Proteomics最新文献

Extracellular vesicles (EVs) are membrane-bound particles secreted by cells, playing crucial roles in intercellular communication. The composition of EVs can undergo changes in response to stress and disease conditions, making them excellent biomarker candidates. However, extracting protein information from EVs can be challenging due to their low abundance in complex biofluids and copurification with contaminant proteins and particles. Techniques to enrich EVs have their strengths and limitations, without one being able to purify EVs to complete homogeneity. This can lead to compromised recovery rates and increased complexity, making data interpretation difficult. In this viewpoint article, we explore the concept that better characterization of EV composition, followed by quantification of EV proteins in complex samples, might be a more viable route for biomarker development. Mass spectrometers can provide reproducible deep coverage of the EV proteome, despite sample impurities. This paradigm shift presents opportunities to integrate advanced bioinformatics tools to refine the EV proteome landscape, identify novel biomarkers, and streamline validation processes in biomarker development. By focusing on leveraging technology rather than achieving absolute purity, this approach can transform current practices and open opportunities for robust biomarker discovery. Herein, we highlight not only such opportunities but also challenges to implement this concept. SUMMARY: Extracellular vesicles (EVs) have enormous potential as biomarkers of diseases, as they can carry signatures of the cells they are derived from and the pathogenesis process. Biofluids, such as blood plasma, are highly complex and contain many components with physicochemical properties similar to those of EVs, making it challenging to obtain pure EV fractions. Challenges in obtaining pure preparations represent a main hurdle for studying EVs, and their components are potential biomarkers. This article explores the concept of studying EV proteins within complex samples, discussing opportunities and needs to move this field forward.

Cells are comprised of a broad spectrum of structures that compartmentalize biochemical and signaling mechanisms. These structures can be comprised of many biomolecules, but especially lipids, proteins, and nucleic acids. Techniques are limited to quantify or discover new subcellular structures. We explored whether a proteomics approach using chemical crosslinking followed by size-exclusion chromatography and mass spectrometry (SEC-MS) of whole cell lysates can address this challenge. Formaldehyde crosslinking was used to preserve the weak molecular interactions responsible for many protein and nucleic acid assemblies. In this study, we perform the first formaldehyde crosslinking-assisted SEC-MS in a bacterial system. We demonstrate that when expressed ectopically in E. coli, large structures of a known assembly protein, FUS, can be detected through SEC-MS. We then show that E. coli proteins are enriched in particles of large or medium size due to formaldehyde crosslinking, which is the first analysis by formaldehyde and SEC-MS for a bacterial system. Last, analysis identified previously characterized E. coli protein assemblies and condensates, as well as potentially novel associations of prokaryote metabolism with large subcellular bodies. We propose this unbiased method can be used to stimulate or supplement targeted methods for discovery of new cellular bodies in a wide range of cell types.

Cobalt is an essential micronutrient but becomes toxic at elevated concentrations, requiring microorganisms to balance acquisition and detoxification. Aeromonas hydrophila, an opportunistic aquatic pathogen, is often encountered in metal-contaminated aquatic environments; however, its adaptive responses to cobalt stress have not been systematically characterized. Here, we applied quantitative proteomics to characterize the global protein response of A. hydrophila under cobalt stress. A total of 2767 proteins were identified, of which 724 were differentially abundant. Enrichment analyses indicated that cobalt exposure was associated with alterations in energy metabolism, oxidative phosphorylation, and ribosome-related pathways. Gene set enrichment analysis suggested an overall upregulation of ribosome-associated functions, accompanied by down regulation of carbon metabolism and the tricarboxylic acid cycle. Protein-protein interaction network mapping identified 15 functional clusters, with core modules linked to oxidative phosphorylation, ABC transport, carbohydrate metabolism, and Fe-S cluster biogenesis. Ten hub proteins associated with respiratory and transport systems were identified based on network topology. Functional validation using seven deletion mutants indicated that genes encoding shikimate kinase, glutaminase, and arsenate reductase contribute to cobalt tolerance. Together, these findings provide a systems-level view of how A. hydrophila adapts to cobalt stress, reveal candidate factors mediating metal resistance, and suggest potential targets for antimicrobial development and bioremediation strategies.

Proteomics is strengthening research in biology and the diversification of the model organisms studied is very promising for fully understanding the complexity of biological principles. However, the lack of protein sequence databases for many species is a major bottleneck. Existing computational solutions are usually incomplete and/or only usable by bioinformaticians. We have built an open-source, user-friendly pipeline, called Brownotate, which allows anyone to generate protein sequence databases for any species as long as sequencing information is available. The pipeline can extract already existing protein sequences, but also automatically annotate any genome assembly or assemble and annotate any DNA sequence dataset. By testing the pipeline with numerous sequencing and assembly datasets covering a large part of the phylogenetic tree, we show that Brownotate generates fragmented but good quality assemblies and good quality annotations when compared to reference data. By comparing the use of protein databases generated by Brownotate or downloaded from NCBI to interpret proteomic data, we show very comparable results. The Brownotate pipeline is, therefore, an important new addition to the proteomics toolbox. The pipeline and its web interface are freely available at https://github.com/LSMBO/Brownotate and https://github.com/LSMBO/brownotate-app, respectively. SUMMARY: This study evaluated the performance of a newly developed pipeline, Brownotate, for the assembly and annotation of sequencing data for multiple species, from prokaryotes to eukaryotes. We compared their fragmentation level (assembly) and completeness based on evolutionary expectations of gene content, and we evaluated their overlap. Brownotate generated fragmented, slightly less complete assemblies. However, the overlap of proteins predicted was very good, despite an excess of predicted sequences of small size with Brownotate. In addition, the interpretation of proteomics data downloaded from PRIDE repository for 27 species was found to lead to very similar results regardless of the origin of the protein sequencing database used, whether it was generated by Brownotate or downloaded from NCBI. Brownotate, made available to the community, will, therefore, be a tool of choice to mitigate the lack of an appropriate protein sequence database for many species, and allow proteomists to analyse without delay samples from species for which only sequencing data are available.

Protein S-palmitoylation, a reversible lipid modification, plays critical roles in regulating protein function and localization. However, its comprehensive role in the rapid reprogramming of macrophages during early immune responses remains incompletely understood. This study investigates the dynamics of the palmitoylome in immortalized bone marrow-derived macrophages (iBMDMs) during the initial phase of lipopolysaccharide (LPS) stimulation. Employing acyl-biotin exchange (ABE) proteomics coupled with a multi-protease digestion strategy (trypsin, AspN, chymotrypsin, or GluC), we significantly enhanced palmitoylation proteome coverage, identifying 2502 putative S-palmitoylated proteins (Log₂ fold change > 2, FDR < 0.05). Notably, this approach uncovered 527 proteins not previously associated with the mouse palmitoylome, including 185 candidates exclusively identified using non-tryptic proteases. In the context of immune cells, this study revealed 1378 proteins not previously reported, with 556 candidates identified exclusively via AspN, chymotrypsin, and/or GluC. Several of these novel candidates are established immune system components and phosphoproteins. Upon stimulation with 100 ng/mL LPS for 30 min, quantitative comparison revealed 648 differentially enriched proteins (308 predominantly detected in untreated, 340 predominantly detected in LPS-treated), indicating dynamic regulation via this posttranslational modification during early innate immune activation. Functional enrichment analysis linked these dynamically regulated proteins to critical pathways: LPS treatment enriched for immune signaling cascades and infection pathways, while untreated cells showed enrichment for metabolic and transport processes. This study provides a comprehensive resource of the macrophage palmitoylome and its dynamic remodeling, offering novel targets for investigating the regulation of macrophage function.

Proteomic experiments, particularly those addressing dynamic proteome properties, time series, or genetic diversity, require the analysis of large sample numbers. Despite significant advancements in proteomic technologies in recent years, further improvements are needed to accelerate measurement and enhance proteome coverage and quantitative performance. Previously, we demonstrated that incorporating a scanning MS2 dimension into data-independent acquisition (DIA) methods (Scanning SWATH, or more generally scanning DIA), but also ion trapping, improves analytical depth and quantitative performance, especially in proteomic methods using fast chromatography. Here, we evaluate the scanning DIA approach combined with ion trapping via the Zeno trap in a method termed ZT Scan DIA, using a ZenoTOF 7600+ instrument (SCIEX). Applying this method to established proteome standards across various analytical setups, enabling intermediate to high sample throughput, we observed a 30%–40% increase in identified precursors. This enhancement extended to overall protein identification and precise quantification. Furthermore, ZT Scan DIA effectively eliminated quantitative bias, as demonstrated by its ability to deconvolute proteomes in multi-species mixtures. We propose that ZT Scan DIA can be used for a broad range of applications in proteomics, particularly in studies requiring high quantitative precision with low sample input and high-throughput workflows.