Journal of Proteome Research最新文献

The aquaculture industry has recently reported the emergence of antibiotic and multimetal-resistant Aeromonas hydrophila strains. However, the intrinsic adaptation mechanisms of A. hydrophila under Zn²⁺ stress remain poorly understood. In this study, we employed a data-independent acquisition (DIA)-based quantitative proteomics approach to investigate global protein expression changes in A. hydrophila exposed to Zn²⁺ stress. A total of 338 proteins were upregulated and 388 were downregulated in response to Zn²⁺ exposure. Bioinformatic analyses revealed that some of the differentially expressed proteins (DEPs) were enriched in several Gene Ontology (GO) terms and KEGG pathways, such as metabolic processes (nucleotide metabolism, ribonucleotide biosynthesis, purine nucleotide metabolism), membrane-associated processes (ABC transporter complex), and signaling and energy pathways (quorum sensing, ABC transporters, and oxidative phosphorylation). Seven genes encoding protein secretion- and signaling-related proteins were selected for RT-qPCR analysis to examine whether their transcriptional responses corresponded to the proteomic trends, providing complementary insight rather than direct validation of the proteomic data. Furthermore, we assessed the tolerance of five gene-deletion mutants to Zn²⁺ stress. Among them, the ΔAHA_2968 mutant, lacking a GGDEF-domain protein, displayed pronounced sensitivity to ZnSO₄, implicating c-di-GMP signaling in zinc stress resistance. These findings have shed light on the intrinsic adaptive mechanisms of A. hydrophila, indicating that they play an important role in resistance to Zn²⁺.

Selecting molecular panels that are applicable to classify the health state of patients is a common task in omics data analysis. Existing software for molecule selection lacks features to select molecule panels from large data sets, requires programming experience, or lacks user-friendly interfaces. We present the Omics Molecule Extractor (OMEx), an open-source web application providing a user-friendly workflow for selecting molecules and molecule panels for sample classification from large data sets. OMEx's user interface provides interactive visualization for exploring input data and analysis results. The feature selection strategy underlying the algorithm is based on machine learning and has not been available in any software with a user interface. Extensive testing using synthetic data sets with known ground truth showed that the algorithm discovers group-separating molecules with high precision. Additionally, OMEx was tested on five real-world omics data sets, demonstrating high reproducibility and overlap with reported molecules from other feature selection methods, while also reporting alternative molecules of interest. OMEx is freely available at https://mdoa-tools.bi.denbi.de/omex/home.

Recent advances in proteomics have enabled the identification of early protein biomarkers and metabolic disturbances associated with type 2 diabetes (T2D), a major global health challenge. This systematic review and meta-analysis synthesize evidence from 27 studies comparing proteomic profiles of individuals with T2D and normoglycemic controls, selected from 2,422 initial records. The QUADOMICS assessment showed good methodological reporting for sample handling and proteomic analysis (>70% of studies), but over 60% lacked information on confounding clinical factors and biomarker validation. A qualitative synthesis focused on 85 recurrently reported proteins (≥8 studies), which showed strong interconnectivity and were involved in immune response, lipid-protein organization, detoxification, proteolysis, and coagulation, key pathways implicated in T2D. An omics-based meta-analysis identified seven promising protein biomarkers for T2D related to lipid/glucose metabolism (Q12907_LMAN2, P02652_POA2, P07602_PSPA, P09622_DLD); cell binding/adhesion (P12109_COL6A1, P12830_CDH1); and translational regulation and mitochondrial function (P35232_PHB). Random-effects meta-analysis revealed variation in effect sizes across studies for previously highlighted biomarkers, but three of them (P02763_ORM1, P00738_HP, P25311_AZGP1) exhibited considerable consistency. To enhance accessibility and further exploration of findings, we provide the interactive web tool metaMarkersT2D: https://jgcurras.shinyapps.io/metaMarkersT2D/.

Brain metabolomics and bioinformatics analyses were applied to investigate metabolic changes in a lipopolysaccharide (LPS)-induced acute inflammation mouse model. Six-week-old C57BL/6 mice received intraperitoneal LPS at 10 mg/kg to establish systemic inflammation. Control and model mice (n = 5 each) were dissected under isoflurane anesthesia, and serum, cerebrum, hippocampus, cerebellum, and hypothalamus were collected. Serum IL-1β levels were significantly elevated in the model group, confirming the establishment of systemic inflammation. Brain metabolomics revealed eight significantly altered metabolites in the cerebrum, whereas no significant changes were observed in the hippocampus, cerebellum, or hypothalamus, thereby demonstrating a region-specific effect of LPS-induced inflammation. A Random Forest model robustly differentiated model mice from control mice. Among the altered metabolites, N-acetylaspartic acid (NAA), a neuronal marker, was significantly decreased. Aspartic acid metabolism was disrupted, urea accumulated via upregulation of the urea cycle, and both aspartic acid and malic acid were reduced, suggesting an impaired function of the malate-aspartate shuttle. These findings indicate that LPS-induced systemic inflammation specifically disrupts cerebral metabolism, characterized by impairments in aspartic acid metabolism and the malate-aspartate shuttle, with NAA and urea emerging as potential biomarkers of neuroinflammation.

The probiotic yeast Saccharomyces boulardii exhibits remarkable resilience to gastrointestinal stress, yet its underlying adaptive mechanisms remain incompletely understood. This study employed multiomics approaches to investigate its stress response systematically. Electron microscopy revealed significant morphological perturbations, including cell shape deformation and compromised cell wall integrity under the stress conditions. Concomitantly, Na⁺/K⁺-ATPase activity exhibited a progressive increase from 0.84 to 3.1 μmol/mg protein, suggesting active ion homeostasis regulation. Proteomic analysis identified 75 differentially expressed proteins under gastric stress and 2470 under intestinal conditions; enhanced pathway enrichment revealed three interconnected regulatory modules. These include upregulated pyruvate metabolism enzymes (e.g., ADE family) for nucleotide/energy production, remodeled nucleocytoplasmic transport for stress-molecule shuttling, and condition-specific hubs, chaperones (APJ1, SSA3) in gastric environments, and glycolytic enzymes (PGK1, TDH3) in intestinal conditions. These findings collectively demonstrate S. boulardii's sophisticated, multifaceted adaptation to gastrointestinal challenges, providing new insights into its probiotic functionality at the molecular level.

Meningioma, the most prevalent primary intracranial tumor, presents significant clinical challenges due to unclear molecular mechanisms underlying its progression from low-grade (LG) to high-grade (HG) and lack of grade-specific biomarkers. Here, we employed high-resolution mass spectrometry-based integrated tissue metabolomics and lipidomics on ∼45 samples. Our findings highlight dysregulated pathways like nucleotide, choline, sphingolipid, and glycerophospholipid metabolism, with purine metabolism-related metabolites notably upregulated in tumor samples. We further performed targeted verification of a subset of purine metabolism-related metabolites using targeted metabolomics. Further, serum lipidomics profiling was performed on ∼75 samples to identify a set of candidate markers. A set of lipid markers was identified as dysregulated in both tissue and serum samples, showing the effects of tumor-associated metabolic changes. The major dysregulated lipid classes were phosphatidylcholines, phosphatidylethanolamines accounting for around 70%, with variations in saturation and carbon chain length. Additionally, machine-learning-based feature selection was used to identify a panel of lipid markers capable of distinguishing HG from LG samples. This analysis identified 18 top classifier lipids, two of which were also dysregulated in tissue samples. Longitudinal analysis of these lipids further emphasized their role in tumor progression. This exploratory study lays the foundation for further validation of candidate markers in a larger cohort of samples.

Epigenetics-related metabolites-substrates or cofactors that regulate epigenetic modifications, that play a critical role in regulating gene expression-are collectively referred to as the "epigenetic metabolome". Here, we developed a comprehensive targeted metabolomic method covering 33 metabolites involved in multiple types of epigenetic modifications. The detection panel included coenzyme A (CoA)/acetyl-CoAs-metabolites in the methionine cycle, those related to nicotinamide adenine dinucleotide (NAD⁺) metabolism─intermediates of carbohydrate metabolism, and acetylglucosamines. These metabolites were analyzed in two liquid chromatography-mass spectrometry runs based on their distinct chemical properties. For most metabolites (over 88%), the limits of quantification were below 16 ng, the dynamic ranges exceeded 3 orders of magnitude, and the precisions were above 80%. We profiled the epigenetic metabolome in a mouse model of diabetic cardiomyopathy and identified 8 significantly altered metabolites linked to various epigenetic modifications, including DNA/histone methylation, acetylation, and O-GlcNAcylation of histones. In conclusion, we established a reliable and sensitive method for detecting alterations in the epigenetic metabolome and demonstrated its applicability to disease-related studies.

High-grade gliomas (HGG) are highly aggressive tumors, which are predominately fatal for adults and pediatric patients. Identifying cancer-selective therapeutic targets remains a critical unmet need. The overexpression of endoplasmic reticulum (ER) chaperones in various cancers is well documented. Moreover, tumor cells exhibit an atypical surface expression of ER chaperones, suggesting the potential for selective targeting. Our study examined the differences in the mRNA, total protein, and surface expression levels of seven key ER chaperones, compared with those in non-neoplastic samples. Notably, a poor correlation was found between mRNA, protein, and surface protein levels, underscoring the limitations of transcriptomics alone in target discovery. We also highlight the limitations of surfaceome studies which exclude noncanonical membrane proteins, such as ectopically expressed ER chaperones, which often escape detection by conventional bioinformatic pipelines. For the first time, this study advances our understanding of the surface expression of ER chaperones in both adult and pediatric HGG. Our findings highlight the importance of surfaceome analysis in the discovery of cancer selective targets against this devastating disease.

In our previous study, we discovered that HNRNPA2B1 exhibited oncogenic activity in multiple myeloma (MM) and protein phosphorylation modifications could play a crucial role in this progression. The aim of this study is to explore the phosphorylation cascades regulated by HNRNPA2B1 in MM and to pinpoint the principal kinase target while clarifying the underlying mechanism. Therefore, quantitative proteome and phosphoproteome analyses were employed to investigate the protein phosphorylation cascades and kinase enrichment analysis was used to predict kinase activity and identify the key kinase target. As a result, in HNRNPA2B1 knockdown myeloma cells, 22 differential kinases and 56 phosphorylation sites were identified and a kinase regulatory network comprising 154 kinase-substrate interactions was constructed. Key kinase targets, CK2 and MAP2K, were identified and validated. The CK2 kinase inhibitor TBB markedly reduced the proliferation of HNRNPA2B1-overexpression MM cells, enhanced cell apoptosis, and triggered ER stress and autophagy activation. In conclusion, this investigation provides a comprehensive overview of the protein phosphorylation cascades regulated by HNRNPA2B1 in MM, identifying CK2 as a crucial kinase target. Inhibiting CK2 kinase not only affects MM cell proliferation and apoptosis but also induces ER stress and autophagy, providing novel insights into MM pathogenesis.

A recent study demonstrated a substantial increase in the peptide signal and corresponding proteome coverage when employing 0.5% acetic acid (AA) as the ion pairing modifier in place of the 0.1% formic acid traditionally used in shotgun proteomics. Given the strictly limited material and counterintuitive observations by others in the emerging field of single-cell proteomics, we chose to investigate this alternative modifier in the analysis of subnanogram proteome dilutions. When digest standards as low as 20 pg total load on the column were evaluated, AA led to increased proteome coverage at every peptide load assessed. Relative improvements were more apparent at lower concentrations, with a 20 pg peptide digest demonstrating a striking 1.8-fold increase to over 2000 protein groups identified in a 30 min analysis. Furthermore, we find that this increase in signal can be leveraged to reduce ramp times, leading to 1.7× more scans across each peak and improvements in quantification, as measured by %CVs. These results can be reproduced on multiple trapped ion mobility instruments. When evaluating single cancer cells, approximately 13% more peptide groups were identified on average when employing AA in the place of FA. These results suggest that ion pairing modifiers and other additives warrant re-evaluation in the context of low-input and single-cell proteomics. All vendor raw and processed data are available through ProteomeXchange as PXD046002 and PXD051590.