Journal of Proteome Research最新文献

{"title":"Cloud-Enabled Scalable Analysis of Large Proteomics Cohorts.","authors":"Harendra Guturu, Andrew Nichols, Lee S Cantrell, Seth Just, Janos Kis, Theodore Platt, Iman Mohtashemi, Jian Wang, Serafim Batzoglou","doi":"10.1021/acs.jproteome.4c00771","DOIUrl":"10.1021/acs.jproteome.4c00771","url":null,"abstract":"<p><p>Rapid advances in depth and throughput of untargeted mass-spectrometry-based proteomic technologies enable large-scale cohort proteomic and proteogenomic analyses. As such, the data infrastructure and search engines required to process data must also scale. This challenge is amplified in search engines that rely on library-free match between runs (MBR) search, which enable enhanced depth-per-sample and data completeness. However, to date, no MBR-based search could scale to process cohorts of thousands or more individuals. Here, we present a strategy to deploy search engines in a distributed cloud environment without source code modification, thereby enhancing resource scalability and throughput. Additionally, we present an algorithm, Scalable MBR, that replicates the MBR procedure of popular DIA-NN software for scalability to thousands of samples. We demonstrate that Scalable MBR can search thousands of MS raw files in a few hours compared to days required for the original DIA-NN MBR procedure and demonstrate that the results are almost indistinguishable to those of DIA-NN native MBR. We additionally show that empirical spectra generated by Scalable MBR better approximates DIA-NN native MBR compared to semiempirical alternatives such as ID-RT-IM MBR, preserving user choice to use empirical libraries in large cohort analysis. The method has been tested to scale to over 15,000 injections and is available for use in the Proteograph Analysis Suite.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"1462-1469"},"PeriodicalIF":3.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143412372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Serum Immune-Response Protein Biomarkers Based on Olink Technology for Diagnosis of Ischemic Stroke.","authors":"Han Wang, Tian Zhao, Jingjing Zeng, Liyuan Pu, Huiqun Yang, Jie Liang, Huina Liu, Xiaomeng Wang","doi":"10.1021/acs.jproteome.4c00900","DOIUrl":"10.1021/acs.jproteome.4c00900","url":null,"abstract":"<p><p>The immune response plays a crucial role in the treatment of ischemic stroke (IS). Our primary objective was to explore immune proteins related to stroke and to develop a noninvasive diagnostic panel. We used the high-throughput Olink immunoassay platform to quantitatively measure 92 proteins in the serum of 88 patients with IS and 88 controls. We first selected feature proteins using least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine (SVM), and then modeled them for external validation of IS. In this study, we found that 53 proteins exhibited significant differences in the IS compared to the control group. We selected GLB1, PRDX5, DDX58, and CLEC4C as potential protein biomarkers to differentiate IS from the control group using LASSO, RF, and SVM. The diagnostic model, which included these four proteins, demonstrated excellent performance in validation data sets, achieving an AUC value of 0.899 (95%CI: 0.848-0.950). Our findings offer valuable insights into both the immune response and the diagnosis of IS. These results offer a novel approach to clinical decision-making in the diagnosis and treatment of IS.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"1296-1305"},"PeriodicalIF":3.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TIMS²Rescore: A Data Dependent Acquisition-Parallel Accumulation and Serial Fragmentation-Optimized Data-Driven Rescoring Pipeline Based on MS²Rescore.","authors":"Arthur Declercq, Robbe Devreese, Jonas Scheid, Caroline Jachmann, Tim Van Den Bossche, Annica Preikschat, David Gomez-Zepeda, Jeewan Babu Rijal, Aurélie Hirschler, Jonathan R Krieger, Tharan Srikumar, George Rosenberger, Claudia Martelli, Dennis Trede, Christine Carapito, Stefan Tenzer, Juliane S Walz, Sven Degroeve, Robbin Bouwmeester, Lennart Martens, Ralf Gabriels","doi":"10.1021/acs.jproteome.4c00609","DOIUrl":"10.1021/acs.jproteome.4c00609","url":null,"abstract":"<p><p>The high throughput analysis of proteins with mass spectrometry (MS) is highly valuable for understanding human biology, discovering disease biomarkers, identifying therapeutic targets, and exploring pathogen interactions. To achieve these goals, specialized proteomics subfields, including plasma proteomics, immunopeptidomics, and metaproteomics, must tackle specific analytical challenges, such as an increased identification ambiguity compared to routine proteomics experiments. Technical advancements in MS instrumentation can mitigate these issues by acquiring more discerning information at higher sensitivity levels. This is exemplified by the incorporation of ion mobility and parallel accumulation and serial fragmentation (PASEF) technologies in timsTOF instruments. In addition, AI-based bioinformatics solutions can help overcome ambiguity issues by integrating more data into the identification workflow. Here, we introduce TIMS²Rescore, a data-driven rescoring workflow optimized for DDA-PASEF data from timsTOF instruments. This platform includes new timsTOF MS²PIP spectrum prediction models and IM2Deep, a new deep learning-based peptide ion mobility predictor. Furthermore, to fully streamline data throughput, TIMS²Rescore directly accepts Bruker raw mass spectrometry data and search results from ProteoScape and many other search engines, including Sage and PEAKS. We showcase TIMS²Rescore performance on plasma proteomics, immunopeptidomics (HLA class I and II), and metaproteomics data sets. TIMS²Rescore is open-source and freely available at https://github.com/compomics/tims2rescore.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"1067-1076"},"PeriodicalIF":3.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11894666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143363181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Searching for Sulfotyrosines (sY) in a HA(pY)STACK.","authors":"Jordan Tzvetkov, Claire E Eyers, Patrick A Eyers, Kerry A Ramsbottom, Sally O Oswald, John A Harris, Zhi Sun, Eric W Deutsch, Andrew R Jones","doi":"10.1021/acs.jproteome.4c00907","DOIUrl":"10.1021/acs.jproteome.4c00907","url":null,"abstract":"<p><p>Protein sulfation can be crucial in regulating protein-protein interactions but remains largely underexplored. Sulfation is nearly isobaric to phosphorylation, making it particularly challenging to investigate using mass spectrometry. The degree to which tyrosine sulfation (sY) is misidentified as phosphorylation (pY) is, thus, an unresolved concern. This study explores the extent of sY misidentification within the human phosphoproteome by distinguishing between sulfation and phosphorylation based on their mass difference. Using Gaussian mixture models (GMMs), we screened ∼45 M peptide-spectrum matches (PSMs) from the PeptideAtlas human phosphoproteome build for peptidoforms with mass error shifts indicative of sulfation. This analysis pinpointed 104 candidate sulfated peptidoforms, backed up by Gene Ontology (GO) terms and custom terms linked to sulfation. False positive filtering by manual annotation resulted in 31 convincing peptidoforms spanning 7 known and 7 novel sY sites. Y47 in calumenin was particularly intriguing since mass error shifts, acidic motif conservation, and MS² neutral loss patterns characteristic of sulfation provided strong evidence that this site is sulfated rather than phosphorylated. Overall, although misidentification of sulfation in phosphoproteomics data sets derived from cell and tissue intracellular extracts can occur, it appears relatively rare and should not be considered a substantive confounding factor in high-quality phosphoproteomics data sets.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"1250-1264"},"PeriodicalIF":3.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11894665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coupling Proximity Biotinylation with Genomic Targeting to Characterize Locus-Specific Changes in Chromatin Environments.","authors":"Pata-Eting Kougnassoukou Tchara, Jérémy Loehr, Jean-Philippe Lambert","doi":"10.1021/acs.jproteome.4c00931","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00931","url":null,"abstract":"<p><p>Regulating gene expression involves significant changes in the chromatin environment at the locus level, especially at regulatory sequences. However, their modulation following pharmacological treatments or pathological conditions remain mostly undetermined. Here, we report versatile locus-specific proteomics tools to address this knowledge gap, which combine the targeting ability of the CRISPR/Cas9 system and the protein-labeling capability of the highly reactive biotin ligases TurboID (in CasTurbo) and UltraID (in CasUltra). CasTurbo and CasUltra enabled rapid chromatin protein labeling at repetitive sequences like centromeres and telomeres, as well as nonamplified genes. We applied CasUltra to A375 melanoma cell lines to decipher the protein environment of the <i>MYC</i> promoter and characterize the molecular effects of the bromodomain inhibitor JQ1, which targets bromodomain and extra-terminal (BET) proteins that regulate <i>MYC</i> expression. We quantified the consequences of BET protein displacement from the <i>MYC</i> promoter and found that it was associated with a considerable reorganization of the chromatin composition. Additionally, BET protein retention at the <i>MYC</i> promoter was consistent with a model of increased JQ1 resistance. Thus, through the combination of proximity biotinylation and CRISPR/Cas9 genomic targeting, CasTurbo and CasUltra have successfully demonstrated their utility in profiling the proteome associated with a genomic locus in living cells.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Myosin Light Chain 12b and MASP1 as Novel Biomarker Candidates in Active Juvenile Idiopathic Arthritis─A Combined Proteomics/Bioinformatics Approach.","authors":"Vanditha Mohan, Sajitha Krishnan, Suma Balan, Sonu Das, Jerry Earali, Evelyn Maria, Devaki Nair, Mathew John","doi":"10.1021/acs.jproteome.4c01054","DOIUrl":"10.1021/acs.jproteome.4c01054","url":null,"abstract":"<p><p>Current diagnostic methods for JIA lack specificity, often failing to distinguish it from other childhood diseases of similar clinical presentations. The present study is a comparative cross-sectional study that identified potential biomarkers using label-free mass spectrometry and bioinformatics. Two differentially expressed proteins (DEPs), Myosin light chain 12b (Myl12b) and Mannose-binding lectin serine protease 1 (MASP1), showed increased expression in JIA patients. Receiver operating characteristic (ROC) analysis revealed strong predictive power (AUC: Myl12b = 0.757, MASP1 = 0.855), validated in a separate cohort via Western blot and ELISA. These findings suggest Myl12b and MASP1 as promising biomarkers for JIA diagnosis and treatment. Data: ProteomeXchange (PXD058863).</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"1439-1448"},"PeriodicalIF":3.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143389513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LC-MS/MS-Profiling of Human Serum Unveils Significant Increase in Neuroinflammation and Carcinogenesis Following Chronic Organophosphate Exposure.","authors":"Vishal Sandilya, Dina El-Gameel, Mojgan Atashi, Thu Nguyen, Mojibola Fowowe, Md Mostofa Al Amin Bhuiyan, Oluwatosin Daramola, Judith Nwaiwu, Noha A Hamdy, Maha Ghanem, Labiba K El-Khordagui, Salwa M Abdallah, Ahmed El-Yazbi, Yehia Mechref","doi":"10.1021/acs.jproteome.4c00995","DOIUrl":"10.1021/acs.jproteome.4c00995","url":null,"abstract":"<p><p>The utilization of organophosphate pesticides (OPs) has escalated in response to the growing global food demand driven by a rapidly increasing population and the environmental disruptions caused by climate change. While acute exposure leads to cholinergic poisoning, chronic OP exposure has been linked to organ dysfunction, inflammation, and carcinogenesis. Serum samples from healthy individuals (<i>n</i> = 11), patients with acute OP exposure (<i>n</i> = 12), and those with chronic OP exposure (<i>n</i> = 31) were analyzed to discern the differentially expressed pathways after acute and chronic OP exposure. Differential expression analysis identified 132 proteins altered in chronic exposure vs control, 86 in acute exposure vs control, and 124 in chronic vs acute exposure. Pathway analysis revealed increased blood coagulation and reduced LXR/RXR activation and DCHR24 signaling in both acute and chronic exposures. Elevated levels of pro-inflammatory proteins, such as S100A8, VWF, and GPIBA, were observed, particularly in chronic exposure, highlighting significant inflammatory effects of OP exposure. These findings provide insights into the pathological mechanisms underlying chronic OP exposure and its contribution to inflammation and long-term health risks.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"1342-1355"},"PeriodicalIF":3.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143187744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Serum Metabolomics Profiling Coupled with Machine Learning Identifies Potential Diagnostic and Prognostic Candidate Markers in Meningioma Using Raman Spectroscopy, ATR-FTIR, and LC-MS/MS.","authors":"Ankit Halder, Priyanka A Jadhav, Archisman Maitra, Arghya Banerjee, Arti Hole, Sridhar Epari, Prakash Shetty, Aliasgar Moiyadi, Murali Krishna Chilkapati, Sanjeeva Srivastava","doi":"10.1021/acs.jproteome.4c00806","DOIUrl":"10.1021/acs.jproteome.4c00806","url":null,"abstract":"<p><p>Meningioma, the most prevalent brain tumor, poses significant challenges due to its unclear transition from low-grade to aggressive forms, with limited knowledge about grade-specific markers. We have utilized vibrational spectroscopic techniques such as ATR-FTIR and Raman spectroscopy, alongside LC-MS/MS-based mass spectrometry to understand the systemic cues and evaluate them for clinical practice. The acquired Raman and ATR-FTIR spectra of 46 meningioma patients (27 low-grade and 19 high-grade) and 8 healthy individuals revealed 98.15% and 83.33% accuracy based on PC-LDA. The grade classification revealed an accuracy of around 70%, implying the presence of subtypes and transition phases. The observed alterations corresponded to lipids, nucleic acids, and proteins. Further, the LC-MS/MS-based study identified different derivatives of cholines, indoles, lipids, sphingosine, tryptophan, and their respective metabolic pathways as contributors in tumorigenesis and progression. Further, PRM-based targeted validation and feature selection was carried out on 43 meningioma patients and 17 healthy controls. Glycochenodeoxycholic acid, indole-3-acetic acid, trans-3-indoleacrylic acid, glycodeoxycholic acid, 5α-dihydrotestosteroneglucornide, and glycocholic acid segregated meningioma samples with an accuracy of around 90% while features like indole-3-acetic acid, stercobilin, sphingosine-1-phosphate, deoxycholic acid, and citric acid could classify grades with around 70% accuracy. These findings suggest that further validation across larger cohorts could enhance its usage in clinical settings.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"1180-1196"},"PeriodicalIF":3.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143497556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EasyPubPlot: A Shiny Web Application for Rapid Omics Data Exploration and Visualization.","authors":"Nguyen Tran Nam Tien, Nguyen Quang Thu, Dong Hyun Kim, Seongoh Park, Nguyen Phuoc Long","doi":"10.1021/acs.jproteome.4c01068","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c01068","url":null,"abstract":"<p><p>Computational toolkits for data exploration and visualization from widely used omics platforms often lack flexibility and customization. While many tools generate standardized output, advanced programming skills are necessary to create high-quality visualizations. Therefore, user-friendly tools that simplify this crucial, yet time-consuming, step are essential. We developed EasyPubPlot (Easy Publishable Plotting), a straightforward, easy-to-use, no-coding, user experience-oriented, open-source, and shiny web application along with its associated R package to streamline data exploration and visualization for functional omics-empowered research. EasyPubPlot generates publishable scores plots, volcano plots, heatmaps, box plots, dot plots, and bubble plots with minimal necessary steps. The tool was designed to guide new users to accurate and efficient navigation. Step-by-step tutorials for each type of plot are also provided. Herein, we demonstrated EasyPubPlot's competent functionality and versatility by showcasing metabolomics, proteomics, and transcriptomics data. Collectively, EasyPubPlot reduces the gap between data analysis and stunning visualization, thereby diminishing friction and focusing on science. The app can be downloaded and installed locally (https://github.com/Pharmaco-OmicsLab/EasyPubPlot) or used through a web application (https://pharmaco-omicslab.shinyapps.io/EasyPubPlot).</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cysteine-Directed Isobaric Labeling Combined with GeLC-FAIMS-MS for Quantitative Top-Down Proteomics.","authors":"Theo Matzanke, Philipp T Kaulich, Kyowon Jeong, Ayako Takemori, Nobuaki Takemori, Oliver Kohlbacher, Andreas Tholey","doi":"10.1021/acs.jproteome.4c00835","DOIUrl":"10.1021/acs.jproteome.4c00835","url":null,"abstract":"<p><p>The quantification of proteoforms, i.e., all molecular forms in which proteins can be present, by top-down proteomics provides essential insights into biological processes at the molecular level. Isobaric labeling-based quantification strategies are suitable for multidimensional separation strategies and allow for multiplexing of the samples. Here, we investigated cysteine-directed isobaric labeling by iodoTMT in combination with a gel- and gas-phase fractionation (GeLC-FAIMS-MS) for in-depth quantitative proteoform analysis. We optimized the acquisition workflow (i.e., the FAIMS compensation voltages, isolation windows, acquisition strategy, and fragmentation method) using a two-proteome mix to increase the number of quantified proteoforms and reduce ratio compression. Additionally, we implemented a mass feature-based quantification strategy in the widely used deconvolution algorithm FLASHDeconv, which improves and facilitates data analysis. The optimized iodoTMT GeLC-FAIMS-MS workflow was applied to quantitatively analyze the proteome of <i>Escherichia coli</i> grown under glucose or acetate as the sole carbon source, resulting in the identification of 726 differentially abundant proteoforms.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":" ","pages":"1470-1480"},"PeriodicalIF":3.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11894657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143062352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}