The aquaculture industry has recently reported the emergence of antibiotic and multimetal-resistant strains. However, the intrinsic adaptation mechanisms of under Zn 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 exposed to Zn stress. A total of 338 proteins were upregulated and 388 were downregulated in response to Zn 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 Zn stress. Among them, the Δ mutant, lacking a GGDEF-domain protein, displayed pronounced sensitivity to ZnSO, implicating c-di-GMP signaling in zinc stress resistance. These findings have shed light on the intrinsic adaptive mechanisms of , indicating that they play an important role in resistance to Zn.

Understanding intratumoral heterogeneity is essential for elucidating tumor biology. Compared to RNA expression, omics-level characterization of cell-type-specific protein expression remains a technical challenge. Bulk mass spectrometry (MS) provides abundant proteomics resources to infer cell-type specificity via data deconvolution; however, it is unclear which proteomic quantification formats are optimal, as they differ from the data types for which most deconvolution methods were designed. Here, leveraging recently generated large-cohort proteogenomics data, we systematically evaluated different MS proteomics quantification formats and preprocessing strategies to resolve cell-type-specific protein expression. Our results indicate that while label-free spectral counts can be used directly, TMT MS1 intensities and MS2 ratios are less suitable and require appropriate data transformation. We demonstrate that a 'min-score' transformation significantly improves MS1 intensity-based deconvolution, providing useful insights for subtyping pancreatic cancer. Moreover, we identified the coefficient of variation (CV) as a robust statistical indicator of deconvolution suitability. Finally, we developed "ProTransDeconv", an R package integrating data transformation, deconvolution, and quality checks for major MS proteomics data formats. This work provides practical guidance for deconvolving bulk proteomics to study cell-type-specific protein-level dysregulation.

Colorectal cancer (CRC) is an aggressive malignant tumor of the digestive system that poses a serious threat to human health. Therefore, there is an urgent need to discover early diagnostic markers and effective therapeutic targets for CRC. In this study, data independent acquisition (DIA) mass spectrometry quantitative technology combined with bioinformatics analysis was used to carry out personalized quantitative proteomics research on abundant protein depletion plasma samples from 48 CRC patients at different TNM stages and healthy individuals. A total of 1089 Protein Groups were identified. By comparing the plasma protein expression profiles between CRC patients and healthy individuals, differentially expressed proteins (DEPs) CRP, FABP1, FABP4 and OSTP with significant changes were screened out, and GO functional, KEGG pathway, and GSEA enrichment analysis were performed. Mfuzz clustering analysis categorized the DEPs in CRC plasma into six expression patterns. Among them, the OSTP protein level in proteomics data and the mRNA level of the gene in TCGA database both showed an upward trend with the progression of the disease, suggesting that it may serve as a diagnostic and prognostic marker in plasma to reflect the disease progression of CRC patients. ROC analysis showed robust predictive performance, and PRM validation cohort correlated well with DIA results, providing potential insights for CRC research.

Vascular Ehlers-Danlos syndrome (vEDS) is a rare connective tissue disorder caused by mutations in the gene, leading to life-threatening vascular complications. This study presents a proteomic analysis of aortic wall tissue from a 60 year-old vEDS patient with a confirmed mutation, who died from an aortic dissection consequent to an abdominal aortic aneurysm. Compared to healthy controls, the patient's tissue showed an imbalance in collagen isoforms such as elevated types I and VI and reduced types VIII, XIV, and XVIII, with no significant change in type III collagen. Alterations were also found in collagen-processing enzymes, nidogens, and matricellular proteins, such as THBS1, THBS2, and fibulins. These findings reveal specific vessel extracellular matrix remodeling and suggest compensatory mechanisms that may contribute to the vascular fragility in vEDS.

Proteins play essential functions through their complex regulations on cell-type-specific expression, localization, and molecular complexes. Protein complexity is further enhanced by proteoforms, which are the diverse molecular forms that each gene can produce through genomic alterations, transcriptional variations, translational regulations, and protein modifications. Profiling of proteoforms is a promising method for gaining a deeper understanding of the role of proteins in biological pathways and disease mechanisms. Here, we developed ProteoformDB, an application tool for generating proteoform databases, and we cataloged a total of over one million unique single-site human proteoforms. We showed that ProteoformDB can serve as a valuable resource to document the experimentally identified proteoforms in a database, supporting protein characterization in quantitative proteomics for both total protein abundances and modified protein forms.

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.

Heavy carbon labeling has emerged as a popular way to study metabolic diseases. However, most carbon labeling techniques use untargeted mass spectrometry, which typically requires dependence on a research core and specialized software. By combining published C labeling patterns and known enzyme reactions, an optimized targeted mass spectrometry method was generated to measure stable isotope labeling with carbon-13 through glycolysis, the tricarboxylic acid cycle, the hexosamine biosynthetic pathway, and glutaminolysis using uniformly labeled glucose or glutamine. This method provides a novel and adaptable approach to investigate pointed hypotheses on the utilization of glucose or glutamine in disease states and models.

Temozolomide (TMZ) is a frontline chemotherapeutic agent for glioblastoma multiforme (GBM); however, approximately half of patients develop resistance to therapy. This study investigates the role of altered cellular bioenergetics and metabolism in the acquired TMZ resistance. Using untargeted metabolomics, we explored the metabolic rewiring in TMZ-resistant GBM cells and identified key alterations in glycolysis, the tricarboxylic acid (TCA) cycle, fatty acid metabolism, and amino acid metabolism, all might be linked to cellular proliferation. Our findings suggest that while glycolysis remains important, increased TCA cycle activity contributes to the drug resistance, supported by increased levels of mitochondrial mass and mitochondrial membrane potential. We observed significantly elevated glutamine levels, which may enhance mitochondrial activity, thereby supporting increased energy production. Furthermore, resistant cells exhibited enhanced NRF2 level in parallel with higher levels of antioxidants, including glutathione and catalase enzyme, and a concomitant decrease in the level of its negative regulator, KEAP1. These factors collectively may contribute to drug resistance by mitigating oxidative stress. These findings indicate that mitochondrial metabolic reprogramming and NRF2/KEAP1-mediated antioxidant defense mechanisms play a crucial role in TMZ resistance, and targeting these pathways may offer a novel strategy to overcome resistance in GBM therapy.

Recent advances in high-throughput molecular analysis have significantly enhanced our understanding of the molecular mechanisms underlying adrenocortical diseases. To identify differences in protein signatures that may reveal insights into disease-specific pathogenesis, we used LC-MS/MS and bioinformatics to compare proteomic profiles of normal human adrenal (NHA) tissue, adrenocortical adenomas (ACA), adrenocortical carcinomas (ACC), and primary macronodular adrenocortical hyperplasia (PMAH) tumors, with and without mutations. In total, 7350 proteins were identified, and 3976 were quantified across all samples. Differentially expressed proteins (DEPs) were found in ACA vs NHA (27 DEPs), ACC vs NHA (49 DEPs), and PMAH vs NHA (81 DEPs). Comparing ACC and ACA revealed 64 upregulated and 48 downregulated DEPs. PMAH with mutations (PMAHw) vs without mutations (PMAHwt) had the fewest DEPs, 12 upregulated and 4 downregulated proteins in PMAHw. These findings were validated using an independent ACC cohort from Seoul National University Hospital, which showed 99.8% overall similarity and with no significant disparities. This comprehensive profiling of NHA, ACA, ACC, and PMAH offers insights into normal adrenal function and tumor-associated changes. Our study presents a high-quality proteomic data set, highlighting potential biomarkers and therapeutic targets, and makes a significant contribution to the understanding of adrenocortical disease mechanisms.

Mass spectrometry (MS) generates large data sets that are stored in increasingly optimized and complex file types, demanding technical expertise to extract information rapidly and easily. We wondered whether a simple structured query language (SQL) database could hold raw MS data and allow for easily readable queries without incurring major penalties in the read time or disk space relative to other popular MS formats. Here, we describe a basic MS schema with intuitive database tables and fields that can outperform other formats for exploratory and interactive analysis according to six data subsets commonly extracted: single scans (both MS and MS), ion chromatograms, retention time ranges, and fragmentation searches (both precursor and fragment search). Additionally, we compare SQLite, DuckDB, and Parquet implementations and find that they can perform these tasks in under a second, even when the files occupy over a gigabyte of data on the disk. We believe that this tidy data schema expands nicely to most forms of MS data and offers a way to transparently query data sets while preserving computational performance.

Nasopharyngeal carcinoma (NPC) represents a malignant tumor linked to Epstein-Barr virus (EBV) that is characterized by distinctive, clinically relevant biological features. As protein N-glycosylation plays critical roles in cancer progression, we applied quantitative glycoproteomics to characterize NPC-specific glycosylation patterns. Using label-free proteomics and intact N-glycoproteomics, we analyzed 17 NPC tissues and 7 normal mucosal epithelia. Integrated analysis with PET/CT imaging and bioinformatics revealed correlations among differentially expressed glycoproteins, glycosyltransferases, and immunophenotypes. Functional validation demonstrated that MANEA, an enzyme linked to high-mannose glycosylation, influences NPC cell proliferation and migration. Furthermore, MANEA likely modulates PD-L1 expression through high-mannose glycans. Our findings indicate that high-mannose glycoproteins are predominant in NPC and may promote tumor progression not only by enhancing malignant behavior but also by facilitating immune escape via PD-L1 regulation, thereby impairing antitumor immunity.

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.

Identifying effective compounds to restore the polarity of absorptive enterocytes (AEs) holds promise for mitigating the severity and duration of small intestinal disorders. Spermidine (SPD) is a natural polyamine; whether it can repair inflammation-induced loss of AE polarity remains unclear. In this study, we employed lipopolysaccharide (LPS)-challenged mice models combined with 4D data-independent acquisition (DIA) proteomics to investigate the mechanisms by which SPD alleviates polarity loss in AEs. Our results demonstrated that SPD supplementation enhanced the antioxidant capacity and improved the villus/crypt ratio in the jejunum of LPS-treated mice. Proteomic analysis revealed that LPS induced acute phase and inflammatory responses, significantly downregulating the expression of cytoskeletal proteins (Pdlim3, Pdlim7) essential for epithelial morphology as well as proteins involved in apical-basal polarity (Pard6b, Pard3, Prkcz, LLGL2), apical membrane integrity (Vil1, Pdims, Akp3, Tjps, Pards), and apical SLC transporters. Conversely, SPD attenuated mucosal- and tissue-specific immune responses and reversed the downregulation of these protein groups. Furthermore, using a Caco-2 cell model, we confirmed the anti-inflammatory effect of SPD and elucidated its role in suppressing AE polarity loss via the regulation of HDAC4 signaling. These findings indicate that SPD effectively alleviates the inflammation-induced loss of AE polarity in the jejunum of LPS-challenged mice.

Nanoflow liquid chromatography capable of delivering consistent and reliable results across extensive sample sets is essential for the advancement of mass spectrometry-based proteomics. Micro-Pillar Array Columns (μPACs) represent a significant breakthrough, offering durability and performance stability. Here, we evaluate the robustness of μPACs by comparing a column used continuously for over 16 months with more than 7000 injections (μPAC1) to a nearly new column (μPAC2). Analysis of two TMT-labeled yeast TKO standards (TKOpro10u, a 10-plex unit-resolved standard; and TKOpro12, a 12-plex isotopolog-inclusive standard) showed that μPAC1 and μPAC2 yielded comparable numbers of unique peptides and proteins, exhibited similar reproducibility, and delivered equivalent chromatographic and spectral quality. Notably, these data showed that μPAC1 maintained high performance with minimal degradation of data quality, highlighting the exceptional durability of the μPAC technology. These findings underscore that μPAC can contribute to reducing workflow disruptions in high-throughput analytical workflows, particularly in proteomics workflows that utilize mass spectrometry.

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.

The probiotic yeast 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., family) for nucleotide/energy production, remodeled nucleocytoplasmic transport for stress-molecule shuttling, and condition-specific hubs, chaperones (, ) in gastric environments, and glycolytic enzymes (, ) in intestinal conditions. These findings collectively demonstrate 's sophisticated, multifaceted adaptation to gastrointestinal challenges, providing new insights into its probiotic functionality at the molecular level.

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.

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 -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.

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.

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 : 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 ( = 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, -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.