The PRIDE database resources in 2023

Nephrology Dialysis Transplantation, vol. 39, pp. 4–6

Abstract

Proteins are essential in all cellular processes, including metabolism, signalling and regulation. Therefore, identifying and characterizing kidney proteins is essential for understanding the molecular basis of kidney function and disease. In recent years, proteomics databases have improved tremendously, becoming an essential link between mass spectrometry analysis and annotated resources. Databases such as UniProt, GenBank and Protein Data Bank (PDB) focus on protein sequence, structure, functional knowledge and existing literature. On the other hand, Open Targets focusses on genome-wide association studies and functional genomics, and Ensembl focusses on evolution, sequence variation and transcriptional regulation. One of the leading data repositories of proteomics data is the ‘Proteomics Identifications’ (PRIDE) database [1] which stores raw proteomics data complimented by detailed annotations. These databases, commonly used in kidney research, provide comprehensive collections of datasets for various applications, such as protein identification, quantification and annotation, even facilitating the reuse and reanalysis of data from multiple cohorts to answer complex biological questions. The most crucial application of proteomics databases in kidney research is for identifying proteins in biological samples, such as urine, serum, plasma, blood or kidney tissue, at a defined status in the progression of the disease. By identifying the changes in protein profiles caused by specific (patho-)physiologic conditions, new insights can be gained into the molecular mechanisms of kidney function and disease. Proteomics databases also facilitate protein quantification, e.g. comparison of the expression of a protein between healthy and diseased kidneys or at different stages of kidney disease. Combining the traditional diagnostic methods with these recently identified markers will tremendously improve risk stratification for kidney disease progression and mortality. General proteomics databases often support kidney proteomics research; however, kidney-specific databases are currently unavailable. Several project-specific proteomics databases which focus on processed data, facilitating the accessibility of bulk mass-spectrometry and protein profile datasets, are available to study the complex molecular signatures underlying kidney pathologies, for example, the ‘Human Protein Atlas’ (HPA) project. The HPA aims to map the entire human proteome using antibody‐based proteomics and integrating various other omics technologies [2]. The HPA has a dedicated kidney-specific database that contains comprehensive information about the human kidney proteome, including protein expression data, subcellular localization and functional annotations. Another project-specific database, ‘Kidney Precision Medicine Project’ (KPMP) aims to improve kidney disease diagnosis and treatment. As part of this project, a proteomics database has been developed that provides a comprehensive overview of the kidney proteome in health and disease. The database contains information from healthy kidneys as well as kidneys from patients with various types of kidney diseases, such as glomerular disease, tubulointerstitial disease and cystic kidney disease. Similarly, the ‘Human Kidney and Urine Proteome Project’ (HKUPP) aims to provide protein expression in normal urine and normal or diseased kidney, supporting the search for proteins from different kidney sections. The HKUPP allows the viewing of two-dimensional gels and query fractions. Even though the platform is still often cited, more easily accessible platforms are available today. ProteomeXchange is a Global Core Biodata Resource with various member repositories such as PRIDE (EMBL-EBI, Cambridge, UK), PeptideAtlas (ISB, Seattle, WA, USA), MassIVE (UCSD, San Diego, CA, USA), jPOST (various institutions, Japan), iProx (National Center for Protein Sciences, Beijing, China) and Panorama Public (University of Washington, Seattle, WA, USA). PRIDE is the European part of this consortium which aims to share proteomics data according to FAIR (Findable, Accessible, Interoperable and Reusable) principles. The PRIDE database is unique in storing and disseminating raw data related to mass spectrometry–based proteomics experiments and is publicly accessible via https://www.ebi.ac.uk/pride/. PRIDE is a comprehensive resource for depositing, storing, analysing and sharing proteomics data which enables reprocessing and reanalysis of data from different laboratories across the globe. Such open sharing platforms facilitate the multi-layered integration and visualization of multi-modal analyses, for instance, to study the common molecular omics of kidney cells [3]. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) reached, on average, around 500 monthly datasets during 2021 [4]. The PRIDE repository encompasses raw mass spectrometry data, processed data and metadata. Besides the proteomics data, the raw files, processed data and metadata are stored in the PRIDE database. A comprehensive collection of files termed ‘Complete submission’ is unique to the PRIDE database and facilitates access to the processed results by users without the installation of specific software used by the submitters of the dataset. In addition, PRIDE experts’ extensive control of the submitted datasets results in high-quality data with multiple quality control reports, which is beyond the scope of other, less extensively managed databases. Figure 1 shows the PRIDE pipeline which ensures strict quality control of the submissions made to the PRIDE database. The PRIDE pipeline (figure adapted from [7]). The data submission process to PRIDE is based on submitting the raw mass spectrometry data and the metadata file, including information such as the experimental design, sample preparation, mass spectrometry parameters and protein identification results, providing a complete picture of the proteomic dataset under consideration. After submission, the data undergo a detailed quality control check by PRIDE experts, including assessing the experimental design, mass spectrometry parameters and data completeness, ensuring the quality of the data available on the PRIDE database. In July 2023, the search term ‘kidney’ lists 838 datasets on the PRIDE Archive, 512 of which are correlated to humans specifically. The PRIDE archive also facilitates access to datasets pertaining to kidney and its comorbidities, such as 3 datasets for cardiovascular system disease, 26 datasets for obesity, 2 datasets for type two diabetes and 22 datasets that are about human kidneys without diseases, among many other possible combinations of kidney-related searches. PRIDE also supports analysis of these numerous datasets, including pre-processing, statistical and pathway analysis. Using the PRIDE database, the analysis of post-translational modifications (PTMs) of proteins and peptides is possible too. PTMs are covalent modifications that occur on proteins after translation, and they play critical roles in regulating protein structure, function, localization and interactions [5]. The PRIDE database encourages users to annotate their submitted data with PTM information. Because of the strong impact of PTM on the onset and genesis of diseases, the PRIDE database data also contains information on post-translational modifications like phosphorylation, acetylation, methylation, ubiquitination and many others, often caused by uraemic toxins [5]. PRIDE provides the possibility of searching for specific PTM types or modified residues, including statistical analyses to gain insights into the distribution and abundance of PTMs across proteins and experimental conditions, which may provide valuable insights, identify new PTM sites, or reveal PTM dynamics under different experimental conditions. Although proteomic approaches are essential for identifying and characterizing proteins involved in kidney function and disease, several proteomics challenges are relevant for the PRIDE databases. All proteomics databases share the primary challenges of sample preparation for kidney research. The kidney contains a heterogeneous mixture of cell types with different proteomes. Therefore, analysing the entire kidney proteome is challenging, as abundant proteins often mask the detection of less abundant proteins. Additionally, post-translational modifications, such as carbamylation or guanidinylation [6], increase the complexity of the kidney proteome. These challenges require specialized sample preparation and analytical techniques, such as multidimensional liquid chromatography and tandem mass spectrometry, to achieve the necessary depth of proteome coverage. Furthermore, the diversity of protein isoforms and splice variants challenge kidney proteomics. These variants elicit different biological functions and may play important roles in kidney function. Unfortunately, these are currently not a specific kidney focus of the PRIDE database. Therefore, developing specialized proteomics techniques and database branches to detect and characterize protein isoforms and splice variants is crucial for advancing kidney research using PRIDE in the future. Although the PRIDE database provides a platform for depositing different types of proteomics data, the completeness of the data is not ensured in PRIDE as well. The data deposited in PRIDE might be expandable, especially regarding information about sample preparation, experimental conditions and quality control measures. In general, protein databases are only as valuable as the data they contain and the tools available for data analysis. The primary challenge is the diversity of data sources, often limiting data integration and analysis of kidney research data. Since 2019, PRIDE has supported the mzTab file format, which facilitates the reanalysis using tools such as Mascot, OpenMS and the protein interface toolkit. However, not all tools support mzTAB as an output tool, but the MzIdentML file format lacks the protein expression/quantification values. Additionally, not all kidney-related datasets and raw data are submitted to the PRIDE database. This necessitates creating further awareness and promoting the benefits of the PRIDE database. Since protein data constantly evolve, continuous data updating is essential for a database. Maintaining protein databases requires continuously updating the latest protein sequence and functional information, and this continuous database updating is the issue for data quality control. The lack of cloud-based software that can analyse bulk data complicates the effective reuse and analysis of data from the PRIDE database. Therefore, developing effective data integration and analysis strategies in the form of specific algorithms and software tools within PRIDE is essential for harnessing the full potential of kidney protein databases. In summary, the PRIDE database serves as a valuable repository for proteomics data, including data related to post-translational modifications. The database has a rigorous curation process that ensures the quality and accuracy of the data. Therefore, PRIDE is a valuable resource for proteomics research, providing a platform for data sharing and collaboration in the kidney field, which needs to be promoted to achieve the full potential of the publicly shared proteomics data. This study was supported by a grant from the German Research Foundation (DFG, SFB/Trr219 Project-ID: 322900939) and CRC1382 Project-ID: 403224013). The authors were supported by the European Union’s Horizon 2020 research and innovation program EU-ITN INTRICARE (722609), CaReSyAn (764474) and the EU-Cost PerMedik (CA21165). This article/publication is based upon work from COST Action Permedik, CA21165, supported by COST (European Cooperation in Science and Technology). The Authors have no conflicts of interest.

Authors 2

1. Shruti Bhargava Aachen Institute of Molecular Cardiovascular Research Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Institute of Molecular Cardiovascular Research, Medical Faculty, RWTH Aachen University, Aachen , Germany

2. Joachim Jankowski corresponding Aachen Institute of Molecular Cardiovascular Research Medical Faculty

   RWTH Aachen University · Universitätsklinikum Aachen · Maastricht University

   Affiliation as printed

   Aachen-Maastricht Institute for Cardiorenal Disease (AMICARE), University Hospital RWTH Aachen , Aachen , Germany

   Experimental Vascular Pathology, Cardiovascular Research Institute Maastricht (CARIM), University of Maastricht, Maastricht , The Netherlands

   Institute of Molecular Cardiovascular Research, Medical Faculty, RWTH Aachen University, Aachen , Germany