Proteomics最新文献

Mass spectrometry is a critical tool to understand complex changes in biological processes. Despite significant advances in search engine technology, many spectra remain unassigned. This research evaluates the performance of three rescoring platforms, Oktoberfest, MS²Rescore, and inSPIRE, using MaxQuant output. The results indicated a substantial increase in identifications at the peptide level (40%-53%) and PSM level (64%-67%). However, some peptides were lost due to limitations in processing posttranslational modifications (PTMs)-with up to 75% of lost peptides exhibiting PTMs. Each platform displayed distinct strengths and weaknesses. For instance, inSPIRE performed best in terms of peptide identifications and unique peptides, while MS²Rescore performed better for PSMs at higher FDR values. Differences in platform performance stemmed from different sources: original search engine feature selection, type of ion series predicted, retention time predictor, and PTMs compatibility. Overall, inSPIRE showed a superior ability to harness original search engine results. Taken all together, rescoring platforms clearly outperformed original search results; however, they demanded additional computation time (up to 77%) and manual adjustments. The findings here underline the necessity of integrating rescoring platforms into current proteomics pipelines but also address some challenges in their implementation and optimization. Future integrated platforms may help enhance adoption.

Metaproteomics enables the large-scale characterization of microbial community proteins, offering crucial insights into their taxonomic composition, functional activities, and interactions within their environments. By directly analyzing proteins, metaproteomics offers insights into community phenotypes and the roles individual members play in diverse ecosystems. Although database-dependent search engines are commonly used for peptide identification, they rely on pre-existing protein databases, which can be limiting for complex, poorly characterized microbiomes. De novo sequencing presents a promising alternative, which derives peptide sequences directly from mass spectra without requiring a database. Over time, this approach has evolved from manual annotation to advanced graph-based, tag-based, and deep learning-based methods, significantly improving the accuracy of peptide identification. This Viewpoint explores the evolution, advantages, limitations, and future opportunities of de novo sequencing in metaproteomics. We highlight recent technological advancements that have improved its potential for detecting unsequenced species and for providing deeper functional insights into microbial communities.

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.