023] 1071633651, 9781071633656

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Methods in Molecular Biology 2700

Francesca Fallarino Marco Gargaro · Giorgia Manni  Editors

Toll-Like Receptors Methods and Protocols

METHODS

IN

MOLECULAR BIOLOGY

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

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

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

Toll-Like Receptors Methods and Protocols

Edited by

Francesca Fallarino, Marco Gargaro, and Giorgia Manni Department of Medicine and Surgery, University of Perugia, Perugia, Italy

Editors Francesca Fallarino Department of Medicine and Surgery University of Perugia Perugia, Italy

Marco Gargaro Department of Medicine and Surgery University of Perugia Perugia, Italy

Giorgia Manni Department of Medicine and Surgery University of Perugia Perugia, Italy

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-3365-6 ISBN 978-1-0716-3366-3 (eBook) https://doi.org/10.1007/978-1-0716-3366-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A. Paper in this product is recyclable.

Preface Through extensive and annotated practical examples, this book describes how to study the biology of Toll-Like Receptors (TLRs) at molecular and cellular level. In particular, this edition is focused on the methods of studying the role of TLRs in immune responses and on the various approaches targeting different TLRs in cancer, autoimmunity, and vaccine development. Perugia, Italy

Francesca Fallarino Marco Gargaro Giorgia Manni

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

PART I

DETECTION AND ANALYSIS OF TOLL LIKE RECEPTORS

1 Modeling of Transmembrane Domain and Full-Length TLRs in Membrane Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alejandra Matamoros-Recio, Marina Mı´nguez-Toral, and Sonsoles Martı´n-Santamarı´a 2 Development of Optimal Virtual Screening Strategies to Identify Novel Toll-Like Receptor Ligands Using the DockBox Suite . . . . . . . . . . . . . . . . . Jordane Preto and Francesco Gentile 3 Use of Fluorescent Chemical Probes in the Study of Toll-like Receptors (TLRs) Trafficking. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ana Rita Franco, Valentina Artusa, and Francesco Peri

PART II

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TOLL LIKE RECEPTORS FUNCTION IN IMMUNE RESPONSES

4 Use of CRISPR/CAS9 Technologies to Study the Role of TLR in Dendritic Cell Subsets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Giulia Mencarelli, Benedetta Pieroni, Kenneth M. Murphy, and Marco Gargaro 5 Endotoxin-Tolerance Mimicking to Study TLR in Promotion of Tolerogenic DCs and Tr1 Cells. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Giulia Scalisi, Doriana Ricciuti, and Giorgia Manni 6 Flow Cytometry Analysis of IL-1 Receptors and Toll-Like Receptors on Platelets and Platelet-Derived Extracellular Vesicles. . . . . . . . . . . . . . . . . . . . . . . Achille Anselmo and Daniela Boselli 7 Methods to Study TLRs in Transplantation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Montserrat Kwan, Martin Sepulveda, and Maria-Luisa Alegre 8 Determining Endosomal Toll-Like Receptors Gene Expression in NK Cells After Stimulation with Specific Agonists . . . . . . . . . . . . . . . . . . . . . . . . Claudia Alicata, Irene Veneziani, Biancamaria Ricci, Lorenzo Moretta, and Enrico Maggi 9 In Vitro Study of TLR4-NLRP3-Inflammasome Activation in Innate Immune Response. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Letizia Mezzasoma, Carsten B. Schmidt-Weber, and Francesca Fallarino

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Contents

PART III 10

11

12

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15 16

TOLL LIKE RECEPTORS AS NOVEL THERAPEUTIC TARGETS FOR DISEASES

Toll-Like Receptor Polymorphisms and the Risk of Cancer: Meta-analysis Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Narttaya Chaiwiang and Teera Poyomtip Unrevealing the Role of TLRs in the Pathogenesis of Autoimmune Disease by Using Mouse Model of Diabetes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eleonora Panfili, Elena Orecchini, and Giada Mondanelli In Vitro and Ex Vivo Methodologies for T-Cell Trafficking Through Blood–Brain Barrier After TLR Activation. . . . . . . . . . . . . . . . . . . . . . . . . Camilla Moliterni, Maria Tredicine, Alessandra Pistilli, Renato Falcicchia, Desire´e Bartolini, Anna Maria Stabile, Mario Rende, Francesco Ria, and Gabriele Di Sante Delineating the Role of Toll-Like Receptors in Inflammatory Bowel Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongbin Liang, Lin Zhang, Bettina Hoden, Bo Qu, David Derubeis, Xiaotong Song, and Dekai Zhang Analysis of Differential TLR Activation in a Mouse Model of Multiple Sclerosis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chiara Suvieri and Claudia Volpi Study of Agonists of TLRs as Vaccine Adjuvants . . . . . . . . . . . . . . . . . . . . . . . . . . . . Francesca Mancini, Francesca Micoli, and Omar Rossi Activation of TLRs by Opportunistic Fungi in Lung Organoids . . . . . . . . . . . . . . Veronika Bosa´kova´, Jan Fricˇ, and Teresa Zelante

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

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Contributors MARIA-LUISA ALEGRE • Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, USA CLAUDIA ALICATA • Tumor Immunology Unit, IRCCS Ospedale Pediatrico Bambino Gesu`, Rome, Italy ACHILLE ANSELMO • Flow Cytometry Resource, Advanced Cytometry Technical Applications Laboratory, San Raffaele Scientific Institute, Milan, Italy VALENTINA ARTUSA • Department of Biotechnology and Biosciences, University of MilanoBicocca, Milan, Italy DESIRE´E BARTOLINI • Department of Pharmaceutical Sciences, University of Perugia, Perugia, Italy VERONIKA BOSA´KOVA´ • International Clinical Research Centre, St. Anne’s University Hospital Brno, Brno, Czech Republic; Department of Biology, Faculty of Medicine, Masaryk University, Brno, Czech Republic DANIELA BOSELLI • Flow Cytometry Resource, Advanced Cytometry Technical Applications Laboratory, San Raffaele Scientific Institute, Milan, Italy NARTTAYA CHAIWIANG • Faculty of Optometry, Ramkhamhaeng University, Bangkok, Thailand DAVID DERUBEIS • Center for Infectious and Inflammatory Diseases, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA GABRIELE DI SANTE • Department of Medicine and Surgery, Section of Human Anatomy, University of Perugia, Perugia, Italy RENATO FALCICCHIA • Department of Translational Medicine and Surgery, Section of ` Cattolica del Sacro Cuore, Rome, Italy General Pathology, Universita FRANCESCA FALLARINO • Department of Medicine and Surgery, University of Perugia, Perugia, Italy ANA RITA FRANCO • Department of Biotechnology and Biosciences, University of MilanoBicocca, Milan, Italy JAN FRICˇ • Department of Biology, Faculty of Medicine, Masaryk University, Brno, Czech Republic; Institute of Haematology and Blood Transfusion, Prague, Czech Republic MARCO GARGARO • Department of Medicine and Surgery, University of Perugia, Perugia, Italy FRANCESCO GENTILE • Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Canada; Ottawa Institute of Systems Biology, Ottawa, Canada BETTINA HODEN • Center for Infectious and Inflammatory Diseases, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA MONTSERRAT KWAN • Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, USA HONGBIN LIANG • Center for Infectious and Inflammatory Diseases, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA ENRICO MAGGI • Tumor Immunology Unit, IRCCS Ospedale Pediatrico Bambino Gesu`, Rome, Italy FRANCESCA MANCINI • GSK Vaccines Institute for Global Health, Siena, Italy

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GIORGIA MANNI • Department of Medicine and Surgery, University of Perugia, Perugia, Italy SONSOLES MARTI´N-SANTAMARI´A • Department of Structural and Chemical Biology, Centro de Investigaciones Biologicas Margarita Salas, CIB-CSIC, Madrid, Spain ALEJANDRA MATAMOROS-RECIO • Department of Structural and Chemical Biology, Centro de Investigaciones Biologicas Margarita Salas, CIB-CSIC, Madrid, Spain GIULIA MENCARELLI • Department of Medicine and Surgery, University of Perugia, Perugia, Italy LETIZIA MEZZASOMA • Department of Medicine and Surgery, University of Perugia, Perugia, Italy FRANCESCA MICOLI • GSK Vaccines Institute for Global Health, Siena, Italy MARINA MI´NGUEZ-TORAL • Department of Structural and Chemical Biology, Centro de Investigaciones Biologicas Margarita Salas, CIB-CSIC, Madrid, Spain CAMILLA MOLITERNI • Department of Translational Medicine and Surgery, Section of ` Cattolica del Sacro Cuore, Rome, Italy; Department of General Pathology, Universita Biology and Biotechnology Charles Darwin, University of Rome Sapienza, Rome, Italy GIADA MONDANELLI • Department of Medicine and Surgery, University of Perugia, Perugia, Italy LORENZO MORETTA • Tumor Immunology Unit, IRCCS Ospedale Pediatrico Bambino Gesu`, Rome, Italy KENNETH M. MURPHY • Department of Pathology and Immunology, Washington University in St. Louis, School of Medicine, St. Louis, MO, USA ELENA ORECCHINI • Department of Onco-Hematology and Cell and Gene Therapy, Bambin Gesu` Children’s Hospital, IRCCS, Rome, Italy ELEONORA PANFILI • Department of Medicine and Surgery, University of Perugia, Perugia, Italy FRANCESCO PERI • Department of Biotechnology and Biosciences, University of MilanoBicocca, Milan, Italy BENEDETTA PIERONI • Department of Medicine and Surgery, University of Perugia, Perugia, Italy ALESSANDRA PISTILLI • Department of Medicine and Surgery, Section of Human Anatomy, University of Perugia, Perugia, Italy TEERA POYOMTIP • Faculty of Optometry, Ramkhamhaeng University, Bangkok, Thailand JORDANE PRETO • Centre de Recherche en Cance´rologie de Lyon, Universite´ Claude Bernard Lyon 1, Lyon, France BO QU • Department of Gastroenterology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China MARIO RENDE • Department of Medicine and Surgery, Section of Human Anatomy, University of Perugia, Perugia, Italy FRANCESCO RIA • Department of Translational Medicine and Surgery, Section of General ` Cattolica del Sacro Cuore, Rome, Italy Pathology, Universita BIANCAMARIA RICCI • Tumor Immunology Unit, IRCCS Ospedale Pediatrico Bambino Gesu`, Rome, Italy DORIANA RICCIUTI • Department of Medicine and Surgery, University of Perugia, Perugia, Italy OMAR ROSSI • GSK Vaccines Institute for Global Health, Siena, Italy GIULIA SCALISI • Department of Medicine and Surgery, University of Perugia, Perugia, Italy

Contributors

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CARSTEN B. SCHMIDT-WEBER • Center for Allergy and Environment (ZAUM), Technical University and Helmholtz Center Munich-Biedersteiner, Munich, Germany MARTIN SEPULVEDA • Department of Medicine, Section of Rheumatology, University of Chicago, Chicago, IL, USA XIAOTONG SONG • Center for Infectious and Inflammatory Diseases, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA ANNA MARIA STABILE • Department of Medicine and Surgery, Section of Human Anatomy, University of Perugia, Perugia, Italy CHIARA SUVIERI • Section of Pharmacology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy MARIA TREDICINE • Department of Translational Medicine and Surgery, Section of General ` Cattolica del Sacro Cuore, Rome, Italy Pathology, Universita IRENE VENEZIANI • Tumor Immunology Unit, IRCCS Ospedale Pediatrico Bambino Gesu`, Rome, Italy CLAUDIA VOLPI • Section of Pharmacology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy TERESA ZELANTE • Department of Medicine and Surgery, University of Perugia, Perugia, Italy DEKAI ZHANG • Center for Infectious and Inflammatory Diseases, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA; Department of Translational Medical Sciences, College of Medicine, Texas A&M University, Houston, TX, USA LIN ZHANG • Center for Infectious and Inflammatory Diseases, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA

Part I Detection and Analysis of Toll Like Receptors

Chapter 1 Modeling of Transmembrane Domain and Full-Length TLRs in Membrane Models Alejandra Matamoros-Recio, Marina Mı´nguez-Toral, and Sonsoles Martı´n-Santamarı´a Abstract Toll-like receptors (TLRs), classified as pattern recognition receptors, have a primordial role in the activation of the innate immunity. In particular, TLR4 binds to lipopolysaccharides (LPS), a membrane constituent of Gram-negative bacteria, and, together with Myeloid Differentiation factor 2 (MD-2) protein, forms a heterodimeric complex which leads to the activation of the innate immune system response. Identification of TLRs has sparked great interest in the therapeutic manipulation of the innate immune system. In particular, TLR4 antagonists may be useful for the treatment of septic shock, certain autoimmune diseases, noninfectious inflammatory disorders, and neuropathic pain, and TLR4 agonists are under development as vaccine adjuvants in antitumoral treatments. Therefore, TLR4 has risen as a promising therapeutic target, and its modulation constitutes a highly relevant and active research area. Deep structural understanding of TLR4 signaling may help in the design and discovery of TLR4-modulating molecules with desirable therapeutic properties. Computational studies of the different independent domains composing the TLR4 were undertaken, to understand the differential domain organization of TLR4 in aqueous and membrane environments, including Liquid-disordered (Ld) and Liquid-ordered (Lo) membrane models, to account for the TLR4 recruitment in lipid rafts over activation. We modeled, by means of all-atom Molecular Dynamics (MD) simulations, the structural assembly of plausible full-length TLR4 models embedded into a realistic plasma membrane, accounting for the active (agonist) state of the TLR4, thus providing an analysis at both atomic/molecular and thermodynamic levels of the TLR4 assembly and biological activity. Our results unveil relevant molecular aspects involved in the mechanism of receptor activation, and adaptor recruitment in the innate immune pathways, and will promote the discovery of new TLR4 modulators and probes. Key words Toll-like receptors, Membrane proteins, Molecular recognition, MD simulations, Molecular docking, Ab initio modeling, Homology modeling, Free energy calculations

1

Introduction

1.1 General Introduction to TollLike Receptors

Toll-like receptors (TLRs) are a class of proteins usually present in the cell wall or nuclear wall of dendritic cells and macrophages. They are related to the activation of the innate immune system participating in the early identification of the invasion of pathogens

Francesca Fallarino et al. (eds.), Toll-Like Receptors: Methods and Protocols, Methods in Molecular Biology, vol. 2700, https://doi.org/10.1007/978-1-0716-3366-3_1, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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by recognizing different Pathogen-Associated Molecular Patterns (PAMPs) and also Damage-Associated Molecular Patterns (DAMPs) [1]. The common mechanism of TLR signaling is that the interaction of a PAMP with the recognition domain of TLR induces the formation of a receptor dimer or changes the conformation of a pre-existing dimer [2]. Identification of TLRs has sparked great interest in the therapeutic manipulation of the innate immune system; TLRs agonists are currently under development for the treatment of cancer, allergies, and viral infections, and also as adjuvants in vaccine development and in cancer immunotherapy, and TLRs antagonists are useful in septic shock and inflammationbased diseases [3]. Specific molecular features of extracellular, transmembrane, and cytoplasmic domains of TLRs are crucial for coordinating the complex innate immune signaling pathways [2]. None of the fulllength structures have been experimentally resolved yet, but experimental NMR [4], X-ray crystallography, and other structural data [5] are currently available for the independent domains for several TLRs (see also chapters about the X-ray crystallographic structure of TLRs, homology modeling and docking studies). However, the study of the independent TLRs domains only provides a partial perspective, as full-length proteins are flexible entities and dynamics play a key role in their functionality. In addition, membrane lipid composition can shape the response of TLRs. In particular, the importance of cholesterol-rich membrane domains known as lipid rafts for TLRs signaling and trafficking has been extensively investigated [6]. On this basis, the full-length models of the activated TLRs dimers in the membrane are definitively required to advance into the complete description of these transmembrane receptors. However, the multicomponent complexity of the receptors in the membrane provides a heterogeneous morphology, where experimental techniques cannot provide a full response [7]. Molecular modeling and computational techniques have proved proficiency in performing realistic simulations of both individual membrane proteins, and more complex membrane systems, yielding invaluable tools to characterize membrane-inserted systems at atomic resolution [8]. Among the TLR family, TLR4 represents an interesting case study for several reasons: (i) it is the only TLR that heterodimerizes with an accessory protein (Myeloid Differentiation factor 2, MD-2) to recognize ligands; (ii) it can activate the immune response within two different signaling pathways, i.e., the MyD88-dependent pathway and the TRIF-dependent pathway [9]; and (iii) it reacts differently to specific PAMP lipopolysaccharides (LPSs), bacterial glycolipids, that act either as TLR4 agonists or antagonists depending on minute variations in their chemical structures [10].

Modeling of Transmembrane Domain and Full-Length TLRs in Membrane Models

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The activation mechanism of the TLR4/MD-2 complex occurs as follows: first, one LPS molecule (or another PAMP or DAMP) binds to one TLR4/MD-2 system, by inserting the fatty acid chains inside the deep MD-2 hydrophobic pocket, and causing molecular rearrangements of the receptor complex resulting in the dimerization of another identical TLR4/MD-2 unit [10]. TLR4 ectodomain dimerization brings together the cytoplasmic Toll/ Interleukin-1 receptor (TIR) domains. Then, the receptor is recruited into lipid rafts, initiating the activation of innate immune system signaling pathways [11]. Recently, our group has provided the most realistic and complete 3D models to date of the active full Toll-like receptor 4 (TLR4) complex embedded into a realistic membrane, through a computational study combining ab initio and homology modeling, molecular docking, molecular dynamics (MD) simulations, and thermodynamics calculations, pushing against the limits of all-atom simulations (Fig. 1) [12]. We first studied each of the different domains composing the TLR4, i.e., the extracellular (ED), the transmembrane (TD), and the intracellular (ID) domains. From the information gathered from our independent TLR4 domain studies, we finally modeled, by means of all-atom MD simulations, the structural assembly of four possible full-length TLR4 models embedded into a realistic plasma membrane, identifying two of them as plausible 3D structures of the full LPS-activated TLR4. Our MD simulations point to a mutual stabilizing role between both extracellular TLR4/MD-2/E. coli LPS units, in the agonist conformation of the receptor (Fig. 1). The transmembrane domain, TD, was simulated in Liquid-ordered (Lo) and Liquiddisordered (Ld) membrane phases [13], showing key aspects of the lipid-raft-embedded secondary structure (Fig. 1.2). The plasticity of the TD hydrophobic region depends on the membrane composition and is determinant for the receptor dimerization, thus explaining the necessity of TLR4 recruitment into lipid rafts during receptor activation [11]. We also identified two potential dimerization interfaces for the TD of the activated TLR4 (TD1 and TD2) (Fig. 1.2). Both dimers could represent distinct active states of the TLR4 TD in different environments, and/or TLR4 cellular localizations [4]. On the other hand, our computational studies reinforce the idea that two dimerization patterns are possible for the TLR4 intracellular TIR domain: symmetric and asymmetric (Fig. 1.3) [5]. The symmetric model is based on the X-ray crystal structure of the TLR10-ID homodimer, which interacts in a “faceto-face” symmetric manner [14]. Alternatively, we suggested a “back-to-face” asymmetric dimerization mode. A remarkable example of the “back-to-face” interaction in a TIR-domain-containing protein is observed in the MAL cryogenic electron microscopy (cryo-EM) structure (PDB ID 5UZB) [15]. Given the high

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Fig. 1 Pipeline for the computational modeling of a full-length model of the activated TLR4, in a Liquid-ordered model, accounting for the receptor recruitment into lipids rafts, during activation. (1–3) The first three steps in the pipeline, consist of the study of each independent domains composing the TLR4, i.e., the extracellular

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conservation of the interface residues among proteins containing TIR domains, including MAL and TLR4 ID [15], we suggest this arrangement as a reasonable alternative mode of dimerization for the intracellular TLR4 TIR domain. Thus, TLR4 intracellular signaling may be regulated by different structural elements of TLR4 ID, pointing to the possibility of pathway-specific interaction surfaces. Finally, we constructed four full-length TLR4 systems based on the asymmetric and the symmetric ID dimers, with both types of TD1 and TD2 models, and simulated them in a Lo model membrane (Fig. 1.4). Analysis of MD simulations supplies the hypothesis from the thermodynamic and molecular points of view, that the full-length TD2-symmetric and TD1-asymmetric models adopt favorable conformations of the full-length receptor in lipid rafts upon LPS binding. Thus, it is suggested that both ways of dimerization could coexist and have a direct implication in the activation of distinct TLR4 pathways. The most favorable energies are observed when the extracellular domain is tilted toward the membrane (Fig. 1.4), interacting with the phospholipids head groups, pointing to a signal-transduction mechanism across the cell membrane, within lipid–TLR4 interactions. In this chapter, we describe the computational procedures adopted in our research group to study full-atom models of the agonist LPS-bound TLR4 dimer in a Lo model of the membrane, as reported by us [12]. We discuss the main tools/protocols to model the structure and dynamics of the receptor, including homology modeling, protein–protein docking, insertion of the complex into the membrane, the MD simulation protocol of the resulting system, and the subsequent estimation of the protein– protein free energy of binding. 1.2

Theory

The following section focuses on the theoretical background of the computational methods required to study an all-atom model of the full-length activated Toll-like receptor 4 dimer in a membrane environment.

1.2.1

PDB

The Protein Data Bank (PDB) is a unified global archive that harbors three-dimensional (3D) structures of biological macromolecules. Deposited structures are mostly determined through X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy,

ä Fig. 1 (continued) (ED) (step 1), the transmembrane (TD) (step 2), and the intracellular (ID) (step 3) domains. (4) In the step 4, the combined analyses of steps 1–3, is used to model, by all-atom MD simulations, the structural assembly of four possible full-length TLR4 models embedded into a realistic plasma membrane. Energy analysis can be used to identify one or more of them as plausible 3D structures of the full agonist LPSbound TLR4. Since most TLR homologs share similar domain patterns, the methodology proposed here can be applied in the modeling of full-length models for other members of the TLR family

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and cryogenic electron microscopy (cryo-EM), and amount to more than 200,000 entries as of early 2023. Back in 1971, particle physicists at the Brookhaven National Laboratory established the archive with the only 7 protein crystal structures available by then. In the 1980s and 1990s, improvements in the crystallization process and X-ray sources, the advent of NMR technologies together with the generalized requirement by journals of a PDB accession code, substantially increased the amount of experimentally determined macromolecular structures. By 1998, with already more than 8000 entries, the management of the PDB was passed on to the Research Collaboratory for Structural Bioinformatics (RCSB; rcsb.org) [16]. In an effort to decentralize the administration of the repository, in 2003, the PDB became the responsibility of an international consortium known as the Worldwide PDB (wwPDB; wwpbd.org) [17] that supervises the validation, curation, and open access distribution of all available structural and related data of macromolecules. Each PDB entry has a unique accession code and a final curated data file containing the structural information. The accession code is a 4-character identifier of the form [0-9][a-z,0-9][a-z,0-9] [a-z,0-9]. The associated ASCII file is named by the accession code followed by the “.pdb” extension that indicates PDB format. This format includes the atomic positions of the macromolecular system in cartesian coordinates in Ångstro¨m units and their experimental method-associated attributes (e.g., B-factors for X-ray crystallography). Within the macromolecular system, atoms are divided into two categories: either belonging to biological polymers of amino acids or nucleotides, or to heterogroups (i.e., metal ions, molecules different to the macromolecule or water molecules). Besides coordinates, PDB files contain associated metadata about the biological context, relevant chemical properties, experimental conditions and authors. Another available structural data format is PDBx/mmCIF (PDB eXtended/macromolecular Crystallographic Information File) with files named as conventional PDB format and the “.cif” extension. This format organizes the information in flexible and extensible clusters of defined data. wwPDB recommends the use of mmCIF format over PDB format [17] as it counteracts the configuration limitations of the latter that cannot consider macromolecular complexes surpassing 99,999 atoms and/or defined by more than 62 chains. Besides, mmCIF format allows broader metadata description. 1.2.2 Homology Modeling

When the 3D structure of a protein sequence has not yet been experimentally resolved, one main computational approach for its prediction is homology modeling (or comparative modeling). Homology modeling predicts the 3D structure of a target protein given an available template protein with known experimental

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structure, and relies on the evolutionary driven fact that protein sequences with significant sequence similarity share similar 3D structure (i.e., homologous sequence) [18]. Other protein structure predictors are based on ab initio strategies (see Subheading 1.2, item 4. Ab initio modeling of transmembrane α-helices) or deeplearning modeling [19]. The workflow of homology-based predictors is usually as follows: identification of template through sequence alignment, model construction, and model assessment. Expected reliability of the final model can be estimated a priori by the target-template sequence identity score. Implementation of the above-mentioned homology modeling procedure varies among programs such as YASARA [20], MODELLER [21], and SWISS-MODEL server [22]. In particular, Swiss-Model template selection uses psiBLAST and HHblits methods, which are based on progressive refinement of sequence profiles and profile hidden Markov models (HMMs) alignments respectively. A profile being a compact representation of a multiplesequence alignment where each sequence position is assigned a probability of observing each of the 20 amino acids in evolutionary related proteins [23]. The rest of the pipeline is ensued as follows. The amino acid core is defined as the conserved amino acids in both query and template. Then query backbone atoms of the core and conserved loops are assigned coordinates according to those of the template. Backbone of nonconserved loops is modeled based on a database of loops extracted from PDB fragments. Servers such as ModLoop are used to model loops in protein structures [24]. Once the backbone is completed, side chains are taken from a rotamer library. Lastly, the structure is optimized to obtain the final 3D model (Fig. 2.1). 1.2.3 Protein–Protein Docking

Computational docking methods aim to predict the geometry of the complex formed by two given 3D molecule structures. Protein– protein docking differs from protein–ligand docking in that there can be multiple interaction surfaces at once. Such surfaces are usually flatter and much larger than those involved in ligand binding, an issue which substantially increases the conformational space that needs to be explored. Moreover, cases involving large conformational changes upon binding with little or no biological data available are currently beyond any correct prediction [25]. Nonetheless, residues in protein–protein interactions are better conserved than others and there are a number of properties computed from the structure that can provide insight into the interaction (e.g., local topography, electrostatic potentials, and surface exposure profiles). Experimental information is generally required for more reliable results as it can filter false positives and rank clusters of solutions, besides the delimitation of the problem and hence computational cost saving. Additionally, docking

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Fig. 2 Workflow for a molecular dynamics simulation of a complex macromolecular system involving proteins, ligands and membranes. It is composed of four main steps: (1) structure retrieval, (2) system assembly, (3) MD calculation, and (4) postprocessing of MD trajectories. Each step contains fundamental procedures (white boxes) with their associated computational methodologies (gray boxes), from which some are exemplified (signaled by dashed arrows) with applications (depicted in the right column) involving the system at work in this chapter

prediction performance for homodimers is superior to that obtained for heterocomplexes [25]. Docking methods use a generalized two-step procedure that involves a search of protein conformations (that usually do not

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account for flexibility, i.e., rigid body search) followed by score calculations and flexibility refinement. Docking methods can be used without additional information beyond the structures, which is known as blind docking (e.g., as implemented in the ClusPro server [26]), but the more common (and preferred) approach is guided docking (e.g., as implemented in the pyDock server [27]) although it requires information on residues presumably involved in the protein–protein interaction. In this chapter, we describe the use of pyDock for the construction of TLR4 intracellular domain homodimer with residue restriction based on experimental data (Fig. 2.2). pyDock uses an improved scoring function to rank rigid body poses in terms of Coulomb electrostatics and implicit desolvation energies [27]. 1.2.4 Ab Initio Modeling of Transmembrane αHelices

Most transmembrane (TM) proteins found in the PDB lack the TM domain because these domains only exist within lipidic bilayers which can rarely be addressed in experimental methods for structural determination. Ab initio modeling predicts the 3D structure exclusively from the amino acid sequence without using structural templates. In the case of TM domains known to fold into a single α-helix (e.g., TLR4 TM domain), TMDOCK [28] and PREDDIMER [29] webservers predict complexes of helical TM homodimers. The TMDOCK algorithm threads an input amino acid sequence through a fast template driven global energy optimization procedure. The PREDDIMER method is based on packing considerations, establishing the maximal complementarity of hydrophobic properties on the helix–helix interface. In order to compare and estimate the better structure given by either predictor, the resulting models are scored with the Qualitative Model Energy ANalysis (QMEAN) server, accessing the scoring function QMEANBrane [30]. QMEANBrane is a local model quality estimation method for membrane proteins that allows evaluating protein structures without knowing the target structure. QMEANBrane determines local (i.e., per residue) and global scores that range from 0 to 1, with “1” being optimal.

1.2.5 All-Atom Molecular Dynamics Simulations

The molecular bases at atomic level of receptor activation dependent on membrane composition remain elusive for experimental techniques. However, computational structural biology is currently able to provide insights into structure, interactions, and function of biomolecules being thus a valuable complement to in vitro and in vivo work. In this regard, molecular dynamics (MD) simulations are a powerful computational tool capable of elucidating, at atomistic resolution, dynamical details on complex biomacromolecular systems [7, 31, 32] like the one addressed in this chapter (Figs. 1 and 2).

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In MD simulations, equations of motion are numerically solved to determine the position and velocity of each atom of the system throughout the whole simulation time (i.e., to obtain the trajectory) [31, 33]. The derivatives of time (dt) are thus replaced by increments of time (Δt). Acceleration is computed via Newton’s second law of Dynamics through first derivatives of energy: forces. A force field is a theoretical framework that is based on an empirical expression that gives energy as a function of atomic coordinates only. It is based on a classical mechanics description where atoms are represented as charged spheres with known mass that interact via bonded-terms, defining covalent interactions (that is, bond stretching, angle bending and dihedral torsions), and nonbonded terms for atom pair interactions, comprising Coulomb electrostatics and van der Waals interactions (usually modeled by a 6-12 Lennard–Jones potential). Atom-specific potentials are parameterized based on the molecular context of said atom. Such parameters are validated using higher-resolution methods (i.e., quantum mechanics calculations) and experimental data [7]. However, force fields differ on parametrization and on availability of parameters for varying molecular types [7, 31], highlighting the significance of choosing an adequate force field specific to the problem at hand. Here we describe the use of the Amber force fields for proteins, lipids, carbohydrates and organic molecules, together with the AmberTools16 package of open-source MD programs implemented in the Amber16 licensed software suite [34, 35]. AmberTools16 contains codes for MD preparation (e.g., LEaP), MD calculations (e.g., pmemd), and MD analysis (e.g., cpptraj). In particular, the LEaP tool is most important to generate the topology and parameters for molecules, compatible with the Amber MD package, and to prepare the molecular system for subsequent simulations (i.e., definition of solvation box, solvation and ion addition). All Amber force fields here mentioned are designed to be fully compatible with one another. 1.2.5.1 ff14SB

Proteins: Amber

Amber ff14SB [36] is the recommended Amber force field for proteins. When experimental scalar coupling data became available, some limitations to its predecessor ff99SB were proven, especially involving parameters for dihedral angles in backbone and side chains. Together with ab initio quantum mechanics calculations, improvements in the characterization of all amino acids were then obtained. Parameters were also generated for alternate protonation states of ionizable side chains.

1.2.5.2

Lipids: Lipid14

Amber Lipid14 [37] is the reference Amber force field for lipids. It presents a major advancement over the previous Amber compatible lipid force fields in that it removes the need for an artificial constant

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surface tension term for lipid bilayer MD simulations in the thermodynamical NPT ensemble (which is the common choice to address biomolecular systems in MD studies). Obtained structural features of lipid bilayers compare favorably with experimental measures such as area per lipid, bilayer thickness, NMR order parameters, scattering data, and lipid lateral diffusion. Parameters for cholesterol were added soon afterward [38]. 1.2.5.3 Carbohydrates: GLYCAM06

Amber GLYCAM06 [39] is the recommended Amber force field for carbohydrates and glycoconjugates. Quantum mechanics calculations and experimental neutron diffraction data were used to build the parametrization set. Importantly, α- and β-carbohydrate anomers were henceforth described with common terms instead of different atom types, allowing interconverting ring forms in MD simulations. GLYCAM06 can be applied to describe monosaccharides and complex oligosaccharides with all carbohydrate ring conformations and sizes.

1.2.5.4 GAFF

GAFF [40] is a general Amber force field for small organic molecules. GAFF has parameters for most organic and pharmaceutical molecules that are composed of H, C, N, O, S, P, and halogens. It uses a simple functional form and a limited number of atom types, but incorporates both empirical and heuristic models to estimate force constants and partial atomic charges. GAFF can be applied to a wide range of molecules in an automatic fashion, making it suitable for rational drug design and database searching.

Small Molecules:

1.2.6 Molecular Dynamics Simulations Protocols

The MD simulations reported in this chapter are mainly performed following two different protocols that are chosen based on the type of system being studied. The protein protocol is applied for systems composed of at least one protein, alone or in complex, with at least one ligand. The membrane protocol is employed for systems that include a lipid bilayer and eventually one or more proteins and/or ligands. Notwithstanding, both protocols follow the standard workflow for MD simulations, that basically consists of: (a) a minimization run to achieve an initial potential energy minimum, (b) an equilibration run to “heat up” the system (usually to biologically relevant 298–310 K), and (c) a production run where a trajectory over the simulation time is obtained (after randomly assigning initial velocities to atoms according to the Maxwell–Boltzmann distribution) by numerically solving the equations of motion. The macromolecular system is immersed in a solvation box replicated in all directions under periodic boundary conditions. These simulation boxes are designed upon adding a pre-established distance (usually around 10–16 Å) from minimum and maximum atom coordinates of the macromolecule, and solvated with the TIP3P water model [41]. For all MD simulations, the particle mesh Ewald method

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[42] is used to calculate long-range electrostatic interactions as implemented in AMBER16. In order to control temperature and pressure, and to keep them constant, different algorithms are applied (thermostat and barostat, respectively). 1.2.6.1 Structure Optimization: Protein Preparation Wizard of the Maestro Package

Refinement of the experimental structure is paramount for correctness of following MD simulations. The Protein Preparation Wizard [43] adds hydrogen bonds, optimizes hydrogen bonding, and performs minimization for protein–ligand systems. It is implemented in the Maestro package [44], a graphical user interface for molecular visualization with other analysis features and molecular building tools. For protein–ligand complexes, a restrained minimization under the matching OPLS3 force field [45] is performed in order to attain a reasonable potential energy minimum.

1.2.6.2 Protein– Membrane System Setup: CHARMM-GUI

The Membrane Builder tool in the CHARMM-GUI website [46, 47] allows for an automated generation of all-atom protein– membrane or membrane-only systems. It covers most lipid types, including sphingolipids and cardiolipin [46], as well as glycolipids and lipoglycans (such as LPS) [47], and can produce mixed and asymmetric lipid bilayers. The webserver implements the “replacement method” to generate a lipid bilayer. When the protein has an irregular shape, the “replacement method” surrounds the protein with lipid-like pseudo atoms that are replaced one at a time with lipid molecules. The user can also decide the size of the simulation box and solvation model. The built system is minimized so that the resulting output files are ready for an equilibration run.

1.2.6.3

The minimization run consists of 1000 steps of steepest descent algorithm followed by 7000 steps of conjugate gradient algorithm while a 100 kcal mol-1 A-2 harmonic potential constraint is applied on the protein. Subsequently, the harmonic potential is progressively lowered (respectively to 10, 5, 2.5, and 0 kcal mol-1 A-2) for 600 steps of conjugate gradient algorithm each time. The equilibration run is ensued as follows. The system is heated from 0 to 100 K applying Langevin thermostat in the canonical ensemble (NVT) under a 20 kcal mol-1 A-2 harmonic potential restraint on the proteins and the ligand. Finally, the system is heated up from 100 to 300 K in the isothermal-isobaric ensemble (NPT) under the same restraint condition, followed by a simulation over 100 ps in which all harmonic restraints are removed. At this point, the system is ready for the production run, which is performed using the Langevin thermostat in the NPT ensemble at 2 fs time step.

Protein Protocol

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1.2.6.4 Membrane Protocol

Minimization of the potential energy of the system is computed with 5000 steepest descent steps followed by 5000 conjugate gradient steps without constraint. The system is then heated from 0 to 100 K for 2500 MD steps in the NVT ensemble while the proteins and the lipids are held still by a 10 kcal mol-1 A-2 harmonic potential. Then, the system is progressively heated from 100 to 303 K for 50,000 steps. Importantly, for lipid bilayer simulations, the choice of temperature for the production run is crucial. Lipid bilayers have experimentally measured phase transition temperatures from highly ordered gel-like phases to liquid phases. One major problem in membrane simulations is to accurately simulate the phase transition of lipids. Later, the system is equilibrated for 5 ns with lipids unrestrained in an anisotropic NPT ensemble and Berendsen barostat. Finally, this step is repeated with no restraints. In all cases, temperature is controlled by a Langevin thermostat. Since anisotropic pressure is needed to account for the surface tension of the membrane, different pressure is set in the tangential direction to the membrane.

1.2.7 Postprocessing of MD Trajectories

Some tools often used to perform a primary analysis of molecular dynamics trajectories are here presented. These tools are used firstly to ensure the accuracy of a simulation and then to understand the dynamic evolution of the overall system, as well as to quantitatively determine its molecular behavior. In this regard, the cpptraj module of AmberTools16 [34, 35] is used for trajectory analysis.

1.2.7.1

Changes in the geometry of a protein system are measured by the root mean square deviation (RMSD) that computes the deviation of the position of certain atoms (e.g. backbone atoms for proteins or nonhydrogen atoms for ligands) with respect to some reference geometry which can be the initial or an average structure. RMSD is given by Eq. 1, where N is the number of atoms, ri is the position of atom i in the target structure, and r R i is the position of atom i in the reference structure R.

RMSD

RMSD =

1 N

N i=1

ri - rR i

2

ð1Þ

In general, it is computed for every sampled frame of the trajectory obtained in the MD simulation and plotted as a function of the simulation time (Fig. 2.4). In such cases, the overall translation and rotation are removed by performing a rigid superposition of every frame with respect to the reference structure. 1.2.7.2

Area Per Lipid

When dealing with MD simulations involving membranes, structural indicators of its stability such as area per lipid (APL), volume per lipid, and membrane thickness can be computed and compared to the documented experimental values. The APL is basically the surface area occupied by each lipid and can be obtained calculating

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the membrane surface area using the simulation box dimensions and dividing it by the number of lipids occupying said area. The area of the protein at the membrane plane must obviously be subtracted for a proper interpretation of the results. APL is generally computed for every sampled frame and plotted as a function of the simulation time. 1.2.7.3 Free Energy of Binding: The MM/GBSA Method

The protein–ligand or protein–protein interaction energy is the essential property of a complex as it describes the affinity between the complexed molecules. At constant P and T, and assuming the enthalpy change associated to complex formation is well represented by the internal energy difference between isolated and complexed molecules ΔEint, the Gibbs free energy (ΔG) of protein– ligand or protein–protein binding can be written as: ΔG = ΔE int þ ΔG sol - T ΔS conf

ð2Þ

where ΔGsol is the free energy change associated to solvation effects on complex formation. In this expression, the total entropy change at constant absolute temperature T is approximated by the entropy difference associated to the conformational changes of the solute molecule upon complex formation, ΔSconf. Given that calculations of entropy changes are still unreliable and entail a huge computational cost, ΔG is commonly approximated by (Eq. 3). ΔG ≈ ΔE int þ ΔG sol

ð3Þ

Free energies of binding given by Eq. 3 can be calculated with methods that employ a Molecular Mechanics (MM) force field to obtain the ΔEint term, which is computed just as the sum of van der Waals and electrostatic contribution estimated from the force field. In other words, bonded terms in the force field are neglected, which means that they are assumed unchanged when the molecules form the complex. The ΔGsol term is in turn obtained resorting to (a) an implicit solvent framework such as the Poisson–Boltzmann equation or the Generalized Born (GB) model to calculate the polar (electrostatic potential) effects, and to (b) an empirical simple formula based on solvent-exposed surface areas (SA) to model the nonpolar solvation effects. The computational methods here presented focus on the “Molecular Mechanics with Generalized Born Surface Area (MM-GBSA)” approach. Irrespective of the particular framework used to obtain internal energy and solvation terms, the binding free-energy change is evaluated as the difference between the bound state and the unbound state between molecules A and B (Eq. 4). ΔG = G complex - ðG moleculeA þ G moleculeB Þ

ð4Þ

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Therefore, all the terms needed to obtain the binding free energy are computed with the chosen approach (here, MM-GBSA as implemented in Amber16 [35]) for the complex, molecule A and molecule B over the sampled frames of the trajectory and then averaged to get the estimated binding free energy of complex formation. In this regard, MD simulations allow for sufficient sampling of interaction geometries at equilibrium to get a reasonable estimate of the binding affinity. 1.2.8 Visualization of MD Trajectories

2 2.1

Rendering of molecular graphics and visual representations of other structural analyses are performed with the programs VMD [48] and PyMOL [49]. While VMD is particularly useful to animate and visualize MD trajectories, PyMOL is especially fitting for generation of high-quality 3D images for publication.

Materials Computer

1. Hardware: minimum specifications are a quad-core machine, e.g., Intel i7, ideally with a compatible CUDA-enabled GPU card, to greatly decrease the time it takes to run explicit and implicit solvent simulation [50], and with at least 1 GB RAM per core and 100 GB disk space. 2. Operating system: Unix operating system. If Windows, run Linux via a virtual machine. The commands in this tutorial are provided assuming a Unix-like environment (Linux/BSD) and user-level knowledge of such systems.

2.2

Software

1. Web servers: TMDOCK [28] for ab initio modeling of the TLR4 transmembrane domain (https://membranome.org/ tmdock), SWISS-MODEL [22] for homology modeling of the monomeric TLR4 intracellular domain (https://swissmodel. expasy.org/), pyDock [27] for protein–protein docking (https://life.bsc.es/servlet/pydock/), MODLOOP [24] for the refinement of the interdomains loops (https://modbase. compbio.ucsf.edu/modloop/), and CHARMM-GUI [46] for TLR4 insertion in a membrane model (https://www.charmmgui.org/). 2. AMBER16 [35]. AMBER is a commercial suite of programs, designed for MD simulations. This software is entirely based on a Command Line Interface (CLI) on a computer with Linux. In this tutorial, we use pmemd (pmemd.CUDA executable) [50], the high performance implementation of the MD engine that can be run with acceleration from GPU. We also introduce essential components of the AMBER package for energy minimization and MD simulations (LEaP and antechamber [51]), basic analytical tools (ptraj/cpptraj command) implemented in

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AmberTools16 [35], and MM-GBSA energy analysis [52]. AMBER package can be purchased at http://ambermd. org. 3. Maestro Schro¨dinger [44]. Maestro is the graphical user interface for Schro¨dinger’s computational programs, including Protein Preparation Wizard [43], and can be purchased at https:// www.schrodinger.com/products/maestro. 4. PyMOL Molecular graphics and modeling [49]. PyMOL is a user-sponsored molecular visualization system on an opensource foundation, maintained and distributed by Schro¨dinger. The program is available for free download for academic use at https://pymol.org/2/. 2.3

Required Data

1. X-Ray crystallographic structure of the extracellular domain of TLR4 in complex with E. coli LPS (PDB ID 3FXI [53]) from Protein Data Bank (PDB, https://www.rcsb.org/) database. 2. TLR4 sequence in FASTA format, retrieved from Uniprot (UniProtKB—O00206) (Uniprot, https://www.uniprot.org/).

3

Methods

3.1 Extracellular Domain of TLR4

Open the Maestro Interface. Maestro displays an empty, temporary (“Scratch”) project when you first launch Maestro. Choose Applications > Protein Preparation Wizard from the top menu. In the Import structure into Workspace dialog, type “3FXI” into the PDB field and click Import; the structure will appear in the Workspace, with a confirmation banner. Click the Preprocess button on the Protein Preparation Wizard. Keep all the parameters as default. After a minute or two, you may get a warning about “Overlapping Atoms”; just click OK. Then, click Optimize on the third tab of the Protein Preparation Wizard (“Refine”) to fix the overlapping atoms. Within the same tab, remove waters with less than 3 H-bonds to nonwaters. Finally, click Minimize for running a Restrained Minimization with the OPLS3 force field, with a convergence parameter to RMSD for heavy atoms kept default at 0.3 Å. Close the Protein Preparation Wizard panel and save the optimized structure in PDB format.

3.2 Dimeric TLR4 Transmembrane Domain (TD2 Model)

Go to the TMDOCK webserver at https://membranome.org/ tmdock. Paste the TLR4 sequence spanning from residue Val620 to residue Gly670, retrieved from Uniprot (UniProtKB—O00206)

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Fig. 3 Predicted TLR4 TD dimer conformations, generated by TMDOCK server, and the calculated data as shown at the TMDOCK server

into the text box of the Amino Acid Sequence (single-letter code) of Peptide (see Note 1). In the File name and Email sections of the form, provide a job name and email address to be notified when the job is complete. Once the job is completed, you will be notified by an e-mail message containing a link to the results page. Click on the link and Download Output File. The output coordinate file includes one or several 3D models of the dimer in the PDB format. They are included as MODEL 1, and so on. The results page also displays the following parameters for every model: (i). Free energy of helix–helix association, ΔGassoc (kcal/mol), (ii). Energy of the alpha-helical dimer relative to helices in water ΔGstb (kcal/mol), (iii). Energy of helix–helix interaction, Eassoc (kcal/mol), (iv). The “symmetry violation” parameter, (Rasym), (v). Interhelical angle and distance, and (vi). Key residues at the helix–helix interface (Fig. 3). According to the TMDOCK original paper [28], models with lower (more negative) values of Eassoc (Easc) may be native-like even if their ΔGassoc is higher. Therefore, MODEL 2 (Easc 32.5 kcal/mol) will be selected as a potential TD dimer, for further construction of the TLR4 full-length model. This TD model corresponds to the TD2 of our original work [12]. Save the MODEL 2 and renumber the file using PyMOL (starting from Lys631). In the PyMOL command line, introduce the following command: alter (all),resi=str(int(resi)+630). 3.3 Intracellular Domain of TLR4 3.3.1 Monomeric TLR4 Intracellular Domain: Homology Modeling

Go to the SWISS-MODEL home page at https://swissmodel. expasy.org/ and click the “Start Modeling” button to start a new modeling project. Paste the TLR4 ID sequence (Asn672-Leu817) retrieved from Uniprot (UniProtKB—O00206) into the text box of the Target Sequence(s) section (see Note 2).

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Fig. 4 Monomeric intracellular domain of TLR4, built with the homology modeling protocol as implemented in SWISS-MODEL server

In the Project Title and Email sections of the form, provide a job name and email address to be notified when the job is complete (see Note 3). Click the Build model button to queue the job on the server (see Note 4). Once the job is completed, you will be notified by an e-mail message containing a link to the results page. The interface provides the top-ranked alignments between the target sequence and the structures in the Protein Data Bank (template library). The highestranked model is selected as default (on the basis of the best template according to Global Model Quality Estimate (GMQE), see Note 5). As observed in Fig. 4, the top-ranked model of the TLR4 ID was constructed based on the crystal structure of the human TLR2 ID (PDB ID 1FYW, chain A) [54]. Click on the “Model 01” button and download the top-ranked homology model for the TLR4 ID in PDB format. Use PyMol to renumber the dimeric TIR domain of TLR4 (starting from Asn672). In the PyMOL command line, introduce the following command: alter (all),resi=str(int(resi)+671). 3.3.2 Dimeric TLR4 Intracellular Domain (Asymmetric Model): Protein–Protein Docking

Go to the pyDock protein–protein docking server, accessible at https://life.bsc.es/servlet/pydock/. In the Project name and Contact email sections of the form, provide a job name and email address to be notified when the job is completed. Select the Upload PDB structures option and upload the homology modeling structure previously downloaded from SWISS-MODEL, as both receptor and ligand. Then, click on Continue (see Note 6).

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Fig. 5 The assembly of the MAL protein forms a stable protofilament consisting of two parallel strands with two types of asymmetric interactions: one within each strand of the protofilament (intrastrand), involving opposite sides of MAL subunits in a BB loop mediated head-to-tail arrangement, and another one between the two strands of the protofilaments (interstrand). On the left: organization of MAL monomers (in gray and salmon pink cartoon) in the cryo-EM MAL filament structure (PDB ID 5UZB). On the right: superimposition of the TLR4 dimer ID asymmetric model (green cartoon) with two adjacent chains (A and C) of the 5ZUB MAL crystal structure (gray cartoon)

Select chain A for both receptor and ligand in the Structure Information tab, and then click on the Select chains button to continue. In the Restraints tab, select residues Asn792, Tyr794, Glu796, Try797, Glu798, and Arg810 for the receptor, and residues Tyr674, Glu698, Arg710, Asp711, Pro714, Arg731, Ser744, Cys747, Tyr751, and Glu752 [55] for the ligand (see Note 7), and click on Next step. Finally, check that you have correctly selected the residues to be restricted during docking, and click on Submit job. Once the job is completed, you will be notified by an e-mail message containing a link to the results page. Access the link and click Download results in Tar.gz format. Use PyMOL for visualization of the top-ten ranked dimers and superimpose them to the MAL structure (PDB ID 5UZB, chains A and C) [15]. Save the best-predicted pose which shows structural overlap with the MAL structure (Fig. 5). 3.4 Molecular Dynamics Simulations and Analysis of the Full-length (TLR4/MD2/LPSEc)2 3.4.1

Model Construction

Open the TLR4 ECD (residues 27–627 of TLR4, plus MD-2 and E. coli LPS), TM (residues 631–662), and TIR (residues 672–817) dimer models in the same PyMOL session. Use the Builder tool implemented in PyMOL to add the missing loops connecting the ED with the TD (residues 628–630) and the TD with the ID (residues 663–671), to the C-termini of the extracellular and transmembrane domains, respectively. Then, align the TLR4 ECD, TM, and TIR domains on a straight axis so that the C-terminus and the N-terminus of each domain face each other within a covalent-bonding distance.

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Save only the protein chains (TLR4 and MD-2 chains) from the resulting molecule as a PDB file. Do not include the ligands (E. coli LPSs). The order of the chains in the full-length TLR4 file should be as follows: TLR4 chain A, TLR4 chain B, MD-2 chain C, and MD-2 chain D (as in the PDB ID 3FXI). Ensure that each residue in the different protein chains follows the correct numeration. Also, ensure that each protein chain is correctly assigned, i.e., TLR4 chain A, TLR4 chain B, MD-2 chain C, and MD-2 chain D. Otherwise, use the PyMOL command alter (e.g., alter (resid 663–671), chain=’A’). Open the resulting PDB in the Maestro Interface. Use the Draw structures tool from the Workspace to connect together the TLR4-independent domains. Connect the C1 (carbon number one) of the C-terminal amino acid of the ED domain, with the N2 (nitrogen number two) of the N-terminus of the TD TLR4 domain (peptide bond). Repeat the same process among the TD (C-terminal) and ID (N-terminal) domains. Interdomain Loops Refinement Finally, access to the MODLOOP server, at https://modbase. compbio.ucsf.edu/MODLOOP/. Complete the required information in the form, including your Email and your Modeller license key (see Note 8). Upload coordinate file, i.e., the PDB file of the full-length receptor created with Maestro. Indicate the TLR4 sequence corresponding to the loops connecting the ED with the TD (residues 628–630) and the TD with the ID (residues 663–671) in the text box of the Enter loop segment (see Note 9). Use the following syntax: Example: sequence of the loop between residues 663 and 675 in TLR4 chain A. 663:A:675:A: Name your model and click on Process. Save the generated model in PDB format and use it as input for a following running of the MODLOOP server. Repeat the process until completing the entire sequence of amino acids that comprises the loops connecting the ED with the TD (residues 628–630) and the TD with the ID (residues 663–652). Full-Length Structure Refinement Refine the resulting structure using the Protein Preparation Wizard in Maestro. Follow the same procedure described in Subheading 3.1. Extracellular Domain of TLR4. Save the full-length TLR4 system in PDB format.

Modeling of Transmembrane Domain and Full-Length TLRs in Membrane Models 3.4.2 Insertion of the Full-Length TLR4 in a Liquid-ordered Model Membrane

23

Access to CHARMM-GUI server, (https://www.charmm-gui. org/) (see Note 10), and Input Generator > Membrane Builder > Bilayer Builder > Protein/Membrane System and Upload PDB File, i.e., the optimized PDB file of the full-length TLR4 generated with Maestro. Click on Next Step: Select Model/Chain. In the Model/ Chain Selection Option, select all the Protein chains and go to the Next Step: Manipulate PDB. In the Terminal group patching of the Manipulate PDB tab, select the ACE option to cap the N-termini of all the protein chains, and the C-TER option for the C-terminal domains. Click in the Next Step: Generate PDB and Orient Molecule. Select the Orientation Options: Align the First Principal Axis Along Z. Then, introduce a random number in the Positioning Options: Translate Molecule along Z axis (e.g., 50 Å). Go to the Next Step Calculate Cross-Sectional Area, and view the structure (step2_orient.pdb). Check that the transmembrane domain of TLR4 is embedded in the membrane. If not, go back to the previous step (Generate PDB and Orient Molecule) and modify the Translate Molecule along Z axis number. Repeat this step as many times as necessary, until TLR4 is correctly positioned in the membrane (Fig. 6). In the next step (Calculate Cross-Sectional Area), keep all the System Size Determination Options as default. The receptor will be inserted in a 150 A2 (X and Y) Liquid-ordered model of membrane. Therefore, the Length of X and Y is 150 (initial guess), and the composition of lipids is 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC)/cholesterol, 60: 40, for both, the Upper and the Lower leaflets of the membrane (see Note 11).

Fig. 6 TLR4 orientation in the membrane. The gray and yellow sheets are the XY planes of membrane. On the left: Side view. On the right: Top view

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Click Show System Info, once completed the Lipids composition table, and check that there are no error messages (see Note 12). Then, go to the Next Step: Determine the Size System. After a couple of minutes, the next step tab will appear. In the Component Building Options, unselect the Include Ions Option and go to the Next Step: Build Components (see Note 13). Once the process is finished, go to the Next Step: Assemble Components. Then, in the next step (step 5), select the set of AMBER Force Fields (i.e., ff14SB for the protein, and Lipid17 for the membrane phospholipids), and the AMBER program, in the Input Generation Options. Keep the other parameters as default, including the temperature (303.15 K). Finally, Download results in Tar.gz format, and access to the AMBER folder. Extract the files step5_input.rst and step5_input.parm7 into a new folder located at your working directory. To simplify the process, the (TLR4/MD-2)2 protein, without the ligand (E. coli LPS), has been used throughout. However, the activated (agonist) form of the receptor is induced only by an agonist ligand, such as E. coli LPS, so its presence in our model is essential. To include the ligand in the system, first, use the step5_input. rst and step5_input.parm7 files to generate a PDB file of the MD simulation input system (created with CHARMM-GUI), by means of the AMBPDB Tool (AMBER16) (see Note 14). $ambpdb -p step5_input.parm7 -c step5_input.rst7 > step5_input.pdb

Then, open the step5_input.pdb and the PDB file of the extracellular domain of TLR4, optimized with the Protein Preparation Wizard tool (Maestro Schrodinger) in Subheading 3.1. Extracellular domain of TLR4. Align the latter to the extracellular domain of the full-length model (step5_input.pdb). Renumber the ligands residues (starting from 1881). In the PyMOL command line, introduce the following command: alter (all),resi=str (int(resi)+1880). Save the new coordinates of the E. coli LPSs ligands. Then use a text editor to open the PDB file containing the E. coli LPS ligands with the new coordinates, and the step5_input. pdb. Copy the coordinates of the E. coli LPS ligands into the step5_input.pdb file where the protein residues end, i.e., after the last atom corresponding to MD-2 (D-chain, residue 1880). Save the changes made in step5_input.pdb.

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Include the Protein Disulfide Bonds Open again the step5_input.pdb with a text editor, and substitute the residue name “CYS” by “CYX” (see Note 15) for the following residues: TLR4 chain A: 4, 15, 256, 281, 365, 366, 558, 560, 584, and 602. TLR4 chain B: 802, 813, 1054, 1079, 1163, 1164, 1356, 1358, 1382, and 1400. MD-2 chain C: 1604, 1630, 1616, 1772, 1674 and 1684. MD-2 chain D: 1772, 1746, 1758, 1869, 1816, and 1826. 3.4.3 MD Simulation of the Full-Length TLR4 in a Liquid-ordered Model Membrane

Build the AMBER Topology and Coordinate Files with LEaP In this section, you will use the file step5_input.pdb into LEaP to prepare an AMBER parameter topology file and initial coordinates file (see Note 16). Due to the nature in which CHARMM-GUI builds lipid bilayers, there will be lipids extending beyond the solvation layers in the X and Y dimensions. It is possible to better estimate the periodic box dimensions by using the coordinates of water molecules. This can be measured using the vmd_box_dims.sh bash/VMD script, accessible at: http://AMBERmd.org/tutorials/advanced/tuto rial16/. Calculate the box dimensions from the water molecules: $vmd_box_dims.sh -i step5_input.pdb -s water

Then, use tleap to generate topology and coordinate files for the step5_input.pdb system. Xleap could also be used for user’s preference. The force fields ff14SB [36], Lipid14 [37], and a combination of GAFF [40], GLYCAM06 [39] and Lipid14 [37] were used to described proteins, membrane phospholipids, and E. coli LPS, respectively. $tleap –f tleap.in > tleap.out

The content of the tleap.in file is: ###SOURCE AMBER FORCE FIELDS source /usr/local/AMBER16/dat/leap/cmd/leaprc.ff14SB source /usr/local/AMBER16/dat/leap/cmd/leaprc.gaff source /usr/local/AMBER16/dat/leap/cmd/leaprc.GLYCAM_06j-1 source /usr/local/AMBER16/dat/leap/cmd/leaprc.phosaa10 source /usr/local/AMBER16/dat/leap/cmd/leaprc.lipid14

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Alejandra Matamoros-Recio et al. ###LOAD THE LPS PARAMETERS loadAMBERparams /YOUR_PATH/LPS.frcmod loadOff /YOUR_PATH/LPS.lib ###LOAD THE PDB INPUT FILE pp = loadpdb step5_input.pdb ###SET THE PERIODIC BOX SIZE (previously calculated with the vmd_box_dims.sh script) set

pp

box

{

150.75700378417969,

150.67399597167969,

226.90799713134766 } ###INDICATE THE DISULFIDE BONDS bond pp.4.SG pp.15.SG bond pp.256.SG pp.281.SG bond pp.365.SG pp.366.SG bond pp.558.SG pp.584.SG bond pp.560.SG pp.602.SG bond pp.802.SG pp.813.SG bond pp.1054.SG pp.1079.SG bond pp.1163.SG pp.1164.SG bond pp.1356.SG pp.1382.SG bond pp.1358.SG pp.1400.SG bond pp.1604.SG pp.1630.SG bond pp.1616.SG pp.1727.SG bond pp.1674.SG pp.1684.SG bond pp.1772.SG pp.1746.SG bond pp.1758.SG pp.1869.SG bond pp.1816.SG pp.1826.SG ###ADD IONS TO NEUTRALIZE THE SYSTEM addions pp Na+ 0 addions pp Cl- 0 ###SAVE AMBER TOPOLOGY AND COORDINATE FILES saveAMBERparm pp SYSTEM.prmtop SYSTEM.inpcrd quit ~

Run MD Simulations of the System For molecular dynamics of proteins inserted in bilayer systems, the following protocol can be used, using Langevin thermostat and

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Berendsen barostat. The particle mesh Ewald (PME) method [56] was used to calculate long-range electrostatic interactions as implemented in AMBER16 [35]. 1. Minimization: minimization on CPU with pmemd. This is advised for membrane systems, which often have bad clashes of lipid chains to resolve. The CPU code is more robust in dealing with these than the GPU [35]. This step is essential to minimize and relax the initially generated structure. Input: 01_Min.in. 2. Heating: After the initial minimization, slowly heat the system to production temperature. The system was heated through two sequential runs to 303 K (see Note 17) while keeping the protein, ligands, and lipids fixed. First the system is heated to 100 K and then slowly to the production temperature. 2.1. Heating: heating to 100 K, with restraints on the receptor, the E. coli LPS ligands, and the membrane lipids. Input: 02_Heat.in. 2.2. Heating 2: heating from 100 to 303 K, with restraints on the receptor, the E. coli LPS ligands, and the membrane lipids. Input: 02_Heat2.in. 3. Equilibration: In order to equilibrate the periodic boundary condition dimensions of the system, it is necessary to run short MD with a barostat. The system’s dimensions and density must equilibrate before proceeding with production MD. 3.1. Equilibration: run 1ns at constant pressure and temperature (NPT, 303 K, 1 atm). Hold restraints on the receptor, the E. coli LPS ligands, and the membrane lipids. Input: 03_Eq.in. 3.2. Equilibration 2: run 1ns at constant pressure and temperature (NPT, 303 K, 1 atm). Hold restraints on the receptor and E. coli LPS ligands. Input: 03_Eq2.in. 3.3. Equilibration 3: run 1ns at constant pressure and temperature (NPT, 303 K, 1 atm). Remove all restraints. Input: 03_Eq3.in. 4. Production: run 350 ns at constant pressure and temperature (NPT, 303 K, 1 atm). Input: 04_Prod.in. The contents of the MD simulations files are (see Note 18): set PROT = "RES 1 1910" #protein and ligand residues set LIP = "RES 1911 3494" #lipids set MASK = "RES 1 3494" #lipids, protein, and ligand residues set MOL = SYSTEM set NBPROC = "8"

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01_Min.in Minimization &cntrl imin=1,maxcyc=10000,ncyc=500, ntb=1,ntp=0, ntf=1,ntc=1, ntpr=50, ntwr=2000, cut=10.0, /

02_Heat.in Heating 100K &cntrl imin=0, ntx=1, irest=0, ntc=2, ntf=2, tol=0.0000001, nstlim=2500, ntt=3, gamma_ln=1.0, ntr=1, ig=-1, ntpr=100, ntwr=10000,ntwx=100, dt=0.002,nmropt=1, ntb=1,ntp=0,cut=10.0,ioutfm=1,ntxo=2, / &wt type=’TEMP0’, istep1=0, istep2=2500, value1=0.0, value2=100.0 / &wt type=’END’ / Hold lipids, protein, and ligand fixed 10.0 ${MASK} &end END

02_Heat2.in Heating 300K &cntrl imin=0, ntx=5, irest=1, ntc=2, ntf=2,tol=0.0000001, nstlim=50000, ntt=3, gamma_ln=1.0, ntr=1, ig=-1, ntpr=100, ntwr=10000,ntwx=100, dt=0.002,nmropt=1, ntb=2,taup=2.0,cut=10.0,ioutfm=1,ntxo=2, ntp=2, / &wt type=’TEMP0’, istep1=0, istep2=50000, value1=100.0, value2=303.0 / &wt type=’END’ /

Modeling of Transmembrane Domain and Full-Length TLRs in Membrane Models Hold lipids protein and ligand fixed 10.0 ${MASK} &end END

03_Eq.in Equilibration 1ns 303K protein and lipids &cntrl imin=0, ntx=5, irest=1, ntc=2, ntf=2, tol=0.0000001, nstlim=500000, ntt=3, gamma_ln=1.0, temp0=303.0, ntpr=5000, ntwr=500000, ntwx=5000, dt=0.002, ig=-1, ntr=1, ntb=2, cut=10.0, ioutfm=1, ntxo=2, ntp=2, / &ewald skinnb=5.0, / &wt type=’END’ / Hold protein fixed 10.0 ${PROT} END Hold lipids fixed 5.0 ${LIP} END END

03_Eq2.in Equilibration 1ns 303K protein &cntrl imin=0, ntx=5, irest=1, ntc=2, ntf=2, tol=0.0000001, nstlim=500000, ntt=3, gamma_ln=1.0, temp0=303.0, ntpr=5000, ntwr=500000, ntwx=5000, dt=0.002, ig=-1, ntr=1, ntb=2, cut=10.0, ioutfm=1, ntxo=2, ntp=2, / &ewald skinnb=5.0, /

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Alejandra Matamoros-Recio et al. &wt type=’END’ / Hold protein fixed 5.0 ${PROT} END END

03_Eq3.in Equilibration 1ns 303K &cntrl imin=0, ntx=5, irest=1, ntc=2, ntf=2, tol=0.0000001, nstlim=500000, ntt=3, gamma_ln=1.0, temp0=303.0, ntpr=5000, ntwr=500000, ntwx=5000, dt=0.002, ig=-1, ntr=1, ntb=2, cut=10.0, ioutfm=1, ntxo=2, ntp=2, / &ewald skinnb=5.0, /

04_Prod.in Production 303K 350ns &cntrl imin=0, ntx=5, irest=1, iwrap=1, ntc=2, ntf=2, tol=0.0000001, nstlim=1750000000, ntt=3, gamma_ln=1.0, temp0=303.0, ntpr=50000, ntwr=50000, ntwx=5000, dt=0.002, ig=-1, ntb=2, cut=10.0, ioutfm=1, ntxo=2, ntp=2, /

Use the following commands to run the MD simulations in a Linux terminal: $mpirun -np ${NBPROC} pmemd.MPI -O -i 01_Min.in -o STEP1.out -c ${MOL}.inpcrd \ -r STEP1.rst -p ${MOL}.prmtop -ref ${MOL}.inpcrd $pmemd.cuda -O -i 02_Heat.in -o STEP2.out -p ${MOL}.prmtop \ -c STEP1.rst -r STEP2.rst -ref STEP1.rst -x STEP2.nc $pmemd.cuda -O -i 02_Heat2.in -o STEP3.out -p ${MOL}.prmtop \ -c STEP2.rst -r STEP3.rst -ref STEP2.rst -x STEP3.nc

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$pmemd.cuda -O -i 03_Eq.in -o STEP4.out -p ${MOL}.prmtop \ -c STEP3.rst -r STEP4.rst -ref STEP3.rst -x STEP4.nc $pmemd.cuda -O -i 03_Eq2.in -o STEP5.out -p ${MOL}.prmtop \ -c STEP4.rst -r STEP5.rst -ref STEP4.rst -x STEP5.nc $pmemd.cuda -O -i 03_Eq3.in -o STEP6.out -p ${MOL}.prmtop \ -c STEP5.rst -r STEP6.rst -ref STEP5.rst -x STEP6.nc $pmemd.cuda -O -i 04_Prod.in -o PRODUCTION.out -p ${MOL}. prmtop \ -c STEP6.rst -r PRODUCTION.rst -ref STEP6.rst -x PRODUCTION. nc

3.4.4 Analysis of MD Simulations

After production, there are several protein and lipid bilayer parameters worth examining including area per lipid, electron density profiles, and deuterium order parameters, among others. Current literature presents many methods of analysis [7]. MM-GBSA Analysis In this section, we describe how to process the results of the simulations focusing on the computation of the Gibbs free energy (ΔG) for TLR4–TLR4 protein–protein interaction (i.e., between TLR4 chain A, and TLR4 chain B), using the MM-GBSA method. More basic analyses, including Root-Mean Square Deviation (RMSD) of protein and ligands, and area per lipid of membrane, can be easily performed with cpptraj tools of AMBER (AMBER tutorial: http://AMBERmd.org/tutorials/advanced/tutorial16). First, three topology files are required, one for the TLR4/ TLR4 dimer, one for the TLR4 chain A, and one for the TLR4 chain B. The topologies must be cross-compatible, i.e., they must have the same charges for the same atoms, be described by the same force field and have the same Bondi radii (PBRadii). For MM-GBSA, several GB models and atomic radii combinations have been tested in bibliography [57]. For our calculations, based on the ff14SB force field, we will select the igb=8 implicit solvent model, with the default PBRadii to the "mbondi3," as described in the AMBER16 reference manual [35]. Additionally, the MD simulations trajectory file is also needed. Here, the trajectory file corresponds to the trajectory of the TLR4 chains extracted from the complete TLR4/MD-2/LPS dimer in the Lo membrane trajectory. Generate the Topology Files The topology file of the system can be created with LEaP:

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First, use a text editor to open the step5_input.pdb created with CHARMM-GUI. Copy the TLR4 chains in a new file and save it in PDB format (TLR4_chains.pdb). Then, run tleap to create the topology file, and set the default PBRadii to the “mbondi3.” Use the following command: $tleap –f tleap.in > tleap.out

The content of the tleap.in file is: ###SOURCE AMBER FORCE FIELDS source /usr/local/AMBER16/dat/leap/cmd/leaprc.ff14SB ###SET THE PDBRADII set default PBRadii mbondi3 ###LOAD THE PDB INPUT FILE pp = loadpdb TLR4_chains.pdb ###INDICATE THE DISULFIDE BONDS bond pp.4.SG pp.15.SG bond pp.256.SG pp.281.SG bond pp.365.SG pp.366.SG bond pp.558.SG pp.584.SG bond pp.560.SG pp.602.SG bond pp.802.SG pp.813.SG bond pp.1054.SG pp.1079.SG bond pp.1163.SG pp.1164.SG bond pp.1356.SG pp.1382.SG bond pp.1358.SG pp.1400.SG ###SAVE AMBER TOPOLOGY AND COORDINATE FILES saveAMBERparm pp TLR4_chains.prmtop TLR4_chains.inpcrd quit ~

In the next step, create the topology files for the independent TLR4 chains (i.e., TLR4_chainA.prmtop and TLR4_chainB. prmtop), starting from the TLR4_chains.prmtop file. To do this, use cpptraj and the following stripping.in files: stripping_TLR4_chainA.in parm TLR4_chains.prmtop parmstrip :1-798 parmwrite out TLR4_chainB.prmtop stripping_TLR4_chainB.in parm TLR4_chains.prmtop

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parmstrip :799-1596 parmwrite out TLR4_chainA.prmtop

Run cpptraj using the command: $cpptraj -i stripping_TLR4_chain.in

Obtain the Trajectory File To obtain the trajectory of only the TLR4 chains, extracted from the trajectory of the whole system simulation, use cpptraj and the following stripping_trajectory.in file; stripping_trajectory.in trajin PRODUCTION.nc strip :!1-1596 trajout TLR4_chains.nc netcdf

Run cpptraj using the command: $cpptraj -p SYSTEM.prmtop -i stripping_trajectory.in

Run the MM-GBSA Analysis To calculate the Gibbs free energy (ΔG) for TLR4–TLR4 protein– protein interaction, use the following input file for MMPBSA.py (mmGBSA.in): &general verbose=1, / &gb igb=8, saltcon=0, / &pb istrng=0,

The following command line will run the script interactively and print the progress of the calculation to STDOUT and any errors or warnings to STDERR. Finally, timings will be printed once the calculation has completed showing the time taken during each step of the calculation. $nohup mpirun -np 4 $AMBERHOME/bin/MMPBSA.py.MPI -O -i mmGBSA. in \ -cp TLR4_chains.prmtop \ -rp TLR4_chainA.prmtop \ -lp TLR4_chainB.prmtop \ -y TLR4_chains.nc \ -o FINAL_RESULTS_mmGBSA_TLR4.dat

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Notes 1. TMDOCK uses amino acid sequence as input (one-letter code). The sequence is expected to be longer than the TM alpha-helix. 2. The amino acid sequence of the target protein can be submitted either as plain text, or in FASTA format. 3. As SWISS-MODEL does not require a user to register before submitting a job, it is important to provide a correct e-mail address. Otherwise, you will not be able to retrieve the results of your job. 4. Successful submission will redirect the user to a page of pending and finished jobs for the account used. Upon submission, the data entered in the form will be validated and the user will be notified of any errors that need to be corrected in a box appearing at the top of the page. 5. Global Model Quality Estimate is a quality estimate which combines properties from the target-template alignment and the template structure [58]. 6. Results from pyDockWEB are offered without warranty for academic noncommercial purposes only. 7. For the construction of the dimeric TLR4 ID asymmetric model, a restricted docking was performed based on the mutagenesis studies reported by Bovijn et al. [55]. 8. Modeller is freely available only for academic use. You need to register for a license of MODELLER. It can be done online and automatically by accessing the MODELLER web site. The web site will email you a MODELLER key. You need to enter this license key here in order to use this server. 9. The total number of residues involved in the loops must not exceed 14. 10. Registration is required. CHARMM-GUI is free for academic and governmental use. Commercial use requires a license and a yearly fee for use. 11. The Lo membrane model represents a membrane raft. Each layer of the Lo model of a mixture of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and cholesterol, approximating a 60:40 ratio. 12. The following message: “the upperleaflet can have more lipids” is not an error message, but a warning, and can be ignored. 13. Ions are added to the system using LEaP, in Subheading 3.4.3 MD Simulation of the Full-Length TLR4 in a Liquid-Ordered Model Membrane.

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14. The step5_input.pdb generated by CHARMM-GUI cannot be used as input for building the AMBER topology and coordinate files with LEaP, since it contains a different numeration and nomenclature of the residues. 15. The residue name used by Leap for regular protonated cysteine residues is CYS, for deprotonated and/or bound to metal atoms it is CYM, and for cysteine residues involved in disulfide bridges and other bonds it is CYX. 16. For the parametrization of E. coli LPS, we combined two force fields from the AMBER family, GLYCAM06 [39] for the saccharide moiety, and Lipid14 [37] for the acyl chains, using the standard Antechamber procedure in AMBER16 (http:// ambermd.org/tutorials/basic/tutorial4b/). To facilitate modular buildup and accurate representation of moieties of different nature in the same molecule, e.g., a glycolipid, a combination of different force fields should be applied for the description of the molecule. The molecule can be split into smaller fragments of the same nature. A force field representing each fragment is then separately parameterized using ab initio data and the resulting force fields are combined to describe the full molecule. To establish a smooth transition between the two domains of atom types that define torsion, angle bending and stretching potential terms, a suitable bridging region should be selected [59]. The set of parameters that describes bonded interactions for this bridging region, and involves a mixed set of atom types, would require either an adaption or a choice for one of the force fields by a suitable (re-)definition of atom types. Redefinition of atom types is not straightforward and requires a deep knowledge of the nature of the molecule to be treated. This task is out of the scope of this chapter. For a better understanding of the parameterization of this type of ligands, please read Banerjee et al. [59]. 17. Choosing a production temperature is important for the lipid bilayer simulation. Lipid bilayers have experimentally measured phase transition temperatures from highly ordered gel-like phases to liquid phases. One major problem in lipid bilayer simulations is accurately simulating the phase transition of lipids. 18. For a complete description of all variables please, see the AMBER16 Manual [35].

Acknowledgments This work was financially supported by the Spanish Ministry for Science and Innovation (grants PID2020-113588RB-I00, PRE2018-086249 for A.M.R. and PRE2021-097247 for M.M.T.).

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Modeling of Transmembrane Domain and Full-Length TLRs in Membrane Models Negi SS, Weng Z, Vreven T, Pierce BG, Borrman TM, Yu J, Ochsenbein F, Guerois R, Vangone A, Rodrigues JPGLM, Van Zundert G, Nellen M, Xue L, Karaca E, Melquiond ASJ, Visscher K, Kastritis PL, Bonvin AMJJ, Xu X, Qiu L, Yan C, Li J, Ma Z, Cheng J, Zou X, Shen Y, Peterson LX, Kim HR, Roy A, Han X, Esquivel-Rodriguez J, Kihara D, Yu X, Bruce NJ, Fuller JC, Wade RC, Anishchenko I, Kundrotas PJ, Vakser IA, Imai K, Yamada K, Oda T, Nakamura T, Tomii K, Pallara C, Romero-Durana M, Jime´nez-Garcı´a B, Moal IH, Fernandez-Recio J, Joung JY, Kim JY, Joo K, Lee J, Kozakov D, Vajda S, Mottarella S, Hall DR, Beglov D, Mamonov A, Xia B, Bohnuud T, Del Carpio CA, Ichiishi E, Marze N, Kuroda D, Roy Burman SS, Gray JJ, Chermak E, Cavallo L, Oliva R, Tovchigrechko A, Wodak SJ (2016) Prediction of homoprotein and heteroprotein complexes by protein docking and templatebased modeling: A CASP-CAPRI experiment. Proteins 84:323–348. 26. Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, Beglov D, Vajda S (2017) The ClusPro web server for protein– protein docking. Nat Protoc 12:255–278. 27. Cheng TM-K, Blundell TL, Fernandez-Recio J (2007) pyDock: electrostatics and desolvation for effective scoring of rigid-body protein-protein docking. Proteins 68:503–515. 28. Lomize AL, Pogozheva ID (2017) TMDOCK: an energy-based method for modeling α-helical dimers in membranes. J Mol Biol 429:390– 398. 29. Polyansky A,A Chugunov AO, Volynsky PE, Krylov NA, Nolde DE, Efremov RG (2013) PREDDIMER: a web server for prediction of transmembrane helical dimers. Bioinformatics 30:889–890. 30. Studer G, Biasini M, Schwede T (2014) Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane). Bioinformatics 30:i505– i511. ˜ i JR, Orozco M, Gelpi JL 31. Hospital A, Gon (2015) Molecular dynamics simulations: advances and applications. Adv Appl Bioinform Chem 8:37-47. 32. Matamoros-Recio A, Franco-Gonzalez JF, Forgione RE, Torres-Mozas A, Silipo A, Martin-Santamaria S (2021) Understanding the antibacterial resistance: computational explorations in bacterial membranes. ACS Omega 6:6041–6054.

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Chapter 2 Development of Optimal Virtual Screening Strategies to Identify Novel Toll-Like Receptor Ligands Using the DockBox Suite Jordane Preto and Francesco Gentile Abstract Toll-like receptors (TLRs) represent attractive targets for developing modulators for the treatment of many pathologies, including inflammation, cancer, and autoimmune diseases. Here, we describe a protocol based on the DockBox package that enables to set up and perform structure-based virtual screening in order to increase the chance of identifying novel TLR ligands from chemical libraries. Key words Molecular docking, Virtual screening, Ligand discovery, Toll-like receptors, Chemical libraries, Small molecules

1

Introduction Toll-like receptors (TLRs) are crucial transmembrane receptors that recognize numerous exogenous and endogenous molecular patterns and trigger the response of the immune system to bacterial infections and inflammation [1, 2]. Members of the TLR family include ten functional types in human (TLR1–10) localized either in the cell or the endosome membrane. Due to their pivotal role in modulating the immune response, TLRs constitute attractive pharmaceutical targets for developing drugs or adjuvants for many human diseases including inflammatory, viral and autoimmune pathologies as well as cancer. Thus, there is a compelling need to develop novel modulators. Over the past 20 years, various TLR ligands have been identified and investigated in clinical trials [3] including two that were approved by the Food and Drug Administration (FDA) agency. Approved drugs include monophosphoryl lipid A, a TLR4 agonist commonly used as a vaccine adjuvant [4], and Imiquimod, a TLR7 agonist recommended for the topical

Francesca Fallarino et al. (eds.), Toll-Like Receptors: Methods and Protocols, Methods in Molecular Biology, vol. 2700, https://doi.org/10.1007/978-1-0716-3366-3_2, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

39

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Jordane Preto and Francesco Gentile

Fig. 1 General structure of full-length TLR7 (accession code: AF-Q9NYK1-F1) as determined by the AlphaFold model [13]. The extracellular domain (ECD), the transmembrane (TM) domain and the Toll/interleukin-1 receptor (TIR) domain are depicted in dark green, light green and cyan, respectively. The ECD guanosine-binding site targeted by Imiquimod and other agonists is depicted as a blue surface

treatment of actinic keratoses, superficial basal cell carcinomas, and genital and perianal warts [5]. At the structural level, TLRs are made of three domains including the extracellular domain (ECD) which presents a typical horseshoe-like shape, the Toll/interleukin-1 receptor (TIR) domain and the transmembrane (TM) domain which connects the ECD and TIR (Fig. 1). Most of TLR agonists bind to the ECD domain, making the structure assemble into a functional dimeric complex. TLR dimerization has been shown to induce structural rearrangement of the TIR enabling its interaction with intracellular partners, which, in turn, initiates the immune cascade [6]. While the majority of TLR-directed drug discovery efforts focuses on the design of agonists that bind to the ECD [7], antagonists structurally derived from agonists, also start to be developed [8]. Moreover, the recent elucidation of antagonistspecific pockets provides new angles for development of this type of ligands [9–11]. The TIR domain can also be targeted to develop inhibitors able to abrogate protein–protein interactions responsible for downstream signaling [12].

Virtual Screening Strategies to Identify TLR Ligands

41

Structure-based computational methods have been rapidly emerging as gold-standard strategies for the identification of novel ligands of clinically relevant proteins [14, 15]. Hence, the availability of high-resolution, experimentally-solved structures for most mammalian TLRs, often complexed with small-molecule ligands, renders this family of receptors a promising target for in silico drug discovery. On this point, many successful virtual screening (VS) and drug design campaigns have been reported [16, 17]. Here, we outline a computational protocol for performing elaborated VS campaigns and increasing the chance of finding novel TLR ligands. Our protocol targets the ECD domain of TLR7, one of the most studied members of the family located in the endosomal membrane, but can easily be applied to other types as well as other protein systems. This chapter covers the preparation of the receptor and ligand structures, the establishment of control calculations to guide the structure selection for docking and to benchmark different VS strategies, the run of the VS campaign, and the analysis of the results. The procedure uses the DockBox suite [18], an open-source program that facilitates the testing and application of several VS approaches based on standard and consensus docking strategies.

2

Materials

2.1 Receptor Structures

Various TLR structures are available from the Protein Data Bank (PDB) database [19] either in the absence (apo form) or in the presence of a ligand (holo form). All the structures considered in this study correspond to the ECD domain of TLR7 and are listed in Table 1. Notably, multiple structures were selected in order to identify the most optimal one to be used in VS campaigns. We recommend to readers who wish to perform molecular docking or VS by themselves, to use structures sharing high sequence identity (>95%) with the organism they are interested in, in order to limit false positives (e.g., human or monkey structures if one is interested in ligands targeting human TLRs, which is the case here). Nevertheless, if the domain of interest is only available from a distant organism but still shares enough identity with the targeted sequence (at least 40% identity [20]), homology modeling can be carried out. Popular programs performing homology modeling include the Molecular Operating Environment (MOE) [21] (2015 version or later) and the SWISS-MODEL webserver (https://swissmodel.expasy.org) [22] which can be used to generate a suitable model for the targeted structure. High-quality, fulllength TLR models can also be obtained from the AlphaFold database [13].

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Jordane Preto and Francesco Gentile

Table 1 ECD structures of TLR7 solved experimentally, available from the PDB. The type of ligand is also specified: AG, agonist; AN, antagonist; Apo, apo structure; KD, dissociation constant. All the structures are related to monkey organism (> 97% sequence identity) except for 7CYN which is a human structure #

PDB ID

Ligand

Type

KD (nM)

Comments

Reference

1

5GMF

Guanosine

AG

2100 –9300

–

[10]

2

5GMG

Loxoribine

AG

5600

–

[10]

3

5GMH

R-848

AG

97a–490

–

[10]

4

5ZSC

IMDQ

AG

–

–

[10]

5

5ZSF

Imiquimod

AG

–

FDA-approved

[10]

6

5ZSG

Gardiquimod

AG

–

–

[10]

7

5ZSH

CL075

AG

–

–

[10]

8

5ZSI

CL097

AG

–

–

[10]

AG

69 –890

–

[10]

9

5ZSJ

GS9620

a

a

10

6IF5

2′,3’-cGMP

AG

80 –1600

–

[10]

11

6LVX

SM-374527

AG

–

–

[11]

12

6LVY

SM-360320

AG

–

–

[11]

13

6LVZ

SM-394830

AG

–

–

[11]

14

6LW0

DSR-139293

AN

10,000

–

[11]

15

6LW1

DSR-139970

AN

7400

Ligand binds to a different site

[11]

16

7CYN

–

Apo

–

–

[23]

a

a

In presence of ssRNA

In the present study, preparation of protein structures was entirely managed by Molecular Operating Environment (MOE) 20181: first, each PDB file was loaded and possible ligand and solvent molecules were removed. Next, MOE’s Structure Preparation wizard was used to correct the receptor structure (e.g., to fix sequence/structure mismatches, to fill small missing gaps (< 8 residues) or to “cap” large missing gaps (≥ 8 residues)) and to protonate it. Protonation was done at an acid pH of 5 which is typical of the mature endosomal environment of TLR7 [11]. Finally, all the complex structures were aligned and superimposed based on their root mean square deviation (RMSD) using the Alignment tool. This step was not mandatory but we encourage readers to do it so that generated docking poses can easily be compared later on

1

Note that other protein preparation tools can be used like Schro¨dinger’s Protein Preparation Wizard [37] or the H++ server (http://newbiophysics.cs.vt.edu/H++) [38]

Virtual Screening Strategies to Identify TLR Ligands

43

between structures. Importantly, the apo structure (PDB ID: 7CYN) included an extra α-helical C-terminal tail on each monomer. These were removed to match the protein sequence of other structures. All the prepared protein structures were saved in PDB format which is required by the DockBox package. 2.2 Ligand Structures

2

The retrospective evaluation of molecular docking strategies against a target of interest using experimentally confirmed ligands (control ligands) provides valuable information to optimally setup a screening campaign [24]. In the present work, we generated two distinct sets of control ligands. The first set was devoted to assessing docking performance in reproducing experimental ligand–protein complex poses and to select an optimal TLR7 structure to be used for VS; this set included the 15 ligands, 13 agonists and 2 antagonists, related to the structures listed in Table 1. Chemical structures of ligands are shown in Fig. 2. For each holo structure, the ligand was extracted and prepared separately with MOE’s Wash utility (Database Viewer > Compute > Molecule > Wash. . .). Only the dominant ionization state at pH 5 was generated for every ligand. Washed ligands were saved in SYBYL MOL2 format [25] which is required by DockBox. The second set of control ligands includes known active compounds of TLR7 and molecular decoys and was built to evaluate the ability of different docking approaches to discriminate between true and false binders. Ligands with confirmed activity against a specific TLR were downloaded from the ChEMBL database (https://www. ebi.ac.uk/chembl) [26]. By queueing TLR7 as target, we accessed the CHEMBL5936 target report card entry relative to the human TLR7, and we selected agonist compounds with reported half maximal effective concentration values (EC50) in the Activity Charts section. From the resulting ensemble (available from ChEMBL in CSV format), we discarded molecules with molecular weight above 500 Da since those are excluded from the decoy generation process. To further reduce the number of active ligands and to facilitate decoy generation while increasing diversity [27], we performed fingerprint-based clustering in MOE on the 411 remaining molecules, by selecting Database Viewer > Compute > Fingerprint > Clusters. . . and by setting Similarity and Overlap thresholds to 75.2 The most potent molecule from each cluster was then selected as representative member, and the resulting 103 ligands were prepared with MOE’s Wash utility as in the first set of control ligands. In order to generate molecular decoys, structures of the ligands were saved in SMILES format utilized as input for decoy generation available at the Directory of Useful

Free tools for compound clustering can be used for the same purpose (for example the best-first clustering scripts available at https://github.com/docking-org/ChemInfTools/tree/master/utils [24] or the diversity filtering scripts from https://www.frdr-dfdr.ca/repo/dataset/f8180e92-a7dd-4c62-b541-33282115d887 [39]).

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Jordane Preto and Francesco Gentile

Fig. 2 Chemical structures, names, and PDB IDs of small-molecule ligands complexed with TLR7 extracted from the PDB database

Decoys - Enhanced - Z (DUDE-Z, https://tldr.docking.org) server [27]. From this procedure, 5850 decoys were obtained from our 103 active compounds that were then prepared just like previous ligands and saved in MOL2 format. 2.3 VS Chemical Library

VS was performed over an ensemble of about 100,000 molecules extracted from “ZINC Is Not Commercial 15” (ZINC15), a free database of commercially-available molecules [28]. In order to generate our set, we first downloaded the 3D structures (in SMILES format) of all the ZINC15 in-stock compounds with lead-like properties (molecular weight between 250 and 350 Da

Virtual Screening Strategies to Identify TLR Ligands

45

and logP ≤3.5) at their pH of reference. At the time of downloading (August 2020), this represented around 2.8 M entries. Next, a python script was written that uses RDKit’s MaxMin Picker [29] to select a set of 100,000 diverse compounds from the downloaded library. Here, Tanimoto similarity based on connectivitybased Morgan fingerprints was considered as distance for picking our compounds. From the generated SMILES strings, we downloaded the corresponding 3D ready-to-dock structures in MOL2 format from ZINC15. As a few SMILES structures were not available in MOL2 format, the final number of compounds was slightly less than expected (96,912). 2.4 DockBox, CD, and SBCD

Docking simulations and VS campaigns were all performed using the DockBox suite [18]. DockBox is a wrapper library that provides a common interface to most popular docking programs including Autodock [30], Autodock Vina [31], DOCK [32], Schro¨dinger’s Glide [33], CCDC GOLD [34], and MOE Dock [21]. Importantly, DockBox not only allows to dock compounds, but it also offers the possibility to rescore already docked ligand poses. This procedure, called rescoring, can be done using the scoring functions of the docking programs mentioned above or with the DSX scoring function [35]. Therefore, using DockBox, one can dock compounds with multiple programs and rescore the docking poses with the same or different programs than the ones originally used for docking. In Subheading 3, we will show that the rescoring step can drastically increase the prediction of actual ligand binding modes while improving the identification of active molecules in VS. Notably, rescoring in DockBox is done by first minimizing the generated docking poses using the Amber molecular dynamics (MD) software. Minimization is run in vacuo on the ligand only, i.e., by constraining the protein structure. This step enables to relax the ligand structure within the pocket to remove possible clashes (and to test the stability of the docked pose) which may hinder proper rescoring. Although all the steps mentioned above are performed automatically by DockBox, docking and scoring programs that are intended to be used must be installed separately and be available in the PATH environment variable. For minimization, the AmberTools package (AmberTools15–18 is currently supported) should also be installed. Please check https://github.com/jp43/ DockBox for more information and for downloading of the DockBox package. In the present work, Autodock, Autodock Vina abbreviated as Vina, and DOCK were used for docking, i.e., to generate ligand poses, while AmberTools17 was applied for minimization. Finally, AutoDock, Vina, DOCK and DSX were selected for rescoring. As detailed in the documentation, DockBox requires an input configuration file specifying all the parameters to be used for docking, minimization and rescoring. The DockBox configuration file used

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Jordane Preto and Francesco Gentile

Fig. 3 DockBox configuration file (INI file) used for redocking crystallographic ligands. Note that the parameters ga_run in AUTODOCK, num_modes in VINA, and nposes in DOCK correspond to the number of docking poses generated by each program, meaning that a maximum of 30 poses can be sampled. Less docking poses can be generated in case docking poses moved outside the docking box after minimization. The origin (center) and the dimensions (boxsize) of the docking box were specified in the SITE section

for redocking crystallographic ligands from Table 1 is shown in Fig. 3. Other configuration files including those used for active molecules and decoys as well as VS are given at the following URL: https://github.com/jp43/DockBox/tree/mas ter/examples/tlr7_chapter. As a multidocking interface, DockBox offers the possibility to perform consensus docking based on two strategies. The first one called standard Consensus Docking (CD) is based on the comparison of the top poses (i.e., the poses with the highest docking score) sampled by different programs. If the top pose of one program is similar to the one obtained by other programs (i.e., within an RMSD of 2 Å), consensus is said to be reached. Compounds that were found to not satisfy the consensus are usually discarded in VS. In this way, CD was demonstrated to discard much more inactive

Virtual Screening Strategies to Identify TLR Ligands

47

compounds than active ones thereby enriching the chemical library. In addition, binding modes satisfying CD were more likely to correspond to the actual binding modes of the tested compounds [36]. The second consensus docking method available in DockBox is Score-Based Consensus Docking (SBCD) and relies on the rescoring step mentioned above. Here, consensus is done by comparing the scores obtained after rescoring all the docking poses. Therefore, consensus in SBCD does not happen between docking programs which sampled the poses, but between the scoring functions which rescored all these poses. Therefore, consensus in SBCD can still be reached when only one docking program generated the correct binding mode which is not the case in CD where all programs should provide the correct mode. In a recent publication, we showed that SBCD was drastically reducing the number of false negative while keeping similar enrichment as CD [18]. This resulted in much more active molecules kept in VS campaigns as compared to CD. Note that the consensus strategies mentioned above are not performed using rundbx but with the extract_dbx_best_poses routine (also available in the DockBox) once docking and rescoring have been done. As detailed in the documentation, extract_dbx_best_poses enables, for a set directories containing completed jobs with the rundbx program, to extract the best docking poses as determined either by (i) standard docking with a given program (ii) rescoring with a given scoring function (iii) CD with multiple docking programs or (iv) SBCD with multiple scoring functions.

3

Methods Our strategy to find novel TLR7 ligands uses a structure-based protocol. First, using a set of available cocrystallized structures of the ECD domain of TLR7 including the protein complexed with small agonists/antagonists as well as the protein apo form, compounds involved in all the 3D structures were redocked to establish which docking strategy was best reproducing the correct binding modes. Moreover, receptor structures that were more likely to bind to active compounds were identified. Second, a set of known active compounds (103) of TLR7 together with decoys (5850) were docked onto two structures selected from the first step. This enabled to evaluate the best docking/rescoring/consensus strategy in order to maximize hit rates in VS campaigns (i.e., the percentage of actives among the top compounds scored by docking). Finally, VS was performed using a set of purchasable compounds (100,000 compounds from the ZINC15 database). Both the best protein structure and the best strategy as highlighted in the first two steps were utilized to identify most promising TLR7 ligands. Results obtained in each of the three steps are provided below.

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3.1 Redocking of Cocrystallized Complexes

3

As summarized in Table 1, a total of 15 holo structures and 1 apo structure were considered for this step, which implies 15 ligands (one from each holo structure) that were redocked on all the receptor conformations resulting in 15 × 16 = 240 jobs performed with the rundbx program. As mentioned above, rundbx was used to dock with three different programs: AutoDock, Vina, DOCK. All the generated poses (ten poses per program at most) were then rescored with four scoring functions: AutoDock, Vina, DOCK and DSX. Different strategies were tested to select the best docking pose including picking (i) the pose with the best score provided by each docking program (referred to as docking) (ii) the pose with the best score provided by each scoring function after rescoring (referred to as rescoring) (iii) the pose satisfying CD, i.e., which relies on the agreement of docking programs and (iv) the pose satisfying SBCD, which relies on the agreement of scoring functions after rescoring. More details about CD and SBCD methods are provided in Subheading 2.4. Note that consensus in CD can be done based on different combinations of docking programs, three pairs: AutoDock-Vina, Autodock-DOCK, and Vina-DOCK; and one triplet: AutoDock-Vina-DOCK. Similarly, SBCD may be run from different combinations of scoring functions for the consensus which, in the present case, includes six pairs, four triplets, and one quadruplet since four scoring functions were applied for rescoring.3 At this stage, best poses for CD and SBCD methods were selected over all the possible combinations, i.e., poses were kept as long as they were satisfying the consensus in any of the combination mentioned above. Note that this was made possible for SBCD because consensus never provided conflicting results (as it would be the case for example if AutoDock-Vina and DOCK-DSX pairs had an agreement on distinct poses). Table 2 shows a summary of the success rates obtained with each method, i.e., the number of correctlypredicted binding modes divided by the total number of ligands.4 For CD and SBCD, we believed it was more reasonable to compute the success rate by dividing by the number of ligands satisfying the consensus as ligands that do not meet this criterion, are systematically discarded in VS. From Table 2, we observe that CD provided the highest success rate with a perfect score of 100%. However, only 3 ligands out the original 15 passed successfully the consensus criterion. This is a source of concern as in VS campaigns this would suggest more false negatives (number of true ligands that were discarded upon consensus). At the same time, SBCD also produced a high success rate

Six pairs: Autodock-Vina, AutoDock-DOCK, AutoDock-DSX, Vina-DOCK, Vina-DSX, DOCK-DSX; four triplets: Autodock-Vina-DOCK, Autodock-Vina-DSX, Autodock-DOCK-DSX, Vina-DOCK-DSX; one quadruplet: Autodock-Vina-DOCK-DSX. 4 A ligand pose was assumed to be correctly predicted if, after spatial superimposition with the corresponding cocrystallized structure, RMSD over ligand atoms was lower than 2.0 Å.

Virtual Screening Strategies to Identify TLR Ligands

49

Table 2 Ability of different strategies to correctly redock ligands according to their cocrystallized complexes. In every case, 15 ligands were tested. The success rate corresponds to the number of ligands correctly predicted over the total number of ligands, i.e., it was directly calculated from the “Ligands correctly predicted” column. For CD and SBCD, the total number of ligands was taken as the number of ligands satisfying the consensus

Strategy for best-pose selection

Ligands satisfying consensus

Ligands correctly predicted

Docking with Autodock

N/A

10/15

66.7

Docking with DOCK

N/A

6/15

40.0

Docking with Vina

N/A

8/15

53.3

Rescoring with Autodock

N/A

8/15

53.3

Rescoring with DOCK

N/A

11/15

73.3

Rescoring with DSX

N/A

7/15

47.7

Rescoring with Vina

N/A

8/15

53.3

CD (any combination)

3/15

3/3

100.0

SBCD (any combination)

11/15

9/11

81.8

Success rate (%)

(81.7%), but the number of ligands satisfying the consensus (11/15) was much higher than CD. Overall, the success rate obtained with each consensus method was higher than any of the standard methods (docking or rescoring) among which rescoring with DOCK gave the highest success rate (73.3%). Regarding receptors, we also wanted to identify which targeted structure was the best to be considered in a VS scenario as, depending on the size of the tested chemical library, VS may require a lot of computational resources and time. In general, it may beneficial to consider multiple structures in VS to allow target site flexibility and to accommodate different types of ligands. In the present case, considering only one or two targets for VS is recommended if one wants to screen a library containing millions of small molecules in a reasonable amount of time (< 1 month, typically). Even so, a computing cluster allowing to run at least a hundred docking jobs in parallel is prescribed. Here, in order to select the best target, we investigated how many times ligands were correctly docked on the same structure, overall and strategy-wise (Table 3). This approach was made possible because a ligand can be correctly redocked even if the associated protein structure did not match exactly the one of the original cocrystallized complex. Therefore, a given receptor structure can serve multiple times as the target of correctlyredocked ligands. Overall, Targets #3 (5GMH) and #12 (6LVY) were found to be the best in identifying correct modes. This was observed over all the strategies and for rescoring and SBCD

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Table 3 Number of unique ligands that were correctly docked on each target using all or a specific strategy among docking, rescoring, CD, and SBCD. Note that the sum computed over each column is greater than 15 (i.e., the total number of ligands) because different programs/scoring functions or combinations of them were used for every strategy. Here, we simply computed how many times a target was involved in the prediction of the correct binding mode irrespective of the programs and scoring functions used #

PDB ID

All strategies

Docking

Rescoring

CD

SBCD

1

5GMF

3/15

1/15

2/15

0/15

1/15

2

5GMG

2/15

2/15

1/15

0/15

0/15

3

5GMH

6/15

1/15

6/15

0/15

4/15

4

5ZSC

3/15

2/15

1/15

0/15

0/15

5

5ZSF

3/15

2/15

1/15

0/15

0/15

6

5ZSG

0/15

0/15

0/15

0/15

0/15

7

5ZSH

4/15

3/15

3/15

1/15

1/15

8

5ZSI

3/15

2/15

2/15

0/15

0/15

9

5ZSJ

0/15

0/15

0/15

0/15

0/15

10

6IF5

0/15

0/15

0/15

0/15

0/15

11

6LVX

1/15

0/15

1/15

0/15

0/15

12

6LVY

6/15

6/15

3/15

1/15

2/15

13

6LVZ

2/15

1/15

1/15

0/15

0/15

14

6LW0

0/15

0/15

0/15

0/15

0/15

15

6LW1

2/15

2/15

1/15

1/15

1/15

16

7CYN

0/15

0/15

0/15

0/15

0/15

methods which are the two methods providing the largest number of correct poses. Therefore, both targets were brought forward in our exercise. Remarkably, 5 targets (5ZSG, 5ZSJ, 6IF5, 6LW0, and 7CYN) did not return any correct binding pose for any strategy, which could be caused by not well-formed binding sites as it is expected for the apo structure. Thus, our procedure enables to easily identify low-performance structures and discard them for subsequent steps. 3.2 Screening of Active Molecules and Decoys

In this step, we investigated the ability of docking, rescoring and consensus strategies to discriminate between active and inactive compounds in a set containing much more inactive molecules,

Virtual Screening Strategies to Identify TLR Ligands

51

similarly to what is expected in VS. Our chemical library was made of 103 TLR7 ligands from CHEMBL and 5,850 decoys built according to the protocol described in Subheading 2.2. Here, two metrics were considered: (i) the hit-rate restrained to the top 100 molecules, i.e., the number of active ligands within the top 100 as determined by each strategy and (ii) the Enrichment Factor (EF) which applies to consensus strategies only and assesses the enrichment of the library based on active/decoys ratio before and after consensus. The latter was computed according to the following formula EF =

TP TP þ FP þ TN þ FP ∙ , TP þ FP TP þ FN

ð1Þ

where TP (True Positives) are active molecules that satisfied the consensus, FN (False Negatives) are active molecules not retained by the consensus, FP (False Positives) are decoys that were kept by the consensus, and TN (True Negatives) are decoys discarded by the consensus. For the interested reader, we provided at https:// github.com/jp43/DockBox/tree/master/examples/tlr7_chapter our in-house script called compute_hit_rates.py to compute both the hit-rate and EF on a set of ligands processed with DockBox’s rundbx and extract_dbx_best_poses. The script allows to calculate these two metrics based on the strategies used by extract_dbx_best_poses, which in the present case are the same as those in Subheading 3.1. Note that we have now considered every combination of program and scoring functions individually for CD and SBCD which was not the case before. Our results are reported in Table 4 for both Targets #3 and #12 selected from the previous step. In line with our previous observations [18], strategies relying on pose rescoring, i.e., rescoring and SBCD, returned the highest hit rates for both structures. Docking and CD generated significantly lower hit-rates, showing as low as half values compared with rescoring methods, consistently with the fact that the latter utilize all the generated poses from docking, thus improving the chance of identifying high scoring poses for actives. Furthermore, rescoring with Dock and SBCD based on Autodock-DSX consensus (using DSX for ranking) showed top performance on both targets. Since SBCD also demonstrated excellent performance in binding-mode prediction (Table 2), we concluded that the Autodock-DSX SBCD scheme using the DSX scoring function was the most suitable strategy for prospective VS. 3.3

VS Campaign

Based on the information gathered from the two previous steps, VS was conducted on a set of 96,912 diverse small molecules extracted from ZINC15. Target #12, which provided satisfactory results so far, was selected as receptor. VS was performed in a two-round process. Round 1 was intended to quickly discard most probable inactive compounds. Since rescoring requires minimization of

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Table 4 Best hit-rates and EF obtained on Targets #3 and #12. The top 5 strategies are displayed for both targets. As all the top strategies either involved rescoring or SBCD, we also showed the best strategy involving either docking or CD and as well as their rank. For SBCD and CD, the scoring function used for building the top 100 is indicated in brackets Target #3

Target #12

Rank Strategy

Hitrate

EF Rank Strategy

Hitrate

EF

1

SBCD AutoDock-DSX (DSX)

44

2.6 1

Rescoring DOCK

51

1.0

2

Rescoring DOCK

39

1.0 2

Rescoring DSX

49

1.0

3

Rescoring DSX

38

1.0 3

SBCD AutoDock-DSX (DSX)

45

2.4

4

SBCD AutoDock-Vina-DSX (DSX)

37

3.7 4

SBCD AutoDock-Vina DSX (DSX)

43

3.8

5

SBCD AutoDock-DOCK-DSX (DSX)

35

6.6 5

SBCD Vina-DSX (DSX)

39

1.7

12

CD DOCK-Vina (DOCK)

27

2.8 10

Docking DOCK

33

1.0

24

Docking DOCK

22

1.0 27

CD Autodock DOCK (DOCK)

19

4.1

ligand poses which may take some extra time to complete, standard docking with DOCK which gave the best performance as a docking-based method (Table 4), was retained. After completion of the first round, the top 1,000 molecules were selected for Round 2. At this stage, AutoDock-DSX SBCD ranked by DSX score was applied as it was among the best methods to determine active molecules. Other good strategies like rescoring with DOCK or DSX could have been used as suggested by Table 4. In Fig. 4a, we displayed the distribution of DSX scores obtained for the top 100 molecules at the end of the second round. The distribution of DSX scores for active molecules identified with the same strategy in Subheading 3.2 was also shown as a comparison. We observed that active molecules have usually a better score (the lower the better) than molecules identified from VS. This was expected as our VS campaign, which was performed as a short application of our protocol, was limited to 100,000 compounds only. We encourage readers interested in conducting more thorough investigation, to use chemical libraries of several million molecules. Nevertheless, seven molecules from our current campaign were found to have a score in the range covered by actives. In addition, three compounds had a score close to the average actives’ score, thus providing interesting candidates to be tested. The structures of the seven compounds are provided in Fig. 4b.

Virtual Screening Strategies to Identify TLR Ligands

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Fig. 4 (a) Score distribution of the top 100 molecules from VS (orange) and active molecules in the top 100 from Subheading 3.2 using Autodock-DSX SBCD molecules ranked by DSX score (blue). (b) Chemical structures, names, and DSX scores of top 7 ranked molecules from VS

4

Summary The chapter outlines a procedure to setting up and perform a prospective VS campaign against a TLR of interest using the DockBox package. The software facilitates the validation and comparison of a large number of different docking and scoring schemes, enabling to run benchmarking calculations to build optimal VS strategies. The first critical step is the target structure selection, as we demonstrated that the apo and a few holo structures of TLR7 failed to recapitulate even a single binding pose of known ligands. On the other hand, our exercise univocally identified the best TLR7

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structures to use for docking. Standard docking strategies provided mediocre binding-mode prediction, i.e., between 40% and 67% of success rate. Consensus strategies were clearly superior in this task. Specifically, the SBCD schemes were able to confidently reproduce crystallographic poses without losing most of the ligands due to consensus filtering, as it happened for CD. This was also observed in the benchmarking VS run, where SBCD strategies returned among the highest top 100 hit-rates. Taken all together, these results confirmed that, while docking is sometimes considered erroneously a trivial task, careful evaluation of multiple target structures and strategies are absolutely necessary in order to obtain meaningful results. The DockBox program encourages precisely this systematic evaluation by providing a user-friendly platform for specialists and nonspecialists alike. Finally, if no small-molecule ligands are known for a specific TLR target, we recommend to consider SBCD as screening tool since it is consistently outperforming other methods while having a similar computational cost to CD. Users that wish to utilize the procedure in their TLR drug discovery campaigns are highly encouraged to gain access to computing clusters in order to scale up their screens to libraries of millions of molecules that can increase the possibility of finding novel, potent active molecules. We hope that the outlined protocol will encourage researchers to explore advanced strategies for docking and VS, and that they will benefit of using DockBox in their TLR drug discovery endeavors.

Acknowledgments We gratefully acknowledge support from the PSMN (Poˆle Scientifique de Mode´lisation Nume´rique) of the ENS de Lyon for the computing resources. F.G. was supported by a University of Ottawa start-up grant and a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant. References 1. Takeda K, Kaisho T, Akira S (2003) Toll-like receptors. Annu Rev Immunol 21:335–376 2. Moresco EMY, LaVine D, Beutler B (2011) Toll-like receptors. Curr Biol 21:R488–R493 3. Anwar MA, Shah M, Kim J et al (2019) Recent clinical trends in toll-like receptor targeting therapeutics. Medicinal Research Reviews 39(3):1053–1090. https://doi.org/10.1002/ med.21553 4. Romero CD, Varma TK, Hobbs JB et al (2011) toll-like receptor 4 agonist The

monophosphoryl lipid a augments innate host resistance to systemic bacterial infection. Infect Immun 79:3576–3587 5. Wagstaff AJ, Perry CM (2007) Topical imiquimod: a review of its use in the management of anogenital warts, actinic keratoses, basal cell carcinoma and other skin lesions. Drugs 67: 2187–2210. https://doi.org/10.2165/ 00003495-200767150-00006 6. El-Zayat SR, Sibaii H, Mannaa FA (2019) Toll-like receptors activation, signaling, and

Virtual Screening Strategies to Identify TLR Ligands targeting: an overview. Bull Natl Res Cent 43: 1–12 7. O’Neill LAJ, Hennessy EJ, Parker AE (2010) Targeting Toll-like receptors: Emerging therapeutics? Nature Rev Drug Discov 9(4): 293–307. https://www.nature.com/articles/ nrd3203 8. Patinote C, Karroum NB, Moarbess G et al (2020) Agonist and antagonist ligands of tolllike receptors 7 and 8: ingenious tools for therapeutic purposes. European J Med Chem 193: 112238 9. Zhang S, Hu Z, Tanji H et al (2018) Smallmolecule inhibition of TLR8 through stabilization of its resting state. Nat Chem Biol 14:58– 64 10. Zhang Z, Ohto U, Shibata T et al (2016) Structural analysis reveals that toll-like receptor 7 is a dual receptor for guanosine and singlestranded RNA. Immunity 45:737–748 11. Tojo S, Zhang Z, Matsui H et al (2020) Structural analysis reveals TLR7 dynamics underlying antagonism. Nat Commun 111(11):1–11 12. Mistry P, Laird MHW, Schwarz RS et al (2015) Inhibition of TLR2 signaling by small molecule inhibitors targeting a pocket within the TLR2 TIR domain. Proc Natl Acad Sci USA 112: 5455–5460 13. Varadi M, Anyango S, Deshpande M et al (2022) AlphaFold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res 50:D439–D444 14. Tuszynski JA, Winter P, White D et al (2014) Mathematical and computational modeling in biology at multiple scales. Theor Biol Med Model 11:52 15. Preto J, Gentile F, Winter P et al (2018) Molecular dynamics and related computational methods with applications to drug discovery. In: Bonilla LL, Kaxiras E, Melnik R (eds) Coupled mathematical models for physical and biological nanoscale systems and their applications. Springer, Cham, pp 267–285 16. Pe´rez-Regidor L, Zarioh M, Ortega L et al (2016) Virtual screening approaches towards the discovery of toll-like receptor modulators. Int J Mol Sci 17(9):1508. https://pubmed. ncbi.nlm.nih.gov/27618029/ 17. Gentile F, Deriu MA, Barakat KH et al (2018) A novel interaction between the TLR7 and a colchicine derivative revealed through a computational and experimental study. Phamaceuticals 11:22 18. Preto J, Gentile F (2019) Assessing and improving the performance of consensus

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docking strategies using the DockBox package. J Comput Aided Mol Des 33:817–829 19. Berman HM (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242 20. Chothia C, Lesk AM (1986) The relation between the divergence of sequence and structure in proteins. EMBO J 5:823–826 21. Chemical Computing Group Inc (2019) Molecular operating environment 2019, http://www.chemcomp.com 22. Arnold K, Bordoli L, Kopp J et al (2006) The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics 22:195–201 23. Ishida H, Asami J, Zhang Z et al (2021) (2021) Cryo-EM structures of toll-like receptors in complex with UNC93B1. Nat Struct Mol Biol 282(28):173–180 24. Lyu J, Wang S, Balius TE et al (2019) Ultralarge library docking for discovering new chemotypes. Nature 566:224–229 25. UC Regents SYBYL MOL2 format, http:// www.csb.yale.edu/userguides/datamanip/ dock/DOCK_4.0.1/html/Manual.41.html 26. Gaulton A, Bellis LJ, Bento AP et al (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100 27. Stein RM, Yang Y, Balius TE et al (2021) Property-unmatched decoys in docking benchmarks. J Chem Inf Model 61(2):699–714 28. Sterling T, Irwin JJ (2015) ZINC 15 - ligand discovery for everyone. J Chem Inf Model 55: 2324–2337 29. The RDKit Documentation — The RDKit 2020.03.1 documentation. https://www. rdkit.org/docs/ 30. Cosconati S, Forli S, Perryman AL et al (2010) Virtual screening with AutoDock: theory and practice. Expert Opin Drug Discov 5:597–607 31. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31: 455–461 32. Allen WJ, Balius TE, Mukherjee S et al (2015) DOCK 6: impact of new features and current docking performance. J Comput Chem 36: 1132–1156 33. Friesner RA, Banks JL, Murphy RB et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749 34. Jones G, Willett P, Glen RC et al (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748

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35. Neudert G, Klebe G (2011) DSX: a knowledge-based scoring function for the assessment of protein-ligand complexes. J Chem Inf Model 51:2731–2745 36. Houston DR, Walkinshaw MD (2013) Consensus docking: improving the reliability of docking in a virtual screening context. J Chem Inf Model 53:384–390 37. Madhavi Sastry G, Adzhigirey M, Day T et al (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual

screening enrichments. J Comput Aided Mol Des 27:221–234 38. Anandakrishnan R, Aguilar B, Onufriev AV (2012) H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations. Nucleic Acids Res 40:W537–W541 39. Gentile F, Fernandez M, Ban F et al (2021) Automated discovery of noncovalent inhibitors of SARS-CoV-2 Main protease by consensus deep docking of 40 billion small molecules. Chem Sci 12:15960–15974

Chapter 3 Use of Fluorescent Chemical Probes in the Study of Toll-like Receptors (TLRs) Trafficking Ana Rita Franco, Valentina Artusa, and Francesco Peri Abstract Fluorescent chemical probes are used nowadays as a chemical resource to study the physiology and pharmacology of several important endogenous receptors. Different fluorescent groups have been coupled with known ligands of these receptors, allowing the visualization of their localization and trafficking. One of the most important molecular players of innate immunity and inflammation are the Toll-Like Receptors (TLRs). These Pattern-Recognition Receptors (PRR) have as natural ligands microbial-derived pathogenassociated molecular patterns (PAMPs) and also endogenous molecules called danger-associated molecular patterns (DAMPs). These ligands activate TLRs to start a response that will determine the host’s protection and overall cell survival but can also lead to chronic inflammation and autoimmune syndromes. TLRs action is tightly related to their subcellular localization and trafficking. Understanding this trafficking phenomenon can enlighten critical molecular pathways that might allow to decipher the causes of different diseases. In this chapter, the study of function, localization and trafficking of TLRs through the use of chemical probes will be discussed. Furthermore, an example protocol of the use of fluorescent chemical probes to study TLR4 trafficking using high-content analysis will be described. Key words Toll-like receptors, Receptor trafficking, Fluorescent chemical probes, Confocal microscopy, High-content analysis, Innate immunity

1

Introduction

1.1 Chemical Fluorescent Probes

The use of receptor-target fluorescent probes targeting receptors is a valuable strategy in drug discovery and receptor pharmacology since it allows for the direct clarification of several questions such as the localization of the receptor, the interaction with its natural ligands, its trafficking within the cell, among others [1]. Furthermore, chemical probes are able to provide a picture of the receptor in its cellular environment without genetic manipulation and use of mutants and offer low cytotoxicity and good cell permeability when comparing to other strategies as inorganic nanomaterials or protein engineering for example [2]. In fact, although the use of antibodies conjugated with a fluorophore or fluorescent labeled proteins has

Francesca Fallarino et al. (eds.), Toll-Like Receptors: Methods and Protocols, Methods in Molecular Biology, vol. 2700, https://doi.org/10.1007/978-1-0716-3366-3_3, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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been very important do define key pharmacological concepts related to receptors, it presented also some limitations that could be eliminated or at least mitigated by the use of chemical probes [3]. These limitations are the toxicity, the need of purification for in vivo studies, the photo-bleaching and the impossibility to study the receptor in its native form [3]. Receptor localization within the cell determines the success of the technique. Membrane receptors are simpler targets than intracellular proteins, since the latter pose numerous challenges due to difficulties in eliminating nonspecific binding inside the cell, the diffusion of the probe in the intracellular environment and the low image resolution due to the presence of a certain percentage of unlabeled receptors [2]. Nevertheless, chemical probes are easier to use to target membrane receptors and offer the possibility of studying receptors of different cell types, including primary cells [4]. Chemical probes for fluorescent imaging techniques are made of a small molecule that targets the receptor, described as the ligand, conjugated with a fluorescent group, the fluorophore, through a linker [1]. The linker’s chemical structure should be carefully projected for instance, by limiting its conformational mobility in order to prevent loss of activity of the ligand due to fluorophore’s interference in the binding. On the other hand, alteration of the fluorescence properties of the fluorophore due to ligand interaction should be avoided. The linker separates the ligand from the fluorescent chemical group by an appropriate number of atoms, thus eliminating or reducing the reciprocal interference of ligand and fluorophore [5]. A ligand with high binding affinity for the target receptor is crucial for having a sensitive and reliable probe [1]. Moreover, the choice of the fluorophore is also essential for a successful binding. Large fluorophores could affect the binding affinity of the small molecular ligand. Therefore, small fluorophores such as 7-nitro1,2,3,-benzoxadiazole (NBD), fluorescein, coumarin, and naphthalimide are usually preferred [1]. However, these fluorophores present the disadvantage of having the excitation and emission wavelengths in the green part of the spectrum. In this region, cells can present some levels of auto-fluorescence [5]. For this reason, near-infrared fluorophores as Cy5 are also more performant when working in a cellular environment [6]. Chemical probes can bind to their target receptor in a covalent or noncovalent mode. In order to establish a covalent bond, the probe has to be designed to have a reactive moiety, normally an electrophilic group called warhead, that reacts with numerous nucleophilic groups on the protein’s surface. This reactive warhead is not specific for a given target, and thus, the same probe can have different applications [4]. One example of covalent probes are activity-based probes (ABP) that are usually used to study active conformations of enzymes [7].

Use of Fluorescent Chemical Probes in the Study of Toll-like Receptors. . . 1.1.1 Receptor Studies Using Chemical Probes

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Fluorescently labeled small molecules have been used as chemical biological tools to understand the physiological and pathological role of some receptors. Such ligands are able to provide information about the localization and conformation of the receptor and to clarify the interactions between the ligand and the receptor. This information can then be used to design new compounds that can modulate the receptor’s activity as agonists or antagonists, thus exerting a pharmacological effect [8]. Furthermore, the receptor’s trafficking and ability to be internalized upon a certain stimulus can also be clarified using chemical probes [8]. When designing a chemical probe, the choice of the ligand should also be based on the characteristic of interest for that particular experiment, since different modulators can have different effects depending on whether they are agonists, antagonists, or even orthosteric or allosteric modulators. For example, if the purpose is to study the internalization or trafficking of a receptor, the probe should contain a fluorescent agonist capable of stimulating the receptor and trigger the proper signaling cascade [3]. The use of fluorescent ligands enables the application of different techniques such as fluorescence polarization, fluorescence correlation spectroscopy (FCS), flow cytometry, fluorescence resonance energy transfer (FRET), bioluminescence resonance energy transfer (BRET), total internal reflection fluorescence microscopy (TIRF), confocal microscopy, and high-content imaging [9]. Recently, different research groups have used chemical fluorescent probes to study several important receptors. For example, morphine-Cy5 derivatives were synthesized in order to study the μ-opioid receptors localization and trafficking upon morphine binding using confocal microscopy [10]. Serotonin1A receptors were investigated using NBD-labeled serotonin analogs that displayed high environmental sensitivity when it comes to its fluorescence [11]. Additionally, another subgroup of serotonin receptors, serotonin2B, were studied using the agonist amphetamine 1-(2,5-dimethoxy-4-iodophenyl)-propan-2-amine labeled with different fluorescent groups. Authors obtained a selective rhodamine derivative that displayed advantageous photophysical features, allowing the observation of the receptor using a high-content imaging instrument [8]. Other authors developed an interesting probe using a click chemistry approach to study adenosine receptors, which are G protein-coupled receptors, and their trafficking and localization [12]. In this study, the authors synthesized clickable orthosteric antagonists with a bicyclo[2.2.2]octyl xanthinebased scaffold and proceeded to the fluorescent labeling in situ using Cy5.5. Receptors associated with metabolic disorders are also a popular target. A high-throughput BRET assay was initially developed for GPR120, a recent target for type 2 diabetes treatment, using naphthalimide and coumarin fluorescent probes based on known sulfonamide ligand GSK137647A [13].

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1.2 Toll-Like Receptors, Innate Immunity, and Inflammation

Innate immunity is a key aspect of host-pathogen interactions upon infection, since it is the organism’s first line of defense and also the beginning of an immune-response [14]. This initial barrier was considered as nonspecific until the discovery of TLRs. TLRs are type I transmembrane proteins with an N-terminal leucine-rich ectodomain, a transmembrane domain and an intracellular Toll interleukin-1 receptor (TIR) domain [15]. These PRRs are synthesized in the endoplasmic reticulum (ER) and transported to the membranes where they are located, either cytoplasmatic or endosomal [14]. TLRs recognize Pathogen-Associated Molecular Patterns (PAMPs) which are conserved microbial components that trigger the consequent signaling pathways and initiate an innate immune response that then will modulate the adaptive response [14]. They are also activated by endogenous molecules, danger-associated molecular patterns (DAMPs), and recent research suggest that TLR/DAMP axis is an emerging target to find therapies against several chronic inflammations and inflammatory diseases that are still lacking specific pharmacological treatment [16]. Each TLR can detect a different PAMP according to its localization and binding pocket characteristics. Specifically, TLRs located in the plasma membrane—TLR1, 2, 4, 5, and 6—have as ligands surface components of the pathogen, while TLRs that are located in the endosome—TLR3, 7, 8, 9, and 11–13 in mice—bind to nucleic acid portions and also to components of intracellular parasites [17]. Surface receptors, TLR1, 2 and 6 recognize different PAMPs such as lipoproteins, peptidoglycans and others. TLR4 binds to the bacterial lipopolysaccharide (LPS), while TLR5 detects bacterial flagellin [18]. In the case of endosomal receptors, it is known that TLR3 recognizes viral double-stranded RNA and self RNAs from damaged cells which are endogenous DAMPs, TLR7 recognizes single-stranded RNA from viruses and also RNA from bacteria like group B streptococcus, whereas TLR8 binds to RNA from bacteria. TLR9 binds to bacterial and viral DNA, namely CpG-DNA motifs and it is also able to detect intracellular parasite’s components [18]. Nucleic acid sensing TLRs’ improper activation is implicated in a variety of auto-immune diseases since they also have the ability to recognize self-RNA and DNA. In fact, TLR7 and 9 are key players in the development of systemic lupus erythematosus and psoriasis [19]. TLR10 function is still not well understood, and some reports suggest that it has an anti-inflammatory effect by counteracting the other TLRs [20]. Interestingly, it was reported that it has a limited role in the induction of trained immunity [21]. Although TLRs respond to different ligands, they induce similar intracellular cascades (Fig. 1). Recognition of the activating PAMP by the receptor induces its dimerization and recruitment of TIR-domain containing adaptor proteins [22]. Essentially, there

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Fig. 1 Simplified scheme of recognition of microbial PAMPs by TLRs and respective signaling. The heterodimers TLR2–1 and TLR2–6 recognize bacterial lipoproteins and recruit TIRAP and MyD88 to start the NF-κB activation. TLR5 binds to bacterial flagellin and through MyD88 also activates NF-κB signaling. TLR4 on the cell surface binds to LPS and recruits TIRAP and MyD88 to induce NF-κB activation. Following internalization into the endosomes, the TLR4 complex recruits TRAM and TRIF and activates IRF3 signaling generating late NF-κB activation. NF-κB-induced transcription leads to the production of proinflammatory cytokines such as TNF-α, IL-1β, and IL-6, while IRF3 leads to type I interferon production. Endosomal TLRs recognize microbial nucleic acids. TLR3 recognizes dsRNA and activates TRIF-dependent pathway leading to IRF3 activation, while TLR7 and 8 bind to viral and bacterial ssRNA, respectively. TLR9 binds to microbial DNA. The three receptors recruit TIRAP and MyD88 to start IRF7 signaling and type I interferon production. All endosomal TLRs are able to also induce NF-κB activation and production of proinflammatory cytokines

are two pathways that can be activated, each one with its main adaptor protein. The MyD88 pathway initiates with the recruitment of MyD88 protein facilitated by TIRAP, that is usually referred to as a bridging adaptor protein, except in the case of endosomal TLRs where this bridging is not always required to start the signaling [23]. The MyD88/TIRAP complex is the beginning of an assembly of different proteins in a supramolecular organized module called the myddosome [24]. This in turn initiates a signaling cascade that leads to the activation and nuclear

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translocation of nuclear-factor κB (NF-κB) and synthesis of proinflammatory cytokines in the case of TLR2, 4, 5, and 6 and to the activation of IFN regulatory factor 7 (IRF7) in the case of TLR7, 8, and 9 [14]. TLR4 is the only TLR that can start another pathway after ligand-induced internalization, by recruiting another TIR-domain containing adaptor protein called TRIF, in a process facilitated by TRAM [23]. The TRAM/TRIF interaction leads to the so called triffosome that relies on another cellular factor called IRF3. TLR3 stimulation by agonists can lead as well to the signaling through the TRAM/TRIF/IRF3 pathway. Activation and nuclear translocation of IRF3 leads to the production of type I interferon and consequent proinflammatory immune responses [14]. Although their activation is necessary to start signal cascades that offer host protection against the pathogen, excessive and prolonged activation of these receptors can lead to exaggerated and chronical inflammation, autoimmune diseases, and even sepsis [25]. Abnormal TLRs stimulation by PAMPs and DAMPs, as well as TLRs polymorphisms in different organs and tissues, have been recently implicated in the development of numerous diseases including some neuroimmune pathologies as multiple sclerosis, Guillain–Barre´ syndrome, and myasthenia gravis [26], as well as the neurodegenerative Alzheimer’s disease [27], inflammatory bowel disease [28], cardiovascular diseases, including heart failure [29], and atherosclerosis [30]. TLRs activation has also been linked to different types of cancer as it is the case of prostate, colorectal, pancreatic and colon cancer, head and neck carcinoma, among others [31]. Nevertheless, it is also worth to note that TLR agonism is also an important option for cancer immunotherapy [32]. Thus, this means that TLRs are pharmacological targets for both agonists, as in the case of TLR4 agonist Monophosphoryl lipid A (MPLA) used as a vaccine adjuvant [33], and antagonist as is the case of TLR7/8 and 9 antagonist IMO-8400 currently in phase 2 clinical trials for the treatment of severe plaque psoriasis [34]. 1.3 Toll-Like Receptors Trafficking

It is clear that understanding TLRs function is essential to clarifying even further the innate immunity’s complex molecular events that constitute the organism’s first defense, and eventually prime acquired immunity in order to offer protection against the pathogen. The other face of the coin is that, in some cases, TLRs activation causes the development of various diseases. One of the very complex phenomena to study is TLRs subcellular localization upon biosynthesis and relocalization as a consequence of ligand activation. These movements within the cell are called trafficking. One of the most studied cases is the endotoxin-stimulated TLR4 internalization by endocytosis and subsequent triggering of the TRAM/ TRIF signaling that is different from the MyD88 signaling directly started by LPS interacting with TLR4/MD-2 dimer localized on

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the plasma membrane [35]. Nevertheless, the regulation of TLR3, 7 and 9 trafficking is also a crucial event in the cell that is of outmost importance for normal TLR functioning and prevention of recognition of self-nucleic acids [17]. 1.3.1 TLR4 and Its Trafficking

After the realization that TLR4 knockout mice did not respond to LPS, TLR4 became the first of this family to be discovered and thus became the best characterized TLR [22]. Therefore, this section will focus on TLR4 trafficking within the cell and the different factors that affect this phenomenon. Upon LPS binding, the TLR4/MD-2 complex is able to dimerize, thus forming the activated (TLR4/MD-2/LPS)2 dimer that starts the MyD88 signaling from the plasma membrane. The TRAM/TRIF pathway is only activated when internalization of the (TLR4/MD-2/LPS)2 complex takes place thanks to the action of CD14 [36]. One of the main proteins involved in the trafficking process is CD14. Indeed, CD14 is a chaperone that presents LPS to the TLR4/MD2 complex, starting the whole TLR4 pathway and it is necessary for TRIF-dependent IFN production but not for the MyD88 pathway at higher LPS concentrations [36]. This interesting observation led the authors to clarify that CD14, which similarly to TLRs is a PRR, is required for LPS-induced endocytosis of TLR4 and, furthermore, that this process is fully dependent on the presence of CD14 but not dependent on TIR adaptor proteins such as MyD88, TRIF, TIRAP, or TRAM, and also has an important participation downstream of the kinases Syk and its effector PLCγ2 [36]. The trafficking of TLR4 is a complex process and different molecular regulators are thought to be important, although many of them are still to be identified. An endogenous regulator of TLR4 trafficking and signaling is prostaglandin E2. It was shown that the release of this prostaglandin and activation of EP4 receptor regulates inflammatory response through a negative-feedback loop by decreasing TLR4 internalization and type I interferon consequent response [37]. In addition, glia maturation factor-γ (GMF-γ) silenced human macrophages displayed increased inflammatory responses after LPS challenge and further evaluation showed that this factor negatively regulates TLR4 response by controlling its trafficking in two different ways [38]. First, it is involved in the endocytosis of TLR4 that although leads to the TRIF pathway activation, decreases the MyD88 one, and second, GMFγ is also a participant in the trafficking from early endosomes to late endosomes which is essential for termination of the proinflammatory response [38]. After internalization and activation of the TRAM/TRIF pathway, TLR4’s inflammatory response is terminated by lysosomal degradation of the receptor. This event was clarified by the observation that reduced trafficking to the lysosomes resulted in

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prolonged TLR4 activation [39]. Interestingly, usually acidification of intracellular compartments initiates the activity of hydrolytic enzymes and promotes the ligand’s dissociation from the receptor. However, this is not the case of TLR4, since it was demonstrated that acidification is required for internalization and thus for the activation of TRAM/TRIF signaling [40]. Indeed, the mechanism of TLR4 degradation is not completely clear and some studies suggest that there is a recycling mechanism involving proteins Rab7a, Rab7b, and Vps35 and a retrograde transport mechanism [41]. 1.3.2 Endosomal TollLike Receptors and Their Trafficking

The intracellular TLRs, namely TLR3, 7, 8, and 9 and 11, 12, and 13 in mice are expressed in specialized compartments namely endosomes. As mentioned above, these receptors recognize nucleic acid, and thus, their localization avoids immune responses to self-RNA and/or DNA because in these structures self-nucleic acids are rarely found [42]. Among this group of receptors, the role of TLR3, 7, and 9 has been more extensively clarified, in respect to the others. These receptors remain in the ER until cells are stimulated with their specific PAMP, although subsequent trafficking is not ligand specific since transport of nucleic acid-sensing TLRs to endosomes has been observed even after LPS stimulation [43]. Regardless, the transport of endosomal TLRs from the ER to the endosomes is a strictly regulated process. Indeed, a simple increase in the number of TLR7 functional receptors inside the endosomes is enough to induce autoimmune diseases such as systemic lupus erythematosus in mice [44]. It is known that one of the most important proteins involved is the chaperone uncoordinated 93 homolog B1 (UNC93B1) that is necessary for the trafficking of nucleic acid-sensing TLRs from the ER where TLRs are synthesized to specialized organelles but nor for cell-surface TLRs [44]. This regulatory factor facilitates the loading of TLR3, 7 and 9 into vesicles although its involvement has been clearly demonstrated only in the case of TLR9’s transport into COPII vesicles [44]. Furthermore, it was demonstrated that UNC93B1 binds to TLR9 and 3 and only dissociates in the endosomes allowing their activation [45]. In contrast, TLR7 remains bound to UNC93B1, and the former is crucial for the recruitment of syntenin-1 that itself signals the need for trafficking of the receptor to multivesicular bodies for the termination of the TLR7 proinflammatory activity and prevention of inadequate autoimmune responses [46]. Some other important proteins include adaptor protein 2 (AP-2) in the case of TLR9 and AP-4 in the case of TLR7 [19]. Indeed, these proteins regulate the endocytosis of the receptors since AP-4 allows TLR7 to be transported directly from the Golgi to the endosomes, while TLR9 has to be first transported to

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the plasma membrane and then into the endosomes through the binding of UNC93B1 to AP-2 [47]. Furthermore, leucine-rich repeat containing protein (LRRC) 59 has been identified as a cofactor for UNC93B1 in the trafficking of TLR3 and 9 from the ER [48]. After this first translocation, these TLRs are transported to the endosomes where they interact with their ligands. It is known that they initiate the signal cascade after proteolytic cleavage by endosomal proteases, and the cell type and the receptor involved determines the location of the reaction and other specific requirements [49]. For this step to happen, it is necessary for the ectodomain to be internalized into the endosome, leaving its membrane that faces the cytosol [42]. Different proteins are implicated in the trafficking of intracellular TLRs after activation of the cell. For example, AP-3 also plays a very important role in the movement of TLR9 to a specialized lysosome-like organelle where it is activated, leading to the production of type I interferon instead of proinflammatory cytokine production, in a process that highlights the receptor’s signaling complexity, since it shows that transport to different compartments can lead to different responses [49]. Interestingly, extensive work on the use of chemical fluorescents probes to study endosomal TLRs trafficking within the cell is still lacking, thus leaving room to interesting future research in this field. 1.4 Chemical Probes to Study TLR4 Trafficking

Although TLR4 trafficking is considered an important event that has an impact in the receptor’s signaling, research work based on the use of fluorescent probes to study the dynamic of TLR4 cellular localization is still limited. Indeed, research groups have used either labeled endotoxin, LPS and lipooligosaccharide (LOS), or labeled glycolipid agonists to visualize TLR4 inside the cell. The combination of LPSCy5 with immunofluorescent antibodies able to label TLR4 is also a viable option to study colocalization and provide mechanistic insights on TLR4 trafficking. Accordingly, two important studies that helped to define this cellular event used this methodology [50, 51]. The first employed livecell confocal microscopy techniques to observe the trafficking of both labeled LPS and TLR4. Indeed, the authors were able to conclude that there was a recycling of the receptors between the membrane and the Golgi apparatus and that LPS followed the same trafficking although this event was not important for activation [50]. These observations illustrate the complexity of TLR4 and related proteins movement between the membrane and specialized compartments of the cell. Further ahead, another group exposed the intricated endosomal movement of TLR4-LPS complex using LPSCy5 and OregonGreen-labeled LPS together with Alexa546labeled TLR4 in order to obtain confocal microscopy data [51]. In

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this exhaustive work, the authors were able to determine that LPS entered the cell through a TLR4-mediated processes and that it later colocalized with the receptors in the endosomes. Authors also observed that signal termination was dependent on the trafficking of both the receptor and LPS to late endosomes and lysosomes [51]. Not only direct trafficking of the receptor can be studied but also the contribution of accessory proteins in the whole process. Accordingly, the role of RAB proteins, a superfamily of GTPases that are implicated in the trafficking of TLR4, was evaluated in a study that hypothesized if RAB11A regulated the transport of TRAM to the endosomes [52]. In reality, the study design allowed for the clarification of the localization of other proteins after LPS stimulation, namely CD14 and TLR4. Although the TLR4 itself was also labeled, the use of LPS labeled with Cy5 as a chemical probe permitted the authors to draw conclusions about colocalization. They determined, using TIRF, that upon binding to LPSCy5, around 70% of the receptors remained stable on the plasma membrane for one and a half minute in a murine cell line and for 2 min in a human cell line, describing what they call an immobile fraction of TLR4 in the plasma membrane [52]. Furthermore, the authors discovered that LPS stimulation induced also an immobile fraction of TRAM in the endocyclic recycling compartment that was dependent on RAB11A and CD14. They also observed that TLR4 accumulated into punctuate structures that were colocalized with CD14 and LPSCy5. Labeled LPS is simpler to use from a practical standpoint since it can even be found commercially. Nevertheless, it is possible to find TLR4 studies using another endotoxin, such as LOS [53], or even synthetic ligands [54]. Indeed, a recent work by our group describes the labeling of LOS with a cyanine moiety, Cy7N, to investigate its interaction with TLR4, localization, and internalization [53]. We described that the chemical probe, as expected due to hindrance, lost activity in respect to the unlabeled LOS, but was able to nevertheless activate TLR4 and initiate signal transduction. As described for LPS, the internalization of LOS was CD14-dependent, we were able to confirm this using confocal microscopy where it was observed that CD14-positive cells had LOSCy7N, while CD14negative cells did not [53]. By using LPS labeled with Alexa Fluor 488, we were able to compare internalization upon stimulus with LPS and LOS and discovered that the latter was faster to be internalized, which means that TLR4 binds to LOS triggers the TRIF pathway faster than LPS. Furthermore, we described a new fluorescent chemical probe with optimal physicochemical and optical properties for cell studies and confocal microscopy [53]. IAXO-102 is a commercially available potent synthetic TLR4 glycolipid antagonist developed by our group, that inhibits MAPK

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and NF-κB phosphorylation, decreasing TLR4 proinflammatory response [55]. This compound was functionalized with a fluorescein unit either directly in the C6 position of glucose or through a glutaryl-diaminoethyl-thiourea linker [54]. Interestingly, the compound with the linker was unable to act as a fluorescent probe while the one directly bound to the fluorescein group was efficient. This fact highlights once again the importance of the linker when designing and choosing a fluorescent chemical probe for a specific receptor. Using confocal microscopy and a competition assay with LPS, the authors were able to establish that labeled IAXO-102 interacted with CD14 and MD-2 proteins and, furthermore, were able to localize the receptor in the membrane [54].

2

Materials Prepare all solutions and handle the reagents always according to the manufacturer’s instructions. Also, proceed to their disposal accordingly. Unless otherwise mentioned, prepare all reagents at room temperature and under sterile atmosphere and keep sterile conditions. 1. THP-1 cells. 2. Sterile Roswell Park Memorial Institute (RPMI) Medium. 3. Sterile Heat-inactivated Fetal Bovine Serum (FBS). 4. Sterile L-Glutamine 100× solution. 5. Sterile Penicillin–Streptomycin 100× Solution. 6. Sterile Phosphate Buffered Saline (PBS). 7. Phorbol 12-myristate 13-acetate (PMA). 8. Paraformaldehyde (PFA) 4% solution. 9. Triton X-100. 10. LPS Alexa Fluor 568 Conjugate (ThermoFisher). 11. Anti-CD14 Mouse antibody (Novus Biologicals 4B4F12). 12. PhenoVue™ Fluor 488 – Goat Anti-Mouse Antibody CrossAdsorbed (PerkinElmer). 13. Cell-Culture Flask with filter cap. 14. 96-well Poly-D lysine-coated PhenoPlate™ 96-well microplates (PerkinElmer). 15. Sterile tubes and vortex to prepare necessary dilutions. 16. Foil to protect plate from the light. 17. Instrumentation: Operetta®CLS™ microplate imager (PerkinElmer) and computer with Harmony® High-Content Imaging Software (PerkinElmer).

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Methods Cell Culture

Culture THP-1 cells according to the manufacturer’s instructions: 1. Culture cells in RPMI supplemented with 10% of heat inactivated FBS, 2 mML-glutamine, 100 U/mL of penicillin and streptomycin. 2. Allow cells to grow at 37 °C in a 5% CO2 and 95% humidity atmosphere in an incubator. 3. Split cells 3 times a week to maintain cell density between 0.5 and 0.6 × 106 cells/mL (see Note 1).

3.2 Plate Preparation and Cell Differentiation

1. Adjust cell density to be able to plate 20,000 cells/200μL/well in a 96-well Poly-D lysine-coated PhenoPlate™ 96-well microplates (PerkinElmer) (see Note 2). 2. Add 100 ng/mL PMA to the cell suspension to ensure differentiation of THP-1 cells into THP-1-derived macrophages (TDM). 3. Seed cells and allow them to differentiate for 3 days at 37 °C in a 5% CO2 and 95% humidity atmosphere. 4. After 3 days, observe cells using a microscope (see Note 3).

3.3

Cell Treatment

1. After cell differentiation, aspirate PMA-containing medium by using an appropriate vacuum pump or a multichannel pipette. 2. Add fresh RPMI medium in an appropriate quantity to obtain desired compound concentration (see Note 4). 3. Prepare dilution of the treatments from stock solutions using RPMI to reach desired concentration (see Note 4). 4. For observation of LPS/CD14 trafficking, perform a timecourse experiment by treating cells with 100 ng/mL of LPS Alexa Fluor 568 Conjugate (ThermoFisher) at different time points: 0, 5 min, 15 min, 30 min, 1 h, 2 h, 4 h, and 6 h, leaving cells at 37 °C in a 5% CO2 and 95% humidity atmosphere in between treatments and avoiding direct light. Start to treat from the longest to the shorter time point (see Note 5). 5. Stop the treatment by removing the medium (see Note 6). 6. Wash briefly and carefully with PBS (see Note 7).

3.4 Fixation and Immunostaining

1. To fix cells, add 50μL of 4% paraformaldehyde (PFA) solution in PBS to each well and incubate for 10 min at room temperature (see Note 8). 2. Aspirate each well and wash with 200μL PBS for 5 min, repeating the process two times for a total of three washes.

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3. To permeabilize the cells, add 50μL of PBS 0.1% Triton X-100 for 10 min. 4. Repeat the aspiration/wash as in step 2. 5. Block by adding 50μL of 1% BSA in PBS for 60 min at room temperature. 6. Repeat the aspiration/wash as in step 2. 7. To proceed with immunostaining, add 50μL of a 1:200 dilution of anti-CD14 antibody (Novus Biologicals 4B4F12) to each well and incubate for 60 min at room temperature (see Note 9). 8. Repeat the aspiration/wash as in step 2. 9. Incubate with 50μL of secondary antibody in a 1:2000 dilution using PhenoVue™ Fluor 488 – Goat Anti-Mouse Antibody Cross-Adsorbed (PerkinElmer) for 60 min at room temperature (see Note 9). 10. Repeat the aspiration/wash as in step 2. 11. Incubate with 50μL of PhenoVue Hoechst 33342 nuclear stain using a 1:5000 dilution for 10 min at room temperature. 12. Repeat the aspiration/wash as in step 2. 13. Add 200–300μL of PBS to each well before reading the plate. If necessary, plate can be sealed with foil and kept at 4 °C and images acquired in the following days (see Note 10). 3.5 High-Content Imaging Analysis

1. Using Operetta®CLS™ microplate imager (PerkinElmer), set up each channel settings (time of exposure, power, height) for observing the different participants: 568 nm for LPS (orange), 488 nm for CD14 (green), HOECHST for nuclei (blue) and Digital Phase Contrast to be able to segment the cells (see Note 11). 2. Acquire several fields of view to ensure reliability of the data using either the 40× or 60× objective or even both. Immersion objectives are suggested. Water objectives in the case of working with Operetta®CLS™ or either oil objectives if present. 3. Using Harmony® High-Content Imaging Software, segment the cell by defining different cellular compartments: membrane, cytoplasm, and nucleus. 4. Calculate the intensity of fluorescence of each channel—568 m and 488 nm—in each of the defined compartments to appreciate changes in the localization of LPS and CD14. 5. Evaluate the results.

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Notes 1. Subculturing at 0.5–0.6 × 106 cells/mL will keep cells at an optimal density for continued growth and will stimulate further proliferation. Maintaining log phase growth will maximize the number of healthy cells for your experiment. It is convenient to change completely the medium every other time by centrifugating at 180 × g (RCF) for 5 min at room temperature and resuspending in an appropriate volume of RPMI. The medium should also be changed in the case that the required volume for splitting is more than half volume of consumed medium. 2. For THP-1-derived macrophages, the determined optimal cell density, by our group, to ensure enough number of analyzed cells but not overcrowding of the well is 20,000 cells/well. Nevertheless, every cell type is different, and thus, this is one of the first parameters to optimize when starting a new experience. It is convenient to plate a test experiment with different densities considering the size of the cells. Too many cells will not allow cell segmentation, while too little cells will lead to lack of differentiation and cell death. 3. THP-1-derived macrophages appear elongated and adherent the bottom of the well, with a flat appearance when observed under a microscope. Ensure that differentiation occurred before starting the actual treatment. 4. Replace the differentiation medium with 150μL of fresh RPMI and then adding 50μL of a 4× treatment solution at the desired time point is a simple manner to speed up the treatment and prevent cell to dry. Also, solutions should be prepared soon before treatment to guarantee optimal dispersion. They should be vortexed when preparing the dilution and right before dispensing them into the well. When doing a time-course treatment from 0 to 6 h, dilutions can be kept at room temperature inside the biological hood. 5. From the moment the fluorescent LPS conjugate is added, the experiment should be performed avoiding direct light. Thus, the hood light should be turned off for the remaining of the experiment. This is valid for every fluorescent conjugate or antibody that might be used. 6. After treatment, instead of discarding them, supernatants can be stored in the freezer for subsequent evaluation of cytokine production in order to correlate the data obtained with the high-content analysis with other biologically relevant information. 7. The first wash before fixing is a limiting step in this type of experiment. We observed that when detachment of the cells

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happen can be in this first wash. Therefore, PBS must be removed very gently, by using a multichannel pipette or inverting the plate and mildly blotting it against clean paper towels. In the subsequent steps after fixation, PBS can be removed by aspiration or by inverting the plate. 8. It is very important to always check the manufacturer’s instructions for the method of permeabilization required to ensure the best primary antibodies performance. 100% Methanol permeabilization at -20 °C can be the preferred method in some cases. 9. Dilutions of primary and secondary antibodies should be optimized for each new experiment set up. Manufacturer’s instructions should always be considered, but observation of the imaging results is also important. If possible, a few tests with different dilutions within the working concentration range should be performed to find the optimal one. 10. To maintain the PBS volume up to 200μL is crucial to avoid the meniscus of the liquid interfere with the light of the laser. In case of acquisition several days after the immunostaining, control PBS did not evaporate and in case add more. 11. It is beyond the scope of this chapter to explain the functioning of the instrument. Nevertheless, practical training should always be performed before starting to work with it. In alterative, a classical confocal microscope and adequate software can be used. References 1. Zhang Y, Li S, Zhang H, Xu H (2021) Design and application of receptor-targeted fluorescent probes based on small molecular fluorescent dyes. Bioconjug Chem 32(1):4–24. https://doi.org/10.1021/acs.bioconjchem. 0c00606 2. Zhu H, Hamachi I (2020) Fluorescence imaging of drug target proteins using chemical probes. J Pharm Anal 10(5):426–433. https://doi.org/10.1016/j.jpha.2020.05.013 3. Iliopoulos-Tsoutsouvas C, Kulkarni RN, Makriyannis A, Nikas SP (2018) Fluorescent probes for G-protein-coupled receptor drug discovery. Expert Opin Drug Discov 13(10): 9 3 3 – 9 4 7 . h t t p s : // d o i . o r g / 1 0 . 1 0 8 0 / 17460441.2018.1518975 4. Heinzlmeir S, Mu¨ller S (2022) Selectivity aspects of activity-based (chemical) probes. Drug Discov Today 27(2):519–528. https:// doi.org/10.1016/j.drudis.2021.10.021 5. Leopoldo M, Lacivita E, Berardi F, Perrone R (2009) Developments in fluorescent probes for

receptor research. Drug Discov Today 14(13): 706–712. https://doi.org/10.1016/j.drudis. 2009.03.015 6. Soave M, Briddon SJ, Hill SJ, Stoddart LA (2020) Fluorescent ligands: bringing light to emerging GPCR paradigms. Br J Pharmacol 177(5):978–991. https://doi.org/10.1111/ bph.14953 7. van der Zouwen AJ, Witte MD (2021) Modular approaches to synthesize activity- and affinity-based chemical probes. Front Chem 9. https://doi.org/10.3389/fchem.2021. 644811 8. Azuaje J, Lo´pez P, Iglesias A, de la Fuente RA, Pe´rez-Rubio JM, Garcı´a D, Ste˛pniewski TM, Garcı´a-Mera X, Brea JM, Selent J, Pe´rez D, Castro M, Loza MI, Sotelo E (2017) Development of fluorescent probes that target serotonin 5-HT2B receptors. Sci Rep 7(1): 10765–10765. https://doi.org/10.1038/ s41598-017-11370-2

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Berlin/Heidelberg, pp 1–14. https://doi. org/10.1007/164_2021_541 21. Mourits VP, Arts RJW, Novakovic B, Matzaraki V, de Bree LCJ, Koeken VACM, Moorlag SJCFM, van Puffelen JH, Groh L, van der Heijden CDCC, Keating ST, Netea MG, Oosting M, Joosten LAB (2020) The role of Toll-like receptor 10 in modulation of trained immunity. Immunology 159(3): 289–297. https://doi.org/10.1111/imm. 13145 22. Luchner M, Reinke S, Milicic A (2021) TLR agonists as vaccine adjuvants targeting cancer and infectious diseases. Pharmaceutics 13(2): 1 4 2 – 1 4 2 . h t t p s : // d o i . o r g / 1 0 . 3 3 9 0 / pharmaceutics13020142 23. Javmen A, Szmacinski H, Lakowicz JR, Toshchakov VY (2018) Blocking TIR domain interactions in TLR9 signaling. J Immunol 201(3): 9 9 5 – 9 9 5 . h t t p s : // d o i . o r g / 1 0 . 4 0 4 9 / jimmunol.1800194 24. Latty SL, Sakai J, Hopkins L, Verstak B, Paramo T, Berglund NA, Cammarota E, Cicuta P, Gay NJ, Bond PJ, Klenerman D, Bryant CE (2018) Activation of Toll-like receptors nucleates assembly of the MyDDosome signaling hub. elife 7:e31377. https://doi.org/10. 7554/eLife.31377 25. Kawai T, Akira S (2010) The role of patternrecognition receptors in innate immunity: update on Toll-like receptors. Nat Immunol 11(5):373–384. https://doi.org/10.1038/ni. 1863 26. Li H, Liu S, Han J, Li S, Gao X, Wang M, Zhu J, Jin T (2021) Role of Toll-like receptors in neuroimmune diseases: therapeutic targets and problems. Front Immunol 12. https:// doi.org/10.3389/fimmu.2021.777606 27. Momtazmanesh S, Perry G, Rezaei N (2020) Toll-like receptors in Alzheimer’s disease. J Neuroimmunol 348:577362–577362. https://doi.org/10.1016/j.jneuroim.2020. 577362 28. Lu Y, Li X, Liu S, Zhang Y, Zhang D (2018) Toll-like receptors and inflammatory bowel disease. Front Immunol 9. https://doi.org/ 10.3389/fimmu.2018.00072 29. Parizadeh SM, Ghandehari M, HeydariMajd M, Seifi S, Mardani R, Parizadeh SM, Ghayour-Mobarhan M, Ferns GA, Hassanian SM, Avan A (2018) Toll-like receptors signaling pathways as a potential therapeutic target in cardiovascular disease. Curr Pharm Des 24(17):1887–1898. https://doi.org/10. 2174/1381612824666180614090224 30. Li B, Xia Y, Hu B (2020) Infection and atherosclerosis: TLR-dependent pathways. Cell Mol

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Part II Toll Like Receptors Function in Immune Responses

Chapter 4 Use of CRISPR/CAS9 Technologies to Study the Role of TLR in Dendritic Cell Subsets Giulia Mencarelli, Benedetta Pieroni, Kenneth M. Murphy, and Marco Gargaro Abstract Dendritic cells (DCs) have a significant role in coordinating both innate and adaptive immunity by serving as sentinels that detect invaders and initiate immune responses to eliminate them, as well as presenting antigens to activate adaptive immune responses that are specific to the antigen and the context in which it was detected. The regulation of DC functions is complex and involves intracellular drivers such as transcription factors and signaling pathways, as well as intercellular interactions with adhesion molecules, chemokines, and their receptors in the microenvironment. Toll-like receptors (TLRs) are crucial for DCs to detect pathogen-associated molecular patterns (PAMPs) and initiate downstream signaling pathways that lead to DC maturation and education in bridging with adaptive immunity, including the upregulation of MHC class II expression, induction of CD80, CD86, and CD40, and production of innate cytokines. Understanding the TLR pathways that DCs use to respond to innate immune stimuli and convert them into adaptive responses is important for new therapeutic targets identification. We present a novel platform that offers a fast and affordable CRISPR-Cas9 screening of genes that are involved in dendritic cells’ TLR-dependent activation. Using CRISPR/Cas9 screening to target individual TLR genes in different dendritic cell subsets allows the identification of TLR-dependent pathways that regulate dendritic cell activation and cytokine production. This approach offers the efficient targeting of TLR driver genes to modulate the immune response and identify novel immune response regulators, establishing a causal link between these regulators and functional phenotypes based on genotypes. Key words CRISPR/CAS9, Retroviral vectors, Dendritic cell subsets, Toll-like receptors, Cytokines

1

Introduction Since its discovery 30 years ago, CRISPR-Cas9 system has been widely used as a powerful tool for genome editing, outperforming other gene editing techniques in terms of efficacy and specificity. Generally, to induce sequence-specific double-strand breaks (DBS) and obtain targeted genome editing, researchers exploit type II CRISPR system from Streptococcus pyogenes [1]. In its basic and most widely used application, two components must be introduced

Francesca Fallarino et al. (eds.), Toll-Like Receptors: Methods and Protocols, Methods in Molecular Biology, vol. 2700, https://doi.org/10.1007/978-1-0716-3366-3_4, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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into and/or expressed in cells or an organism: the Cas9 nuclease and a RNA guide (sgRNA). sgRNA comprehend a sequence of twenty nucleotides at the 5′ end, which confers target specificity, and a scaffold required to bind Cas9 endonuclease [2]. In order to perform a DSB in the gene of interest, the 20 bp sequence must pair with the desired target DNA site using standard RNA–DNA complementarity base-pairing rules, and additionally, these target sites must lie immediately 5′ of a Protospacer Adjacent Motif sequence that matches the canonical form 5′-NGG; only then Cas9 enzyme can perform the cleavage. Thus, with this system, Cas9 nuclease activity can be directed to any DNA sequence of the form N20-NGG simply by altering the first 20 nt of the gRNA to correspond to the target DNA sequence [3, 4]. After cleavage, DSBs can be repaired mainly through either the nonhomologous end joining (NHEJ) pathway or homologydirected repair (HDR) [5, 6]. NHEJ typically leads to short insertion/deletion (indels) near the cutting site, whereas HDR can be used to introduce specific sequences into the cutting site if exogenous template DNA is provided. CRISPR-Cas9 system has proved to be a game changer approach in dendritic cells studies, to the point of allowing DCs therapeutically engineering in allergies [7] and tumors [8] and reaching a high efficiency of gene knock-out also in human mo-DCs [9]. Such system represents however a challenging tool, especially when used in vivo both in mouse [10] and human studies [11], where off-target effects are always around the corner. Recently also on-target repercussions were shown in mouse embryos where CRISPR-Cas9 germline genome editing gave rise to karyotype alterations [12]. All considered it is simple to see why this powerful and easy-to-use genome editing technique is constantly evolving and going under refinement. In this context, enzyme variants have been developed such as evoCas9 [13], which exhibits a strongly limited nonspecific cleavage, as well as novel approaches that switch from DNA to RNA editing [14]. An open need in genome editing research is still deepening overall knowledge on CRISPR-Cas9 system. Creating new platforms is crucial to facilitate its use in everyday studies (Fig. 1).

2

Materials

2.1 Retroviral Particles Production

Plasmids containing either scramble RNA or guide RNA for the desired gene were transfected into Plat-E packaging cells in order to generate retroviral particles that could be subsequently used to infect c-kit+ progenitors. It is important to carry with you the scramble RNA as a transfection and infection control.

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From staminal cell to engineering tools CRISPR Cas9 systems allow the creation of a novel genetic engineering platform

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Fig. 1 Workflow of CRISPR Cas9 platform. c-kit+ staminal progenitor cells are obtained from Cas9 mice bone marrow and infected with retroviral particles containing a vector expressing silencing guides. These cells are then differentiated in specific immune cells and finally analyzed for multiple parameters. Created with BioRender 2.1.1 sgRNA Vector Generation

Thy1.1-hU6-gRNA-Bbs1 retroviral vector was generated as described by Theisen et al. [15]. sgRNA can be designed using various tools (see Note 3), and oligos for their generation as well as primers for validation (see Subheading 2.4) are designed using Primer3, source RRID: SCR_003139 https://bioinfo.ut.ee/primer3-0.4.0/ (see Fig. 2). Once designed, oligos and primers were ordered from Sigma. Below are listed the materials used for sgRNA vector generation. 1. 1X TE buffer: 10 mM Tris–HCl, pH 8, 1 mM EDTA in ddH2O. 2. 100 μM oligo stock (Sigma) in 1X TE buffer. 3. 10X T4 DNA Ligase (#M0202, NEB) and T4 DNA Ligase buffer (#B0202, NEB). 4. BbsI enzyme (#R0539, NEB).

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Fig. 2 Primers for guide validation. Each guide’s ability to cut target DNA can be validated designing primers upstream and downstream the cleavage site. If the cut occurred, Forward and Reverse primers will be in sufficient proximity to give an amplicon. Created with BioRender

5. NEB 2.1 buffer (#B7202, NEB). 6. NEB® 5-alpha Competent ciency) (#C2987H, NEB).

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7. LB Agar (#22700025, Thermo Fisher). 8. LB Broth Base (#12780052, Thermo Fisher). 9. Amplicillin sodium salt (#A9518-25G, Sigma Aldrich). 10. Genelute plasmid miniprep kit (#PLN70-1KT, Sigma Aldrich). 2.1.2 Cell Isolation and Culture

1. Platinum-E (Plat-E) Retroviral Packaging Cell Line (Cell BioLabs) (see Note 1). 2. I10F medium: Iscove’s Modified Dulbecco’s Media (IMDM, #12440053, Thermo Fisher) supplemented with 0.1 mM Nonessential amino acids (#11140–035 Thermo Fisher), 1 mM Sodium Pyruvate (#11360–070, Thermo Fisher), 5 mM L-Glutamine (#25030–024, Thermo Fisher), 50 μM 2-Mercaptoethanol (#31350–010, Thermo Fisher), 100 U/mL Penicillin, 100 g/mL Streptomycin (#15140–122, Thermo Fisher), 10% FBS (#10270–106, Thermo Fisher), 1 μg/mL Puromycin (#P8833–25, Merck), and 10 μg/mL Basticidin (#SBR00022, Merck). 3. 1X Phosphate Buffer Saline (PBS). 4. 0.05% Trypsin-EDTA (ethylenediaminetetraacetic acid). 5. 0.4% Trypan Blue Solution (#15250061, Thermo Fisher). 6. 6-well plates for cell cultures (#140675, Thermo Fisher).

2.1.3

Cell Transfection

1. Opti-MEM medium (#31985062, Thermo Fisher, see Note 2). 2. TransIT-LT1 (#MIR2300, Mirus Bio, see Note 2).

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3. sgRNA vectors containing the specific sgRNA guide prepared in Subheading 2.1.1 (see Note 3). 4. I10F medium (see Subheading 2.1.2). 2.2 Isolation of Murine Hematopoietic and Progenitor Stem Cells (HSPCs) 2.2.1

/Cas9-GFP Rosa26Cas9-GFP mice (B6J.129(Cg)-Gt(ROSA) tm1.1(CAG-cas9*,-EGFP)Fezh 26Sor /J, JAX: 026179) are from The Jackson Laboratory Breeding (see Note 4).

Mice

2.2.2 HSPCs Isolation and Stimulation

1. 5-Fluoro Uracil solution: 10 g/l 5-FU (#6627, Sigma Aldrich) and 0.9% NaCl are dissolved in double-distilled H2O (dd H2O) (see Note 5). 2. Magnetic-Activated Cell-Sorting (MACS) buffer: 1X PBS + 0.5% Bovine Serum Albumin (# A3059, Sigma Aldrich) + 2 mM EDTA (# E4884, Sigma Aldrich), pH 7.2 (see Note 5). 3. Ammonium-Chloride-Potassium (ACK) buffer: 0.15 M Ammonium Chloride, 10 mM Potassium Carbonate (see Note 5). 4. 1X Phosphate Buffer Saline (PBS). 5. 70 μm cell strainer, scissors, forceps, 16G 1 ½ inch needle, 1.5 and 0.5 mL tubes. 6. Antibodies: FcR blocking (2.4G2, Bioceros), Biotin α CD11b (BioLegend), Biotin α CD5 (BioLegend), Biotin α B220 (BioLegend), Biotin α GR-1 (BioLegend), Biotin α Ter119 (BioLegend). 7. Anti-Biotin MicroBeads (#130–090-485, Miltenyi) and LS column (#130–042-401, Miltenyi). 8. CD117 magnetic beads (#130–091-224, Miltenyi). 9. I10F medium (see Subheading 2.1.2). 10. 100 ng/mL Flt3-L (#250–31, PeproTech). 11. 6-well plates for cell cultures (#140675, Thermo Fisher).

2.2.3 Cells

Purity of c-Kit+

1. Antibodies: Lineage: CD3 Biotin (BioLegend), CD19 Biotin (BioLegend), B220 Biotin (BioLegend), CD105 Biotin (Thermo Fisher), Ly6G Biotin (BioLegend), Ter119 Biotin (BioLegend) + qDOT BV605 (Thermo Fisher), Fc block (2.4G2, Bioceros), and c-Kit PE (BD Biosciences). 2. Magnetic-Activated Cell-Sorting (MACS) buffer: 1X PBS + 0.5% Bovine Serum Albumin +2 mM EDTA, pH 7.2 (see Note 5).

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3. 1% Paraformaldehyde (# 15714, Electron Microscopy Science). 4. Flow cytometry is performed on a FACS Fortessa (BD) and analyzed using FlowJo analysis software (Tree Star). 2.3

Infection

1. 1 mL syringe and 0.45 μm filter. 2. 8 μg/mL Hexadimethrine bromide (polybrene) (#H9268, Sigma-Aldrich). 3. 5 mL Facs tubes with caps. 4. Anti-Thy1.1 APC (#17–0900-82, BioLegend). 5. Paraformaldehyde 1% (# 15714, Electron Microscopy Science). 6. I10F medium (see Subheading 2.1.2). 7. 100 ng/mL Flt3-L (#250–31, PeproTech). 8. Flow cytometry is performed on a FACS Fortessa (BD) and analyzed using FlowJo analysis software (Tree Star).

2.4

Cell Sorting

1. FACS Aria Fusion instrument (BD). 2. FlowJo software (Tree Star). 3. Magnetic-Activated Cell-Sorting (1X PBS + 0.5% BSA + 2 mM EDTA).

(MACS)

buffer

4. Antibodies: Thy1.1 PE (BioLegend), B220 BV786 (BD Biosciences), Bst2 APC (eBioscience), CD11c APCcy7 (Thermo Fisher), MHCII BV421 (BioLegend), CD24 PEcy7 (BioLegend), Sirp-α (CD172) PerCP-Cy5.5 (eBioscience). 2.5

Guide Validation

1. Genomic DNA purification kit (#T3010, NEB).

2.5.1 Genomic DNA Extraction 2.5.2 Polymerase Chain Reaction

2.6 TLR Ligands Stimulation

Polymerase chain reaction (PCR) is performed on gDNA using the primer designed as in Note 11. GoTaq DNA Polymerase (# 9PIM300, Promega) kit can be used to validate each guide. 1. 6-well plates for cell cultures (#140675, Thermo Fisher). 2. I10F medium (see Subheading 2.1.2). 3. 96-well V-bottom plates for cell cultures (#167008, Thermo Fisher). 4. Brefeldin A (#00-4506-51, eBioscience). 5. 10 ng/mL Pam3CSK4 (# 506350, Sigma Aldrich, see Note 7). 6. 25 μg/μL Polyinosinic:polycytidylic acid (Poly I:C, BioFab Research, see Note 7).

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7. 250 ng/mL Lipopolysaccharide (LPS) 055:B5 (# L288010MG, Sigma Aldrich, see Note 7). 8. 100 ng/mL Flagellin (# ALX-522-058-3010; Enzo Life Science, see Note 7). 9. 5 μg/mL Resiquimod (# SML0196; Sigma, see Note 7). 10. 10 μg/mL Phosphorothioate oligodeoxyribonucleotides containing CpG motifs (CpG ODN 1826) (BioFab Research, see Note 7). 11. 100 ng/mL Profilin (TgPRF, # SRP8050, Sigma, see Note 7). 12. 1 μg/μL Soluble tachyzoite antigen (STAg) (see Note 7). 13. 1 μg/mL S. aureus 23S rRNA derived oligoribonucleotide (ORN Sa19) (#tlrl-orn19, InvivoGen, see Note 7). 2.7 Cytokine Analysis

Cytokine analysis is performed with Luminex xMAP technology according to the manufacturer protocol.

2.7.1 Luminex xMAP Technology

1. Immune Monitoring 48-Plex Mouse ProcartaPlex™ Panel (#EPX480–20834-901, Thermo Fisher). https://www. thermofisher.com/order/catalog/product/EPX480-20 834-901

2.7.2 Intracellular Staining

1. Antibodies: Fc block (2.4G2, Bioceros), CD11c PE Cy7 (BioLegend), MHCII BV421 (BioLegend), CD24 BV605 (BioLegend), Sirp-α (CD172) PerCP-Cy5.5 (eBioscience), IL-12 APC (BD Biosciences), TNFα APCy7 (BD Biosciences), IL-6 PE (BD Biosciences), IL-23 AF488 (Thermo Fisher). 2. Magnetic-Activated Cell-Sorting (MACS) buffer: 1X PBS + 0.5% Bovine Serum Albumin +2 mM EDTA, pH 7.2 (see Note 5). 3. 1% and 2% Paraformaldehyde (# 15714, Electron Microscopy Science). 4. 0.5% and 0.05% Saponin (# 8047-15-2, Sigma Aldrich, see Note 6). 5. FACS Fortessa (BD). 6. FlowJo software (Tree Star).

3

Methods

3.1 Retroviral Particles Production 3.1.1 sgRNA Vector Generation

Thy1.1-hU6-gRNA-Bbs1 retroviral vector was generated as described by Theisen et al. [15] and used to produce retroviral guide plasmids. sgRNA can be designed using various tools (see Subheading 2.1.1 and Note 3) and annealed oligonucleotides are generated as follows:

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1. Prepare a PCR mixture containing 1 μL of each primer (Subheading 2.1.1, 1 μL of 10X T4 DNA ligase buffer and 7 μL of ddH2O. 2. Incubate in thermocycler under these conditions: 37 °C 30′, 95 °C 5′, 90 °C 1′, 85 °C 1′, 80 °C 1′, 75 °C 1′, 70 °C 1′, 65 °C 1′, 60 °C 1′, 55 °C 1′, 50 °C 1′, 45 °C 1′, 40 °C 1′, 35 °C 1′, 30 °C 1′. 3. Place annealed oligo in fridge. 4. Digest 1 μg of Thy1.1-hU6-gRNA-Bbs1 retroviral vector with 1 μL BbsI enzyme adding 2 μL of NEB buffer 2.1 for a total volume of 20 μL, incubating 1 h at 37 °C. 5. Directly add to the digested vector 1 μL of annealed oligos (containing Bbs1 compatible overhangs), 2.5 μL 10X T4 DNA ligase buffer, and 1.5 μL T4 DNA ligase and incubate 1 h at 37 °C. 6. Use 2 μL of ligation product to transform 25 μL of DH5α competent bacteria following manufacturer protocol. 7. Plate on LB agar plates containing 50 μg/mL Ampicillin and incubate at 37 °C O/N. 8. Colonies were propagated on a liquid medium, incubated at 37 °C O/N and plasmid DNA was purified the following day (Subheading 2.1.1). 9. Quantify pDNA and use it to transfect PLAT-E packaging cell line (see Subheading 3.1.3). 3.1.2 Cell Isolation and Culture

1. Thaw 5 × 106 Platinum-E cells in I10F medium. 2. When reaching 70% to 80% of confluence, wash the cells with PBS 1X and use Trypsin-EDTA 0.05% to detach them. 3. Count cells diluting 1:10 with Trypan Blue Solution and plate at the concentration of 0.54 × 106/2 mL in I10F medium on a 6-well plate for 24 h.

3.1.3

Cell Transfection

1. Prepare transfection mix as follows: 250 μL of filtered OptiMEM (see Note 2), 2.5 μg of DNA and 7.5 μL TransIT-LT1. 2. Pipet gently. 3. Incubate transfection mix at room temperature for 20 min. 4. Drop transfection mix on cells. 5. Incubate O/N at 37 °C. 6. Carefully change transfected Plat-E medium. 7. Leave cells at 32 °C for 24 h.

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Fig. 3 Bone marrow cells purification. (a) From left to right, we have pelvis, femur, and tibia. (b) Drill the 0.5 mL eppendorf with a 16 G 1 ½ inch needle. Cut the edges of bones (c) and (d), insert (e) two of them inside the smaller eppendorf previously put in a 1.5 mL eppendorf, and close (f) the latter for the centrifugation. After 30 s, the bone marrow is flushed by centrifugation (g), and we can remove the bones containing eppendorf (h) to further proceed with purification 3.2 Isolation of Murine Hematopoietic and Progenitor Stem Cells (HSPCs)

1. Inject intravenously (IV) 300 μL of 10 mg/mL 5-Fluorouracil (5-FU) for a 20 g adult mouse Cas9 mice (150 mg/kg) to obtain a HSPCs enrichment (see Note 9). 2. 4 days after mice injection, euthanize mice through cervical dislocation and take femur, tibias and pelvis (see Fig. 3). 3. Prepare a series of 1.5 mL eppendorf: inside each, collocate a 0.5 mL eppendorf previously drilled with a 16 G 1 ½-inch needle (see Fig. 3). 4. Clean bones and cut one of the edges (see Fig. 3). 5. Place 2 bones within every 0.5 mL eppendorf, so that the cut edge is close to the hole (see Fig. 3). 6. Centrifuge at 10,000 rpm for 30 s (see Note 10). 7. Remove the smaller eppendorf and resuspend bone marrow in 1 mL of MACS buffer. 8. Pass through a 70 μm cell strainer and centrifuge at 1400 rpm for 5 min (see Fig. 3). 9. Resuspend cells with ACK buffer and incubate 4 min at 4 °C; add MACS buffer and centrifuge at 1400 rpm for 5 min.

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10. Resuspend in 4 mL of MACS buffer and stratify into 4 mL of Histopaque 1119 and centrifuge at 1800 rpm for 20 min without break. 11. Harvest the cells on the ring between the two phases. 12. Add PBS 1X until 50 mL and centrifuge at 1400 rpm for 5 min. 13. Resuspend in 10 mL of MACS buffer and count. 14. Centrifuge at 1400 rpm for 5 min resuspend 100 × 106/mL and add the following antibodies (diluted 1:200) for lineage depletion: FcR blocking, Biotin α CD11b, Biotin α CD5, Biotin α B220, Biotin α GR-1, Biotin α Ter119. 15. Incubate 30 min at 4 °C, wash with 5 mL of MACS buffer and centrifuge at 1400 rpm for 5 min. 16. Resuspend 100 × 106/mL and add α Biotin Microbeads 10 μL/107. 17. Incubate 20 min at 4 °C, wash with 5 mL of MACS buffer and centrifuge at 1400 rpm for 5 min. 18. Resuspend in 3 mL of MACS buffer. 19. Load cells in LS column by filtering with 70 μM strainer. 20. Wash column 5 times and count negative fraction, containing lineage negative cells. 21. Enrich c-Kit+ HSPCs using CD117 magnetic beads and LS column following manufacturer’s instructions. 22. Resuspend 1 × 106/mL in I10F medium with 100 ng/mL Flt3-L on a single well (6 well plate). 23. Incubate O/N at 37 °C 5% CO2. 24. Harvest by pipetting and washing well with 1 mL of I10F medium, count and proceed with infection. 25. Perform a purity of c-Kit+ by cytofluorimetric analysis. 3.3

Infection

1. After 24 h at 32 °C, recover transfected Plat-E (see Note 8) on 5 mL Facs Tubes with caps. 2. Centrifuge at 1400 rpm for 5 min and recover the viruscontaining supernatant. 3. Filter all the supernatant using a syringe and a 0.45 μm filter. 4. In order to evaluate Plat-E transfection efficiency, label cells with anti-Thy1.1 APC for 30 min at 4 °C and fix with 1% Paraformaldehyde. 5. HSPCs cells should be approximately 70%–80% confluent. 6. Add 200 μL of retrovirus and 8 μg/mL polybrene directly to HSPCs and mix pipetting up and down. 7. Incubate 30 min at 37 °C, 5% CO2.

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Fig. 4 Characterization of infected cells. Infection efficiency is evaluated gating on Thy1.1+ events. These cells are further characterized and as shown, excluding plasmacytoid dendritic cells (B220+) most of progenitor differentiated in conventional dendritic cells (B220-CD11c+MHCII+), comprising cDC1 (CD24+) and cDC2 (CD172+) subsets

8. Centrifuge the plate at 500 g for 90 min at 32 °C. 9. Incubate infected cells at 37 °C, 5% CO2, O/N. 10. Remove the virus and polybrene-containing medium. 11. Add 200 μL of complete IMDM (I10 F) supplemented with 100 ng/mL Flt3-L and plate on a 96-well tissue culture. 12. Incubate at 37 °C, 5% CO2 for 9 days. 13. Take a small aliquot to evaluate infection efficiency (%Thy1.1+ events) using FACS Fortessa [16] (Fig. 4). 3.4

Cell Sorting

Flt3L bone marrow derived dendritic cells are sorted as Thy1.1+B220+Bst2+ (pDC), Thy1.1+B220-MHCII+CD11c+CD24+ CD172α (cDC1), and Thy1.1+B220-MHCII+CD11c+CD24CD172α+ (cDC2) using FACS Aria Fusion Instrument (BD) and analyzed using FlowJo analysis software (Tree Star, see Note 12).

3.5

Guide Validation

Extract the genomic DNA of cells infected with Cas9 and sgRNA containing vector, as follows: 1. Resuspend 0.2 × 106 cells in 100 μL PBS 1X. 2. Add 1 μL Proteinase K and 3 μL RNase A to the resuspended pellet and mix by vortexing briefly. 3. Add 100 μL Cell Lysis Buffer and vortex immediately and thoroughly. The solution will rapidly become viscous. 4. Incubate for 5 min at 56 °C in a thermal mixer with agitation at full speed (~1400 rpm). 5. Add 400 μL gDNA Binding Buffer to the sample and mix thoroughly by pulse-vortexing for 5–10 s. 6. Transfer the lysate/binding buffer mix (~600 μL) to a gDNA Purification Column preinserted into a collection tube. 7. Close the cap and centrifuge: first for 3 min at 1000 × g to bind the gDNA and then for 1 min at maximum speed (>12,000 × g) to clear the membrane. Discard the flowthrough and the collection tube.

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Table 1 GoTaq DNA polymerase protocol Component

Final volume

5X green buffer

10 μL

PCR nucleotides mix 10 mM

1 μL

Primer Fw

0.1–1 μM

Primer rev

0.1–1 μM

GoTaq DNA polymerase

0.25 μL

Template DNA

12,000 × g) to elute the gDNA. 14. Perform PCR on gDNA following the manufacturer protocol. The following table (Table 1) can be used as a template. 3.6 TLR Ligands Stimulation

1. Plate 1 × 106 pDC or cDC1 or cDC2 into each well in I10F, in a 48-well plate. 2. Add stimuli: 250 ng/mL LPS, 10 μg/mL CpG ODN, 1 μg/ mL STAg, 25 μg/mL Poly I:C, 10 ng/mL Pam3CSK4, 100 ng/mL Flagellin, 5 μg/mL Resiquimod, 100 ng/mL TgPRF, and 1 μg/mL ORN Sa19. 3. Incubate the cells at 37 °C, 5% CO2, for 2 h. 4. Collect cells, resuspend in 150 μL of I10F medium containing 3 μg/mL of Brefeldin A and plate on a 96-well V-bottom plate.

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5. Incubate 2–3 h at 37 °C, 5% CO2 and perform an intracellular staining (see Subheading 3.7.1). 6. Repeat the same experimental setting incubating cells at 37 °C, 5% CO2, for 24 and 48 h. 7. Recover supernatants for cytokine analysis by Luminex xMAP technology (see Subheading 3.7.2). 3.7 Cytokine Analysis 3.7.1 Intracellular Staining

Cytokine analysis is the readout to functionally validate our engineered cells, exploiting a novel multiplex protein platform. 1. On cells incubated with Brefeldin, perform an intracellular staining (see Subheading 2.7.2) to obtain a multiparametric cytoanalysis using the following protocol: 2. Wash cells in MACS buffer and centrifuge at 1400 rpm for 5 min. 3. Add extracellular antibodies mix. 4. Incubate 30 min at 4 °C, covered from light. 5. Wash with MACS buffer and centrifuge at 1400 rpm for 5 min. 6. Incubate 20 min with 2% PFA in PBS 1X at room temperature. 7. Centrifuge at 1400 rpm for 5 min. 8. Wash cells in MACS buffer and centrifuge at 1400 rpm for 5 min. 9. Incubate with 0.05% Saponin for 5 min at room temperature. 10. Centrifuge at 1400 rpm for 5 min. 11. Add intracellular antibodies mix, prepared in 0.5% Saponin in PBS 1X. 12. Incubate 30 min at 4 °C, covered from light. 13. Centrifuge at 1400 rpm for 5 min. 14. Wash with 0.5% Saponin in PBS 1X and centrifuge at 1400 rpm for 5 min. 15. Fix with 1% PFA in MACS buffer.

3.7.2 Luminex xMAP Technology

1. Collect supernatants and centrifuge at 1500 rpm and 4 °C for 10 min to remove particulates. 2. Assay immediately or aliquot supernatant and store at -80 °C. 3. Cytokines of interest are measured using Luminex xMAP technology.

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Notes 1. Platinum-E cells can be kept in good condition for at least 4 months in the presence of drug selection, and can produce retroviruses with an average titer of 1 × 107 infectious units/ml by transient transfection. After thawing, do not change the culture medium during the first 3 days. It is normal to see some cells floating after the first 24 h and do not culture cells to complete confluence, reach 70–90%. 2. Warm Opti-MEM and Trans-IT at room temperature; it is optimal to filter Opti-MEM with a 0.22 μm filter. 3. When designing sgRNAs, choose the most 5′ exon that is common to all isoforms. Some common tools are CRISPOR (http://crispor.tefor.net/) and CHOPCHOP (https:// chopchop.cbu.uib.no/); we used the latter. 4. These CRISPR/Cas9 knock-in mice constitutively express CRISPR associated protein 9 (cas9) endonuclease, a 3X-FLAG epitope tag and EGFP in a widespread fashion under the direction of a CAG promoter. 5. 5-FU: dissolve in a 55 °C water bath with vigorous vortexing and filter 0.22 μm. MACS buffer: dissolve by stirring and degas for at least 2 h. ACK buffer: filter 0.22 μm. 6. 5% saponin is prepared in 1x PBS, while 0.5% and 0.05% dilutions are made in MACS buffer. 7. LPS: 100 ng/mL stock. CpG ODN: 1 mg/mL stock. CpG ODN contains phosphorothioate backbone to increase its in vitro half-life. STAg: 1 mg/mL stock [17]. PolyIC: 1 mg/mL stock. Flagellin: 0.1 mg/mL stock. Pam3CSK4: 2 mg/mL stock. Resiquimod (R848): 1 mg/mL stock. TgPRF: 10 μg/mL stock. ORN Sa19: 133 μg/mL stock. 8. Normally transfected cells should be already detached; therefore, pipetting is sufficient to recover them all. 9. Typically, 5 to 10 × 106 HSPCs, if there are no further enrichments, could be obtained from a mouse at 4 days after 5-FU injection. To obtain enough cells for viral infection and subsequent transplantation, pool cells isolated from 2–3 mice in one well.

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10. After the centrifuge, check if bones are white. 11. Guide validation primers should be designed as in Fig. 1 with the cleavage site in between. Designing the primers ourselves, we acknowledge the amplicon length; therefore, we expect that whenever a guide is cleaving, this amplicon will be smaller or at least have a different molecular weight. Sequencing the gene of interest will be important to assess whether the cleavage was in the correct position and how the sequence has rearranged. If the PCR product has the expected molecular weight, it is also recommendable to proceed with its sequencing. 12. Operation of sorting procedure on the cytometer and software is standardized with a detailed instruction provided by the company. Before and after cell sorting, we evaluate subset purity through cytofluorimetric analysis. References 1. Ishino Y, Shinagawa H, Makino K, Amemura M, Nakata A (1987) Nucleotide sequence of the iap gene, responsible for alkaline phosphatase isozyme conversion in Escherichia coli, and identification of the gene product. J Bacteriol 169:5429 2. Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA, Charpentier E (2012) A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337: 816 3. Platt RJ, Chen S, Zhou Y, Yim MJ, Swiech L, Kempton HR, Dahlman JE, Parnas O, Eisenhaure TM, Jovanovic M, Graham DB, Jhunjhunwala S, Heidenreich M, Xavier RJ, Langer R, Anderson DG, Hacohen N, Regev A, Feng G, Sharp PA, Zhang F (2014) CRISPR-Cas9 knockin mice for genome editing and cancer modeling. Cell 159:440–455 4. De Souza N (2013) RNA-guided gene editing. Nat Methods 10:189 5. Xue C, Greene EC (2021) DNA repair pathway choices in CRISPR-Cas9-mediated genome editing. Trends Genet 37:639 6. Cong L, Ran FA, Cox D, Lin S, Barretto R, Habib N, Hsu PD, Wu X, Jiang W, Marraffini LA, Zhang F (2013) Multiplex genome engineering using CRISPR/Cas systems. Science 339:819 7. Kim B, Lee YE, Yeon JW, Go GY, Byun J, Lee K, Lee HK, Hur JK, Jang M, Kim TH (2021) A novel therapeutic modality using CRISPR-engineered dendritic cells to treat allergies. Biomaterials 273:120798

8. Perez CR, De Palma M (2019) Engineering dendritic cell vaccines to improve cancer immunotherapy. Nat Commun 10:5408 9. Jost M, Jacobson AN, Hussmann JA, Cirolia G, Fischbach MA, Weissman JS (2021) CRISPR-based functional genomics in human dendritic cells. Elife 10:e65856 10. Zischewski J, Fischer R, Bortesi L (2017) Detection of on-target and off-target mutations generated by CRISPR/Cas9 and other sequence-specific nucleases. Biotechnol Adv 35:95 11. Saha K (2023) Accounting for diversity in the design of CRISPR-based therapeutic genome editing. Nat Genet 55:6 12. Papathanasiou S, Markoulaki S, Blaine LJ, Leibowitz ML, Zhang C-Z, Jaenisch R, Pellman D (2021) Whole chromosome loss and genomic instability in mouse embryos after CRISPRCas9 genome editing. Nat Commun 12:5855 13. Casini A, Olivieri M, Petris G, Montagna C, Reginato G, Maule G, Lorenzin F, Prandi D, Romanel A, Demichelis F, Inga A, Cereseto A (2018) A highly specific SpCas9 variant is identified by in vivo screening in yeast. Nat Biotechnol 36:265 14. Tao J, Bauer DE, Chiarle R (2023) Assessing and advancing the safety of CRISPR-Cas tools: from DNA to RNA editing. Nat Commun 14: 212 ˜ o CG, 15. Theisen DJ, Davidson JT 4th, Brisen Gargaro M, Lauron EJ, Wang Q, Desai P, Durai V, Bagadia P, Brickner JR, Beatty WL, Virgin HW, Gillanders WE,

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Mosammaparast N, Diamond MS, Sibley LD, Yokoyama W, Schreiber RD, Murphy TL, Murphy KM (2018) WDFY4 is required for crosspresentation in response to viral and tumor antigens. Science 362(6415):694–699 ˜ o CG, 16. Gargaro M, Scalisi G, Manni G, Brisen Bagadia P, Durai V, Theisen DJ, Kim S, Castelli M, Xu CA, Meyer Zu Ho¨rste G, Servillo G, Della Fazia MA, Mencarelli G, Ricciuti D, Padiglioni E, Giacche` N, Colliva C, Pellicciari R, Calvitti M, Zelante T, Fuchs D, Orabona C, Boon L, Bessede A, Colonna M, Puccetti P, Murphy TL, Murphy KM, Fallarino F (2022) Indoleamine

2,3-dioxygenase 1 activation in mature cDC1 promotes tolerogenic education of inflammatory cDC2 via metabolic communication. Immunity 55:1032 17. Tussiwand R, Lee WL, Murphy TL, Mashayekhi M, Kc W, Albring JC, Satpathy AT, Rotondo JA, Edelson BT, Kretzer NM, Wu X, Weiss LA, Glasmacher E, Li P, Liao W, Behnke M, Lam SS, Aurthur CT, Leonard WJ, Singh H, Stallings CL, Sibley LD, Schreiber RD, Murphy KM (2012) Compensatory dendritic cell development mediated by BATF-IRF interactions. Nature 490:502

Chapter 5 Endotoxin-Tolerance Mimicking to Study TLR in Promotion of Tolerogenic DCs and Tr1 Cells Giulia Scalisi, Doriana Ricciuti, and Giorgia Manni Abstract Dendritic cells (DCs) are key regulators of immunogenic and tolerogenic immune responses. Both these immune responses require DCs respectively to activate effector T cells or to induce their anergy and T regulatory activity. Modifications of DCs in the laboratory and several pharmacological agents can enhance and stabilize their tolerogenic properties. Recent evidences demonstrate that activation of specific toll-like receptors (TLRs) can be involved in induction of DCs with tolerogenic properties able to initiate T regulatory cell responses. In the present chapter, we show a detail protocol to obtain in vitro regulatory conventional DCs (cDCs) in response to repeated exposure to lipopolysaccharide (LPS), a ligand of TLR4, by mimicking the mechanism of endotoxin tolerance. Subsequently, the protective effect of cDCs’ conditionate with LPS will be describe in in vivo inflammatory model of endotoxemia. Finally, we illustrate the method to study the ability of LPS-conditionate cDCs to promote T regulatory cells in ex vivo system. Key words Immune tolerance, Bone marrow dendritic cells, Toll-like receptors, LPS, Treg polarization, Endotoxin tolerance

1

Introduction

1.1 Tolerogenic Dendritic Cells

Although the central role of dendritic cells (DCs) is to orchestrate the initiation of immune responses presenting specific antigen to T cells, DCs carry out an important function in maintaining immune tolerance. The balance between immune activation and tolerance is determined by the status of DCs that in turn depends on the applied stimuli and the maturation conditions. By default, immature DCs (iDCs) are considered tolerogenic while mature DCs are immunogenic [1, 2]. This classification is based on expression level of specific surface molecules. Specifically, iDCs exhibit low MHC-II (major histocompatibility complex class II), costimulatory molecules (CD80, CD86, and CD40), and CCR7 surface expression [3, 4]. Although tolerogenic DCs exhibit a low ratio of costimulatory to inhibitory signals, they are able to present antigens to T

Francesca Fallarino et al. (eds.), Toll-Like Receptors: Methods and Protocols, Methods in Molecular Biology, vol. 2700, https://doi.org/10.1007/978-1-0716-3366-3_5, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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cells, attenuating T-cell activation and proliferation [5]. Upon activation triggered by specific stimuli, mature DCs upregulate their surface molecules, among which Toll-like receptors (TLRs) and promote adaptive immunity [6–8]. Nevertheless, also mature DCs could have tolerogenic properties whose effector responses depend on the several stimuli that modified their phenotype [9–11]. It has been reported that tolerogenic activity of DCs directly depend on the ability of these cells to produce several mediators, among which regulatory cytokines, including TGF-β and IL-10, that are critical for the induction of Foxp3+ T regulatory cells (Treg), or IL-10 and IL-27 for the induction of Tr1 cells (type 1 regulatory T cells), as well as immunoregulatory molecules as the tryptophan catabolizing enzyme indoleamine 2,3 dioxygenase1 (IDO1) [2, 11, 12, 13]. Different agents proved to be potent inducers of tolerogenic DCs phenotype. In addition, the tolerogenic properties of DCs can be depending also by the different DC populations. Both human and murine DCs consist of two main cell populations: plasmacytoid DC (pDC) and conventional DC (cDC), that in turn are divided into cDC1 and cDC2 [14, 15, 16] (Box 1). Both pDCs and cDCs induce tolerance by promoting immunosuppressive Treg differentiation and function [17]. In mice, pDCs have been identified to be crucial for tolerance in several autoimmune disease models (rheumatoid arthritis, asthma, type 1 diabetes) where it was demonstrated that pDCs tolerogenic functions predominantly depend on IDO1 and resulting in Treg induction and expansion [1, 18, 19, 20]. cDC1 and cDC2 also contribute to peripheral tolerance in certain autoimmune diseases, in particular in experimental autoimmune encephalomyelitis (EAE) [5, 13, 21, 22, 23]. Although activated DCs acquire strong phenotypic changes and enhance effector T-cell function and inflammatory cytokine production, DCs can also exert regulatory function under inflammatory situations. For instance, it was demonstrated that cDCs stimulated twice with inflammatory stimuli lipopolysaccharide (LPS), are able to promote a regulatory response capable to induce IDO1, one of the most effective mediators of anti-inflammatory activity by DCs, and TGF-β which expression by DCs results in activation of T regulatory cells [24, 25]. Moreover, LPS conditioned cDCs in vitro protect mice from endotoxic shock [26], pointing on tolerogenic cDCs as a new option for controlling potentially harmful responses to TLR4 signaling. All of these approaches generating a state of immune tolerance help to prevent autoimmune diseases or other hyperreactivities disorders. Thus, understanding these tolerogenic mechanisms is of most importance for therapeutic approaches to treat immune pathologies, tumors, and infections.

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Based on data from literature, the present chapter aims to describe a detail protocol in order to study the role of TLR4 in generating tolerogenic cDCs (tol-DCs) and subsequently T regulatory responses. Specifically, we will report the method to obtained tol-DCs in vitro and the methodologies to validate the cDCs tolerogenic phenotype. In order to verify the protective role of LPS-conditioned cDCs, we will illustrate in vivo model of endotoxin tolerance and ex vivo methodologies to assess the effect of TLR activation on cDCs in controlling the development of Treg cells by using Foxp3YFP/cre-mutant mice. 1.2 An Overview of Different Dendritic Cells Subsets

Dendritic cells (DCs) were first described from Paul Langerhans in 1868 as cells with a “tree-like” morphology [27]. In 1973, Ralph Steinman introduced DCs as a new class of cells involved in the initiation and control of immune responses [28]. DCs are considered as the most potent antigen presenting cells (APC), governing T-cell-mediated immunity and are indispensable for maintaining central and peripheral tolerance. All dendritic cells originate from hematopoietic stem cells that differentiate into various subsets under the control of many factors. Four major types of DCs can be distinguished based on differences in surface markers phenotype, key gene signature, critical transcription factors and TLRs expression [29]. Currently, we can broadly classify DCs in conventional or classical dendritic cells (cDCs), plasmacytoid DC (pDCs), Langerhans cells (LCs) and monocyte-derived DCs (moDCs) (Fig. 1, created with BioRender.com) [30]. cDCs are professional antigen presenting cells that sense and capture pathogens and cellular debris and trigger a maturation process leading to the

pDCs

cDCs cDC1

Innate defenses against viruses

Crosspresentation of exogenous antigens to CD8+T cells; Tolerance to self/ Cellular immunity to intracelllular pathogens and tumors

moDCs

Langerhans DCs

cDC2

Presentation of exogenous antigens to CD4+T cells;

Innate defenses against infections

skin defenses against infections

Cellular and humoral immunity to extratracelllular pathogens

Fig. 1 Dendritic cell subsets. Dendritic cells are divided into four main classes: conventional or classical dendritic cells (cDCs), plasmacytoid DC (pDCs), Langerhans cells (LCs), and monocyte-derived DCs (MoDCs)

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migration of cDCs to the T-cell zones of secondary lymphoid organs, where they present antigen-derived peptides to T lymphocytes and induce T-cell-mediated immune responses. Depending on the context, cDCs control the balance between immunity and tolerance. cDCs are classified in two different subsets: type 1 conventional DCs (cDC1) and type 2 cDCs (cDC2), each performing specific immune function. cDC1 differentiation is modulated by a combination of transcription factors, especially interferon regulatory factor 8 (IRF8) and basic leucine zipper ATF-like transcription factor 3 (BATF3) [14]. They are identified on the expression of MHC II, CD11c and CD8a or CD103, Clec9a, and XCR1. cDC1 cross-present exogenous antigens (e.g., tumor antigens) to CD8+ T cells and are key cells for the generation of cytotoxic effector T-cell responses, what is important in antitumor and antivirus immunity [31]. cDC2 are mainly dependent on transcription factors interferon regulatory factor 4 (IRF4) and are characterized by high level expression of MHC II, CD11c, CD1c, and SIRPA [15]. cDC2 express wide range of plasma surface and endosomal TLRs (such as TLR2, TLR4 and TLR8, TLR9) through which they recognize ssRNA base analogous and DNA CpG. Despite cDC1, which are well characterized, the functions of cDC2 need to be better investigated. Recently, some studies revealed that cDC2 have both anti- and protumoral immune responses [32]. pDCs have a spherical shape, characteristic of antibodysecreting plasma cells. They have not phagocytic function and maintain a high rate of MHC class II turnover, making them inefficient at presenting exogenous antigen. pDCs are mainly defined by the expression of surface marker B220, Siglec-H, and BST2. pDCs recognize nonself-nucleic acids derived from bacteria or viruses and respond to viral infection with production of high quantities of type I and type III interferons e secrete cytokines [33]. pDCs present antigens and induce immunogenic T-cell responses through differentiation of cytotoxic CD8+ T cells and effector CD4+ T cells, leading to Ag-specific Th1 response, via BST-2, and Th17 responses, promoted by TGF-β. pDCs also exhibit strong tolerogenic functions by inducing CD8+ T-cell deletion, CD4+ T-cell anergy, and Treg differentiation [1]. LCs are distinct from other DCs ontogenetically, they populate the epidermal layer of the skin and express high levels of the C-type lectin langerin, CD1a, and EpCAM and low levels of CD11c, CD11b, and CD103. When LCs migrate into the afferent lymphatic vessels, in inflammatory conditions, they mature into potent crosspresenting DC and produce high levels of IL-15, acquiring the ability to present mycobacterial glycolipid antigens and stimulate CD8 T cells, inducing Th1, Th2, and Th17 cytotoxic responses [34]. Lastly, monocyte-derived DCs or moDCs are also a distinct DC subset that originate from monocytes during infection and inflammation, though the specific mechanism that drives the

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differentiation needs additional investigation [35]. moDCs are also called “inflammatory moDCs,” and they appear to perform an essential role in defense mechanisms against pathogens by participating in the induction of both adaptive and innate immune responses [36].

2

Materials

2.1 Murine Bone Marrow-Derived Dendritic Cells (BMDCs) Differentiation

1. 6–8th weeks old C57BL/6 mice. 2. Sterile instrument: scissors, tweezers, mortar, and pestle. 3. 70% ethanol. 4. 70 μm cell strain. 5. 1X PBS, Phosphate Buffer Saline: 137 mM NaCl, 10 mM Na2HPO4, 2.7 mM KCl, and 1.8 mM KH2PO4. 6. BSA, Bovine serum albumin. 7. 0.5 M EDTA, Ethylenediaminetetraacetic acid. 8. Macs Buffer: 1X PBS supplemented with 0.5% (w/v) BSA and 2 mM EDTA (see Note 1). 9. ACK lysis buffer: 0.15 M Ammonium Chloride, 10 mM Potassium Carbonate. Store at 4 °C. 10. Histopaque-1119: sterile, endotoxin tested, ready-to-use medium, pH 8.8–9.0 (Sigma-Aldrich). 11. Complete IMDM medium: Iscove’s Modified Dulbecco’s Media (IMDM) supplemented with 10% FBS (v/v), 100 U/mL penicillin, 100 g/mL streptomycin, 5 mM glutamine, 0.1 mM Nonessential amino acids, 1 mM Sodium Pyruvate, 50 μM 2-Mercaptoethanol supplemented with 10% FBS, 100 U/mL penicillin, 100 g/mL streptomycin, 5 mM glutamine, 0.1 mM Nonessential amino acids, 1 mM Sodium Pyruvate, and 50 μM 2-Mercaptoethanol. Store at 4 °C. 12. Fms-like tyrosine kinase receptor 3 ligand (Flt3-L). Store at -20 °C. 13. 50 mL conical tube. 14. 6-well plate. 15. Trypan blue 0.4% (Thermo Fisher Scientific). 16. Counting cells Burker chamber.

2.2

cDCs Purification

1. 50 mL conical tubes. 2. Macs Buffer. 3. Magnetic support for cells separation. 4. Anti-FcR II/III antibody (FcR block): 1.25 μg/mL in Macs Buffer. Store at 4 °C (see Note 2).

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5. 5 μg/mL Biotin anti-mouse CD317 (clone 927, BioLegend). Store at 4 °C. 6. 5 μg/mL Biotin anti mouse/human CD45/B220 (clone RA3-6B2, BioLegend). Store at 4 °C. 7. MagniSort Streptavidin-Negative Selection Beads. Store at 4 °C. 8. 0.4% Trypan blue (Thermo Fisher Scientific). 9. Counting cells Burker chamber. 2.3 cDCs Phenotyping

1. Polystyrene round-bottom tubes for Cytofluorimetric analysis. 2. Macs Buffer. 3. FcR block. 4. Antibodies: anti-mouse/human CD45R/B220 (clone RA3-6B2, BioLegend), anti-mouse CD317 (clone eBio927, Invitrogen), anti-mouse I-A/I-E (clone M5/114.15.2, BioLegend), anti-mouse CD11c (clone N418, Invitrogen), antimouse CD24 (clone M1/69, BioLegend), anti-mouse CD172a (clone P84, Invitrogen), anti-mouse/rat XCRI (clone ZET, BioLegend). Store at 4 °C. 5. Paraformaldehyde (PFA, Electron Microscopy Science) solution 1% (v/v) in Macs Buffer. Store at 4 °C.

2.4 In Vitro Induction of Tolerogenic cDCs via TLR4

Perform these procedures in sterile condition. Keep all reagent cold (2 to 8 °C) until use. 1. 48- and 12-well plate. 2. 1.5 mL tubes. 3. 50 mL conical tubes. 4. 0.2 μm syringe filter. 5. Complete IMDM medium. 6. Lipopolysaccharide from Escherichia coli, LPS (055: B5) (Sigma Aldrich) (see Note 3).

2.4.1 Extracellular and Intracellular Staining

1. Polystyrene round-bottom tubes for Cytofluorimetric analysis. 2. Macs Buffer. 3. FcR block. 4. Extracellular antibodies: anti-mouse/human CD45R/B220 (clone RA3-6B2, BioLegend), anti-mouse CD317 (clone eBio927, Invitrogen), anti-mouse I-A/I-E (clone M5/114.15.2, BioLegend), anti-mouse CD11c (clone N418, Invitrogen), anti-mouse CCR7 (clone 4B12, BD bioscience), anti-mouse CD40 (clone 1C10, Invitrogen), antimouse CD80 (clone 16-10A1, BioLegend), anti-mouse

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CD86 (clone GL-1, BD bioscience), anti-mouse PDL1(clone MIH 5, Invitrogen), and anti-mouse LAP (cloneTW7-16B4, BD bioscience). Store at 4 °C. 5. 1X PBS. 6. 4% (v/v) PFA solution in 1X PBS. Store at 4 °C. 7. Permeabilization buffer: 1X PBS with 0.4 mM EDTA, 0.1% (w/v) BSA, and 0.1% (w/v) saponin. Store at 4 °C. 8. Intracellular antibodies: anti-mouse IDO1 (clone 8G11, Merk), IgG2a,k Isotype Ctrl (clone, RTK2758), anti-mouse IgG (clone Poly4053, BioLegend). 9. 1% PFA solution in Macs buffer. 2.4.2

Cytokines Analysis

1. IL-6, IL-10 (Thermo Fisher Scientific), and TGF-β (R&D) ELISA kit. 2. Wash buffer: 1X PBS with 0.05% (v/v) Tween. 3. Tecan microplate reader Spark.

2.5 Administration of Tolerogenic cDCs in Endotoxin Mouse Model

1. 8–10th weeks old male C57BL/6 mice.

2.5.1 Ex Vivo Analysis of Regulatory T Cells

1. Sterile instrument: scissors, tweezers.

2.5.1.1 Tissue Collection and Processing

3. 1.5 mL tubes.

2. 1 mL syringes. 3. Lipopolysaccharide from Escherichia coli (055: B5) (SigmaAldrich).

2. 70% Alcohol. 4. Complete IMDM medium. 5. Collagenase B from Clostridium histolyticum (Roche) 250 mg/ mL. 6. Deoxyribonuclease I (DNase I) from bovine pancreas (Sigma) 30 U/mL. 7. GentleMACS™ Octo Dissociator (see Note 4). 8. GentleMACS™ C Tubes. 9. Macs buffer. 10. 50 mL conical tubes. 11. 40 μm cell strain. 12. 0.4% Trypan blue (Thermo Fisher Scientific).

2.5.2

Blood Collection

1. Tiletamine/zolazepam (50 mg/kg per mouse). 2. 3.2% (w/v) Sodium Citrate anticoagulant solution. 3. 1 mL syringes. 4. 1.5 mL tubes. 5. Benchtop microfuge.

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2.5.2.1 Serum Cytokines Analysis

1. ProcartaPlex Mo cytokine/Chemokines Panel 1A 36plex (Thermo Fisher Scientific). 2. Hand-Held Magnetic Plate washer. 3. Luminex MagPix w/Xponent 4.2.

2.5.2.2 Analysis of Regulatory T Cells by Flow Cytometry

1. 8–10th weeks old male B6.129(Cg)-Foxp3tm4(YFP/icre)Ayr/J (Jackson Laboratory). 2. Macs buffer. 3. 96-well V bottom. 4. 50 ng/mL PMA (phorbol 12-myristate 13-acetate) (SigmaAldrich). 5. 800 ng/mL Ionomycin (Sigma-Aldrich). 6. 3 μg/mL Brefeldin (Thermo Fisher Scientific). 7. FcR block. 8. 2% (v/v) PFA solution in 1X PBS. Store a 4 °C. 9. Saponin 0.5% (w/v) in Macs buffer (see Note 5). 10. 0.05% Saponin: take 5 mL of 0.5% saponin and add 45 mL of MACS Buffer. 11. Antibodies: anti-mouse CD3 (clone 145-2C11, BioLegend), anti-mouse CD4 (clone GK1.5, BioLegend), anti-mouse CD8 (clone 53–6.7, BD bioscience), anti-moues CD25 (clone PC61, BioLegend), anti-mouse CD44 (clone IM7, BD bioscience), anti-mouse LAP (cloneTW7-16B4, BD bioscience), and anti-mouse IL-10 (JES5-16E3 BD bioscience). Store at 4 °C. 12. 1% PFA solution in Macs buffer.

3

Methods

3.1 Murine Bone Marrow-Derived DCs (BMDCs) Differentiation

The following protocol provides a simple method to harvest the major leg bones from a mouse using a bone crushing technique, in order to isolate cell progenitors from bone marrow (Fig. 2; created with BioRender.com). The entire procedure should be performed in a sterile condition using a laminar flow hood and 70% of EtOH to sterilize surgical equipment. 1. Sacrifice C57BL/6 mice and remove the tibias, femurs and hips under sterile conditions. 2. Remove the muscular and fibrous tissues from the bones using small dissecting scissors (see Note 6). 3. Harvest bone marrow (BM) from femur, tibia and pelvis using a mortar and pestle in approximately 2 mL of Macs buffer. 4. Crush the bone fragments vigorously with pestle in Macs buffer.

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Mouse tissue collection

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4 Resuspend cell pellet in complete media

Resuspend cell pellets in MACS buffer A

Processing bones by mortar and pestle

IMDM

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Dissect and cut

Count cells

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Dilute cells at 2x10 6/ ml and add FlT3-L

hip

5%of FlT3-L

+ D

Remove muscle and clean carefully the bones

Plate 8ml of cell suspension on 6 well plate

Harvest cell ring

9 days of culture

Fig. 2 Bone marrow dendritic cells isolation and differentiation. pDCs and cDCs were isolated from bone marrow of wild-type mice by using mortar and pestle to crush bones and by density gradient centrifugation. Subsequently, stem cells derived cells were differentiated in vitro adding specific FlT3-ligand cytokine

5. Break the clumps in the supernatant by resuspending. 6. Collect supernatant from mortar and filter into a 50 mL conical tube using a 70 μm cell strainer (see Note 7). 7. Repeat step 5 until bones result white. 8. Rinse the remaining bone fragments with an equal volume of Macs buffer and filter into the same 50 mL conical tube. 9. Centrifuge at 1500 rpm for 5 min. 10. Discard the supernatant. 11. Lyse red blood cells with ACK lysis buffer incubating at 4 °C for 3–4 min. After this time, add Macs buffer to stop the reaction and centrifuge the cells at 1500 rpm for 5 min. 12. Discard the supernatant. 13. Resuspend cells in 4 mL of Macs buffer. 14. Add cells to a new conical tube containing an equal volume of Histopaque-1119 and centrifuge at 1800 rpm for 20 min (acceleration 3, brake 1) (see Note 8). 15. After cell density gradient separation, recover the cells ring in a new conical tube. Add Macs buffer until 50 mL and centrifuge the cell suspension at 1500 rpm for 5 min.

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16. Discard the supernatant. 17. Resuspend cells in 10 mL of complete IMDM medium and count them. 18. Resuspend cells 2 × 106/mL in complete IMDM medium containing 5% murine Flt3-L (see Note 9). 19. Seed 8 mL/well in 6-well plate tissue culture, plates at 37 °C and 5% CO2 for approximately 9 days. 3.2

cDCs Purification

The majority of suspension or loosely adherent cells will be dendritic cells, but other cell types may be present. Hence, in order to obtain a highly pure fraction of dendritic cells, and in particular cDCs, it is recommended to take measures to deplete other cell subsets and enrich the population for CD11c+MHCII+ cDCs. To do this, it is possible to use an immunomagnetic cell separation system that involves the use of biotinylated antibodies, which binds to cells expressing the corresponding epitope, and magnetic microbeads conjugated with streptavidin for the indirect magnetic labeling. When the cell suspension is placed in a magnetic field, the magnetically labeled cells are retained, while the unlabeled cells can be removed and recovered. 1. After the indicated time, recover nonadherent cells in the culture supernatant by gently pipetting. 2. Centrifuge at 1500 rpm for 5 min. 3. Discard the supernatant and resuspend cells in 10 mL of Macs buffer. 4. Count them and approximately set aside 0.25 × 106 cells for purity analysis by Flow Cytometry before cDCs enrichment. 5. Centrifuge as in step 3 and then resuspend them 100 × 106/ mL. 6. Before staining, incubate the cells with FcR block for 5 min at room temperature (RT). 7. Add to cell suspension 5 μg/mL anti-CD317 and anti-B220 biotinylated antibodies, and incubate for 30 min at 4 °C, flicking each 5 min (see Note 10). 8. After this time, add approximately 5 mL of Macs buffer to make a cell washing. Centrifuge at 1500 rpm for 5 min, discard supernatant and resuspend the cells 100 × 106/mL. 9. Incubate cells with MagniSort Streptavidin Negative Selection Beads 25 μL/100 × 106 cells, for 15 min at 4 °C flicking each 5 min (see Note 11). 10. Add approximately 5 mL of Macs buffer and centrifuge at 1500 rpm for 5 min. 11. Resuspend cells in 3 mL of Macs buffer.

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Biotin Detection antibody

Streptavidin CD317

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Magnet

CD137 and B220 negative cells

Fig. 3 Purification of cDCs. Conventional dendritic cells were purified from bone marrow cell culture by magnetic sorting

12. Prepare a magnetic support containing three tubes. Sequentially pass the cells through the three tubes in order to retain magnetically labeled samples due to a magnetic field. Perform two washes of the tubes with 3 mL of Macs buffer (Fig. 3; created with BioRender.com). 13. Recover the cells not retained by the magnet in a new tube and count them. 3.3

cDC Phenotyping

DC subsets is characterized by the expression of a specific cell surface markers. It is possible to use Fluorescence-activated cell sorting (FACS) technique to validate the purity of cDCs purification. 1. Collect 0.25 × 106 cells from each steps of cell purification (see Subheading 3.2) in a polystyrene round-bottom tubes. 2. Centrifuge the cells at 1500 rpm for 5 min. 3. Discard the supernatant. 4. Prepare a mix of antibody diluted in Macs buffer: 0.5 μg/mL B220, 1 μg/mL BST2, CD11c, MHC II, CD24, CD172, and XCRI (see Notes 12–13). 5. Distribute 25 μL of antibody cocktail for each sample (see Note 10). 6. Protect the samples from light and incubate for 30 min at 4 °C. 7. Add approximately 1 mL of Macs buffer to make a cell washing.

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8. Centrifuge the cells at 1500 rpm for 5 min. 9. Discard the supernatant. 10. Fix the samples with PFA solution 1% and analyze by flow cytometry. 3.4 Induction of Tolerogenic cDCs In Vitro via TLR4

Upon stimulation with LPS, TLR4 mediates signals promoting transcription of proinflammatory cytokines. The majority of innate immune cells develop tolerance to secondary TLR stimuli [37, 38, 39, 40]. The phenotype of endotoxin tolerance has been extensively studied in monocytes and macrophages. This protocol includes conventional dendritic cells. 1. Suspend cDCs, obtained as described in Subheading 3.2 in complete IMDM medium at concentration of 1 × 106 cells/mL. 2. Plate 1 × 106 cells per well in a 48-well plate and stimulate one well with LPS 0.5 μg/mL (low dose) and two wells with vehicle as controls, for 24 h. 3. After 24 h, harvest the samples in sterile 1.5 mL tube and centrifuge at 6000 rpm for 5 min. 4. Eliminate the supernatant and wash with 1 mL of complete IMDM medium. 5. Centrifuge at 6000 rpm for 5 min and discard supernatant. 6. Resuspend in 1 mL of complete IMDM medium and plate in a 48-well plate. 7. Restimulate for an additional 24 h as follow: vehicle (untreated control), LPS 1 μg/mL (unprimed control, sample that not received LPS at first stimulation), LPS 1 μg/mL (high dose) (primed cDCs) (Fig. 4; created with BioRender.com).

3.4.1 Validation of Tolerogenic cDCs Phenotype In Vitro

3.4.1.1 Extracellular Staining: Cytofluorimetric Analysis

Tolerogenic cDCs, with a regulatory phenotype, are characterized by low levels of activation markers (CD40, CD80, CD86 and CCR7) and high levels of regulatory molecules such as PDL1, LAP, and IDO1. Moreover, tol-DCs are able to produce the antiinflammatory cytokines TGF-β and IL-10 and not proinflammatory cytokine IL-6. To validate the tolerogenic signature of double LPS exposure on cDCs, different analysis can be performed. Surface markers CD40, CD80, CD86 and CCR7, PDL1, and LAP can be detected by cytofluorimetric analysis by using extracellular staining. IDO1 expression are detected by cytofluorimetric analysis with an intracellular staining. TGF-β, IL-10, and IL-6 production is assessed by the ELISA test. 1. Harvest the samples stimulated as in step 3.4 in 1.5 mL tube and centrifuge at 6000 rpm for 5 min. 2. Wash cell pellets with 1 mL of cold 1X PBS.

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LPS-unprimed cDCs

3 LP S0

,5u g/

m

l, 2

LPS-primed cDCs 4h

LPS 0,5ug/ ml, 24h

Fig. 4 Schematic representation of cDCs treatment. Purified cDCs were stimulated in vitro as follow: (1) Untreated cDCs were stimulated only with PBS as vehicle; (2) LPS-unprimed cDCs were stimulated 24 h with PBS and subsequently with high dose of LPS (1 μg/mL) for other 24 h; (3) LPS-primed cDCs were stimulated twice with low doses of LPS (0.5 μg/mL)

3. Remove PBS, resuspend in 150 μL of Macs Buffer and transfer in 96-well V bottom plate. 4. Centrifuge at 1500 rpm for 5 min. 5. Dump by quickly inverting the 96-well V bottom plate. Blot on paper towel to remove any residual solution (Dump/blot). 6. Resuspend samples in 50 μL of Macs buffer containing the following antibodies:, 0.5 μg/mL B220, 1.25 μg/mL MHC II, 2.5 μg/mL CD86, 1 μg/mL BST2, CD11c, CCR7, CD40, CD80, PD-L1, and LAP (see Note 13). 7. Incubate for 30 min at 4 °C, protecting from the light. After this time, add approximately 150 μL of Macs buffer to make a cell washing. 8. Centrifuge the cells at 1500 rpm for 5 min. Dump/blot. 9. Wash with 150 μL of cold 1X PBS by centrifuge at 1500 rpm for 5 min. 10. Fix extracellular staining with 200 μL of PFA solution 4% and incubate 20 min at room temperature (RT), protecting form the light. 11. Centrifuge at 1500 rpm for 5 min. Dump/blot.

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12. Wash with 150 μL of Macs buffer by centrifuges at 1500 rpm for 5 min. 13. Dump/Blot. 14. Resuspend samples in 200 μL of Permeabilization buffer and incubate 5 min at RT, protecting from the light. 15. Centrifuge at 1500 rpm for 5 min. Dump/blot. 3.4.1.2 IDO1 Intracellular Staining

1. Incubate cells from step 15 of 3.4.1.1 with 0.5 μg of IDO1 antibody/well in 100 μL of Permeabilization buffer, 1 h at 4 °C on the dark (see Notes 13–14). 2. Centrifuge at 1500 rpm for 5 min. Dump/blot. 3. Wash twice with 150 μL of Permeabilization buffer and centrifuge at 1500 rpm for 5 min. Dump/blot. 4. Add 100 μL of Permeabilization buffer containing the appropriate secondary antibody (see Note 15), 30 min at 4 °C on the dark. 5. Centrifuge at 1500 rpm for 5 min. Dump/blot. 6. Wash twice with 150 μL of Permeabilization buffer by centrifuge at 1500 rpm for 5 min. Dump/blot. 7. Fix in 200 μL of PFA solution 1% and store at 4 °C. 8. Acquire the samples by a flow cytometer and analyze the data by specific software.

3.4.2

Cytokines Analysis

Perform TGF-β, IL-10, and IL-6 ELISA test according to the manufacturer’s datasheet. 1. Collect supernatants of cDCs stimulated as described in step 3.4. Store at -80 °C until the use. 2. Coat ELISA plate with 100 μL/well of capture antibody, seal the plate and incubate overnight at 4 °C (IL-10 and IL-6) or RT (TGF-β), according to manufacturer’s datasheet. 3. Aspirate wells and wash 3 times with >250 μL/well Wash Buffer. 4. Block wells with 200 μL of ELISA/ELISPOT Diluent. Incubate at room temperature for 1 h. 5. Aspirate and wash at least once with Wash Buffer. 6. Add 100 μL/well of samples (see Notes 16–17) and standard curve to the appropriate wells. Seal the plate and incubate at room temperature for 2 h. 7. Aspirate and wash. Repeat for a total of three to five washes. 8. Add 100 μL/well diluted Detection Antibody to all wells. Seal the plate and incubate at room temperature for 1 h. 9. Aspirate and wash. Repeat for a total of three to five washes.

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10. Add 100 μL/well of diluted Avidin-HRP. Seal the plate and incubate at room temperature for 30 min. 11. Aspirate and wash. Repeat for a total of five to seven washes. 12. Add 100 μL/well of TMB Solution. Incubate at room temperature for 15 min. 13. Add 100 μL/well of Stop Solution. 14. Read plate at 450 nm and 570 nm, subtracting the latter value as nonspecific plate absorbance. 3.5 Administration of Tolerogenic cDCs in Endotoxin Mouse Model

Endotoxin tolerance is a complex pathophysiological process regarded as the regulatory mechanism of the host against excessive inflammation. This process plays an important role in reducing the mortality of sepsis, endotoxin shock, and other endotoxin-related diseases [41]. Involvement of TLR4 signaling pathway in a mouse model of endotoxin tolerance is well described [41, 42], but the exact cellular mechanism remains elusive up to date. Following, it is described the method to study the role of tolerogenic cDCs in in vivo model of “LPS infectious tolerance” (Fig. 5; created with BioRender.com). The protective role of LPS-conditioned cDCs is evaluate monitoring mice survival after exposure to lethal LPS challenge and serum cytokines are evaluate by ProcartaPlex Multiplex panels over time. We used eight-ten old male C57BL/6 black mice. All in vivo procedures are performed in accordance with the respective institutional guideline and with protocols approved by animal care and use committee. 1. Harvest cDCs, stimulated as described in Subheading 3.4 (see Note 18), in a 50 mL conical tube, by gently pipetting. 2. Centrifuge at 1500 rpm for 5 min. 3. Discard supernatant and resuspend in 5 mL of complete IMDM medium and count them (see Note 19). 4. Resuspend in the appropriate volume of sterile PBS in order to have a concentration of 1 × 106 in 200 μL. 5. Inject 200 μL of cells per mouse via retro-orbital injection (see Note 20). 6. After 48 h from cDCs injection, treat recipient mice with 30 mg/Kg LPS or vehicle intraperitoneally. Use at least 16 mice per group (see Note 21). 7. Monitor mice every day, for 72 h, for the presence of signs of moribundity, including lack of responsiveness to manual stimulation, immobility, and inability to eat or drink.

3.5.1 Serum Cytokines Analysis Over Time

Mouse cytokines and chemokines are measured in the serum from blood of mice adoptively transferred with tol-DCs and challenged with LPS by ProcartaPlex Mo cytokine/Chemokines Panel 1A

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Fig. 5 Schematic representation of mouse model of LPS infectious tolerance. Wild-type mice were injected intravenously with: (1) untreated cDCs; (2) LPS-unprimed cDCs; and (3) LPS-primed cDCs. Two days after cell injection, mice were intraperitoneal inoculated with lethal doses of LPS (30 mg/kg) and monitored daily for 3–7 days. Blood and spleen collection were used for cytokines analysis and Treg evaluation

36plex. This panel perform the analysis of 36 target proteins including 9 chemokines and 27 cytokines, in a single well using Luminex xMAP technology. Blood was collecting every day for 3 days from 2 mice at each time point. We use 16 mice from each group. 3.5.1.1

Blood Collection

1. Deeply anesthetize the animal with the selected anesthetic agent prior to sample collection procedures. 2. Place the animal in dorsal recumbency once the animal has reached an appropriate plane of anesthesia. 3. Attach appropriately sized needle to a syringe and insert bevel up at a 30–40° angle through the diaphragm, with syringe parallel to the midline of the mouse. 4. Insert needle slightly left of and under the sternum, directed toward the animal’s head. The needle can be angled slightly toward the left shoulder. 5. Retract the plunger slightly to create a vacuum inside the syringe and gently advance needle until blood flash appears in needle hub.

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6. Immobilize the needle and continue to aspirate until a sufficient amount of blood has been collected (see Note 22). 7. Euthanize the animal immediately upon completion of blood collection. 8. Collect blood in a 1.5 mL tube contain Sodium Citrate 3.2% anticoagulant solution. 9. Centrifuge blood samples at 2000 × g for 10 min at RT. 10. Transfer the resulting serum into a clean tube using pipette. 11. Store at -80 °C until use (see Note 23). 3.5.1.2 Multiplex Analysis

1. Thaw frozen serum plasma on ice. 2. Mix well by vortexing and then centrifuge at 10,000 × g for 10 min at 4 °C to pellet out particulates. 3. Prepare antigen standard instructions.

according to

manufacturer’s

4. Prepare Capture Antibody Magnetic Beads by vortexing 30 s and add 50 μL of the capture beads to each well. 5. Seat the plate for 2 min in the Hand-Held Magnetic Plate washer. 6. Remove liquid by quickly inverting the plate in the Hand-Held Magnetic Plate washer. 7. Wash the plate with 150 μL of washing buffer, wait 30 s and remove liquid by quickly inverting the plate in the Hand-Held Magnetic Plate washer. 8. Add samples and standards: for serum add 25 μL of Universal Assay Buffer and 25 μL of standards or samples. 9. Seal the plate and incubate with shaking at 500 rpm for 2 h at RT. 10. Wash plate three times as in step 7. 11. Add detection antibody mix. 12. Seal the plate and incubate with shaking at 500 rpm for 30 min at RT. 13. Wash plate three times as in step 7. 14. Add Streptavidin-PE and incubate with shaking at 500 rpm for 30 min at RT. 15. Wash plate three times as in step 7. 16. Add Reading Buffer and incubate for 5 min at RT, shaking at 500 rpm. 17. Acquire data Luminex MagPix w/Xponent 4.2 system.

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3.6 Ex Vivo Analysis of Regulatory T Cells

Mouse model of endotoxemia was repeated by using Foxp3YFP/cremutant mice. These mice express a knocked-in yellow fluorescent protein/iCre recombinase (fusion protein from the Foxp3 locus without disrupting expression of the endogenous Foxp3 gene). These mice are useful for studying regulatory T cells without performing intracellular staining for FoxP3. All in vivo procedures are performed in accordance with the respective institutional guideline and with protocols approved by animal care and use committee. 1. Administer LPS conditioned DCs as described in step 3.5. 2. After 8–10 days [43] from endotoxemia induction sacrifice mice by cervical dislocation. 3. Wet fur on left side of sacrificed mouse using 70% ethanol. 4. Cut away the fur along the left side of the mouse, about halfway between the front and back legs. 5. Cut open the body cavity. 6. Remove the spleen using the forceps.

3.6.1 Isolation of Murine Splenocytes

1. Place the spleen into GentleMACS™ C Tubes, cut with sterile scissors and add 5 mL of complete IMDM medium supplemented with Collagenase B and DNase I. 2. Put the tubes on GentleMACS™ Octo Dissociator and homogenize spleen by starting opportuning program. 3. Transfer homogenized spleen into 40 μm cell strainer to filter. 4. Wash GentleMACS™ C Tubes with 5 mL of MACS Buffer e transfer again into cell strainer to filter. 5. Centrifuge at 1500 rpm for 5 min at RT. 6. Discard supernatant and resuspend pellet in 1 mL of ACK lysis buffer. Incubate at 4 °C for 3–4 min. Add 4 mL Macs Buffer and spin as before (step 5). 7. Discard supernatant and resuspend pellet in 10 mL of Macs Buffer. 8. Count cells.

3.6.2 Analysis of Regulatory T Cells: Treg and Tr1

1. Take 1 × 106 splenocytes from each sample and centrifuge at 1500 rpm for 5 min. 2. Resuspend in 150 μL complete IMDM supplemented with PMA, Ionomycin, and Brefeldin. 3. Transfer in 96-well V bottom plate and incubate 4 h at 37 °C. 4. Centrifuge 1500 rpm for 5 min. Dump/blot. 5. Resuspend in 25 μL of Macs Buffer containing FcR block reagent and the appropriate fluorescent antibodies to analyze

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typical surface markers of T cells: CD3, CD4, CD8, CD44, and LAP (use all antibodies at 1 μg/mL). Incubate 30 min at 4 °C protecting form the light. 6. Centrifuge at 1500 rpm for 5 min. Dump/Blot. 7. Then, wash as in step 4. Dump/blot. 8. Fix extracellular staining by adding PFA solution 2% and incubate for 15 min at RT on the dark. 9. Centrifuge at 1500 rpm for 5 min. Dump/Blot. 10. Add 100 μL of saponin 0.05% and incubate 5 min at room temperature, protecting from the light (see Note 24). 11. Wash by adding 100 μL of saponin 0.05% and centrifuge as in step 4. Dump/Blot. 12. Add 50 μL of saponin 0.5% containing IL-10 antibody and incubate 30 min at 4 °C on the dark. 13. Wash twice with 100 μL of saponin 0.05% by centrifuge at 1500 rpm for 5 min. 14. Fix with 200 μL of PFA solution 1% and store at 4 °C. 15. Acquire the samples by a flow cytometer and analyze the data by specific software.

4

Notes 1. Add 1 L of 1X PBS to a graduated cylinder. Weigh 5 g of BSA and transfer to the cylinder. Add 4 mL of EDTA (stock 0.5 M) to cylinder. Degas with the pump for 2 h, under magnetic agitation. Then, filter with a 0.22 μm filter. Store at 4 °C. 2. FcR block is an antibody for CD16/CD32 used to block unwanted binding of antibodies to Fc receptor express into murine cells. This reagent increases the specificity of labeling with antibodies or microBeads and thereby improves the specificity of immunofluorescent staining and purity of magnetically isolated target cells. We used a homemade reagent (stock 0.250 μg/μL), used 1.25 μg/mL. Alternatively, you can use commercially available FcR block reagents, like Clear block (MBL) or FcR block reagent (Miltenyi Biotech) following the manufacture datasheet. 3. Wear FFP3 mask when resuspend LPS and perform under wood. Avoid inhalation, ingestion and contact with skin and eyes, since LPS cab activate innate immune response. 4. The gentleMACS™ Dissociator is a benchtop instrument for the semiautomated dissociation of tissues into single-cell suspensions or thorough homogenates. The standardized tissue dissociation or homogenization procedures make for reliable

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and reproducible results. There are several gentleMACS Programs for a variety of specific applications (mouse or human tumor, mouse spleen, mouse or rat neonatal heart, neural tissue, lung, muscle, epidermis, or liver mouse or human skin). 5. Wearing a FFP3 mask when weight saponin and avoid inhalation, ingestion and contact with skin and eyes. 6. It’s very important to remove any remaining muscle and clean carefully in order to completely expose the bones and not to cut bones as this will compromise the sterility of the BM. 7. Remember to previously prepare the filter by rinsing with 2 mL of Macs buffer. 8. Histopaque-1119 is used to enrich viable mononuclear cells and granulocytes and to remove cellular debris by a gradient centrifugation. Brake should be off in order to prevent the cell ring disruption. 9. Flt3-L is known as the primary factor which drive proliferation, differentiation from progenitor and survival of dendritic cells. 10. Gently mix the antibodies before adding them to the suspension of cells. 11. Use MagniSort™ Streptavidin Negative Selection Beads following the manufacture instruction and perform two-fold series dilution as recommended, in order to find the optimal condition. 12. Each of these subsets can be identified by flow cytometry based on the expression of a specific group of cell surface markers. In particular pDCs were sorted for B220+BST2+ and cDCs were sorted for B220-CD11c+MHCII+CD24+CD172α+. 13. Use the fluorescent antibodies following the manufacture instruction and perform a titration to assess the optimal dilution starting from the manufacture’s datasheet suggestion. 14. As negative control, a separate set of samples should be stained with an isotype control. 15. Skip this step if the IDO1 primary antibody is conjugate with a fluorochrome. 16. Dilute the samples in Diluent buffer. Different dilution factors are required for different cytokines. IL-10 are detected in undiluted samples; otherwise, for IL-6, samples should be diluted 1:2. 17. Before added to plate, samples used to detect TGF-β are activated to obtain the immunoreactive form of TGF-β. Activation consists of addition of acid solution for 10 min and subsequently neutralization solution in order to stabilize pH value at 7.2–7.6 following manufacturer’s conditions.

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18. For in vivo administration we usually resuspend cDCs at 1 × 106/mL and plate 3 mL in 12-well plate. Do not exceed 4 mL volume in 12-well plate since an optimal cell confluence is important to maintain the cell viability. In case you want to stimulate more than 4 × 106/well, use 6-well plate. 19. The number of cDCs administered per mouse is 1 × 106 cells. To ensure to have enough cDCs for all mice, stimulate twice the cell number needed, since cDCs could be loss during the incubations. 20. Resuspend the cells carefully, not leaving clumps. Filter the cells with 70 μm cells strainer, since the presence of clumps during i.v. injection could cause mice dead. 21. In general, we used 10 mice to monitoring survival and 6 mice to evaluate serum cytokines. 22. Blood collection via cardiac puncture is a terminal procedure. This procedure must be performed under deep general anesthesia. 23. The samples should be maintained at 2–8 °C while handling. If the serum is not analyzed immediately, the serum should be apportioned into 0.5 mL aliquots, stored, and transported at 20 °C or lower. It is important to avoid freeze-thaw cycles because this is detrimental to many serum components. Samples which are hemolyzed, icteric, or lipemic can invalidate certain tests. 24. Prepare a surplus sample without intracellular staining since the fixing and the permeabilization could change the light scatter prolife of the cells and affect the brightness of the extracellular antibodies. References 1. Gue´ry L, Hugues S (2013) Tolerogenic and activatory plasmacytoid dendritic cells in autoimmunity. Front Immunol 4:59. https://doi. org/10.3389/fimmu.2013.00059 2. Takenaka MC, Quintana FJ (2017) Tolerogenic dendritic cells. Semin Immunopathol 39(2):113–120. https://doi.org/10.1007/ s00281-016-0587 3. Kim MK, Kim J (2019) Properties of immature and mature dendritic cells: phenotype, morphology, phagocytosis, and migration. RSC Advances 9(20):11230–11238. https://doi. org/10.1039/c9ra00818g 4. Xing F, Wang J, Hu M, Yu Y, Chen G, Liu J (2011) Comparison of immature and mature bone marrow-derived dendritic cells by atomic force microscopy. Nanoscale Res Lett 6(1):

455. https://doi.org/10.1186/1556-276X6-455 5. Audiger C, Rahman MJ, Yun TJ, Tarbell KV, Lesage S (2017) The Importance of Dendritic Cells in Maintaining Immune Tolerance. J Immunol 198(6):2223–2231. https://doi. org/10.4049/jimmunol.1601629 6. Chamorro S, Garcı´a-Vallejo JJ, Unger WW, Fernandes RJ, Bruijns SC, Laban S, Roep BO, BA t H, van Kooyk Y (2009) TLR triggering on tolerogenic dendritic cells results in TLR2 up-regulation and a reduced proinflammatory immune program. J Immunol 183(5): 2984–2994. https://doi.org/10.4049/ jimmunol.0801155 7. Jensen SS, Gad M (2010) Differential induction of inflammatory cytokines by dendritic

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cells treated with novel TLR-agonist and cytokine based cocktails: targeting dendritic cells in autoimmunity. J Inflamm 7:37. https://doi. org/10.1186/1476-9255-7-37 8. Popov A, Driesen J, Abdullah Z, Wickenhauser C, Beyer M, Debey-Pascher S, Saric T, Kummer S, Takikawa O, Domann E, Chakraborty T, Kro¨nke M, Utermo¨hlen O, Schultze JL (2008) Infection of myeloid dendritic cells with Listeria monocytogenes leads to the suppression of T cell function by multiple inhibitory mechanisms. J Immunol 181(7): 4976–4988. https://doi.org/10.4049/ jimmunol.181.7.4976 9. Vogel A, Martin K, Soukup K, Halfmann A, Kerndl M, Brunner JS, Hofmann M, Oberbichler L, Korosec A, Kuttke M, Datler H, Kieler M, Musiejovsky L, Dohnal A, Sharif O, Schabbauer G (2022) JAK1 signaling in dendritic cells promotes peripheral tolerance in autoimmunity through PD-L1-mediated regulatory T cell induction. Cell Rep 38(8):110420. https://doi.org/10. 1016/j.celrep.2022.110420 10. Steinman RM, Nussenzweig MC (2002) Avoiding horror autotoxicus: the importance of dendritic cells in peripheral T cell tolerance. Proc Natl Acad Sci USA 99(1):351–358. https://doi.org/10.1073/pnas.231606698 11. Xing Y, Hogquist KA (2012) T-cell tolerance: central and peripheral. Cold Spring Harbor Perspect Biol 4(6):a006957. https://doi.org/ 10.1101/cshperspect.a006957 12. Zhou F, Zhang GX, Rostami A (2018) Distinct Role of IL-27 in Immature and LPS-Induced Mature Dendritic Cell-Mediated Development of CD4+ CD127+3G11+Regulatory T Cell Subset. Front Immunol 9:2562. https://doi. org/10.3389/fimmu.2018.02562 ˜ o CG, 13. Gargaro M, Scalisi G, Manni G, Brisen Bagadia P, Durai V, Theisen DJ, Kim S, Castelli M, Xu CA, Zu Ho¨rste GM, Servillo G, Della Fazia MA, Mencarelli G, Ricciuti D, Padiglioni E, Giacche` N, Colliva C, Pellicciari R, Calvitti M, Zelante T, Fuchs D, Orabona C, Boon L, Bessede A, Colonna M, Puccetti P, Murphy TL, Murphy KM, Fallarino F (2022) Indoleamine 2,3-dioxygenase 1 activation in mature cDC1 promotes tolerogenic education of inflammatory cDC2 via metabolic communication. Immunity 55(6):1032–1050.e14. https://doi. org/10.1016/j.immuni.2022.05.013 14. Murphy TL, Grajales-Reyes GE, Wu X, ˜ o CG, Iwata A, Kretzer Tussiwand R, Brisen NM, Durai V, Murphy KM (2016) Transcriptional Control of Dendritic Cell Development. Annu Rev Immunol 34:93–119. https://doi.

org/10.1146/annurev-immunol032713-120204 15. Durai V, Murphy KM (2016) Functions of Murine Dendritic Cells. Immunity 45(4): 719–736. https://doi.org/10.1016/j. immuni.2016.10.010 16. Guilliams M, Dutertre CA, Scott CL, McGovern N, Sichien D, Chakarov S, Van Gassen S, Chen J, Poidinger M, De Prijck S, Tavernier SJ, Low I, Irac SE, Mattar CN, Sumatoh HR, Low GHL, Chung TJK, Chan DKH, Tan KK, Hon TLK, Fossum E, Bogen B, Choolani M, Chan JKY, Larbi A, Luche H, Henri S, Saeys Y, Newell EW, Lambrecht BN, Malissen B, Ginhoux F (2016) Unsupervised High-Dimensional Analysis Aligns Dendritic Cells across Tissues and Species. Immunity 45(3):669–684. https://doi. org/10.1016/j.immuni.2016.08.015 17. Iberg CA, Jones A, Hawiger D (2017) Dendritic Cells As Inducers of Peripheral Tolerance. Trends Immunol 38(11):793–804. https://doi.org/10.1016/j.it.2017.07.007 18. Swiecki M, Colonna M (2015) The multifaceted biology of plasmacytoid dendritic cells. Nat Rev Immunol 15(8):471–485. https://doi. org/10.1038/nri3865 19. Price JD, Tarbell KV (2015) The Role of Dendritic Cell Subsets and Innate Immunity in the Pathogenesis of Type 1 Diabetes and Other Autoimmune Diseases. Front Immunol 6: 288. https://doi.org/10.3389/fimmu.2015. 00288 20. Ali S, Mann-Nu¨ttel R, Schulze A, Richter L, Alferink J, Scheu S (2019) Sources of Type I Interferons in Infectious Immunity: Plasmacytoid Dendritic Cells Not Always in the Driver’s Seat. Front Immunol 10:778. https://doi. org/10.3389/fimmu.2019.00778 21. Hasegawa H, Matsumoto T (2018) Mechanisms of Tolerance Induction by Dendritic Cells In Vivo. Front Immunol 9:350. https://doi. org/10.3389/fimmu.2018.00350 22. Ring S, Maas M, Nettelbeck DM, Enk AH, Mahnke K (2013) Targeting of autoantigens to DEC205+ dendritic cells in vivo suppresses experimental allergic encephalomyelitis in mice. J Immunol 191(6):2938–2947. h t t p s : // d o i . o r g / 1 0 . 4 0 4 9 / j i m m u n o l . 1202592 23. Tabansky I, Keskin DB, Watts D, Petzold C, Funaro M, Sands W, Wright P, Yunis EJ, Najjar S, Diamond B, Cao Y, Mooney D, Kretschmer K, Stern JNH (2018) Targeting DEC-205-DCIR2+dendritic cells promotes immunological tolerance in proteolipid protein-induced experimental autoimmune encephalomyelitis. Mole Med 24(1):17.

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Chapter 6 Flow Cytometry Analysis of IL-1 Receptors and Toll-Like Receptors on Platelets and Platelet-Derived Extracellular Vesicles Achille Anselmo and Daniela Boselli Abstract Flow cytometry is largely used for the immunophenotyping and quantification of several cell types or related components including platelets and extracellular vesicles. Platelets and platelet-derived extracellular vesicles (PEVs) are receiving increased interest in inflammatory diseases including sepsis. Thus, in this chapter, we will describe protocols for the flow cytometry analysis of platelets, platelet/neutrophils hetero aggregates, and PEVs mainly focusing on the evaluation of the surface expression of some IL-1 receptor (ILR) and Toll-like receptor (TLR) family members. Key words Flow cytometry, Platelets, Extracellular vesicles, Instrument setting, Antibody, Immunophenotyping, Inflammation

1

Introduction

1.1 Polychromatic Flow Cytometry

Flow cytometry is an evolving fluorescence-based technology largely used in basic research as well as in translational research and clinical practice. This technology is less qualitative as compared to the imaging techniques, but it has the major distinct feature of being an excellent quantitative approach for the assessment of physical and chemical features in single cells, organelles, and particles. Moreover, due to the increasing instrument performances, the flow cytometry sensitivity for the identification and quantification of very rare populations is now comparable with those coming from molecular tools such as next-generation sequencing (NGS) or Allele-Specific Oligonucleotide quantitative PCR (ASOqPCR) [1]. Fluorophores are fluorescent chemical compounds able to re-emit light upon laser excitation. They are a wide world of molecules with different chemical structures but with similar chemical features. Indeed, they are characterized by a massive electron

Francesca Fallarino et al. (eds.), Toll-Like Receptors: Methods and Protocols, Methods in Molecular Biology, vol. 2700, https://doi.org/10.1007/978-1-0716-3366-3_6, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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delocalization due to the presence of aromatic rings and/or conjugated double bonds. Upon laser excitation, fluorophores release energy in the form of photons with a specific wavelength, longer than the excitation source. In a conventional flow cytometry instrument the emitted photons pass through the collection lens and are split and steered down specific channels with the use of long pass and band pass filters. Finally, the photons coming from a single event are collected by photodiodes or the more sensitive photomultipliers in which the optical signal is converted into a voltage pulse and visualized through specific software. Conventional flow cytometers can look at different fluorescence signals depending on the laser sources and on the optical bench composition, including the two morphological parameters: Forward SCatter (FSC) and Side SCatter (SSC). The FSC signal provides a measure of the relative cell size and refractive index (membrane permeability) whereas the SSC signal is related to the sample internal complexity (i.e., the amount of cytosolic structure in the cells). Both parameters are detected upon excitation at 488 nm and their combination allows the discrimination of particles, higher than 0.3 μm, from the background noise. The capability of conventional flow cytometers to identify particles lower 0.3 μm is physically limited by the amount of light scattered from each particle that is directly proportional to the diameter of the particle and inversely proportional to the wavelength of the light being used to detect it. Thus, for several years, the flow cytometry analysis of micro- and nanoparticles has represented a big challenge. To overcome this issue, some new generation analyzers are equipped with a violet side scatter (VSSC) (measured upon laser excitation at 405 nm) able to clearly discriminate 80 nm polystyrene and 150 nm silica nanoparticles from background noise. This new come out technological implementation helps to amplify the difference in the refractive index between the particles and their surrounding media, and in turn increases the ability to detect micro- and nanoparticles. Flow cytometry has been known for an incredible growth in the last years. Indeed, the development of new fluorophores, together with the implementation of instruments equipped with an increasing number of laser sources, has contributed to the growth of the so-called next-generation flow. Thus, the number of researchers approaching with multicolor panels using more than 15 parameters is increasing day by day. More recently, instruments with peculiar features have been developed and marketed. These are newly built spectral analyzers incorporating a unique combination of patentpending innovative technologies that has the capability of measuring the entire emission spectra of the fluorescent dyes excited by multiple lasers installed on the instrument. The full spectrum capture enables the discrimination of fluorophores with the same

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excitation and emission spectra such as FITC and Alexa-488 or Pacific blue and BV421. As a result, by exploiting this technology is now possible the simultaneous analysis of up to 40 different labels. In this chapter we will discuss and apply advanced flow cytometry to platelet and platelet-derived EV analysis. 1.2

Platelets

1.3 Extracellular Vesicles (EVs)

Platelets are small non nucleated cell fragments that circulate in the blood. They originate from megakaryocytes and exist in circulation for 5–7 days having as a primary function, the regulation of the hemostasis and thrombosis. Indeed, the traditional role of platelets is to detect blood vessel injury and become activated by expressing on their surface several molecules such as CD62P and the active form of the GPα2bβ3 both playing a role in thrombus formation [2]. In addition to their well-described hemostatic function, platelets are active players in the immune response to microbial organisms and during inflammation [3]. Indeed, several lines of evidence have demonstrated a wide repertoire of functional toll-like receptors (TLRs) in platelets [4, 5]. These receptors are involved in platelet activation, platelet-mediated leukocyte activation through hetero aggregate formation, and in platelet-dependent antimicrobial activity [6, 7]. More in detail, platelet TLR4 is involved in neutrophil activation and degranulation, neutrophil extracellular traps (NETs) formation, and bacteria trapping [6]. Moreover, platelets express functional IL-1R1, IL-18R [8, 9], and high levels of IL-1R8 which plays a nonredundant role as regulator of thrombocyte function in vitro and in vivo [9]. Nowadays, several research groups are focusing their efforts on the study of the role of platelets in the pathogenesis of the severe acute respiratory syndrome coronavirus disease 2019 (COVID19), caused by the new human coronavirus SARS coronavirus 2 (SARS-CoV-2). Indeed, COVID 19 patients with a severe form of the disease often show thrombotic complications, including venous or arterial thromboembolism [10, 11]. Recent studies describe increased platelet activation and platelet-leukocyte aggregates through mechanisms involving P-selectin and integrin αIIb/ β3-dependent signaling in patients with a severe form of the disease, thus shedding light on pathological mechanisms involving platelets in COVID-19 [12]. Extracellular vesicles (EV) are intercellular communication particles released under basal and stress conditions [13–15]. They can be classified based on the physical characteristics, such as density or size—for example small EVs denoting particles 200 nm—or the cell/condition of origin—i.e., platelet EVs, hypoxic EVs, or apoptotic bodies [16]. EVs can be detected in different body fluids such as blood

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plasma, serum, urine, saliva, breast milk, bronchial lavage fluid, amniotic fluid, cerebrospinal fluid, and malignant ascites [17]. They can be shed or budded from the plasma membrane of several cell types, such as erythrocytes, platelets, endothelial cells, and leukocytes [13–15], or they can originate from intracellular compartments such as multivesicular bodies [18]. Upon fusion with the plasma membrane, EVs can acquire specific antigens of the cell of origin together with phospholipids and membraneassociated glycoproteins but they can also carry other active biomolecules in the “intravesicular” compartment, such as proteins, prostanoids, mRNAs, microRNAs (miRNAs), and lipids [19]. PEVs originating from plasma membrane share the same antigens as their parent platelet, such as CD41, CD61, and CD42a [13–20]. They mainly have a size above 0.2 μm and are more likely to contain and transfer functional receptors from platelets to other cell types [21]. Whereas PEVs of endosomal origin are generally smaller than the former and are mainly enriched in miRNA and mRNA [22]. PEVs are receiving increased interest in inflammatory diseases, sepsis, and cardiovascular disorders (CVD) [23–25]. Indeed, PEVs contribute to inflammation during infection through a direct recruitment of leukocytes, via chemokine release and by promoting the interaction between monocytes and endothelial cells [25]. LPS and staphylococcal superantigen-like protein (SSL) directly stimulate PEV formation via activation of platelet TLRs [26, 27]. Also in viral infections, PEV release increases and contributes to the activation of the host’s immune system [28]. More recently, PEVs have attracted interest as prognostic biomarkers and players in COVID-19. Indeed, a growing number of independent studies found PEVs together with other types of blood-derived EVs as potential biomarkers of COVID-19 severity and as predictors of disease outcome [29–31]. Although EVs have drawn the attention of both biological researchers and clinical physicians due to their function and potential value as biomarker in several pathological conditions; their identification, characterization and quantification is still challenging. 1.4 EV Validation Through Flow Cytometry

The international society of extracellular vesicles has published a Minimum Information for Studies of EVs (MISEV) guidelines. The manuscript provides updates about the EV nomenclature, collection, isolation, characterization, and functional studies in the effort to sensitize researchers in providing more insights in manuscripts that report EV data [16]. Moreover, even the analysis of EVs through flow cytometry has been implemented by the publication of the Minimum Information about a flow cytometry (FC) experiment (MIFlowCyt) standard in

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an EV-FC-specific reporting framework (MIFlowCyt-EV). This toll aims to support researchers in sample staining procedures, EV detection and measurement, and experimental design, thus improving consistency in EV-FC studies [32].

2

Materials To limit background noise during sample acquisition and analysis, all the buffers including instrument sheath fluid should be filtered using 0.22 μm filter.

2.1 Isolation of Platelets from Peripheral Blood

1. Whole blood. 2. 19-gauge needle. 3. EDTA-containing Vacutainer tubes. 4. PGE and apyrase buffer: 1 μg/μL PGE1 and 1 U/mL apyrase final concentration. 5. HEPES-Tyrode buffer: 134 mM NaCl, 0.34 mM Na2HPO4, 2.9 mM KCL, 12 mM NaHCO3, 20 mM HEPES, 5 mM glucose, 1 mM MgCl2, pH 7.3. 6. 5 mL Round Bottom Polystyrene Tubes.

2.2 Flow Cytometry Analysis of TLRs and ILRs on Resting Platelets

1. Biotinylated Systems).

goat anti-human IL-1R8/SI-GIRR (R&D

2. Biotinylated normal goat IgG (R&D Systems). 3. Purified mouse anti-human CD284 (TLR4) (Clone HTA125) (eBioscience). 4. Purified mouse anti-human CD282 (TLR2) (Clone TL2.1) (eBioscience). 5. Purified mouse (eBioscience).

anti-human

IL-18Rα

(Clone

2B7E6)

6. Purified mouse IgG2a (eBioscience). 7. Purified goat anti-human IL-1R1 (R&D Systems). 8. Purified normal goat IgG (R&D Systems). 9. Alexa-647-conjugated Invitrogen).

streptavidin

(Molecular

Probes,

10. Alexa-647-conjugated goat anti-mouse antibody (Molecular Probes, Invitrogen). 11. Alexa-647-conjugated rabbit anti-goat antibody (Molecular Probes, Invitrogen). 12. 5 mL Round Bottom Polystyrene Tube.

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2.3 Platelet/ Neutrophil (Plt/PMN) Hetero Aggregate Evaluation Through Flow Cytometry

1. Whole blood. 2. 19-gauge needle. 3. EDTA-containing Vacutainer tubes. 4. FITC Mouse Anti-Human (BD Biosciences).

CD41a

(Clone

HIP8)

5. ACK (Ammonium-Chloride-Potassium) Lysis Buffer Solution: 0.15 M Ammonium Chloride, 0.01 M Potassium Bicarbonate, 0.0001 M EDTA. 6. LPS 0127/B8 1 (Sigma-Aldrich). 7. IL-1β (R&D Systems). 8. IL-18 (R&D Systems). 9. Adenosine DiPhosphate (ADP) (Sigma-Aldrich). 2.4 Isolation and Immunophenotyping of Platelet-Derived EVs (PEVs)

1. Whole blood. 2. 19-gauge needle. 3. EDTA-containing Vacutainer tubes. 4. PBS 1× without Ca2+/Mg2+. 5. Calcium-free HBSS supplemented with 4 mM EDTA, pH 6.4. 6. LPS 0127/B8 1 (Sigma-Aldrich). 7. Adenosine DiPhosphate (ADP) (Sigma-Aldrich). 8. SYTOX (Invitrogen). 9. Phalloidin (Invitrogen). 10. Biotinylated goat anti-human IL-1R8/SI-GIRR (R&D Systems). 11. Biotinylated normal goat IgG (R&D Systems). 12. Purified mouse anti-human CD284 (TLR4) (Clone HTA125) (eBioscience). 13. Purified mouse anti-human CD282 (TLR2) (Clone TL2.1) (eBioscience). 14. Purified mouse IgG2a (eBioscience). 15. Purified goat anti-human IL-1R1 (R&D Systems). 16. Purified normal goat IgG (R&D Systems). 17. Purified mouse (eBioscience).

anti-human

18. Alexa-647-conjugated Invitrogen).

IL-18Rα

streptavidin

(Clone

2B7E6)

(Molecular

Probes,

19. Alexa-647-conjugated goat anti-mouse antibody (Molecular Probes, Invitrogen). 20. Alexa-647-conjugated rabbit anti-goat secondary antibody (Molecular Probes, Invitrogen).

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21. FITC Mouse anti-human CD61 (Clone VI-PL2) (Biolegend). 22. 5 mL Round Bottom Polystyrene Tube. 23. BD Trucount Absolute Counting Tubes. 2.5 Flow Cytometry Instrument Setting for Platelet and PEV Analysis

1. Megamix-Plus FSC beads (mix fluorescent beads with different sizes: 0.1 μm, 0.3 μm, 0.5 μm, 0.9 μm) (Biocytex). 2. PBS 1× without Ca2+/Mg2+. 3. VersaComp Antibody Capture Kit (Beckman Coulter). 4. CytoFLEX Daily QC Fluorospheres (Beckman Coulter). 5. BD FACSDiva™ CS&T Research Beads (BD Biosciences). 6. Spherotech Rainbow Calibration Particles (Spherotech). 7. 5 mL Round Bottom Polystyrene Tubes.

3

Methods

3.1 Isolation of Platelets from Peripheral Blood 3.1.1

Collection of Blood

Normal platelet count ranges from 150,000 to 450,000 per microliter of blood. Platelets can be easily separated from the other blood cells upon centrifugation (Fig. 1). 1. Collect blood by venipuncture of antecubital vein by using a 19-gauge needle without venous stasis in Vacutainer tubes containing EDTA. 2. Discard the first ~2 mL of blood drawn to minimize red cell hemolysis that may lead to platelet activation. 3. Cap the tube and mix blood and anticoagulant by inverting the tube gently three times. 4. Process sample within 15 min of blood collection. In the interim, blood should be kept at room temperature (see Note 1).

Fig. 1 Isolation of platelets from peripheral blood. Schematic representation of platelet isolation steps from whole blood

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3.1.2 Preparation of Platelet-Rich Plasma (PRP)

1. Centrifuge anticoagulated whole blood at 160 × g for 15 min without brake at room temperature. 2. To avoid leukocyte contamination, aspirate the top third of the PRP and place it in fresh tubes. The PRP volume will be approximately 50% of the whole blood volume but it is dependent on the hematocrit value of the donor (see Note 2) (Fig. 1).

3.1.3 Preparation of Washed Platelets

1. Collect PRP in a fresh tube. 2. Add PGE1 and apyrase buffer, and centrifuge at 600 × g for 20 min at room temperature to obtain a platelet pellet (see Note 3) (Fig. 1). 3. Remove the supernatant and resuspend the pellet in HEPESTyrode’s buffer containing PGE1 and apyrase (Fig. 1). 4. Repeat step 3. 5. Assess for leukocyte contamination through optical microscopy: leukocyte should be less than 104/mL of PRP.

3.2 Flow Cytometry Analysis of TLRs and ILRs on Resting Platelets

Platelets express the IL-1R family members IL-1R1, IL-1R8, and IL-18R as well as a wide repertoire of toll-like receptors (TLRs) [4–7]. 1. Incubate 1–3 × 106 washed platelets with appropriate saturating concentrations of the following unconjugated antibodies and relative isotype controls: biotinylated goat anti-human IL-1R8/SI-GIRR, biotinylated normal goat IgG, purified mouse anti-human CD284 (TLR4), purified mouse antihuman CD282 (TLR2), purified mouse anti-human IL-18R and purified mouse IgG2a, purified goat anti-human IL-1R1, and purified normal goat IgG. Of note, incubate each sample with one antibody. 2. Incubate for 20 min at room temperature, in the dark. 3. Wash platelets by centrifuging at 600 × g for 20 min at room temperature. 4. Collect the pellet and add Alexa-647-conjugated streptavidin or Alexa-647-conjugated goat anti-mouse or rabbit anti-goat secondary antibodies. 5. Incubate for 20 min at room temperature, in the dark. 6. Wash platelets by centrifuging at 600 × g for 20 min at room temperature. 7. Analyze by flow cytometry. If preferred include an unstained control.

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3.3 Platelet Neutrophil (Plt/PMN) Hetero Aggregate Evaluation Through Flow Cytometry

Platelets actively modulate innate and adaptive immune responses during inflammation and host defense. An important prerequisite for platelet-mediated changes of immune functions involves direct interaction with different types of leukocytes such as monocytes and neutrophils. Indeed, platelet/ neutrophil hetero aggregates within the circulation and/or locally at the site of inflammation are involved in the pathogenesis of several thrombo-inflammatory diseases, including sepsis [33].

3.3.1 Plt/PMN Hetero Aggregate Assessment

1. Incubate 100 μL of whole blood with saturating concentration of anti CD41a antibody for 20 min at room temperature in the dark. 2. Lysate red blood cells with ACK lysis buffer. 3. Analyze by flow cytometry.

3.3.2 Plt/PMN Hetero Aggregate In Vitro Formation Upon Stimulation with LPS, IL1-β, and IL-18

1. Stimulate 100 μL of whole blood by adding LPS (1 μg/mL), IL-1β (12.5 ng/mL), or IL-18 (100 ng/mL) and incubate for 15 min at room temperature. Include a positive control ADP (20 μM) and a vehicle control (sample treated with the buffer in which the molecules are dissolved). 2. Incubate with appropriately saturating concentration of anti CD41a antibody for 20 min at room temperature in the dark. 3. Lysate red blood cells with ACK lysis buffer. 4. Analyze by flow cytometry.

3.4 Isolation and Immunophenotyping of Platelet-Derived EVs (PEVs)

3.4.1 Platelet-Free Plasma (PFP) Preparation

Platelet-derived EVs (PEVs) are the most abundant EV subpopulation detectable in plasma together with erythroid-derived EVs [34]. PEVs carry several surface molecules from platelet surface. Moreover, PEV release has been associated with both noninfectious chronic inflammatory diseases (i.e., atherosclerosis, diabetes, coronary artery disease, and hypertension) [23–25] and infectious diseases (i.e., influenza and COVID-19) [29]. 1. Centrifuge blood at 1200 × g for 20 min at room temperature to remove all blood cells and platelets. 2. Centrifuge again at 500 × g for 30 min at 4 °C and collect the upper two third of plasma (Fig. 2).

3.4.2 Isolation of Total EVs from PFP

1. Centrifuge the obtained platelet-free plasma at 12,000 × g for 45 min at 4 °C, remove the supernatant, leaving a pellet containing EVs (Fig. 2). 2. Resuspend the pellet in 75 μL of filtered PBS 1× without Ca2+/Mg2+ and immediately process or freeze (see Note 4).

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Fig. 2 Isolation of PEVs from peripheral blood. Schematic representation of PEV isolation steps from whole blood

3.4.3 Isolation of PEVs from LPS-Stimulated Washed Platelets

1. Resuspend the washed platelets into calcium-free HBSS supplemented with 4 mM EDTA, pH 6.4. 2. Dilute platelets to 4 × 108/mL and adjust volumes according to the number of conditions and replicates. 3. Activate 10 × 106 platelets by adding LPS 100 ng/mL at a final volume of 100 μL and incubate for 20 min at 37 °C. 4. Centrifuge washed platelets at 500 × g for 30 min at room temperature and collect the supernatant. 5. Include a positive control ADP (20 μM) and a vehicle control.

3.4.4 PEV Immunophenotyping Protocol

1. Incubate PEVs using SYTOX, phalloidin, and the following unconjugated antibodies and relative isotype controls: biotinylated goat anti-human IL-1R8/SI-GIRR, biotinylated normal goat IgG, purified mouse anti-human CD284 (TLR4), Purified mouse anti-human CD282 (TLR2), purified mouse antihuman IL-18Ra and purified mouse IgG2a, purified goat antihuman IL-1R1 and purified normal goat IgG. Of note, incubate each sample with one antibody including in all tubes SYTOX, phalloidin. 2. Wash PEVs by centrifuging at 12,000 × g for 45 min at 4 °C and remove the supernatant. 3. Collect the pellet and add Alexa-647-conjugated streptavidin or Alexa-647-conjugated goat anti-mouse or rabbit anti-goat secondary antibodies. 4. Incubate for 20 min at room temperature, in the dark. 5. Wash PEVs by centrifuging at 12,000 × g for 45 min at 4 °C and remove the supernatant. 6. Incubate EVs with saturating concentration of CD61 antibody for 20 min at room temperature in the dark. 7. Wash PEVs by centrifuging at 12,000 × g for 45 min at 4 °C and remove the supernatant.

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8. Analyze EVs by flow cytometry. Only SYTOX-/phalloidin-/ CD61+ events are analyzed. 9. Include an unstained control, an FMO control for CD61, and a PBS sample stained following the same procedures as the samples, thus excluding false positive events due to the formation of antibody aggregates from the analysis. 3.4.5 Absolute Count of EVs

Count of EVs can be performed using different methods: 1. By Trucount Absolute Counting Tubes following the manufacturer’s protocol. In brief, the lyophilized pellet in the tube dissolves, releasing a known number of fluorescent beads. During analysis, the absolute number (cells/μL) of positive cells in the sample can be determined by comparing cellular events to bead events. 2. By using volumetric cell counting: thanks to peristaltic pump fluidic based system, new-generation flow cytometers have the capability to directly measure the acquisition volume, thus calculating final cell concentration.

3.5 Flow Cytometry Instrument Setting for Platelet and PEV Analysis

In this session, we will describe some specific advice to perform a correct analysis of both platelets, platelet/PMN aggregates and PEVs. More in detail, we will focus on the discrimination of doublets, evaluation of physical parameters with particular attention to the determination of the threshold, exclusion of apoptotic bodies and cell fragments, isotypic controls and FMO, choice of the best fluorochromes, antibodies titration, and compensation. Finally, we will focus on gating strategy and data acquisition and analysis.

3.5.1 Doublet Discrimination

A doublet is an event that consists of two independent particles. The cytometer is not able to distinguish the two particles forming the single event since they are too close. Doublets, characterized by a high value of intrinsic autofluorescence due to the sum of the autofluorescence of each cell forming the aggregate, may overlap with the truly positive events, thus leading to a massive error in data interpretation. The fluorescence signal coming from both morphological parameters (FSC and SSC) can be quantified and visualized as Area (a measure of all the fluorescence signal collected from a single event), Height (a measure of the peak of the fluorescence signal collected from a single event) and Width (a measure of the fluorescence signal collected from a single event related to the time spent to pass through the laser beam). In single cells, Area or Width measurements increase proportionally with the Height, whereas aggregates will show a clear deviation from the linearity. When a doublet passes in front of a laser beam, the Area and the Width of the electronic pulse increase more than the Height; hence, the deviation from linearity occurs.

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Doublets can be identified by plotting FSC-A versus FSC-H as a cloud of events with an altered ratio between Area and Height. The same is valid for SSC-W and SSC-H. The doublets exclusion is recommended in platelets analysis; however, due to PEV heterogeneity, we suggest skipping this step during their flow cytometry analysis. 3.5.2 Setting of Morphological Parameters

The size of platelets typically ranges between 1 and 4 μm in diameter, while the size of PEVs ranges from 100 nm up to 1 μm. When studying small events, the light scatter parameters (SSC-A and FSC-A) are expressed in logarithmic scale instead of linear. If possible, the use of the Violet Side scatter (VSSC) has to be preferred from the Blue Side Scatter (SSC). The standard FSC threshold commonly applied for leukocyte analysis is not appropriate for platelets and PEVs analysis and it needs to be substantially reduced to visualize small events (see Note 5). For PEVs analysis use specific beads to calibrate scatter channels as follow: 1. Acquire Megamix-Plus FSC beads (a mixture of four fluorescent beads ranging between 0.1 and 0.9 μm) and increase/ decrease the voltage (or the gain) in order to see the bead populations in an SSC-A/VSSC-A versus FITC-A dot plot. 2. Set four regions around the four populations of beads. 3. In the SSC-A/VSSC-A versus FSC-A dot plot, set a region including the 0.9 μm and the 0.1 μm bead populations. Label this region as “PEV Gate,” and use this setting for sample analysis.

3.5.3 Exclusion of Cell Fragments and Apoptotic Bodies During PEV Analysis

PEVs are sensitive to preanalytical conditions including procedures such as blood collection, centrifugation, and storage. Cell disruption at these phases can cause the appearance of cell fragments and apoptotic/necrotic bodies leading to artifact during measurement. Moreover, fragments and apoptotic bodies can nonspecifically bind monoclonal antibodies, potentially leading to false positive events. The use of fluorochrome-labeled phalloidin, a molecule possessing high affinity to filamentous actin (f-actin) is recommended to exclude actin-exposing cell fragments from the analysis. Whereas the use of SYTOX reagent, a cell-impermeant dye able to bind DNA, is recommended to exclude apoptotic bodies from the analysis.

3.5.4 Definition of Background/Unspecific Signal

The definition of the background is essential for the quantification of the relative antigen expression and for the cell/EV subset gating. Two approaches can be used: isotype and fluorescence minus one (FMO) control.

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Historically, isotype control has represented the most widely used staining control to define the threshold of positivity from background fluorescence due to unspecific binding. The rationale behind using isotype controls is the assumption that positive staining with isotype controls may be an indication of an antibody a specific staining via Fc receptors. However, recent guidelines on flow cytometry have shown how isotypic control could be different from the staining antibody, with different amino acid composition in the variable region, different fluorochrome/antibody ratio, and different concentrations, thus limiting their application [35]. For cell staining using an unconjugated or a biotinylated primary antibody (i.e., IL-1R8, TLR4, IL-1R1, and IL-18Ra), the right negative control is a sample stained using the same immunoglobulins, at the same concentration, as the primary antibody followed by the staining using the same conjugated secondary antibody or streptavidin as the full stained sample [9]. More recently, the use of the isotype control has been implemented with the Fluorescence Minus One (FMO) negative control [36]. The FMO control is a sample stained with all the fluorophores, included into the antibody panel, minus one, thus allowing the operator to assess the spread of all the fluorophores into the missing fluorescence channel. Of note, the number of FMO controls has to be the same as the number of fluorophores included into the multicolor panel. 3.5.5 Choice of Fluorochromes

The fluorescent dyes can be subdivided into five main classes: • Large protein-based molecules (i.e., phycobiliproteins like phycoerythrin and allophycocyanin). • Small organic dyes (i.e., fluorescein isothiocyanate). • Inorganic fluorescent nanocrystals (i.e., quantum dots). • π-Conjugated polymers (i.e., Brilliant Dyes). • Tandem dyes (i.e., PE-Cy5 and APC-Cy7). The successful set up of a multicolor panel depends on some rules mainly based on the biological and physical proprieties of the antigen/fluorochrome combination, and on the instrument configuration that is used for sample analysis. Hereafter the four main golden rules: • Use bright fluorochromes for low expressed antigens. • Use dim fluorochromes for highly expressed antigens. • Choose antibodies conjugated with fluorochromes with minimal spectral overlap to other fluorochromes, for the detection of dimly expressed and/or sensitive antigens.

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• If possible, do not select fluorochromes which are sensitive to photobleaching, alcohols or pH changes. Photobleaching is a catabolic photochemical reaction that results in a reduction in fluorescence intensity. 3.5.6

Antibody Titration

All antibodies must be titrated before being used. This allows the achievement of best results in terms of resolution and sensitivity. 1. Centrifuge antibody 10 min at 15,000 × g at 4 °C to remove antibody aggregates. Discard pellet. 2. Make serial dilutions of your antibody in PBS 1× without Ca2+/Mg2+. 3. Incubate cells with the different antibody concentrations for 20 min at room temperature, in the dark. 4. Analyze by flow cytometry. Include unstained control. 5. To define the best antibody concentration calculate the signal to noise ratio for each antibody titer using the following formula: Signal to noise ratio: MFI positive cells=MFI negative cells 6. Plot the values as a function of antibody dilution: the highest ratio is the optimal titer since this value provides the greatest discrimination between positive and negative cells, regardless of the absolute value of fluorescence intensity. The antibody titration is influenced by the cell number, so antibody titration should be performed using the same cell number as those required for sample analysis. For PEVs, refer to sample volume instead of PEV absolute count for antibody titration [37].

3.5.7

Compensation

The term compensation refers to the process of correcting the fluorescence spillover, i.e., removing the signal of a given fluorochrome from all the detectors. Compensation can be performed manually or automatically. The former is susceptible to operator bias, whereas the latter is more recommended especially in case of complex multiparametric analysis. Indeed, the automatic compensation calculates the percentage of spillover of each fluorochromes into the other fluorescence channels, thus generating a compensation matrix [38]. To this aim, it is possible to acquire a series of a single stained compensation beads stained with the fluorophore-conjugate antibodies that are used in the immunophenotyping panel. Importantly, single stained controls should be brighter than sample, thus minimizing undercompensated data.

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Versa Comp Bead Preparation 1. Label a number of 5 mL Round Bottom Polystyrene Tubes as the number of fluorochrome-conjugated antibodies to be tested plus a tube for the unstained control. 2. Add 100 μL of PBS 1× without Ca2+/Mg2+ to each tube. 3. Add one drop of beads to each tube. 4. Add the antibodies to be tested in the corresponding labeled tubes. 5. Incubate at room temperature in the dark for 15 min. 6. Add 1 mL of PBS 1× without Ca2+/Mg2+ to each test tube and centrifuge at 200 × g for 10 min. 7. Remove the supernatant from each tube. 8. Add 500 μL of PBS 1× without Ca2+/Mg2+ to each tube and vortex before the acquisition (see Note 6). 3.5.8 Gating Strategy for Platelet Analysis

The gating strategy for the analysis of platelet activation state and TLR and ILR expression on platelets is described below: 1. Create a dot plot SSC-A log scale versus FSC-A log scale for morphological parameter assessment. On this dot plot, draw a region that identify the cell population (Fig. 3). 2. Create a dot plot SSC-A log scale versus SSC-W log scale for doublets discrimination. Draw a second region excluding aggregates (Fig. 3 near here). 3. Create a histogram plot for each parameter that has to be analyzed (i.e., IL-1R8, TLR4, TLR2, IL-1R1). Use isotype control sample as negative control. Results should be expressed as Relative MFI especially in case of nonbimodal expression curves.

Fig. 3 Morphological parameter setting for platelet analysis. Platelets from PRP can be identified through an SSC-A log scale versus FSC-A log scale dot plot (R1). Single cells are discriminated from platelet aggregates through a second dot plot SSC-A log versus SSC-W log scale (R2)

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Fig. 4 Schematic representation of PMN/Plt hetero aggregate formation. PMNs identified through the gate R1 are discriminated from other leukocytes by exploiting their morphological features (big size and high internal complexity). Single cells are discriminated from aggregates through the gate R2. PMN/Plt hetero aggregates (R3) are identified through the expression of CD41a on PMN surface and can easily discriminated from non-aggregate PMNs (R4) 3.5.9 Gating Strategy for Plt/PMN Hetero Aggregate Analysis

The gating strategy for hetero aggregate analysis is described below (Fig. 4): 1. Create a dot plot SSC-A linear scale versus FSC-A linear scale for morphological parameter assessment: PMNs are discriminated from other leukocytes by exploiting their morphological features (big size and high internal complexity). Create a region surrounding PMN-like cells. 2. Create a dot plot SSC-A linear scale versus SSC-W linear scale for doublets discrimination. Draw a second region excluding aggregates. 3. Create a dot plot SSC-A linear scale versus CD41a-A log scale. Draw a third region including PMNs expressing CD41a on their surface.

3.5.10 Gating Strategy for PEV Analysis

The gating strategy for the analysis of TLR and ILR expression on PEVs is described below: 1. Create a dot plot SSC-A/VSSC-A log scale versus FITC channel to identify Megamix Beds (Fig. 5 near here). 2. Create a dot plot SSC-A/VSSC-A log scale versus FSC-A log scale for morphological parameter assessment of beads and use this setting for PEV analysis (Fig. 6). 3. Create a dot plot SYTOX versus phalloidin for cell fragments and apoptotic bodies’ exclusion. Create a region that identify SYTOX-/phalloidin- events (Fig. 6). 4. Create a dot plot FSC-A/ log scale versus CD61 for PEVs identification. Results can be expressed as percentage of positive events (Fig. 6 near here). 5. Create a histogram plot for each parameter that has to be analyzed (i.e., IL-1R8, TLR4, TLR2, IL-1R1). Use isotype

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Fig. 5 Flow cytometry analysis of Megamix beads. Beads are visualized in a VSSC-A log scale versus FITC dot plot: various sizes of beads are color-marked: 0.9 μm—red, 0.5 μm—blue, 0.3 μm—yellow and 0.1 μm—green dots. In a second dot plot (VSSC-A log scale vs. FSC-A log scale) beads are characterized for their morphological parameters (the smallest beads overlap with the instrument background)

Fig. 6 Flow cytometry analysis of PEVs. PEVs are visualized using a VSSC-A log scale versus FSC-A log scale dot plot. Region R1 has been set using Magamix Beads. Through the region R2 (SYTOX-/Phalloidin-) cell fragments and apoptotic bodies are excluded from the analysis. Finally, PEVs are identified through gate R3

control sample as negative control. Results should be expressed as Relative MFI especially in case of nonbimodal expression curves. 3.5.11 Platelet Acquisition Procedure

1. Acquire the unstained sample and/or the sample stained with the isotype control and adjust the voltage (or the gain) of the morphological parameters to correctly visualize platelets. Decrease the FSC standard threshold value to better visualize small events. Perform all the acquisition at low flow rate, thus minimizing event abort. 2. For the other fluorescence channels refer to the instrument setting defined using QC beads (see Note 7). 3. Acquire stained samples using the same setting previously defined.

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3.5.12 Platelet/PMN Hetero Aggregate Acquisition Procedure

1. Acquire the unstained sample and adjust the voltage (or the gain) of the morphological parameters to correctly visualize total leukocytes. Modify the FSC standard threshold value to minimize debris from the analysis. Perform all the acquisition at low flow rate, thus minimizing event abort. 2. For the other fluorescence channels refer to the instrument setting defined using QC beads (see Note 7). 3. Acquire stained samples using the same setting previously defined.

3.5.13 PEV Acquisition Procedure

1. Run filtered, distilled water to control the instrument cleaning (less than 1000 ev/s). Instrument cleaning procedures are essential to better visualize small events and to minimize background noise (see Note 8). 2. Decrease the FSC standard threshold value to better visualize small events. Perform all the acquisition at low flow rate, thus minimizing event abort. 3. Acquire Megamix-Plus FSC beads as previously described. 4. Acquire the unstained sample and/or the sample stained with the isotype control using the same voltage (or the gain) of the morphological parameters defined using Megamix-Plus FSC beads. 5. For the other fluorescence channels refer to the instrument setting defined using QC beads. 6. Acquire stained samples using the same setting previously defined.

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Notes 1. Platelet activation due to mishandling and refrigeration. Whole blood can be kept at room temperature if sample preparation starts within 30 min; otherwise, it should be rested at 37 °C to preserve platelet activation. Blood should be properly mixed with anticoagulant, avoiding unnecessary agitation prior to testing [39]. Platelet storage at 4 °C cause deep modifications in platelet shape and function for this reason it is preferable to store the platelets at room temperature. 2. Hematocrit value influences the PRP volume. The PRP volume is highly influenced by hematocrit and platelet count [39]. Several factors can influence the hematocrit: living at a high altitude, pregnancy, significant recent blood loss, recent blood transfusion, severe dehydration.

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Moreover, hematocrit differ between man and woman and change during the life. This should be considered when preparing the PRP. 3. How prevent activation and aggregation of platelets. To prevent the activation of platelets during the sample preparation, strong mechanical force (i.e., fast pipetting, vigorous shaking) should be avoided. In addition, platelet activation and aggregation inhibitors can be added to the platelet suspension. One is prostaglandin E1 (PGE1) that stimulates adenylcyclase activity in platelets and increases cyclic AMP concentrations, which inhibit Ca2+ release and platelet aggregation induced by P2Y1 receptor activation. Apyrase (adenosine 5′-triphosphate di-phosphohydrolase) inhibits platelet activation by degrading ATP or ADP. Platelet suspension including PGE1, and Apyrase are stable for 5–8 h at 37 °C. 4. Stability of frozen EVs. The EV pellet can be frozen at -80 °C and is stable for 1 week. Whereas the PFP can be directly frozen at -80 °C within 2 h from blood collection; concentration and diameter of EVs are not significantly influenced by a single plasma freeze/thaw cycle within 1 year of plasma collection [40]. 5. Threshold. A low threshold value sets on morphological parameters (preferentially FSC) improves the EV identification. 6. Versa comp beads. Versa comp beads can be stained using the antibody that will be used in the experiment or using a different antibody that is conjugated with the same fluorochrome. This is true for all the fluorophores commercially available except for tandem dyes in which the donor/acceptor ratio may vary between the different antibodies and lot by lot. 7. Instrument QC. Quality control beads can be used to define the instrument setting to reach the best separation between positive and negative signals as well as to verify the instrument performances. Each instrument uses its own quality control beads: CytoFLEX Daily QC Fluorospheres for Beakman Coulter CytoFLEX equipped with CytExpert software and BD FACSDiva™ CS&T Research Beads, for BD instrument equipped with FACS DIVA software. Moreover, instrument performances can be monitored day by day using Spherotech Rainbow Calibration Particles.

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8. Instrument cleaning procedures before EV analysis. Wash the instrument by using 10% Contrad solution for 10 min, followed by 70% Ethanol solution for 10 min, and filtered milli Q H2O for 10 min. Run filtered, distilled water to control the instrument cleaning (less than 1000 events/s should be displayed). References 1. Flores-Montero J, Sanoja-Flores L et al (2017) Next generation flow for highly sensitive and standardized detection of minimal residual disease in multiple myeloma. Leukemia 31(10): 2094–2103 2. Cimmino G, Golino P (2013) Platelet biology and receptor pathways. J Cardiovasc Transl Res 6(3):299–309 3. Semple JW, Italiano JE Jr, Freedman J (2011) Platelets and the immune continuum. Nat Rev Immunol 11(4):264–274 4. Beaulieu LM, Freedman JE (2009) The role of inflammation in regulating platelet production and function: toll-like receptors in platelets and megakaryocytes. Thromb Res 125(3): 205–209 5. Andonegui G, Kerfoot SM et al (2005) Platelets express functional Toll-like receptor-4. Blood 106(7):2417–2423 6. Stephen RC, Ma AC et al (2007) Platelet TLR4 activates neutrophil extracellular traps to ensnare bacteria in septic blood. Nat Med 13(4):463–469 7. Blair P, Rex S et al (2009) Stimulation of Tolllike receptor 2 in human platelets induces a thromboinflammatory response through activation of phosphoinositide 3-kinase. Circ Res 104(3):346–354 8. Brown GT, Narayanan P et al (2013) Lipopolysaccharide stimulates platelets through an IL-1β autocrine loop. J Immunol 191(10): 5196–5203 9. Anselmo A, Riva F et al (2016) Expression and function of IL-1R8 (TIR8/SIGIRR): a regulatory member of the IL-1 receptor family in platelets. Cardiovasc Res 111(4):373–384 10. Middeldorp S, Coppens M et al (2020) Incidence of venous thromboembolism in hospitalized patients with COVID-19. J Thromb Haemost 18(8):1995–2002 11. Klok FA, Kruip MJHA et al (2020) Incidence of thrombotic complications in critically ill ICU patients with COVID-19. Thromb Res 191:145–147 12. Hottz ED, Azevedo-Quintanilha IG et al (2020) Platelet activation and platelet-

monocyte aggregate formation trigger tissue factor expression in patients with severe COVID-19. Blood 136(11):1330–1341 13. Anselmo A, Frank D et al (2021) Myocardial hypoxic stress mediates functional cardiac extracellular vesicle release. Eur Heart J 42(28):2780–2792 ˜ ez-Mo´ M, Siljander PR et al (2015) 14. Ya´n Biological properties of extracellular vesicles and their physiological functions. J Extracell Vesicles 4:27066 15. Vion AC, Ramkhelawon B et al (2013) Shear stress regulates endothelial microparticle release. Circ Res 112(10):1323–1333 16. The´ry C, Witwer KW et al (2018) Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles 7(1):1535750 17. Xu R, Greening DW et al (2016) Extracellular vesicle isolation and characterization: toward clinical application. J Clin Invest 126(4): 1152–1162 18. The´ry C, Zitvogel L et al (2002) Exosomes: composition, biogenesis and function. Nat Rev Immunol 2(8):569–579 19. Diehl P, Fricke A et al (2012) Microparticles: major transport vehicles for distinct microRNAs in circulation. Cardiovasc Res 93(4): 633–644 20. Mobarrez F, Antovic J et al (2010) A multicolor flow cytometric assay for measurement of platelet-derived microparticles. Thromb Res 125(3):110–116 21. Rozmyslowicz T, Majka M et al (2003) Platelet- and megakaryocyte-derived microparticles transfer CXCR4 receptor to CXCR4-null cells and make them susceptible to infection by X4-HIV. AIDS 17(1):33–42 22. Pienimaeki-Roemer A, Konovalova T et al (2017) Transcriptomic profiling of platelet senescence and platelet extracellular vesicles. Transfusion 57(1):144–156

Flow Cytometry Analysis of IL-1 Receptors and Toll-Like Receptors 23. Loyer X, Vion AC et al (2014) Microvesicles as cell-cell messengers in cardiovascular diseases. Circ Res 114(2):345–353 24. Robbins PD, Dorronsoro A et al (2016) Regulation of chronic inflammatory and immune processes by extracellular vesicles. J Clin Invest 126(4):1173–1180 25. Kerris EWJ, Hoptay C et al (2020) Platelets and platelet extracellular vesicles in hemostasis and sepsis. J Investig Med 68(4):813–820 26. Bei JJ, Liu C et al (2016) Staphylococcal SSL5induced platelet microparticles provoke proinflammatory responses via the CD40/TRAF6/ NFκB signalling pathway in monocytes. Thromb Haemost 115(3):632–645 27. Brown GT, McIntyre TM (2011) Lipopolysaccharide signaling without a nucleus: kinase cascades stimulate platelet shedding of proinflammatory IL-1β-rich microparticles. J Immunol 186(9):5489–5496 28. Boilard E, Pare´ G (2014) Influenza virus H1N1 activates platelets through FcγRIIA signaling and thrombin generation. Blood 123(18):2854–2863 29. Puhm F, Flamand L et al (2022) Platelet extracellular vesicles in COVID-19: potential markers and makers. J Leukoc Biol 111(1):63–74 30. Cappellano G, Raineri D et al (2021) Circulating platelet-derived extracellular vesicles are a Hallmark of Sars-Cov-2 infection. Cell 10(1): 85 31. Guervilly C, Bonifay A et al (2021) Dissemination of extreme levels of extracellular vesicles: tissue factor activity in patients with severe COVID-19. Blood Adv 5(3):628–634

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32. Welsh JA, Van Der Pol E et al (2020) MIFlowCyt-EV: a framework for standardized reporting of extracellular vesicle flow cytometry experiments. J Extracell Vesicles 9(1): 1713526 33. Clark SR, Ma AC et al (2007) Platelet TLR4 activates neutrophil extracellular traps to ensnare bacteria in septic blood. Nat Med 13(4):463–469 34. Arraud N, Linares R et al (2014) Extracellular vesicles from blood plasma: determination of their morphology, size, phenotype and concentration. J Thromb Haemost 12(5):614–627 35. Cossarizza A, Chang HD et al (2019) Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition). Eur J Immunol 49(10):1457–1973 36. Roederer M (2002) Compensation in flow cytometry. Curr Protoc Cytom Chapter 1: Unit 1.14 37. Anselmo A, Colombo FS (2021) Flow cytometry instrument setting as a crucial checkpoint for optimal T-cell analysis and sorting. Methods Mol Biol 2325:1–27 38. Szalo´ki G, Goda K (2015) Compensation in multicolor flow cytometry. Cytometry A 87(11):982–985 39. Andrade MG, de Freitas Branda˜o CJ et al (2008) Evaluation of factors that can modify platelet-rich plasma properties. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 105(1):e5 40. Yuana Y, Bo¨ing AN et al (2015) Handling and storage of human body fluids for analysis of extracellular vesicles. J Extracell Vesicles 11(4):29260

Chapter 7 Methods to Study TLRs in Transplantation Montserrat Kwan, Martin Sepulveda, and Maria-Luisa Alegre Abstract Toll-like receptors (TLRs) are key regulators of immune responses, including alloimmune responses. In this chapter, we present protocols to study whether and/or how TLRs can contribute to solid-organ transplant rejection. We describe methods to reduce heterogeneity in microbiome variations between animals before beginning experiments to limit confounding factors, protocols using TLR agonists to prevent antiCD154/donor splenocyte transfer-mediated tolerance, and recipes to heat-kill microbes or use hosts genetically deficient in TLR-dependent pathways to distinguish between TLR-dependent and live bacteria-dependent effects. Key words Solid-organ transplantation, Toll-like receptors, Costimulation blockade, Microbiome, Microbiota, Heart transplantation, Skin transplantation

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Introduction Solid-organ transplantation is a life-saving treatment for end-stage organ failure. The use of immunosuppressive drugs helps promote graft acceptance, but depending on the type of allograft, rejection due to acute and chronic alloimmune responses still occurs. Barrier organs such as intestine, skin, and lung are colonized by communities of microorganisms collectively called the microbiota, and are associated with a shorter half-life following transplantation, compared to more sterile organs, such as kidney, heart, and liver (Organ Procurement and Transplantation Network (OPTN), https:// optn.transplant.hrsa.gov/data/). Barrier organs are exposed to the environment, and microorganisms or microbial-derived products can modify the milieu in the transplanted organ or can translocate during or after surgery, potentially impacting the survival of the graft. For instance, we have shown that donor commensal bacteria in a skin graft can augment the effector function of alloreactive T cells when these T cells infiltrate the graft, resulting in faster rejection of colonized than germ-free skin grafts in mice [1]. Moreover, we have shown that hosts of colonized skin grafts mount not only

Francesca Fallarino et al. (eds.), Toll-Like Receptors: Methods and Protocols, Methods in Molecular Biology, vol. 2700, https://doi.org/10.1007/978-1-0716-3366-3_7, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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an alloimmune response to the transplanted tissue but also an anticommensal response to the accompanying donor microbiota and that both responses cooperate to damage the graft [2]. Notably, if the graft recipient harbors memory responses to the donor commensals, the host–anticommensal response is stronger and can result in resistance to immunosuppression of the graft recipient [2]. In addition to the commensals that accompany a colonized organ, intestinal commensals of the host can also impact transplant outcome. Reducing or eliminating the intestinal microbiota via oral antibiotics or using germ-free mice respectively, can delay graft rejection, while reconstitution of germ-free mice with fecal microbiome transfer from colonized mice can accelerate graft rejection [3]. Conversely, some bacterial taxa can dominantly prolong graft survival [4–6]. Thus, both intestinal and transplant-associated microbiota can influence the kinetics of graft rejection. The microbiota is made up of a complex microbial ecology, including bacteria, viruses and fungi, and comprises multiple microorganism-specific molecules. These molecules are called microbial-associated molecular patterns (MAMPS) and can be recognized by families of different mammalian receptors including toll-like receptors (TLRs), which can sense these microbial patterns at both the plasma membrane and in intracellular compartments. Following ligation, TLRs dimerize and relay signals throughout the cell via adaptor molecules, transducing signals that have central functions in coordinating innate and adaptive immune responses in host defense [7, 8]. In the setting of transplantation, TLRs play a key role during ischemia/reperfusion injury, initiation of allograft rejection, and during the effector phase of rejection (and in graftvs.-host disease) [9]. Sensing TLR agonists like LPS and poly I:C can prevent the induction of donor-specific transplant tolerance in mouse models of skin and cardiac transplantation, in a myeloid differentiation factor 88 (MyD88)-dependent manner [10–13], suggesting TLRs as potential targets of new therapeutic strategies to promote allograft acceptance. Multiple conventional practices can be implemented to reduce interindividual variability of the host microbiota in experimental models of transplantation or of diseases influenced by the immune system. The practice of randomizing animals by cohousing them is an effective strategy to help control for “cage effects” caused by the microbiome in studies of animal phenotypes. Cage effects refer to variation of microbial communities between cages even in a specific breeding room within a specific pathogen-free facility. As mice are coprophagic, cohousing is a method to reduce microbiota variability between animals prior to and during experiments [14]. Upon cohousing, gut, and likely skin, and microbes are passively transferred between animals in the same cage, which homogenizes the microbiota among the cohoused mice [15]. Many animal

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husbandry-related factors are potential sources of heterogeneity that can alter the composition of the microbiota, and may confound the interpretation of studies centered on the role of TLRs. Examples of these various factors include breeding environment (different rooms and interfacility transfers), changes in staff as they can introduce new human commensals, genetic backgrounds, different nutrient composition of the chow, variation in water sterilization methods, sex, and age. Additionally, mice of similar genetic backgrounds but bred in separate rooms within the same facility can harbor different microbial communities (including taxa with heterogeneous resistance to certain antibiotics, or resistant to colonization by certain pathogens). Therefore, it is important to decrease microbiota variations among animals before starting experiments. The use of littermate controls is another valuable strategy to limit microbiome variations among experimental animals. If phenotypic differences are observed between wild-type control and mutant mice when they are bred independently or when one group is sourced from an external vendor, it cannot be accurately distinguished whether they are due to a difference in host genetics, a difference in microbiota composition, or both [16]. Including littermate controls (i.e., born from the same dam or at least grandmother) into experimental designs is a robust way to equalize the microbiota in the progeny to investigate the effects of the host genotype on the phenotype of interest without the confounder of genotype-unrelated disparate starting microbiota. Interindividual variability among animals may still be present despite the use of the conventional strategies described. While important, these practices are not without limitations: cohousing is constrained by the number of mice that can be housed per cage, and in the case of male mice, adults become aggressive if multiple different litters are cohoused. Furthermore, while using littermates is a gold standard, it is often challenging to accrue a sufficiently large number of age-matched animals per experimental group. Including a prestudy bedding transfer protocol can address some of these limitations by further normalizing microbiome differences among animals in large cohort studies and relieving challenges that arise from cohousing adult male mice from different litters [17]. In this protocol, soiled bedding is mixed and distributed equally among pups at weaning age (3 weeks old) until the start of experiments (6 weeks old and beyond) among multiple cages of mice. This procedure encourages uniformity of microbes across cages prior to beginning a study, serving to further minimize individual variability and cage differences. To investigate whether TLR engagement is sufficient for certain commensals or pathogens to exert immunomodulatory effects in transplantation studies, one approach is to heat-inactivate the bacteria of interest. The use of live versus heat-killed bacteria can dissect whether specific bacteria drive a phenotype solely via TLR

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engagement, which may be retained following heat inactivation, or via active metabolic pathways such as production of a metabolite that would be disabled by heat inactivation. Moist heat inactivation is more effective than dry heat for heat-killing microbes [18]. Specifically, moist heat damages microbial membranes by denaturation and protein coagulation while dry heat causes protein denaturation and oxidative damage. A growing body of evidence indicates that TLRs and their downstream cytoplasmic signaling adaptor protein, MyD88, are key components that contribute to driving allograft inflammation and the onset of acute and chronic rejection [10, 19–23]. MyD88 is an adaptor protein that is required for signaling by most TLRs and IL-1R and -18R, and when engaged, can prevent the induction of transplantation tolerance [10]. We and others have reported that genetic deficiency of MyD88 in both donor and recipient mice facilitated anti-CD154 + donor splenocyte transfer (DST) (antiCD154/DST)-mediated long-term acceptance of skin allografts, a very immunogenic tissue [10, 21]. Mechanistically, TLR signals generated after skin transplantation, such as the nuclear translocation of NF-kB, causes dendritic cells and other antigen-presenting cells (APCs) to mature, thereby upregulating expression of costimulatory ligands and release of proinflammatory cytokines [8]. This process facilitates acute rejection, as donor APCs can then migrate into the draining lymph nodes of the recipient and promote Th1 alloreactivity [19]. Using genetic deletion of individual TLRs or more targeted ablation of specific signaling components of TLR-mediated signaling pathways can provide insight into whether and/or how TLRs are involved in solid-organ transplant rejection. Although TLR signaling converges onto shared downstream effectors, some TLRs display unique requirements. All TLRs require MyD88 to signal except TLR3, which utilizes the TIR domain-containing adaptor molecule 1 (TRIF), while TLR4 engages both MyD88 and TRIF [24]. Therefore, individual TLR knockouts, targeted knockouts of MyD88, TRIF, or TRIF/MyD88 double knockout mice, and C3H/HEJ mice that have defective TLR4 signaling are all powerful tools to dissect the contribution of TLRs to solidorgan transplant rejection [10, 19, 22, 25]. In this chapter, we present protocols used to study TLRs in the context of transplant immunology. We describe methods to reduce heterogeneity in microbiome variations between animals before beginning experiments to limit confounding factors, protocols using TLR agonists to prevent anti-CD154/DST-mediated tolerance, and recipes to heat-kill microbes or use hosts genetically deficient in TLR-dependent pathways to distinguish between TLR-dependent and live bacteria-dependent effects.

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Materials The preparation and storage of all reagents is at room temperature, unless indicated otherwise.

2.1 DonorSplenocyte Transfer (DST)

1. 1× PBS: Dilute 10× Dulbecco’s Phosphate-Buffered Saline (DPBS) using MilliQ sterile water (see Note 1). 2. Dissecting tools. 3. 40 mm cell strainer. 4. 3 mL disposable syringe. 5. 50 mL conical tube. 6. Centrifuge with 50 mL conical tube adaptor.

2.2 Costimulation Blockade

1. Anti-CD154 (CD40L): Clone, MR1, 500 mg/dose, 3 doses. Store at -80 °C.

2.3 TLR Agonist Preparation

1. CpG: TLR9 agonist, 100 μg first dose, 50 μg following 2 doses. Store at -20 °C. 2. PAM3CysK4: TLR2 agonist, 200 mg/dose, 2 doses. Store at 20 °C. 3. LPS: TLR4 agonist, 100 mg/dose, 1 dose. Store at -20 °C. 4. Polyinosinic:Polycytidylic acid (Poly I:C): TLR3 agonist, 50 mg/dose, 1 dose. Store at -20 °C.

2.4 Injection into Mice

1. 0.5 mL Insulin syringe, 29G ½.

2.5 Inactivation of Commensal Microbes by Heat-Killing

1. Liquid bacterial culture media. 2. 1× PBS: Dilute 10× Dulbecco’s Phosphate-Buffered Saline (DPBS) using sterile deionized water (see Note 1). 3. 50 mL conical tube. 4. Centrifuge with 50 mL conical tube adaptor. 5. Heat block or water bath. 6. Sterile, heat resistant, and airtight screw top polypropylene screw-top tubes. 7. Agar plates.

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Methods

3.1 Generation of Donor-Specific Tolerance 3.1.1

DST Preparation

1. Euthanize an organ donor-matched mouse (BALB/c) with CO2 asphyxiation and proceed with a secondary method of euthanasia (in agreement with the institutional Animal Care and Use Committee and according to the National Institutes of Health guidelines for animal use) (see Note 2). 2. Harvest and dissect the spleen. 3. Under sterile conditions, smash the spleen utilizing the plunger of a 3 mL syringe against a 40 mm cell strainer (see Note 3). 4. Strain into a 50 mL conical tube to obtain a single-cell suspension. 5. Centrifuge at 370 g for 5 min. 6. Resuspend splenocyte pellet in 800 mL of 1× PBS, and store in ice until use. 7. Load 200 mL (1/4 spleen) in an insulin syringe.

3.1.2 Heart Allograft Tolerance Induction

1. After BALB/c heterotrophic heart transplant surgery into a C57Bl/6 mouse [26], and while the mouse is under general anesthesia, inject i.v. 500 mg of anti-CD154 and 200 mL of DST (day 0 in reference to transplantation). 2. Inject i.p. 500 mg of anti-CD154 on days 7 and 14 posttransplantation. 3. Monitor transplanted heart survival over time. 4. The day of rejection is defined as the last day a detectable heartbeat can be felt in the graft.

3.1.3 Skin Allograft Tolerance Induction

1. On day -7 (relative to day of transplantation), inject i.v. 200 mL of DST and i.p. 500 mg of anti-CD154. 2. On day -4, inject i.p. 500 mg of anti-CD154. 3. After BALB/c skin transplantation on a C57Bl/6 mouse, and while the mouse is under general anesthesia [27], inject i.p. 500 mg of anti-CD154. 4. Inject i.p. 500 mg of anti-CD154 on day 4 posttransplantation. 5. Remove bandages on day 7 posttransplantation and monitor skin graft survival over time. 6. The day of rejection is defined as the day that less than 20% of the graft remains healthy.

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3.2 Prevention of Donor-Specific Tolerance Using TLR Agonists 3.2.1 Heart Allograft Prevention of Anti-CD154/ DST-Mediated Tolerance

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1. CpG: Inject i.v. 100 mg of CpG-B on the day of transplantation (at the same time anti-CD154/DST). Afterward, inject i.p. 50 mg on day 1 and 2 posttransplantation. 2. PAM3CysK4: Inject i.v. 200 mg of PAM3CysK4 on the day of transplantation (at the same time as anti-CD154/DST). Afterward, inject i.p. 200 mg on day 2 posttransplantation. 3. Monitor transplanted heart survival over time. 4. The day of rejection is defined as the last day a detectable heartbeat can be felt in the graft.

3.2.2 Skin Allograft Prevention of Anti-CD154/ DST-Mediated Tolerance

1. LPS: Inject i.p. 100 mg within 1 h of the anti-CD154/DST injections on day -7. 2. Poly I:C: Inject i.p. 50 mg within 1 h of the anti-CD154/DST injections on day -7. 3. Remove bandages on day 7 posttransplantation and monitor skin graft survival over time. 4. The day of rejection is defined as the day that less than 20% of the graft remains healthy.

3.3 Reducing Microbiota Variations Among Animals Before Experiments 3.3.1 Randomizing Mice Between Cages by Cohousing Upon Arrival to Investigator’s Animal Facility

1. In the first 72 h after arrival to the investigator’s animal facility, animals should be left undisturbed to allow them to acclimate after shipment. 2. If working in a specific pathogen-free animal facility, wear standard vivarium personal protective equipment (disposable gown, gloves, hair net, face mask, and shoe covers) and follow standard barrier practices for biological safety cabinet (BSC) sanitization: thoroughly sterilize the BSC twice in between handling each experimental group by spraying down with Clidox® (a chlorine dioxide-based sterilant) and allow it to sit for approximately 3 min between each cleaning and wiping down with fresh paper towels. If working in a BSL2 facility, wear standard vivarium personal protective equipment and sterilize twice with a 1:10 dilution of bleach followed once with 70% isopropyl alcohol. 3. Change into two pairs of fresh gloves and sleeve covers after handling each experimental group. 4. Depending on the number of mice cohoused together, randomly transfer 1–2 mice per cage between experimental groups as many times as possible until different treatment conditions no longer permit microbiota exchange (see Note 4). 5. If appropriate, collect two fecal pellets from each mouse at each sampling point: one pellet to track 16S rRNA gene sequencing to determine microbial community composition and one pellet to monitor colonization load to plate on differential agar if an exogenous microbe will be introduced (see Note 5).

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3.3.2

Littermate Controls

1. Use littermate controls whenever possible to control for the effects of the microbiome and metagenome. Breeding littermate controls then testing for phenotypic differences between controls and genetic mutants will ensure similar microbiota colonization at birth and determine if phenotypic differences are due to the genetic alteration rather than host microbiota differences.

3.3.3

Breeding In-House

1. Breeding genetic lines within the institution’s animal facility rather than purchasing from external animal vendors whenever possible will further control for microbiome variability.

3.3.4

Bedding Transfer

1. Following a 14-day cycle in between normal cage changes, transfer and redistribute a portion of soiled bedding into new cages at 3–4 days and again at 8–10 days. 2. At each bedding transfer time point, collect approximately one-quarter of the soiled bedding from each cage, combine the bedding from all cages in an autoclaved sterilized container, then mix in a sanitized BSC in the same room where animals are housed. 3. Wear standard vivarium personal protective equipment (disposable gown, gloves, hair net, face mask, and shoe covers) and follow standard barrier practices for BSC sanitization: sanitize the BSC by spraying down with Clidox® (a chlorine dioxidebased sterilant) and allow it to sit for approximately 3 min before wiping down with fresh paper towels. 4. The mixed soiled bedding should then be redistributed across all cages, while working in the sanitized BSC. 5. This bedding transfer protocol can start as soon as mice are weaned and continue until the start of fecal sample collections.

3.4 Inactivation of Commensal Microbes by Heat-Killing

1. Grow bacterial cultures following appropriate culturing conditions for the microbe of interest (i.e., anaerobic/aerobic, suitable nutrient broth/temperature, specific shaking rate if necessary). 2. Centrifuge the culture at 3220 g for 10 min at 4 °C to precipitate a bacterial pellet. 3. Remove the culture supernatant, being careful not to disturb the pellet. 4. Pellet can be washed with PBS if desired. 5. Resuspend pellet in desired volume of PBS depending on the desired bacterial cell density. 6. Dry or moist heat may be used to heat-kill the bacteria. Heat aliquots of resuspended bacterial cells to 95 °C for 1 h in a heat block or in a water bath immediately before oral gavage to mice or other treatment (see Note 6).

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7. Verify complete killing of bacterial cells by streaking heat-killed bacterial cells on the appropriate agar plate and incubating overnight. No colonies should grow on plates. 3.5 Use of Transplant Hosts and/or Recipients Genetically Deficient in Individual TLRs or Modules of TLR-Dependent Signaling Pathways

1. Either mutant donors, mutant recipients, or both mutant donors and mutant recipient animals can be used in experiments.

3.5.1 Use Animals with Targeted Genetic Deficiency of Both Copies of Individual TLR Genes Such as TLR2, TLR4, etc., or C3H/HeJ Mice That Express a Nonfunctional TLR4 Gene (Defective Signaling)

4. Conditional knockout animals can also be generated to study the involvement of individual TLRs in specific tissues or cell types in transplantation by using mice expressing LoxP sitesflanked TLR genes crossed to mice in which tissue or cellspecific promoters drive expression of Cre recombinase in order to excise the TLR in the tissue or cell of interest. Further genetic approaches may permit temporal in addition to spatialrestricted excision of the gene of interest.

3.5.2 Use Animals with Targeted Genetic Deletion of Both Copies of Adaptor Molecule Genes Required for TLR-Mediated Signaling Pathways

1. Perform transplantation experiments using mice deficient in MyD88, TRIF, or both, as recipients, graft donors, or both donor and recipient.

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2. For experimental control groups, use wild-type littermate controls. 3. Use C3H/HeN wild-type control mice as a control for C3H/ HeJ-mutant mice.

2. For experimental control groups, use wild-type littermate controls or Cre recombinase-negative mice. 3. Test whether the absence of genes encoding adaptor molecules required for TLR-dependent signaling can facilitate graft acceptance following anti-CD154/DST treatment in models using immunogenic grafts that normally resist tolerance induction with this costimulation blockade-based regimen.

Notes 1. Ensure all reagents utilized are sterile. Filter sterilized reagents using 0.22 mm filters. 2. Always prepare DST fresh. 3. Do not utilize a red blood cell lysis buffer to remove erythrocytes from the spleen. 4. If treating animals with antibiotics and a vehicle-treated control group will be used, always handle the control group last after the treatment group during treatments and when randomly transferring mice between cages. This will prevent the unwanted transfer of microbes from control animals to antibiotic-treated animals.

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5. Immediately store fecal pellets for 16S rRNA gene sequencing on dry ice after collection then store at -80 °C until sequencing. For fecal pellets used immediately after collection for plating on differential agar, store on ice until plating to prevent bacterial cell proliferation. 6. Screw top tubes should be sterile, heat resistant, and airtight.

Acknowledgment This work was supported by R01 AI115716 to MLA and T32 AI7090-41 to MK and MS. References 1. Lei YM et al (2019) Skin-restricted commensal colonization accelerates skin graft rejection. JCI Insight 4(15). https://doi.org/10.1172/ jci.insight.127569 2. Pirozzolo I et al (2022) Host-versus-commensal immune responses participate in the rejection of colonized solid organ transplants. https://www.jci.org/articles/view/153403/ pdf 3. Lei YM et al (2016) The composition of the microbiota modulates allograft rejection. J Clin Invest 126(7):2736–2744. https://doi.org/ 10.1172/JCI85295 4. Bromberg JS et al (2018) Gut microbiotadependent modulation of innate immunity and lymph node remodeling affects cardiac allograft outcomes. JCI Insight 3(19):4. https://doi.org/10.1172/jci.insight.121045 5. McIntosh CM, Chen L, Shaiber A, Eren AM, Alegre M-L (2018) Gut microbes contribute to variation in solid organ transplant outcomes in mice. Microbiome 6(1):96. https://doi. org/10.1186/s40168-018-0474-8 6. Li Z et al (2023) Oral administration of the commensal Alistipes onderdonkii prolongs allograft survival. Am J Transplant 23(2):272–277. https://doi.org/10.1016/j.ajt.2022.11.011 7. Rakoff-Nahoum S, Medzhitov R (2008) Innate immune recognition of the indigenous microbial flora. Mucosal Immunol 1(1):Art. no. 1. https://doi.org/10.1038/mi.2008.49 8. Medzhitov R (2001) Toll-like receptors and innate immunity. Nat Rev Immunol 1(2): 1 3 5 – 1 4 5 . h t t p s : // d o i . o r g / 1 0 . 1 0 3 8 / 35100529 9. Alegre M-L et al (2008) The multiple facets of Toll-like receptors in transplantation biology. Transplantation 86(1):1–9. https://doi.org/ 10.1097/TP.0b013e31817c11e6

10. Chen L et al (2006) TLR engagement prevents transplantation tolerance. Am J Transplant 6(10):2282–2291. https://doi.org/10.1111/ j.1600-6143.2006.01489.x 11. Chen L et al (2009) TLR signals promote IL-6/IL-17-dependent transplant rejection. J Immunol 182(10):6217–6225. https://doi. org/10.4049/jimmunol.0803842 12. Thornley TB et al (2006) TLR agonists abrogate costimulation blockade-induced prolongation of skin allografts. J Immunol 176(3): 1561–1570. https://doi.org/10.4049/ jimmunol.176.3.1561 13. Thornley TB et al (2007) Type 1 IFN mediates cross-talk between innate and adaptive immunity that abrogates transplantation tolerance. J Immunol 179(10):6620–6629. https://doi. org/10.4049/jimmunol.179.10.6620 14. Oliveira RA et al (2020) Klebsiella michiganensis transmission enhances resistance to Enterobacteriaceae gut invasion by nutrition competition. Nat Microbiol 5(4):630–641. https://doi.org/10.1038/s41564-0190658-4 15. Franklin CL, Ericsson AC (2017) Microbiota and reproducibility of rodent models. Lab Anim (NY) 46(4):114–122. https://doi.org/ 10.1038/laban.1222 16. Stappenbeck TS, Virgin HW (2016) Accounting for reciprocal host-microbiome interactions in experimental science. Nature 534(7606):191–199. https://doi.org/10. 1038/nature18285 17. Miyoshi J et al (2018) Minimizing confounders and increasing data quality in murine models for studies of the gut microbiome. PeerJ 6: e5166. https://doi.org/10.7717/peerj.5166 18. Smelt JPPM, Brul S (2014) Thermal inactivation of microorganisms. Crit Rev Food Sci

TLRs in Transplantation Nutr 54(10):1371–1385. https://doi.org/10. 1080/10408398.2011.637645 19. Goldstein DR, Tesar BM, Akira S, Lakkis FG (2003) Critical role of the Toll-like receptor signal adaptor protein MyD88 in acute allograft rejection. J Clin Invest 111(10):9. https://doi.org/10.1681/ASN.2012010052 20. Tesar BM, Zhang J, Li Q, Goldstein DR (2004) TH1 immune responses to fully MHC mismatched allografts are diminished in the absence of MyD88, a Toll-like receptor signal adaptor protein. Am J Transplant 4(9): 1429–1439. https://doi.org/10.1111/j. 1600-6143.2004.00544.x 21. Walker WE, Nasr IW, Camirand G, Tesar BM, Booth CJ, Goldstein DR (2006) Absence of innate MyD88 signaling promotes inducible allograft acceptance. J Immunol 177(8): 5307–5316. https://doi.org/10.4049/ jimmunol.177.8.5307 22. Wang S et al (2010) Recipient Toll-like receptors contribute to chronic graft dysfunction by both MyD88- and TRIF-dependent signaling. Dis Model Mech 3(1–2):92–103. https://doi. org/10.1242/dmm.003533

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23. Wu H et al (2012) Absence of MyD88 signaling induces donor-specific kidney allograft tolerance. J Am Soc Nephrol 23(10):1701–1716. https://doi.org/10.1681/ASN.2012010052 24. Pandey S, Kawai T, Akira S (2014) Microbial sensing by Toll-like receptors and intracellular nucleic acid sensors. Cold Spring Harb Perspect Biol 7(1):a016246. https://doi.org/10. 1101/cshperspect.a016246 25. Chong AJ et al (2004) Toll-like receptor 4 mediates ischemia/reperfusion injury of the heart. J Thorac Cardiovasc Surg 128(2):170–179. https://doi.org/10.1016/j.jtcvs.2003. 11.036 26. Corry RJ, Winn HJ, Russell PS (1973) Primarily vascularized allografts of hearts in mice: the role of H-2D, H-2K and Non-H-2 antigens in rejection. Transplantation 16(4):343–350. h t t p s : // d o i . o r g / 1 0 . 1 0 9 7 / 0 0 0 0 7 8 9 0 197310000-00010 27. Markees TG et al (1997) Prolonged survival of mouse skin allografts in recipients treated with donor splenocytes and antibody to CD40L. Transplantation 64(2):329–335. https://doi. org/10.1097/00007890-199707270-00026

Chapter 8 Determining Endosomal Toll-Like Receptors Gene Expression in NK Cells After Stimulation with Specific Agonists Claudia Alicata, Irene Veneziani, Biancamaria Ricci, Lorenzo Moretta, and Enrico Maggi Abstract Poor knowledge is currently available about the biology of Toll-like receptors (TLRs) in natural killer (NK) cells. This is particularly due to the old belief that NK cells are unable to specifically eliminate microbes without presensitization. On the contrary, it has been clearly demonstrated that not only they can be activated through the engagement of Toll-like receptors (TLRs) by microbial molecules, but also that this interaction induces NK cells to release cytokines that, in turn, activate other cells of both innate and adaptive immunity. For this reason, immunotherapy based on local infusion of TLRs ligands is currently considered as a novel potential strategy to treat solid tumors. Here, we provide a protocol to efficiently stimulate NK cells via endosomal TLRs agonists and to determine endosomal TLRs gene expression level. This protocol can be used for in vitro investigation into endosomal TLRs function in NK cells under different conditions. Key words Toll-like receptors, TLRs gene expression, TLRs agonists, NK cells, TLRs triggering

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Introduction Natural killer (NK) cells are effector lymphocytes of the innate immune system that play an important role in eliminating tumor cells and in the early response to viral infections by limiting their spread and subsequent tissue damage [1]. NK cells display their cytotoxic activity against cellular targets through the release of lytic granules that contain granzymes and perforin [2]. The latter create pores in target’s cellular membrane, through which granzymes can enter the cytoplasm by inducing apoptosis through activation of caspases and cleavage of other substrates [3]. After activation, NK cells can also produce cytokines and chemokines that modulate the function of other cells of the innate and adaptive immunity [4].

Francesca Fallarino et al. (eds.), Toll-Like Receptors: Methods and Protocols, Methods in Molecular Biology, vol. 2700, https://doi.org/10.1007/978-1-0716-3366-3_8, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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NK cells represent 5–15% of peripheral blood lymphocytes, develop in the bone marrow from CD34+ hematopoietic progenitor cells and complete their differentiation in the peripheral lymphoid tissues [1, 5, 6]. NK cells are generally defined as CD56+CD3- and classified into two major populations: CD56dim and CD56bright. The first subset is considered the cytotoxic population while the second one is known for their proinflammatory cytokine release. In the peripheral blood, approximately 90% of the NK cells are represented by mature CD56dimCD16+, while approximately 10% are immature CD56brightCD16- [7]. NK cell function is dictated by a fine balance between signals that they receive from an array of germline DNA-encoded activating and inhibitory cell receptors enabling these lymphocytes to discriminate between self and non-self [8, 9]. NK cells were initially speculated to nonspecifically eliminate microbes without presensitization; by contrast they are able to recognize microbial-associated or pathogen-associated molecular patterns (PAMPs) through their pattern-recognition receptors (PRRs), including Toll-like receptors (TLRs) [10]. The interaction between PAMPs and their cognate TLRs induces in NK cells numerous pathways activating innate immune-related genes, including those encoding inflammatory cytokines, antimicrobial mediators, costimulatory, and adhesion molecules [11, 12]. TLR-activated NK cells, in turn, promote maturation of dendritic cells (DCs) and induce these cells to produce proinflammatory cytokine leading to the activation of both innate and adaptive immune responses [13]. The immunotherapy based on the cross-talk between DCs and NK cells represents a promising approach to the treatment of infectious diseases and cancer [14, 15]. In particular, intratumor therapy with TLR ligands has been established as a novel potential strategy to treat solid tumors [16]. Based on cellular distribution, two types of TLRs are present on NK cells: cell surface types (TLR1, 2, 4, 5, 6, and 10) which are primarily devoted to recognizing extracellular macromolecules from bacteria and fungi, and endosomal types (TLR3, 7, 8, 9) recognizing foreign nucleic acids from intracellular pathogens [11]. Current efforts are addressed to better characterize the mechanism by which TLRs triggering lead to NK cells activation. In the last decades, it has been clarified that the NK cell response to endosomal TLR agonists depends on their activation state and that only cytokine-activated NK cells can respond to TLR ligands (TLR-Ls) [17, 18]. More recently it has been demonstrated that specific endosomal TLR agonists increases cytokine production and cytotoxic activity specifically of CD56brightCD16- NK cells in both healthy donors and cancer patients [19] highlighting the potential value of TLR in NK cells as a new target for immunotherapy of cancer. Although these new discoveries greatly improve our knowledge, many aspects of the endosomal TLR biology of NK cells remain still unclear and need further investigations.

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In this chapter, we describe a method to stimulate NK cells via endosomal TLR agonists and to assess endosomal TLR gene expression.

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Materials PBMC Isolation

1. Whole blood sample. 2. Ficoll, or other density gradient medium with density of 1.077 g/cm3. 3. PBS-FBS: Dulbecco’s Phosphate-Buffered Saline (PBS) supplemented with 2% fetal bovine serum. 4. Complete RPMI (Roswell Park Memorial Institute): RPMI supplemented with 2 mM L-glutamine, 1% penicillin, 1% streptomycin, and 10% fetal bovine serum (FBS). 5. Interleukin (IL)-2 20 U/mL, IL-12 1 ng/mL, IL-15 1 ng/ mL, IL-18 20 ng/mL. 6. FACS tubes: round-bottom, polypropylene tubes, 2 mL. 7. Centrifuge. 8. Flow cytometer. 9. Flow cytometry analysis software.

2.2 NK Cells Purification

1. Complete RPMI (Roswell Park Memorial Institute): RPMI supplemented with 2 mM L-glutamine, 1% penicillin, 1% streptomycin, and 10% fetal bovine serum (FBS). 2. Kit targeting non-NK cells for removal with antibodies specific for cell surface markers. In particular, antibody cocktail crosslinking unwanted cells to multiple red blood cells (RosetteSep human NK cell enrichment cocktail, Stemcell Technologies). 3. Dulbecco’s Phosphate-Buffered Saline (PBS). 4. Ficoll, or other density gradient medium with density of 1.077 g/cm3. 5. Centrifuge. 6. FACS tubes: round-bottom, polypropylene tubes, 2 mL. 7. Anti-CD56, anti-CD3, anti-CD19, anti-CD16 antibodies. 8. Flow cytometer. 9. Flow cytometry analysis software.

2.3 NK Cells Stimulation with Endosomal TLR Agonists

1. Complete RPMI. 2. Round-bottom 96-well plate. 3. IL-2, IL-12.

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4. R848 (TLR7-8 agonist), Poly-I:C (TLR3 agonist), ODN 2395 (TLR9 agonist), Imiquimod (TLR7 agonist), TL8-506 (TL8 agonist), and Loxoribine (TLR7 agonist). 5. RNase free 1.5 mL tubes. 6. Centrifuge. 7. Trizol. 2.4

RNA Extraction

1. RNase free 1.5 mL tubes. 2. Chloroform. 3. Qiagen RNeasy micro kit or Qiagen RNeasy mini kit, depending on the amount of input cells. For ≤500 × 103 cells, use MinElute spin column included in RNeasy micro kit. For ≥500 × 103 cells, use MinElute spin column included in RNeasy mini kit. 4. 96–100%, 70%, and 80% RNA quality Ethanol. 5. RPE buffer included in Qiagen RNeasy kit. 6. RNase free water. 7. Centrifuge. 8. NanoDrop instrument.

2.5

cDNA Synthesis

1. PCR tubes. 2. Thermocycler. 3. High-Capacity cDNA Reverse Transcription Kit.

2.6 Real-Time PCR of Endosomal TLR Genes

1. Applied Biosystem PowerUp Sybr Green Master Mix. 2. qPCR thermal cycler. 3. MicroAmp Fast Optical 96-well reaction plate. 4. MicroAmp Optical plate seals. 5. Primers: Biorad Real-time PCR primer assay designed for SYBR Green gene expression analysis. Following scheme indicates Unique Assay ID corresponding to each TLR-specific primer pair. Target gene

Biorad primer pair Unique Assay ID

TLR3

qHsaCID0013344

TLR7

qHsaCID0015054

TLR8

qHsaCED0036379

TLR9

qHsaCED0003672

GAPDH

qHsaCID0015464

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Methods PBMC Isolation

1. Dilute the blood sample to a 1:1 volume ratio with PBS + 2% FBS. 2. Add a volume of Ficoll to a fresh tube (see Note 1). Recommended volumes are 1/3 of density gradient medium and 2/3 of diluted blood (e.g., 15 mL of density gradient medium and 30 mL of diluted blood in a 50 mL tube). 3. Gently layer the diluted blood on top of the Ficoll. Take care not to mix the two layers. 4. Centrifuge at 800× g for 20 min at room temperature with the brake OFF. 5. Carefully harvest the cells (Peripheral blood mononuclear cells PBMC) by inserting the pipette directly through the upper plasma layer to the mononuclear cells at the interface. Alternatively, first remove the upper layer and then collect the cells (see Note 2). 6. Wash the harvested cells twice by adding 3 volumes of either complete RPMI or PBS-FBS and centrifuge at 500× g for 5 min at room temperature. Discard the supernatant each time and finally resuspend the pellet in 1–2 mL of either complete RPMI or PBS-FBS. Cells are now ready for downstream applications. 7. To evaluate the amount of PBMC, transfer 20 μL of PBMC in a FACS tube, add 180 μL of PBS (1:10 dilution). 8. Perform citofluorimetric analysis aimed at evaluating the quality and amount of PBMC (Fig. 1). To perform subsequent NK cells purification, red cells are needed. 9. Collect red cells from the bottom of the gradient column and transfer in a fresh tube. 10. Wash red cells by adding 2 volumes of either complete RPMI or PBS-FBS and centrifuge at 1500× g for 5 min at room temperature and remove the supernatant (see Note 3). Perform this step twice. 11. Store the resulted red cells on ice for immediate use or overnight a 4 °C.

3.2 NK Cells Purification

1. In a single 15 mL tube, transfer 100 × 106 PBMC resuspended in 2 mL complete RPMI. 2. Add 2 mL of red cells previously washed and 200 μL of RosetteSep cocktail (see Note 4) (The total volume is 4.2 mL). Incubate 15 min a room temperature (see Note 5). 3. After the incubation, dilute sample 1:1 by adding 4.2 mL of PBS to the mix of PBMC with red cells and RosetteSep.

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Fig. 1 Identification of PBMC (orange gate) based on the physical parameters of the cells in the sample (FSC-A vs. SSC-A plot). P3 refers to cell gate and numbers refer to percentage of cells in the gate

4. Add 4 mL of Ficoll in a 15 mL fresh tube and gently layer the sample previously diluted (see Note 6). 5. Centrifuge for 20 min at 800× g at room temperature with the brake OFF. 6. Carefully harvest the cells (NK cells) by inserting the pipette directly through the upper layer to the NK cells at the interface. Alternatively, first remove the upper layer and then collect the cells. 7. Wash cells twice by adding 2 volumes of complete RPMI. Centrifuge for 5 min at 500× g at room temperature. Discard the supernatant each time and resuspend the pellet in 1 mL of complete RPMI. 8. Transfer 10 μL of washed NK cells into a 2 mL FACS tube and stain with anti-CD56, anti-CD3, anti-CD19, anti-CD16 antibodies. 9. Perform citofluorimetric analysis aimed at evaluating the quality and mount of NK cells (Fig. 2) (see Note 7). 3.3 NK Cell Stimulation with Endosomal TLRs Agonists

1. Seed 180,000 NK cells in 200 μL complete RPMI in a 96-well round-bottom plate (see Note 8). Each sample needs to be seeded in two different wells, one will not be treated (NT) and one will be added by the TLR agonist (Table 1). 2. Add interleukin (IL)-2 at 20 U/mL final concentration and IL-12 at 1 ng/mL final concentration in all wells. 3. Add TLRs agonists to the treated sample wells according to the following scheme (Table 2) and coculture for 20 h.

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Fig. 2 Gating strategy to analyze NK cells. (a) A first gate on NK cells is designed according to physical parameters of the cells (FSC-A vs. SSC-A plot). NK cells are gated as CD3-CD19- (b) and are divided into two major subsets: NK CD56brightCD16- and CD56dimCD16+ (c). Percentage of NK cells in the samples refers to the CD56+CD3-CD19- cell population (d). P1–P4 refer to cell gates, numbers refer to percentage of cells in the corresponding gate and Q1-UL/UR/LL/LR correspond to plot quadrants

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Table 1 Culturing more than 500,000 NK cells Total cell number

Medium volume

Culture plate

Well diameter

2.5 × 10

5

0.5 mL

48 well

1.13 cm

5

1 mL

24 well

1.6 cm

8 × 105

2 mL

12 well

2.26 cm

2 × 10

5 mL

6 well

3.5 cm

4 × 10

6

Table 2 TLRs agonists TLR agonist

Final concentrations

R848

6 μM

Poly-I:C

10 ng/mL

ODN 2395

1 μM

TL8–506

100 μM

IMIQUIMOD

10 μM

LOXORIBINE

1 μM

4. After 20 h, gently pipette up and down samples in each well, transfer the sample to a fresh RNase free 1.5 mL tube and centrifuge for 5 min at 500× g at room temperature. 5. Remove the supernatant and add 700 μL of Trizol. Store samples a -80 °C or immediately proceed to the next step. 3.4

RNA Extraction

This protocol is optimized for small number of cells (from 50 × 103 cells) and can be used for up to 2 × 106 cells (see Note 9). 1. Thaw Trizol cells on ice. If cells are not frozen, skip this step and directly pass to the next one. 2. Add 200 μL Chloroform to Trizol cells and mix vigorously for 15 s. 3. Keep on ice 10 min and centrifuge at maximum speed for 15 min at 4 °C. 4. Transfer 450 μL aqueous phase to a new RNase free tube not disrupting interphase by inserting tip deeper or making turbulent flow (see Note 10). 5. Add 450 μL RNA quality 70% Ethanol and mix thoroughly by pipette. 6. Transfer 700 μL of sample to Qiagen RNeasy spin column. Centrifuge at 8000× g for 15 s at room temperature.

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7. Dump flow-through (see Note 11). Add rest of sample to spin column. Spin 15 s at 8000× g at room temperature 8. Dump flow-through. Transfer column to new clean RNase free tube. Add 500 μL RPE buffer (see Note 12). Centrifuge at 8000× g for 15 s at room temperature. 9. Dump flow-through. Transfer column to new RNase free clean tube. Add 500 μL of 80% RNA quality Ethanol. Centrifuge at 8000× g for 2 min at room temperature. 10. Dump flow-through. Transfer column to new tube. Spin 5 min at maximum speed at room temperature to dry column membrane. 11. Discard the tube and transfer the column to a fresh RNase free tube. 12. Add 17 μL RNase-free water directly to the center of the column membrane and spin 1 min at full speed at room temperature (see Note 13). 13. Measure RNA concentration using the NanoDrop instrument. 3.5

cDNA Synthesis

RNA (500 ng) is reverse transcribed with the High-Capacity cDNA Reverse Transcription Kit following the manufacturer’s instructions. In particular: Prepare the reaction mix as following indicated Components

Volume/Reaction (μL)

10 × RTbuffer

2

25 × dNTP mix (100 mM)

0.8

10 × Random primers

2

MultiScribe reverse transcriptase

1

RNase inhibitor

1

Nuclease-free water

3.2

RNA (500 ng)

10

Total

20

Mix components all together in a PCR tube and conduct the reaction as follows: Step

1

2

3

4

Temperature (°C)

25

37

85

4

Time

10 min

120 min

5s

1

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3.6 Real-Time PCR of Endosomal TLR Genes

cDNA (25 ng) is mixed with Power SYBR Green PCR Master Mix and a mix of forward and reverse primers. Each sample will be amplified with the primer pair specific for the following genes: TLR3, TLR7, TLR8, TLR9 and GAPDH as internal calibrator. 1. Mix 25 ng of cDNA with 10 μL of PowerUp SYBR Green PCR Master mix, 2 μL of either TLR-specific Biorad primer mix or GAPDH Biorad primer mix and water up to 20 μL per reaction (see Note 14). 2. To generate technical replicates, scale the volumes to dispense each mix into two/three wells of a 96-well reaction plate. 3. Perform exponential amplification in Fast cycling mode according to the following program: Step

Temperature

Duration

Dual loch DNA polymerase

95 °C

20 s

Denature

95 °C

1s

Anneal/extend

60 °C

20 s

Cycles

40

4. An example of Amplification plot is shown in Fig. 3. 5. Data can be exported and analyzed in Microsoft Excel.

Fig. 3 Representative amplification plots relative to the four endosomal TLRs expressed in NK cells. Horizontal lines in the bottom indicate threshold values for GAPDH gene (upper line) and TLR gene (lower line)

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Notes 1. Allow Ficoll to equilibrate to room temperature for approximately 30 min before use. 2. Save the gradient column because red cells on the bottom will be used subsequently. 3. Do not invert the tube, instead suck the supernatant out by pipetting. 4. Use 50 μL RosetteSep for 25 × 106 PBMC. 5. Every 5 min invert the tube twice. 6. Let set the Ficoll 15 min before layering the sample. 7. NK cells are defined as CD3-CD19- and segregate into two major subsets: CD56brightCD16- and CD56dimCD16+. 8. When culturing more than 500,000 NK cells, refer to Table 1, which is based on our experience. 9. For more than 2 × 106 cells, we recommend using the appropriate Qiagen RNA extraction kit. 10. Set P200 pipetter at 150 μL and pipette 150 μL three times. 11. Aspirate flow-through instead of pouring to prevent waste from getting on lip tube. 12. Buffer RPE is supplied by Qiagen kit as a concentrate. Before using for the first time, add 4 volumes of ethanol (96–100%) as indicated on the bottle to obtain a working solution. 13. Final volume will be around 15 μL. 14. Due to the low expression level of TLRs in NK cells, we warmly recommend using at least 20 ng of cDNA per reaction.

Acknowledgments Enrico Maggi is supported by a grant from the Ministero della Salute (grant no. 5X1000 OPBG), and Lorenzo Moretta is funded by Associazione Italiana per la Ricerca sul Cancro (project no. 5x1000 2018 Id 21147 and project no. IG 2017 Id 19920). Irene Veneziani is supported by FIRC-AIRC fellowship for Italy; Claudia Alicata and Biancamaria Ricci are recipients of grants awarded by Fondazione Umberto Veronesi. References 1. Caligiuri MA (2008) Human natural killer cells. Blood 112:461–469 2. Ewen CL, Kane KP, Bleackley RC (2012) A quarter century of granzymes. Cell Death Differ 19:28–35

3. Prager I, Watzl C (2019) Mechanisms of natural killer cell-mediated cellular cytotoxicity. J Leukoc Biol 105:1319–1329 4. Cooper MA, Fehninger TA, Turner SC et al (2001) Human natural killer cells: a unique

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innate immunoregulatory role for the CD56 bright subset. Blood 97(10):3146–3151 5. Mingari MC, Vitale C, Cantoni C et al (1997) Interleukin-15-induced maturation of human natural killer cells from early thymic precursors: selective expression of CD94/NKG2-A as the only HLA class I-specific inhibitory receptor. Eur J Immunol 27(6):1374–1380 6. Poggi A, Sargiacomo M, Biassoni R et al (1993) Extrathymic differentiation of T lymphocytes and natural killer cells from human embryonic liver precursors. Proc Natl Acad Sci U S A 90(10):4465–4469 7. Freud AG, Mundy-Bosse BL, Yu J et al (2017) The broad spectrum of human natural killer cell diversity. Immunity 47(5):820–833 8. Moretta A, Bottino C, Vitale M et al (1996) Receptors for HLA class-I molecules in human natural killer cells. Annu Rev Immunol 14: 619–648 9. Moretta A, Bottino C, Vitale M et al (2001) Activating receptors and coreceptors involved in human natural killer cell-mediated cytolysis. Annu Rev Immunol 19:197–223 10. Kawai T, Akira S (2010) The role of patternrecognition receptors in innate immunity: update on Toll-like receptors. Nat Immunol 11(5):373–384 11. Alexopoulou L, Holt AC, Medzhitov R et al (2001) Recognition of doublestranded RNA and activation of NF-κB by Toll-like receptor 3. Nature 413:732–738 12. Poltorak A, He X, Smirnova I et al (1998) Defective LPS signaling in C3H/ HeJ and C57BL/10ScCr mice: mutations in Tlr4 gene. Science 282:2085–2088

13. Jacobs B, Ullrich E (2012) The interaction of Nk cells and dendritic cells in the tumor environment: how to enforce Nk Cell & Dc Action under immunosuppressive conditions? Curr Med Chem 19:1771–1779 14. Dudek AZ, Yunis C, Harrison LI et al (2007) First in human phase I trial of 852A, a novel systemic Toll-like receptor 7 agonist, to activate innate immune responses in patients with advanced cancer. Clin Cancer Res 13:7119– 7125 15. Link BK, Ballas ZK, Weisdorf D et al (2006) Oligodeoxynucleotide CpG 7909 delivered as intravenous infusion demonstrates immunologic modulation in patients with previously treated non-Hodgkin lymphoma. J Immunother 29:558–568 16. Gadkaree SK, Fu J, Sen R et al (2017) Induction of tumor regression by intratumoral sting agonists combined with anti-programmed death-L1 blocking antibody in a preclinical squamous cell carcinoma model. Head Neck 39:1086–1094 17. Della Chiesa M, De Maria A, Muccio L et al (2019) Human NK cells and herpesviruses: mechanisms of recognition, response and adaptation. Front Microbiol 10:2297 18. Cooper M, Fehniger TA, Fuchs A (2004) Nk cell and DC interactions. Trends Immunol 25: 47–52 19. Veneziani I, Alicata C, Pelosi A et al (2022) Toll-like receptor 8 agonists improve NK-cell function primarily targeting CD56bright CD16- subset. J Immunother Cancer 10(3): e003385corr1

Chapter 9 In Vitro Study of TLR4-NLRP3-Inflammasome Activation in Innate Immune Response Letizia Mezzasoma, Carsten B. Schmidt-Weber, and Francesca Fallarino Abstract Toll-like receptors (TLRs) are pivotal players in mediating immune responses. TLR4 is the main receptor for LPS, a strong activator of immune cells. LPS/TLR4-dependent pathway, by inducing NF-κB activation, is responsible for the release of several mediators, including IL-1β, one of the most powerful cytokines deeply involved in inflammatory and immune responses. The same pathway is also involved in NLRP3inflammasome activation, essential for IL-1β maturation. NLRP3 is a major component of innate immune responses, being a crucial player of host immune defense against virus, bacterial, or fungal infections. NLRP3-inflammasome and IL-1β hyperactivation have been associated to the pathogenesis of a wide range of disorders and represent therapeutic targets for the development of new treatments of inflammasomedriven inflammatory and autoimmune diseases. Here, we describe an in vitro protocol to induce LPS/TLR4-dependent NLRP3-inflammasome/IL-1β activation in immune cells, in order to provide a useful assay to study the efficacy of different antiinflammatory/immune-modulatory agents. Key words Toll-like receptor 4, LPS, NF-κB, IL-1β, NLRP3-inflammasome, Autoimmune diseases, Inflammatory diseases

1

Introduction Toll-like receptors (TLRs) are a family of extracellular or intracellular pattern-recognition receptors (PRRs) and represent pivotal players in mediating immune responses. TLRs are expressed in immune and nonimmune cells and are activated by a broad spectrum of stimuli. Upon activation, the intracellular signaling cascade is responsible for the expression and secretion of proinflammatory cytokines, chemokines, and other mediators that orchestrate the immune host defense [1]. Among TLRs, TLR4 plays a crucial role in mediating innate immune responses by the recognition of pathogenic profiles in the extracellular environment. TLR4 is widely expressed on the cell

Francesca Fallarino et al. (eds.), Toll-Like Receptors: Methods and Protocols, Methods in Molecular Biology, vol. 2700, https://doi.org/10.1007/978-1-0716-3366-3_9, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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surface of the mononuclear phagocyte system cells, such as monocytes, dendritic cells and macrophages and its activation by pathogen-related molecular patterns (PAMPs) and damageassociated molecular patterns (DAMPs), provides the first line of defense against infections and offers a fundamental contributor to the tissue homeostatic responses during cell injury [1–3]. TLR4 structure comprise an extracellular leucine-rich repeats (LRRs) domain, devoted to PAMPs/DAMPs recognition, a transmembrane domain, and a Toll/IL-1 receptor domain (TIR domain), crucial for the TLR4 downstream signaling cascade activation [1]. TLR4 is the main receptor for lipopolysaccharide (LPS), a component of the outer membrane of Gram-negative bacteria and a strong activator of immune cells [2, 4]. LPS binding to TLR4 requires a coordinated involvement of accessory proteins such as LPS-binding protein (LBP), CD14, and the Myeloid Differentiation-2 (MD-2) protein, this latter associated with the extracellular domain of TLR4 [5] (Fig. 1). LPS binding promotes

LPS

LPS/TLR4-dependent pathway

MD-2

CD14

CD14

LRRs

CD14

LPS

LPS

TIR

TIRAP

MD-2

MyD-88

NF-κB activation

Pro-inflammatory cytokines

Fig. 1 LPS/TLR4-dependent pathway. LPS stimulation of immune cells requires the participation of several proteins including LBP, CD14, MD-2 which interactions lead to TLR4 dimerization and the subsequent activation of the intracellular cascade culminating in NF-kB activation, responsible for cytokines production

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the dimerization of TLR4/MD-2 which induces the recruitments of cytoplasmic adaptors proteins which initiate the signal transduction pathways. Among these, TIR domain-containing Adaptor Protein (TIRAP) and Myeloid Differentiation factor 88 (MyD88) are the proteins involved in the intracellular cascade leading to the activation of the transcription factor Nuclear Factor-Kappa-lightchain-enhancer of activated B cells (NF-κB), responsible for the expression of the target pro-inflammatory cytokines [6] (Fig. 1). Among the cytokines produced via LPS/TLR4-dependent pathway, Interleukin (IL)-1β represents a powerful pro- inflammatory mediator able to exert pleiotropic actions in inflammatory and immune responses, including the recruitment of innate immune cells to the site of infection and the modulation of adaptive immune response [7–11]. To provide an adequate immune protection without causing damage to the host tissues, IL-1β production is tightly controlled via a transcriptional and a posttranslational mechanism [12]. LPS/TLR-4 signaling, by inducing NF-κB activation, is responsible for the transcriptional up-regulation of the immature pro-IL-1β, whereas inflammasome-mediated caspase-1 activation is responsible for IL-1β maturation [12]. Inflammasomes are cytoplasmic multiprotein complexes essential for host defense, deeply involved in innate immune responses and in immune tolerance [13]. Inflammasomes sense DAMPs and PAMPs via nucleotidebinding oligomerization domain (NOD)-like receptor (NLRs) and absent in melanoma 2 (AIM2)-like receptors (ALRs). For the NLRs family, the pyrin domain-containing 3 (NLRP3) inflammasome is the best characterized. Upon activation, NLRP3 recruits the effector pro-caspase-1 via the adaptor apoptosis-associated speck like protein containing a caspase-recruitment domain (ASC), leading to the inflammasome platform assembly and caspase-1 activation, responsible for IL-1β maturation [12]. NLRP3 activation is fine-tuned regulated. LPS/TLR4-dependent pathway, via NF-κB activation is responsible for the transcriptional up-regulation of the NLRP3, whereas activation signals, inducing posttranslational modification, drive the oligomerization/activation of the platform [14–18] (Fig. 2). NLRP3-inflammasome and IL-1β hyperactivation have been associated with the pathogenesis of a wide range of disorders including Crohn’s diseases, cryopyrin-associated periodic syndromes, psoriasis, neurological diseases, diabetes, gout, silicosis, cardio-metabolic disorders, atherosclerosis, and cancer [8–11, 18–20]. The clinical relevance of such dysregulation highlights the request of new agents for the treatments of inflammasome-driven inflammatory and autoimmune diseases [21]. Herein, we will describe an in vitro protocol to induce LPS/ TLR4-dependent NLRP3-inflammasome/IL-1β activation in immune cells, in order to offer a useful assay to study the efficacy of different compounds on this immune-modulatory pathway.

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Canonical NLRP3-inflammasome activation pathway Signal 1:Priming LPS

Signal 2:Activation ATP

P2X7R

TIRAP

TLR4

INFLAMMASOME PLATFORM OLIGOMERIZATION

MyD-88

NF-κB ACTIVATION

NLRP3 ASC P-NF-κB

Pro-Caspase-1

Caspase-1 NLRP3 Pro-IL-1β

IL-1β

Fig. 2 Canonical NLRP3-inflammasome activation pathway. The canonical NLRP3-inflammasome activation requires two signals: the first is represented TLR4-LPS engagement that, via NF-κB activation, culminates in the up-regulation of NLRP3 and pro-IL-1β; the second, induced by activation signals, drives the oligomerization of the inflammasome platform, leading to caspase-1 activation

2

Materials Prepare all reagents using appropriate personal protective equipment and recommendations according to the data sheet of each reagent and follow all waste disposal regulations when disposing waste materials.

2.1 Immune Cell Stimulation

1. THP-1 cell line. 2. RPMI 1640 medium with L-glutamine and sodium bicarbonate, liquid, sterile-filtered, suitable for cell culture.

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3. Fetal Bovine Serum (FBS) sterile-filtered, suitable for cell culture. 4. Sodium pyruvate 100 mM, sterile-filtered, suitable for cell culture. 5. Penicillin-streptomycin solution stabilized, with 10,000 units penicillin and 10 mg streptomycin/mL sterile-filtered, suitable for cell culture. 6. Nonessential amino acids liquid 100×, sterile-filtered, suitable for cell culture. 7. Trypan blue. 8. Bi-distilled water sterile. 9. Phorbol-12-myristate-13-acetate (PMA). 10. Dimethyl sulfoxide (DMSO), suitable for cell culture. 11. Trypsin, suitable for cell culture. 12. Lipopolysaccharide (LPS) from E. coli O111:B4, for cell culture. 13. Adenosine 5′-triphosphate disodium salt hydrate (ATP), Grade I, ≥99%, from microbial. 14. Appropriate personal protective equipment. 15. T 25 or T 75 sterile flasks with vented caps. 16. 12-well sterile cell culture plates. 17. 5 mL or 10 mL capacity serological pipette. 18. 20 μL, 100 μL, and 1000 μL sterile pipette tips. 19. 10 mL or 50 mL sterile conical tube. 20. Cell counting chamber. 21. Coverslips. 22. Laminar flow hood. 23. Humidified cell culture incubator set to 37 °C and 5% CO2. 24. Water bath set to 37 °C or 56 °C. 25. Centrifuge with swinging-bucket rotor and adaptors for 50 mL conical tubes. 26. Upright microscope with 10× objective. 27. Pipette-aid. 2.2 NLRP3Inflammasome and NF-kB Activation Analysis

1. PBS buffer (NaCl: 137 mM, KCl: 2.7 mM, Na2HPO4: 10 mM, KH2PO4: 1.8 mM). Place a beaker with a magnetic stir bar on stirrer and add 8 g of NaCl to 800 mL of bi-distilled water and allow to mix until NaCl is dissolved. Add 0.2 g of KCl, 1.44 g of Na2HPO4, 0.245 g of KH2PO4. Adjust pH to 7.4 and add bi-distilled water until the volume is 1000 mL.

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2. RIPA Lysis Buffer pH 7.4 complete with protease inhibitors. RIPA Lysis Buffer (Tris–HCl 50 mM, pH 8.0; NaCl 150 mM; Triton X-100 1%; SDS 0.1%): Place a beaker with a magnetic stir bar on stirrer under a chemical hood and add 0.6 g of Tris Base to 50 mL of bi-distilled water and allow to mix until the Tris–HCl is dissolved. Add 0.86 g of NaCl, 1 mL of Triton X-100 and 0.1 g SDS. Adjust pH to 7.4 and add bi-distilled water to 100 mL. Add protease inhibitors (Pefabloc 2 mM, NaF 20 mM, Aprotinin 0.008 TIU/mL, Bacitracin 0.1 mg/ mL, EDTA 2 mM): add 0.048 g of Pefabloc, 0.083 g of NaF, Aprotinin 0.008 TIU/mL, 0.01 g of Bacitracin and 400 μL of EDTA 0.5 M. Aliquot and store at -20 °C until use. 3. Protein quantitation assay materials. 4. Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) materials. 5. Prestained protein standard with protein bands ranging in molecular weight from 3.5 to 260 kDa. 6. Electro-transfer blotter materials. 7. TBS-Tween 20 (TBS-T) (Tris Base 0.5 M; NaCl 9%; Tween 20 0.5%; pH 8.4) washing buffer 10×: Place a beaker with magnetic stir bar on stirrer and add 61 g of Tris–HCl and 90 g of NaCl into 900 mL of distilled water. Mix to dissolve, adjust pH to 8.4, add 5 mL of Tween 20 and add bi-distilled water to 1000 mL. Store this solution at room temperature. Before use, dilute 100 mL of this stock solution with 900 mL distilled water to get TBS-T 1× concentrated. 8. Ponceau S. 9. ROTI block. 10. Antibodies against human: NLRP3, pro-caspase-1 and active caspase-1 p20 subunit, ASC, pro-IL-1β, mature IL-1β, Phospho-p65-NF-kB, p65-NF-kB, β-actin and the appropriate horseradish peroxidase (HRP)-conjugated secondary antibodies. 11. Enhanced Chemiluminescence (ECL) WB substrate. 12. Human-IL-1β ELISA kit. 13. Appropriate personal protective equipment. 14. Laboratory glassware. 15. 1 mL, 5 mL, or 10 mL capacity pipette. 16. 100 μL and 1000 μL pipette tips. 17. 1.5 mL microcentrifuge tubes. 18. Ice. 19. Pipette-aid.

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20. Magnetic stir bar. 21. Stirrer. 22. Bench top refrigerated microcentrifuge. 23. Vortex. 24. Chemical balance. 25. pH sensor. 26. Chemical hood. 27. Platform shaker. 28. Enhanced Chemiluminescence revealing system. 29. Microplate reader with a wavelength of 450 nm. 2.3 NLRP3Inflammasome Assembly Analysis by Chemical Crosslinking of ASC Oligomers

1. PBS buffer pH 7.2. 2. Dimethyl sulfoxide (DMSO). 3. Bis-N-succinimidyl-(pentaethylene gylcol) (BS(PEG)5). 4. Tris–HCl 0.5 M pH 7.5. 5. Sample loading buffer. 6. Appropriate personal protective equipment. 7. 1.5 microcentrifuge tubes. 8. 10 μL, 100 μL, and 1000 μL pipette tips. 9. Ice. 10. Pipette-aid. 11. Bench-top refrigerated microcentrifuge. 12. Chemical hood.

3

Methods

3.1 Immune Cell Stimulation

This protocol is performed by using the human monocytic cell line THP-1 which represent a valuable tool for the research in immune system disorder, immunology and toxicology fields and a widely used model for investigating monocyte and macrophage biology. THP-1 designates a spontaneously immortalized monocyte-like cell line, isolated from peripheral blood of an acute monocytic leukemia patient [22]. THP-1 cells may be also easily differentiated into macrophages-like cells by 20 nM Phorbol-12-myristate-13acetate (PMA). The induction of canonical NLRP3-inflammasome activation is performed by a two-step protocol. THP-1 cells are pretreated with the TLR4-ligand LPS (priming step) and are subsequently treated with ATP (activation step) to trigger the NLRP3-inflammasome platform assembly/activation [12, 16, 17] (Fig. 3).

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Fig. 3 Schematic representation of LPS/TLR4-dependent NLRP3-inflammasome/IL-1β activation protocol. THP-1 cells are stimulated with priming signals and subsequently with activation signals. NLRP3inflammasome activation is analyzed by cross-linking of ASC oligomers; WB of p-NF-kB and inflammasome components; ELISA of IL-1β secreted

All the steps must be performed using aseptic techniques under sterile conditions using appropriate personal protective equipment and recommendation according to the data sheet of each reagent and follow all waste disposal regulations when disposing waste materials. 1. Transfer FBS in 50 mL sterile conical tube and inactivate the complement proteins, that are part of the immune response, by heating for 1 h in water bath set to 56 °C. 2. Prepare complete RPMI 1640 medium by supplementing RPMI 1640 medium with inactivated FBS to a final concentration of 10%, sodium pyruvate 1 mM, nonessential amino acids 1×, penicillin 100 units/mL, streptomycin 0.1 mg/mL. Store at +4 °C until use. 3. Solubilize LPS in sterile bi-distilled water at a final concentration of 1 mg/mL (stock solution), aliquot and store at -20 °C until use. 4. Defrost THP-1 cells by transferring 10 mL of preheated at 37 °C complete RPMI 1640 into a 50 mL conical tube and keep warm. Defrost the vial of frozen cells as quickly as possible by using a water bath set to 37 °C until just defrosted. Transfer the cells into the warmed medium and centrifuge at 800× g for 5 min to remove the DMSO. Carefully remove the supernatant without disturbing the cell pellet. Add 5 mL of preheated at 37 °C complete RPMI 1640 medium gently to the side of the tube and slowly pipette up and down 2–3 times to resuspend the cell pellet. Transfer cells into a T25 flask and incubate overnight. The next day count the cells and check the viability by using dye Trypan blue. Adjust cell density accordingly.

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5. Propagate THP-1 cells (see Note 1) by starting cell cultures at 1 × 105 cells/mL in 20 mL complete RPMI 1640 medium into T75 flasks, let grow to approximately 1 × 106 cells/mL by allowing to grow for 3 days. Split 1/5 by centrifuging cells at 800× g for 5 min. Carefully remove the supernatant without disturbing the cell pellet. Add 5 mL of preheated at 37 °C complete RPMI 1640 medium gently to the side of the tube and slowly pipette up and down 2–3 times to resuspend the cell pellet. Resuspend THP-1 cells to a concentration of 3 × 106 cells/mL in complete RPMI 1640 medium. Prepare the volume needed based on the experimental condition you will be use. 6. Transfer 1 mL of cell suspension in 12-well cell culture plates. Perform each test in triplicate. The same condition will be used for: NLRP3-inflammasome activation analysis; IL-1β secretion analysis; NLRP3-inflammasome assembly evaluation by crosslinking of ASC oligomers. 7. Incubate plate/s for 1 h in a 5% CO2 humidified cell culture incubator at 37 °C. 8. Add LPS at a concentration of 10 μg/mL (10 μL of the stock solution for 1 mL of cell suspension). To distribute LPS, immediately mix the cell suspension by pipetting up and down 3 times. 9. Incubate plate/s for 40 min a 5% CO2 humidified cell culture incubator at 37 °C. 10. Prepare 500 mM ATP solution by dissolving the ATP powder in sterile bi-distilled water. Prepare ATP immediately before use in ice and, at the end of the incubation time, add to the wells at a concentration of 5 mM (10 μL for 1 mL of cell suspension). To distribute the ATP, immediately mix the cell suspension by pipetting up and down 3 times. 11. Incubate plate/s in a 5% CO2 humidified cell culture incubator at 37 °C for: 20 min for the NLRP3-inflammasome activation analysis; 24 h and 48 h for IL-1β secretion analysis; 20 min for the NLRP3-inflammasome assembly evaluation by crosslinking of ASC oligomers. 3.1.1 THP-1 Differentiation into Macrophages-Like Cells by Phorbol-12-Myristate-13Acetate (PMA)

1. Centrifuge THP-1 cells at 800× g for 5 min at RT. 2. Resuspend in complete RPMI medium to a concentration of 1 × 106 cells/mL. Prepare the volume needed based on the experimental condition you will be use. 3. Transfer 1 mL of cell suspension in 12-well cell culture plates. Perform each test in triplicate. 4. Incubate plate/s for 1 h in a 5% CO2 incubator at 37 °C.

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5. Prepare PMA 1 mM by dissolving 1 mg of PMA in 1.612 mL of DMSO (stock solution that can be aliquoted and stored dark at -20 °C for at least 6 months). 6. Prepare PMA 2 μM by adding 1 μL of PMA 1 mM (stock solution) for 500 μL of complete RPMI (aliquot and store dark at -20 °C for at least 6 months). 7. Use a final concentration of PMA 20 nM to differentiate THP-1 cells by adding 10 μL of PMA 2 μM for 1 mL of cell suspension. 8. Allow THP-1 cells to differentiate for 3 days (adding PMA on Friday, the cells will be ready on Monday). 9. The third day assess the acquisition of a macrophage-like phenotype (cell adhesion, spreading, and increased cytoplasmic volume), by the microscopy evaluation. 10. Remove medium, wash gently 3 times with complete RPMI medium and perform the evaluation of LPS/TLR4-induced NLRP3-inflammasome/IL-1β activation, according to the experimental protocol described above. 11. To lift cells, use trypsin, or a nonenzymatic cell dissociation solution. 3.2 NLRP3Inflammasome and NF-kB Activation Analysis

The NLRP3-inflammasome activation is under intensive investigation given its involvement in several human diseases [9, 18– 20]. The most prominent function of NLRP3-inflammasome is the processing and activation of pro-IL-1β via caspase-1 activation. This protocol evaluates the canonical NLRP3-inflammasome activation and the related NF-kB involvement in LPS/ATP-stimulated THP-1 cells by Western Blot (WB) analysis of phospho-p65-NF-kB and of all the components of the platform: NLRP3; ASC; pro-Caspase-1; active Caspase-1; pro-IL-1β; mature IL-1β; and by ELISA analysis of secreted IL-1β concentration in the culture medium of THP-1 cells.

3.2.1 Total Protein Extraction

All the steps for the protein extraction must be performed at 2–8 °C. 1. Collect THP-1 cell suspension after the stimulation with LPS and ATP (see Subheading 3.1) (or THP-1 differentiated into macrophages by trypsinization), and transfer into 1.5 mL sterile microcentrifuge tubes. Place on ice. 2. Pellet cells by centrifugation at 800× g for 5 min at 4 °C in a chilled microcentrifuge. 3. Discard the supernatant and wash the cell pellet by adding ice-cold PBS (500 μL). Resuspend pellet by pipetting up and down for 3 times.

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4. Pellet cells by centrifugation at 800× g for 5 min at 4 °C in a chilled microcentrifuge. Discard the supernatant and make another centrifugation at 800× g for 5 min at 4°C to completely remove the supernatants. 5. Add ice-cold complete RIPA-lysis buffer to the cell pellet. For 3 × 106 cells, use 50 μL of complete RIPA-lysis buffer. Promote cell lysis by pipetting up and down 3 times. Collect together the triplicates, place on ice for 30 min and vortex for six periods of 20 s each. 6. At the end of the incubation time, freeze at -80 °C. 7. After thawing, centrifuge the tubes at 10,000× g for 15 min at 4 °C in a chilled microcentrifuge. 8. Collect the supernatant that contains the total proteins in a fresh tube. 9. Place on ice and take 2 μL to perform protein quantitation according to a standard protein determination protocol. 10. Store protein at -20 °C until use. 3.2.2 Western Blot Analysis

1. Separate 15 μg of total proteins by a standard protocol of 10% or 12% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE). As marker use a prestained protein standard with protein bands ranging in molecular weight from 3.5 to 260 kDa. Transfer the proteins from gel to a nitrocellulose membrane by using a standard electro-transfer blotter system protocol. 2. After transfer, wash membrane with bi-distilled water for 5 min. 3. Detect protein bands on nitrocellulose membranes by using a standard Ponceau S staining. 4. Wash membrane with bi-distilled water for 5 min. 5. Wash membrane 3× with 15 mL TBS-T, 5 min each time. 6. Block the nonspecific binding sites with 10 mL ROTI block for 1 h at room temperature (RT), in a gentle continuous shaking. 7. Incubate with 10 mL of the appropriate anti-human antibodies (Abs) diluted 1:1000 in ROTI block over night at 4 °C, in a gentle continuous shaking. 8. Wash membrane 3× with 25 mL TBS-T, 10 min each time. 9. Incubate with 10 mL of the appropriate HRP-conjugated secondary Abs diluted 1:2000 in ROTI block for 1 h at RT, in a gentle continuous shaking. 10. Wash membrane 3× with 25 mL TBS-T, 10 min each time. 11. Reveal using the enhanced chemiluminescence (ECL) WB substrate and the appropriate revealing system.

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3.2.3 IL-1β Secretion Analysis

1. Collect the medium of THP-1 cells stimulated with LPS and ATP for 24 h and 48 h (see Subheading 3.1) and place into 1.5 mL microcentrifuge tubes. 2. Centrifugate at 800× g for 5 min at 4 °C in a chilled microcentrifuge. 3. Collect the supernatant and use immediately for ELISA assay or aliquoted and stored at -80 °C. 4. Vortex the medium samples prior to their use in IL-1β ELISA assays. 5. Follow instructions provided by the manufacturer of the ELISA kits. 6. Read the absorbance of the wells on a microplate reader with a wavelength of 450 nm.

3.3 NLRP3Inflammasome Assembly Analysis by Chemical Crosslinking of ASC Oligomers

The NLRP3-inflammasome canonical activation relies upon inflammasome components assembly induced by posttranslational modification [16]. The key element responsible for inflammasome assembly is the adapter protein ASC, which bridges inflammasome NLRP3 receptors and caspase-1. Inflammasome activation triggers supramolecular oligomerization of ASC dimers into relatively large protein aggregates which are termed “ASC-specks” [23]. The detection of this unique feature of ASC constitutes a simple and reliable upstream readout for inflammasome activation. Here, we describe a method to detect the oligomerization of ASC by chemical crosslinking of ASC oligomers. The chemical reagent used in this assay is Bis-N-succinimidyl-(pentaethylene gylcol) BS(PEG)5, a homobifunctional, amine reactive crosslinkers with polyethylene glycol (PEG) spacer arms, which covalently bounds amine-containing molecules. 1. Prepare BS(PEG)5 solution carefully follow data sheet protocol recommendations (see Note 2). 2. Collect THP-1 cell suspension after the stimulation with LPS and ATP (see Subheading 3.1) and transfer it into 1.5 mL microcentrifuge tubes. Place on ice. 3. Pellet cells by centrifugation at 800× g for 5 min at 4 °C in a chilled microcentrifuge. 4. Discard the supernatant and suspend cells in ice-cold PBS pH 7.2 (500 μL). 5. Froze at -80 °C for 10 min and thaw for 5 cycles. 6. Centrifuge at 10,000× g for 10 min at 4 °C in a chilled microcentrifuge. 7. Suspend very gently the pellet with PBS pH 7.2 (50 μL). 8. Add 1 μL of BS(PEG)5 250 mM (stock solution).

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9. Incubate for 30 min at room temperature (RT). 10. Quench by adding 5 μL Tris–HCl 0.5 M pH 7.5. 11. Incubate for 15 min RT. 12. Centrifuge at 10,000× g for 10 min at 4 °C in a chilled microcentrifuge. 13. Discard supernatants. 14. Directly resuspend pellets in sample loading buffer (20 μL). 15. Resolve on a 12% SDS-PAGE (see Subheading 2.2). 16. Visualize by immunoblotting with the anti-ASC antibody (see Subheading 2.2).

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Notes 1. THP-1 cells grown in suspension with a doubling time of approximately 24 h. THP-1 cells grow faster at higher concentration and the best propagation density is between 1 × 105 cells/mL and 1 × 106 cells/mL. THP-1 best use for experiments is between passage numbers 5 and 10. 2. BS(PEG)5 is a viscous pale liquid difficult to weight and distribute. To facilitate handling, prepare a Crosslinker stock solution 250 mM immediately before first use by dissolving 100 mg of BS(PEG)5 (entire contents of the vial, ~100 μL) with 650 μL of DMSO. Store unused stock solution in a moisture-free condition at -20 °C (stable for three months). Equilibrate reagent vial to room temperature before opening to avoid moisture condensation inside the container. Minimize exposure to air by keeping the stock solution capped by a septum through which reagent can be taken by a syringe.

References 1. Gay NJ, Gangloff M (2007) Structure and function of Toll receptors and their ligands. Annu Rev Biochem 76:141–165. https://doi. org/10.1146/annurev.biochem.76.060305. 151318 2. Ohto U, Fukase K, Miyake K, Shimizu T (2012) Structural basis of species-specific endotoxin sensing by innate immune receptor TLR4/MD-2. Proc Natl Acad Sci U S A 109(19):7421–7426. https://doi.org/10. 1073/pnas.1201193109 3. Zhang X, Mosser DM (2008) Macrophage activation by endogenous danger signals. J Pathol 214(2):161–178. https://doi.org/10. 1002/path.2284

4. Meng F, Lowell CA (1997) Lipopolysaccharide (LPS)-induced macrophage activation and signal transduction in the absence of Src-family kinases Hck, Fgr, and Lyn. J Exp Med 185(9): 1661–1670. https://doi.org/10.1084/jem. 185.9.1661 5. Nagai Y, Akashi S, Nagafuku M, Ogata M, Iwakura Y, Akira S, Kitamura T, Kosugi A, Kimoto M, Miyake K (2002 Jul) Essential role of MD-2 in LPS responsiveness and TLR4 distribution. Nat Immunol 3(7): 667–672. https://doi.org/10.1038/ni809 6. Lu YC, Yeh WC, Ohashi PS (2008 May) LPS/TLR4 signal transduction pathway. Cytokine 42(2):145–151. https://doi.org/10. 1016/j.cyto.2008.01.006

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7. Dinarello CA (2018) Overview of the IL-1 family in innate inflammation and acquired immunity. Immunol Rev 281(1):8–27. https://doi.org/10.1111/imr.12621 8. Dinarello CA (2011) Interleukin-1 in the pathogenesis and treatment of inflammatory diseases. Blood 117(14):3720–3732. https:// doi.org/10.1182/blood-2010-07-273417 9. Jain A, Irizarry-Caro RA, McDaniel MM, Chawla AS, Carroll KR, Overcast GR, Philip NH, Oberst A, Chervonsky AV, Katz JD, Pasare C (2020) T cells instruct myeloid cells to produce inflammasome-independent IL-1β and cause autoimmunity. Nat Immunol 21(1): 65–74. https://doi.org/10.1038/s41590019-0559-y 10. Zhang W, Borcherding N, Kolb R (2020) IL-1 signaling in tumor microenvironment. Adv Exp Med Biol 1240:1–23. https://doi.org/ 10.1007/978-3-030-38315-2_1 11. Migliorini P, Italiani P, Pratesi F, Puxeddu I, Boraschi D (2020) The IL-1 family cytokines and receptors in autoimmune diseases. Autoimmun Rev 19(9):102617. https://doi.org/ 10.1016/j.autrev.2020.102617 12. Martinon F, Burns K, Tschopp J (2002) The inflammasome: a molecular platform triggering activation of inflammatory caspases and processing of pro IL-beta. Mol Cell 10(2):417–426. https://doi.org/10.1016/s1097-2765(02) 00599-3 13. Huber S, Gagliani N, Zenewicz LA, Huber FJ, Bosurgi L, Hu B, Hedl M, Zhang W, O’Connor W Jr, Murphy AJ, Valenzuela DM, Yancopoulos GD, Booth CJ, Cho JH, Ouyang W, Abraham C, Flavell RA (2012) IL-22BP is regulated by the inflammasome and modulates tumorigenesis in the intestine. Nature 491(7423):259–263. https://doi.org/10. 1038/nature11535 14. Mezzasoma L, Antognelli C, Talesa VN (2016) Atrial natriuretic peptide down-regulates LPS/ATP-mediated IL-1β release by inhibiting NF-kB, NLRP3 inflammasome and caspase-1 activation in THP-1 cells. Immunol Res 64(1):303–312. https://doi.org/10. 1007/s12026-015-8751-0 15. Mezzasoma L, Antognelli C, Talesa VN (2017) A novel role for brain natriuretic peptide:

inhibition of IL-1β secretion via downregulation of NF-kB/Erk 1/2 and NALP3/ASC/ caspase-1 activation in human THP-1 monocyte. Mediat Inflamm 2017:5858315. https:// doi.org/10.1155/2017/5858315 16. Kelley N, Jeltema D, Duan Y, He Y (2019) The NLRP3 inflammasome: an overview of mechanisms of activation and regulation. Int J Mol Sci 20(13):3328. https://doi.org/10. 3390/ijms20133328 17. Mezzasoma L, Talesa VN, Romani R, Bellezza I (2020) ANP and BNP exert antiinflammatory action via NPR-1/cGMP axis by interfering with canonical, non-canonical, and alternative routes of inflammasome activation in human THP1 cells. Int J Mol Sci 22(1): 24. https://doi.org/10.3390/ijms22010024 18. Menu P, Vince JE (2011) The NLRP3 inflammasome in health and disease: the good, the bad and the ugly. Clin Exp Immunol 166(1): 1 – 1 5 . h t t p s : // d o i . o r g / 1 0 . 1 1 1 1 / j . 1365-2249.2011.04440.x 19. Guo H, Callaway JB, Ting JP (2015) Inflammasomes: mechanism of action, role in disease, and therapeutics. Nat Med 21(7):677–687. https://doi.org/10.1038/nm.3893 20. Gabay C, Lamacchia C, Palmer G (2010) IL-1 pathways in inflammation and human diseases. Nat Rev Rheumatol 6(4):232–241. https:// doi.org/10.1038/nrrheum.2010.4 21. Yang Y, Wang H, Kouadir M, Song H, Shi F (2019) Recent advances in the mechanisms of NLRP3 inflammasome activation and its inhibitors. Cell Death Dis 10(2):128. https://doi. org/10.1038/s41419-019-1413-8 22. Tsuchiya S, Yamabe M, Yamaguchi Y, Kobayashi Y, Konno T, Tada K (1980) Establishment and characterization of a human acute monocytic leukemia cell line (THP-1). Int J Cancer 26(2):171–176. https://doi.org/10. 1002/ijc.2910260208 23. Fernandes-Alnemri T, Wu J, Yu JW, Datta P, Miller B, Jankowski W, Rosenberg S, Zhang J, Alnemri ES (2007) The pyroptosome: a supramolecular assembly of ASC dimers mediating inflammatory cell death via caspase-1 activation. Cell Death Differ 14(9):1590–1604. https://doi.org/10.1038/sj.cdd.4402194

Part III Toll Like Receptors as Novel Therapeutic Targets for Diseases

Chapter 10 Toll-Like Receptor Polymorphisms and the Risk of Cancer: Meta-analysis Study Narttaya Chaiwiang and Teera Poyomtip Abstract A systematic review and meta-analysis is a useful method to summarize the different results from primary data, which can then provide an evidence-based outcome. Meta-analysis generates quantitative data by calculating effect sizes, which include odd ratios, relative risks, proportions, correlation coefficients, and so forth. The study of single-nucleotide polymorphisms (SNPs) and the association with the interested outcome is one discipline that has resulted in inconsistent relations. Therefore, the meta-analysis aimed to summarize the relevant data on SNPs associated with the outcome of interest. Herein, we describe a comprehensive meta-analysis on Toll-like receptor-9 polymorphism and the risk of cervical cancer. Key words Meta-analysis, Gene polymorphisms, Toll-like receptor-9, Cervical cancer

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Introduction Genetic association study (GAS) aims to identify single-nucleotide polymorphism (SNP), which is related to phenotypes such as treatment responses, risk of diseases, and disease progression. To date, a large number of GAS have been undertaken, though some genetic variants may not usually be repeatable between studies because of differences in experimental designs, genotyping methods, sample size, genetic backgrounds, and others, resulting in often inconsistent outcomes and thus uncertainty. These limitations may cause biological plausibility [1, 2]. A systematic review coupled with meta-analysis is a useful approach to solve problems by combining the results across studies, thus leading to increased statistical power while providing a summary of the interested gene [3]. In this chapter, we provided an example of guidelines for a comprehensive systematic review and meta-analysis to study the association between gene polymorphisms and cancer.

Francesca Fallarino et al. (eds.), Toll-Like Receptors: Methods and Protocols, Methods in Molecular Biology, vol. 2700, https://doi.org/10.1007/978-1-0716-3366-3_10, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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Materials To summarize the different GAS results, the original studies should be considered as materials for meta-analysis. In doing so, the unbiased selection of publications provides high-quality meta-analysis, which should also be conducted in accordance with international standards, guidelines, and protocols. Regarding the gene association study, we recommend registering the protocol in the INPLASY database or PROSPERO database.

2.1 Identification of Study Types

The first step in performing a systematic review and meta-analysis is to identify the type of study. The study types should be related to: 1. Human subject. 2. Case–control study design. 3. Publication in English. 4. Publication in the full article. A study was excluded if it was a brief report, short communication, conference report, review article, or thesis.

2.2 Identification of Participants

The types of participants are the definition of case and study. Occasionally, there are different definitions of case or control between included studies, so the clearly defined case and control in this step reduce error in interpretation of meta-analysis results.

2.3

Keywords or search terms are the words that identify the main concepts of your study. Targeting your search to specific keywords can be a powerful tool to summarize your conclusion. In case, without the right keywords, it can be difficult to find relevant articles in the databases. Therefore, the keyword-setting process is very important to the systemic literature search. The characteristics of keywords include the following:

Keywords

1. A broad term: A broad term refers to a broader of search termsincrease resulting to various outcomes. For example, if your study is related to depression, you can use the broader term “disease” or “illness.” 2. Synonyms or alternative keywords: A word that has the same or nearly the same meaning can be helpful to determine relevant keywords for the systemic literature search. Some studies have many different words that describe the content, synonyms, or alternative keywords that can be used in the systemic literature search. 3. Specific terms: A word with a clear meaning can be helpful to find all of the relevant results. It is also used to combine multiple keywords using Boolean terms (AND, OR, and NOT) to create a higher percentage of efficient searching.

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4. A relationship words or judgment words: Many words can be unhelpful to determine relevantly search particularly relationship words or judgment words. These words may exclude relevant articles on your systemic literature search. 5. Abbreviations: It is a shortened form of words that will help ensure you are finding all of the relevant articles. For example, CNS is an abbreviation for the central nervous system. 6. Concise terms: A keyword must be a concise term. Using keywords that are many words of phrase results in more information and are not relevant to the topic. In this example, we searched the following keywords in PubMed on January 24, 2022: ((Cervical Cancer) AND (variants OR Genetic Polymorphisms OR Genetic Polymorphism OR genotyping OR SNP OR SNPs OR Single-Nucleotide Polymorphism OR Single-Nucleotide Polymorphisms OR Polymorphisms OR Polymorphism OR Nucleotide Polymorphism) AND (TLR-9 OR TLR9 OR Receptor, TLR9 OR TLR9 Receptor OR Toll 9 Receptor OR Toll-9 Receptor OR Toll Like Receptor 9)). 2.4 Searching Strategy

3

Two independent researchers apply the keywords that cover the objective of the study to search in the databases. In general, two investigators independently perform searching. At least three databases were acceptable for systematic review. In this example, we suggested PubMed, Web of Science, and EMBASE. After getting the search results, the investigators screened the title and abstract to include and exclude the studies following the criteria as shown in Subheading 2.1 and fill the Preferred Reporting Item for Systematic Review and Meta-analysis (Fig. 1) [4]. However, our example was performed only on PubMed, in which the result showed 27 relevant articles.

Methods

3.1 Quality Assessments

This is an important step when analyzing data because the low quality in a primary study may affect the summarized results. Therefore, appropriate studies are essential for registration in meta-analysis. Presently, there are several forms of quality assessments that researchers can apply to evaluate the primary study, such as the STrengthening the REporting of Genetic Association Studies (STREGA) [5]. Ye et al. presented a quality assessment based on this tool, which rates the original study using a 2-point scale (0 = no, 1 = yes) following 9 questions [6]: 1. Is a description of genotyping techniques available? 2. Is there an evaluation of population stratification?

Identification

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….articles from International Database (PubMed, EMBASE, and Web of Science)

Screening

….articles were removed as duplication

….articles excluded for the following reasons: ….articles screened

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Non-relevant studied Review; Abstract; Report; Poster presentation Other pathogens infection Unavailable data for genetic model calculation Etc.

Eligibility

….full text articles assessed for eligibility

….additional studies were identified from relevant review

Included

….studies included in quality synthesis

Experimental studies investigating SNPs and cancers (….articles)

Fig. 1 Flow diagram of eligible articles selected in this meta-analysis

3. Is there a description of genotype inference methods? 4. Is there an explanation of how Hardy–Weinberg Equilibrium (HWE) works in terms of controls? 5. Is it stated if the study is a first report, a replication attempt, or both? 6. Is it possible to get information on the eligibility and matching criteria for matched case–control studies?

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7. Is there a description of statistical methods and software versions used? 8. Is there a description to address and adjust for subjectrelatedness? 9. Is the information sufficient? 3.2

Data Extraction

3.3 Statistical Analysis

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The primary data was extracted separately by two independent researchers. Any discrepancies were resolved via discussion. A third person would be invited to give an opinion when the authors failed to reach a consensus agreement. Necessary information was expected as follows: the first author’s name, year of publication, ethnicity, male:female ratio, number of subjects, genotyping methods, and genotype frequency. To examine selection bias, Hardy–Weinberg Equilibrium (HWE) is calculated in the control group via the Chi-square test. The association between SNPs and risk of cancer is calculated by odds ratio (OR) with 95% confidence intervals (95% CIs). All allelic models (allele contrast, homozygous comparison, heterozygous comparison, dominant model, recessive model, and overdominant model) are determined for the association, and the p value is calculated from multiple testing with the Bonferroni method. The heterogeneous study is evaluated by I2 value. I2 > 50% coupled with statistical significance is considered as heterogeneity, and the pool-OR is generated using a random-effect model. Publication bias is tested by funnel plot and Egger’s regression test. Finally, sensitivity analysis is performed by subsequently omitting the study to observe the stability of this meta-analysis. We suggested MetaGenyo [7] to use for analyzing all statistic models.

Notes 1. Research question must be clearly understood. The key elements are PICO (Population, Intervention, Comparator and, Outcomes) resulting in an effective answer. 2. The exclusion of articles during the study must be justifiable. Otherwise, it may impair the research results. 3. The two main investigators had different opinions, may need a third person to resolve the issue. 4. In data extraction, the study location should be considered to ensure the data are not double-counted. 5. The major allele and minor allele of interest should be identified when performing a meta-analysis of gene association study to indicate the risk allele before analysis.

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6. When the study focuses on multiple genetic models, it should indicate the dominant and recessive models. For example, given that A and B in haploid and A is the risk allele, the classical genetic model should be categorized into three groups: Dominant model (AA + AB versus BB), Recessive model (AA versus AB + BB), and Additive model (AA versus AB versus BB) [8]. 7. The one-layer omitting (ignore one study for analysis) is called sensitivity analysis, which is used to observe the robustness of the results. 8. When I2 is lower than 50%, the fixed-effect model should be used for analysis 9. When the I2 is higher than 50%, subgroup analysis should be performed to identify the source of heterogeneity. Subgroup analysis may depend on the type of study (i.e., case–control study versus cohort study), ethnicity (i.e., Asian versus European), genotyping method (i.e., PCR-RFLP versus DNA sequencing), the biology of the disease (i.e., disease stages), and others. 10. The data for the included study may be missing or insufficient. Attempts should be made to contact the corresponding authors to get the necessary data by e-mail before these publications are excluded from the analysis. 11. It is important to state its publication before the start of the meta-analysis study in the method section. 12. In the discussion, the limitation and protocol deviation of meta-analysis in the field of interest should be considered. 13. MetaGenyo [7] is a web-based interactive platform that is userfriendly. Other programs can also perform the meta-analysis, such as RevMen and Stata. References 1. Van der Velden WJ, Feuth T, Stevens WB, Donnelly JP, Blijlevens NM (2011) Issues in genetic association studies: limitations of statistical analysis and biological plausibility. Bone Marrow Transplant 46(6):906–907. https://doi.org/ 10.1038/bmt.2010.211 2. Colhoun HM, McKeigue PM, Davey Smith G (2003) Problems of reporting genetic associations with complex outcomes. Lancet 361(9360):865–872. https://doi.org/10. 1016/s0140-6736(03)12715-8 3. Trikalinos TA, Salanti G, Zintzaras E, Ioannidis JP (2008) Meta-analysis methods. Adv Genet 60:311–334. https://doi.org/10.1016/ S0065-2660(07)00413-0

4. Moher D, Liberati A, Tetzlaff J, Altman DG, Group P (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6(7): e1000097. https://doi.org/10.1371/journal. pmed.1000097 5. Little J, Higgins JP, Ioannidis JP, Moher D, Gagnon F, von Elm E, Khoury MJ, Cohen B, Davey-Smith G, Grimshaw J, Scheet P, Gwinn M, Williamson RE, Zou GY, Hutchings K, Johnson CY, Tait V, Wiens M, Golding J, van Duijn C, McLaughlin J, Paterson A, Wells G, Fortier I, Freedman M, Zecevic M, King R, Infante-Rivard C, Stewart A, Birkett N (2009) STrengthening the

Toll-Like Receptor Polymorphisms and the Risk of Cancer: Meta-analysis Study REporting of Genetic Association Studies (STREGA) – an extension of the STROBE statement. Genet Epidemiol 33(7):581–598. https://doi.org/10.1002/gepi.20410 6. Ye ZM, Luo MB, Zhang C, Zheng JH, Gao HJ, Tang YM (2020) A comprehensive evaluation of single nucleotide polymorphisms associated with osteosarcoma risk: a protocol for systematic review and network meta-analysis. Medicine (Baltimore) 99(26):e20486. https://doi.org/ 10.1097/MD.0000000000020486

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7. Martorell-Marugan J, Toro-Dominguez D, Alarcon-Riquelme ME, Carmona-Saez P (2017) MetaGenyo: a web tool for meta-analysis of genetic association studies. BMC Bioinf 18(1):563. https://doi.org/10.1186/s12859017-1990-4 8. Zhao F, Song M, Wang Y, Wang W (2016) Genetic model. J Cell Mol Med 20(4):765. https://doi.org/10.1111/jcmm.12751

Chapter 11 Unrevealing the Role of TLRs in the Pathogenesis of Autoimmune Disease by Using Mouse Model of Diabetes Eleonora Panfili, Elena Orecchini, and Giada Mondanelli Abstract Toll-like receptors (TLRs) are receptors of the innate immune system specialized in recognizing conserved molecular pattern of pathogens and initiating an appropriate immune response. Along with the recognition of foreign materials, TLRs have also been shown to respond to endogenous molecules, thus mediating the development of autoimmune diseases. Type 1 diabetes (T1D) is a prototypic autoimmune disease in which TLRs play a pathogenic role. We here describe a protocol to study the role of TLRs in the development and progression of T1D by resorting to the nonobese diabetic (NOD) mouse model. Key words Toll-like receptors, Type 1 diabetes, Nonobese diabetic (NOD) mice, Autoimmune diseases

1

Introduction Toll-like receptors (TLRs) are pathogen-recognition receptors (PRRs) mainly expressed by innate immune cells and specialized in recognizing molecules commonly expressed by infectious pathogens, known as pathogen-associated molecular patterns (PAMPs) [1]. Upon ligand binding, these receptors activate several signaling pathways that lead to production of proinflammatory cytokines and upregulation of costimulatory molecules, which, in turn, stimulate adaptive immune system to react against the invading pathogens [2]. Along with the recognition of foreign materials, TLRs have also been shown to sense damage-associated molecular patterns (DAMPs; i.e., endogenous molecules from dead or dying cells, such as low-density lipoproteins and free fatty acids) [1]. Several data from both murine models and humans have implicated TLRs activation in the pathogenesis of autoimmune diseases [3]. For instance, self-nucleic acids—that are ligands of specific TLRs—have been observed in sera of patients with systemic lupus erythematosus and contribute to the activation of an aberrant

Francesca Fallarino et al. (eds.), Toll-Like Receptors: Methods and Protocols, Methods in Molecular Biology, vol. 2700, https://doi.org/10.1007/978-1-0716-3366-3_11, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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adaptive immune response [4]. Fibronectin fragments and heatshock proteins, which activate TLR4, have been measured in both the joints and sera of patients with rheumatoid arthritis [5, 6]. TLRs play a pivotal role in the pathogenesis of type 1 diabetes (T1D) as well. T1D is a chronic autoimmune disease characterized by the progressive destruction of β-cells in the pancreatic islets of Langerhans, which results in insulin deficiency and hyperglycemia. The mechanism triggering islet destruction is not yet fully understood, but several data suggest that β-cell death is mainly mediated by self-reactive cytotoxic T lymphocytes (CTLs). Specifically, the inappropriate activation of TLRs by self-antigens confers a proinflammatory phenotype on dendritic cells (DCs) that, in turn, direct CTLs activity against pancreatic β-cells [7]. Given the similarities with the human pathology, the nonobese diabetic (NOD) mouse is a widely used preclinical model of T1D, providing insights into genetic susceptibility, mechanisms of disease pathogenesis and in the translational development of novel immunotherapies [8]. Although the role of TLRs in the pathogenesis of autoimmune diseases, including T1D, is not completely understood, TLRs and their signaling pathways remain valuable therapeutic targets [9]. Herein, we will describe a protocol to study the role of TLRs in the contest of T1D. Specifically, we will report the in vivo and ex vivo methodologies to assess the effect of TLRs targeting in controlling the course of diabetes in NOD mice (Fig. 1).

Fig. 1 Schematic representation of experimental parameters evaluated on NOD mice after the administration of the TLRs inhibitors

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Materials

2.1 Housing of Animals

1. Specific pathogen-free (SPF) facilities. 2. Isolator or scantainer with appropriate, sterile cages. 3. Sterile bedding. 4. Sterile food. 5. Sterile, acid (pH 2.5–3.0, adjust using HCl) or hyperchlorinated (10 ppm sodium hypochlorite) water. 6. Personal protective equipment, including gloves, scrubs or overalls, hair net, face mask, and shoe covers. 7. 10–14 weeks of age NOD/ShiLtJ strain, a polygenic model for T1D, originally developed at Shionogi Research Laboratories in Osaka by Makino and colleagues [10].

2.2 Monitoring of Diabetes Onset

1. Diastix urine test strips. 2. Anesthetic spray. 3. Scalpel or surgical scissors. 4. Glucometer and test strips.

2.3 Monitoring of Serum Cytokines

1. Restraint device. 2. 25 G needles. 3. Heparinized capillary tubes. 4. Centrifuge. 5. Multiplex cytokines panel.

2.4 Tissue Processing for FFPE

1. 10% Neutral-Buffered Formalin (NBF): add 900 mL of distilled water to 100 mL Formalin (37–40% stock solution). Bring the pH up to 7.4 with 4 g/L NaH2PO4 (monobasic) and 6.5 g/L Na2HPO4 (dibasic/anhydrous) (see Note 1). 2. 70% EtOH: add 700 mL of EtOH to 300 mL of distilled water. 3. 80% EtOH: add 800 mL of EtOH to 200 mL of distilled water. 4. 95% EtOH: add 950 mL of EtOH to 50 mL of distilled water. 5. 100% EtOH. 6. Xylene. 7. Paraffin wax.

2.5

H&E Staining

1. Alum hematoxylin: add 200 g of aluminum ammonium sulfate into 1000 mL of distilled water. Place the flask on a heater/ stirrer, turn on the heater and allow to mix until the alum dissolves (usually, this takes about 15 min). Allow the solution to cool down and then add the remaining 1800 mL of distilled water. Add 20 g of hematoxylin powder to 40 mL of EtOH and

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dissolve by shaking for a few minutes. Mix the alcoholic solution of hematoxylin with the cooled alum solution and stir to ensure all the powder is dissolved (this takes several hours, preferably overnight). Add 4 g of sodium iodate, 80 mL of acetic acid, and finally 1200 mL of glycerol. Mix well and store. 2. 0.3% Acid alcohol: mix 2800 mL of commercial grade EtOH with 1200 mL of distilled water and 12 mL of hydrochloric acid. 3. Scott’s tap water substitute: dissolve 10 g of sodium hydrogen carbonate and 100 g of magnesium sulfate in 5 L of distilled water. Store stock solution at room temperature. 4. Alcohol acetified eosin/phloxine: add 400 mL of tetrabromofluorescein, disodium salt (Yellowish eosin; 1%) to 3100 mL of EtOH. Then, add 40 mL of aqueous phloxine and 16 mL of glacial acetic acid. 5. A toluene-based synthetic resin mounting medium. 6. Tap water. 7. Coverslips. 2.6 Immunofluorescence for Insulin and Immune Cells Detection

1. Antibodies: anti-insulin AF488 and Phycoerythrin (PE) antimouse CD45. 2. TBS-Tween 20 10X: 0.5 M Tris Base, 9% NaCl, 0.5% Tween 20, pH 8.4. Add 61 g of Tris and 90 g of NaCl into 1000 mL of distilled water. Mix to dissolve, adjust pH to 8.4 using concentrated HCl and then add 5 mL of Tween 20. Store this solution at room temperature. Before use, dilute 100 mL of this stock solution with 900 mL distilled water to get TBS-tween 1X concentrated. 3. Citrate buffer, 10 mM Tri-sodium citrate, 0.05% Tween 20, pH 6: add 2.92 g of Tri-sodium citrate to 1000 mL of distilled water. Mix to dissolve, adjust pH to 6 using concentrated HCl and then add 500 μL of Tween 20. Store this solution at room temperature. 4. Blocking solution: TBS-tween 20 1X, 5% BSA. Add 500 mg of BSA into 10 mL of TBS-Tween 20 1X. 5. Tap water. 6. DAPI (4′,6-diamidino-2-phenylindole). 7. Antifade mounting medium. 8. Xylene, EtOH 100%, 90%, 70%, 50%. 9. Hydrophobic marking pen. 10. Coverslips.

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Methods

3.1 Housing of Animals

The NOD/ShiLtJ strain (commonly known as NOD) is a polygenic model for T1D originally developed at Shionogi Research Laboratories in Osaka by Makino and colleagues [10]. At around 10–14 weeks of age, mice develop overt diabetes, which is more prevalent in female mice with an incidence ranging from 60% to 90%, whereas the incidence in males ranges from 10% to 30% in most colonies. Unlike to humans, development of diabetes in NOD mouse is negatively associated with microbial infections. Exposure to pinworms, Salmonella typhimurium, Schistosoma mansoni, mouse hepatitis virus (MHV), lymphocytic choriomeningitis virus (LCMV), lactate dehydrogenase virus (LDHV), encephalomyocarditis virus (EMCV), and Sendai virus has been associated with a decrease of diabetes incidence [11]. Thus, mice should be kept in specific pathogen-free (SPF) conditions to maintain the proper diabetes incidence. 1. House and handle mice according to local ethical guidelines. 2. Wear protective equipment, including hair and shoe coverers, gloves, overalls, and masks to prevent the spread of pathogens to the mice and the exposure to allergens to humans. 3. Bred and maintain mice in SPF facilities and carefully monitor for the presence of mouse pathogens (see Note 2). 4. Sterilize or disinfect food, water, bedding, cages, and other materials that will contact mice. Use acid or hyperchlorinated water to control Pseudomonas species contamination. 5. A peculiar feature of diabetes is hyperglycemia, which leads to polydipsia and polyuria. Therefore, closely monitor water availability providing ad libitum access to water and change cages more frequently.

3.2

TLRs Targeting

Since their involvement in a broad range of pathological conditions spanning from cancer to autoimmunity, TLRs represent promising drug candidates. Efforts have thus been made to develop specific compounds capable of targeting these receptors. Both agonist and antagonist of TLRs are currently available and a large number of these molecules has already reached different stages of clinical trials [12]. By enhancing the immune system, TLRs agonists are promising agents for the treatment of cancer, allergies, and infectious diseases. On the other hand, TLRs antagonists are potential therapeutic tools useful to regulate the immune responses in chronic inflammatory disorders and autoimmune diseases. Examples of the most relevant compounds targeting TLRs include monophosphoryl lipid A (a FDA-approved TLR4 agonist utilized as an adjuvant in vaccines against human papilloma virus and

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hepatitis B virus), Imiquimod (a TLR7 agonist approved for the treatment of human papilloma virus-induced genital and perianal warts) and M5049 (a small-molecule antagonist of TLR7 and TLR8, recently tested in clinical trials against COVID-19 infection). As an alternative to molecules, genetically modified NOD strains can be used. These mice are deficient for the receptor/s of interest or for specific, critical component of their signal pathway, thus abrogating receptor/s activity. To pharmacologically target a specific TLR, NOD mice are treated with a receptor agonist or antagonist, whose usage requires specific considerations. 1. Before use, verify physiological properties and solubility of the molecule (see Note 3). Avoid toxic or irritating compounds that cause tissues damages or discomfort to animals. 2. Prepare molecule solutions or suspensions aseptically and use sterile equipment for administration. 3. Chose the appropriate route and volume of administration. Various sources are available to get information on good practice of administration and well-tolerated volumes [13]. 4. Determine the optimal time and dose considering the pharmacokinetics and pharmacodynamic properties of the selected molecule. If possible, perform a preliminary treatment study (see Note 4). 5. Start the treatment with the molecule either before the diabetes onset (preventive study) or when the blood glucose level is above 250 mg/dL (interventional study), depending on research purpose. Carefully monitor animals for diabetes incidence and the occurrence of side effects (see Subheading 3.3.1). 6. In the experiment, include a negative control group using NOD mice treated with vehicle alone. 3.3

In Vivo Analysis

3.3.1 Monitoring of Diabetes Onset

To get insights into the involvement of TLRs in the T1D pathogenesis, treated NOD mice are compared with control animals (i.e., mice receiving vehicle alone) in terms of blood glucose levels (glycaemia) and glucose concentration in the urine (glycosuria). 1. Glucose levels in the urine is measured once a week, by using Diastix (Bayer, Germany) or similar reagent strips and proceeding as follow (see Note 5). 2. Allow a small drop of urine to place on the test area of the reagent strip. 3. Observe the change in color of the strip comparing it to the scale provided by the manufacturer. If there is no color change, the mouse is likely to be nondiabetic. If the color changes, the

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mouse should be retested 24 h later. If a positive analysis is confirmed, then it is recommended to measure blood glucose level. 4. Blood glucose levels are measured every other days, by using a glucometer according to the manufacturer’s instructions: 5. Spray the mouse’s tail with anesthetic spray. 6. Using a sterile scalpel, cut a tiny piece of the tail. 7. Place a small drop of blood on the test strip mounted in the glucometer and read blood glucose concentration (see Note 6). A positive diagnosis of diabetes is usually performed with a blood glucose above 250 mg/dL. 8. Record diabetes onset and establish the incidence curve. 3.3.2 Monitoring of Serum Cytokines

It is well established that cytokines play a significant role in T1D onset and development acting as inflammatory molecules that mediate insulitis and β-cell destruction [14]. Several cytokines are kwon to be involved in the pathogenesis of diabetes, such as interleukin (IL)-1, IL-6, IL-21, tumor necrosis factor α (TNFα), and the interferon (IFN) family. More recently, members of the IL-12 family (including IL-12, IL-23, IL-27 and IL-35) and of the Th17 (namely, IL-17, IL-22 and IL-25) have been described as potential immunotherapeutic targets of T1D [15]. Comparing specific cytokines levels in the sera of treated and control mice provides additional information about the progression of diabetes and the mechanism through which TLRs influence the disease. 1. Hold mice in a restraint device, insert a 25 G needle into the lateral tail vein and collect the resulting blood droplets in heparinized capillary tubes (see Note 7). 2. Centrifuge blood at 2000 relative centrifugal force (rcf) for 20 min at 4 °C, collect serum and store at -80 °C until analysis. 3. Analyze the serum cytokines concentration using a Multiplex Cytokines Panel optimized for diabetes research, according to the manufacturer’s instructions.

3.4 Ex Vivo Evaluation of Inflammatory Cells Infiltrating Pancreas

The histopathological hallmark of T1D, being an organ-specific autoimmune disorder, is defined by insulitis, which is an inflammatory lesion consisting of immune cell infiltrates around and within the islets of Langerhans. Usually, 4-week-old NOD mice show an infiltrate surrounding the pancreatic islet (per-insulitis), which is mainly constituted by DCs and macrophages. As the disease evolves, the lymphocytes become predominant and invade the islet (namely, insulitis). To assess insulitis, Formalin-Fixed, Paraffin-Embedded (FFPE) pancreatic sections are stained with hematoxylin and eosin (H&E).

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Specifically, hematoxylin turns the cell nuclei blue, while eosin allows the counterstaining of the cytoplasm in red-purple. The degree of inflammation can be scored by examining at least 30 islets for each pancreas and evaluating the presence of small mononuclear cells, whose nuclei are colored blue after H&E staining. Insulitis scoring is performed according to the following criteria: 0— absence of cell infiltration; 1—peri-insulitis, immune cells are around the islet; 2—less than 50% of the islet area is infiltrated; 3—severe insulitis, more that 50% of the islets are infiltrated and their structure is disrupted [16]. By recording the percentages of islets of a given score over total number of islets, it is possible to compare treated versus control NOD mice and thus appreciate the involvement of the targeted TLR in the disease course. Animals are anesthetized with Ketamine/Xylazine (100/20 mg/kg) by intraperitoneal injection. Pancreatic tissue samples are harvested and fixed in 10% Neutral-Buffered Formalin (NBF) to preserve tissue from natural decomposition. Subsequently, tissues are included in paraffin (i.e., a waxy, lipid compound) to give a consistency necessary for their maintenance and processing. The FFPE tissue is then cut into very tiny sections (3–5 μm) by means of a microtome and placed on a glass slide. To remove the paraffin and thus proceed with the staining with hydrophilic reagents, the slides are exposed to xylene and then rehydrated incubating them with EtOH solutions at different percentages of water. Alternatively, it is possible to identify and distinguish the different cellular components thanks to the use of antibodies that specifically recognize and bind proteins, lipids or carbohydrates that are typically expressed by each cell type [17]. 3.4.1 Tissue Processing for FFPE

1. Fix pancreas with 10% NBF for 24 h at room temperature (see Note 8). Make sure you have enough fixative to cover tissues. Usually, fixative volume should be 5 times of tissue volume. 2. Place fixed pancreas into embedding cassettes. 3. Before paraffin embedding, the tissue is dehydrated by incubating in EtOH solutions with decreasing concentration of water. Samples are then exposed to xylene, which is miscible with paraffin. The embedding schedule is as follow: – 1 h in 70% Ethanol, for two times; – 1 h in 80% Ethanol, one change; – 1 h in 95% Ethanol, one change; – 1 h in 100% Ethanol, for three times; – 1 h in Xylene, for three changes; – 2 h in Paraffin wax (58–60 °C), for two changes. 4. Embed tissues into paraffin blocks.

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5. Trim paraffin blocks at 3–5 μm with a microtome. 6. Place paraffin ribbon in water bath at about 40–45 °C. 7. Mount paraffin sections on glass slides. 8. Allow sections to air dry and then bake in 45 °C oven, overnight (see Note 9). 9. To remove the paraffin, incubate sections in three changes of xylene, 10 min each. 10. Then, rehydrate samples with 2 changes of 100% ethanol for 3 min each, followed by 95%, 80%, and 70% EtOH for 5 min each. Finally, rinse the sections in distilled water. 3.4.2

H&E Staining

1. Remove paraffin and rehydrate sections as stated in step 9 (see Subheading 3.4.1). 2. Incubate sections in alum hematoxylin solution for 5 min. 3. Rinse in running tap water for 5 min. 4. Differentiate by dipping the samples in 0.3% acid alcohol solution for 10 times (see Note 10). 5. Rinse in running tap water for 2 min. 6. Rinse in Scott’s tap water substitute (see Note 11). 7. Rinse in tap water for 2 min. 8. Incubate sections in alcohol acetified eosin/phloxine solution (see Note 12) for 1 min. 9. Dehydrate with one change of 70% ethanol for 3 min, followed by 80%, 95%, and 100% EtOH for 5 min each. 10. Incubate sections in xylene for 10 min. 11. Place a drop of mounting medium on the slide and let the coverslip to fall gently onto the slide, taking care to leave no bubbles. 12. Dry overnight in the hood.

3.4.3 Immunofluorescence for Insulin and Immune Cells Detection

1. Remove paraffin and rehydrate sections as stated in step 9 (see Subheading 3.4.1). 2. Bring citrate buffer at 95 °C using water bath. 3. Dip the slides into warm citrate buffer and incubate at 95 °C for 30 min. The heat-induced epitope retrieval is fundamental to break the bridges between proteins and proteins/nucleic acids formed during the fixation with 10% NBF. The antigenic sites are thus unmasked, becoming available to bind specific antibodies. 4. Let the samples to cool down for 15 min. 5. Wash sections with TBS-tween 1X for two changes, 2 min each.

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6. Using a special marking pen, draw a circle around the specimen on the slide. This water repellent barrier keeps reagents localized on the tissue section and allows use of less reagents per section. 7. To block nonspecific binding sites, add 100 μL of TBS-tween 1X, 5% BSA, and incubate sections for 1 h at room temperature. 8. Wash sections with TBS-tween 1X for 2 min. 9. Dilute primary antibodies in blocking buffer (100 μL for each section) following the manufacturer’s instructions. 10. Incubate sections with anti-insulin AF488 and PE anti-mouse CD45 for 2 h, at room temperature. 11. Wash sections with TBS-tween 1X for three changes, 5 min each. 12. Wash samples with tap water for 2 min. 13. To label nuclei, add 100 μL of DAPI and incubate for 15 min, at room temperature. DAPI is a blue-fluorescent dye that binds to AT-rich regions of DNA. 14. Wash sections with tap water for three changes, 5 min each. 15. Place a drop of antifade mounting medium on the slide and let the coverslip to fall gently onto the slide, taking care to leave no bubbles. 16. Dry overnight and then visualize under a fluorescence microscope.

4

Notes 1. The addition of 900 mL of water to 100 mL of Formalin (37–40% stock solution) makes an unbuffered solution with a pH of 3–4, which cannot be used as such. Indeed, in acid condition, the tissue hemoglobin produces dark brown precipitates that interfere with the histological interpretation. Thus, the pH of the solution has to be adjusted up to 7.2 by adding sodium phosphate. 2. If mice come from an outside distributor, verify their SPF status before passing them in the SPF facility. Set up sentinel cages within each isolator to monitor for infections. 3. Where possible, prefer compounds soluble in water (sterile water for injections, 0.9% saline or 5% dextrose/saline) that is close to physiological pH, as highly acid or basic solutions can be irritant. If needed, organic solvents (e.g., dimethyl sulfoxide, DMSO) should not exceed 10% of the injected volume, while detergents (e.g., Tween), solubilizers or emulsifiers should not exceed 20% of the injected volume. Some

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compounds may not be soluble and require administration as a suspension; it must be shaken before using to assure a proper dosage. 4. A preliminary investigational treatment study typically uses two mice per dose with a doubling dose-escalation or dose-halving de-escalation in a range centered on the hypothesized dose. 5. The monitoring of glycosuria should be done at about the same time of the day in order to not introduce variables. 6. Blood is drawn up by capillary action. Ensure there is enough blood to fill the test strip; otherwise, an error will occur, and test will need to be repeated. Carefully place the blood in the area where the test strip indicated, and avoid blood spill over the area, which will also lead to error readings [18]. 7. This procedure is facilitated by promoting vasodilatation at the level of the murine tail. It can be achieved by holding the mouse in hand and dipping its tail in hot water at 42 °C for 20 s. This blood collection method is minimally invasive and allows repeated sampling of small volumes from the same animal without anesthesia. 8. The 10% NBF contains 4% of formaldehyde, which induces the formation of cross-links between proteins, and between proteins and nucleic acids. Cross-linking by formaldehyde is a slow process that requires 24–48 h to be completed. Therefore, the incubation with 10% NBF should not be less than 24 h; otherwise, the early interruption of the fixation leaves the center of the tissue section not fixed. On the other hand, prolonged formalin fixation may lead to weak or absent staining due to an irreversible damage of some epitopes. 9. Pay attention to the baking temperature, which should not go higher than 50 °C to avoid the sections crack. 10. Hematin is the oxidation product that is responsible for the staining properties of hematoxylin. In acid condition, hematin binds to lysine residues of nuclear histones through a linkage with aluminum mordant. Usually, to achieve complete saturation of the binding sites, the incubation in hematoxylin solution is leave longer than necessary. The overstained tissues are then incubated in an alcoholic acidic solution that removes the excess of coloration (a process known as differentiation). 11. The differentiation process is blocked by incubating samples in an alkaline solution (bluing step). Indeed, in Scott’s tap water substitute, hematin (which is soluble, red molecule) becomes insoluble and thus stains the cell nuclei with a blue hue. 12. Eosin staining allows the visualization of full cellular detail. Phloxine is a halogenated fluorescein that is added into eosin solution to enhance the counterstaining. Moreover, the glacial acetic acid is added to make the coloration even more intense and specific.

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References 1. Aluri J, Cooper MA, Schuettpelz LG (2021) Toll-like receptor signaling in the establishment and function of the immune system. Cell 10:1374. https://doi.org/10.3390/ cells10061374 2. Wong FS, Hu C, Zhang L et al (2008) The role of Toll-like receptors 3 and 9 in the development of autoimmune diabetes in NOD mice. Ann N Y Acad Sci 1150:146–148. https://doi. org/10.1196/annals.1447.039 3. Duffy L, O’Reilly SC (2016) Toll-like receptors in the pathogenesis of autoimmune diseases: recent and emerging translational developments. Immunotargets Ther 5:69–80. https://doi.org/10.2147/ITT.S89795 4. Celhar T, Magalha˜es R, Fairhurst A-M (2012) TLR7 and TLR9 in SLE: when sensing self goes wrong. Immunol Res 53:58–77. https:// doi.org/10.1007/s12026-012-8270-1 5. Brentano F, Schorr O, Gay RE et al (2005) RNA released from necrotic synovial fluid cells activates rheumatoid arthritis synovial fibroblasts via Toll-like receptor 3. Arthritis Rheum 52:2656–2665. https://doi.org/10. 1002/art.21273 6. Roelofs MF, Boelens WC, Joosten LAB et al (2006) Identification of small heat shock protein B8 (HSP22) as a novel TLR4 ligand and potential involvement in the pathogenesis of rheumatoid arthritis. J Immunol 176:7021– 7027. https://doi.org/10.4049/jimmunol. 176.11.7021 7. Lee AS, Ghoreishi M, Cheng WK et al (2011) Toll-like receptor 7 stimulation promotes autoimmune diabetes in the NOD mouse. Diabetologia 54:1407–1416. https://doi.org/10. 1007/s00125-011-2083-y 8. Pearson JA, Wong FS, Wen L (2016) The importance of the Non Obese Diabetic (NOD) mouse model in autoimmune diabetes. J Autoimmun 66:76–88. https://doi.org/10. 1016/j.jaut.2015.08.019 9. Anwar MA, Shah M, Kim J, Choi S (2019) Recent clinical trends in Toll-like receptor targeting therapeutics. Med Res Rev 39:1053– 1090. https://doi.org/10.1002/med.21553

10. Makino S, Kunimoto K, Muraoka Y et al (1980) Breeding of a non-obese, diabetic strain of mice. Jikken Dobutsu 29:1–13. https://doi. org/10.1538/expanim1978.29.1_1 11. Leiter EH (2001) The NOD mouse: a model for insulin-dependent diabetes mellitus. Curr Protoc Immunol Chapter 15:Unit 159. https://doi.org/10.1002/0471142735. im1509s24 12. Farooq M, Batool M, Kim MS, Choi S (2021) Toll-like receptors as a therapeutic target in the era of immunotherapies. Front Cell Dev Biol 9: 756315. https://doi.org/10.3389/fcell. 2021.756315 13. Diehl KH, Hull R, Morton D et al (2001) A good practice guide to the administration of substances and removal of blood, including routes and volumes. J Appl Toxicol 21:15–23. https://doi.org/10.1002/jat.727 14. Eizirik DL, Colli ML, Ortis F (2009) The role of inflammation in insulitis and beta-cell loss in type 1 diabetes. Nat Rev Endocrinol 5:219– 226. https://doi.org/10.1038/nrendo. 2009.21 15. Lu J, Liu J, Li L et al (2020) Cytokines in type 1 diabetes: mechanisms of action and immunotherapeutic targets. Clin Transl Immunol 9: e1122. https://doi.org/10.1002/cti2.1122 16. Mondanelli G, Albini E, Pallotta MT et al (2017) The proteasome inhibitor bortezomib controls indoleamine 2,3-dioxygenase 1 breakdown and restores immune regulation in autoimmune diabetes. Front Immunol 8:428. https://doi.org/10.3389/fimmu.2017. 00428 17. Anquetil F, Mondanelli G, Gonzalez N et al (2018) Loss of IDO1 expression from human pancreatic β-cells precedes their destruction during the development of type 1 diabetes. Diabetes 67:1858–1866. https://doi.org/10. 2337/db17-1281 18. Chen D, Thayer TC, Wen L, Wong FS (2020) Mouse models of autoimmune diabetes: the Nonobese Diabetic (NOD) mouse. Methods Mol Biol 2128:87–92. https://doi.org/10. 1007/978-1-0716-0385-7_6

Chapter 12 In Vitro and Ex Vivo Methodologies for T-Cell Trafficking Through Blood–Brain Barrier After TLR Activation Camilla Moliterni, Maria Tredicine, Alessandra Pistilli, Renato Falcicchia, Desire´e Bartolini, Anna Maria Stabile, Mario Rende, Francesco Ria, and Gabriele Di Sante Abstract This chapter describes ex vivo isolation of human T cells and of naı¨ve splenocytes respectively collected from multiple sclerosis patients and healthy controls and experimental autoimmune encephalomyelitis-affected mice. After the magnetic sorting of naı¨ve and activated T helper lymphocytes, we provide details about the cell cultures to measure the interaction with extracellular matrix proteins using standard cell invasion or hand-made in vitro assays, upon different stimuli, through Toll-like receptor(s) ligands, T-cell activators, and cell adhesion molecules modulators. Finally, we describe the methods to harvest and recover T cells to evaluate the properties associated with their trafficking ability. Key words Toll-like receptors, T-cell trafficking, Multiple Sclerosis, Experimental Autoimmune Encephalomyelitis, In vitro model of blood–brain barrier

1

Introduction It has been demonstrated that Mycobacterium tuberculosis (Mtb)derived products determine trafficking properties of antigenspecific T cells through its interaction with Toll Like Receptor 2 (TLR2) [1–6]. T-cell trafficking is a complex process, highly regulated by the fine coordination of integrins, selectins, matrixassociated proteins, chemokines, and related receptors, all with specific roles to guide subsets of T cells to the most appropriate site for action [7–9]. The understanding of these mechanisms could represent an interesting and efficient tool for therapeutic intervention in different diseases involving both inappropriate immune responses such as autoimmune disorders [10, 11] and cancer progression. This chapter is intended to describe ex vivo/in vitro methodologies to test and modulate T-cell trafficking, using the example of an autoimmune disorder and its mouse models,

Francesca Fallarino et al. (eds.), Toll-Like Receptors: Methods and Protocols, Methods in Molecular Biology, vol. 2700, https://doi.org/10.1007/978-1-0716-3366-3_12, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

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Table 1 Principal actively induced EAE mouse models Mouse strain

Immunogen peptide or protein

SJL/J

PLP (rat/bovine) PLP139–151 PLP178–191 PLP180–199 MOG92–106 PLP139–151 MBP (mouse/rat/ bovine) MBP84–104

Days before symptoms

Disease severity

Relapsing–remitting Relapsing–remitting Relapsing–remitting Relapsing–remitting Relapsing–remitting/ chronic Relapsing–remitting Relapsing–remitting

7–14 7–14 11–21 11–21 11–21

Severe Severe Moderate Moderate Moderate

7–14 7–14

Severe Moderate

Relapsing–remitting

7–14

Moderate

Disease type

C57BL/6

MBP MBP84–104 MOG MOG35–55

Monophasic/chronic Monophasic/chronic Relapsing–remitting Relapsing–remitting

10–14 10–14 10–14 10–14

Mild Mild Moderate Moderate

PL/J, B10. PL

MBPAc1–11

Monophasic

–

Moderate

Balb/c

PLP (bovine) PLP180–199

Monophasic/chronic Monophasic/chronic

10–18 10–21

Moderate Moderate

B10.S

MBP MBP87–106

Monophasic Monophasic

10–11 10–11

Moderate Mild

Chronic relapsing Chronic relapsing Chronic relapsing

10–14 10–14 10–11

MOG8–22

Chronic relapsing

10–18

MOG35–55

Chronic relapsing

10–14

Acute-mild Acute-mild Chronic relapsing Chronic relapsing Chronic relapsing

PLP (rat or bovine) PLP109–209 PLP215–232

Chronic/atypical Chronic/atypical Chronic/atypical

10–14 10–21 10–21

Biozzi ABH MBP12–26 MAG97–112 PLP56–70

C3H/HeJ

Acute-mild Acute-mild Acute-mild

This list does not include transgenic actively induced mouse strain susceptible to develop EAE and exclude passively induced EAE mice

Multiple Sclerosis (MS) and Experimental Autoimmune Encephalomyelitis (EAE) (Table 1). One of the main challenges in the study of disease pathogenesis of MS is the difficult prediction of the distribution of lesions within the CNS. Several factors have been called to play a role including antigen (Ag) density, T-cell phenotype and local milieu at the site of infiltration [12, 13] (Fig. 1), and host–pathogen interaction(s) [14–19]. The in vitro simulation of blood–brain barrier (BBB) can help to dissect and regulate the different aspects of T-cell trafficking (Fig. 2).

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Fig. 1 T-cell trafficking. Trafficking is a highly regulated event that has a central function in the immune response. The complexity of this mechanism is proportional to the multiplicity of factors that can influence the process, including chemokines, integrins, selectins, matrix-associated proteins, and related receptors

Previous works have described the important role of pathogenrecognition receptors (PRR) in the modulation of trafficking of T lymphocytes [6, 20–23] (Fig. 3). Their mobilization crucially depends both on the genetic background and on the stimuli from the environment. Indeed, T-cell trafficking in mouse strains differing in Toll-like receptor 2 (TLR2, 82Ile in SJL/J versus 82Met in C57Bl/6 mice) is regulated by the concentration of Mycobacterium Tuberculosis (Mtb) in the adjuvant [3]: high amount of Mtb in the adjuvant is responsible of early mobilization of activated T cells in the SJL/J strain, but not in C57Bl/6 [3, 5]. Moreover, antigen (Ag) driven activated T cells are regulated by pathogens through direct binding of TLR2, associating with a different distribution of CNS lesions during EAE [5], whereas the modulation of their Th phenotype relies on indirect activation through TLR2 expressed by APCs [22, 25, 26]. Recently we demonstrated that TLR2 modulates trafficking properties of T cells, focusing on its ability to reshuffle the role of CD44, an adhesion molecule involved in the regulation of cell– extracellular matrix (ECM) interactions, lymphopoiesis, and cell activation and homing [27, 28]. Through the methodology here

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Fig. 2 Trafficking of T cells to CNS during MS. The environment could lead an imbalance, also acting on the ability of T cells to move. The modulation of the expression of certain adhesion molecules through the stimulation of TLRs pathways can explain the ability of T cells to infiltrate into the central nervous system of patients affected by MS, crossing the blood–brain barrier. This effect seems to be associated with microbial products, able to modulate adhesion molecules on T cells, that regulate their ability to move and interact with ECM protein(s). (Created with Biorender.com)

described we showed that the stimulation of T cells by LPS or CpG (activating other TLRs also expressed by T cells) led to a repertoire of CD44 isoforms-specific mRNAs that varied specifically depending on the type of stimulus, causing the infiltration of distinct areas of the CNS during EAE [6, 29–31]. These works described the specific role of CD44 in the pathogenesis of MS, in line with other immunomodulated disorders [29, 32–34].

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Fig. 3 Known TLRs and their standard ligands. In the context of PAMPs, different TLRs respond to specific classes of pathogens: TLR2, both as homodimers or as TLR2–TLR1 and TLR2–TLR6 heterodimers, has several ligands (e.g., lipopeptides, peptidoglycans, lipoarabinomannan, GPI from T. cruzi, and Zymosan from yeast). TLR4 mainly binds lypopolysaccharide (LPS) derived from Gram-negative bacteria [24] and lipoteichoic acids from Gram-positives. TLR5 is activated by bacterial flagellin (generally in the gut epithelium). TLR3 responds to viral double-stranded RNA (dsRNA), while TLR7 and TLR8 bind single-stranded RNA (ssRNA) and synthetic imidazoquinolines. TLR9 can bind unmethylated CpG DNA from bacterial and viral genomes. TLR11 (only murine) responds to profilin. This image was created with Biorender software

2

Materials Prepare and store all reagents at room temperature (unless indicated otherwise). Diligently follow all the about waste materials. For experiments with murine cells, it is mandatory the planning. EAE induction should follow the appropriate procedures depending on mouse strain and disease forms (e.g., progressive, chronic, relapsing-remitting) [35–37]. The collection of human cells requires good coordination with clinicians, in order to collect the appropriate quantity of sample(s) at the appropriate timepoint(s).

2.1 Isolation of Murine Naı¨ve and Activated T Cells

1. Buffers: Cold-PBS (4–10 °C), MACS Tissue Storage Solution (Miltenyi Biotec™) or alternatively cold PBS containing 0.5% of bovine serum albumin (BSA) and 2 mM of Ethylenediaminetetraacetic acid (EDTA). Hybri-Max Red Blood Cell Lysing Buffer (Merck™) or an alternative hand-made ACK lysis buffer (8.02 g of Ammonium chloride, 1 g of Potassium bicarbonate and 0.0372 g of Disodium EDTA in 1 L of distilled water with pH between 7.2 and 7.4). Bambanker® (Nippongenetics™) or alternative freezing solution composed by fetal calf serum (FCS, 90%) and dimethyl sulfoxide (DMSO, 10%).

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2. Plastics: 6-well plate(s) or Petri dish(es), 2 mL syringes, 15 and 50mL tubes, 0.2 and 1 mL tips, serological pipettes and 70 μm MACS SmartStrainer (Miltenyi Biotec™) or same size filters. 3. Instruments: Surgical equipment and supplies, pipettor, vortex, sterile hood, refrigerated centrifuge appropriate for 15/50 mL tubes, ice machine or cooler, -4 °C and -20 °C fridges. 4. Kits: Miltenyi Biotec™ “CD4+CD62L+ T Cell Isolation Kit, mouse” and all the materials and instrumentation required for this protocol (see Notes 1 and 2). 2.2 Isolation of Human T Cells

1. Buffers: PBS (4–10 °C and at room temperature), density gradient solution (see Note 3), MACS Buffer (Miltenyi Biotec™) or alternatively cold PBS containing 0.5% of bovine serum albumin (BSA) and 2 mM of ethylenediaminetetraacetic acid (EDTA). Hybri-Max Red Blood Cell Lysing Buffer (Merck™)or an alternative hand-made ACK lysis buffer (8.02 g of Ammonium chloride, 1 g of Potassium bicarbonate and 0.0372 g of Disodium EDTA in 1 L of distilled water with pH between 7.2 and 7.4). 2. Plastics: vacuum blood collection tubes containing lithium heparin or sodium heparin (green cap) or alternatively EDTAviolet cap tubes (see Note 4), 15 and 50 mL tubes, 0.2 and 1 mL tips, 5, 10, and 25 mL serological pipettes and 3 mL sterile Pasteur pipettes. 3. Instruments: oscillating platform, pipettor, vortex, sterile hood, refrigerated centrifuge appropriate for 15/50 mL tubes, ice machine or cooler, -4 °C and - 20 °C fridges. 4. Miltenyi Biotec™ “CD14 MicroBeads, human” and “CD4+ Microbeads, human,” and all the materials and instrumentation required for these protocols (see Note 5).

2.3 Murine Cell Cultures

1. Plastics: 6-, 24-, and 96-well plate(s), 96 transwells (Corning Life Sciences, NY, USA) with 5 mm pores and relative supports, 15 and 50 mL tubes, 0.01, 0.02, 0.2, and 1 mL tips, serological and Pasteur pipettes. 2. Instruments: pipettor, vortex, sterile hood, refrigerated centrifuge appropriate for 15/50 mL tubes, incubator with CO2 at 5%, ice machine or cooler, -4 °C, -20 °C, and -80 °C fridges and liquid nitrogen storage tank. 3. Buffers: Medium with serum (RPMI 1640 supplemented with 0.2% mouse serum (see Note 6), 40 μg/mL gentamicin, 50 μM 2-mercaptoethanol, and 2 mM glutamine) and without serum (RPMI 1640 supplemented with 40 μg/mL gentamicin, 50 μM 2-mercaptoethanol, and 2 mM glutamine). Corning Cell Recovery solution.

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4. Cells and kits: Primary murine splenocyte cultures of magnetically sorted CD4+/CD62L+ and CD4+/CD62L- T lymphocytes; T Cell Activation/Expansion Kit mouse (αCD3/α CD28) Miltenyi Biotec™. 5. Chemicals, peptides, and recombinant proteins: Pam2CSK4 (100 ng/mL) InvivoGen™ tlrl-pm2s-1, Pam3CSK4 (100 ng/mL) InvivoGen™ tlrl-pms, LPS-B5 Ultrapure (20 mg/mL) InvivoGen™ tlrl-pb5lps, ODN 1585 (CpG) (2 ng/mL) InvivoGen™ tlrl-1585, Peptidoglycan (PGN) extracted from Staphylococcus aureus (10 μg/mL) 77,140 (Merck™). 2.4 Primary Human CD4+ T-Cell Cultures

1. Primary Human CD4+ T cells and CD14+ monocytes cultures enriched (Miltenyi Biotec™) from PBMC extracted with density gradient from blood sample of healthy donors and MS patients [38–40]. 2. Instruments: pipettor, vortex, sterile hood, refrigerated centrifuge appropriate for 15/50 mL tubes, incubator with CO2 at 5%, ice machine or cooler, -4 °C, -20 °C, and -80 °C fridges and liquid nitrogen storage tank. 3. Buffers: Medium with serum (RPMI 1640 supplemented with 2% human serum, 40 μg/mL gentamicin, 50 μM 2-mercaptoethanol and 2 mM-glutamine) and without serum (RPMI 1640 supplemented with 40 μg/mL gentamicin, 50 μM 2-mercaptoethanol and 2 mM-glutamine); Matrigel Basement Membrane Matrix (Corning 356,234); Corning Cell Recovery solution. Bambanker® (Nippongenetics™) or alternative freezing solution composed by fetal calf serum (FCS, 90%) and dimethyl sulfoxide (DMSO, 10%). 4. Cells and kits: Primary murine splenocyte cultures of magnetically sorted CD4+/CD62L+ and CD4+/CD62L- T lymphocytes; T Cell Activation/Expansion Kit human (αCD2/α CD3/αCD28) Miltenyi Biotec. 5. Chemicals, peptides, and recombinant proteins: human Collagen, Type I (Merck™); human Osteopontin (Merck™); Pam2CSK4 (100 ng/mL, InvivoGen™); Pam3CSK4 (100 ng/mL, InvivoGen™); LPS (20 mg/mL, InvivoGen™); ODN 1585 (CpG, 2 ng/mL, InvivoGen™); Peptidoglycan extracted from Staphylococcus aureus (10 μg/mL, Merck™). 6. Modulators of adhesion molecules: αCD44 mAb (preferably IgG2a, clone MJ-64); αLFA1 (αCD11a, preferably IgG2a, clone TIB-13); αVLA4 (αCD49d, preferably IgG2a, clone PS/2); αL-selectin (αCD62L, preferably IgG2a, clone MEL-14).

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2.5 In Vitro BBB Simulation

1. Cell cultures: bEnd.3 cells (ATCC®, catalogue number: CRL-2299TM), expand and store cell stocks in liquid nitrogen. 2. Plastics: 15 mL centrifuge tubes, Transwell® (Corning™), T-75 flasks (CellTreat™, catalog number: 229341). 3. Buffers: Growth factor reduced (GFR) Matrigel® (Corning™), fetal bovine serum (FBS), Dulbecco’s modified Eagle’s medium (DMEM) Medium with serum (RPMI 1640 supplemented with 2% human serum, 40 μg/mL gentamicin, 50 μM 2-mercaptoethanol, and 2 mM-glutamine) and without serum (RPMI 1640 supplemented with 40 μg/mL gentamicin, 50 μM 2-mercaptoethanol and 2 mM-glutamine); DPBS (Ca2+/Mg2+ free); 0.05% Trypsin/EDTA solution; Trypsin Neutralizing solution (Lonza™); Phenol red-free medium (Gibco™) HEPES-buffered Krebs-Ringer solution, Mouse serum (Merck™ or freshly collected from animals).

3

Methods

3.1 Isolation of Murine T Cells (Fig. 4)

1. After euthanasia of EAE-affected mice as described in [36, 41– 43], collect spleens under sterile conditions, immerse them individually in 15 mL tubes containing 2 mL of MACS Tissue Storage Solution, and then place the tubes on ice. 2. To obtain a primary murine splenocytes’ suspension, wash spleens in Petri dishes with 3 mL of PBS and cut them into small pieces with sterile scissors in a 6-well plate on ice in a sterile environment (see Note 7). 3. With 5 mL of cold PBS prewet a 70 μm MACS SmartStrainer (Miltenyi Biotec™) placed on top of a 50 mL tube on ice, in sterile conditions and then discard the flow-through (see Note 8). 4. Place the previously minced spleens into the cell strainer and, continuously adding cold PBS (10 mL in total), gently dissociate the pieces with a 2 mL syringe plunger (on the rubber side), with circular moves and light pressures. This process should not take more than 3 min/spleen (see Note 8). Wash all excess cells off, using 2 mL of cold PBS (approximately). 5. Centrifuge the tube(s) at 4 °C for 8 min at 300 g. Carefully aspirate the supernatant and place on ice the tube(s) with the cell pellet. 6. Using the Hybri-Max Red Blood Cell Lysing Buffer (roughly 1 mL/spleen), pipet the pellet with intermittent swirling, to lyse red blood cells (RBCs) for 1 min. Ensure that the cells suspension(s) is(are) placed on ice in between swirls (see Note 9).

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Fig. 4 T-cell sorting from human and murine samples. Protocol scheme of the extraction and magnetic sorting T cells

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7. Fill the tube with cold PBS to interfere with cell lysis and centrifuge at 4 °C for 8 min at 300 g with (standard acceleration and brake/deceleration). 8. Accurately and carefully aspirate the supernatant, resuspend cell pellet with 0.5 mL of cold PBS, avoiding exaggerated turbulences and high pressures, and fill the tube with cold PBS to wash cells from any residues of cell lysis buffer. Centrifuge at 4 °C for 8 min at 300 g with (standard acceleration and brake/deceleration). Repeat this step two times (see Note 10). 9. Dilute a small cell fraction 1:10 with Trypan Blue, place 10 μL of the cell solution + Trypan Blue on a Burker chamber by exploiting the capillarity between the chamber and a coverslip, then count the cells appearing in a chamber using an optical microscope one counts them (see Note 11). 10. Aspirate supernatants and perform CD4+CD62L+ T Cell Isolation Kit, mouse applying the manufacturer’s protocol to magnetically isolate murine naı¨ve CD4+/CD62L+ and activated CD4+/CD62L- T lymphocytes (see Note 12). 11. After magnetic isolation, murine T cells can be (totally or partially) alternatively stored in 1 mL of Bambanker® to proceed to cryopreservation for future experiments or directly cultured as described below. 12. Resuspend splenocytes in 40 μL of precooled MACS buffer and 10 μL of Antibody Cocktail with biotin (the cocktail contains various biotinylated antibodies such as CD8, CD56, CD19, and CD14, with affinity for all splenocytes except CD4). 13. Incubate for 5 min at 2–8 °C. 14. Carefully resuspend the cell solution with 30 μL of buffer +20 μL of Anti-Biotin MicroBeads (after vortexing since MicroBeads tend to settle to the bottom). The microbeads in addition to binding biotin are provided with a magnetic bead. 15. Incubate for 10 min at 2–8 °C, in the meantime activate the LS (Miltenyi Biotec™) column, placing it inside the corresponding magnet for its size and adding 3 mL of cold MACS buffer. In the bottom of the LS column, place a 15 mL tube to collect the negative fraction. The first eluate will be negative for the chosen antibod(ies)y, but in this case will be CD4-positive T cells (our target). The column throughout the procedure must never dry out. 16. When the buffer leaves the column, load the suspension of stained cells with the antibody cocktail and the magnetic MicroBeads bound to them. 17. When the column is empty, add 1 mL of cold MACS buffer. Repeat for three times.

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18. Remove the column from the magnetic holder and place a second 15 mL tube in the bottom to collect the positive fraction, which in this case will be CD4-negative T cells. 19. Add 3 mL of buffer and apply through the plunger of the column a compressive force is whereby the buffer will result in the elution of the cells, which, being no longer subjected to a magnetic force, will be collected and isolated. 20. Next, repeat the on CD4+ T cells (negative fraction) with a CD62L antibody already bound to magnetic microbeads. 21. Incubate for 15 min in cold MACS buffer. 22. Wash the exceeding/unbound antibodies with a centrifugation at 300 × g for 5 min at 4 °C. 23. Proceed to magnetic separation on a smaller column (MS, Miltenyi Biotec™), placing it inside the corresponding magnet for its size and adding 1 mL of cold MACS buffer. In the bottom of the MS column, place a 15 mL tube to collect the negative fraction (CD4+CD62L-). In this case, the column must never dry out throughout the procedure. 24. When the buffer leaves the column, load the suspension of cells stained with CD62L MicroBeads. 25. When the column is empty, add 0.5 mL of cold MACS buffer. Wash 3 times. 26. Remove the column from the magnetic holder and place a second 15 mL tube in the bottom to collect the positive fraction, which in this case will be CD4+CD62L+. 27. Add 1 mL of cold MACS buffer and apply through the plunger of the column a compressive force is whereby the buffer will result in the elution of the cells, which, being no longer subjected to a magnetic force, will be collected and isolated. Two subpopulations will be obtained: CD4+/CD62L+ or naı¨ve T cells and CD4+/CD62L- or activated T helper lymphocytes. 28. Check that the purity of the selected subpopulation measuring it with flow Cytometry. Preferably a purity