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Supplementary Table from Phosphorylation of SHP2 at Tyr62 Enables Acquired Resistance to SHP2 Allosteric Inhibitors in FLT3-ITD–Driven AML

Supplementary Data from Phosphorylation of SHP2 at Tyr62 Enables Acquired Resistance to SHP2 Allosteric Inhibitors in FLT3-ITD–Driven AML

The protein tyrosine phosphatase SHP2 is crucial for oncogenic transformation of acute myeloid leukemia (AML) cells expressing mutated receptor tyrosine kinases. SHP2 is required for full RAS-ERK activation to promote cell proliferation and survival programs. Allosteric SHP2 inhibitors act by stabilizing SHP2 in its autoinhibited conformation and are currently being tested in clinical trials for tumors with overactivation of the RAS/ERK pathway, alone and in various drug combinations. In this study, we established cells with acquired resistance to the allosteric SHP2 inhibitor SHP099 from two FLT3-ITD (internal tandem duplication)-positive AML cell lines. Label-free and isobaric labeling quantitative mass spectrometry–based phosphoproteomics of these resistant models demonstrated that AML cells can restore phosphorylated ERK (pERK) in the presence of SHP099, thus developing adaptive resistance. Mechanistically, SHP2 inhibition induced tyrosine phosphorylation and feedback-driven activation of the FLT3 receptor, which in turn phosphorylated SHP2 on tyrosine 62. This phosphorylation stabilized SHP2 in its open conformation, preventing SHP099 binding and conferring resistance. Combinatorial inhibition of SHP2 and MEK or FLT3 prevented pERK rebound and resistant cell growth. The same mechanism was observed in a FLT3-mutated B-cell acute lymphoblastic leukemia cell line and in the inv(16)/KitD816Y AML mouse model, but allosteric inhibition of Shp2 did not impair the clonogenic ability of normal bone marrow progenitors. Together, these results support the future use of SHP2 inhibitor combinations for clinical applications.Significance:These findings suggest that combined inhibition of SHP2 and FLT3 effectively treat FLT3-ITD–positive AML, highlighting the need for development of more potent SHP2 inhibitors and combination therapies for clinical applications.

Supplementary Figure from Phosphorylation of SHP2 at Tyr62 Enables Acquired Resistance to SHP2 Allosteric Inhibitors in FLT3-ITD–Driven AML

Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Some methods only impute assuming the limit of detection (LOD) was not passed and therefore impute missing values with too low or too high intensities, potentially leading to biased results in downstream statistical analysis. Here we test how self supervised deep learning models can impute missing values in the context of LFQ at different levels: precursors, aggregated peptides or protein groups. We demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can be used to reconstruct missing values and can make more relevant features available for downstream analysis compared to current approaches. Additionally, we show that deep learning approaches can model data in its entirety for imputation and offer an approach for controlled evaluation of imputation approaches. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. We identified 49 additional proteins (+23.6%) that are significantly differentially abundant across disease stages compared to traditional methods and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics and provide workflows for these.

Mass spectrometry-based phosphoproteomics is currently the leading methodology for the study of global kinase signaling. The scientific community is continuously releasing technological improvements for sensitive and fast identification of phosphopeptides, and their accurate quantification. To interpret large-scale phosphoproteomics data, numerous bioinformatic resources are available that help understanding kinase network functional role in biological systems upon perturbation. Some of these resources are databases of phosphorylation sites, protein kinases and phosphatases; others are bioinformatic algorithms to infer kinase activity, predict phosphosite functional relevance and visualize kinase signaling networks. In this review, we present the latest experimental and bioinformatic tools to profile protein kinase signaling networks and provide examples of their application in biomedicine.

The protein tyrosine phosphatase SHP2 is crucial for oncogenic transformation of acute myeloid leukemia (AML) cells expressing mutated receptor tyrosine kinases. SHP2 is required for full RAS-ERK activation to promote cell proliferation and survival programs. Allosteric SHP2 inhibitors act by stabilizing SHP2 in its autoinhibited conformation and are currently being tested in clinical trials for tumors with overactivation of the RAS/ERK pathway, alone and in various drug combinations. In this study, we established cells with acquired resistance to the allosteric SHP2 inhibitor SHP099 from two FLT3-ITD (internal tandem duplication)-positive AML cell lines. Label-free and isobaric labeling quantitative mass spectrometry–based phosphoproteomics of these resistant models demonstrated that AML cells can restore phosphorylated ERK (pERK) in the presence of SHP099, thus developing adaptive resistance. Mechanistically, SHP2 inhibition induced tyrosine phosphorylation and feedback-driven activation of the FLT3 receptor, which in turn phosphorylated SHP2 on tyrosine 62. This phosphorylation stabilized SHP2 in its open conformation, preventing SHP099 binding and conferring resistance. Combinatorial inhibition of SHP2 and MEK or FLT3 prevented pERK rebound and resistant cell growth. The same mechanism was observed in a FLT3-mutated B-cell acute lymphoblastic leukemia cell line and in the inv(16)/KitD816Y AML mouse model, but allosteric inhibition of Shp2 did not impair the clonogenic ability of normal bone marrow progenitors. Together, these results support the future use of SHP2 inhibitor combinations for clinical applications. Significance: These findings suggest that combined inhibition of SHP2 and FLT3 effectively treat FLT3-ITD–positive AML, highlighting the need for development of more potent SHP2 inhibitors and combination therapies for clinical applications.

Mycobacterium tuberculosis is the causative agent of tuberculosis and remains one of the most widespread and deadliest bacterial pathogens in the world. A distinguishing feature of mycobacteria that sets them apart from other bacteria is the unique architecture of their cell wall, characterized by various species-specific lipids, most notably mycolic acids (MAs). Therefore, targeted inhibition of enzymes involved in MA biosynthesis, transport, and assembly has been extensively explored in drug discovery. Additionally, more recent evidence suggests that many enzymes in the MA biosynthesis pathway are regulated by kinase-mediated phosphorylation, thus opening additional drug-development opportunities. However, how phosphorylation regulates MA production remains unclear. Here, we used genetic strategies combined with lipidomics and phosphoproteomics approaches to investigate the role of protein phosphorylation in Mycobacterium. The results of this analysis revealed that the Ser/Thr protein kinase PknB regulates the export of MAs and promotes the remodeling of the mycobacterial cell envelope. In particular, we identified the essential MmpL3 as a substrate negatively regulated by PknB. Taken together, our findings add to the understanding of how PknB activity affects the mycobacterial MA biosynthesis pathway and reveal the essential role of protein phosphorylation/dephosphorylation in governing lipid metabolism, paving the way for novel antimycobacterial strategies.

Phosphorylation-driven cell signaling governs most biological functions and is widely studied using mass-spectrometry-based phosphoproteomics. Identifying the peptides and localizing the phosphorylation sites within them from the raw data is challenging and can be performed by several algorithms that return scores that are not directly comparable. This increases the heterogeneity among published phosphoproteomics data sets and prevents their direct integration. Here we compare 22 pipelines implemented in the main software tools used for bottom-up phosphoproteomics analysis (MaxQuant, Proteome Discoverer, PeptideShaker). We test six search engines (Andromeda, Comet, Mascot, MS Amanda, SequestHT, and X!Tandem) in combination with several localization scoring algorithms (delta score, D-score, PTM-score, phosphoRS, and Ascore). We show that these follow very different score distributions, which can lead to different false localization rates for the same threshold. We provide a strategy to discriminate correctly from incorrectly localized phosphorylation sites in a consistent manner across the tested pipelines. The results presented here can help users choose the most appropriate pipeline and cutoffs for their phosphoproteomics analysis.

T cells recognize a few high-affinity antigens among a vast array of lower affinity antigens. According to the kinetic proofreading model, antigen discrimination properties could be explained by the gradual amplification of small differences in binding affinities as the signal is transduced downstream of the T cell receptor. Which early molecular events are affected by ligand affinity, and how, has not been fully resolved. Here, we used time-resolved high-throughput proteomic analyses to identify and quantify the phosphorylation events and protein–protein interactions encoding T cell ligand discrimination in antigen-experienced T cells. Although low-affinity ligands induced phosphorylation of the Cd3 chains of the T cell receptor and the interaction of Cd3 with the Zap70 kinase as strongly as high-affinity ligands, they failed to activate Zap70 to the same extent. As a result, formation of the signalosome of the Lat adaptor was severely impaired with low- compared with high-affinity ligands, whereas formation of the signalosome of the Cd6 receptor was affected only partially. Overall, this study provides a comprehensive map of molecular events associated with T cell ligand discrimination.

Data analysis of the DIA data associated with the manuscript "Tyr62 phosphorylation of the phosphatase SHP2 enables acquired resistance to SHP2 allosteric inhibitors in FLT3-ITD-driven AML". This repository is associated with the TMT data analysis that can be found here:http://doi.org/10.5281/zenodo.4434421.

The amount of public proteomics data is increasing at an extraordinary rate. Hundreds of datasets are submitted each month to ProteomeXchange repositories, representing many types of proteomics studies, focusing on different aspects such as quantitative experiments, post-translational modifications, protein-protein interactions, or subcellular localization, among many others. For every proteomics dataset, two levels of data are captured: the dataset description, and the data files (encoded in different file formats). Whereas the dataset description and data file formats are supported by all ProteomeXchange partner repositories, there is no standardized format to properly describe the sample metadata and their relationship with the dataset files in a way that fully allows their understanding or re-analysis. It is left to the user’s choice whether to provide or not an ad hoc document containing this information. Therefore, in many cases, understanding the study design and data requires going back to the associated publication. This can be tedious and may be restricted in the case of non-open access publications. In many cases, this problem limits the generalization and reuse of public proteomics data.Here we present a standard representation for sample metadata tailored to proteomics datasets produced by the HUPO Proteomics Standards Initiative and supported by ProteomeXchange resources. We repurposed the existing data format MAGE-TAB used routinely in the transcriptomics field to represent and annotate proteomics datasets. MAGETAB-Proteomics defines a set of annotation rules that the datasets submitted to ProteomeXchange should follow, ranging from sample properties to data analysis protocols. We also introduce a crowdsourcing project that enabled the manual curation of over 200 public datasets using MAGE-TAB-Proteomics. In addition, we describe an ecosystem of tools and libraries that were developed to validate and submit sample metadata-related information to ProteomeXchange. We expect that these tools will improve the reproducibility of published results and facilitate the reanalysis and integration of public proteomics datasets.

The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets., The number of publicly available proteomics datasets is growing rapidly, but a standardized approach for describing the associated metadata is lacking. Here, the authors propose a format and a software pipeline to present and validate metadata, and integrate them into ProteomeXchange repositories.

The European Bioinformatics Community for Mass Spectrometry (EuBIC‐MS; eubic-ms.org) was founded in 2014 to unite European computational mass spectrometry researchers and proteomics bioinformaticians working in academia and industry. EuBIC‐MS maintains educational resources (proteomics-academy.org) and organises workshops at national and international conferences on proteomics and mass spectrometry. Furthermore, EuBIC‐MS is actively involved in several community initiatives such as the Human Proteome Organization's Proteomics Standards Initiative (HUPO‐PSI). Apart from these collaborations, EuBIC‐MS has organised two Winter Schools and two Developers' Meetings that have contributed to the strengthening of the European mass spectrometry network and fostered international collaboration in this field, even beyond Europe. Moreover, EuBIC‐MS is currently actively developing a community‐driven standard dedicated to mass spectrometry data annotation (SDRF‐Proteomics) that will facilitate data reuse and collaboration. This manuscript highlights what EuBIC‐MS is, what it does, and what it already has achieved. A warm invitation is extended to new researchers at all career stages to join the EuBIC‐MS community on its Slack channel (eubic.slack.com).

T‐cell receptor (TCR) ligation‐mediated protein phosphorylation regulates the activation, cellular responses, and fates of T cells. Here, we used time‐resolved high‐resolution phosphoproteomics to identify, quantify, and characterize the phosphorylation dynamics of thousands of phosphorylation sites in primary T cells during the first 10 min after TCR stimulation. Bioinformatic analysis of the data revealed a coherent orchestration of biological processes underlying T‐cell activation. In particular, functional modules associated with cytoskeletal remodeling, transcription, translation, and metabolic processes were mobilized within seconds after TCR engagement. Among proteins whose phosphorylation was regulated by TCR stimulation, we demonstrated, using a fast‐track gene inactivation approach in primary lymphocytes, that the ITSN2 adaptor protein regulated T‐cell effector functions. This resource, called LymphoAtlas, represents an integrated pipeline to further decipher the organization of the signaling network encoding T‐cell activation. LymphoAtlas is accessible to the community at: https://bmm-lab.github.io/LymphoAtlas., The study presents LymphoAtlas, a phosphoproteomic dataset enabling the identification and visualization of phosphorylation dynamics during the first 10 min after TCR stimulation of primary mouse T cells.

Trehalose polyphleates (TPP) are high-molecular-weight, surface-exposed glycolipids present in a broad range of nontuberculous mycobacteria. These compounds consist of a trehalose core bearing polyunsaturated fatty acyl substituents (called phleic acids) and a straight-chain fatty acid residue and share a common basic structure with trehalose-based glycolipids produced byMycobacterium tuberculosis. TPP production starts in the cytosol with the formation of a diacyltrehalose intermediate. An acyltransferase, called PE, subsequently catalyzes the transfer of phleic acids onto diacyltrehalose to form TPP, and an MmpL transporter promotes the export of TPP or its precursor across the plasma membrane. PE is predicted to be an anchored membrane protein, but its topological organization is unknown, raising questions about the subcellular localization of the final stage of TPP biosynthesis and the chemical nature of the substrates that are translocated by the MmpL transporter. Here, using genetic, biochemical, and proteomic approaches, we established that PE ofMycobacterium smegmatisis exported to the cell envelope following cleavage of its signal peptide and that this process is required for TPP biosynthesis, indicating that the last step of TPP formation occurs in the outer layers of the mycobacterial cell envelope. These results provide detailed insights into the molecular mechanisms controlling TPP formation and transport to the cell surface, enabling us to propose an updated model of the TPP biosynthetic pathway. Because the molecular mechanisms of glycolipid production are conserved among mycobacteria, these findings obtained with PE fromM. smegmatismay offer clues to glycolipid formation inM. tuberculosis.

Hydrogen-Deuterium eXchange coupled to Mass Spectrometry (HDX-MS) is now common practice in structural biology. However, it is most of the time applied to rather small oligomeric complexes. Here, we report on the use of HDX-MS to investigate conformational differences between the human standard 20S (std20S) and immuno 20S (i20s) proteasomes alone or in complex with PA28αβ or PA28γ activators. Their solvent accessibility is analyzed through a dedicated bioinformatic pipeline including stringent statistical analysis and 3D visualization. These data confirm the existence of allosteric differences between the std20S and i20S at the surface of the α-ring triggered from inside the catalytic β-ring. Additionally, binding of the PA28 regulators to the 20S proteasomes modify solvent accessibility due to conformational changes of the β-rings. This work is not only a proof-of-concept that HDX-MS can be used to get structural insights on large multi-protein complexes in solution, it also demonstrates that the binding of the std20S or i20S subtype to any of its PA28 activator triggers allosteric changes that are specific to this 20S/PA28 pair., Immune cells express immunoproteasomes (i20S), which bind to specialized regulators, contain different catalytic subunits and generate immunogenic peptides. HDX-MS—based assessment of the differences between the conformational dynamics of standard and i20s reveals specific, allosteric changes in i20S and upon regulator binding.

The 2020 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers’ meeting was held from January 13th to January 17th 2020 in Nyborg, Denmark. Among the participants were scientists as well as developers working in the field of computational mass spectrometry (MS) and proteomics. The 4-day program was split between introductory keynote lectures and parallel hackathon sessions. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts, and to actively contribute to highly relevant research projects. We successfully produced several new tools that will be useful to the proteomics community by improving data analysis as well as facilitating future research. All keynote recordings are available on https://doi.org/10.5281/zenodo.3890181.

Here are the R scripts and MaxQuant search results ofthe analysis of OT1 CD8+ T cells after TCR stimulation. This is published alongside a similar analysis of CD4+ T cells after antibody-based stimulation, and is published in "LymphoAtlas: a dynamic and integrated phosphoproteomic resource of TCR signaling in primary T cells reveals ITSN2 as a regulator of effector functions".

The 2019 European Bioinformatics Community (EuBIC) Winter School was held from January 15th to January 18th 2019 in Zakopane, Poland. This year’s meeting was the third of its kind and gathered international researchers in the field of (computational) proteomics to discuss (mainly) challenges in proteomics quantification and data independent acquisition (DIA). Here, we present an overview of the scientific program of the 2019 EuBIC Winter School. Furthermore, we can already give a small outlook to the upcoming EuBIC 2020 Developer’s Meeting. Keywords: EuBIC, Computational proteomics, Bioinformatics, Protein quantification, Date independent acquisition

Summary With the advent of fully automated sample preparation robots for Hydrogen–Deuterium eXchange coupled to Mass Spectrometry (HDX-MS), this method has become paramount for ligand binding or epitope mapping screening, both in academic research and biopharmaceutical industries. However, bridging the gap between commercial HDX-MS software (for raw data interpretation) and molecular viewers (to map experiment results onto a 3D structure for biological interpretation) remains laborious and requires simple but sometimes limiting coding skills. We solved this bottleneck by developing HDX-Viewer, an open-source web-based application that facilitates and quickens HDX-MS data analysis. This user-friendly application automatically incorporates HDX-MS data from a custom template or commercial HDX-MS software in PDB files, and uploads them to an online 3D molecular viewer, thereby facilitating their visualization and biological interpretation. Availability and implementation The HDX-Viewer web application is released under the CeCILL (http://www.cecill.info) and GNU LGPL licenses and can be found at https://masstools.ipbs.fr/hdx-viewer. The source code is available at https://github.com/david-bouyssie/hdx-viewer.

International audience; The rise of intact protein analysis by mass spectrometry (MS) was accompanied by an increasing need for flexible tools allowing data visualization and analysis. These include inspection of the deconvoluted molecular weights of the proteoforms eluted alongside liquid chromatography (LC) through their representation in three-dimensional (3D) liquid chromatography coupled to mass spectrometry (LC-MS) maps (plots of deconvoluted molecular weights, retention times, and intensity of the MS signal). With this aim, we developed a free and open-source web application named VisioProt-MS (https://masstools.ipbs.fr/mstools/visioprot-ms/). VisioProt-MS is highly compatible with many algorithms and software developed by the community to integrate and deconvolute top-down and intact protein MS data. Its dynamic and userfriendly features greatly facilitate analysis through several graphical representations dedicated to MS and tandem mass spectrometry (MS/MS) analysis of proteoforms in complex samples. Here, we will illustrate the importance of LC-MS map visualization to optimize top-down acquisition/ search parameters and analyze intact protein MS data. We will go through the main features of VisioProt-MS using the human proteasomal 20S core particle as a user-case.

The inaugural European Bioinformatics Community (EuBIC) developer's meeting was held from January 9th to January 12th 2018 in Ghent, Belgium. While the meeting kicked off with an interactive keynote session featuring four internationally renowned experts in the field of computational proteomics, its primary focus were the hands-on hackathon sessions which featured six community-proposed projects revolving around three major topics: (i) quality control; (ii) workflows, protocols, and guidelines; and (iii) quantification. Here, we present an overview of the scientific program of the EuBIC developer's meeting and provide a starting point for follow-up on the covered projects.

The 2017 EuBIC Winter School was held from January 10th to January 13th 2017 in Semmering, Austria. This meeting gathered international researchers in the fields of bioinformatics and proteomics to discuss current challenges in data analysis and biological interpretation. This article outlines the scientific program and exchanges that took place on this occasion and presents the current challenges of this ever-growing field. Biological significance The EUPA bioinformatics community (EuBIC) organized its first winter school in January 2017. This successful event illustrates the growing need of the bioinformatics community in proteomics to gather and discuss current and future challenges in the field. In addition to the organization of yearly meetings, the young and active EuBIC community aims to develop new collaborative open source projects, spread bioinformatics knowledge in Europe, and actively promote data sharing through public repositories.

Regulatory T cells (Treg) represent a minor subpopulation of T lymphocytes that is crucial for the maintenance of immune homeostasis. Here, we present a large-scale quantitative mass spectrometry study that defines a specific proteomic "signature" of Treg. Treg and conventional T lymphocyte (Tconv) subpopulations were sorted by flow cytometry and subjected to global proteomic analysis by single-run nanoLC-MS/MS on a fast-sequencing Q-Exactive mass spectrometer. Besides "historical" proteins that characterize Treg, our study identified numerous new proteins that are up- or downregulated in Treg versus Tconv. We focused on Themis1, a protein particularly under-represented in Treg, and recently described as being involved in the pathogenesis of immune diseases. Using a transgenic mouse model overexpressing Themis1, we provided in vivo and in vitro evidence of its importance for Treg suppressive functions, in an animal model of inflammatory bowel disease and in coculture assays. We showed that this enhanced suppressive activity in vitro is associated with an accumulation of Tregs. Thus, our study highlights the usefulness of label free quantitative methods to better characterize the Treg cell lineage and demonstrates the potential role of Themis1 in the suppressive functions of these cells.

The exit of metastasizing tumor cells from the vasculature, extravasation, is regulated by their dynamic interactions with the endothelial cells that line the internal surface of vessels. To elucidate signals controlling tumor cell adhesion to the endothelium and subsequent transendothelial migration, we performed phosphoproteomic analysis to map cell-specific changes in protein phosphorylation that were triggered by contact between metastatic MDA-MB-231 breast cancer cells and endothelial cells. From the 2669 unique phosphorylation sites identified, 77 and 43 were differentially phosphorylated in the tumor cells and endothelial cells, respectively. The receptor tyrosine kinase ephrin type A receptor 2 (EPHA2) exhibited decreased Tyr(772) phosphorylation in the cancer cells upon endothelial contact. Knockdown of EPHA2 increased adhesion of the breast cancer cells to human umbilical vein endothelial cells (HUVECs) and their transendothelial migration in coculture cell assays, as well as early-stage lung colonization in vivo. EPHA2-mediated inhibition of transendothelial migration of breast cancer cells depended on interaction with the ligand ephrinA1 on HUVECs and phosphorylation of EPHA2-Tyr(772). When EPHA2 phosphorylation dynamics were compared between cell lines of different metastatic ability, EPHA2-Tyr(772) was rapidly dephosphorylated after ephrinA1 stimulation specifically in cells targeting the lung. Knockdown of the phosphatase LMW-PTP reduced adhesion and transendothelial migration of the breast cancer cells. Overall, cell-specific phosphoproteomic analysis provides a bidirectional map of contact-initiated signaling between tumor and endothelial cells that can be further investigated to identify mechanisms controlling the transendothelial cell migration of cancer cells.

International audience; Proteogenomics, the alliance of proteomics, transcriptomics, genomics and bioinformatics, was first proposed for refining genome annotation using experimental data acquired on gene products. With high-throughput analysis of proteins made possible with next-generation tandem mass spectrometers, proteogenomics is greatly improving human genome annotation per se, and is helping to decrypt the numerous gene and protein modifications occurring during development, aging, illness and cancer progression. Further efforts are required to obtain a comprehensive picture of human genes, their products, functions, and drift over time or in reaction to microbiota and pathogen stimuli. This should be performed not only to obtain a general overview of the human population, but also to gain specific information at the individual level. This review focuses on the clinical implications of proteogenomics: novel biological insights into fundamental biology, better characterization of pathogens and parasites, discovery of novel diagnostic approaches for cancer, and personalized medicine.

Sequence analysis of the human genome and the association of genetic aberrations with diseases have provided a rough framework whereby the impact of individual genotypes can be assessed. To fully understand the effect of individual and co-occurring genetic aberrations, as well as their individual and collected contribution to the development of diseases, it is critical to analyse the matching proteome and to determine how the organisation, expression level and function of protein networks are affected. Sensitive mass spectrometric platforms in combination with innovative workflows allow qualitative and quantitative analyses of the cellular as well as the extracellular proteome. Importantly, in addition to specifically identifying the content of the proteome, several aspects of the proteomic organisation can be analysed including protein complexes, protein modifications, enzymatic activities and subcellular/organelle localisation. Together, these measurements will provide novel insight into the biological effect of disease-causing mutations ultimately coupling genotype and phenotype.