Mechanistic safety assessment via multi-omic characterisation of systemic pathway perturbations following in vivo MAT2A inhibition.

The tumour suppressor p16/CDKN2A and the metabolic gene, methyl-thio-adenosine phosphorylase (MTAP), are frequently co-deleted in some of the most aggressive and currently untreatable cancers. Cells with MTAP deletion are vulnerable to inhibition of the metabolic enzyme, methionine-adenosyl transferase 2A (MAT2A), and the protein arginine methyl transferase (PRMT5). This synthetic lethality has paved the way for the rapid development of drugs targeting the MAT2A/PRMT5 axis. MAT2A and its liver- and pancreas-specific isoform, MAT1A, generate the universal methyl donor S-adenosylmethionine (SAM) from ATP and methionine. Given the pleiotropic role SAM plays in methylation of diverse substrates, characterising the extent of SAM depletion and downstream perturbations following MAT2A/MAT1A inhibition (MATi) is critical for safety assessment. We have assessed in vivo target engagement and the resultant systemic phenotype using multi-omic tools to characterise response to a MAT2A inhibitor (AZ'9567). We observed significant SAM depletion and extensive methionine accumulation in the plasma, liver, brain and heart of treated rats, providing the first assessment of both global SAM depletion and evidence of hepatic MAT1A target engagement. An integrative analysis of multi-omic data from liver tissue identified broad perturbations in pathways covering one-carbon metabolism, trans-sulfuration and lipid metabolism. We infer that these pathway-wide perturbations represent adaptive responses to SAM depletion and confer a risk of oxidative stress, hepatic steatosis and an associated disturbance in plasma and cellular lipid homeostasis. The alterations also explain the dramatic increase in plasma and tissue methionine, which could be used as a safety and PD biomarker going forward to the clinic.

Proteomics of prostate cancer serum and plasma using low and high throughput approaches.

Despite progress, MS-based proteomics in biofluids, especially blood, faces challenges such as dynamic range and throughput limitations in biomarker and disease studies. In this work, we used cutting-edge proteomics technologies to construct label-based and label-free workflows, capable of quantifying approximately 2,000 proteins in biofluids. With 70µL of blood and a single depletion strategy, we conducted an analysis of a homogenous cohort (n = 32), comparing medium-grade prostate cancer patients (Gleason score: 7(3 + 4); TNM stage: T2cN0M0, stage IIB) to healthy donors. The results revealed dozens of differentially expressed proteins in both plasma and serum. We identified the upregulation of Prostate Specific Antigen (PSA), a well-known biomarker for prostate cancer, in the serum of cancer cohort. Further bioinformatics analysis highlighted noteworthy proteins which appear to be differentially secreted into the bloodstream, making them good candidates for further exploration.

Proprotein convertase PCSK9 affects expression of key surface proteins in human pancreatic beta cells via intracellular and extracellular regulatory circuits.

Proprotein convertase subtilisin/kexin type 9 (PCSK9) is involved in the degradation of the low-density lipoprotein receptor. PCSK9 also targets proteins involved in lipid metabolism (very low-density lipoprotein receptor), immunity (major histocompatibility complex I), and viral infection (cluster of differentiation 81). Recent studies have also indicated that PCSK9 loss-of-function mutations are associated with an increased incidence of diabetes; however, the expression and function of PCSK9 in insulin-producing pancreatic beta cells remain unclear. Here, we studied PCSK9 regulation and function by performing loss- and gain-of-function experiments in the human beta cell line EndoC-ßH1. We demonstrate that PCSK9 is expressed and secreted by EndoC-ßH1 cells. We also found that PCSK9 expression is regulated by cholesterol and sterol regulatory element-binding protein transcription factors, as previously demonstrated in other cell types such as hepatocytes. Importantly, we show that PCSK9 knockdown using siRNA results in deregulation of various elements of the transcriptome, proteome, and secretome, and increases insulin secretion. We also observed that PCSK9 decreases low-density lipoprotein receptor and very low-density lipoprotein receptor levels via an extracellular signaling mechanism involving exogenous PCSK9, as well as levels of cluster of differentiation 36, a fatty acid transporter, through an intracellular signaling mechanism. Finally, we found that PCSK9 regulates the cell surface expression of PDL1 and HLA-ABC, proteins involved in cell-lymphocyte interaction, also via an intracellular mechanism. Collectively, these results highlight PCSK9 as a regulator of multiple cell surface receptors in pancreatic beta cells.

Characterization of the Secretome, Transcriptome, and Proteome of Human ß Cell Line EndoC-ßH1.

Early diabetes research is hampered by limited availability, variable quality, and instability of human pancreatic islets in culture. Little is known about the human ß cell secretome, and recent studies question translatability of rodent ß cell secretory profiles. Here, we verify representativeness of EndoC-ßH1, one of the most widely used human ß cell lines, as a translational human ß cell model based on omics and characterize the EndoC-ßH1 secretome. We profiled EndoC-ßH1 cells using RNA-seq, data-independent acquisition, and tandem mass tag proteomics of cell lysate. Omics profiles of EndoC-ßH1 cells were compared to human ß cells and insulinomas. Secretome composition was assessed by data-independent acquisition proteomics. Agreement between EndoC-ßH1 cells and primary adult human ß cells was ∼90% for global omics profiles as well as for ß cell markers, transcription factors, and enzymes. Discrepancies in expression were due to elevated proliferation rate of EndoC-ßH1 cells compared to adult ß cells. Consistently, similarity was slightly higher with benign nonmetastatic insulinomas. EndoC-ßH1 secreted 783 proteins in untreated baseline state and 3135 proteins when stressed with nontargeting control siRNA, including known ß cell hormones INS, IAPP, and IGF2. Further, EndoC-ßH1 secreted proteins known to generate bioactive peptides such as granins and enzymes required for production of bioactive peptides. EndoC-ßH1 secretome contained an unexpectedly high proportion of predicted extracellular vesicle proteins. We believe that secretion of extracellular vesicles and bioactive peptides warrant further investigation with specialized proteomics workflows in future studies.

An integrated landscape of protein expression in human cancer.

Using 11 proteomics datasets, mostly available through the PRIDE database, we assembled a reference expression map for 191 cancer cell lines and 246 clinical tumour samples, across 13 lineages. We found unique peptides identified only in tumour samples despite a much higher coverage in cell lines. These were mainly mapped to proteins related to regulation of signalling receptor activity. Correlations between baseline expression in cell lines and tumours were calculated. We found these to be highly similar across all samples with most similarity found within a given sample type. Integration of proteomics and transcriptomics data showed median correlation across cell lines to be 0.58 (range between 0.43 and 0.66). Additionally, in agreement with previous studies, variation in mRNA levels was often a poor predictor of changes in protein abundance. To our knowledge, this work constitutes the first meta-analysis focusing on cancer-related public proteomics datasets. We therefore also highlight shortcomings and limitations of such studies. All data is available through PRIDE dataset identifier PXD013455 and in Expression Atlas.

Using Deep Learning to Extrapolate Protein Expression Measurements.

Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for imputations of proteins measured in at least some of the samples. In silico methods for comprehensively estimating abundances across all proteins are still missing. Here, a novel method is proposed using deep learning to extrapolate the observed protein expression values in label-free MS experiments to all proteins, leveraging gene functional annotations and RNA measurements as key predictive attributes. This method is tested on four datasets, including human cell lines and human and mouse tissues. This method predicts the protein expression values with average R2 scores between 0.46 and 0.54, which is significantly better than predictions based on correlations using the RNA expression data alone. Moreover, it is demonstrated that the derived models can be "transferred" across experiments and species. For instance, the model derived from human tissues gave a R2=0.51 when applied to mouse tissue data. It is concluded that protein abundances generated in label-free MS experiments can be computationally predicted using functional annotated attributes and can be used to highlight aberrant protein abundance values.

The functional landscape of the human phosphoproteome.

Protein phosphorylation is a key post-translational modification regulating protein function in almost all cellular processes. Although tens of thousands of phosphorylation sites have been identified in human cells, approaches to determine the functional importance of each phosphosite are lacking. Here, we manually curated 112 datasets of phospho-enriched proteins, generated from 104 different human cell types or tissues. We re-analyzed the 6,801 proteomics experiments that passed our quality control criteria, creating a reference phosphoproteome containing 119,809 human phosphosites. To prioritize functional sites, we used machine learning to identify 59 features indicative of proteomic, structural, regulatory or evolutionary relevance and integrate them into a single functional score. Our approach identifies regulatory phosphosites across different molecular mechanisms, processes and diseases, and reveals genetic susceptibilities at a genomic scale. Several regulatory phosphosites were experimentally validated, including identifying a role in neuronal differentiation for phosphosites in SMARCC2, a member of the SWI/SNF chromatin-remodeling complex.

Expression Atlas update: from tissues to single cells.

Expression Atlas is EMBL-EBI's resource for gene and protein expression. It sources and compiles data on the abundance and localisation of RNA and proteins in various biological systems and contexts and provides open access to this data for the research community. With the increased availability of single cell RNA-Seq datasets in the public archives, we have now extended Expression Atlas with a new added-value service to display gene expression in single cells. Single Cell Expression Atlas was launched in 2018 and currently includes 123 single cell RNA-Seq studies from 12 species. The website can be searched by genes within or across species to reveal experiments, tissues and cell types where this gene is expressed or under which conditions it is a marker gene. Within each study, cells can be visualized using a pre-calculated t-SNE plot and can be coloured by different features or by cell clusters based on gene expression. Within each experiment, there are links to downloadable files, such as RNA quantification matrices, clustering results, reports on protocols and associated metadata, such as assigned cell types.

The ProteomeXchange consortium in 2020: enabling 'big data' approaches in proteomics.

The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) has standardized data submission and dissemination of mass spectrometry proteomics data worldwide since 2012. In this paper, we describe the main developments since the previous update manuscript was published in Nucleic Acids Research in 2017. Since then, in addition to the four PX existing members at the time (PRIDE, PeptideAtlas including the PASSEL resource, MassIVE and jPOST), two new resources have joined PX: iProX (China) and Panorama Public (USA). We first describe the updated submission guidelines, now expanded to include six members. Next, with current data submission statistics, we demonstrate that the proteomics field is now actively embracing public open data policies. At the end of June 2019, more than 14 100 datasets had been submitted to PX resources since 2012, and from those, more than 9 500 in just the last three years. In parallel, an unprecedented increase of data re-use activities in the field, including 'big data' approaches, is enabling novel research and new data resources. At last, we also outline some of our future plans for the coming years.

Quantifying the impact of public omics data.

The amount of omics data in the public domain is increasing every year. Modern science has become a data-intensive discipline. Innovative solutions for data management, data sharing, and for discovering novel datasets are therefore increasingly required. In 2016, we released the first version of the Omics Discovery Index (OmicsDI) as a light-weight system to aggregate datasets across multiple public omics data resources. OmicsDI aggregates genomics, transcriptomics, proteomics, metabolomics and multiomics datasets, as well as computational models of biological processes. Here, we propose a set of novel metrics to quantify the attention and impact of biomedical datasets. A complete framework (now integrated into OmicsDI) has been implemented in order to provide and evaluate those metrics. Finally, we propose a set of recommendations for authors, journals and data resources to promote an optimal quantification of the impact of datasets.

Quantitative Proteomics Data in the Public Domain: Challenges and Opportunities.

Mass spectrometry based proteomics is no longer only a qualitative discipline, and can be successfully employed to obtain a truly multidimensional view of the proteome. In particular, systematic protein expression profiling is now a routine part of many studies in the field and beyond. The large growth in the number of quantitative studies is accompanied by a trend to share publicly the associated analysis results and the underlying raw data. This trend, established and strongly supported by public repositories such as the PRIDE database at the European Bioinformatics Institute, opens up enormous possibilities to explore the data beyond the original publications, for instance by reusing, reanalyzing, and performing different flavors of meta-analysis studies. To help researchers and scientists realize about this potential, here we describe the mainstream public proteomics resources containing quantitative proteomics data, including the processed analysis results and/or the underlying raw data. We then present and discuss the most important points to consider when attempting to (re)use proteomics data in the public domain. We conclude by highlighting potential pitfalls of (re)using quantitative data and discuss some of our own experiences in this context.

Integrative Omic Profiling Reveals Unique Hypoxia Induced Signatures in Gastric Cancer Associated Myofibroblasts.

Although hypoxia is known to contribute to several aspects of tumour progression, relatively little is known about the effects of hypoxia on cancer-associated myofibroblasts (CAMs), or the consequences that conditional changes in CAM function may have on tumour development and metastasis. To investigate this issue in the context of gastric cancer, a comparative multiomic analysis was performed on populations of patient-derived myofibroblasts, cultured under normoxic or hypoxic conditions. Data from this study reveal a novel set of CAM-specific hypoxia-induced changes in gene expression and secreted proteins. Significantly, these signatures are not observed in either patient matched adjacent tissue myofibroblasts (ATMs) or non-cancer associated normal tissue myofibroblasts (NTMs). Functional characterisation of different myofibroblast populations shows that hypoxia-induced changes in gene expression not only enhance the ability of CAMs to induce cancer cell migration, but also confer pro-tumorigenic (CAM-like) properties in NTMs. This study provides the first global mechanistic insight into the molecular changes that contribute to hypoxia-induced pro-tumorigenic changes in gastric stromal myofibroblasts.

The PRIDE database and related tools and resources in 2019: improving support for quantification data.

The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.

A quantitative and temporal map of proteostasis during heat shock in Saccharomyces cerevisiae.

Temperature fluctuation is a common environmental stress that elicits a molecular response in order to maintain intracellular protein levels. Here, for the first time, we report a comprehensive temporal and quantitative study of the proteome during a 240 minute heat stress, using label-free mass spectrometry. We report temporal expression changes of the hallmark heat stress proteins, including many molecular chaperones, tightly coupled to their protein clients. A notable lag of 30 to 120 minutes was evident between transcriptome and proteome levels for differentially expressed genes. This targeted molecular response buffers the global proteome; fewer than 15% of proteins display significant abundance change. Additionally, a parallel study in a Hsp70 chaperone mutant (ssb1Δ) demonstrated a significantly attenuated response, at odds with the modest phenotypic effects that are observed on growth rate. We cast the global changes in temporal protein expression into protein interaction and functional networks, to afford a unique, time-resolved and quantitative description of the heat shock response in an important model organism.

Expression Atlas: gene and protein expression across multiple studies and organisms.

Expression Atlas (http://www.ebi.ac.uk/gxa) is an added value database that provides information about gene and protein expression in different species and contexts, such as tissue, developmental stage, disease or cell type. The available public and controlled access data sets from different sources are curated and re-analysed using standardized, open source pipelines and made available for queries, download and visualization. As of August 2017, Expression Atlas holds data from 3,126 studies across 33 different species, including 731 from plants. Data from large-scale RNA sequencing studies including Blueprint, PCAWG, ENCODE, GTEx and HipSci can be visualized next to each other. In Expression Atlas, users can query genes or gene-sets of interest and explore their expression across or within species, tissues, developmental stages in a constitutive or differential context, representing the effects of diseases, conditions or experimental interventions. All processed data matrices are available for direct download in tab-delimited format or as R-data. In addition to the web interface, data sets can now be searched and downloaded through the Expression Atlas R package. Novel features and visualizations include the on-the-fly analysis of gene set overlaps and the option to view gene co-expression in experiments investigating constitutive gene expression across tissues or other conditions.

Using the PRIDE Database and ProteomeXchange for Submitting and Accessing Public Proteomics Datasets.

The ProteomeXchange (PX) Consortium is the unifying framework for world-leading mass spectrometry (MS)-based proteomics repositories. Current members include the PRIDE database (U.K.), PeptideAtlas/PASSEL, and MassIVE (U.S.A.), and jPOST (Japan). The Consortium standardizes submission and dissemination of public proteomics data worldwide. This is achieved through implementing common data submission guidelines and enforcing metadata requirements by each of the members. Furthermore, the members use a common identifier space. Each dataset receives a unique (PXD) accession number and is publicly accessible as soon as the associated scientific publications are released. The two basic protocols provide a step-by-step guide on how to submit data to the PRIDE database, and describe how to access the PX portal (called ProteomeCentral), which can be used to search datasets available in any of the PX members. © 2017 by John Wiley & Sons, Inc.

Analysis of Intrinsic Peptide Detectability via Integrated Label-Free and SRM-Based Absolute Quantitative Proteomics.

Quantitative mass spectrometry-based proteomics of complex biological samples remains challenging in part due to the variability and charge competition arising during electrospray ionization (ESI) of peptides and the subsequent transfer and detection of ions. These issues preclude direct quantification from signal intensity alone in the absence of a standard. A deeper understanding of the governing principles of peptide ionization and exploitation of the inherent ionization and detection parameters of individual peptides is thus of great value. Here, using the yeast proteome as a model system, we establish the concept of peptide F-factor as a measure of detectability, closely related to ionization efficiency. F-factor is calculated by normalizing peptide precursor ion intensity by absolute abundance of the parent protein. We investigated F-factor characteristics in different shotgun proteomics experiments, including across multiple ESI-based LC-MS platforms. We show that F-factors mirror previously observed physicochemical predictors as peptide detectability but demonstrate a nonlinear relationship between hydrophobicity and peptide detectability. Similarly, we use F-factors to show how peptide ion coelution adversely affects detectability and ionization. We suggest that F-factors have great utility for understanding peptide detectability and gas-phase ion chemistry in complex peptide mixtures, selection of surrogate peptides in targeted MS studies, and for calibration of peptide ion signal in label-free workflows. Data are available via ProteomeXchange with identifier PXD003472.

Quantitative proteomics and network analysis of SSA1 and SSB1 deletion mutants reveals robustness of chaperone HSP70 network in Saccharomyces cerevisiae.

Molecular chaperones play an important role in protein homeostasis and the cellular response to stress. In particular, the HSP70 chaperones in yeast mediate a large volume of protein folding through transient associations with their substrates. This chaperone interaction network can be disturbed by various perturbations, such as environmental stress or a gene deletion. Here, we consider deletions of two major chaperone proteins, SSA1 and SSB1, from the chaperone network in Sacchromyces cerevisiae. We employ a SILAC-based approach to examine changes in global and local protein abundance and rationalise our results via network analysis and graph theoretical approaches. Although the deletions result in an overall increase in intracellular protein content, correlated with an increase in cell size, this is not matched by substantial changes in individual protein concentrations. Despite the phenotypic robustness to deletion of these major hub proteins, it cannot be simply explained by the presence of paralogues. Instead, network analysis and a theoretical consideration of folding workload suggest that the robustness to perturbation is a product of the overall network structure. This highlights how quantitative proteomics and systems modelling can be used to rationalise emergent network properties, and how the HSP70 system can accommodate the loss of major hubs.