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

VGLL3 operates via TEAD1, TEAD3 and TEAD4 to influence myogenesis in skeletal muscle

VGLL proteins are transcriptional co-factors that bind TEAD family transcription factors to regulate events ranging from wing development in fly, to muscle fibre composition and immune function in mice. Here, we characterise Vgll3 in skeletal muscle. We found that mouse Vgll3 was expressed at low levels in healthy muscle but that its levels increased during hypertrophy or regeneration; in humans, VGLL3 was highly expressed in tissues from patients with various muscle diseases, such as in dystrophic muscle and alveolar rhabdomyosarcoma. Interaction proteomics revealed that VGLL3 bound TEAD1, TEAD3 and TEAD4 in myoblasts and/or myotubes. However, there was no interaction with proteins from major regulatory systems such as the Hippo kinase cascade, unlike what is found for the TEAD co-factors YAP (encoded by YAP1) and TAZ (encoded by WWTR1). Vgll3 overexpression reduced the activity of the Hippo negative-feedback loop, affecting expression of muscle-regulating genes including Myf5, Pitx2 and Pitx3, and genes encoding certain Wnts and IGFBPs. VGLL3 mainly repressed gene expression, regulating similar genes to those regulated by YAP and TAZ. siRNA-mediated Vgll3 knockdown suppressed myoblast proliferation, whereas Vgll3 overexpression strongly promoted myogenic differentiation. However, skeletal muscle was overtly normal in Vgll3-null mice, presumably due to feedback signalling and/or redundancy. This work identifies VGLL3 as a transcriptional co-factor operating with the Hippo signal transduction network to control myogenesis.

Summary: VGLL3 interacts with TEAD transcription factors to direct expression of crucial muscle regulatory genes and contribute to the control of skeletal myogenesis.

Novel interconnections of HOG signaling revealed by combined use of two proteomic software packages

Modern quantitative mass spectrometry (MS)-based proteomics enables researchers to unravel signaling networks by monitoring proteome-wide cellular responses to different stimuli. MS-based analysis of signaling systems usually requires an integration of multiple quantitative MS experiments, which remains challenging, given that the overlap between these datasets is not necessarily comprehensive. In a previous study we analyzed the impact of the yeast mitogen-activated protein kinase (MAPK) Hog1 on the hyperosmotic stress-affected phosphorylome. Using a combination of a series of hyperosmotic stress and kinase inhibition experiments, we identified a broad range of direct and indirect substrates of the MAPK. Here we re-evaluate this extensive MS dataset and demonstrate that a combined analysis based on two software packages, MaxQuant and Proteome Discoverer, increases the coverage of Hog1-target proteins by 30%. Using protein-protein proximity assays we show that the majority of new targets gained by this analysis are indeed Hog1-interactors. Additionally, kinetic profiles indicate differential trends of Hog1-dependent versus Hog1-independent phosphorylation sites. Our findings highlight a previously unrecognized interconnection between Hog1 signaling and the RAM signaling network, as well as sphingolipid homeostasis.

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering

The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data-driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of 'omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data-driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system-scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.

Meeting the challenge of developing food crops with improved nutritional quality and food safety: leveraging proteomics and related omics techniques

Eliminating malnutrition remains an imminent priority in our efforts to achieve food security and providing adequate calories, proteins, and micronutrients to the growing world population. Malnutrition may be attributed to socio-economic factors (poverty and limited accessibility to nutritional food), dietary preferences, inherent nutrient profiles of traditional food crops, and to a combination of all such factors. Modern advancements in "omics" technology have made it possible to reliably predict, diagnose, and suggest ways to remedy the low protein content and bioavailability of key micronutrients in food crops. In this review, we briefly describe how proteomics techniques can potentially be used for improving the nutrient profile of major crops, through high throughput multiplexed assays. Food safety is another important issue where proteomics and related platforms can offer solution for absolute quantitation of food allergens and mycotoxins present in the plant-based food. The purpose of the present review is to discuss the proteomic-based strategies in food crops to meet the challenges of overcoming malnutrition throughout the world.

Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences

Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.

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.

Leveraging Multilayered "Omics" Data for Atopic Dermatitis: A Road Map to Precision Medicine

Atopic dermatitis (AD) is a complex multifactorial inflammatory skin disease that affects ~280 million people worldwide. About 85% of AD cases begin in childhood, a significant portion of which can persist into adulthood. Moreover, a typical progression of children with AD to food allergy, asthma or allergic rhinitis has been reported (“allergic march” or “atopic march”). AD comprises highly heterogeneous sub-phenotypes/endotypes resulting from complex interplay between intrinsic and extrinsic factors, such as environmental stimuli, and genetic factors regulating cutaneous functions (impaired barrier function, epidermal lipid, and protease abnormalities), immune functions and the microbiome. Though the roles of high-throughput “omics” integrations in defining endotypes are recognized, current analyses are primarily based on individual omics data and using binary clinical outcomes. Although individual omics analysis, such as genome-wide association studies (GWAS), can effectively map variants correlated with AD, the majority of the heritability and the functional relevance of discovered variants are not explained or known by the identified variants. The limited success of singular approaches underscores the need for holistic and integrated approaches to investigate complex phenotypes using trans-omics data integration strategies. Integrating omics layers (e.g., genome, epigenome, transcriptome, proteome, metabolome, lipidome, exposome, microbiome), which often have complementary and synergistic effects, might provide the opportunity to capture the flow of information underlying AD disease manifestation. Overlapping genes/candidates derived from multiple omics types include FLG, SPINK5, S100A8, and SERPINB3 in AD pathogenesis. Overlapping pathways include macrophage, endothelial cell and fibroblast activation pathways, in addition to well-known Th1/Th2 and NFkB activation pathways. Interestingly, there was more multi-omics overlap at the pathway level than gene level. Further analysis of multi-omics overlap at the tissue level showed that among 30 tissue types from the GTEx database, skin and esophagus were significantly enriched, indicating the biological interconnection between AD and food allergy. The present work explores multi-omics integration and provides new biological insights to better define the biological basis of AD etiology and confirm previously reported AD genes/pathways. In this context, we also discuss opportunities and challenges introduced by “big omics data” and their integration.

Proteomics based analysis of the nicotine catabolism in Paenarthrobacter nicotinovorans pAO1

Paenarthrobacter nicotinovorans is a nicotine-degrading microorganism that shows a promising biotechnological potential for the production of compounds with industrial and pharmaceutical importance. Its ability to use nicotine was linked to the presence of the catabolic megaplasmid pAO1. Although extensive work has been performed on the molecular biology of nicotine degradation in this bacterium, only half of the genes putatively involved have been experimentally linked to nicotine. In the current approach, we used nanoLC-MS/MS to identify a total of 801 proteins grouped in 511 non-redundant protein clusters when P. nicotinovorans was grown on citrate, nicotine and nicotine and citrate as the only carbon sources. The differences in protein abundance showed that deamination is preferred when citrate is present. Several putative genes from the pAO1 megaplasmid have been shown to have a nicotine-dependent expression, including a hypothetical polyketide cyclase. We hypothesize that the enzyme would hydrolyze the N1-C6 bond from the pyridine ring with the formation of α-keto- glutaramate. Two chromosomally-encoded proteins, a malate dehydrogenase, and a D-3-phosphoglycerate dehydrogenase were shown to be strongly up-regulated when nicotine was the sole carbon source and could be related to the production the α-keto-glutarate. The data have been deposited to the ProteomeXchange with identifier PXD008756.

Fast Open Modification Spectral Library Searching through Approximate Nearest Neighbor Indexing

Open modification searching (OMS) is a powerful search strategy that identifies peptides carrying any type of modification by allowing a modified spectrum to match against its unmodified variant by using a very wide precursor mass window. A drawback of this strategy, however, is that it leads to a large increase in search time. Although performing an open search can be done using existing spectral library search engines by simply setting a wide precursor mass window, none of these tools have been optimized for OMS, leading to excessive runtimes and suboptimal identification results. We present the ANN-SoLo tool for fast and accurate open spectral library searching. ANN-SoLo uses approximate nearest neighbor indexing to speed up OMS by selecting only a limited number of the most relevant library spectra to compare to an unknown query spectrum. This approach is combined with a cascade search strategy to maximize the number of identified unmodified and modified spectra while strictly controlling the false discovery rate as well as a shifted dot product score to sensitively match modified spectra to their unmodified counterparts. ANN-SoLo achieves state-of-the-art performance in terms of speed and the number of identifications. On a previously published human cell line data set, ANN-SoLo confidently identifies more spectra than SpectraST or MSFragger and achieves a speedup of an order of magnitude compared with SpectraST. ANN-SoLo is implemented in Python and C++. It is freely available under the Apache 2.0 license at https://github.com/bittremieux/ANN-SoLo .

Evaluation of Unconventional Protein Secretion by Saccharomyces cerevisiae and other Fungi

Development of proteome analysis of extracellular proteins has revealed that a wide variety of proteins, including fungal allergens are present outside the cell. These secreted allergens often do not contain known secretion signal sequences. Recent research progress shows that some fungal allergens are secreted by unconventional secretion pathways, including autophagy- and extracellular-vesicle-dependent pathways. However, secretion pathways remain unknown for the majority of extracellular proteins. This review summarizes recent data on unconventional protein secretion in Saccharomyces cerevisiae and other fungi. Particularly, methods for evaluating unconventional protein secretion are proposed for fungal species, including S. cerevisiae, a popular model organism for investigating protein secretion pathways.

Approaches to the discovery of non-invasive urinary biomarkers of prostate cancer

Prostate cancer (PCa) continues to be one of the most common cancers in men worldwide. Prostate specific antigen (PSA) measured in blood has been used for decades as an aid for physicians to detect the presence of prostate cancer. However, the PSA test has limited sensitivity and specificity, leading to unnecessary biopsies, overdiagnosis and overtreatment of patients. For these reasons, there is an urgent need for more accurate PCa biomarkers that can detect PCa with high sensitivity and specificity. Urine is a unique source of potential protein biomarkers that can be measured in a non-invasive way. This review comprehensively summarizes state of the art approaches used in the discovery and validation of urinary biomarkers for PCa. Numerous strategies are currently being used in the discovery of urinary biomarkers for prostate cancer including gel-based separation techniques, mass spectrometry, activity-based proteomic assays and software approaches. Antibody-based approaches remain preferred method for validation of candidate biomarkers with rapidly advancing multiplex immunoassays and MS-based targeted approaches. In the last decade, there has been a dramatic acceleration in the development of new techniques and approaches in the discovery of protein biomarkers for prostate cancer including computational, statistical and data mining methods. Many urinary-based protein biomarkers have been identified and have shown significant promise in initial studies. Examples of these potential biomarkers and the methods utilized in their discovery are also discussed in this review.

Computational Methods for Understanding Mass Spectrometry–Based Shotgun Proteomics Data

Computational proteomics is the data science concerned with the identification and quantification of proteins from high-throughput data and the biological interpretation of their concentration changes, posttranslational modifications, interactions, and subcellular localizations. Today, these data most often originate from mass spectrometry–based shotgun proteomics experiments. In this review, we survey computational methods for the analysis of such proteomics data, focusing on the explanation of the key concepts. Starting with mass spectrometric feature detection, we then cover methods for the identification of peptides. Subsequently, protein inference and the control of false discovery rates are highly important topics covered. We then discuss methods for the quantification of peptides and proteins. A section on downstream data analysis covers exploratory statistics, network analysis, machine learning, and multiomics data integration. Finally, we discuss current developments and provide an outlook on what the near future of computational proteomics might bear.

Application of targeted mass spectrometry in bottom-up proteomics for systems biology research

The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).

An update on sORFs.org: a repository of small ORFs identified by ribosome profiling

sORFs.org (http://www.sorfs.org) is a public repository of small open reading frames (sORFs) identified by ribosome profiling (RIBO-seq). This update elaborates on the major improvements implemented since its initial release. sORFs.org now additionally supports three more species (zebrafish, rat and Caenorhabditis elegans) and currently includes 78 RIBO-seq datasets, a vast increase compared to the three that were processed in the initial release. Therefore, a novel pipeline was constructed that also enables sORF detection in RIBO-seq datasets comprising solely elongating RIBO-seq data while previously, matching initiating RIBO-seq data was necessary to delineate the sORFs. Furthermore, a novel noise filtering algorithm was designed, able to distinguish sORFs with true ribosomal activity from simulated noise, consequently reducing the false positive identification rate. The inclusion of other species also led to the development of an inner BLAST pipeline, assessing sequence similarity between sORFs in the repository. Building on the proof of concept model in the initial release of sORFs.org, a full PRIDE-ReSpin pipeline was now released, reprocessing publicly available MS-based proteomics PRIDE datasets, reporting on true translation events. Next to reporting those identified peptides, sORFs.org allows visual inspection of the annotated spectra within the Lorikeet MS/MS viewer, thus enabling detailed manual inspection and interpretation.

Computational Analysis of the Chaperone Interaction Networks

We provide computational protocols to identify chaperone interacting proteins using a combination of both physical (protein-protein) and genetic (gene-gene or epistatic) interaction data derived from the published large-scale proteomic and genomic studies for the budding yeast Saccharomyces cerevisiae. Using these datasets, we discuss bioinformatic analyses that can be employed to build comprehensive high-fidelity chaperone interaction networks. Given that many proteins typically function as complexes in the cell, we highlight various step-wise approaches for combining both the genetic and physical interaction datasets to decipher intra- and inter-connections for distinct chaperone- and non-chaperone-containing complexes in the network. Together, these informatics procedures will aid in identifying protein complexes with distinctive functional specializations in the cell that yield a very broad and diverse set of interactions. The described procedures can also be leveraged to datasets from other eukaryotes, including humans.

Significance estimation for large scale metabolomics annotations by spectral matching

The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate (FDR) for 70 public metabolomics data sets. We show that the spectral matching settings need to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from -92 up to +5705%) when compared with a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to assess the scoring criteria for large scale analysis of mass spectrometry based metabolomics data that has been essential in the advancement of proteomics, transcriptomics, and genomics science.

Intersection of Proteomics and Genomics to "Solve the Unsolved" in Rare Disorders such as Neurodegenerative and Neuromuscular Diseases

Despite recent rapid advances in sequencing technologies, a significant proportion of patients with rare genetic disorders do not receive a genetic diagnosis after exhaustive testing, and even fewer have a potential therapeutic target identified. Taking rare neuromuscular and neurodegenerative disorders as a paradigm that can be extended to other rare Mendelian disorders, this viewpoint explores the opportunities that are brought about by the integration of genomics and proteomics, as well as the limitations and remaining challenges of this newly emerging field of proteogenomics. The relevance of combining proteomic findings with genetic results for diagnosis and gene discovery is illustrated, highlighting the insights the combined analysis provides into the underlying biology and aetiology as well as the limitations of the experimental techniques. A final discussion focuses on the importance of mechanisms to enable the sharing, reuse, and analysis of source experimental data and describes some of the international initiatives that are making progress in this area.

OLS Client and OLS Dialog: Open Source Tools to Annotate Public Omics Datasets

The availability of user-friendly software to annotate biological datasets and experimental details is becoming essential in data management practices, both in local storage systems and in public databases. The Ontology Lookup Service (OLS, http://www.ebi.ac.uk/ols) is a popular centralized service to query, browse and navigate biomedical ontologies and controlled vocabularies. Recently, the OLS framework has been completely redeveloped (version 3.0), including enhancements in the data model, like the added support for Web Ontology Language based ontologies, among many other improvements. However, the new OLS is not backwards compatible and new software tools are needed to enable access to this widely used framework now that the previous version is no longer available. We here present the OLS Client as a free, open-source Java library to retrieve information from the new version of the OLS. It enables rapid tool creation by providing a robust, pluggable programming interface and common data model to programmatically access the OLS. The library has already been integrated and is routinely used by several bioinformatics resources and related data annotation tools. Secondly, we also introduce an updated version of the OLS Dialog (version 2.0), a Java graphical user interface that can be easily plugged into Java desktop applications to access the OLS. The software and related documentation are freely available at https://github.com/PRIDE-Utilities/ols-client and https://github.com/PRIDE-Toolsuite/ols-dialog.

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.

Rapid and Sensitive Detection of Breast Cancer Cells in Patient Blood with Nuclease-Activated Probe Technology

A challenge for circulating tumor cell (CTC)-based diagnostics is the development of simple and inexpensive methods that reliably detect the diverse cells that make up CTCs. CTC-derived nucleases are one category of proteins that could be exploited to meet this challenge. Advantages of nucleases as CTC biomarkers include: (1) their elevated expression in many cancer cells, including cells implicated in metastasis that have undergone epithelial-to-mesenchymal transition; and (2) their enzymatic activity, which can be exploited for signal amplification in detection methods. Here, we describe a diagnostic assay based on quenched fluorescent nucleic acid probes that detect breast cancer CTCs via their nuclease activity. This assay exhibited robust performance in distinguishing breast cancer patients from healthy controls, and it is rapid, inexpensive, and easy to implement in most clinical labs. Given its broad applicability, this technology has the potential to have a substantive impact on the diagnosis and treatment of many cancers.

MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics

There is a need to better understand and handle the 'dark matter' of proteomics-the vast diversity of post-translational and chemical modifications that are unaccounted in a typical mass spectrometry-based analysis and thus remain unidentified. We present a fragment-ion indexing method, and its implementation in peptide identification tool MSFragger, that enables a more than 100-fold improvement in speed over most existing proteome database search tools. Using several large proteomic data sets, we demonstrate how MSFragger empowers the open database search concept for comprehensive identification of peptides and all their modified forms, uncovering dramatic differences in modification rates across experimental samples and conditions. We further illustrate its utility using protein-RNA cross-linked peptide data and using affinity purification experiments where we observe, on average, a 300% increase in the number of identified spectra for enriched proteins. We also discuss the benefits of open searching for improved false discovery rate estimation in proteomics.

Experimental design and data-analysis in label-free quantitative LC/MS proteomics: A tutorial with MSqRob

Label-free shotgun proteomics is routinely used to assess proteomes. However, extracting relevant information from the massive amounts of generated data remains difficult. This tutorial provides a strong foundation on analysis of quantitative proteomics data. We provide key statistical concepts that help researchers to design proteomics experiments and we showcase how to analyze quantitative proteomics data using our recent free and open-source R package MSqRob, which was developed to implement the peptide-level robust ridge regression method for relative protein quantification described by Goeminne et al. MSqRob can handle virtually any experimental proteomics design and outputs proteins ordered by statistical significance. Moreover, its graphical user interface and interactive diagnostic plots provide easy inspection and also detection of anomalies in the data and flaws in the data analysis, allowing deeper assessment of the validity of results and a critical review of the experimental design. Our tutorial discusses interactive preprocessing, data analysis and visualization of label-free MS-based quantitative proteomics experiments with simple and more complex designs. We provide well-documented scripts to run analyses in bash mode on GitHub, enabling the integration of MSqRob in automated pipelines on cluster environments (https://github.com/statOmics/MSqRob).

SIGNIFICANCE

The concepts outlined in this tutorial aid in designing better experiments and analyzing the resulting data more appropriately. The two case studies using the MSqRob graphical user interface will contribute to a wider adaptation of advanced peptide-based models, resulting in higher quality data analysis workflows and more reproducible results in the proteomics community. We also provide well-documented scripts for experienced users that aim at automating MSqRob on cluster environments.

Proteomic Approach for Extracting Cytoplasmic Proteins from Streptococcus sanguinis using Mass Spectrometry

Streptococcus sanguinis is a commensal and early colonizer of oral cavity as well as an opportunistic pathogen of infectious endocarditis. Extracting the soluble proteome of this bacterium provides deep insights about the physiological dynamic changes under different growth and stress conditions, thus defining "proteomic signatures" as targets for therapeutic intervention. In this protocol, we describe an experimentally verified approach to extract maximal cytoplasmic proteins from Streptococcus sanguinis SK36 strain. A combination of procedures was adopted that broke the thick cell wall barrier and minimized denaturation of the intracellular proteome, using optimized buffers and a sonication step. Extracted proteome was quantitated using Pierce BCA Protein Quantitation assay and protein bands were macroscopically assessed by Coomassie Blue staining. Finally, a high resolution detection of the extracted proteins was conducted through Synapt G2Si mass spectrometer, followed by label-free relative quantification via Progenesis QI. In conclusion, this pipeline for proteomic extraction and analysis of soluble proteins provides a fundamental tool in deciphering the biological complexity of Streptococcus sanguinis.

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.

The Promise of Proteomics in the Study of Oncogenic Viruses

Oncogenic viruses are responsible for about 15% human cancers. This article explores the promise and challenges of viral proteomics in the study of the oncogenic human DNA viruses, HPV, McPyV, EBV and KSHV. These viruses have coevolved with their hosts and cause persistent infections. Each virus encodes oncoproteins that manipulate key cellular pathways to promote viral replication and evade the host immune response. Viral proteomics can identify cellular pathways perturbed by viral infection, identify cellular proteins that are crucial for viral persistence and oncogenesis, and identify important diagnostic and therapeutic targets.

The diverse and expanding role of mass spectrometry in structural and molecular biology

The emergence of proteomics has led to major technological advances in mass spectrometry (MS). These advancements not only benefitted MS-based high-throughput proteomics but also increased the impact of mass spectrometry on the field of structural and molecular biology. Here, we review how state-of-the-art MS methods, including native MS, top-down protein sequencing, cross-linking-MS, and hydrogen-deuterium exchange-MS, nowadays enable the characterization of biomolecular structures, functions, and interactions. In particular, we focus on the role of mass spectrometry in integrated structural and molecular biology investigations of biological macromolecular complexes and cellular machineries, highlighting work on CRISPR-Cas systems and eukaryotic transcription complexes.

Fuzzy-FishNET: a highly reproducible protein complex-based approach for feature selection in comparative proteomics

Background

The hypergeometric enrichment analysis approach typically fares poorly in feature-selection stability due to its upstream reliance on the t-test to generate differential protein lists before testing for enrichment on a protein complex, subnetwork or gene group.

Methods

Swapping the t-test in favour of a fuzzy rank-based weight system similar to that used in network-based methods like Quantitative Proteomics Signature Profiling (QPSP), Fuzzy SubNets (FSNET) and paired FSNET (PFSNET) produces dramatic improvements.

Results

This approach, Fuzzy-FishNET, exhibits high precision-recall over three sets of simulated data (with simulated protein complexes) while excelling in feature-selection reproducibility on real data (based on evaluation with real protein complexes). Overlap comparisons with PFSNET shows Fuzzy-FishNET selects the most significant complexes, which are also strongly class-discriminative. Cross-validation further demonstrates Fuzzy-FishNET selects class-relevant protein complexes.

Conclusions

Based on evaluation with simulated and real datasets, Fuzzy-FishNET is a significant upgrade of the traditional hypergeometric enrichment approach and a powerful new entrant amongst comparative proteomics analysis methods.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-016-0228-z) contains supplementary material, which is available to authorized users.

The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition

The ProteomeXchange (PX) Consortium of proteomics resources (http://www.proteomexchange.org) was formally started in 2011 to standardize data submission and dissemination of mass spectrometry proteomics data worldwide. We give an overview of the current consortium activities and describe the advances of the past few years. Augmenting the PX founding members (PRIDE and PeptideAtlas, including the PASSEL resource), two new members have joined the consortium: MassIVE and jPOST. ProteomeCentral remains as the common data access portal, providing the ability to search for data sets in all participating PX resources, now with enhanced data visualization components.

We describe the updated submission guidelines, now expanded to include four members instead of two. As demonstrated by data submission statistics, PX is supporting a change in culture of the proteomics field: public data sharing is now an accepted standard, supported by requirements for journal submissions resulting in public data release becoming the norm. More than 4500 data sets have been submitted to the various PX resources since 2012. Human is the most represented species with approximately half of the data sets, followed by some of the main model organisms and a growing list of more than 900 diverse species. Data reprocessing activities are becoming more prominent, with both MassIVE and PeptideAtlas releasing the results of reprocessed data sets. Finally, we outline the upcoming advances for ProteomeXchange.

Proteomics: Technologies and Their Applications

Proteomics involves the applications of technologies for the identification and quantification of overall proteins present content of a cell, tissue or an organism. It supplements the other "omics" technologies such as genomic and transcriptomics to expound the identity of proteins of an organism, and to cognize the structure and functions of a particular protein. Proteomics-based technologies are utilized in various capacities for different research settings such as detection of various diagnostic markers, candidates for vaccine production, understanding pathogenicity mechanisms, alteration of expression patterns in response to different signals and interpretation of functional protein pathways in different diseases. Proteomics is practically intricate because it includes the analysis and categorization of overall protein signatures of a genome. Mass spectrometry with LC-MS-MS and MALDI-TOF/TOF being widely used equipment is the central among current proteomics. However, utilization of proteomics facilities including the software for equipment, databases and the requirement of skilled personnel substantially increase the costs, therefore limit their wider use especially in the developing world. Furthermore, the proteome is highly dynamic because of complex regulatory systems that control the expression levels of proteins. This review efforts to describe the various proteomics approaches, the recent developments and their application in research and analysis.

Undiscovered Physiology of Transcript and Protein Networks

The past two decades have witnessed a rapid evolution in our ability to measure RNA and protein from biological systems. As a result, new principles have arisen regarding how information is processed in cells, how decisions are made, and the role of networks in biology. This essay examines this technological evolution, reviewing (and critiquing) the conceptual framework that has emerged to explain how RNA and protein networks control cellular function. We identify how future investigations into transcriptomes, proteomes, and other cellular networks will enable development of more robust, quantitative models of cellular behavior whilst also providing new avenues to use knowledge of biological networks to improve human health. © 2016 American Physiological Society. Compr Physiol 6:1851-1872, 2016.

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.

Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge

Hybrid analytical instrumentation constructed around mass spectrometry (MS) is becoming the preferred technique for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements used to infer systems-level information. In this article, we describe multidimensional MS configurations as technologies that are big data drivers and review some new and emerging strategies for mining information from large-scale datasets. We discuss the information content that can be obtained from individual dimensions, as well as the unique information that can be derived by comparing different levels of data. Finally, we summarize some emerging data visualization strategies that seek to make highly dimensional datasets both accessible and comprehensible.

Unveiling alterative splice diversity from human oligodendrocyte proteome data

Oligodendrocytes produce and maintain the myelin sheath of axons in the central nervous system. Because misassembled myelin sheaths have been associated with brain disorders such as multiple sclerosis and schizophrenia, recent advances have been made towards the description of the oligodendrocyte proteome. The identification of splice variants represented in the proteome is as important as determining the level of oligodendrocyte-associated proteins. Here, we used an oligodendrocyte proteome dataset deposited in ProteomeXchange to search against a customized protein sequence file containing computationally predicted splice variants. Our approach resulted in the identification of 39 splice variants, including one variant from the GTPase KRAS gene and another from the human glutaminase gene family. We also detected the mRNA expression of five selected splice variants and demonstrated that a fraction of these have their canonical proteins participating in direct protein-protein interactions. In conclusion, we believe our findings contribute to the molecular characterization of oligodendrocytes and may encourage other research groups working with central nervous system disorders to investigate the biological significance of these splice variants. The splice variants identified in this study may encode proteins that could be targeted in novel treatment strategies and diagnostic methods.

SIGNIFICANCE

Several disorders of the central nervous system (CNS) are associated with misassembled myelin sheaths, which are produced and maintained by oligodendrocytes (OL). Recently, the OL proteome has been explored to identify key proteins and molecular functions associated with CNS disorders. We developed an innovative approach to select, with a higher level of confidence, a relevant list of splice variants from a proteome dataset and detected the mRNA expression of five selected variants: EEF1D, KRAS, MFF, SDR39U1, and SUGT1. We also described splice variants extracted from OL proteome data. Among the splice variants identified, some are from genes previously linked to CNS and related disorders. Our findings may contribute to oligodendrocyte characterization and encourage other research groups to investigate the biological role of splice variants and to improve current treatments and diagnostic methods for CNS disorders.

Genic insights from integrated human proteomics in GeneCards

GeneCards is a one-stop shop for searchable human gene annotations (http://www.genecards.org/). Data are automatically mined from ∼120 sources and presented in an integrated web card for every human gene. We report the application of recent advances in proteomics to enhance gene annotation and classification in GeneCards. First, we constructed the Human Integrated Protein Expression Database (HIPED), a unified database of protein abundance in human tissues, based on the publically available mass spectrometry (MS)-based proteomics sources ProteomicsDB, Multi-Omics Profiling Expression Database, Protein Abundance Across Organisms and The MaxQuant DataBase. The integrated database, residing within GeneCards, compares favourably with its individual sources, covering nearly 90% of human protein-coding genes. For gene annotation and comparisons, we first defined a protein expression vector for each gene, based on normalized abundances in 69 normal human tissues. This vector is portrayed in the GeneCards expression section as a bar graph, allowing visual inspection and comparison. These data are juxtaposed with transcriptome bar graphs. Using the protein expression vectors, we further defined a pairwise metric that helps assess expression-based pairwise proximity. This new metric for finding functional partners complements eight others, including sharing of pathways, gene ontology (GO) terms and domains, implemented in the GeneCards Suite. In parallel, we calculated proteome-based differential expression, highlighting a subset of tissues that overexpress a gene and subserving gene classification. This textual annotation allows users of VarElect, the suite’s next-generation phenotyper, to more effectively discover causative disease variants. Finally, we define the protein–RNA expression ratio and correlation as yet another attribute of every gene in each tissue, adding further annotative information. The results constitute a significant enhancement of several GeneCards sections and help promote and organize the genome-wide structural and functional knowledge of the human proteome.

Database URL: http://www.genecards.org/

Urine Proteome Biomarkers in Kidney Diseases. I. Limits, Perspectives, and First Focus on Normal Urine

Urine proteome is a potential source of information in renal diseases, and it is considered a natural area of investigation for biomarkers. Technology developments have markedly increased the power analysis on urinary proteins, and it is time to confront methodologies and results of major studies on the topics. This is a first part of a series of reviews that will focus on the urine proteome as a site for detecting biomarkers of renal diseases; the theme of the first review concerns methodological aspects applied to normal urine. Main issues are techniques for urine pretreatment, separation of exosomes, use of combinatorial peptide ligand libraries, mass spectrometry approaches, and analysis of data sets. Available studies show important differences, suggesting a major confounding effect of the technologies utilized for analysis. The objective is to obtain consensus about which approaches should be utilized for studying urine proteome in renal diseases.

g2pDB: A Database Mapping Protein Post-Translational Modifications to Genomic Coordinates

Large scale proteomics have made it possible to broadly screen samples for the presence of many types of post-translational modifications, such as phosphorylation, acetylation, and ubiquitination. This type of data has allowed the localization of these modifications to either a specific site on a proteolytically generated peptide or to within a small domain on the peptide. The resulting modification acceptor sites can then be mapped onto the appropriate protein sequences and the information archived. This paper describes the usage of a very large archive of experimental observations of human post-translational modifications to create a map of the most reproducible modification observations onto the complete set of human protein sequences. This set of modification acceptor sites was then directly translated into the genomic coordinates for the codons for the residues at those sites. We constructed the database g2pDB using this protein-to-codon site mapping information. The information in g2pDB has been made available through a RESTful-style API, allowing researchers to determine which specific protein modifications would be perturbed by a set of observed nucleotide variants determined by high throughput DNA or RNA sequencing.

A year (2014-2015) of plants in Proteomics journal. Progress in wet and dry methodologies, moving from protein catalogs, and the view of classic plant biochemists

The present review is an update of the previous one published in Proteomics 2015 Reviews special issue [Jorrin-Novo, J. V. et al., Proteomics 2015, 15, 1089-1112] covering the July 2014-2015 period. It has been written on the bases of the publications that appeared in Proteomics journal during that period and the most relevant ones that have been published in other high-impact journals. Methodological advances and the contribution of the field to the knowledge of plant biology processes and its translation to agroforestry and environmental sectors will be discussed. This review has been organized in four blocks, with a starting general introduction (literature survey) followed by sections focusing on the methodology (in vitro, in vivo, wet, and dry), proteomics integration with other approaches (systems biology and proteogenomics), biological information, and knowledge (cell communication, receptors, and signaling), ending with a brief mention of some other biological and translational topics to which proteomics has made some contribution.

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.

Exploring the potential of public proteomics data

In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS-based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re-)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.