Proteomics最新文献

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.

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.

Alzheimer's disease (AD) is a leading cause of dementia, but the pathogenesis mechanism is still elusive. Advances in proteomics have uncovered key molecular mechanisms underlying AD, revealing a complex network of dysregulated pathways, including amyloid metabolism, tau pathology, apolipoprotein E (APOE), protein degradation, neuroinflammation, RNA splicing, metabolic dysregulation, and cognitive resilience. This review examines recent proteomic findings from AD brain tissues and biological fluids, highlighting potential biomarkers and therapeutic targets. By examining the proteomic landscape of them, we aim to deepen our understanding of the disease and support developing precision medicine strategies for more effective interventions.

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.

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.

One of the key processes that forms the basis of fertilisation is the tight interaction between sperm and egg. Both sperm and egg proteomes are known to evolve and diverge rapidly even between closely related species. Understanding the sperm proteome therefore provides key insights into the proteins that underpin the mechanisms involved during fertilisation and the fusion between sperm and egg, and how they can differ across individuals of the same species. Despite being a commonly used model organism for reproductive research, little is currently understood about the zebrafish Danio rerio sperm proteome. We performed nanoLC-MS/MS proteomics analysis after off-line sample fractionation with six pooled samples containing sperm from ten males each. We confidently identified 5410 proteins, from which a total of 3900 GeneIDs were generated leading to 1720 Gene Ontology terms.

Naturally occurring fragments of collagen type I alpha 1 chain (COL1A1) have been previously associated with chronic kidney disease (CKD), with some fragments showing positive and others negative associations. Using urinary peptidome data from healthy individuals (n = 1131) and CKD patients (n = 5585) this aspect was investigated in detail. Based on the hypothesis that many collagen peptides are derived not from the full, mature collagen molecule, but from (larger) collagen degradation products, relationships between COL1A1 peptides containing identical sequences were investigated, with the smaller (offspring) peptide being a possible degradation product of the larger (parent) one. The strongest correlations were found for relationships where the parent differed by a maximum of three amino acids from the offspring, indicating an exopeptidase-regulated stepwise degradation process. Regression analysis indicated that CKD affects this degradation process. A comparison of matched CKD patients and control individuals (n = 612 each) showed that peptides at the start of the degradation process were consistently downregulated in CKD, indicating an attenuation of COL1A1 endopeptidase-mediated degradation. However, as these peptides undergo further degradation, likely mediated by exopeptidases, this downregulation can become less significant or even reverse, leading to an upregulation of later-stage fragments and potentially explaining the inconsistencies observed in previous studies.

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.

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.

The human gut microbiota (GM) is a community of microorganisms that resides in the gastrointestinal (GI) tract. Recognized as a critical element of human health, the functions of the GM extend beyond GI well-being to influence overall systemic health and susceptibility to disease. Among the other omic sciences, metaproteomics highlights additional facets that make it a highly valuable discipline in the study of GM. Indeed, it allows the protein inventory of complex microbial communities. Proteins with associated taxonomic membership and function are identified and quantified from their constituent peptides by liquid chromatography coupled to mass spectrometry analyses and by querying specific databases (DBs). The aim of this review was to compile comprehensive information on metaproteomic studies of the human GM, with a focus on the bacterial component, to assist newcomers in understanding the methods and types of research conducted in this field. The review outlines key steps in a metaproteomic-based study, such as protein extraction, DB selection, and bioinformatic workflow. The importance of standardization is emphasized. In addition, a list of previously published studies is provided as hints for researchers interested in investigating the role of GM in health and disease states.