Simultaneous targeted and discovery-driven clinical proteotyping using hybrid-PRM/DIA.

BACKGROUND: Clinical samples are irreplaceable, and their transformation into searchable and reusable digital biobanks is critical for conducting statistically empowered retrospective and integrative research studies. Currently, mainly data-independent acquisition strategies are employed to digitize clinical sample cohorts comprehensively. However, the sensitivity of DIA is limited, which is why selected marker candidates are often additionally measured targeted by parallel reaction monitoring. METHODS: Here, we applied the recently co-developed hybrid-PRM/DIA technology as a new intelligent data acquisition strategy that allows for the comprehensive digitization of rare clinical samples at the proteotype level. Hybrid-PRM/DIA enables enhanced measurement sensitivity for a specific set of analytes of current clinical interest by the intelligent triggering of multiplexed parallel reaction monitoring (MSxPRM) in combination with the discovery-driven digitization of the clinical biospecimen using DIA. Heavy-labeled reference peptides were utilized as triggers for MSxPRM and monitoring of endogenous peptides. RESULTS: We first evaluated hybrid-PRM/DIA in a clinical context on a pool of 185 selected proteotypic peptides for tumor-associated antigens derived from 64 annotated human protein groups. We demonstrated improved reproducibility and sensitivity for the detection of endogenous peptides, even at lower concentrations near the detection limit. Up to 179 MSxPRM scans were shown not to affect the overall DIA performance. Next, we applied hybrid-PRM/DIA for the integrated digitization of biobanked melanoma samples using a set of 30 AQUA peptides against 28 biomarker candidates with relevance in molecular tumor board evaluations of melanoma patients. Within the DIA-detected approximately 6500 protein groups, the selected marker candidates such as UFO, CDK4, NF1, and PMEL could be monitored consistently and quantitatively using MSxPRM scans, providing additional confidence for supporting future clinical decision-making. CONCLUSIONS: Combining PRM and DIA measurements provides a new strategy for the sensitive and reproducible detection of protein markers from patients currently being discussed in molecular tumor boards in combination with the opportunity to discover new biomarker candidates.

Proteogenetic drug response profiling elucidates targetable vulnerabilities of myelofibrosis.

Myelofibrosis is a hematopoietic stem cell disorder belonging to the myeloproliferative neoplasms. Myelofibrosis patients frequently carry driver mutations in either JAK2 or Calreticulin (CALR) and have limited therapeutic options. Here, we integrate ex vivo drug response and proteotype analyses across myelofibrosis patient cohorts to discover targetable vulnerabilities and associated therapeutic strategies. Drug sensitivities of mutated and progenitor cells were measured in patient blood using high-content imaging and single-cell deep learning-based analyses. Integration with matched molecular profiling revealed three targetable vulnerabilities. First, CALR mutations drive BET and HDAC inhibitor sensitivity, particularly in the absence of high Ras pathway protein levels. Second, an MCM complex-high proliferative signature corresponds to advanced disease and sensitivity to drugs targeting pro-survival signaling and DNA replication. Third, homozygous CALR mutations result in high endoplasmic reticulum (ER) stress, responding to ER stressors and unfolded protein response inhibition. Overall, our integrated analyses provide a molecularly motivated roadmap for individualized myelofibrosis patient treatment.

Ex vivo drug response heterogeneity reveals personalized therapeutic strategies for patients with multiple myeloma.

Multiple myeloma (MM) is a plasma cell malignancy defined by complex genetics and extensive patient heterogeneity. Despite a growing arsenal of approved therapies, MM remains incurable and in need of guidelines to identify effective personalized treatments. Here, we survey the ex vivo drug and immunotherapy sensitivities across 101 bone marrow samples from 70 patients with MM using multiplexed immunofluorescence, automated microscopy and deep-learning-based single-cell phenotyping. Combined with sample-matched genetics, proteotyping and cytokine profiling, we map the molecular regulatory network of drug sensitivity, implicating the DNA repair pathway and EYA3 expression in proteasome inhibitor sensitivity and major histocompatibility complex class II expression in the response to elotuzumab. Globally, ex vivo drug sensitivity associated with bone marrow microenvironmental signatures reflecting treatment stage, clonality and inflammation. Furthermore, ex vivo drug sensitivity significantly stratified clinical treatment responses, including to immunotherapy. Taken together, our study provides molecular and actionable insights into diverse treatment strategies for patients with MM.

Integrated multi-omics reveals anaplerotic rewiring in methylmalonyl-CoA mutase deficiency.

Methylmalonic aciduria (MMA) is an inborn error of metabolism with multiple monogenic causes and a poorly understood pathogenesis, leading to the absence of effective causal treatments. Here we employ multi-layered omics profiling combined with biochemical and clinical features of individuals with MMA to reveal a molecular diagnosis for 177 out of 210 (84%) cases, the majority (148) of whom display pathogenic variants in methylmalonyl-CoA mutase (MMUT). Stratification of these data layers by disease severity shows dysregulation of the tricarboxylic acid cycle and its replenishment (anaplerosis) by glutamine. The relevance of these disturbances is evidenced by multi-organ metabolomics of a hemizygous Mmut mouse model as well as through identification of physical interactions between MMUT and glutamine anaplerotic enzymes. Using stable-isotope tracing, we find that treatment with dimethyl-oxoglutarate restores deficient tricarboxylic acid cycling. Our work highlights glutamine anaplerosis as a potential therapeutic intervention point in MMA.

Kinase Interaction Network Expands Functional and Disease Roles of Human Kinases.

Protein kinases are essential for signal transduction and control of most cellular processes, including metabolism, membrane transport, motility, and cell cycle. Despite the critical role of kinases in cells and their strong association with diseases, good coverage of their interactions is available for only a fraction of the 535 human kinases. Here, we present a comprehensive mass-spectrometry-based analysis of a human kinase interaction network covering more than 300 kinases. The interaction dataset is a high-quality resource with more than 5,000 previously unreported interactions. We extensively characterized the obtained network and were able to identify previously described, as well as predict new, kinase functional associations, including those of the less well-studied kinases PIM3 and protein O-mannose kinase (POMK). Importantly, the presented interaction map is a valuable resource for assisting biomedical studies. We uncover dozens of kinase-disease associations spanning from genetic disorders to complex diseases, including cancer.

Multi-layered proteomic analyses decode compositional and functional effects of cancer mutations on kinase complexes.

Rapidly increasing availability of genomic data and ensuing identification of disease associated mutations allows for an unbiased insight into genetic drivers of disease development. However, determination of molecular mechanisms by which individual genomic changes affect biochemical processes remains a major challenge. Here, we develop a multilayered proteomic workflow to explore how genetic lesions modulate the proteome and are translated into molecular phenotypes. Using this workflow we determine how expression of a panel of disease-associated mutations in the Dyrk2 protein kinase alter the composition, topology and activity of this kinase complex as well as the phosphoproteomic state of the cell. The data show that altered protein-protein interactions caused by the mutations are associated with topological changes and affected phosphorylation of known cancer driver proteins, thus linking Dyrk2 mutations with cancer-related biochemical processes. Overall, we discover multiple mutation-specific functionally relevant changes, thus highlighting the extensive plasticity of molecular responses to genetic lesions.

Complex-centric proteome profiling by SEC-SWATH-MS for the parallel detection of hundreds of protein complexes.

Most catalytic, structural and regulatory functions of the cell are carried out by functional modules, typically complexes containing or consisting of proteins. The composition and abundance of these complexes and the quantitative distribution of specific proteins across different modules are therefore of major significance in basic and translational biology. However, detection and quantification of protein complexes on a proteome-wide scale is technically challenging. We have recently extended the targeted proteomics rationale to the level of native protein complex analysis (complex-centric proteome profiling). The complex-centric workflow described herein consists of size exclusion chromatography (SEC) to fractionate native protein complexes, data-independent acquisition mass spectrometry to precisely quantify the proteins in each SEC fraction based on a set of proteotypic peptides and targeted, complex-centric analysis where prior information from generic protein interaction maps is used to detect and quantify protein complexes with high selectivity and statistical error control via the computational framework CCprofiler (https://github.com/CCprofiler/CCprofiler). Complex-centric proteome profiling captures most proteins in complex-assembled state and reveals their organization into hundreds of complexes and complex variants observable in a given cellular state. The protocol is applicable to cultured cells and can potentially also be adapted to primary tissue and does not require any genetic engineering of the respective sample sources. At present, it requires ~8 d of wet-laboratory work, 15 d of mass spectrometry measurement time and 7 d of computational analysis.

Multi-omic measurements of heterogeneity in HeLa cells across laboratories.

Reproducibility in research can be compromised by both biological and technical variation, but most of the focus is on removing the latter. Here we investigate the effects of biological variation in HeLa cell lines using a systems-wide approach. We determine the degree of molecular and phenotypic variability across 14 stock HeLa samples from 13 international laboratories. We cultured cells in uniform conditions and profiled genome-wide copy numbers, mRNAs, proteins and protein turnover rates in each cell line. We discovered substantial heterogeneity between HeLa variants, especially between lines of the CCL2 and Kyoto varieties, and observed progressive divergence within a specific cell line over 50 successive passages. Genomic variability has a complex, nonlinear effect on transcriptome, proteome and protein turnover profiles, and proteotype patterns explain the varying phenotypic response of different cell lines to Salmonella infection. These findings have implications for the interpretation and reproducibility of research results obtained from human cultured cells.

ASPP proteins discriminate between PP1 catalytic subunits through their SH3 domain and the PP1 C-tail.

Serine/threonine phosphatases such as PP1 lack substrate specificity and associate with a large array of targeting subunits to achieve the requisite selectivity. The tumour suppressor ASPP (apoptosis-stimulating protein of p53) proteins associate with PP1 catalytic subunits and are implicated in multiple functions from transcriptional regulation to cell junction remodelling. Here we show that Drosophila ASPP is part of a multiprotein PP1 complex and that PP1 association is necessary for several in vivo functions of Drosophila ASPP. We solve the crystal structure of the human ASPP2/PP1 complex and show that ASPP2 recruits PP1 using both its canonical RVxF motif, which binds the PP1 catalytic domain, and its SH3 domain, which engages the PP1 C-terminal tail. The ASPP2 SH3 domain can discriminate between PP1 isoforms using an acidic specificity pocket in the n-Src domain, providing an exquisite mechanism where multiple motifs are used combinatorially to tune binding affinity to PP1.

Complex-centric proteome profiling by SEC-SWATH-MS.

Proteins are major effectors and regulators of biological processes that can elicit multiple functions depending on their interaction with other proteins. The organization of proteins into macromolecular complexes and their quantitative distribution across these complexes is, therefore, of great biological and clinical significance. In this paper, we describe an integrated experimental and computational technique to quantify hundreds of protein complexes in a single operation. The method consists of size exclusion chromatography (SEC) to fractionate native protein complexes, SWATH/DIA mass spectrometry to precisely quantify the proteins in each SEC fraction, and the computational framework CCprofiler to detect and quantify protein complexes by error-controlled, complex-centric analysis using prior information from generic protein interaction maps. Our analysis of the HEK293 cell line proteome delineates 462 complexes composed of 2,127 protein subunits. The technique identifies novel sub-complexes and assembly intermediates of central regulatory complexes while assessing the quantitative subunit distribution across them. We make the toolset CCprofiler freely accessible and provide a web platform, SECexplorer, for custom exploration of the HEK293 proteome modularity.

Systematic Analysis of Human Protein Phosphatase Interactions and Dynamics.

Coordinated activities of protein kinases and phosphatases ensure phosphorylation homeostasis, which, when perturbed, can instigate diseases, including cancer. Yet, in contrast to kinases, much less is known about protein phosphatase functions and their interactions and complexes. Here, we used quantitative affinity proteomics to assay protein-protein interactions for 54 phosphatases distributed across the three major protein phosphatase families, with additional analysis of their 12 co-factors. We identified 838 high-confidence interactions, of which 631, to our knowledge, have not been reported before. We show that inhibiting the activity of phosphatases PP1 and PP2A by okadaic acid disrupts their specific interactions, supporting the potential of therapeutics that target these proteins. Additional analyses revealed candidate physical and functional interaction links to phosphatase-based regulation of several signaling pathways and to human cancer. Our study provides an initial glimpse of the protein interaction landscape of phosphatases and their functions in cellular regulation.

Interaction proteome of human Hippo signaling: modular control of the co-activator YAP1.

Tissue homeostasis is controlled by signaling systems that coordinate cell proliferation, cell growth and cell shape upon changes in the cellular environment. Deregulation of these processes is associated with human cancer and can occur at multiple levels of the underlying signaling systems. To gain an integrated view on signaling modules controlling tissue growth, we analyzed the interaction proteome of the human Hippo pathway, an established growth regulatory signaling system. The resulting high-resolution network model of 480 protein-protein interactions among 270 network components suggests participation of Hippo pathway components in three distinct modules that all converge on the transcriptional co-activator YAP1. One of the modules corresponds to the canonical Hippo kinase cassette whereas the other two both contain Hippo components in complexes with cell polarity proteins. Quantitative proteomic data suggests that complex formation with cell polarity proteins is dynamic and depends on the integrity of cell-cell contacts. Collectively, our systematic analysis greatly enhances our insights into the biochemical landscape underlying human Hippo signaling and emphasizes multifaceted roles of cell polarity complexes in Hippo-mediated tissue growth control.

The protein interaction landscape of the human CMGC kinase group.

Cellular information processing via reversible protein phosphorylation requires tight control of the localization, activity, and substrate specificity of protein kinases, which to a large extent is accomplished by complex formation with other proteins. Despite their critical role in cellular regulation and pathogenesis, protein interaction information is available for only a subset of the 518 human protein kinases. Here we present a global proteomic analysis of complexes of the human CMGC kinase group. In addition to subgroup-specific functional enrichment and modularity, the identified 652 high-confidence kinase-protein interactions provide a specific biochemical context for many poorly studied CMGC kinases. Furthermore, the analysis revealed a kinase-kinase subnetwork and candidate substrates for CMGC kinases. Finally, the presented interaction proteome uncovered a large set of interactions with proteins genetically linked to a range of human diseases, including cancer, suggesting additional routes for analyzing the role of CMGC kinases in controlling human disease pathways.

Interlaboratory reproducibility of large-scale human protein-complex analysis by standardized AP-MS.

The characterization of all protein complexes of human cells under defined physiological conditions using affinity purification-mass spectrometry (AP-MS) is a highly desirable step in the quest to understand the phenotypic effects of genomic information. However, such a challenging goal has not yet been achieved, as it requires reproducibility of the experimental workflow and high data consistency across different studies and laboratories. We systematically investigated the reproducibility of a standardized AP-MS workflow by performing a rigorous interlaboratory comparative analysis of the interactomes of 32 human kinases. We show that it is possible to achieve high interlaboratory reproducibility of this standardized workflow despite differences in mass spectrometry configurations and subtle sample preparation-related variations and that combination of independent data sets improves the approach sensitivity, resulting in even more-detailed networks. Our analysis demonstrates the feasibility of obtaining a high-quality map of the human protein interactome with a multilaboratory project.