Analysis of high accuracy, quantitative proteomics data in the MaxQB database

Linear polyubiquitylation of Gli protein regulates its protein stability and facilitates tumor growth in colorectal cancer

The linear ubiquitin chain assembly complex (LUBAC) mediates the linear ubiquitination of various proteins and is involved in NF-κB signaling and immune regulation. However, the function and mechanism of linear ubiquitination in regulating oncogenic signaling and tumor growth have remained poorly understood. Herein, we identified Gli proteins, key transcription factors in the Hedgehog (Hh) signaling pathway, as novel substrates of LUBAC. Linear ubiquitination stabilizes Gli proteins, leading to the noncanonical activation of Hh signaling in CRC cells. Furthermore, LUBAC facilitates tumor growth in CRC cells. Additionally, elevated expression of LUBAC components in CRC tissues was observed, and higher expression levels of these components correlated with poor prognosis in CRC patients. Interestingly, inhibition of LUBAC using either a small molecule agonist or RNA silencing specifically suppressed cell growth in CRC cells but had no effect on normal intestinal cells. Taken together, aberrant expression of LUBAC components activates Hh signaling noncanonically by mediating linear ubiquitination, promoting tumor growth in CRC, demonstrating the novel function of linear ubiquitination in regulating the protein stability of its substrates and highlighting the potential of targeting LUBAC as a therapeutic strategy in CRC.

Biopsy Proteome Scoring to Determine Mucosal Remodeling in Celiac Disease

BACKGROUND & AIMS

Histologic evaluation of gut biopsies is a cornerstone for diagnosis and management of celiac disease (CeD). Despite its wide use, the method depends on proper biopsy orientation, and it suffers from interobserver variability. Biopsy proteome measurement reporting on the tissue state can be obtained by mass spectrometry analysis of formalin-fixed paraffin-embedded tissue. Here we aimed to transform biopsy proteome data into numerical scores that give observer-independent measures of mucosal remodeling in CeD.

METHODS

A pipeline using glass-mounted formalin-fixed paraffin-embedded sections for mass spectrometry-based proteome analysis was established. Proteome data were converted to numerical scores using 2 complementary approaches: a rank-based enrichment score and a score based on machine learning using logistic regression. The 2 scoring approaches were compared with each other and with histology analyzing 18 patients with CeD with biopsies collected before and after treatment with a gluten-free diet as well as biopsies from patients with CeD with varying degree of remission (n = 22). Biopsies from individuals without CeD (n = 32) were also analyzed.

RESULTS

The method yielded reliable proteome scoring of both unstained and H&E-stained glass-mounted sections. The scores of the 2 approaches were highly correlated, reflecting that both approaches pick up proteome changes in the same biological pathways. The proteome scores correlated with villus height-to-crypt depth ratio. Thus, the method is able to score biopsies with poor orientation.

CONCLUSIONS

Biopsy proteome scores give reliable observer and orientation-independent measures of mucosal remodeling in CeD. The proteomic method can readily be implemented by nonexpert laboratories in parallel to histology assessment and easily scaled for clinical trial settings.

Landscape of protein-protein interactions during hepatitis C virus assembly and release

Assembly of infectious hepatitis C virus (HCV) particles requires multiple cellular proteins including for instance apolipoprotein E (ApoE). To describe these protein-protein interactions, we performed an affinity purification mass spectrometry screen of HCV-infected cells. We used functional viral constructs with epitope-tagged envelope protein 2 (E2), protein (p) 7, or nonstructural protein 4B (NS4B) as well as cells expressing a tagged variant of ApoE. We also evaluated assembly stage-dependent remodeling of protein complexes by using viral mutants carrying point mutations abrogating particle production at distinct steps of the HCV particle production cascade. Five ApoE binding proteins, 12 p7 binders, 7 primary E2 interactors, and 24 proteins interacting with NS4B were detected. Cell-derived PREB, STT3B, and SPCS2 as well as viral NS2 interacted with both p7 and E2. Only GTF3C3 interacted with E2 and NS4B, highlighting that HCV assembly and replication complexes exhibit largely distinct interactomes. An HCV core protein mutation, preventing core protein decoration of lipid droplets, profoundly altered the E2 interactome. In cells replicating this mutant, E2 interactions with HSPA5, STT3A/B, RAD23A/B, and ZNF860 were significantly enhanced, suggesting that E2 protein interactions partly depend on core protein functions. Bioinformatic and functional studies including STRING network analyses, RNA interference, and ectopic expression support a role of Rad23A and Rad23B in facilitating HCV infectious virus production. Both Rad23A and Rad23B are involved in the endoplasmic reticulum (ER)-associated protein degradation (ERAD). Collectively, our results provide a map of host proteins interacting with HCV assembly proteins, and they give evidence for the involvement of ER protein folding machineries and the ERAD pathway in the late stages of the HCV replication cycle.IMPORTANCEHepatitis C virus (HCV) establishes chronic infections in the majority of exposed individuals. This capacity likely depends on viral immune evasion strategies. One feature likely contributing to persistence is the formation of so-called lipo-viro particles. These peculiar virions consist of viral structural proteins and cellular lipids and lipoproteins, the latter of which aid in viral attachment and cell entry and likely antibody escape. To learn about how lipo-viro particles are coined, here, we provide a comprehensive overview of protein-protein interactions in virus-producing cells. We identify numerous novel and specific HCV E2, p7, and cellular apolipoprotein E-interacting proteins. Pathway analyses of these interactors show that proteins participating in processes such as endoplasmic reticulum (ER) protein folding, ER-associated protein degradation, and glycosylation are heavily engaged in virus production. Moreover, we find that the proteome of HCV replication sites is distinct from the assembly proteome, suggesting that transport process likely shuttles viral RNA to assembly sites.

Ion Mobility-Based Enrichment-Free N-Terminomics Analysis Reveals Novel Legumain Substrates in Murine Spleen

Aberrant levels of the asparaginyl endopeptidase legumain have been linked to inflammation, neurodegeneration, and cancer, yet our understanding of this protease is incomplete. Systematic attempts to identify legumain substrates have been previously confined to in vitro studies, which fail to mirror physiological conditions and obscure biologically relevant cleavage events. Using high-field asymmetric waveform ion mobility spectrometry (FAIMS), we developed a streamlined approach for proteome and N-terminome analyses without the need for N-termini enrichment. Compared to unfractionated proteomic analysis, we demonstrate FAIMS fractionation improves N-termini identification by >2.5 fold, resulting in the identification of >2882 unique N-termini from limited sample amounts. In murine spleens, this approach identifies 6366 proteins and 2528 unique N-termini, with 235 cleavage events enriched in WT compared to legumain-deficient spleens. Among these, 119 neo-N-termini arose from asparaginyl endopeptidase activities, representing novel putative physiological legumain substrates. The direct cleavage of selected substrates by legumain was confirmed using in vitro assays, providing support for the existence of physiologically relevant extra-lysosomal legumain activity. Combined, these data shed critical light on the functions of legumain and demonstrate the utility of FAIMS as an accessible method to improve depth and quality of N-terminomics studies.

On-Tissue Spatial Proteomics Integrating MALDI-MS Imaging with Shotgun Proteomics Reveals Soy Consumption-Induced Protein Changes in a Fragile X Syndrome Mouse Model

Fragile X syndrome (FXS), the leading cause of inherited intellectual disability and autism, is caused by the transcriptional silencing of the FMR1 gene, which encodes the fragile X messenger ribonucleoprotein (FMRP). FMRP interacts with numerous brain mRNAs that are involved in synaptic plasticity and implicated in autism spectrum disorders. Our published studies indicate that single-source, soy-based diets are associated with increased seizures and autism. Thus, there is an acute need for an unbiased protein marker identification in FXS in response to soy consumption. Herein, we present a spatial proteomics approach integrating mass spectrometry imaging with label-free proteomics in the FXS mouse model to map the spatial distribution and quantify levels of proteins in the hippocampus and hypothalamus brain regions. In total, 1250 unique peptides were spatially resolved, demonstrating the diverse array of peptidomes present in the tissue slices and the broad coverage of the strategy. A group of proteins that are known to be involved in glycolysis, synaptic transmission, and coexpression network analysis suggest a significant association between soy proteins and metabolic and synaptic processes in the Fmr1KO brain. Ultimately, this spatial proteomics work represents a crucial step toward identifying potential candidate protein markers and novel therapeutic targets for FXS.

Balance between Pro- and Antifibrotic Proteins in Mesenchymal Stromal Cell Secretome Fractions Revealed by Proteome and Cell Subpopulation Analysis

Multipotent mesenchymal stromal cells (MSCs) regulate tissue repair through paracrine activity, with secreted proteins being significant contributors. Human tissue repair commonly results in fibrosis, where fibroblast differentiation into myofibroblasts is a major cellular mechanism. MSCs' paracrine activity can inhibit fibrosis development. We previously demonstrated that the separation of MSC secretome, represented by conditioned medium (CM), into subfractions enriched with extracellular vesicles (EV) or soluble factors (SF) boosts EV and SF antifibrotic effect. This effect is realized through the inhibition of fibroblast-to-myofibroblast differentiation in vitro. To unravel the mechanisms of MSC paracrine effects on fibroblast differentiation, we performed a comparative proteomic analysis of MSC secretome fractions. We found that CM was enriched in NF-κB activators and confirmed via qPCR that CM, but not EV or SF, upregulated NF-κB target genes (COX2, IL6, etc.) in human dermal fibroblasts. Furthermore, we revealed that EV and SF were enriched in TGF-β, Notch, IGF, and Wnt pathway regulators. According to scRNAseq, 11 out of 13 corresponding genes were upregulated in a minor MSC subpopulation disappearing in profibrotic conditions. Thus, protein enrichment of MSC secretome fractions and cellular subpopulation patterns shift the balance in fibroblast-to-myofibroblast differentiation, which should be considered in studies of MSC paracrine effects and the therapeutic use of MSC secretome.

Decoding Systems Biology of Inflammation Signatures in Cancer Pathogenesis: Pan-Cancer Insights from 12 Common Cancers

Chronic inflammation is an important contributor to tumorigenesis in many tissues. However, the underlying mechanisms of inflammatory signaling in the tumor microenvironment are not yet fully understood in various cancers. Therefore, this study aimed to uncover the gene expression signatures of inflammation-associated proteins that lead to tumorigenesis, and with an eye to discovery of potential system biomarkers and novel drug candidates in oncology. Gene expression profiles associated with 12 common cancers (e.g., breast invasive carcinoma, colon adenocarcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma) from The Cancer Genome Atlas were retrieved and mapped to inflammation-related gene sets. Subsequently, the inflammation-associated differentially expressed genes (i-DEGs) were determined. The i-DEGs common in all cancers were proposed as tumor inflammation signatures (TIS) after pan-cancer analysis. A TIS, consisting of 45 proteins, was evaluated as a potential system biomarker based on its prognostic forecasting and secretion profiles in multiple tissues. In addition, i-DEGs for each cancer type were used as queries for drug repurposing. Narciclasine, parthenolide, and homoharringtonine were identified as potential candidates for drug repurposing. Biomarker candidates in relation to inflammation were identified such as KNG1, SPP1, and MIF. Collectively, these findings inform precision diagnostics development to distinguish individual cancer types, and can also pave the way for novel prognostic decision tools and repurposed drugs across multiple cancers. These new findings and hypotheses warrant further research toward precision/personalized medicine in oncology. Pan-cancer analysis of inflammatory mediators can open up new avenues for innovation in cancer diagnostics and therapeutics.

Development of Potent and Selective Coactivator-Associated Arginine Methyltransferase 1 (CARM1) Degraders

CARM1 is amplified or overexpressed in many cancer types, and its overexpression correlates with poor prognosis. Potent small-molecule inhibitors for CARM1 have been developed, but the cellular efficacy of the CARM1 inhibitors is limited. We herein report the development of the proteolysis targeting chimera (PROTAC) for CARM1, which contains a CARM1 ligand TP-064, a linker, and a VHL E3 ligase ligand. Compound 3b elicited potent cellular degradation activity (DC50 = 8 nM and Dmax > 95%) in a few hours. Compound 3b degraded CARM1 in VHL- and proteasome-dependent manner and was highly selective for CARM1 over other protein arginine methyltransferases. CARM1 degradation by 3b resulted in potent downregulation of CARM1 substrate methylation and inhibition of cancer cell migration in cell-based assays. Thus, CARM1 PROTACs can be used to interrogate CARM1's cellular functions and potentially be developed as therapeutic agents for targeting CARM1-driven cancers.

Proteome-wide Profiling of Asymmetric Dimethylated Arginine in Human Breast Tumors

Arginine methylation catalyzed by protein arginine methyltransferases (PRMTs) is a prevalent post-translational modification (PTM) that regulates diverse cellular processes. Aberrant expression of type I PRMTs that catalyze asymmetric arginine dimethylation (ADMA) is often found in cancer, though little is known about the ADMA status of substrate proteins in tumors. Using LC-MS/MS along with pan-specific ADMA antibodies, we performed global mapping of ADMA in five patient-derived xenograft (PDX) tumors representing different subtypes of human breast cancer. In total, 403 methylated sites from 213 proteins were identified, including 322 novel sites when compared to the PhosphositesPlus database. Moreover, using peptide arrays in vitro, approximately 70% of the putative substrates were validated to be methylated by PRMT1, PRMT4, and PRMT6. Notably, when compared with our previously identified ADMA sites from breast cancer cell lines, only 75 ADMA sites overlapped between cell lines and PDX tumors. Collectively, this study provides a useful resource for both PRMT and breast cancer communities for further exploitation of the functions of PRMT dysregulation during breast cancer progression.

UniProt and Mass Spectrometry-Based Proteomics-A 2-Way Working Relationship

The human proteome comprises of all of the proteins produced by the sequences translated from the human genome with additional modifications in both sequence and function caused by nonsynonymous variants and posttranslational modifications including cleavage of the initial transcript into smaller peptides and polypeptides. The UniProtKB database (www.uniprot.org) is the world's leading high-quality, comprehensive and freely accessible resource of protein sequence and functional information and presents a summary of experimentally verified, or computationally predicted, functional information added by our expert biocuration team for each protein in the proteome. Researchers in the field of mass spectrometry-based proteomics both consume and add to the body of data available in UniProtKB, and this review highlights the information we provide to this community and the knowledge we in turn obtain from groups via deposition of large-scale datasets in public domain databases.

The Cdc48 N-terminal domain has a molecular switch that mediates the Npl4-Ufd1-Cdc48 complex formation

Cdc48 (VCP/p97) is a major AAA-ATPase involved in protein quality control, along with its canonical cofactors Ufd1 and Npl4 (UN). Here, we present novel structural insights into the interactions within the Cdc48-Npl4-Ufd1 ternary complex. Using integrative modeling, we combine subunit structures with crosslinking mass spectrometry (XL-MS) to map the interaction between Npl4 and Ufd1, alone and in complex with Cdc48. We describe the stabilization of the UN assembly upon binding with the N-terminal-domain (NTD) of Cdc48 and identify a highly conserved cysteine, C115, at the Cdc48-Npl4-binding interface which is central to the stability of the Cdc48-Npl4-Ufd1 complex. Mutation of Cys115 to serine disrupts the interaction between Cdc48-NTD and Npl4-Ufd1 and leads to a moderate decrease in cellular growth and protein quality control in yeast. Our results provide structural insight into the architecture of the Cdc48-Npl4-Ufd1 complex as well as its in vivo implications.

Control of atypical PKCι membrane dissociation by tyrosine phosphorylation within a PB1-C1 interdomain interface

Atypical PKCs are cell polarity kinases that operate at the plasma membrane where they function within multiple molecular complexes to contribute to the establishment and maintenance of polarity. In contrast to the classical and novel PKCs, atypical PKCs do not respond to diacylglycerol cues to bind the membrane compartment. Until recently, it was not clear how aPKCs are recruited; whether aPKCs can directly interact with membranes or whether they are dependent on other protein interactors to do so. Two recent studies identified the pseudosubstrate region and the C1 domain as direct membrane interaction modules; however, their relative importance and coupling are unknown. We combined molecular modeling and functional assays to show that the regulatory module of aPKCι, comprising the PB1 pseudosubstrate and C1 domains, forms a cooperative and spatially continuous invariant membrane interaction platform. Furthermore, we show the coordinated orientation of membrane-binding elements within the regulatory module requires a key PB1-C1 interfacial β-strand (beta-strand linker). We show this element contains a highly conserved Tyr residue that can be phosphorylated and that negatively regulates the integrity of the regulatory module, leading to membrane release. We thus expose a hitherto unknown regulatory mechanism of aPKCι membrane binding and release during cell polarization.

Prognostic Biomarkers Based on Proteomic Technology in COPD: A Recent Review

Chronic obstructive pulmonary disease (COPD) is a common heterogeneous respiratory disease which is characterized by persistent and incompletely reversible airflow limitation. Due to the heterogeneity and phenotypic complexity of COPD, traditional diagnostic methods provide limited information and pose a great challenge to clinical management. In recent years, with the development of omics technologies, proteomics, metabolomics, transcriptomics, etc., have been widely used in the study of COPD, providing great help to discover new biomarkers and elucidate the complex mechanisms of COPD. In this review, we summarize the prognostic biomarkers of COPD based on proteomic studies in recent years and evaluate their association with COPD prognosis. Finally, we present the prospects and challenges of COPD prognostic-related studies. This review is expected to provide cutting-edge evidence in prognostic evaluation of clinical patients with COPD and to inform future proteomic studies on prognostic biomarkers of COPD.

High-quality and robust protein quantification in large clinical/pharmaceutical cohorts with IonStar proteomics investigation

Robust, reliable quantification of large sample cohorts is often essential for meaningful clinical or pharmaceutical proteomics investigations, but it is technically challenging. When analyzing very large numbers of samples, isotope labeling approaches may suffer from substantial batch effects, and even with label-free methods, it becomes evident that low-abundance proteins are not reliably measured owing to unsufficient reproducibility for quantification. The MS1-based quantitative proteomics pipeline IonStar was designed to address these challenges. IonStar is a label-free approach that takes advantage of the high sensitivity/selectivity attainable by ultrahigh-resolution (UHR)-MS1 acquisition (e.g., 120-240k full width at half maximum at m/z = 200) which is now widely available on ultrahigh-field Orbitrap instruments. By selectively and accurately procuring quantitative features of peptides within precisely defined, very narrow m/z windows corresponding to the UHR-MS1 resolution, the method minimizes co-eluted interferences and substantially enhances signal-to-noise ratio of low-abundance species by decreasing noise level. This feature results in high sensitivity, selectivity, accuracy and precision for quantification of low-abundance proteins, as well as fewer missing data and fewer false positives. This protocol also emphasizes the importance of well-controlled, robust experimental procedures to achieve high-quality quantification across a large cohort. It includes a surfactant cocktail-aided sample preparation procedure that achieves high/reproducible protein/peptide recoveries among many samples, and a trapping nano-liquid chromatography-mass spectrometry strategy for sensitive and reproducible acquisition of UHR-MS1 peptide signal robustly across a large cohort. Data processing and quality evaluation are illustrated using an example dataset ( http://proteomecentral.proteomexchange.org ), and example results from pharmaceutical project and one clinical project (patients with acute respiratory distress syndrome) are shown. The complete IonStar pipeline takes ~1-2 weeks for a sample cohort containing ~50-100 samples.

Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression

Targeted proteomics enables hypothesis-driven research by measuring the cellular expression of protein cohorts related by function, disease, or class after perturbation. Here, we present a pathway-centric approach and an assay builder resource for targeting entire pathways of up to 200 proteins selected from >10,000 expressed proteins to directly measure their abundances, exploiting sample multiplexing to increase throughput by 16-fold. The strategy, termed GoDig, requires only a single-shot LC-MS analysis, ~1 µg combined peptide material, a list of up to 200 proteins, and real-time analytics to trigger simultaneous quantification of up to 16 samples for hundreds of analytes. We apply GoDig to quantify the impact of genetic variation on protein expression in mice fed a high-fat diet. We create several GoDig assays to quantify the expression of multiple protein families (kinases, lipid metabolism- and lipid droplet-associated proteins) across 480 fully-genotyped Diversity Outbred mice, revealing protein quantitative trait loci and establishing potential linkages between specific proteins and lipid homeostasis.

Integrating Multiple Quantitative Proteomic Analyses Using MetaMSD

MetaMSD is a proteomic software that integrates multiple quantitative mass spectrometry data analysis results using statistical summary combination approaches. By utilizing this software, scientists can combine results from their pilot and main studies to maximize their biomarker discovery while effectively controlling false discovery rates. It also works for combining proteomic datasets generated by different labeling techniques and/or different types of mass spectrometry instruments. With these advantages, MetaMSD enables biological researchers to explore various proteomic datasets in public repositories to discover new biomarkers and generate interesting hypotheses for future studies. In this protocol, we provide a step-by-step procedure on how to install and perform a meta-analysis for quantitative proteomics using MetaMSD.

The LRRK2 signaling network converges on a centriolar phospho-Rab10/RILPL1 complex to cause deficits in centrosome cohesion and cell polarization

The Parkinson's-disease-associated LRRK2 kinase phosphorylates multiple Rab GTPases including Rab8 and Rab10, which enhances their binding to RILPL1 and RILPL2. The nascent interaction between phospho-Rab10 and RILPL1 blocks ciliogenesis in vitro and in the intact brain, and interferes with the cohesion of duplicated centrosomes in dividing cells. We show here that regulators of the LRRK2 signaling pathway including vps35 and PPM1H converge upon causing centrosomal deficits. The cohesion alterations do not require the presence of other LRRK2 kinase substrates including Rab12, Rab35 and Rab43 or the presence of RILPL2. Rather, they depend on the RILPL1-mediated centrosomal accumulation of phosphorylated Rab10. RILPL1 localizes to the subdistal appendage of the mother centriole, followed by recruitment of the LRRK2-phosphorylated Rab proteins to cause the centrosomal defects. The centrosomal alterations impair cell polarization as monitored by scratch wound assays which is reverted by LRRK2 kinase inhibition. These data reveal a common molecular pathway by which enhanced LRRK2 kinase activity impacts upon centrosome-related events to alter the normal biology of a cell.

Summary: The Parkinson's disease LRRK2 signaling pathway converges upon the formation of a complex at the subdistal appendage of the mother centriole which causes centrosomal deficits and impairs appropriate cell polarization.

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.

Genome-wide RNAi screen identifies novel players in human 60S subunit biogenesis including key enzymes of polyamine metabolism

Ribosome assembly is an essential process that is linked to human congenital diseases and tumorigenesis. While great progress has been made in deciphering mechanisms governing ribosome biogenesis in eukaryotes, an inventory of factors that support ribosome synthesis in human cells is still missing, in particular regarding the maturation of the large 60S subunit. Here, we performed a genome-wide RNAi screen using an imaging-based, single cell assay to unravel the cellular machinery promoting 60S subunit assembly in human cells. Our screen identified a group of 310 high confidence factors. These highlight the conservation of the process across eukaryotes and reveal the intricate connectivity of 60S subunit maturation with other key cellular processes, including splicing, translation, protein degradation, chromatin organization and transcription. Intriguingly, we also identified a cluster of hits comprising metabolic enzymes of the polyamine synthesis pathway. We demonstrate that polyamines, which have long been used as buffer additives to support ribosome assembly in vitro, are required for 60S maturation in living cells. Perturbation of polyamine metabolism results in early defects in 60S but not 40S subunit maturation. Collectively, our data reveal a novel function for polyamines in living cells and provide a rich source for future studies on ribosome synthesis.

ProteomicsDB: toward a FAIR open-source resource for life-science research

ProteomicsDB (https://www.ProteomicsDB.org) is a multi-omics and multi-organism resource for life science research. In this update, we present our efforts to continuously develop and expand ProteomicsDB. The major focus over the last two years was improving the findability, accessibility, interoperability and reusability (FAIR) of the data as well as its implementation. For this purpose, we release a new application programming interface (API) that provides systematic access to essentially all data in ProteomicsDB. Second, we release a new open-source user interface (UI) and show the advantages the scientific community gains from such software. With the new interface, two new visualizations of protein primary, secondary and tertiary structure as well an updated spectrum viewer were added. Furthermore, we integrated ProteomicsDB with our deep-neural-network Prosit that can predict the fragmentation characteristics and retention time of peptides. The result is an automatic processing pipeline that can be used to reevaluate database search engine results stored in ProteomicsDB. In addition, we extended the data content with experiments investigating different human biology as well as a newly supported organism.

Quantitative Detection of Protein Splice Variants by Selected Reaction Monitoring (SRM) Mass Spectrometry

Molecular diversification of the cellular proteome through alternative splicing has emerged as an important biological principle. However, the lack of tools to specifically detect and quantify proteoforms (Smith et al., Nat Methods 10:186-187, 2013) is a major impediment to functional studies. Recently, biological mass spectrometry (MS) has undergone impressive advances (Mann, Nat Rev Mol Cell Biol 17:678, 2016), including the generation of a highly diverse set of biological applications (Aebersold and Mann, Nature 537:347-355, 2016), and has demonstrated to be an essential tool to address many biological questions (Savitski et al., Science 346:1255784, 2014; Rinner et al., Nat Methods 5:315-318, 2008). In particular, targeted LC-MS, with its high selectivity and specificity, is ideally suited for the precise and sensitive quantification of specific proteins and their proteoforms (Picotti and Aebersold, Nat Methods 9:555-566, 2012). We describe in detail the application of this workflow applied to dissect the molecular diversity of the synaptic adhesion proteins and their splicing-derived proteoforms (Schreiner et al., Elife 4:e07794, 2015).

Comprehensive comparison of sample preparation workflows for proteomics

Mass spectrometry-based proteomics experiments can be subject to a large variability, which forms an obstacle to obtaining deep and accurate protein identification. Here, to obtain an optimal sample preparation workflow...

Single-Step Affinity Purification (ssAP) and Mass Spectrometry of Macromolecular Complexes in the Yeast S. cerevisiae

Cellular functions are mostly defined by the dynamic interactions of proteins within macromolecular networks. Deciphering the composition of macromolecular complexes and their dynamic rearrangements is the key to get a comprehensive picture of cellular behavior and to understand biological systems. In the past two decades, affinity purification coupled to mass spectrometry has become a powerful tool to comprehensively study interaction networks and their assemblies. To overcome initial limitations of the approach, in particular, the effect of protein and RNA degradation, loss of transient interactors, and poor overall yield of intact complexes from cell lysates, various modifications to affinity purification protocols have been devised over the years. In this chapter, we describe a rapid single-step affinity purification method for the efficient isolation of dynamic macromolecular complexes. The technique employs cell lysis by cryo-milling, which ensures nondegraded starting material in the submicron range, and magnetic beads, which allow for dense antibody-conjugation and thus rapid complex isolation, while avoiding loss of transient interactions. The method is epitope tag-independent, and overcomes many of the previous limitations to produce large interactomes with almost no contamination. The protocol as described here has been optimized for the yeast S. cerevisiae.

AKIRIN2 controls the nuclear import of proteasomes in vertebrates

Protein expression and turnover are controlled through a complex interplay of transcriptional, post-transcriptional and post-translational mechanisms to enable spatial and temporal regulation of cellular processes. To systematically elucidate such gene regulatory networks, we developed a CRISPR screening assay based on time-controlled Cas9 mutagenesis, intracellular immunostaining and fluorescence-activated cell sorting that enables the identification of regulatory factors independent of their effects on cellular fitness. We pioneered this approach by systematically probing the regulation of the transcription factor MYC, a master regulator of cell growth^1-3. Our screens uncover a highly conserved protein, AKIRIN2, that is essentially required for nuclear protein degradation. We found that AKIRIN2 forms homodimers that directly bind to fully assembled 20S proteasomes to mediate their nuclear import. During mitosis, proteasomes are excluded from condensing chromatin and re-imported into newly formed daughter nuclei in a highly dynamic, AKIRIN2-dependent process. Cells undergoing mitosis in the absence of AKIRIN2 become devoid of nuclear proteasomes, rapidly causing accumulation of MYC and other nuclear proteins. Collectively, our study reveals a dedicated pathway controlling the nuclear import of proteasomes in vertebrates and establishes a scalable approach to decipher regulators in essential cellular processes.

Music of metagenomics-a review of its applications, analysis pipeline, and associated tools

This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."

Variants in LSM7 impair LSM complexes assembly, neurodevelopment in zebrafish and may be associated with an ultra-rare neurological disease

Leukodystrophies, genetic neurodevelopmental and/or neurodegenerative disorders of cerebral white matter, result from impaired myelin homeostasis and metabolism. Numerous genes have been implicated in these heterogeneous disorders; however, many individuals remain without a molecular diagnosis. Using whole-exome sequencing, biallelic variants in LSM7 were uncovered in two unrelated individuals, one with a leukodystrophy and the other who died in utero. LSM7 is part of the two principle LSM protein complexes in eukaryotes, namely LSM1-7 and LSM2-8. Here, we investigate the molecular and functional outcomes of these LSM7 biallelic variants in vitro and in vivo. Affinity purification-mass spectrometry of the LSM7 variants showed defects in the assembly of both LSM complexes. Lsm7 knockdown in zebrafish led to central nervous system defects, including impaired oligodendrocyte development and motor behavior. Our findings demonstrate that variants in LSM7 cause misassembly of the LSM complexes, impair neurodevelopment of the zebrafish, and may be implicated in human disease. The identification of more affected individuals is needed before the molecular mechanisms of mRNA decay and splicing regulation are added to the categories of biological dysfunctions implicated in leukodystrophies, neurodevelopmental and/or neurodegenerative diseases.

Non-canonical function of DGCR8 in DNA double-strand break repair signaling and tumor radioresistance

In response to DNA double-strand breaks (DSBs), repair proteins are recruited to the damaged sites. Ubiquitin signaling plays a critical role in coordinating protein recruitment during the DNA damage response. Here, we find that the microRNA biogenesis factor DGCR8 promotes tumor resistance to X-ray radiation independently of its Drosha-binding ability. Upon radiation, the kinase ATM and the deubiquitinase USP51 mediate the activation and stabilization of DGCR8 through phosphorylation and deubiquitination. Specifically, radiation-induced ATM-dependent phosphorylation of DGCR8 at serine 677 facilitates USP51 to bind, deubiquitinate, and stabilize DGCR8, which leads to the recruitment of DGCR8 and DGCR8’s binding partner RNF168 to MDC1 and RNF8 at DSBs. This, in turn, promotes ubiquitination of histone H2A, repair of DSBs, and radioresistance. Altogether, these findings reveal the non-canonical function of DGCR8 in DSB repair and suggest that radiation treatment may result in therapy-induced tumor radioresistance through ATM- and USP51-mediated activation and upregulation of DGCR8.

The molecular mechanisms underlying cancer cell radioresistance need to be elucidated. In this study, the authors show that the microRNA biogenesis factor DGCR8 is stabilized by USP51 and ATM upon irradiation and by consequence it promotes the repair of DNA double-strand breaks and radioresistance by recruiting RNF168 to sites of damage.

Quantitative mapping of human hair greying and reversal in relation to life stress

Background:

Hair greying is a hallmark of aging generally believed to be irreversible and linked to psychological stress.

Methods:

Here, we develop an approach to profile hair pigmentation patterns (HPPs) along individual human hair shafts, producing quantifiable physical timescales of rapid greying transitions.

Results:

Using this method, we show white/grey hairs that naturally regain pigmentation across sex, ethnicities, ages, and body regions, thereby quantitatively defining the reversibility of greying in humans. Molecularly, grey hairs upregulate proteins related to energy metabolism, mitochondria, and antioxidant defenses. Combining HPP profiling and proteomics on single hairs, we also report hair greying and reversal that can occur in parallel with psychological stressors. To generalize these observations, we develop a computational simulation, which suggests a threshold-based mechanism for the temporary reversibility of greying.

Conclusions:

Overall, this new method to quantitatively map recent life history in HPPs provides an opportunity to longitudinally examine the influence of recent life exposures on human biology.

Funding:

This work was supported by the Wharton Fund and NIH grants GM119793, MH119336, and AG066828 (MP).

Hair greying is a visible sign of aging that affects everyone. The loss of hair color is due to the loss of melanin, a pigment found in the skin, eyes and hair. Research in mice suggests stress may accelerate hair greying, but there is no definitive research on this in humans. This is because there are no research tools to precisely map stress and hair color over time. But, just like tree rings hold information about past decades, and rocks hold information about past centuries, hairs hold information about past months and years.

Hair growth is an active process that happens under the skin inside hair follicles. It demands lots of energy, supplied by structures inside cells called mitochondria. While hairs are growing, cells receive chemical and electrical signals from inside the body, including stress hormones. It is possible that these exposures change proteins and other molecules laid down in the growing hair shaft. As the hair grows out of the scalp, it hardens, preserving these molecules into a stable form. This preservation is visible as patterns of pigmentation. Examining single-hairs and matching the patterns to life events could allow researchers to look back in time through a person’s biological history.

Rosenberg et al. report a new way to digitize and measure small changes in color along single human hairs. This method revealed that some white hairs naturally regain their color, something that had not been reported in a cohort of healthy individuals before. Aligning the hair pigmentation patterns with recent reports of stress in the hair donors’ lives showed striking associations. When one donor reported an increase in stress, a hair lost its pigment. When the donor reported a reduction in stress, the same hair regained its pigment. Rosenberg et al. mapped hundreds of proteins inside the hairs to show that white hairs contained more proteins linked to mitochondria and energy use. This suggests that metabolism and mitochondria may play a role in hair greying. To explore these observations in more detail Rosenberg et al. developed a mathematical model that simulates the greying of a whole head of hair over a lifetime, an experiment impossible to do with living people. The model suggested that there might be a threshold for temporary greying; if hairs are about to go grey anyway, a stressful event might trigger that change earlier. And when the stressful event ends, if a hair is just above the threshold, then it could revert back to dark.

The new method for measuring small changes in hair coloring opens up the possibility of using hair pigmentation patterns like tree rings. This could track the influence of past life events on human biology. In the future, monitoring hair pigmentation patterns could provide a way to trace the effectiveness of treatments aimed at reducing stress or slowing the aging process. Understanding how ‘old’ white hairs regain their ‘young’ pigmented state could also reveal new information about the malleability of human aging more generally.

Deciphering Human Leukocyte Antigen Susceptibility Maps From Immunopeptidomics Characterization in Oncology and Infections

Genetic variability across the three major histocompatibility complex (MHC) class I genes (human leukocyte antigen [HLA] A, B, and C) may affect susceptibility to many diseases such as cancer, auto-immune or infectious diseases. Individual genetic variation may help to explain different immune responses to microorganisms across a population. HLA typing can be fast and inexpensive; however, deciphering peptides loaded on MHC-I and II which are presented to T cells, require the design and development of high-sensitivity methodological approaches and subsequently databases. Hence, these novel strategies and databases could help in the generation of vaccines using these potential immunogenic peptides and in identifying high-risk HLA types to be prioritized for vaccination programs. Herein, the recent developments and approaches, in this field, focusing on the identification of immunogenic peptides have been reviewed and the next steps to promote their translation into biomedical and clinical practice are discussed.

Simultaneously monitoring endogenous MAPK members in single living cells by multi-channel fluorescence correlation spectroscopy

The mitogen-activated protein kinase (MAPK) pathway is a major module for cellular signal transduction. The dysregulation of the MAPK pathway has been involved in the pathogenesis of multiple diseases ranging from cancers to chronic inflammations. So far, we have not fully understood the influences of external factors and signaling networks on the MAPK pathway due to the lack of in situ methods for simultaneous detection of multiple kinases in the pathway in living cells. Herein, we present a new strategy for in situ and simultaneously monitoring MAPK pathway kinases in single living cells combining multi-channel fluorescence correlation spectroscopy (FCS) with affinity fluorescent probes. We chose rapidly growing fibrosarcoma kinase (RAF), mitogen-activated protein kinase (MEK), and extracellular signal-regulated kinase (ERK) as representative members in the MAPK pathway. We designed and synthesized three fluorescent affinity probes and experimental results demonstrated that the three probes specifically targeted endogenous BRAF, MEK1/2, and ERK1/2 in living cells. Based on the multi-channel FCS system, we studied the influences of biological substances, drugs and oxidative stress on the activities of endogenous MAPK kinases and the cross-talk between the MAPK and PI3K-mTOR pathways. We have found that serum, sorafenib, and hydrogen peroxide can regulate multiple MAPK kinases and the effects of external stimuli can transmit to the MAPK pathway; furthermore, we have observed that the MAPK pathway can be activated by modulating the PI3K-mTOR pathway. Our results illustrated the complexity of a cellular signal network and the necessity of in situ and simultaneous determination of biomolecules in living cells.

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.

Differential Interactome Proposes Subtype-Specific Biomarkers and Potential Therapeutics in Renal Cell Carcinomas

Although many studies have been conducted on single gene therapies in cancer patients, the reality is that tumor arises from different coordinating protein groups. Unveiling perturbations in protein interactome related to the tumor formation may contribute to the development of effective diagnosis, treatment strategies, and prognosis. In this study, considering the clinical and transcriptome data of three Renal Cell Carcinoma (RCC) subtypes (ccRCC, pRCC, and chRCC) retrieved from The Cancer Genome Atlas (TCGA) and the human protein interactome, the differential protein–protein interactions were identified in each RCC subtype. The approach enabled the identification of differentially interacting proteins (DIPs) indicating prominent changes in their interaction patterns during tumor formation. Further, diagnostic and prognostic performances were generated by taking into account DIP clusters which are specific to the relevant subtypes. Furthermore, considering the mesenchymal epithelial transition (MET) receptor tyrosine kinase (PDB ID: 3DKF) as a potential drug target specific to pRCC, twenty-one lead compounds were identified through virtual screening of ZINC molecules. In this study, we presented remarkable findings in terms of early diagnosis, prognosis, and effective treatment strategies, that deserve further experimental and clinical efforts.

Genome-Wide Proteomics and Phosphoproteomics Analysis of Trypanosoma cruzi During Differentiation

Trypanosoma cruzi is a pathogenic protozoan that still has an impact on public health, despite the decrease in the number of infection cases along the years. T. cruzi possesses an heteroxenic life cycle in which it differentiates in at least four forms. Among the differentiation processes, metacyclogenesis has been exploited in different views by researchers. An intriguing question that rises is how metacyclogenesis is triggered and controlled by cell signaling and which are the differentially expressed proteins and posttranslational modifications involved in this process. An important cell signaling pathway is the protein phosphorylation, and it is reinforced in T. cruzi in which the gene expression control occurs almost exclusively posttranscriptionally. Additionally, the number of protein kinases in T. cruzi is relatively high compared to other organisms. A way to approach these questions is evaluating the cells through phosphoproteomics and proteomics. In this chapter, we will describe the steps from the cell protein extraction, digestion and fractionation, phosphopeptide enrichment, to LC-MS/MS analysis as well as a brief overview on peptide identification. In addition, a published method for in vitro metacyclogenesis will be detailed.

UniProt: the universal protein knowledgebase in 2021

The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.

UniProt: the universal protein knowledgebase in 2021

The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.

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.

Proteomic profiling of the monothiol glutaredoxin Grx3 reveals its global role in the regulation of iron dependent processes

Iron is an essential nutrient required as a cofactor for many biological processes. As a fungal commensal-pathogen of humans, Candida albicans encounters a range of bioavailable iron levels in the human host and maintains homeostasis with a conserved regulatory circuit. How C. albicans senses and responds to iron availability is unknown. In model yeasts, regulation of the iron homeostasis circuit requires monothiol glutaredoxins (Grxs), but their functions beyond the regulatory circuit are unclear. Here, we show Grx3 is required for virulence and growth on low iron for C. albicans. To explore the global roles of Grx3, we applied a proteomic approach and performed in vivo cross-linked tandem affinity purification coupled with mass spectrometry. We identified a large number of Grx3 interacting proteins that function in diverse biological processes. This included Fra1 and Bol2/Fra2, which function with Grxs in intracellular iron trafficking in other organisms. Grx3 interacts with and regulates the activity of Sfu1 and Hap43, components of the C. albicans iron regulatory circuit. Unlike the regulatory circuit, which determines expression or repression of target genes in response to iron availability, Grx3 amplifies levels of gene expression or repression. Consistent with the proteomic data, the grx3 mutant is sensitive to heat shock, oxidative, nitrosative, and genotoxic stresses, and shows growth dependence on histidine, leucine, and tryptophan. We suggest Grx3 is a conserved global regulator of iron-dependent processes occurring within the cell.

Mammalian pathogens occupy a diverse set of niches within the host organism. These niches vary in iron and oxygen availability. As a commensal and pathogen of humans, its ability to regulate iron uptake and utilization in response to bioavailable iron level is critical for its survival in different host environments encompassing a broad range of iron levels. This study aims to understand how C. albicans senses and responds to iron level to regulate multiple aspects of its biology. The cytosolic monothiol glutaredoxin Grx3 is a critical regulator of C. albicans iron homeostasis and virulence. Taking a proteomic approach, we identified a large list of Grx3 associated proteins of diverse functions, including iron-sulfur trafficking, iron homeostasis, metabolism redox homeostasis, protein translation, DNA maintenance and repair. In support of these protein associations, Grx3 is important for all these processes. Thus, Grx3 is a global regulator of iron homeostasis and other iron dependent cellular processes.

CRISPR-Cas12a-assisted PCR tagging of mammalian genes

Here we describe a time-efficient strategy for endogenous C-terminal gene tagging in mammalian tissue culture cells. An online platform is used to design two long gene-specific oligonucleotides for PCR with generic template cassettes to create linear dsDNA donors, termed PCR cassettes. PCR cassettes encode the tag (e.g., GFP), a Cas12a CRISPR RNA for cleavage of the target locus, and short homology arms for directed integration via homologous recombination. The integrated tag is coupled to a generic terminator shielding the tagged gene from the co-inserted auxiliary sequences. Co-transfection of PCR cassettes with a Cas12a-encoding plasmid leads to robust endogenous expression of tagged genes, with tagging efficiency of up to 20% without selection, and up to 60% when selection markers are used. We used target-enrichment sequencing to investigate all potential sources of artifacts. Our work outlines a quick strategy particularly suitable for exploratory studies using endogenous expression of fluorescent protein-tagged genes.

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.

STAT3-dependent analysis reveals PDK4 as independent predictor of recurrence in prostate cancer

Prostate cancer (PCa) has a broad spectrum of clinical behavior; hence, biomarkers are urgently needed for risk stratification. Here, we aim to find potential biomarkers for risk stratification, by utilizing a gene co-expression network of transcriptomics data in addition to laser-microdissected proteomics from human and murine prostate FFPE samples. We show up-regulation of oxidative phosphorylation (OXPHOS) in PCa on the transcriptomic level and up-regulation of the TCA cycle/OXPHOS on the proteomic level, which is inversely correlated to STAT3 expression. We hereby identify gene expression of pyruvate dehydrogenase kinase 4 (PDK4), a key regulator of the TCA cycle, as a promising independent prognostic marker in PCa. PDK4 predicts disease recurrence independent of diagnostic risk factors such as grading, staging, and PSA level. Therefore, low PDK4 is a promising marker for PCa with dismal prognosis.

The "adductome": A limited repertoire of adducted proteins in human cells

Proteins form adducts with nucleic acids in a variety of contexts, and these adducts may be cytotoxic if not repaired. Here we apply a proteomic approach to identification of proteins adducted to DNA or RNA in normally proliferating cells. This approach combines RADAR fractionation of proteins covalently bound to nucleic acids with quantitative mass spectrometry (MS). We demonstrate that "RADAR-MS" can quantify induction of TOP1- or TOP2-DNA adducts in cells treated with topotecan or etoposide, respectively, and also identify intermediates in physiological adduct repair. We validate RADAR-MS for discovery of previously unknown adducts by determining the repertoires of adducted proteins in two different normally proliferating human cell lines, CCRF-CEM T cells and GM639 fibroblasts. These repertoires are significantly similar with one another and exhibit robust correlations in their quantitative profiles (Spearman r = 0.52). A very similar repertoire is identified by the classical approach of CsCl buoyant density gradient centrifugation. We find that in normally proliferating human cells, the repertoire of adducted proteins - the "adductome" - is comprised of a limited number of proteins belonging to specific functional groups, and that it is greatly enriched for histones, HMG proteins and proteins involved in RNA splicing. Treatment with low concentrations of formaldehyde caused little change in the composition of the repertoire of adducted proteins, suggesting that reactive aldehydes generated by ongoing metabolic processes may contribute to protein adduction in normally proliferating cells. The identification of an endogenous adductome highlights the importance of adduct repair in maintaining genomic structure and the potential for deficiencies in adduct repair to contribute to cancer.

Hypothesis: protein and RNA attributes are continuously optimized over time

Background

Little is known why proteins and RNAs exhibit half-lives varying over several magnitudes. Despite many efforts, a conclusive link between half-lives and gene function could not be established suggesting that other determinants may influence these molecular attributes.

Results

Here, I find that with increasing gene age there is a gradual and significant increase of protein and RNA half-lives, protein structure, and other molecular attributes that tend to affect protein abundance. These observations are accommodated in a hypothesis which posits that new genes at ‘birth’ are not optimized and thus their products exhibit low half-lives and less structure but continuous mutagenesis eventually improves these attributes. Thus, the protein and RNA products of the oldest genes obtained their high degrees of stability and structure only after billions of years while the products of younger genes had less time to be optimized and are therefore less stable and structured. Because more stable proteins with lower turnover require less transcription to maintain the same level of abundance, reduced transcription-associated mutagenesis (TAM) would fixate the changes by increasing gene conservation.

Conclusions

Consequently, the currently observed diversity of molecular attributes is a snapshot of gene products being at different stages along their temporal path of optimization.

Rearrangement of the Protein Phosphatase 1 Interactome During Heart Failure Progression

BACKGROUND

Heart failure (HF) is a complex disease with a rising prevalence despite advances in treatment. Protein phosphatase 1 (PP1) has long been implicated in HF pathogenesis, but its exact role is both unclear and controversial. Most previous studies measured only the PP1 catalytic subunit (PP1c) without investigating its diverse set of interactors, which confer localization and substrate specificity to the holoenzyme. In this study, we define the PP1 interactome in cardiac tissue and test the hypothesis that this interactome becomes rearranged during HF progression at the level of specific PP1c interactors.

METHODS

Mice were subjected to transverse aortic constriction and grouped on the basis of ejection fraction into sham, hypertrophy, moderate HF (ejection fraction, 30%-40%), and severe HF (ejection fraction <30%). Cardiac lysates were subjected to affinity purification with anti-PP1c antibodies followed by high-resolution mass spectrometry. PP1 regulatory subunit 7 (Ppp1r7) was knocked down in mouse cardiomyocytes and HeLa cells with adeno-associated virus serotype 9 and siRNA, respectively. Calcium imaging was performed on isolated ventricular myocytes.

RESULTS

Seventy-one and 98 PP1c interactors were quantified from mouse cardiac and HeLa lysates, respectively, including many novel interactors and protein complexes. This represents the largest reproducible PP1 interactome data set ever captured from any tissue, including both primary and secondary/tertiary interactors. Nine PP1c interactors with changes in their binding to PP1c were strongly associated with HF progression, including 2 known (Ppp1r7 and Ppp1r18) and 7 novel interactors. Within the entire cardiac PP1 interactome, Ppp1r7 had the highest binding to PP1c. Cardiac-specific knockdown in mice led to cardiac dysfunction and disruption of calcium release from the sarcoplasmic reticulum.

CONCLUSIONS

PP1 is best studied at the level of its interactome, which undergoes significant rearrangement during HF progression. The 9 key interactors that are associated with HF progression may represent potential targets in HF therapy. In particular, Ppp1r7 may play a central role in regulating the PP1 interactome by acting as a competitive molecular "sponge" of PP1c.

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.

The Encyclopedia of Proteome Dynamics: a big data ecosystem for (prote)omics

Driven by improvements in speed and resolution of mass spectrometers (MS), the field of proteomics, which involves the large-scale detection and analysis of proteins in cells, tissues and organisms, continues to expand in scale and complexity. There is a resulting growth in datasets of both raw MS files and processed peptide and protein identifications. MS-based proteomics technology is also used increasingly to measure additional protein properties affecting cellular function and disease mechanisms, including post-translational modifications, protein-protein interactions, subcellular and tissue distributions. Consequently, biologists and clinicians need innovative tools to conveniently analyse, visualize and explore such large, complex proteomics data and to integrate it with genomics and other related large-scale datasets. We have created the Encyclopedia of Proteome Dynamics (EPD) to meet this need (https://peptracker.com/epd/). The EPD combines a polyglot persistent database and web-application that provides open access to integrated proteomics data for >30 000 proteins from published studies on human cells and model organisms. It is designed to provide a user-friendly interface, featuring graphical navigation with interactive visualizations that facilitate powerful data exploration in an intuitive manner. The EPD offers a flexible and scalable ecosystem to integrate proteomics data with genomics information, RNA expression and other related, large-scale datasets.

The Proteins API: accessing key integrated protein and genome information

The Proteins API provides searching and programmatic access to protein and associated genomics data such as curated protein sequence positional annotations from UniProtKB, as well as mapped variation and proteomics data from large scale data sources (LSS). Using the coordinates service, researchers are able to retrieve the genomic sequence coordinates for proteins in UniProtKB. This, the LSS genomics and proteomics data for UniProt proteins is programmatically only available through this service. A Swagger UI has been implemented to provide documentation, an interface for users, with little or no programming experience, to 'talk' to the services to quickly and easily formulate queries with the services and obtain dynamically generated source code for popular programming languages, such as Java, Perl, Python and Ruby. Search results are returned as standard JSON, XML or GFF data objects. The Proteins API is a scalable, reliable, fast, easy to use RESTful services that provides a broad protein information resource for users to ask questions based upon their field of expertise and allowing them to gain an integrated overview of protein annotations available to aid their knowledge gain on proteins in biological processes. The Proteins API is available at (http://www.ebi.ac.uk/proteins/api/doc).

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.

Intelligence Algorithms for Protein Classification by Mass Spectrometry

Mass spectrometry (MS) is an important technique in protein research. Effective classification methods by MS data could contribute to early and less-invasive diagnosis and also facilitate developments in the bioinformatics field. As MS data is featured by high dimension, appropriate methods which can effectively deal with the large amount of MS data have been widely studied. In this paper, the applications of methods based on intelligence algorithms have been investigated. Firstly, classification and biomarker analysis methods using typical machine learning approaches have been discussed. Then those are followed by the Ensemble strategy algorithms. Clearly, simple and basic machine learning algorithms hardly addressed the various needs of protein MS classification. Preprocessing algorithms have been also studied, as these methods are useful for feature selection or feature extraction to improve classification performance. Protein MS data growing with data volume becomes complicated and large; improvements in classification methods in terms of classifier selection and combinations of different algorithms and preprocessing algorithms are more emphasized in further work.

Revealing the cellular degradome by mass spectrometry analysis of proteasome-cleaved peptides

Cellular function is critically regulated through degradation of substrates by the proteasome. To enable direct analysis of naturally cleaved proteasomal peptides under physiological conditions, we developed mass spectrometry analysis of proteolytic peptides (MAPP), a method for proteasomal footprinting that allows for capture, isolation and analysis of proteasome-cleaved peptides. Application of MAPP to cancer cell lines as well as primary immune cells revealed dynamic modulation of the cellular degradome in response to various stimuli, such as proinflammatory signals. Further, we performed analysis of minute amounts of clinical samples by studying cells from the peripheral blood of patients with systemic lupus erythematosus (SLE). We found increased degradation of histones in patient immune cells, thereby suggesting a role of aberrant proteasomal degradation in the pathophysiology of SLE. Thus, MAPP offers a broadly applicable method to facilitate the study of the cellular-degradation landscape in various cellular conditions and diseases involving changes in proteasomal degradation, including protein aggregation diseases, autoimmunity and cancer.