How to submit MS proteomics data to ProteomeXchange via the PRIDE database

The PRIDE database at 20 years: 2025 update

The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's leading mass spectrometry (MS)-based proteomics data repository and one of the founding members of the ProteomeXchange consortium. This manuscript summarizes the developments in PRIDE resources and related tools for the last three years. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 534 datasets per month. This has been possible thanks to continuous improvements in infrastructure such as a new file transfer protocol for very large datasets (Globus), a new data resubmission pipeline and an automatic dataset validation process. Additionally, we will highlight novel activities such as the availability of the PRIDE chatbot (based on the use of open-source Large Language Models), and our work to improve support for MS crosslinking datasets. Furthermore, we will describe how we have increased our efforts to reuse, reanalyze and disseminate high-quality proteomics data into added-value resources such as UniProt, Ensembl and Expression Atlas.

AMPK activator 991 specifically activates SnRK1 and thereby affects seed germination in rice

Sucrose non-fermenting-1-related protein kinase 1 (SnRK1) and AMP-activated protein kinase (AMPK) are highly conserved. Compound 991 is an AMPK activator in mammals. However, whether 991 also activates SnRK1 remains unknown. The addition of 991 significantly increased SnRK1 activity in desalted extracts from germinating rice seeds in vitro. To determine whether 991 has biological activity, rice seeds were treated with different concentrations of 991. Germination was promoted at low concentrations but inhibited at high concentrations. The effects of 991 on germination were similar to those of OsSnRK1a overexpression. To explore whether 991 affects germination by specifically affecting SnRK1, germination of an snrk1a mutant and the wild type under 1 μM 991 treatment was compared. The snrk1a mutant was insensitive to 991. Phosphoproteomic analysis showed that the differential phosphopeptides induced by 991 and OsSnRK1a overexpression largely overlapped. Furthermore, SnRK1 might regulate rice germination in a dosage-dependent manner by regulating the phosphorylation of three phosphosites, namely S285-PIP2;4, S1013-SOS1, and S110-ABI5. These results indicate that 991 is a specific SnRK1 activator in rice. The promotion and inhibition of germination by 991 also occurred in wheat seeds. Thus, 991 is useful for exploring SnRK1 function and the chemical regulation of growth and development in crops.

Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation

Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

Peptidomics

Peptides are biopolymers, typically consisting of 2-50 amino acids. They are biologically produced by the cellular ribosomal machinery or by non-ribosomal enzymes and, sometimes, other dedicated ligases. Peptides are arranged as linear chains or cycles, and include post-translational modifications, unusual amino acids and stabilizing motifs. Their structure and molecular size render them a unique chemical space, between small molecules and larger proteins. Peptides have important physiological functions as intrinsic signalling molecules, such as neuropeptides and peptide hormones, for cellular or interspecies communication, as toxins to catch prey or as defence molecules to fend off enemies and microorganisms. Clinically, they are gaining popularity as biomarkers or innovative therapeutics; to date there are more than 60 peptide drugs approved and more than 150 in clinical development. The emerging field of peptidomics comprises the comprehensive qualitative and quantitative analysis of the suite of peptides in a biological sample (endogenously produced, or exogenously administered as drugs). Peptidomics employs techniques of genomics, modern proteomics, state-of-the-art analytical chemistry and innovative computational biology, with a specialized set of tools. The complex biological matrices and often low abundance of analytes typically examined in peptidomics experiments require optimized sample preparation and isolation, including in silico analysis. This Primer covers the combination of techniques and workflows needed for peptide discovery and characterization and provides an overview of various biological and clinical applications of peptidomics.

Analysis of affinity purification-related proteomic data for studying protein-protein interaction networks in cells

During intracellular signal transduction, protein-protein interactions (PPIs) facilitate protein complex assembly to regulate protein localization and function, which are critical for numerous cellular events. Over the years, multiple techniques have been developed to characterize PPIs to elucidate roles and regulatory mechanisms of proteins. Among them, the mass spectrometry (MS)-based interactome analysis has been increasing in popularity due to its unbiased and informative manner towards understanding PPI networks. However, with MS instrumentation advancing and yielding more data than ever, the analysis of a large amount of PPI-associated proteomic data to reveal bona fide interacting proteins become challenging. Here, we review the methods and bioinformatic resources that are commonly used in analyzing large interactome-related proteomic data and propose a simple guideline for identifying novel interacting proteins for biological research.

E. coli Toxin YjjJ (HipH) Is a Ser/Thr Protein Kinase That Impacts Cell Division, Carbon Metabolism, and Ribosome Assembly

Protein Ser/Thr kinases are posttranslational regulators of key molecular processes in bacteria, such as cell division and antibiotic tolerance. Here, we characterize the E. coli toxin YjjJ (HipH), a putative protein kinase annotated as a member of the family of HipA-like Ser/Thr kinases, which are involved in antibiotic tolerance. Using SILAC-based phosphoproteomics we provide experimental evidence that YjjJ is a Ser/Thr protein kinase and its primary protein substrates are the ribosomal protein RpmE (L31) and the carbon storage regulator CsrA. YjjJ activity impacts ribosome assembly, cell division, and central carbon metabolism but it does not increase antibiotic tolerance as does its homologue HipA. Intriguingly, overproduction of YjjJ and its kinase-deficient variant can activate HipA and other kinases, pointing to a cross talk between Ser/Thr kinases in E. coli. IMPORTANCE Adaptation to growth condition is the key for bacterial survival, and protein phosphorylation is one of the strategies adopted to transduce extracellular signal in physiological response. In a previous work, we identified YjjJ, a putative kinase, as target of the persistence-related HipA kinase. Here, we performed the characterization of this putative kinase, complementing phenotypical analysis with SILAC-based phosphoproteomics and proteomics. We provide the first experimental evidence that YjjJ is a Ser/Thr protein kinase, having as primary protein substrates the ribosomal protein RpmE (L31) and the carbon storage regulator CsrA. We show that overproduction of YjjJ has a major influence on bacterial physiology, impacting DNA segregation, cell division, glycogen production, and ribosome assembly.

The 'ForensOMICS' approach for postmortem interval estimation from human bone by integrating metabolomics, lipidomics, and proteomics

The combined use of multiple omics allows to study complex interrelated biological processes in their entirety. We applied a combination of metabolomics, lipidomics and proteomics to human bones to investigate their combined potential to estimate time elapsed since death (i.e., the postmortem interval [PMI]). This 'ForensOMICS' approach has the potential to improve accuracy and precision of PMI estimation of skeletonized human remains, thereby helping forensic investigators to establish the timeline of events surrounding death. Anterior midshaft tibial bone was collected from four female body donors before their placement at the Forensic Anthropology Research Facility owned by the Forensic Anthropological Center at Texas State (FACTS). Bone samples were again collected at selected PMIs (219-790-834-872days). Liquid chromatography mass spectrometry (LC-MS) was used to obtain untargeted metabolomic, lipidomic, and proteomic profiles from the pre- and post-placement bone samples. The three omics blocks were investigated independently by univariate and multivariate analyses, followed by Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO), to identify the reduced number of markers describing postmortem changes and discriminating the individuals based on their PMI. The resulting model showed that pre-placement metabolome, lipidome and proteome profiles were clearly distinguishable from post-placement ones. Metabolites in the pre-placement samples suggested an extinction of the energetic metabolism and a switch towards another source of fuelling (e.g., structural proteins). We were able to identify certain biomolecules with an excellent potential for PMI estimation, predominantly the biomolecules from the metabolomics block. Our findings suggest that, by targeting a combination of compounds with different postmortem stability, in the future we could be able to estimate both short PMIs, by using metabolites and lipids, and longer PMIs, by using proteins.

Proteomic Discovery and Validation of Novel Fluid Biomarkers for Improved Patient Selection and Prediction of Clinical Outcomes in Alzheimer’s Disease Patient Cohorts

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease characterized by progressive cognitive decline. The two cardinal neuropathological hallmarks of AD include the buildup of cerebral β amyloid (Aβ) plaques and neurofibrillary tangles of hyperphosphorylated tau. The current disease-modifying treatments are still not effective enough to lower the rate of cognitive decline. There is an urgent need to identify early detection and disease progression biomarkers that can facilitate AD drug development. The current established readouts based on the expression levels of amyloid beta, tau, and phospho-tau have shown many discrepancies in patient samples when linked to disease progression. There is an urgent need to identify diagnostic and disease progression biomarkers from blood, cerebrospinal fluid (CSF), or other biofluids that can facilitate the early detection of the disease and provide pharmacodynamic readouts for new drugs being tested in clinical trials. Advances in proteomic approaches using state-of-the-art mass spectrometry are now being increasingly applied to study AD disease mechanisms and identify drug targets and novel disease biomarkers. In this report, we describe the application of quantitative proteomic approaches for understanding AD pathophysiology, summarize the current knowledge gained from proteomic investigations of AD, and discuss the development and validation of new predictive and diagnostic disease biomarkers.

Proteomic repository data submission, dissemination, and reuse: key messages

INTRODUCTION

The creation of ProteomeXchange data workflows in 2012 transformed the field of proteomics, consisting of the standardization of data submission and dissemination and enabling the widespread reanalysis of public MS proteomics data worldwide. ProteomeXchange has triggered a growing trend toward public dissemination of proteomics data, facilitating the assessment, reuse, comparative analyses, and extraction of new findings from public datasets. By 2022, the consortium is integrated by PRIDE, PeptideAtlas, MassIVE, jPOST, iProX, and Panorama Public.

AREAS COVERED

Here, we review and discuss the current ecosystem of resources, guidelines, and file formats for proteomics data dissemination and reanalysis. Special attention is drawn to new exciting quantitative and post-translational modification-oriented resources. The challenges and future directions on data depositions including the lack of metadata and cloud-based and high-performance software solutions for fast and reproducible reanalysis of the available data are discussed.

EXPERT OPINION

The success of ProteomeXchange and the amount of proteomics data available in the public domain have triggered the creation and/or growth of other protein knowledgebase resources. Data reuse is a leading, active, and evolving field; supporting the creation of new formats, tools, and workflows to rediscover and reshape the public proteomics data.

Sucrose nonfermenting-1-related protein kinase 1 regulates sheath-to-panicle transport of nonstructural carbohydrates during rice grain filling

The remobilization of nonstructural carbohydrates (NSCs) reserved in rice (Oryza sativa) sheaths is essential for grain filling. This assimilate distribution between plant tissues and organs is determined by sucrose non-fermenting-1-related protein kinase 1 (SnRK1). However, the SnRK1-mediated mechanism regulating the sheath-to-panicle transport of NSCs in rice remains unknown. In this study, leaf cutting treatment was used to accelerate NSC transport in the rice sheaths. Accelerated NSC transport was accompanied by increased levels of OsSnRK1a mRNA expression, SnRK1a protein expression, catalytic subunit phosphorylation of SnRK1, and SnRK1 activity, indicating that SnRK1 activity plays an important role in sheath NSC transport. We also discovered that trehalose-6-phosphate, a signal of sucrose availability, slightly reduced SnRK1 activity in vitro. Since SnRK1 activity is mostly regulated by OsSnRK1a transcription in response to low sucrose content, we constructed an snrk1a mutant to verify the function of SnRK1 in NSC transport. NSCs accumulated in the sheaths of snrk1a mutant plants and resulted in a low seed setting rate and grain weight, verifying that SnRK1 activity is essential for NSC remobilization. Using phosphoproteomics and parallel reaction monitoring, we identified 20 SnRK1-dependent phosphosites that are involved in NSC transport. In addition, the SnRK1-mediated phosphorylation of the phosphosites directly affected starch degradation, sucrose metabolism, phloem transport, sugar transport across the tonoplast, and glycolysis in rice sheaths to promote NSC transport. Therefore, our findings reveal the importance, function, and possible regulatory mechanism of SnRK1 in the sheath-to-panicle transport of NSCs in rice.

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.

STAT3 contributes to cisplatin resistance, modulating EMT markers, and the mTOR signaling in lung adenocarcinoma

Lung cancer is the second leading cause of cancer death worldwide and is strongly associated with cisplatin resistance. The transcription factor signal transducer and activator of transcription 3 (STAT3) is constitutively activated in cancer cells and coordinates critical cellular processes as survival, self-renewal, and inflammation. In several types of cancer, STAT3 controls the development, immunogenicity, and malignant behavior of tumor cells while it dictates the responsiveness to radio- and chemotherapy. It is known that STAT3 phosphorylation at Ser727 by mechanistic target of rapamycin (mTOR) is necessary for its maximal activation, but the crosstalk between STAT3 and mTOR signaling in cisplatin resistance remains elusive. In this study, using a proteomic approach, we revealed important targets and signaling pathways altered in cisplatin-resistant A549 lung adenocarcinoma cells. STAT3 had increased expression in a resistance context, which can be associated with a poor prognosis. STAT3 knockout (SKO) resulted in a decreased mesenchymal phenotype in A549 cells, observed by clonogenic potential and by the expression of epithelial-mesenchymal transition markers. Importantly, SKO cells did not acquire the mTOR pathway overactivation induced by cisplatin resistance. Consistently, SKO cells were more responsive to mTOR inhibition by rapamycin and presented impairment of the feedback activation loop in Akt. Therefore, rapamycin was even more potent in inhibiting the clonogenic potential in SKO cells and sensitized to cisplatin treatment. Mechanistically, STAT3 partially coordinated the cisplatin resistance phenotype via the mTOR pathway in non-small cell lung cancer. Thus, our findings reveal important targets and highlight the significance of the crosstalk between STAT3 and mTOR signaling in cisplatin resistance. The synergic inhibition of STAT3 and mTOR potentially unveil a potential mechanism of synthetic lethality to be explored for human lung cancer treatment.

Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases

This article describes some use case studies and self-assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR (findable, accessible, interoperable and reusable) data principles in a large distributed bioinformatics infrastructure. We address the challenge of heterogeneity of wet lab technologies, data, metadata, software, computational workflows and the levels of implementation and monitoring of FAIR principles within the different bioinformatics sub-disciplines joint in de.NBI. On the one hand, this broad service landscape and the excellent network of experts are a strong basis for the development of useful research data management plans. On the other hand, the large number of tools and techniques maintained by distributed teams renders FAIR compliance challenging.

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.

Mitochondrial peptide BRAWNIN is essential for vertebrate respiratory complex III assembly

The emergence of small open reading frame (sORF)-encoded peptides (SEPs) is rapidly expanding the known proteome at the lower end of the size distribution. Here, we show that the mitochondrial proteome, particularly the respiratory chain, is enriched for small proteins. Using a prediction and validation pipeline for SEPs, we report the discovery of 16 endogenous nuclear encoded, mitochondrial-localized SEPs (mito-SEPs). Through functional prediction, proteomics, metabolomics and metabolic flux modeling, we demonstrate that BRAWNIN, a 71 a.a. peptide encoded by C12orf73, is essential for respiratory chain complex III (CIII) assembly. In human cells, BRAWNIN is induced by the energy-sensing AMPK pathway, and its depletion impairs mitochondrial ATP production. In zebrafish, Brawnin deletion causes complete CIII loss, resulting in severe growth retardation, lactic acidosis and early death. Our findings demonstrate that BRAWNIN is essential for vertebrate oxidative phosphorylation. We propose that mito-SEPs are an untapped resource for essential regulators of oxidative metabolism.

Comparing Peptide Spectra Matches Across Search Engines

Mass spectrometry is extremely efficient for sequencing small peptides generated by, for example, a trypsin digestion of a complex mixture. Current instruments have the capacity to generate 50-100 K MSMS spectra from a single run. Of these ~30-50% is typically assigned to peptide matches on a 1% FDR threshold. The remaining spectra need more research to explain. We address here whether the 30-50% matched spectra provide consensus matches when using different database-dependent search pipelines. Although the majority of the spectra peptide assignments concur across search engines, our conclusion is that database-dependent search engines still require improvements.

Solution to Dark Matter Identified by Mass-Tolerant Database Search

Recently a mass-tolerant search approach was proposed which suggested novel types of abundant modification with delta masses that left many scratching their heads. These surprising new findings which were hard to explain were later referred to as dark matter of mass spectrometry-based proteomics. Rewards were promised for those who could solve these intriguing new findings. We propose here simple solutions to the novel delta masses identified by mass-tolerant database search.

Toward a Consensus on Applying Quantitative Liquid Chromatography-Tandem Mass Spectrometry Proteomics in Translational Pharmacology Research: A White Paper

Quantitative translation of information on drug absorption, disposition, receptor engagement, and drug-drug interactions from bench to bedside requires models informed by physiological parameters that link in vitro studies to in vivo outcomes. To predict in vivo outcomes, biochemical data from experimental systems are routinely scaled using protein quantity in these systems and relevant tissues. Although several laboratories have generated useful quantitative proteomic data using state-of-the-art mass spectrometry, no harmonized guidelines exit for sample analysis and data integration to in vivo translation practices. To address this gap, a workshop was held on September 27 and 28, 2018, in Cambridge, MA, with 100 experts attending from academia, the pharmaceutical industry, and regulators. Various aspects of quantitative proteomics and its applications in translational pharmacology were debated. A summary of discussions and best practices identified by this expert panel are presented in this "White Paper" alongside unresolved issues that were outlined for future debates.

Proteometabolomics of Melphalan Resistance in Multiple Myeloma

Drug resistance remains a critical problem for the treatment of multiple myeloma (MM), which can serve as a specific example for a highly prevalent unmet medical need across almost all cancer types. In MM, the therapeutic arsenal has expanded and diversified, yet we still lack in-depth molecular understanding of drug mechanisms of action and cellular pathways to therapeutic escape. For those reasons, preclinical models of drug resistance are developed and characterized using different approaches to gain insights into tumor biology and elucidate mechanisms of drug resistance. For MM, numerous drugs are used for treatment, including conventional chemotherapies (e.g., melphalan or L-phenylalanine nitrogen mustard), proteasome inhibitors (e.g., Bortezomib), and immunomodulators (e.g., Lenalidomide). These agents have diverse effects on the myeloma cells, and several mechanisms of drug resistance have been previously described. The disparity of these mechanisms and the complexity of these biological processes lead to the formation of complicated hypotheses that require omics approaches for efficient and effective analysis of model systems that can then be interpreted for patient benefit. Here, we describe the combination of metabolomics and proteomics to assess melphalan resistance in MM by examining three specific areas: drug metabolism, modulation of endogenous metabolites to assist in therapeutic escape, and changes in protein activity gauged by ATP probe uptake.

Genomes of trombidid mites reveal novel predicted allergens and laterally transferred genes associated with secondary metabolism

Background

Trombidid mites have a unique life cycle in which only the larval stage is ectoparasitic. In the superfamily Trombiculoidea ("chiggers"), the larvae feed preferentially on vertebrates, including humans. Species in the genus Leptotrombidium are vectors of a potentially fatal bacterial infection, scrub typhus, that affects 1 million people annually. Moreover, chiggers can cause pruritic dermatitis (trombiculiasis) in humans and domesticated animals. In the Trombidioidea (velvet mites), the larvae feed on other arthropods and are potential biological control agents for agricultural pests. Here, we present the first trombidid mites genomes, obtained both for a chigger, Leptotrombidium deliense, and for a velvet mite, Dinothrombium tinctorium.

Results

Sequencing was performed using Illumina technology. A 180 Mb draft assembly for D. tinctorium was generated from two paired-end and one mate-pair library using a single adult specimen. For L. deliense, a lower-coverage draft assembly (117 Mb) was obtained using pooled, engorged larvae with a single paired-end library. Remarkably, both genomes exhibited evidence of ancient lateral gene transfer from soil-derived bacteria or fungi. The transferred genes confer functions that are rare in animals, including terpene and carotenoid synthesis. Thirty-seven allergenic protein families were predicted in the L. deliense genome, of which nine were unique. Preliminary proteomic analyses identified several of these putative allergens in larvae.

Conclusions

Trombidid mite genomes appear to be more dynamic than those of other acariform mites. A priority for future research is to determine the biological function of terpene synthesis in this taxon and its potential for exploitation in disease control.

Current state of the art for enhancing urine biomarker discovery

INTRODUCTION

Urine is a highly desirable biospecimen for biomarker analysis because it can be collected recurrently by non-invasive techniques, in relatively large volumes. Urine contains cellular elements, biochemicals, and proteins derived from glomerular filtration of plasma, renal tubule excretion, and urogenital tract secretions that reflect, at a given time point, an individual's metabolic and pathophysiologic state.

AREAS COVERED

High-resolution mass spectrometry, coupled with state of the art fractionation systems are revealing the plethora of diagnostic/prognostic proteomic information existing within urinary exosomes, glycoproteins, and proteins. Affinity capture pre-processing techniques such as combinatorial peptide ligand libraries and biomarker harvesting hydrogel nanoparticles are enabling measurement/identification of previously undetectable urinary proteins. Expert commentary: Future challenges in the urinary proteomics field include a) defining either single or multiple, universally applicable data normalization methods for comparing results within and between individual patients/data sets, and b) defining expected urinary protein levels in healthy individuals.

The mzIdentML Data Standard Version 1.2, Supporting Advances in Proteome Informatics

The first stable version of the Proteomics Standards Initiative mzIdentML open data standard (version 1.1) was published in 2012-capturing the outputs of peptide and protein identification software. In the intervening years, the standard has become well-supported in both commercial and open software, as well as a submission and download format for public repositories. Here we report a new release of mzIdentML (version 1.2) that is required to keep pace with emerging practice in proteome informatics. New features have been added to support: (1) scores associated with localization of modifications on peptides; (2) statistics performed at the level of peptides; (3) identification of cross-linked peptides; and (4) support for proteogenomics approaches. In addition, there is now improved support for the encoding of de novo sequencing of peptides, spectral library searches, and protein inference. As a key point, the underlying XML schema has only undergone very minor modifications to simplify as much as possible the transition from version 1.1 to version 1.2 for implementers, but there have been several notable updates to the format specification, implementation guidelines, controlled vocabularies and validation software. mzIdentML 1.2 can be described as backwards compatible, in that reading software designed for mzIdentML 1.1 should function in most cases without adaptation. We anticipate that these developments will provide a continued stable base for software teams working to implement the standard. All the related documentation is accessible at http://www.psidev.info/mzidentml.

Proteogenomics: Key Driver for Clinical Discovery and Personalized Medicine

Proteogenomics is a multi-omics research field that has the aim to efficiently integrate genomics, transcriptomics and proteomics. With this approach it is possible to identify new patient-specific proteoforms that may have implications in disease development, specifically in cancer. Understanding the impact of a large number of mutations detected at the genomics level is needed to assess the effects at the proteome level. Proteogenomics data integration would help in identifying molecular changes that are persistent across multiple molecular layers and enable better interpretation of molecular mechanisms of disease, such as the causal relationship between single nucleotide polymorphisms (SNPs) and the expression of transcripts and translation of proteins compared to mainstream proteomics approaches. Identifying patient-specific protein forms and getting a better picture of molecular mechanisms of disease opens the avenue for precision and personalized medicine. Proteogenomics is, however, a challenging interdisciplinary science that requires the understanding of sample preparation, data acquisition and processing for genomics, transcriptomics and proteomics. This chapter aims to guide the reader through the technology and bioinformatics aspects of these multi-omics approaches, illustrated with proteogenomics applications having clinical or biological relevance.

Comprehensive proteomic characterization of stem cell-derived extracellular matrices

In the stem-cell niche, the extracellular matrix (ECM) serves as a structural support that additionally provides stem cells with signals that contribute to the regulation of stem-cell function, via reciprocal interactions between cells and components of the ECM. Recently, cell-derived ECMs have emerged as in vitro cell culture substrates to better recapitulate the native stem-cell microenvironment outside the body. Significant changes in cell number, morphology and function have been observed when mesenchymal stem cells (MSC) were cultured on ECM substrates as compared to standard tissue-culture polystyrene (TCPS). As select ECM components are known to regulate specific stem-cell functions, a robust characterization of cell-derived ECM proteomic composition is critical to better comprehend the role of the ECM in directing cellular processes. Here, we characterized and compared the protein composition of ECM produced in vitro by bone marrow-derived MSC, adipose-derived MSC and neonatal fibroblasts from different donors, employing quantitative proteomic methods. Each cell-derived ECM displayed a specific and unique matrisome signature, yet they all shared a common set of proteins. We evaluated the biological response of cells cultured on the different matrices and compared them to cells on standard TCPS. The matrices lead to differential survival and gene-expression profiles among the cell types and as compared to TCPS, indicating that the cell-derived ECMs influence each cell type in a different manner. This general approach to understanding the protein composition of different tissue-specific and cell-derived ECM will inform the rational design of defined systems and biomaterials that recapitulate critical ECM signals for stem-cell culture and tissue engineering.

A Golden Age for Working with Public Proteomics Data

Data sharing in mass spectrometry (MS)-based proteomics is becoming a common scientific practice, as is now common in the case of other, more mature ‘omics’ disciplines like genomics and transcriptomics. We want to highlight that this situation, unprecedented in the field, opens a plethora of opportunities for data scientists. First, we explain in some detail some of the work already achieved, such as systematic reanalysis efforts. We also explain existing applications of public proteomics data, such as proteogenomics and the creation of spectral libraries and spectral archives. Finally, we discuss the main existing challenges and mention the first attempts to combine public proteomics data with other types of omics data sets.

The field of proteomics has matured and diversified substantially over the past 10 years.

Proteomics data are increasingly shared through centralized, public repositories.

Standardization efforts have ensured that a large proportion of these public data can be read and processed by any interested researcher.

Because any proteomics data set is only partially understood, there is great opportunity for (orthogonal) reuse of public data.

While public proteomics data has so far remained outside ethics and privacy discussions, recent work indicates that there is an inherent risk.

Equipping Physiologists with an Informatics Tool Chest: Toward an Integerated Mitochondrial Phenome

Understanding the complex involvement of mitochondrial biology in disease development often requires the acquisition, analysis, and integration of large-scale molecular and phenotypic data. An increasing number of bioinformatics tools are currently employed to aid in mitochondrial investigations, most notably in predicting or corroborating the spatial and temporal dynamics of mitochondrial molecules, in retrieving structural data of mitochondrial components, and in aggregating as well as transforming mitochondrial centric biomedical knowledge. With the increasing prevalence of complex Big Data from omics experiments and clinical cohorts, informatics tools have become indispensable in our quest to understand mitochondrial physiology and pathology. Here we present an overview of the various informatics resources that are helping researchers explore this vital organelle and gain insights into its form, function, and dynamics.

Surface proteomics on nanoparticles: a step to simplify the rapid prototyping of nanoparticles

Engineered nanoparticles for biomedical applications require increasing effectiveness in targeting specific cells while preserving non-target cells' safety. We developed a surface proteomics method for a rapid and systematic analysis of the interphase between the nanoparticle protein corona and the targeted cells that could implement the rapid prototyping of nanomedicines. Native nanoparticles entering in a protein-rich liquid medium quickly form a macromolecular structure called protein corona. This protein structure defines the physical interaction between nanoparticles and target cells. The surface proteins compose the first line of interaction between this macromolecular structure and the cell surface of a target cell. We demonstrated that SUSTU (SUrface proteomics, Safety, Targeting, Uptake) provides a qualitative and quantitative analysis from the protein corona surface. With SUSTU, the spatial dynamics of the protein corona surface can be studied. Data from SUSTU would ascertain the nanoparticle functionalized groups exposed at a destiny that could circumvent preliminary in vitro experiments. Therefore, this method could implement in the analysis of nanoparticle targeting and uptake capability and could be integrated into a rapid prototyping strategy which is a major challenge in nanomaterials science. Data are available via ProteomeXchange with the identifier PXD004636.

Detection of Missing Proteins Using the PRIDE Database as a Source of Mass Spectrometry Evidence

The current catalogue of the human proteome is not yet complete, as experimental proteomics evidence is still elusive for a group of proteins known as the missing proteins. The Human Proteome Project (HPP) has been successfully using technology and bioinformatic resources to improve the characterization of such challenging proteins. In this manuscript, we propose a pipeline starting with the mining of the PRIDE database to select a group of data sets potentially enriched in missing proteins that are subsequently analyzed for protein identification with a method based on the statistical analysis of proteotypic peptides. Spermatozoa and the HEK293 cell line were found to be a promising source of missing proteins and clearly merit further attention in future studies. After the analysis of the selected samples, we found 342 PSMs, suggesting the presence of 97 missing proteins in human spermatozoa or the HEK293 cell line, while only 36 missing proteins were potentially detected in the retina, frontal cortex, aorta thoracica, or placenta. The functional analysis of the missing proteins detected confirmed their tissue specificity, and the validation of a selected set of peptides using targeted proteomics (SRM/MRM assays) further supports the utility of the proposed pipeline. As illustrative examples, DNAH3 and TEPP in spermatozoa, and UNCX and ATAD3C in HEK293 cells were some of the more robust and remarkable identifications in this study. We provide evidence indicating the relevance to carefully analyze the ever-increasing MS/MS data available from PRIDE and other repositories as sources for missing proteins detection in specific biological matrices as revealed for HEK293 cells.

Mass Spectrometry-Based Bottom-Up Proteomics: Sample Preparation, LC-MS/MS Analysis, and Database Query Strategies

Recent technological advances in mass spectrometry (MS) have made possible the investigation and quantification of complex mixtures of biomolecules. The exceptional sensitivity and resolving power of today's mass spectrometers allow for the detection of proteins and peptides at low femtomole quantities; however, these attributes demand high sample purity to minimize artifacts and achieve the highest degree of biomolecule identification. Tissue preparation for proteomic studies is particularly challenging due to their heterogeneity in cell type, presence of insoluble biomaterials, and wide diversity of biomolecules. The workflow described herein details sample preparation from tissues through protein extraction, proteolysis, and purification to generate peptides for MS analysis. Increased peptide resolution and a corresponding increase in protein identification is accomplished using polarity-based fractionation (C18 resin) at the peptide level. Additionally, approaches to instrument set up, including the use of nanoscale liquid chromatography and quadrupole Orbitrap MS, along with database searching, are described. © 2016 by John Wiley & Sons, Inc.

Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 2.1

Every data-rich community research effort requires a clear plan for ensuring the quality of the data interpretation and comparability of analyses. To address this need within the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), we have developed through broad consultation a set of mass spectrometry data interpretation guidelines that should be applied to all HPP data contributions. For submission of manuscripts reporting HPP protein identification results, the guidelines are presented as a one-page checklist containing 15 essential points followed by two pages of expanded description of each. Here we present an overview of the guidelines and provide an in-depth description of each of the 15 elements to facilitate understanding of the intentions and rationale behind the guidelines, for both authors and reviewers. Broadly, these guidelines provide specific directions regarding how HPP data are to be submitted to mass spectrometry data repositories, how error analysis should be presented, and how detection of novel proteins should be supported with additional confirmatory evidence. These guidelines, developed by the HPP community, are presented to the broader scientific community for further discussion.

Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets

Mass spectrometry (MS) is the main technology used in proteomics approaches. However, on average 75% of spectra analysed in an MS experiment remain unidentified. We propose to use spectrum clustering at a large-scale to shed a light on these unidentified spectra. PRoteomics IDEntifications database (PRIDE) Archive is one of the largest MS proteomics public data repositories worldwide. By clustering all tandem MS spectra publicly available in PRIDE Archive, coming from hundreds of datasets, we were able to consistently characterize three distinct groups of spectra: 1) incorrectly identified spectra, 2) spectra correctly identified but below the set scoring threshold, and 3) truly unidentified spectra. Using a multitude of complementary analysis approaches, we were able to identify less than 20% of the consistently unidentified spectra. The complete spectrum clustering results are available through the new version of the PRIDE Cluster resource (http://www.ebi.ac.uk/pride/cluster). This resource is intended, among other aims, to encourage and simplify further investigation into these unidentified spectra.

Stage-specific Proteomes from Onchocerca ochengi, Sister Species of the Human River Blindness Parasite, Uncover Adaptations to a Nodular Lifestyle

Despite 40 years of control efforts, onchocerciasis (river blindness) remains one of the most important neglected tropical diseases, with 17 million people affected. The etiological agent, Onchocerca volvulus, is a filarial nematode with a complex lifecycle involving several distinct stages in the definitive host and blackfly vector. The challenges of obtaining sufficient material have prevented high-throughput studies and the development of novel strategies for disease control and diagnosis. Here, we utilize the closest relative of O. volvulus, the bovine parasite Onchocerca ochengi, to compare stage-specific proteomes and host-parasite interactions within the secretome. We identified a total of 4260 unique O. ochengi proteins from adult males and females, infective larvae, intrauterine microfilariae, and fluid from intradermal nodules. In addition, 135 proteins were detected from the obligate Wolbachia symbiont. Observed protein families that were enriched in all whole body extracts relative to the complete search database included immunoglobulin-domain proteins, whereas redox and detoxification enzymes and proteins involved in intracellular transport displayed stage-specific overrepresentation. Unexpectedly, the larval stages exhibited enrichment for several mitochondrial-related protein families, including members of peptidase family M16 and proteins which mediate mitochondrial fission and fusion. Quantification of proteins across the lifecycle using the Hi-3 approach supported these qualitative analyses. In nodule fluid, we identified 94 O. ochengi secreted proteins, including homologs of transforming growth factor-β and a second member of a novel 6-ShK toxin domain family, which was originally described from a model filarial nematode (Litomosoides sigmodontis). Strikingly, the 498 bovine proteins identified in nodule fluid were strongly dominated by antimicrobial proteins, especially cathelicidins. This first high-throughput analysis of an Onchocerca spp. proteome across the lifecycle highlights its profound complexity and emphasizes the extremely close relationship between O. ochengi and O. volvulus The insights presented here provide new candidates for vaccine development, drug targeting and diagnostic biomarkers.

Protein profile changes in the frontotemporal lobes in human severe traumatic brain injury

Severe traumatic brain injury (sTBI) is a serious public health issue with high morbidity and mortality rates. Previous proteomic studies on sTBI have mainly focused on human cerebrospinal fluid and serum, as well as on brain protein changes in murine models. However, human proteomic data in sTBI brain is still scarce. We used proteomic and bioinformatic strategies to investigate variations in protein expression levels in human brains after sTBI, using samples from the Department of Neurosurgery, Affiliated Hospital of Hebei University (Hebei, China). Our proteomic data identified 4031 proteins, of which 160 proteins were overexpressed and 5 proteins were downregulated. Bioinformatics analysis showed significant changes in biological pathways including glial cell differentiation, complement activation and apolipoprotein catalysis in the statin pathway. Western blot verification of protein changes in a subset of the available tissue samples showed results that were consistent with the proteomic data. This study is one of the first to investigate the whole proteome of human sTBI brains, and provide a characteristic signature and overall landscape of the sTBI brain proteome.

2016 update of the PRIDE database and its related tools

The PRoteomics IDEntifications (PRIDE) database is one of the world-leading data repositories of mass spectrometry (MS)-based proteomics data. Since the beginning of 2014, PRIDE Archive (http://www.ebi.ac.uk/pride/archive/) is the new PRIDE archival system, replacing the original PRIDE database. Here we summarize the developments in PRIDE resources and related tools since the previous update manuscript in the Database Issue in 2013. PRIDE Archive constitutes a complete redevelopment of the original PRIDE, comprising a new storage backend, data submission system and web interface, among other components. PRIDE Archive supports the most-widely used PSI (Proteomics Standards Initiative) data standard formats (mzML and mzIdentML) and implements the data requirements and guidelines of the ProteomeXchange Consortium. The wide adoption of ProteomeXchange within the community has triggered an unprecedented increase in the number of submitted data sets (around 150 data sets per month). We outline some statistics on the current PRIDE Archive data contents. We also report on the status of the PRIDE related stand-alone tools: PRIDE Inspector, PRIDE Converter 2 and the ProteomeXchange submission tool. Finally, we will give a brief update on the resources under development 'PRIDE Cluster' and 'PRIDE Proteomes', which provide a complementary view and quality-scored information of the peptide and protein identification data available in PRIDE Archive.

Temporal lobe in human aging: A quantitative protein profiling study of samples from Chinese Human Brain Bank

The temporal lobe is a portion of the cerebral cortex with critical functionality. The age-related protein profile changes in the human temporal lobe have not been previously studied. This 4-plex tandem mass tag labeled proteomic study was performed on samples of temporal lobe from Chinese donors. Tissue samples were assigned to four age groups: Group A (the young, age: 34±13 years); Group B (the elderly, 62±5 years); Group C (the aged, 84±4 years) and Group D (the old, 95±1 years). Pooled samples from the different groups were subjected to proteomics and bioinformatics analysis to identify age-related changes in protein expression and associated pathways. We isolated 5072 proteins, and found that 67 proteins were downregulated and 109 proteins were upregulated in one or more groups during the aging process. Western blotting assays were performed to verify the proteomic results. Bioinformatic analysis identified proteins involved in neuronal degeneration, including proteins involved in neuronal firing, myelin sheath damage, and cell structure stability. We also observed the accumulation of extracellular matrix and lysosomal proteins which imply the occurrence of fibrosis and autophagy. Our results suggest a series of changes across a wide range of proteins in the human temporal lobe that may relate to aging and age-related neurodegenerative disorders.

Making proteomics data accessible and reusable: current state of proteomics databases and repositories

Compared to other data-intensive disciplines such as genomics, public deposition and storage of MS-based proteomics, data are still less developed due to, among other reasons, the inherent complexity of the data and the variety of data types and experimental workflows. In order to address this need, several public repositories for MS proteomics experiments have been developed, each with different purposes in mind. The most established resources are the Global Proteome Machine Database (GPMDB), PeptideAtlas, and the PRIDE database. Additionally, there are other useful (in many cases recently developed) resources such as ProteomicsDB, Mass Spectrometry Interactive Virtual Environment (MassIVE), Chorus, MaxQB, PeptideAtlas SRM Experiment Library (PASSEL), Model Organism Protein Expression Database (MOPED), and the Human Proteinpedia. In addition, the ProteomeXchange consortium has been recently developed to enable better integration of public repositories and the coordinated sharing of proteomics information, maximizing its benefit to the scientific community. Here, we will review each of the major proteomics resources independently and some tools that enable the integration, mining and reuse of the data. We will also discuss some of the major challenges and current pitfalls in the integration and sharing of the data.

The need to revisit published data: A concept and framework for complementary proteomics

Tandem proteomic strategies based on large-scale and high-resolution mass spectrometry have been widely applied in various biomedical studies. However, protein sequence databases and proteomic software are continuously updated. Proteomic studies should not be ended with a stable list of proteins. It is necessary and beneficial to regularly revise the results. Besides, the original proteomic studies usually focused on a limited aspect of protein information and valuable information may remain undiscovered in the raw spectra. Several studies have reported novel findings by reanalyzing previously published raw data. However, there are still no standard guidelines for comprehensive reanalysis. In the present study, we proposed the concept and draft framework for complementary proteomics, which are aimed to revise protein list or mine new discoveries by revisiting published data.

Introducing the PRIDE Archive RESTful web services

The PRIDE (PRoteomics IDEntifications) database is one of the world-leading public repositories of mass spectrometry (MS)-based proteomics data and it is a founding member of the ProteomeXchange Consortium of proteomics resources. In the original PRIDE database system, users could access data programmatically by accessing the web services provided by the PRIDE BioMart interface. New REST (REpresentational State Transfer) web services have been developed to serve the most popular functionality provided by BioMart (now discontinued due to data scalability issues) and address the data access requirements of the newly developed PRIDE Archive. Using the API (Application Programming Interface) it is now possible to programmatically query for and retrieve peptide and protein identifications, project and assay metadata and the originally submitted files. Searching and filtering is also possible by metadata information, such as sample details (e.g. species and tissues), instrumentation (mass spectrometer), keywords and other provided annotations. The PRIDE Archive web services were first made available in April 2014. The API has already been adopted by a few applications and standalone tools such as PeptideShaker, PRIDE Inspector, the Unipept web application and the Python-based BioServices package. This application is free and open to all users with no login requirement and can be accessed at http://www.ebi.ac.uk/pride/ws/archive/.

Enrichment or depletion? The impact of stool pretreatment on metaproteomic characterization of the human gut microbiota

To date, most metaproteomic studies of the gut microbiota employ stool sample pretreatment methods to enrich for microbial components. However, a specific investigation aimed at assessing if, how, and to what extent this may impact on the final taxonomic and functional results is still lacking. Here, stool replicates were either pretreated by differential centrifugation (DC) or not centrifuged. Protein extracts were then processed by filter-aided sample preparation, single-run LC, and high-resolution MS, and the metaproteomic data were compared by spectral counting. DC led to a higher number of identifications, a significantly richer microbial diversity, as well as to reduced information on the nonmicrobial components (host and food) when compared to not centrifuged. Nevertheless, dramatic differences in the relative abundance of several gut microbial taxa were also observed, including a significant change in the Firmicutes/Bacteroidetes ratio. Furthermore, some important microbial functional categories, including cell surface enzymes, membrane-associated proteins, extracellular proteins, and flagella, were significantly reduced after DC. In conclusion, this work underlines that a critical evaluation is needed when selecting the appropriate stool sample processing protocol in the context of a metaproteomic study, depending on the specific target to which the research is aimed. All MS data have been deposited in the ProteomeXchange with identifier PXD001573 (http://proteomecentral.proteomexchange.org/dataset/PXD001573).

Shared resources, shared costs--leveraging biocuration resources

The manual curation of the information in biomedical resources is an expensive task. This article argues the value of this approach in comparison with other apparently less costly options, such as automated annotation or text-mining, then discusses ways in which databases can make cost savings by sharing infrastructure and tool development. Sharing curation effort is a model already being adopted by several data resources. Approaches taken by two of these, the Gene Ontology annotation effort and the IntAct molecular interaction database, are reviewed in more detail. These models help to ensure long-term persistence of curated data and minimizes redundant development of resources by multiple disparate groups.

Database URL:http://www.ebi.ac.uk/intact and http://www.ebi.ac.uk/GOA/