Mass Spectrometry Proteomics Data Research Articles

Global Co-regulatory Cross Talk Between m6A and m5C RNA Methylation Systems Coordinate Cellular Responses and Brain Disease Pathways.

N6 adenosine and C5 cytosine modification of mRNAs, tRNAs and rRNAs are regulated by the behaviour of distinct sets of writer, reader and eraser effector proteins which are conventionally considered to function independently. Here, we provide evidence of global cross-regulatory and functional interaction between the m6A and m5C RNA methylation systems. We first show that m6A and m5C effector protein transcripts are subject to reciprocal base modification supporting the existence of co-regulatory post-transcriptional feedback loops. Using global mass spectrometry proteomic data generated after biological perturbation to identify proteins which change in abundance with effector proteins, we found novel co-regulatory cellular response relationships between m6A and m5C proteins such as between the m6A eraser, ALKBH5, and the m5C writer, NSUN4. Gene ontology analysis of co-regulated proteins indicated that m6A and m5C RNA cross-system control varies across cellular processes, e.g. proteasome and mitochondrial mechanisms, and post-translational modification processes such as SUMOylation and phosphorylation. We also uncovered novel relationships between effector protein networks including contributing to intellectual disability pathways. Finally, we provided in vitro confirmation of colocalisation between m6A-RNAs and the m5C reader protein, ALYREF, after synaptic NMDA activation. These findings have important implications for understanding control of RNA metabolism, cellular proteomic responses, and brain disease mechanisms.

Mendelian randomization and multi-omics approach analyses reveal impaired glucose metabolism and oxidative phosphorylation in visceral adipose tissue of women with polycystic ovary syndrome.

What is the significance of visceral adipose tissue (VAT) in the pathogenesis of polycystic ovary syndrome (PCOS) and its impact on the regulation of metabolic disorders in women with PCOS? We revealed a potentially causal relationship between increased genetically predicted VAT and PCOS-related traits, and found that VAT exhibited impaired glucose metabolism and mitochondrial oxidative phosphorylation (OXPHOS) in women with PCOS. PCOS is a common reproductive endocrine disorder accompanied by many metabolic abnormalities. Adipose tissue is a metabolically active endocrine organ that regulates multiple physiological processes, and VAT has a much stronger association with metabolism than subcutaneous adipose tissue does. Mendelian randomization (MR) analysis was used to investigate the potential causal association between genetically predicted VAT and the risk of PCOS. Data for MR analysis were extracted from European population cohorts. VAT samples from sixteen PCOS patients and eight control women who underwent laparoscopic surgery were collected for proteomics and targeted metabolomics analyses. PCOS was diagnosed according to the 2003 Rotterdam Criteria. The control subjects were women who underwent laparoscopic investigation for infertility or benign indications. Proteomics was performed by TMT labeling and liquid chromatography-tandem mass spectrometry analysis, and targeted metabolomics was performed by ultra-performance liquid chromatography-tandem mass spectrometry analysis. The key differentially expressed proteins (DEPs) were validated by immunoblotting. MR analysis revealed a potentially causal relationship between increased genetically predicted VAT and PCOS, as well as related traits, such as polycystic ovaries, total testosterone, bioavailable testosterone, and anti-Müllerian hormone, while a negative relationship was found with sex hormone-binding globulin. Enrichment pathway analysis of DEPs indicated the inhibition of glycolysis and activation of mitochondrial OXPHOS in the VAT of PCOS patients. MR analysis revealed that key DEPs involved in glycolysis and OXPHOS were significantly linked to PCOS and its related traits. Dot blot assay confirmed a significant decrease in glycolysis enzymes PKM2 and HK1, and an increase in mitochondrial Complex I and III subunits, NDUFS3 and UQCR10. Moreover, metabolomics analysis confirmed down-regulated metabolites of energy metabolic pathways, in particular glycolysis. Further analysis of PCOS and control subjects of normal weight revealed that dysregulation of glucose metabolism and OXPHOS in VAT of women with PCOS was independent of obesity. The mass spectrometry proteomics data have been deposited to the iProX database (http://www.iprox.org) with the iProX accession: IPX0005774001. There may be an overlap in some exposure and outcome data, which might affect the results in the MR analysis. The changes in protein expression of key enzymes affect their activities and disrupt the energy metabolic homeostasis in VAT, providing valuable insight for identifying potential intervention targets of PCOS. This work was supported by the National Key Research and Development Project of China (2021YFC2700402), the National Natural Science Foundation of China (82071608, 82271665), the Key Clinical Projects of Peking University Third Hospital (BYSY2022043), and the CAMS Innovation Fund for Medical Sciences (2019-I2M-5-001). All authors report no conflict of interest. N/A.

Imputation of cancer proteomics data with a deep model that learns from many datasets.

Missing values are a major challenge in the analysis of mass spectrometry proteomics data. Missing values hinder reproducibility, decrease statistical power for identifying differentially expressed (DE) proteins and make it challenging to analyze low-abundance proteins. We present Lupine, a deep learning-based method for imputing, or estimating, missing values in tandem mass tag (TMT) proteomics data. Lupine is, to our knowledge, the first imputation method that is designed to learn jointly from many datasets, and we provide evidence that this approach leads to more accurate predictions. We validated Lupine by applying it to TMT data from >1,000 cancer patient samples spanning ten cancer types from the Clinical Proteomics Tumor Atlas Consortium (CPTAC). Lupine outperforms the state of the art for TMT imputation, identifies more DE proteins than other methods, corrects for TMT batch effects, and learns a meaningful representation of proteins and patient samples. Lupine is implemented as an open source Python package.

Serum Extracellular Vesicles Cargo Approach in Bitches with Mammary Tumors.

This study investigated serum extracellular vesicles (EVs) in bitches with mammary neoplasms, in order to understand their size, shape, and concentration, as well as their association with tumor malignancy. Thirty bitches were categorized into control (n = 10), mammary tumor grades I and II (GI, n = 13), and grade III (GII, n = 7). Serum was separated from blood collected during mastectomy, and EVs were isolated using size exclusion chromatography. The analysis revealed no significant differences in EV concentrations among groups, with similar concentrations for control, GI, and GII. Ninety-one proteins were identified in EV-enriched samples, with six showing varied abundance across groups. Notably, keratin 18 was highly abundant in GI, while sushi domain-containing protein, EvC ciliary subunit 2, and the joining chain of multimeric IgM and IgA were increased in GII. Additionally, protocadherin 17 and albumin were upregulated in both GI and GII. ROC curves identified potential biomarkers for differentiating tumor grades. Enrichment pathway analysis revealed AFP gene upregulation in the GI. Mass spectrometry proteomics data were deposited in Mendeley Data. The study provides valuable insights into serum EV characterization in bitches, suggesting keratin 18 and protocadherin 17 as potential biomarkers for canine mammary neoplasia, with implications for future diagnostic and therapeutic strategies.

Understanding bacterial pathogen diversity: A proteogenomic analysis and use of an array of genome assemblies to identify novel virulence factors of the honey bee bacterial pathogen Paenibacillus larvae.

Mass spectrometry proteomics data are typically evaluated against publicly available annotated sequences, but the proteogenomics approach is a useful alternative. A single genome is commonly utilized in custom proteomic and proteogenomic data analysis. We pose the question of whether utilizing numerous different genome assemblies in a search database would be beneficial. We reanalyzed raw data from the exoprotein fraction of four reference Enterobacterial Repetitive Intergenic Consensus (ERIC) I-IV genotypes of the honey bee bacterial pathogen Paenibacillus larvae and evaluated them against three reference databases (from NCBI-protein, RefSeq, and UniProt) together with an array of protein sequences generated by six-frame direct translation of 15 genome assemblies from GenBank. The wide search yielded 453 protein hits/groups, which UpSet analysis categorized into 50 groups based on the success of protein identification by the 18 database components. Nine hits that were not identified by a unique peptide were not considered for marker selection, which discarded the only protein that was not identified by the reference databases. We propose that the variability in successful identifications between genome assemblies is useful for marker mining. The results suggest that various strains of P. larvae can exhibit specific traits that set them apart from the established genotypes ERIC I-V.

Proteogenomic profiling of clonal hematopoiesis in the solid tumor microenvironment.

2567 Background: Clonal hematopoiesis (CH) is caused by somatic mutations that provide a fitness advantage in hematopoietic stem cells, contributing to inflammation and disease. CH is common in solid tumor patients, and has shown context-dependent associations with survival; however, its contribution to the tumor microenvironment (TME) remains unclear. Here, we employ proteogenomic methods to define CH-associated alterations in the TME. Methods: We tested 1,550 patients across 10 primary, treatment-naïve cancers in the Clinical Proteomic Tumor Analysis Consortium cohort. CH calls were derived from peripheral blood and tumour whole exome sequencing (WES) data, and CH was defined as the presence of a somatic driver mutation at variant allele frequency (VAF) ≥2% in blood. Overall survival (OS) analysis was conducted using Cox proportional hazard models, controlled for age, sex, tumor type, metastatic status, and smoking. Tumor bulk RNA-sequencing and mass spectrometry proteomics data were processed for differential expression and gene set enrichment analyses. Abundance of immune cell populations was estimated with CibersortX. Results: 349 CH mutations were identified in 283 patients (18.3%). CH was strongly associated with age and mutations were mostly found in the epigenetic regulators DNMT3A(37.8%, n=132) and TET2(20.6%, n=72). CH was most prevalent in ovarian cancer (30%, n=27/90) and colorectal cancer (CRC; 28.3%, n=30/106). 103 blood CH mutations were also detected in tumor WES (CHTum), with presence in the tumor associated with higher tumor immune infiltration and peripheral blood VAF ≥10%. CHTum, but not CH, was associated with worse OS (CHTum HR = 1.74 [1.13-2.69]; CH HR = 1.12 [0.83-1.50]). CHTum was also associated with a reduced likelihood of patients being classified as tumor free at follow up (OR = 0.39 [0.19-0.82]). We did not identify a pan-cancer proteogenomic signature of CH in the TME. At the tumor-specific level, we consistently observed associations between CH and its subtypes with dysregulated inflammation, with high transcriptomic-proteomic concordance. In CRC, TET2-mutant CH was associated with greater infiltration of CD4+ T cells, monocyte/macrophages, NK cells, and B cells, alongside an inflammatory response characterized by IL6/JAK/STAT3 signalling, TNF signalling via NFκB, and IL2/STAT5 signalling. Conclusions: CH is common, even prior to therapy, in solid tumor patients and the infiltration of CH-mutant immune clones into the TME is linked with poor outcomes. Beyond confounding molecular tumor diagnostics, CH in the TME also dysregulates the anti-tumor immune response, highlighting the value of a blood reference in precision oncology. The lack of a pan-cancer CH signature in the TME supports a tumor-specific influence of CH. Further study is needed for mechanistic discovery and biomarker development to realize the potential of CH in immuno-oncology and improve patient outcomes.

Meta-Analysis of Rice Phosphoproteomics Data to Understand Variation in Cell Signaling Across the Rice Pan-Genome.

Phosphorylation is the most studied post-translational modification, and has multiple biological functions. In this study, we have reanalyzed publicly available mass spectrometry proteomics data sets enriched for phosphopeptides from Asian rice (Oryza sativa). In total we identified 15,565 phosphosites on serine, threonine, and tyrosine residues on rice proteins. We identified sequence motifs for phosphosites, and link motifs to enrichment of different biological processes, indicating different downstream regulation likely caused by different kinase groups. We cross-referenced phosphosites against the rice 3,000 genomes, to identify single amino acid variations (SAAVs) within or proximal to phosphosites that could cause loss of a site in a given rice variety and clustered the data to identify groups of sites with similar patterns across rice family groups. The data has been loaded into UniProt Knowledge-Base─enabling researchers to visualize sites alongside other data on rice proteins, e.g., structural models from AlphaFold2, PeptideAtlas, and the PRIDE database─enabling visualization of source evidence, including scores and supporting mass spectra.

Comparative proteomic analysis of the ovarian fluid and eggs of Siberian sturgeon

BackgroundSturgeon species are living fossils that exhibit unique reproductive characteristics, and elucidation of the molecular processes governing the formation and quality of sturgeon eggs is crucial. However, comprehensive data on the protein composition of sturgeon ovarian fluid (OF) and eggs and their functional significance are lacking. To address this knowledge gap, the aim of the present study was to conduct a comprehensive comparative proteomic analysis of Siberian sturgeon OF and eggs using liquid chromatography–mass spectrometry (LC–MS/MS).ResultsA total of 617 proteins were identified in OF, and 565 proteins were identified in eggs. A total of 772 proteins showed differential abundance. Among the differentially abundant proteins, 365 were more abundant in OFs, while 407 were more abundant in eggs. We identified 339 proteins unique to OFs and 287 proteins specific to eggs, and further investigated the top 10 most abundant proteins in each. The functional annotation of the OF proteins highlighted their predominant association with immune system processes, including the complement and coagulation cascade, neutrophil and leukocyte-mediated immunity, cholesterol metabolism, and regulation of the actin cytoskeleton. Analysis of egg proteins revealed enrichment in metabolic pathways, such as oxidative phosphorylation and fatty acid metabolism, and protein ubiquitination and translation. OF-specific proteins included extracellular matrix and secretory vesicles, and eggs were enriched in proteins localized to mitochondria and ribosome components.ConclusionsThis study presents the first comprehensive characterization of the protein composition of sturgeon OF and eggs and elucidates their distinct functional roles. These findings advance our understanding of sturgeon reproduction, OF-egg signaling and the origin of OF proteins. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium with the dataset identifier PXD044168 to ensure accessibility for further research.

Comparative Proteomics Identified EXOSC1 as a Target Protein of Anticancer Peptide LVTX-8 in Nasopharyngeal Carcinoma Cells.

Nasopharyngeal carcinoma (NPC) is a prevalent malignancy that usually occurs among the nose and throat. Due to mild initial symptoms, most patients are diagnosed in the late stage, and the recurrence rate of tumors is high, resulting in many deaths every year. Traditional radiotherapy and chemotherapy are prone to causing drug resistance and significant side effects. Therefore, searching for new bioactive drugs including anticancer peptides is necessary and urgent. LVTX-8 is a peptide toxin synthesized from the cDNA library of the spider Lycosa vittata, which is consisting of 25 amino acids. In this study, a series of in vitro cell experiments such as cell toxicity, colony formation, and cell migration assays were performed to exam the anticancer activity of LVTX-8 in NPC cells (5-8F and CNE-2). The results suggested that LVTX-8 significantly inhibited cell proliferation and migration of NPC cells. To find the potential molecular targets for the anticancer capability of LVTX-8, high-throughput proteomic and bioinformatics analysis were conducted on NPC cells. The results identified EXOSC1 as a potential target protein with significantly differential expression levels under LVTX-8+/LVTX-8- conditions. The results in this research indicate that spider peptide toxin LVTX-8 exhibits significant anticancer activity in NPC, and EXOSC1 may serve as a target protein for its anticancer activity. These findings provide a reference for the development of new therapeutic drugs for NPC and offer new ideas for the discovery of biomarkers related to NPC diagnosis. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository with the data set identifier PXD050542.

Succinylation of Park7 activates a protective metabolic response to acute kidney injury.

Acute kidney injury (AKI) is extremely prevalent among hospitalizations and presents a significant risk for the development of chronic kidney disease and increased mortality. Ischemia caused by shock, trauma, and transplant are common causes of AKI. To attenuate ischemic AKI therapeutically, we need a better understanding of the physiological and cellular mechanisms underlying damage. Instances of ischemia are most damaging in proximal tubule epithelial cells (PTECs) where hypoxic signaling cascades, and perhaps more rapidly, posttranslational modifications (PTMs), act in concert to change cellular metabolism. Here, we focus on the effects of the understudied PTM, lysine succinylation. We have previously shown a protective effect of protein hypersuccinylation on PTECs after depletion of the desuccinylase sirtuin5. General trends in the results suggested that hypersuccinylation led to upregulation of peroxisomal activity and was protective against kidney injury. Included in the list of changes was the Parkinson's-related deglycase Park7. There is little known about any links between peroxisome activity and Park7. In this study, we show in vitro and in vivo that Park7 has a crucial role in protection from AKI and upregulated peroxisome activity. These data in combination with published results of Park7's protective role in cardiovascular damage and chronic kidney disease lead us to hypothesize that succinylation of Park7 may ameliorate oxidative damage resulting from AKI and prevent disease progression. This novel mechanism provides a potential therapeutic mechanism that can be targeted.NEW & NOTEWORTHY Succinylation is an understudied posttranslational modification that has been shown to increase peroxisomal activity. Furthermore, increased peroxisomal activity has been shown to reduce oxidative stress and protect proximal tubules after acute kidney injury. Analysis of mass spectrometry succinylomic and proteomic data reveals a novel role for Parkinson's related Park7 in mediating Nrf2 antioxidant response after kidney injury. This novel protection pathway provides new insights for kidney injury prevention and development of novel therapeutics.

Analysis of Tandem Mass Spectrometry Data with CONGA: Combining Open and Narrow Searches with Group-Wise Analysis.

Searching for tandem mass spectrometry proteomics data against a database is a well-established method for assigning peptide sequences to observed spectra but typically cannot identify peptides harboring unexpected post-translational modifications (PTMs). Open modification searching aims to address this problem by allowing a spectrum to match a peptide even if the spectrum's precursor mass differs from the peptide mass. However, expanding the search space in this way can lead to a loss of statistical power to detect peptides. We therefore developed a method, called CONGA (combining open and narrow searches with group-wise analysis), that takes into account results from both types of searches─a traditional "narrow window" search and an open modification search─while carrying out rigorous false discovery rate control. The result is an algorithm that provides the best of both worlds: the ability to detect unexpected PTMs without a concomitant loss of power to detect unmodified peptides.

In-Depth Proteome Coverage of In Vitro-Cultured Treponema pallidum and Quantitative Comparison Analyses with In Vivo-Grown Treponemes.

Previous mass spectrometry (MS)-based global proteomics studies have detected a combined total of 86% of all Treponema pallidum proteins under infection conditions (in vivo-grown T. pallidum). Recently, a method was developed for the long-term culture of T. pallidum under in vitro conditions (in vitro-cultured T. pallidum). Herein, we used our previously reported optimized MS-based proteomics approach to characterize the T. pallidum global protein expression profile under in vitro culture conditions. These analyses provided a proteome coverage of 94%, which extends the combined T. pallidum proteome coverage from the previously reported 86% to a new combined total of 95%. This study provides a more complete understanding of the protein repertoire of T. pallidum. Further, comparison of the in vitro-expressed proteome with the previously determined in vivo-expressed proteome identifies only a few proteomic changes between the two growth conditions, reinforcing the suitability of in vitro-cultured T. pallidum as an alternative to rabbit-based treponemal growth. The MS proteomics data have been deposited in the MassIVE repository with the data set identifier MSV000093603 (ProteomeXchange identifier PXD047625).

Differential CpG methylation at Nnat in the early establishment of beta cell heterogeneity

Aims/hypothesisBeta cells within the pancreatic islet represent a heterogenous population wherein individual sub-groups of cells make distinct contributions to the overall control of insulin secretion. These include a subpopulation of highly connected ‘hub’ cells, important for the propagation of intercellular Ca2+ waves. Functional subpopulations have also been demonstrated in human beta cells, with an altered subtype distribution apparent in type 2 diabetes. At present, the molecular mechanisms through which beta cell hierarchy is established are poorly understood. Changes at the level of the epigenome provide one such possibility, which we explore here by focusing on the imprinted gene Nnat (encoding neuronatin [NNAT]), which is required for normal insulin synthesis and secretion.MethodsSingle-cell RNA-seq datasets were examined using Seurat 4.0 and ClusterProfiler running under R. Transgenic mice expressing enhanced GFP under the control of the Nnat enhancer/promoter regions were generated for FACS of beta cells and downstream analysis of CpG methylation by bisulphite sequencing and RNA-seq, respectively. Animals deleted for the de novo methyltransferase DNA methyltransferase 3 alpha (DNMT3A) from the pancreatic progenitor stage were used to explore control of promoter methylation. Proteomics was performed using affinity purification mass spectrometry and Ca2+ dynamics explored by rapid confocal imaging of Cal-520 AM and Cal-590 AM. Insulin secretion was measured using homogeneous time-resolved fluorescence imaging.ResultsNnat mRNA was differentially expressed in a discrete beta cell population in a developmental stage- and DNA methylation (DNMT3A)-dependent manner. Thus, pseudo-time analysis of embryonic datasets demonstrated the early establishment of Nnat-positive and -negative subpopulations during embryogenesis. NNAT expression is also restricted to a subset of beta cells across the human islet that is maintained throughout adult life. NNAT+ beta cells also displayed a discrete transcriptome at adult stages, representing a subpopulation specialised for insulin production, and were diminished in db/db mice. ‘Hub’ cells were less abundant in the NNAT+ population, consistent with epigenetic control of this functional specialisation.Conclusions/interpretationThese findings demonstrate that differential DNA methylation at Nnat represents a novel means through which beta cell heterogeneity is established during development. We therefore hypothesise that changes in methylation at this locus may contribute to a loss of beta cell hierarchy and connectivity, potentially contributing to defective insulin secretion in some forms of diabetes.Data availabilityThe mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD048465.Graphical

A data analysis framework for combining multiple batches increases the power of isobaric proteomics experiments.

We present a framework for the analysis of multiplexed mass spectrometry proteomics data that reduces estimation error when combining multiple isobaric batches. Variations in the number and quality of observations have long complicated the analysis of isobaric proteomics data. Here we show that the power to detect statistical associations is substantially improved by utilizing models that directly account for known sources of variation in the number and quality of observations that occur across batches.In a multibatch benchmarking experiment, our open-source software (msTrawler) increases the power to detect changes, especially in the range of less than twofold changes, while simultaneously increasing quantitative proteome coverage by utilizing more low-signal observations. Further analyses of previously published multiplexed datasets of 4 and 23 batches highlight both increased power and the ability to navigate complex missing data patterns without relying on unverifiable imputations or discarding reliable measurements.

Temporal Profiling of Host Proteome against Different M. tuberculosis Strains Reveals Delayed Epigenetic Orchestration.

Apart from being preventable and treatable, tuberculosis is the deadliest bacterial disease afflicting humankind owing to its ability to evade host defence responses, many of which are controlled by epigenetic mechanisms. Here, we report the temporal dynamics of the proteome of macrophage-like host cells after infecting them for 6, 18, 30, and 42 h with two laboratory strains (H37Ra and H37Rv) and two clinical strains (BND433 and JAL2287) of Mycobacterium tuberculosis (MTB). Using SWATH-MS, the proteins characterized at the onset of infection broadly represented oxidative stress and cell cytoskeleton processes. Intermediary and later stages of infection are accompanied by a reshaping of the combination of proteins implicated in histone stability, gene expression, and protein trafficking. This study provides strain-specific and time-specific variations in the proteome of the host, which might further the development of host-directed therapeutics and diagnostic tools against the pathogen. Also, our findings accentuate the importance of proteomic tools in delineating the complex recalibration of the host defence enabled as an effect of MTB infection. To the best of our knowledge, this is the first comprehensive proteomic account of the host response to avirulent and virulent strains of MTB at different time periods of the life span of macrophage-like cells. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE repository with the dataset identifier PXD022352.

Data-independent acquisition proteomics of cerebrospinal fluid implicates endoplasmic reticulum and inflammatory mechanisms in amyotrophic lateral sclerosis.

While unbiased proteomics of human cerebrospinal fluid (CSF) has been used successfully to identify biomarkers of amyotrophic lateral sclerosis (ALS), high-abundance proteins mask the presence of lower abundance proteins that may have diagnostic and prognostic value. However, developments in mass spectrometry (MS) proteomic data acquisition methods offer improved protein depth. In this study, MS with library-free data-independent acquisition (DIA) was used to compare the CSF proteome of people with ALS (n = 40), healthy (n = 15) and disease (n = 8) controls. Quantified protein groups were subsequently correlated with clinical variables. Univariate analysis identified 7 proteins, all significantly upregulated in ALS versus healthy controls, and 9 with altered abundance in ALS versus disease controls (FDR < 0.1). Elevated chitotriosidase-1 (CHIT1) was common to both comparisons and was proportional to ALS disability progression rate (Pearson r = 0.41, FDR-adjusted p = 0.035) but not overall survival. Ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1; upregulated in ALS versus healthy controls) was proportional to disability progression rate (Pearson r = 0.53, FDR-adjusted p = 0.003) and survival (Kaplan Meier log-rank p = 0.013) but not independently in multivariate proportional hazards models. Weighted correlation network analysis was used to identify functionally relevant modules of proteins. One module, enriched for inflammatory functions, was associated with age at symptom onset (Pearson r = 0.58, FDR-adjusted p = 0.005) and survival (Hazard Ratio = 1.78, FDR = 0.065), and a second module, enriched for endoplasmic reticulum proteins, was negatively correlated with disability progression rate (r = -0.42, FDR-adjusted p = 0.109). DIA acquisition methodology therefore strengthened the biomarker candidacy of CHIT1 and UCHL1 in ALS, while additionally highlighted inflammatory and endoplasmic reticulum proteins as novel sources of prognostic biomarkers.

In-depth proteome analysis of brain tissue from Ewsr1 knockout mouse by multiplexed isobaric tandem mass tag labeling

EWS RNA binding protein 1 (EWSR1) is a multifunctional protein whose epigenetic signatures contribute to the pathogenesis of various human diseases, such as neurodegenerative disorders, skin development, and tumorigenic processes. However, the specific cellular functions and physiological characteristics of EWSR1 remain unclear. In this study, we used quantitative mass spectrometry-based proteomics with tandem mass tag labeling to investigate the global proteome changes in brain tissue in Ewsr1 knockout and wild-type mice. From 9115 identified proteins, we selected 118 differentially expressed proteins, which is common to three quantitative data processing strategies including only protein level normalizations and spectrum-protein level normalization. Bioinformatics analysis of these common differentially expressed proteins revealed that proteins up-regulated in Ewsr1 knockout mouse are mostly related to the positive regulation of bone remodeling and inflammatory response. The down-regulated proteins were associated with the regulation of neurotransmitter levels or amino acid metabolic processes. Collectively, these findings provide insight into the physiological function and pathogenesis of EWSR1 on protein level. Better understanding of EWSR1 and its protein interactions will advance the field of clinical research into neuronal disorders. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD026994.

MS Annika 2.0 Identifies Cross-Linked Peptides in MS2-MS3-Based Workflows at High Sensitivity and Specificity.

Cross-linking mass spectrometry has become a powerful tool for the identification of protein-protein interactions and for gaining insight into the structures of proteins. We previously published MS Annika, a cross-linking search engine which can accurately identify cross-linked peptides in MS2 spectra from a variety of different MS-cleavable cross-linkers. In this publication, we present MS Annika 2.0, an updated version implementing a new search algorithm that, in addition to MS2 level, only supports the processing of data from MS2-MS3-based approaches for the identification of peptides from MS3 spectra, and introduces a novel scoring function for peptides identified across multiple MS stages. Detected cross-links are validated by estimating the false discovery rate (FDR) using a target-decoy approach. We evaluated the MS3-search-capabilities of MS Annika 2.0 on five different datasets covering a variety of experimental approaches and compared it to XlinkX and MaXLinker, two other cross-linking search engines. We show that MS Annika detects up to 4 times more true unique cross-links while simultaneously yielding less false positive hits and therefore a more accurate FDR estimation than the other two search engines. All mass spectrometry proteomics data along with result files have been deposited to the ProteomeXchange consortium via the PRIDE partner repository with the dataset identifier PXD041955.

Proteogenomic data and resources for pan-cancer analysis.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

Deep Proteomics Network and Machine Learning Analysis of Human Cerebrospinal Fluid in Japanese Encephalitis Virus Infection.

Japanese encephalitis virus is a leading cause of neurological infection in the Asia-Pacific region with no means of detection in more remote areas. We aimed to test the hypothesis of a Japanese encephalitis (JE) protein signature in human cerebrospinal fluid (CSF) that could be harnessed in a rapid diagnostic test (RDT), contribute to understanding the host response and predict outcome during infection. Liquid chromatography and tandem mass spectrometry (LC-MS/MS), using extensive offline fractionation and tandem mass tag labeling (TMT), enabled comparison of the deep CSF proteome in JE vs other confirmed neurological infections (non-JE). Verification was performed using data-independent acquisition (DIA) LC-MS/MS. 5,070 proteins were identified, including 4,805 human proteins and 265 pathogen proteins. Feature selection and predictive modeling using TMT analysis of 147 patient samples enabled the development of a nine-protein JE diagnostic signature. This was tested using DIA analysis of an independent group of 16 patient samples, demonstrating 82% accuracy. Ultimately, validation in a larger group of patients and different locations could help refine the list to 2-3 proteins for an RDT. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD034789 and 10.6019/PXD034789.