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

Carotenoids in Health as Studied by Omics-Related Endpoints

Carotenoids have been associated with risk reduction for several chronic diseases, including the association of their dietary intake/circulating levels with reduced incidence of obesity, type 2 diabetes, certain types of cancer, and even lower total mortality. In addition to some carotenoids constituting vitamin A precursors, they are implicated in potential antioxidant effects and pathways related to inflammation and oxidative stress, including transcription factors such as nuclear factor κB and nuclear factor erythroid 2-related factor 2. Carotenoids and metabolites may also interact with nuclear receptors, mainly retinoic acid receptor/retinoid X receptor and peroxisome proliferator-activated receptors, which play a role in the immune system and cellular differentiation. Therefore, a large number of downstream targets are likely influenced by carotenoids, including but not limited to genes and proteins implicated in oxidative stress and inflammation, antioxidation, and cellular differentiation processes. Furthermore, recent studies also propose an association between carotenoid intake and gut microbiota. While all these endpoints could be individually assessed, a more complete/integrative way to determine a multitude of health-related aspects of carotenoids includes (multi)omics-related techniques, especially transcriptomics, proteomics, lipidomics, and metabolomics, as well as metagenomics, measured in a variety of biospecimens including plasma, urine, stool, white blood cells, or other tissue cellular extracts. In this review, we highlight the use of omics technologies to assess health-related effects of carotenoids in mammalian organisms and models.

Challenges and opportunities in sharing microbiome data and analyses

Microbiome data, metadata and analytical workflows have become 'big' in terms of volume and complexity. Although the infrastructure and technologies to share data have been established, the interdisciplinary and multi-omic nature of the field can make resources difficult to identify and use. Following best practices for data deposition requires substantial effort, with sometimes little obvious reward. Gaps remain where microbiome-specific resources for data sharing or reproducibility do not yet exist. We outline available best practices, challenges to their adoption and opportunities in data sharing in microbiome research. We showcase examples of best practices and advocate for their enforcement and incentivization for data sharing. This includes recognition of data curation and sharing endeavours by individuals, institutions, journals and funders. Opportunities for progress include enabling microbiome-specific databases to incorporate future methods for data analysis, integration and reuse.

Spectroscape enables real-time query and visualization of a spectral archive in proteomics

In proteomics, spectral archives organize the enormous amounts of publicly available peptide tandem mass spectra by similarity, offering opportunities for error correction and novel discoveries. Here we adapt an indexing algorithm developed by Facebook for organizing online multimedia resources to tandem mass spectra and achieve practically instantaneous retrieval and clustering of approximate nearest neighbors in a large spectral archive. An interactive web-based graphical user interface enables the user to view a query spectrum in its clustered neighborhood, which facilitates contextual validation of peptide identifications and exploration of the dark proteome.

Mass spectrometry-based retina proteomics

A subfield of neuroproteomics, retina proteomics has experienced a transformative growth since its inception due to methodological advances in enabling chemical, biochemical, and molecular biology techniques. This review focuses on mass spectrometry's contributions to facilitate mammalian and avian retina proteomics to catalog and quantify retinal protein expressions, determine their posttranslational modifications, as well as its applications to study the proteome of the retina in the context of biology, health and diseases, and therapy developments.

Bioinformatics Tools and Knowledgebases to Assist Generating Targeted Assays for Plasma Proteomics

In targeted proteomics experiments, selecting the appropriate proteotypic peptides as surrogate for the target protein is a crucial pre-acquisition step. This step is largely a bioinformatics exercise that involves integrating information on the peptides and proteins and using various software tools and knowledgebases. We present here a few resources that automate and simplify the selection process to a great degree. These tools and knowledgebases were developed primarily to streamline targeted proteomics assay development and include PeptidePicker, PeptidePickerDB, MRMAssayDB, MouseQuaPro, and PeptideTracker. We have used these tools to develop and document thousands of targeted proteomics assays, many of them for plasma proteins with focus on human and mouse. An important aspect in all these resources is the integrative approach on which they are based. Using these tools in the first steps of designing a singleplexed or multiplexed targeted proteomic experiment can reduce the necessary experimental steps tremendously. All the tools and knowledgebases we describe here are Web-based and freely accessible so scientists can query the information conveniently from the browser. This chapter provides an overview of these software tools and knowledgebases, their content, and how to use them for targeted plasma proteomics. We further demonstrate how to use them with the results of the HUPO Human Plasma Proteome Project to produce a new database of 3.8 k targeted assays for known human plasma proteins. Upon experimental validation, these assays should help in the further quantitative characterizing of the plasma proteome.

Variability analysis of LC-MS experimental factors and their impact on machine learning

BACKGROUND

Machine learning (ML) technologies, especially deep learning (DL), have gained increasing attention in predictive mass spectrometry (MS) for enhancing the data-processing pipeline from raw data analysis to end-user predictions and rescoring. ML models need large-scale datasets for training and repurposing, which can be obtained from a range of public data repositories. However, applying ML to public MS datasets on larger scales is challenging, as they vary widely in terms of data acquisition methods, biological systems, and experimental designs.

RESULTS

We aim to facilitate ML efforts in MS data by conducting a systematic analysis of the potential sources of variability in public MS repositories. We also examine how these factors affect ML performance and perform a comprehensive transfer learning to evaluate the benefits of current best practice methods in the field for transfer learning.

CONCLUSIONS

Our findings show significantly higher levels of homogeneity within a project than between projects, which indicates that it is important to construct datasets most closely resembling future test cases, as transferability is severely limited for unseen datasets. We also found that transfer learning, although it did increase model performance, did not increase model performance compared to a non-pretrained model.

Discovery and characterization of a new class of O-linking oligosaccharyltransferases from the Moraxellaceae family

Bacterial protein glycosylation is commonly mediated by oligosaccharyltransferases (OTases) that transfer oligosaccharides en bloc from preassembled lipid-linked precursors to acceptor proteins. Natively, O-linking OTases usually transfer a single repeat unit of the O-antigen or capsular polysaccharide to the side chains of serine or threonine on acceptor proteins. Three major families of bacterial O-linking OTases have been described: PglL, PglS, and TfpO. TfpO is limited to transferring short oligosaccharides both in its native context and when heterologously expressed in glycoengineered Escherichia coli. On the other hand, PglL and PglS can transfer long-chain polysaccharides when expressed in glycoengineered E. coli. Herein, we describe the discovery and functional characterization of a novel family of bacterial O-linking OTases termed TfpM from Moraxellaceae bacteria. TfpM proteins are similar in size and sequence to TfpO enzymes but can transfer long-chain polysaccharides to acceptor proteins. Phylogenetic analyses demonstrate that TfpM proteins cluster in distinct clades from known bacterial OTases. Using a representative TfpM enzyme from Moraxella osloensis, we determined that TfpM glycosylates a C-terminal threonine of its cognate pilin-like protein and identified the minimal sequon required for glycosylation. We further demonstrated that TfpM has broad substrate tolerance and can transfer diverse glycans including those with glucose, galactose, or 2-N-acetyl sugars at the reducing end. Last, we find that a TfpM-derived bioconjugate is immunogenic and elicits serotype-specific polysaccharide IgG responses in mice. The glycan substrate promiscuity of TfpM and identification of the minimal TfpM sequon renders this enzyme a valuable additional tool for expanding the glycoengineering toolbox.

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.

Identification of partial denitrification granulation enhanced by low C/N ratio in the aspect of metabolomics and quorum sensing

Partial denitrification granular sludge (PDGS) and denitrification granular sludge (DGS) play an important role in nitrogen removal from wastewater. However, the inherent cause of aggregation capacity related to the ratio of COD to nitrogen (COD/N) is still unclear. In this study, metabolomics analysis was combined with microbiological analyses, granular performance and extracellular polymeric substances (EPS) structure to explore the granulation mechanism at different influent COD/N ratios. The results showed that the higher COD/N ratio selectively enhanced the gluconeogenesis pathway, purine and pyrimidine metabolism pathway, resulting in more extracellular polysaccharide (PS) excretion and floc sludge. The absence of carbon source weakened tricarboxylic acid cycle (TCA) reaction, resulting in NAD⁺ and ADP decrease, nitrite accumulation and change of microbial community structure. The amino acids biosynthesis pathway was enhanced under low COD/N ratio, which promoted the hydrophobicity of EPS. PDGS had stronger Acyl-homoserine lactones (AHLs)-based quorum sensing (QS) than DGS during the operational period. CO8-HSL, C8-HSL and C6-HSL, as the main form of AHLs, played a dominating role in DGS and PDGS. Batch tests illustrated that adding AHLs obviously improved the synthesis of the amino acids, threonine (Thr), tryptophan (Trp), methionine (Met) and glycine (Gly). Dosing AHLs regulated PS synthesis only at a high COD/N ratio. The glucose-6P, glycerate-3p and UDP-Glc were up-regulated only in DSG, which increased the hydrophilic groups in EPS. The results not only provided the new insights into the metabolism of denitrifying granular sludge, but also indicated the application potential of the technologies regarding start-up and operation of granule sludge.

A proteomics sample metadata representation for multiomics integration and big data analysis

The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.

Targeted liquid chromatography-tandem mass spectrometry analysis of proteins: Basic principles, applications, and perspectives

Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is now the main analytical method for the identification and quantification of peptides and proteins in biological samples. In modern research, identification of biomarkers and their quantitative comparison between samples are becoming increasingly important for discovery, validation, and monitoring. Such data can be obtained following specific signals after fragmentation of peptides using multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) methods, with high specificity, accuracy, and reproducibility. In addition, these methods allow measurement of the amount of post-translationally modified forms and isoforms of proteins. This review article describes the basic principles of MRM assays, guidelines for sample preparation, recent advanced MRM-based strategies, applications and illustrative perspectives of MRM/PRM methods in clinical research and molecular biology.

Methods for Proteomic Analyses of Mycobacteria

The use of proteomic technologies to characterize and study the proteome of mycobacteria has provided important information in terms of function, diversity, protein-protein interactions, and host-pathogen interactions in Mycobacterium spp. There are many different mass spectrometry methodologies that can be applied to proteomics studies of mycobacteria and microorganisms in general. Sample processing and appropriate study design are critical to generating high-quality data regardless of the mass spectrometry method applied. Appropriate study design relies on statistical rigor and data curation using bioinformatics approaches that are widely applicable regardless of the organism or system studied. Sample processing, on the other hand, is often a niched process specific to the physiology of the organism or system under investigation. Therefore, in this chapter, we will provide protocols for processing mycobacterial protein samples for the specific application of Top-down and Bottom-up proteomic analyses.

Proteogenomics Reveals Orthologous Alternatively Spliced Proteoforms in the Same Human and Mouse Brain Regions with Differential Abundance in an Alzheimer's Disease Mouse Model

Alternative splicing (AS) may increase the number of proteoforms produced by a gene. Alzheimer's disease (AD) is a neurodegenerative disease with well-characterized AS proteoforms. In this study, we used a proteogenomics strategy to build a customized protein sequence database and identify orthologous AS proteoforms between humans and mice on publicly available shotgun proteomics (MS/MS) data of the corpus callosum (CC) and olfactory bulb (OB). Identical proteotypic peptides of six orthologous AS proteoforms were found in both species: PKM1 (gene PKM/Pkm), STXBP1a (gene STXBP1/Stxbp1), Isoform 3 (gene HNRNPK/Hnrnpk), LCRMP-1 (gene CRMP1/Crmp1), SP3 (gene CADM1/Cadm1), and PKCβII (gene PRKCB/Prkcb). These AS variants were also detected at the transcript level by publicly available RNA-Seq data and experimentally validated by RT-qPCR. Additionally, PKM1 and STXBP1a were detected at higher abundances in a publicly available MS/MS dataset of the AD mouse model APP/PS1 than its wild type. These data corroborate other reports, which suggest that PKM1 and STXBP1a AS proteoforms might play a role in amyloid-like aggregate formation. To the best of our knowledge, this report is the first to describe PKM1 and STXBP1a overexpression in the OB of an AD mouse model. We hope that our strategy may be of use in future human neurodegenerative studies using mouse models.

Old World Vipers-A Review about Snake Venom Proteomics of Viperinae and Their Variations

Fine-tuned by millions of years of evolution, snake venoms have frightened but also fascinated humanity and nowadays they constitute potential resources for drug development, therapeutics and antivenoms. The continuous progress of mass spectrometry techniques and latest advances in proteomics workflows enabled toxinologists to decipher venoms by modern omics technologies, so-called ‘venomics’. A tremendous upsurge reporting on snake venom proteomes could be observed. Within this review we focus on the highly venomous and widely distributed subfamily of Viperinae (Serpentes: Viperidae). A detailed public literature database search was performed (2003–2020) and we extensively reviewed all compositional venom studies of the so-called Old-World Vipers. In total, 54 studies resulted in 89 venom proteomes. The Viperinae venoms are dominated by four major, four secondary, six minor and several rare toxin families and peptides, respectively. The multitude of different venomics approaches complicates the comparison of venom composition datasets and therefore we differentiated between non-quantitative and three groups of quantitative workflows. The resulting direct comparisons within these groups show remarkable differences on the intra- and interspecies level across genera with a focus on regional differences. In summary, the present compilation is the first comprehensive up-to-date database on Viperinae venom proteomes and differentiating between analytical methods and workflows.

Integrating Proteomics for Facilitating Drug Identification and Repurposing During an Emerging Virus Pandemic

The coronavirus disease 2019 (COVID-19) pandemic has disrupted global healthcare and economic systems throughout 2020 with no clear end in sight. While the pandemic continues to have deleterious effects across the globe, mechanisms for disrupting disease transmission have relied on behavioral controls (e.g., social distancing, masks, and hygiene) as there are currently no vaccines approved for use and limited therapeutic options. As this pandemic has demonstrated our vulnerability to newly emerging viruses, there has been strong interest in utilizing proteomics approaches to identify targets for repurposed drugs as novel therapeutic candidates that could be fast-tracked for human use. Building on a previous discussion on the combination of proteomics technologies with clinical data for combating emerging viruses, we discuss how these technologies are being employed for COVID-19 and the current state of knowledge regarding repurposed drugs in these efforts.

DiagnoTop: A Computational Pipeline for Discriminating Bacterial Pathogens without Database Search

Pathogen identification is crucial to confirm bacterial infections and guide antimicrobial therapy. Although MALDI-TOF mass spectrometry (MS) serves as foundation for tools that enable rapid microbial identification, some bacteria remain challenging to identify. We recently showed that top-down proteomics (TDP) could be used to discriminate closely related enterobacterial pathogens (Escherichia coli, Shigella, and Salmonella) that are indistinguishable with tools rooted in the MALDI-TOF MS approach. Current TDP diagnostic relies on the identification of specific proteoforms for each species through a database search. However, microbial proteomes are often poorly annotated, which complicates the large-scale identification of proteoforms and leads to many unidentified high-quality mass spectra. Here, we describe a new computational pipeline called DiagnoTop that lists discriminative spectral clusters found in TDP data sets that can be used for microbial diagnostics without database search. Applied to our enterobacterial TDP data sets, DiagnoTop could easily shortlist high-quality discriminative spectral clusters, leading to increased diagnostic power. This pipeline opens new perspectives in clinical microbiology and biomarker discovery using TDP.

Proteomic Analyses of Vitreous in Proliferative Diabetic Retinopathy: Prior Studies and Future Outlook

Vitreous fluid is becoming an increasingly popular medium for the study of retinal disease. Numerous studies have demonstrated that proteomic analysis of the vitreous from patients with proliferative diabetic retinopathy yields valuable molecular information regarding known and novel proteins and pathways involved in this disease. However, there is no standardized methodology for vitreous proteomic studies. Here, we share a suggested protocol for such studies and outline the various experimental and analytic methods that are currently available. We also review prior mass spectrometry-based proteomic studies of the vitreous from patients with proliferative diabetic retinopathy, discuss common pitfalls of these studies, and propose next steps for moving the field forward.

Enhancing the chondrogenic potential of chondrogenic progenitor cells by deleting RAB5C

Osteoarthritis (OA) is the most prevalent chronic joint disease that affects a large proportion of the elderly population. Chondrogenic progenitor cells (CPCs) reside in late-stage OA cartilage tissue, producing a fibrocartilaginous extracellular matrix; these cells can be manipulated in vitro to deposit proteins of healthy articular cartilage. CPCs are under the control of SOX9 and RUNX2. In our earlier studies, we showed that a knockdown of RUNX2 enhanced the chondrogenic potential of CPCs. Here we demonstrate that CPCs carrying a knockout of RAB5C, a protein involved in endosomal trafficking, exhibited elevated expression of multiple chondrogenic markers, including the SOX trio, and increased COL2 deposition, whereas no changes in COL1 deposition were observed. We report RAB5C as an attractive target for future therapeutic approaches designed to increase the COL2 content in the diseased joint.

Big data, integrative omics and network biology

A cell integrates various signals through a network of biomolecules that crosstalk to synergistically regulate the replication, transcription, translation and other metabolic activities of a cell. These networks regulate signal perception and processing that drives biological functions. The biological complexity cannot be fully captured by a single -omics discipline. The holistic study of an organism-in health, perturbation, exposure to environment and disease, is studied under systems biology. The bottom-up molecular approaches (genes, mRNA, protein, metabolite, etc.) have laid the foundation of current biological knowledge covering the horizon from viruses, bacteria, fungi, plants and animals. Yet, these techniques provide a rather myopic view of biology at the molecular level. To understand how the interconnected molecular components are formed and rewired in disease or exposure to environmental stimuli is the holy grail of modern biology. The omics era was heralded by the genomics revolution but advanced sequencing techniques are now also ubiquitous in transcriptomics, proteomics, metabolomics and lipidomics. Multi-omics data analysis and integration techniques are driving the quest for deeper insights into how the different layers of biomolecules talk to each other in diverse contexts.

Proteome analysis using machine learning approaches and its applications to diseases

With the tremendous developments in the fields of biological and medical technologies, huge amounts of data are generated in the form of genomic data, images in medical databases or as data on protein sequences, and so on. Analyzing this data through different tools sheds light on the particulars of the disease and our body's reactions to it, thus, aiding our understanding of the human health. Most useful of these tools is artificial intelligence and deep learning (DL). The artificially created neural networks in DL algorithms help extract viable data from the datasets, and further, to recognize patters in these complex datasets. Therefore, as a part of machine learning, DL helps us face all the various challenges that come forth during protein prediction, protein identification and their quantification. Proteomics is the study of such proteins, their structures, features, properties and so on. As a form of data science, Proteomics has helped us progress excellently in the field of genomics technologies. One of the major techniques used in proteomics studies is mass spectrometry (MS). However, MS is efficient with analysis of large datasets only with the added help of informatics approaches for data analysis and interpretation; these mainly include machine learning and deep learning algorithms. In this chapter, we will discuss in detail the applications of deep learning and various algorithms of machine learning in proteomics.

An Update on MRMAssayDB: A Comprehensive Resource for Targeted Proteomics Assays in the Community

Precise multiplexed quantification of proteins in biological samples can be achieved by targeted proteomics using multiple or parallel reaction monitoring (MRM/PRM). Combined with internal standards, the method achieves very good repeatability and reproducibility enabling excellent protein quantification and allowing longitudinal and cohort studies. A laborious part of performing such experiments lies in the preparation steps dedicated to the development and validation of individual protein assays. Several public repositories host information on targeted proteomics assays, including NCI’s Clinical Proteomic Tumor Analysis Consortium assay portals, PeptideAtlas SRM Experiment Library, SRMAtlas, PanoramaWeb, and PeptideTracker, with all offering varying levels of details. We introduced MRMAssayDB in 2018 as an integrated resource for targeted proteomics assays. The Web-based application maps and links the assays from the repositories, includes comprehensive up-to-date protein and sequence annotations, and provides multiple visualization options on the peptide and protein level. We have extended MRMAssayDB with more assays and extensive annotations. Currently it contains >828 000 assays covering >51 000 proteins from 94 organisms, of which >17 000 proteins are present in >2400 biological pathways, and >48 000 mapping to >21 000 Gene Ontology terms. This is an increase of about four times the number of assays since introduction. We have expanded annotations of interaction, biological pathways, and disease associations. A newly added visualization module for coupled molecular structural annotation browsing allows the user to interactively examine peptide sequence and any known PTMs and disease mutations, and map all to available protein 3D structures. Because of its integrative approach, MRMAssayDB enables a holistic view of suitable proteotypic peptides and commonly used transitions in empirical data. Availability: http://mrmassaydb.proteincentre.com.

Improving Exposure Assessment Using Non-Targeted and Suspect Screening: The ISO/IEC 17025: 2017 Quality Standard as a Guideline

The recent advances of novel methodologies such as non-targeted and suspect screening based on high-resolution mass spectrometry (HRMS) have paved the way to a new paradigm for exposure assessment. These methodologies allow to profile simultaneously thousands of small unknown molecules present in environmental and biological samples, and therefore hold great promises in order to identify more efficiently hazardous contaminants potentially associated with increased risks of developing adverse health outcomes. In order to further explore the potential of these methodologies and push the transition from research applications towards regulatory purposes, robust harmonized quality standards have to be implemented. Here, we discuss the feasibility of using ISO/IEC 17025: 2017 as a guideline to implement non-targeted and suspect screening methodologies in laboratories, whether it is for accreditation purposes or not. More specifically, we identified and then discussed how specificities of non-targeted HRMS methodology can be accounted for in order to comply with the specific items of ISO/IEC 17025: 2017. We also discussed other specificities of HRMS methodologies (e.g., need for digital storage capacity) that are so far not included in the ISO/IEC 17025 requirements but should be considered. This works aims to fuel and expand the discussion in order to subsidize new opportunities of harmonization for non-targeted and suspect screening.

IL-4 absence triggers distinct pathways in apical periodontitis development

Dental pulp is a specialized tissue able to respond to infectious processes. Nevertheless, infection progress and root canal colonization trigger an immune-inflammatory response in tooth-surrounding tissues, leading to apical periodontitis and bone tissue destruction, further contributing to tooth loss. In order to shed some light on the effects of IL-4 on periradicular pathology development modulation, microtomographic, histological and proteomic analyses were performed using 60 mice, 30 wild type and 30 IL-4^-/^-. For that, 5 animals were used for microtomographic and histological analysis, and another 5 for proteomic analysis for 0, 7 and 21 days with/without pulp exposure. The periapical lesions were established in WT and IL-4^-/^- mice without statistical differences in their volume, and the value of p < 0.05 was adopted as significant in microtomographic and histological analyses. Regarding histological analysis, IL-4^-/^- mice show aggravation of pulp inflammation compared to WT. By using proteomic analysis, we have identified 32 proteins with increased abundance and 218 proteins with decreased abundance in WT animals after 21 days of pulp exposure, compared to IL-4^-/^- animals. However, IL-4^-/^- mice demonstrated faster development of apical periodontitis. These animals developed a compensatory mechanism to overcome IL-4 absence, putatively based on the identification of upregulated proteins related to immune system signaling pathways. Significance: IL-4 might play a protective role in diseases involving bone destruction and its activity may contribute to host protection, mainly due to its antiosteoclastogenic action.

Identification of Cytosolic Protein Targets of Catechol Estrogens in Breast Cancer Cells Using a Click Chemistry-Based Workflow

Catechol estrogens (CEs) are known to be toxic metabolites and the initiators of the oncogenesis of breast cancers via forming covalent adducts with DNAs. CEs shall also react with proteins, but their cellular protein targets remain unexplored. Here, we reported the identification of protein targets of CEs in the soluble cytosol of estrogen-sensitive breast cancer cells by multiple comparative proteomics using liquid chromatography-tandem mass spectrometry (LC-MS/MS) coupled with an improved click chemistry-based workflow. Multiple comparative proteomics composed of an experimental pair (probe versus solvent) and two control pairs (solvent versus solvent and probe versus solvent without enrichment) were studied using stable isotope dimethyl labeling. The use of 4-hydroxyethynylestradiol (4OHEE2) probe with an amide-free linker coupled with on-bead digestion and redigestion of the proteins cleaved from the beads was shown to greatly improve the recovery and identification of CE-adducted peptides. A total of 310 protein targets and 40 adduction sites were repeatedly (n ≥ 2) identified with D/H (probe/solvent) ratio >4 versus only one identified with D/H >4 from the two control pairs, suggesting that our workflow imposes only a very low background. Meanwhile, multiple comparative D/H ratios revealed that CEs may downregulate many target proteins involved in the metabolism or detoxification, suggesting a negative correlation between CE-induced adduction and expression of proteins acting on the alleviation of stress-induced cellular damages. The reported method and data will provide opportunities to study the progression of estrogen metabolism-derived diseases and biomarkers.

piNET: a versatile web platform for downstream analysis and visualization of proteomics data

Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.

Proteomic Mapping of Multifunctional Complexes Within Triatomine Saliva

Triatomines are hematophagous insects that transmit Trypanosoma cruzi, the etiological agent of Chagas disease. This neglected tropical disease represents a global health issue as it is spreading worldwide. The saliva of Triatominae contains miscellaneous proteins crucial for blood feeding acquisition, counteracting host's hemostasis while performing vasodilatory, anti-platelet and anti-coagulant activities, besides modulating inflammation and immune responses. Since a set of biological processes are mediated by protein complexes, here, the sialocomplexomes (salivary protein complexes) of five species of Triatominae were studied to explore the protein-protein interaction networks. Salivary multiprotein complexes from Triatoma infestans, Triatoma dimidiata, Dipetalogaster maxima, Rhodnius prolixus, and Rhodnius neglectus were investigated by Blue-Native- polyacrylamide gel electrophoresis coupled with liquid chromatography tandem mass spectrometry. More than 70 protein groups, uncovering the landscape of the Triatominae salivary interactome, were revealed. Triabin, actin, thioredoxin peroxidase and an uncharacterized protein were identified in sialocomplexes of the five species, while hexamerin, heat shock protein and histone were identified in sialocomplexes of four species. Salivary proteins related to triatomine immunity as well as those required during blood feeding process such as apyrases, antigen 5, procalins, and nitrophorins compose different complexes. Furthermore, unique proteins for each triatomine species were revealed. This study represents the first Triatominae sialocomplexome reference to date and shows that the approach used is a reliable tool for the analysis of Triatominae salivary proteins assembled into complexes.

Castro M, Sidoli S, Schwämmle V, Luz IS, Roepstorff P, Fontes W. Phosphoproteomic Analysis of Rat Neutrophils Shows the Effect of Intestinal Ischemia/Reperfusion and Preconditioning on Kinases and Phosphatases

Intestinal ischemia reperfusion injury (iIRI) is a severe clinical condition presenting high morbidity and mortality worldwide. Some of the systemic consequences of IRI can be prevented by applying ischemic preconditioning (IPC), a series of short ischemia/reperfusion events preceding the major ischemia. Although neutrophils are key players in the pathophysiology of ischemic injuries, neither the dysregulation presented by these cells in iIRI nor the protective effect of iIPC have their regulation mechanisms fully understood. Protein phosphorylation, as well as the regulation of the respective phosphatases and kinases are responsible for regulating a large number of cellular functions in the inflammatory response. Moreover, in previous work we found hydrolases and transferases to be modulated in iIR and iIPC, suggesting the possible involvement of phosphatases and kinases in the process. Therefore, in the present study, we analyzed the phosphoproteome of neutrophils from rats submitted to mesenteric ischemia and reperfusion, either submitted or not to IPC, compared to quiescent controls and sham laparotomy. Proteomic analysis was performed by multi-step enrichment of phosphopeptides, isobaric labeling, and LC-MS/MS analysis. Bioinformatics was used to determine phosphosite and phosphopeptide abundance and clustering, as well as kinases and phosphatases sites and domains. We found that most of the phosphorylation-regulated proteins are involved in apoptosis and migration, and most of the regulatory kinases belong to CAMK and CMGC families. An interesting finding revealed groups of proteins that are modulated by iIR, but such modulation can be prevented by iIPC. Among the regulated proteins related to the iIPC protective effect, Vamp8 and Inpp5d/Ship are discussed as possible candidates for control of the iIR damage.

Exploring the functional impact of alternative splicing on human protein isoforms using available annotation sources

In recent years, the emphasis of scientific inquiry has shifted from whole-genome analyses to an understanding of cellular responses specific to tissue, developmental stage or environmental conditions. One of the central mechanisms underlying the diversity and adaptability of the contextual responses is alternative splicing (AS). It enables a single gene to encode multiple isoforms with distinct biological functions. However, to date, the functions of the vast majority of differentially spliced protein isoforms are not known. Integration of genomic, proteomic, functional, phenotypic and contextual information is essential for supporting isoform-based modeling and analysis. Such integrative proteogenomics approaches promise to provide insights into the functions of the alternatively spliced protein isoforms and provide high-confidence hypotheses to be validated experimentally. This manuscript provides a survey of the public databases supporting isoform-based biology. It also presents an overview of the potential global impact of AS on the human canonical gene functions, molecular interactions and cellular pathways.

Glycomics and glycoproteomics of viruses: Mass spectrometry applications and insights toward structure-function relationships

The advancement of viral glycomics has paralleled that of the mass spectrometry glycomics toolbox. In some regard the glycoproteins studied have provided the impetus for this advancement. Viral proteins are often highly glycosylated, especially those targeted by the host immune system. Glycosylation tends to be dynamic over time as viruses propagate in host populations leading to increased number of and/or "movement" of glycosylation sites in response to the immune system and other pressures. This relationship can lead to highly glycosylated, difficult to analyze glycoproteins that challenge the capabilities of modern mass spectrometry. In this review, we briefly discuss five general areas where glycosylation is important in the viral niche and how mass spectrometry has been used to reveal key information regarding structure-function relationships between viral glycoproteins and host cells. We describe the recent past and current glycomics toolbox used in these analyses and give examples of how the requirement to analyze these complex glycoproteins has provided the incentive for some advances seen in glycomics mass spectrometry. A general overview of viral glycomics, special cases, mass spectrometry methods and work-flows, informatics and complementary chemical techniques currently used are discussed. © 2020 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd. Mass Spec Rev.

Paternal Resistance Training Modulates Calcaneal Tendon Proteome in the Offspring Exposed to High-Fat Diet

The increase in high-energy dietary intakes is a well-known risk factor for many diseases, and can also negatively impact the tendon. Ancestral lifestyle can mitigate the metabolic harmful effects of offspring exposed to high-fat diet (HF). However, the influence of paternal exercise on molecular pathways associated to offspring tendon remodeling remains to be determined. We investigated the effects of 8 weeks of paternal resistance training (RT) on offspring tendon proteome exposed to standard diet or HF diet. Wistar rats were randomly divided into two groups: sedentary fathers and trained fathers (8 weeks, three times per week, with 8–12 dynamic movements per climb in a stair climbing apparatus). The offspring were obtained by mating with sedentary females. Upon weaning, male offspring were divided into four groups (five animals per group): offspring from sedentary fathers were exposed either to control diet (SFO-C), or to high-fat diet (SFO-HF); offspring from trained fathers were exposed to control diet (TFO-C) or to a high-fat diet (TFO-HF). The Nano-LC-MS/MS analysis revealed 383 regulated proteins among offspring groups. HF diet induced a decrease of abundance in tendon proteins related to extracellular matrix organization, transport, immune response and translation. On the other hand, the changes in the offspring tendon proteome in response to paternal RT were more pronounced when the offspring were exposed to HF diet, resulting in positive regulation of proteins essential for the maintenance of tendon integrity. Most of the modulated proteins are associated to biological pathways related to tendon protection and damage recovery, such as extracellular matrix organization and transport. The present study demonstrated that the father’s lifestyle could be crucial for tendon homeostasis in the first generation. Our results provide important insights into the molecular mechanisms involved in paternal intergenerational effects and potential protective outcomes of paternal RT.

Dynamic proteomic analysis of Aedes aegypti Aag-2 cells infected with Mayaro virus

Background

Mayaro virus (MAYV) is responsible for a mosquito-borne tropical disease with clinical symptoms similar to dengue or chikungunya virus fevers. In addition to the recent territorial expansion of MAYV, this virus may be responsible for an increasing number of outbreaks. Currently, no vaccine is available. Aedes aegypti is promiscuous in its viral transmission and thus an interesting model to understand MAYV-vector interactions. While the life-cycle of MAYV is known, the mechanisms by which this arbovirus affects mosquito host cells are not clearly understood.

Methods

After defining the best conditions for cell culture harvesting using the highest virus titer, Ae. aegypti Aag-2 cells were infected with a Brazilian MAYV isolate at a MOI of 1 in order to perform a comparative proteomic analysis of MAYV-infected Aag-2 cells by using a label-free semi-quantitative bottom-up proteomic analysis. Time-course analyses were performed at 12 and 48 h post-infection (hpi). After spectrum alignment between the triplicates of each time point and changes of the relative abundance level calculation, the identified proteins were annotated and using Gene Ontology database and protein pathways were annotated using the Kyoto Encyclopedia of Genes and Genomes.

Results

After three reproducible biological replicates, the total proteome analysis allowed for the identification of 5330 peptides and the mapping of 459, 376 and 251 protein groups, at time 0, 12 hpi and 48 hpi, respectively. A total of 161 mosquito proteins were found to be differentially abundant during the time-course, mostly related to host cell processes, including redox metabolism, translation, energy metabolism, and host cell defense. MAYV infection also increased host protein expression implicated in viral replication.

Conclusions

To our knowledge, this first proteomic time-course analysis of MAYV-infected mosquito cells sheds light on the molecular basis of the viral infection process and host cell response during the first 48 hpi. Our data highlight several mosquito proteins modulated by the virus, revealing that MAYV manipulates mosquito cell metabolism for its propagation.

The jPOST environment: an integrated proteomics data repository and database

Rapid progress is being made in mass spectrometry (MS)-based proteomics, yielding an increasing number of larger datasets with higher quality and higher throughput. To integrate proteomics datasets generated from various projects and institutions, we launched a project named jPOST (Japan ProteOme STandard Repository/Database, https://jpostdb.org/) in 2015. Its proteomics data repository, jPOSTrepo, began operations in 2016 and has accepted more than 10 TB of MS-based proteomics datasets in the past two years. In addition, we have developed a new proteomics database named jPOSTdb in which the published raw datasets in jPOSTrepo are reanalyzed using standardized protocol. jPOSTdb provides viewers showing the frequency of detected post-translational modifications, the co-occurrence of phosphorylation sites on a peptide and peptide sharing among proteoforms. jPOSTdb also provides basic statistical analysis tools to compare proteomics datasets.

Bioinformatics Resources for Plant Abiotic Stress Responses: State of the Art and Opportunities in the Fast Evolving -Omics Era

Abiotic stresses are among the principal limiting factors for productivity in agriculture. In the current era of continuous climate changes, the understanding of the molecular aspects involved in abiotic stress response in plants is a priority. The rise of -omics approaches provides key strategies to promote effective research in the field, facilitating the investigations from reference models to an increasing number of species, tolerant and sensitive genotypes. Integrated multilevel approaches, based on molecular investigations at genomics, transcriptomics, proteomics and metabolomics levels, are now feasible, expanding the opportunities to clarify key molecular aspects involved in responses to abiotic stresses. To this aim, bioinformatics has become fundamental for data production, mining and integration, and necessary for extracting valuable information and for comparative efforts, paving the way to the modeling of the involved processes. We provide here an overview of bioinformatics resources for research on plant abiotic stresses, describing collections from -omics efforts in the field, ranging from raw data to complete databases or platforms, highlighting opportunities and still open challenges in abiotic stress research based on -omics technologies.

Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows

Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.

SeQuery: an interactive graph database for visualizing the GPCR superfamily

The rate at which new protein and gene sequences are being discovered has grown explosively in the omics era, which has increasingly complicated the efficient characterization and analysis of their biological properties. In this study, we propose a web-based graphical database tool, SeQuery, for intuitively visualizing proteome/genome networks by integrating the sequential, structural and functional information of sequences. As a demonstration of our tool’s effectiveness, we constructed a graph database of G protein-coupled receptor (GPCR) sequences by integrating data from the UniProt, GPCRdb and RCSB PDB databases. Our tool attempts to achieve two goals: (i) given the sequence of a query protein, correctly and efficiently identify whether the protein is a GPCR, and, if so, define its sequential and functional roles in the GPCR superfamily; and (ii) present a panoramic view of the GPCR superfamily and its network centralities that allows users to explore the superfamily at various resolutions. Such a bottom-up-to-top-down view can provide the users with a comprehensive understanding of the GPCR superfamily through interactive navigation of the graph database. A test of SeQuery with the GPCR2841 dataset shows that it correctly identifies 99 out of 100 queried protein sequences. The developed tool is readily applicable to other biological networks, and we aim to expand SeQuery by including additional biological databases in the near future.

Recommendations for the collection and use of multiplexed functional data for clinical variant interpretation

Variants of uncertain significance represent a massive challenge to medical genetics. Multiplexed functional assays, in which the functional effects of thousands of genomic variants are assessed simultaneously, are increasingly generating data that can be used as additional evidence for or against variant pathogenicity. Such assays have the potential to resolve variants of uncertain significance, thereby increasing the clinical utility of genomic testing. Existing standards from the American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) and new guidelines from the Clinical Genome Resource (ClinGen) establish the role of functional data in variant interpretation, but do not address the specific challenges or advantages of using functional data derived from multiplexed assays. Here, we build on these existing guidelines to provide recommendations to experimentalists for the production and reporting of multiplexed functional data and to clinicians for the evaluation and use of such data. By following these recommendations, experimentalists can produce transparent, complete, and well-validated datasets that are primed for clinical uptake. Our recommendations to clinicians and diagnostic labs on how to evaluate the quality of multiplexed functional datasets, and how different datasets could be incorporated into the ACMG/AMP variant-interpretation framework, will hopefully clarify whether and how such data should be used. The recommendations that we provide are designed to enhance the quality and utility of multiplexed functional data, and to promote their judicious use.

ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion

The field of computational proteomics is approaching the big data age, driven both by a continuous growth in the number of samples analyzed per experiment as well as by the growing amount of data obtained in each analytical run. In order to process these large amounts of data, it is increasingly necessary to use elastic compute resources such as Linux-based cluster environments and cloud infrastructures. Unfortunately, the vast majority of cross-platform proteomics tools are not able to operate directly on the proprietary formats generated by the diverse mass spectrometers. Here, we present ThermoRawFileParser, an open-source, cross-platform tool that converts Thermo RAW files into open file formats such as MGF and the HUPO-PSI standard file format mzML. To ensure the broadest possible availability and to increase integration capabilities with popular workflow systems such as Galaxy or Nextflow, we have also built Conda package and BioContainers container around ThermoRawFileParser. In addition, we implemented a user-friendly interface (ThermoRawFileParserGUI) for those users not familiar with command-line tools. Finally, we performed a benchmark of ThermoRawFileParser and msconvert to verify that the converted mzML files contain reliable quantitative results.

Utilization of the Proteome Data Deposited in SRMAtlas for Validating the Existence of the Human Missing Proteins in GPM

The Human Proteome Project (HPP) has made great efforts to clarify the existing evidence of human proteins since 2012. However, according to the recent release of neXtProt (2019-1), approximately 10% of all human genes still have inadequate or no experimental evidence of their translation at the protein level. They were categorized as missing proteins (PE2-PE4). To further the goal of HPP, we developed a two-step bioinformatic strategy addressing the utilization of the SRMAtlas synthetic peptides corresponding to the missing proteins as an exclusive reference in order to explore their natural counterparts within GPM. In the first step, we searched the GPM for the non-nested SRMAtlas peptides corresponding to the missing proteins, taking under consideration only those detected via ≥2 non-nested unitypic/proteotypic peptides "Stranded peptides" with length ≥9 amino acids in the same proteomic study. As a result, 51 missing proteins were newly detected in 35 different proteomic studies. In the second step, we validated these newly detected missing proteins based on matching the spectra of their synthetic and natural peptides in SRMAtlas and GPM, respectively. The results showed that 23 of the missing proteins with ≥2 non-nested peptides were validated by careful spectral matching.

Bioinformatics approaches for improving seminal plasma proteome analysis

Reproduction efficiency of male animals is one of the key factors influencing the sustainability of livestock. Mass spectrometry (MS) based proteomics has become an important tool for studying seminal plasma proteomes. In this review, we summarize bioinformatics analysis strategies for current proteomics approaches, for identifying novel biomarkers of reproductive robustness.

Detection and Characterization of Catechol Quinone-Derived Protein Adducts Using Biomolecular Mass Spectrometry

The catechol quinone (CQ) motif is present in many biologically relevant molecules throughout endogenous metabolic products, foods, drugs, and environmental pollutants. The CQ derivatives may undergo Michael addition, and has been shown to yield covalent bonds with nucleophilic sites of cysteine, lysine, or histidine residue of proteins. The CQ-adducted proteins may exhibit cytotoxicity or biological functions different from their un-adducted forms. Identification, characterization, and quantification of relevant protein targets are essential but challenging goals. Mass spectrometry (MS) is well-suited for the analysis of proteins and protein modifications. Technical development of bottom-up proteomics has greatly advanced the field of biomolecular MS, including protein adductomics. This mini-review focuses on the use of biomolecular MS in (1) structural and functional characterization of CQ adduction on standards of proteins, (2) identification of endogenous adduction targets, and (3) quantification of adducted blood proteins as exposure index. The reactivity and outcome of CQ adduction are discussed with emphases on endogenous species, such as dopamine and catechol estrogens. Limitations and advancements in sample preparation, MS instrumentation, and software to facilitate protein adductomics are also discussed.

ProteomicsDB

ProteomicsDB (https://www.ProteomicsDB.org) is a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. ProteomicsDB was first released in 2014 to enable the interactive exploration of the first draft of the human proteome. To date, it contains quantitative data from 78 projects totalling over 19k LC–MS/MS experiments. A standardized analysis pipeline enables comparisons between multiple datasets to facilitate the exploration of protein expression across hundreds of tissues, body fluids and cell lines. We recently extended the data model to enable the storage and integrated visualization of other quantitative omics data. This includes transcriptomics data from e.g. NCBI GEO, protein–protein interaction information from STRING, functional annotations from KEGG, drug-sensitivity/selectivity data from several public sources and reference mass spectra from the ProteomeTools project. The extended functionality transforms ProteomicsDB into a multi-purpose resource connecting quantification and meta-data for each protein. The rich user interface helps researchers to navigate all data sources in either a protein-centric or multi-protein-centric manner. Several options are available to download data manually, while our application programming interface enables accessing quantitative data systematically.

Interactive Peptide Spectral Annotator: A Versatile Web-based Tool for Proteomic Applications

Here we present IPSA, an innovative web-based spectrum annotator that visualizes and characterizes peptide tandem mass spectra. A tool for the scientific community, IPSA can visualize peptides collected using a wide variety of experimental and instrumental configurations. Annotated spectra are customizable via a selection of interactive features and can be exported as editable scalable vector graphics to aid in the production of publication-quality figures. Single spectra can be analyzed through provided web forms, whereas data for multiple peptide spectral matches can be uploaded using the Proteomics Standards Initiative file formats mzTab, mzIdentML, and mzML. Alternatively, peptide identifications and spectral data can be provided using generic file formats. IPSA provides supports for annotating spectra collecting using negative-mode ionization and facilitates the characterization of experimental MS/MS performance through the optional export of fragment ion statistics from one to many peptide spectral matches. This resource is made freely accessible at http://interactivepeptidespectralannotator.com, whereas the source code and user guides are available at https://github.com/coongroup/IPSA for private hosting or custom implementations.

Quantifying the impact of public omics data

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

Increasing amount of public omics data are important and valuable resources for the research community. Here, the authors develop a set of metrics to quantify the attention and impact of biomedical datasets and integrate them into the framework of Omics Discovery Index (OmicsDI).

Visual mass-spec share (vMS-Share): a new public web-based mass spectrometry visualization and data mining repository

Herein we introduce the Visual Mass-Spec Share (vMS-Share), a new public mass spectrometric (MS) repository and data mining website/resource freely accessible at https://vmsshare.nist.gov. vMS-Share is a web-based application developed for instant visualization of raw MS data with integrated display of metadata optimized for the sharing of proteomics and metabolomics experimental results. Each MS-based identification is linked to a given experiment and the entire experimental data can then be viewed using the link associated with a given peptide and/or small molecule. Interactive and user-friendly visualizations are provided to the user via variety of easily accessible search filters.

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.