Integrated View of Baseline Protein Expression in Human Tissues

The availability of proteomics datasets in the public domain, and in the PRIDE database, in particular, has increased dramatically in recent years. This unprecedented large-scale availability of data provides an opportunity for combined analyses of datasets to get organism-wide protein abundance data in a consistent manner. We have reanalyzed 24 public proteomics datasets from healthy human individuals to assess baseline protein abundance in 31 organs. We defined tissue as a distinct functional or structural region within an organ. Overall, the aggregated dataset contains 67 healthy tissues, corresponding to 3,119 mass spectrometry runs covering 498 samples from 489 individuals. We compared protein abundances between different organs and studied the distribution of proteins across these organs. We also compared the results with data generated in analogous studies. Additionally, we performed gene ontology and pathway-enrichment analyses to identify organ-specific enriched biological processes and pathways. As a key point, we have integrated the protein abundance results into the resource Expression Atlas, where they can be accessed and visualized either individually or together with gene expression data coming from transcriptomics datasets. We believe this is a good mechanism to make proteomics data more accessible for life scientists.

Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas

The number of mass spectrometry (MS)-based proteomics datasets in the public domain keeps increasing, particularly those generated by Data Independent Acquisition (DIA) approaches such as SWATH-MS. Unlike Data Dependent Acquisition datasets, the re-use of DIA datasets has been rather limited to date, despite its high potential, due to the technical challenges involved. We introduce a (re-)analysis pipeline for public SWATH-MS datasets which includes a combination of metadata annotation protocols, automated workflows for MS data analysis, statistical analysis, and the integration of the results into the Expression Atlas resource. Automation is orchestrated with Nextflow, using containerised open analysis software tools, rendering the pipeline readily available and reproducible. To demonstrate its utility, we reanalysed 10 public DIA datasets from the PRIDE database, comprising 1,278 SWATH-MS runs. The robustness of the analysis was evaluated, and the results compared to those obtained in the original publications. The final expression values were integrated into Expression Atlas, making SWATH-MS experiments more widely available and combining them with expression data originating from other proteomics and transcriptomics datasets.

Expression Atlas update: gene and protein expression in multiple species

The EMBL-EBI Expression Atlas is an added value knowledge base that enables researchers to answer the question of where (tissue, organism part, developmental stage, cell type) and under which conditions (disease, treatment, gender, etc) a gene or protein of interest is expressed. Expression Atlas brings together data from >4500 expression studies from >65 different species, across different conditions and tissues. It makes these data freely available in an easy to visualise form, after expert curation to accurately represent the intended experimental design, re-analysed via standardised pipelines that rely on open-source community developed tools. Each study's metadata are annotated using ontologies. The data are re-analyzed with the aim of reproducing the original conclusions of the underlying experiments. Expression Atlas is currently divided into Bulk Expression Atlas and Single Cell Expression Atlas. Expression Atlas contains data from differential studies (microarray and bulk RNA-Seq) and baseline studies (bulk RNA-Seq and proteomics), whereas Single Cell Expression Atlas is currently dedicated to Single Cell RNA-Sequencing (scRNA-Seq) studies. The resource has been in continuous development since 2009 and it is available at https://www.ebi.ac.uk/gxa.

The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences

The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. 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 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas.

A comprehensive analysis of candidate genes in familial pancreatic cancer families reveals a high frequency of potentially pathogenic germline variants

BACKGROUND

The 5-year survival rate of patients with pancreatic ductal adenocarcinoma (PDAC) is around 5% due to the fact that the majority of patients present with advanced disease that is treatment resistant. Familial pancreatic cancer (FPC) is a rare disorder that is defined as a family with at least two affected first degree relatives, with an estimated incidence of 4%-10%. The genetic basis is unknown in the majority of families although around 10%-13% of families carry germline mutations in known genes associated with hereditary cancer and pancreatitis syndromes.

METHODS

Panel sequencing was performed of 35 genes associated with hereditary cancer in 43 PDAC cases from families with an apparent hereditary pancreatic cancer syndrome.

FINDINGS

Pathogenic variants were identified in 19% (5/26) of PDAC cases from pure FPC families in the genes MLH1, CDKN2A, POLQ and FANCM. Low frequency potentially pathogenic VUS were also identified in 35% (9/26) of PDAC cases from FPC families in the genes FANCC, MLH1, PMS2, CFTR, APC and MUTYH. Furthermore, an important proportion of PDAC cases harboured more than one pathogenic, likely pathogenic or potentially pathogenic VUS, highlighting the multigene phenotype of FPC.

INTERPRETATION

The genetic basis of familial or hereditary pancreatic cancer can be explained in 21% of families by previously described hereditary cancer genes. Low frequency variants in other DNA repair genes are also present in 35% of families which may contribute to the risk of pancreatic cancer development.

FUNDING

This study was funded by the Instituto de Salud Carlos III (Plan Estatal de I + D + i 2013-2016): ISCIII (PI09/02221, PI12/01635, PI15/02101 and PI18/1034) and co-financed by the European Development Regional Fund ''A way to achieve Europe'' (ERDF), the Biomedical Research Network in Cancer: CIBERONC (CB16/12/00446), Red Temática de investigación cooperativa en cáncer: RTICC (RD12/0036/0073) and La Asociación Española contra el Cáncer: AECC (Grupos Coordinados Estables 2016).

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.

Early detection of lysosomal diseases by screening of cases of idiopathic splenomegaly and/or thrombocytopenia with a next-generation sequencing gene panel

Lysosomal diseases (LD) are a group of about 70 rare hereditary disorders (combined incidence 1:5000) in which diverse lysosomal functions are impaired, impacting multiple organs and systems. The first clinical signs and symptoms are usually unspecific and shared by hundreds of other disorders. Diagnosis of LD traditionally relies on performing specific enzymatic assays, if available, upon clinical suspicion of the disorder. However, the combination of the insidious onset of LD and the lack of awareness on these rare diseases among medical personnel results in undesirable diagnostic delays, with unchecked disease progression, appearance of complications and a worsened prognosis. We tested the usefulness of a next‐generation sequencing‐based gene panel for quick, early detection of LD among cases of idiopathic splenomegaly and/or thrombocytopenia, two of the earliest clinical signs observed in most LD. Our 73‐gene panel interrogated 28 genes for LD, 1 biomarker and 44 genes underlying non‐LD differential diagnoses. Among 38 unrelated patients, we elucidated eight cases (21%), five with LD (GM1 gangliosidosis, Sanfilippo disease A and B, Niemann‐Pick disease B, Gaucher disease) and three with non‐LD conditions. Interestingly, we identified three LD patients harboring pathogenic mutations in two LD genes each, which may result in unusual disease presentations and impact treatment. Turnaround time for panel screening and genetic validation was 1 month. Our results underline the usefulness of resequencing gene panels for quick and cost‐effective screening of LDs and disorders sharing with them early clinical signs.

A comprehensive evaluation of omega-3 fatty acid supplementation in cystic fibrosis patients using lipidomics

The evaluation of the benefits of omega-3 fatty acid supplementation in humans requires the identification and characterization of suitable biomarkers of its incorporation in the body. The reference method for the evaluation of omega-3, gas chromatography, is difficult to apply in clinical practice because of its low throughput and does not provide information about the incorporation of specific fatty acids in lipid species and the potential effects of supplementation on lipid classes. We used a quantitative lipidomic approach to follow the incorporation of omega-3 fatty acids into plasma lipids in cystic fibrosis patients (n=50) from a randomized controlled clinical trial after the supplementation of seaweed oil enriched with docosahexaenoic acid (DHA). Lipidomic analysis accurately showed the distribution of fatty acids in different lipid classes after omega-3 supplementation, and the performance in determining the compliance to supplementation was similar to that of gas chromatography coupled to mass spectrometry. Twelve months after fatty acid supplementation, DHA was predominantly incorporated into highly unsaturated cholesteryl esters (110.9±16.2 vs. 278.6±32.6 μM, mean±S.E.M.) and phosphatidylcholine (142.4±11.9 vs. 272.9±21.4 μM) and, to a lesser extent, into phosphatidylethanolamine (9.4±0.8 vs. 15.5±1.5 μM) and triglycerides (0.4±0.04 vs. 1.1±0.12 μM). In addition, a technique was developed for the fast measurement of the DHA/arachidonic acid ratio to simplify the follow-up of nutritional intervention with DHA-enriched foods. We conclude that lipidomics is a suitable approach for monitoring the incorporation of omega-3 fatty acids in nutritional studies.

Quantitative lipidomic analysis of plasma and plasma lipoproteins using MALDI-TOF mass spectrometry

Knowledge of the plasma lipid composition is essential to clarify the specific roles of different lipid species in various pathophysiological processes. In this study, we developed an analytical strategy combining high-performance liquid chromatography with evaporative light scattering detection (HPLC-ELSD) and off-line coupling with matrix-assisted laser desorption/ionization with time-of-flight mass spectrometry (MALDI-TOF/MS) to determine the composition of plasma and major lipoproteins at two levels, lipid classes and lipid species. We confirmed the suitability of MALDI-TOF/MS as a quantitative measurement tool studying the linearity and repeatability for triglycerides (TG), phosphatidylethanolamine (PE) and phosphatidylcholine (PC). Moreover, data obtained with this method were correlated with other lipid classes and species measurements using currently available technologies. To establish the potential utility of our approach, human plasma very low density- (VLDL), low density- (LDL) and high density- (HDL) lipoproteins from 10 healthy donors were separated using ultracentrifugation, and compositions of nine lipid classes, cholesteryl esters (CE), TG, free cholesterol (FC), PE, phosphatidylinositol (PI), sulfatides (S), PC, lysophosphatidylcholine (LPC) and sphingomyelin (SM), analyzed. In total, 157 lipid species in plasma, 182 in LDL, 171 in HDL, and 148 in VLDL were quantified. The lipidomic profile was consistent with known differences in lipid classes, but also revealed unexpected differences in lipid species distribution of lipoproteins, particularly for LPC and SM. In summary, the methodology developed in this study constitutes a valid approach to determine the lipidomic composition of plasma and lipoproteins.