Research Collection Doctoral Thesis Modular organization of proteomes: new insights into tissue homeostasis and epigenetic control Author(s): Hauri, Simon Karl Publication Date: 2013 Permanent Link: https://doi.org/10.3929/ethz-a-010105188 Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library DISS. ETH NO. 21312 Modular Organization of Proteomes: New Insights into Tissue Homeostasis and Epigenetic Control A dissertation submitted to ETH ZURICH for the degree of Doctor of Sciences presented by SIMON KARL HAURI MSc, University of Basel born January 4, 1983 citizen of Hirschthal AG accepted on the recommendation of Prof. Dr. Ruedi Aebersold, examiner Dr. Matthias Gstaiger, co-examiner Dr. Christian Beisel, co-examiner Dr. Nic Tapon, co-examiner 2013 I. Zusammenfassung In der Vergangenheit wurde die Funktion eines Proteins aufgrund von genom-weiten phänotypischen Analysen in Modellorganismen bestimmt. Heute wissen wir, dass die meisten Proteine ihre Funktion im Zusammenhang mit anderen Proteinen in makromolekularen Protein Komplexen ausführen. In vielen Fällen ist die Bildung dieser Komplexe ein dynamischer Prozess und die molekulare Funktion kann durch die Assoziation oder Dissoziation von Proteinen beeinflusst werden. In Abhängigkeit des zellulären Zustandes können Proteinkomplexe Zusammensetzung ändern und ihre Funktion den biologischen Bedingungen anpassen. In vielen genetisch verursachten Krankheiten, wie Krebs, kann die korrekte Bildung von Proteinkomplexen durch genetische Mutationen verhindert werden. Daher ist es von grosser Wichtigkeit, die molekularen Mechanismen der Proteinkomplexbildung besser zu verstehen. Klassische biochemische Proteinanalyse ist aufwendig und die Charakterisierung eines Proteinkomplexes allein kann oftmals eine ganze Doktorarbeit umfassen. Um aber einen globales Verständnis zu erlangen, wie Proteine in Komplexen organisiert sind, brauchen wir Methoden die gleichzeitig viele Proteininteraktionsdaten messen können. „Proteomics“ ist die Lehre aller Proteine und deren Eigenschaften innerhalb eines zellulären Systems. In den letzten Jahren hat dieser Forschungsbereich vor allem durch die Proteinbestimmung mittels Massenspektrometrie enorme Fortschritte gemacht. Gekoppelt mit Protein-Affinitätsaufreinigung (engl. affinity purification mass spectrometry, oder „AP-MS“) ermöglicht es diese potente Technologie Proteininteraktionen systematisch und quantitativ mit einer Genauigkeit zu studieren wie noch nie zuvor. Während meiner Doktorarbeit habe ich eine etablierte AP-MS Methode benutzt um zwei umfangreiche Proteininteraktionsnetzwerke zu studieren. Den menschlichen Hippo Signalweg und das Kompendium menschlicher Polycomb Group (PcG) Proteinkomplexe. Der Hippo Signalweg kontrolliert Gewebehomeostase und Organgrösse in Vielzellern. PcG Proteine sind Chromatin-regulatoren und beteiligt an epigenetischen Kontrollmechanismen der Genexpression. Für beide Systeme haben wir neue und bekannte Proteininteraktionen gefunden und konnten mithilfe der Netzwerktopologie neue funktionell angereicherte Module und Proteinkomplexe zu bestimmen. I Zusammengefasst, befasst sich diese Doktorarbeit mit den Herausforderungen der massenspektrometrischen Bestimmung von Proteinkomplexen und präsentiert zwei hochauflösende Interaktionsnetzwerke von momentan grösstem biologischem Interesse. Zusätzlich war ich an zwei weiteren, AP-MS bezogene, Studien beteiligt: Die Bestimmung von Reproduzierbarkeit von AP-MS Daten zwischen verschieden Labors und die Entwicklung einer Datenbank für die Identifikation von unspezifischen Kontaminantenproteinen in AP-MS Experimenten. II II. Summary In the past, the function of a protein has been determined based on genome wide phenotypic screens in model organisms. Today we know that most proteins carry out their function in the context of protein complexes. In many cases, protein complexes are dynamic systems and their molecular function can be affected by association and dissociation of proteins. Dependent on the cellular state, protein complexes can change in their composition and adapt their function to overcome biological challenges. In many genetic diseases, including cancer, the proper formation of protein complexes can be disturbed by genetic mutations of associating proteins. Therefore, it is of great importance to study the molecular mechanisms involved in protein complex formation. Classical biochemical analysis of proteins is a tedious task and more often than not, the characterization of one protein complex was topic in an entire PhD thesis. To reach a global comprehension of how proteins are organized in the cell, we need methods capable of measuring many protein interactions at the same time. Proteomics is the large-scale study of proteins and their properties within a living organism. In the last few years, proteomics was subjected to tremendous advances thanks to protein identification by mass spectrometry. Combined with affinity purification (AP-MS) this potent technology allows to perform systematic quantitative studies of protein interactions at near physiological conditions and at unprecedented resolution. During my PhD thesis I incorporated an integrated AP-MS workflow to study two large protein interaction networks: The human Hippo growth signaling pathway and the human Polycomb Group (PcG) protein complexes. The Hippo pathway controls tissue homeostasis and organ size in developing organisms. PcG protein complexes are chromatin regulators involved in epigenetic control. For both systems we identified many novel and known protein interactions and were able to determine a network topology that allowed us to define functionally enriched modules and novel protein complex assemblies. In summary this PhD thesis discusses the challenges of mass spectrometry based interaction proteomics and presents two high resolution protein interaction networks of great biological importance. I was also involved in two additional studies: The assessment of inter-laboratory reproducibility of our AP-MS protocol and the assembly of a repository for common contaminant proteins in AP-MS experiments. III III. Abbreviations ABCP apico-basal cell polarity AP-MS affinity purification mass spectrometry CID collision induced dissociation CMV cytomegalovirus co-IP co-immunoprecipitation ComPASS Comparative Proteomics Analysis Software Suite DIA data independent acquisition DLR dual luciferase assay DUB deubiquitinating enzyme ESI electrospray ionization FDR false discovery rate FERM four-one, Ezrin, Radixin Moesin domain GFP green fluorescent protein H3K27me3 histone H3 lysine-27 trimethylation HCIP high confidence interacting protein Hpo Hippo L27 Lin2 Lin7 domain LC-MS/MS liquid chromatography tandem mass spectrometry LOD limit of detection LOQ limit of quantification LTQ linear ion trap m/z mass to charge ratio NSAF normalized spectral count abundance factor PcG Polycomb group PCP planar cell polarity PDZ PDT-85, Dlg, ZO-1 domain PINA protein interaction network analysis platform PPI protein-protein interaction PRC Polycomb repressive complex QUBIC Quantitative BAC Interactomics RFP ref fluorescent protein SAINT Significance Analysis of Interactome SARAH Salvador Rassf Hippo domain SEC size exclusion chromatography SRM selected reaction monitoring STRIPAK striatin-interacting phosphatase and kinase complex TPP trans-proteomic pipeline Y2H yeast two hybrid IV V. Table of Contents I. Zusammenfassung I II. Summary III III. Abbreviations IV V. Table of Contents V Chapter 1 Introduction to Interaction Proteomics 1 1.1. Abstract 2 1.2. Introduction 3 1.3. Mass spectrometry based Interaction proteomics 4 1.3.1. Protein Identification 4 1.3.2. Protein quantification 6 1.3.3. Targeted proteomics by SRM and SWATH-MS 7 1.3.4. Analysis of Protein interactions by Affinity purification mass spectrometry 9 1.3.5. Data filtering strategies for unspecific interacting proteins 11 1.3.6. Generating large scale interaction maps to guide upcoming and targeted studies 13 1.3.7. Inference of Protein complex stoichiometry by absolute quantification 13 1.3.8. Profiling of dynamic changes in interaction proteomes. 13 1.4. Cross-linking and AP-MS 14 1.5. Sub-fractionation of full cell lysates and purified complexes 14 1.6. Conclusions and Outlook 15 1.7. References 177 Chapter 2 Interaction Proteome of Human Hippo Signaling: Modular Control of the Transcriptional Co-activator YAP1 21 2.1. Abstract 22 2.2. Introduction 23 2.3. Results and Discussion 25 2.3.1. Characterization of the human Hippo interaction proteome by a systematic AP-MS approach 25 2.3.2. Hierarchical clustering assigns Hpo pathway components to interaction modules 27 V 2.3.3. Topology of the Hippo Core Kinase Complex 29 2.3.4. The PP1-ASPP module provides links to apico-basal and planar cell polarity 32 2.3.5. PP1/ASPP2 complexes promote YAP1 activity 33 2.3.6. A Cell Polarity Network linked to L2GL1, Kibra, Merlin and YAP1 36 2.4. Conclusions 39 2.5. Experimental Procedures 41 2.6. References 45 2.7. Supplementary Materials 48 2.7.1. Supplementary tables 48 2.7.2. Supplementary figures 49 Chapter 3 Characterization of the Human Polycomb Group Interaction Proteome 55 3.1. Abstract 56 3.2. Introduction 57 3.3. Results 58 3.3.1. Systematic profiling of the human PcG interaction proteome 58 3.3.2. Hierarchical clustering assigns HCIPs to PcG complexes 61 3.3.3. The PRC1 module 63 3.3.4. The PRC2 module