Proteomics最新文献

{"title":"Addressing Sample Mix-Ups: Tools and Approaches for Large-Scale Multi-Omics Studies","authors":"Yingxue Fu,&nbsp;Zuo-Fei Yuan,&nbsp;Long Wu,&nbsp;Junmin Peng,&nbsp;Xusheng Wang,&nbsp;Anthony A. High","doi":"10.1002/pmic.202400271","DOIUrl":"10.1002/pmic.202400271","url":null,"abstract":"<div>\u0000 \u0000 <p>Advances in high-throughput omics technologies have enabled system-wide characterization of biological samples across multiple molecular levels, such as the genome, transcriptome, and proteome. However, as sample sizes rapidly increase in large-scale multi-omics studies, sample mix-ups have become a prevalent issue, compromising data integrity and leading to erroneous conclusions. The interconnected nature of multi-omics data presents an opportunity to identify and correct these errors. This review examines the potential sources of sample mix-ups and evaluates the methodologies and tools developed for detecting and correcting these errors, with an emphasis on approaches applicable to proteomics data. We categorize existing tools into three main groups: expression/protein quantitative trait loci-based, genotype concordance-based, and gene/protein expression correlation-based approaches. Notably, only a handful of tools currently utilize the proteogenomics approach for correcting sample mix-ups at the proteomics level. Integrating the strengths of current tools across diverse data types could enable the development of more versatile and comprehensive solutions. In conclusion, verifying sample identity is a critical first step to reduce bias and increase precision in subsequent analyses for large-scale multi-omics studies. By leveraging these tools for identifying and correcting sample mix-ups, researchers can significantly improve the reliability and reproducibility of biomedical research.</p>\u0000 </div>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 1-2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142805723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An In-Depth Coho Salmon (Oncorhynchus kisutch) Ovarian Follicle Proteome Reveals Coordinated Changes Across Diverse Cellular Processes during the Transition From Primary to Secondary Growth","authors":"Emma Timmins-Schiffman,&nbsp;Jennifer Telish,&nbsp;Chelsea Field,&nbsp;Chris Monson,&nbsp;José M. Guzmán,&nbsp;Brook L. Nunn,&nbsp;Graham Young,&nbsp;Kristy Forsgren","doi":"10.1002/pmic.202400311","DOIUrl":"10.1002/pmic.202400311","url":null,"abstract":"<div>\u0000 \u0000 <p>Teleost fishes are a highly diverse, ecologically essential group of aquatic vertebrates that include coho salmon (<i>Oncorhynchus kisutch</i>). Coho are semelparous and all ovarian follicles develop synchronously. Owing to their ubiquitous distribution, teleosts provide critical sources of food worldwide through subsistence, commercial fisheries, and aquaculture. Enhancement of hatchery practices requires detailed knowledge of teleost reproductive physiology. Despite decades of research on teleost reproductive processes, an in-depth proteome of teleost ovarian development has yet to be generated. We have described a coho salmon ovarian proteome of over 5700 proteins, generated with data independent acquisition, revealing the proteins that change through the transition from primary to secondary ovarian follicle development. This transition is critical during the onset of puberty and for determining egg quality and embryonic development. Primary follicle development was marked by differential abundances of proteins in carbohydrate metabolism, protein turnover, and the complement pathway, suggesting elevated metabolism as the follicles develop through stages of oogenesis. The greatest proteomic shift occurred during the transition from primary to secondary follicle growth, with increased abundance of proteins underlying cortical alveoli formation, extracellular matrix reorganization, iron binding, and cell–cell signaling. This work provides a foundation for identifying biomarkers of salmon oocyte stage and quality.</p>\u0000 </div>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 5-6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reduction of Chemokine CXCL9 Expression by Omega-3 Fatty Acids via ADP-Ribosylhydrolase ARH3 in MIN6 Insulin-Producing Cells","authors":"Youngki You,&nbsp;Soumyadeep Sarkar,&nbsp;Cailin Deiter,&nbsp;Emily C. Elliott,&nbsp;Carrie D. Nicora,&nbsp;Raghavendra G. Mirmira,&nbsp;Lori Sussel,&nbsp;Ernesto S. Nakayasu","doi":"10.1002/pmic.202400053","DOIUrl":"10.1002/pmic.202400053","url":null,"abstract":"<p>Type 1 diabetes (T1D) results from the autoimmune destruction of the insulin-producing β cells of the pancreas. Omega-3 fatty acids protect β cells and reduce the incidence of T1D, but the mechanism is poorly understood. We have shown that omega-3 fatty acids reduce pro-inflammatory cytokine-mediated β-cell apoptosis by upregulating the expression of the ADP-ribosylhydrolase ARH3. Here, we further investigate the β-cell protection mechanism of ARH3 by performing siRNA analysis of its gene <i>Adprhl2</i> in MIN6 insulin-producing cells, subsequent treatment with a cocktail of the pro-inflammatory cytokines IL-1β + IFN-γ + TNF-α, followed by proteomics analysis. ARH3 regulated proteins from several pathways related to the nucleus (splicing, RNA surveillance, and nucleocytoplasmic transport), mitochondria (metabolic pathways), and endoplasmic reticulum (protein folding). ARH3 also regulated the levels of proteins related to antigen processing and presentation, and the chemokine-signaling pathway. We further studied the role of ARH3 in regulating the chemokine CXCL9. We found that ARH3 reduces the cytokine-induced expression of CXCL9, which is dependent on omega-3 fatty acids. In conclusion, we demonstrate that omega-3 fatty acids regulate CXCL9 expression via ARH3, which may have a role in protecting β cells from immune attack thereby preventing T1D development.</p><p><i>Significance of the Study</i>: Omega-3 fatty acids have a variety of health benefits. In type 1 diabetes, omega-3 fatty acids reduce the islet autoimmune response and the disease development. Here, we studied the pathways regulated by the adenosine diphosphate (ADP)-ribosylhydrolase ARH3, a protein whose expression is regulated by omega-3 fatty acids. We showed that ARH3 reduces the expression of chemokines in response to omega-3 fatty acids. This represents an anti-inflammatory mechanism of omega-3 fatty acids that might be involved with protection against type 1 diabetes development.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202400053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Plant Resistance Proteins Using Alignment-Based and Alignment-Free Approaches","authors":"Pushpendra Singh Gahlot,&nbsp;Shubham Choudhury,&nbsp;Nisha Bajiya,&nbsp;Nishant Kumar,&nbsp;Gajendra P. S. Raghava","doi":"10.1002/pmic.202400261","DOIUrl":"10.1002/pmic.202400261","url":null,"abstract":"<div>\u0000 \u0000 <p>Plant disease resistance (PDR) proteins are critical in identifying plant pathogens. Predicting PDR protein is essential for understanding plant–pathogen interactions and developing strategies for crop protection. This study proposes a hybrid model for predicting and designing PDR proteins against plant-invading pathogens. Initially, we tried alignment-based approaches, such as Basic Local Alignment Search Tool (BLAST) for similarity search and MERCI for motif search. These alignment-based approaches exhibit very poor coverage or sensitivity. To overcome these limitations, we developed alignment-free or machine learning (ML)-based methods using compositional features of proteins. Our ML-based model, developed using compositional features of proteins, achieved a maximum performance area under the receiver operating characteristic curve (AUROC) of 0.91. The performance of our model improved significantly from AUROC of 0.91–0.95 when we used evolutionary information instead of protein sequence. Finally, we developed a hybrid or ensemble model that combined our best ML model with BLAST and obtained the highest AUROC of 0.98 on the validation dataset. We trained and tested our models on a training dataset and evaluated them on a validation dataset. None of the proteins in our validation dataset are more than 40% similar to proteins in the training dataset. One of the objectives of this study is to facilitate the scientific community working in plant biology. Thus, we developed an online platform for predicting and designing plant resistance proteins, “PlantDRPpred” (https://webs.iiitd.edu.in/raghava/plantdrppred).</p>\u0000 </div>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 5-6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142708782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urinary Proteomics and Systems Biology Link Eight Proteins to the Higher Risk of Hypertension and Related Complications in Blacks Versus Whites.","authors":"De-Wei An, Dries S Martens, Gontse G Mokwatsi, Yu-Ling Yu, Babangida S Chori, Agnieszka Latosinska, Godsent Isiguzo, Susanne Eder, Dong-Yan Zhang, Gert Mayer, Ruan Kruger, Jana Brguljan-Hitij, Christian Delles, Catharina M C Mels, Katarzyna Stolarz-Skrzypek, Marek Rajzer, Peter Verhamme, Aletta E Schutte, Tim S Nawrot, Yan Li, Harald Mischak, Augustine N Odili, Jan A Staessen","doi":"10.1002/pmic.202400207","DOIUrl":"https://doi.org/10.1002/pmic.202400207","url":null,"abstract":"<p><p>Blacks are more prone to salt-sensitive hypertension than Whites. This cross-sectional analysis of a multi-ethnic cohort aimed to search for proteins potentially involved in the susceptibility to salt sensitivity, hypertension, and hypertension-related complications. The study included individuals enrolled in African Prospective Study on the Early Detection and Identification of Cardiovascular Disease and Hypertension (African-PREDICT), Flemish Study of the Environment, Genes and Health Outcomes (FLEMENGHO), Prospective Cohort Study in Patients with Type 2 Diabetes Mellitus for Validation of Biomarkers (PROVALID)-Austria, and Urinary Proteomics Combined with Home Blood Pressure Telemonitoring for Health Care Reform Trial (UPRIGHT-HTM). Sequenced urinary peptides detectable in 70% of participants allowed the identification of parental proteins and were compared between Blacks and Whites. Of 513 urinary peptides, 300 had significantly different levels among healthy Black (n = 476) and White (n = 483) South Africans sharing the same environment. Analyses contrasting 582 Blacks versus 1731 Whites, and Sub-Saharan Blacks versus European Whites replicated the findings. COL4A1, COL4A2, FGA, PROC, MGP, MYOCD, FYXD2, and UMOD were identified as the most likely candidates underlying the racially different susceptibility to salt sensitivity, hypertension, and related complications. Enriched pathways included hemostasis, platelet activity, collagens, biology of the extracellular matrix, and protein digestion and absorption. Our study suggests that MGP and MYOCD being involved in cardiovascular function, FGA and PROC in coagulation, FYXD2 and UMOD in salt homeostasis, and COL4A1 and COL4A2 as major components of the glomerular basement membrane are among the many proteins potentially incriminated in the higher susceptibility of Blacks compared to Whites to salt sensitivity, hypertension, and its complication. Nevertheless, these eight proteins and their associated pathways deserve further exploration in molecular and human studies as potential targets for intervention to reduce the excess risk of hypertension and cardiovascular complications in Blacks versus Whites.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400207"},"PeriodicalIF":3.4,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142708789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"(Prote)omics for Superior Management of Kidney and Cardiovascular Disease-A Thought-Provoking Impulse From Nephrology.","authors":"Joachim Beige, Michael Masanneck","doi":"10.1002/pmic.202400143","DOIUrl":"https://doi.org/10.1002/pmic.202400143","url":null,"abstract":"<p><p>Chronic kidney disease (CKD) and cardiovascular disease (CVD) are complex conditions often managed by nephrologists. This viewpoint paper advocates for a multi-omics approach, integrating clinical symptom patterns, non-invasive biomarkers, imaging and invasive diagnostics to enhance diagnosis and treatment. Early detection of molecular changes, particularly in collagen turnover, is crucial for preventing disease progression. For instance, urinary proteomics can detect early molecular changes in diabetic kidney disease (DKD), heart failure (HF) and coronary artery disease (CAD), enabling proactive interventions and reducing the need for invasive procedures like renal biopsies. For example, urinary proteomic patterns can differentiate between glomerular and extraglomerular pathologies, aiding in the diagnosis of specific kidney diseases. Additionally, urinary peptides can predict CKD progression and HF development, offering a non-invasive alternative to traditional biomarkers like eGFR and NT-proBNP. The integration of multi-omics data with artificial intelligence (AI) holds promise for personalised treatment strategies, optimizing patient outcomes. This approach can also reduce healthcare costs by minimizing unnecessary invasive procedures and hospitalizations. In conclusion, the adoption of multi-omics and non-invasive biomarkers in nephrology and cardiology can revolutionize disease management, enabling early detection, personalised treatment and improved patient outcomes.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400143"},"PeriodicalIF":3.4,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142708779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent Advances in Labeling-Based Quantitative Glycomics: From High-Throughput Quantification to Structural Elucidation","authors":"Zicong Wang,&nbsp;Jingwei Zhang,&nbsp;Lingjun Li","doi":"10.1002/pmic.202400057","DOIUrl":"10.1002/pmic.202400057","url":null,"abstract":"<p>Glycosylation, a crucial posttranslational modification (PTM), plays important roles in numerous biological processes and is linked to various diseases. Despite its significance, the structural complexity and diversity of glycans present significant challenges for mass spectrometry (MS)-based quantitative analysis. This review aims to provide an in-depth overview of recent advancements in labeling strategies for N-glycomics and O-glycomics, with a specific focus on enhancing the sensitivity, specificity, and throughput of MS analyses. We categorize these advancements into three major areas: (1) the development of isotopic/isobaric labeling techniques that significantly improve multiplexing capacity and throughput for glycan quantification; (2) novel methods that aid in the structural elucidation of complex glycans, particularly sialylated and fucosylated glycans; and (3) labeling techniques that enhance detection ionization efficiency, separation, and sensitivity for matrix-assisted laser desorption/ionization (MALDI)-MS and capillary electrophoresis (CE)-based glycan analysis. In addition, we highlight emerging trends in single-cell glycomics and bioinformatics tools that have the potential to revolutionize glycan quantification. These developments not only expand our understanding of glycan structures and functions but also open new avenues for biomarker discovery and therapeutic applications. Through detailed discussions of methodological advancements, this review underscores the critical role of derivatization methods in advancing glycan identification and quantification.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 1-2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142708787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial Board: Proteomics 21–22'24","authors":"","doi":"10.1002/pmic.202470172","DOIUrl":"https://doi.org/10.1002/pmic.202470172","url":null,"abstract":"","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 21-22","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202470172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contents: Proteomics 21–22'24","authors":"","doi":"10.1002/pmic.202470173","DOIUrl":"https://doi.org/10.1002/pmic.202470173","url":null,"abstract":"","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 21-22","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202470173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Standard abbreviations","authors":"","doi":"10.1002/pmic.202470174","DOIUrl":"10.1002/pmic.202470174","url":null,"abstract":"","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 21-22","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142674591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}