Wujie Chen, Qihua Ye, Biying Zhang, Zhenhua Ma, Hanxiao Tu
{"title":"Identification of FGG as a Biomarker in Early Gastric Cancer via Tissue Proteomics and Clinical Verification.","authors":"Wujie Chen, Qihua Ye, Biying Zhang, Zhenhua Ma, Hanxiao Tu","doi":"10.1021/acs.jproteome.4c00624","DOIUrl":"10.1021/acs.jproteome.4c00624","url":null,"abstract":"<p><p>Early and accurate diagnosis of gastric cancer (GC) is essential for reducing mortality and improving patient well-being. However, methods for the early diagnosis of GC are still lacking. In this study, by isobaric tagging for relative and absolute quantitation (iTRAQ), we identified 336 proteins that overlapped among the upregulated differentially expressed proteins (DEPs) in early gastric cancer (EGC) versus progressive gastric cancer (PGC), upregulated DEPs in EGC versus nongastric cancer (NGC), and nonsignificant proteins in EGC versus NGC. These DEPs were involved primarily in the neutrophil-related immune response. Network analysis of proteins and pathways revealed that fibrinogen α (FGA), β (FGB), and γ (FGG) are candidates for distinguishing EGC. Furthermore, parallel reaction monitoring (PRM), immunohistochemistry (IHC), and Western blot (WB) assays of clinical samples confirmed that, compared with that in PGC and NGC, only FGG was uniquely and significantly upregulated in the gastric mucosa of EGC. Our results demonstrated that FGG in the gastric mucosa could be a novel biomarker to diagnose EGC patients via endoscopy.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142453388","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}
Nathan R Zuniga, Dustin C Frost, Karsten Kuhn, Myungsun Shin, Rebecca L Whitehouse, Ting-Yu Wei, Yuchen He, Shane L Dawson, Ian Pike, Ryan D Bomgarden, Steven P Gygi, Joao A Paulo
{"title":"Achieving a 35-Plex Tandem Mass Tag Reagent Set through Deuterium Incorporation.","authors":"Nathan R Zuniga, Dustin C Frost, Karsten Kuhn, Myungsun Shin, Rebecca L Whitehouse, Ting-Yu Wei, Yuchen He, Shane L Dawson, Ian Pike, Ryan D Bomgarden, Steven P Gygi, Joao A Paulo","doi":"10.1021/acs.jproteome.4c00668","DOIUrl":"10.1021/acs.jproteome.4c00668","url":null,"abstract":"<p><p>Mass spectrometry-based sample multiplexing with isobaric tags permits the development of high-throughput and precise quantitative biological assays with proteome-wide coverage and minimal missing values. Here, we nearly doubled the multiplexing capability of the TMTpro reagent set to a 35-plex through the incorporation of one deuterium isotope into the reporter group. Substituting deuterium frequently results in suboptimal peak coelution, which can compromise the accuracy of reporter ion-based quantification. To counteract the deuterium effect on quantitation, we implemented a strategy that necessitated the segregation of nondeuterium and deuterium-containing channels into distinct subplexes during normalization procedures, with reassembly through a common bridge channel. This multiplexing strategy of \"design independent sub-plexes but acquire together\" (DISAT) was used to compare protein expression differences between human cell lines and in a cysteine-profiling (i.e., chemoproteomics) experiment to identify compounds binding to cysteine-113 of Pin1.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142386403","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}
Yongseok Kim, Katherine Kokkinias, Anice Sabag-Daigle, Ikaia Leleiwi, Mikayla Borton, Michael Shaffer, Maryam Baniasad, Rebecca Daly, Brian M M Ahmer, Kelly C Wrighton, Vicki H Wysocki
{"title":"Time-Resolved Multiomics Illustrates Host and Gut Microbe Interactions during <i>Salmonella</i> Infection.","authors":"Yongseok Kim, Katherine Kokkinias, Anice Sabag-Daigle, Ikaia Leleiwi, Mikayla Borton, Michael Shaffer, Maryam Baniasad, Rebecca Daly, Brian M M Ahmer, Kelly C Wrighton, Vicki H Wysocki","doi":"10.1021/acs.jproteome.4c00172","DOIUrl":"10.1021/acs.jproteome.4c00172","url":null,"abstract":"<p><p><i>Salmonella</i> infection, also known as <i>Salmonellosis</i>, is one of the most common food-borne illnesses. <i>Salmonella</i> infection can trigger host defensive functions, including an inflammatory response. The provoked-host inflammatory response has a significant impact on the bacterial population in the gut. In addition, <i>Salmonella</i> competes with other gut microorganisms for survival and growth within the host. Compositional and functional alterations in gut bacteria occur because of the host immunological response and competition between <i>Salmonella</i> and the gut microbiome. Host variation and the inherent complexity of the gut microbial community make understanding commensal and pathogen interactions particularly difficult during a <i>Salmonella</i> infection. Here, we present metabolomics and lipidomics analyses along with the 16S rRNA sequence analysis, revealing a comprehensive view of the metabolic interactions between the host and gut microbiota during <i>Salmonella</i> infection in a CBA/J mouse model. We found that different metabolic pathways were altered over the four investigated time points of <i>Salmonella</i> infection (days -2, +2, +6, and +13). Furthermore, metatranscriptomics analysis integrated with metabolomics and lipidomics analysis facilitated an understanding of the heterogeneous response of mice, depending on the degree of dysbiosis.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142386409","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}
Hamid Hachemi, Jean Armengaud, Lucia Grenga, Olivier Pible
{"title":"LineageFilter: Improved Proteotyping of Complex Samples Using Metaproteomics and Machine Learning.","authors":"Hamid Hachemi, Jean Armengaud, Lucia Grenga, Olivier Pible","doi":"10.1021/acs.jproteome.4c00184","DOIUrl":"10.1021/acs.jproteome.4c00184","url":null,"abstract":"<p><p>Metaproteomics is a powerful tool to characterize how microbiota function by analyzing their proteic content by tandem mass spectrometry. Given the complexity of these samples, accurately assessing their taxonomical composition without prior information based solely on peptide sequences remains a challenge. Here, we present LineageFilter, a new python-based AI software for refined proteotyping of complex samples using metaproteomics interpreted data and machine learning. Given a tentative list of taxa, their abundances, and the scores associated with their identified peptides, LineageFilter computes a comprehensive set of features for each identified taxon at all taxonomical ranks. Its machine-learning model then assesses the likelihood of each taxon's presence based on these features, enabling improved proteotyping and sample-specific database construction.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142453390","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}
Carmen Peña-Bautista, Lourdes Álvarez-Sánchez, Ángel Balaguer, Luis Raga, Lorena García-Vallés, Miguel Baquero, Consuelo Cháfer-Pericás
{"title":"Defining Alzheimer's Disease through Proteomic CSF Profiling.","authors":"Carmen Peña-Bautista, Lourdes Álvarez-Sánchez, Ángel Balaguer, Luis Raga, Lorena García-Vallés, Miguel Baquero, Consuelo Cháfer-Pericás","doi":"10.1021/acs.jproteome.4c00590","DOIUrl":"10.1021/acs.jproteome.4c00590","url":null,"abstract":"<p><p>Alzheimer disease (AD) is the main cause of dementia, and its complexity is not yet completely understood. Proteomic profiles can provide useful information to explore the pathways involved and the heterogeneity among AD patients. A proteomic analysis was performed in cerebrospinal fluid (CSF) samples from mild cognitive impairment due to AD (MCI-AD) and control individuals; both groups were classified by amyloid β42/amyloid β40 levels in CSF (data available in BioStudies database (S-BSST1456)). The analysis based on PLS regression and volcano plot identified 7 proteins (FOLR2, PPP3CA, SMOC2, STMN1, TAGLN3, TMEM132B, and UCHL1) mainly related to protein phosphorylation, structure maintenance, inflammation, and protein degradation. Enrichment analysis revealed the involvement of different biological processes related to neuronal mechanisms and synapses, lipid and carbohydrate metabolism, immune system and inflammation, vascular, hormones, and response to stimuli, and cell signaling and adhesion. In addition, the proteomic profile showed some association with the levels of AD biomarkers in CSF. Regarding the subtypes, two MCI-AD subgroups were identified: one could be related to synapsis and neuronal functions and the other to innate immunity. The study of the proteomic profile in the CSF of AD patients reflects the heterogeneity of biochemical pathways involved in AD.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142379414","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":"Multiomics Studies on Metabolism Changes in Alcohol-Associated Liver Disease.","authors":"Liqing He, Raobo Xu, Xipeng Ma, Xinmin Yin, Eugene Mueller, Wenke Feng, Michael Menze, Seongho Kim, Craig J McClain, Xiang Zhang","doi":"10.1021/acs.jproteome.4c00451","DOIUrl":"10.1021/acs.jproteome.4c00451","url":null,"abstract":"<p><p>Metabolic dysfunction in the liver represents a predominant feature in the early stages of alcohol-associated liver disease (ALD). However, the mechanisms underlying this are only partially understood. To investigate the metabolic characteristics of the liver in ALD, we did a relative quantification of polar metabolites and lipids in the liver of mice with experimental ALD using untargeted metabolomics and untargeted lipidomics. A total of 99 polar metabolites had significant abundance alterations in the livers of alcohol-fed mice. Pathway analysis revealed that amino acid metabolism was the most affected by alcohol in the mouse liver. Metabolites involved in glycolysis and the TCA cycle were decreased, while glycerol 3-phosphate (G3P) and long-chain fatty acids were increased. Relative quantification of lipids unveiled an upregulation of multiple lipid classes, suggesting that alcohol consumption drives metabolism toward lipid synthesis. Results from enzyme expression and activity detection indicated that the decreased activity of mitochondrial glycerol 3-phosphate dehydrogenase contributed to the disordered metabolism.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142453392","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":"Meta-Analysis and DIA-MS-Based Proteomic Investigation of COPD Patients and Asymptomatic Smokers in the Indian Population.","authors":"Gautam Sharma, Debarghya Pratim Gupta, Koustav Ganguly, Mahesh Padukudru Anand, Sanjeeva Srivastava","doi":"10.1021/acs.jproteome.4c00463","DOIUrl":"10.1021/acs.jproteome.4c00463","url":null,"abstract":"<p><p>Chronic obstructive pulmonary disease (COPD) is India's second largest cause of death and is largely caused by smoking. Asymptomatic smokers develop COPD due to genetic, environmental, and molecular variables, making early screening crucial. Data-independent acquisition mass spectrometry (DIA-MS) based-proteomics offers an unbiased method to analyze proteomic profiles. This study is the first to use DIA-based proteomics to analyze individual serum samples from three distinct male cohorts: healthy individuals (<i>n</i> = 10), asymptomatic smokers (<i>n</i> = 10), and COPD patients (<i>n</i> = 10). This comprehensive approach identified 667 proteins with a 1% false discovery rate. Differentially expressed proteins included 40 in the normal versus asymptomatic comparison, 88 in the COPD versus normal comparison, and 40 in the COPD versus asymptomatic comparison. Among them, protein-associated genes such as <i>PRDX6</i>, <i>ELANE</i>, <i>PRKCSH</i>, <i>PRTN3</i>, and <i>MNDA</i> could help differentiate COPD from asymptomatic smokers, while <i>ELANE</i>, <i>H3-3A</i>, <i>IGHE</i>, <i>SLC4A1</i>, and <i>SERPINA11</i> could differentiate COPD from healthy subjects. Pathway enrichment and protein-protein interaction analyses revealed significant alterations in hemostasis, immune system functions, fibrin clot formation, and post-translational protein modifications. Key proteins were validated using a parallel reaction monitoring assay. DIA data are available via ProteomeXchange with identifier PXD055242. Our findings reveal key protein classifiers in COPD patients, asymptomatic smokers, and healthy individuals, helping clinicians understand disease pathobiology and improve disease management and quality of life.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142491152","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}
Louise Bundgaard, Filip Årman, Emma Åhrman, Marie Walters, Ulrich Auf dem Keller, Johan Malmström, Stine Jacobsen
{"title":"An Equine Protein Atlas Highlights Synovial Fluid Proteome Dynamics during Experimentally LPS-Induced Arthritis.","authors":"Louise Bundgaard, Filip Årman, Emma Åhrman, Marie Walters, Ulrich Auf dem Keller, Johan Malmström, Stine Jacobsen","doi":"10.1021/acs.jproteome.4c00125","DOIUrl":"10.1021/acs.jproteome.4c00125","url":null,"abstract":"<p><p>In human proteomics, substantial efforts are ongoing to leverage large collections of mass spectrometry (MS) fragment ion spectra into extensive spectral libraries (SL) as a resource for data independent acquisition (DIA) analysis. Currently, such initiatives in equine research are still missing. Here we present a large-scale equine SL, comprising 6394 canonical proteins and 89,329 unique peptides, based on data dependent acquisition analysis of 75 tissue and body fluid samples from horses. The SL enabled large-scale DIA-MS based quantification of the same samples to generate a quantitative equine protein distribution atlas to infer dominant proteins in different organs and body fluids. Data mining revealed 163 proteins uniquely identified in a specific type of tissue or body fluid, serving as a starting point to determine tissue-specific or tissue-type-specific proteins. We showcase the SL by highlighting proteome dynamics in equine synovial fluid samples during experimental lipopolysaccharide-induced arthritis. A fuzzy c-means cluster analysis pinpointed SERPINB1, ATRN, NGAL, LTF, MMP1, and LBP as putative biomarkers for joint inflammation. This SL provides an extendable resource for future equine studies employing DIA-MS.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11536436/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142453372","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}
Alex Zelter, Michael Riffle, David D Shteynberg, Guo Zhong, Ellen B Riddle, Michael R Hoopmann, Daniel Jaschob, Robert L Moritz, Trisha N Davis, Michael J MacCoss, Nina Isoherranen
{"title":"Detection and Quantification of Drug-Protein Adducts in Human Liver.","authors":"Alex Zelter, Michael Riffle, David D Shteynberg, Guo Zhong, Ellen B Riddle, Michael R Hoopmann, Daniel Jaschob, Robert L Moritz, Trisha N Davis, Michael J MacCoss, Nina Isoherranen","doi":"10.1021/acs.jproteome.4c00663","DOIUrl":"10.1021/acs.jproteome.4c00663","url":null,"abstract":"<p><p>Covalent protein adducts formed by drugs or their reactive metabolites are risk factors for adverse reactions, and inactivation of cytochrome P450 (CYP) enzymes. Characterization of drug-protein adducts is limited due to lack of methods identifying and quantifying covalent adducts in complex matrices. This study presents a workflow that combines data-dependent and data-independent acquisition (DDA and DIA) based liquid chromatography with tandem mass spectrometry (LC-MS/MS) to detect very low abundance adducts resulting from CYP mediated drug metabolism in human liver microsomes (HLMs). HLMs were incubated with raloxifene as a model compound and adducts were detected in 78 proteins, including CYP3A and CYP2C family enzymes. Experiments with recombinant CYP3A and CYP2C enzymes confirmed adduct formation in all CYPs tested, including CYPs not subject to time-dependent inhibition by raloxifene. These data suggest adducts can be benign. DIA analysis showed variable adduct abundance in many peptides between livers, but no concomitant decrease of unadducted peptides. This study sets a new standard for adduct detection in complex samples, offering insights into the human adductome resulting from reactive metabolite exposure. The methodology presented will aid mechanistic studies to identify, quantify and differentiate between adducts that result in adverse drug reactions and those that are benign.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11537226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142491148","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}
Valeriia Vasylieva, Ihor Arefiev, Francis Bourassa, Félix-Antoine Trifiro, Marie A Brunet
{"title":"Proteomics Can Rise to the Challenge of Pseudogenes' Coding Nature.","authors":"Valeriia Vasylieva, Ihor Arefiev, Francis Bourassa, Félix-Antoine Trifiro, Marie A Brunet","doi":"10.1021/acs.jproteome.4c00116","DOIUrl":"https://doi.org/10.1021/acs.jproteome.4c00116","url":null,"abstract":"<p><p>Throughout the past decade, technological advances in genomics and transcriptomics have revealed pervasive translation throughout mammalian genomes. These putative proteins are usually excluded from proteomics analyses, as they are absent from common protein repositories. A sizable portion of these noncanonical proteins is translated from pseudogenes. Pseudogenes are commonly termed defective copies of coding genes unable to produce proteins. Here, we suggest that proteomics can help in their annotation. First, we define important terms and review specific examples underlining the caveats in pseudogene annotation and their coding potential. Then, we will discuss the challenges inherent to pseudogenes that have thus far rendered complex their confidence in omics data. Finally, we identify recent developments in experimental procedures, instrumentation, and computational methods in proteomics that put the field in a unique position to solve the pseudogene annotation conundrum.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563533","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}