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

Decrypting lysine deacetylase inhibitor action and protein modifications by dose-resolved proteomics

Lysine deacetylase inhibitors (KDACis) are approved drugs for cutaneous T cell lymphoma (CTCL), peripheral T cell lymphoma (PTCL), and multiple myeloma, but many aspects of their cellular mechanism of action (MoA) and substantial toxicity are not well understood. To shed more light on how KDACis elicit cellular responses, we systematically measured dose-dependent changes in acetylation, phosphorylation, and protein expression in response to 21 clinical and pre-clinical KDACis. The resulting 862,000 dose-response curves revealed, for instance, limited cellular specificity of histone deacetylase (HDAC) 1, 2, 3, and 6 inhibitors; strong cross-talk between acetylation and phosphorylation pathways; localization of most drug-responsive acetylation sites to intrinsically disordered regions (IDRs); an underappreciated role of acetylation in protein structure; and a shift in EP300 protein abundance between the cytoplasm and the nucleus. This comprehensive dataset serves as a resource for the investigation of the molecular mechanisms underlying KDACi action in cells and can be interactively explored online in ProteomicsDB.

Chemical proteomics reveals the target landscape of 1,000 kinase inhibitors

Medicinal chemistry has discovered thousands of potent protein and lipid kinase inhibitors. These may be developed into therapeutic drugs or chemical probes to study kinase biology. Because of polypharmacology, a large part of the human kinome currently lacks selective chemical probes. To discover such probes, we profiled 1,183 compounds from drug discovery projects in lysates of cancer cell lines using Kinobeads. The resulting 500,000 compound-target interactions are available in ProteomicsDB and we exemplify how this molecular resource may be used. For instance, the data revealed several hundred reasonably selective compounds for 72 kinases. Cellular assays validated GSK986310C as a candidate SYK (spleen tyrosine kinase) probe and X-ray crystallography uncovered the structural basis for the observed selectivity of the CK2 inhibitor GW869516X. Compounds targeting PKN3 were discovered and phosphoproteomics identified substrates that indicate target engagement in cells. We anticipate that this molecular resource will aid research in drug discovery and chemical biology.

Evidence that CD36 is expressed on red blood cells and constitutes a novel blood group system of clinical importance

BACKGROUND AND OBJECTIVES

Polymorphic molecules expressed on the surface of certain blood cells are traditionally categorized as blood groups and human platelet or neutrophil antigens. CD36 is widely considered a platelet antigen (Naka) and anti-CD36 can cause foetal/neonatal alloimmune thrombocytopenia (FNAIT) in CD36-negative pregnant women. CD36 is used as a marker of differentiation in early erythroid culture. During the experimental culture of CD34+ cells from random blood donors, we observed that one individual lacked CD36. We sought to investigate this observation further and determine if CD36 fulfils the International Society of Blood Transfusion criteria for becoming a blood group.

MATERIALS AND METHODS

Surface markers were monitored by flow cytometry on developing cells during the erythroid culture of CD34+ cells. Genetic and flow cytometric analyses on peripheral blood cells were performed. Proteomic datasets were analysed, and clinical case reports involving anti-CD36 and foetal anaemia were scrutinized.

RESULTS

Sequencing of CD36-cDNA identified homozygosity for c.1133G>T/p.Gly378Val in the CD36-negative donor. The minor allele frequency of rs146027667:T is 0.1% globally and results in abolished CD36 expression. CD36 has been considered absent from mature red blood cells (RBCs); however, we detected CD36 expression on RBCs and reticulocytes from 20 blood donors. By mining reticulocyte and RBC datasets, we found evidence for CD36-derived peptides enriched in the membrane fractions. Finally, our literature review revealed severe cases of foetal anaemia attributed to anti-CD36.

CONCLUSIONS

Based on these findings, we conclude that CD36 fulfils the criteria for becoming a new blood group system and that anti-CD36 is implicated not only in FNAIT but also foetal anaemia.

Reporting and reproducibility: Proteomics of fish models in environmental toxicology and ecotoxicology

Environmental toxicology and ecotoxicology research efforts are employing proteomics with fish models as New Approach Methodologies, along with in silico, in vitro and other omics techniques to elucidate hazards of toxicants and toxins. We performed a critical review of toxicology studies with fish models using proteomics and reported fundamental parameters across experimental design, sample preparation, mass spectrometry, and bioinformatics of fish, which represent alternative vertebrate models in environmental toxicology, and routinely studied animals in ecotoxicology. We observed inconsistencies in reporting and methodologies among experimental designs, sample preparations, data acquisitions and bioinformatics, which can affect reproducibility of experimental results. We identified a distinct need to develop reporting guidelines for proteomics use in environmental toxicology and ecotoxicology, increased QA/QC throughout studies, and method optimization with an emphasis on reducing inconsistencies among studies. Several recommendations are offered as logical steps to advance development and application of this emerging research area to understand chemical hazards to public health and the environment.

SPDB: a comprehensive resource and knowledgebase for proteomic data at the single-cell resolution

The single-cell proteomics enables the direct quantification of protein abundance at the single-cell resolution, providing valuable insights into cellular phenotypes beyond what can be inferred from transcriptome analysis alone. However, insufficient large-scale integrated databases hinder researchers from accessing and exploring single-cell proteomics, impeding the advancement of this field. To fill this deficiency, we present a comprehensive database, namely Single-cell Proteomic DataBase (SPDB, https://scproteomicsdb.com/), for general single-cell proteomic data, including antibody-based or mass spectrometry-based single-cell proteomics. Equipped with standardized data process and a user-friendly web interface, SPDB provides unified data formats for convenient interaction with downstream analysis, and offers not only dataset-level but also protein-level data search and exploration capabilities. To enable detailed exhibition of single-cell proteomic data, SPDB also provides a module for visualizing data from the perspectives of cell metadata or protein features. The current version of SPDB encompasses 133 antibody-based single-cell proteomic datasets involving more than 300 million cells and over 800 marker/surface proteins, and 10 mass spectrometry-based single-cell proteomic datasets involving more than 4000 cells and over 7000 proteins. Overall, SPDB is envisioned to be explored as a useful resource that will facilitate the wider research communities by providing detailed insights into proteomics from the single-cell perspective.

CancerProteome: a resource to functionally decipher the proteome landscape in cancer

Advancements in mass spectrometry (MS)-based proteomics have greatly facilitated the large-scale quantification of proteins and microproteins, thereby revealing altered signalling pathways across many different cancer types. However, specialized and comprehensive resources are lacking for cancer proteomics. Here, we describe CancerProteome (http://bio-bigdata.hrbmu.edu.cn/CancerProteome), which functionally deciphers and visualizes the proteome landscape in cancer. We manually curated and re-analyzed publicly available MS-based quantification and post-translational modification (PTM) proteomes, including 7406 samples from 21 different cancer types, and also examined protein abundances and PTM levels in 31 120 proteins and 4111 microproteins. Six major analytical modules were developed with a view to describe protein contributions to carcinogenesis using proteome analysis, including conventional analyses of quantitative and the PTM proteome, functional enrichment, protein-protein associations by integrating known interactions with co-expression signatures, drug sensitivity and clinical relevance analyses. Moreover, protein abundances, which correlated with corresponding transcript or PTM levels, were evaluated. CancerProteome is convenient as it allows users to access specific proteins/microproteins of interest using quick searches or query options to generate multiple visualization results. In summary, CancerProteome is an important resource, which functionally deciphers the cancer proteome landscape and provides a novel insight for the identification of tumor protein markers in cancer.

Illuminating phenotypic drug responses of sarcoma cells to kinase inhibitors by phosphoproteomics

Kinase inhibitors (KIs) are important cancer drugs but often feature polypharmacology that is molecularly not understood. This disconnect is particularly apparent in cancer entities such as sarcomas for which the oncogenic drivers are often not clear. To investigate more systematically how the cellular proteotypes of sarcoma cells shape their response to molecularly targeted drugs, we profiled the proteomes and phosphoproteomes of 17 sarcoma cell lines and screened the same against 150 cancer drugs. The resulting 2550 phenotypic profiles revealed distinct drug responses and the cellular activity landscapes derived from deep (phospho)proteomes (9-10,000 proteins and 10-27,000 phosphorylation sites per cell line) enabled several lines of analysis. For instance, connecting the (phospho)proteomic data with drug responses revealed known and novel mechanisms of action (MoAs) of KIs and identified markers of drug sensitivity or resistance. All data is publicly accessible via an interactive web application that enables exploration of this rich molecular resource for a better understanding of active signalling pathways in sarcoma cells, identifying treatment response predictors and revealing novel MoA of clinical KIs.

External Validation of a Novel Multimarker GFR Estimating Equation

Key Points

Using multiple markers may improve GFR estimation especially in settings where creatinine and cystatin C are known to be limited. Panel eGFR is a novel multimarker eGFR equation consisting of age, sex, cystatin C, and nuclear magnetic resonance–measured creatinine, valine, and myo-inositol. eGFR-Cr and eGFR-Cr-CysC may underestimate measured GFR, while panel eGFR was unbiased among younger Black male individuals.

Background

Using multiple markers may improve accuracy in GFR estimation. We sought to externally validate and compare the performance of a novel multimarker eGFR (panel eGFR) equation among Black and White persons using the Genetic Epidemiology Network of Arteriopathy cohort.

Methods

We included 224 sex, race/ethnicity, and measured GFR (mGFR) category–matched persons, with GFR measured using urinary clearance of iothalamate. We calculated panel eGFR using serum creatinine, valine, myo-inositol, cystatin C, age, and sex. We compared its reliability with current eGFR equations (2021 CKD Epidemiology Collaboration creatinine [eGFR-Cr] and creatinine with cystatin C [eGFR-Cr-CysC]) using median bias, precision, and accuracy metrics. We evaluated each equation's performance in age, sex, and race subgroups.

Results

In the overall cohort, 49% were Black individuals, and mean mGFR was 79 ml/min per 1.73 m2. Panel eGFR overestimated mGFR (bias: −2.4 ml/min per 1.73 m2; 95% confidence interval [CI], −4.4 to −0.7), eGFR-Cr-CysC underestimated mGFR (bias: 4.8 ml/min per 1.73 m2; 95% CI, 2.1 to 6.7), while eGFR-Cr was unbiased (bias: 2.0 ml/min per 1.73 m2; 95% CI, −1.1 to 4.6). All equations had comparable accuracy. Among Black male individuals younger than 65 years, both eGFR-Cr (bias: 17.0 ml/min per 1.73 m2; 95% CI, 8.6 to 23.5) and eGFR-Cr-CysC (bias: 14.5 ml/min per 1.73 m2; 95% CI, 6.0 to 19.7) underestimated mGFR, whereas panel eGFR was unbiased (bias: 1.7 ml/min per 1.73 m2; 95% CI, −3.4 to 10.0). Metrics of accuracy for all eGFRs were acceptable in all subgroups except for panel eGFR in Black female individuals younger than 65 years (P30: 73.3%).

Conclusions

Panel eGFR can be used to estimate mGFR and may have utility among Black male individuals younger than 65 years where current CKD Epidemiology Collaboration equations are biased.

Roles of USP9X in cellular functions and tumorigenesis (Review)

Ubiquitin-specific peptidase 9X (USP9X) is involved in certain human diseases, including malignancies, atherosclerosis and certain diseases of the nervous system. USP9X promotes the deubiquitination and stabilization of diverse substrates, thereby exerting a versatile range of effects on pathological and physiological processes. USP9X serves vital roles in the processes of cell survival, invasion and migration in various types of cancer. The present review aims to highlight the current knowledge of USP9X in terms of its structure and the possible mediatory mechanisms involved in certain types of cancer, providing a thorough introduction to its biological functions in carcinogenesis and further outlining its oncogenic or suppressive properties in a diverse range of cancer types. Finally, several perspectives regarding USP9X-targeted pharmacological therapeutics in cancer development are discussed.

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

Proteogenomics in Cancer: Then and Now

For years, the paths of sequencing technologies and mass spectrometry have occurred in isolation, with each developing its own unique culture and expertise. These two technologies are crucial for inspecting complementary aspects of the molecular phenotype across the central dogma. Integrative multiomics strives to bridge the analysis gap among different fields to complete more comprehensive mechanisms of life events and diseases. Proteogenomics is one integrated multiomics field. Here in this review, we mainly summarize and discuss three aspects: workflow of proteogenomics, proteogenomics applications in cancer research, and the SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of proteogenomics in cancer research. In conclusion, proteogenomics has a promising future as it clarifies the functional consequences of many unannotated genomic abnormalities or noncanonical variants and identifies driver genes and novel therapeutic targets across cancers, which would substantially accelerate the development of precision oncology.

Tissue-based absolute quantification using large-scale TMT and LFQ experiments

Relative and absolute intensity-based protein quantification across cell lines, tissue atlases and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same samples. This new TMT method substitutes reporter ion intensities for MS1 feature intensities in the iBAQ framework. Additionally, we compared LFQ-iBAQ values to TMT-iBAQ values from two independent large-scale tissue atlas datasets (one LFQ and one TMT) using robust bottom-up proteomic identification, normalisation and quantitation workflows.

Deciphering Protein Secretion from the Brain to Cerebrospinal Fluid for Biomarker Discovery

Cerebrospinal fluid (CSF) is an essential matrix for the discovery of neurological disease biomarkers. However, the high dynamic range of protein concentrations in CSF hinders the detection of the least abundant protein biomarkers by untargeted mass spectrometry. It is thus beneficial to gain a deeper understanding of the secretion processes within the brain. Here, we aim to explore if and how the secretion of brain proteins to the CSF can be predicted. By combining a curated CSF proteome and the brain elevated proteome of the Human Protein Atlas, brain proteins were classified as CSF or non-CSF secreted. A machine learning model was trained on a range of sequence-based features to differentiate between CSF and non-CSF groups and effectively predict the brain origin of proteins. The classification model achieves an area under the curve of 0.89 if using high confidence CSF proteins. The most important prediction features include the subcellular localization, signal peptides, and transmembrane regions. The classifier generalized well to the larger brain detected proteome and is able to correctly predict novel CSF proteins identified by affinity proteomics. In addition to elucidating the underlying mechanisms of protein secretion, the trained classification model can support biomarker candidate selection.

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.

RhoGAP6 interacts with COPI to regulate protein transport

RhoGAP6 is the most highly expressed GTPase-activating protein (GAP) in platelets specific for RhoA. Structurally RhoGAP6 contains a central catalytic GAP domain surrounded by large, disordered N- and C-termini of unknown function. Sequence analysis revealed three conserved consecutive overlapping di-tryptophan motifs close to the RhoGAP6 C-terminus which were predicted to bind to the mu homology domain (MHD) of δ-COP, a component of the COPI vesicle complex. We confirmed an endogenous interaction between RhoGAP6 and δ-COP in human platelets using GST-CD2AP which binds an N-terminal RhoGAP6 SH3 binding motif. Next, we confirmed that the MHD of δ-COP and the di-tryptophan motifs of RhoGAP6 mediate the interaction between both proteins. Each of the three di-tryptophan motifs appeared necessary for stable δ-COP binding. Proteomic analysis of other potential RhoGAP6 di-tryptophan motif binding partners indicated that the RhoGAP6/δ-COP interaction connects RhoGAP6 to the whole COPI complex. 14-3-3 was also established as a RhoGAP6 binding partner and its binding site was mapped to serine 37. We provide evidence of potential cross-regulation between 14-3-3 and δ-COP binding, however, neither δ-COP nor 14-3-3 binding to RhoGAP6 impacted RhoA activity. Instead, analysis of protein transport through the secretory pathway demonstrated that RhoGAP6/δ-COP binding increased protein transport to the plasma membrane, as did a catalytically inactive mutant of RhoGAP6. Overall, we have identified a novel interaction between RhoGAP6 and δ-COP which is mediated by conserved C-terminal di-tryptophan motifs, and which might control protein transport in platelets.

Decrypting drug actions and protein modifications by dose- and time-resolved proteomics

Although most cancer drugs modulate the activities of cellular pathways by changing posttranslational modifications (PTMs), little is known regarding the extent and the time- and dose-response characteristics of drug-regulated PTMs. In this work, we introduce a proteomic assay called decryptM that quantifies drug-PTM modulation for thousands of PTMs in cells to shed light on target engagement and drug mechanism of action. Examples range from detecting DNA damage by chemotherapeutics, to identifying drug-specific PTM signatures of kinase inhibitors, to demonstrating that rituximab kills CD20-positive B cells by overactivating B cell receptor signaling. DecryptM profiling of 31 cancer drugs in 13 cell lines demonstrates the broad applicability of the approach. The resulting 1.8 million dose-response curves are provided as an interactive molecular resource in ProteomicsDB.

NAGS, CPS1, and SLC25A13 (Citrin) at the Crossroads of Arginine and Pyrimidines Metabolism in Tumor Cells

Urea cycle enzymes and transporters collectively convert ammonia into urea in the liver. Aberrant overexpression of carbamylphosphate synthetase 1 (CPS1) and SLC25A13 (citrin) genes has been associated with faster proliferation of tumor cells due to metabolic reprogramming that increases the activity of the CAD complex and pyrimidine biosynthesis. N-acetylglutamate (NAG), produced by NAG synthase (NAGS), is an essential activator of CPS1. Although NAGS is expressed in lung cancer derived cell lines, expression of the NAGS gene and its product was not evaluated in tumors with aberrant expression of CPS1 and citrin. We used data mining approaches to identify tumor types that exhibit aberrant overexpression of NAGS, CPS1, and citrin genes, and evaluated factors that may contribute to increased expression of the three genes and their products in tumors. Median expression of NAGS, CPS1, and citrin mRNA was higher in glioblastoma multiforme (GBM), glioma, and stomach adenocarcinoma (STAD) samples compared to the matched normal tissue. Median expression of CPS1 and citrin mRNA was higher in the lung adenocarcinoma (LUAD) sample while expression of NAGS mRNA did not differ. High NAGS expression was associated with an unfavorable outcome in patients with glioblastoma and GBM. Low NAGS expression was associated with an unfavorable outcome in patients with LUAD. Patterns of DNase hypersensitive sites and histone modifications in the upstream regulatory regions of NAGS, CPS1, and citrin genes were similar in liver tissue, lung tissue, and A549 lung adenocarcinoma cells despite different expression levels of the three genes in the liver and lung. Citrin gene copy numbers correlated with its mRNA expression in glioblastoma, GBM, LUAD, and STAD samples. There was little overlap between NAGS, CPS1, and citrin sequence variants found in patients with respective deficiencies, tumor samples, and individuals without known rare genetic diseases. The correlation between NAGS, CPS1, and citrin mRNA expression in the individual glioblastoma, GBM, LUAD, and STAD samples was very weak. These results suggest that the increased cytoplasmic supply of either carbamylphosphate, produced by CPS1, or aspartate may be sufficient to promote tumorigenesis, as well as the need for an alternative explanation of CPS1 activity in the absence of NAGS expression and NAG.

Multi-Omics Studies in Historically Excluded Populations: The Road to Equity

Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non-coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi-omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non-coding region, other machine learning and omics approaches have been developed to identify the potential causative single-nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome-wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome-wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi-omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.

Surfaceome Profiling of Cell Lines and Patient-Derived Xenografts Confirm FGFR4, NCAM1, CD276, and Highlight AGRL2, JAM3, and L1CAM as Surface Targets for Rhabdomyosarcoma

Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children. The prognosis for patients with high-grade and metastatic disease is still very poor, and survivors are burdened with long-lasting side effects. Therefore, more effective and less toxic therapies are needed. Surface proteins are ideal targets for antibody-based therapies, like bispecific antibodies, antibody-drug conjugates, or chimeric antigen receptor (CAR) T-cells. Specific surface targets for RMS are scarce. Here, we performed a surfaceome profiling based on differential centrifugation enrichment of surface/membrane proteins and detection by LC-MS on six fusion-positive (FP) RMS cell lines, five fusion-negative (FN) RMS cell lines, and three RMS patient-derived xenografts (PDXs). A total of 699 proteins were detected in the three RMS groups. Ranking based on expression levels and comparison to expression in normal MRC-5 fibroblasts and myoblasts, followed by statistical analysis, highlighted known RMS targets such as FGFR4, NCAM1, and CD276/B7-H3, and revealed AGRL2, JAM3, MEGF10, GPC4, CADM2, as potential targets for immunotherapies of RMS. L1CAM expression was investigated in RMS tissues, and strong L1CAM expression was observed in more than 80% of alveolar RMS tumors, making it a practicable target for antibody-based therapies of alveolar RMS.

MS Ana: Improving Sensitivity in Peptide Identification with Spectral Library Search

Spectral library search can enable more sensitive peptide identification in tandem mass spectrometry experiments. However, its drawbacks are the limited availability of high-quality libraries and the added difficulty of creating decoy spectra for result validation. We describe MS Ana, a new spectral library search engine that enables high sensitivity peptide identification using either curated or predicted spectral libraries as well as robust false discovery control through its own decoy library generation algorithm. MS Ana identifies on average 36% more spectrum matches and 4% more proteins than database search in a benchmark test on single-shot human cell-line data. Further, we demonstrate the quality of the result validation with tests on synthetic peptide pools and show the importance of library selection through a comparison of library search performance with different configurations of publicly available human spectral libraries.

Fresh Medium or L-Cystine as an Effective Nrf2 Inducer for Cytoprotection in Cell Culture

The Nrf2 gene encodes a transcription factor best known for regulating the expression of antioxidant and detoxification genes. A long list of small molecules has been reported to induce Nrf2 protein via Keap1 oxidation or alkylation. Many of these Nrf2 inducers exhibit off-target or toxic effects due to their nature as electrophiles. In searching for non-toxic Nrf2 inducers, we found that a culture medium change to fresh DMEM is capable of inducing Nrf2 protein in HeLa, HEK293, AC16 and MCF7 cells. Testing the components of DMEM led to the discovery of L-Cystine as an effective Nrf2 inducer. L-Cystine induces a dose-dependent increase of Nrf2 protein, from 0.1 to 1.6 mM. RNA-seq analyses and RT-PCR revealed an induction of multiple Nrf2 downstream genes, including NQO1, HMOX1, GCLC, GCLM, SRXN1, TXNRD1, AKR1C and OSGIN1 by 0.8 mM L-Cystine. The induction of Nrf2 protein was dependent on L-Cystine entering cells via the cystine/glutamate antiporter and the presence of Keap1. The half-life of Nrf2 protein increased from 19.4 min to 30.9 min with 0.8 mM L-Cystine treatment. L-Cystine was capable of eliciting cytoprotection by reducing ROS generation and protecting against oxidant- or doxorubicin-induced apoptosis. As an amino acid derivative, L-Cystine is considered a non-toxic Nrf2 inducer that exhibits the potential for protection against oxidative stress and tissue injury.

The ProteomeXchange consortium at 10 years: 2023 update

Mass spectrometry (MS) is by far the most used experimental approach in high-throughput proteomics. The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) was originally set up to standardize data submission and dissemination of public MS proteomics data. It is now 10 years since the initial data workflow was implemented. In this manuscript, we describe the main developments in PX since the previous update manuscript in Nucleic Acids Research was published in 2020. The six members of the Consortium are PRIDE, PeptideAtlas (including PASSEL), MassIVE, jPOST, iProX and Panorama Public. We report the current data submission statistics, showcasing that the number of datasets submitted to PX resources has continued to increase every year. As of June 2022, more than 34 233 datasets had been submitted to PX resources, and from those, 20 062 (58.6%) just in the last three years. We also report the development of the Universal Spectrum Identifiers and the improvements in capturing the experimental metadata annotations. In parallel, we highlight that data re-use activities of public datasets continue to increase, enabling connections between PX resources and other popular bioinformatics resources, novel research and also new data resources. Finally, we summarise the current state-of-the-art in data management practices for sensitive human (clinical) proteomics data.

Reanalysis of ProteomicsDB Using an Accurate, Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups

Estimating false discovery rates (FDRs) of protein identification continues to be an important topic in mass spectrometry-based proteomics, particularly when analyzing very large datasets. One performant method for this purpose is the Picked Protein FDR approach which is based on a target-decoy competition strategy on the protein level that ensures that FDRs scale to large datasets. Here, we present an extension to this method that can also deal with protein groups, that is, proteins that share common peptides such as protein isoforms of the same gene. To obtain well-calibrated FDR estimates that preserve protein identification sensitivity, we introduce two novel ideas. First, the picked group target-decoy and second, the rescued subset grouping strategies. Using entrapment searches and simulated data for validation, we demonstrate that the new Picked Protein Group FDR method produces accurate protein group-level FDR estimates regardless of the size of the data set. The validation analysis also uncovered that applying the commonly used Occam's razor principle leads to anticonservative FDR estimates for large datasets. This is not the case for the Picked Protein Group FDR method. Reanalysis of deep proteomes of 29 human tissues showed that the new method identified up to 4% more protein groups than MaxQuant. Applying the method to the reanalysis of the entire human section of ProteomicsDB led to the identification of 18,000 protein groups at 1% protein group-level FDR. The analysis also showed that about 1250 genes were represented by ≥2 identified protein groups. To make the method accessible to the proteomics community, we provide a software tool including a graphical user interface that enables merging results from multiple MaxQuant searches into a single list of identified and quantified protein groups.

Is DIA proteomics data FAIR? Current data sharing practices, available bioinformatics infrastructure and recommendations for the future

Data independent acquisition (DIA) proteomics techniques have matured enormously in recent years, thanks to multiple technical developments in e.g. instrumentation and data analysis approaches. However, there are many improvements that are still possible for DIA data in the area of the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include more tailored data sharing practices and open data standards, since public databases and data standards for proteomics were mostly designed with DDA data in mind. Here we first describe the current state of the art in the context of FAIR data for proteomics in general, and for DIA approaches in particular. For improving the current situation for DIA data, we make the following recommendations for the future: (i) development of an open data standard for spectral libraries; (ii) make mandatory the availability of the spectral libraries used in DIA experiments in ProteomeXchange resources; (iii) improve the support for DIA data in the data standards developed by the Proteomics Standards Initiative; and (iv) improve the support for DIA datasets in ProteomeXchange resources, including more tailored metadata requirements. This article is protected by copyright. All rights reserved.

PDS5A and PDS5B differentially affect gene expression without altering cohesin localization across the genome

BACKGROUND

Cohesin is an important structural regulator of the genome, regulating both three-dimensional genome organization and gene expression. The core cohesin trimer interacts with various HEAT repeat accessory subunits, yielding cohesin complexes of distinct compositions and potentially distinct functions. The roles of the two mutually exclusive HEAT repeat subunits PDS5A and PDS5B are not well understood.

RESULTS

Here, we determine that PDS5A and PDS5B have highly similar localization patterns across the mouse embryonic stem cell (mESC) genome and they show a strong overlap with other cohesin HEAT repeat accessory subunits, STAG1 and STAG2. Using CRISPR/Cas9 genome editing to generate individual stable knockout lines for PDS5A and PDS5B, we find that loss of one PDS5 subunit does not alter the distribution of the other PDS5 subunit, nor the core cohesin complex. Both PDS5A and PDS5B are required for proper gene expression, yet they display only partially overlapping effects on gene targets. Remarkably, gene expression following dual depletion of the PDS5 HEAT repeat proteins does not completely overlap the gene expression changes caused by dual depletion of the STAG HEAT repeat proteins, despite the overlapping genomic distribution of all four proteins. Furthermore, dual loss of PDS5A and PDS5B decreases cohesin association with NIPBL and WAPL, reduces SMC3 acetylation, and does not alter overall levels of cohesin on the genome.

CONCLUSIONS

This work reveals the importance of PDS5A and PDS5B for proper cohesin function. Loss of either subunit has little effect on cohesin localization across the genome yet PDS5A and PDS5B are differentially required for gene expression.

In Depth Exploration of the Alternative Proteome of Drosophila melanogaster

Recent studies have shown that hundreds of small proteins were occulted when protein-coding genes were annotated. These proteins, called alternative proteins, have failed to be annotated notably due to the short length of their open reading frame (less than 100 codons) or the enforced rule establishing that messenger RNAs (mRNAs) are monocistronic. Several alternative proteins were shown to be biologically active molecules and seem to be involved in a wide range of biological functions. However, genome-wide exploration of the alternative proteome is still limited to a few species. In the present article, we describe a deep peptidomics workflow which enabled the identification of 401 alternative proteins in Drosophila melanogaster. Subcellular localization, protein domains, and short linear motifs were predicted for 235 of the alternative proteins identified and point toward specific functions of these small proteins. Several alternative proteins had approximated abundances higher than their canonical counterparts, suggesting that these alternative proteins are actually the main products of their corresponding genes. Finally, we observed 14 alternative proteins with developmentally regulated expression patterns and 10 induced upon the heat-shock treatment of embryos, demonstrating stage or stress-specific production of alternative proteins.

The 2022 Nucleic Acids Research database issue and the online molecular biology database collection

The 2022 Nucleic Acids Research Database Issue contains 185 papers, including 87 papers reporting on new databases and 85 updates from resources previously published in the Issue. Thirteen additional manuscripts provide updates on databases most recently published elsewhere. Seven new databases focus specifically on COVID-19 and SARS-CoV-2, including SCoV2-MD, the first of the Issue's Breakthrough Articles. Major nucleic acid databases reporting updates include MODOMICS, JASPAR and miRTarBase. The AlphaFold Protein Structure Database, described in the second Breakthrough Article, is the stand-out in the protein section, where the Human Proteoform Atlas and GproteinDb are other notable new arrivals. Updates from DisProt, FuzDB and ELM comprehensively cover disordered proteins. Under the metabolism and signalling section Reactome, ConsensusPathDB, HMDB and CAZy are major returning resources. In microbial and viral genomes taxonomy and systematics are well covered by LPSN, TYGS and GTDB. Genomics resources include Ensembl, Ensembl Genomes and UCSC Genome Browser. Major returning pharmacology resource names include the IUPHAR/BPS guide and the Therapeutic Target Database. New plant databases include PlantGSAD for gene lists and qPTMplants for post-translational modifications. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Our latest update to the NAR online Molecular Biology Database Collection brings the total number of entries to 1645. Following last year's major cleanup, we have updated 317 entries, listing 89 new resources and trimming 80 discontinued URLs. The current release is available at http://www.oxfordjournals.org/nar/database/c/.