IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-10 DOI: 10.1002/pmic.202400108

Marta B Lopes, Roberta Coletti, Flore Duranton, Griet Glorieux, Mayra Alejandra Jaimes Campos, Julie Klein, Matthias Ley, Paul Perco, Alexia Sampri, Aviad Tur-Sinai

{"title":"The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease.","authors":"Marta B Lopes, Roberta Coletti, Flore Duranton, Griet Glorieux, Mayra Alejandra Jaimes Campos, Julie Klein, Matthias Ley, Paul Perco, Alexia Sampri, Aviad Tur-Sinai","doi":"10.1002/pmic.202400108","DOIUrl":"https://doi.org/10.1002/pmic.202400108","url":null,"abstract":"Chronic kidney disease (CKD) poses a significant and growing global health challenge, making early detection and slowing disease progression essential for improving patient outcomes. Traditional diagnostic methods such as glomerular filtration rate and proteinuria are insufficient to capture the complexity of CKD. In contrast, omics technologies have shed light on the molecular mechanisms of CKD, helping to identify biomarkers for disease assessment and management. Artificial intelligence (AI) and machine learning (ML) could transform CKD care, enabling biomarker discovery for early diagnosis and risk prediction, and personalized treatment. By integrating multi-omics datasets, AI can provide real-time, patient-specific insights, improve decision support, and optimize cost efficiency by early detection and avoidance of unnecessary treatments. Multidisciplinary collaborations and sophisticated ML methods are essential to advance diagnostic and therapeutic strategies in CKD. This review presents a comprehensive overview of the pipeline for translating CKD omics data into personalized treatment, covering recent advances in omics research, the role of ML in CKD, and the critical need for clinical validation of AI-driven discoveries to ensure their efficacy, relevance, and cost-effectiveness in patient care.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400108"},"PeriodicalIF":3.4,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142942016","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}

引用次数: 0

The Proteomic Landscape of the Coronary Accessible Heart Cell Surfaceome. 冠状动脉可达性心脏细胞表面体的蛋白质组学研究。

IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-10 DOI: 10.1002/pmic.202400320

Iasmin Inocencio, Alin Rai, Daniel Donner, David W Greening

{"title":"The Proteomic Landscape of the Coronary Accessible Heart Cell Surfaceome.","authors":"Iasmin Inocencio, Alin Rai, Daniel Donner, David W Greening","doi":"10.1002/pmic.202400320","DOIUrl":"https://doi.org/10.1002/pmic.202400320","url":null,"abstract":"Cell surface proteins (surfaceome) represent key signalling and interaction molecules for therapeutic targeting, biomarker profiling and cellular phenotyping in physiological and pathological states. Here, we employed coronary artery perfusion with membrane-impermeant biotin to label and capture the surface-accessible proteome in the neo-native (intact) heart. Using quantitative proteomics, we identified 701 heart cell surfaceome accessible by the coronary artery, including receptors, cell surface enzymes, adhesion and junctional molecules. This surfaceome comprises to 216 cardiac cell-specific surface proteins, including 29 proteins reported in cardiomyocytes (CXADR, CACNA1C), 12 in cardiac fibroblasts (ITGA8, COL3A1) and 63 in multiple cardiac cell types (ICAM1, SLC3A2, CDH2). Further, this surfaceome comprises to 53 proteins enriched in heart tissue compared to other tissues in humans and implicated in cardiac cell signalling networks involving cardiomyopathy (CDH2, DTNA, PTKP2, SNTA1, CAM, K2D/B), cardiac muscle contraction and development (ENG, SNTA1, SGCG, MYPN), calcium ion binding (SGCA, MASP1, THBS4, FBLN2, GSN) and cell metabolism (SDHA, NUDFS1, GYS1, ACO2, IDH2). This method offers a powerful tool for dissecting the molecular landscape of the coronary artery accessible heart cell surfaceome, its role in maintaining cardiac and vascular function, and potential molecular leads for studying cardiac cell interactions and systemic delivery to the neo-native heart.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400320"},"PeriodicalIF":3.4,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142942018","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}

引用次数: 0

Proteomic Insight Into Alzheimer's Disease Pathogenesis Pathways. 蛋白质组学洞察阿尔茨海默病的发病途径。

IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-10 DOI: 10.1002/pmic.202400298

Taekyung Ryu, Kyungdo Kim, Nicholas Asiimwe, Chan Hyun Na

引用次数: 0

Decoding Microbial Plastic Colonisation: Multi-Omic Insights Into the Fast-Evolving Dynamics of Early-Stage Biofilms. 解码微生物塑料定植：多组学洞察早期生物膜的快速发展动态。

IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-06 DOI: 10.1002/pmic.202400208

Charlotte E Lee, Lauren F Messer, Ruddy Wattiez, Sabine Matallana-Surget

{"title":"Decoding Microbial Plastic Colonisation: Multi-Omic Insights Into the Fast-Evolving Dynamics of Early-Stage Biofilms.","authors":"Charlotte E Lee, Lauren F Messer, Ruddy Wattiez, Sabine Matallana-Surget","doi":"10.1002/pmic.202400208","DOIUrl":"https://doi.org/10.1002/pmic.202400208","url":null,"abstract":"Marine plastispheres represent dynamic microhabitats where microorganisms colonise plastic debris and interact. Metaproteomics has provided novel insights into the metabolic processes within these communities; however, the early metabolic interactions driving the plastisphere formation remain unclear. This study utilised metaproteomic and metagenomic approaches to explore early plastisphere formation on low-density polyethylene (LDPE) over 3 (D3) and 7 (D7) days, focusing on microbial diversity, activity and biofilm development. In total, 2948 proteins were analysed, revealing dominant proteomes from Pseudomonas and Marinomonas, with near-complete metagenome-assembled genomes (MAGs). Pseudomonas dominated at D3, whilst at D7, Marinomonas, along with Acinetobacter, Vibrio and other genera became more prevalent. Pseudomonas and Marinomonas showed high expression of reactive oxygen species (ROS) suppression proteins, associated with oxidative stress regulation, whilst granule formation, and alternative carbon utilisation enzymes, also indicated nutrient limitations. Interestingly, 13 alkanes and other xenobiotic degradation enzymes were expressed by five genera. The expression of toxins, several type VI secretion system (TVISS) proteins, and biofilm formation proteins by Pseudomonas indicated their competitive advantage against other taxa. Upregulated metabolic pathways relating to substrate transport also suggested enhanced nutrient cross-feeding within the more diverse biofilm community. These insights enhance our understanding of plastisphere ecology and its potential for biotechnological applications.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400208"},"PeriodicalIF":3.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930182","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}

引用次数: 0

Fecal Metaproteomics as a Tool to Monitor Functional Modifications Induced in the Gut Microbiota by Ketogenic Diet: A Case Study. 粪便宏蛋白质组学作为监测生酮饮食引起的肠道微生物群功能改变的工具：一个案例研究。

IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-05 DOI: 10.1002/pmic.202400191

Alessandro Tanca, Simona Masia, Piero Giustacchini, Sergio Uzzau

{"title":"Fecal Metaproteomics as a Tool to Monitor Functional Modifications Induced in the Gut Microbiota by Ketogenic Diet: A Case Study.","authors":"Alessandro Tanca, Simona Masia, Piero Giustacchini, Sergio Uzzau","doi":"10.1002/pmic.202400191","DOIUrl":"https://doi.org/10.1002/pmic.202400191","url":null,"abstract":"Metaproteomics is a valuable approach to characterize the biological functions involved in the gut microbiota (GM) response to dietary interventions. Ketogenic diets (KDs) are very effective in controlling seizure severity and frequency in drug-resistant epilepsy (DRE) and in the weight loss management in obese/overweight individuals. This case study provides proof of concept for the suitability of metaproteomics to monitor changes in taxonomic and functional GM features in an individual on a short-term very low-calorie ketogenic diet (VLCKD, 4 weeks), followed by a low-calorie diet (LCD). A marked increase in Akkermansia and Pseudomonadota was observed during VLCKD and reversed after the partial reintroduction of carbohydrates (LCD), in agreement with the results of previous metagenomic studies. In functional terms, the relative increase in Akkermansia was associated with an increased production of proteins involved in response to stress and biosynthesis of gamma-aminobutyric acid. In addition, VLCKD caused a relative increase in enzymes involved in the synthesis of the beta-ketoacid acetoacetate and of the ketogenic amino acid leucine. Our data support the potential of fecal metaproteomics to investigate the GM-dependent effect of KD as a therapeutic option in obese/overweight individuals and DRE patients.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400191"},"PeriodicalIF":3.4,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930184","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}

引用次数: 0

Deep learning methods for protein function prediction. 用于蛋白质功能预测的深度学习方法。

IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-01 Epub Date: 2024-07-12 DOI: 10.1002/pmic.202300471

Frimpong Boadu, Ahhyun Lee, Jianlin Cheng

{"title":"Deep learning methods for protein function prediction.","authors":"Frimpong Boadu, Ahhyun Lee, Jianlin Cheng","doi":"10.1002/pmic.202300471","DOIUrl":"10.1002/pmic.202300471","url":null,"abstract":"Predicting protein function from protein sequence, structure, interaction, and other relevant information is important for generating hypotheses for biological experiments and studying biological systems, and therefore has been a major challenge in protein bioinformatics. Numerous computational methods had been developed to advance protein function prediction gradually in the last two decades. Particularly, in the recent years, leveraging the revolutionary advances in artificial intelligence (AI), more and more deep learning methods have been developed to improve protein function prediction at a faster pace. Here, we provide an in-depth review of the recent developments of deep learning methods for protein function prediction. We summarize the significant advances in the field, identify several remaining major challenges to be tackled, and suggest some potential directions to explore. The data sources and evaluation metrics widely used in protein function prediction are also discussed to assist the machine learning, AI, and bioinformatics communities to develop more cutting-edge methods to advance protein function prediction.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e2300471"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597982","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}

引用次数: 0

Population Proteomics: A Tool to Gain Insights Into the Inflamed Periodontium. 群体蛋白质组学：一种深入了解发炎牙周组织的工具。

IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-01 Epub Date: 2024-12-30 DOI: 10.1002/pmic.202400055

Stefan Lars Reckelkamm, Sebastian-Edgar Baumeister, Daniel Hagenfeld, Zoheir Alayash, Michael Nolde

{"title":"Population Proteomics: A Tool to Gain Insights Into the Inflamed Periodontium.","authors":"Stefan Lars Reckelkamm, Sebastian-Edgar Baumeister, Daniel Hagenfeld, Zoheir Alayash, Michael Nolde","doi":"10.1002/pmic.202400055","DOIUrl":"10.1002/pmic.202400055","url":null,"abstract":"Periodontitis, characterized by inflammatory loss of tooth-supporting tissues associated with biofilm, is among the most prevalent chronic diseases globally, affecting approximately 50% of the adult population to a moderate extent and cases of severe periodontitis surpassing the one billion mark. Proteomics analyses of blood, serum, and oral fluids have provided valuable insights into the complex processes occurring in the inflamed periodontium. However, until now, proteome analyses have been primarily limited to small groups of diseased versus healthy individuals. The emergence of population-scale analysis of proteomic data offers opportunities to uncover disease-associated pathways, identify potential drug targets, and discover biomarkers. In this review, we will explore the applications of proteomics in population-based studies and discuss the advancements it brings to our understanding of periodontal inflammation. Additionally, we highlight the challenges posed by currently available data and offer perspectives for future applications in periodontal research. This review aims to explain the ongoing efforts in leveraging proteomics for elucidating the complexities of periodontal diseases and paving the way for clinical strategies.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400055"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142908804","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}

引用次数: 0

Computational Methods for Data Integration and Imputation of Missing Values in Omics Datasets. 组学数据集的数据集成与缺失值的计算方法。

IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-01 Epub Date: 2024-12-30 DOI: 10.1002/pmic.202400100

Yannis Schumann, Antonia Gocke, Julia E Neumann

{"title":"Computational Methods for Data Integration and Imputation of Missing Values in Omics Datasets.","authors":"Yannis Schumann, Antonia Gocke, Julia E Neumann","doi":"10.1002/pmic.202400100","DOIUrl":"10.1002/pmic.202400100","url":null,"abstract":"Molecular profiling of different omic-modalities (e.g., DNA methylomics, transcriptomics, proteomics) in biological systems represents the basis for research and clinical decision-making. Measurement-specific biases, so-called batch effects, often hinder the integration of independently acquired datasets, and missing values further hamper the applicability of typical data processing algorithms. In addition to careful experimental design, well-defined standards in data acquisition and data exchange, the alleviation of these phenomena particularly requires a dedicated data integration and preprocessing pipeline. This review aims to give a comprehensive overview of computational methods for data integration and missing value imputation for omic data analyses. We provide formal definitions for missing value mechanisms and propose a novel statistical taxonomy for batch effects, especially in the presence of missing data. Based on an automated document search and systematic literature review, we describe 32 distinct data integration methods from five main methodological categories, as well as 37 algorithms for missing value imputation from five separate categories. Additionally, this review highlights multiple quantitative evaluation methods to aid researchers in selecting a suitable set of methods for their work. Finally, this work provides an integrated discussion of the relevance of batch effects and missing values in omics with corresponding method recommendations. We then propose a comprehensive three-step workflow from the study conception to final data analysis and deduce perspectives for future research. Eventually, we present a comprehensive flow chart as well as exemplary decision trees to aid practitioners in the selection of specific approaches for imputation and data integration in their studies.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400100"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142908798","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}

引用次数: 0

The role of protein corona in advancing plasma proteomics. 蛋白质电晕在推进血浆蛋白质组学方面的作用。

IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-01 Epub Date: 2024-09-02 DOI: 10.1002/pmic.202400028

Amir Ata Saei, Liangliang Sun, Morteza Mahmoudi

{"title":"The role of protein corona in advancing plasma proteomics.","authors":"Amir Ata Saei, Liangliang Sun, Morteza Mahmoudi","doi":"10.1002/pmic.202400028","DOIUrl":"10.1002/pmic.202400028","url":null,"abstract":"The protein corona, a layer of biomolecules forming around nanoparticles in biological environments, critically influences nanoparticle interactions with biosystems, affecting pharmacokinetics and biological outcomes. Initially, the protein corona presented challenges for nanomedicine and nanotoxicology, such as nutrient depletion in cell cultures and masking of nanoparticle-targeting species. However, recent advancements have highlighted its potential in environmental toxicity, proteomics, and immunology. This viewpoint focuses on leveraging the protein corona to enhance the depth of plasma proteome analysis, addressing challenges posed by the high dynamic range of protein concentrations in plasma. The protein corona simplifies sample preparation, enriches low-abundance proteins, and improves proteome coverage. Innovations include using diverse nanoparticles and spiking small molecules to increase the number of quantified proteins. Reproducibility issues across core facilities necessitate standardized protocols. Moreover, top-down proteomics enables proteoform-specific measurements, providing deeper insights into protein corona composition. Future research should aim at improving top-down proteomics techniques and integrating protein corona studies and proteomics for personalized medicine and advanced diagnostics.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e2400028"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735278/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142102726","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}

引用次数: 0

Addressing Sample Mix-Ups: Tools and Approaches for Large-Scale Multi-Omics Studies. 解决样本混淆：大规模多组学研究的工具和方法。

IF 3.4 4区生物学

Proteomics Pub Date : 2025-01-01 Epub Date: 2024-12-10 DOI: 10.1002/pmic.202400271

Yingxue Fu, Zuo-Fei Yuan, Long Wu, Junmin Peng, Xusheng Wang, Anthony A High

{"title":"Addressing Sample Mix-Ups: Tools and Approaches for Large-Scale Multi-Omics Studies.","authors":"Yingxue Fu, Zuo-Fei Yuan, Long Wu, Junmin Peng, Xusheng Wang, Anthony A High","doi":"10.1002/pmic.202400271","DOIUrl":"10.1002/pmic.202400271","url":null,"abstract":"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.","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400271"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","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}

引用次数: 0

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