Journal of Proteome Research最新文献

Atherosclerotic plaque rupture is a major cause of cardiovascular events. Plaque destabilization is associated with extracellular matrix (ECM) modification involving proteases which generate protein fragments with new N-termini. We hypothesized that rupture-prone plaques would contain elevated fragment levels, and their sequences would allow identification of active proteases and target proteins. Plaques from 21 patients who underwent surgery for symptomatic carotid artery stenosis were examined in an observational/cross-sectional study. Plaques were analyzed by liquid chromatography-mass spectrometry for the presence of N-terminal fragments. 33920 peptides were identified, with 17814 being N-terminal species. 5735 distinct N-terminal peptides were quantified and subjected to multidimensional scaling analysis and consensus clustering. These analyses indicated three clusters, which correlate with gross macroscopic plaque morphology (soft/mixed/hard), ultrasound classification (echolucent/echogenic), and the presence of hemorrhage/ulceration. Differences in the fragment complements are consistent with plaque-type-dependent turnover and degradation pathways. Identified peptides include signal and pro-peptides from synthesis and those from protein fragmentation. Sequence analysis indicates that targeted proteins include ECM species and responsible proteases (meprins, cathepsins, matrix metalloproteinases, elastase, and kallikreins). This study provides a large data set of peptide fragments and proteases present in plaques of differing stability. These species may have potential as biomarkers for improved atherosclerosis risk profiling.

Prokaryotes and eukaryotes secrete extracellular vesicles (EVs) into the surrounding milieu to preserve and transport elevated concentrations of biomolecules across long distances. EVs encapsulate metabolites, DNA, RNA, and proteins, whose abundance and composition fluctuate depending on environmental cues. EVs are involved in eukaryote-to-prokaryote communication owing to their ability to navigate different ecological niches and exchange molecular cargo between the two domains. Among the different bacterium-host relationships, rhizobium-legume symbiosis is one of the closest known to nature. A crucial developmental stage of symbiosis is the formation of N₂-fixing root nodules by the plant. These nodules contain endocytosed rhizobia─called bacteroids─confined by plant-derived peribacteroid membranes. The unrestricted interface between the bacterial external membrane and the peribacteroid membrane is the peribacteroid space. Many molecular aspects of symbiosis have been studied, but the interbacterial and interdomain molecule trafficking by EVs in the peribacteroid space has not been questioned yet. Here, we unveil intensive EV trafficking within the symbiosome interface of several rhizobium-legume dual systems by developing a robust EV isolation procedure. We analyze the EV-encased proteomes from the peribacteroid space of each bacterium-host partnership, uncovering both conserved and differential traits of every symbiotic system. This study opens the gates for designing EV-based biotechnological tools for sustainable agriculture.

Post-translational modifications are crucial in regulating biological functions across both prokaryotes and eukaryotes. In Aeromonas hydrophila, CobQ, a recently identified novel deacetylase, plays a significant role in lysine deacetylation, influencing bacterial metabolism and stress responses. The present study utilized quantitative proteomics to investigate the impact of cobQ deletion on the global protein expression profile in A. hydrophila. Through data-independent acquisition mass spectrometry, we identified 233 upregulated and 41 downregulated proteins in the cobQ deletion mutant (ΔahcobQ) strain compared to the wild-type (WT) strain. Key differentially expressed proteins were involved in oxidative phosphorylation, bacterial secretion, and ribosomal function. Additionally, phenotypic assays demonstrated that the ΔahcobQ strain exhibited an increased resistance to oxidative phosphorylation inhibitors, suggesting a pivotal role for AhCobQ in energy metabolism. Outer membrane proteins and efflux pumps also showed altered expression, indicating potential implications for membrane permeability and antibiotic resistance. These results suggested that AhCobQ plays a vital regulatory role in maintaining metabolic homeostasis and responding to environmental stress, highlighting its potential as a target for therapeutic interventions against A. hydrophila infections.

Humoral immunity plays a critical role in clearing SARS-CoV-2 during viral invasion. However, the proteome-wide characteristics of antibody responses in individuals infected with Omicron variant, both asymptomatic and symptomatic, remain poorly understood. We profiled the serum antibodies from 108 individuals, including healthy controls and those infected with Omicron BA.2, using a SARS-CoV-2 proteome microarray at the amino acid resolution. We constructed a landscape of B-cell epitopes across the SARS-CoV-2 proteome in symptomatic and asymptomatic individuals. Immunodominant epitopes were mainly derived from S, N, Nsp3, M, and ORF3a proteins, with some epitopes overlapping with T-cell epitopes. Using machine learning, we identified a proteomic signature capable of distinguishing asymptomatic individuals from healthy controls in both training and validation cohorts, achieving AUCs of 0.988 and 0.857, respectively. These findings provide crucial immunological insights into BA.2 infections of the Omicron and have implications for future COVID-19 diagnostics and therapeutics.

Top-down proteomics, the characterization of intact proteoforms by tandem mass spectrometry, is the principal method for proteoform characterization in complex samples. Top-down proteomics relies on precursor isolation and subsequent gas-phase fragmentation to make proteoform identifications. While this strategy can produce highly detailed molecular information, the reliance on time-intensive tandem MS limits the speed with which proteoforms can be identified. We suggest that once proteoforms have been identified by top-down analysis in a system of interest, and archived in a system-specific Proteoform Atlas, subsequent analyses in that system can utilize the Atlas information to enable simpler and faster MS1-only identifications. We explore this idea here, using the E. coli ribosome as a model system of limited complexity. We used deep top-down analysis to construct an E. coli ribosomal Proteoform Atlas containing 2099 proteoforms from 52 of the 54 proteins that make up the E. coli ribosome. We show that using the Atlas enables confident MS1-only identifications of E. coli ribosomal proteoforms from E. coli that were perturbed by exposure to cold. Furthermore, this Atlas strategy identifies proteoforms up to 77% more rapidly compared to top-down identifications that require acquisition of both MS1 and MS2 spectra.

Label-free proteomics expression data sets often exhibit data heterogeneity and missing values, necessitating the development of effective normalization and imputation methods. The selection of appropriate normalization and imputation methods is inherently data-specific, and choosing the optimal approach from the available options is critical for ensuring robust downstream analysis. This study aimed to identify the most suitable combination of these methods for quality control and accurate identification of differentially expressed proteins. In this study, we developed nine combinations by integrating three normalization methods, locally weighted linear regression (LOESS), variance stabilization normalization (VSN), and robust linear regression (RLR) with three imputation methods: k-nearest neighbors (k-NN), local least-squares (LLS), and singular value decomposition (SVD). We utilized statistical measures, including the pooled coefficient of variation (PCV), pooled estimate of variance (PEV), and pooled median absolute deviation (PMAD), to assess intragroup and intergroup variation. The combinations yielding the lowest values corresponding to each statistical measure were chosen as the data set's suitable normalization and imputation methods. The performance of this approach was tested using two spiked-in standard label-free proteomics benchmark data sets. The identified combinations returned a low NRMSE and showed better performance in identifying spiked-in proteins. The developed approach can be accessed through the R package named 'lfproQC' and a user-friendly Shiny web application (https://dabiniasri.shinyapps.io/lfproQC and http://omics.icar.gov.in/lfproQC), making it a valuable resource for researchers looking to apply this method to their data sets.

Long-term memory (LTM) formation relies on de novo protein synthesis; however, the full complement of proteins crucial to LTM formation remains unknown in any system. Using an aversive operant conditioning model of aerial respiratory behavior in the pond snail mollusk, Lymnaea stagnalis (L. stagnalis), we conducted a transcriptome-guided proteomic analysis on the central nervous system (CNS) of LTM, no LTM, and control animals. We identified 366 differentially expressed proteins linked to LTM formation, with 88 upregulated and 36 downregulated in LTM compared to both no LTM and controls. Functional annotation highlighted the importance of balancing protein synthesis and degradation for LTM, as indicated by the upregulation of proteins involved in proteasome activity and translation initiation, including EIF2D, mRNA levels of which were confirmed to be upregulated by conditioning and implicated nuclear factor Y as a potential regulator of LTM-related transcription in this model. This study represents the first transcriptome-guided proteomic analysis of LTM formation ability in this model and lays the groundwork for discovering orthologous proteins critical to LTM in mammals.

Peg-IFNα is one of the current therapeutic strategies for Hepatitis B virus (HBV) seroclearance. Nevertheless, the underlying mechanisms are not yet adequately understood. The objective of this study was to explore the potential mechanisms using multiomics approach. For the first time, we revealed the transcriptomic, proteomic, and metabolomic characterizations of Peg-IFNα-induced HBsAg seroclearance. We found that Peg-IFNα caused significant changes during the treatment. Patients who achieved HBsAg seroclearance were characterized as having decreased transcriptional activity of genes involved in fatty acid metabolism and the glycolysis/gluconeogenesis pathway, with up-regulated expression of fatty acid degradation-related proteins. Consistently, mitochondrial TCA cycle metabolites, including citric, isocitric, and malic acids, were significantly elevated in patients who achieved HBsAg seroclearance. We also observed up-regulated transcriptional activity of NK cell-mediated cytotoxicity, positive regulation of B cell activation, immunoglobulin production, and T cell receptor complex in functional-cured patients. Conversely, the metabolites associated with unsaturated fatty acid biosynthesis were increased in HBsAg persistent patients, and the transcriptional activity of immunoglobulin production and T cell receptor complex was down-regulated after 48 weeks of Peg-IFNα treatment. Our findings provided valuable resources to better understand the process of HBsAg seroclearance and shed new light on the pathways to facilitate higher functional cure rates for CHB.

CKD is frequently diagnosed only after a significant progression. GFR is the most common indicator of kidney function but is limited in detecting early CKD cases and distinguishing glomerular, tubular, and global CKD. Aiming to provide a glomeruli specific biomarker assay, we developed a peptide immunoaffinity targeted mass spectrometry method for the quantitation of three podocyte specific proteins in human urine: nephrin, podocalyxin, and podocin. Proteins in urine were precipitated, stable isotope labeled peptide standards incorporated, and digested with trypsin. Target peptides were enriched using an online antibody column prior to LC-MS/MS. The performance metrics for nephrin, podocalyxin, and podocin were evaluated: The lower limits of quantitation were 3.8, 22.0, and 5.4 pM, respectively. The intraplate relative error (RE) was within ±10.6%, ± 10.4%, and ±16.1%, and coefficient of variation (CV) was ≤27.2%, ≤ 14.1%, and ≤20.7% accordingly. The interplate RE was within ±7.0%, ± 3.8%, and ±3.0%, and CV was ≤17.2%, ≤ 12.1%, and ≤20.0% for the three analytes. The urinary nephrin, podocalyxin, and podocin concentrations in 60 healthy volunteers and 20 disease samples was measured, thereby establishing the basal levels of these protein and enabling future evaluation of their roles as noninvasive biomarkers of glomerular injury in the clinic.

In targeted proteomics utilizing Selected Reaction Monitoring (SRM), the precise detection of specific peptides within complex mixtures remains a significant challenge, particularly due to noise and interference in chromatograms. Existing methodologies, such as isotopic labeling and scoring algorithms, offer partial solutions but are constrained by high run times and elevated false discovery rates. To address these limitations, we have developed ProPickML a machine learning-based tool designed to accurately identify peptide peaks across diverse data sets, independent of the assumed presence of the peptide. This model was trained on a manually labeled data set and subsequently validated to assess its predictive accuracy. The results demonstrate that the model reliably identifies peptide peaks in the presence of noise, achieving a Matthews correlation coefficient (MCC) of 0.81 on an independent test data set, surpassing mProphet's MCC of 0.71. Implemented in R as ProPickML, this tool offers a competitive, cost-effective alternative to existing techniques, significantly reducing reliance on isotopic labeling and enhancing the accuracy of peptide identification in SRM workflows.