Ras Activity and Signaling: Methods and Protocols (Methods in Molecular Biology, 2262) 1071611895, 9781071611890

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Methods in Molecular Biology 2262

Ignacio Rubio Ian Prior Editors

Ras Activity and Signaling 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.

Ras Activity and Signaling Methods and Protocols

Edited by

Ignacio Rubio Department Anesthesiology and Intensive Care, Jena University Hospital, Jena, Thüringen, Germany

Ian Prior Cellular and Molecular Physiology, University of Liverpool, Liverpool, UK

Editors Ignacio Rubio Department Anesthesiology and Intensive Care Jena University Hospital Jena, Thu¨ringen, Germany

Ian Prior Cellular and Molecular Physiology University of Liverpool Liverpool, UK

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-1189-0 ISBN 978-1-0716-1190-6 (eBook) https://doi.org/10.1007/978-1-0716-1190-6 © Springer Science+Business Media, LLC, part of Springer Nature 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface Following the description of the biochemical properties and the delineation of the major effector pathways of Ras in the 1980s and 1990s, it seemed that it would only be a matter of time before effective Ras-targeted therapies would be developed. While these proved to be elusive, the introduction of improved imaging, isoform-specific reagents, and genetically engineered models spurred deeper understanding of the differences between different Ras isoforms that might underlie their different contributions to normal and disease-associated phenotypes. The launch of the NIH/NCI “Ras Initiative” (www.cancer.gov/research/keyinitiatives/ras) in 2013 was intended to reboot the stalled progress in therapeutic targeting and led to a wider renaissance of interest in Ras function. This encouraged a systematic approach to studying the biology of all Ras variants and the generation of reagents and standards for the benefit of the whole field. Many researchers have been motivated to resume the task of developing approaches to assay and target Ras activity. Consequently, this book aims to compile many of the new experimental approaches and concepts that have emerged in recent years focusing mostly, but not solely, on ways and means to manipulate and regulate Ras activity and its downstream signaling output. In addition to these novel methods, we also revisit standard methodologies such as the use of affinity probes for biochemical Ras-GTP determination, probably still the most widely used assay in Ras laboratories. This book therefore aims at all bench workers, including beginners to the Ras GTPase field seeking a guide to traditional and novel methods, but it equally targets principal investigators that may profit from an updated and bundled compilation of methodologies for studying Ras activity and signaling. The book begins with two non-methodological review chapters that revisit our current state of knowledge with regard to general Ras activity control and the question of Ras variant-specific functions in biology and human disease. This is followed by 24 methodological chapters subdivided into four different parts: (1) biochemical methods, (2) Ras processing/trafficking/localization, (3) Ras signaling/inhibition, and (4) in vivo models for studying Ras function. In these chapters, experts of various fields of Ras biology and biochemistry present their methodologies wrapped as step-by-step lab protocols for maximal insight and easy implementation, especially thinking of the less-experienced lab workers. References to explanatory notes elaborating on specific issues are spiked into the text at places in which long discursive passages would disrupt the text flow. In summary, the method chapters provide sound, genuine laboratory protocols together with sufficient supporting information to correctly capture all the necessary technical background information. It has been a pleasure and incredibly rewarding for us to edit this book, compiling this collection of lab methodologies written by such a distinguished panel of experts in the Ras field. We express our warmest thanks to all authors that have contributed to this book. We sincerely hope that the methods described herein will provide a meaningful support and guide to lab workers both in their everyday routine work on Ras GTPases and in the design of new projects requiring novel methodologies. Liverpool, UK ¨ ringen, Germany Jena, Thu

Ian Prior Ignacio Rubio

v

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

PART I

GENERIC RAS BIOLOGY

1 Ras Variant Biology and Contributions to Human Disease . . . . . . . . . . . . . . . . . . . Ian Prior 2 Regulation of the Small GTPase Ras and Its Relevance to Human Disease . . . . . Kayla R. Kulhanek, Jeroen P. Roose, and Ignacio Rubio

PART II

v xi

3 19

METHODS IN RAS BIOCHEMISTRY

3 Precise Characterization of KRAS4B Proteoforms by Combining Immunoprecipitation with Top-Down Mass Spectrometry . . . . . . . . . . . . . . . . . . . Lauren M. Adams, Caroline J. DeHart, and Neil L. Kelleher 4 Absolute Quantitation of GTPase Protein Abundance . . . . . . . . . . . . . . . . . . . . . . . Fiona E. Hood, Yasmina M. Sahraoui, Rosalind E. Jenkins, and Ian Prior 5 Validation of Isoform- and Mutation-Specific RAS Antibodies . . . . . . . . . . . . . . . . Andrew M. Waters and Channing J. Der 6 Production and Membrane Binding of N-Terminally Acetylated, C-Terminally Farnesylated and Carboxymethylated KRAS4b . . . . . . . . . . . . . . . . . Simon Messing, Constance Agamasu, Matt Drew, Caroline J. DeHart, Andrew G. Stephen, and William K. Gillette 7 Active GTPase Pulldown Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Martin J. Baker and Ignacio Rubio 8 Methods to Monitor Ras Activation State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kari Kopra and Harri H€ a rm€ a 9 NMR Detection Methods for Profiling RAS Nucleotide Cycling. . . . . . . . . . . . . . Ryan C. Killoran and Matthew J. Smith

47 65 91

105

117 137 169

PART III METHODS IN RAS PROCESSING, TRAFFICKING, AND LOCALIZATION 10

11 12

Ras Diffusion and Interactions with the Plasma Membrane Measured by FRAP Variations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Orit Gutman, Marcelo Ehrlich, and Yoav I. Henis Understanding Ras Spatial Cycles Through Reaction-Diffusion Simulations . . . 199 Malte Schmick and Philippe I. H. Bastiaens Super-Resolution Imaging and Spatial Analysis of RAS on Intact Plasma Membrane Sheets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 Yong Zhou and John F. Hancock

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viii

13

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15

Contents

FLIM-FRET Analysis of Ras Nanoclustering and Membrane-Anchorage . . . . . . 233 Hanna Parkkola, Farid Ahmad Siddiqui, Christina Oetken-Lindholm, and DanielAbankwa Assessment of Plasma Membrane Fatty Acid Composition and Fluidity Using Imaging Flow Cytometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Natividad R. Fuentes, Michael L. Salinas, Xiaoli Wang, Yang-Yi Fan, and RobertS. Chapkin Spatiotemporal Imaging of Small GTPase Activity Using Conformational Sensors for GTPase Activity (COSGA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Yao-Wen Wu

PART IV 16 17 18 19

20

21

Using BioID to Characterize the RAS Interactome . . . . . . . . . . . . . . . . . . . . . . . . . Hema Adhikari and Christopher M. Counter Probing RAS Function with Monobodies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imran Khan and John P. O’Bryan Detection of Endogenous RASSF1A Interacting Proteins. . . . . . . . . . . . . . . . . . . . Howard Donninger, Desmond Harrell-Stewart, and Geoffrey J. Clark Mathematical Modeling to Study KRAS Mutant-Specific Responses to Pathway Inhibition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Edward C. Stites A Facile Method to Engineer Mutant Kras Alleles in an Isogenic Cell Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Konstantin Budagyan and Jonathan Chernoff RASless MEFs as a Tool to Study RAS-Dependent and RAS-Independent Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Carmen G. Lechuga, Marina Salmon, Guillem Paniagua, Carmen Guerra, Mariano Barbacid, and Matthias Drosten

PART V 22

23 24

25

26

METHODS IN RAS SIGNALING AND INHIBITION 271 281 303

311

323

335

METHODS IN VIVO RAS BIOLOGY

Generation of Patient-Derived Colorectal Cancer Organoids for RAS Studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dirk Schumacher, Joseph L. Regan, Dorothea Przybilla, and Reinhold Sch€ a fer Ras GEF Mouse Models for the Analysis of Ras Biology and Signaling . . . . . . . . Alberto Ferna´ndez-Medarde and Eugenio Santos Studying Metabolic Abnormalities in the Costello Syndrome HRAS G12V Mouse Model: Isolation of Mouse Embryonic Fibroblasts and Their In Vitro Adipocyte Differentiation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Miray Fidan, Saravanakkumar Chennappan, and Ion Cristian Cirstea Dissecting Oncogenic RAS Signaling in Melanoma Development in Genetically Engineered Zebrafish Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrew P. Badrock and Adam Hurlstone Ras, Ral, and Rap1 in C. elegans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Neal R. Rasmussen and David J. Reiner

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

349 361

397

411 423 437

Contributors DANIEL ABANKWA • Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland; Cancer Cell Biology and Drug Discovery Group, Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg LAUREN M. ADAMS • Department of Molecular Biosciences, Northwestern University, Evanston, IL, USA HEMA ADHIKARI • Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC, USA CONSTANCE AGAMASU • NCI RAS Initiative, Frederick National Laboratory for Cancer Research, Frederick, MD, USA ANDREW P. BADROCK • Faculty of Biology, Medicine and Health, School of Biological Sciences, The University of Manchester, Manchester, UK MARTIN J. BAKER • Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA MARIANO BARBACID • Molecular Oncology Programme, Centro Nacional de Investigaciones Oncologicas (CNIO), Madrid, Spain PHILIPPE I. H. BASTIAENS • Department of Systemic Cell Biology, Max Planck Institute of Molecular Physiology, Dortmund, Germany; Faculty of Chemistry and Chemical Biology, TU Dortmund, Dortmund, Germany KONSTANTIN BUDAGYAN • Drexel University College of Medicine, Philadelphia, PA, USA ROBERT S. CHAPKIN • Program in Integrative Nutrition and Complex Diseases, Texas A&M University, College Station, TX, USA; Department of Nutrition, Texas A&M University, College Station, TX, USA; Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, TX, USA; Center for Environmental Health Research, Texas A&M University, College Station, TX, USA SARAVANAKKUMAR CHENNAPPAN • Masonic Medical Research Institute, Utica, NY, USA JONATHAN CHERNOFF • Fox Chase Cancer Center, Philadelphia, PA, USA ION CRISTIAN CIRSTEA • Institute of Comparative Molecular Endocrinology, Ulm University, Ulm, Germany GEOFFREY J. CLARK • James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA CHRISTOPHER M. COUNTER • Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC, USA; Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA CAROLINE J. DEHART • NCI RAS Initiative, Frederick National Laboratory for Cancer Research, Frederick, MD, USA CHANNING J. DER • Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA HOWARD DONNINGER • Department of Medicine, University of Louisville, Louisville, KY, USA; James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA

x

Contributors

MATT DREW • NCI RAS Initiative, Frederick National Laboratory for Cancer Research, Frederick, MD, USA MATTHIAS DROSTEN • Molecular Oncology Programme, Centro Nacional de Investigaciones Oncologicas (CNIO), Madrid, Spain MARCELO EHRLICH • Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel YANG-YI FAN • Program in Integrative Nutrition and Complex Diseases, Texas A&M University, College Station, TX, USA; Department of Nutrition, Texas A&M University, College Station, TX, USA ALBERTO FERNA´NDEZ-MEDARDE • Centro de Investigacion del Ca´ncer—Instituto de Biologı´a Molecular y Celular del Ca´ncer (CSIC—Universidad de Salamanca) and CIBERONC, Salamanca, Spain MIRAY FIDAN • Institute of Comparative Molecular Endocrinology, Ulm University, Ulm, Germany NATIVIDAD R. FUENTES • Program in Integrative Nutrition and Complex Diseases, Texas A&M University, College Station, TX, USA; Department of Nutrition, Texas A&M University, College Station, TX, USA; Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, TX, USA WILLIAM K. GILLETTE • NCI RAS Initiative, Frederick National Laboratory for Cancer Research, Frederick, MD, USA CARMEN GUERRA • Molecular Oncology Programme, Centro Nacional de Investigaciones Oncologicas (CNIO), Madrid, Spain ORIT GUTMAN • Department of Neurobiology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel JOHN F. HANCOCK • Department of Integrative Biology and Pharmacology, University of Texas Health Science Center at Houston, Houston, TX, USA HARRI HA€ RMA€ • Department of Chemistry, Chemistry of Drug Development, University of Turku, Turku, Finland DESMOND HARRELL-STEWART • Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA YOAV I. HENIS • Department of Neurobiology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel FIONA E. HOOD • Division of Cellular and Molecular Physiology, Institute of Translational Medicine, University of Liverpool, Liverpool, UK ADAM HURLSTONE • Faculty of Biology, Medicine and Health, School of Biological Sciences, The University of Manchester, Manchester, UK ROSALIND E. JENKINS • Centre for Drug Safety Science Bioanalytical Facility, Division of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, UK NEIL L. KELLEHER • Department of Molecular Biosciences, Northwestern University, Evanston, IL, USA; Proteomics Center of Excellence, Northwestern University, Evanston, IL, USA; Department of Chemistry, Northwestern University, Evanston, IL, USA IMRAN KHAN • Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Hollings Cancer Center, Medical University of South Carolina, Charleston, SC, USA; Ralph H. Johnson VA Medical Center, Charleston, SC, USA RYAN C. KILLORAN • Institute for Research in Immunology and Cancer, Universite´ de Montre´ al, Montre´al, QC, Canada

Contributors

xi

KARI KOPRA • Department of Chemistry, Chemistry of Drug Development, University of Turku, Turku, Finland KAYLA R. KULHANEK • Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA CARMEN G. LECHUGA • Molecular Oncology Programme, Centro Nacional de Investigaciones Oncologicas (CNIO), Madrid, Spain SIMON MESSING • NCI RAS Initiative, Frederick National Laboratory for Cancer Research, Frederick, MD, USA JOHN P. O’BRYAN • Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Hollings Cancer Center, Medical University of South Carolina, Charleston, SC, USA; Ralph H. Johnson VA Medical Center, Charleston, SC, USA CHRISTINA OETKEN-LINDHOLM • Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland GUILLEM PANIAGUA • Molecular Oncology Programme, Centro Nacional de Investigaciones Oncologicas (CNIO), Madrid, Spain HANNA PARKKOLA • Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland IAN PRIOR • Division of Cellular and Molecular Physiology, Institute of Translational Medicine, University of Liverpool, Liverpool, UK DOROTHEA PRZYBILLA • Charite´ Comprehensive Cancer Center, Charite´ Universit€ a tsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), German Cancer Research Center Heidelberg, Heidelberg, Germany NEAL R. RASMUSSEN • Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA JOSEPH L. REGAN • Charite´ Comprehensive Cancer Center, Charite´ Universit€ a tsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), German Cancer Research Center Heidelberg, Heidelberg, Germany DAVID J. REINER • Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA JEROEN P. ROOSE • Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA IGNACIO RUBIO • Institute of Molecular Cell Biology, Center for Molecular Biomedicine, University Hospital Jena, Jena, Germany; Clinic for Anaesthesiology and Intensive Care, University Hospital Jena, Jena, Germany YASMINA M. SAHRAOUI • Division of Cellular and Molecular Physiology, Institute of Translational Medicine, University of Liverpool, Liverpool, UK MICHAEL L. SALINAS • Program in Integrative Nutrition and Complex Diseases, Texas A&M University, College Station, TX, USA; Department of Nutrition, Texas A&M University, College Station, TX, USA MARINA SALMO´N • Molecular Oncology Programme, Centro Nacional de Investigaciones Oncologicas (CNIO), Madrid, Spain EUGENIO SANTOS • Centro de Investigacion del Ca´ncer—Instituto de Biologı´a Molecular y Celular del Ca´ncer (CSIC—Universidad de Salamanca) and CIBERONC, Salamanca, Spain REINHOLD SCHA€ FER • Charite´ Comprehensive Cancer Center, Charite´ Universit€ atsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), German Cancer Research Center Heidelberg, Heidelberg, Germany

xii

Contributors

MALTE SCHMICK • Department of Systemic Cell Biology, Max Planck Institute of Molecular Physiology, Dortmund, Germany DIRK SCHUMACHER • Charite´ Comprehensive Cancer Center, Charite´ Universit€ atsmedizin Berlin, Berlin, Germany; German Cancer Consortium (DKTK), German Cancer Research Center Heidelberg, Heidelberg, Germany FARID AHMAD SIDDIQUI • Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland MATTHEW J. SMITH • Institute for Research in Immunology and Cancer, Universite´ de Montre´al, Montre´al, QC, Canada; Department of Pathology and Cell Biology, Faculty of Medicine, Universite´ de Montre´al, Montre´al, QC, Canada ANDREW G. STEPHEN • NCI RAS Initiative, Frederick National Laboratory for Cancer Research, Frederick, MD, USA EDWARD C. STITES • Integrative Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA XIAOLI WANG • Program in Integrative Nutrition and Complex Diseases, Texas A&M University, College Station, TX, USA; Department of Nutrition, Texas A&M University, College Station, TX, USA ANDREW M. WATERS • Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA YAO-WEN WU • Department of Chemistry, Umea˚ Centre for Microbial Research, Umea˚ University, Umea˚, Sweden YONG ZHOU • Department of Integrative Biology and Pharmacology, University of Texas Health Science Center at Houston, Houston, TX, USA

Part I Generic Ras Biology

Chapter 1 Ras Variant Biology and Contributions to Human Disease Ian Prior Abstract Analysis of cancer and RASopathy genetic databases reveals that ~19% of all cancer cases and ~4% of developmental disorders contain Ras mutations. Ras isoform and mutation variants differentially contribute to these diseases and provide an opportunity for deeper understanding of Ras function. The putative mechanisms underpinning these differences, new approaches that are being applied, and some of the key questions and challenges that remain are discussed. Key words HRAS, KRAS, NRAS, COSMIC, TCGA, RASopathy, Cancer

1

Introduction Ras proteins are GTPases that control a network of signaling cascades controlling a wide range of cellular functions (Fig. 1). They are principally associated with promoting cell proliferation via the RAF-MAP kinase (MAPK) pathway and cell survival via the PtdIns3 kinase (PI3K)-AKT pathway. Activated (GTP-bound) Ras proteins undergo a conformational change that allows engagement with a variety of effectors via their Ras-binding (RBD) or Ras association (RA) domains [1, 2]. The affinities of different effectors for Ras vary, with Raf proteins exhibiting >tenfold higher affinity for Ras than most other effectors [3–5]. Most members of the Ras network are present as multiple isoforms that also display variable propensities for engagement with or activation by Ras [6]. Multiple variants of Ras are also present in all cells, and their isoform- and mutation-specific properties provide important opportunities for improved understanding of Ras function and for developing therapeutic strategies targeting aberrant Ras signaling.

Ignacio Rubio and Ian Prior (eds.), Ras Activity and Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2262, https://doi.org/10.1007/978-1-0716-1190-6_1, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Ian Prior

Fig. 1 The Ras signaling network. Ras cycling between inactive (GDP-bound) and active (GTP-bound) states is controlled by GEFs and GAPs. GTP binding results in a conformational change in Ras that allows interaction with effectors controlling many cellular functions

2

Ras Variants In humans, three Ras genes encode four ubiquitously expressed proteins: HRAS, NRAS, KRAS4A, and KRAS4B. These protein variants are almost identical over the majority of their sequence, including within the regions involved in GDP/GTP binding and hydrolysis and effector engagement (Fig. 2). The C-terminal hypervariable region (HVR) contains the distinguishing features of each isoform and results in their differential localization. The HVR is post-translationally modified via farnesylation, proteolysis of the final three amino acids, and then carboxymethylation of the C-terminal cysteine [7]. This provides weak membrane affinity; additional motifs generate more stable membrane association and enable plasma membrane localization where the majority of Ras signaling takes place [8–10]. For HRAS, NRAS, and KRAS4A, this comprises palmitoylation and hydrophobic/basic residues [10, 11], while for KRAS4B, a hexalysine polybasic patch facilitates electrostatic membrane interactions (Fig. 2). At the plasma membrane, Ras variants adopt a variety of activation-dependent orientations regulated by electrostatic membrane interactions with basic residues within the HVR and the allosteric region [12, 13]. These are thought to facilitate interactions with different Ras effectors. There is also evidence that activated KRAS4B can dimerize via the α4-β6-α5 interface [14, 15],

Ras Variant Biology

5

Fig. 2 Ras isoforms. Three ubiquitously expressed Ras genes encode four proteins that are almost identical except for their hypervariable regions (HVR). The HVR is post-translationally modified to enable membrane interactions and specify correct localization. At the cell surface, Ras proteins adopt monomeric or clustered arrangements that are thought to facilitate isoform-specific cell signaling

although no evidence for dimerization was seen in cell-free experiments using purified proteins and artificial lipid bilayers [16]. KRAS4B dimerization was inhibited with a D154Q mutation, with consequent inhibitory effects on Ras-dependent MAPK signaling and tumor initiation [14]. The putative dimerization interface represents a potential drug target that has been successfully inhibited using monobodies [17].

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Ian Prior

A further type of Ras cell surface presentation is via nanoclusters (Fig. 2). These are short-lived, 5–10 nm specialized clusters of proteins and lipids that are distinct for each Ras isoform [18, 19]. Nanoclustering is controlled via HVR interactions with membrane lipids and a suite of partner proteins [19–23]. Ras structural changes caused by GDP or GTP binding are translated into altered HVR-membrane interactions that result in changed affinity for specific types of nanocluster [24–27]. Lipid contributions are also important, HRAS and NRAS variously rely on cholesterol for nanoclustering depending on their activation state [19, 28], whereas KRAS4B interacts with anionic lipids and displays a strong preference for phosphatidylserine [27, 29, 30]. These interactions are exquisitely sensitive to the precise arrangement of charged residues, with changes to this sequence resulting in different associations with anionic lipid and the extent of saturation seen in the acyl chains of these lipids [30]. Membrane interactions of Ras have long been the focus for potential therapeutic targeting; farnesyl transferase inhibitors (FTIs) are specific for HRAS [31], and clinical trials are ongoing in a range of HRAS mutant cancers. Interfering with Ras nanoclustering represents an emergent field, and a variety of compounds that interfere with cell surface phosphatidylserine levels have shown efficacy in disrupting KRAS4B nanoclustering and signaling [32–35]. The largely non-overlapping distributions of Ras isoforms between these nanoscale domains are thought to at least in part underlie the isoform-specific biology associated with these Ras variants. These phenotypic differences include the fact that only KRAS4B is essential for mouse development, and different cancer types and developmental disorders are associated with particular Ras isoforms [36–39]. In vitro data indicated that different effector pathways appear to be preferentially coupled to each isoform [40, 41], although analysis of endogenous Ras signaling has not observed such consistent links [42]. Nevertheless, research using in vivo Ras-driven cancer models identified different signaling programs associated with KRAS versus NRAS tumorigenesis, with KRAS more able to engage proliferative pathways and promote tumor stem cell expansion while NRAS promote an anti-apoptotic program via non-canonical MAPK pathways [43–45]. While it’s clear that each Ras isoform exhibits different contributions to cell, development, and disease biology, the mechanisms underpinning this largely remain to be formally demonstrated. Ras mutations represent a second Ras variant feature that together with isoform-specific biology provides diverse options for tuning specific outputs leading to different disease contributions. Almost all Ras mutations found in cancer patients are located at codons 12, 13, and 61 [2, 39]. They result in impaired GAP binding and/or reduced nucleotide affinity, leading to enhanced GTP binding and prolonged Ras activation [1]. All mutation

Ras Variant Biology

7

options tested in these codons are activating and capable of transforming cells [46–48]; therefore, it has generally been assumed that they are equivalent. More recently it has become clear that all mutations are not equal and, while they might all be activating, they have significantly different effects on signaling outputs and the ability to promote disease [49–52]. The best example of this was a recent library CRISPR study measuring relative oncogenicity of KRAS codon 12 and 13 mutants [53]. Simultaneous introduction of 12 different mutations that have been observed in various cancer types into the endogenous KRAS locus in mouse lungs revealed that only 4 could promote lung tumorigenesis and that background genetic context determined which mutations were oncogenic. Further evidence for mutational differences came from A146T and G12D KRAS mouse models that found that A146T promoted a proliferative program in colon but not pancreas while G12D was oncogenic in both tissue types [54]. Therefore, it’s not as simple as the presence of an activating mutation inevitably leading to an oncogenic program; the wider cell and tissue biological context is a key defining influence on whether the Ras signaling generated is going to lead to disease. It’s also important that there is widespread recognition that Ras mutants are unlikely to be equivalently responsive and this should be factored into clinical trial design and interpretation.

3

Ras Contributions to Disease Aberrant activation of the Ras pathway is associated with a wide range of cancer types and with a group of related developmental disorders found in up to 1/1000 live births. Ras isoforms appear to be differentially associated with these two types of disease (Fig. 3a), for reasons that remain to be fully understood. In general, the differences in Ras mutation and isoform prevalence associated with disease subtypes are thought to be due to the interplay between Ras dosage, signaling specificity, and cellular/tissue context that results in narrow windows of activity permissive for driving initiation and progression of each disease [60, 61].

3.1 RASopathies: Developmental Disorders

Germline mutations that result in upregulation of the Ras/MAPK pathway cause RASopathies. These represent a group of eight related diseases that share many common traits exemplified by the most prevalent disorder: Noonan syndrome [38]. These include distinctive cranio-facial features, cardiac defects, learning disabilities, skeletal abnormalities, short stature, and increased cancer risk [38]. More than 15 Ras/MAPK pathway genes have been variously implicated in causing these diseases [62]; however, Ras isoforms themselves are mutated in ~4% of all RASopathy cases (Fig. 3a). This overall figure is dominated by Noonan syndrome cases where

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Fig. 3 Distinctive Ras variant contributions to RASopathies and cancer. (a) Ras isoforms display differential associations with cancer versus RASopathies and within each RASopathy where they are observed to be mutated. (b) The patterns of favored codon mutations are not equivalent between disease types; the top ten most frequently mutated codons that are depicted are altered in 99% of all cancer and 94% of all RASopathy cases. (c) Both cancers and RASopathies favor mutations of codon 12; however, the frequencies of each of the 6 possible mutants that can result at each position are highly divergent between disease types. Cancer data source, COSMIC v85; RASopathy data source, NSEuroNet. In both cases, mutation frequencies within each database for each isoform and codon were first normalized to the relative incidence of each disease in which they were observed (see also Table 1)

PTPN11 is the predominant Ras pathway driver; in contrast, the much rarer Costello syndrome exhibits strong direct coupling to Ras with ~95% of patients screened harboring a mutant HRAS gene. These statistics are derived from the NSEuroNet database that has screened more than 3700 RASopathy patients for the presence of mutations in 15 key Ras pathway genes [63, 64]. The size of the NSEuroNet database allows more detailed analysis of the types of mutations present in each disease type for the Ras mutant subpopulation of RASopathy patients. When the Ras variant mutation frequency values in each RASopathy subtype are normalized to the prevalence of each disease, an estimate is generated of the relative pan-RASopathy abundance of each isoform/codon mutation (Fig. 3). It is immediately apparent that the spectrum of mutations preferred in RASopathies is largely distinct from those commonly seen in cancer (Fig. 3b). While cancerassociated mutations exclusively reside within locations involved in regulating nucleotide binding/hydrolysis, RASopathy mutations are also located in other regions that regulate effector interactions or with a less obvious functional impact on Ras activity. Analysis and normalization of NSEuroNet data for disease subtype incidence reveal that over half of all Ras mutant RASopathy patients harbor one of the following four mutations: D153V (17.6%), G12S, G60E, and T58I (each ~13%). These tend to be

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Table 1 Top 20 most frequently observed Ras mutant cancer types. Mutation frequencies are based on COSMIC v85 data together with selected publicly available COAD (H, K, N), READ (H, K, N), LUAD (K), and PAAD (K) data from the Foundation Medicine database [55–57]. Mutation frequencies are applied to the most recent American Cancer Society data on cancer incidence [58] to estimate new cancer cases per year in the USA, and GLOBOCAN 2018 cancer statistics [59] are used to extrapolate the approximate number of global Ras mutant cancer cases

associated with a particular Ras isoform. For example, G12S is almost exclusively associated with HRAS in Costello syndrome where this Ras variant combination was found in ~80% of all patients screened. T58I is the second most abundant KRAS

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mutation (~25% of KRAS cases), whereas for HRAS it represents fewer than 2% of cases and is rarely seen in NRAS mutant patients. G60E is predominantly found in KRAS and NRAS mutant Noonan cases and never observed in HRAS mutant cases. The most commonly observed mutant, D153V, is only found in KRAS mutant patients because the other Ras isoforms encode a glutamic acid at this position. Notably E153 is not a target for mutations in HRAS or NRAS RASopathies. D153V is interesting because it is adjacent to D154Q mutation site in the α5 helix that prevented KRAS4B dimerization [14], suggesting that altered associations between KRAS molecules might underpin a subset of RASopathies. The likely functional difference between RASopathy and cancer mutations can best be understood by looking at the most commonly shared mutation location. Most codon 12 mutations in RASopathies are in HRAS and encode G12S, whereas most G12 mutants in cancer are in KRAS and exhibit a wider spectrum of mutations (Fig. 3c). The cancer-associated mutations are considered to be too toxic for normal development. This was seen for germline heterozygous KRAS G12D mice that exhibited placental insufficiency and early embryonic lethality between E9.5 and E11.5 [65]. Germline heterozygous expression of NRAS G12D in mice was also embryonically lethal [66], while germline heterozygous HRAS G12V mice were initially viable but ~80% died within 14 days of birth [67]. G12S is the most commonly observed codon 12 mutation in RASopathies, and the library CRISPR study that measured relative oncogenicity of codon 12 and 13 KRAS mutants revealed G12S to be the weakest [53]. The notion that RASopathy mutants are somehow weaker than cancer-associated Ras mutants is supported by biochemical evidence that revealed that G12D is more active than RASopathyassociated T58I and V14I due to lower GTPase activity/lower GAP responsiveness [68]. Further work to biochemically characterize the other RASopathy mutants and to understand what relative differences in GTP loading/nucleotide cycling mean in terms of Ras network activity is required to properly understand the qualitative differences that are observed between these classes of disease-causing mutants. 3.2

Oncogenic Ras

Several cancer genetics databases collate mutation and copy number data for genes of interest across a wide range of cancer types. The Catalogue of Somatic Mutations in Cancer (COSMIC) is the largest database, containing data on almost ten million coding mutations in ~1.5 million samples that have been collated via literature searches [69]. The Cancer Genome Atlas (TCGA) is the most refined publicly available database in that the ~12,000 curated samples are included based on stringent diagnostic criteria [70]. Extensive molecular characterization is applied to TCGA samples, including genome-wide sequencing and copy number

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Fig. 4 Ras isoform and Ras pathway genetic alterations in cancer. (a) Ras isoforms exhibit different preferences for codon 12, 13, and 61 mutations in cancer. (b) Distinct patterns of amplification and deletion (+2/ 2 change in copy number) are also seen for Ras isoforms, with KRAS typically exhibiting amplification and HRAS favoring loss of copy number. (c) Across a wide range of cancer types, ~30–95% of patient samples contain mutation, amplification, or deletion of at least 1 out of 85 Ras pathway genes (gene list derived from [71]). Ras contributions to each cancer type are highly variable (altered in 200 mg recommended), maintain in liquid nitrogen (LN2) until just before cryogrinding. Subject to two rounds of grinding (frequency 30 Hz, 2 min), keeping samples immersed in LN2 between rounds. Transfer pulverized lysate to a 50 mL conical tube pre-chilled in LN2. Allow lysate to thaw on ice, followed by resuspension in 25 mL chilled lysis buffer. 4. Incubate lysate on ice for 30 min. Pipette the lysate every 10 min to ensure even resuspension. Do not vortex. If lysing on dish, use a cell scraper and serological pipette to transfer lysate to a clean conical tube. 5. Sonicate the lysate on ice at 50% amplitude for 30 2-s cycles, with incubation on ice for 5 s in between each cycle. 6. Clarify the lysate by centrifugation for 30 min at 16,000  g and 4  C.

Fig. 2 Experimental workflow schematic for KRAS4B IP-TDMS assays employing either agarose or custom magnetic immunoprecipitation (IP) beads (figure modified from Ntai et al.) [7]. Day 1 of the SOP workflow entails cancer cell or tissue lysis, followed by whole cell lysate incubation with IP beads overnight (see Subheadings 3.1 and 3.2). Day 2 of the SOP workflow involves completion of the IP, desalting and concentration of the immunoenriched KRAS4B protein, and proteoform identification by top-down LC-MS/ MS (see Subheadings 3.1–3.4)

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7. Equilibrate 50 μL of IP beads (100 μL of 50% slurry) in 1 mL of lysis buffer in a 15 mL conical tube on ice for 30 min. Remove the majority of lysis buffer while still leaving a visible layer of buffer on top of the beads (see Note 1). 8. Take gel aliquots (for western blot) from the lysate supernatant, and store at 20  C or 80  C (see Note 2). 9. Add lysate to the IP beads. For lysate from tissue samples (25 mL), divide evenly between two aliquots of IP beads (12.5 mL/50 μL IP beads). Incubate for 16–20 h at 4  C with constant rotation. 10. Prepare one autosampler vial per IP by adding 200 μL of 2 mg/mL BSA. Store vial in a closed container at 4  C until use during Day 2 of IP (see Note 3). Day 2 11. Prepare IP wash buffer 1 and 2. Chill on ice.

12. Collect lysate flow through centrifugation for 5 min at 120  g and 4  C (see Note 4). 13. Take gel aliquots from the flow through supernatant and store at 20  C or 80  C. 14. Remove supernatant, leaving a visible layer of liquid covering the beads (see Note 1). 15. Wash the IP beads three times with 5 mL of wash buffer 1. Pellet beads by centrifugation for 5 min at 120  g and 4  C. Remove supernatant, leaving the beads covered by liquid. 16. Wash the IP beads three times with 5 mL of wash buffer 2. Pellet beads by centrifugation for 5 min at 120  g and 4  C (see Note 5). Remove supernatant, leaving the beads covered by liquid. 17. Transfer IP beads from the 15 mL conical tube to a 1.5 mL LoBind tube using a blunted pipette tip. Centrifuge for 5 min at 120  g and RT. Remove supernatant, leaving the beads covered by liquid, and place LoBind tube on ice. 18. Make a fresh solution of elution buffer (see Notes 6 and 7). 19. Elute KRAS4B from beads using 250 μL of elution buffer at 900 rpm for 20 min at RT (ThermoMixer). Check at 5-min intervals to ensure that beads are evenly resuspended, mixing with a blunted pipette tip if settled. Transfer eluent to new 1.5 mL LoBind tube and store on ice. 20. Repeat the elution step two to three times, keeping each recovered fraction separate in a clean LoBind tube on ice. 21. Take separate gel aliquots from all elutions (e.g., E1 and E2). Add 50 μL of gel sample loading buffer to the remaining IP beads. Store at 20  C or 80  C.

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22. Prepare a 1.5 mL LoBind tube for the ZipTip elution by adding 5 μL of C4 ZipTip elution buffer and storing on ice. 23. Prepare for final sample dilution and vial conditioning by chilling HPLC Solvent A on ice. 24. Concentrate and desalt the eluted KRAS4B protein via C4 ZipTip. First, add a C4 ZipTip to a p10 pipette, and activate in six 10 μL volumes of the C4 ZipTip activation buffer, dispensing each to waste. Wash the C4 ZipTip in six 10 μL volumes of the C4 ZipTip wash buffer, dispensing each to waste. Transfer the C4 ZipTip to the end of a p200 tip, and bind KRAS4B by pipetting 200 μL of each elution fraction up and down ten times, taking extreme care to not pass air through the resin. Combine multiple elution fractions onto one C4 ZipTip by repeating this step for each fraction collected (e.g., E1, followed by E2). Return the C4 ZipTip to the p10 pipette, and wash in ten 10 μL volumes of the C4 ZipTip wash buffer, dispensing each to waste. Elute KRAS4B from C4 ZipTip by pipetting 5 μL C4 ZipTip elution buffer up and down at least ten times, taking extreme care to not pass bubbles through the resin. Dilute 1:5 with HPLC Solvent A (see Notes 6 and 8). 25. Remove BSA from autosampler vials, and wash four times in 200 μL of ice-cold HPLC Solvent A, dispensing to waste each time. 26. Transfer sample from LoBind tube containing the final sample from Step #24 into autosampler vial (see Note 9). 3.2 KRAS4B Magnetic Bead Immunoprecipitation (Fig. 2)

Day 1 1. Wash 75 μL (150 μL of 50% slurry) of protein A/G magnetic beads in 1 mL of 1  TBS in a 2 mL tube. Pellet beads using a magnet and remove supernatant. Repeat twice more.

2. Resuspend beads in 1 mL of 0.1 mg/mL BSA in 1X TBS. 3. Add 15 μg of α-v-HRAS antibody to the bead solution. Incubate for 4 h at RT with constant rotation (see Note 10). 4. Following incubation, use a magnet to pellet beads at the bottom of the tube, and remove as much of the supernatant as possible while still leaving a visible layer on top of the beads. Wash beads two times in 1 mL of 1  TBS, each time pelleting the beads on the bottom of the tube and removing as much of the buffer as possible while still leaving a visible layer on top of the beads. Add 1 mL of 1  TBS and transfer beads from 2 mL tube to a 15 mL tube. Pellet beads at the bottom of the tube and remove supernatant. Equilibrate beads in 1 mL of lysis buffer for 30 min on ice.

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5. Prepare lysis buffer and chill on ice. Add HALT Inhibitor Cocktail only to chilled buffer immediately prior to lysis. 6. For cell culture (~1  108 cells recommended), rinse cells twice with ice-cold PBS, and store on ice. Add ~1 mL of lysis buffer per ~1  107 cells (for a 15 cm cell culture dish, this is approximately 2 mL per dish). For cell pellets, thaw cells on ice, followed by resuspension in 8 mL chilled lysis buffer. 7. For cryopreserved tissue samples (>200 mg recommended), maintain in liquid nitrogen (LN2) until just before cryogrinding. Subject to two rounds of grinding (frequency 30 Hz, 2 min), keeping samples immersed in LN2 between rounds. Transfer pulverized lysate to a 50 mL conical tube pre-chilled in LN2. Allow lysate to thaw on ice, followed by resuspension in 25 mL chilled lysis buffer. 8. Incubate lysate on ice for 30 min. Pipette the lysate every 10 min to ensure even resuspension. Do not vortex. If lysing on dish, use a cell scraper and serological pipette to transfer lysate to a clean conical tube. 9. Sonicate the lysate on ice at 50% amplitude for 30 2-s cycles, with incubation on ice for 5 s in between each cycle. 10. Clarify the lysate by centrifugation for 30 min at 16,000  g and 4  C. 11. Take gel aliquots (for western blot) from the lysate supernatant, and store at 20  C or 80  C (see Note 2). 12. Remove the majority of lysis buffer while still leaving a visible layer of buffer on top of the beads (see Note 1). 13. Add lysate to the IP beads. For lysate from tissue samples (25 mL), divide evenly between two aliquots of IP beads (12.5 mL/50 μL IP beads). Incubate for 16–20 h at 4  C with constant rotation. 14. Prepare one autosampler vial per IP by adding 200 μL of 2 mg/mL BSA. Store vial in a closed container at 4  C until use during Day 2 of IP (see Note 3). Day 2 15. Prepare IP wash buffer 1 and 2. Chill on ice.

16. Collect lysate flow through by pelleting beads by magnet (see Note 4). 17. Take gel aliquots from the flow through supernatant and store at 20  C or 80  C. 18. Remove supernatant, leaving the beads covered by liquid (see Note 1). 19. Wash the IP beads three times with 5 mL of wash buffer 1. Pellet beads by magnet. Remove supernatant, leaving the beads covered by liquid.

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20. Wash the IP beads three times with 5 mL of wash buffer 2. Pellet beads by magnet. Remove supernatant, leaving the beads covered by liquid (see Note 5). 21. Transfer IP beads from the 15 mL tube to a 1.5 mL LoBind tube using a blunted pipette tip. Pellet with magnet. Remove supernatant, leaving the beads covered by liquid. 22. Make a fresh solution of elution buffer (see Notes 6 and 7). 23. Elute KRAS4B from beads using 250 μL of elution buffer at 900 rpm for 20 min at RT (ThermoMixer). Check at 5-min intervals to ensure that beads are evenly resuspended, mixing with a blunted pipette tip if settled. Transfer eluent to new 1.5 mL LoBind tube and store on ice. 24. Repeat elution step two to three times, keeping each recovered fraction separate in a clean LoBind tube on ice. 25. Take separate gel aliquots from all elutions (e.g., E1 and E2). Add 50 μL of gel sample loading buffer to the remaining IP beads. Store at 20  C or 80  C. 26. Prepare a 1.5 mL LoBind tube for the ZipTip elution by adding 5 μL of C4 ZipTip elution buffer and storing on ice. 27. Prepare for final sample dilution and vial conditioning by chilling HPLC Solvent A on ice. 28. Concentrate and desalt the eluted KRAS4B protein via C4 ZipTip. First, add a C4 ZipTip to a p10 pipette, and activate in six 10 μL volumes of the C4 ZipTip activation buffer, dispensing each to waste. Wash the C4 ZipTip in six 10 μL volumes of the C4 ZipTip wash buffer, dispensing each to waste. Transfer the C4 ZipTip to the end of a p200 tip, and bind KRAS4B by pipetting 200 μL of each elution fraction up and down ten times, taking extreme care to not pass air through the resin. Combine multiple elution fractions onto one C4 ZipTip by repeating this step for each fraction collected (e.g., E1, followed by E2). Return the C4 ZipTip to the p10 pipette, and wash in ten 10 μL volumes of the C4 ZipTip wash buffer, dispensing each to waste. Elute KRAS4B from C4 ZipTip by pipetting 5 μL C4 ZipTip elution buffer up and down at least ten times, taking extreme care to not pass bubbles through the resin. Dilute 1:5 with HPLC Solvent A (see Notes 6 and 8). 29. Remove BSA from autosampler vials, and wash four times in 200 μL of ice-cold HPLC Solvent A, dispensing to waste each time. 30. Transfer sample from LoBind tube containing the final sample from Step #28 into autosampler vial (see Note 9).

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1. KRAS4B proteoforms should be further resolved by reversephase nanocapillary liquid chromatography (LC) prior to introduction into the mass spectrometer. The parameters provided herein are for a Dionex Ultimate 3000 LC system coupled with a Q-Exactive HF BioPharma mass spectrometer (Thermo Fisher Scientific). 2. The LC should use the described (Methods) trap and nanocapillary analytical columns coupled to a vented tee setup and nanospray emitter [10]. For the alternative commercial columns and source listed in Methods, employ a trap-in-valve configuration and an extender line to the NanoFlex ion source, with parameters as described in [11]. 3. Maintain the LC temperature at 45  C throughout LC-MS/ MS analysis. Load an injection volume of 5 μL of KRAS4B sample (prepared by IP less than 24 h prior to LC-MS/MS, with samples prepared immediately prior to LC-MS/MS recommended; see Note 9) onto the trap column, and wash in HPLC Solvent A for 10 min at a flow rate of 2.5 μL/min. Elute KRAS4B proteins into the mass spectrometer at a flow rate of 0.3 μL/min by the following gradient: 5% HPLC Solvent B at 0 min, 30% Solvent B at 5 min, 45% Solvent B at 25 min, 95% Solvent B from 28–31 min, and 5% Solvent B from 34 to 50 min. 4. Acquire partial intact mass (MS1) spectra using a 200 m/z window to determine both the most abundant charge states of KRAS4B and the location of KRAS4B within the chromatogram. This step can be performed simultaneously with step 5 within a single LC-MS/MS run. 5. MS1 scan method parameters: (a) Protein Mode. (b) Resolving power (r.p.): 120,000 (at 200 m/z). (c) Scan window: 750–950 m/z. (d) Average of four microscans. (e) Automatic gain control (AGC) target: 1E+06. (f) Maximum ion injection time: 50 ms. 6. Acquire targeted intact mass (tMS1) spectra using a selected ion monitoring (SIM) method targeting individual charge states of KRAS4B (e.g., 23+, Table 1). SIM focuses the maximum of allowed ion current through a narrow (10 m/z) window, providing a dramatic increase in sensitivity and facilitating detection of low-abundance endogenous KRAS4B proteoforms. 7. tMS1 scan method parameters: (a) Protein Mode.

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Table 1 KRAS4B charge states and their expected tMS1 (10 m/z) windows KRAS4B charge state

tMS1 (10 m/z) window

23

920–930

24

881–891

25

846–856

(b) Resolving power (r.p.): 120,000 (at 200 m/z). (c) Isolation window: 10 m/z (e.g., 920.0–930.0 for the KRAS4B 23+ charge state). (d) Average of four microscans. (e) Automatic gain control (AGC) target: 5E+04. (f) Maximum ion injection time: 400 ms. 8. Fragment ion (MS2) spectra are used to confirm proteoform sequence and localize post-translational modifications (PTMs). MS2 data is similarly collected by targeting a list of pre-selected values with increasingly narrow m/z windows to provide diagnostic fragment ions to be used in proteoform quantitation and comparison (tMS2). 9. For tMS2 target peak selection, open the Global Parameters tab in the method editor. Select Inclusion List from the dropdown menu, and then enter the m/z value, charge, and retention time range for each proteoform target. It is recommended that three or fewer abundant proteoforms be targeted per retention time range using the given parameters. 10. tMS2 scan method parameters: (a) Protein Mode. (b) Default Charge: 23+ (value corresponding to example targeted charge state). (c) r.p.: 60,000 (at 200 m/z), (d) Isolation window: 8 m/z (wide) or 3 m/z (narrow). (e) Average of four microscans. (f) Minimum m/z: 400. (g) AGC target: 1E+06. (h) Maximum ion injection time: 800 ms. (i) High-energy collisional dissociation (HCD) normalized collision energy (NCE): applied in 2% steps between 19 and 25%. 11. Additional MS parameters: (a) Heated transfer capillary temperature: 320 ˚C.

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(b) S-lens RF amplitude: 50%. (c) 15 V in-source dissociation. 3.4 Data Analysis (see Note 11; Figs. 3 and 4)

1. To analyze the data in ProSight Lite, the monoisotopic masses for both the precursor and the fragmentation scans will be needed. 2. Open the .raw file in Xcalibur QualBrowser. 3. Average across the most abundant portion of the MS1 chromatographic peak corresponding to the KRAS4B proteoform of interest. Click Export and then the Xtract function. Select the following parameters for deconvolution and deisotoping: (a) Generate mass mode: M. (b) Resolution@400: 120000.0. (c) S/N threshold: 3. (d) Max charge: 30. (e) Averagine table: Averagine.

Fig. 3 Example intact mass (MS1) spectra (top) and MS2 graphical fragment maps (bottom) supporting the precise characterization of intact KRAS4B proteoforms: PFR 249914, PFR 249915, PFR 249917, and PFR249918 in HCT-116 Par (WT/G13D) cells by the SOP IP protocol

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Fig. 4 Visual guide on how to use ProSight Lite [9] to characterize KRAS4B proteoforms from MS1 and MS2 data collected during TDMS

4. A new window will appear. Use the left and right keyboard arrow keys to reach Scan #2 of PostXtract results, where the monoisotopic, zero-charge (13C0) mass spectrum can be found. Click Export and then Clipboard (Exact Mass). Paste into a spreadsheet (i.e., Microsoft Excel), and save the file with a title including MS1. 5. Repeat steps 1–3 but with the HCD fragmentation data files and the Resolution@400 set to 60,000. Save the spreadsheet with a title including MS2. 6. Open ProSight Lite (Fig. 4) [9]. 7. Select Add Experimental Data on the home page of ProSight Lite. Copy the monoisotopic mass from the MS1 spreadsheet, and paste under Precursor. Copy Mass data from the MS2 spreadsheet file and paste under Fragments. Set the following parameters: (a) Precursor mass type: Monoisotopic. (b) Mass mode: M (neutral). (c) Fragmentation methods: HCD. (d) Fragmentation tolerance: 10 ppm. 8. Copy and paste the KRAS4B sequence under Add Candidate Sequence tab on the home page of ProSight Lite (can be found at https://www.uniprot.org/uniprot/P01116-2.fasta). From the sequence, you will need to remove the N-terminal methionine (M) and the C-terminal valine (V), isoleucine (I), and

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methionine (M) residues. For Fixed Modifications, use No Modification for cysteine and methionine. 9. Common modifications include adding an acetylation modification to the N-terminus (Thr 2 of KRAS4B) as well as a farnesylation and/or carboxymethylation to the C-terminus (Cys 185 of KRAS4B, proteoform-dependent). Common and custom modifications can be added to residues within the sequence as required. 10. Detailed explanation of the results can be found in DeHart et al. [12]. In short, the Mass Diff. between the Theoretical Mass (calculated from sequence and modification masses) and the Observed Mass (from MS1 spreadsheet file) is reported in both Da and ppm. The p-score describes the probability of the fragments matching the proteoform due to random chance; the smaller the p-score, the less likely it is that the fragments match the proteoform due to random chance [13].

4

Notes 1. Curtail oxidation of KRAS4B proteoforms by limiting exposure of IP beads to the air. It is recommended that a thin layer of buffer be maintained on top of the IP beads during all steps of the IP protocols. 2. It is possible that the IP may fail. A good way to assess the points of failure is to perform a western blot of the IP fractions using an α-RAS primary antibody (Fig. 5). All bands corresponding to KRAS4B should be around the 21 kDa molecular weight marker. (a) If lysis was unsuccessful, a faint band or absence of one will occur in the input (IN) fraction. To fix this, try an increased volume of lysis buffer and/or additional repetitions of sonication. It is also possible that the starting concentration of cells or tissue used was not sufficient to

Fig. 5 Visualization of an example SOP IP by SDS-PAGE and subsequent α-Pan RAS (abcam 52939) immunoblot. IP fractions shown include the input/clarified whole cell lysate (IN; 1:1000), flow through (FT; 1:1000), elution (E1; 1:100), and molecular weight marker (MW). The far left lane contains 50 ng of recombinant processed KRAS4B standard (Std) to mark the expected MW and estimate the quantity of recovered RAS within the IP elution. Note that a band is still present in the FT fraction, suggesting that not all KRAS4B was depleted from the input. Increasing the IP bead to cell lysate protein concentration ratio can improve depletion

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detect KRAS4B proteoforms via IP-TDMS. If this is the case, increase the concentration of starting material. (b) The flow through (FT) fraction should have a minimal band or no band present. If a strong band is present, increase the ratio of IP beads to lysate volume. (c) The elution fraction (E) should have a faint to strong band. If no band is present, the elution was unsuccessful. The pH of the elution may not have been acidic enough if too thick of a layer of wash buffer 2 was left on top of the IP beads when the elution buffer was added. This can be avoided by removing as much liquid as possible from the covered beads immediately prior to elution and ensuring that the elution buffer is close to pH 2 (e.g., by dispensing an aliquot onto pH paper) prior to the elution step. (d) To verify complete elution of KRAS4B from the IP beads, a boiled beads control can be incorporated into the diagnostic immunoblot. This control may have KRAS4B remaining if the elution steps did not work properly, if more elution steps are needed, or if the elution composition needs to be altered depending on the KRAS4B proteoform population. For populations with more hydrophobic KRAS4B, consider eluting with a buffer composed of 0.5% TFA and 10% ACN. 3. Coat all LC-MS/MS vials in 2 mg/mL BSA for at least 4 h prior to use. This reduces loss of hydrophobic KRAS4B proteoforms on the sides of the LC vial. 4. Limit loss of IP beads throughout the protocols by paying special attention to the progress of bead pelleting. If the supernatant has a considerable quantity of agarose IP beads still present following centrifugation, repeat the centrifugation step, or leave the tube on ice until fewer beads remain in the supernatant. For magnetic IP beads, the supernatant will have a brown tint if too many IP beads remain. Continue pelleting magnetic IP beads with a magnet until the supernatant is clear. 5. If the concentration of polymer contaminants from residual detergent in the final sample precludes KRAS4B proteoform characterization, perform three to five additional wash steps with IP wash buffer 2. 6. Use only Optima-grade ACN and H2O for all buffers during and after the elution steps. Do not store these reagents in plastic. 7. Use only fresh TFA when preparing the elution buffers. It is recommended to use single-use 1 mL ampules of concentrated TFA when preparing buffers and to make the elution buffer immediately prior to the elution step. Do not store concentrated TFA in plastic tubes.

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8. To increase the quantity of KRAS4B proteoforms in the final elution, consider performing the C4 ZipTip protocol twice. Elute KRAS4B from both C4 ZipTips into the same final 5 μL elution. 9. The KRAS4B proteoforms can last at 4  C for 24 h post-final elution; however, considerable loss in signal will occur after 24 h. Do not freeze and thaw samples, or delay sample preparation at any step throughout the IP protocols. It is highly recommended that LC-MS/MS analysis be performed immediately following completion of the C4 ZipTip cleanup and final dilution steps. If this is not feasible, samples should be stored in washed, BSA-conditioned vials on ice at 4  C until they can be run. 10. Not all antibodies are created equal. Keep this in mind when using antibodies other than the anti-v-HRAS antibody used in this protocol. Antibody concentration, magnetic bead epitope (i.e., Protein A/G, Protein G, etc.), incubation times, and temperatures may need to be optimized accordingly. 11. For a more exhaustive explanation of how to analyze top-down proteomics data using Xtract and ProSight Lite, refer to DeHart et al. [12].

Acknowledgments We thank Kevin Haigis for providing an agarose bead-based IP protocol and the following coauthors of the Ntai et al. [7] manuscript for their significant contributions to the initial IP-TDMS method development: Ioanna Ntai and Luca Fornelli. This work was supported by federal funds from the National Cancer Institute (Office of Cancer Clinical Proteomics Research), National Institutes of Health, under Contract HHSN261200800001E, and Leidos Biomedical Research under Contract HHSN261200800001E and was carried out in collaboration with the National Resource for Translational and Developmental Proteomics under National Institutes of Health Grant P41 GM108569. L.M.A. is supported by T32GM008382. References 1. Nussinov R, Tsai CJ, Chakrabarti M, Jang H (2016) A new view of Ras isoforms in cancers. Cancer Res 76(1):18–23. https://doi.org/10. 1158/0008-5472.Can-15-1536 2. Barbacid M (1987) ras genes. Annu Rev Biochem 56:779–827. https://doi.org/10.1146/ annurev.bi.56.070187.004023 3. Willumsen BM, Christensen A, Hubbert NL, Papageorge AG, Lowy DR (1984) The p21 ras

C-terminus is required for transformation and membrane association. Nature 310 (5978):583–586. https://doi.org/10.1038/ 310583a0 4. Wright LP, Philips MR (2006) Thematic review series: lipid posttranslational modifications. CAAX modification and membrane targeting of Ras. J Lipid Res 47(5):883–891.

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https://doi.org/10.1194/jlr.R600004JLR200 5. Cox AD, Der CJ, Philips MR (2015) Targeting RAS membrane association: back to the future for anti-RAS drug discovery? Clin Cancer Res 21(8):1819–1827. https://doi.org/10.1158/ 1078-0432.Ccr-14-3214 6. Toby TK, Fornelli L, Kelleher NL (2016) Progress in top-down proteomics and the analysis of proteoforms. Annu Rev Anal Chem (Palo Alto, Calif) 9(1):499–519. https://doi. org/10.1146/annurev-anchem-071015041550 7. Ntai I, Fornelli L, DeHart CJ, Hutton JE, Doubleday PF, LeDuc RD, van Nispen AJ, Fellers RT, Whiteley G, Boja ES, Rodriguez H, Kelleher NL (2018) Precise characterization of KRAS4b proteoforms in human colorectal cells and tumors reveals mutation/modification cross-talk. Proc Natl Acad Sci U S A 115(16):4140–4145. https:// doi.org/10.1073/pnas.1716122115 8. Smith LM, Kelleher NL (2018) Proteoforms as the next proteomics currency. Science 359 (6380):1106–1107. https://doi.org/10. 1126/science.aat1884 9. Fellers RT, Greer JB, Early BP, Yu X, LeDuc RD, Kelleher NL, Thomas PM (2015) ProSight lite: graphical software to analyze

top-down mass spectrometry data. Proteomics 15(7):1235–1238. https://doi.org/10.1002/ pmic.201570050 10. Fornelli L, Durbin KR, Fellers RT, Early BP, Greer JB, LeDuc RD, Compton PD, Kelleher NL (2017) Advancing top-down analysis of the human proteome using a benchtop quadrupole-Orbitrap mass spectrometer. J Proteome Res 16(2):609–618. https://doi. org/10.1021/acs.jproteome.6b00698 11. Toby TK, Fornelli L, Srzentic´ K, DeHart CJ, Levitsky J, Friedewald J, Kelleher NL (2019) A comprehensive pipeline for translational top-down proteomics from a single blood draw. Nat Protoc 14(1):119–152. https:// doi.org/10.1038/s41596-018-0085-7 12. DeHart CJ, Fellers RT, Fornelli L, Kelleher NL, Thomas PM (2017) Bioinformatics analysis of top-down mass spectrometry data with ProSight lite. Methods Mol Biol 1558:381–394. https://doi.org/10.1007/ 978-1-4939-6783-4_18 13. Meng F, Cargile BJ, Miller LM, Forbes AJ, Johnson JR, Kelleher NL (2001) Informatics and multiplexing of intact protein identification in bacteria and the archaea. Nat Biotechnol 19(10):952–957. https://doi.org/10. 1038/nbt1001-952

Chapter 4 Absolute Quantitation of GTPase Protein Abundance Fiona E. Hood, Yasmina M. Sahraoui, Rosalind E. Jenkins, and Ian Prior Abstract Ras proteins and other small molecular weight GTPases are molecular switches controlling a wide range of cellular functions. High homology and functional redundancy between closely related family members are commonly observed. Antibody-based methods are commonly used to characterize their protein expression. However, these approaches are typically semi-quantitative, and the requirement to use different antibodies means that this strategy is not suited for comparative analysis of the relative expression of proteins expressed by different genes. We present a mass spectrometry-based method that precisely quantifies the protein copy number per cell of a protein of interest. We provide detailed protocols for the generation of isotopically labeled protein standards, cell/tissue processing, mass-spectrometry optimization, and subsequent utilization for the absolute quantitation of the abundance of a protein of interest. As examples, we provide instructions for the quantification of HRAS, KRAS4A, KRAS4B, NRAS, RALA, and RALB in cell line and tissue-derived samples. Key words GTPase, Protein abundance, Proteomics, PSAQ, RAS

1

Introduction Small GTPases of the Ras family are involved in a wide range of cellular processes and pathways [1]. The Ras proteins (HRas, NRas, KRas4A, and KRas4B) are proto-oncogenes, and activating mutations are found in 19% of cancer patients [2]. RalA and RalB are Ras effectors that influence cell adhesion and migration and contribute to Ras-dependent transformation in cancer [3]. Certain Ras isoforms and mutants are more strongly associated with certain cancers [4]. The reasons for this pattern have yet to be fully elucidated, but one key component is thought to be the expression level and relative dosage of different Ras isoforms and the relative abundance of mutant versus wildtype Ras [5, 6]. It is likely that relative

Fiona E. Hood and Yasmina M. Sahraoui contributed equally to this work. Ignacio Rubio and Ian Prior (eds.), Ras Activity and Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2262, https://doi.org/10.1007/978-1-0716-1190-6_4, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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expression levels are important regulators of the biology of other GTPases; however accurately quantifying relative protein abundance can be challenging. Mass spectrometry (MS) can be used to absolutely quantify the levels of specific endogenous proteins in a sample by comparison with spiked in isotopically heavy peptide or protein standards [7, 8]. These can be distinguished from the unlabeled counterpart by a mass difference that is easily detectable in the mass spectrometer. Diagnostic peptides are used that are specific for individual isoforms; it is also possible to detect and quantify individual mutants versus wild type proteins. The benefits of this approach versus antibody-based approaches are the accuracy of the quantitation and the unambiguous identification of the protein variant of interest. Here we describe a method for quantifying endogenous GTPases in cells using protein standards absolute quantification (PSAQ) [9, 10]. This involves spiking in isotopically labeled fulllength protein standards into a cell lysate. Adding a full-length protein standard at the start of the process controls against potential sample extraction, processing and handling artifacts that would otherwise result in inaccurate quantitation. The lysate containing the labeled standards is then fractionated using SDS-PAGE, the proteins are digested, and the peptides are then processed for highperformance liquid chromatography (HPLC) fractionation and triple quadrupole-based MS analysis. Diagnostic peptides for the protein of interest are detected, and both the endogenous (light) and protein standard (heavy) peptides are distinguishable due to their different masses. The ratio between the areas of the two sets of peptide peaks allows the accurate estimation of the endogenous protein copy number per mg of cell lysate and per cell. An overview of the method can be seen in Fig. 1. The method that we describe can be applied to any protein of interest including all GTPases. As examples, we provide specific details for quantifying Ras and Ral isoforms. We use heavy isotope-labeled arginine and lysine to label full-length, His-tagged protein standards since trypsin digestion will result in one of these amino acids being present in all diagnostic peptides. However, other heavy amino acids and protease combinations can be chosen to generate suitable diagnostic peptides for your protein of interest. We also describe selected reaction monitoring (SRM) analysis using an Sciex QTRAP 6500 hybrid triple-quad mass spectrometer; however, any triple-quad MS would be suitable once the conditions for identifying the diagnostic peptides have been optimized on the instrument.

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Fig. 1 An overview of the PSAQ method for quantify protein abundance

2

Materials All steps involving bacteria or bacterial culture media should be performed using standard laboratory aseptic technique using sterile containers and consumables. Use ultrapure water for all steps up to gel fixation or staining, from which point onward use liquid chromatography-mass spectrometry (LCMS) grade solvents. The generation of protein standards relies on access to routine bacterial culture and protein production equipment including conical flasks, a shaking incubator and a spectrophotometer. Recombinant protein standards, cell pellets, lysates, and in-gel digests are processed and stored in Eppendorf protein LoBind plastics to prevent loss of proteins and peptides. Buffers for

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HPLC, in-gel digests, and ZipTips are made in glass that has never been washed in detergent (see Note 1). All waste must be disposed by the appropriate local procedures for genetically modified organisms, biological and chemical waste. 2.1 Production of Heavy Labeled His-Tagged Ras/Ral Standards 2.1.1 Expression of Heavy His-Ras/His-Ral Recombinant Protein in AT713 Bacteria

Protein standards are made containing isotopically labeled arginine and lysine. In order to avoid unlabeled arginine or lysine being incorporated into the standards, we use the AT713 E. coli strain that is unable to synthesize these amino acids. We also make and use a minimal M9 media that ensures that only the heavy isotopes of arginine and lysine will be available for making the recombinant protein standards. The 5
M9 salt stock solution for this medium can be made and autoclaved in advance, but the complete media must be made up on the first day of use and used up within a few days. It is important that the media is made in the order described below. We make up a stock of unlabeled hydrophilic amino acids, a stock of hydrophobic amino acids, and individual stocks of the heavy labeled lysine and arginine. We use His-tagged protein standards to facilitate the generation of high-purity standards. Other tags can be used; however, it is important that the tag and linker are small enough to migrate similarly to the endogenous protein on SDS-PAGE for this method to work efficiently. Alternatively, the tag and linker should be cleaved off prior to spike-in. 1. AT713 E. coli bacteria (Yale Coli Genetic Stock Centre, New Haven, USA). These are auxotrophic for lysine, arginine, and cysteine (see Note 2). 2. Plasmids encoding inducible His-tagged Ras or Ral. 3. 5
M9 media stock solution: Make up 1 L of 120 mM Na2HPO4, 55 mM KH2PO4, 21.5 mM NaCl, 10 mM NH4Cl, pH 7.4. Autoclave stock. 4. L-amino acids: All are unlabeled “light” amino acids unless otherwise stated. Make up the following stocks on the day of use (see Note 2): (a) Make a hydrophobic amino acids mix by combining the following at 2 mg/mL each in water: L-Alanine, L-Isoleucine, L-Leucine, L-Methionine, L-Phenylalanine, L-Proline, L-Tryptophan, L-Valine. Stir extensively on a magnetic stirrer until they are dissolved. (b) Make a hydrophilic amino acid mix by combining the following at 0.5 mg/mL each in water: L-Aspartic Acid, L-Asparagine, L-Cysteine, L-Glutamic acid, L-Glutamine, Glycine, L-Histidine, L-Serine, L-Threonine, and L-Tyrosine. Stir extensively with very slow dropwise addition of hydrochloric acid until they are dissolved (see Note 2).

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(c) Make up heavy L-Arginine (L-arginine-U-13C6-15N4) and heavy L-Lysine (L-lysine-U-13C6-15N2) at 100 mg/mL each in water. (d) 1
complete M9 media: 1
M9 stock, 0.4% glucose, 1 μg/mL thiamine-HCl, 1 mM MgSO4, 0.1 mM CaCl2, 20 μM ZnSO4, 10 μM FeCl3, 1
Trace Metals A5 with Co (92949; Sigma Aldrich, USA), 200 μg/mL hydrophobic amino acids mix, 100 μg/mL hydrophilic amino acids mix, 200 μg/mL heavy Lysine, 200 μg/mL heavy arginine, 100 μg/mL ampicillin (see Note 3). Make up 1 L on first day of use in this order (see Note 4): l

l

l

l

Add 4 g glucose, 1 mL 1 mg/mL thiamine-HCl, 1 mL 1 M MgSO4, and stir to dissolve. Add 100 μL 1 M CaCl2 dropwise while stirring constantly using a magnetic stirrer, and continue stirring until any precipitate re-dissolves. Add 200 μL 0.1 M ZnSO4 and 1 mL trace metal mix.

l

Add amino acids (200 mg hydrophobic amino acids mix, 100 mg hydrophilic amino acids mix, 200 mg each of heavy lysine and arginine).

l

Adjust pH back to 7.4.

l

Add ampicillin to 100 μg/mL.

l

l

l

2.1.2 Affinity Purification Using His-Trap Columns

Add 200 mL 5
M9 stock and dilute with ultrapure water up to 600 mL.

Make up to a final volume of 1 L, and filter using a 0.22 μM vacuum filter flask. Isopropyl β-D-1-thiogalactopyranoside (IPTG): dissolve in 1–2 mL of M9 immediately prior to use. Phosphate buffered saline (PBS): 2.7 mM KCl, 1.47 mM KH2PO4, 138 mM NaCl, 8.1 mM Na2HPO4, pH 7.4.

1. Resuspension buffer: 20 mM Tris–HCl, pH 7.4, 0.5 M NaCl. 2. Bacterial protease inhibitor cocktail (P8456; Sigma Aldrich, USA). 3. Lysozyme. 4. Ultracentrifuge: capable of spinning at 82,000
g. 5. His affinity column or beads: His-Trap HP 1 mL (89,870; GE Healthcare, USA) (see Note 5). 6. Automated HPLC and fractionator: AKTA purifier equipped with Frac950 and UPC900, operated using Unicorn software (optional, see Note 5). 7. Binding Buffer (“Buffer A”): 20 mM Tris–HCl, pH 7.4, 0.5 M NaCl, 20 mM imidazole.

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8. Elution buffer (“Buffer B”): 20 mM Tris-HCl, pH 7.4, 0.5 M NaCl, 500 mM imidazole. 9. Buffers A and B can be stored at 4–8  C for a few days. 10. Eppendorf protein LoBind 96-well collection plates (1–2 mL well capacity). 11. Hot lysis buffer: 50 mM Tris–HCl, pH 6.8, 2% SDS, 10% glycerol. 12. 10 X hot lysis buffer gel loading dye: 1 M DTT, 1% Bromophenol blue. 2.1.3 Purification Check: Confirm Purity and Distribution of His-Ras/ His-Ral Protein

1. Rainbow marker: Choose any molecular weight marker that contains pre-stained bands close to the molecular weight of your target protein to allow you to accurately cut the as small a gel fragment as possible from an unstained gel without risking leaving behind any residue of your protein of interest. 2. Primary antibodies: Anti-His tag (H1029; Sigma Aldrich), anti-pan-Ras EP1125Y (ab52939; AbCam), anti-RALA (#3526; Cell Signalling Technology), anti-RALB (#3523; Cell Signalling Technology).

2.1.4 Size-Based Purification by Gel Filtration

1. Concentration and buffer exchange column: Amicon Ultra-15 Centrifugal filter unit, 10 kDa molecular weight cut off (Millipore). 2. Gel filtration column: Superdex Increase 200 10/300 GL (GE Healthcare, USA). 3. Automated HPLC and fractionator: AKTA purifier equipped with Frac950 and UPC900, operated using Unicorn software. 4. Gel filtration buffer: 40 mM Tris–HCl (pH 7.4), 50 mM NaCl, 5 mM MgCl2.

2.1.5 Storage and Concentration Determination of the Standard

2.2 Mass Spectrometric Characterization of GTPases Such as Ras/Ral Isoforms 2.2.1 SDS Polyacrylamide Gel Electrophoresis (SDS-PAGE)

1. Concentration column: Amicon Ultra-0.5 mL, 10 kDa MWCO (UFC501024; Millipore). 2. Protein concentration assay kit: bicinchoninic acid (BCA) assay kit. Any SDS-PAGE format can be used; we provide details below of the system we use for making gels that has given us more consistent results for Ras and Ral quantitation than pre-cast gradient gel systems. 1. 4
ProtoGel Resolving Buffer (Geneflow, UK): 1.5 M Tris– HCl, 0.4% SDS, pH 8.8. 2. 4
ProtoGel Stacking Buffer (Geneflow, UK): 0.5 M Tris– HCl, 0.4% SDS, pH 6.8. 3. Ultrapure ProtoGel (Geneflow, UK): 30% (w/v) acrylamide: 0.8% (w/v) Bis-acrylamide stock solution (37.5:1).

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4. Ammonium persulfate (APS): 10% solution in water. 5. N,N,N,N0 -Tetramethyl-ethylenediamine (TEMED). 6. 10
Tris/Glycine/SDS running buffer (Geneflow, UK): 0.25 M Tris, 1.92 M glycine, 1% SDS. 7. 5
SDS-PAGE sample buffer: 0.3 M Tris–HCl (pH 6.8), 10% SDS, 25% β-mercaptoethanol, 0.1% bromophenol blue, 45% glycerol. 8. Short plates and space plates with 1 mm integrated spacers (see Note 6). 9. 15 well, 1 mm combs. 2.2.2 In-Gel Digestion and C-18 Desalt

Acetonitrile (ACN) is light sensitive, so store in a dark/covered bottle. Only use approved mass spectrometry tips (no colors, no autoclaving, no UV). Gel loading tips are used to avoid accidentally pipetting up the gel pieces at the bottom of the tube. All in-gel digestion and C-18 clean up solutions should be made on the day. 1. Fix solution: 10% acetic acid/ 50% methanol. 2. Sterile stainless steel scalpels. 3. 100 mM ammonium bicarbonate (Ambic). 4. 50 mM Ambic/50% acetonitrile (ACN). 5. 10 mM dithiothreitol (DTT) in 100 mM Ambic. 6. 55 mM iodoacetamide (IAM) in 100 mM Ambic. 7. Reaction buffer (for trypsin): 40 mM Ambic, 9% ACN. 8. Trypsin Gold: 5 ng/μL in reaction buffer. 9. 1% Formic acid. 10. 100% ACN. 11. 0.1% trifluoroacetic acid (TFA). 12. 75% ACN/ 0.1% TFA. 13. ZipTip with 0.6 μL C18 resin (ZTC18S096, Millipore).

2.2.3 Mass Spectrometric Characterization of Ras Isoforms

Any MS can be used; we provide details on the system that we employ: 1. Mass spectrometer: TripleTOF 6600 (Sciex). 2. In-line liquid chromatography system: Eksigent NanoLC 400 System (Sciex) mounted with a NanoAcquity 5 μm particle size, 180 μm
20 mm C18 trap and 1.7 μm, particle size 75 μm
250 mm C18 analytical column (Waters). Columns are maintained at 50  C. 3. Ion source: NanoSpray III source fitted with PicoTip emitter (New Objective).

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4. Mobile phase A: 2% acetonitrile/0.1% formic acid. 5. Mobile phase B: 98% acetonitrile/0.1% formic acid. 6. Loading buffer: 2% acetonitrile/0.5% formic acid. 2.2.4 Optimization of MRM Transitions and Quantitation of Ras and Ral Isoforms

1. Mass spectrometer: QTRAP 6500 (Sciex). 2. In-line liquid chromatography system: Dionex U3000 nanoLC system (Thermo) mounted with a NanoAcquity 5 μm particle size, 180 μm
20 mm C18 trap and 1.7 μm particle size, 75 μm
100 mm C18 analytical column (Waters). Columns are maintained at 40  C. 3. Ion source: NanoSpray III source fitted with a PicoTip emitter (New Objective).

2.3 Quantification of Endogenous Ras or Ral Levels in Cell Lysates

3

1. Cells of interest. 2. Hemocytometer. 3. NP40 lysis buffer: 50 mM Tris, pH 7.5, 150 mM NaCl, 1% NP40 substitute, 1/250 mammalian protease inhibitor cocktail (P8340, Sigma). Store at 4  C.

Methods

3.1 Production of Heavy Labeled His-Tagged Ras/Ral Standards 3.1.1 Expression of Heavy His-Ras/His-Ral Recombinant Protein in AT713 Bacteria

1. Select a single colony (or a scrape from a glycerol stock) of AT713 transformed with the plasmid of interest to inoculate 5 mL of LB + 100 μg/mL ampicillin. Grow at 220 rpm, 37  C in a shaking incubator for approximately 6 h. 2. Make 1 L of complete 1
M9 media. Of this, 25 mL will be used for an overnight culture, 5 mL kept aside for blanks and resuspension of IPTG, and 970 mL for the final protein induction culture. Keep at 4  C until needed. 3. Use 20 μL of the AT713 LB culture to inoculate 25 mL of complete 1
M9 media. Grow at 220 rpm, 37  C in a shaking incubator for approximately 16 h (see Note 7). Store the rest of the complete M9 media at 4–8  C until needed. Check the M9 media carefully for precipitation prior to use and make fresh media if necessary. 4. Inoculate 970 mL complete 1
M9 media with 20 mL of the overnight AT713 culture in a sterile 2 L conical flask loosely sealed with foil. Incubate at 220 rpm, 37  C in a shaking incubator until the OD600 ¼ 0.600–0.800. 5. Purification check: Once the desired OD600 has been reached, take aside 1 mL as a pre-induction sample. Microfuge at 700 rpm for 2 min at room temperature. Discard the supernatant, add 120 μL hot lysis buffer to the pellet, and vortex and store at 20  C for later use in SDS-PAGE.

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6. Induce protein expression in the remainder of the culture by adding IPTG to 1 mM final concentration. 7. Incubate at 220 rpm, 37  C in a shaking incubator for 3 h. 8. Purification check: Take aside a 600 μL post-induction sample, and collect, resuspend, and store as in step 4. 9. Centrifuge the remaining culture at 3000
g for 15 min at 4  C. 10. Discard the supernatant and resuspend on ice in 20 mL of ice cold PBS, and transfer the resuspended bacterial cell pellet into a pre-weighed 50 mL centrifuge tube. 11. Centrifuge at 3000
g for 10 min. 12. Discard the supernatant and weigh the pellet. 13. The sample can be snap frozen and stored at 80  C for future use, or you can proceed directly with the next steps to affinity purify the proteins (see Note 8). 3.1.2 Affinity Purification Using His-Trap Columns

1. Resuspend the bacterial cell pellet on ice in 20 mL of ice cold resuspension buffer supplemented with bacterial protease inhibitor cocktail (1 mL per 4 g bacterial pellet). 2. Add lysozyme to a final concentration of 1 mg/mL, and incubate on ice for 30 min with frequent agitation. 3. Using a probe sonicator set to 100% output, sonicate 10 times for 20 s with 30-s intervals on ice in between to avoid heating (increase ice rest if necessary). 4. Centrifuge at 82,000
g for 30 min at 4  C. Transfer supernatant into a fresh tube. 5. Purification check: Take aside a 40 μL sample of the cleared lysate supernatant, and mix with 40 μL hot lysis buffer. Also scrape ~1 mm diameter piece of the pellet into 200 μL of hot lysis buffer. Store at 20  C for later use in SDS-PAGE. 6. Spike buffer B into the cleared supernatant to raise the imidazole concentration to 20 mM (see Note 9). 7. Filter the lysate using a 0.2 μm syringe filter. 8. Purification check: Take aside a 40 μL sample of the filtered lysate into 40 μL hot lysis buffer. Store at 20  C for later use in SDS-PAGE. 9. Wash the His-Trap column at 1 mL/min with 5 column volumes (CVs) of Buffer A, then 5 CVs of buffer B. 10. Equilibrate the His-Trap column with 10 CVs of buffer A. 11. Load the filtered lysate (step 7) onto the His-Trap column by running buffer A through either a superloop or a smaller sample loop (see Note 10).

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12. Purification check: Collect a 40 μL sample of the flow-through, and add to 40 μL of hot lysis buffer. Store at 20  C for later use in SDS-PAGE. 13. Equilibrate the His-Trap column with 10–15 CVs of buffer A. 14. Run the imidazole gradient: (a) Monitor protein elution by reading absorbance at 280 nm using a UV module throughout. (b) 1 mL/min flow rate throughout. (c) 0–60% buffer B (20–300 mM imidazole) over 21.5 CVs. (d) 0.25 mL elution fractions. (e) 3 mL gradient delay. 15. Purification check: Set aside 12 μL of each fraction of interest, and add to 3 μL of 5
SDS-PAGE sample buffer. Proceed to SDS-PAGE check (see Subheading 3.1.3). 16. Store the fractions at 4  C; while this test is run, we typically limit this to one overnight storage step. 3.1.3 Purification Check: Sample Preparation for SDS-PAGE

The samples collected during the purification are now checked for successful enrichment of the protein of interest using SDS-PAGE and Coomassie staining/Western blotting. General samples

1. Dilute the protein suspension into SDS-PAGE sample buffer to a final concentration of 1
sample buffer and vortex. 2. Boil at 98  C for 5 min. 3. Microfuge briefly, vortex to mix, and microfuge again for 1 min at 17,000
g. 4. Avoid touching the bottom of the tube with tip when loading gel to avoid insoluble material that may have pelleted. Hot lysis

1. For hot lysis samples, mix the protein suspension/pellet/bacterial culture with hot lysis buffer, and boil at 110  C for 15 min with frequent vortexing. 2. Sonicate using a probe sonicator for 10 s (set to 25% output). 3. Microfuge for 5 min at 17,000
g. 4. Take 36 μL of supernatant, and mix with 4 μL of 10
SDS-PAGE sample buffer. 5. Follow steps 2–4 described for general samples. 3.1.4 Purification Check: Confirm Purity and Distribution of His-Ras/ His-Ral Protein

The first time that the procedure is performed, we recommend selecting fractions from all observed UV peaks for analysis using SDS-PAGE. These should be tested using immunoblotting to confirm the presence of the correct full-length tagged protein and

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by Coomassie protein stain to confirm purity (see Note 11 for comments about the desired level of purity). The pre-elution samples can be tested using immunoblotting if low/no yield of the desired protein is observed (we do not typically see enough induced protein to detect over the background bacterial proteins by Coomassie stain). 1. Run 7 μL per lane of all samples, on replicate 4–12% BisTris NuPAGE gels using MES running buffer. Run replicate lanes to allow immunoblotting with antibodies for the tag and the protein of interest. 2. Stain one gel using Coomassie total protein stain, and transfer one onto membrane (0.9 mA, 1 h using wet transfer) for immunoblotting. 3. Probe with anti-His tag and antibodies specific for the protein of interest. 3.1.5 Size-Based Purification by Gel Filtration

If sufficient purity has not been obtained, further purification by size-based gel filtration can be performed. If purity is satisfactory, perform buffer exchange and concentration according to step 1, followed by skipping straight to step 2 of the next section (see Subheading 3.1.6). 1. Concentrate and perform buffer exchange by combining the desired fractions on a 10 kDa molecular weight cut-off Amicon 15 column and centrifuging according to the manufacturer’s instructions: Reduce the volume to 1 mL before adding 5 mL of gel filtration buffer. Repeat at least 3 times, on the last round continuing to spin until the final volume reaches 0.4–0.5 mL. 2. Run the gel filtration method: (a) Monitor protein elution by measuring absorbance at 280 nm using a UV module throughout. (b) 0.5 mL/min flow rate throughout. (c) Equilibration of the Superdex 200 column with 1.5 CVs of gel filtration buffer. (d) Sample injection. (e) 1.25 CVs elution with elution fraction size of 0.25 mL. 3. Purification check: Collect 12 μL of each fraction of interest (based on the peak observed in UV trace), and add 3 μL of 5
SDS-PAGE sample buffer. Identify which fractions contain the protein of interest and their relative purity using SDS-PAGE and Coomassie staining/Western blotting. Figure 2 gives representative examples of chromatography column elutions of Ras protein during the His-tag affinity purification and size-exclusion-based enrichment steps to show the final purity that it is necessary to achieve.

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Fig. 2 Representative elution profiles for His-tag and size exclusion-based chromatography of GTPase purifications. Nickel-based enrichment of His-tagged proteins typically retains contaminating proteins that require further purification steps to achieve sufficient purity for use as an isotope-labeled protein standard for use in protein quantitation 3.1.6 Storage and Concentration Determination of the Standard

These steps will set the absolute concentration of your standards that will be used to ratiometrically determine your endogenous protein abundance. It is important that the determination of the protein concentration of the standard is as accurate as possible. Changes in concentration during storage will also influence your subsequent endogenous protein quantitation, so it is important to store samples in a format to avoid protein adsorption/precipitation. We use SDS-PAGE sample buffer so that they can be directly added to cell lysates and we avoid multiple re-freezing of samples (see Note 12). Figure 3 shows representative examples of purified Ras and Ral isoforms (see Note 13). 1. Combine and concentrate the desired fractions using 10 kDa molecular weight cut-off 0.5 mL Centricon columns until the final volume of the protein standard is 150–400 μL (see Note 14). 2. Perform a BCA assay and/or OD280 absorbance-based measurements on five replicates in order to determine the concentration as accurately as possible (see Note 15). 3. Once reproducible readings to determine concentrations are obtained, the protein standard can be stored (see Notes 11 and 16 for comments on purity and yield). 4. Dilute the protein standard using 5
SDS-PAGE sample buffer to a final concentration of 1
sample buffer.

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Fig. 3 High-purity Ras and Ral protein standards. Coomassie staining of the whole gel reveals no contaminating proteins. 200 ng of each protein loaded per lane

5. Boil 5 min 98  C. 6. Centrifuge to collect all liquid at the bottom of the tube, and vortex and centrifuge again. 7. Aliquot, snap freeze on liquid nitrogen, and store at 80  C. 3.2 Mass Spectrometric Characterization of GTPases Such as Ras/Ral Isoforms

To confirm the presence and purity of the correct full-length proteins, a sample of each is processed by in-gel digestion and analyzed using data-dependent acquisition. We describe the protocol used in our studies, but this can be performed using any in-house methods and equipment.

3.2.1 Confirming Efficient Labeling of Recombinant His-Ras/His-Ral Protein

It is important to confirm that the protein standard has efficiently incorporated heavy arginine and lysine. SDS-PAGE and in-gel digest are performed followed by analysis on the mass spectrometer to determine the extent to which “light” peptides are present that would be indicative of incomplete incorporation.

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1. Run 100–200 ng of protein standard on an SDS-PAGE gel and Coomassie stain if required to aid identification of region of the gel where it has migrated to. 2. Cut out the protein band, and slice into 1 mm cubes as described in Subheading 3.2.2. 3. If Coomassie has been used, destain thoroughly using 50 mM Ambic/50% ACN, 37  C, 10 min, 900 rpm on a thermoshaker. Repeat until fully destained. 4. Proceed with in-gel digest as described in Subheading 3.2.2. 5. Analyze using standard LC-MS or MRM, and check the percentage of heavy labeling using ProteinPilot5 or similar software (see Subheading 3.3.4). 6. We consider ≧95% labeling to be acceptable. Unlabeled standard must be accounted for in the final copy number calculations. 3.2.2 In-Gel Digestion

To avoid contamination with keratin, perform all the pipetting steps in a laminar flow hood if possible, and change gloves frequently. Keratin contamination is less of an issue after peptide extraction, as whole keratin proteins will not interfere with MS analysis of peptides. The volumes indicated below are estimates, but each should be in relation to the actual gel volume in the tube. Aim to cover all the gel pieces with each solution. 1. Separate proteins and/or lysates by SDS-PAGE. 2. Fix with 10% Acetic acid/50% methanol for 10 min at room temperature with gentle rocking (see Note 17). 3. Wash in HPLC grade water 3
5 min. 4. Move to a laminar flow hood, and use new sterile scalpel blades for each section that will be excised from the unstained gel. Cut this piece further into approximately 1 mm cubes, and add to a LoBind Eppendorf (see Note 18). 5. Dehydrate samples by adding 300 μL of 100% ACN to cover the gel pieces. Incubate at 37  C for 10 min, 900 rpm on a thermoshaker. Pieces should shrink and become completely opaque, white, and hard. Repeat with fresh ACN until this is observed. 6. Dry tubes completely using a SpeedVac for 5 min at 37  C. 7. Reduce samples by adding 300 μL (plenty of excess) of 10 mM DTT, and incubate at 56  C for 1 h at 900 rpm on a thermoshaker. Discard supernatant, and allow samples to cool at room temperature for 5 min before proceeding to the next step.

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8. Alkylate samples by adding 300 μL of 50 mM IAM. Incubate in the dark for 30 min, at room temperature, 900 rpm on a thermoshaker. Discard the supernatant. 9. Wash samples by adding 300 μL of 100 mM Ambic. Incubate for 15 min at room temperature, 900 rpm on a thermoshaker. Discard the supernatant. 10. Wash samples by adding 300 μL of 50 mM Ambic/50% ACN. Incubate for 15 min at room temperature, 900 rpm on a thermoshaker. Discard the supernatant. 11. Dehydrate the gel slices by adding 300 μL of 100% ACN. Incubate for 5 min at room temperature, 900 rpm on a thermoshaker. Discard the supernatant. Repeat this step as many times as necessary until the pieces are small, hard, and opaque white. 12. Dry tube completely by SpeedVac for 5–10 min at 37  C. 13. Make up a single stock of 2.5 ng/μL trypsin to cover all samples (typically 60–90 μL per sample), plus excess, in reaction buffer (see Note 19). 14. Add 1 volume (~60–90 μL) trypsin to each tube, and incubate for ~16 h at 37  C, checking after the first hour that all pieces are still covered (see Note 20). If they are not, top up with reaction buffer by adding the minimum possible volume to cover all pieces. There is some flexibility in total incubation time, but aim to stick between 14–18 h. Do not throw away any supernatant now; it contains the peptides. 15. Add 1 volume (i.e., the total volume of trypsin/reaction buffer that was added) of 100% ACN. Incubate for 30 min at 30  C, 900 rpm on a thermoshaker. 16. Transfer the supernatant to a fresh LoBind Eppendorf. Repeat step 15 if pieces are not completely dehydrated. 17. Add 1 volume of freshly prepared 1% formic acid to gel pieces. Incubate at room temperature for 20 min at 900 rpm on a thermoshaker. Transfer the supernatant to the same Eppendorf as in step 16. 18. Repeat step 17. 19. Add 1 volume of 100% ACN to the gel pieces. Incubate at room temperature for 10 min at 900 rpm on a thermoshaker. Transfer the supernatant to the same Eppendorf as in step 16. Repeat until gel pieces shrink and turn white. 20. SpeedVac the peptides at 37  C to dry them (usually takes overnight). Do not leave them drying longer than necessary. These can be stored at 20  C.

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3.2.3 C-18 Desalting Using ZipTip with C18 Resin

Cleaning up of samples can be carried out on a general laboratory work bench. Throughout the procedure, you must be careful to maintain a small amount of liquid above the C18 bed at all times to stop the resin drying out and compromising peptide yield. Two ZipTips are needed per sample, in which 10 μL aliquots of each sample are processed one at a time and the eluents pooled into a fresh LoBind Eppendorf. 1. Resuspend extracted peptides in 10–20 μL 0.1% TFA. 2. Wet the C18 bed by aspirating and ejecting 2
10 μL 100% ACN. 3. Equilibrate by aspirating and ejecting 3
10 μL 0.1% TFA. 4. Bind the peptides to the bed by aspirating and ejecting in a controlled manner (not at full pipetting speed) 10–12 times. 5. Wash the bed with 5
10 μL 0.1% TFA. 6. Elute the peptides by aspirating 10 μL 75% ACN/0.1% TFA and pumping up and down 6–7 times in a collection tube. Push the last one through fully, and repeat with second ZipTip. 7. Dry down samples in SpeedVac (~1 h, do not over-dry), and store at 4  C until Mass Spec analysis (see Subheading 3.3.4).

3.2.4 Identification of Suitable Peptides and Transition Ions

1. Process 100–200 ng of each protein by SDS-PAGE and in-gel digest. 2. Resuspend dried peptides in 0.1% formic acid. 3. Wash trap with 2% acetonitrile/0.5% formic acid for 10 min at a flow rate of 2.5 μL/min before switching in-line with the analytical column. 4. Run a gradient of 5–50% acetonitrile/0.1% formic acid (v/v) over 90 min at a flow rate of 300 nL/min. 5. Clean the column by increasing ACN concentration to 80% for 10 min, and then re-equilibrate with 5% acetonitrile/0.1% formic acid for 10 min. 6. Operate the mass spectrometer in positive ion mode (Analyst TF1.7) with survey scans of 250 ms, MS/MS accumulation time of 100 ms, and with monitoring of the 25 most intense ions (total cycle time 2.75 s). 7. Search data using ProteinPilot 5 software (Sciex) against the latest UniProt database with biological modifications allowed and iodoacetamide as the cysteine alkylating reagent. Use the reversed database as a decoy to determine the false discovery rate (FDR) for protein identification. 8. Use these empirical data to select potential transitions for multiple reaction monitoring (MRM) of the Ras isoforms. Choose proteotypic peptides that are either present in all

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isoforms or are specific to each isoform or mutant variant. Select up to three different charge states and up to seven different fragment ions for optimization of the mass spectrometric parameters. 3.2.5 Optimization of MRM Transitions

In order to achieve the greatest sensitivity and specificity of the MRM detection of targets, the mass spectrometry method can be fine-tuned for each analyte. A range of settings for parameters such as the collision energy, declustering potential, etc. is applied to a test sample, and those settings giving optimal signal-to-noise are then incorporated into the final working method. The use of a QTRAP instrument capable of MRM-triggered full-scan MS/MS acquisition increases the confidence that the quantification has been performed on the correct targets. 1. Run 100–200 ng of each heavy isoform. For example, in four tubes, mix a heavy HRAS, a heavy KRAS4A, a heavy KRAS4B, or a heavy NRAS each with light KRAS4B. Process by SDS-PAGE and in-gel digest, followed by desalting. 2. Reconstitute dried peptides in 0.1% formic acid. 3. Wash the trap with mobile A for 5 min at a flow rate of 15 μL/ min before switching in-line with the analytical column. 4. Run a gradient of 2–50% mobile B over 45 min at a flow rate of 300 nL/min. 5. Clean the columns by increasing mobile B to 99% for 15 min, and then re-equilibrate with mobile A for 15 min. 6. Operate the mass spectrometer in positive ion mode using Analyst TF1.6 software (Sciex), and use the MIDAS approach (MRM-initiated detection and sequencing) to quantify and confirm the identity of the analytes of interest. An enhanced resolution scan at 250 Da/s is used to calculate the optimal collision energy on the fly for up to 3 MS/MS scans at 10,000 Da/s. 7. A range of different settings for collision energy, collision cell exit potential, and declustering potential should be assessed for each of the transitions chosen from empirical MS/MS data. The transitions and settings that provided the greatest sensitivity of detection can be carried forward to the final MS method. Figure 4 depicts the positions of the diagnostic peptides that we use for Ras and Ral. Figure 5 gives an example of the transition spectra produced from a diagnostic Ras peptide. Table 1 details all of the transitions that we use for each diagnostic Ras and Ral peptide.

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Fig. 4 Selected proteotypic RAS and RAL peptides. All peptides produced following trypsin digestion are indicated. The resultant peptides that are specific for individual isoforms or shared by all isoforms (Pan) are highlighted. Peptides that are used to quantify protein abundance are shown together with their sequences. The dotted line in the first RALA-specific peptide indicates a missed cleavage that is consistently observed 3.2.6 Calibration Curves to Determine Spike-In Concentrations

Before quantification of endogenous protein levels can be determined, linearity between the target protein peptide abundance and MS response needs to be confirmed. Mixing each heavy standard at a 0.05 to 1 ratio of 10 ng with cell lysates to generate a calibration curve will allow you to deduce the appropriate concentration to spike in samples when compared with the endogenous peptide readouts. For the quantitation experiments, the endogenous proteins should fall within the range of this calibration curve. 1. Mix 100 ng of each heavy and 100 ng of a single light isoform, such that each sample contains 1 heavy isoform and 1 light isoform with the same light protein used for comparison and calibration across all the heavy isoforms. For example, in four tubes mix a heavy HRAS, a heavy KRAS4A, a heavy KRAS4B, or a heavy NRAS each with light KRAS4B.

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Fig. 5 MS/MS transition spectra for the diagnostic NRAS peptide. Transitions are chosen based on signal-to-noise and sensitivity to allow robust discrimination versus other peptides with similar m/z in the cell lysate. Generally, higher mass range transitions are preferred due to the higher likelihood that they will be selective for the parent peptide

2. Carry out an in-gel digestion as outlined in Subheading 3.2.2 and run on MS as described above (see Subheading 3.2.5). 3. Use the ratio of a common heavy peptide to the corresponding light peptide to calibrate all the different heavy protein stocks to the single light standard. For example, if a heavy peptide signal is 0.9
that of the light standard, then it is 0.9
as concentrated as previously thought. Use these calibrated values moving forward. 4. Make separate samples containing 20 μg cell lysate with 0.05, 0.25, 0.5, 1, 2, 5, and 10 ng of each heavy Ras/Ral proteins from the calibrated concentrations in step 3 (see Note 21). 5. Carry out an in-gel digestion as outlined in Subheading 3.2.2 and run on MS as described above (see Subheading 3.2.5). 6. For Ras, 1 ng each of HRas, KRas4A, and NRas and 2 ng of KRas4B are used, and for Ral, 1 ng of RalA and 1 ng of RalB will be spiked into each 20 μg lysate sample. 7. Plot the curves as mean area under curve (AUC) vs ng or moles of heavy standards (see Subheading 3.3.5 for description of molar calculations from grams). The equation of the line of best fit is used to calculate the endogenous protein concentrations in the quantitation experiments in Subheading 3.3.

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Table 1. Transitions for proteotypic Ras and Ral peptides. Transitions are generated from the indicated peptides representing endogenous proteins (Lys0 Arg0) and isotope-labeled (Lys8 Arg10) standards.

3.3 Quantification of Endogenous Ras or Ral Levels in Cell Lysates 3.3.1 Harvesting Cells

Once the amounts of heavy standards to spike into a given amount of lysate have been determined, the quantitation experiments can be performed.

1. All types of adherent or suspension cells can be assayed. For adherent cells, grow in 10 cm dishes to 80% confluence (or note confluence if different to ensure assaying of equivalent confluence between biological repeats). Typically, a minimum of 1,000,000 cells per assay point are used. 2. Dissociate cells using your preferred reagent: for example, 1 mL of 1
0.5% trypsin-EDTA.

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3. Gently pellet the cells by centrifuging at 150
g for 5 min. 4. Aspirate the supernatant, and resuspend the cell pellet in 9 mL of medium supplemented with the serum conditions that the cells are normally maintained in. Pipette thoroughly to break up clumps. 5. Transfer 9 mL immediately to a fresh centrifuge tube for collecting the cell pellet (see Subheading 3.3.2). 6. Use the well-mixed remainder for counting. Do the counts quickly while the 9 mL of cells are being centrifuged. Using a hemocytometer, take an average of two counts (typically these should be within 5% of each other). Cell counting is optional but is necessary if you want to calculate protein copy number per cell. 3.3.2 Collecting Cell Pellets

1. Centrifuge the cells at 150
g for 5 min at room temperature. 2. Aspirate off the media. 3. Flick the pellet to loosen, and then resuspend in 10 mL of ice cold PBS. 4. Centrifuge again at 150
g for 5 min at 4  C. 5. Flick pellet to loosen, and resuspend gently in 300 μL of ice cold PBS using wide bore tip or a 1 mL tip with the end snipped off. Transfer to a LoBind Eppendorf on ice. 6. Remove the tip and set aside. Use a fresh tip to add 300 μL ice cold PBS to the centrifuge tube. Add the wide bore/trimmed tip back onto the pipette, mix the PBS well, and add to the same Eppendorf. 7. Spin at 1000
g for 5 min, 4  C. 8. Remove all PBS (use gel loading tips for last bit to avoid aspirating cells). 9. Either (a) snap freeze in liquid nitrogen and store at 80  C or (b) lyse on ice in NP40 lysis buffer.

3.3.3 Cell Lysis and Spike-in

Before proceeding with experiments, perform a Western blot on 10% self-poured SDS-PAGE gels to determine the location of the endogenous protein of interest on the gel in relation to the pre-stained markers, and define which markers to cut at (see Note 13). This calibration step is important for all subsequent MS analysis runs when gel fragments have to be excised that are as small as possible but contain all of the protein of interest. 1. Resuspend cells in 140–300 μL of NP40 lysis buffer (see Note 22). 2. Incubate on ice for 10 min. 3. Centrifuge at 17,000
g for 15 min at 4  C.

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4. Collect the supernatant and transfer to a new Eppendorf being careful not to disturb the pellet. 5. Determine protein concentration by BCA assay. 6. Spike-in pre-determined concentrations (see concentration checks Subheading 3.1.6) of heavy standards with 20 μg of cell lysate (we typically make 1.2
sample to allow excess). 7. Prepare sample for gel by boiling as described in Subheading 3.1.3. 8. Run sample on a self-poured 15 well, 1 mm, 10% BisTris SDS-PAGE gel at 100 V until all rainbow markers can just be seen in the gel (the pink 12 kDa marker at the bottom will just appear out of the dye front). Run alternating lanes of samples and rainbow marker so that you can easily cut out the right segment of the sample lane (12–38 kDa for Ras, 17–31 kDa for Ral). Figure 6 shows the migration of endogenous and His-tagged Ras proteins as an example. 9. Snap freeze the remainder of the lysate and store at 80  C.

Fig. 6 Determining gel migration for accurate excision of target protein and standards. The His-tag results in a band shift that must be accounted for when determining where to cut the gel. Pre-stained ladders guide the gel excision and also separate the lanes to avoid cross-contamination of samples. Note that the addition of the protein standard to the lysate does not result in a noticeable additional band on the Coomassie gel. For the Western blots, 5 ng Ras/Ral and 15 μg SW48 lysate are loaded in the indicated lanes, and for the Coomassie gels, 40 ng Ras and 8 μg SW48 lysate are loaded in the indicated lanes

Quantifying Ras/Ral Abundance 3.3.4 Mass Spectrometric Quantification of Endogenous Proteins and Spiked-In Standards

3.3.5 Calculation of Endogenous Protein Concentration from MRM Data

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1. Reconstitute desalted samples in 11 μL of 0.1% formic acid, and deliver 5 μL aliquots into the MS as described above. In our experiments, the dwell time for each transition was 20 ms. As before, the charge status of each precursor ion was determined using an enhanced resolution scan at 250 Da/s, and up to 3 MS/MS scans at 10,000 Da/s were triggered with dynamic fill time. This gave a total cycle time of 3.9 s. The calibration curves established in Subheading 3.2.6 are used to determine the endogenous concentration from the AUC calculated in Subheading 3.3.4. 1. Use the equation of the calibration curve to determine the number of moles of protein per lane. As the calibration curve was based on ng of heavy standards, the accurate molecular weight of the heavy standards and endogenous protein is needed. This can be obtained by entering the protein sequence into ProtParam on the ExPasy webpage. As the heavy standard is larger than the endogenous protein, the quantity of endogenous protein in ng determined from the line of best fit needs to be adjusted by multiplying by the ratio of Mwendogenous/ Mwstandard. Use the Mw to determine the number of moles per lane (protein in grams divided by Mw in Daltons) and then the number of moles per μg lysate (divide moles per lane by μg lysate per lane). 2. Determine the number of cells per μg lysate generated in Subheading 3.3.3 (divide the number of cells in the pellet by the total no. of μg lysate yielded). 3. Calculate the number of moles per cell by dividing the result of step 1 by the result of step 2. 4. Finally determine the number of molecules of the small GTPase per cell by multiplying the result of step 3 by Avogadro’s number. 5. The total number of molecules in the Pan peptide should approximate the sum of those from the isoform-specific peptides if all isoforms have been accounted for. 6. We typically generate a mean value from three experiments for our quantitation.

4

Notes 1. Detergent can contaminate the samples, damaging the chromatography column. 2. Some amino acids are not easily soluble. We find that making a dilute solution and stirring for 30–60 min help, and for the

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hydrophilic mixture, specifically that HCl helps (add this very slowly with plenty of stirring since too much HCl could affect the final osmolarity of the media). Cysteine oxidizes rapidly to cystine in aqueous solution so we make the amino acid solutions up on the first day of use. Lysine and arginine are also stable when reconstituted in PBS at 75–100 mg/mL and frozen at 20  C for a few months. 3. Our plasmids for protein expression in bacteria all carry an ampicillin resistance gene, so we have assumed ampicillin resistance throughout, but if using plasmids carrying additional or alternative resistance genes, the method should be altered accordingly. 4. We found that AT713 upregulate ZinT in response to zinc starvation. This protein is approximately 25 kDa and interacts with various metal ions. It can be erroneously purified on His-Trap columns and easily mistaken for Ras and Ral proteins due to the similar molecular weight. Addition of Zinc ions to the media resolved this issue. The presence of other trace metals and thiamine further improves growth of the bacteria. 5. The use of an automated HPLC system and fractionator is described here, but in the absence of access to this, a gravitybased column or batch approach could be used. The buffers described are appropriate for the His-Trap columns used, but if alternative affinity resin is used, the relevant manufacturer’s instructions should be followed. 6. Only wash short and spacer plates with water and ethanol to avoid detergent contamination of protein samples run on gels. 7. A small initial culture in LB is used to get the bacteria started, but only a minimal amount of this is carried over into an overnight culture with the heavy media in order to minimize the amount of unlabeled material that may appear in the final prep. 8. Imidazole is inhibitory to lysozyme so must be added after this stage. 9. If you plan to process the pellets immediately after induction, the next stopping point will be after the elutions have been collected from the His-Trap columns. 10. A superloop can be used to load a large volume of lysate (we typically yield approximately 24 mL after centrifugation and filtration) in a single batch. Alternatively, a smaller sample loop can be used to repeatedly apply batches of lysate by until all has been loaded. Either works effectively for an affinity column. 11. We accepted the purity to be sufficient if no additional bands were observed by eye on a Coomassie stained gel. If it proves

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impossible to eliminate additional bands, an estimate should be made of what percentage of the total protein concentration is due to contaminant proteins and should be adjusted for in concentration determination. 12. Proteins can be stored in other buffers if desired. We found that storage directly in sample buffer helped to minimize loss of these low concentration pure proteins due to precipitation during thawing. In our hands, aliquots could be freeze thawed a few times without issue, but we recommend minimizing this. 13. Note that the migration of Ras and Ral bands in Fig. 3 is higher than in Fig. 6 due to the use of a NuPAGE gradient gel system in Fig. 3 versus a self-poured, non-gradient gel format in Fig. 6. It is important to formally verify the migration of endogenous target proteins and tagged-protein standards in the same gel system that you intend to use for the final absolute quantitation experiments so that the correct piece of gel is excised to capture all of the target proteins and standards. 14. We selected two batches of final protein stock after gel filtration, based on the presence of apparent additional bands. The fractions to combine should be empirically determined based on maximizing yield and minimizing contamination. 15. We find that checking a small volume using a nanodrop spectrophotometer is helpful in reducing wastage by informing us of the approximate volumes needed for the protein assay. 16. In our experience, the amount of pure protein yield varies between His-Ras proteins. We obtained the lowest yield for His-HRas and His-NRas (15 μg across batches 1 and 2 and 28 μg across batches 1 and 2, respectively). We found that His-KRas4B wildtype generated the highest yield (400 μg across batches 1 and 2). With all the proteins, there is a compromise to be struck between yield and purity. By prioritizing purity in our experiment, we accepted a low yield. 17. If you wish to observe the proteins on the gel, stain with Coomassie. Gels can still undergo in-gel digestion following destaining with several changes of 50 mM Ambic/50%ACN washes (fixing is not required). 18. Move the gel onto the lid with a little bit of HPLC water to keep the gel slightly moist when cutting; otherwise the pieces become charged with static electricity and can be easily lost. Place a clean white sheet of paper underneath the dish for easier visibility. It is recommended to use a new sterile scalpel for cutting out each band to avoid protein cross-contamination. 19. Decide the volume per sample based on the volumes needed thus far, bearing in mind that the gel pieces will soak in the buffer to rehydrate.

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20. Preferably keep samples in trypsin incubating where lid is kept warm, such as a cell incubator, to prevent evaporation of samples. 21. Using 20 μg of cell lysate is the optimal quantity for both PSAQ and loading into the self-poured gels. Including cell lysate in the generation of the calibration curve does make the heavy standards more difficult to detect, especially at the lower ratios, giving an accurate representation of detection levels in an experiment. However, if you wish to run the standards alone, a polynomial trendline may be observed as opposed to linear. 22. NP40 lysis buffer is used instead of RIPA because we experienced contamination of our LC columns from Triton X-100 that had not been successfully removed from the samples during the in-gel digestion process. It should be noted that lysate concentration and subsequent protein quantification can vary between different lysis buffers due to different levels of extraction from different organelles, for example, RIPA buffer disrupts nuclei whereas NP40 does not. References 1. Stephen AG, Esposito D, Bagni RK, McCormick F (2014) Dragging Ras back in the ring. Cancer Cell 25(3):272–281. https://doi.org/ 10.1016/j.ccr.2014.02.017 2. Mo SP, Coulson JM, Prior IA (2018) RAS variant signalling. Biochem Soc Trans 46 (5):1325–1332. https://doi.org/10.1042/ BST20180173 3. Gentry LR, Martin TD, Reiner DJ, Der CJ (2014) Ral small GTPase signaling and oncogenesis: more than just 15minutes of fame. Biochim Biophys Acta 1843(12):2976–2988. https://doi.org/10.1016/j.bbamcr.2014.09. 004 4. Prior IA, Lewis PD, Mattos C (2012) A comprehensive survey of Ras mutations in cancer. Cancer Res 72(10):2457–2467 5. Li S, Balmain A, Counter CM (2018) A model for RAS mutation patterns in cancers: finding the sweet spot. Nat Rev Cancer 18 (12):767–777. https://doi.org/10.1038/ s41568-018-0076-6 6. Pershing NL, Lampson BL, Belsky JA, Kaltenbrun E, MacAlpine DM, Counter CM (2015) Rare codons capacitate Kras-driven de

novo tumorigenesis. J Clin Invest 125 (1):222–233. https://doi.org/10.1172/ JCI77627 7. Brun V, Masselon C, Garin J, Dupuis A (2009) Isotope dilution strategies for absolute quantitative proteomics. J Proteome 72(5):740–749. https://doi.org/10.1016/j.jprot.2009.03. 007 8. Ankney JA, Muneer A, Chen X (2018) Relative and absolute quantitation in mass spectrometry-based proteomics. Annu Rev Anal Chem (Palo Alto, Calif) 11(1):49–77. https://doi.org/10.1146/annurev-anchem061516-045357 9. Mageean CJ, Griffiths JR, Smith DL, Clague MJ, Prior IA (2015) Absolute quantification of endogenous Ras isoform abundance. PLoS One 10(11):e0142674. https://doi.org/10. 1371/journal.pone.0142674 10. Lebert D, Dupuis A, Garin J, Bruley C, Brun V (2011) Production and use of stable isotopelabeled proteins for absolute quantitative proteomics. Methods Mol Biol 753:93–115. https://doi.org/10.1007/978-1-61779-1482_7

Chapter 5 Validation of Isoform- and Mutation-Specific RAS Antibodies Andrew M. Waters and Channing J. Der Abstract Validation of antibody specificity is essential for the accurate evaluation of protein expression. For antibodies that recognize the gene products of the RAS family of oncogenes (HRAS, KRAS, and NRAS), an important challenge is the determination of selectivity for the four nearly identical HRAS, KRAS4A, KRAS4B, and NRAS proteins. With increasing appreciation for the distinct roles of the different RAS proteins in normal and neoplastic cells, there is a need for well-validated antibodies to evaluate the function and expression of the different RAS isoforms. Here we describe our experimental approaches to characterize RAS antibodies for their isoform- and mutant-specificity for use in immunoblot analyses. Key words RAS, KRAS, HRAS, NRAS, Immunoblots, Western blots, Antibodies, Mouse embryonic fibroblasts, MEFs

1

Introduction Only 6 of 53 landmark cancer research papers from the early 2000s could be replicated [1], and a major cause of the “reproducibility crisis” in biomedical research is poorly characterized antibodies [2– 4]. $800 million each year is wasted on poor quality antibodies [4], and roughly half of the commercially available antibodies have been estimated to not recognize their advertised target [3]. These issues are recognized and discussed by the International Working Group on Antibody Validation and at the bi-annual International Antibody Validation meetings [5, 6]. The National Institutes of Health and many scientific journals also mandate the use of better validated experimental reagents. However, despite greater recognition of this issue, the highly variable degree and rigor of validation of commercially available antibodies remains a major challenge to improving the rigor and reproducibility of published research studies. The four RAS proteins encoded by the three RAS genes share 82–90% identity in amino acid sequence [7]. Despite their substantial sequence, biochemical, and conformational similarities, there is

Ignacio Rubio and Ian Prior (eds.), Ras Activity and Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2262, https://doi.org/10.1007/978-1-0716-1190-6_5, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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now greater recognition for RAS isoform-distinct functional and biological differences [8]. One indication for these differences is that missense mutation frequencies in different cancers are strikingly variable [9]. For example, 100% of the RAS mutations in pancreatic cancer are in the KRAS gene, whereas 94% of the RAS mutations in cutaneous melanoma are in NRAS, and 86% of the RAS mutations in head and neck cancer are HRAS mutations [10]. KRAS is mutated in 94% of pancreatic cancers, 52% of colon cancers, and 32% of lung cancers [7, 10], which are the three most deadly human cancers in the United States [11]. Highlighting the mutation preferences among the RAS isoforms, 83% of KRAS mutations are at the glycine 12 (G12) position, whereas 62% of NRAS mutations occur at the glutamine 61 position (Q61) [10]. There are also tissue-specific mutation preferences within an isoform, as indicated by the 43% frequency of G12C mutation in KRAS-mutant lung adenocarcinoma, but only the 1–2% frequency of G12C mutation in KRAS-mutant pancreatic ductal adenocarcinoma (PDAC) [7, 10, 12]. Additionally, KRASG12R mutations, which exhibit unique structural and biochemical features, are the third most common KRAS mutation found in KRAS -mutant pancreatic cancer (16%), yet rarely occur in other KRAS-mutant cancers [7, 10, 13]. In KRAS -mutant PDAC, particular KRAS mutations are prognostic indicators. For example, Q61 mutations have a more favorable overall survival than G12 mutations [14]. Further, G12D mutations are associated with a shorter survival [15]. Finally, it is now clear specific KRAS mutations like G12C can be therapeutically exploited [16, 17], with the onset of five clinical trials using KRASG12C inhibitors for KRAS -mutant cancer patients harboring G12C mutations (NCT03600883, NCT03785249, NCT04006301, NCT04165031, NCT04185883). Importantly, initial results from these trials are promising [18, 19]. For these reasons, it is imperative RAS researchers have reagents that are properly validated. To provide clarity to the RAS field, we chose a panel of 22 commonly used RAS isoform- and mutation-specific antibodies and qualified them in several assays [20]. However, at least as many additional RAS antibodies exist, and many others have been released since our study was completed that were not included in our assessment. Although most of the antibodies we tested came with a guarantee for immunoblotting from the manufacturer, we found most antibodies to be poorly selective. However, we did qualify at least one isoform-specific antibody for each RAS protein and two mutant-specific RAS antibodies. Subsequently, these qualified antibodies have been used successfully for RAS co-immunoprecipitations [21]. Here we describe an immunoblotting method using “RASless” mouse embryonic fibroblast (MEF) cell lysates rescued with human RAS isoforms [22] (see also

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Drosten and Barbacid chapter in this book) to validate RAS antibodies for isoform- and mutant-specificity. This chapter can be used as a guideline for immunoblotting and as a resource to validate RAS antibodies from commercial suppliers.

2 2.1

Materials Cell Lines

All MEFs used in the study were obtained from the RAS Initiative at the Frederick National Laboratory for Cancer Research and maintained in Dulbecco’s modified minimum essential medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and appropriate selection antibiotic (puromycin, blasticidin). 1. BRAFV600E-rescued MEFs (negative control). 2. RAS-rescued MEFs (see Note 1).

2.2 Reagents, Chemicals

1. Ice bucket with ice. 2. Tissue culture-treated 6-well plate. 3. Microfuge tubes (see Note 2). 4. Cell scrapers. 5. Bicinchoninic Acid (BCA) protein assay (see Note 3). 6. Bovine serum albumin (BSA). 7. 96-well flat bottom plate. 8. Phosphate-buffered saline (PBS): 137 mM NaCl, 2.7 mM KCl, 4.3 mM Na2HPO4, 1.47 mM KH2PO4, pH 7.4. 9. Triton lysis buffer: 1% Triton X-100, 150 mM NaCl, 20 mM Tris pH 7.4, 1 mM EDTA, protease inhibitors, phosphatase inhibitors. Store at 4  C for up to 1 week (see Note 4). 10. 4X LDS sample buffer loading dye. 11. Denaturing agent (1 M DTT, β-mercaptoethanol, or TCEP). 12. Tris-glycine-SDS running buffer: 25 mM Tris, 192 mM glycine, 0.1% SDS, pH 8.3. 13. Transfer Buffer: 25 mM Tris, 192 mM Glycine, 20% (v/v) methanol. 14. TTBS: 20 mM Tris pH 7.4, 137 mM NaCl, 0.1% Tween-20. 15. Blotting sponges. 16. Filter paper. 17. PVDF membranes. 18. Methanol. 19. Blotting cartridges. 20. Non-fat dry milk powder.

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21. KRAS 4B monoclonal WH0003845M1).

mouse

antibody

(Sigma

22. KRAS 4A polyclonal rabbit antibody (Millipore ABC1442). 23. KRAS 4A/4B monoclonal mouse antibody (Millipore OP24 or Santa Cruz Biotechnology SC-30). 24. HRAS polyclonal rabbit antibody (ProteinTech 18295-1-AP). 25. NRAS monoclonal mouse antibody (Santa Cruz Biotechnology SC-31). 26. Pan-RAS monoclonal mouse antibody (Millipore 05-1072). 27. G12D monoclonal rabbit antibody (Cell Signaling Technology 14429S). 28. G12V monoclonal rabbit antibody (Cell Signaling Technology 14412S). 29. HRP-conjugated rabbit secondary antibody. 30. HRP-conjugated mouse secondary antibody. 31. Forceps. 2.3

Equipment

1. Tissue culture incubator. 2. Refrigerated microcentrifuge (see Note 5). 3. Plate reader. 4. Heat block. 5. Power supply. 6. Electrophoresis chamber. 7. Orbital shaker. 8. Imaging system.

3

Methods

3.1 Lysate Generation, Protein Quantification, and Normalization

1. Following standard tissue culture procedure, plate 3  105 viable cells for each MEF cell line into a pre-warmed (37  C) tissue culture-treated 6-well plate containing 2 ml of DMEM +10% FBS with appropriate selection antibiotic. 2. Incubate cells overnight in a humidity-controlled tissue culture incubator at 37  C, 5% CO2. 3. After ~16–24 h, aspirate medium. 4. Wash cells twice with ice-cold PBS (see Note 6). 5. Aliquot 75 μl ice-cold lysis buffer per well, rocking 6-well plate gently back and forth to ensure coverage of the well. 6. Incubate for ~5 min on ice or at 4  C.

RAS Antibody Validation

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7. Place the 6-well plate in an ice bucket and tilt plate at ~30 angle and firmly scrape cells towards the bottom of the well (see Note 7). 8. Transfer lysates from each well of the 6-well plate to pre-chilled microfuge tubes and centrifuge at >10,000  g for 15 min at 4  C. 9. Transfer the supernatant to a new pre-chilled microfuge tube, discarding the insoluble pellet, and keep clarified lysates on ice. 10. After making two-fold serial dilutions of BSA from 2 mg/ml to 0.125 mg/ml (2, 1, 0.5, 0.25, 0.125, 0), aliquot 25 μl of each standard into a 96-well flat bottom plate. For the “0” or “blank” sample, use PBS. 11. Aliquot 5 μl of each lysate into separate wells of the 96-well flat bottom plate. This difference in volume from the previous step will be accounted for in step 18. 12. Mix reagents A and B from BCA assay (50:1 ratio, should result in distinct green color) and aliquot 200 μl of mixture into each standard and test sample in the 96-well plate. 13. Incubate the 96-well plate for ~30 min at 37  C. 14. Remove the plate and read the absorbance at 562 nm in a plate reader (see Note 8). 15. Using Microsoft Excel, enter the absorbances, subtracting the “0” (blank) sample from each standard and test reading, and graph the standard curve of the BSA samples. Insert an x-y scatter plot, with the x-axis set to [BSA] and the y-axis set to absorbance at 562 nm. Set the y-intercept to 0. 16. Fit a linear trendline to the points graphed in step 15 and display the equation on the chart. The R2 value is generally >0.95 (see Notes 9 and 10). 17. To determine the protein concentration of the lysate samples, divide by the slope of the line displayed on the scatter chart and multiply by 5 (see Note 11). 18. Add 100 μg of lysate, 25 μl 4 loading dye, and 5 μl of denaturing agent per sample into a new microtube, in 100 μl of total sample, making up the difference with water. If some lysate samples do not have 100 μg of protein, normalize all samples to the lowest protein concentration (Fig. 1). 19. Heat samples to 95  C for 5 min in a heat block and proceed with SDS-PAGE gel electrophoresis or store samples (4  C for short-term storage, 20  C for long-term storage) (see Note 12).

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Fig. 1 Protein quantification and normalization. BSA standard curve is graphed on an x-y scatter plot, setting the y-intercept to 0, and a linear trendline is fit to the data, displaying the equation and R2 value. Absorbances are divided by the slope of the line and multiplied by 5, and then diluted in 4X loading dye and reducing agent (see Subheading 3.1, steps 10–18) 3.2

Immunoblotting

1. Load SDS-PAGE gels into electrophoresis chamber and cover with Tris-Glycine-SDS running buffer. Remove combs from gels (see Notes 13 and 14). 2. Load 15 μg of each protein sample (15 μl) flanked by protein ladders on both sides (see Note 15). 3. Run the gel for 45 min at 200 V toward the anode (red, positive) oriented at the bottom of the gel. Ensure that the loading dye does not run off the bottom of the gel (see Note 16). 4. After gel electrophoresis, remove SDS-PAGE gel from casing and place in water while preparing blotting sandwich. 5. Pre-wet porous sponges in transfer buffer. 6. Activate PVDF membranes by soaking briefly in methanol (see Note 17). 7. (See Note 18). Place open blotting cartridge (black side down) in container and cover with transfer buffer.

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8. Place blotting sponge on top of the black side of the blotting cartridge. 9. Place one piece of filter paper on top of blotting sponge. 10. Remove SDS-PAGE gel from water and place on top of filter paper. 11. Remove PVDF membrane and gently place on top of SDS-PAGE gel, rolling out any air bubbles. 12. Place one piece of filter paper on top of PVDF membrane, rolling out any air bubbles. 13. Place blotting sponge on top of filter paper (see Note 19). 14. Close and secure blotting sandwich and place in electrophoresis chamber. 15. Cover blot with transfer buffer. Cool the solution by placing an ice pack in electrophoresis chamber. 16. Run the transfer, transferring toward the anode (red, positive) at 100 V for 75 min on the benchtop (see Notes 20 and 21). 17. Open the blotting sandwich and remove the PVDF membrane from the cassette. The protein ladder bands should have transferred to the membrane indicating a successful transfer. 18. Place the blots (transferred PVDF membranes) into 5% non-fat dry milk powder dissolved in TTBS (blocking buffer) and gently rock on an orbital for 30–60 min at room temperature. 3.3 Antibodies and Developing Blots

1. Add 10 μg of the desired antibody to 10 ml of fresh blocking buffer (see Note 22). 2. Incubate blots in blocking buffer/antibody mixture overnight at 4  C on orbital shaker (see Note 23). 3. Collect antibody and store at 4  C for short-term storage or 20  C for long-term storage (see Note 24). 4. Wash blots 4 times with TTBS for 2–5 min each. 5. Incubate blots in 1:2000–1:10000 secondary antibody (HRP-conjugated rabbit or HRP-conjugated mouse as appropriate) for 1 h at room temperature (see Note 25). 6. Decant secondary antibody and wash blots 4 times with TTBS for 2–5 min each. 7. Mix chemiluminescent substrates in a 1:1 ratio and add to blot, incubating for 1–2 min (see Note 26). 8. Using forceps, remove the blot from the chemiluminescent substrate and place directly on developer (see Note 27). 9. Expose the blot with the imaging system’s software (see Notes 28 and 29) (Figs. 2 and 3).

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Fig. 2 BRAF- or RAS-rescued MEF immunoblots. Lysates from RASless MEFs rescued with BRAFV600E, KRAS 4B, KRAS 4A, HRAS, and NRAS as indicated (top) were incubated with validated isoform-specific antibodies as indicated (left) and developed. (Adapted from [Waters AM, Ozkan-Dagliyan I, Vaseva AV, Fer N, Strathern LA, Hobbs GA, Tessier-Cloutier B, Gillette WK, Bagni R, Whiteley GR, Hartley JL, McCormick F, Cox AD, Houghton PJ, Huntsman DG, Philips MR, Der CJ (2017) Evaluation of the selectivity and sensitivity of isoform- and mutation-specific RAS antibodies. Science signaling 10 (498). doi:https://doi.org/10.1126/scisignal. aao3332]. Reprinted with permission from AAAS)

4

Notes 1. The RAS-rescued MEFs may be rescued with wild-type or mutant H-, N-, or K- RAS. 2. Pre-label three complete sets of microfuge tubes, placing two sets at 20  C or on wet ice and the other set at room temperature. The first two sets will be used for scraping and clarifying the lysates, and the third will be used for protein normalization and denaturation of the lysates. Chilled tubes, solutions, and equipment are important to prevent the action of phosphatases and proteases present in cell lysates. 3. Bradford protein concentration assays can also be used for protein quantification in place of BCA protein assays. In this case, follow manufacturer’s recommendations for reagent preparation and read absorbance at 595 nm. 4. Prepare 10 ml of cold lysis buffer and discard after 1 week. SDS- and NP-40 based lysis buffers can also be used in place of a Triton X-100 based lysis buffer. 5. A microcentrifuge without temperature control can be used provided it is prechilled to 4  C in a cold room or refrigerator. 6. Before the second PBS wash, tilt plates at a ~30 angle to allow residual wash buffer to drain for about 15 s before aspirating. This helps to keep the lysis buffer from being diluted and to keep your protein concentrations high. 7. Each well should not require more than 5–10 s of scraping. 8. BSA standard samples should range from a pale green to a dark purple with increasing protein concentration.

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Fig. 3 RAS-rescued MEF immunoblots. Lysates from RASless MEFs rescued with the indicated BRAF or KRAS isoforms (top) or KRAS-mutant cancer cell lines as indicated (top) were incubated with validated mutant-specific antibodies as indicated (right) and developed. (From [Waters AM, Ozkan-Dagliyan I, Vaseva AV, Fer N, Strathern LA, Hobbs GA, Tessier-Cloutier B, Gillette WK, Bagni R, Whiteley GR, Hartley JL, McCormick F, Cox AD, Houghton PJ, Huntsman DG, Philips MR, Der CJ (2017) Evaluation of the selectivity and sensitivity of isoformand mutation-specific RAS antibodies. Science signaling 10 (498). doi:https://doi. org/10.1126/scisignal.aao3332]. Reprinted with permission from AAAS)

9. Blank readings for the “0” PBS well generally range from 0.07 to 0.11 and the standards increase in absorbance linearly with the 2 mg/ml standard generally having an absorbance between 2 and 3.

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10. Many plate readers can be formatted to calculate the protein concentration automatically, replacing steps 15–18, Subheading 3.1. 11. Adding 5 μl of lysate and 25 μl of the standard helps to minimize sample loss and ensure lysate absorbance values are within the linear range of the BSA standard line. If the protein concentrations fall outside of the range of the standard line (this should be very rare), repeat the protein concentration assay with a more appropriate dilution. 12. After denaturing the sample with loading dye and reducing agent, this is a good stopping point. The best practice for preventing degradation of protein or phosphate groups is to not lyse cells unless they can be denatured by the end of the day. 13. If samples have been stored from a previous day, thaw to room temperature and reheat to 95  C for 5 min. 14. This procedure works for both gradient and fixed percentage gels, and for self-poured gels. Because RAS proteins are ~21 kDa, better separation is achieved with higher percentage gels (10.5–20% gradient gels, or 12% and 14% fixed percentage gels), although most standard gel percentages should work reasonably well. 15. Loading different volumes of protein ladder on the left and right sides of the gel will help later for proper orientation. 16. Time and voltage can be liberally adjusted provided some level of current is always running through the gel and the protein of interest does not run off the bottom of the gel. 17. Nitrocellulose membranes also work well with this procedure and do not require methanol activation. 18. Steps 7–14 of Subheading 3.2 have been adapted to Bio-Rad transfer systems, and alternative systems may have different orientations. Please refer to manufacturer guidelines in these instances. 19. The entire blotting sandwich should be covered in transfer buffer at this point. 20. This procedure also works well if the transfer is run at 4  C, or at constant amperage instead of constant voltage. If running at constant amperage, maintaining the current at 200–400 mA per blot is adequate for 75 min. 21. This procedure also works well with semi-dry transfer systems such as the Powerblotter, or dry transfer systems such as the Iblot. 22. Antibodies generally range from 0.1 mg/ml to 1 mg/ml, so a 0.2 mg/ml antibody would be diluted 1:200 (common for

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Santa Cruz Biotechnology), whereas a 1 mg/ml antibody would be diluted 1:1000 (common for Cell Signaling Technology). 23. Blots may be cut above 25 kDa and higher molecular weight proteins of interest can be probed for if necessary. 24. Most antibodies can be stored in TTBS/blocking buffer at 4  C for several weeks, until the solution eventually becomes contaminated. Expect longer exposure times for similar signal with each reuse as antibody is gradually depleted over time. Most antibodies can be frozen in TTBS/blocking buffer at 20  C for up to a year and re-used at least once. 25. The LICOR Odyssey system may also be used as an imaging system. This system uses fluorescent secondary antibodies that emit in red or green channels. Because each color is read in a separate channel, both can be read at the same time in the same blot. For example, you could measure levels of phosphorylated and total protein at the same time. In this case, each primary antibody must be raised from a unique species (commonly, mouse and rabbit). 26. Different chemiluminescent substrates have different sensitivities, which may require some optimization for incubation time. 27. This protocol has also been used successfully by placing the blot in a transparent sleeve and placing the sleeve containing the blot on the developer, but use caution as this has a tendency to cause increased nonspecific background signal. 28. If possible, incubate the blot in chemiluminescent substrate in front of the imaging system, as this provides more user control and fine tuning if the blot needs to be incubated longer in substrate. 29. Take multiple exposures if desired and for publications, use an exposure that contains no over-saturated bands. If the chemiluminescent signal is too strong to image without overexposing bands, rapidly remove the blot from the developer and rinse in TTBS before placing back on the developer. Do not re-incubate with chemiluminescent substrate. References 1. Begley CG, Ellis LM (2012) Drug development: raise standards for preclinical cancer research. Nature 483(7391):531–533. https://doi.org/10.1038/483531a 2. Baker M (2015) Reproducibility crisis: blame it on the antibodies. Nature 521 (7552):274–276. https://doi.org/10.1038/ 521274a

3. Berglund L, Bjorling E, Oksvold P, Fagerberg L, Asplund A, Szigyarto CA, Persson A, Ottosson J, Wernerus H, Nilsson P, Lundberg E, Sivertsson A, Navani S, Wester K, Kampf C, Hober S, Ponten F, Uhlen M (2008) A genecentric human protein atlas for expression profiles based on antibodies. Mol Cell Proteomics 7

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(10):2019–2027. https://doi.org/10.1074/ mcp.R800013-MCP200 4. Bradbury A, Pluckthun A (2015) Reproducibility: standardize antibodies used in research. Nature 518(7537):27–29. https://doi.org/ 10.1038/518027a 5. Bordeaux J, Welsh A, Agarwal S, Killiam E, Baquero M, Hanna J, Anagnostou V, Rimm D (2010) Antibody validation. BioTechniques 48(3):197–209. https://doi.org/10.2144/ 000113382 6. Uhlen M, Bandrowski A, Carr S, Edwards A, Ellenberg J, Lundberg E, Rimm DL, Rodriguez H, Hiltke T, Snyder M, Yamamoto T (2016) A proposal for validation of antibodies. Nat Methods 13(10):823–827. https:// doi.org/10.1038/nmeth.3995 7. Waters AM, Der CJ (2018) KRAS: the critical driver and therapeutic target for pancreatic cancer. Cold Spring Harb Perspect Med 8(9): a031435. https://doi.org/10.1101/ cshperspect.a031435 8. Colicelli J (2004) Human RAS superfamily proteins and related GTPases. Sci STKE 2004 (250):Re13. https://doi.org/10.1126/stke. 2502004re13 9. Prior IA, Lewis PD, Mattos C (2012) A comprehensive survey of Ras mutations in cancer. Cancer Res 72(10):2457–2467. https://doi. org/10.1158/0008-5472.CAN-11-2612 10. Hobbs GA, Der CJ, Rossman KL (2016) RAS isoforms and mutations in cancer at a glance. J Cell Sci 129(7):1287–1292. https://doi.org/ 10.1242/jcs.182873 11. Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69(1):7–34. https://doi.org/10.3322/caac.21551 12. Cox AD, Fesik SW, Kimmelman AC, Luo J, Der CJ (2014) Drugging the undruggable RAS: Mission possible? Nat Rev Drug Discov 13(11):828–851. https://doi.org/10.1038/ nrd4389 13. Hobbs GA, Baker NM, Miermont AM, Thurman RD, Pierobon M, Tran TH, Anderson AO, Waters AM, Diehl JN, Papke B, Hodge RG, Klomp JE, Goodwin CM, DeLiberty JM, Wang J, Ng RWS, Gautam P, Bryant KL, Esposito D, Campbell SL, Petricoin EF 3rd, Simanshu DK, Aguirre AJ, Wolpin BM, Wennerberg K, Rudloff U, Cox AD, Der CJ (2019) Atypical KRAS(G12R) mutant is impaired in PI3K signaling and macropinocytosis in pancreatic cancer. Cancer Discov 10 (1):104–123. https://doi.org/10.1158/ 2159-8290.cd-19-1006 14. Witkiewicz AK, McMillan EA, Balaji U, Baek G, Lin WC, Mansour J, Mollaee M,

Wagner KU, Koduru P, Yopp A, Choti MA, Yeo CJ, McCue P, White MA, Knudsen ES (2015) Whole-exome sequencing of pancreatic cancer defines genetic diversity and therapeutic targets. Nat Commun 6:6744. https://doi. org/10.1038/ncomms7744 15. Bournet B, Muscari F, Buscail C, Assenat E, Barthet M, Hammel P, Selves J, Guimbaud R, Cordelier P, Buscail L (2016) KRAS G12D mutation subtype is a prognostic factor for advanced pancreatic adenocarcinoma. Clin Transl Gastroenterol 7:e157. https://doi. org/10.1038/ctg.2016.18 16. Ostrem JM, Peters U, Sos ML, Wells JA, Shokat KM (2013) K-Ras(G12C) inhibitors allosterically control GTP affinity and effector interactions. Nature 503(7477):548–551. https://doi.org/10.1038/nature12796 17. Lim SM, Westover KD, Ficarro SB, Harrison RA, Choi HG, Pacold ME, Carrasco M, Hunter J, Kim ND, Xie T, Sim T, Janne PA, Meyerson M, Marto JA, Engen JR, Gray NS (2014) Therapeutic targeting of oncogenic K-Ras by a covalent catalytic site inhibitor. Angew Chem Int Ed Engl 53(1):199–204. https://doi.org/10.1002/anie.201307387 18. Canon J, Rex K, Saiki AY, Mohr C, Cooke K, Bagal D, Gaida K, Holt T, Knutson CG, Koppada N, Lanman BA, Werner J, Rapaport AS, San Miguel T, Ortiz R, Osgood T, Sun JR, Zhu X, McCarter JD, Volak LP, Houk BE, Fakih MG, O’Neil BH, Price TJ, Falchook GS, Desai J, Kuo J, Govindan R, Hong DS, Ouyang W, Henary H, Arvedson T, Cee VJ, Lipford JR (2019) The clinical KRAS(G12C) inhibitor AMG 510 drives anti-tumour immunity. Nature 575(7781):217–223. https://doi. org/10.1038/s41586-019-1694-1 19. Hallin J, Engstrom LD, Hargis L, Calinisan A, Aranda R, Briere DM, Sudhakar N, Bowcut V, Baer BR, Ballard JA, Burkard MR, Fell JB, Fischer JP, Vigers GP, Xue Y, Gatto S, Fernandez-Banet J, Pavlicek A, Velastagui K, Chao RC, Barton J, Pierobon M, Baldelli E, Patricoin EF 3rd, Cassidy DP, Marx MA, Rybkin II, Johnson ML, Ou SI, Lito P, Papadopoulos KP, Janne PA, Olson P, Christensen JG (2019) The KRAS(G12C) inhibitor MRTX849 provides insight toward therapeutic susceptibility of KRAS-mutant cancers in mouse models and patients. Cancer Discov 10 (1):54–71. https://doi.org/10.1158/21598290.cd-19-1167 20. Waters AM, Ozkan-Dagliyan I, Vaseva AV, Fer N, Strathern LA, Hobbs GA, TessierCloutier B, Gillette WK, Bagni R, Whiteley GR, Hartley JL, McCormick F, Cox AD, Houghton PJ, Huntsman DG, Philips MR,

RAS Antibody Validation Der CJ (2017) Evaluation of the selectivity and sensitivity of isoform- and mutation-specific RAS antibodies. Sci Signal 10(498):eaao3332. https://doi.org/10.1126/scisignal.aao3332 21. Terrell EM, Durrant DE, Ritt DA, Sealover NE, Sheffels E, Spencer-Smith R, Esposito D, Zhou Y, Hancock JF, Kortum RL, Morrison DK (2019) Distinct binding preferences between Ras and Raf family members and the impact on oncogenic Ras signaling. Mol Cell

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76(6):872–884.e875. https://doi.org/10. 1016/j.molcel.2019.09.004 22. Drosten M, Dhawahir A, Sum EY, Urosevic J, Lechuga CG, Esteban LM, Castellano E, Guerra C, Santos E, Barbacid M (2010) Genetic analysis of Ras signalling pathways in cell proliferation, migration and survival. EMBO J 29(6):1091–1104. https://doi.org/ 10.1038/emboj.2010.7

Chapter 6 Production and Membrane Binding of N-Terminally Acetylated, C-Terminally Farnesylated and Carboxymethylated KRAS4b Simon Messing, Constance Agamasu, Matt Drew, Caroline J. DeHart, Andrew G. Stephen, and William K. Gillette Abstract Recombinant mammalian proteins are routinely produced in E. coli and thus lack post-translational modifications. KRAS4b is processed at both the N- and C-terminus, resulting in an acetylation of the N-terminus (at Thr, after aminopeptidase removal of the original N-term Met) and farnesylation/carboxymethylation of the C-terminal Cys (after proteolytic cleavage of the original C-terminal three amino acids, Val-Iso-Met). Processing of KRAS enables it to associate with the plasma membrane and fulfill its function in cell signaling. We describe here the production of recombinant KRAS4b from our modified baculovirus/ insect cell expression system that accurately incorporates these in vivo modifications to allow experiments that anchor KRAS4b to membrane mimetics (e.g., nanodiscs and liposomes). Key words KRAS, Protein purification, N-terminal acetylation, Farnesylation, Carboxymethylation, Membrane binding, Nanodiscs

1

Introduction The three RAS genes, HRAS , NRAS , and KRAS , encode four different proteins (HRAS, NRAS, and the splice variants KRAS4a and KRAS4b) that belong to a family of small GTPases. These GTPases function as molecular switches in cell signaling pathways [1, 2]. These RAS proteins share a highly conserved G-domain (residues 1–166, 90% identity) that binds to GTP in the active state and is hydrolyzed to GDP resulting in the inactive state. The C-terminal hypervariable region (HVR), which shows minimal sequence similarity between the four proteins, is required for localizing RAS to the plasma membrane [3]. Membrane localization is essential for RAS engagement with effector proteins [4]. Point mutations in KRAS are drivers in ~20% of human cancers [5]; thus there is significant interest in KRAS as a therapeutic target.

Ignacio Rubio and Ian Prior (eds.), Ras Activity and Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2262, https://doi.org/10.1007/978-1-0716-1190-6_6, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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KRAS4b (herein after referred to as KRAS), the primary isoform in human cells, is post-translationally modified at the C-terminus via the CaaX prenylation pathway. The addition of a 15-carbon farnesyl group to cysteine 185 in the cytoplasm is catalyzed by farnesyltransferase (FNT). Following prenylation, the protein is trafficked to the surface of the endoplasmic reticulum where the RAS converting enzyme, RCE1, cleaves the three C-terminal residues of the protein. The final step in processing is the carboxymethylation of the C-terminal farnesylcysteine residue by the ER membrane protein, isoprenylcysteine methyl transferase (ICMT). Previously, we developed a protocol to express and purify authentic C-terminally processed KRAS in insect cells [6]. In addition to C-terminal processing, KRAS is also modified at the N-terminus by acetylation [7–9]. The cleavage of the N-terminal methionine residue followed by N-acetylation is the most common post-translational modification in eukaryotes [10]. Methionine cleavage is catalyzed by specific methionine aminopeptidases and followed by acetylation of the second amino acid by N-terminal acetyltransferases (NATs) [11], which in the case of KRAS is threonine. The addition of the acetyl group at the threonine neutralizes the positive charge and increases the hydrophobicity, which may change the interaction interface of this part of the protein. We have developed a protocol to express and purify N-terminally acetylated and C-terminally farnesylated and carboxymethylated KRAS that recapitulates the authentic version of the protein that is expressed in mammalian cells (see Note 1).

2 2.1

Materials Buffers

Buffer A: 20 mM MES, pH 6.0, 300 mM NaCl, 5 mM MgCl2, 1 mM TCEP. Buffer B: 20 mM MES, pH 6.0, 1 M NaCl, 5 mM MgCl2, 1 mM TCEP. Buffer C: 20 mM MES, pH 6.0, 200 mM NaCl, 5 mM MgCl2, 1 mM TCEP. Buffer D: 20 mM HEPES, pH 7.3, 300 mM NaCl, 2 mM MgCl2, 1 mM TCEP, 1 mM β-cyclodextrin. Buffer E: 20 mM HEPES, pH 7.3, 300 mM NaCl, 2 mM MgCl2, 1 mM TCEP. Buffer S: 20 mM HEPES, pH 7.2, 150 mM NaCl, 1 mM MgCl2.

2.2 Baculovirus Protein Expression

1. Sf-900 III SFM (serum-free medium) (Thermo Fisher Scientific, Waltham, MA).

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2. 2.8 L Thomson Optimum Growth™ flask (Thomson Instrument, Oceanside, CA). 3. High titer (
1  108 pfu/mL) Hs.KRAS4b(1-188) DE35 baculovirus stock [6] prepared and titer determined as described previously [12]. 4. Tni-FNL cell line [13, 14]. 5. 500 mL conical centrifuge bottles (Corning Life Sciences, Tewksbury, MA). 2.3

Lysis

1. Microfluidizer M-110EH (Microfluidics Corp, Newton, MA). 2. Protease Inhibitor St. Louis, MO).

Cocktail

P8829

(Sigma

Aldrich,

3. 0.45 μm Whatman polyethersulfone syringe filters (GE Healthcare, Chicago, IL). 2.4 Protein Purification

1. NGC Discover Medium-Pressure Chromatographic System (Bio-Rad Laboratories, Hercules, CA). 2. HiTrap Heparin Sepharose High Performance 5 mL pre-packed column (GE Healthcare, Chicago, IL). 3. 10–20% Criterion™ Tris–HCl protein gel, 26 well, 15 μL (Bio-Rad Laboratories, Hercules, CA). 4. HiTrap SP Sepharose High Performance 5 mL pre-packed column (GE Healthcare, Chicago, IL). 5. HiLoad 16/600 Superdex 75 pg preparative size exclusion chromatography column (GE Healthcare, Chicago, IL). 6. SnakeSkin™ dialysis tubing, 10K MWCO, 35 mm (Thermo Fisher Scientific, Waltham, MA). 7. Amicon Ultra-15 centrifugal filter unit, 10K MWCO, regenerated cellulose membrane (Millipore Sigma, Burlington, MA). 8. Slide-A-Lyzer™ dialysis cassettes, 10K MWCO, 3 mL, regenerated cellulose membrane (Thermo Fisher Scientific, Waltham, MA).

2.5 Surface Plasmon Resonance (SPR)

1. Biacore T200 Instrument, Series S Sensor Chip CM5, Gilson vials (7  14 mm polypropylene tubes), and amine coupling kit composed of 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC), N-hydroxy succinimide (NHS), and ethanolamine-HCl buffer (all from GE Healthcare, Chicago, IL). 2. Acetate buffer pH 5.0 and 0.85% v/v phosphoric acid solution (Bio-Rad Laboratories, Hercules, CA). 3. Freshly prepared nanodiscs, comprised of 50 mM 70:30 1-palmitoyl-2-oleyl-sn-glycerol-3-phosphocoline (POPC):1-

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palmitoyl-2-oleyl-sn-glycerol-phosphoserine MSP1D1 as described [12, 15].

(POPS)

His6-

4. 1 mg/mL anti-His antibody (Abcam, Cambridge, UK). This is used to capture the nanodisc via the His6-MSP1D1.

3

Methods

3.1 Protein Expression

The N-acetylated KRAS clone was generated using a KRAS4b construct containing amino acids 1–188 optimized for expression in insect cells (ATUM, Newark, CA) which was introduced into a Gateway Entry clone and subsequently subcloned into a pFastBacstyle native expression vector (pDest-8, Thermo Fisher Scientific, Waltham, MA). Bacmid DNA was generated using the Bac-to-Bac system (Thermo Fisher Scientific, Waltham, MA) using the manufacturer’s instructions. 1. Set 1 L of Tni-FNL cells at 7.5  105 viable cells/mL and shake for 24 h at 105 RPM at 27  C (see Note 2). 2. Remove a small sample of culture from the overnight growth for cell count and viability determination. 3. Based on cell count, calculate the amount of virus needed for an infection at a MOI of 3 using the equation below. Virus (mL) ¼ ((Viable cells/mL)  (Volume of culture to be infected (mL))  3)/titer of virus stock (pfu/mL). 4. Add the calculated amount of virus to the culture. 5. Set culture to shake for 72 h at 105 RPM at 21  C. 6. After 72 h, remove a small sample of the culture for cell count and viability determination (see Note 3). 7. Add 500 mL of culture to conical centrifuge bottles, and centrifuge at 1400  g for 15 min at 4  C. 8. Decant the supernatant into 10% v/v sodium hypochlorite for decontamination. 9. Repeat steps 7–8 with the remaining culture, adding the remaining culture on top of the first pellet. 10. Cell pellet may be frozen at 80  C or processed for purification immediately (see Fig. 1 for approximate purification timeframe).

3.2

Lysis

1. Thaw cell pellet (from 1 L of culture), and resuspend cells with 100 mL of Buffer A amended with 1:100 (v/v) protease inhibitor. 2. Lyse cells by mechanical lysis (see Note 4). Pass the homogenized cells twice through an M-110EH Microfluidizer at 7000 psi.

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Fig. 1 Schematic of protein purification protocol. The major steps of the purification protocol are noted with the corresponding approximate time of the protocol indicated below. Asterisk notes the point in the protocol at which the process may be paused, and the material stored at 80  C

3. Clarify the lysate by centrifugation at 100,000  g (RCF average) for 30 min at 4  C (see Note 5). 4. Filter the lysate through 0.45 μm Whatman polyethersulfone syringe filters (GE Healthcare, Chicago, IL) (see Note 6). 5. Clarified lysate can be used immediately or frozen at 80  C for future use. If frozen, a high-speed spin at 4  C may be necessary to remove particulates that form during the freeze/ thaw process. 3.3 Protein Purification

All purification steps were performed on an NGC chromatography system at room temperature (see Note 7) using a flow rate of 1.5 mL/min (see Note 8) except as noted otherwise. 1. Thaw lysate in a room temperature (~23  C) water bath, and promptly move to ice when thawed. 2. Prepare the HiTrap Heparin HP column with 3 column volumes (CV) of Buffer A. 3. Load the lysate to the column and wash with 3 CV of Buffer A. Collect the column flow through and wash. 4. Elute proteins from the column with a 10 CV gradient from 0% to 100% Buffer B while collecting the eluate in 5 mL fractions. 5. Analyze the results of the chromatography by SDS-PAGE/ Coomassie Blue staining (Fig. 2a). 6. Pool positive fractions (see Note 9) and dialyze against Buffer C. Typical pool size is 20–30 mL. 7. Equilibrate the HiTrap SP HP column with four CV of Buffer C, and load the dialyzed heparin pool, collecting the flow through as a precaution. Use a flow rate of 2 mL/min for all steps of this cation exchange chromatography (see Note 10). 8. Wash the column with 5 CV of buffer C, collecting 7.5 mL fractions as a precaution.

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Fig. 2 SDS-PAGE/Coomassie staining analysis of the chromatographic steps. (a) Heparin column chromatography. (b) Cation exchange chromatography (SP Sepharose High Performance column). (c) Size exclusion chromatography (Hi Load 16/600 Superdex 75 pg preparative column). Size of molecular weight markers are noted in kilodaltons in each panel. M, molecular weight ladder; T, total protein; L, clarified lysate/column load; F, column flow through; W, column wash. Additional lanes are elution fractions from increasing NaCl concentration in panels a and b. Black bars indicate fractions combined for pools

9. Elute proteins from the column with a 20 CV gradient from 0% to 40% Buffer B, collecting 5 mL fractions. 10. Analyze the results of the chromatography by SDS-PAGE/ Coomassie Blue staining (Fig. 2b). 11. Pool positive fractions (see Note 11), with a typical pool size of 10–20 mL, and concentrate using an Amicon centrifugal filter unit (3000  g, ~15 min) to a final volume of 5 mL for injection onto the size exclusion column. 12. Equilibrate the SEC column (see Note 12) with 2 CV of Buffer D, inject the sample, and develop the column (all steps at 1 mL/min) with 1 CV of Buffer D, collecting 1 mL fractions. 13. Analyze the results of the chromatography by SDS-PAGE/ Coomassie Blue staining and pool positive fractions (Fig. 2c). 14. Concentrate the final pool using an Amicon Centrifugal Filter (3000  g, ~15 min) to ~2–3 mg/mL (see Note 13). 15. Add the sample to a Slide-A-Lyzer dialysis cassette and dialyze for 4–6 h against Buffer E. 16. Determine the final protein concentration, dispense in aliquots of no larger than 0.25 mL, flash-freeze in liquid nitrogen, and store at 80  C (see Fig. 3 for final protein QC).

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Fig. 3 QC of protein. (a) SDS-PAGE/Coomassie Blue staining analysis of the final protein. Size of molecular weight markers are noted in kilodaltons. M—molecular weight ladder. 1 and 5 micrograms were analyzed as noted. (b) Intact mass (MS1) spectrum (top) and graphical fragment map (bottom) providing MS2-based confirmation of the sequence, N-terminal acetylation, Cys 185 farnesylation, and C-terminal carboxymethylation of the final protein. These data were obtained by top-down LC-MS/MS on an Orbitrap Fusion Lumos mass spectrometer and analyzed with both Xcalibur and ProSight Lite 3.4 SPR: Anti-His Chip Build Using Amine Coupling Chemistry

1. Insert the Series S Sensor Chip CM5 into the Biacore T200 Instrument. Prime the system with Buffer S for ~10 min at 30 μL/min. 2. Add 10 μL of 1 mg/mL anti-His antibody to 90 μL of acetate buffer, pH 5.0, and mix the contents carefully. Centrifuge the contents for 1 min at 2000  g and transfer contents into a Gilson tube. 3. Place 100 μL of EDC, NHS, and ethanolamine-HCl solutions into three separate Gilson tubes. 4. Follow the steps outlined in the immobilization protocol provided in the Biacore T-200 software to amine couple the anti-His antibody to the CM5 chip. Specifically, activate the surface with EDC/NHS for 420 s at a flow rate of 10 μL/min. Capture the anti-His antibody onto flow cells (FC) 1 and 2 with 420 s contact time at a flow rate of 3 μL/min. This method usually leads to a capture level of about ~8000–12,000 response units (RU) of anti-His antibody.

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3.5 SPR: Sample Preparation

1. Prepare a 2:1 dilution series (using Buffer S as diluent) for KRAS4b-FMe:GDP, Ac-KRAS4b-FMe:GDP, Ac-KRAS4bFMe:GNP, and Ac-KRAS4b (2–169) GDP from 10 μM – 0.16 μM in 100 μL total volume in 1.0 mL Eppendorf tubes. Two buffer samples should be prepared and included as part of the KRAS titration series. Centrifuge samples for 60 s at 2000 x g and transfer contents into the Gilson tubes. 2. Place 100 μL of 10 μM 70:30 POPC:POPS His6-MSP1D1 nanodisc into a separate Gilson tube (see Note 14).

3.6 SPR: Biacore T200 Setup

1. Prepare an automated method for the Biacore T200 as outlined below. 2. Condition the CM5 chip by injecting 10 start-up buffer injections of 60 s over FC 1 and FC 2 with Buffer S at 30 μL/min (see Note 15). 3. Set the data collection rate to 10 Hz, and use dual detection mode to record data on FC 1 and FC 2. 4. Capture nanodiscs on FC 2 with a contact time of 60 s and flow rate of 10 μL/min. Using a 10 μM concentration of nanodisc, between 1000 and 3000 RU are captured onto FC 2 (see Note 16). 5. Each KRAS sample is injected over FC 1 and FC 2 with a contact time and dissociation time of 60 s and a flow rate of 30 μL/min. 6. After each KRAS injection the surface is regenerated using the phosphoric acid solution with a contact time of 30 s and flow rate of 30 μL/min over both FCs. This regeneration step dissociates the captured nanodisc (and any bound KRAS) from the amine captured anti-His antibody on the chip (see Note 17). 7. After regeneration, FC 2 is ready for capture of nanodiscs, followed by the next concentration of KRAS sample, injected over FC 1 and FC 2. Once the KRAS cycle is complete, the surface is regenerated with phosphoric acid. This set of cycles is repeated until all the KRAS concentrations have been injected. 8. As part of the KRAS concentration series, at least two buffer injections should be included using the same flow rate, contact time, and dissociation time as the rest of the KRAS samples. These buffer injections are used for buffer subtraction in the data analysis (see Note 18). 9. All samples are loaded into the sample rack and maintained at 25  C. The instrument temperature is set to 25  C for the running of the experiment. 10. Once the run is complete, the data is processed in the Biacore T200 evaluation software. Processed sensorgrams can be fit

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Fig. 4 SPR analysis of membrane binding. (a) SPR sensorgrams obtained upon titration of KRAS4b-FMe:GDP (blue), Ac-KRAS4b-FMe:GDP (red), Ac-KRAS4b-FMe:GDP (black), and Ac-KRAS4b:GDP (2–169) (green) to 70:30 POPC:POPS nanodiscs. The binding response was normalized via the capture level of nanodisc to the anti-His antibody prior to the injection of all the KRAS4b constructs. Data were fit using a steady state binding model and reported apparent binding affinities of ~7.0 μM, 9.7 μM, and 6.0 μM for KRAS4b-FMe:GDP, Ac-KRAS4b-FMe:GDP, and Ac-KRAS4b-FMe:GNP, respectively. In addition, Ac-KRAS4b (2–169) does not bind to the membrane consistent with previous data that show the HVR and the farnesyl group are required for membrane binding [6, 16]. (b) SPR fit curves obtained from binding data

using a steady state binding model to calculate an apparent dissociation binding constant (KDapp) (see Note 19 and Fig. 4).

4

Notes 1. While this method is specific to the production of KRAS4b that is modified on both the N- and C-terminus, the N-terminal modification of acetylation can be obtained by expressing the protein with its native N-terminus [8]. 2. We determine cell counts using a CedEx HiRes automated cell counter and determine viability using Trypan Blue dye. 3. Cell count just before harvest should be near the initial infection count as the baculovirus will stop cell doubling after infection. Cell viability should be between 75% and 90%, and you will often notice an increase in cell size as well. 4. Lysis of baculovirus-infected insect cells is not difficult; however, we have not investigated alternative methods to the highpressure approach described here. The choice of high-pressure

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was adopted to both reduce the need for excipients (e.g., detergents) and also to have a single method in the lab for the lysis of all expression material. 5. The centrifugation step greatly reduces the amount of lipid present in the sample. This is seen as a floating white pellicle and also typically coats the walls of the upper portion of the centrifuge tubes. Failure to remove this material can lead to back pressure during chromatography. 6. Despite the extra steps taken to clarify the lysate, the filters still become clogged fairly rapidly. We typically use 3–4 filters for a lysate from 1 L of expression material. 7. In previous work [6], we have determined that performing the ion exchange chromatography at room temperature was important to obtain consistent resolution of the desired target species. We have not evaluated the role of temperature in this protocol but have maintained the room temperature protocol. 8. A 1.5 mL/min flow rate for the load of the heparin column is necessary to ensure appropriate contact time for the sample to be bound and should not be exceeded. 9. The N-acetylated, farnesylated, and methylated KRAS generally elutes off the heparin column after 50% Buffer B and should not be confused or pooled with earlier material in wash or early gradient (see Fig. 2a). This early material does not have all the post translational modifications. 10. The 2 mL/min flow rate for the cation exchange is necessary to ensure binding to the column as well as for good resolution between the two elution peaks containing RAS (see Note 11). 11. There are two KRAS4b species that elute from the cation exchange column (+/ carboxymethylation). It is important to take the species that elutes later in the salt gradient, as the earlier eluting protein lacks methylation (Fig. 2b). 12. The size exclusion chromatography may be performed at 4  C or room temperature. The inclusion of β-cyclodextrin in Buffer D is important to prevent loss of the protein to the column and improves the elution profile of the protein through reduced tailing. 13. The protein will contain bound GDP and thus calculations of protein concentration that are based on A280 measurements should account for the extinction coefficient of the nucleotide (7765) as well as the protein (11,920). 14. When prepared as described [12, 15], the MSP1D1:lipid ratio reparation is ~2:130, and the 10 μM nanodisc concentration used here refers to the concentration of MSP1D1. 15. Buffer injections of 60 s contact time condition the dextran surface and improve the reproducibility of injections. This is

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especially important for consistent buffer responses which are used for referencing purposes. A minimum of ten injections should be included before any samples are injected over the flow cells. 16. Do not capture nanodisc onto FC 1 since this FC is used as a reference to subtract non-specific binding of KRAS samples to the dextran and anti-His antibody. 17. The regeneration step removes all nanodisc and bound KRAS from FC 1 and FC 2. The anti-His antibody on FC 2 is ready for capture of a further round of nanodisc, followed by the next KRAS injection. 18. Buffer subtraction is an important step in data processing as it removes small refractive index differences between the samples and the running buffer. If the buffer referencing step is not included, these small refractive index mismatches will contribute to the binding signal. 19. In the calculation of a dissociation binding constant (KD), the stoichiometry of complex formation is usually 1:1. However, when KRAS binds to lipid mimetics such as nanodiscs, there are multiple binding sites available on each bilayer. The stoichiometry of KRAS:nanodisc complex formation is complex with multiple KRAS molecules binding. The steady-state analysis provided in the Biacore evaluation software assumes a 1:1 binding stoichiometry. This software can be used to fit the data (Fig. 4), but the values obtained should be reported as an apparent KD (KDapp).

Acknowledgments The authors thank Allison Coward, Peter Frank, Jennifer Mehalko, and Shelly Perkins for assistance in protein purification. This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government. References 1. Simanshu DK, Nissley DV, McCormick F (2017) RAS proteins and their regulators in human disease. Cell 170(1):17–33. https:// doi.org/10.1016/j.cell.2017.06.009

2. Stephen AG, Esposito D, Bagni RK, McCormick F (2014) Dragging ras back in the ring. Cancer Cell 25(3):272–281. https://doi.org/ 10.1016/j.ccr.2014.02.017

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3. Magee AI, Newman CM, Giannakouros T, Hancock JF, Fawell E, Armstrong J (1992) Lipid modifications and function of the ras superfamily of proteins. Biochem Soc Trans 20(2):497–499 4. Khosravi-Far R, Cox AD, Kato K, Der CJ (1992) Protein prenylation: key to ras function and cancer intervention? Cell Growth Differ 3 (7):461–469 5. Prior IA, Lewis PD, Mattos C (2012) A comprehensive survey of Ras mutations in cancer. Cancer Res 72(10):2457–2467. https://doi. org/10.1158/0008-5472.CAN-11-2612 6. Gillette WK, Esposito D, Abreu Blanco M, Alexander P, Bindu L, Bittner C, Chertov O, Frank PH, Grose C, Jones JE, Meng Z, Perkins S, Van Q, Ghirlando R, Fivash M, Nissley DV, McCormick F, Holderfield M, Stephen AG (2015) Farnesylated and methylated KRAS4b: high yield production of protein suitable for biophysical studies of prenylated protein-lipid interactions. Sci Rep 5:15916. https://doi.org/10.1038/srep15916 7. Ntai I, Fornelli L, DeHart CJ, Hutton JE, Doubleday PF, LeDuc RD, van Nispen AJ, Fellers RT, Whiteley G, Boja ES, Rodriguez H, Kelleher NL (2018) Precise characterization of KRAS4b proteoforms in human colorectal cells and tumors reveals mutation/modification cross-talk. Proc Natl Acad Sci U S A 115(16):4140–4145. https:// doi.org/10.1073/pnas.1716122115 8. Dharmaiah S, Tran TH, Messing S, Agamasu C, Gillette WK, Yan W, Waybright T, Alexander P, Esposito D, Nissley DV, McCormick F, Stephen AG, Simanshu DK (2019) Structures of N-terminally processed KRAS provide insight into the role of N-acetylation. Sci Rep 9(1):10512. https:// doi.org/10.1038/s41598-019-46846-w 9. Buser CA, Dinsmore CJ, Fernandes C, Greenberg I, Hamilton K, Mosser SD, Walsh ES, Williams TM, Koblan KS (2001) Highperformance liquid chromatography/mass spectrometry characterization of Ki4B-Ras in PSN-1 cells treated with the prenyltransferase inhibitor L-778,123. Anal Biochem 290 (1):126–137. https://doi.org/10.1006/abio. 2000.4972

10. Bonissone S, Gupta N, Romine M, Bradshaw RA, Pevzner PA (2013) N-terminal protein processing: a comparative proteogenomic analysis. Mol Cell Proteomics 12(1):14–28. https://doi.org/10.1074/mcp.M112. 019075 11. Bradshaw RA, Brickey WW, Walker KW (1998) N-terminal processing: the methionine aminopeptidase and N alpha-acetyl transferase families. Trends Biochem Sci 23(7):263–267. https://doi.org/10.1016/s0968-0004(98) 01227-4 12. Agamasu C, Ghirlando R, Taylor T, Messing S, Tran TH, Bindu L, Tonelli M, Nissley DV, McCormick F, Stephen AG (2019) KRAS Prenylation is required for bivalent binding with Calmodulin in a nucleotide-independent manner. Biophys J 116(6):1049–1063. https:// doi.org/10.1016/j.bpj.2019.02.004 13. Talsania K, Mehta M, Raley C, Kriga Y, Gowda S, Grose C, Drew M, Roberts V, Cheng KT, Burkett S, Oeser S, Stephens R, Soppet D, Chen X, Kumar P, German O, Smirnova T, Hautman C, Shetty J, Tran B, Zhao Y, Esposito D (2019) Genome assembly and annotation of the Trichoplusia ni Tni-FNL insect cell line enabled by long-read technologies. Genes (Basel) 10(2):79. https://doi.org/ 10.3390/genes10020079 14. Gillette W, Frank P, Perkins S, Drew M, Grose C, Esposito D (2019) Production of Farnesylated and methylated proteins in an engineered insect cell system. Methods Mol Biol 2009:259–277. https://doi.org/10. 1007/978-1-4939-9532-5_20 15. Bayburt TH, Sligar SG (2003) Self-assembly of single integral membrane proteins into soluble nanoscale phospholipid bilayers. Protein Sci 12 (11):2476–2481. https://doi.org/10.1110/ ps.03267503 16. Lakshman B, Messing S, Schmid EM, Clogston JD, Gillette WK, Esposito D, Kessing B, Fletcher DA, Nissley DV, McCormick F, Stephen AG, Jean-Francois FL (2019) Quantitative biophysical analysis defines key components modulating recruitment of the GTPase KRAS to the plasma membrane. J Biol Chem 294(6):2193–2207. https://doi. org/10.1074/jbc.RA118.005669

Chapter 7 Active GTPase Pulldown Protocol Martin J. Baker and Ignacio Rubio Abstract Ras and its related small GTPases are important signalling nodes that regulate a wide variety of cellular functions. The active form of these proteins exists in a transient GTP bound state that mediates downstream signalling events. The dysregulation of these GTPases has been associated with the progression of multiple diseases, most prominently cancer and developmental syndromes known as Rasopathies. Determining the activation state of Ras and its relatives has hence been of paramount importance for the investigation of the biochemical functions of small GTPases in the cellular signal transduction network. This chapter describes the most broadly employed approach for the rapid, label-free qualitative and semi-quantitative determination of the Ras GTPase activation state, which can readily be adapted to the analysis of other related GTPases. The method relies on the affinity-based isolation of the active GTP-bound fraction of Ras in cellular extracts, followed by its visualization via western blotting. Specifically, we describe the production of the recombinant affinity probes or baits that bind to the respective active GTPases and the pulldown method for isolating the active GTPase fraction from adherent or non-adherent cells. This method allows for the reproducible measurement of active Ras or Ras family GTPases in a wide variety of cellular contexts. Key words Ras, GTPase, Rin/Rit, Arf, Rab, Rac, Cdc42, Ral, Ran, Rap, Rho, Activation, RBD, Pulldown

1

Introduction In 1982 four laboratories simultaneously reported mutations in H-Ras as oncogenic lesions in human bladder cancer cell lines, followed shortly later by the mapping of mutations to N-Ras in neuroblastoma, making Ras (collectively for H-, K-, and N-Ras) the first identified human oncogene (reviewed in [1, 2]). Around the same time, Scolnick and co-workers showed that Ras proteins bind tightly to guanine nucleotides and that the bound guanine nucleotide critically determines Ras function [3]. Almost 40 years later, we now understand that the signalling activity of Ras and most other related small G-proteins is governed by their GDP/GTP loading status, the GTP-loaded form representing the biologically active version. The nucleotide loading status of Ras is tightly controlled by a combination of environmental signals, growth factors, and

Ignacio Rubio and Ian Prior (eds.), Ras Activity and Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2262, https://doi.org/10.1007/978-1-0716-1190-6_7, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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intracellular cues acting via changes in the activity of nucleotide exchange factors (GEFs) and/or GTPase activating proteins (GAPs) (see Chapter 2). These concepts remain for the most part valid, even though there are exceptions to these rules. For example, Rho family Rnd GTPases are constitutively GTP-loaded, they do not hydrolyze GTP, and thus their activation state must be governed by means other than the control of their nucleotide loading [4]. Similarly, some GTPases transmit their biological signals in their GDP-bound conformation, notably some Rag GTPases [5] and also recently described for Rho in Dictyostelium discoideum cells [6]. Notwithstanding these exceptional cases, methods to determine the activation state of Ras GTPases have always involved assessing the extent of loading with GTP and/or GDP. Over the years, several approaches that measure the amount of GTP-bound (i.e., active) Ras either as fold-of-control or as % of total GDP + GTP-bound Ras have been developed. Originally and over many of the pioneering years of Ras research, Ras activation was monitored by counting the amount of radioactive 32P-GDP and 32P-GTP bound to Ras immunoprecipitated from cells labelled metabolically with inorganic 32P-phosphate [7]. This protocol was, for instance, employed for the first documentation of Ras activation by extracellular agonists in T-cells and NIH3T3 fibroblasts in 1990 [8, 9]. This basic protocol has evolved and improved over the years to include alternative means for label detection (e.g., TLC or HPLC) and quantification of Ras-bound 32P-GDP/GTP (phosphorimager versus online radioactive scintillation counting) [10]. In addition, this methodology has been adapted for the analysis of Ras-bound GDP/GTP levels at pre-steady-state conditions in permeabilized cells [11, 12], an approach used to measure the nucleotide exchange rate on cellular Ras. More recently, non-radioactive approaches have taken center stage. Thus, an enzyme-based method was devised that allows for the absolute determination of label-free GDP/GTP levels on Ras [13]. In an accompanying chapter in this book, Kopra and H€arm€a describe a novel approach based on the use of quenching of resonance energy transfer (QRET) for high-throughput screening of the activation status of Ras or other GTPases. While all traditional and other more modern methodologies remain in use and are convenient for particular applications, they suffer from limitations that render them impractical for everyday use in cell biological studies. For example, metabolic 32PO4-labelling protocols are tedious and require relatively high amounts of radioactivity. Moreover, activation status measurements on GTPases other than Ras are often precluded by the lack of suitable IP antibodies that must prevent nucleotide loss, exchange, or hydrolysis during sample processing (as achieved by the rat monoclonal Y13-259 anti-Ras Ab [7, 14]). Today, metabolic 32PO4-labelling and enzymatic approaches have been widely replaced by the so-called Ras-GTP

Active GTPase Pulldown Protocol

A

Active GTPase

Bait

Sepharose Bead

GSH GST

RBD Ras GTP

119

Inactive GTPase (does not bind)

Ras GDP

B 1. Add lysis buffer bait solution to cells

3. Add Supernatant to glutathione beads

Ras GDP Ras GDP Ras GDP

GST

RBD

GST

RBD Ras GTP

4. Incubate supernatant with glutathione beads on rotator Bead

GSH GST

RBD Ras GTP

2. Spin to clarify lysate

45 min

Lysis buffer bait solution

Non adherent Cells in lysis Cells buffer bait solution

Clarified lysate

Prewashed beads

5. Wash beads 2 x with lysis buffer

6. Boil beads and detect GTPase via Western blotting or ELISA

Fig. 1 Schematic of the active GTPase pulldown procedure. (a) Schematic representation of the GST-bait mediating the interaction between the GSH-sepharose beads and an active GTPase. In this case a recombinant GST-RBD binding to Ras-GTP. Also shown is the inactive GTPase not binding. (b) Schematic representation of the workflow for the pulldown procedure with non-adherent cells described in Subheading 3

pulldown methodology for the routine assessment of Ras activation [15, 16]. This method relies on the use of the Ras-binding domain (RBD) of the Ras effector c-Raf for the isolation of active, GTP-bound Ras. As a Ras effector, by definition, the RBD exhibits strong selectivity toward Ras-GTP to the detriment of Ras-GDP (over 1000-fold higher affinity [17]). A recombinantly produced RBD “bait” is thus used to capture and isolate Ras-GTP from cell extracts, which is subsequently visualized or detected, e.g., by western blotting or ELISA (Fig. 1). By virtue of its strong preference for Ras-GTP, the same RBD has been used in imaging approaches for the visualization of Ras-GTP in live or fixed cells [18–21]. Importantly, by permuting the “bait” domain used to capture the active GTPase, the assay can be easily adapted to determine the activation status of multiple small GTPases (see Table 1 and Fig. 2). Numerous commercial providers meanwhile offer kits for the determination of the activation status of various GTPases (mostly Ras, Rap, Ral, Rac, and Rho) based on the pulldown method described herein. Some kits include the GST-bait immobilized on

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Table 1 Recommended recombinant baits for the specific pulldown of different activated GTPases GTPase

GST-tagged bait

K-, H-, N-Ras

RBD domain of Raf (aa 51–131) [22, 23] (see Note 37)

R-Ras2 (TC21)/R-Ras3 RBD domain of Raf (aa 51–131) [19, 24]; RA domain of RIN2 [25]; RA (M-Ras) domain of NORE1 [26] Rin/Rit

RBD domain of Raf (aa 51–131) [27]

Arf

GGA3 GAT domain (aa 165–237) [28, 29]

Rab

Rab5 binding domains from Rabaptin-5 (aa 739–862), Rabenosyn-5 (aa 1–40), or EEA1 (aa 1–209) [30–33]

Rac/Cdc42

PAK CRIB domain (aa 67–150) [34, 35]

RalA/RalB

Ral binding domain of RalBP1 (aa 393–446) [36]

Ran

Importin-β [37] or RanBP1 [38, 39]

Rap

RA domain of RalGDS (aa 1–97) [35, 40]

RhoA/RhoB/RhoC

Rho binding domains of Rhotekin (aa 7–89) [35, 41] or citron (mouse citron aa 1124–1286) [42]

RhoG

ELMO1 [43]; RhoGIP122 [44]

PaK1 WASP

Citron

NORE1 RIN2 Af6/Afadin RalGDS Rhotekin mDia

P110 γ

Raf

Marker

Aa amino acids, RA domain Ras-association domain, RBD Ras-binding domain

175 83 62 42 32.5

Fig. 2 Coomassie staining of recombinant GST-tagged baits. Coomassie staining of the indicated recombinant GST-tagged baits purified as described in Subheading 3.1. Red arrows mark the expected size of each bait

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a resin (e.g., Merck, Abcam, and Cytoskeleton), while a few providers offer the GST-bait in soluble form for direct supplementation in the lysis buffer (e.g., Thermo Fisher, Cell Signaling Technology, and Jena Bioscience). In this chapter we describe in detail the preparation of the bait (exemplarily for GST-RafRBD as a bait for Ras-GTP) as well as the procedure for the active GTPase pulldown assay, again exemplarily for Ras. Specifically, we describe the procedure using soluble bait protein, as this has a number of experimental benefits, as outlined below.

2 2.1

Materials Bait Production

1. Glutathione-sepharose or glutathione-agarose beads. 2. Glutathione elution buffer: 50 mM Tris HCl pH 8, 10 mM reduced L-glutathione, 10 mM DTT. Prepare fresh each time prior to experiment. 3. Lysozyme solution: 50 mg/ml lysozyme in PBS prepared fresh. 4. Isopropyl β-D-1-thiogalactopyranoside (IPTG). 5. PBS. 6. 500 mM EDTA pH 8.0. 7. 10% TritonX-100. 8. LB broth with appropriate antibiotic for bacterial resistance. 9. BL21(De3)pLysS or AD202 E. coli. 10. PD10 Desalting Column (GE), or similar product. 11. Liquid nitrogen. 12. Bacteria incubator. 13. Ultracentrifuge. 14. Tabletop refrigerated centrifuge. 15. Rotator and/or rocker.

2.2 Active GTPase Pulldown

This protocol provides the option of two lysis buffers that have each been optimized for the pulldown of different GTPases by different groups. Lysis buffer A is known to work well for Ras/Rap/RalGTP pulldowns, and lysis buffer B has been optimized for Ras/Rac/Cdc42-GTP pulldowns. Moreover, depending on their function and subcellular distribution, some GTPases may require harsher or special conditions for lysis and solubilization in particular cellular backgrounds (e.g., a RIPA-like lysis buffer for Rap-GTP assays in platelets [40]).

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1. Lysis buffer A: 50 mM Tris HCl pH 7.5, 50 mM NaCl, 5 mM MgCl2, 1 mM EGTA, 1% NP40 alternative, 10% glycerol (optional, see Note 1). Adjust pH to 7.5 at 4
C using HCl (see Notes 2 and 3), and add protease inhibitors or protease inhibitor cocktail. Optional: If protein phosphorylation is to be assessed from the same extracts, also include phosphatase inhibitors as appropriate, but avoid the use of EDTA, as this will complex and scavenge Mg2+ (see Note 1), and NaF, as this can affect GTPase nucleotide loading (see Note 4). Keep on ice. 2. Lysis buffer B: 20 mM Tris HCl pH 7.5, 150 mM NaCl, 5 mM MgCl2, 0.5% NP40 alternative, 5 mM β-glycerophosphate, 1 mM DTT. Adjust pH to 7.4 at 4
C using HCl (see Notes 2 and 3), and add protease inhibitors or protease inhibitor cocktail. Optional: add phosphatase inhibitors as appropriate but avoid NaF (see Note 4). Keep on ice. 3. Prewashed bead slurry: Prepare with 10 μl glutathione sepharose 4B (glutathione agarose works equally well) beads per sample (alternatively, beads may be mixed with 2 vol. of empty sepharose-4B beads for easier handling when aspirating/washing (see Note 5)). Wash the beads with an addition of 10 greater volume of lysis buffer, and pellet beads in tabletop centrifuge at 4
C at 500  g for 2 min or a few seconds at full speed, and remove supernatant. Repeat the wash step for a minimum of two washes. Finally resuspend the beads with lysis buffer so that there is 10 μl of glutathione beads in every 100 μl of lysis buffer (10% slurry). Distribute 100 μl of prewashed bead slurry into separate 1.5 ml microcentrifuge tubes for each condition (10 μl of glutathione beads per condition) (see Note 6). Keep aliquoted prewashed beads on ice. 4. Lysis buffer-bait solution: Use 0.6–1.0 ml of lysis buffer A or B (see Note 7) and 10–20 μg recombinant GST-tagged bait per a sample (see Table 1) (see Notes 8 and 9). The addition of 100 μM GDP is also advisable for some GTPases and/or cell lines with high basal GEF activity (e.g., for Ras in hematopoietic cell lines [45]), as excess GDP will prevent post lytic GTP loading. Keep the lysis buffer-bait solution on ice. 5. (for adherent cells) Prepare 1 PBS and keep on ice. 6. (for cells in suspension) Prepare an appropriate phosphatebuffered saline for the cell type being used, and keep at 37
C. 7. pH Meter. 8. 1.5 ml microcentrifuge tubes. 9. Gel-loading-like pipette tips for bead washing steps. 10. Aspirator connected to vacuum, piston pump, or peristaltic pump for bead washing steps. 11. Refrigerated microcentrifuge set to 4
C.

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12. Rotator capable of holding 1.5 ml microcentrifuge tubes. Place in 4
C cold room. 13. 4 Laemmli sample buffer. 14. (for adherent cells) Cell scrapers. 15. Western blotting system and antibodies for detection of the GTPase being studied. 16. Ponceau stain solution.

3

Methods

3.1 Bait Production Method

As described above the active GTPase pulldown requires a recombinant bait. The following section describes its production. We express most of our bait proteins routinely in BL21(De3)pLysS bacteria. These allow for IPTG inducible expression of the recombinant bait protein and possess other features that make them an ideal expression host. Occasionally we use AD202 E. coli (a protease-negative strain) for the expression of proteolysissensitive bait protein variants (such as GST-RalBD) [46] following essentially the same protocol as described below. 1. (to be done prior to bait production) Transform expression plasmid coding for GST-bait fusion protein in BL21 or AD202 E. coli by standard procedures (see Note 10), and store aliquots of bacteria in glycerol at 80
C for future bait productions. 2. Add a glycerol stock of transformed bacteria or one agar plate colony (each containing bait expression plasmid) to 5 ml of LB with appropriate selection antibiotic, and grow at 37
C throughout the day for at least 5 h. 3. Transfer the starter culture to 250 ml of LB with appropriate selection antibiotic, and grow overnight at 37
C. 4. Dilute culture into 3 l of LB with appropriate selection antibiotic (we found that it is easiest to split this evenly in 3  2 l flasks), and grow cells in a large incubator shaker at 37
C until the OD A600 ¼ 0.7. 5. Induce fusion protein expression at this point by adding IPTG to a final concentration of 0.5 mM (optional, take a sample of the uninduced bacteria culture as a control for later SDS-PAGE check of bait expression). Incubate for 4 h to overnight with shaking at room temperature (see Note 11). For GST-RBD, 4–5 h is sufficient. (Optional, take a sample after incubation to check for the induction of bait production by SDS-PAGE). 6. Sediment bacteria at 4000  g for 10 min and discard supernatant.

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7. Resuspend bacteria pellets in 60 ml ice-cold PBS (+protease inhibitors) total. Divide equally between two 50 ml tubes (30 ml in each). 8. Lyse the bacteria by subjecting this suspension to 3 freezethaw cycles as follows: snap-freeze in liquid nitrogen (optional, pellet may be stored at 80
C at this stage), wait 5 min, and thaw by placing in beaker with warm water. Alternatively, bacteria can be lysed with 5  15 s of sonication on ice using a probe sonicator, with 1 min on ice between each sonication cycle. 9. After bacteria lysis, transfer suspension to appropriate ultracentrifugation tubes. 10. Add 200 μl of lysozyme solution to bacterial suspension, dividing equally between tubes. Incubate 20 min at room temperature. Mix the tube periodically by inverting. 11. Add a total of 2 ml [500 mM EDTA pH 8.0] dividing equally between tubes, and mix carefully. Add a total of 6 ml [10% TritonX-100] dividing equally between tubes, and mix carefully. Incubate 20 min at room temperature on rotator or with periodic mixing by inversion. 12. Centrifuge at 35,000  g for 20 min to pellet lysis debris. 13. Transfer supernatant to new precooled tubes (2 tubes). Make up to 10 mM DTT by adding from a 1 M DTT stock (see Note 12). 14. Add 2 ml of 50% prewashed glutathione-sepharose or agarose bead slurry to each tube (see Note 13). Prewash beads as follows: (a) 1.33 ml of glutathione bead slurry (>75%). (b) Wash 3 with 10 ml ice-cold PBS (+protease inhibitors +10 mM DTT). (c) Resuspend in 2 ml of ice-cold PBS (+protease inhibitors +10 mM DTT). 15. Incubate for 1–2 h or up to overnight at 4
C on rotator or with gentle rocking (see Note 14). 16. Pellet glutathione beads and remove supernatant. 17. Wash GST-beads 3 with 10 ml ice-cold PBS (+protease inhibitors +10 mM DTT). During this process pool beads into one 15 ml tube. 18. Add 1 ml of room temperature glutathione elution buffer, and incubate at room temperature for 10 min with constant rocking or shaking. 19. Centrifuge and transfer supernatant to a new tube, and this is your eluate; keep on ice.

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20. Repeat steps 18 and 19 for a total of 3, pooling the eluate into one tube (should be 3 ml). 21. Wash a PD10 column 5 with 5 ml room temperature PBS (+protease inhibitors) (see Notes 15 and 16). 22. Add reduced glutathione/eluted bait solution from step 20 to the PD10 column, and allow to enter by gravity flow (see Note 17). 23. Add 3.5 ml room temperature PBS (+protease inhibitor) collecting the flow through. Collect the first 500 μl in one tube (this will have low concentration of the bait) and the remaining 3 ml in a 15 ml tube (this is the cleaned eluted GST-bait). Keep on ice. 24. Measure protein concentration using spectrophotometer at 280 nm (1 Å ¼ approximately 0.5 mg/ml for GST-PAKCRIB) (see Note 18). Alternatively, or in addition, measure protein concentration by standard protein concentration assays (e.g., Bradford or CBA) (see Note 19). 25. Keep aliquot for protein purity assessment on Coomassie gel (recommended for your first preparations). 26. Purified GST-bait proteins can be stored at 80
C following one of the procedures outlined below: (a) Divide into appropriately sized aliquots to avoid repeated freeze-thaw cycles (enough for an average number of samples in your experiments) (see Note 20). Snap-freeze in liquid nitrogen and store at 80
C. Stored in this way, GST-bait proteins are stable for several years. (b) Alternatively, add 3 ml of glycerol to create a 50% glycerol/bait solution. Gently mix, aliquot, and store at 20
C. This is a mild way of storing proteins, as the solution is kept cold but does not solidify at 20
C due to the 50% glycerol. Stored in this way, GST-bait proteins are stable for several months. 3.2 Active GTPase Pulldown Method

3.2.1 Cells in Suspension

This protocol should be performed on ice, except where indicated, and as quickly as possible due to the intrinsic GTP hydrolysis activity of GTPases. It is possible to do this protocol with cells in suspension or adherent cells by following the steps of Subheading 3.2.1 or Subheading 3.2.2, respectively. 1. Starve cells as appropriate (see Note 21). 2. Wash twice, and resuspend at 2  107 cells/ml in a prewarmed appropriate buffered saline, such as HBSS. Place cells on ice for >10 min.

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3. Aliquot 1 ml of cell suspension to a microcentrifuge tube for each condition, and incubate at 37
C for >5 min prior to stimulation/treatment. Briefly invert every few minutes to avoid sedimentation of the cells (see Note 22). 4. Treat/stimulate cells as appropriate. 5. Stop stimulations by spinning down cells a few seconds at full speed in a tabletop centrifuge. Aspirate off medium while ensuring that the pellet is not disturbed. Proceed directly to the next step. 6. Add 0.6–1.0 ml (consistent with item 4 of Subheading 2.2) of ice-cold lysis buffer-bait solution. For some cell types, vigorous vortexing may be required to assist the lysis. Always check that the pellet has been successfully suspended and homogenized. Keep on ice. 7. Proceed quickly to Subheading 3.2.3. Do not freeze samples. 3.2.2 Adherent Cells

1. The number of cells required for the assay depends on the cell type, GTPase expression, and stimulation conditions. However, seeding 1.5–6  105 cells in a well of a 6-well plate 24–48 h before the experiment is known to work well for Ras or Rap activation assays in routinely used cell lines. 2. Some treatments or stimulations may require serum starvation prior to the pulldown. This is cell type and treatment/stimulation dependent and should be done as appropriate (see Note 23). 3. Stimulate cells (if appropriate) with the addition of 10 stimuli at an appropriate incubation temperature and time (see Note 24). 4. Aspirate medium thoroughly from the culture vessel, and proceed immediately to the next step on ice. The removal of residual media is very important. 5. Optional: wash cells on ice with ice-cold PBS (see Note 25), and completely remove the PBS by aspirating. The removal of residual PBS is very important (see Note 26). Proceed rapidly to the next step. 6. Add the appropriate volume (consistent with item 4 of Subheading 2.2) of ice-cold lysis buffer-bait solution directly onto the cells in each plate. 7. Scrape cells in the lysis buffer-bait solution on ice, and transfer to precooled labelled microcentrifuge tubes on ice (see Note 27). Some cell types will require thorough vortexing to assist the lysis. 8. Proceed quickly to Subheading 3.2.3. Do not freeze samples.

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1. Spin lysates at 4
C, >12,000 rpm for 10 min. Keep spun lysates on ice. 2. Transfer 75% of the supernatant (e.g., 450 μl if 0.6 ml of lysis buffer bait solution was used to lyse the cells or 750 μl if 1 ml was used) to a tube containing the 100 μl of prewashed bead slurry, prepared in item 3 of Subheading 2.2, for each sample. Be careful not to transfer any pellet material and keep the residual lysates on ice. Proceed directly to the next step. The activation of two or more GTPases can be assessed from the same pulldown by one of two methods: (a) For the analysis of two GTPases that bind to the same bait, the user can split the supernatant between two tubes each containing 100 μl of prewashed bead slurry prepared in item 3 of Subheading 2.2 (see Note 28). (b) Alternatively, several GTPases sharing affinity for the same bait may be assessed sequentially from one western blot (see examples in [19, 47]). 3. Incubate this lysate/prewashed bead solution at 4
C on rotator for 45 min. 4. During incubation transfer 90 μl of the supernatant from step 1 to a new microcentrifuge tube, and add 30 μl of 4 Laemmli sample buffer. Boil for 5 min. This post-clarification lysate is for the measurement of total GTPase levels and can be stored at 20
C if western blotting is to be performed on a different day. 5. After the incubation of step 3, spin down beads by centrifuging a few seconds at full speed, aspirate off supernatant, and wash beads with the addition of 1 ml of lysis buffer. Ensure that the beads are fully resuspended; this can be achieved by directly pipetting the lysis buffer onto the beads or with a brief vortex. Do not pipette up and down (see Note 29). When aspirating, take extra care to avoid removing any of the beads (see Note 30). 6. Repeat the wash step (step 5), if required, for a total of two washes (see Notes 31 and 32). 7. After the final wash, aspirate off the supernatant, aspirating close to the beads (draining the beads with a fine needle or tip is also possible). Add 10 μl of 2 Laemmli sample buffer (adding a 1:1 ratio of beads to 2 sample buffer). Boil for 5 min, or, alternatively, incubate from 2 h to overnight at room temperature (see Note 33). This is your pulled-down active GTPase fraction. Proceed to Subheading 3.2.4, or store at 20
C if western blotting is to be performed on a different day.

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3.2.4 Western Blotting

1. Western blotting should be performed with 12% or higher denaturing polyacrylamide gels. 2. Equal amounts of total lysate from step 4 of Subheading 3.2.3, should be loaded into separate wells for each sample. 3. The boiled beads from Subheading 3.2.3, step 7 containing the active GTPase fraction should be loaded in their entirety to separate wells for each sample (see Note 34). Alternatively, some researchers prefer to spin down the beads and load only the supernatant. Either option works as long as the complete sample or equivalent amounts are loaded for each condition. 4. Run western gels and transfer following usual western blotting procedure. 5. Some of the GST-tagged baits can be detected by antibodies for the GTPase of interest (e.g., the GST-Pak-CRIB bait can be detected by some Rac1 antibodies; conceivably, the large amount of GST-bait present may result in non-specific decoration by the antibody). Therefore, the post-transfer membrane should be ponceau stained and cut directly below the visible GST-bait (see Note 35). Alternatively the gel can be cut below the GST-bait, prior to the transfer, allowing the transfer of only the lower portion. This is highly recommended as it can significantly improve gel-to-membrane transfer of the GTPase as the GST-tagged bait is present in large excess over the pulled-down GTPases. 6. Probe membrane with primary antibody specific to the GTPase of interest and appropriate secondary antibody. 7. Quantitative western blotting allows the ratio of active GTPase/total GTPase to be calculated from the pulled-down GTPase signal and total GTPase signal. The percentage of GTPase active in the cells can also be estimated from the known volume added to the beads and the volume of total lysate used for western blot (see Note 36).
 1 Pulldown signal  VV bt @
A  100 Vt Total lysate signal  0:75V g 0

¼ %Active GTPase

ð1Þ

Calculation of the percentage of active GTPase assuming that the complete pulldown was loaded for each sample. Vt ¼ total volume of lysis buffer bait solution used to lyse cells. Vb ¼ volume of clarified lysate added to beads. Vg ¼ volume of total lysate loaded into gel. There are many other effector proteins that could be used as baits for active GTPases. For example, the catalytic PI3K subunit

Active GTPase Pulldown Protocol

A

B

EGF (min) 0 K-Ras H/N-Ras K-Ras H/N-Ras

5

10

EGF (min) 0

IB: K-Ras

1 Active Rac1 Total Rac1

IB: N-Ras

Active Ras

Active Cdc42 Total Cdc42

K-Ras H/N-Ras

129

IB: H-Ras

K-Ras H/N-Ras

IB: Pan-Ras

Total Ras

Fig. 3 Representative blot of pulldown assay. (a) HeLa cells were serum starved and stimulated with 100 ng/ ml EGF for the indicated time. GST-Raf-RBD bait was used to pulldown active Ras, and the samples run on Western blot. The pulldown membrane was sequentially stripped and probed for each Ras isoform as illustrated (first K-Ras, second N-Ras, third H-Ras). Total Ras load was probed with a pan-Ras antibody that detects all three isoforms. Reproduced with modifications from [56] with permission of cell cycle (Taylor & Francis). (b) C4-2 cells were serum starved and stimulated with 100 ng/ml EGF for the indicated time. GSTPak-CRIB bait was used to pulldown active Rac1 and active Cdc42 and the samples run on Western blot. The samples were split as per step 2a of Subheading 3.2.3. to allow for the analysis of both Rac1 and Cdc42

p110γ interacts with active Ras [48], Af6/afadin interacts with active Rap [49], mDia interacts with active RhoA [50], and WASP interacts with active Cdc42 [51, 52]. The bait simply needs to have selectivity for the active conformation of the GTPase being studied. This allows the protocol to be applied to a wide variety of situations if a specific bait is available. Figure 3 shows representative pulldown blots.

4

Notes 1. The presence of 5 mM MgCl2 in the buffer during lysis and washing steps is essential to keep nucleotides bound to the GTPases. Also, the addition of glycerol has been found to improve the pulldown of active Rap and Ral GTPases. 2. A dilution of 1:40 concentrated HCl to water provides a useful stock solution for the gradual addition of HCl when adjusting the pH. 3. The addition of 3 M NaOH can be used to readjust the lysis buffer pH if the addition of HCl reduces it below the required pH. However, the addition of more than 2 μl is not advised as this will increase the salt concentration of the buffer.

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4. Fluoride, in the presence of trace amounts of aluminum, may bind to Ras-GDP as a mimic of the γ-phosphate and stabilize the GTP hydrolysis transition state of Ras in the presence of GAPs [53, 54]. 5. If the user has difficulty aspirating close to the beads in later steps of the protocol due to the small volume beads, then the amount of beads can be increased. However, it needs to remain a suitable volume to allow loading into a gel in Subheading 3.2.4. To save GSH-sepharose, empty sepharose-CL-4b beads may be used to increase the bead slurry volume. 6. Prepare the bead slurry in excess to ensure that there is enough for each condition. 7. Adjust the lysis buffer volume to the size of the culture dish surface and the number of cells. For example, if assaying more than 5  106 cells from a 10 cm diameter dish, use 1–1.2 ml. 8. 10 μg is usually sufficient; however, it is important to have the GST-tagged bait in excess. For some cell types or stimuli, it may be necessary to perform a titration of the bait to ensure that it is present in excess. 9. Some protocols use bait already bound to beads. However, the addition of it in the lysis buffer allows for capture of the active GTPase in the fastest possible manner as the cells lyse. This precludes post-lytic Ras GTP hydrolysis and hence loss of signal. 10. The transformation efficiency of AD202 bacteria is low compared to BL21 and other conventional strains. This should be taken into account when choosing the amount of plasmid for transformation or when plating bacteria onto agar plates. 11. The duration may depend on the particular bait produced. In the case of GST-RafRBD, 5 h or overnight works equally well, while for other baits (e.g., GST-PAKCRIB) 4 h gives better yields. 12. We supplement all buffers from here on with DTT to ensure that the glutathione stays in its reduced form. 13. Alternatively to the batch procedure described here, GST-bait may be purified by column chromatography either by gravity flow on bench using self-casted GSH-sepharose columns or on automated chromatography systems (e.g., FPLC). 14. 2 h is sufficient for most GST-bait proteins. 15. This step (size-exclusion chromatography using a commercial PD10 column) is critical to remove excess free GSH from the elution step. Removal of excess GSH is essential, as this would otherwise prevent capture of the GST-bait on the immobilized GSH-beads in the GTPase activation assays.

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16. As an alternative to the PD10 size-exclusion chromatography, excess GSH may be removed by dialysis against PBS (works well for GST-RafRBD and GST-RalGDS). We use floating microdialysis devices with a sample capacity of 1–5 ml (e.g., Spectra/Por®Float-A-Lyzer® from Roth). 17. If flow is slow and the procedure gets tedious, flow velocity can be easily increased by appending a long tubing to the end of the PD10 column. This results in an increase in the height of the liquid column, higher hydrostatic pressure, and increased liquid flow. 18. We calculate absorbance extinction coefficients according to [55]. 19. Additionally, protein concentration and purity may be assessed by SDS-PAGE: run coomassie gel with BSA protein standards of 0.5 μg, 1 μg, 2.5 μg, 5 μg, and 10 μg along with three different volumes of produced bait protein to allow concentration to be calculated from Coomassie staining by comparing to the BSA standards. 20. Many GST-bait fusion proteins are rather robust and can withstand several cycles of freeze-thaw (e.g., GST-RafRBD, GST-RalGDS-RA). Others, however, are less stable under these conditions and should not be subjected to repeated thawing and freezing (e.g., GST-PAK, GST-Rhotekin). To keep damage to the proteins to a minimum, we recommend rapid thawing of frozen aliquots in a 37
C water bath and rapid snap freezing in liquid nitrogen. 21. The serum starvation of cells is required to increase their sensitivity to a stimulus and is advisable if stimulationdependent responses are being tested. 22. Incubations and treatments of cells in suspension at 37
C are easiest in a water bath. 23. For growth factor stimulation, a serum starvation of 8–24 h is known to work well. 24. Do not replace media with stimulation media for this step as GTPases can be activated or deactivated in response to subtle changes in media composition or mechanical stress of replacing the media. The stimulation of cells at different temperatures can also yield different results, so for most situations this should be done at a physiologically relevant temperature of 37
C. 25. Add the PBS to the wall of the plate so that cells are not dislodged by the PBS. The volume of PBS does not need to be accurate but should be a volume that covers the entire plate. 26. The removal of residual PBS is very important as it can dilute the sample and reduce the accuracy of active/total GTPase

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ratio calculations at the end of the procedure. Briefly tilting the plate into the aspirator can help at this step. 27. Do not scrape too vigorously as this can cause some sample to be splashed out of the plate and lost. 28. The splitting of the sample should only be done if the two GTPases will give adequate signal from the reduced lysate being added to the beads. The GTPases both need to bind to the recombinant GST-tagged bait being used in the lysis buffer-bait solution. 29. Do not pipette up and down as this will result in loss of sample as beads will adhere to surface of the plastic tip. 30. It is important not to aspirate any of the beads in this step, and several things should be done to minimize the chances of this happening, for example, not using round bottom microcentrifuge tubes (e.g., 2 ml vials) as these will give a less compact pellet. The use of a fine tip on the aspirator is also extremely helpful. 31. Some GTPases, such as the three Ras proteins, are extremely clean proteins in terms of their performance in affinity precipitations, and thus one round of washing is enough to wash away non-bound GDP-associated GTPases while minimizing loss of Ras-GTP. For other more abundant and/or sticky GTPases (e.g., Rap), two to three rounds of washing may provide cleaner results. 32. In most cases one or two washes are adequate for western blot analysis. However, if this protocol is to be applied to isolate active GTPases for mass spectrometry analysis, then a minimum of five washes should be undertaken. 33. We have found that denaturing samples in SDS-Laemmli buffer at room temperature for > 2h, rather than boiling them, produces better results for Ras and other GTPases. 34. Do not use narrow loading tips for this step as the beads will block them. A 20 μl pipette works well for this step. Gentle pipetting up and down will be required to resuspend the beads, but if some residual beads remain, these can be resuspended and collected with the addition of a few microliters of gel running buffer and then added to the well of the gel. 35. Most GTPases are approximately 21 kDa, so ensure that the membrane is not cut below 25 kDa in this situation. For example, the GST-Pak-CRIB used in the Rac/Cdc42 pulldown appears as a double or triple band around 34 kDa (see Fig. 2).

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36. For some western blot developers or scanners, the total lysate and pulldown blots should be developed/scanned at the same time to allow calculations of active/total to be truly representative. Alternatively, a quantitative western blot imaging system can be used. 37. Some researchers have employed engineered Raf-RBD domains with enhanced affinity for Ras-GTP (A85KRBD) [57].

Acknowledgments We acknowledge the generous provision of GST-bait expression plasmids by Shuh Narumiya, Julian Downward, Gary Bokoch, Piero Crespo, Fried Zwartkruis, and Pablo Rodriguez-Viciana. Martin J. Baker would also like to acknowledge the mentorship and guidance of Marcelo G. Kazanietz in the optimization of the pulldown protocol. References 1. Malumbres M, Barbacid M (2003) RAS oncogenes: the first 30 years. Nat Rev Cancer 3:459–465 2. Cox AD, Der CJ (2010) Ras history: the saga continues. Small GTPases 1:2–27. https://doi. org/10.4161/sgtp.1.1.12178 3. Scolnick EM, Papageorge AG, Shih TY (1979) Guanine nucleotide-binding activity as an assay for src protein of rat-derived murine sarcoma viruses. Proc Natl Acad Sci U S A 76:5355–5359. https://doi.org/10.1073/ pnas.76.10.5355 4. Riou P, Villalonga P, Ridley AJ (2010) Rnd proteins: multifunctional regulators of the cytoskeleton and cell cycle progression. BioEssays 32:986–992. https://doi.org/10.1002/ bies.201000060 5. Hatakeyama R, De Virgilio C (2016) Unsolved mysteries of Rag GTPase signaling in yeast. Small GTPases 7:239–246 6. Senoo H, Kamimura Y, Kimura R et al (2019) Phosphorylated Rho–GDP directly activates mTORC2 kinase towards AKT through dimerization with Ras–GTP to regulate cell migration. Nat Cell Biol 21:867–878. https://doi. org/10.1038/s41556-019-0348-8 7. Gibbs JB (1995) Determination of guanine nucleotides bound to Ras in mammalian cells. Methods Enzymol 255:118–125. https://doi. org/10.1016/S0076-6879(95)55014-3

8. Gibbs JB, Marshall MS, Scolnick EM et al (1990) Modulation of guanine nucleotides bound to ras in NIH3T3 cells by oncogenes, growth factors, and the GTPase activating protein (GAP). J Biol Chem 265:20437–20442 9. Downward J, Graves JD, Warne PH et al (1990) Stimulation of p21ras upon T-cell activation. Nature 346:719–723. https://doi. org/10.1038/346719a0 10. Rubio I, Pusch R, Wetzker R (2004) Quantification of absolute Ras-GDP/GTP levels by HPLC separation of Ras-bound [(32)P]labelled nucleotides. J Biochem Biophys Methods 58:111–117. https://doi.org/10.1016/j. jbbm.2003.07.001 11. de Vries-Smits AMM, van der Voorn L, Downward J, Bos JL (1995) Measurements of GTP/GDP exchange in permeabilized fibroblasts. Methods Enzymol 255:156–161. https://doi.org/10.1016/S0076-6879(95) 55019-4 12. Rubio I, Rennert K, Wittig U, Wetzker R (2003) Ras activation in response to lysophosphatidic acid requires a permissive input from the epidermal growth factor receptor. Biochem J 376:571–576. https://doi.org/10.1042/ BJ20031410 13. Scheele JS, Rhee JM, Boss GR (1995) Determination of absolute amounts of GDP and GTP bound to Ras in mammalian cells: comparison of parental and Ras-overproducing NIH 3T3 fibroblasts. Proc Natl Acad Sci U S

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A 92:1097–1100. https://doi.org/10.1073/ pnas.92.4.1097 14. Downward J (1995) Measurement of nucleotide exchange and hydrolysis activities in immunoprecipitates. Methods Enzymol 255:110–117. https://doi.org/10.1016/ S0076-6879(95)55013-5 15. van Triest M, Bos JL (2004) Pull-down assays for guanoside 50 -triphosphate-bound Ras-like guanosine 50 -triphosphatases. Methods Mol Biol 250:97–102. https://doi.org/10.1385/ 1-59259-671-1:97 16. Taylor SJ, Resnick RJ, Shalloway D (2001) Nonradioactive determination of Ras-GTP levels using activated Ras interaction assay. Methods Enzymol 333:333–342. https://doi. org/10.1016/S0076-6879(01)33067-7 17. Herrmann C, Martin GA, Wittinghofer A (1995) Quantitative analysis of the complex between p21ras and the Ras-binding domain of the human Raf-1 protein kinase. J Biol Chem 270:2901–2905. https://doi.org/10. 1074/jbc.270.7.2901 18. Sherman LS, Ratner N (2001) Immunocytochemical assay for Ras activity. Methods Enzymol 333:348–356. https://doi.org/10.1016/ S0076-6879(01)33069-0 19. Augsten M, Pusch R, Biskup C et al (2006) Live-cell imaging of endogenous Ras-GTP illustrates predominant Ras activation at the plasma membrane. EMBO Rep 7:46–51. https://doi.org/10.1038/sj.embor.7400560 20. Chiu VK, Bivona T, Hach A et al (2002) Ras signalling on the endoplasmic reticulum and the Golgi. Nat Cell Biol 4:343–350. https:// doi.org/10.1038/ncb783 21. Mochizuki N, Yamashita S, Kurokawa K (2001) Spatio-temporal images of growthfactor-induced activation of Ras and Rap1. Nature 411:1065–1068. https://doi.org/10. 1038/35082594 22. de Rooij J, Bos JL (1997) Minimal Ras-binding domain of Raf1 can be used as an activationspecific probe for Ras. Oncogene 14:623–625. https://doi.org/10.1038/sj.onc.1201005 23. van Triest M, Bos JL (2004) Pull-down assays for guanoside 50 -triphosphate-bound ras-like guanosine 50 -triphosphatases. In: MAP kinase signaling protocols. Humana Press, Totowa, NJ, pp 97–102 24. Delgado P, Cubelos B, Calleja E et al (2009) Essential function for the GTPase TC21 in homeostatic antigen receptor signaling. Nat Immunol 10:880–888. https://doi.org/10. 1038/ni.1749 25. Rodriguez-Viciana P, Sabatier C, McCormick F (2004) Signaling specificity by Ras family GTPases is determined by the full spectrum of

effectors they regulate. Mol Cell Biol 24:4943–4954. https://doi.org/10.1128/ MCB.24.11.4943-4954.2004 26. Ehrhardt A, David MD, Ehrhardt GRA, Schrader JW (2004) Distinct mechanisms determine the patterns of differential activation of H-Ras, N-Ras, K-Ras 4B, and M-Ras by receptors for growth factors or antigen. Mol Cell Biol 24:6311–6323. https://doi.org/10.1128/ MCB.24.14.6311-6323.2004 27. Shi G-X, Han J, Andres DA (2005) Rin GTPase couples nerve growth factor signaling to p38 and b-Raf/ERK pathways to promote neuronal differentiation. J Biol Chem 280:37599–37609. https://doi.org/10. 1074/jbc.M507364200 28. Santy LC, Casanova JE (2001) Activation of ARF6 by ARNO stimulates epithelial cell migration through downstream activation of both Rac1 and phospholipase D. J Cell Biol 154:599–610. https://doi.org/10.1083/jcb. 200104019 29. Collins BM, Watson PJ, Owen DJ (2003) The structure of the GGA1-GAT domain reveals the molecular basis for ARF binding and membrane association of GGAs. Dev Cell 4:321–332. https://doi.org/10.1016/ S1534-5807(03)00037-6 30. Qi Y, Liang Z, Wang Z et al (2015) Determination of Rab5 activity in the cell by effector pull-down assay. Methods Mol Biol 1298:259–270. https://doi.org/10.1007/ 978-1-4939-2569-8_22 31. Zhu G, Zhai P, Liu J et al (2004) Structural basis of Rab5-Rabaptin5 interaction in endocytosis. Nat Struct Mol Biol 11:975–983. https://doi.org/10.1038/nsmb832 32. Nielsen E, Christoforidis S, UttenweilerJoseph S et al (2000) Rabenosyn-5, a novel Rab5 effector, is complexed with Hvps45 and recruited to endosomes through a Fyve finger domain. J Cell Biol 151:601–612. https://doi. org/10.1083/jcb.151.3.601 33. Simonsen A, Lippe R, Christoforidis S et al (1998) EEA1 links PI(3)K function to Rab5 regulation of endosome fusion. Nature 394:494–498. https://doi.org/10.1038/ 28879 34. Benard V, Bohl BP, Bokoch GM (1999) Characterization of rac and cdc42 activation in chemoattractant-stimulated human neutrophils using a novel assay for active GTPases. J Biol Chem 274:13198–13204. https://doi. org/10.1074/jbc.274.19.13198 35. Jennings RT, Knaus UG (2014) Rho family and Rap GTPase activation assays. Humana Press, Totowa, NJ, pp 79–88

Active GTPase Pulldown Protocol 36. Hofer F, Berdeaux R, Steven Martin G (1998) Ras-independent activation of Ral by a Ca2+dependent pathway. Curr Biol 8:839–844. https://doi.org/10.1016/S0960-9822(98) 70327-6 37. Ly TK, Wang J, Pereira R et al (2010) Activation of the Ran GTPase is subject to growth factor regulation and can give rise to cellular transformation. J Biol Chem 285:5815–5826. https://doi.org/10.1074/jbc.M109.071886 38. Coutavas E, Ren M, Oppenheim JD et al (1993) Characterization of proteins that interact with the cell-cycle regulatory protein Ran/TC4. Nature 366:585–587. https://doi. org/10.1038/366585a0 39. Dasso M (2001) Running on Ran: nuclear transport and the mitotic spindle. Cell 104:321–324. https://doi.org/10.1016/ s0092-8674(01)00218-5 40. Franke B, Akkerman JN, Bos JL (1997) Rapid Ca2+-mediated activation of Rap1 in human platelets. EMBO J 16:252–259. https://doi. org/10.1093/emboj/16.2.252 41. Ren X-D, Schwartz MA (2000) Determination of GTP loading on Rho. Methods Enzymol 325:264–272. https://doi.org/10.1016/ S0076-6879(00)25448-7 42. Kimura K, Tsuji T, Takada Y et al (2000) Accumulation of GTP-bound RhoA during cytokinesis and a critical role of ECT2 in this accumulation. J Biol Chem 275:17233–17236. https://doi.org/10. 1074/jbc.C000212200 43. Meller J, Vidali L, Schwartz MA (2008) Endogenous RhoG is dispensable for integrinmediated cell spreading but contributes to Rac-independent migration. J Cell Sci 121:1981–1989. https://doi.org/10.1242/ jcs.025130 44. Blangy A, Vignal E, Schmidt S et al (2000) TrioGEF1 controls Rac- and Cdc42dependent cell structures through the direct activation of rhoG. J Cell Sci 113 (Pt 4):729–739 45. Rubio I, Wetzker R (2000) A permissive function of phosphoinositide 3-kinase in Ras activation mediated by inhibition of GTPaseactivating proteins. Curr Biol 10:1225–1228 46. Van Triest M, De Rooij J, Bos JL (2001) Measurement of GTP-bound Ras-like GTPases by activation-specific probes. Methods Enzymol 333:343–348. https://doi.org/10.1016/ S0076-6879(01)33068-9 47. Hennig A, Markwart R, Wolff K et al (2016) Feedback activation of neurofibromin terminates growth factor-induced Ras activation.

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Cell Commun Signal 14:5. https://doi.org/ 10.1186/s12964-016-0128-z 48. Rodriguez-Viciana P, Warne PH, Dhand R et al (1994) Phosphatidylinositol-3-OH kinase direct target of Ras. Nature 370:527–532. https://doi.org/10.1038/370527a0 49. Su L, Hattori M, Moriyama M et al (2003) AF-6 controls integrin-mediated cell adhesion by regulating Rap1 activation through the specific recruitment of Rap1GTP and SPA-1. J Biol Chem 278:15232–15238. https://doi. org/10.1074/jbc.M211888200 50. Palazzo AF, Cook TA, Alberts AS, Gundersen GG (2001) mDia mediates Rho-regulated formation and orientation of stable microtubules. Nat Cell Biol 3:723–729. https://doi.org/10. 1038/35087035 51. Kim AS, Kakalis LT, Abdul-Manan N et al (2000) Autoinhibition and activation mechanisms of the Wiskott–Aldrich syndrome protein. Nature 404:151–158. https://doi.org/10. 1038/35004513 52. Burbelo PD, Drechsel D, Hall A (1995) A conserved binding motif defines numerous candidate target proteins for both Cdc42 and Rac GTPases. J Biol Chem 270:29071–29074. https://doi.org/10.1074/jbc.270.49.29071 53. Gremer L, Gilsbach B, Reza Ahmadian M, Wittinghofer A (2008) Fluoride complexes of oncogenic Ras mutants to study the Ras-RasGAP interaction. Biol Chem 389:1163–1171. https://doi.org/10.1515/ BC.2008.132 54. Mittal R, Ahmadian MR, Goody RS, Wittinghofer A (1996) Formation of a transitionstate analog of the Ras GTPase reaction by Ras-GDP, tetrafluoroaluminate, and GTPaseactivating proteins. Science 273:115–117. https://doi.org/10.1126/science.273.5271. 115 55. Nick C, Gray T (1995) How to measure and predict the molar absorption coefficient of a protein. Protein Sci 4:2411–2423. https:// doi.org/10.1002/pro.5560041120 56. Vasjari L, Bresan S, Biskup C et al (2019) Ras signals principally via Erk in G1 but cooperates with PI3K/Akt for Cyclin D induction and S-phase entry. Cell Cycle 18:204–225. https://doi.org/10.1080/15384101.2018. 1560205 57. Fridman M, Maruta H, Gonez J et al (2000) Point mutants of c-Raf-1 RBD with elevated binding to v-Ha-Ras. J Biol Chem 275:30363–30371. https://doi.org/10. 1074/JBC.M003193200

Chapter 8 Methods to Monitor Ras Activation State Kari Kopra and Harri H€arm€a Abstract Various biochemical methods have been introduced to detect and characterize small GTPases and Ras. Luminescence-based techniques cover most of the currently used methods, utilizing single- or multiluminophore-conjugated molecules and external probes. Here we describe methods enabling Ras activity and activation state monitoring in vitro. This chapter focuses mainly on luminescence-based techniques. Key words Environment-sensitive labels, Fluorescence polarization (FP), Fo¨rster resonance energy transfer (FRET), GTP- and GDP-specific antibodies, Quenching resonance energy transfer (QRET), Ras, Ras-binding domain (RBD), Small GTPases, Thermal stability assay (TSA), Time-resolved fluorescence resonance energy transfer (TR-FRET)

1

Introduction Luminescence is the most widely used signaling technique in bioanalytical assays. Luminescence can be defined as radiation emitted by a luminophore following the excitation from ground state to the higher energy level [1]. Especially photoluminescence, such as fluorescence and phosphorescence, is utilized for biomolecular sensing and also monitoring of Ras and related reaction [1, 2]. In this chapter, we mainly concentrate on homogeneous luminescence-based assays applicable for Ras studies. Multiple small guanosine triphosphate (GTP)-binding proteins are already known today, and they interact with a variety of regulatory proteins, e.g., guanine nucleotide exchange factors (GEFs), GTPase-activating proteins (GAPs), and some with guanine nucleotide dissociation inhibitors (GDIs) [2–5]. As the function of small GTPases is affected by a multitude of interactions, the activity can also be monitored in several different levels as Ras cycles between inactive GDP-bound and active GTP-bound states. Ras activity status is controlled by GEFs, which induce activation of Ras and enable GTP-loaded Ras to interact with its downstream effectors, e.g., Raf and phosphoinositide 3-kinases (PI3Ks), and GAPs, which

Ignacio Rubio and Ian Prior (eds.), Ras Activity and Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2262, https://doi.org/10.1007/978-1-0716-1190-6_8, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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catalyzes the GTP hydrolysis reversing the Ras activity status back to inactive GDP-Ras form [3–5]. These two reactions determine the status of Ras and are by far the most studied reactions in terms of Ras. Many of the Ras interactions were originally defined by laborious assay systems, e.g., two-hybrid analysis, blot overlay, or glutathione S-transferase (GST) pulldown. These methods have been continuously applied to study cellular samples. However, collection of detailed real-time data is ideally enabled with in vitro assays in a luminescence detection mode. Especially homogeneous wash-free assays fulfill the criteria [1]. Luminescence-based homogeneous assays can be divided as label-free methods and single- or multilabel approaches. Intrinsic fluorescence from the tryptophan (Trp) moiety and external environment-sensitive probes are examples of the label-free assays [6, 7]. However, Trp is not often used in the case of Ras, as there are no naturally occurring Trp. External probes, on the other hand, are extremely useful in Ras thermal stability assays (TSA). Single-label techniques are increasingly applied to study Ras (in)activation. These methods include fluorescence polarization (FP), environment-sensitive labels, and quenching resonance energy transfer (QRET) [2, 3, 8–11]. Multi-label approaches are often based on the energy transfer between organic donor and acceptor label as in Fo¨rster resonance energy transfer (FRET)-type detection [2, 3, 10]. Additionally, lanthanide labels have shown to be efficient donors in time-resolved FRET (TR-FRET) processes [2, 12, 13]. FRET techniques are highly suitable to study Ras (in)activation but also Ras interactions with other proteins. Several methods to study Ras nucleotide exchange, GTPase cycling, effector interactions, and thermal stability are introduced in the following section.

2

Materials Prepare all solutions using ultrapure water (prepared by purifying deionized water, to attain a sensitivity of 18 MΩ-cm at 25
C) and analytical grade reagents. Prepare and store all proteins at 80
C and other reagents at +4
C (unless indicated otherwise). Diligently follow all waste disposal regulations when disposing of waste materials.

2.1

Ras Preloading

1. Ras nucleotide exchange buffer: 20 mM HEPES (pH 7.5), 10 mM EDTA, 25 mM NaCl, 1 mM DTT. Weigh 0.48 g HEPES (238.3 g/mol), 0.37 g EDTA (372.2 g/mol), 0.15 g NaCl (58.44 g/mol), 0.02 g DTT (154.3 g/mol), and dilute in 100 mL of water (see Note 1). Adjust pH to 7.5 using NaOH.

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2. Ras loading buffer: 20 mM HEPES (pH 7.5), 25 mM NaCl, 100 mM MgCl2, 1 mM DTT. Weigh 0.48 g HEPES, 2.04 g MgCl2 (203.3 g/mol), 0.15 g NaCl, 0.02 g DTT, and dilute in 100 mL of water (see Note 2). Adjust pH to 7.5 using NaOH. 3. Ras storage buffer: 20 mM HEPES (pH 7.5), 1 mM MgCl2, 100 mM NaCl, 1 mM TCEP. Weigh 0.48 g HEPES, 0.02 g MgCl2, 0.58 g NaCl, 0.03 g TCEP (286.7 g/mol), and dilute in 100 mL of water (see Note 3). Adjust pH to 7.5 using NaOH. 4. Loaded Ras protein (see Note 4). 5. Loaded GDP or GTP or nucleotide analog conjugated with the appropriate label (see Note 5). 6. Appropriate purification column (see Note 6). 2.2 Nucleotide Exchange 2.2.1 EnvironmentSensitive Nucleotide Analogs

1. Nucleotide exchange buffer 1: 25 mM HEPES (pH 7.5), 1 mM MgCl2, 10 mM NaCl, 1 mM DTT. Weigh 0.60 g HEPES, 0.02 g MgCl2, 0.06 g NaCl, 0.02 g DTT, and dilute in 100 mL of water (see Note 7). Adjust pH to 7.5 using NaOH and store at 4
C. 2. Ras and SOS or other GEF protein (see Notes 8 and 9). 3. Label (e.g., Mant or BODIPY) conjugated GDP and GTP analog (see Note 10). 4. Ras inhibitors, activators, or interactor molecules (see Note 11). 5. Multiwell plate (see Note 12).

2.2.2 QRET

1. Nucleotide exchange buffer 2: 25 mM HEPES (pH 7.5), 1 mM MgCl2, 10 mM NaCl, 0.01% γ-globulins, 0.01% Triton X-100. Weigh 0.60 g HEPES, 0.02 g MgCl2, 0.06 g NaCl, 0.01 g γ-globulins (160 kDa), 0.01 g Triton X-100 (647 g/ mol), and dilute in 100 mL of water (see Note 13). Adjust pH to 7.5 using NaOH and store at 4
C. 2. Ras and SOS or other GEF protein (see Note 8). 3. 20 /30 -GTP-Eu3+ or 20 /30 -GDP-Eu3+ (see Note 14). 4. Modulator (MT2) solution (see Note 15). 5. Ras inhibitors, activators, or interactor molecules (see Note 11). 6. Multiwell plate (see Note 16).

2.2.3 FRET and TR-FRET

1. Nucleotide exchange buffer 2: 25 mM HEPES (pH 7.5), 1 mM MgCl2, 10 mM NaCl, 0.01% γ-globulins, 0.01% Triton X-100 (see Notes 7, 13, and 17). 2. Ras and SOS or other GEF protein (see Note 18).

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3. Acceptor- or donor-conjugated GDP/GTP analog (see Note 19). 4. Acceptor- or Note 20).

donor-conjugated

streptavidin

(SA)

(see

5. Ras inhibitors, activators, or interactor molecules (see Note 11). 6. Multiwell plate (see Note 21). 2.3 GTPase Cycling and GTP Hydrolysis 2.3.1 GTP Detection

1. GTPase cycling buffer 1: 25 mM HEPES (pH 7.5), 1 mM MgCl2, 1 mM NaCl, 0.01% γ-globulins, 0.01% Triton X-100. Weigh 0.60 g HEPES, 0.02 g MgCl2, 0.006 g NaCl, 0.01 g γ-globulins, 0.01 g Triton X-100, and dilute in 100 mL of water. Adjust pH to 7.5 using NaOH and store at 4
C (see Note 22). 2. Ras, GEF, and GAP proteins (see Note 23). 3. Ras inhibitors, activators, or interactor molecules (see Note 11). 4. GTP detection components (see Note 24). 5. Multiwell plate (see Notes 16 and 25).

2.3.2 GDP Detection

1. GTPase cycling buffer 2: 25 mM HEPES (pH 7.5), 5 mM MgCl2, 10 mM NaCl, 0.01% BSA, 0.01% Triton X-100. Weigh 0.60 g HEPES, 0.10 g MgCl2, 0.06 g NaCl, 0.01 g BSA (66 kDa), 0.01 g Triton X-100, and dilute in 100 mL of water (see Note 26). Adjust pH to 7.5 using NaOH and store at 4
C. 2. Ras, GEF, and GAP proteins (see Note 23). 3. GDP detection kit (see Note 27). 4. Multiwell plate (see Note 28).

2.3.3 Phosphate Detection

1. GTPase cycling buffer 2: 25 mM HEPES (pH 7.5), 5 mM MgCl2, 10 mM NaCl, 0.01% BSA, 0.01% Triton X-100 (see Notes 26 and 29). 2. Ras, GEF, and GAP proteins (see Notes 23 and 30). 3. Phosphate detection kit (see Note 31). 4. Multiwell plate (see Note 32).

2.4 Ras Loading State and Stability 2.4.1 FRET- and TRFRET-Based GTPRas Assay

1. Nucleotide exchange buffer 2: 25 mM HEPES (pH 7.5), 1 mM MgCl2, 10 mM NaCl, 0.01% γ-globulins, 0.01% Triton X-100 (see Note 17). 2. Ras and SOS or other GEF protein (see Note 33). 3. Acceptor- or donor-conjugated Raf-RBD or pan-Ras antibody (see Note 34).

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4. Acceptor- or donor-conjugated GTP analog or streptavidin (SA) (see Note 35). 5. Ras inhibitors, activators, or interactor molecules (see Note 11). 6. Multiwell plate (see Note 21). 2.4.2 Ras Thermal Stability

1. Thermal stability buffer: 25 mM HEPES (pH 7.5), 1 mM MgCl2, 20 mM NaCl, 0.001% Triton X-100. Weigh 0.60 g HEPES, 0.02 g MgCl2, 0.12 g NaCl, 0.001 g Triton X-100, and dilute in 100 mL of water. Adjust pH to 7.5 using NaOH (see Note 36). 2. Ras protein (see Note 37). 3. External fluorescent probe (see Note 38). 4. Ras inhibitors, activators, or interactor molecules (see Note 11). 5. Multiwell plate (see Note 21).

3

Methods Carry out all procedures at room temperature unless otherwise specified. Ras and other proteins should be kept on ice at all times before addition to plate. Luminophores should be protected from light at all times. All concentrations given are calculated to final 20 μL volume and are indicative depending on, e.g., the activity of the Ras batch used.

3.1

Ras Preloading

1. Calculate the amount of protein you need to perform the assay. Take account of the estimated loss of Ras during the nucleotide loading and purification (see Note 39). Example: The need for 4 Ras solution for 100 individual assay points in mant-GTP assay is 550 μL assuming the 10% extra solution for pipetting. If Ras concentration in well (20 μL) is 1 μM (~23 μg/mL), the added 4 solution is 4 μM (~93 μg/mL). Taking account of the estimated 20% loss during loading and purification, the minimum amount of Ras used in preloading is approximately 62 μg (see Note 39). 2. Mix Ras and 50-fold excess of nucleotide/analog in a nucleotide exchange buffer (see Note 40). If Ras is available in a suitable buffer, concentration, and volume, nucleotide can be added and mixed directly with Ras and EDTA without any other change in buffer (see Note 41). 3. Incubate Ras in a nucleotide exchange buffer for 30 min (see Note 42).

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4. Add nucleotide loading buffer or MgCl2 to enable Ras reloading with the selected nucleotide/analog (see Note 43). 5. Ras reloading occurs rapidly after MgCl2 addition, and buffer exchange can be started soon after MgCl2 addition (see Note 44). 6. If loaded Ras is used directly after buffer exchange, store it on ice at all times to preserve enzyme activity (see Note 45). If Ras is not used immediately, Ras stock should be snap freeze in liquid nitrogen and stored at 80
C. In all cases, Ras concentration and the loading efficiency should be determined (see Note 46). 3.2 Nucleotide Exchange 3.2.1 EnvironmentSensitive Nucleotide Analogs

1. Calculate the needed volume, and add 10% extra volume of all assay components prepared as 4 concentration (see Note 47). Prepare all assay components in the nucleotide exchange buffer 1, by starting from the most stable component and keeping nucleotide analogs protected from light at all times (see Note 48). All proteins should be kept on ice at all times before addition to the plate (see Note 49). Nucleotide analogpreloaded Ras is preferred to be freshly prepared (see Note 50). Assay is described in 20 μL final volume (see Note 51). Simplified presentation of nucleotide exchange reactions using environment-sensitive labels is depicted in Fig. 1. 2. Add 5 μL Ras inhibitors, activators, or interactor molecules first to the plate (see Notes 11 and 52). 3. Add 5 μL Ras protein (1 μM) to the plate and incubate for 15 min (see Note 53). 4. Add 5 μL fluorescent nucleotide analog (1 μM), if not preloaded to Ras (see Notes 53 and 54). 5. Add 5 μL SOS or other GEF (0.5 μM) last to the plate, and monitor the signal (see Note 55).

Fig. 1 Nucleotide exchange using environment-sensitive labels. Fluorophore environment sensitivity can be utilized to monitor nucleotide exchange with Ras. (a) Some GTPases naturally have suitable tryptophan (Trp) in their sequence, but in case of Ras, it needs to be mutationally added. Fluorophores, as BODIPY (b) or Mant (c), can be also attached to GTP or GDP structure at 20 - or 30 -position, and the nucleotide exchange is monitored from the change in fluorescence signal upon binding. Fluorescent nucleotide analogs can also be used as a FRET acceptor with Trp donor or label in fluorescence polarization

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1. Calculate the needed volume, and add 10% extra volume of all assay components prepared as 4 concentration (see Note 47). Prepare all assay components in the nucleotide exchange buffer 2, by starting from the most stable component (see Note 48). Prepare Ras and SOS (or other GEF) solution last, and keep solution on ice before addition to the plate (see Note 49). Assay is described in 20 μL final volume (see Note 51). Simplified presentation of nucleotide exchange reaction using QRET technique and the expected results are depicted in Fig. 2a, b. 2. Add 5 μL Ras inhibitors, activators, or interactor molecules first to the plate (see Notes 52 and 56). 3. Add 5 μL Ras protein (100 nM) to the plate, and incubate for 15 min (see Note 57).

Fig. 2 Nucleotide exchange using QRET and TR-FRET. Nucleotide exchange can be monitored using non-modified Ras and GTP-Eu3+ (QRET) or biotinylated Ras, SA-Eu3+, and GTP-Alexa647 (TR-FRET). (a) In the QRET assay, Ras (100 nM) loading is monitored after SOScat (20 nM) addition from the increasing signal. (b) SOScat initiates the nucleotide exchange, and the Ras-bound GTP-Eu3+ (10 nM) is protected from the MT2 modulator producing high signal. Dissociation can be initiated by addition of non-modified GTP (10 μM). (c) In the TR-FRET assay, the GTP-Alexa647 (10 nM) associated to biotin-Ras (25 nM) forms a TR-FRET pair with the Ras-bound SA-Eu3+ (5 nM). (D) High TR-FRET signal is observed after SOScat (5 nM) addition, and the dissociation kinetics can be monitored after GTP (10 μM) addition. Data is demonstrated using triplicates in 10 μL reaction volume, and signals are monitored using Tecan Spark 20 M plate reader

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4. Add 5 μL detection solution containing Eu3+-GTP or Eu3+GDP (10 nM) and the modulator (2.2 μM MT2) to the plate (see Note 58). 5. Add 5 μL SOS or other GEF (20 nM) last to the plate, and monitor the signal (see Note 59). Expected result from nucleotide association and dissociation assay is presented in Fig. 2b. 3.2.3 FRET and TR-FRET

1. Calculate the needed volume, and add 10% extra volume of all assay components prepared as 4 concentration (see Note 47). Prepare all assay components in the nucleotide exchange buffer 2, by starting from the most stable component (see Note 48). All proteins should be kept on ice at all times before addition to the plate (see Note 49). Assay is described in 20 μL final volume (see Note 51). Simplified presentation of nucleotide exchange reaction and expected results using TR-FRET technique is depicted in Fig. 2c, d. 2. Add 5 μL Ras inhibitors, activators, or interactor molecules first to the plate (see Note 11). 3. Add 5 μL Ras protein (25 nM biotinylated Ras) to the plate, and incubate for 15 min (see Note 60). 4. Add 5 μL acceptor- and donor-conjugated nucleotide analog and SA (see Notes 19 and 20). 5. Add 5 μL SOS or other GEF (5 nM) last to the plate, and monitor the signal (see Note 61). Expected result from nucleotide association and dissociation assay is presented in Fig. 2d.

3.3 GTPase Cycling and GTP Hydrolysis 3.3.1 GTP Detection

1. Calculate the needed volume, and add 10% extra volume of all assay components prepared as 4 concentration (see Notes 47 and 62). Prepare all assay components in the GTPase cycling buffer 1 (or the buffer provided by the kit manufacturer) by starting from the most stable component (see Note 48). Prepare Ras, SOS (or other GEF), and GAP solutions last, and keep solution on ice before addition to the plate (see Notes 22, 49, and 63). Assay is described in 20 μL final volume (see Note 51). Simplified presentation of GTPase cycling detection using QRET technique and expected results is depicted in Fig. 3. 2. Add 5 μL Ras inhibitors, activators, or interactor molecules first to the plate (see Notes 11, 25, 52, and 64). 3. Add 5 μL Ras protein (400 nM) together with GTP to the plate, and incubate for 10 min (see Notes 23 and 65). 4. Add 5 μL SOS or other GEF (100 nM) and GAP, e.g., p120GAP or NF1 (100 nM), to the plate, and incubate reaction for 30 min (see Notes 23 and 66).

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Fig. 3 GTP hydrolysis utilizing GTP detection. GTPase cycle converts GTP to GDP and Pi. Using anti-GTP antibody, Ras cycling is monitored from the decrease in GTP concentration. The GTPase cycling assays using 200 nM Ras (wild type and mutants G12D, G12C, G13D, and Q61R), SOScat (100 nM), p120RasGAP or NF1 (200 nM), and GTP (2 μM) were performed in a 384-well plate in 5 μL volume with 60 min incubation. Thereafter, the detection was performed using QRET method and 15 nM anti-GTP antibody and MT2 quencher. Competitive GTP detection assay is demonstrated in 10 μL reaction volume, and signals were monitored with PerkinElmer Victor 1420 plate reader. Data represent mean  SD (n ¼ 3)

5. In the QRET GTPase assay, add 5 μL detection solution containing Eu3+-GTP, anti-GTP antibody, and the modulator to the plate, and monitor signal (see Note 67). In case of GTPaseGlo, detection is performed in two steps, and signal is monitored 30 min after final component addition (see Notes 62 and 68). Expected results from the QRET GTPase cycling assay using Ras mutants and two GAP proteins are presented in Fig. 3. 3.3.2 GDP Detection

1. Calculate the needed volume and add 10% extra volume of all assay components prepared as 8 concentration (see Notes 47 and 69). Prepare all assay components in the GTPase cycling buffer 2, by starting from the most stable component (see Note 48). Prepare Ras, SOS (or other GEF), and GAP solutions last, and keep solution on ice before addition to the plate (see Notes 49 and 63). Assay is described in 20 μL final volume in which

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Fig. 4 GTP hydrolysis utilizing GDP and Pi detection. GTPase cycle converts GTP to GDP and Pi, and either of the reaction products can be monitored. By using 10 μM phosphate sensor, low μM Pi (KH2PO4 standard) can be monitored in the presence of 0.5 mM GTP. GDP detection can be performed utilizing anti-GDP antibody, showing over 100-fold GDP specificity over GTP in fluorescence polarization (FP) mode. Keeping total nucleotide concentration at 100 μM, high nM GDP can be detected with 4 nM Alexa633-GDP and 25 μg/ mL anti-GDP antibody. Phosphate Sensor Assay is demonstrated using 20 μL reaction volume and Tecan Infinite 200 plate reader and the GDP detection using 20 μL reaction and Tecan Spark 20 M plate reader. Data represent mean  SD (n ¼ 3)

the GTPase cycling is performed in 10 μL and detection by doubling the volume with detection solution (see Note 69). Simplified presentation of GTPase cycling detection using Transcreener GDP detection kit and its sensitivity is depicted in Fig. 4. 2. Add 2.5 μL Ras inhibitors, activators, or interactor molecules first to the plate (see Notes 11 and 52). 3. Add 2.5 μL Ras (400 nM) protein to the plate (see Notes 8 and 23). 4. Add 2.5 μL GTP (10 μM) to the plate and incubate for 15 min (see Note 70). 5. Add 2.5 μL SOS or other GEF (100 nM) and GAP, e.g., p120GAP or NF1 (100 nM), to the plate and incubate reaction for 30 min (see Notes 66 and 71). 6. Add detection solution in 10 μL, and monitor signal with an appropriate plate reader (see Note 72).

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1. Calculate the needed volume, and add 10% extra volume of all assay components prepared as 8 concentration (see Notes 47 and 69). Prepare all assay components in the GTPase cycling buffer 2, by starting from the most stable component (see Note 48). Prepare Ras, SOS (or other GEF), and GAP solutions last, and keep solution on ice before addition to the plate (see Notes 49 and 63). Assay is described in 20 μL final volume in which the GTPase cycling is performed in 10 μL and detection by doubling the volume with detection solution (see Note 69). Simplified presentation of GTPase cycling detection using phosphate sensor and its sensitivity is depicted in Fig. 4. 2. Add 2.5 μL Ras inhibitors, activators, or interactor molecules first on the plate (see Notes 11 and 52). 3. Add 2.5 μL Ras protein to the plate (see Notes 8, 23, and 73). 4. Add 2.5 μL GTP on the plate, and incubate for 15 min (see Note 70). 5. Add 2.5 μL SOS (or other GEF) and GAP (e.g., p120GAP or NF1) to the plate, and incubate reaction for 30 min (see Notes 66, 71, and 73). 6. Add detection solution in 10 μL, and monitor signal with an appropriate plate reader (see Note 74).

3.4 Ras Loading State and Stability 3.4.1 FRET and TRFRET-Based GTPRas Assay

1. Calculate the needed volume, and add 10% extra volume of all assay components prepared as 4 concentration (see Note 47). Prepare all assay components in the nucleotide exchange buffer 2, by starting from the most stable component. All proteins should be kept on ice at all times before addition to the plate (see Note 49). Assay is described in 20 μL final volume (see Note 51). Simplified presentation of nucleotide exchange reaction and expected results using TR-FRET technique is depicted in Fig. 5. 2. Add 5 μL Ras inhibitors, activators, or interactor molecules first to the plate (see Notes 11 and 21). 3. Add 5 μL Ras protein (25 nM) and GDP or GTP (2 μM) to the plate, and incubate for 15 min (see Notes 33 and 75). 4. Add 5 μL acceptor- and donor-conjugated Raf-RBD (25 nM) and SA or pan-Ras antibody (see Notes 34, 35, and 76). 5. Add 5 μL SOS or other GEF (5 nM) last on the plate, and monitor the signal (see Note 77). Expected result from Raf-RBD binding interaction with GTP-Ras and the interaction blocking is presented in Fig. 5b.

3.4.2 Ras Thermal Tability

1. Calculate the needed volume, and add 10% extra volume of all Ras and stabilizing molecule prepared as 4 concentration (see Note 47). External probe is added from 2 stock from the

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Fig. 5 GTP-Ras detection with TR-FRET readout. Using biotinylated Ras and Alexa680-RBD, Ras loading state is monitored from the increase in TR-FRET signal. Biotinylated Ras (25 nM), GTP (2 μM), SA-Eu3+ (5 nM), and Alexa680-RBD (25 nM) are first mixed, and the reaction is initiated by SOScat (5 nM) addition. GTP loading can be kinetically monitored, and Alexa680-RBD dissociation can also be monitored after GTP-Ras-specific K55 DARPin (2 μM) addition. Assay is demonstrated using 10 μL reaction volume and Tecan Spark 20 M plate reader. Data represent mean  SD (n ¼ 3)

selected label concentration (see Note 78). Prepare all assay components in the thermal stability buffer, by starting from the most stable component. Ras should be kept on ice at all times before addition to the plate (see Note 49). Assay is described in 20 μL final volume in which the Ras stability reaction is performed in 10 μL and detection by doubling the volume with detection solution (see Note 78). Simplified presentation of Ras thermal stability using SYPRO Orange external probe is depicted in Fig. 6. 2. Add 5 μL Ras inhibitors, activators, or interactor molecules first to the plate (see Note 79). 3. Add 5 μL Ras protein (5 μM) to the plate, and incubate for 15 min (see Note 80). 4. Add 10 μL external probe to the plate, perform heating cycle, and monitor the signal (see Note 81).

4

Notes 1. Ras nucleotide exchange buffer is selected to enable efficient chelation of magnesium and thus removal of Ras-bound nucleotide to create nucleotide-free apo-Ras. If Ras storage buffer contains over 1 mM MgCl2, the EDTA concentration in exchange buffer must be increased to enable 10 molar excess of EDTA over MgCl2 in a final volume. EDTA is preferred to be used from the 0.5–1 M stock which pH is adjusted to 8 to enable the solubility in water. Long buffer storage (over 1 week

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Fig. 6 Ras thermal stability with SYPRO Orange. In thermal stability assay (TSA), Ras is heated in the presence of denaturation sensing dye, e.g., SYPRO Orange, and its denaturation is monitored from the increased signal. Ras melting temperature does not change when assayed using SYPRO Orange (5) either with 3 μM (dashed) or 10 μM (solid) Ras concentration, but the signal-tobackground ratio is more prominent. Assay is demonstrated using 10 μL reaction volume and Tecan Spark 20 M plate reader. Data represent mean  SD (n ¼ 3)

at +4
C) after DTT addition should be avoided to preserve DTT function as a reducing agent. DTT can also be freshly added to the buffer from 1 M stock stored at 20
C. 2. Ras loading buffer is selected to enable Ras loading with the selected nucleotide or nucleotide analog (e.g., luminophoreconjugated GDP or GTP). Loading buffer must contain 5 or higher excess of MgCl2 over EDTA in the final volume. Especially with high EDTA concentration, it is often more practical to add MgCl2 directly from the 1 M stock to minimize increase in reaction volume. 3. Ras storage buffer is selected to enable long-term storage without loss of enzymatic activity. Buffer composition can be quite freely selected, but it should contain low concentration of MgCl2 and reducing agent (TCEP or DTT). TCEP is preferred in long-term Ras storage at 80
C as it does not contain thiol and it also can work as chelating agent.

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4. For Ras reloading with nucleotide or its analogs, high concentration of protein (100–500 μM) in storage buffer containing low MgCl2 and reducing agent concentration is recommended to maintain the enzymatic activity. 5. Any Ras-binding nucleotide or nucleotide analog can be loaded, but the loading efficiency varies depending on the nucleotide/analog affinity on Ras. Most often Ras is loaded with luminescent nucleotide analogs, e.g., BODIPY-GDP and Mant-GDP, which are commercially available. In case of Ras, the label should be preferably conjugated to 20 /30 -positions or either position individually. With other GTP-binding proteins, the optimal label position may vary, e.g., for tubulin-like proteins 8-position and for trimeric G proteins γ-phosphate position are preferred. 6. As excess of nucleotide analog is used, near-complete loading is expected with wild-type Ras and most common nucleotide analogs. Thus, any column separating free nucleotide and loaded protein can be used. Depending on the volume and protein concentration, NAP-5 gel filtration column or spin desalting column (~7 kDa) are applicable. Column should be selected to enable Ras storage or functional use in an appropriate concentration. 7. Both with Mant- and BODIPY-analogs, simplified buffers are preferred, as both are responding to change in their environment. Concentrations of NaCl and MgCl2 can usually be freely selected between 0–150 mM and 1–10 mM, respectively. As signal baseline is always adjusted, buffer can contain low concentration of detergent, glycerol, and reducing agent without losing assay functionality. In addition to HEPES, e.g., Tris-HCl (pH 7.5) can be used instead as a buffering agent. 8. Ras and GEF (e.g., SOScat) are available commercially or can be produced in-house. GEF proteins are more constant in terms of functionality than Ras, but different GEFs can have significant effects on nucleotide exchange rate. The quality and functionality of the Ras proteins can vary significantly depending on the source, construct, and mutations. Ras constructs with amino acids 1–188 or 1–169 are highly recommended to be used over shorter Ras constructs (amino acids 1–166). DDT and additional GDP may improve Ras stability, and those are often present in commercial protein solutions. 9. In case of environment-sensitive labels, analog-preloaded Ras is often preferred. Preloading with the luminescent nucleotide analog decreases the assay background as the non-bound analog is removed after the preloading. However, nucleotide exchange can also be performed without preloading with careful optimization of the luminescent analog concentration.

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10. Mant-GDP/mant-GTP are the traditionally used luminescent analogs in which the Mant-label is conjugated to 20 -/30 -positions in ribose (Fig. 1a) [14]. However, 20 - and 30 -isomers are not functionally identical, and thus 20 -deoxy derivative, in which the Mant-label is conjugated unambiguously to the 30 -position of GTP or GDP (mdGDP), can improve the quality of the results especially in kinetic mode [15]. Ras-binding BODIPY-FL-GDP and BODIPY-TR-GTP are luminophore-conjugated also at 20 -/30 -positions. Unbound BODIPY-guanine nucleotides are internally quenched, and upon binding to some GTPases, e.g., Ras, a large increase in the fluorescent quantum yield occurs, which is monitored as increased signal (Fig. 1b). Due to internal quenching, BODIPY-analogs possess lower background signal compared to Mant-analogs, and thus signal-to-background (S/B) ratio is improved. Also, some other analogs such as Tamra-GTP have been introduced [2, 16]. 11. Small molecules affecting Ras activity should preferably be in 100% DMSO to secure the solubility and functionality. In the assay, DMSO in all wells should be adjusted to the same concentration, usually approximately 1%, to enable direct comparison between treated and non-treated Ras. Too high DMSO concentration will have an effect on signal, and DMSO concentration should not exceed 10% in any point of the assay to avoid protein denaturation. 12. In most of the Ras assays, black polystyrene 384-well microplates are preferred, but assays can be performed also in 96-well plates or in cuvette. Plates are well suited for end-point assays and monitoring of slow-rate kinetics (>10 s). Mant-analogs are also used in stopped-flow measurement to monitor extremely fast reactions (1e-8 && normVal>0) imData¼normVal.*diffIm+imData; end imData(~mask)¼0; returnVal¼imData; end

integration of reaction diffusion systems as previously employed [3] to understand how Ras isoforms are maintained at the subcellular locations where they can actuate signal transduction (Fig. 2). In a nutshell, Ras isoforms share a farnesyl moiety the confers a moderate membrane affinity (around a 2:1 partitioning between endomembranes and the soluble pool). This lipid anchor can be shielded by binding to a GDI-like solubilization factor (GSF) such as PDEδ, to enhance the amount of soluble Ras [8]. This dimerization can be countered by interaction with Arl2 in its “active” GTP-bound form, which releases the farnesyl moiety and allows for a higher probability of binding to membranes [9]. However, the surface area of endomembranes available for binding far outweighs the target area that needs to be reached, i.e., inner leaflet of the plasma membrane in the case of KRas. This protein has a C-terminal polybasic stretch in its KRas4B variant that decreases the effective

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cell geometry template for simulation

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secretory pathway

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Fig. 2 Left: Scheme of the Ras localization mechanism. Ras at the plasma membrane is redistributed to endomembranes via endocytic processes and dissociation. A significant fraction is solubilized via a GSF (e.g., PDEδ) and displaced from the GSF predominantly in the perinuclear area by Arl2 activity. Here, high chance to get kinetically or electrostatically trapped the Golgi or recycling endosome, respectively, allows vesicular transport directed at the plasma membrane to reinstate enrichment there. Right: Template for the geometry in which reaction-diffusion simulations are performed

dissociation rate from negatively charged membranes, such as the inner leaflet of the plasma membrane. One can demonstrate, however, that even a substantial decrease in dissociation cannot outcompete the availability of endomembrane surface area [3]. Additionally, endocytic processes actively redistribute parts of the plasma membrane, especially in epithelial cell lines like MDCK cells that have a higher prevalence for secretory activity. To counter this redistribution activity, a localization mechanism must necessarily also be energy-consuming. For KRas this takes three forms: (1) Arl2-GTP is localized in the perinuclear region in an out-ofequilibrium enrichment, where (2) it can interact with KRas-PDEδ dimers to actively promote dissociation of these complexes. As a result, the prenyl moiety of KRas can either rebind PDEδ or interact with perinuclear membranes. Among these, the recycling endosome shares a negative surface charge with the plasma membrane [10]. This enables KRas to become trapped at the recycling endosome by electrostatic interaction, which maintains (3) directed vesicular transport toward the plasma membrane. As PDEδ unspecifically binds farnesylated proteins, this generic mechanism also works for palmitoylated HRas and NRas and Rheb. HRas and NRas require reversible palmitoylation to increase membrane residence time [11]. As the palmityl transferases are localized at the Golgi apparatus, HRas and NRas can utilize its active vesicular transport toward the plasma membrane to generate an

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out-of-equilibrium enrichment there, which is sufficiently fast for twice-palmitoylated HRas to be localized in amounts comparable with KRas, while NRas has only one palmitoylatable cysteine and is therefore less highly concentrated [12]. If palmitoylation was irreversible, N-/HRas would slowly equilibrate to all membranes, as lipidation just confers unspecific membrane affinity. Endocytosis would tremendously increase the speed of this re-equilibration. Instead, palmitoyl-protein thioesterase activity removes this lipid moiety [13], which not only speeds up equilibration but facilitates interaction with PDEδ. In this rapidly diffusing mode of depalmitoylated Ras, interaction and re-palmitoylation at the Golgi then constitutes a kinetic trap [14]. For KRas, this step is passive, as endocytic vesicles soon lose their negative surface charge, which increases KRas dissociation from membranes and its interaction with PDEδ to match that of depalmitoylated N-/HRas. For Rheb, the lack of a secondary membrane-association motive means a stronger enrichment on perinuclear membranes, where it has easy access to bind to the mTor complex 1 on perinuclear lysosomes [15]. It was also shown that proto-oncogenic tyrosine kinases like the polybasic stretch containing Src or palmitoylated Fyn utilize analogous mechanisms for maintaining their enrichment at the plasma membrane based on a different solubilizer Unc119 that binds the N-terminal myristoyl group [16].

4

Disrupting the KRas Localization Cycle A model describing the mechanism described above for maintaining a significant population of KRas at the plasma membrane is comprised of many parameters: association/dissociation rate constants, diffusion coefficients, catalytic activities, protein concentrations, and more. To ensure that this many parameters represent the mechanism and not metaphorically fit an elephant [17], one should try to build such a model piece-by-piece and independently measure, constrain, or confirm these parameters. Having access to an in silico representation allows to independently vary each aspect and iteratively improve one’s understanding of the system, which aids in the subsequent design of more meaningful experiments. Indeed, it is intuitively clear that depriving the Ras localization mechanism of its solubilizer should result in a lot of mislocalized KRas on endomembranes. By changing the corresponding parameters in the simulation, this can be confirmed (Fig. 3a, upper branch), while a downregulation of PDEδ chemically or genetically is a much more difficult endeavor. Similarly, the simulation confirms that a lack of Arl2GTP interaction to displace Ras from PDEδ will keep KRas locked in solubilized form (Fig. 3a, lower branch). A third pillar that can be disrupted would be the perinuclear activity of the Arl2 GTP/GDP hydrolysis/exchange cycle (Fig. 3a, middle branch).

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b

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no Arl2 rate of endocytosis

Fig. 3 (a) Starting from the working KRas localization mechanism (left), three aspects are inhibited to verify a mislocalization of KRas: removal of PDEδ (top, red arrow, and graph), mislocalization of Arl2 from perinuclear membranes to the cytosol (middle, blue arrow, and graph), and removal of Arl2 (bottom, green arrow, and graph). The three graphs demonstrate the reinstating of KRas localization under a decrease of the rate of endocytosis by a factor of 10 (Right: final steady-state distribution of KRas in the cell). (b) Varying the concentration of PDEδ or the general diffusive mobility of all cytosolic species in the simulation by 3–4 orders of magnitude around the measured values shows the effect on KRas localization at the plasma membrane

Expressing a mutated variant Arl2Q70L, which is a constitutively active form whose activity is not restricted to the perinuclear area, indeed shows a reduction in plasma membrane enrichment [3]. As our simulations confirm by varying the respective parameters, the latter case shows the weakest effect in redistributing KRas and therefore appears to be least suited as a pharmacological target, like its process of becoming GTP-loaded. However, one can push the predictive power of our simulation further and test which parameter would be best suited to “rescue” these mislocalized phenotypes. Slowing down endocytosis turns out to be surprisingly effective in restoring KRas localization (Fig. 3a). If a cell were to maintain a defined shape and therefore constant plasma membrane surface area, any excess surface that arises from exocytic events must be balanced by constitutive endocytosis. It stands to reason that cells with a less active secretory pathway would also have a reduced level of constitutive endocytosis. Even though inhibiting PDEδ by competitive binding of a small molecule to the farnesyl binding pocket was a feasible and successful endeavor [18], these simulations predict that this should hardly affect KRas localization in cell types with a naturally slower balance of endocytosis and exocytosis. On the other hand, in pancreatic cell lines with their high prevalence of KRas mutations (such as human pancreatic ductal adeno carcinoma [19], hPDAC), secretion is a major purpose of the tissue and results in an elevated vesicular turnover of the plasma membrane. As a result, the KRas localization cycle is more essential in such cell types and therefore constitutes an important pharmacological target. As one of the hallmarks of cancer, metastatic behavior relies on an elevated migratory behavior that is also linked to Ras at the plasma membrane [20] and similarly requires increased

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vesicular dynamics, which should make them susceptible to interference with Ras spatial cycles to affect oncogenic signaling. In the absence of endocytosis, the KRas localization mechanism can increase the amount of KRas at the membrane to levels much higher than the typically observed ~10:1 ratio. A variation of single parameters confirmed that no other aspect of the mechanism can achieve a similar effect. For example, simply increasing the intracellular concentration of PDEδ hardly improves the levels of KRas at the plasma membrane, as PDEδ is close to saturating the cycle and interaction with Arl2 and vesicular transport from the recycling endosome are bottlenecks in this situation. However, an experimentally almost impossible variation of the diffusion coefficient for all cytosolic proteins yields a surprising result. In case of faster diffusion, one could assume more mislocalization of KRas, but the increased opportunity to become electrostatically trapped at the recycling endosome allows for a roughly 20% increased level of KRas at the plasma membrane (Fig. 3b). Both perturbations clearly show that reducing the diffusive exploration of the cell and the mobility in the cytoplasm counteracts enrichment of KRas at the plasma membrane. In that scenario, only a tiny fraction of KRas could be activated in case of an extracellular signal, and the lack of binding opportunities of KRas effectors could interfere with proliferative or survival signaling. The aforementioned competitive binding of a small molecule to PDEδ in an attempt to interfere with KRas-dependent signaling gave rise to another surprise: earlier iterations of inhibitors were shown to bind to their target with an in-cell KD of ~40 nM [5]. However, long-term treatment of cells required much higher doses (~5 μM), before the pharmacological interference with PDEδ had a measurable effect on proliferative or survival signaling to reflect as a change in growth rates. To shine light on this seeming contradiction, the CA simulations can also be utilized to “measure” titration curves and analyze the effect of increasing levels of inhibitor in the cell. While an inhibitor affinity for PDEδ of 1 μM also shows an IC50 of roughly 1 μM, improving the affinity of the inhibitor by 2 orders of magnitude only results in an IC50 of ~100 nM in accordance with the in vivo findings (Fig. 4a). This result was obtained assuming that Arl2GTP does not displace the inhibitor from PDEδ. Adding this feature to the simulation results in a further increase of IC50 to almost 0.5 μM (Fig. 4b). A possible explanation for this effect is underscored by simulation as well: the concentration of free PDEδ is highest in the perinuclear area, where intense membrane hopping of KRas enables an efficient kinetic trapping at the recycling endosome, even if more than 80% of PDEδ is occupied by the inhibitor (Fig. 4c). Recently, a picomolar affinity inhibitor was developed which appears to bind tightly enough so that Arl2 cannot displace it from PDEδ anymore [21]. Comparatively looking at the mislocalization of Rheb and

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Fig. 4 (a) Titration curves of normalized KRas localization under the influence of a competitive PDEδ inhibitor with different affinities (blue, KD ¼ 1 μM; red, KD ¼ 10 nM). (b) Expanding the model to allow the release of the inhibitor from PDEδ by Arl2GTP results in an even worse IC50 for the higher affinity inhibitor (red, KD ¼ 10 nM, same graph as in a; blue, KD ¼ 10 nM with Arl2GTP-mediated release of the inhibitor). (c) Top: Comparing the efficacy of the KD ¼ 1 μM inhibitor on KRas and Rheb in the same cell as titration curves (blue, KRas, same as in a; red, Rheb; green, mean fraction of PDEδ-KRas complex at the different inhibitor concentrations; magenta, same as the green graph, but for PDEδ-Rheb complex; y-axis labels for blue/red graph on the left; y-axis labels for green/magenta on the right). Bottom: exemplary images of protein distribution at the indicated concentrations (gray arrows). Top row, KRas; middle, Rheb; bottom, free PDEδ (ranging from 75% white to 0% black)

KRas (by simulating them at the same time in the same cell) offers a clue as to why the inhibitor is unexpectantly effective for some cell types. For an 1 μM inhibitor that disrupts KRas localization by 50% at 1 μM concentration, the IC50 of Rheb is already 200 nM, even though almost twice as much PDEδ is concurrently loaded with Rheb than with KRas. The reason for this imbalance in PDEδ binding is that KRas has a reasonable chance to become trapped at the recycling endosome, while no such trapping occurs for Rheb. This lack translates to the finding that even though twice as much Rheb is in complex with PDEδ and therefore should get enriched on perinuclear membranes by being displaced by Arl2GTP, this additional work by the PDEδ-Arl2 mechanism fails to generate a local perinuclear concentration for Rheb but is sufficient for KRas, which sustains its out-of-equilibrium localization even for low availability of PDEδ.

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Discussion Work in a molecular biology lab is becoming increasingly interdisciplinary and relies more and more on computation and integration of knowledge from diverse fields. This chapter introduces a readily available approach to implement reaction-diffusion simulation as a data-analysis tool, as well as a hypothesis testing sand box for experimental design and gaining deeper insights into the data provided by experiments. Informing one’s intuition with simulations can also lead to recursively optimizing one’s experiments: better insight into the data will prompt the questions that lead to the next round of improved experiments. In preparation of experiments, simulations can be used to test whether, e.g., a pharmacological interference with an aspect of a localization mechanism has a chance of a phenotypic effect or at what range concentration interfering with protein interactions should be tested. It was also elucidated that simulations can help to identify which parts of a reaction-diffusion system are the most sensitive to interfere with, meaning be the least invasive dose that has the largest change in behavior. Even though computational resources are a limiting factor, this type of simulation is modular and can be extended from a well-characterized model to more in-depth systems. One area of current investigation in the context of oncogenic Ras-driven malignancy is the dose-dependent interaction between wild-type and oncogenic Ras at the plasma membrane that affects its collective signalling activty. This will build on the running Ras localization cycle that interacts with downstream effectors to be simulated as well. Simulations will give a valuable insight on whether pharmacologically limiting the amount of Ras at the plasma membrane can stop RasGTP driven survival signaling in oncogenic Ras addicted cancer cells.

6

Notes 1. To maintain a constant sum of all image pixel values, image filtering kernels are normalized so that the sum of all elements is 1. This means that the width σ of the Gaussian kernel values is not exactly proportional to the diffusion coefficient D. This would only be true for very large kernel sizes that impact the speed of filtering. Typically, the size of the kernel is approximated by rounding up 2·σ as the half-width or n ¼ 2·ceil (2·σ) + 1. Due to this normalization, a 3  3 filter for σ < 0.3 produces the same result as no filtering. 2. For large kernels it can be extremely beneficial not to use the naı¨ve method of calculating the weighted average of each pixel in its neighborhood. Instead, more advanced functions—like

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“imgaussfilt” of the commercial software MATLAB—convolve the kernel with the image. This is realized by converting the spatial information of an image to the frequency domain via Fourier transformation, multiplying with the Fourier transform of the kernel and back-transforming the result into an image. The computational cost of this approach does not depend on the size of the kernel. 3. For simplicity and due to the timescales involved, Euler’s method of integration is acceptable to use in these examples. Nonetheless, it is straightforward to implement more complicated integration schemes, and the reader is encouraged to do so. In more complicated examples, not only accuracy but also numerical stability must be considered. By verifying that the result of the simulation does not change upon varying Δt (necessitating an adjustment of reaction and diffusion parameters), one can ensure that it is appropriate to use simplistic approaches. 4. GNU OCTAVE (https://www.gnu.org/software/octave) is a freely redistributable, modular software available for multiple platforms that is continually improved by a centrally maintained plethora of “packages” of code to extend its base functionality. Due to its compatibility with Mac OS X, the older version OCTAVE 4.4.1 was used for this publication. In order to utilize image filtering functions, the following command must be executed once: “pkg install -forge image,” in order to download and install that package. Line 1 in the scripts (Tables 1 and 2) has to be executed once whenever OCTAVE is freshly started but is included in the scripts to avoid an error message. OCTAVE developers strive to maintain script compatibility with MATLAB. Indeed, all scripts in this chapter can also be executed in MATLAB after removing the first line and have been tested with version R2019b but require the IMAGE PROCESSSING TOOLBOX to be installed. 5. Line-by-line explanation of the commands: (1) Loading of the “image” package containing commands “imfilter” and “fspecial”. (2) Defining the value of σ ¼ 0.5 in units of pixels. (3) Generating a normalized 3  3 Gaussian kernel of width σ ¼ 0.5. (4) Defining constraints of the simulation: maximum number of iterations; sizes of the image; and x-position of the membrane. (5) Defining dynamic parameters of membrane association and dissociation for a 2:1 membrane partitioning.

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(6) Initializing the protein concentration in solution and at the membrane. (7) A loop counting from 1 to the maximum number of iteration encapsulating the actual four lines of simulation code. (8)–(9) Calculating the symmetric change of concentrations as the difference between association rate and dissociation rate at the membrane pixels. (10) Subtracting this change from the concentration of soluble protein and (11) Adding it to the concentration at the membrane. (12) Computing the diffusion of the soluble concentration by filtering the image with the previously defined Gaussian kernel. (13) End of the for-loop beginning in (7). (14)–(16) Sample code to display the result of the simulation. 6. This normalization is only correct for a homogenously filled area. If the area has varying concentrations, an approximation can be utilized to correct for that: (a) the total amount of diffusive loss across the boundary is computed; (b) the difference due to normalization is calculated; and (c) this difference is normalized to match the result of (a) and added to the uncorrected result of diffusion. This approximation is reasonably accurate, conserves the total concentration, and is much faster to calculate than tracking the number and position of each individual pixel’s neighborhood. References 1. Hancock JF, Paterson H, Marshall CJ (1990) A polybasic domain or palmitoylation is required in addition to the CAAX motif to localize p21ras to the plasma membrane. Cell 63 (1):133–139. https://doi.org/10.1016/ 0092-8674(90)90294-o 2. Yeung T, Gilbert GE, Shi J, Silvius J, Kapus A, Grinstein S (2008) Membrane phosphatidylserine regulates surface charge and protein localization. Science 319(5860):210–213. https://doi.org/10.1126/science.1152066 3. Schmick M, Vartak N, Papke B, Kovacevic M, Truxius DC, Rossmannek L, Bastiaens PIH (2014) KRas localizes to the plasma membrane by spatial cycles of solubilization, trapping and vesicular transport. Cell 157(2):459–471. https://doi.org/10.1016/j.cell.2014.02.051 4. Prior IA, Lewis PD, Mattos C (2012) A comprehensive survey of Ras mutations in cancer.

Cancer Res 72(10):2457–2467. https://doi. org/10.1158/0008-5472.CAN-11-2612 5. Zimmermann G, Papke B, Ismail S, Vartak N, Chandra A, Hoffmann M, Hahn SA, Triola G, Wittinghofer A, Bastiaens PI, Waldmann H (2013) Small molecule inhibition of the KRAS-PDEdelta interaction impairs oncogenic KRAS signalling. Nature 497(7451):638–642. https://doi.org/10.1038/nature12205 6. Saxton RA, Sabatini DM (2017) mTOR signaling in growth, metabolism, and disease. Cell 168(6):960–976. https://doi.org/10.1016/j. cell.2017.02.004 7. Atkinson KE (1978) An introduction to numerical analysis. Wiley, New York 8. Chandra A, Grecco HE, Pisupati V, Perera D, Cassidy L, Skoulidis F, Ismail SA, Hedberg C, Hanzal-Bayer M, Venkitaraman AR, Wittinghofer A, Bastiaens PI (2011) The

Understanding Ras Spatial Cycles Through Reaction-Diffusion Simulations GDI-like solubilizing factor PDEdelta sustains the spatial organization and signalling of Ras family proteins. Nat Cell Biol 14(2):148–158. https://doi.org/10.1038/ncb2394 9. Ismail SA, Chen YX, Rusinova A, Chandra A, Bierbaum M, Gremer L, Triola G, Waldmann H, Bastiaens PI, Wittinghofer A (2011) Arl2-GTP and Arl3-GTP regulate a GDI-like transport system for farnesylated cargo. Nat Chem Biol 7(12):942–949. https://doi.org/10.1038/nchembio.686 10. Chen B, Jiang Y, Zeng S, Yan J, Li X, Zhang Y, Zou W, Wang X (2010) Endocytic sorting and recycling require membrane phosphatidylserine asymmetry maintained by TAT-1/CHAT1. PLoS Genet 6(12):e1001235. https://doi. org/10.1371/journal.pgen.1001235 11. Hancock JF, Magee AI, Childs JE, Marshall CJ (1989) All ras proteins are polyisoprenylated but only some are palmitoylated. Cell 57 (7):1167–1177. https://doi.org/10.1016/ 0092-8674(89)90054-8 12. Schroeder H, Leventis R, Rex S, Schelhaas M, Nagele E, Waldmann H, Silvius JR (1997) S-acylation and plasma membrane targeting of the farnesylated carboxyl-terminal peptide of N-ras in mammalian fibroblasts. Biochemistry 36(42):13102–13109. https://doi.org/10. 1021/bi9709497 13. Camp LA, Hofmann SL (1993) Purification and properties of a palmitoyl-protein thioesterase that cleaves palmitate from H-Ras. J Biol Chem 268(30):22566–22574 14. Rocks O, Gerauer M, Vartak N, Koch S, Huang ZP, Pechlivanis M, Kuhlmann J, Brunsveld L, Chandra A, Ellinger B, Waldmann H, Bastiaens PI (2010) The palmitoylation machinery is a spatially organizing system for peripheral membrane proteins. Cell 141(3):458–471. https://doi.org/10.1016/j. cell.2010.04.007 15. Sancak Y, Bar-Peled L, Zoncu R, Markhard AL, Nada S, Sabatini DM (2010) Ragulator-Rag

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complex targets mTORC1 to the lysosomal surface and is necessary for its activation by amino acids. Cell 141(2):290–303. https:// doi.org/10.1016/j.cell.2010.02.024 16. Konitsiotis AD, Rossmannek L, Stanoev A, Schmick M, Bastiaens PIH (2017) Spatial cycles mediated by UNC119 solubilisation maintain Src family kinases plasma membrane localisation. Nat Commun 8(1):114. https:// doi.org/10.1038/s41467-017-00116-3 17. Mayer J, Khairy K, Howard J (2010) Drawing an elephant with four complex parameters. Am J Phys 78(6):648–649. https://doi.org/10. 1119/1.3254017 18. Klein CH, Truxius DC, Vogel HA, Harizanova J, Murarka S, Martin-Gago P, Bastiaens PIH (2019) PDEdelta inhibition impedes the proliferation and survival of human colorectal cancer cell lines harboring oncogenic KRas. Int J Cancer 144 (4):767–776. https://doi.org/10.1002/ijc. 31859 19. Cox AD, Fesik SW, Kimmelman AC, Luo J, Der CJ (2014) Drugging the undruggable RAS: mission possible? Nat Rev Drug Discov 13(11):828–851. https://doi.org/10.1038/ nrd4389 20. Devreotes P, Horwitz AR (2015) Signaling networks that regulate cell migration. Cold Spring Harb Perspect Biol 7(8):a005959. https://doi.org/10.1101/cshperspect. a005959 21. Martin-Gago P, Fansa EK, Klein CH, Murarka S, Janning P, Schurmann M, Metz M, Ismail S, Schultz-Fademrecht C, Baumann M, Bastiaens PI, Wittinghofer A, Waldmann H (2017) A PDE6delta-KRas inhibitor chemotype with up to seven H-bonds and picomolar affinity that prevents efficient inhibitor release by Arl2. Angew Chem Int Ed Engl 56(9):2423–2428. https://doi.org/10.1002/anie.201610957

Chapter 12 Super-Resolution Imaging and Spatial Analysis of RAS on Intact Plasma Membrane Sheets Yong Zhou and John F. Hancock Abstract The function of lipid-anchored small GTPases RAS proteins is mostly compartmentalized to the plasma membrane (PM). Complex biophysical interactions between the C-terminal membrane-anchoring domains of RAS isoforms and PM lipids drive spatial segregation of RAS molecules in the formation of nanometersized domains, termed as nanoclusters. These RAS/lipid proteolipid nano-assemblies are the main sites for efficient effector recruitment and signal transduction. Here, we describe a super-resolution imaging method to quantify the nanometer-sized nanoclustering of RAS over a length scale between 8 and 240 nm on intact PM sheets of mammalian cells. Detailed molecular spatial distribution parameters, including the extent of nanoclustering, average cluster size, clustered fraction, and population distribution can be obtained by the univariate spatial distribution analysis. Intermolecular associations between different RAS isoforms, RAS and various PM lipids, as well as RAS and diverse effectors can be quantified via bivariate co-localization analysis. Key words Electron microscopy, Spatial distribution, Small GTPases, RAS, Gold nanoparticles, Nanoclusters, Ripley’s K function

1

Introduction RAS proteins, including HRAS, NRAS, and splice variants KRAS4A and KRAS4B, must localize to the plasma membrane (PM) for efficient signaling and function [1–4]. The C-terminal membrane-anchoring domain of each RAS isoform contains a two-factor signal to anchor to the PM. In addition to a C-terminal farnesyl chain commonly shared by all RAS isoforms, HRAS has two more palmitoyl chains attached to Cysteine 181 and Cysteine 184, NRAS has one palmitoyl chain attached to Cysteine 181, KRAS4B has a hexa-lysine polybasic domain (lysines 175–180), while KRAS4A has a mixed polybasic domain composed of lysines and arginines as well as a palmitoyl chain attached to Cysteine 180 [2, 5, 6]. The distinct RAS membrane anchors interact with select PM lipids, which drives the spatial distribution of

Ignacio Rubio and Ian Prior (eds.), Ras Activity and Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2262, https://doi.org/10.1007/978-1-0716-1190-6_12, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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RAS on the PM and the formation of nanometer-sized domains called nanoclusters [5–9]. Most RAS effectors contain specific lipidbinding domains [10]. RAS nanoclusters are important for RAS function because efficient effector recruitment requires synergistic association of active GTP-bound RAS and specific PM lipids [5, 6]. For instance, signaling of a constitutively active mutant KRAS.G12V depends on the clustered fraction of KRAS.G12V on the PM, without changing total levels of the GTP-bound KRAS. G12V in cells and on the PM [11]. Thus, it is important to characterize RAS nanoclustering on the PM for better understanding RAS function. We now describe a super-resolution quantitative imaging technique that calculates the immunogold-labeled GFP-tagged RAS on intact PM sheets [11–15].

2

Materials

2.1 Solutions for Generating Gold Nanoparticles

1. 1% Trisodium citrate: 0.5 g of sodium citrate in 50 ml of deionized water (stored at room temperature). 2. 1% Tannic acid: 0.5 g Aleppo tannin in 50 ml of deionized water (stored at room temperature). 3. 25 mM Potassium carbonate: 0.173 g of potassium carbonate in deionized water (stored at room temperature). 4. 1% Gold chloride ! 1 g of gold chloride in 100 ml deionized water (stored at 4  C). 5. Reducing solution (a working solution used for generating gold nanoparticles): dissolve 1% trisodium citrate, 1% tannic acid, and 25 mM potassium carbonate into deionized water to reach a final volume of 10 ml. Different amount of the stock solutions will be needed for different sized gold nanoparticles (see Table 1 for the exact amount, and see Note 1 for molar ratios of tannic acid/gold chloride). 6. Gold solution (working solution) ! combining 0.5 ml of 1% gold chloride with 39.5 ml deionized water. 7. 0.5 M NaOH solution: 0.99 g of NaOH in 50 ml of deionized water (stored at room temperature).

2.2 Solutions for PM Sheet Preparation

1. Stock solution of bovine serum albumin (BSA at 10%): 2 g of BSA in 20 ml of PBS (stored at 4  C). 2. Working solution of BSA (0.1%): 0.5 ml of 10% BSA diluted with PBS to reach a final volume of 50 ml. 3. Glycerol solutions (for establishing a gradient, 10–40%): different volumes of glycerol (see Table 2) and 50 μl BSA (0.1%) to reach a final volume of 5 ml.

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Table 1 Amounts of tannic acid and potassium carbonate vary for different sized gold nanoparticles Gold nanoparticle diameter (nm)

2–3

4

4.5

5.5

6

7.5

1% Tannic acid (ml)

2.5

1.25

0.75

0.5

0.25

0.13

25 mM Potassium carbonate (ml)

2.5

1.25

0.75

0.5

0.25

0.13

Tannic acid/gold chloride ratio

1

0.5

0.33

0.2

0.1

0.05

Table 2 Different amounts of glycerol is needed to established a proper glycerol gradient for further refining the gold particle sizes % Glycerol

10

15

20

25

30

35

40

PBS (ml)

4.45

4.2

3.95

3.7

3.45

3.2

2.95

10% BSA (ml)

0.05

0.05

0.05

0.05

0.05

0.05

0.05

Glycerol (ml)

0.5

0.75

1

1.25

1.5

1.75

2

4. Methyl cellulose (2%): 2 g of methyl cellulose in 98 ml of deionized water (stored at 4  C). 5. KOAc buffer (10, stock solution): dissolve 11.92 g HEPES, 22.58 g potassium acetate, and 1.02 g magnesium chloride deionized water to reach a final volume of 180 ml (stock solution stored at 20  C). 6. Fixative: 2 ml of 16% paraformaldehyde, 32 μl of 25% glutaraldehyde, and 0.8 ml of 10 KOAc stock combined with 5.17 ml of deionized water (stored at 4  C in the dark). 7. Glycine (25 mM): dissolve 93.8 mg glycine in 1 PBS to reach a final volume of 50 ml. Aliquots of 350 μl stored at 20  C. 8. Fish skin gelatin (10%): slowly dissolve 3 ml of 45% commercial fish skin gelatin solution in 11.5 ml of PBS and stored at 4  C. 9. Blocking solution: combine 100 μl of 10% fish skin gelatin, 100 μl of 10% BSA, and 9.6 ml of 1 KOAc buffer. 10. Uranyl acetate (3%): dissolve 1.5 g of uranyl acetate deionized water to reach a final volume of 50 ml of and stored at 4  C. 2.3 Preparation of Copper and Gold EM Grids 2.3.1 Pioloform Coating

1. Dissolve 0.4 g of pioloform powder in 50 ml of EM-grade chloroform and shake well to mix. 2. Add the pioloform/chloroform solution to a 25-ml cylindrical separatory funnel to half-full and place a clean glass slide into the separatory funnel and half-submerged in the pioloform/ chloroform solution.

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3. Drain the pioloform/chloroform solution out of the funnel to allow thin films of pioloform to form on either side of the glass slide. 4. Cut lines along the edges of the pioloform films on the glass slide using a razor blade. 5. Slowly dip the glass slide covered with pioloform just below the surface of deionized water in a small bowl. The pioloform films should slide off the glass slide as the glass slide is slowly dipped into water. 6. With the thin pioloform films floating on the water surface, carefully place copper or gold EM grids onto the pioloform film. EM grids contain two sides: a shiny side and a dull side. Either side can be coated with pioloform. However, to be consistent, only one side should be chosen for all the grids. Approximately 40–50 grids can be placed on each pioloform film. Do not cover the entire pioloform film with grids and leave a section of the film uncovered. 7. Position another glass slide coated with a self-adhesive paper vertically above the uncovered section of the pioloform film. Quickly push vertical glass slide into water and allow the pioloform film (with the coated grids) to adhere to the self-adhesive paper on the glass slide. The pioloform-coated grids are dried overnight at room temperature. The pioloform film can be coated on the grids ahead of time and stored at room temperature for several months (see Note 2). 2.3.2 Poly-L-Lysine Coating

1. Place the pioloform-coated EM grids on a 2 ml droplet of polyL-lysine solution (0.1%), with the pioloform-coated side exposed to the droplet. 2. Incubate the grids on the poly-L-lysine droplet for 5 min; this is followed with two additional steps of washing 2 ml deionized water droplets. 3. The poly-L-lysine-coated grids are dried overnight at room temperature. 4. The poly-L-lysine coating should be applied on the day before the experiments. The pioloform- and poly-L-lysine-coated copper grids can be stored on the bench. Gold grids should be placed in the tissue culture hood overnight under UV light to sterilize. Poly-L-lysine coating should be performing on the day before experiments (see Note 2).

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2.4 Preparation of Antibody-Coated Gold Nanoparticles of Univariate Spatial Analysis

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1. Heat 10 ml of reducing solution (prepared in Subheading 2.1, item 5) and 40 ml of gold solution (prepared in Subheading 2.1, item 6) in separate flasks to ~60  C on a hot plate. Gold particle generation should be conducted in dedicated glassware (see Note 3). For univariate spatial distribution analyses, we typically use 4.5 nm gold particles. For bivariate co-localization, we generate 2 nm and 6 nm gold particles. 2. At 60  C, combine two solutions above into one flask, first vigorously shake the flask with hands to mix the solutions and continue to heat the combined solution to boiling and maintain boiling for 5 min under constant swirling using stirring bars on a magnetic hot plate. A maroon color indicates proper formation of gold colloid. 3. Place the flask on ice to cool down quickly. 4. Gradually add 0.5 M NaOH solution (first adding ~150 μl and followed by 10 μl increments) to adjust the pH of the gold solution to ~8.5. Take care not to exceed pH of 9. For monitoring the pH values, please use pH paper only, but not pH electrodes because gold nanoparticles damage pH electrodes. 5. Titrate optimal antibody levels by combining varying levels of anti-GFP antibody (0–5 μg) with deionized water for a final volume of 20 μl, which is then mixed with 250 μl of gold solution. To further optimize the labeling efficiency, commercial antibodies can be affinity-purified (see Note 4). 6. After incubation at room temperature for 5 min, add 100 μl of 10% NaCl to the mix. Colors of the solutions will be used to judge optimal antibody concentration, where the lowest antibody concentration to yield a stable blue color indicates the optimal antibody concentration. 7. Based on the titration results, appropriate amount anti-GFP antibody is mixed with gold solution. Typically, approximately 80 μg of antibody is needed for 20 ml of gold solution. 8. Incubate the gold/antibody mix for 10 min at room temperature and add appropriate amount of 10% BSA to reach a final concentration of 0.1%. 9. Centrifuge the gold/antibody solutions at 100,000  g (for gold nanoparticle diameters of 4–6 nm) or 120,000  g (for gold nanoparticle diameters of 2–3 nm) for 1 h at 4  C. 10. Immediately after the ultracentrifugation, the antibody-coated gold particles are collected. Inside of each ultracentrifuge tube, two patches of gold pallets are visible: a loose patch located on the lower tube wall (antibody-coated gold) and a solid spherical patch on the bottom of the tube (uncoated gold). Take care to collect only the loose patch without disturbing the solid uncoated gold on the bottom of the tube.

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2.5 Further Refining the Gold Particle Sizes for Bivariate Co-localization Analysis

For bivariate co-localization analysis, GFP and RFP on intact PM sheets will be co-labeled with two populations of gold nanoparticles, respectively. Thus, the requirement for accurate gold particle size is stricter for better differentiation of the two gold populations during analysis. 1. After the first round of ultracentrifugation as described in the above section, the collected gold particles are loaded on top of a glycerol gradient (40% to 10%) in 5 ml ultracentrifuge tubes (see Table 2). 2. Centrifuge the gold particles on the glycerol gradient at 100,000  g (for 6 nm gold) or 120,000  g (for 2–3 nm gold) for 1 h at 4  C. 3. Portions of gold particles in the top half of the glycerol gradient are collected for analysis. 4. To carefully calibrate the gold size distribution, pipette 10 μl of the gold solution onto a Parafilm sheet, and place a pioloformand poly-L-lysine-coated EM copper grid on the droplet. Incubate 5 min and let dry for at least 30 min before EM imaging. 5. Open the EM images in ImageJ and count the number of gold particles as a function of pixel sizes of gold particles. 6. For both univariate and bivariate analyses, the antibody-coated nanoparticles themselves should not cluster. For further verification of the newly generated antibody-coated gold particles, see Note 5.

3

Methods

3.1 Apical and Basolateral PM Rip-Off and Immunogold-Labeling

For apical PM sheet preparations, either copper or gold grids can be used. On the other hand, only gold grids can be used for basolateral PM sheets rip-off. This is because copper is toxic to cells that should be seeded on the grids for attaching basolateral PM sheets to the grids. The initial steps in Subheadings 3.1.1 and 3.1.2 differ slightly between the apical and basolateral PM rip-off techniques, while the following steps (Subheading 3.1.3 and after) are identical for both preparations. Additionally, the isolated apical and basolateral PM sheets attached to the copper/gold grids will be treated with a series of droplets of reagent solutions placed on a clean Parafilm sheet.

3.1.1 Apical PM Rip-Off

1. Cells transiently or stably expressing a GFP-tagged RAS construct are seeded on glass coverslips to reach a desired confluency (see Note 6). 2. After washing 2 with PBS, two pioloform- and poly-L-lysinecoated copper EM grids are placed over the cells, with the pioloform and poly-L-lysine coating facing the apical surfaces of the cells.

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3. Firm attachment of the apical PM onto the copper grids is achieved via pressing the grids down onto the cells using a rubber bung. 4. 100 μl 1 KOAc solution is added on the glass coverslip around the copper EM grids. Surface tension of the KOAc solution pops the copper grids along with the attached apical PM off the glass coverslip, thus separating the apical PM from the rest of the cells. 3.1.2 Basolateral PM Rip-Off

1. Basolateral PM sheets are usually collected for studying RAS spatial distribution on the basolateral PM of polarized cells. Before seeding cells, carefully position the pioloform- and polyL-lysine-coated gold EM grids on the bottom of a 3.5 cm tissue culture dish. Ensure that the pioloform- and poly-L-lysinecoated side of the grids face up. 2. Seed cells directly in the dish over the gold EM grids. Take care not to disturb the grids on the bottom of the dishes. 3. When cell density reaches a desired confluency (see Note 6), carefully pick up the gold grids with a pair of tweezers and wash the gold EM grids 2 with PBS. 4. Place the gold grids on a piece of clean filter paper, and cover the grids with a piece of filter paper pre-wetted with PBS solution. 5. Slowly remove the wet filter paper to peel off the remaining of the cells, thus leaving only the basal PM attached to the gold EM grids. 6. Place a PBS-wetted glass coverslip over the gold EM grids and press down on the coverslip using a rubber bung to better remove endomembrane material from the basolateral PM sheets attached to the gold grids.

3.1.3 The Following Fixation and Labeling Procedures Are Identical for Both Apical and Basolateral PM Sheets Prepared Above and Will be Conducted on a Clean Parafilm Sheet as a Clean Work Surface (See Note 7)

1. Place EM grids on a droplet of 50 μl fixative (prepared in Subheading 2.2) for 10 min. 2. Position the EM grids over a piece of clean filter paper at a 45 angle to carefully blot off the fixative from the grids. Careful not to allow the EM grids to directly touch the filter paper to avoid deforming the grids. 3. Wash the grids by placing them on a droplet of 100 μl PBS for 5 min. 4. Quench the intact PM sheets on the EM grids by placing them consecutively on three 100 μl droplets of 1 glycine solution diluted in 1 KOAc (prepared in Subheading 2.2) for 5 min each. 5. Place the EM grids on a droplet of 100 μl blocking solution (prepared in Subheading 2.2) for 20 min.

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6. Position the EM grids on a small droplet of 10 μl antibodycoated gold nanoparticles for 30 min. The stock antibodycoated gold solutions are typically very concentrated (deep maroon in color). For efficient immunogold-labeling, the stock gold solutions should be diluted using the blocking solution (prepared in Subheading 2.2). Typical dilution factors should be between 10- and 40-fold. 7. For univariate analysis, one round of 30 min incubation with 4.5 nm anti-GFP gold is needed. 8. For bivariate experiments, the EM grids are first placed over a 10 μl droplet containing large 6 nm gold conjugated with antiGFP antibody, followed with incubation with blocking solution for another 5 min and a second round of 30 min incubation with 2 nm small gold coated with anti-RFP antibody. 9. Place the grids on five consecutive 100 μl droplets of blocking solution, each for 5 min. For optimal blocking to avoid nonspecific gold-labeling, the copper/gold grids should be blotted with clean filter papers (see Note 8). 10. Position the grids on five consecutive 100 μl droplets of deionized water. The grids can be moved from one water droplet to the next without spending time for incubation. Ensure to thoroughly rinse the handling tweezers with deionized water between each move to eliminate salts left on the tweezers. Complete elimination of possible salts on the grids is important to avoid potential precipitation of uranyl acetate in the following step (see Note 9). 11. Move grids onto 100 μl uranyl acetate droplet (10:1 dilution using methyl cellulose) on ice for 9 min. Ensure to blot the residual water off the grids before placing them on the uranyl acetate droplet. Extra care should be taken when handling uranyl acetate (see Note 10). 12. Use a grid loop to scoop the grids from uranyl acetate droplets and slowly slide the scoop across a piece of filter paper to blot residual uranyl acetate off the grids. 13. Dry the grids on scoops overnight at room temperature. 3.2 TEM Imaging and Spatial Analysis

1. Image EM grids using a transmission EM at the magnification of 100,000 (Fig. 1a, see Note 11). 2. To assign x, y coordinates to gold particles for univariate analysis of the 4.5 nm gold conjugated to anti-GFP antibody, open an electron micrograph in ImageJ, and select a 1 μm2 PM area with even distribution of gold particles by cropping (Fig. 1b). 3. Smoothen the selected 1 μm2 PM area in ImageJ. 4. Subtract background of the smoothened image by using a rolling ball with the radius of 6 pixels.

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Fig. 1 Univariate spatial analysis characterizes the lateral spatial point pattern of the immunogold-labeled RAS on intact PM sheets. (a) An EM image shows a piece of intact PM sheet on a pioloform- and poly-L-lysinecoated copper EM grid. The PM sheet was isolated from baby hamster kidney (BHK) cells expressing GFP-KRAS.G12V and was subsequently immunolabeled with 4.5 nm gold conjugated to anti-GFP antibody. Gold particles were imaged using transmission EM at 100,000 magnification. Yellow arrows point to the edge of the PM sheet, which is extensively gold labeled. On the other hand, the empty grid area not covered by PM sheets is free of gold labeling. A 1 μm2 PM area was cropped for further processing and analysis. (b) The 1 μm2 PM area cropped from above EM image was smoothened and background-subtracted for assigning x,y coordinates for the spatial analysis. (c) The extent of nanoclustering, L(r)  r, was plotted against the length scale, r, in nanometer for the same image shown in a and b. L(r)  r values above the 99% confidence interval (99%C.I.) indicate statistically meaningful clustering. (d) A spatial distribution heat map was generated for the same EM image in a and b and calculation in c. Blue dots indicate monomers; yellow dots indicate dimers; orange dots indicate trimers; red dots indicate higher-ordered oligomers

5. Adjust the threshold function to specifically select the gold particles (see Note 12). 6. The x, y coordinates of each gold particle are assigned by using the Analyze Particle function in ImageJ. Ensure to set the particle size range to 2–80 pixel2. 7. To assign x, y coordinates for two populations of gold particles (6 nm the anti-GFP-coated gold and 2 nm anti-RFP-coated gold) for bivariate analysis, EM images are cropped, smoothened, background-subtracted, and threshold-selected as described (Fig. 2a).

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Fig. 2 Bivariate co-clustering analysis quantifies the spatial pattern of co-localization between dual-goldlabeled constituents on intact PM sheets. (a) An EM image shows a 1 μm2 intact PM area. The PM sheet, isolated from BHK cells co-expressing GFP-LactC2 (a specific phosphatidylserine-binding domain) and RFP-KRAS.G12V, was co-immunolabeled with 6 nm gold-labeling GFP and 2 nm gold-labeling RFP. (b) After processing, x/y coordinates of the gold particles were used to quantify the co-localization between 6 nm and 2 nm gold. Smaller red dots indicate 2 nm gold-labeling RFP; larger black dots indicate 6 nm goldlabeling GFP. (c) The extent of co-clustering, Lbiv(r)  r, was plotted as a function of r in nanometer. Lbiv(r)  r values above the 95%C.I. indicate statistically meaningful co-localization between the two gold populations at the corresponding r values

8. The x, y coordinates of 6 nm big and 2 nm small gold particles are assigned separately by setting distinct particle size ranges (typically 2–15 pixel2 for 2 nm small gold and 22–60 pixel2 for 6 nm small gold, Fig. 2b). The optimal particle size ranges are set based on the gold particle distribution curves generated in Subheading 3.3. 9. Univariate spatial analysis: Ripley’s K function quantifies the nanoclustering of a single species of 4.5 nm gold particles on the intact PM sheets [16] (Eqs. 1 and 2, all macros are available upon request): K ðr Þ ¼ An2

X    w ij 1 x i  x j   r

ð1Þ

i6¼j

rffiffiffiffiffiffiffiffiffiffiffi K ðr Þ L ðr Þ  r ¼ r π

ð2Þ

where K(r) denotes the univariate K function for a single population of gold nanoparticles in a PM sheet, which possesses an area of A and contain a total number of gold particles as n; r indicates the length between two gold particles (with a typical range between 0 and 240 nm); the Euclidean distance, denoted by || . ||, possesses a value of 1 if ||xi  xj||  r and a value of 0 if ||xi  xj|| > r; for edge correction, we draw a circle with xi as the center and ||xi  xj|| as the radius, where a parameter wij1 describes the fraction of the circumference of the circle; and L(r)  r is a standardization of the

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K-function. Monte Carlo simulations then estimate the 99% confidence interval (C.I.). L(r)  r is plotted as a function of r to indicate the extent of nanoclustering as a function of distance (Fig. 1c). As the K function analysis tests a null hypothesis that the point pattern distributes randomly, L(r)  r values above the 99% C.I. indicate statistically meaningful clustering for the corresponding r, while L(r)  r values between 1 and 1 illustrate random distribution. L(r)  r values below 1 represent de-clustering. Either the peak L(r)  r value or integration of the L(r)  r curve can be used to summarize the clustering statistics, as both parameters correlate directly and linearly [14]. Using the resulting K-function analysis, a heat map of the point distribution pattern for the EM images can be generated. Figure 1d illustrates a highly heterogeneous spatial distribution, with the clustering radius set at 15 nm, for the same PM sheet shown in Fig. 1a, b and the quantification shown in Fig. 1c. The extent of nanoclustering is independent of gold labeling densities (see Note 13). Typically, a minimum of 15–20 PM sheets are imaged and analyzed for each condition. To evaluate statistical significance between different conditions, we use bootstrap tests, where replicated point patterns are generated to compare with the sample point patterns [8, 17]. This analysis also estimates the extent of PM localization by counting the number of gold particles in the same 1 μm2 PM area used in the nanoclustering analysis. To distinguish the changes in PM localization and total expression levels of RAS, Western blotting should also be performed to validate RAS total expression level. The statistical significance for the gold labeling density is evaluated via one-way ANOVA. 10. Bivariate K function: a derivative of the above Ripley’s K function quantifies the co-localization of two different populations of gold particles (Fig. 2a, b, 6 nm big gold conjugated to anti-GFP and 2 nm gold coupled to anti-RFP) [12, 13, 18] (Eqs. 3–6, all macros are available upon request): K biv ðr Þ ¼ ðnb þ ns Þ1 ½nb K sb ðr Þ þ ns K bs ðr Þ

ð3Þ

nb X ns    A X wij 1 x i  x j   r nb ns

ð4Þ

nb ns X    A X wij 1 x i  x j   r nb ns

ð5Þ

K bs ðr Þ ¼

i¼1

K sb ðr Þ ¼

i¼1

j ¼1

j ¼1

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi K biv ðr Þ L biv ðr Þ  r ¼ r π

ð6Þ

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where Kbs(r) denotes the distribution pattern of all the 6 nm (b: big) gold particles with respect to a particular 2 nm (s: small) gold particle and Ksb(r) describes the distribution of all the small gold particles with respect to a particular big gold particle. Kbiv(r) is a unified estimator function that combines Kbs(r) and Ksb(r), where nb is the number of big (6 nm) gold and ns is the number of small (2 nm) gold. The remaining notations are identical as described in Eqs. 1 and 2. Lbiv(r)  r is a standardization of Kbiv(r) function, with a 95% confidence interval estimated by Monte Carlo simulations. The bivariate K function tests a null hypothesis that the two populations of point patterns distribute independent of each other. Thus, Lbiv(r)  r values between 1 and 1 confirm this hypothesis. Lbiv(r)  r values above the 95%C.I. indicate statistically meaningful co-clustering. Larger Lbiv(r)  r values indicate more extensive co-localization between the point populations. To better summarize the statistic of co-localization, we integrate the Lbiv(r)  r curves to yield a parameter of L-functionbivariate-integrated (LBI) [13]: Z 110 LBI ¼ Std L biv ðr Þ  r:dr ð7Þ 10

For each condition, a minimum of 15–20 PM sheets are imaged and analyzed. Bootstrap tests described above are applied to evaluate the statistical significance of the bivariate data sets.

4

Notes 1. Tannic acid has been used commonly as a reducing agent in gold nanoparticle synthesis [19, 20]. The molar ratios of tannic acid (M.W. 1701 g/mole)/gold chloride (357.8 g/mole) determine the sizes of the resulted gold nanoparticles. Listed in Table 1, lower tannic acid/gold chloride ratios yield larger gold nanoparticle sizes. 2. Pioloform coating is relatively stable. As such, the pioloformcoated grids can last for at least a few months. However, the poly-L-lysine coating should be conducted on the day before experiment and leave to dry overnight at room temperature to ensure efficient attachment of the intact PM sheets on the EM grids. 3. For making the antibody-coated gold nanoparticles in Subheading 2.4, dedicated glassware, stirring bars, and thermometers should be used to avoid potential cross-contamination of other lab equipment with nanoparticles. 4. We typically use anti-GFP and anti-RFP antibodies that we purify in-house. Although the presence of azide in commercial

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antibodies has been shown to interfere with labeling efficiencies, our latest attempts to directly use commercial antibodies for gold coating have been successful [21, 22]. To further optimize the labeling efficiency, the antibodies can affinity purified. 5. The antibody-coated gold particles alone should not cluster, so as not to artificially influence the spatial distribution analysis. Thus, the distribution of the antibody-coated gold particles alone must be validated before use. An empty pioloform- and poly-L-lysine-coated copper or gold grid (with no PM sheets) is placed on top of a 10-μl droplet of antibody gold solution for 5 min, followed by a 5-min incubation on top of a 100-μl droplet of methyl cellulose. The grid is then scooped out of the methyl cellulose and dried at room temperature for at least 30 min on a grid loop. Once the grid is dry, EM imaging is conducted. The spatial distribution of the gold particle is analyzed as described in Subheading 3.5. The L(r)  r curve should be between 1 and 1 to indicate a randomly distributed point pattern. If L(r)  r value for an antibody-gold preparation is above the 99%C.I. (indicating that some of the gold particles are clustered together), the gold nanoparticles cannot be used for experiments and need to be further processed. (a) To further process the clustered antibody-gold preparation, the gold suspension is combined with 20 ml of 1 KOAC solution containing 0.1% BSA. (b) The suspension is then ultracentrifuged at 100,000  g (for 6 nm gold) or 120,000  g (for 2–3 nm gold) for 1 h at 4  C. (c) The loose gold pallet is collected for another round of imaging and spatial analysis, as described above. (d) The process should be repeated until the spatial analysis yields a randomly distribution to indicate that the antibody-coated gold particles do not cluster themselves and can be used for experiments. 6. Cell confluency is also important for effective rip-off and imaging. For basolateral PM sheet rip-off, a monolayer of polarized cells should be reached. For apical PM sheet rip-off, approximately 80–90% confluency should be optimal. Too few cells should yield not enough PM sheets for imaging. Too many cells will yield too many PM sheets overlapping each other, which will make imaging difficult. 7. The isolated intact PM sheets attached to the copper/gold grids should be treated with reagent droplets placed on a clean Parafilm sheet as a clean workspace (Subheading 3.1). The paper sheet covering the Parafilm sheet should not be removed during the handling of the Parafilm sheet on the

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bench to avoid potential contamination. The paper sheet should be removed only before reagent droplets are ready to be positioned on the Parafilm sheet. Do not touch the Parafilm sheet with fingers to avoid dirt and stain precipitation and background labeling. The Parafilm sheets should not reused. 8. To avoid nonspecific gold labeling on the PM sheets, the blocking treatments before and after gold labeling step are important. The blocking treatment before gold labeling should be at least 20 min. Gold solution should also be diluted in the blocking solution. The edge of the EM grids should be blotted with filter papers to drain residual solution from the grids between gold labeling and the following blocking treatment, as well as between blocking treatments. Care should be taken not to allow the grids to directly touch the filter paper to avoid damage to the grids. 9. Uranyl acetate can precipitate out of solution in the presence of salt. Thus, the last five steps of washing with deionized water are critical. Between steps of water washing, the tweezers must be thoroughly rinsed with deionized water. 10. Uranyl acetate is radioactive and must be handled with care. Especially, the mouth of the conical tube containing the uranyl acetate solution is typically covered with a yellow film of dried uranyl acetate. Opening and closing the cap of the tube may disturb the uranyl acetate film and spread the radioactive uranyl acetate powder into the air. As such, opening and closing the uranyl acetate tube must be conducted in a chemical hood. Handling of the uranyl acetate solution must also be conducted in the chemical hood. 11. To ensure the EM grids are not nonspecifically labeled, the intact PM sheets and empty grid areas not covered with PM sheets must be imaged and compared during EM imaging. A clear edge separating the extensively gold-labeled PM sheets and non-labeled empty grid areas must be observed (an example shown in Fig. 1a, where yellow arrows point to the edges of a gold-labeled PM sheet). If extensive gold labeling is observed in the empty grid areas, imaging of this particular grid should be stopped. 12. While digitizing and processing the EM images, care should be taken not to over- or under-highlight the gold particles. The threshold function in ImageJ should be adjusted several times to compare highlighted and unhighlighted gold particles, which is to ensure that all the gold particles have been highlighted. At the same time, small gray/dark dots (membrane debris but not gold particles) in the background should not be highlighted.

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13. Although some of our data shows a direct correlation between nanoclustering and gold-labeling density, our more carefully controlled experiments suggest that the nanoclustering is independent of gold density on the PM over a wide range of labeling densities (40–1500 gold particles/1 μm2 PM area) [9, 15]. This is also consistent with recent findings by Lee et al. who showed that KRAS spatiotemporal organization is independent of KRAS levels well below, at, or well above the endogenous KRAS expression levels [23].

Acknowledgments This work was supported by the Cancer Prevention and Research Institute of Texas (CPRIT) (RP130059 and RP170233) to J.F.H. and the NIH (P30 DK 56338) to Y.Z. Disclosures: Y.Z. and J.F.H. declare that they have no conflict of interest. References 1. Cox AD, Fesik SW, Kimmelman AC, Luo J, Der CJ (2014) Drugging the undruggable RAS: mission possible? Nat Rev Drug Discov 13(11):828–851. https://doi.org/10.1038/ nrd4389 2. Cox AD, Der CJ, Philips MR (2015) Targeting RAS membrane association: back to the future for anti-RAS drug discovery? Clin Cancer Res 21(8):1819–1827. https://doi.org/10.1158/ 1078-0432.CCR-14-3214 3. Downward J (2003) Targeting RAS signalling pathways in cancer therapy. Nat Rev Cancer 3 (1):11–22. https://doi.org/10.1038/nrc969 4. Hancock JF (2003) Ras proteins: different signals from different locations. Nat Rev Mol Cell Biol 4(5):373–384. https://doi.org/10. 1038/nrm1105 5. Zhou Y, Hancock JF (2015) Ras nanoclusters: versatile lipid-based signaling platforms. Biochim Biophys Acta 1853(4):841–849. https://doi.org/10.1016/j.bbamcr.2014.09. 008 6. Zhou Y, Hancock JF (2017) Deciphering lipid codes: K-Ras as a paradigm. Traffic. https:// doi.org/10.1111/tra.12541 7. Prior IA, Muncke C, Parton RG, Hancock JF (2003) Direct visualization of Ras proteins in spatially distinct cell surface microdomains. J Cell Biol 160:165–170 8. Plowman SJ, Muncke C, Parton RG, Hancock JF (2005) H-ras, K-ras, and inner plasma membrane raft proteins operate in nanoclusters with

differential dependence on the actin cytoskeleton. Proc Natl Acad Sci U S A 102 (43):15500–15505. https://doi.org/10. 1073/pnas.0504114102 9. Tian T, Harding A, Inder K, Plowman S, Parton RG, Hancock JF (2007) Plasma membrane nanoswitches generate high-fidelity Ras signal transduction. Nat Cell Biol 9(8):905–914. https://doi.org/10.1038/ncb1615 10. Ghosh S, Strum JC, Sciorra VA, Daniel L, Bell RM (1996) Raf-1 kinase possesses distinct binding domains for phosphatidylserine and phosphatidic acid. Phosphatidic acid regulates the translocation of Raf-1 in 12-O-tetradecanoylphorbol-13-acetate-stimulated MadinDarby canine kidney cells. J Biol Chem 271 (14):8472–8480 11. Zhou Y, Wong CO, Cho KJ, van der Hoeven D, Liang H, Thakur DP, Luo J, Babic M, Zinsmaier KE, Zhu MX, Hu H, Venkatachalam K, Hancock JF (2015) SIGNAL TRANSDUCTION. Membrane potential modulates plasma membrane phospholipid dynamics and K-Ras signaling. Science 349(6250):873–876. https://doi. org/10.1126/science.aaa5619 12. Prior IA, Parton RG, Hancock JF (2003) Observing cell surface signaling domains using electron microscopy. Sci STKE 2003 (177):PL9. https://doi.org/10.1126/stke. 2003.177.pl9

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13. Zhou Y, Liang H, Rodkey T, Ariotti N, Parton RG, Hancock JF (2014) Signal Integration by lipid-mediated spatial cross talk between Ras nanoclusters. Mol Cell Biol 34(5):862–876. https://doi.org/10.1128/MCB.01227-13 14. Zhou Y, Prakash P, Liang H, Cho KJ, Gorfe AA, Hancock JF (2017) Lipid-sorting specificity encoded in K-Ras membrane anchor regulates signal output. Cell 168(1–2):239–251. e216. https://doi.org/10.1016/j.cell.2016. 11.059 15. Liang H, Mu H, Jean-Francois F, Lakshman B, Sarkar-Banerjee S, Zhuang Y, Zeng Y, Gao W, Zaske AM, Nissley DV, Gorfe AA, Zhao W, Zhou Y (2019) Membrane curvature sensing of the lipid-anchored K-Ras small GTPase. Life Sci Alliance 2(4):e201900343. https://doi. org/10.26508/lsa.201900343 16. Ripley BD (1977) Modeling spatial patterns. J R Stat Soc Series B Stat Methodol 39 (2):172–192 17. Diggle PJ, Mateu J, Clough HE (2000) A comparison between parametric and non-parametric approaches to the analysis of replicated spatial point patterns. Adv Appl Probab 32(2):331–343 18. Zhou Y, Cho KJ, Plowman SJ, Hancock JF (2012) Nonsteroidal anti-inflammatory drugs alter the spatiotemporal organization of Ras proteins on the plasma membrane. J Biol Chem 287(20):16586–16595. [pii] M112.348490

19. Ahmad T, Wani IA, Manzoor N, Ahmed J, Asiri AM (2013) Biosynthesis, structural characterization and antimicrobial activity of gold and silver nanoparticles. Colloids Surf B Biointerfaces 107:227–234. https://doi.org/10. 1016/j.colsurfb.2013.02.004 20. Wani IA, Ahmad T, Manzoor N (2013) Size and shape dependant antifungal activity of gold nanoparticles: a case study of Candida. Colloids Surf B Biointerfaces 101:162–170. https:// doi.org/10.1016/j.colsurfb.2012.06.005 21. Sutton MN, Lu Z, Li YC, Zhou Y, Huang T, Reger AS, Hurwitz AM, Palzkill T, Logsdon C, Liang X, Gray JW, Nan X, Hancock J, Wahl GM, Bast RC Jr (2019) DIRAS3 (ARHI) blocks RAS/MAPK signaling by binding directly to RAS and disrupting RAS clusters. Cell Rep 29(11):3448–3459.e3446. https:// doi.org/10.1016/j.celrep.2019.11.045 22. Baameur F, Singhmar P, Zhou Y, Hancock JF, Cheng X, Heijnen CJ, Kavelaars A (2016) Epac1 interacts with importin beta1 and controls neurite outgrowth independently of cAMP and Rap1. Sci Rep 6:36370. https:// doi.org/10.1038/srep36370 23. Lee Y, Phelps C, Huang T, Mostofian B, Wu L, Zhang Y, Tao K, Chang YH, Stork PJ, Gray JW, Zuckerman DM, Nan X (2019) Highthroughput single-particle tracking reveals nested membrane domains that dictate KRas (G12D) diffusion and trafficking. elife 8. https://doi.org/10.7554/eLife.46393

Chapter 13 FLIM-FRET Analysis of Ras Nanoclustering and Membrane-Anchorage Hanna Parkkola, Farid Ahmad Siddiqui, Christina Oetken-Lindholm, and Daniel Abankwa Abstract On the plasma membrane, Ras is organized into laterally segregated proteo-lipid complexes called nanoclusters. The extent of Ras nanoclustering correlates with its signaling output, positioning nanocluster as dynamic signaling gain modulators. Recent evidence suggests that stacked dimers of Ras and Raf are elemental units at least of one type of Ras nanocluster. However, it is still incompletely understood, in which physiological contexts nanoclustering is regulated and which constituents are parts of nanocluster. Nonetheless, disruption of nanoclustering faithfully diminishes Ras activity in cells, suggesting Ras nanocluster as potential drug targets. While there are several methods available to study Ras nanocluster, fluorescence or Fo¨rster resonance energy transfer (FRET) between fluorescently labeled, nanoclustered Ras proteins is a relatively simple readout. FRET measurements using fluorescence lifetime imaging microscopy (FLIM) have proven to be robust and sensitive to determine Ras nanoclustering changes. Loss of FRET that emerges due to nanoclustering reports on all processes upstream of Ras nanoclustering, i.e., also on proper trafficking or lipid modification of Ras. Here we report our standard FLIM-FRET protocol to measure nanoclusteringdependent FRET of Ras in mammalian cells. Importantly, nanoclustering-dependent FRET is one of the few methods that can detect differences between the Ras isoforms. Key words Ras, GTPase, FRET, FLIM, Nanocluster, Plasma membrane

1

Introduction

1.1 Targeting Ras Membrane Anchorage

Three RAS genes (HRAS, NRAS, KRAS) are frequently mutated in human cancers and in developmental diseases called RASopathies and have therefore attracted high research interest for more than 30 years [1]. Currently, the first direct Ras inhibitors are entering the clinic and first results confirm that Ras inhibition can effectively block tumor growth in human patients [2]. Ras has to localize to the plasma membrane in order to become activated and to recruit effectors, such as the initiator kinase of the MAPK pathway, Raf [1]. The characteristic plasma membrane

Ignacio Rubio and Ian Prior (eds.), Ras Activity and Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2262, https://doi.org/10.1007/978-1-0716-1190-6_13, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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localization of Ras is realized through an elaborate vesicular trafficking cycle [3, 4]. This requires collection of Ras from cellular membranes and trapping on the Golgi (K-Ras4A, N-Ras, H-Ras) or recycling endosomes (K-Ras4A) for vesicular transport to the plasma membrane. Affinity for cellular membranes comes from the farnesyl tail and in the case of K-Ras4A, N-Ras, and H-Ras additional palmitoylation on their C-termini [5]. Several indirect drug-targeting approaches have aimed at disrupting Ras plasma membrane localization. Farnesyl transferase inhibitors (FTI) were the first clinically evaluated drugs against Ras. They block the attachment of the farnesyl-tail, leaving Ras proteins cytoplasmic and therefore inactive [1]. Other approaches have focused on trafficking chaperones, such as PDE6D (also known as PDEδ) [6] or calmodulin (CaM) [7, 8]. These proteins shield the farnesyl tail from the aqueous cytoplasm and in the case of PDE6D release their cargo in a regulated fashion [9]. Three generations of PDE6D inhibitors, such as Deltarasin, have demonstrated activity against mutant KRAS in cancer cells [10]. Recently, we published newly designed PDE6D inhibitors, the Deltaflexins, which overcome the 1000-fold in vitro to in cellulo-potency gap seen with past inhibitors [11]. Inhibitors against CaM were already under development in the 1980s [12]. Given the association of a K-Ras4B/CaM-signaling axis in cancer cell stemness, CaM inhibitors may experience a renaissance [13, 14]. 1.2 Isoform-Specific Ras Nanocluster as Signaling Hubs

On the plasma membrane, Ras proteins are laterally segregated into nanoscale proteo-lipid complexes, called nanoclusters [15, 16]. Importantly, nanoclustering correlates with Ras-signaling output, thus representing a commonly neglected level of Ras activity regulation. Lateral segregation is primarily driven by the lipid modified C-terminal hypervariable region [17]. Recent work illustrated how the interaction between specific (basic) amino acids and (acidic) membrane lipids position K-Ras4B (hereafter K-Ras) in a specific lipid nano-environment [18]. In particular phosphatidylserine (PS) emerged as an important determinant of K-Ras nanoclustering. Moreover, a specific PS concentration is necessary to maintain lateral segregation and correct functioning of K-Ras and H-Ras [19]. Intriguingly, active H-Ras can negatively regulate K-Ras4B nanoclustering by segregating PS in the membrane [19], suggesting partly opposing functions of these two Ras isoforms. Likewise, caveolae can segregate PS and thus regulate Ras nanoclustering cell type and differentiation state specifically [20]. In addition, we showed that another lipid, phosphatidic acid, can oppositely regulate nanoclustering and signaling output of K-Ras and H-Ras [21]. The exact structure of Ras nanocluster complexes is still elusive. However, recent data point toward dimers of Ras assembling with dimers of Raf as minimal signaling units [22, 23] (see Chapter 17).

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In these complexes, significant cooperativity between Raf engagement and Ras-nanocluster stabilization appears to be relevant for effective downstream signaling [24]. This module appears to be the target of the best-characterized class of nanocluster- modulators or -scaffolds, the galectins. Remarkably, H-Ras and K-Ras seem to have their cognate set of nanocluster-stabilizing galectins. Galectin-1 stabilizes H-Ras-nanoclustering but destabilizes KRas-nanoclustering [23, 25], while galectin-3 stabilizes K-Ras nanocluster [26]. Interestingly, dimeric galectin-1 was found to bind to the Ras binding domain of Raf and therefore has the potential to stabilize dimers of Raf, thus allosterically enhancing H-Ras nanocluster [23]. In this context it may be interesting that dimerization of Ras proceeds via certain interfaces; hence the distribution of conformational states that permit dimerization is another factor that can impact on nanoclustering [27, 28]. Such “Ras-intrinsic” determinants appear to be perturbed by rare, cancer-associated mutations [11, 29]. 1.3 Ras-Nanocluster Detection Methods

Electron microscopy was the first technique that successfully detected Ras nanoclusters [30]. Several fluorescence microscopybased methods have helped to elaborate the nanocluster biology of Ras. Raster image correlation spectroscopy (RICS)/number and brightness (N&B) analysis [31], single molecule tracking [32], fluorescence recovery after photobleaching (FRAP), and stimulated emission depletion (STED)-fluorescence correlation spectroscopy (FCS) analysis [33] allow for the further characterization of Ras nanocluster. The latter three approaches exploit the fact that active Ras becomes transiently immobilized in Ras nanocluster. Fluorescence resonance energy transfer (FRET)-based analysis of nanoclustering exploits the dense packing of Ras in nanocluster. For all of the above approaches, Ras has to be labeled with antibodies or fluorescent tags. This inevitable perturbation appears however remarkably negligible, given that a good correlation of nanocluster properties can be seen between the different nanocluster detection methods.

1.4 Fluorescence Lifetime Imaging Microscopy (FLIM)-FRET to Detect Ras Membrane-Anchorage and Nanoclustering

The high level of nanoclustering- or oligomerization-dependent FRET of membrane anchored proteins has been exploited by us to construct membrane-anchorage FRET-biosensors for Rho and Rab small GTPases [34], heterotrimeric G proteins [35, 36], Src-family kinases [37], and membrane receptors [38]. The dense packing of Ras in isoform-specific nanoclusters likewise gives rise to FRET. FRET emerges between two identical Ras proteins tagged with donor (e.g., mGFP) or acceptor (e.g., mCherry) fluorophores (Fig. 1a). The FRET efficiency depends on the Fo¨rster radius, which typically ranges in the nanometer regime for fluorescent

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Fig. 1 Nanoclustering-dependent FRET reports on Ras membrane trafficking and nanoscale Ras membrane organization changes. (a) Schematic representation of the nanoclustering-dependent FRET changes that can be observed. We typically tag Ras N-terminally with fluorescent donor (mGFP) or acceptor (mCherry) and co-express these constructs. High FRET emerges due to the organization of Ras into nanocluster (middle). Note that donor and acceptor constructs randomly combine, i.e., half of the pairs will be FRET-inactive donordonor or acceptor-acceptor combinations, while here for simplicity two FRET pairs are shown. Disruption of nanoclustering can lower the FRET (right), while a complete loss of Ras membrane anchorage would decrease the FRET to background levels. (b) Inhibition of K-Ras trafficking and nanoclustering can be monitored using Ras membrane-FRET. (1) Farnesyltransferase inhibitors (e.g., FTI277) block the C-terminal farnesylation of nascent Ras, which also prevents all of the following C-terminal modifications by RCE1 (Ras-converting

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proteins (ca. 5 nm for mGFP/mCherry). It steeply drops with the sixth power of the inverse distance between donor and acceptor fluorophores. The precise amount of FRET in our mGFP-Ras/mCherry-Ras system depends on a number of factors that differ somewhat from a classical receptor-ligand type of FRET system [39]. The distance between donor- and acceptor-tagged Ras proteins is not known, even though models for di-/oligmeric Ras in nanoclusters exist [27]. Thus, based on the size of Ras and the fluorescent protein tag, the average distance can be expected to be in the order of 5 nm in a densely packed nanocluster. Due to the random encounter of donor- and acceptor-tagged Ras proteins in the cell membrane, however, only half of the expressed Ras proteins find each other in productive FRET pairs (i.e., mGFP-Ras encounters an mCherryRas), while the other half (i.e., mGFP-Ras/mGFP-Ras or mCherry-Ras/mCherry-Ras) consists of nonproductive pairs that effectively remain background. Moreover, some of the tagged Ras proteins might be sequestered by other binding partners or be otherwise inaccessible for FRET. And given the oligomeric nature of nanocluster, FRET is higher, if a donor is surrounded by more acceptors, further complicating the exact prediction of Ras nanoclustering-FRET. Therefore, it is assumed that within each cell, at a given expression level of donor and acceptor, tagged Ras proteins stochastically distribute in a similar fashion. Furthermore, the major fraction is in plasma membrane nanoclusters that may become ever more frequent as the overall Ras expression level increases. A higher nanoclustered population would result in relatively more FRET-enabled Ras proteins. Interestingly, an apparent saturation of cellular nanoclustering systems can be recognized when FRET is monitored versus the expression level [36]. In conclusion, the effective FRET in a cellular nanocluster system depends on the acceptor expression level, while the donor-to-acceptor ratio further impacts on the FRET efficiency [40]. These parameters should therefore be determined experimentally or be constrained through stable experimental or analysis conditions [28, 39]. The high level of nanoclustering-dependent FRET is a sensitive indicator not merely of Ras being in nanoclusters but of all upstream events as well, i.e., Ras being anchored to the plasma membrane, lipid modified, etc. This is important to remember, as ä Fig. 1 (continued) enzyme 1), ICMT (isoprenylcysteine carboxyl methyltransferase), and palmitoylation, leaving Ras cytoplasmic. (2) Calmodulin inhibitors (including calmidazolium, chlorpromazine, trifluoperazine, and the covalent inhibitor ophiobolin A) and (3) PDE6D inhibitors (such as Deltarasin or Deltaflexins) block trafficking chaperones, which facilitate the transport of K-Ras throughout the cytoplasm. (4) K-Ras nanoclustering depends on the plasma membrane lipid composition. K-Ras nanoclustering is disrupted by perturbing the phosphatidylserine (PS) distribution (by, e.g., salinomycin). (5) Ras nanocluster modulators or scaffolds such as galectin-3 can further stabilize K-Ras nanocluster

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loss of nanoclustering-dependent FRET cannot be readily interpreted in whole cell measurements, as it may stem from Ras being less on the membrane or less in nanoclusters. We therefore in short refer to this nanoclustering-dependent FRET as nanoclusteringFRET or more broadly membrane-FRET, depending on the context. In any case, loss of membrane-FRET indicates a loss of functionality; let it be through loss of nanoclustering, di-/ oligomerization, or proper subcellular distribution. Whether nanoclustering and/or membrane anchorage as such have been perturbed, e.g., by a treatment with a drug, requires additional methods to confirm either. Typically, electron microscopy or fluorescence fluctuation methods (nanoclustering) and confocal microscopy (subcellular distribution) provide this information [41]. Here we describe the use of a frequency-domain FLIM system with a resolution of individual cells, but no subcellular details, for measuring nanoclustering-dependent FRET of Ras. It should be noted that time-domain FLIM systems and systems with a better subcellular resolution could potentially allow for even more detailed analysis than described here, taking more parameters at pixel resolution into account, such as the individual lifetime components [42]. In FLIM-FRET, the drop in the average fluorescence lifetime quantifies the extent of FRET. This can then be converted into the apparent FRET efficiency of the examined sample. The apparent FRET efficiency is an integrative measure for the fraction of FRETcompetent complexes in the sample. FLIM-FRET has several advantages over intensity-based methods, which often suffer from insufficient spectral separation, when detecting donor and acceptor fluorophores [43]. Ultimately, it is very sensitive in determining even subtle differences in FRET efficiency. Automation of image acquisition can furthermore greatly enhance sample throughput, which can allow for novel high-content FRET assays [44]. In the following, we will describe our standard protocol to prepare FLIMFRET samples of Ras and the nanoclustering-/membrane-FRET data analysis procedure.

2 2.1

Materials Cell Culture

1. Human embryonic kidney (HEK) 293 cells (ATCC® CRL-1573™, see Note 1). 2. 100 mm cell culture dishes, autoclaved glass pipettes and plastic pipette tips, hemocytometer for cell counting under a light microscope.

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Table 1 Ras nanocluster modulator/scaffold constructs

Construct

DNA amount/ 12-well plate

Effect on Ras nanoclustering

K-Ras- vs. H-Ras selectivity

pcDNA3-Gal-1

0.3–0.6 μg

Increase; decrease

H-Ras (increase) K-Ras (decrease)

pcDNA3-Gal-3

0.6 μg

Increase

K-Ras

LGALS3-siRNA

25 nM

Decrease

K-Ras

3. Cell culture medium: Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% FBS and L-glutamine (see Note 2). 4. 0.25% trypsin–EDTA, phosphate-buffered saline (PBS). 5. Round 16 mm coverslips, at least #1 (0.13–0.17 mm) or #1.5 if confocal resolution is possible, sterilized in a hot air oven at 160
C for 2 h. 6. 12-well cell culture plates. 7. Plasmids encoding mGFP- and mCherry-tagged K-RasG12V and H-RasG12V mutants (see Note 3): pmGFP-K-RasG12V, pmCherry-K-RasG12V, pmGFP-H-RasG12V, and pmCherryH-RasG12V. As FRET-control plasmids, constructs expressing mGFP and mCherry alone, as well as an mGFP-mCherry fusion protein as high-FRET control, can be used. 8. Plasmids encoding nanocluster modulators/scaffolds (Table 1), galectin-1 (pcDNA3-Gal-1), galectin-3 (pcDNA3Gal-3), and siRNA against galectin-3 (LGALS3-siRNA, Santa Cruz Biotechnology, Inc.; Cat. No. sc-155994). A pcDNA empty plasmid as control for overexpression studies and scramble siRNA as siRNA transfection control. 9. jetPrime transfection reagent for plasmid transfections (see Note 1). 10. Compounds (Table 2) were dissolved in DMSO at 0.5–20 mM. These stock solutions were then further diluted with culture medium to the desired concentration for analysis. A 0.1% DMSO dilution equal to the maximal DMSO percentage of compound dilutions was used as control. 11. 4% formaldehyde (PFA) in PBS for fixing cells. 12. Microscope slides. 13. Mowiol 4-88 for mounting coverslips on microscope slides. 14. 20 μM fluorescein in Tris buffer, pH 10. 15. 35 mm glass bottom dishes.

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Table 2 Ras membrane organization inhibitors against targets described in Fig. 1b

Compound

Test concentration

Drug disrupts Ras. . .

K-Rasvs. H-Ras-selectivity

FTI277

0.5 μM

Membrane anchorage

H-Ras > K-Ras

Deltarasin

5 μM

Intracellular trafficking

K-Ras

Calmidazolium

5 μM

Intracellular trafficking

K-Ras

Trifluoperazine

0.4 μM

Intracellular trafficking

K-Ras

Intracellular trafficking

K-Ras

Chlorpromazine 4 μM Ophiobolin A

0.5 μM

Intracellular trafficking

K-Ras

Salinomycin

1 μM

Co-nanoclustering with membrane lipids (PS)

K-Ras > H-Ras

2.2

FLIM Setup

2.3 FLIM-Data Analysis Software

3

The settings and filters described here are appropriate for measuring FRET between mGFP and mCherry with a Lambert Instruments FLIM Attachment on an inverted fluorescence microscope (Zeiss AXIO Observer D1). This setup was described previously [44]. In brief, using a temperature-stabilized multi-LED system (Lambert Instruments), samples were excited with sinusoidally modulated (40 MHz) epi-illumination at 470 nm (3 W). A 63, NA 1.4 oil objective and a GFP filter set (excitation bandpass 470/40; beam splitter FT 495; emission bandpass 525/50) was used to find and image the cells to acquire the fluorescence lifetime of the donor/mGFP-constructs (alone or in the presence of a FRET-acceptor). The fluorescence lifetimes (phase and modulation derived) per pixel were determined against a fluorescein reference sample from images acquired at 12 phase settings using the manufacturer’s software (see below). Lambert FLIM software Li-FLIM (version 1.2.25) that was provided with the FLIM-setup.

Methods This standard protocol describes the characterization of the effects of the test compounds and of nanocluster modulator/scaffold protein overexpression/knockdown (Tables 1 and 2) on K-Ras and H-Ras nanoclustering-dependent FRET (Fig. 2). The protocol can be modified to suit other experimental questions.

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Fig. 2 Standard assay workflow. (a) Coverslips are placed in 12-well plates and (b) cells are plated. (c) Cells are transfected with mGFP- (donor) and mCherry- (acceptor) FRET pair constructs or control constructs. Cells can be simultaneously co-transfected with nanocluster modulators. After 5 h, transfection medium is replaced with fresh culture medium, and cells are incubated for additional 19 h. (d) 24 h after transfections, cells are treated with test compounds followed by (e) PFA fixing. (f) After fixation wells are washed with PBS, and (g) coverslips with the fixed and washed cells are mounted on microscopy slides. FLIM analysis is performed within 1–7 days

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3.1 Cell Transfections and Treatments

1. Perform cell culture in the aseptic environment of a laminar flow cabinet. Use sterile cell culture ware (dishes, glass pipettes, and autoclaved pipette tips) and solutions. 2. Use cell culture media, PBS, and trypsin-EDTA preheated to 37
C. 3. Allow cells to reach a confluency of 80% in 100 mm cell culture dishes. Remove the media and gently wash the cells with 10 ml of PBS. Remove PBS and add 1 ml of trypsin-EDTA to detach the cells at 37
C for a maximum of 5 min. 4. Add 9 ml of culture medium to detached cells and suspend by pipetting. 5. Centrifuge at 250 rcf for 5 min to pellet the cells. Remove trypsin-EDTA-containing media, and resuspend the cell pellet in 10 ml of culture media. 6. Count the number of cells using a hemocytometer. Add 10 μl of cell suspension to each of the counting areas. Use a light microscope to count the cells. 7. To each well of a sterile 12-well cell culture plate, add first a sterilized cover slip (Fig. 3a). Then add 80,000 cells from the

Fig. 3 Nanoclustering-dependent FRET detects selective inhibition of K-Ras and H-Ras. (a, b) Selective inhibition by trafficking inhibitors of K-Ras- (a) but not H-Ras- (b) nanoclustering-FRET is observed for the K-Ras selective inhibitors. FTI277 affects less potently K-Ras, as its membrane anchorage can be rescued by alternative geranylgeranylation. (c, d) Salinomycin selectively disrupts the K-Ras nanoscale organization in the membrane. (a–c) Coloring of the bars matches that of the targets in Fig. 1b. Tested inhibitor concentrations are shown in Table 2. Samples were prepared as described in the standard protocol. Data show averages SEM of 1–3 independent experimental repeats and the number of analyzed cells indicated on the bars. Statistical differences to the DMSO control are annotated as *p < 0.05; ***p < 0.001; ****p < 0.0001. (e) Representative FLIM-FRET images of treated HEK293 cells expressing the donor only (left) or the K-RasG12V FRET pair without (control) or with indicated treatment. Color look-up table shows fluorescence lifetimes. The fluorescence lifetime is lower in the FRET situation

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cell suspension and fresh culture medium to a final volume of 1000 μl (Fig. 3b, see Note 4). 8. After 24 h, transfect the cells following the transfection kit manual. Transfections with jetPrime are described here. Prepare DNA dilutions with 50 μl of jetPrime buffer per well and the following set of samples using the amount of DNA specified for each plasmid (Fig. 2c, see Note 5). Donor only sample (amounts per well): (a) 0.8 μg of pmGFP-K-rasG12V. (b) 0.4 μg of pmGFP-H-rasG12V. Donor + acceptor FRET pairs (amounts per well): (c) K-Ras FRET pair: 0.2 μg of pmGFP-K-rasG12V + 0.6 μg pmCherry-K-rasG12V (total amount 0.8 μg; donor/ acceptor ratio 1:3). (d) H-Ras FRET pair: 0.1 μg of pmGFP-H-rasG12V + 0.3 μg pmCherry-H-rasG12V (total amount 0.4 μg; donor/ acceptor ratio 1:3). Vortex and spin the DNA dilutions. Add 2 μl of jetPrime transfection reagent for 1 μg of total DNA to complete the transfection mixes. Vortex the transfection mixes, and spin and incubate for 10 min at room temperature. Add the transfection mixes to the wells drop-wise, gently rock the plate, and place it back in the incubator. For compound treatments continue to step 9. For nanocluster modulator protein overexpression and knockdown studies, perform the following transfections at the same time as FRET pair transfections are done. Combine nanocluster modulator constructs (Table 1) with the FRET pair transfection mixes (see 8. above), adjusting the volume of jetPrime transfection reagent to match the amount of total DNA. Then continue to step 9 (Fig. 4). Galectin-1 overexpression: (e) Add 0.6 μg pcDNA3-Gal-1 in the K-Ras FRET pair transfection mix. (f) Add 0.3 μg pcDNA3-Gal-1 in the H-Ras FRET pair transfection mix. Galectin-3 overexpression: (g) Add 0.6 μg pcDNA3-Gal-3 in the K-Ras FRET pair transfection mix. Galectin-3 knockdown: (h) 25 nM LGALS3-siRNA in the K-Ras and H-Ras FRET pair transfection mix.

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Fig. 4 Nanoclustering-dependent FRET detects selective modulation of K-Ras and H-Ras nanoclustering by nanocluster modulator/scaffold overexpression or knockdown. (a) K-Ras nanocluster modulator galectin-3 overexpression increases K-Ras nanoclustering-FRET, and consistently its knockdown with siRNA (siLGALS3) reduces FRET. It is currently not understood how galectin-1 decreases K-Ras nanoclustering. (b) For H-Ras, however, galectin-1 overexpression increases H-Ras nanoclustering-FRET. Samples were prepared as described in the standard protocol. Data show averages SEM of 1–3 independent experimental repeats and the number of analyzed cells indicated on the bars. Statistical differences to the DMSO control are annotated as ****p < 0.0001

9. 5 h after transfection, remove the media from the wells, and add gently 1000 μl of culture media. Incubate the plate at 37
C for additional 19 h. For compound treatments, continue to step 10. For nanocluster modulator protein overexpression/knockdown studies, continue to Subheading 3.2. 10. 24 h post-transfection, treat the cells with test compounds (Figs. 2d and 4). Prepare compound dilutions (Table 2) in culture medium, 100 μl per well, taking into account the volume (1000 μl) already in the wells. Add compound dilutions gently to wells (see Note 6). Incubate for 24 h (see Note 7). 3.2 Sample Preparation for FLIM

1. 24 h after treatment or 48 h of nanocluster modulator overexpression/knockdown, remove the media from wells. Fix the cells on the coverslips by gently adding 1 ml of 4% PFA to the edges of the well. Incubate at RT for 10–15 min. Remove the PFA and add 1 ml PBS to wash the cells.

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2. Mount coverslips on microscope slides using Mowiol. To dry the mounted slides, heat at 55
C for 35 min. Store slides in the dark at RT overnight for further drying. For a maximum of 1 week, slides can be stored at 4
C in the dark until FLIMFRET analysis (see Note 8). 3.3 FLIM Data Acquisition

1. In order to do calibrated FLIM measurements, the Li-FLIM requires a fluorescence lifetime standard with known fluorescence lifetime. We standardly employ fluorescein. Pipette a drop of the 20 μM fluorescein solution on a glass bottom dish. Set exposure time to 10 ms and voltage gain to 600 V. Place the dish on the sample holder and adjust the focus position inside the drop and record the image stack that will serve as a fluorescence lifetime reference (4.0 ns for fluorescein). Renew the reference every two to three samples. 2. Measure the donor fluorescence lifetime of all other samples. Add a drop of oil on the objective and put the microscope slide on the sample holder. Set exposure time (10 ms for donor only; up to 100 ms for samples co-expressing the FRET pair) and voltage gain (450–600) depending on the sample. Importantly, software settings like exposure time and voltage gain should largely remain the same for all samples (FRET pairs) that should be compared later on. Use color scale Jet2 from 1.6 to 2.4 (see Note 9). 3. Select cells for analysis, processing fields of view containing a dozen or so cells expressing constructs as described above; see Note 10). 4. For each sample, determine the fluorescence lifetime of 40–60 cells using region of interest (ROI) markup within the software. 5. Export fluorescence lifetime data of measured ROIs (i.e., cells) into an excel file.

3.4 FLIM-FRET Data Analysis

1. Gather all fluorescence lifetime phase values of cells with the same treatment into one excel sheet. 2. Calculate per FRET pair and treatment condition the apparent FRET efficiency (Eapp in %) using the obtained average fluorescence lifetimes of the FRET pair samples (τDA) and the average fluorescence lifetime of the donor-only (τD) samples, using: Eapp ¼ (1  τDA/τD)  100% (see Note 11). 3. Determine significant differences between the samples using GraphPad Prism. Employ analysis of variance (ANOVA) complemented by Tukey’s multiple comparisons test or unpaired t test, depending on the number of treatment groups. Generate result plots to your liking.

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Notes 1. HEK293 (ATCC® CRL-1573™) are recommended for this assay. In our group we typically used the HEK293-EBNA variant under adherent growth conditions [45]. Other cell lines, such as BHK-21 (DSMZ, #ACC 61), have been used for FLIM-FRET analysis [14, 33]. jetPrime and FuGENE 6 transfection reagents have been employed for these cell lines, resulting in reproducibly high protein expression levels. If using other cell lines, donor and acceptor transfection efficiencies should be confirmed as being sufficient for the FRET analysis. 2. Penicillin (100 U/ml) and streptomycin (100 μg/ml) can be used to supplement cell culture medium, if needed. However, they are not necessary, if cells remain free from contaminations while handled in culture. 3. The FRET pairs described here allow demonstrating that test compounds or nanocluster modulators can act Ras isoform (K-Ras and H-Ras) specifically. Other Ras constructs, such as derived from N-Ras or even other small GTPases, also show high FRET [34, 46]. 4. Coverslips can be pretreated for better attachment of cells. HEK293 cells detach easily by mechanical force and need to be treated gently when adding reagents or washing the cells. To prevent excessive detachment, coverslips can be rinsed with sterile H2O or PBS, or pretreated with HNO3, according to the following protocol: (a) Place 100 coverslips in a 1 L beaker, and cover with 65% HNO3. (b) Incubate at RT for 5 min with agitation. (c) Wash under running water for 10 min. (d) Rinse 6 times with MilliQ H2O. (e) Wash with MeOH. (f) Wash with EtOH. (g) Spread out for drying. (h) Sterilize before use. 5. With the FRET pairs described here, a donor-acceptor DNA ratio 1:3 was found to be optimal to achieve suitable FRET measurements using the jetPrime transfection reagent. It also ascertains that donor-expressing cells have the acceptor present. For other FRET plasmid pairs of choice, it is recommendable to determine the best ratio experimentally. The H-Ras FRET pair expresses at higher levels than the K-Ras pair; if expressed too high, it can lead to cell senescence and problems with analysis. 6. DMSO concentration in the cell culture medium should be kept as low as possible in order to avoid toxic effects to cells. In our assays, the maximum concentration is 0.1%, which is generally accepted to be nontoxic.

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7. Compound or construct concentrations should be adjusted to avoid excessive cell damage. Treatments and transfections may cause too much toxicity to cells. Analyzing essentially dead cells can lead to inaccurate FRET results. Therefore, it is recommended to check cell morphology under a light microscope prior to fixing the cells for FLIM analysis. 8. FLIM analysis should always be performed at the same time after sample preparation. The optimal time for imaging is 1 day after sample mounting. Samples should not be stored for longer than 7 days prior to analysis. Let samples stored in the cool reach RT before analysis. 9. Fluorescence lifetime values for mGFP donor-only constructtransfected cells should be stable and close to the fluorescence lifetime of mGFP of 2.2 ns. 10. When imaging, only well-defined cells, clearly in focus and of a similar intensity should be analyzed. Clustered cells, or cells in the middle of larger aggregates with abnormal morphology, should be excluded. 11. For mGFP- and mCherry-K-RasG12V-transfected (untreated) control cells, we typically find apparent FRET efficiencies between 10 and 12%. For mGFP- and mCherry-HRasG12V-transfected control cells, the efficiency should be between 12 and 16%.

Acknowledgments This work was supported by a CIMO fellowship to F.A.S. D.A. received financial support from the Sigrid Juselius Foundation and Jane and Aatos Erkkos Foundation. Figures may contain elements from Servier Medical Art templates, which are licensed under a Creative Commons Attribution 3.0 Unported License (https://smart.servier.com). References 1. Stephen AG, Esposito D, Bagni RK, McCormick F (2014) Dragging Ras back in the ring. Cancer Cell 25:272–281. https://doi.org/10. 1016/j.ccr.2014.02.017 2. Canon J, Rex K, Saiki AY, Mohr C, Cooke K, Bagal D, Gaida K, Holt T, Knutson CG, Koppada N, Lanman BA, Werner J, Rapaport AS, Miguel TS, Ortiz R, Osgood T, Sun J-R, Zhu X, McCarter JD, Volak LP, Houk BE, Fakih MG, O’Neil BH, Price TJ, Falchook GS, Desai J, Kuo J, Govindan R, Hong DS, Ouyang W, Henary H, Arvedson T, Cee VJ, Lipford JR (2019) The clinical KRAS(G12C) inhibitor AMG 510 drives anti-tumour

immunity. Nature 575:1–26. https://doi. org/10.1038/s41586-019-1694-1 3. Schmick M, Vartak N, Papke B, Kovacevic M, Truxius DC, Rossmannek L, Bastiaens PIH (2014) KRas localizes to the plasma membrane by spatial cycles of solubilization, trapping and vesicular transport. Cell 157:459–471. https://doi.org/10.1016/j.cell.2014.02.051 4. Rocks O, Gerauer M, Vartak N, Koch S, Huang Z-P, Pechlivanis M, Kuhlmann J, Brunsveld L, Chandra A, Ellinger B, Waldmann H, Bastiaens PIH (2010) The palmitoylation machinery is a spatially organizing system for peripheral membrane proteins. Cell

248

Hanna Parkkola et al.

141:458–471. https://doi.org/10.1016/j. cell.2010.04.007 5. Ahearn IM, Haigis K, Bar-Sagi D, Philips MR (2012) Regulating the regulator: posttranslational modification of RAS. Nat Rev Mol Cell Biol 13:39–51. https://doi.org/10. 1038/nrm3255 6. Chandra A, Grecco HE, Pisupati V, Perera D, Cassidy L, Skoulidis F, Ismail SA, Hedberg C, Hanzal-Bayer M, Venkitaraman AR, Wittinghofer A, Bastiaens PIH (2011) The GDI-like solubilizing factor PDEδ sustains the spatial organization and signalling of Ras family proteins. Nat Cell Biol 14:148–158. https://doi.org/10.1038/ncb2394 7. Agamasu C, Ghirlando R, Taylor T, Messing S, Tran TH, Bindu L, Tonelli M, Nissley DV, McCormick F, Stephen AG (2019) KRAS prenylation is required for bivalent binding with CaM in a nucleotide independent manner. Biophys J 116:1–47. https://doi.org/10.1016/j. bpj.2019.02.004 8. Fivaz M, Meyer T (2005) Reversible intracellular translocation of KRas but not HRas in hippocampal neurons regulated by Ca2+/calmodulin. J Cell Biol 170:429–441. https:// doi.org/10.1083/jcb.200409157 9. Ismail SA, Chen Y-X, Rusinova A, Chandra A, Bierbaum M, Gremer L, Triola G, Waldmann H, Bastiaens PIH, Wittinghofer A (2011) Arl2-GTP and Arl3-GTP regulate a GDI-like transport system for farnesylated cargo. Nat Chem Biol 7:942–949. https:// doi.org/10.1038/nchembio.686 10. Zimmermann G, Papke B, Ismail S, Vartak N, Chandra A, Hoffmann M, Hahn SA, Triola G, Wittinghofer A, Bastiaens PIH, Waldmann H (2013) Small molecule inhibition of the KRASPDEδ interaction impairs oncogenic KRAS signalling. Nature 497:638–642. https://doi. org/10.1038/nature12205 11. Siddiqui FA, Alam C, Rosenqvist P, Ora M, Sabt A, Manoharan GB, Bindu L, Okutachi S, Catillon M, Taylor T, Abdelhafez OM, Lo¨nnberg H, Stephen AG, Papageorgiou AC, Virta P, Abankwa D (2019) PDE6D Inhibitors with a new design principle selectively block K-Ras activity. ACS Omega. 5(1):832–842. https:// doi.org/10.1021/acsomega.9b03639. eCollection 2020 Jan 14. PMID: 31956834 12. Hait WN, Lazo JS (1986) Calmodulin: a potential target for cancer chemotherapeutic agents. J Clin Oncol 4:994–1012 13. Wang M-T, Holderfield M, Galeas J, Delrosario R, To MD, Balmain A, McCormick F (2015) K-Ras promotes tumorigenicity through suppression of non-canonical Wnt

signaling. Cell 163:1237–1251. https://doi. org/10.1016/j.cell.2015.10.041 14. Najumudeen AK, Jaiswal A, Lectez B, OetkenLindholm C, Guzman C, Siljam€aki E, Posada IMD, Lacey E, Aittokallio T, Abankwa D (2016) Cancer stem cell drugs target K-ras signaling in a stemness context. Oncogene 35:5248–5262. https://doi.org/10.1038/ onc.2016.59 15. Zhou Y, Hancock JF (2015) Ras nanoclusters: versatile lipid-based signaling platforms. Biochim Biophys Acta 1853:841–849. https:// doi.org/10.1016/j.bbamcr.2014.09.008 16. Abankwa D, Gorfe AA (2020) Mechanisms of ras membrane organization and signaling: Ras rocks again. Biomolecules. 10(11):1522. https://doi.org/10.3390/biom10111522. PMID: 33172116 17. Abankwa D, Hanzal-Bayer M, Ariotti N, Plowman SJ, Gorfe AA, Parton RG, McCammon JA, Hancock JF (2008) A novel switch region regulates H-ras membrane orientation and signal output. EMBO J 27:727–735. https://doi. org/10.1038/emboj.2008.10 18. Zhou Y, Prakash P, Liang H, Cho K-J, Gorfe AA, Hancock JF (2016) Lipid-sorting specificity encoded in K-Ras membrane anchor regulates signal output. Cell 168(1-2):239–251. e16. https://doi.org/10.1016/j.cell.2016. 11.059 19. Zhou Y, Liang H, Rodkey T, Ariotti N, Parton RG, Hancock JF (2014) Signal integration by lipid-mediated spatial cross talk between Ras nanoclusters. Mol Cell Biol 34:862–876. https://doi.org/10.1128/MCB.01227-13 20. Ariotti N, Fernandez-Rojo MA, Zhou Y, Hill MM, Rodkey TL, Inder KL, Tanner LB, Wenk MR, Hancock JF, Parton RG (2014) Caveolae regulate the nanoscale organization of the plasma membrane to remotely control Ras signaling. J Cell Biol 18:599. https://doi.org/10. 1083/jcb.201307055.dv 21. Posada IMD, Lectez B, Siddiqui FA, OetkenLindholm C, Sharma M, Abankwa D (2017) Opposite feedback from mTORC1 to H-ras and K-ras4B downstream of SREBP1. Sci Rep 7:8944. https://doi.org/10.1038/s41598017-09387-8 22. Nan X, Tamgu¨ney TM, Collisson EA, Lin L-J, Pitt C, Pitt C, Galeas J, Galeas J, Lewis S, Gray JW, McCormick F, Chu S (2015) Ras-GTP dimers activate the mitogen-activated protein kinase (MAPK) pathway. Proc Natl Acad Sci 112:7996–8001. https://doi.org/10.1073/ pnas.1509123112 23. Blazevitsˇ O, Mideksa YG, Solman M, Ligabue A, Ariotti N, Nakhaeizadeh H, Fansa

Ras Nanoclustering-Dependent FRET EK, Papageorgiou AC, Wittinghofer A, Ahmadian MR, Abankwa D (2016) Galectin-1 dimers can scaffold Raf-effectors to increase H-ras nanoclustering. Sci Rep 6:24165. https://doi.org/10.1038/srep24165 24. Jin T, Lavoie H, Sahmi M, David M, Hilt C, Hammell A, Therrien M (2017) RAF inhibitors promote RAS-RAF interaction by allosterically disrupting RAF autoinhibition. Nat Commun 8:1–16. https://doi.org/10.1038/ s41467-017-01274-0 25. Posada IMD, Lectez B, Sharma M, OetkenLindholm C, Yetukuri L, Zhou Y, Aittokallio T, Abankwa D (2017) Rapalogs can promote cancer cell stemness in vitro in a Galectin-1 and H-ras-dependent manner. Oncotarget 8:44550–44566. https://doi. org/10.18632/oncotarget.17819 26. Shalom-Feuerstein R, Plowman SJ, Rotblat B, Ariotti N, Tian T, Hancock JF, Kloog Y (2008) K-ras nanoclustering is subverted by overexpression of the scaffold protein galectin-3. Cancer Res 68:6608–6616. https://doi.org/ 10.1158/0008-5472.CAN-08-1117 27. Prakash P, Sayyed-Ahmad A, Cho K-J, Dolino DM, Chen W, Li H, Grant BJ, Hancock JF, Gorfe AA (2017) Computational and biochemical characterization of two partially overlapping interfaces and multiple weak-affinity K-Ras dimers. Sci Rep 7:40109. https://doi. org/10.1038/srep40109 28. Abankwa D, Gorfe AA, Inder K, Hancock JF (2010) Ras membrane orientation and nanodomain localization generate isoform diversity. Proc Natl Acad Sci 107:1130–1135. https:// doi.org/10.1073/pnas.0903907107 29. Sˇolman M, Ligabue A, Blazˇevitsˇ O, Jaiswal A, Zhou Y, Liang H, Lectez B, Kopra K, Guzma´n C, H€arm€a H, Hancock JF, Aittokallio T, Abankwa D (2015) Specific cancer-associated mutations in the switch III region of Ras increase tumorigenicity by nanocluster augmentation. Elife. 4:e08905. https://doi.org/ 10.7554/eLife.08905. PMID: 26274561 30. Prior IA, Muncke C, Parton RG, Hancock JF (2003) Direct visualization of Ras proteins in spatially distinct cell surface microdomains. J Cell Biol 160:165–170. https://doi.org/10. 1083/jcb.200209091 31. (2013) Nanoclustering and heterogeneous membrane diffusion of Ras studied by FRAP and RICS analysis. In: Methods in molecular biology. Humana Press, Totowa, NJ, pp 307–326 32. Cho K-J, Kasai RS, Park J-H, Chigurupati S, Heidorn SJ, van der Hoeven D, Plowman SJ, Kusumi A, Marais R, Hancock JF (2012) Raf inhibitors target Ras spatiotemporal dynamics.

249

Curr Biol 22:945–955. https://doi.org/10. 1016/j.cub.2012.03.067 33. Guzma´n C, Solman M, Ligabue A, Blazevitsˇ O, Andrade DM, Reymond L, Eggeling C, Abankwa D (2014) The efficacy of Raf kinase recruitment to the GTPase H-ras depends on H-ras membrane conformer-specific nanoclustering. J Biol Chem 289:9519–9533. https:// doi.org/10.1074/jbc.M113.537001 34. Ko¨hnke M, Schmitt S, Ariotti N, Piggott AM, Parton RG, Lacey E, Capon RJ, Alexandrov K, Abankwa D (2012) Design and application of in vivo FRET biosensors to identify protein prenylation and nanoclustering inhibitors. Chem Biol 19:866–874. https://doi.org/10. 1016/j.chembiol.2012.05.019 35. Crouthamel M, Abankwa D, Zhang L, DiLizio C, Manning DR, Hancock JF, Wedegaertner PB (2010) An N-terminal polybasic motif of Gαq is required for signaling and influences membrane nanodomain distribution. Mol Pharmacol 78:767–777. https://doi. org/10.1124/mol.110.066340 36. Abankwa D, Vogel H (2007) A FRET map of membrane anchors suggests distinct microdomains of heterotrimeric G proteins. J Cell Sci 120:2953–2962. https://doi.org/10.1242/ jcs.001404 37. Najumudeen AK, Ko¨hnke M, Solman M, Alexandrov K, Abankwa D (2013) Cellular FRET-biosensors to detect membrane targeting inhibitors of N-myristoylated proteins. PLoS One 8:e66425. https://doi.org/10. 1371/journal.pone.0066425 38. Sykes AM, Palstra N, Abankwa D, Hill JM, Skeldal S, Matusica D, Venkatraman P, Hancock JF, Coulson EJ (2012) The effects of transmembrane sequence and dimerization on cleavage of the p75 neurotrophin receptor by γ-secretase. J Biol Chem 287:43810–43824. https://doi.org/10.1074/jbc.M112.382903 39. Najumudeen AK, Guzma´n C, Posada IMD, Abankwa D (2015) Rab-NANOPS: FRET biosensors for Rab membrane nanoclustering and prenylation detection in mammalian cells. In: Li G (ed) Rab GTPases. Springer, New York, NY, pp 29–45 40. Berney C, Danuser G (2003) FRET or no FRET: a quantitative comparison. Biophys J 84:3992–4010. https://doi.org/10.1016/ S0006-3495(03)75126-1 41. Guzma´n C, Solman M, Abankwa D (2014) Nanoclustering and heterogeneous membrane diffusion of Ras studied by FRAP and RICS analysis. Methods Mol Biol 1120:307–326. https://doi.org/10.1007/978-1-62703-7914_20

250

Hanna Parkkola et al.

42. Digman MA, Caiolfa VR, Zamai M, Gratton E (2008) The phasor approach to fluorescence lifetime imaging analysis. Biophys J 94: L14–L16. https://doi.org/10.1529/ biophysj.107.120154 43. Jares-Erijman EA, Jovin TM (2003) FRET imaging. Nat Biotechnol 21:1387–1395. https://doi.org/10.1038/nbt896 44. Guzma´n C, Oetken-Lindholm C, Abankwa D (2016) Automated high-throughput fluorescence lifetime imaging microscopy to detect protein-protein interactions. J Lab Autom

21:238–245. https://doi.org/10.1177/ 2211068215606048 45. Meissner P, Pick H, Kulangara A, Chatellard P, Friedrich K, Wurm FM (2001) Transient gene expression: recombinant protein production with suspension-adapted HEK293-EBNA cells. Biotechnol Bioeng 75:197–203. https://doi.org/10.1002/bit.1179 46. Posada IMD, Serulla M, Zhou Y, OetkenLindholm C, Abankwa D, Lectez B (2016) ASPP2 is a novel Pan-Ras nanocluster scaffold. PLoS One 11:e0159677. https://doi.org/10. 1371/journal.pone.0159677

Chapter 14 Assessment of Plasma Membrane Fatty Acid Composition and Fluidity Using Imaging Flow Cytometry Natividad R. Fuentes, Michael L. Salinas, Xiaoli Wang, Yang-Yi Fan, and Robert S. Chapkin Abstract Phospholipid fatty acid (FA) composition influences the biophysical properties of the plasma membrane and plays an important role in cellular signaling. Our previous work has demonstrated that plasma membrane fatty acid composition is an important determinant of oncogenic Ras signaling and that dietary (exogenous) modulation of membrane composition may underlie the chemoprotective benefits of long chain n-3 polyunsaturated fatty acids (PUFA). In this chapter, we describe in vitro methods to modulate membrane phospholipid fatty acid composition of cultured cells using fatty acids complexed to bovine serum albumin (BSA). Furthermore, we describe a method to quantify the biophysical properties of plasma membranes in live cells using Di-4-ANEPPDHQ (Di4) and image-based flow cytometry. Key words n-3 PUFA, DHA, Membrane order, Lipid rafts, Di-4-ANNEPDHQ, Imaging flow cytometry

1

Introduction The plasma membrane (PM) is a dynamic cellular structure composed of a myriad of lipids and proteins [1]. The coalescence of these membrane components into structured domains regulates many cellular signals, including Ras [2]. Therefore, disruption of these structured domains may serve as a complementary strategy to inhibit aberrant Ras signaling [3]. Recently, we demonstrated that membrane enrichment with long-chain n-3 polyunsaturated fatty acids (n-3 PUFA), e.g., eicosapentaenoic acid (EPA, 20:5 Δ5,8,11,14,17 ) and docosahexaenoic acid (DHA, 22:6 Δ4,7,10,13,16,19 ), attenuates oncogenic signaling by altering KRas proteolipid composition [4]. The physiological delivery of fatty acids to mammalian cells occurs via non-esterified fatty acids bound to serum albumin or by their incorporation into circulating lipoproteins. Thus, the

Ignacio Rubio and Ian Prior (eds.), Ras Activity and Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2262, https://doi.org/10.1007/978-1-0716-1190-6_14, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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delivery of PUFA by bovine serum albumin (BSA) provides an efficient strategy to manipulate membrane composition. The ability to assess the biophysical properties of the plasma membrane is of great interest since plasma membrane rigidity is linked to plasma membrane receptor function and downstream cellular signaling [5–7]. A commonly used method to assess membrane order involves confocal-based spectral ratiometric imaging of polarity-sensitive membrane dyes, e.g., Laurdan and Di-4ANEPPDHQ [8, 9]. This technique has been recently extended to flow cytometry-based applications [10, 11]. Here, we describe methods that combine imaging and flow cytometry to assess membrane order [5, 12, 13]. This application has the advantage of rapid single cell analysis similar to flow cytometry while also providing images which can be masked and thresholded to determine organelle-specific membrane order. Dietary n-3 PUFA play an under-appreciated role in human health and disease as modulators of membrane structure and function [12–14]. To elucidate the interrelationships between longchain fatty acids and membrane rigidification, we describe protocols to modulate membrane fatty acid composition via the delivery of albumin-complexed fatty acids to cells in culture. Furthermore, we describe a quantitative method (imaging flow cytometry) to determine the biophysical impact of fatty acid incorporation on plasma membrane order.

2

Materials

2.1 Delivery of Fatty Acids

1. Baked spatulas, 250 mL beaker, and 2 mL glass conical V-bottom vials. 2. Bovine Serum Albumin Fraction V, heat shock, fatty acid-free (Sigma, 3117057001), MW: 68,000 g/mol. 3. DHA [52.56 mg/mL in EtOH], (Nu-Chek Prep, U-84-A) MW: 328.57 g/mol. 4. 0.05 M Na2CO3 buffer: 0.053 g Na2CO3 in 10 mL sterile H2O. 5. 15% BSA solution: Add 20 mL of RPMI 1640 medium into 50 mL beaker. Gently layer 3 g of FA-free BSA onto the surface of the medium. Do not stir, let the BSA powder slowly dissolve into the medium.

2.2 Determining Membrane Order

1. We are providing details of our set-up using an Amnis® FlowSight®. However, an equivalent image-based flow cytometer could be used as long as it has the following specifications: A 488 nm laser and capable of collecting emissions 480–560 nm (ordered) and 640–745 (disordered) to analyze Di-4ANEPPDHQ (see Note 1).

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2. Live Cell Imaging Solution (Thermo Fisher, A14291DJ). 3. Di-4-ANEPPDHQ (Thermo Fisher, D36802). 4. 10 mM MβCD cholesterol depletion buffer: 0.066 g MβCD (Sigma, C4555) into 5 mL RPMI 1640.

3

Methods

3.1 Preparation of BSA-FA

1. Dissolve the fatty acids in 100% ethanol (see Note 2). 2. Add 5 mg of FA into a 2 mL glass vial. 3. Carefully dry down the FA under a stream of N2. 4. After drying down the FA, add 1 mL of 0.05 M Na2CO3 to the vial. Flush the vial with N2 gas. Vortex for 30 s. Let the vials sit at room temperature (RT) for 1 h. During the 1 h incubation, vortex the vials every 10 min to aid the dissolution of the FA. 5. Calculate the materials needed to generate a 2.5 mM FA-BSA complex at the FA:BSA (3:1) molar ratio (2.5 mM refer to FA concentration). Example: DHA (MW: 328.57 g/mol): 5 mg in 1 mL 0.05 M Na2CO3 BSA (MW: 68,000 g/mol): 15% solution 6. Calculate the volume of 15% BSA solution needed for 5 mg DHA to obtain FA:BSA 3:1 molar ratio. Example: 0:005 g DHA 1   68, 000 g=mol ðBSA MW Þ 328:57 g=mol ðDHA MW Þ 3 100 15 ¼ 2:3 mL 

7. Calculate the total volume of solution needed for 5 mg DHA to make 2.5 mM DHA-BSA complex. Example: 5 mg DHA ¼ 0:006087 L ¼ 6:09 mL 328:57 g=mol ðDHA MW Þ  2:5 mM 8. Calculate the volume of basal medium needed for making the 2.5 mM DHA-BSA complex. Example: 6.09 mL (total volume) – 1 mL (FA in 0.05 M Na2CO3) – 2.3 mL (15% BSA) ¼ 2.79 mL.

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DHA 15% BSA Medium 1 mL

2.3 mL

2.5 mM DHA-BSA 2.79 mL

6.09 mL (total volume)

9. It is difficult to completely retrieve the 1 mL FA-Na2CO3 from the glass vial. Therefore, take only 95% of the solution to make the DHA-BSA complex. Example: 0.95 mL of DHA + 2.19 mL of 15% BSA + 2.65 mL of medium. 10. Add the following mL of FA-Na2CO3, 15% BSA, and medium to 15 mL conical tube for respective FA. Ex:

FA-Na2CO3

15% BSA

Medium

(FA-BSA)

0.95 mL

2.19 mL

2.65 mL

(5.79) mL

11. Flush the tubes with N2. Shake the tubes on a belly dancer shaker for at least 0.5 h. 12. Filter sterilize using a 0.2μm low protein binding syringe filter and aliquot the FA-BSA complex under a sterile hood (see Note 3). 13. Store at 20  C for up to a month. 3.2 Treatment of Cells

1. Seed 5  104 young adult mouse colonic (YAMC) cells per well of a 24-well plate. Remember to have an extra well to use as a cholesterol depletion control (see Note 4). 2. Incubate cells with desired concentration of FA-BSA for 24 h (see Note 5).

3.3 Trypsinization and Labeling with Di-4-ANEPPDHQ

1. To deplete cholesterol, remove media and replace with RPMI media containing 10 mM methyl-β-cyclodextrin (MβCD) for 30 min at 33  C (see Note 6). 2. For a 24-well plate, aspirate media and rinse with 1 mL DPBS. Add 250μL of 0.05% trypsin-EDTA for 3–5 min. 3. After 3–5 min, add 1 mL of media to stop the trypsinization. Transfer the cell suspension into a 2 mL tube. Centrifuge cells at 200  g for 5 min to pellet cells. 4. After centrifugation, aspirate media and resuspend cells in 50μL of live cell imaging solution (LCIS) or other imaging media. Keep cells on ice. 5. When ready to image, add 4μL of 10μM Di4 in LCIS media to an aliquot of 36μL of cells in LCIS (Final Di4 concentration:

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Fig. 1 Representative FlowSight gating strategy

1μM). Pipette up and down gently, to mix and avoid generating bubbles. 6. Immediately run sample on the FlowSight (see Note 7). 3.4

Imaging

1. Perform the startup and calibration of the FlowSight according to manufacturer’s recommendation. Takes ~30 min. 2. Set a gate to collect whole cells as in Fig. 1 (see Note 8). 3. Adjust the power of the 488 nm laser to minimize saturated max pixels. 4. Collect at least 5000 events (see Note 9).

3.5

Analysis

1. Analysis is performed using commercial software associated with the flow cytometer. On our machine, we use Amnis IDEAS® (Version 6.2). 2. For every cell, the mean intensity of CH02 (ordered, O) and CH05 (disordered, D) is used to generate a generalized polarization (GP) value by applying the equation below [9]. GP ¼

IO  ID IO þ ID

where IO and ID are the intensities of ordered channel and disordered channel, respectively.The GP value ranges from +1 to 1, where higher values indicate increased membrane order. The analysis template used is available upon request.

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Fig. 2 DHA enrichment and cholesterol depletion by MβCD reduce membrane order. (a) Representative bright field (BF), ordered (O) and disordered (D) channel images of YAMC cells. Plasma membrane, whole cell, or intracellular membrane masks overlaid in blue highlight. When images are acquired quickly (0.5 and area >100 are used. However, this may need to be optimized for other cell types. 9. The number of cells collected is dependent on the concentration of the sample. Typically, collection times are limited to no more than 10 min to avoid internalization of the dye.

Acknowledgments This work was supported by the Allen Endowed Chair in Nutrition and Chronic Disease Prevention and the National Institutes of Health (R35-CA197707 and P30-ES029067). Natividad R. Fuentes is supported by the Texas A&M University Regulatory Science in Environmental Health and Toxicology Training Grant (T32-ES026568) and former recipient of a Predoctoral Fellowship in Pharmacology/Toxicology from the PhRMA Foundation. Michael L. Salinas is a recipient of the National Science Foundation Texas A&M University System Louis Stokes Alliance for Minority Participation (TAMUS LSAMP) Bridge to the Doctorate Fellowship (HRD-1612776). References 1. Barrera NP, Zhou M, Robinson CV (2013) The role of lipids in defining membrane protein interactions: insights from mass spectrometry. Trends Cell Biol 23:1–8 2. Zhou Y, Hancock JF (2014) Ras nanoclusters: versatile lipid-based signaling platforms. Biochim Biophys Acta 1853:841–849 3. Cho K, Hancock JF (2013) Ras nanoclusters: a new drug target? Small GTPases 4:57–60 4. Fuentes NR, Mlih M, Barhoumi R et al (2018) Long-chain n-3 fatty acids attenuate oncogenic KRas-driven proliferation by altering plasma membrane nanoscale proteolipid composition. Cancer Res 78:3899–3912

5. Salinas ML, Fuentes NR, Choate R et al (2019) AdipoRon attenuates Wnt signaling by reducing cholesterol-dependent plasma membrane rigidity. Biophys J. https://doi.org/10.1016/ j.bpj.2019.09.009 6. Sezgin E, Levental I, Mayor S, Eggeling C (2017) The mystery of membrane organization: composition, regulation and roles of lipid rafts. Nat Rev Mol Cell Biol 18:361–374 7. Sezgin E, Azbazdar Y, Ng XW, Teh C et al (2017) Binding of canonical Wnt ligands to their receptor complexes occurs in ordered plasma membrane environments. FEBS J 284:2513–2526

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8. Ashdown GW, Owen DM (2015) Imaging membrane order using environmentally sensitive fluorophores. Methods Mol Biol 1232:115–122 9. Owen DM, Rentero C, Magenau A et al (2012) Quantitative imaging of membrane lipid order in cells and organisms. Nat Protoc 7:24–35 10. Miguel L, Owen DM, Lim C et al (2011) Primary human CD4+ T cells have diverse levels of membrane lipid order that correlate with their function. J Immunol 186:3505–3516 11. Waddington KE, Pineda-Torra I, Jury EC (2019) Analyzing T-cell plasma membrane lipids by flow cytometry. Methods Mol Biol 1951:209–216 12. Fuentes NR, Kim E, Fan YY et al (2018) Omega-3 fatty acids, membrane remodeling and cancer prevention. Mol Asp Med 64:79–91 13. Fuentes NR, Salinas ML, Kim E et al (2017) Emerging role of chemoprotective agents in the dynamic shaping of plasma membrane

organization. Biochim Biophys Acta 1859:1668–1678 14. Erazo-Oliveras A, Fuentes NR, Wright RC et al (2018) Functional link between plasma membrane spatiotemporal dynamics, cancer biology, and dietary membrane-altering agents. Cancer Metastasis Rev 37:519–544 15. Sezgin E, Sadowski T, Simons K (2014) Measuring lipid packing of model and cellular membranes with environment sensitive probes. Langmuir 30:8160–8166 16. Alsabeeh N, Chausse B, Kakimoto PA et al (2018) Cell culture models of fatty acid overload: problems and solutions. Biochim Biophys Acta Mol Cell Biol Lipids 1863:143–151 17. Kim W, McMurray DN, Chapkin RS (2010) n-3 polyunsaturated fatty acids—physiological relevance of dose. Prostaglandins Leukot Essent Fat Acids 82:155–158 18. Conquer JA, Holub BJ (1998) Effect of supplementation with different doses of DHA on the levels of circulating DHA as non-esterified fatty acid in subjects of Asian Indian background. J Lipid Res 39:286–292

Chapter 15 Spatiotemporal Imaging of Small GTPase Activity Using Conformational Sensors for GTPase Activity (COSGA) Yao-Wen Wu Abstract Small GTPases cycle between active GTP bound and inactive GDP bound forms in live cells. They act as molecular switches and regulate diverse cellular processes at different times and locations in the cell. Spatiotemporal visualization of their activity provides important insights into dynamics of cellular signaling. Conformational sensors for GTPase activity (COSGAs) are based on the conserved GTPase fold and have been used as a versatile approach for imaging small GTPase activity in the cell. Conformational changes upon GDP/GTP binding can be visualized directly in solution, on beads, or in live cells using COSGA by fluorescence lifetime imaging microscopy (FLIM) technique. Herein, we describe the construction of COSGA for imaging K-Ras GTPase activity in live cells. Key words COSGA, Conformational sensor, Protein labeling, Native chemical ligation, FRET, FLIM, GTPases, K-Ras

1

Introduction Small GTP binding proteins (GTPases) such as Ras, Rac, and Rho proteins are involved in numerous crucial biological events in cells, including membrane trafficking, signal transduction, and cytoskeleton rearrangement. The Ras superfamily of small GTPases consists of over 150 members in human genome [1]. These proteins act as molecular switches by cycling between active GTP-bound and inactive GDP-bound forms, a process known as the GTPase cycle. The GTPase cycle is tightly regulated by guanine nucleotide exchange factors (GEFs) that catalyze exchange of GDP for GTP and GTPase activating proteins (GAPs) that stimulate GTP hydrolysis [2, 3]. The active GTP-bound small GTPases mediate downstream signaling by binding to different effectors. Spatiotemporal detection of the nucleotide binding state of small GTPases in the cell provides valuable information regarding the function and dynamics of signal transduction. Traditional approaches such as pull down assays only give information about

Ignacio Rubio and Ian Prior (eds.), Ras Activity and Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2262, https://doi.org/10.1007/978-1-0716-1190-6_15, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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relative quantities of GTP and GDP bound forms with no spatial and poor temporal resolution [4]. The advanced approaches for detection of GTPase activity in cells mostly rely on specific binding of an effector domain to the active GTP-bound GTPase. The binding can be monitored by intermolecular Fo¨rster resonance energy transfer (FRET) [5] or intramolecular FRET (Raichu sensor), as shown for H-Ras and Rap1 [6], Rho [7], Rab5 [8] and others. However, quantification of FRET requires calibration of the concentrations of the fluorescent species and undesired signals from donor emission bleed-through as well as direct acceptor excitation. In another method, the CRIB domain of the effector WASP labeled with an environment-sensitive dye displays an increase in fluorescence intensity upon binding to the activated Cdc42 GTPase [9, 10]. However, these strategies require a domain that binds specifically to the active GTPase. Consequently, these sensors remain limited to certain small GTPases, because for each target a binding domain has to be identified and optimized [11, 12]. In many cases there are no suitable effectors available. Furthermore, such domains may have to compete with endogenous effectors. As a result, they either fail to bind to the activated GTPase or titrate out endogenous ligands [13, 14]. Ideal probes would allow the direct observation of GTPase activation instead of indirect readout from the binding to effector domains. All small GTPases comprise a conserved guanosine nucleotide binding domain, called the G domain, which switches between the GTP- and GDP-bound form causing conformational changes in the two switch regions (Fig. 1a) [15, 16]. These conformational changes could be exploited to visualize the GTP/GDP-binding status of small GTPases in the cell. In order to detect the conformational changes, we developed novel conformational sensors for GTPase activity (COSGA) using a combination of chemical labeling and protein engineering [17]. The conformational changes can be detected by Fo¨rster resonance energy transfer (FRET) using fluorescence lifetime imaging microscopy (FLIM) (Fig. 1b). Energy transfer from a donor fluorophore to an acceptor fluorophore leads to the decrease of the fluorescence lifetime of the donor. The FRET efficiency can be derived from the measurements of the fluorescence lifetime of the donor. Fluorescence lifetime is independent of fluorescence intensity. Thus, FLIM-FRET measurement does not need to be corrected for artifacts resulted from changes in local fluorophore concentration or emission intensity and the cross-talk due to donor bleed-trough or acceptor photobleaching [18, 19]. We utilized the COSGA sensors to visualize Rab1 and K-Ras activation, deactivation, and effector binding in solution, on beads and in live cells by FLIM. Herein, we describe the method for construction of COSGA sensor for K-Ras and imaging K-Ras activity in live cells.

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Fig. 1 COSGA sensor. (a) Conformational changes in switch regions of the conserved G domain of small GTPases. The dashed line represents a disorder region. GppNHp: non-hydrolysable analog of GTP. (b) The intramolecular FRET sensor combines an N-terminal fluorescent protein (donor) with a small organic dye (acceptor) that is introduced into the protein by site-specific chemical labeling

2 2.1

Materials Plasmids

2.2 Protein Expression and Purification

1. Wild type or mutant K-Ras (D30C and E31C) (see Note 1) with C-terminal truncation of the CAAX motif (ΔCVIM) and N-terminal fusion to EGFP or mCitrine cloned in a modified pTWIN-HisTag vector (NEB) (see Note 2). 1. E. coli BL21 (DE3) or E. coli BL21-CodonPlus (DE3)-RIL cells. 2. Isopropyl-β-D-1-thiogalactopyranoside Nr. 367-93-1, Sigma Aldrich).

(IPTG)

(CAS

3. Triton X-100 (CAS Nr. 9002-93-1, Sigma Aldrich). 4. Sodium 2-mercaptoethanesulfonate Nr. 19767-45-4, Sigma Aldrich).

(MESNA)

(CAS

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5. Thiol-free bug buffer: 50 mM NaH2PO4 pH 8.0, 300 mM NaCl, 1 mM MgCl2, 10 μM GDP, 1 mM phenylmethylsulfonylfluoride (PMSF). 6. Buffer A: 50 mM NaH2PO4 pH 8.0, 300 mM NaCl, 1 mM MgCl2, 10 μM GDP. 7. Buffer B (50 mM NaH2PO4 pH 8.0, 300 mM NaCl, 1 mM MgCl2, 10 μM GDP, 500 mM imidazole). 8. Elution Buffer: 20 mM HEPES pH 7.5, 20 mM NaCl, 1 mM MgCl2, 10 μM GDP. 9. Ni-NTA column (Hi-Trap® 5 mL, GE Healthcare). 10. Superdex 200 SEC column (GE Healthcare). 11. 0.2 μm ZapCap filter (Nalgene). 2.3 Protein Labeling and Ligation

1. Tide Fluor 3 and Tide Fluor 4 (TF3/TF4) maleimide (AAT Bioquest) (see Note 3). 2. CVIM peptide prepared by solid peptide synthesis (SPPS). 3. 4-Mercaptophenylacetic acid (MPAA) (CAS Nr. 39161-84-7, Sigma Aldrich). 4. Labeling Buffer: 20 mM HEPES pH 7.5, 20 mM NaCl, 1 mM MgCl2, 10 μM GDP. 5. Ligation Buffer: 20 mM HEPES pH 7.5, 20 mM NaCl, 1 mM MgCl2, 10 μM GDP, 10 mM MESNA. 6. GTPase Buffer: 20 mM HEPES pH 7.5, 20 mM NaCl, 1 mM MgCl2, 10 μM GDP and 2 mM DTE. 7. Size-exclusion concentrator (molecular-weight cutoff 30 kDa, Millipore).

2.4 Cell Culture and Microinjection

1. HeLa, Cos-7, and MDCK cells (ATCC® CCL-2, CRL-1651, CCL-34). 2. Tissue culture vessels, e.g., 10 cm diameter dishes or T75 flask (Sarstedt). 3. Sterile single packed pipets (2, 5, 10 and 25 mL) (Sarstedt). 4. Pipette controller. 5. Sterile microliter pipets and pipet tips (10, 200, and 1000μL). 6. Glass bottom imaging vessels such as 35 mm MatTek dishes (MatTek). 7. 15 mL, 50 mL falcon tubes, sterile (Sarstedt). 8. 1.5 mL Eppendorf tubes, sterile (Eppendorf). 9. Inverted light microscope (Leica). 10. Cell culture medium: DMEM supplemented with 10% (v/v) FBS, 1% (v/v) non-essential amino acid (NEAA), 1% (v/v) sodium pyruvate, 1% (v/v) penicillin-streptomycin (Thermo Fisher).

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11. Opti-MEM medium, serum free (Thermo Fisher). 12. X-tremeGENE HP transfection reagent (Roche). 13. 0.25% Trypsin-EDTA (Thermo Fisher). 14. Phosphate-buffered saline (PBS) (Sigma Aldrich). 15. Eppendorf Transjector Micromanipulator 5171. 2.5 Confocal Microscopy and FLIM

5246

and

Eppendorf

1. Laser scanning confocal microscope FlouView FV 1000 (Olympus) equipped with time correlated single photon counting LSM Upgrade Kit (PicoQuant). 2. 60/1.35 UPlanSApo oil immersion objective (Olympus Deutschland GmbH). 3. Live-cell imaging chamber with control of humidity, temperature of 37  C, and 5% CO2.

3

Methods

3.1 Preparation of Recombinant K-Ras Thioester Proteins

1. Transform a EGFP/mCitrine-KRasΔCVIM-intein-His plasmid (in the pTWIN-HisTag vector) containing wild-type or mutant K-Ras with C-terminal truncation of CAAX motif into E. coli BL21 (DE3) or E. coli BL21-CodonPlus (DE3)-RIL cells. 2. Induce expression by 0.2 mM IPTG at 18  C for 5–12 h when the cell density (absorbance at 600 nm, OD600) reached 0.5–0.7. 3. Harvest cells by centrifugation at 5400  g, 4  C for 20 min, and wash the cells with PBS by centrifugation at 3000  g, 4  C for 15 min. 4. Suspend the bacterial cell pellet in thiol-free bug buffer (see Note 4). 5. Lyse cells and add 1% Triton X-100 into the cell lysate. 6. Centrifuge the cell lysate at 35,000 rpm, 4  C for 30 min, and filter the supernatant through a 0.2 μm ZapCap filter. 7. Load the cell lysate to Ni-NTA column (Hi-Trap 5 mL) that has been equilibrated with the Buffer A and elute the protein with a gradient of 2–100% Buffer B. 8. Identify and collect fractions of interest by SDS-PAGE. 9. Add sodium 2-mercaptoethanesulfonate (MESNA) to the protein solution to a concentration of 0.5 M and incubate overnight at 20  C (see Note 5). 10. Dilute solution with fivefold volume of Buffer A.

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11. Load them onto a Ni-NTA column (Hi-Trap 5 mL) equilibrated with the Buffer A containing 100 mM MESNA. Collect flow-through. 12. Elute column with 2–5% Buffer B containing 100 mM MESNA. Collect and concentrate protein. 13. Run a size exclusion chromatography on a Superdex 200 column using Elution Buffer. 14. Identify and collect fractions of interest by SDS-PAGE. Concentrate protein and snap-freeze in liquid nitrogen. Store protein at 80  C. 3.2 Labeling and Native Chemical Ligation of K-Ras Thioester Proteins

1. Prepare 10 mM TF3/TF4 maleimide stock solution in DMSO. 2. Incubate K-Ras thioester proteins with 1.5–2 equivalents of dyes at 25  C for 30–120 min in Labeling Buffer. Monitor the reaction by ESI-MS (see Note 6). 3. Remove excess dyes using a desalting column pre-equilibrated with Ligation Buffer. 4. Incubate K-Ras thioester proteins (5–10 mg/mL) with 2 mM CVIM peptide and 50 mM MPAA at 4  C overnight. Monitor the reaction by ESI-MS (see Note 7). 5. Remove excess peptides and exchange buffer to GTPase Buffer using a size-exclusion concentrator (molecular-weight cutoff 30 kDa). Concentrate protein to 6–10 mg/mL and snapfreeze in liquid nitrogen. Store protein at 80  C.

3.3 Imaging of Ras Activity in Live Cells

1. Centrifuge the protein at 20000  g for 5 min using a benchtop centrifuge (see Note 8). 2. Microinject the protein into 40–70 cells and incubate cells at 37  C, 5% CO2 for 1 h (see Note 9). 3. Image cells with confocal microscopy to examine the localization of K-Ras COSGA proteins (at the plasma membrane). 4. For FLIM, excite samples with a 470-nm pulsed diode laser (LDH 470) at a repetition rate of 40 MHz. 5. Collect the photons in a single-photon counting avalanche photodiode (PDM Series, MPD) by using a time-correlated single-photon counting module (PicoHarp 300) after being spectrally filtered using a narrow-band emission filter (HQ 525/15). 6. Carry out fluorescence lifetime imaging of the donor (EGFP or mCitrine) before and after EGF stimulation (see Note 10) (Fig. 2).

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Fig. 2 Imaging of K-Ras activity in cells. Confocal images of (a) mCitrine and (b) TF4. FLIM images of serum starved MDCK cells microinjected with mCitrine-KRasE31C-TF4-CVIM (c) before and (d) 5 min after EGF stimulation [17]

4

Notes 1. The residues (mutation site) involved in large conformational changes upon nucleotide exchange were identified by molecular dynamics (MD) simulations based on the root mean square fluctuation (RMSF) in GTP- and GDP-bound K-Ras structures. MD simulations of K-Ras were based on the X-ray structure of K-RasQ61H bound to GppNHP (PDB: 3GFT). The Q61H mutation was restored, and GppNHP was changed to either GDP or GTP. The labeling position should not affect GTPase functionality. Thus, those residues involved in binding with GEFs, GAPs, and effectors should be avoided. The labeling sites in K-Ras are D30 and E31 in switch I. 2. These constructs are used to reconstitute the CAAX motif. The CBD tag in the original pTWIN vector was replaced by the His tag. 3. TF3 and TF4 are thiol-reactive red dyes that serve as the acceptor for EGFP and mCitrine, respectively. 4. GDP and PMSF should be added freshly. Don’t add any reducing substances.

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5. MESNA mediates thiolysis of EGFP/mCitrine-KRasΔCVIMintein-His fusion protein to produce EGFP/mCitrineKRasΔCVIM-thioester proteins and release of intein-His. 6. The labeling should be performed under non-oxidative conditions, e.g., under argon. 7. The C-terminal CAAX motif is reconstituted by native chemical ligation (NCL). After ligation the labeled K-Ras become fully functional. MPAA substantially increases the efficacy of NCL. The concentration of peptide should not be lower in order to achieve efficient NCL. 8. The protein for microinjection should be clean to avoid blocking microinjection needle. The centrifugation is to remove any solid in the solution. 9. The health of cells should be monitored before and after microinjection. Care must be taken to avoid any contamination. 10. Use the donor only construct (EGFP/mCitrine-KRas-thioester) as a control.

Acknowledgments This work was supported by the European Research Council (ChemBioAP), Vetenskapsra˚det (Nr. 2018-04585), and The Knut and Alice Wallenberg Foundation. References 1. Wennerberg K, Rossman KL, Der CJ (2005) The Ras superfamily at a glance. J Cell Sci 118 (Pt 5):843–846 2. Bos JL, Rehmann H, Wittinghofer A (2007) GEFs and GAPs: critical elements in the control of small G proteins. Cell 129(5):865–877 3. Cherfils J, Zeghouf M (2013) Regulation of small GTPases by GEFs, GAPs, and GDIs. Physiol Rev 93(1):269–309. https://doi.org/ 10.1152/physrev.00003.2012 4. Li F, Yi L, Zhao L, Itzen A, Goody RS, Wu YW (2014) The role of the hypervariable C-terminal domain in Rab GTPases membrane targeting. Proc Natl Acad Sci U S A 111 (7):2572–2577. https://doi.org/10.1073/ pnas.1313655111 5. Kraynov VS, Chamberlain C, Bokoch GM, Schwartz MA, Slabaugh S, Hahn KM (2000) Localized Rac activation dynamics visualized in living cells. Science 290(5490):333–337 6. Mochizuki N, Yamashita S, Kurokawa K, Ohba Y, Nagai T, Miyawaki A, Matsuda M

(2001) Spatio-temporal images of growthfactor-induced activation of Ras and Rap1. Nature 411(6841):1065–1068 7. Pertz O, Hodgson L, Klemke RL, Hahn KM (2006) Spatiotemporal dynamics of RhoA activity in migrating cells. Nature 440 (7087):1069–1072. https://doi.org/10. 1038/nature04665 8. Kitano M, Nakaya M, Nakamura T, Nagata S, Matsuda M (2008) Imaging of Rab5 activity identifies essential regulators for phagosome maturation. Nature 453(7192):241–245. https://doi.org/10.1038/nature06857 9. MacNevin CJ, Toutchkine A, Marston DJ, Hsu CW, Tsygankov D, Li L, Liu B, Qi T, Nguyen DV, Hahn KM (2016) Ratiometric imaging using a single dye enables simultaneous visualization of Rac1 and Cdc42 activation. J Am Chem Soc 138(8):2571–2575. https://doi. org/10.1021/jacs.5b09764 10. Nalbant P, Hodgson L, Kraynov V, Toutchkine A, Hahn KM (2004) Activation of

COSGA: Construction and Application endogenous Cdc42 visualized in living cells. Science 305(5690):1615–1619 11. Nakamura T, Kurokawa K, Kiyokawa E, Matsuda M (2006) Analysis of the spatiotemporal activation of rho GTPases using Raichu probes. Methods Enzymol 406:315–332. https://doi. org/10.1016/S0076-6879(06)06023-X 12. Yoshizaki H, Ohba Y, Kurokawa K, Itoh RE, Nakamura T, Mochizuki N, Nagashima K, Matsuda M (2003) Activity of Rho-family GTPases during cell division as visualized with FRET-based probes. J Cell Biol 162 (2):223–232. https://doi.org/10.1083/jcb. 200212049 13. Pertz O, Hahn KM (2004) Designing biosensors for Rho family proteins—deciphering the dynamics of Rho family GTPase activation in living cells. J Cell Sci 117(Pt 8):1313–1318. https://doi.org/10.1242/jcs.01117 14. Yasuda R, Harvey CD, Zhong H, Sobczyk A, van AL, Svoboda K (2006) Supersensitive Ras activation in dendrites and spines revealed by two-photon fluorescence lifetime imaging. Nat Neurosci 9(2):283–291. https://doi.org/10. 1038/nn1635

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15. Vetter IR, Wittinghofer A (2001) The guanine nucleotide-binding switch in three dimensions. Science 294(5545):1299–1304 16. Wittinghofer A, Vetter IR (2011) Structurefunction relationships of the G domain, a canonical switch motif. Annu Rev Biochem 80:943–971. https://doi.org/10.1146/ annurev-biochem-062708-134043 17. Voss S, Kruger DM, Koch O, Wu YW (2016) Spatiotemporal imaging of small GTPases activity in live cells. Proc Natl Acad Sci U S A 113(50):14348–14353. https://doi.org/10. 1073/pnas.1613999113 18. Berezin MY, Achilefu S (2010) Fluorescence lifetime measurements and biological imaging. Chem Rev 110(5):2641–2684. https://doi. org/10.1021/cr900343z 19. Sun Y, Day RN, Periasamy A (2011) Investigating protein-protein interactions in living cells using fluorescence lifetime imaging microscopy. Nat Protoc 6(9):1324–1340. https://doi.org/10.1038/nprot.2011.364

Part IV Methods in Ras Signaling and Inhibition

Chapter 16 Using BioID to Characterize the RAS Interactome Hema Adhikari and Christopher M. Counter Abstract Identifying the proteins that associate with RAS oncoproteins has great potential, not only to elucidate how these mutant proteins are regulated and signal but also to identify potential therapeutic targets. Here we describe a detailed protocol to employ proximity labeling by the BioID methodology, which has the advantage of capturing weak or transient interactions, to identify in an unbiased manner those proteins within the immediate vicinity of oncogenic RAS proteins. Key words BioID, BirA, Proximity labeling, Interactome, KRAS, HRAS, NRAS

1

Introduction The BioID-mediated proximity-labeling approach is an emerging proteomic methodology for unbiased detection of potential protein-protein interactions [1]. This technology is based on fusing BirA*, the E. coli biotin ligase BirA harboring an R118G mutation, in-frame to a protein of interest to covalently affix biotin to proteins within 10 nm when cells are treated with exogenous biotin, a physiological distance indicative of protein interactions [2–4]. This is further improved with a new generation of BirA biotin ligases, TurboID and miniTurboID, that have a faster biotinylation kinetics and increased efficiency [5]. Biotinylated proteins are then affinity captured with streptavidin and identified by liquid chromatography coupled to tandem mass spectrometry (LC/MS/MS). One of the distinctive feature of BioID in comparison to more traditional approaches, such as coimmunoprecipitation, is detection of weak and transient interactions, as there is no need to maintain a protein complex throughout the enrichment process [4]. Given these advantages, we employed BioID to identify proteins proximal to each of the oncogenic (G12V) RAS isoforms KRAS(4b), NRAS, and HRAS (Fig. 1). We then benchmarked labeled proteins against a common RAS effector, Raf1, to nominate proteins to the RAS interactomes [6]. This approach reduced the complexity of the

Ignacio Rubio and Ian Prior (eds.), Ras Activity and Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2262, https://doi.org/10.1007/978-1-0716-1190-6_16, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Fig. 1 A flow chart showing the overall method to perform RAS BioID. HEK-HT cells are transfected with myc-BirA*-HRASG12V, myc-BirA*-NRASG12V, and myc-BirA*-KRASG12V plasmid constructs in three biological replicates. Exogenous biotin is added to the cells and incubated for 24 h. Cells are then lysed and subjected to streptavidin affinity capture, and eluates processed for mass spectrometry analysis to identify the protein interactomes of RAS oncoproteins. All samples are processed in three independent biological replicates until the mass spectrometry step

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proteome to those proteins most likely to mediate oncogenic RAS function, namely those within the immediate vicinity. Identifying the RAS interactome in such a manner can then inform any number of research directions. For example, the small size of the interactome is ideal for generating sgRNA libraries for unbiased screening, as we did to identify KRAS-specific mediators of oncogenic signaling [6], for in vivo tumor studies, or dual-targeting sgRNA libraries, to name a few. Given the many uses of this approach, we describe here how we performed BioID with oncogenic RAS mutant proteins.

2

Materials Prepare all solutions using nuclease-free water for downstream mass spectrometry analysis (Invitrogen). Prepare and store all reagents at room temperature (unless indicated otherwise). 1. pcDNA3.1 myc-BioID vector system (Addgene# 35700). 2. pBABE-puro vector (Addgene# 1764). 3. Fugene 6 Transfection Reagent (Promega Corporation). 4. HEK-HT cells [7], although other cell lines can be employed for analysis by optimizing culture conditions. 5. Biotin solution: 1 mM biotin (Sigma# B4501) dissolve in serum-free DMEM medium for exogenous addition of biotin to cells. Prepare fresh each time before use. 6. 1 PBS. 7. Cell Lysis Buffer: 50 mM Tris HCl pH 7.5; 500 mM NaCl; 0.2% SDS; protease inhibitor tablet-EDTA free (Roche); 1 mM EDTA; 1 mM PMSF; 1 mM DTT. Prepare fresh cell lysis buffer before use. PMSF and DTT should be added right before use. 8. Triton X-100. 9. Table-top centrifuge. 10. 1.5 ml and 2 ml tubes. 11. Heat block. 12. Magnetic stand. 13. Rotator mixer. 14. Wash Buffer 1: 2% SDS. Prepare fresh before use. 15. Wash Buffer 2: 0.5% Triton X-100; 1 mM EDTA; 500 mM NaCl; 50 mM Tris HCl pH 7.5. Prepare fresh before use. 16. Wash Buffer 3: 0.5% IPEGAL-CA630; 1 mM EDTA; 50 mM Tris HCl pH 7.5. Prepare fresh before use. 17. Dynabeads My One Streptavidin C1 Beads (Thermo Fisher #65001).

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18. 2 Laemmli Sample Buffer. 19. BSA Blocking buffer: 5% BSA in 1 TBSTX (1 Tris-buffered saline; 0.1% Triton X-100). 20. Powdered Milk Blocking Buffer: 5% powdered milk in 1 TBST (1 Tris-buffered saline; 0.1% Tween 20). Prepare fresh before use. 21. 1 TBSTX (20 mM Tris HCl pH 7.5; 150 mM NaCl; 0.1% w/v Triton X-100). 22. 1 TBST (20 mM Tris HCl pH 7.5; 150 mM NaCl; 0.1% w/v Tween 20). 23. Myc-tag antibody (Cell Signaling Technology #2276). 24. Pierce High Sensitivity Streptavidin-HRP (Thermo Fisher# 21130). 25. Trifluroacetic acid [TFA]. 26. Acetonitrile LC/MS grade [ACN]. 27. Maximum Recovery LC vials. 28. Yeast alcohol dehydrogenase (Sigma). 29. NanoACQUITY UPLC system (Waters) coupled to a QExactive Plus high resolution accurate mass tandem mass spectrometer (Thermo Fisher). 30. Software Packages: Rosetta Elucidator v.4 (Rosetta Biosoftware, Inc.); Swissprot database.

3

Methods

3.1 myc-BirA*RAS Fusion Expression Constructs

1. Myc-BirA*RAS fusion proteins are available at Addgene. 2. Each of the three RAS transgenes were cloned into the multiple cloning site (BamH1 and EcoRI restriction sites) of myc-BirA* pcDNA3.1 vector system. 3. A similar cloning strategy can be employed to introduce any desired RAS cDNA into the pcDNA3.1-myc-BirA* expression vector.

3.2 Transfection of myc-BirA*-RASG12V Constructs and Biotin Addition

1. Split HEK-HT cells one day before transfection (see Note 1). For each condition, a total of three 10 cm tissue culture plates are required to capture the maximum number of interactors with statistical significance and one plate is needed for immunoblot analysis (see Note 2). 2. At ~80–90% confluency, transfect 6μg of the desired pcDNA3.1 myc-BirA*-RASG12V construct(s) using 18μl of the Fugene 6 reagent according to the manufacturer’s

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protocol. As a control, transfect 6μg of the pcDNA3.1-mycBirA* empty vector into a separate plate of cells. 3. After 24 h, add 50μM of 1 mM Biotin Solution, and incubate for another 16–24 h (see Note 3). 3.3 Lysis of Cultured Cells for Immunoblot Analysis

1. Wash cells with 10 ml 1 PBS three times to remove any excess biotin present in the culture medium. 2. Add 500μl of Cell Lysis Buffer to each 10 cm plate and swirl plate for 3–5 min at room temperature (see Note 4). 3. Transfer lysed cells from each plate to a pre-chilled 1.5 ml microfuge tube (one tube per plate). 4. Add 200μl 2% Triton-X100 and pipette the solution to mix the lysate. 5. Incubate the mixture by end-to-end rotation for 30 min at 4  C. 6. After 30 min of incubation, add equal volume of chilled 50 mM Tris HCl pH 7.5 and mix by repeated pipetting. 7. Subject the lysate to centrifugation at 12,500  g for 20 min at 4  C. 8. Transfer the lysates to new, pre-chilled 2 ml microfuge tubes and proceed to bead incubation step. 9. Protein quantification should be performed at this point to ensure equal amounts of total protein are incubated in all of the samples to be tested. A minimum of 1 mg of total protein lysate is required but higher concentrations (5–10 mg) yield better results (see Note 5).

3.4 Streptavidin Bead Preparation

1. This step should be performed immediately after the cell lysis step above. Transfer 50μl of streptavidin magnetic beads in a fresh 1.5 ml microfuge tube for each sample. Incubate the tubes on a magnetic stand for 3 min at room temperature and discard the clear solution. 2. Remove the tubes from the magnetic stand and equilibrate the beads with addition of 1 ml of Cell Lysis Buffer followed by end-to-end rotation for 5 min at room temperature. 3. Place the tubes on the magnetic stand and incubate again for 3 min. Discard the lysis buffer, and transfer the lysate into a new 2 ml microfuge tubes containing the equilibrated beads. 4. Incubate the bead-lysate mixture by end-to-end rotation overnight at 4  C.

3.5 Pull Down of Biotinylated Proteins

1. Perform all steps at room temperature unless indicated otherwise. Incubate tubes on the magnetic stand for 5 min. Carefully discard all the lysate without disturbing the beads.

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2. Wash the beads by adding 1 ml of Wash Buffer 1, followed by end-to-end rotation for 5 minutes. 3. Incubate the mixture on the magnetic stand for 3 min, then carefully remove buffer with a micropipette. 4. Wash the beads by adding 1 ml of Wash Buffer 2, followed by end-to-end rotation for 5 min. 5. Incubate the mixture on the magnetic stand for 3 min, then carefully remove buffer with a micropipette. 6. Wash the beads by adding 1 ml of Wash Buffer 3, followed by end-to-end rotation for 5 min. 7. Incubate the mixture on the magnetic stand for 3 min, then carefully remove buffer with a micropipette. 8. Wash the beads by adding 1 ml of 50 mM Tris HCl pH 7.5, followed by end-to-end rotation for 5 min. 9. Incubate the mixture on the magnetic stand for 3 min, then carefully remove buffer with a micropipette. 10. Add 50μl 2 Lamelli Sample Buffer supplemented with 2 mM biotin to the beads and mix by pipetting the mixture up and down 5 times using a cut 200μl pipette tip (see Note 6). 11. Heat the beads at 90  C in a pre-heated heating block for 5 min to release the bound proteins from the beads (see Note 7). 12. Place the tubes on the magnetic stand for 5 min and then transfer the eluate to a pre-chilled 1.5 ml microfuge tube and immediately freeze on dry-ice (see Note 8). 13. Prior to performing LC/MS/MS analysis, analyze the eluate for biotin labeling (Fig. 2) and protein-expression, as described next (see Notes 9 and 10). 3.6 Confirming the Biotin-Labeling Activity of the myc-BirA*-RASG12V Fusion Protein (s) by Immunoblot

1. Load ~5–10μl of the eluate from one tube per condition into two 10% SDS-polyacrylamide gels, one for detecting biotinlabeled proteins as described here and one for detecting myc-BirA*-RASG12V fusion protein as described next in Subheading 3.7. Separate proteins by electrophoresis. 2. Perform protein transfer to a PVDF membrane from both gels using a Turbo Blot system (BioRad) according to the manufacturer’s protocol. 3. Remove and then block one membrane in BSA Blocking Buffer for 30 min to 1 h at room temperature; the other is processed as described next in Subheading 3.7 (see Note 11). 4. To detect biotin-labeled proteins, incubate the membrane with the Strep-HRP antibody diluted 1:20,000 in BSA Blocking Buffer for 1 h at room temperature. 5. Wash the membrane three times in 1 TBST, each for 10 min.

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V

H

N

K

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kDa 250 150

strep-HRP

100 75 50 37 25 20 50

actin Fig. 2 Immunoblot confirming the biotinylation profiles of cells expressing myc-BirA* vector alone (V), myc-BirA*-HRASG12V (H), myc-BirA*-NRASG12V (N), and myc-BirA*-KRASG12V (K) on exogenous addition of biotin that is detected by streptavidin-HRP antibody. Actin is used as a loading control

6. Process the membrane for chemiluminescence detection either on X-ray film or by digital acquisition. 3.7 Confirming Expression of the mycBirA*-RASG12V Fusion Protein (s) by Immunoblot

1. Gel electrophoresis and transfer of proteins are performed as described above. 2. Incubate the membrane in Powdered Milk Blocking Buffer for 1 h. 3. To detect expression of the fusion protein, incubate the membrane with the myc antibody diluted 1:5000 in Powdered Milk Blocking Buffer for either 1 h at room temperature or overnight at 4  C. 4. Wash the membrane three times in 1 TBST, each for 10 min. 5. Process the membrane for chemiluminescence detection either on X-ray film or by digital acquisition.

3.8 Quantitative LC MS/MS Analysis

1. Resolve 50μl of each sample (namely, the three remaining samples per condition) by SDS-PAGE (4–12% Invitrogen NuPAGE; MES buffer) for 3 min, followed by fixation and Coomassie staining. 2. Excise bands and perform in-gel tryptic digestion using standard methodologies (see https://genome.duke.edu/sites/ genome.duke.edu/files/In-gelDigestion Protocolrevised_0. pdf). 3. Dry peptides in a SpeedVac, and then reconstitute each sample in 40μl of 1% TFA/2% ACN containing 25 fmol/μl yeast

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alcohol dehydrogenase surrogate standard and transferred to Maximum Recovery LC vials. A quality control (QC) pool is prepared by mixing equal volumes of all samples. 4. Analyze samples using a nanoACQUITY UPLC system coupled to a QExactive Plus high resolution accurate mass tandem mass spectrometer via a nanoelectrospray ionization source. 5. Import data into Rosetta Elucidator v.4 (Rosetta Biosoftware, Inc.). Align based on the accurate mass and retention time of detected ions (“features”) using PeakTeller algorithm in Elucidator. Calculate relative peptide abundance based on area under the curve (AUC) of the selected ion chromatograms of the aligned features across all runs. 6. Search MS/MS data against a custom Swissprot database with Homo sapiens taxonomy with additional proteins, including yeast ADH1 and bovine serum albumin, as well as an equal number of reversed-sequence “decoys” for false discovery rate determination (40,546 total entries). Search database and score peptides using the PeptideProphet algorithm, annotate data at a 0.9% peptide false discovery rate. 3.9

Data Analysis

1. In order to assess technical reproducibility, % coefficient of variation (%CV) for each protein is calculated across the four injections of a QC pool interspersed throughout the samples. 2. To assess biological variability, %CVs are measured for each protein across the individual analyses. 3. It is possible that differences in expression across treatment groups reflect the biotin capture efficiency or the overall expression level of BirA fusion proteins. 4. To account for these variables, two additional normalizations of the data are required. 5. First, to control for the variability in streptavidin pulldown, normalize data to the mean intensity of peptides from endogenously-biotinylated carboxylases (see Note 12). 6. Second, normalize the data to the levels of BirA* protein across each sample. 7. However, we found that normalization to BirA* was effective in normalizing the total intensity of Ras proteins (including shared peptides) across the samples; therefore we suggest using the BirA*-normalized data for initial statistical analysis. 8. As an initial statistical analysis, calculate fold changes between groups based on the average fold changes for each comparison (e.g., HRAS versus control, HRAS versus NRAS, etc.) for proteins in which a minimum of 2 peptides are recovered.

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9. An unpaired t-test (e.g., in Excel) on the log2-transformed data should be performed for each of these comparisons. 10. The resulting dataset can then be analyzed to identify proteins specific to different RAS isoforms. For example, to select for proteins specifically enriched in HRAS-expressing cells, proteins were filtered to include those quantified by 15 min. 5. Prepare cells for top agar layer (see Note 22). 6. Warm cell suspension briefly to 45
C (2 min). 7. Add twice the volume of 0.5% agar/medium solution at 45
C (final agar 0.33%) to each tube of cells. 8. Carefully layer 3 ml agar/cell suspension onto bottom agar layer. 9. Let plates set at room temperature in tissue culture hood for at least 30 min prior to returning to 37
C incubator, 5% CO2. 10. Feed cells 1–2 per week by careful dropwise addition of growth media to top layer. Remove old media prior to subsequent feedings. Let cells grow for approximately 3 weeks until visible colonies emerge. 11. Colonies are quantified following staining with MTT. Overlay soft agar plates with 100 μl solution of MTT (2 mg/ml water), and incubate at 37
C for 4 h (Fig. 5b). 12. Colonies are quantified using NIH ImageJ software. Briefly, open a digital image (TIFF) of each plate with ImageJ, and use the line tool to draw a line across the width of a well. Go to image settings > adjust > set the threshold. This results in a black and white binary image where the colonies are shown as black dots on a white background. Use the circle tool to draw around the well you want to measure. Next select “Analyze” > “Measure Particles”. Set the scale to measure colonies

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in the range of 0–infinity and circularity 0.00–1.00, and then select “Show” and then “Outlines.” Check the Display Results box, and then select “OK.” This will measure the number of colonies in the well. Simply move your circle to the next well and repeat. 3.11 Xenograft Tumor Assays

Nude mouse tumor assays represent a robust biological assay to assess the efficacy of anti-RAS therapies. In this assay, human cancer cells are injected subcutaneously into the flanks of immunocompromised mice, and the response of emergent tumors to therapeutic regimes is evaluated. To determine the anti-tumor effects of Mbs, human cancer lines generated in Subheading 3.8.2 are injected into athymic nude mice and assessed for their ability to form tumors in the absence or presence of Mb expression following DOX administration. 1. Grow the requisite number of cells in 150 mm2 dishes (see Note 23). 2. Wash cells with PBS (10 ml per plate) and then harvest by trypsinization. 3. Combine cells, and wash thoroughly with complete media to remove residual trypsin. 4. Aspirate media and combine pellets in 30 ml serum-free media. 5. Resuspend cells, dilute 10 μl of cell suspension in 490 μl media (1:50 dilution), and count. 6. Aliquot 7.5  107 cells to a tube, centrifuge at 1500 RPM for 8 min, resuspend cell pellet in 900 μl media, and cool on ice for 5 min (number of cells assumes 5  106 cells injected per mouse). 7. Thaw Matrigel O/N at 4
C and aliquot to individual pre-cooled tubes on ice. Add 60 μl of cell suspension to each 60 μl aliquot of Matrigel tube and place on ice. 8. Pre-cool 1 ml syringes and 26-gauge needles to ensure that the Matrigel does not begin to solidify prior to injecting mice. 9. Transfer cell suspensions to ice, along with needles and syringes. 10. Draw 120 μl cell suspension into syringe. 11. Insert needle, and bevel side up, under mouse skin. Gently lift needle, and continue to insert about 1 cm under the skin with point slightly angled toward the body of the mouse being careful that needle remains under the skin. 12. Carefully inject the entire cell suspension, and then gradually withdraw the needle (see Note 24).

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13. To induce Mb expression, a cohort of animals are provided 150 ml DOX water (2 mg/ml DOX) 24 h. Following cell injections. Drinking bottles are changed every 2–3 days (see Note 25). 14. To assess whether Mbs promotes tumor regression, mice are provided DOX water once tumors reach 50–200 mm3 volume. 15. Mice are observed 2 per week until tumors begin to emerge, and then tumor volumes are measured every other day until human endpoints are reached (see Note 26). 16. Upon reaching experimental endpoints, tumors are harvested from animals (see Note 27). 3.12 Tumor Cell Line Generation

1. Euthanize mouse and spray with 70% ethanol. 2. Harvest a small amount of tumor (~0.5–1 mm), and transfer tumor fragments into 60 mm2 dishes with 10 ml enzyme solution (collagenase c.f. 1 mg/ml, elastase c.f. ~0.744 units/ ml, Pen-Strep c.f. 1%) in HBSS buffer (c.f. final concentration). 3. Use sterile scalpel or scissors to cut tumors into fine fragments, and let the tumor fragments incubate in enzyme solution at 5% CO2, 37
C for 2–3 h. Mix the tumor fragment solution several times during the incubation period with a sterile 5 ml pipette to further aid in cell dissociation. 4. Centrifuge the enzyme solution at 450  g for 6 min to pellet dissociated cells. 5. Remove supernatant and then resuspend pellet with fresh 25 ml culture medium. Centrifuge at 1500 rpm for 6 min to pellet cells. 6. Aspirate media and then resuspend cell pellet in 10 ml of desired culture media. 7. Transfer to 100 mm2 dishes with fungizone (1 μg/ml) and antibiotic selection for Mb expression construct (e.g., puromycin). This selection will also eliminate any murine cells. 8. Let cells seed O/N. On the following day, wash with PBS, and add culture media with fungizone and antibiotic selection (see Note 28). Normally cell should be ready to passage in a couple of days. However, we typically culture for about a week with fungizone to prevent fungal growth and then remove from routine culturing of cells. 9. Tumor cell lines can then be frozen after about 2 weeks in antibiotic selection for later analysis (see Note 29).

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Notes 1. PEI stored at 80
C can be used at least 2 after freeze-thaw without a decrease in efficacy. 2. The PCR product should be verified on an agarose gel. 3. Alternative approaches to subcloning can be used. We have observed greater than 90% success rate in isolating clones with inserts using this approach. 4. We typically use a small volume PCR reaction (20–25 μl/per colony) with a low-cost thermostable polymerase such as Taq polymerase. To select a colony, use a sterile pipette tip to gently touch the bacterial colony, and then transfer a small portion to the PCR reaction. Reactions are subjected to the following cycling parameter: 1 cycle 94
C, 5 min, 50
C, 30 s, 72
C, 1 min; 30 cycles 94
C, 30 s, 50
C, 30 s, 72
C 1 min; 1 cycle 94
C, 30 s, 72
C 10 min. Soak at 4
C. Approximately 5–7 μl of the reaction is analyzed on an 0.8% agarose gel in 1TAE buffer. Clones with inserts will result in an ~390 bp fragment, depending on the Mb. Given the success rate of the Gibson Assembly Kit, we screen between 12 and 24 colonies for inserts. 5. Cells are passaged at least once after thawing from liquid nitrogen, adapted to the media, and free of any mycoplasma contamination. 6. Opti-MEM and cell growth media are warmed to 37
C prior to use. The volume of Opti-MEM used is 1/10 the final volume of serum-free media added to cells. 7. Amount of RAS expression vectors to be transfected depends on the RAS isoform being studied. Equivalent μg amounts of HRAS and NRAS express equally, while KRAS expression is typically lower. To achieve equal expression among various isoforms, we normally use two fold more KRAS vs H/NRAS. 8. Avoid incubating HEK293 cells with PEI transfection mixture for more than 3 h to reduce cell toxicity and death. 9. Amount of PLC depends on the size of tissue culture plate. We usually use 500 μl for 35 cm2 plate, 750 μl for 60 cm2 plate, and 1 ml for 100 cm2 plate. 10. Lysates can be used directly for protein estimation and analysis or stored at 80
C for later use. 11. Choice of Protein A vs Protein G beads is dependent on the immunoglobulin subtype and the species in which the antibody was generated. Protein A agarose beads work optimally for antibodies raised in rabbits, whereas Protein G agarose beads are optimal for antibodies raised in mice.

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12. When aspirating the supernatant, it is best to leave a small volume of buffer (~50 μl) in each tube to avoid aspirating beads which will result in uneven results. After the last wash, carefully remove as much supernatant as possible leaving equivalent amounts in all tubes. 13. MYC-ERK is co-transfected to measure the effects of Mbs on ERK activation only in transfected cells. Activation of endogenous ERK can be measured by Western blot analysis of cell lysates directly with anti-pERK and total ERK antibodies. However, this approach may underestimate the effects of the Mbs on RAS-mediated signaling. To assess the effects of Mbs on RAS-mediated activation of PI3K-AKT pathway, experiments are performed essentially as described for analysis of ERK except HA-AKT is substituted for MYC-ERK in the co-transfections. HA immunoprecipitates are then analyzed for total AKT and pAKT with activation specific antibodies to Ser473 and Thr308 of AKT. 14. RASless MEFs are genetically modified mouse embryonic fibroblasts that lack all three RAS genes [25]. However, these cells can be engineered to express only a single RAS isoform or a constitutively activated BRAF(V600E). Thus, a RAS-specific Mb should inhibit MAPK activity only in cells expressing the RAS isoform targeted by the Mb but not cells expressing BRAF (V600E). 15. For optimal inhibition of RAS signaling, Mb expression needs to be >2.5 RAS expression [3]. This is determined by co-transfection of RAS and Mb constructs with the same epitope tags. Careful titration of Mb expression levels can then be assessed by Western blot analysis of transfected cells to determine the ratio of Mb to RAS for optimal inhibition. 16. We use Image Studio Lite (v5.2.5, LI-COR Biosciences, USA) to quantify images obtained on a BioRad Gel Doc Image station. However, alternative approaches can be used to quantify gel bands including ImageJ or other imaging software. 17. Longer incubations of NIH/3T3 cells in serum-free media lead to cell death due to serum deprivation. Incubation times should be determined empirically to maximize transfection efficiency while minimizing cell loss. 18. Assays should be stopped before foci grow too large and begin to merge with one another. 19. To obtain optimal infection of recipient cells, replace media with the same media used to culture cells that will be infected with virus. 20. Instrument settings depend on the manufacturer.

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21. It is critical that all components are warmed to 45
C so that the agar does not begin to solidify before plating. 22. The number of cells plated on top layer of soft agar is dependent on the specific growth kinetics of the cell line being assayed. Several concentrations of cells are plated to determine optimal number. We typically seed 5–10  103 cells per well. As an example, for cell line A where 5  103 cells per well is used, dilute 17.5  103 of cells (for 3 wells) without DOX in 3.5 ml media without DOX. Prepare an identical sample with DOX. 23. The number of cells injected depends on the growth kinetics of the cell line. We typically use 5  106 to 1  107 cells per injection site per animal. A power analysis should be used to determine the number of animals needed. We typically use six animals per condition. 24. A small bolus of cell suspension will remain under the skin. This lump will gradually dissipate in a few days. 25. Alternatively, mice can be fed commercially prepared chow containing DOX. 26. Tumor size is measured by recording length and width of the tumor using digital caliper, and volumes are calculated using the formula: length  width2  0.52. 27. Tumors may be used for the generation of cell lines and lysates for biochemical analyses and sectioned for pathological and immunohistochemical analysis. Mb expression is typically confirmed by Western blot analysis of tumor lysates or by immunofluorescence using anti-FLAG or anti-GFP antibodies. 28. Cell lines generated from mice treated with DOX to induce Mb expression should normally be positive for fluorescence on the day after plating. However, this fluorescence will dissipate over time if DOX is not added to cell culture media. 29. Tumors that emerge in the RAS Mb-expressing cohort may have acquired resistance to RAS inhibition. These tumor lines can serve as useful tools in the identification of potential resistance mechanisms.

Acknowledgments This work was supported in part by a Merit Review Award (1I01BX002095) from the United States (US) Department of Veterans Affairs Biomedical Laboratory Research and Development Service, NIH awards (CA212608 and CA138313), and start-up funds from the Hollings Cancer Center at MUSC to J.P.O. The contents of this article do not represent the views of the US Department of Veterans Affairs or the US government.

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References 1. Sha F, Salzman G, Gupta A, Koide S (2017) Monobodies and other synthetic binding proteins for expanding protein science. Protein Sci 26(5):910–924 2. Spencer-Smith R, Koide A, Zhou Y, Eguchi RR, Sha F, Gajwani P et al (2017) Inhibition of RAS function through targeting an allosteric regulatory site. Nat Chem Biol 13(1):62–68 3. Spencer-Smith R, Li L, Prasad S, Koide A, Koide S, O’Bryan JP (2017) Targeting the α4-α5 interface of RAS results in multiple levels of inhibition. Small GTPases:1–10 4. Khan I, Spencer-Smith R, O’Bryan JP (2019) Targeting the α4-α5 dimerization interface of K-RAS inhibits tumor formation in vivo. Oncogene 38(16):2984–2993 5. Lee K-Y, Fang Z, Enomoto M, Seabrook GG, Zheng L, Koide S et al (2020) Two distinct structures of membrane-associated homodimers of GTP- and GDP-bound KRAS4B revealed by paramagnetic relaxation enhancement. Angew Chem Int Ed Engl 59 (27):11037–11045 6. Freeman AK, Ritt DA, Morrison DK (2013) Effects of Raf dimerization and its inhibition on normal and disease associated Raf signaling. Mol Cell 49(4):751–758 7. Koide A, Koide S (2007) Monobodies. In: Arndt KM, Mu¨ller KM (eds) Protein engineering protocols. Humana Press, Totowa, NJ, pp 95–109. (Methods in Molecular Biology™) 8. Koide S, Koide A, Lipovsˇek D (2012) Targetbinding proteins based on the 10th human fibronectin type III domain (10Fn3). Methods Enzymol 503:135–156 9. Koide A, Koide S (2012) Affinity maturation of single-domain antibodies by yeast surface display. Methods Mol Biol 911:431–443 10. Hopp TP, Prickett KS, Price VL, Libby RT, March CJ, Pat Cerretti D et al (1988) A short polypeptide marker sequence useful for recombinant protein identification and purification. Nat Biotechnol 6(10):1204–1210 11. Koide A, Wojcik J, Gilbreth RN, Hoey RJ, Koide S (2012) Teaching an old scaffold new tricks: monobodies constructed using alternative surfaces of the FN3 scaffold. J Mol Biol 415(2):393–405 12. Clark SG, McGrath JP, Levinson AD (1985) Expression of normal and activated human Ha-ras cDNAs in Saccharomyces cerevisiae. Mol Cell Biol 5(10):2746–2752

13. Longo PA, Kavran JM, Kim M-S, Leahy DJ (2013) Transient mammalian cell transfection with polyethylenimine (PEI). Methods Enzymol 529:227–240 14. Akinc A, Thomas M, Klibanov AM, Langer R (2005) Exploring polyethylenimine-mediated DNA transfection and the proton sponge hypothesis. J Gene Med 7(5):657–663 15. Rajalingam K, Schreck R, Rapp UR, Albert S (2007) Ras oncogenes and their downstream targets. Biochim Biophys Acta 1773 (8):1177–1195 16. Spencer-Smith R, O’Bryan JP (2019) Direct inhibition of RAS: quest for the Holy Grail? Semin Cancer Biol 54:138–148 17. Khan I, Rhett JM, O’Bryan JP (2020) Therapeutic targeting of RAS: new hope for drugging the “undruggable”. Biochim Biophys Acta, Mol Cell Res 1867(2):118570 18. Rajakulendran T, Sahmi M, Lefranc¸ois M, Sicheri F, Therrien M (2009) A dimerizationdependent mechanism drives RAF catalytic activation. Nature 461(7263):542–545 19. Bondzi C, Grant S, Krystal GW (2000) A novel assay for the measurement of Raf-1 kinase activity. Oncogene 19(43):5030–5033 20. Clark GJ, Cox AD, Graham SM, Der CJ (1995) Biological assays for Ras transformation. Methods Enzymol 255:395–412 21. Estis LF, Temin HM (1979) Suppression of multiplication of avian sarcoma virus by rapid spread of transformation-defective virus of the same subgroup. J Virol 31(2):389–397 22. Kangas L, Gro¨nroos M, Nieminen AL (1984) Bioluminescence of cellular ATP: a new method for evaluating cytotoxic agents in vitro. Med Biol 62(6):338–343 23. Crouch SP, Kozlowski R, Slater KJ, Fletcher J (1993) The use of ATP bioluminescence as a measure of cell proliferation and cytotoxicity. J Immunol Methods 160(1):81–88 24. Borowicz S, Van Scoyk M, Avasarala S, Karuppusamy Rathinam MK, Tauler J, Bikkavilli RK et al (2014) The soft agar colony formation assay. J Vis Exp (92):e51998 25. Drosten M, Dhawahir A, Sum EYM, Urosevic J, Lechuga CG, Esteban LM et al (2010) Genetic analysis of Ras signalling pathways in cell proliferation, migration and survival. EMBO J 29(6):1091–1104

Chapter 18 Detection of Endogenous RASSF1A Interacting Proteins Howard Donninger, Desmond Harrell-Stewart, and Geoffrey J. Clark Abstract RASSF1A is a Ras effector that promotes the anti-proliferative properties of Ras. It acts as a scaffold protein that regulates several pro-apoptotic signaling pathways, thereby linking Ras to their regulation. However, accumulating evidence suggests that RASSF1A functions as a regulator of other additional biological processes, such as DNA repair and transcription, thereby implicating Ras in the modulation of these biological processes. The mechanisms by which RASSF1A modulates these processes is not fully understood but likely involves interacting with other effectors associated with these functions and coordinating their activity. Thus, to fully understand how RASSF1A manifests its activity, it is critical to identify RASSF1A interacting partners. Unfortunately, the reagents available for the detection of RASSF1A are of poor quality and also exhibit low sensitivity. Here we describe an immunoprecipitation protocol, taking into consideration the limitations of currently available reagents, that can reliably detect the endogenous interaction between RASSF1A and its binding partners. Key words Ras, RASSF1A, Immunoprecipitation, Immunoblotting, Antibodies

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Introduction The Ras oncoprotein stimulates a wide variety of signaling cascades in response to mitogenic signals that synergize to promote growth and transformation [1–3]. These growth-promoting properties of Ras manifest due to Ras interactions with various effector proteins, most notably the Raf kinases, PI-3 kinase, and the Ral-GEF family [1, 4, 5]. Paradoxically, Ras can also mediate growth inhibition and induce apoptosis [6]; however the mechanisms and signaling pathways Ras employs to inhibit proliferation are not nearly as well defined as the proliferative signals emanating from Ras. The growth inhibitory properties of Ras are likely mediated through the interaction of Ras with a set of negative Ras effectors or “death” effectors such as the RASSF family [7–9], the best characterized of which is RASSF1A [10, 11]. RASSF1A is a bona fide Ras effector and can be found in an endogenous complex with Ras [12, 13]. It does not exhibit any

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enzymatic activity and acts as a scaffold protein regulating multiple growth inhibitory process and tumor suppressor pathways [7, 10], including the HIPPO signaling pathway [13–15], apoptosis [16, 17], DNA damage repair [18, 19], microtubule dynamics [20–22], and transcription via modulation of nuclear actin localization [23]. The interaction of RASSF1A with the mediators of these processes is frequently regulated by Ras, thereby implicating Ras in the regulation of these various growth inhibitory mechanisms. There are likely additional biological processes that Ras impacts to inhibit cell growth, and identifying and confirming the interaction of RASSF1A with the effectors of these pathways/biological processes is critical to understanding and elucidating the mechanisms that Ras employs to subvert growth. In this chapter we describe in detail an immunoprecipitation protocol for confirming the endogenous interaction of RASSF1A with its binding partners. Given that the antibodies against RASSF1A exhibit low sensitivity and are of relatively poor quality, and that RASSF1A is expressed at low levels due to its frequent methylation in human tumors and tumor cell lines [10], we have optimized the immunoprecipitation procedure to account for the limitations of current RASSF1A antibodies.

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Materials Prepare all reagents using ultrapure water (18 mΩ cm) and analytical grade reagents. Prepare and store all reagents at room temperature unless otherwise indicated.

2.1 Immunoprecipitation

1. Lysis buffer: 10 mM Tris–HCl (pH 7.5), 150 mM NaCl, 0.5 mM EDTA, 0.5% Tergitol (70% aqueous solution) (see Note 1). Add 0.5 ml of 1 M Tris–HCl (pH 7.5), 1.5 ml of 5 M NaCl, 50 μl 0.5 M EDTA to a 50 ml polypropylene tube. Add water to a volume of approximately 40 ml. Then add 250 μl of the melted NP-40/Tergitol (see Note 2). Once the Tergitol has completely dissolved, add water to a volume of 50 ml. Store at 4  C. 2. Dilution/wash buffer: 10 mM Tris–HCl (ph 7.5), 150 mM NaCl, 0.5 mM EDTA. Add 0.5 ml 1 M Tris–HCl (pH 7.5), 1.5 ml 5 M NaCl, 50 μl 0.5 M EDTA to a 50 ml polypropylene tube. Add water to a volume of 50 ml. Store at 4  C. 3. 1 phosphate-buffered saline (PBS). 4. Antibody specific to the target protein of choice (see Note 3). 5. Species-specific IgG immunoprecipitation beads (Protein A and Protein G agarose beads).

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6. RIPA buffer: 50 mM Tris–HCl, pH 8.0, 150 mM NaCl, 1.0% Igepal CA-630 (NP-40), 0.5% sodium deoxycholate, 0.1% sodium dodecyl sulfate (SDS). Store at 4  C. 7. LDS (lithium dodecyl sulfate) sample buffer (4) (Invitrogen): 106 mM Tris–HCl, 141 mM Tris base, 2% LDS, 10% glycerol, 0.51 mM EDTA, 0.22 mM SERVA Blue G250, 0.175 mM Phenol Red, pH 8.5 (see Note 4). Store at room temperature. 8. Protease inhibitor cocktail (Sigma Aldrich). Aliquot and store at 20  C. 9. 100 mM Na orthovanadate (Na3VO4) made up in water. Aliquot and store at 20  C. 10. Cell scrapers. 2.2

Immunoblotting

1. Nitrocellulose membranes. 2. Whatman 3MM blotting paper. 3. NuPage Novex electrophoresis system (Invitrogen), or equivalent. 4. NuPage Novex 4–12% Bis-Tris gels (Invitrogen), or equivalent. Store at 4  C. 5. NuPage MOPS SDS running buffer (20) (Invitrogen), or equivalent: 50 mM MOPS, 50 mM Tris base, 0.1% SDS, 1 mM EDTA, pH 7.7. Dilute 20 SDS running buffer to a final concentration of 1 with ultrapure water. 1 SDS running buffer can be re-used at least five times. Store at room temperature. 6. NuPage transfer buffer (20) (Invitrogen), or equivalent: 25 mM Bicine, 25 mM Bis-Tris (free base), 1 mM EDTA, pH 7.2. Dilute 50 ml 20 transfer buffer in 500 ml water. Add 200 ml methanol. Adjust the volume to 1 L with water. 1 transfer buffer can be re-used at least five times. Store at room temperature. 7. Tris-buffered saline containing 0.05% Tween 20 (TBST; 10): 1.5 M NaCl, 0.5 M Tris–HCl, pH 7.5, 0.5% Tween 20. Store at room temperature. 8. Blocking solution: 5% non-fat milk in 1 TBST. Add 2.5 g non-fat milk powder to 50 ml 1 TBST in a 50 ml conical tube. Gently mix to dissolve the milk. Ensure the milk powder is completely dissolved before use. Store at 4  C. 9. Mouse monoclonal (clone 3F3) anti-RASSF1A antibody (the one that currently works the best). 10. Anti-mouse/rabbit-HRP secondary antibodies. 11. Pre-stained protein standards. 12. Plastic containers.

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2.3 Sources of Proteins

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1. Mammalian cell lines. 2. Mammalian tissue.

Methods Carry out all procedures at room temperature unless otherwise stated.

3.1 Preparation of Lysates

1. Add protease inhibitors and Na3VO4 to the lysis buffer (see Note 5). Add 10 μl of the protease inhibitor cocktail and 100 mM Na3VO4 per milliliter of lysis buffer. Vortex well and keep the solution on ice. 2. Lyse cells or tissue in a suitable volume of lysis buffer (see Note 6). 3. Clarify the lysate by passing the lysate through a 21 G needle 3–5 times. Centrifuge the lysate in a microfuge at 13,000 rpm (10,000  g), 4  C for 3 min and transfer the supernatant to a fresh Eppendorf tube. 4. Determine the protein concentration using either a Bradford or BCA Protein Assay.

3.2 Immunoprecipitation

1. Transfer 1 mg of lysate into a fresh Eppendorf tube and add dilution/wash buffer (containing protease inhibitors and Na3VO4) to a final volume of 1 ml. Add the appropriate amount (as suggested by the manufacturer) of primary antibody (typically 2–4 μg) to the target of interest (see Note 7). Rotate the sample at 4  C for 16 h. It is important to also include the following controls: (1) lysate plus control IgG and (2) primary antibody plus dilution/wash buffer. 2. Add IgG immunoprecipitation beads (10 μl) to each sample and incubate the samples at 4  C for 2–3 h with gentle rotation. 3. Centrifuge the samples in a microfuge at 3000 rpm (600  g) (see Note 8) for 2 min at 4  C to pellet the IgG beads/antibody/protein complexes. 4. Carefully aspirate off the supernatant without disturbing the beads (see Note 9). 5. Wash beads by adding 500 μl dilution/wash buffer to each tube and gently inverting 5–6 times. Pellet the beads by centrifugation in a microfuge at 3000 rpm (600  g) for 2 min at 4  C. 6. Aspirate the supernatant and repeat the wash step a total of three times.

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7. Remove as much of the supernatant as possible after the final wash without disturbing the beads. Add 15 μl of RIPA buffer and 5 μl of 4 LDS sample buffer to each tube and vortex for 10 s (see Note 10). 3.3

Immunoblotting

1. Heat the samples at 95  C for 10 min. Include an aliquot of the lysates (30–50 μg; containing LDS loading dye at a final concentration of 1) to determine input levels. Vortex the immunoprecipitated samples again for 10 s. Centrifuge the heated samples for 20 s in a microfuge at 13,000 rpm (10,000  g) to pellet any condensate. Place samples immediately back on ice. Load the samples on the polyacrylamide gel, together with 5 μl of pre-stained protein standards. Electrophorese at 120 V until the dye front reaches the bottom of the gel. 2. After electrophoresis, pry the gel plates apart with a spatula and remove the top portion of the gel containing the wells. Carefully transfer the gel to a glass dish containing transfer buffer (see Note 11), and rinse the gel in the transfer buffer for approximately 5 min. 3. Pre-cut two pieces of Whatman 3MM paper and one piece of nitrocellulose membrane to the same size as the gel. Set up a sandwich transfer as follows: Completely wet two sponges with the transfer buffer and place in the lower half of the transfer cassette. Gently float the gel onto one of the pieces of Whatman paper, pre-soaked in transfer buffer. Place this on top of the two sponges in the transfer cassette. Place the pre-cut nitrocellulose membrane, also soaked in transfer buffer, on top of the gel. Use the spatula to gently squeeze out any air bubbles between the membrane and the gel. Place the other piece of Whatman paper (pre-soaked in transfer buffer) on top of the nitrocellulose membrane and gently squeeze any air bubbles from between the Whatman paper and the membrane using the spatula. Place a stack of sponges (pre-soaked in transfer buffer) on top of the Whatman/nitrocellulose/gel “sandwich.” Use as many sponges as required such that a tight fit forms between the top and bottom parts of the transfer cassette. 4. Place the transfer cassette in the transfer chamber with the appropriate electrodes aligned correctly. Fill the chamber with chilled 1 transfer buffer to completely cover the transfer cassette. Transfer the proteins from the gel on to the nitrocellulose at 30 V for 3 h, at room temperature. 5. Following transfer, remove the transfer cassette from the transfer chamber and gently pull apart the gel-membrane “sandwich.” Cut away excess membrane using a scalpel blade. Rinse the membrane with 1 TBST for 5 min with gentle agitation.

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6. Block the membrane with blocking buffer for 1 h. 7. Transfer the membrane to a 50 ml conical tube and add the RASSF1A primary antibody (see Note 12). 8. Incubate the membrane with the RASSF1A primary antibody overnight at 4  C with gentle agitation. 9. Rinse the membrane 3  5 min and 1  10 min with 1 TBST. 10. Add anti-mouse secondary antibody conjugated to horse radish peroxidase (HRP), diluted 1:5000–1:10,000 in 1 TBST, and incubate the membrane at room temperature with gentle agitation for 1 h (see Note 13). 11. Wash as in step 9. 12. Drain as much of the TBST off the membrane as possible by holding it against some paper towel and place the membrane face up on a piece of clear plastic wrap. 13. Add ECL detection reagent to the membrane for 2–5 min with gentle agitation (see Note 14). 14. Drain excess ECL reagent from the membrane as described in step 12. 15. Place the membrane face down on a fresh piece of clear plastic wrap. Fold the plastic wrap over such that the membrane is completely sealed, ensuring there are no air bubbles. 16. Tape the sealed membrane face up in an X-ray cassette and expose to X-ray film (see Note 15).

4

Notes 1. Melt the Tergitol for approximately 1 h at 40–50  C on a hot plate. Place the bottle of Tergitol in a beaker filled one-third with water to facilitate melting of the Tergitol. Remove the Tergitol from the heat immediately after it has completely melted, and add the required amount to the lysis buffer. 2. The melted Tergitol will form a precipitate once added to the solution. It is important to completely dissolve the precipitate by gently agitating the solution and warming it to 37  C. 3. It is important to choose a primary antibody that is compatible with immunoprecipitation. 4. Add 2-mercaptoethanol to a final concentration of 10% and store buffer at 4  C. 5. Add the protease inhibitor and Na3VO4 to the lysis buffer fresh each time and prepare this directly prior to lysing the cells or tissue.

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6. For mammalian cell lines, typically a 100 mm cell culture dish will yield sufficient lysate. A volume of 500 μl will result in efficient cell lysis while ensuring the lysate is concentrated. Wash the cells with 1 PBS, aspirate as much of the PBS as possible then add the lysis buffer directly to the culture dish. Scrape the cells off the dish and transfer the lysate into an Eppendorf tube. To ensure efficient lysis, rotate the lysate at 4  C for 3–4 h. 7. Since the quality and sensitivity of RASSF1A antibodies is poor, it is preferable to immunoprecipitate the target protein of interest and then detect RASSF1A in the immunoprecipitate by immunoblotting with a RASSF1A antibody, to confirm the interaction of the two proteins. 8. Do not exceed 3000 rpm (600  g) when pelleting IgG beads as this will shatter the beads. 9. When aspirating the supernatant, using a gel loading tip with a fine tip (0.57 mm opening diameter) will limit bead loss. 10. Adding the RIPA buffer to the beads after washing and vortexing aids in removing the protein from the beads. 11. Chill the transfer buffer at 20  C while performing the electrophoresis. Using ice cold transfer buffer during the transfer helps excessive heating of the system. 12. The quality of RASSF1A antibodies is generally very poor. Dilute the primary antibody 1:100 in blocking buffer. 13. The heavy and light chains of the immunoprecipitating antibodies may obscure the RASSF1A band on the western blot, particularly for longer exposures. It is therefore preferable to use a secondary antibody whose cross reactivity with immunoglobulin heavy and light chains is minimal, such as the Mouse TrueBlot® ULTRA secondary antibody (Rockland). 14. ECL detection reagents typically come as two solutions. Mix equal volumes of each solution directly prior to adding it to the membrane. 15. Expose the membrane to the X-ray film for 1 min initially, and based on the signal obtained, perform additional exposures, either longer or shorter, to obtain the desired image. References 1. Pylayeva-Gupta Y, Grabocka E, Bar-Sagi D (2011) RAS oncogenes: weaving a tumorigenic web. Nat Rev Cancer 11(11):761–774. https://doi.org/10.1038/nrc3106 2. Nussinov R, Tsai CJ, Jang H (2018) Oncogenic Ras isoforms signaling specificity at the membrane. Cancer Res 78(3):593–602.

https://doi.org/10.1158/0008-5472.CAN17-2727 3. Shields JM, Pruitt K, McFall A, Shaub A, Der CJ (2000) Understanding Ras: ‘it ain’t over ‘til it’s over. Trends Cell Biol 10(4):147–154. https://doi.org/10.1016/s0962-8924(00) 01740-2

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4. Downward J (2003) Targeting RAS signalling pathways in cancer therapy. Nat Rev Cancer 3 (1):11–22. https://doi.org/10.1038/nrc969 5. Vojtek AB, Der CJ (1998) Increasing complexity of the Ras signaling pathway. J Biol Chem 273(32):19925–19928. https://doi.org/10. 1074/jbc.273.32.19925 6. Cox AD, Der CJ (2003) The dark side of Ras: regulation of apoptosis. Oncogene 22 (56):8999–9006. https://doi.org/10.1038/ sj.onc.1207111 7. Donninger H, Schmidt ML, Mezzanotte J, Barnoud T, Clark GJ (2016) Ras signaling through RASSF proteins. Semin Cell Dev Biol 58:86–95. https://doi.org/10.1016/j. semcdb.2016.06.007 8. van der Weyden L, Adams DJ (2007) The Ras-association domain family (RASSF) members and their role in human tumourigenesis. Biochim Biophys Acta 1776(1):58–85. https://doi.org/10.1016/j.bbcan.2007.06. 003 9. Volodko N, Gordon M, Salla M, Ghazaleh HA, Baksh S (2014) RASSF tumor suppressor gene family: biological functions and regulation. FEBS Lett 588(16):2671–2684. https://doi. org/10.1016/j.febslet.2014.02.041 10. Donninger H, Vos MD, Clark GJ (2007) The RASSF1A tumor suppressor. J Cell Sci 120 (Pt 18):3163–3172. https://doi.org/10. 1242/jcs.010389 11. Agathanggelou A, Cooper WN, Latif F (2005) Role of the Ras-association domain family 1 tumor suppressor gene in human cancers. Cancer Res 65(9):3497–3508. https://doi. org/10.1158/0008-5472.CAN-04-4088 12. Calvisi DF, Ladu S, Gorden A, Farina M, Conner EA, Lee JS, Factor VM, Thorgeirsson SS (2006) Ubiquitous activation of Ras and Jak/Stat pathways in human HCC. Gastroenterology 130(4):1117–1128. https://doi.org/10. 1053/j.gastro.2006.01.006 13. Matallanas D, Romano D, Al-Mulla F, O’Neill E, Al-Ali W, Crespo P, Doyle B, Nixon C, Sansom O, Drosten M, Barbacid M, Kolch W (2011) Mutant K-Ras activation of the proapoptotic MST2 pathway is antagonized by wild-type K-Ras. Mol Cell 44 (6):893–906. https://doi.org/10.1016/j. molcel.2011.10.016 14. Donninger H, Allen N, Henson A, Pogue J, Williams A, Gordon L, Kassler S, Dunwell T, Latif F, Clark GJ (2011) Salvador protein is a tumor suppressor effector of RASSF1A with hippo pathway-independent functions. J Biol Chem 286(21):18483–18491. https://doi. org/10.1074/jbc.M110.214874

15. Fallahi E, O’Driscoll NA, Matallanas D (2016) The MST/hippo pathway and cell death: a non-canonical affair. Genes (Basel) 7(6). https://doi.org/10.3390/genes7060028 16. Vos MD, Dallol A, Eckfeld K, Allen NP, Donninger H, Hesson LB, Calvisi D, Latif F, Clark GJ (2006) The RASSF1A tumor suppressor activates Bax via MOAP-1. J Biol Chem 281(8):4557–4563. https://doi.org/ 10.1074/jbc.M512128200 17. Baksh S, Tommasi S, Fenton S, Yu VC, Martins LM, Pfeifer GP, Latif F, Downward J, Neel BG (2005) The tumor suppressor RASSF1A and MAP-1 link death receptor signaling to Bax conformational change and cell death. Mol Cell 18(6):637–650. https://doi.org/10. 1016/j.molcel.2005.05.010 18. Donninger H, Clark J, Rinaldo F, Nelson N, Barnoud T, Schmidt ML, Hobbing KR, Vos MD, Sils B, Clark GJ (2015) The RASSF1A tumor suppressor regulates XPA-mediated DNA repair. Mol Cell Biol 35(1):277–287. https://doi.org/10.1128/MCB.00202-14 19. Hamilton G, Yee KS, Scrace S, O’Neill E (2009) ATM regulates a RASSF1A-dependent DNA damage response. Curr Biol 19 (23):2020–2025. https://doi.org/10.1016/j. cub.2009.10.040 20. Donninger H, Clark JA, Monaghan MK, Schmidt ML, Vos M, Clark GJ (2014) Cell cycle restriction is more important than apoptosis induction for RASSF1A protein tumor suppression. J Biol Chem 289 (45):31287–31295. https://doi.org/10. 1074/jbc.M114.609537 21. Dallol A, Agathanggelou A, Fenton SL, Ahmed-Choudhury J, Hesson L, Vos MD, Clark GJ, Downward J, Maher ER, Latif F (2004) RASSF1A interacts with microtubuleassociated proteins and modulates microtubule dynamics. Cancer Res 64(12):4112–4116. https://doi.org/10.1158/0008-5472.CAN04-0267 22. Vos MD, Martinez A, Elam C, Dallol A, Taylor BJ, Latif F, Clark GJ (2004) A role for the RASSF1A tumor suppressor in the regulation of tubulin polymerization and genomic stability. Cancer Res 64(12):4244–4250. https:// doi.org/10.1158/0008-5472.CAN-04-0339 23. Chatzifrangkeskou M, Pefani DE, Eyres M, Vendrell I, Fischer R, Pankova D, O’Neill E (2019) RASSF1A is required for the maintenance of nuclear actin levels. EMBO J 38(16): e101168. https://doi.org/10.15252/embj. 2018101168

Chapter 19 Mathematical Modeling to Study KRAS Mutant-Specific Responses to Pathway Inhibition Edward C. Stites Abstract This chapter will describe how mathematical modeling allows the RAS pathway to be studied with computational experiments. The mathematical model utilized simulates the biochemical reactions that regulate RAS signaling. This type of model incorporates knowledge of reaction mechanisms, including measured quantitative parameters that characterize these reactions for both wild-type and mutant RAS proteins. For an illustrative example, this chapter focuses on how modeling provided new insights that helped solve a problem that challenged the RAS community for nearly a decade: why do colorectal cancers with the KRAS G13D mutation, but not the other common KRAS mutations, benefit from EGFR inhibition? The methods described include computational dose-response experiments and the use of “computational chimeric” RAS mutants. Key words Computational biology, Systems biology, Systems pharmacology, Targeted therapy

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Introduction The RAS pathway has been exceptionally well-studied and is wellunderstood. However, unexpected pathway behaviors are still encountered. One such example involves how KRAS mutations influence the response to EGFR inhibitors in colorectal cancer. Activation of EGFR by its ligands can initiate multiple intracellular signaling events, including RAS activation, which in turn can activate the RAF/MEK/ERK MAPK cascade that can drive cellular proliferation (Fig. 1). As cancer is a disease of excessive and uncontrolled cellular proliferation within the malignant cells, agents that target this pathway to impair proliferation have been developed and have demonstrated benefit in a variety of cancers, including colorectal cancer. Approximately 40–50% of colorectal cancer patients harbor oncogenic KRAS mutations [1] (see accompanying Chapter 1 by Prior, I.). As oncogenic KRAS mutant proteins are constitutively active and can initiate downstream signaling in the absence of

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Fig. 1 KRAS G13D colorectal cancer and its unexplained response to EGFR inhibition. Activation of EGFR by its ligands leads to activation of the RAS/ERK pathway that drives cellular proliferation. This pathway is believed to be overactivated in colorectal cancer. Patients with KRAS WT colon cancers benefit from anti-EGFR agents that prevent the activation of EGFR. Patients with constitutively active KRAS mutants do not benefit from antiEGFR agents, as the constitutively active RAS mutants can still drive proliferation. The constitutively active KRAS G13D mutation is an exception; patients with this mutation have been shown to benefit from anti-EGFR agents, although a mechanism to explain why this constitutively active mutation behaved differently had been unknown

EGFR stimulation, it was logical to hypothesize that EGFR inhibitors would offer less benefit to KRAS mutant colorectal cancer patients. Indeed, Phase 3 clinical trials revealed that the patients with a KRAS mutation, as a group, did not receive any benefit from treatment with EGFR inhibitors [2]. Many different KRAS mutations have been observed in colorectal cancer, with KRAS G12D, KRAS G12V, and KRAS G13D being the three most common [1]. An analysis of the Phase 3 clinical trial data that investigated whether any specific KRAS mutants behaved differently made the surprising observation that patients with the KRAS G13D tended to benefit from EGFR inhibitors, in contrast to the set of all other KRAS mutant colorectal cancers [3]. This was reproduced in cellular and in mouse xenograft experiments, and other, subsequent, studies also observed that KRAS G13D cancers appeared to be more sensitive to EGFR inhibitors [4, 5]. However, it has also been controversial as it appears to contradict the well-accepted dogma on RAS biology, and studies have also been presented that claim KRAS G13D colorectal cancers

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treated with EGFR inhibitors responded the same as other KRAS mutant colorectal cancers [6, 7]. The application of a computational model to a problem like this allows the problem to be studied from a new perspective. Additionally, the utilization of a mathematical model that simulates the biochemical reactions that regulate RAS signaling allows for one to investigate whether it is even consistent with the known principles of RAS biology for cancers with different constitutively active KRAS mutants to be more (or less) sensitive than one another when treated with an inhibitor. The incorporation of the fundamental biochemical properties, like reaction rate constants, that are specific to the different mutants allows one to investigate whether the known biochemical differences between these mutants are sufficient to cause the diverging responses to treatment. For example, KRAS G13D has been most notable for having fast, spontaneous, nucleotide dissociation relative to wild-type (WT) KRAS, which allows for increased GTP loading and autoactivation that is not GEF-dependent [8–11]. Mutations like this have been referred to as “fast cycling.” Whether fast cycling, the most well-known biochemical distinction of KRAS G13D, could mechanistically cause increased sensitivity to EGFR inhibition was unknown, but such a question can be investigated with a mechanism-based mathematical model. The first approach described here is the simulation of drug dose responses for modeled cellular conditions with different KRAS mutations present. The second approach discussed here is the development and utilization of chimeric RAS mutants to isolate specific biochemical parameters and determine which are responsible for specific phenotypes.

2 2.1

Materials RAS Model

If one wanted to study the effects of a drug on RAS mutant cancers in an animal model, that scientist might choose an established genetically engineered mouse model [12]. If one wanted to study the effects of a therapeutic on RAS mutant cancers with a cellular model, they might choose from existing cancer cell lines that are known to harbor an oncogenic RAS mutant. Similarly, to study RAS computationally, it may be possible to use an existing mathematical model. One advantage of using an existing model is that it may have already demonstrated an ability to make novel insights. If the available models could not be adapted to a specific problem, however, it may be necessary to develop a new model. The process of building a mass-action model of RAS has been described previously [13]. To study KRAS G13D colorectal cancer and its response to EGFR inhibition, a previously developed model that has been used to study KRAS G12V, KRAS G12D, and KRAS G12C [14, 15] was

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Fig. 2 The biochemical reactions of the RAS model. The model focuses on the reactions the directly influence RAS-GTP and RAS-GTP-effector binding reactions. Each reaction (arrow) is modeled with mass-action or enzymatic kinetics. The same equations apply to wild-type and mutant proteins, but the specific parameter values (e.g., reaction rate constants) will vary based on whether the equation is describing the activity of a WT or mutant RAS protein. For example, KRAS G12V, G12D, and G13D have slower intrinsic GTPase reaction rate constants than WT RAS. When comparing KRAS G13D to KRAS G12V and KRAS G12D, the most notable differences are KRAS G13D has a more rapid rate of intrinsic nucleotide dissociation and KRAS G13D binds less well to the RAS GAP NF1

utilized and extended to include KRAS G13D. The model focuses on the reactions that directly interact with RAS to influence the RAS nucleotide state (Fig. 2). It includes RAS GTPases, RAS GEFs, RAS GAPs, RAS effectors, and guanine nucleotides GTP and GDP. For RAS, intrinsic GTPase activities and spontaneous (non-GEFmediated) nucleotide dissociation and association are included and described with mass-action kinetics. Mass-action kinetics are also used to describe interactions between RAS and its effectors. GAP-facilitated GTP hydrolysis is modeled with Michaelis-Menten kinetics. GEF-facilitated nucleotide exchange is modeled with reversible Michaelis-Menten kinetics. For these enzymatic processes, competitive forms of the corresponding Michaelis-Menten equations are used to account for wild-type and mutant RAS proteins that may compete to bind to the same GEFs and GAPs. The parameters of these reactions (reaction rate constants and Michaelis-Menten kcat and Km terms) are obtained from the experimental literature for wild-type RAS, and changes in these terms that follow from mutations are also obtained from the experimental literature. Computer code for this model can be downloaded as

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part of the supplementary material from one publication that utilized the model [15], and thorough descriptions of model development are also available [13, 14, 16]. The model allows for investigations of how the various reactions and their properties contribute to RAS activation and effector binding, such as how changes in the abundances and/or activity of different proteins in the network influence signaling, as well as how different RAS mutations respond to a specific cellular state. 2.2 Data on the Relevant Mutants

Our goal is to use our computational model to investigate differences between KRAS G13D, KRAS G12D, and KRAS G12V. The original publication that described the RAS model and its development includes rate constant for G12D and G12V mutants, but not for G13D [14]. Although new data options continue to become available [8], we have chosen to utilize the original parameters to communicate the general value of the model—it does not need to be continually retuned, but can provide numerous insights in its current form. However, one could choose to update the parameters to newer data, if they choose, and we needed to specify parameters for KRAS G13D in order to model that mutant. To find these parameters, a search of manuscripts that characterized the biophysical properties of KRAS G13D uncovered data that report nucleotide dissociation rates 3.6625 times faster than for WT KRAS [11] (see Note 1) and that report poor binding of KRAS G13D to the RAS GAP NF1, leading us to estimate that the Km for binding to NF1 is approximately 100 times weaker than for WT KRAS [17]. Additionally, it has also been long appreciated that KRAS hotspot mutations at codons 12, 13, and 61 are partially impaired at nucleotide hydrolysis and severely impaired at GAP-mediated nucleotide hydrolysis. At the time we began our study, we could not find a value for the KRAS G13D GTPase reaction, so we utilized the impaired value of KRAS G12D as an estimate. For impaired GAP-mediated GTP hydrolysis, we assume that the GAP cannot increase GTP hydrolysis, as we do for other mutants (like G12D and G12V). Without clear data to define other parameter values, we began our modeling with a goal of determining whether these parameters were sufficient to explain the observed behavior of KRAS G13D. Alternatively, one may experimentally measure the other parameters or computationally explore the possible effects of the other parameters. Whatever is chosen for parameters, these are inserted into the model to specify the KRAS mutant, such as KRAS G13D.

2.3 Software to Implement the Model and Hardware for Simulations

The RAS model utilized here was simulated in MATLAB, and a version of the model that was written using MATLAB can be downloaded as part of a related publication [15]. The model can be implemented in other languages, such as Python or Mathematica. The RAS model is fairly limited in scope and does not present a computational challenge—it can quickly be simulated and studied

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on a common laptop computer. It is notable that even though the scope of the system may appear limited and less overwhelming than large pathway models, the model has revealed non-obvious behaviors within this pathway that have also been experimentally confirmed.

3

Methods

3.1 Simulate the Effects of an EGFR Inhibitor Dose Response

1. Specify the basal conditions of the model. The original RAS model was parameterized for basal RAS signaling in the absence of induced activation. As RAS WT cancers are sensitive to EGFR inhibition, we assume colorectal cancers commonly have elevated EGFR activation that in turn activates RAS. The model thus needs to be adjusted to model the conditions of elevated EGFR-driven RAS activation. When we originally developed our RAS model, we found a 10 increase in our basal level of GEF activity mimicked conditions of experimental receptor tyrosine kinase-mediated RAS activation [14]. Thus, a value of basal GEF activation that is 10 the value used in the original model appears to be a good approximation (see Note 2). 2. Specify the conditions that may approximate an EGFR inhibitor dose response. The activation of EGFR is believed to lead to RAS activation through RAS GEFs like SOS1. Treatment with an EGFR inhibitor is assumed to result in reduced EGFR activation and reduced RAS GEF recruitment and activation. Thus, to simulate an EGFR inhibitor dose response with our model, one can consider different levels of GEF activity, ranging from the value used to mimic conditions of EGFR activation down through lower levels, such as the unstimulated level used in the original RAS model. 3. Specify a simulation protocol. The goal is to find the steadystate level of RAS signal, i.e., the level of RAS-GTP observed under typical conditions. This can be done by simulating the model for a period of time sufficiently long enough that RAS-GTP levels are no longer changing. Levels of total RAS-GTP, WT RAS-GTP, and mutant RAS-GTP should be saved as outputs for each level of simulated GEF activity. 4. Simulate a dose response for conditions with no RAS mutation. To model a cell with all wild-type RAS, 100% of the total RAS abundance is specified with WT RAS biochemical parameters. The simulation protocol can then be followed for each level of GEF activity. 5. Simulate dose responses for conditions with a KRAS G12D, KRAS G12V, or KRAS G13D mutation. To model a cell with a

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KRAS mutation, 25% of total RAS is set to the mutant (which is specified with the mutant parameters), and 75% of total RAS is set to WT RAS (which is specified with the WT RAS parameters). The simulation protocol can then be followed for each level of GEF activity for each modeled RAS mutant. 6. Plot the resultant outputs (see Note 3). 7. Plot the simulation dose responses for the WT and KRAS mutant subsets of total RAS (see Note 4). 3.2 Simulate KRAS Mutant Chimeras to Evaluate the Contribution of Nucleotide Cycling Rate

1. Generate chimeric mutants between KRAS G13D and KRAS G12V and between KRAS G13D and KRAS G12D that alternate the nucleotide dissociation rate constants. For the chimeric G13D with G12V cycling, the GDP and GTP dissociation rate constants from KRAS G13D are replaced with the (slower) rate constants for the same reactions from G12V. For the chimeric G12V with G13D cycling, the dissociation rate constants for GDP and GTP from KRAS G12V are replaced with the (faster) rate constants for the same reactions from G13D. The process for chimeras between KRAS G13D and KRAS G12D is similar. 2. Simulate EGFR dose responses, as in Subheading 3.1, but utilizing the chimeric mutants (Fig. 3). 3. Compare to the simulated dose responses for the KRAS G12D, KRAS G12V, and KRAS G13D mutants to determine whether the change in nucleotide cycling rate made it more or less sensitive to EGFR inhibitors (see Note 5).

3.3 Simulate KRAS Mutant Chimeras to Evaluate the Contribution of Impaired NF1 Binding

1. Generate chimeric mutants between KRAS G13D and KRAS G12V and between KRAS G13D and KRAS G12D that alternate the property that specifies the strength of binding to the RAS GAP NF1. For the chimeric G13D that has a G12V-like affinity for NF1, the Km for the NF1-G13D interaction is replaced with the value for the Km between NF1 and KRAS G12V. For the chimeric G12V that has a G13D-like affinity for NF1, the Km for the NF1-G12V interaction is replaced with the value for the Km between NF1 and KRAS G13D. The process for chimeras between KRAS G13D and KRAS G12D is similar. 2. Simulate EGFR dose responses, as in Subheading 3.1, but utilizing the chimeric mutants (Fig. 4). 3. Compare to the simulated dose responses for the KRAS G12D, KRAS G12V, and KRAS G13D mutants to determine whether the change in nucleotide cycling rate made it more or less sensitive to EGFR inhibitors (see Note 6).

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Fig. 3 Fast cycling (increased nucleotide exchange) does not explain why KRAS G13D responds to EGFR inhibition. Simulated anti-EGFR dose responses for the RAS model with chimeric mutants involving the substitution of nucleotide properties between G13D, G12D, and G12V. Solid lines indicate the simulated dose responses for KRAS G12D (blue), KRAS G12V (green), and KRAS G13D (red). Dashed lines indicate the simulated dose responses for chimeric mutants. The color of the line indicates the mutant with which the chimera featured in the panel is most similar. The y-axis indicates the percentage of all RAS in the model (both WT and mutant) that is bound to GTP

Fig. 4 Impaired binding to NF1 explains why KRAS G13D responds to EGFR inhibition. Simulated anti-EGFR dose responses for the RAS model with chimeric mutants involving the substitution of the Km for RAS-NF1 binding between G13D, G12D, and G12V. Solid lines indicate the simulated dose responses for KRAS G12D (blue), KRAS G12V (green), and KRAS G13D (red). Dashed lines indicate the simulated dose response for chimeric mutants. The color of the line indicates the mutant with which the chimera featured in the panel is most similar. The y-axis indicates the percentage of all RAS in the model (both WT and mutant) that is bound to GTP

4

Notes 1. More recent measurements of parameters for KRAS G13D were published [8–10] after we began our work [18]. The manuscripts report KRAS G13D to have nucleotides dissociate at a rate that is 10–15 times larger than WT, in contrast to the approximately 4 times larger change we used based on the earlier publication with exchange rate data [11]. It would be

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possible to simulate with these alternative values. Of note, we do not believe values from the RAS GAP RASA1 can be used in place of NF1 as some studies have observed the biochemical consequences of RAS mutations can vary between these GAPS [19], and NF1 appears to be an essential GAP in colorectal cancer, as suggested by its frequent mutation rate within patient tumors. 2. The activation of wild-type RAS in colorectal cancer is undoubtedly more complex than RAS mutants and EGFR. Other receptor tyrosine kinases and other mutations have been shown to play a role, as well. The present study however investigates how KRAS G13D may be sensitive to EGFR inhibition, so we focused on KRAS mutations and EGFR activity level as the known variables to determine whether they alone may be sufficient to explain the observed and confusing clinical responses to EGFR inhibitors. Similarly, processes such as negative feedback, which are known to play an important role in the biology of RAS pathway mutations in colorectal cancer, are not included in the model [20, 21]. That the insights generated by the model were then experimentally observable and that these data confirmed the model-based predictions suggests that the computational model can generate new, non-obvious, and experimentally verifiable insights even when it necessarily limits itself to a subset of the RAS signaling network. 3. This revealed that the total RAS output decreased more rapidly for KRAS G13D than for KRAS G12D and KRAS G12V. This suggests first that it is perfectly consistent with the known principles of RAS signal regulation for different RAS mutants to respond with different intensities to EGFR inhibitors. This is notable, as disbelief of original clinical trial only makes sense if one assumes all mutants must behave essentially equivalently. Second, it is notable because the available data for KRAS G13D was sufficient to suggest it is more sensitive. There may be additional biochemical parameters that differ that have yet to be measured, but of the limited number that was measured, they were sufficient to cause an increased sensitivity. This is also notable, as once the G13D exceptional response was observed, the field wondered how this could be possible. That the data needed to explain increased sensitivity was readily available yet the mechanism was not elucidated until computational modeling was applied to the problem highlights both the complexity of the system and the value of computational modeling. 4. The further subdivision of total RAS GTP into WT RAS GTP and mutant RAS GTP shows that the differences in EGFR/ GEF inhibition fall within the WT RAS GTP pool of RAS

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within a KRAS mutant cancer and shows little to no change in mutant RAS GTP for any of the three KRAS mutants [16]. 5. These chimeras reveal that the higher nucleotide dissociation rate of KRAS G13D would actually make it less sensitive to EGFR inhibitors—the opposite of the observed behavior— thus suggesting that the known fast-cycling activity of KRAS G13D does not explain why it is sensitive to EGFR inhibitors. 6. These chimeras reveal that impaired binding to NF1 makes a RAS mutant more sensitive to EGFR inhibition.

Acknowledgments This work was supported by NIH grant K22CA216318 and P30CA014195. References 1. Cancer Genome Atlas N (2012) Comprehensive molecular characterization of human colon and rectal cancer. Nature 487(7407):330–337. https://doi.org/10.1038/nature11252 2. Karapetis CS, Khambata-Ford S, Jonker DJ, O’Callaghan CJ, Tu D, Tebbutt NC, Simes RJ, Chalchal H, Shapiro JD, Robitaille S, Price TJ, Shepherd L, Au HJ, Langer C, Moore MJ, Zalcberg JR (2008) K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med 359 (17):1757–1765. https://doi.org/10.1056/ NEJMoa0804385 3. De Roock W, Jonker DJ, Di Nicolantonio F, Sartore-Bianchi A, Tu D, Siena S, Lamba S, Arena S, Frattini M, Piessevaux H, Van Cutsem E, O’Callaghan CJ, KhambataFord S, Zalcberg JR, Simes J, Karapetis CS, Bardelli A, Tejpar S (2010) Association of KRAS p.G13D mutation with outcome in patients with chemotherapy-refractory metastatic colorectal cancer treated with cetuximab. JAMA 304(16):1812–1820. https://doi.org/ 10.1001/jama.2010.1535 4. Tejpar S, Celik I, Schlichting M, Sartorius U, Bokemeyer C, Van Cutsem E (2012) Association of KRAS G13D tumor mutations with outcome in patients with metastatic colorectal cancer treated with first-line chemotherapy with or without cetuximab. J Clin Oncol 30 (29):3570–3577. https://doi.org/10.1200/ JCO.2012.42.2592 5. Nakamura M, Aoyama T, Ishibashi K, Tsuji A, Takinishi Y, Shindo Y, Sakamoto J, Oba K, Mishima H (2017) Randomized phase II

study of cetuximab versus irinotecan and cetuximab in patients with chemo-refractory KRAS codon G13D metastatic colorectal cancer (G13D-study). Cancer Chemother Pharmacol 79(1):29–36. https://doi.org/10.1007/ s00280-016-3203-7 6. Peeters M, Douillard JY, Van Cutsem E, Siena S, Zhang K, Williams R, Wiezorek J (2013) Mutant KRAS codon 12 and 13 alleles in patients with metastatic colorectal cancer: assessment as prognostic and predictive biomarkers of response to panitumumab. J Clin Oncol 31(6):759–765. https://doi.org/10. 1200/JCO.2012.45.1492 7. Segelov E, Thavaneswaran S, Waring PM, Desai J, Robledo KP, Gebski VJ, Elez E, Nott LM, Karapetis CS, Lunke S, Chantrill LA, Pavlakis N, Khasraw M, Underhill C, Ciardiello F, Jefford M, Wasan H, Haydon A, Price TJ, van Hazel G, Wilson K, Simes J, Shapiro JD (2016) Response to cetuximab with or without irinotecan in patients with refractory metastatic colorectal cancer harboring the KRAS G13D mutation: Australasian gastrointestinal trials group ICECREAM study. J Clin Oncol 34(19):2258–2264. https://doi. org/10.1200/JCO.2015.65.6843 8. Hunter JC, Manandhar A, Carrasco MA, Gurbani D, Gondi S, Westover KD (2015) Biochemical and structural analysis of common cancer-associated KRAS mutations. Mol Cancer Res 13(9):1325–1335. https://doi.org/ 10.1158/1541-7786.MCR-15-0203 9. Smith MJ, Neel BG, Ikura M (2013) NMR-based functional profiling of

Mathematical Modeling of KRAS Mutants RASopathies and oncogenic RAS mutations. Proc Natl Acad Sci U S A 110 (12):4574–4579. https://doi.org/10.1073/ pnas.1218173110 10. Johnson CW, Lin YJ, Reid D, Parker J, Pavlopoulos S, Dischinger P, Graveel C, Aguirre AJ, Steensma M, Haigis KM, Mattos C (2019) Isoform-specific destabilization of the active site reveals a molecular mechanism of intrinsic activation of KRas G13D. Cell Rep 28(6):1538–1550.e1537. https://doi.org/10. 1016/j.celrep.2019.07.026 11. Palmioli A, Sacco E, Airoldi C, Di Nicolantonio F, D’Urzo A, Shirasawa S, Sasazuki T, Di Domizio A, De Gioia L, Martegani E, Bardelli A, Peri F, Vanoni M (2009) Selective cytotoxicity of a bicyclic Ras inhibitor in cancer cells expressing K-Ras (G13D). Biochem Biophys Res Commun 386 (4):593–597. https://doi.org/10.1016/j. bbrc.2009.06.069 12. Jackson EL, Willis N, Mercer K, Bronson RT, Crowley D, Montoya R, Jacks T, Tuveson DA (2001) Analysis of lung tumor initiation and progression using conditional expression of oncogenic K-ras. Genes Dev 15 (24):3243–3248. https://doi.org/10.1101/ gad.943001 13. Stites EC, Ravichandran KS (2012) Mathematical investigation of how oncogenic ras mutants promote ras signaling. Methods Mol Biol 880:69–85. https://doi.org/10.1007/978-161779-833-7_5 14. Stites EC, Trampont PC, Ma Z, Ravichandran KS (2007) Network analysis of oncogenic Ras activation in cancer. Science 318 (5849):463–467. https://doi.org/10.1126/ science.1144642

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15. Stites EC, Shaw AS (2018) Quantitative systems pharmacology analysis of KRAS G12C covalent inhibitors. CPT Pharmacometrics Syst Pharmacol 7(5):342–351. https://doi. org/10.1002/psp4.12291 16. McFall T, Diedrich JK, Mengistu M, Littlechild SL, Paskvan KV, Sisk-Hackworth L, Moresco JJ, Shaw AS, Stites EC (2019) A systems mechanism for KRAS mutant allele-specific responses to targeted therapy. Sci Signal 12 (600):eaaw8288. https://doi.org/10.1126/ scisignal.aaw8288 17. Gremer L, Gilsbach B, Ahmadian MR, Wittinghofer A (2008) Fluoride complexes of oncogenic Ras mutants to study the Ras-RasGap interaction. Biol Chem 389(9):1163–1171. https://doi.org/10.1515/BC.2008.132 18. Stites EC (2014) Differences in sensitivity to EGFR inhibitors could be explained by described biochemical differences between oncogenic Ras mutants. bioRxiv. https://doi. org/10.1101/005397 19. Donovan S, Shannon KM, Bollag G (2002) GTPase activating proteins: critical regulators of intracellular signaling. Biochim Biophys Acta 1602(1):23–45 20. Prahallad A, Sun C, Huang S, Di Nicolantonio F, Salazar R, Zecchin D, Beijersbergen RL, Bardelli A, Bernards R (2012) Unresponsiveness of colon cancer to BRAF (V600E) inhibition through feedback activation of EGFR. Nature 483(7387):100–103. https://doi.org/10.1038/nature10868 21. Stites EC (2012) The response of cancers to BRAF inhibition underscores the importance of cancer systems biology. Sci Signal 5(246): pe46. https://doi.org/10.1126/scisignal. 2003354

Chapter 20 A Facile Method to Engineer Mutant Kras Alleles in an Isogenic Cell Background Konstantin Budagyan and Jonathan Chernoff Abstract Oncogenic KRAS mutations are common in colorectal cancer (CRC), found in ~50% of tumors, and are associated with poor prognosis and resistance to therapy. There is substantial diversity of KRAS mutations observed in CRC. Importantly, emerging clinical and experimental analysis of relatively common KRAS mutations at amino acids G12, G13, A146, and Q61 suggest that each mutation differently influences the clinical properties of a disease and response to therapy. Although clinical evidence suggests biological differences between mutant KRAS alleles, these differences and the mechanisms underlying them are not well understood, and further exploration of allele-specific differences may provide evidence for individualized therapeutics. One approach to study allelic variation involves the use of isogenic cell lines that express different endogenous KRAS mutants. Here we developed an assay using fluorescent co-selection for CRISPR-driven gene editing to generate various Kras mutations in an isogenic murine colon epithelial cell line background. This assay involves generation of a cell line stably expressing Cas9 linked to BFP and simultaneous introduction of single-guide RNAs (sgRNAs) to two different gene loci resulting in doubleediting events. Single-stranded donor oligonucleotides are introduced for a GFP gene and a Kras mutant allele of our choice as templates for homologous recombination (HDR). Cells that successfully undergo HDR are GFP-positive and have a higher probability of containing the desired Kras mutation. Therefore, selection for GFP-positive cells allows us to identify those with phenotypically silent Kras edits. Ultimately, this method allows us to toggle between different mutant alleles and preserve the wild-type allele while maintaining an isogenic background. Key words KRAS, Small G proteins, Signal transduction, Cancer, Isogenic cells, Gene editing, Coselection, Epithelial cells

1

Introduction KRAS is the most frequently mutated oncogene in cancer and is commonly associated with colorectal cancer (CRC), pancreatic ductal adenocarcinoma (PDAC), and non-small cell lung cancer (NSCLC) [1]. The most common sites of oncogenic mutations of KRAS are located at codons 12, 13, 61, 117, and 146, showing differing frequency of specific alleles depending on cancer type. CRC is unique for its substantial diversity of KRAS alleles, with

Ignacio Rubio and Ian Prior (eds.), Ras Activity and Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2262, https://doi.org/10.1007/978-1-0716-1190-6_20, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Table 1 Most common KRAS mutations in colorectal cancer, their biochemical properties, and allele-specific therapeutic options Mutation Frequency Biochemistry

Therapeutics

G12D

~28% [12] Decreased intrinsic and GAP-mediated hydrolysis

G12V

~20% [12] Decreased intrinsic and GAP-mediated hydrolysis

G12C

~7% [12] Decreased intrinsic and GAP-mediated hydrolysis

Allele-specific inhibitors [13–15]

G12R

~1% [12] Decreased PI3K binding [16]

Potentially sensitive to autophagy inhibitors [16]

G13D

~18% [12] ~7
decrease in hydrolysis ~14
increase in nucleotide exchange [17]

Q61L

~5% [12] Lowest rate of hydrolysis [17, 19]

A146T

~6% [12] ~1000
nucleotide exchange [20] Lower activation of MAPK signaling than G12D [20]

Potentially sensitive to EGFR inhibition [18]

much higher frequency of mutations at codons 13, 117, and 146 relative to other cancers. Clinical evidence suggests important biological differences between mutant KRAS alleles; however these are yet to be fully elucidated [2, 3] (Table 1). Further exploration of allele-specific biological differences between mutant alleles may prove essential for identification of therapeutic targets. Early attempts to study allelic variation involved the use of ectopically expressed forms of KRAS [4–6]. However, this approach is of limited utility because overexpression of KRAS can produce artifactual results. A more modern approach, enabled by new gene editing techniques, is to study endogenous alleles using isogenic cell lines that express different KRAS mutants. Here we describe a rapid and facile method of CRISPR-enabled gene engineering to generate an allelic series of Kras mutations in an isogenic murine colon epithelial cell line background. This approach can also be readily adapted to other oncogenes and other cell types. The assay is based on fluorescent co-selection for CRISPRdriven KRAS editing. This assay involves simultaneous introduction of single-guide RNAs (sgRNAs) to two different gene loci resulting in double-editing events, combined with two singlestranded donor oligonucleotides (ssODNs) designed to edit the sequences of interest. Such co-selection schemes are based on the idea that a cell that is competent to undergo HDR at one site is more likely to also undergo HDR at additional sites [7–9]. We first introduced a Cas9-blue fluorescent protein (BFP) fusion into a

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Fig. 1 Schematic of a fluorescent co-selection for CRISPR-driven Kras point editing. Mouse colon epithelial cells are first transduced with a lentivirus containing Cas9 linked to BFP. Following transduction, BFP-positive cells are sorted, and stable expression of Cas9 is confirmed using immunoblotting. BFP-positive cells are then transfected with a plasmid containing two sgRNAs targeting BFP and Kras G12D allele (green), along with two ssODN designed as templates for HDR. Resulting amino acid changes (red) lead to simultaneous conversion of BFP to GFP and KRAS G12D to G12V. A silent mutation (cyan) is introduced close to the PAM site (blue) to prevent continuous cutting of the G12D locus. Kras G12D allele contains a previously constructed point mutation (orange) generating a HindIII restriction site; the wild-type allele does not. Additional silent mutations create a NarI site (purple) for screening by restriction digestion. Following transfection, GFP-positive cells are sorted and analyzed for Kras editing events

mouse colon epithelial cell line deleted for Apc and also containing a heterozygous Kras G12D allele (i.e., Apc+/;KrasG12D/WT). We then used sgRNAs targeting BFP and the mutant Kras G12D allele, along with ssODN HDR templates to edit BFP to GFP and to edit Kras G12D to G12V, G12C, etc., while sparing the WT Kras allele. Cells that successfully undergo double HDR are GFP-positive and contain the desired Kras mutation (Fig. 1). Therefore, selection for GFP-positive cells allows us to enrich and isolate cells that contain phenotypically silent Kras edits. Further edits can be done in a similar manner, using an sgRNA and appropriate ssODN template to change GFP back to BFP, and the Kras mutation into a different mutation or to WT. This method allows us to toggle between different mutant alleles while preserving the remaining WT allele, all in an isogenic background. Such isogenic cells can then be directly compared for mRNA expression, epigenetic status, basal signaling, and responses to environmental perturbations such as drug treatments, etc.

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Materials

2.1 Lentiviral Infection Components

1. Lenti-X 293T cells (Takara). 2. TransDux MAX Biosciences).

virus

transduction

reagent

(System

3. Opti-MEM. 4. DMEM. 5. Lenti-X 293T cell medium: DMEM with high glucose (4.5 g/ L), 4 mM L-glutamine, and sodium bicarbonate, 10% Fetal Bovine Serum (FBS), 100 U/mL penicillin G sodium, 100 μg/mL streptomycin sulfate, 1 mM sodium pyruvate. 6. Mouse colon epithelium KRASWT/G12D medium: RPMI 1640, 10% FBS, 1% Antibiotic-Antimycotic, 4 mM L-glutamine. 7. Lipofectamine 3000 (Invitrogen). 8. 45 μm syringe filter. 9. Second-generation packaging plasmids: psPAX2 (Addgene, cat. no. 12260), pMD2.G (Addgene, cat. no. 12259). 10. Lentivirus plasmid containing the Cas9-P2A-BFP flanked by long terminal repeats: pLentiCas9-P2A-BFP (Addgene, cat. no. 149447). 11. 10 cm and 24-well culture plates. 2.2 CRISPR/ Cas9-Mediated Genome Editing in Mouse Colon Epithelial Cells 2.2.1 Electroporation Components

1. Neon Transfection System (Thermo Fisher). 2. Single-strand DNA oligonucleotides for GFP and KrasG12V. 3. Trypsin-EDTA (0.25%) (Thermo Fisher). 4. sgRNA plasmid: U6::sgBFP-U6::sgKrasG12D-pX333-red plasmid (Addgene, cat. no. 149448). 5. GFP ssODN: 50 -CACCACCGGCAAGCTGCCCGTGCCC TGGCCCACCCTCGTGACCACCCTGACCTACGGCGTG CAGTGCTTCAGCCGCTACCCCGACCACATGAAGCAG CAC-30 . 6. Kras G12V ssODN: 50 - TGCTGAAAATGACTGAGTA TAAGCTTGTGGTGGTTGGCGCCGTTGGAGTAGGCAA GAGCGCCTTGACGATACAGCTAATTCA-30 . 7. Fluorescence microscope.

2.2.2 Clonal Selection Components

1. NarI restriction enzyme (NEB) and associated buffer. 2. NucleoSpin Tissue genomic DNA extraction kit (Takara) or any other compatible genomic DNA extraction kit. 3. DNA polymerase and accompanying buffers. 4. TAE buffer: 40 mM Tris (pH 7.6), 20 mM acetic acid, 1 mM EDTA.

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5. 1.5% agarose gel in TAE buffer. 6. Ethidium bromide. 7. Ras (G12V Mutant Specific) (D2H12) antibody (Cell Signaling 14412). 8. Ras (G12D Mutant Specific) (D8H7) antibody (Cell Signaling 14429). 9. Kras forward primer: 50 0 TAGCTGTCGACAAGC-3 .

GGGTAGGTGTTGGGA

10. Kras reverse primer: 50 -CCTTTACAAGCGCACGCAGACTG TAGAGC-30 . 11. Kras sequencing primer: 50 -TCTTGTGTGAGACATG-30 . 12. Kras sgRNA top primer: 50 - CACCGTGGTTGGAGCT GATGGCGT-30 . 13. Kras sgRNA bottom primer: 50 - AAACACGCCATCAGCTC CAACCAC-30 . 14. BFP sgRNA top primer: 0 CACCCCGTGGCTCA-3 .

50 -

CACCGCACTG

15. BFP sgRNA bottom primer: 50 -AAACTGAGCCACGGGGTG CAGTGC-30 .

3

Methods

3.1 Design and Generation of CRISPR/Cas9 Vectors

This assay involves simultaneous introduction of single-guide RNAs (sgRNAs) to two different gene loci resulting in doubleediting events (see Note 1 regarding sgRNA design). This is done by initially establishing stable expression of BFP in the target cell (see Note 2). This will serve as a fluorescent marker that will allow for screening of cells in which homologous recombination events have occurred. We use a pLentiCas9-P2A-BFP plasmid to establish stable cells with Cas9 and BFP expression. Once the stable cell line expressing Cas9 and BFP is established, the cells are transfected with a plasmid containing two sgRNAs, one targeting BFP and another targeting a Kras allele (Fig. 1). Here we started with a cell line that contains one mutant Kras allele (KrasWT/G12D); therefore, the sgRNA is specifically designed to target the mutant but not the WT allele (see Note 3). The delivery of sgRNAs is done via a plasmid containing two sgRNAs: U6::sgBFP-U6::sgKRASG12D as well as a fluorescent (HcRed) selection marker to facilitate transfection efficiency (see Note 4). ssODN templates encoding the desired edits (i.e., BFP ! GFP and KrasG12D ! G12V) are co-transfected along with the dual sgRNA plasmid. We use ssODNs with a ~35 bp homology region up- and downstream of desired nucleotide changes (see Note 5). When editing the Kras allele, we generated

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three silent mutations close to the modified nucleotide, two of which generated a NarI restriction site, and another close to the protospacer adjacent motif (PAM) sequence to block guide RNA binding to the repaired sequence (see Note 6). This setup allows for efficient screening for Kras homologous recombination events (Fig. 1). 3.2 Establishment of Stable Cell Lines Expressing Cas9 and BFP

1. Generate lentivirus using a second-generation lentiviral assembly system. Second-generation packaging plasmids (pMD2.G, psPAX2) along with a lentivirus shuttle vector (pLentiCas9P2A-BFP) were transfected using Lipofectamine 3000 transfection reagent. 2. Prepare early passage Lenti-X 293T cells in a 10 cm culture plate in DMEM with 10% FBS. These cells should be about 70% confluent. Prior to transfection, change the media to antibiotic-free media. 3. Dilute plasmids (7.5 μg of psPAX2, 2.5 μg of pMD2.G, and 10 μg of pLentiCas9-P2A-BFP) in 500 μL of Opti-MEM, and add 40 μL of P3000 Reagent. In a separate tube, mix 500 μL of Opti-MEM with 45 μL of Lipofectamine 3000 Reagent. 4. Mix the diluted DNA tube with diluted Lipofectamine tube together at 1:1 ratio, and incubate for 15 min at room temperature. 5. Carefully transfer the transfection mix by adding it dropwise to the plate of Lenti-X 293T cells. 6. 16 h later, remove the medium and replace with fresh complete medium. Incubate for 24 h. 7. Next day (~48 h post-transfection), harvest the supernatant containing the virus. Centrifuge at 200
g or 5 min at 4  C to remove any cells. Filter the supernatant through a 0.45 μm syringe filter. 8. (Optional) Concentrate the virus using an ultracentrifuge at 10,500
g at 4  C overnight. Discard the supernatant into 10% bleach. Resuspend the viral pellet overnight in 50–200 μL of Opti-MEM. 9. Plate mouse colon epithelial cells containing a deletion of Apc and a KrasWT/G12D mutation in a 24-well plate the day before infection (~50,000 cells per well). Cells should be 50–70% at the day of infection. 10. Use the TransDux MAX transduction reagent according to manufacturer’s protocol. Combine 2.5 μL of TransDux and 100 μL of MAX Enhancer to 400 μL of culture medium, and transfer to each well. 11. Add virus to each well and mix.

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12. Incubate for 72 h. Check for BFP fluorescence using a fluorescent microscope. 13. Sort for BFP-positive cells. 14. Expand the BFP-positive cells and confirm expression of Cas9 using immunoblotting technique. 3.3 Electroporation of Mouse Colon Epithelial Cells with sgRNAs and ssODN

We recommend also reading the Neon Transfection System protocol on the Thermo Fisher webpage: http://tools.thermofisher. com/content/sfs/manuals/neon_device_man.pdf (see Note 7). 1. On the day of transfection, harvest BFP+ mouse colon epithelial Apc/;KrasWT/G12D cells using 0.25% trypsin, and wash in phosphate-buffered saline (PBS) without Ca2+ and Mg2+. 2. Resuspend the cells in Resuspension Buffer R at a final density of 0.5–1
105 per well in a 24-well plate. 3. Prepare a 24-well plate by adding 500 μL of RPMI 1640 with 10% FBS (without antibiotics) to each well used for transfection. 4. Prepare DNA for the transfection (1 μg U6::sgBFP-U6:: sgKrasG12D-pX333-HcRed plasmid, 1 μL of 10 μM ssODN (GFP), and 1 μL of 10 μM ssODN (KrasG12V). 5. Using a 10 mL tip, transfect according to Neon Transfection System protocol at the following settings (see Note 8): (a) Pulse voltage (v): 1300. (b) Pulse width (ms): 20. (c) Pulse number: 2. 6. Place the electroporated cells in a well of a 24-well plate. 7. Following 16 h incubation, change the medium to a fresh medium with antibiotics. 8. Three days after transfection, look under a fluorescent microscope for GFP-positive cells, a marker of HR events. 9. Sort and expand GFP-positive cells. 10. After expansion, isolate genomic DNA for PCR analysis. For isolation you can use the NucleoSpin Tissue genomic DNA extraction kit from Macherey-Nagel or other standard genomic DNA isolation procedures. 11. Analyze the pooled GFP-positive cells by PCR and Sanger sequencing to verify that KrasG12D allele was edited (Fig. 2a). Use primers to amplify a ~500 bp region of Kras that contains the desired edit and sequence the resulting PCR product with a Kras sequencing primer (see Note 9). 12. Perform single-cell cloning of GFP-positive cells. After expansion of clones, analyze by PCR and Sanger sequencing to verify the presence of a wild-type and KrasG12D allele (Fig. 2b, c).

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Fig. 2 Analysis of Kras editing events. (a) Pooled GFP-positive cells are analyzed for Kras edits using Sanger sequencing. Point mutations generating NarI restriction site are highlighted by double peaks. Three peaks are seen at the 12th codon indicating the presence of a wild-type, G12D, and G12V alleles in the pooled

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13. Confirm the expression of mutant and wild-type Kras alleles using immunoblotting, if possible. Allele-specific antibodies were used to detect KrasG12V and KrasG12D mutant alleles (Fig. 2d).

4

Notes 1. When designing sgRNAs, several parameters were considered. Ideally, the PAM site should be near the location where desired mutations are to be introduced, within 5–10 bases. Previous studies have shown that HDR rate decreases dramatically when cut sites are farther away [10]. Benchling was used to evaluate the on-target and off-target efficiency of sgRNAs (https:// benchling.com). Based on that analysis, several sgRNAs were evaluated in HEK293 cells for their cutting efficiency. 2. Prior to the procedure, ensure that the cells are clear of mycoplasma or all other contaminants. This protocol is compatible for use with most mammalian cells including primary and stem cells. The efficacy of this protocol depends on efficient delivery of plasmid with sgRNAs and HDR template into the chosen cell line. 3. Here we used a cell line that is heterozygous for the Kras mutation. This allows for selective targeting of the mutant allele. It is also possible to start with a cell line that has a target sequence present in both alleles. When genotyping single cell clones, we found both single-allelic and bi-allelic edits. The frequency of single-allelic cells depends on the targeting efficiency. We saw bi-allelic cells even when we started with a heterozygous cell line, suggesting a high targeting efficiency with the sgRNA used in this protocol. 4. If the edit efficiency is extremely low ( Ral, during the development of the vulva and other tissues. We additionally describe the use of these principles to delineate the function of the close Ras relative, RAP-1. The worm continues to lead the way in clarifying otherwise poorly understood functions of Ras during animal development. Key words LIN-45, RGL-1, RAL-1, ERK, MPK-1, MAP kinase, EGF, EGFR, FGF, FGFR

1

Introduction Genetic analysis of C. elegans development has greatly informed our knowledge of Ras function in vivo. Mutations altering vulval cell fate, resulting in vulvaless (Vul) and multivulval (Muv) animals, were isolated and characterized [1–3]. Subsequent molecular genetic analyses and study of genetic interactions led to a deeper understanding of how signal transduction operates. Critical advances included initial characterization of loss-, gain-, and dominant-negative mutations in RasLET-60 in animal development [4–6] and connection of Grb2SEM-5 to the EGFRLET-23 > RasLET60 signal [7, 8]. Also of note were the co-discovery of the KSRKSR-1 scaffold of RafLIN-45 and MEKMEK-2 [9, 10] and the discovery of Zinc channels CDF-2 and SUR-7 in the regulation of RasLET60 > RafLIN-45 > MEKMEK-2 > ERKMPK-1 signaling [11, 12]. The former was striking because of functional redundancy of two KSR-encoding genes, such that a single mutant for KSRKSR-1 resulted in virtually no phenotype. The latter discovery of Zinc channels CDF-1 and SUR-7 was striking because of redundancy and that they do not physically interact with the Ras > Raf cascade

Ignacio Rubio and Ian Prior (eds.), Ras Activity and Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2262, https://doi.org/10.1007/978-1-0716-1190-6_26, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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and yet are required for the Muv phenotype conferred by constitutively activated RasLET-60. The C. elegans vulva is induced by EGFLIN-3 secreted by the anchor cell (AC), located in the ventral gonad (reviewed in [13]). Six initially equipotent vulval precursor cells are induced to form the 3
–3
–2
–1
–2
–3
pattern of cells fates, with 1
fate induced in the cell located closest to the AC. This pattern is generated by a mechanism termed sequential induction. Developmental and molecular genetic analyses indicate that the EGFLIN-3 > EGFRLET-23 > RasLET-60 > RafLIN-45 > MEKMEK-2 > ERKMPK-1 canonical MAP kinase cascade is necessary and sufficient for induction of 1
fate (reviewed in [14]). Induced 1
cells produce redundant DSL ligands for the Notch receptor [15], and NotchLIN-12 is necessary and sufficient for induction of 2
cells flanking the initially induced 1
cell. Initially equipotent VPCs are induced to form the 3
–3
–2
–
1 –2
–3
pattern of cell fates [16]. That VPCs are equipotent and initially naive raises the possibility of conflicting signals within the same VPC. Indeed, signaling mechanisms exist to exclude the activity of inappropriate signals for a presumptive cell type. For example, inappropriate activation of ERKMPK-1 in an initially specified 2
cell is quenched by expression of MKPLIP-1, a DUSP (ERK phosphatase) that is a transcriptional client gene of NotchLIN-12 [17]. Conversely, NotchLIN-12 is internalized and degraded in initially specified 1
cells [18, 19]. We refer to this phenomenon as “mutual antagonism” between signals promoting contradictory VPC fates (reviewed in [20]). Classic developmental biology and genetic experiments suggest that there exists a graded EGFLIN-3 signal that regulates VPC fate patterning in addition to the aforementioned mechanism of sequential induction [21–25]. While the canonical RasLET-60 effector, RafLIN-45, is necessary and sufficient for 1
fate induction, the poorly understood RasLET-60 effector, RalGEFRGL-1, signaling through the Ras cousin, RalRAL-1, had not been investigated. Our lab exploited the principle of mutual antagonism to investigate the role of the poorly understood Ras effector, RalGEFRGL-1 > RalRAL-1, in fate patterning of VPCs. We found that RasLET-60 dynamically switches effectors during VPC fate patterning, from RafLIN-45 to RalGEFRGL-1. Furthermore, RasLET-60 > RalGEFRGL-1 > RalRAL-1 signaling mediates a 2
-promoting EGF signal, apparently modulatory, that supports the main 2
-promoting signal of NotchLIN-12 [26]. We further utilized this system to characterize a 2
-promoting signal propagated downstream of RalRAL-1 signals through the MAP4KGCK-2 and downstream p38PMK-1 MAP kinase [27]. We have used the VPC fate patterning system for other purposes. Rap1 (Ras proximal) is a Ras relative with identical core effector-binding sequences [28], suggesting that Ras and Rap1 could engage the same effectors. The role of Rap1 in activating

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Raf has long been enigmatic, with contradictory results afflicting the field. We used CRISPR-dependent genome editing to mutationally activate the endogenous Rap1RAP-1 gene, thereby showing that Rap1RAP-1 is sufficient to induce ectopic 1
cells, albeit at a level much lower than activated RasLET-60 [29]. We additionally found that activated Rap1RAP-1 causes duplication of the excretory duct cell, also at a frequency lower than activated RasLET-60. There are several advantages to developmental genetic analysis in C. elegans. Generally, nematodes harbor a single gene compared to multiple paralogs of that gene in vertebrates. Thus, analysis in nematodes avoids issues of isoform redundancy that plague genetic analysis in mammalian cell-based systems. Genetic analysis of endogenous genes also avoids caveats associated with expression of ectopic protein. Transgenics are typically routine to generate, and sophisticated genetic analysis and CRISPR/Cas9-dependent genome editing [30] are possible. Analysis in live animals is also an advantage. Disadvantages include the frequent inability to measure signaling outcomes biochemically (e.g., western blot analysis of phospho-ERK or phospho-Akt); the VPCs are estimated to comprise less than 0.1% of the volume of the animal, with large tissues like the germline containing ample activation of ERKMPK-1 that drowns out a signal in other tissues. Here we describe methods of genetic analysis of RasLET-60 signaling in C. elegans, as well as analysis of close relatives Rap1RAP-1 and RalRAL-1. We describe sensitized backgrounds, strains, and fluorescent reporters to be used (Table 1).

2

Materials 1. All animals should be grown under standard culturing conditions, on 5 cm NG agar plates seeded with OP50 bacteria at 20
C, unless stated otherwise [31]. 2. Media and culturing conditions for C. elegans can be found at WormBook [32]. (a) NG agar: animals are grown on plates with a spot of E. coli OP50 bacteria [32]. (b) OP50 bacteria are the standard food [32], though others are available for specific applications, like HT115 for bacterially mediated RNA interference [33]. (c) M9 buffer (not to be confused with M9 minimal medium) [32]. (d) Wild-type (“N2”) C. elegans genomic DNA can be purchased commercially. (e) A platinum worm pick is used for controlled transfer of animals between plates [32]. (f) Halocarbon oil 700 (Sigma-Aldrich).

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Table 1 Key genetic reagents Strain Ras

Genotype

Phenotypes

LET-60

MT2124 let-60(n1046gf) IV SD551

let-60(ga89gf) IV

NH2531 let-60(ay75gf)/dpy-20(e1362) IV

Multivulva, duct cell duplication Multivulva, fertility Multivulva, fluid homestasis (Clr), fertility

EGFRLET-23 PS1524

unc-4(e120) let-23(sa62gf)/mnC1 dpy-10(e128) unc-52 (e444) II

Multivulva

Rap1RAP-1 DV3313 rap-1(re180gf) IV

Multivulva, duct cell duplication

NotchLIN-12 DV2443 lin-12(n379d) II; him-8(e1489) IV

Vulvaless, ectopic 2
psuedovulvae, males

EGF-dependent 2
induction DV2449 lin-12(n379d) II; lin-15(n765ts) X

Vulvaless, ectopic 2
psuedovulvae at 15
C

Vulval-specific RNAi JU2039

mfIs70[Plin-31::rde-1(+), Pmyo-2::gfp] IV; rde-1(ne219) V

JU2058

rrf-3(pk1426) II; mfIs70[Plin-31::rde-1(+), Pmyo-2::gfp] IV; Vulva-specific RNAi, RNAi rde-1(ne219) V hyper-sensitive

Vulva-specific RNAi

Cell fate reporters

a

CM117

unc-119(e2498) III; saIs14[Plin-48::GFP, unc-119(+)]

Duct cell fate reporter

GS3582

arIs92[Pegl-17::NLS-CFP-LacZ, Pttx-3::GFP, unc-4(+)] mrt(re58)a V

1
VPC transcriptional reporter

GS4892

arIs131[Plag-2::YFP, Pceh-22::GFP, pha-1(+)] III

1
VPC transcriptional reporter

GS5231

arIs116[Plst-5::2xNLS::tag-RFP, Pttx-3::GFP, pha-1(+)] X

2
VPC transcriptional reporter

GS8729

arSi12[Pmex-5::ERK::KTR::GFP(smu-1 introns)::T2A:: mCherry::his-11::tbb-2 30 UTR]

ERK activity reporter

This strain may harbor a “mortal germline” (Mrt) mutation, since animals progressively become sterile over many generations. In the lab, we refresh this strain frequently from a starved, parafilmed plate or re-thaw as needed

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Fig. 1 Raslet-60 and Notchlin-12 signaling in vulval development. Genotypes are listed across the top, stage on the right. (a–c) Late L4 stage. (d–f) Adults with fertilized embryos in the uterus. (g) Vulvaless “bag of worms” Notchlin-12(n379d) animal, where larvae have hatched inside the mother. White triangles ¼ normal vulva. Black triangles ¼ ectopic pseudovulvae. Scale bar ¼ 10 μm

3. Microscopy. (a) Manipulating C. elegans cultured on NG pates [32] is best performed with a stereomicroscope (i.e., the “dissecting microscope” of undergraduate laboratories) with a minimum 6 zoom ratio and a transmitted light base. Either actual zoom or rotating carousel is acceptable. Scoring ectopic pseudovulvae in adults can be performed on these instruments. (b) More accurate scoring of ectopic pseudovulvae is done at the late L4 stage, when the morphogenetic invagination is present (Fig. 1). L4 animals are typically mounted on agar pads on slides. Scoring ectopic vulval invaginations at the L4 stage is more reliable than scoring pseudovulvae in adults due to potentially confounding morphogenetic effects in the adult. But for higher-throughput though potentially less accurate analysis, animals can be scored on the plate and in the adult (see Note 1). (c) Scoring L4s requires a bright-field compound microscope equipped with differential interference contrast (DIC)/ Nomarski optics and a 60 oil objective.

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(d) Epifluorescent imaging of fate reporters (see below) is feasible on the above compound microscope equipped with a standard filer set for fluorescent proteins RFP, GFP, and CFP. (YFP is visible with standard GFP or FITC filter sets.) Confocal microscopy is not required for these assays. 4. RasLET-60 sensitizing mutations: Various mutations in Raslet-60 provide tools for dissecting changes in RasLET-60 signaling in C. elegans. We highlight here three gain-of-function alleles that allow for overlapping and discrete interrogation of RasLET-60 signaling. (a) Raslet-60(n1046gf) causes a G13E substitution, resulting in a dominant gain-of-function allele and consistent with predominant mutant forms of oncogenic Ras in humans [34]. This allele results in both multivulval (Muv) (Fig. 2a–c; see Note 2) and excretory duct cell duplication phenotypes [4, 35]. (b) The Raslet-60(ga89gf, ts) allele causes a L19F substitution. However, ga89gf differs from n1046gf in that it is strongly temperature-sensitive (ts) and mostly impacts fertility rather than 1
VPC induction (see Note 3). Animals are almost sterile at 25
C, with a significant reduction in brood size at 20
C, and are nearly wild type at 15
C. A small fraction is Muv at 25 and 20
C, but animals are wild type at 15
C [36]. (c) Raslet-60(ay75gf,ts) causes a G60R substitution. The mutant phenotypes are temperature-sensitive (ts) and exhibit a clear (Clr) phenotype, due to defective homeostasis of electrolytes, which causes the body cavity to fill will liquid (see Note 4). Fluid homeostasis is governed by FGFREGL-15 rather than EGFRLET-23, but both receptor tyrosine kinases use the Ras > Raf > MEK > ERK signaling cascade [37]. Similar to ga89, ay75 also impacts fertility, with mutants sterile at 25
C, severely reduced fertility at 20
C, and fertile at 15
C (see Note 4). A partially penetrant Muv phenotype is visible in animals grown at 15
C [37]. 5. EGFRLET-23 sensitizing mutation. (a) The EGFRlet-23(sa62gf) dominant gain-of-function mutation provides an additional tool for investigating Ras signaling aside from Raslet-60 itself. As with activated Ras Raslet-60, EGFRlet-23(sa62gf) animals are Muv. Unlike mutant for activated Raslet-60, EGFRlet-23(sa62gf) animals suffer a modest growth delay, requiring an additional day before any vulval counts [22].

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Fig. 2 A wiring diagram of competing signals in C. elegans VPCs. (a) EGFRLET-23 > RasLET-60 (blue) and NotchLIN-12 (rose) signals are necessary and sufficient for induction of 1
and 2
VPCs, respectively. Modulatory 1
-promoting PI3KAGE-1 > PDKPDK-1 > AktAKT-1/2 or 2
-promoting RasLET-60 > RalGEFRGL-1 > RalRAL-1 signals are shown in light blue/light rose and smaller lettering, respectively. (b) Mutational activation of RasLET-60 (asterisk) results in increased activation of ERK (bold) and transformation of presumptive 3
to 1
VPCs. (Not all are transformed due to the lateral signal of NotchLIN-12.) (c) In the activated RasLET-60 background, reduction of the RasLET-60 > RalGEFRGL-1 > RalRAL-1 signals reduces 2
-promoting signal, thus enhancing the strength of the RasLET-60 > RafLIN-45 1
-promoting signal and transformed 1
phenotype. (d) Weakly activated Notchlin-12(n379d) abrogates the development of the anchor cell (AC), resulting in a vulvaless phenotype, as well as weakly inducing ectopic 2
cells. (e) In the absence of EGF, these animals are agnostic to the loss of RasLET-60 > RalGEFRGL-1 > RalRAL-1, but (f) activation of RasLET-60 > RalGEFRGL-1 > RalRAL-1 is sufficient to increase the frequency of ectopic 2
cells

6. Rap1RAP-1 sensitizing mutation. (a) The Rap1rap-1(re180gf) allele was generated via CRISPR/ Cas9 genome editing, introducing a G12V activating mutation. This allele, like activating mutations in Raslet60 , results in Muv and duplicated duct cell phenotypes but to a lower degree [29]. 7. NotchLIN-12 sensitizing mutation. (a) The weakly activating Notchlin-12(n379d) allele provides a valuable resource to analyze VPC cell fate patterning independently of EGF signaling. In 95% of mutant animals,

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AC does not develop, and hence EGFLIN-3 signaling is not present [3]. As such the animals are vulvaless and sensitive to perturbation of the 2
but not 1
EGFLIN-3-dependent VPC cell fate inductive signaling (Fig. 2d; [20, 26]). 8. A double mutant that confers EGFLIN-3-dependent induction of 2
VPCs. (a) Single mutant strains harboring the lin-15(n765ts) mutation are superficially Muv at 25
C but wild type at 15
C. LIN-15 was previously shown to negatively regulate levels of EGFLIN-3 [38], and thus the temperature-sensitive n765 mutation permits the user to titrate EGFLIN-3 levels to levels optimal for experimentation. In combination with Notchlin-12(n379d), above, lin-15(n765ts) at 15
C results in strong induction of 2
cells that is dependent on the Raslet-60 > RalGEFRGL-1 > RalRAL-1 signaling module (Fig. 2c–e; [26, 27]). 9. Cell fate/transcriptional reporter strains: While interpretation of phenotypic changes in vulval development served to establish our early understanding of RasLET-60 signaling in C. elegans, it has since been augmented by the use of various transgenic reporters that are integrated into a chromosome (see Notes 5–7). We will review here several of the key reporters for RasLET-60-dependent cell fate decisions in the excretory duct cell and the vulva. (a) The saIs14[Plin-48::GFP] reporter expresses GFP in the excretory duct cell. Excessive EGFRLET-23 > RasLET-60 signaling results in duplication of the duct cell, which can be readily visualized via epifluorescence by two GFP spots ventral to the pharynx in the first larval (L1) stage [39]. (b) Two key 1
VPC transcriptional reporters have been used within in the field: arIs92[Pegl-17::NLS-CFP-LacZ] and arIs122[Plag-2::NLS-YFP]. While both serve as reporters for 1
fate induction, arIs122[Plag-2::YFP] in addition marks the anchor cell [40, 41] (see Note 7). (c) arIs116[Plst-5::2xNLS::tag-RFP] serves as a sensitive reporter of 2
VPC fate induction [42] (see Note 5). (d) arTi85[Plin-31::ERK::KTR(NLS3)::mClover::T2A:: mCherry::H2B] is a unique entry in reporters of VPC signaling. This strain allows for quantitative analysis of phosphorylation of ERKMPK-1 substrate, through the use of a fluorescent kinase translocation reporter compared to an internal control nuclear fluorescence of mCherry::H2B fusion. However, this tool does require expertise in imaging and analysis [43].

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10. An understudied aspect of vulval induction is the role of genes that are otherwise essential for viability or fertility. RNAi specifically targeting the VPCs and lineal progeny could reveal heretofore uncharacterized roles of genes in vulval induction and patterning. (a) RNAi depletion only in the VPCs was conferred by transgenic rescue of mutant ArgonauteRDE-1 by expression of wild-type ArgonauteRDE-1 in VPCs [44].

3

Methods

3.1 Standard Growth and Assays

1. Maintain strains on 5 cm plates spotted with OP50 bacteria [32]. Pick one adult and one L4 of a strain of interest, transfer to 20
C to generate an asynchronous population. 2. For more accurate DIC analysis, score animals 3 days after founding, either picking late L4 animals to NG agar pads on slides for scoring invaginations of ectopic pseudovulvae using DIC/Nomarski [45] microscopy (more accurate but slower) or scoring pseudovulval protrusions of adults on the plate (less accurate but quicker). 3. Bacterially mediated RNAi interference (RNAi). Streak and handle RNAi strains (see above and as reported [33, 46]). (a) Day 0: Spot two RNAi plates (NG + carbenillicin + IPTG) for each gene to be tested with 80 μl RNAi culture. (b) Day 1: Pick a variable number of L4s (2–10, depending on the fecundity of strain) to a transfer plate without food. Use a tiny drop of halocarbon oil 700 to the foodless plate to facilitate picking up animals without food. Transfer the needed number of animals to the RNAi plate. (This practice minimized OP50 bacteria picked to the RNAi plate.) (c) Day 2: Transfer all animals to the second RNAi plate for each genotype (see Note 8). (d) Day 4: 48 h after transferring, pick L4s to slide for scoring. For scoring adults, wait an additional 12–24 h as needed. 4. For examination of a genetic relationship between your gene of interest (GOI) and any of the above-described strains, they can either be crossed together or knocked down through the use of bacterially mediated RNA interference. Standard approaches for genetic crosses and bacterially mediated RNA interference are described in WormBook chapters. http://wormbook.org/toc_geneticsgenomics.html. http://wormbook.org/chapters/www_ introreversegenetics/introreversegenetics.html.

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5. Analysis of EGFLIN-3 > RasLET-60-dependent 1
-promoting genes. (a) For analysis of EGFRLET-23 > RasLET-60-dependent 1
-promoting genes, the Raslet-60(n1046gf) and EGFRlet-23(sa62gf) mutations provide ideal sensitized backgrounds. As both strains are Muv, this allows for interrogation of GOI via either reduction or enhancement of frequency of cell fate transformations. (b) A minimum of at least 40 L4 stage animals per genotype should be imaged for the vulval count assay. (c) If alterations of the GOI within these sensitized backgrounds lead to an observed statistically significant difference within the rate of Muv, additional epistasis experiments can determine if the GOI is acting in parallel or as part of the EGF > Ras cascade, e.g., [27, 47]. (d) The excretory duct cell provides a secondary simple binary cell fate decision that is also RasLET-60 dependent. As Raslet-60 (n1046gf) duct cell duplication occurs at ~95% animals, the use of this assay lends itself either to genes reducing RasLET-60 activity or solely as positive control comparison. 6. Analysis of NotchLIN-12-dependent 2
-promoting genes. (a) Similarly to RasLET-60 above, Notchlin-12(n379d) animals can be used to assess Notch signaling. A series of stronger mutations in Notchlin-12 (e.g., n676, n950) can be used to generate backgrounds better for analyzing positive or negative regulation. (b) Mutant animals are vulvaless, so brood size is limited due to larvae hatching inside the number. 40 or more animals scored per genotype should reveal genetic interactions of positive or negative regulators of Notchlin-12 signaling. 7. Analysis of EGF > Ras-dependent 2º-promoting genes. (a) Notchlin-12(n379d); lin-15(n765ts) animals should be grown at 15
C, where a high frequency of small ectopic pseudovulvae should be observed. As with other backgrounds, scoring in the late L4 is more reliable than in the adult. 8. Transgenic expression of GOIs within the VPCs uses the lin-31 promoter, and silencing can be an issue. Two approaches can address this: complex transgenic arrays or miniMos. Complex transgenic arrays require the addition of purified blunt cut C. elegans genomic DNA to represent a large fraction of DNA in the injection mix, thus generating a complex extrachromosomal array [48]. This addition of genomic DNA fragments prevents silencing by interspersing random DNA

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sequences with repetitive plasmid sequences in the resulting multicopy transgenic extrachromosomal array. miniMos or mosSCI, transposon-mediated methods, improves the likelihood of expression by incorporation of the single- or low-copy insertions into the chromosome [49, 50], thus avoiding repetitive sequences.

4

Notes 1. Scoring of cell fate can be done via recording of VPCs lineages in real time [51]. This process represents the earliest methodology in C. elegans to determine changes in cell fate and is feasible due to the invariant nature of cell lineages in C. elegans. However, this approach is highly specialized and time-consuming, and is not recommended for beginners or even intermediate C. elegans experimentalists. 2. Additional care must be used of any strains carrying the Raslet60 (n1046gf) allele due to genetic drift under continuous culturing, resulting in increased phenotypic strength [47]. For consistent results, a parafilmed, starved reference strain should be made immediately upon thawing or construction of a strain. All strains should then be reestablished from the reference strain each week for all subsequent experiments. Neither Rap1rap-1(re180gf) nor EGFRlet-23(sa62gf) has exhibited similar genetic drift [29]. 3. The effects of RasLET-60 signaling in the germline are quite apparent in the observed phenotypes of sterility or disorganized germlines [52]. However, as the cellular changes represent continuous and not discrete cell fate changes, quantification can be difficult. Although ERKMPK-1 is an established player in germline development as in vulval development, germline ERKMPK-1 relies on noncanonical outputs and as such established cell fate reporters are unhelpful. 4. While defective fluid homeostasis leads to easily distinguishable clear (Clr) animals, RasLET-60 signaling in this context is difficult to quantify, as there are currently no known reporters for this activity. 5. Precise timing of EGFLIN-3 induction is difficult to determine, since established reporters for this activity all require transcription of downstream genes. Consequently, scoring of expression of cell fate reporters should occur in L3 animals at the Pn.px (two-cell) stage, which guarantees that inductive signaling has already occurred. Consistent exposure times should be used across all animals.

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6. Currently there are no known reporters for the modulatory cascades: PI3KAGE-1 > PDKPDK-1 > AktAKT-1/2 1
-promoting [53] or RasLET-60 > RalGEFRGL-1 > RalRAL-1 > MAP4KGCK2 >>> p38PMK-1 2
-promoting signals. 7. Imaging and scoring of the Plst-5::2xNLS::tag-RFP reporter does require an extended exposure time of 4 s to capture both high and low expressing VPCs [29]. 8. In backgrounds harboring the Notchlin-12(n379d) mutation, the AC fails to develop. Hence these animals do not form a functional vulva, and fertilized embryos hatch inside the mother, leading to the “bag of worms” phenotype. Consequently, these animals yield a small (~30), partially synchronized brood.

Acknowledgments This work was supported by R01GM121625 and R21HD090707 to D.J.R. References 1. Ferguson EL, Horvitz HR (1985) Identification and characterization of 22 genes that affect the vulval cell lineages of the nematode Caenorhabditis elegans. Genetics 110 (1):17–72 2. Ferguson EL, Sternberg PW, Horvitz HR (1987) A genetic pathway for the specification of the vulval cell lineages of Caenorhabditis elegans. Nature 326(6110):259–267. https:// doi.org/10.1038/326259a0 3. Greenwald IS, Sternberg PW, Horvitz HR (1983) The lin-12 locus specifies cell fates in Caenorhabditis elegans. Cell 34(2):435–444. https://doi.org/10.1016/0092-8674(83) 90377-x 4. Beitel GJ, Clark SG, Horvitz HR (1990) Caenorhabditis elegans ras gene let-60 acts as a switch in the pathway of vulval induction. Nature 348(6301):503–509. https://doi. org/10.1038/348503a0 5. Han M, Aroian RV, Sternberg PW (1990) The let-60 locus controls the switch between vulval and nonvulval cell fates in Caenorhabditis elegans. Genetics 126(4):899–913 6. Han M, Sternberg PW (1990) let-60, a gene that specifies cell fates during C. elegans vulval induction, encodes a ras protein. Cell 63 (5):921–931. https://doi.org/10.1016/ 0092-8674(90)90495-z

7. Clark SG, Stern MJ, Horvitz HR (1992) C. elegans cell-signalling gene sem-5 encodes a protein with SH2 and SH3 domains. Nature 356(6367):340–344. https://doi.org/10. 1038/356340a0 8. Pawson T (1992) Cell signalling. Conviction by genetics. Nature 356(6367):285–286. https://doi.org/10.1038/356285a0 9. Kornfeld K, Hom DB, Horvitz HR (1995) The ksr-1 gene encodes a novel protein kinase involved in Ras-mediated signaling in C. elegans. Cell 83(6):903–913 10. Sundaram M, Han M (1995) The C. elegans ksr-1 gene encodes a novel Raf-related kinase involved in Ras-mediated signal transduction. Cell 83(6):889–901 11. Bruinsma JJ, Jirakulaporn T, Muslin AJ, Kornfeld K (2002) Zinc ions and cation diffusion facilitator proteins regulate Ras-mediated signaling. Dev Cell 2(5):567–578. https://doi. org/10.1016/s1534-5807(02)00151-x 12. Yoder JH, Chong H, Guan KL, Han M (2004) Modulation of KSR activity in Caenorhabditis elegans by Zn ions, PAR-1 kinase and PP2A phosphatase. EMBO J 23(1):111–119. https://doi.org/10.1038/sj.emboj.7600025 13. Sternberg PW (2005) Vulval development. WormBook:1–28. https://doi.org/10.1895/ wormbook.1.6.1

Ras in C. elegans 14. Sundaram MV (2005) The love-hate relationship between Ras and Notch. Genes Dev 19 (16):1825–1839. https://doi.org/10.1101/ gad.1330605 15. Chen N, Greenwald I (2004) The lateral signal for LIN-12/Notch in C. elegans vulval development comprises redundant secreted and transmembrane DSL proteins. Dev Cell 6 (2):183–192 16. Braendle C, Felix MA (2008) Plasticity and errors of a robust developmental system in different environments. Dev Cell 15(5):714–724. https://doi.org/10.1016/j.devcel.2008.09. 011 17. Berset T, Hoier EF, Battu G, Canevascini S, Hajnal A (2001) Notch inhibition of RAS signaling through MAP kinase phosphatase LIP-1 during C. elegans vulval development. Science 291(5506):1055–1058. https://doi.org/10. 1126/science.1055642 18. Shaye DD, Greenwald I (2002) Endocytosismediated downregulation of LIN-12/Notch upon Ras activation in Caenorhabditis elegans. Nature 420(6916):686–690. https://doi.org/ 10.1038/nature01234 19. Shaye DD, Greenwald I (2005) LIN-12/ Notch trafficking and regulation of DSL ligand activity during vulval induction in Caenorhabditis elegans. Development 132 (22):5081–5092. https://doi.org/10.1242/ dev.02076 20. Shin H, Reiner DJ (2018) The signaling network controlling C. elegans vulval cell fate patterning. J Dev Biol 6(4). https://doi.org/10. 3390/jdb6040030 21. Katz WS, Hill RJ, Clandinin TR, Sternberg PW (1995) Different levels of the C. elegans growth factor LIN-3 promote distinct vulval precursor fates. Cell 82(2):297–307 22. Katz WS, Lesa GM, Yannoukakos D, Clandinin TR, Schlessinger J, Sternberg PW (1996) A point mutation in the extracellular domain activates LET-23, the Caenorhabditis elegans epidermal growth factor receptor homolog. Mol Cell Biol 16(2):529–537 23. Sternberg PW, Horvitz HR (1986) Pattern formation during vulval development in C. elegans. Cell 44(5):761–772 24. Sternberg PW, Horvitz HR (1988) lin-17 mutations of Caenorhabditis elegans disrupt certain asymmetric cell divisions. Dev Biol 130(1):67–73 25. Sternberg PW, Horvitz HR (1989) The combined action of two intercellular signaling pathways specifies three cell fates during vulval induction in C. elegans. Cell 58(4):679–693

435

26. Zand TP, Reiner DJ, Der CJ (2011) Ras effector switching promotes divergent cell fates in C. elegans vulval patterning. Dev Cell 20 (1):84–96. https://doi.org/10.1016/j. devcel.2010.12.004 27. Shin H, Kaplan REW, Duong T, Fakieh R, Reiner DJ (2018) Ral signals through a MAP4 kinase-p38 MAP kinase cascade in C. elegans cell fate patterning. Cell Rep 24 (10):2669–2681.e2665. https://doi.org/10. 1016/j.celrep.2018.08.011 28. Reiner DJ, Lundquist EA (2018) Small GTPases. WormBook:1–65. https://doi.org/ 10.1895/wormbook.1.67.2 29. Rasmussen NR, Dickinson DJ, Reiner DJ (2018) Ras-dependent cell fate decisions are reinforced by the RAP-1 small GTPase in Caenorhabditis elegans. Genetics. https://doi.org/ 10.1534/genetics.118.301601 30. Dickinson DJ, Ward JD, Reiner DJ, Goldstein B (2013) Engineering the Caenorhabditis elegans genome using Cas9-triggered homologous recombination. Nat Methods 10 (10):1028–1034. https://doi.org/10.1038/ nmeth.2641 31. Brenner S (1974) The genetics of Caenorhabditis elegans. Genetics 77(1):71–94 32. Stiernagle T (2006) Maintenance of C. elegans. WormBook:1–11. https://doi.org/10.1895/ wormbook.1.101.1 33. Timmons L, Court DL, Fire A (2001) Ingestion of bacterially expressed dsRNAs can produce specific and potent genetic interference in Caenorhabditis elegans. Gene 263 (1–2):103–112. https://doi.org/10.1016/ s0378-1119(00)00579-5 34. Pylayeva-Gupta Y, Grabocka E, Bar-Sagi D (2011) RAS oncogenes: weaving a tumorigenic web. Nat Rev Cancer 11(11):761–774. https://doi.org/10.1038/nrc3106 35. Yochem J, Sundaram M, Han M (1997) Ras is required for a limited number of cell fates and not for general proliferation in Caenorhabditis elegans. Mol Cell Biol 17(5):2716–2722. https://doi.org/10.1128/mcb.17.5.2716 36. Eisenmann DM, Kim SK (1997) Mechanism of activation of the Caenorhabditis elegans ras homologue let-60 by a novel, temperaturesensitive, gain-of-function mutation. Genetics 146(2):553–565 37. Schutzman JL, Borland CZ, Newman JC, Robinson MK, Kokel M, Stern MJ (2001) The Caenorhabditis elegans EGL-15 signaling pathway implicates a DOS-like multisubstrate adaptor protein in fibroblast growth factor signal transduction. Mol Cell Biol 21

436

Neal R. Rasmussen and David J. Reiner

(23):8104–8116. https://doi.org/10.1128/ MCB.21.23.8104-8116.2001 38. Cui M, Chen J, Myers TR, Hwang BJ, Sternberg PW, Greenwald I, Han M (2006) SynMuv genes redundantly inhibit lin-3/EGF expression to prevent inappropriate vulval induction in C. elegans. Dev Cell 10(5):667–672. https://doi.org/10.1016/j.devcel.2006.04. 001 39. Johnson AD, Fitzsimmons D, Hagman J, Chamberlin HM (2001) EGL-38 Pax regulates the ovo-related gene lin-48 during Caenorhabditis elegans organ development. Development 128(15):2857–2865 40. Yoo AS, Bais C, Greenwald I (2004) Crosstalk between the EGFR and LIN-12/Notch pathways in C. elegans vulval development. Science 303(5658):663–666. https://doi.org/10. 1126/science.1091639 41. Zhang X, Greenwald I (2011) Spatial regulation of lag-2 transcription during vulval precursor cell fate patterning in Caenorhabditis elegans. Genetics 188(4):847–858. https:// doi.org/10.1534/genetics.111.128389 42. Li J, Greenwald I (2010) LIN-14 inhibition of LIN-12 contributes to precision and timing of C. elegans vulval fate patterning. Curr Biol 20 (20):1875–1879. https://doi.org/10.1016/j. cub.2010.09.055 43. de la Cova C, Townley R, Regot S, Greenwald I (2017) A real-time biosensor for ERK activity reveals signaling dynamics during C. elegans cell fate specification. Dev Cell 42 (5):542–553.e544. https://doi.org/10. 1016/j.devcel.2017.07.014 44. Barkoulas M, van Zon JS, Milloz J, van Oudenaarden A, Felix MA (2013) Robustness and epistasis in the C. elegans vulval signaling network revealed by pathway dosage modulation. Dev Cell 24(1):64–75. https://doi.org/ 10.1016/j.devcel.2012.12.001 45. Yochem JK (2006) Nomarski images for learning the anatomy, with tips for mosaic analysis. WormBook:1–47. https://doi.org/10. 1895/wormbook.1.100.1

46. Kamath RS, Martinez-Campos M, Zipperlen P, Fraser AG, Ahringer J (2001) Effectiveness of specific RNA-mediated interference through ingested double-stranded RNA in Caenorhabditis elegans. Genome Biol 2(1): RESEARCH0002. https://doi.org/10. 1186/gb-2000-2-1-research0002 47. Shin H, Braendle C, Monahan KB, Kaplan REW, Zand TP, Mote FS, Peters EC, Reiner DJ (2019) Developmental fidelity is imposed by genetically separable RalGEF activities that mediate opposing signals. PLoS Genet 15(5): e1008056. https://doi.org/10.1371/journal. pgen.1008056 48. Kelly WG, Xu S, Montgomery MK, Fire A (1997) Distinct requirements for somatic and germline expression of a generally expressed Caernorhabditis elegans gene. Genetics 146 (1):227–238 49. Merritt C, Seydoux G (2010) Transgenic solutions for the germline. WormBook:1–21. https://doi.org/10.1895/wormbook.1. 148.1 50. Nance J, Frokjaer-Jensen C (2019) The Caenorhabditis elegans transgenic toolbox. Genetics 212(4):959–990. https://doi.org/10. 1534/genetics.119.301506 51. Sulston JE, Horvitz HR (1977) Postembryonic cell lineages of the nematode, Caenorhabditis elegans. Dev Biol 56(1):110–156. https://doi.org/10.1016/0012-1606(77) 90158-0 52. Arur S, Ohmachi M, Nayak S, Hayes M, Miranda A, Hay A, Golden A, Schedl T (2009) Multiple ERK substrates execute single biological processes in Caenorhabditis elegans germ-line development. Proc Natl Acad Sci U S A 106(12):4776–4781. https://doi.org/10. 1073/pnas.0812285106 53. Nakdimon I, Walser M, Frohli E, Hajnal A (2012) PTEN negatively regulates MAPK signaling during Caenorhabditis elegans vulval development. PLoS Genet 8(8):e1002881. https://doi.org/10.1371/journal.pgen. 1002881

INDEX A ADP-ribosylation factor (Arf) ................ 20, 21, 120, 152 Antibodies ............... 13, 50, 53, 60, 63, 70, 75, 91–101, 108, 111, 112, 115, 123, 128, 129, 140, 145, 147, 153–155, 162–165, 189, 193, 195, 196, 221, 224, 225, 229, 235, 274, 276, 277, 281, 283, 287–289, 291, 292, 295, 299–301, 304–306, 308, 309, 327, 331, 345, 355, 357, 373–376, 380–382, 385 Arl2 .............................................................. 206, 209, 210

B Baculovirus .................................................. 106, 107, 113 Behavioral studies.......................................................... 366

C Caenorhabditis elegans ......................................... 423–434 Carboxymethylation............................................... 61, 114 Cdc42 ........................................... 20, 120, 129, 132, 260 Conformational Sensors for GTPase Activity (COSGA) 259–266 Costello.................................................. 8, 9, 36, 397–408 CRISPR ..............7, 9, 14, 326–327, 332, 363, 425, 429

D Developmental syndromes ............................................. 36 Drosophila ...................................................................... 363

E EGFR inhibitors ..................... 311, 312, 316, 317, 319, 320 Egg................................................................................. 415 Electron microscopy ................................... 235, 238, 382 Embryos .................. 363, 367, 368, 384, 389, 398, 401, 405, 412, 414, 415, 417, 419, 420, 427, 434 Endocytosis .................................. 21, 202, 204, 208–210 Endomembranes ..........................................206–208, 223 Environment sensitive labels ...................... 138, 142, 150 Epidermal growth factor (EGF)................ 129, 189, 190, 194, 264, 289, 351, 352, 357, 368, 378, 386, 424, 429, 432

Extracellular signal-regulated kinase (ERK) .......... 29, 30, 32, 35, 283, 289, 290, 295, 300, 311, 312, 337, 368, 424, 426, 428–430

F Farnesyl transferase (FTase)............................... 6, 24, 234 FGFR ............................................................................. 428 Fibroblast growth factor (FGF) .......................... 357, 378 Flow cytometry ..................................251–257, 365, 367, 373, 380 Fluorescence lifetime imaging microscopy (FLIM)........................... 235, 237–239, 245, 247, 260, 263, 264 Fluorescence polarization (FP) ........................... 138, 154 Fluorescence recovery after photobleaching (FRAP) 185–196, 235 Fo¨rster resonance energy transfer (FRET).................... 13, 138, 139, 144, 147, 152, 153, 155, 161, 165, 235, 237–239, 242, 245–247, 260

G Gene editing ........................................................... 13, 324 Gene targeting...................................................... 363, 378 GTPase activating proteins (GAPs) ................... 4, 22, 35, 37, 118, 137, 162, 170, 176, 180, 259, 265, 314, 319, 336 Guanine nucleotide exchange factors (GEFs)..................... 4, 22, 28, 37, 118, 137, 150, 170, 176, 180, 259, 265, 314, 316, 335, 336, 361–363, 365–369, 378, 380, 387

H Histology ................................... 365–367, 372–375, 379, 380, 383

I Immunoblots.......................................61, 62, 98, 99, 274 Immunoprecipitation.................... 47–63, 285, 288, 291, 304, 306, 308 Isogenic cells ........................................................ 323–332

Ignacio Rubio and Ian Prior (eds.), Ras Activity and Signaling: Methods and Protocols, Methods in Molecular Biology, vol. 2262, https://doi.org/10.1007/978-1-0716-1190-6, © Springer Science+Business Media, LLC, part of Springer Nature 2021

437

RAS ACTIVITY

438 Index

AND

SIGNALING: METHODS

AND

PROTOCOLS

K

Proximity-labeling......................................................... 271

KRas mutants .....................8, 25, 234, 292, 311, 312, 317, 324, 325, 345 proteoforms ......................................................... 47–63

Q

L Lentivirus.............................................................. 326, 328 LIN-45 ........................................................ 423, 424, 429 Liposome ....................................................................... 332 Luminescence .............................137, 138, 159, 163, 296

M Mammalian target of rapamycin (mTOR).......... 201, 208 MAP kinases .................................................................. 424 Mass spectrometry (MS) Top-down MS ............................................ 47–63, 111 Mathematical modeling ....................................... 311–320 MATLAB ....................................199, 203, 204, 213, 314 Microinjections.................. 266, 379, 414, 415, 417, 420 Models ..........................6, 13, 14, 31, 32, 113, 163, 165, 208, 211, 212, 237, 313–316, 318, 319, 349, 350, 363, 365–368, 371, 379, 384, 386–388, 411–421 Monobodies ..................................................... 5, 281–301 Mouse embryonic fibroblast (MEF) RASless ...................... 92, 98, 99, 289, 300, 335–345 Mouse models ................. 7, 30, 313, 361–389, 397–408

N Nanoclusters .................6, 218, 234, 235, 237–239, 242, 245, 246 Nanodiscs ...................................107, 108, 112, 114, 115 Nanoparticles..................... 218, 221, 222, 224–226, 229 NF1 ........................ 31, 32, 35, 144, 146, 147, 153, 162, 314, 317–320, 411 Nuclear magnetic resonance (NMR) ........ 169, 170, 172, 174–179 Nucleotide affinities.......6, 22, 26, 119, 150, 153, 156, 157, 170 cycling ........... 34, 138, 153, 164, 169–181, 259, 317

O OCTAVE .............................................199, 201–204, 213 Organoids ............................................................. 349–359

P PDEδ ..............................................................25, 206–211 Phosphoinositide 3-kinase (PI3K) ..................... 3, 12, 22, 23, 128, 137, 290, 300, 324, 337, 368, 411, 412, 429, 434 Post-translational modifications ............................ 58, 114

Quenching resonance energy transfer (QRET)................. 118, 138, 139, 143–145, 152, 153, 160, 162

R Rac ..........................................20, 29, 119–121, 259, 412 Raf ............................ 3, 20, 22, 120, 137, 200, 233–235, 289–292, 303, 311, 337, 423, 424, 428 Ral ............................66, 68–89, 119, 120, 337, 423–434 Rap ..................................... 119–121, 126, 129, 362, 365 Ras-like proteins in brain (Rab) ............. 20, 21, 120, 235 Rat sarcoma (Ras) activation ........................... 3, 6–8, 13, 20, 23, 27–29, 31, 32, 34, 36, 118, 126, 137–166, 170, 291, 315, 319, 361, 365, 368, 397 activities ........................... 7–9, 20–24, 26, 28, 29, 32, 33, 36, 117, 122, 137, 141, 142, 149–151, 153, 156, 158, 160–162, 200, 207, 208, 234, 291, 315, 337, 345, 362, 365, 387 binding domains ............................................ 235, 337 in cancers ...........................6, 8, 9, 11, 23–25, 30, 35, 65, 234, 367 carboxymethylation.............................................4, 106 conformations .................................25, 118, 129, 169 dimers .......................................................20, 207, 234 effectors ........................ 3, 4, 6, 8, 13, 21, 22, 24, 25, 30, 65, 105, 128, 137, 138, 161, 169, 200, 212, 218, 233, 259, 271, 285, 288, 290, 303, 314, 315, 336, 337, 424 imaging ................................... 13, 119, 217–231, 264 immunoprecipitation ...............................49, 285, 288 isoforms ........................................3–9, 11, 13, 14, 21, 24, 47, 65, 66, 76, 80–82, 92, 99, 106, 129, 187, 206, 217, 234, 246, 271, 279, 285, 286, 288, 299, 300, 336, 337, 345 LET-60 .......................................... 423–430, 432–434 localizations .........................4, 21, 24, 27, 28, 30, 47, 105, 200, 201, 207, 208, 212, 227, 234, 345 mutants ..............................7–9, 14, 65, 81, 145, 152, 156, 157, 170, 187, 218, 273, 286, 288–290, 313, 319, 320, 387, 413 palmitoylation.............................................4, 187, 234 post-translational modifications ............................. 4, 5 prenylation............................................................... 106 processing .................................................20, 106, 118 proteoforms ............................................................... 48 spatial cycles.................................................... 199–211 subcellular distribution ........................................... 238 Ras-association domain family (RASSF)...................... 303 RASA1 .................................................31, 32, 34, 35, 319

RAS ACTIVITY Ras-binding (RBD).......................... 3, 27, 119, 120, 133 RasGRPs ............................ 22, 27, 28, 30, 336, 365, 368 Ras homolog enriched in brain (Rheb) ...................... 34, 201, 207, 208, 210, 211 Ras homologous (Rho) .............. 20, 118–120, 151, 235, 259, 260, 351, 388 Rasless .................... 92, 98, 99, 289, 300, 336–340, 342, 344, 345 Rasopathies .............................7–9, 14, 35, 233, 397, 398 Reaction-diffusion simulations............................ 199–214 Rin.................................................................................. 120 Rit .................................................................................. 120

AND

SIGNALING: METHODS

AND

PROTOCOLS Index 439

Spatial analysis ...................................................... 217–231 Spatiotemporal imaging....................................... 259–266

T Thermal stability assay (TSA) .............138, 156, 165, 166 Transgenics ..................30, 368, 412, 413, 425, 430–433

W Western blots...............................53, 55, 60, 85, 86, 127, 129, 132, 133, 175, 287–289, 291, 295, 300, 301, 309, 425

S

X

Sensory perception........................................................ 366 Single-guide RNAs (sgRNAs) ............................ 324, 325, 327, 329, 331, 338, 340 Sos isoforms ................................................................... 369 transgenic animals ................................................... 368

Xenograft ..................................................... 297, 298, 312

Z Zebrafish ............................................................... 411–421