High-resolution protein correlation profiling to resolve subcellular proteome organization
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Author
Date
2020Type
- Doctoral Thesis
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Abstract
Systems biology is a holistic approach which studies the relations of the different layers of networks across all scales within any given biological system. Instead of following a reductionist approach by taking layers apart and study each single biomolecule, a system biology approach tries to identify the relationship existing within a system and to integrate these information to generate system-level blueprints of molecular mechanisms. These approaches are especially useful to understand how biomolecules organize in greatly specialized hierarchical networks, or pathways. This is achieved by perturbation of the cell in combination with high-resolution and high-throughput measurements. Thereby spatial and temporal insights into the architecture of networks and layers of networks (or "networks of networks") are gained and new hypothesis can be formulated. System biology thereby helps to link genetic perturbations back to its phenotypic manifestation. One of the most important biomolecule of this networks of networks in a cell are proteins, as they are involved in almost all cellular processes. Proteomics is the omics methods which studies proteins, whereas mass spectrometry (MS) based proteomics methods are the most widely applied techniques to identify and quantify proteins. Proteomics allows further to study the relation and organization of proteins into interaction networks. The interactome encompass all protein-protein interaction within a cell, and can be probed by affinity purification mass spectrometry (AP-MS). Higher order organization, such as protein complexes, can be investigated by combinations of biochemical fractionation with quantitative MS. While proteins interact at the molecular scale (i.e interactions), the cellular scale (i.e organelles) greatly contributes to cellular diversity and mediates homeostasis by specialization of organelles into cellular functions. By employing subcellular fractionation techniques combined with MS it is possible to shed light on the spatial distribution of the proteins across these organelles. These systems approaches to study the organization of the proteome were enabled by rapid developments in instrumentation and data analysis strategies. One of the developed techniques, data independent acquisition (DIA) was especially useful for systems approaches, as it enables increased continuity and completeness across large datasets. As protein correlation profiling (PCP) as tool to elucidate protein organization across cellular states, is becoming more widely used, developments focusing on fractionation techniques, throughput in sample preparation, and robust and high-throughput data acquisition schemes are needed. Increased throughput however results in highly-complex datasets, and thus the current data analysis strategies need to be improved. This thesis describes biochemical and computational advances for the analysis of protein complexes from cellular extracts and subcellular enriched fractions by applying native fractionation techniques combined with high-resolution "bottom-up" mass spectrometry based proteomics. To study protein-protein interactions and protein complexes on a system-wide level, native biochemical fractionation methods were established. These methods record the quantitative elution profile of each protein across a gradient, and by subsequent correlation based analysis, allow to identify protein complexes. The established methods are mostly based on combinations of size exclusion chromatography (SEC) with quantitative mass spectrometry approaches. In the first part of the thesis we present the ongoing efforts to increase throughput, sample quality, and yield within large-scale protein complex profiling experiments. We summarize the developed biochemical and mass spectrometer acquisition techniques which enables the study of hundreds of complexes across several cell-states and replicates at a throughput not yet reported in the field. These methodological developments are the basis for other projects presented in this thesis. The subcellular organization of the human proteome is of great importance for cellular processes. However, despite many studies investigating the subcellular location of proteins, there is a clear lack of knowledge how the protein is organized within organelles. We developed and integrated workflow to study protein localization and protein associations in the form of protein complexes on a system-level, by investigating 12 organelles. We employed high-resolution separation techniques combined with robust data acquisition by SWATH-MS, which enabled together with a novel machine learning approach, the mapping of >4500 proteins to specific subcellular location. SEC-SWATH allowed us further to investigate the assembly state within the organelles. Together, this enabled us to outline a map of the organization of the human proteome on subcellular level. Next, we implemented a method to experimentally identify all co-purified protein complexes within an AP-MS experiment. A single AP-MS yields binary protein-protein interactions (PPIs), and often multiple AP-MS experiments are combined to generate high-density interaction networks from these binary interaction networks. These networks are then used to derive the intrinsic modularity in the functional assemblies. We set out to directly probe all co-purified bait containing protein complexes of specific affinity purified samples. We combined an affinity purification with subsequent separation by blue native PAGE, a well known powerful tool for separating proteins and multimeric assemblies. To limit the samples losses and increase robustness, we developed a high-throughput sample preparation protocol, with similar or better yields than established methods. The data acquisition time was reduced, by employing short gradients, allowing us to measure a gel within one day. To ensure high-quality of quantitation, despite the 21 minutes gradient, we optimized the MS acquisition and developed a window scheme which allows to employ close to optimum duty cycle and fill time. We further introduced a novel data analysis strategy for protein-correlation data, which allowed us to retrieve not only the modularity, but also the PPIs and filter the interactions without the need for additional controls. The workflow is introduced on the example of the Prefoldin (PFD) and the R2TP/Prefoldin-like (R2TP/PFDL) protein complexes. We were able to resolve the complexes and including sub-modules. We further identified a yet not reported canonical PFD assemblies. The data allowed us to identify clients of the PFD and PFDL protein complex. In collaborative efforts we employed AP-MS to study mutant transcription factor interactions in cancer disease models, thereby identifying potential therapeutic targets. Further, we elucidated with a multi-layered proteomics and phosphoproteomics approach in a cellular model potential effects of cancer mutations on the associations and assembly state of a kinase complex. In two further projects we studied the effects of perturbations on the assembly state by employing global protein complex analysis across cellular states. These studies showed, that SEC-SWATH enabled the study of proteome reorganization in mitotic cells and to reveal the organization in the proteome upon dosage compensation in mouse embryonic stem cells. In conclusion, this thesis presents biochemical and computational methods to analyse complexes on subcellular level. This was achieved by combining fractionation techniques with DIA mass spectrometry. These approaches were employed to draw a spatially resolved map of the human protein complex landscape and thereby enabling new insights into the functional organization of the proteome. Together in collaborations, we demonstrate the applicability of interaction proteomics, protein complex profiling for differential assembly states analysis and the usefulness of combining affinity purification with protein co-elution profiling. Show more
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https://doi.org/10.3929/ethz-b-000467807Publication status
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Contributors
Examiner: Aebersold, Ruedi
Examiner: Gstaiger, Matthias
Examiner: Picotti, Paola
Examiner: Gingras, Anne-Claude
Publisher
ETH ZurichSubject
PROTEOMICS (PROTEINS AND PEPTIDES)Organisational unit
03663 - Aebersold, Rudolf (emeritus) / Aebersold, Rudolf (emeritus)
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