Proteomics最新文献

{"title":"A complementary metaproteomic approach to interrogate microbiome cultivated from clinical colon biopsies.","authors":"Van-An Duong, Altai Enkhbayar, Nobel Bhasin, Lakmini Senavirathna, Eva C Preisner, Kristi L Hoffman, Richa Shukla, Robert R Jenq, Kai Cheng, Mary P Bronner, Daniel Figeys, Robert A Britton, Sheng Pan, Ru Chen","doi":"10.1002/pmic.202400078","DOIUrl":"10.1002/pmic.202400078","url":null,"abstract":"<p><p>The human gut microbiome plays a vital role in preserving individual health and is intricately involved in essential functions. Imbalances or dysbiosis within the microbiome can significantly impact human health and are associated with many diseases. Several metaproteomics platforms are currently available to study microbial proteins within complex microbial communities. In this study, we attempted to develop an integrated pipeline to provide deeper insights into both the taxonomic and functional aspects of the cultivated human gut microbiomes derived from clinical colon biopsies. We combined a rapid peptide search by MSFragger against the Unified Human Gastrointestinal Protein database and the taxonomic and functional analyses with Unipept Desktop and MetaLab-MAG. Across seven samples, we identified and matched nearly 36,000 unique peptides to approximately 300 species and 11 phyla. Unipept Desktop provided gene ontology, InterPro entries, and enzyme commission number annotations, facilitating the identification of relevant metabolic pathways. MetaLab-MAG contributed functional annotations through Clusters of Orthologous Genes and Non-supervised Orthologous Groups categories. These results unveiled functional similarities and differences among the samples. This integrated pipeline holds the potential to provide deeper insights into the taxonomy and functions of the human gut microbiome for interrogating the intricate connections between microbiome balance and diseases.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e2400078"},"PeriodicalIF":3.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141198517","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":"https://doi.org/10.1002/pmic.202470174","url":null,"abstract":"","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 21-22","pages":"e2470174"},"PeriodicalIF":3.4,"publicationDate":"2024-11-01","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}

{"title":"A cross-omics data analysis strategy for metabolite-microbe pair identification.","authors":"Tao Sun, Dongnan Sun, Junliang Kuang, Xiaowen Chao, Yihan Guo, Mengci Li, Tianlu Chen","doi":"10.1002/pmic.202400035","DOIUrl":"10.1002/pmic.202400035","url":null,"abstract":"<p><p>Given the pivotal roles of metabolomics and microbiomics, numerous data mining approaches aim to uncover their intricate connections. However, the complex many-to-many associations between metabolome-microbiome profiles yield numerous statistically significant but biologically unvalidated candidates. To address these challenges, we introduce BiOFI, a strategic framework for identifying metabolome-microbiome correlation pairs (Bi-Omics). BiOFI employs a comprehensive scoring system, incorporating intergroup differences, effects on feature correlation networks, and organism abundance. Meanwhile, it establishes a built-in database of metabolite-microbe-KEGG functional pathway linking relationships. Furthermore, BiOFI can rank related feature pairs by combining importance scores and correlation strength. Validation on a dataset of cesarean-section infants confirms the strategy's validity and interpretability. The BiOFI R package is freely accessible at https://github.com/chentianlu/BiOFI.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e2400035"},"PeriodicalIF":3.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141589067","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":"Open-source large language models in action: A bioinformatics chatbot for PRIDE database.","authors":"Jingwen Bai, Selvakumar Kamatchinathan, Deepti J Kundu, Chakradhar Bandla, Juan Antonio Vizcaíno, Yasset Perez-Riverol","doi":"10.1002/pmic.202400005","DOIUrl":"10.1002/pmic.202400005","url":null,"abstract":"<p><p>We here present a chatbot assistant infrastructure (https://www.ebi.ac.uk/pride/chatbot/) that simplifies user interactions with the PRIDE database's documentation and dataset search functionality. The framework utilizes multiple Large Language Models (LLM): llama2, chatglm, mixtral (mistral), and openhermes. It also includes a web service API (Application Programming Interface), web interface, and components for indexing and managing vector databases. An Elo-ranking system-based benchmark component is included in the framework as well, which allows for evaluating the performance of each LLM and for improving PRIDE documentation. The chatbot not only allows users to interact with PRIDE documentation but can also be used to search and find PRIDE datasets using an LLM-based recommendation system, enabling dataset discoverability. Importantly, while our infrastructure is exemplified through its application in the PRIDE database context, the modular and adaptable nature of our approach positions it as a valuable tool for improving user experiences across a spectrum of bioinformatics and proteomics tools and resources, among other domains. The integration of advanced LLMs, innovative vector-based construction, the benchmarking framework, and optimized documentation collectively form a robust and transferable chatbot assistant infrastructure. The framework is open-source (https://github.com/PRIDE-Archive/pride-chatbot).</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e2400005"},"PeriodicalIF":3.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140331349","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":"Identification of RNA‐dependent liquid‐liquid phase separation proteins using an artificial intelligence strategy.","authors":"Zahoor Ahmed, Kiran Shahzadi, Yanting Jin, Rui Li, Biffon Manyura Momanyi, Hasan Zulfiqar, Lin Ning, Hao Lin","doi":"10.1002/pmic.202400044","DOIUrl":"10.1002/pmic.202400044","url":null,"abstract":"<p><p>RNA-dependent liquid-liquid phase separation (LLPS) proteins play critical roles in cellular processes such as stress granule formation, DNA repair, RNA metabolism, germ cell development, and protein translation regulation. The abnormal behavior of these proteins is associated with various diseases, particularly neurodegenerative disorders like amyotrophic lateral sclerosis and frontotemporal dementia, making their identification crucial. However, conventional biochemistry-based methods for identifying these proteins are time-consuming and costly. Addressing this challenge, our study developed a robust computational model for their identification. We constructed a comprehensive dataset containing 137 RNA-dependent and 606 non-RNA-dependent LLPS protein sequences, which were then encoded using amino acid composition, composition of K-spaced amino acid pairs, Geary autocorrelation, and conjoined triad methods. Through a combination of correlation analysis, mutual information scoring, and incremental feature selection, we identified an optimal feature subset. This subset was used to train a random forest model, which achieved an accuracy of 90% when tested against an independent dataset. This study demonstrates the potential of computational methods as efficient alternatives for the identification of RNA-dependent LLPS proteins. To enhance the accessibility of the model, a user-centric web server has been established and can be accessed via the link: http://rpp.lin-group.cn.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e2400044"},"PeriodicalIF":3.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141198523","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":"Proteomics analysis of round and wrinkled pea (Pisum sativum L.) seeds during different development periods.","authors":"Sintayehu D Daba, Punyatoya Panda, Uma K Aryal, Alecia M Kiszonas, Sean M Finnie, Rebecca J McGee","doi":"10.1002/pmic.202300363","DOIUrl":"https://doi.org/10.1002/pmic.202300363","url":null,"abstract":"<p><p>Seed development is complex, influenced by genetic and environmental factors. Understanding proteome profiles at different seed developmental stages is key to improving seed composition and quality. We used label-free quantitative proteomics to analyze round and wrinkled pea seeds at five growth stages: 4, 7, 12, 15, and days after anthesis (DAA), and at maturity. Wrinkled peas had lower starch content (30%) compared to round peas (47%-55%). Proteomic analysis identified 3659 protein groups, with 21%-24% shared across growth stages. More proteins were identified during early seed development than at maturity. Statistical analysis found 735 significantly different proteins between wrinkled and round seeds, regardless of the growth stage. The detected proteins were categorized into 31 functional classes, including metabolic enzymes, proteins involved in protein biosynthesis and homeostasis, carbohydrate metabolism, and cell division. Cell division-related proteins were more abundant in early stages, while storage proteins were more abundant later in seed development. Wrinkled seeds had lower levels of the starch-branching enzyme (SBEI), which is essential for amylopectin biosynthesis. Seed storage proteins like legumin and albumin (PA2) were more abundant in round peas, whereas vicilin was more prevalent in wrinkled peas. This study enhances our understanding of seed development in round and wrinkled peas. The study highlighted the seed growth patterns and protein profiles in round and wrinkled peas during seed development. It showed how protein accumulation changed, particularly focusing on proteins implicated in cell division, seed reserve metabolism, as well as storage proteins and protease inhibitors. These findings underscore the crucial role of these proteins in seed development. By linking the proteins identified to Cameor-based pea reference genome, our research can open avenues for deeper investigations into individual proteins, facilitate their practical application in crop improvement, and advance our knowledge of seed development.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e2300363"},"PeriodicalIF":3.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142542423","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":"Characterization of Trivalently Crosslinked C-Terminal Telopeptide of Type I Collagen (CTX) Species in Human Plasma and Serum Using High-Resolution Mass Spectrometry.","authors":"Justine Demeuse, William Determe, Elodie Grifnée, Philippe Massonnet, Matthieu Schoumacher, Loreen Huyghebeart, Thomas Dubrowski, Stéphanie Peeters, Caroline Le Goff, Etienne Cavalier","doi":"10.1002/pmic.202400027","DOIUrl":"https://doi.org/10.1002/pmic.202400027","url":null,"abstract":"<p><p>With an aging population, the increased interest in the monitoring of skeletal diseases such as osteoporosis led to significant progress in the discovery and measurement of bone turnover biomarkers since the 2000s. Multiple markers derived from type I collagen, such as CTX, NTX, PINP, and ICTP, have been developed. Extensive efforts have been devoted to characterizing these molecules; however, their complex crosslinked structures have posed significant analytical challenges, and to date, these biomarkers remain poorly characterized. Previous attempts at characterization involved gel-based separation methods and MALDI-TOF analysis on collagen peptides directly extracted from bone. However, using bone powder, which is rich in collagen, does not represent the true structure of the peptides in the biofluids as it was cleaved. In this study, our goal was to characterize plasma and serum CTX for subsequent LC-MS/MS method development. We extracted and characterized type I collagen peptides directly from human plasma and serum using a proteomics workflow that integrates preparative LC, affinity chromatography, and HR-MS. Subsequently, we successfully identified numerous CTX species, providing valuable insights into the characterization of these crucial biomarkers.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400027"},"PeriodicalIF":3.4,"publicationDate":"2024-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142491727","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":"SWATH-MS Based Secretome Proteomic Analysis of Pseudomonas aeruginosa Against MRSA.","authors":"Yi-Feng Zheng, Yu-Sheng Lin, Jing-Wen Huang, Kuo-Tung Tang, Cheng-Yu Kuo, Wei-Chen Wang, Han-Ju Chien, Chih-Jui Chang, Nien-Jen Hu, Chien-Chen Lai","doi":"10.1002/pmic.202300649","DOIUrl":"https://doi.org/10.1002/pmic.202300649","url":null,"abstract":"<p><p>The study uses Sequential Window Acquisition of All Theoretical Fragment Ion Mass Spectra (SWATH)-MS in conjunction with secretome proteomics to identify key proteins that Pseudomonas aeruginosa secretes against methicillin-resistant Staphylococcus aureus (MRSA). Variations in the inhibition zones indicated differences in strain resistance. Multivariate statistical methods were applied to filter the proteomic results, revealing five potential protein biomarkers, including Peptidase M23. Gene ontology (GO) analysis and sequence alignment supported their antibacterial activity. Thus, SWATH-MS provides a comprehensive understanding of the secretome of P. aeruginosa in its action against MRSA, guiding future antibacterial research.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202300649"},"PeriodicalIF":3.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142454346","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":"Contents: Proteomics 20'24","authors":"","doi":"10.1002/pmic.202470163","DOIUrl":"https://doi.org/10.1002/pmic.202470163","url":null,"abstract":"","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 20","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202470163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142429818","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":"Astronaut proteomics: Japan leads the way for transformative studies in space","authors":"Alexia Tasoula,&nbsp;Nathaniel Szewczyk","doi":"10.1002/pmic.202300645","DOIUrl":"10.1002/pmic.202300645","url":null,"abstract":"","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 20","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398872","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}