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PubMed2026

Molecular mechanisms underlying umami taste perception: A DIA-based proteomic analysis of Agrocybe aegerita peptides.

The mechanisms underlying the modulation of the salivary perception of umami peptides remain poorly understood. Herein, three umami peptides (DDL, DEL, and ENG) obtained from Agrocybe aegerita were used to investigate the regulatory role of saliva in umami taste perception via a combined approach involving sensory evaluation and proteomics analysis based on 4D-DIA technology. The results revealed that umami intensity peaked at 10 s after ingestion and was accompanied by a significant increase in saliva secretion (p < 0.05). Further proteomics analysis revealed that lactotransferrin and proline-rich proteins are closely associated with the sensory perception of umami peptides. Differentially expressed proteins were mainly enriched in pathways related to saliva secretion and proteasome function. This study provides new insights from the perspectives of salivary proteomics and dynamic salivary secretion, contributing to a deeper understanding of the mechanisms by which saliva regulates umami perception.

Humans↗

PubMed2025

Four-dimensional data independent acquisition proteomics and metabolomics reveal mechanisms of hydrogen-rich water at Zusanli (ST36) point against triple-negative breast cancer in mice.

OBJECTIVE: To develop a safe and effective green therapy for triple-negative breast cancer, this study combines hydrogen-rich water with acupuncture point injection, and finds that it can prevent tumor growth and minimize cancer metastasis. METHODS: After 21 d of hydrogen rich water injection treatment on 4T1 (mouse breast cancer cells) xenograft mice, in order to systematically identify differentially expressed proteins in tumor samples between the model group and the Zusanli (ST36) group injected with hydrogen rich water at acupoints, with a focus on functional proteins or signaling pathways related to tumor occurrence and development, researchers conducted four-dimensional data independent acquisition (4D-DIA) proteomic analysis on tumor tissues. In order to further investigate the dynamic changes of metabolites after therapeutic intervention, researchers conducted liquid chromatography-tandem mass spectrometry untargeted metabolomics identification and analysis on mouse serum. The results of the joint proteomics-metabolomics analysis were validated using experimental methods such as immunofluorescence, Western blotting, and quantitative reverse transcription polymerase chain reaction detection. RESULTS: Injecting hydrogen-rich water into acupoints significantly inhibited tumor growth (P < 0.05). 4D-DIA proteomics and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses uncovered pathways such as T helper 1 cell (Th1) and T helper 2 cell (Th2) cell differentiation. The KEGG metabolic pathways identified in the metabolomics analysis included galactose metabolism along with fructose and mannose metabolism. Based on the combined proteomics and metabolomics analysis, the key pathways included the C-type lectin receptor signaling pathway. The major cancer-related differential proteins detected in Th1 and Th2 cell differentiation [interleukin 6 signal transducer, nuclear factor of activated T cells 4, recombinant mitogen activated protein kinase 10 (MAPK10), and MAPK11] were upregulated after the injection of hydrogen-rich water into the Zusanli (ST36) acupoint, whereas Linker for activation of T cells (Lat), signal transducer and activator of transcription 1, and protein kinase C, theta were downregulated. CONCLUSION: The injection of hydrogen-rich water into the Zusanli (ST36) acupoint effectively inhibited the hyperplasia of 4T1 BC cells and enhanced their apoptosis, potentially exerting a therapeutic effect through multiple pathways and targeting various sites.

Animals↗

PubMed2026

The AAA+ chaperone ClpB contributes to stress tolerance and pathogenesis in Mycoplasma bovis.

ClpB, an ATP-dependent molecular chaperone belonging to the Hsp100/Clp subfamily of AAA+ ATPases, plays a crucial role in protein disaggregation, thereby enhancing bacterial survival under stress conditions. Despite its well-conserved function in prokaryotes, the specific contributions of ClpB to the pathogenesis of the ruminant pathogen Mycoplasma bovis remain largely unexplored. In this study, we identified and functionally characterized a ClpB homolog in M. bovis. Biochemical assays confirmed that the recombinant ClpB protein exhibits intrinsic ATPase activity and, in cooperation with the DnaK chaperone system, efficiently mediates protein disaggregation in vitro. Through genome-wide transposon mutagenesis of the M. bovis HB0801 strain, we generated ClpB-deficient mutants that maintained normal growth kinetics and morphology at 37 °C but exhibited significant growth defects under thermal and oxidative stress conditions. Phenotypic analysis demonstrated that ClpB disruption attenuated key virulence traits, including impaired adhesion to host cells, marked reduction in biofilm formation, diminished pro-inflammatory cytokines (IL-1β, IL-6, TNF-α) expression in BoMac cells. Furthermore, the reduced virulence of the ClpB mutant was investigated by DIA proteomic analyses, which revealed that the ClpB mutant strain altered distinct protein expression patterns related to proteostasis, including phosphotransferase system, serine-type peptidase activity, serine hydrolase activity, and chaperone-mediated protein folding that contribute to the stress response and virulence. These findings collectively demonstrate that ClpB serves as a multifunctional virulence determinant in M. bovis, orchestrating stress adaptation, host-pathogen interactions, and pathogenic potential through modulation of both protein quality control systems and virulence-associated pathways.

Mycoplasma bovis↗

PubMed2026

P1D6 inhibits FnBP-induced extracellular proteome remodeling: proteomic evidence for a novel intervention strategy in atopic dermatitis.

Atopic dermatitis (AD) is an inflammatory skin disorder characterized by skin barrier impairment, chronic inflammation, and intense pruritus. Staphylococcus aureus (S. aureus) critically contributes to its pathogenesis; however, the mechanistic role of its virulence factor fibronectin-binding protein (FnBP) in keratinocytes remains poorly understood. This study used bibliometric analysis and quantitative proteomics to examine the relationship. We first performed a bibliometric analysis, revealing a sustained increase in publications on S. aureus and AD, peaking at 99 articles in 2023, with hotspots focused on skin barrier function, immune inflammation, and pediatrics. Quantitative proteomics was employed to investigate how FnBP reshapes the extracellular proteome and whether the anti-α5 integrin antibody P1D6 exerts interventional effects. HaCaT cells were stimulated with recombinant FnBP alone or in combination with P1D6, followed by data-independent acquisition (DIA)-based proteomic analysis of secretome changes. Proteomic analysis identified FnBP-induced differentially expressed proteins enriched in immune- and barrier-related pathways, including cell adhesion, cell junctions, and VEGFA-VEGFR2 signaling. P1D6 intervention significantly inhibited the secretome profile and identified 241 core responsive proteins, of which approximately 52% returned to baseline levels after intervention (P > 0.05). These proteins were primarily enriched in pathways governing protein homeostasis, folding, proteasomal degradation, and interleukin-7 signaling. Notably, P1D6 modulated the downregulation of ATP5F1B and P4HB, key effectors within the interleukin-7 pathway. This study demonstrates that FnBP remodels the keratinocyte secretome by disrupting protein homeostasis, consequently inducing barrier injury and chronic inflammation related to AD, which can be effectively blocked by P1D6. Combined with bibliometric trends and proteomic evidence, this study focuses on FnBP, an underexplored virulence factor, and provides novel insights into AD pathogenesis and therapeutic interventions.

Humans↗

PubMed2026

DiaReport: reproducible workflow for differential expression analysis and interactive reporting in DIA-based proteomics.

MOTIVATION: Data-independent acquisition (DIA) has become the preferred data acquisition method for mass spectrometry-based proteomics, yet, reproducible workflows for differential expression (DE) analysis and results reporting remain limited. We present DiaReport, an R package that performs precursor- and protein-level DE analysis from DIA-NN output using MSqRob and QFeatures, while generating high-quality, interactive HTML reports through Quarto. DiaReport integrates precursor data, filtering of missing values, normalization, protein summarization and statistical modeling within a single function, supporting both simple pairwise as well as complex experimental designs. The package provides structured outputs and configuration files to ensure computational reproducibility across different studies. To accommodate diverse research needs, DiaReport includes multiple reporting templates tailored to different proteomic applications. Applying DiaReport to an extracellular vesicle (EV) proteomics dataset demonstrates its ability to efficiently analyze DIA data and provide rapid insights into sample quality and protein level differences. AVAILABILITY: DiaReport is an open-source R package available at https://github.com/Gevaert-Lab/diareport (DOI: 10.5281/zenodo.20120604). The package is platform-independent and distributed under the MIT license. Reports are generated using Quarto and require only standard R dependencies. Detailed documentation, installation guides and usage vignettes are provided within the repository. The interactive HTML reports discussed in this study, including the UPS2 benchmark and EV case study, are archived on Zenodo (10.5281/zenodo.20122506 and 10.5281/zenodo.20123378).

Proteomics↗

PubMed2026

A Comprehensive Analysis of Differential Protein Expression in the Plasma of Rheumatoid Arthritis Patients Utilizing Data-Independent Acquisition (DIA) Proteomics Technology.

BACKGROUND: Rheumatoid Arthritis (RA) is a Prevalent Autoimmune Disorder Affecting Millions of People Worldwide. A Thorough Understanding of Its Clinical and Pathological Features Is Essential to Improve Patient Outcomes. METHODS: This Study Combined Data-Independent Acquisition Proteomics and Enzyme-Linked Immunosorbent Assay (ELISA) to Identify and Validate Potential Plasma Protein Biomarkers for the Early Diagnosis of RA. RESULTS: Differential Proteomic Analysis Identified Differentially Expressed Proteins Between Patients With RA and Healthy Controls and Characterized Their Functions. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes Enrichment Analyses Were Performed to Explore Protein Functions and Associated Biological Pathways. The STRING Database and the Metascape Platform Were Used to Conduct an in-Depth Analysis of the Protein-Protein Interaction Network, Highlighting the Functional Attributes and Interconnections of Upregulated Proteins and Identifying Key Protein Complexes Involved in RA. ELISA Analysis of Plasma Samples Revealed Significantly Elevated SERPINA3 Levels in Patients With RA, Which Were Positively Correlated With Disease Activity Indicators-Including Erythrocyte Sedimentation Rate, C-Reactive Protein, and Disease Activity Score 28-But Were Not Correlated With Rheumatoid Factor or Its Subtypes. CONCLUSIONS: This Study Provides New Insights and Identifies Potential Biomarkers for the Early Diagnosis of RA.

Humans↗

PubMed2025

SpectroPipeR-a streamlining post Spectronaut® DIA-MS data analysis R package.

SUMMARY: Proteome studies frequently encounter challenges in down-stream data analysis due to limited bioinformatics resources, rapid data generation, and variations in analytical methods. To address these issues, we developed SpectroPipeR, an R package designed to streamline data analysis tasks and provide a comprehensive, standardized pipeline for Spectronaut® DIA-MS data. This novel package automates various analytical processes, including XIC plots, ID rate summary, normalization, batch and covariate adjustment, relative protein quantification, multivariate analysis, and statistical analysis, while generating interactive HTML reports for e.g. ELN systems. AVAILABILITY AND IMPLEMENTATION: The SpectroPipeR package (manual: https://stemicha.github.io/SpectroPipeR/) was written in R and is freely available on GitHub (https://github.com/stemicha/SpectroPipeR).

Software↗

PubMed2026

A Parallel Accumulation-Mobility Aligned Fragmentation Strategy Utilizing High-Resolution Ion Mobility for High-Performance Proteomics Analysis.

Here we present a novel data-independent acquisition (DIA) mass spectrometry (MS) operating mode termed parallel accumulation-mobility aligned fragmentation (PAMAF) that offers enhanced speed and sensitivity of ion fragmentation analysis for discovery workflows such as bottom-up proteomics. This mode of operation leverages high-resolution ion mobility (HRIM) separation capabilities of the structures for lossless ion manipulation technology to achieve HRIM-based precursor isolation in place of traditional quadrupole filtering approaches. PAMAF mode increases the number of features that can be identified per MS1/MS2 acquisition cycle by employing mobility-based time alignment to associate fragment ions with their corresponding precursor ions. By using a high-speed, lossless separation technique for precursor isolation instead of the comparatively slow and wasteful quadrupole filtering, ion losses are avoided while simultaneously increasing the rate at which precursor ions are sequentially fragmented and detected. In addition, by accumulating ions while the previous packet of ions is being analyzed, the PAMAF mode achieves ∼100% ion utilization efficiency. Benchmarking results of LC-PAMAF-MS analysis of a whole cell protein digest showed ∼6× more protein group identifications compared to a standard data-dependent acquisition analysis without HRIM on the same QTOF instrument, and >100 x improvement for low-load workflows. Quantitative evaluations demonstrated that PAMAF mode could quantify low abundance peptides, including those undetectable by data-dependent acquisition. In addition, since precursor isolation in PAMAF mode is size-based rather than m/z-based, coeluting isobars and isomers can be resolved prior to fragmentation, eliminating chimeric spectra that compromise identification accuracy. We also explored the benefits of combining HRIM and quadrupole isolation to achieve improved specificity termed DIA-PAMAF mode, which enabled the detection of over 8000 protein groups from a HeLa digest analysis. PAMAF mode brings a powerful new technique to the field of proteomics with the potential to improve the sensitivity and selectivity of mass spectrometry-based proteomics.

Proteomics↗

PubMed2026

Pan-cancer multi-omics machine learning defines a lactylation-associated immune-excluded tumor state with proteomic and experimental corroboration.

BACKGROUND: Histone lactylation links lactate metabolism to chromatin regulation, but whether lactylation-program-associated transcriptional patterns delineate recurrent pan-cancer tumor states remains unclear. METHODS: We integrated mRNA, lncRNA, and miRNA profiles from 9712 TCGA tumors across 33 cancer types with GTEx references, six GEO cohorts, IMvigor210, and an institutional clear-cell renal cell carcinoma (ccRCC) cohort used for exploratory DIA-NN proteomic corroboration. Random-effects co-expression meta-analysis, multi-omics consensus clustering, regulon inference, immune deconvolution, TIDE, oncoPredict, and SHAP-based machine learning were applied. hsa-miR-431-5p was functionally evaluated as a proof-of-concept CS2-associated miRNA in bladder cancer models. RESULTS: LacCoEx-Atlas comprised 398,491 lactylation-related co-expression pairs across 24,667 RNA features under a random-effects framework (median I² = 88.6%). Consensus clustering identified two subtypes: CS2 showed glycolytic-mesenchymal-immune-excluded features, M2 macrophage enrichment, CD8⁺ T-cell depletion, elevated HDAC4/NSD3/KDM6B activity, and worse survival, whereas CS1 showed oxidative, sirtuin-active programs. CS2 had fewer predicted ICI responders (18.3% vs. 52.0%) and a lower observed ORR in IMvigor210 (15.3% vs. 24.0%). oncoPredict identified NU7441 as a hypothesis-generating CS2-associated sensitivity signal (Hedges' g = 1.17). DIA-NN proteomics in 50 ccRCC specimens provided exploratory support for CS2-associated hypoxia, ECM degradation, and metastasis programs. The 10-feature mRNA LARItools model achieved an apparent AUC of 0.9413, while a separate multi-omics model achieved 0.971; neither was independently validated. LARItools reproduced prognostic separation across six GEO cohorts. miR-431-5p promoted malignant phenotypes and EMT in bladder cancer cells, with concordant CMU4h expression findings. CONCLUSIONS: Lactylation-program-associated transcriptional patterns delineate a recurrent immune-excluded pan-cancer tumor state associated with adverse prognosis, reduced predicted immunotherapy responsiveness, exploratory single-cancer protein-level support, and testable DNA damage response-targeting hypotheses. LacCoEx-Atlas and LARItools provide open resources for lactylation-program-associated tumor-state stratification and future translational research.

Humans↗

PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗

PubMed2026

11-O-galloylbergenin alleviates LPS-stimulated inflammation in RAW 264.7 macrophages by targeting Grb2, RhoA, and Cdc42 in the RAS signaling pathway.

OBJECTIVE: This study aimed to explore the anti-inflammatory mechanism of 11-O-galloylbergenin in macrophages. METHODS: Lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages were treated with 11-O-galloylbergenin. Cytotoxicity was assessed by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, and cytokine secretion was measured by enzyme-linked immunosorbent (ELISA) assay. Data-independent acquisition (DIA)-based proteomics, Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, western blotting, and molecular docking were also performed. RESULTS: 11-O-Galloylbergenin (≤50 μg/mL) was non-cytotoxic and significantly suppressed tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) secretion. Proteomics analysis identified 209 differentially expressed proteins (DEPs) that showed opposite expression trends before and after 11-O-galloylbergenin treatment. Seven of these DEPs were enriched in the RAS pathway: RhoA, Cdc42, Grb2, RalB, Calm3, Gnb2, and Pla2g4a. Western blotting confirmed that 11-O-galloylbergenin downregulated RhoA, Cdc42, and Grb2 expression. Molecular docking revealed good binding affinity of 11-O-galloylbergenin to RhoA, Cdc42, and Grb2. CONCLUSION: 11-O-Galloylbergenin alleviates LPS-stimulated inflammation in RAW 264.7 macrophages by inhibiting the RAS signaling pathway.

Animals↗

PubMed2026

pmultiqc: An Open-Source, Lightweight, and Metadata-Oriented QC Reporting Library for MS Proteomics.

The increasing scale and complexity of proteomics data demand robust, scalable, and interpretable quality control (QC) frameworks to ensure data reliability and reproducibility. Here, we present pmultiqc, an open-source Python package that standardizes and generates web-based QC reports across multiple proteomics data analysis platforms. Built on top of the widely adopted MultiQC framework, pmultiqc offers specialized modules tailored to mass spectrometry workflows, with full initial support for quantms, DIA-NN, MaxQuant/MaxDIA, FragPipe, and mzIdentML/mzML-based pipelines. The package computes a wide range of QC metrics, including raw intensity distributions, identification rates, retention time consistency, and missing value patterns, and presents them in interactive, publication-ready reports. By leveraging sample metadata in the Sample and Data Relationship Format format, pmultiqc enables metadata-aware QC and introduces, for the first time in proteomics, QC reports and metrics guided by standardized sample metadata. Its modular architecture allows easy extension to new workflows and formats. Alongside comprehensive documentation and examples for running pmultiqc locally or integrated into existing workflows, we offer a cloud-based service that enables users to generate QC reports from their own data or public PRIDE datasets.

Proteomics↗

PubMed2026

Plasma Proteomic Profiling Identifies Candidate Biomarkers for Pancreatic Ductal Adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy that is often diagnosed after curative treatment is no longer feasible. Existing biomarkers, particularly CA19-9, have limited sensitivity and specificity. Plasma proteins that capture tumor-associated biological alterations may therefore provide useful signals for earlier detection. METHODS: Plasma samples from 99 patients with PDAC and 30 healthy controls were analyzed using data-independent acquisition (DIA) proteomics. Differentially expressed proteins were identified using predefined statistical thresholds and further examined by functional enrichment analysis. Selected candidate biomarkers were validated by ELISA in an independent subset. RESULTS: Among 565 quantified plasma proteins, 52 were differentially expressed between PDAC and controls. These proteins were enriched in extracellular processes, cholesterol metabolism, complement and coagulation cascades, and pancreatic secretion pathways. ELISA validation confirmed higher plasma levels of Cathepsin S, CTRB2, MARCO, PIGR, PRDX6, REG1A, Trypsin-2, and PEP-FAP in patients with PDAC compared with healthy controls. ROC analyses showed moderate-to-good discriminatory performance for several candidates, and the MARCO + PEP-FAP model improved classification compared with either marker alone. CONCLUSION: These findings reveal circulating proteins linked to key PDAC-related biological processes and identify eight candidates for further evaluation in multi-protein diagnostic panels. Larger validation studies incorporating clinically relevant disease control groups are warranted to determine their diagnostic specificity and clinical utility.

Humans↗

PubMed2025

SynchroSep-MS: Parallel LC Separations for Multiplexed Proteomics.

Achieving high throughput remains a challenge in MS-based proteomics for large-scale applications. We introduce SynchroSep-MS, a novel method for parallelized, label-free proteome analysis that leverages the rapid acquisition speed of modern mass spectrometers. This approach employs multiple liquid chromatography columns, each with an independent sample, simultaneously introduced into a single mass spectrometer inlet. A precisely controlled retention time offset between sample injections creates distinct elution profiles, facilitating unambiguous analyte assignment. We modified the DIA-NN workflow to effectively process these unique parallelized data, accounting for retention time offsets. Using a dual-column setup with mouse brain peptides, SynchroSep-MS detected approximately 16,700 unique protein groups, nearly doubling the peptide information obtained from a conventional single proteome analysis. The method demonstrated excellent precision and reproducibility (median protein %RSDs less than 4%) and high quantitative linearity (median R2 greater than 0.96) with minimal matrix interference. SynchroSep-MS represents a new paradigm for data collection and the first example of label-free multiplexed proteome analysis via parallel LC separations, offering a direct strategy to accelerate throughput for demanding applications such as large-scale clinical cohorts and single-cell analyses without compromising peak capacity or causing ionization suppression.

Proteomics↗

PubMed2025

Preliminary screening of urinary host protein biomarkers for Schistosomiasis haematobium: A proteome profiling study identifying candidate diagnostic targets in school-aged children.

Schistosomiasis is a major public health challenge and a globally neglected tropical disease. Schistosoma haematobium, the causative agent of urogenital schistosomiasis, is endemic in African countries; with school-aged children ages 7-15 years being the most vulnerable population. Current diagnostic methods rely on microscopy to identify parasite eggs in urine; which is labor-intensive, requires specialized skills, and often lacks sensitivity, especially in mild infections. To address these limitations, we explored host disease-related biomarkers as a promising avenue for advancing diagnosis and detection. We recruited 135 children ages 7-15 years from Zanzibar, a known transmission hotspot, and used data-independent acquisition (DIA) proteomics combined with machine learning to identify potential host protein biomarkers in urine samples from individuals infected with Schistosoma haematobium. Proteomic analysis identified 823 common host proteins in urine samples from the infected group. Machine learning algorithms highlighted candidate discriminative proteins; which were validated using enzyme-linked immunosorbent assays (ELISA). Machine learning emphasized SYNPO2, CD276, α2M, LCAT, and hnRNPM as the most discriminating biomarkers for Schistosoma haematobium infection. ELISA validation confirmed the differential expression trends of these proteins, while machine learning further validated LCAT and α2M, underscoring their diagnostic potential. Our study focused on host-derived proteins and identified key urinary protein biomarkers associated with Schistosoma haematobium infection, and offers new insights into host-parasite interactions and potential tools for non-invasive diagnostics. While validated in African pediatric populations from transmission hotspots, this host-protein approach inherently overcomes geographic limitations of parasite-based diagnostics; which is a critical advantage for surveillance in non-endemic regions where imported cases threaten gains toward elimination. These findings lay the groundwork for developing novel diagnostic approaches that could significantly improve the detection and surveillance of schistosomiasis, particularly in high-risk populations.

Humans↗

PubMed2026

Enzyme-Metabolite Network Analysis of Endometrial Cancer-Derived Extracellular Vesicles Through Integrated Proteomics and Metabolomics.

Endometrial cancer (EC) is the most common gynecological malignancy in high-income countries. Extracellular vesicles (EVs) are key mediators of intercellular communication and metabolic reprogramming, but their molecular cargo in EC remains poorly characterized. EVs were isolated from four EC cell lines representing Type I and Type II subtypes (AN3CA, ISHIKAWA, HEC1A, and KLE). Untargeted metabolomics was performed by HILIC-LC-MS/MS, proteomics by data-independent acquisition (DIA) mass spectrometry, and multi-omics integration using MetaboAnalyst and OmicsNet. Metabolomic profiling identified 1463 annotated features and revealed significant differences among EC cell lines (PERMANOVA, p = 0.002). Twenty-eight differentially abundant metabolites, including lactic acid, succinic acid, and uric acid, were identified. Proteomic analysis quantified 8513 proteins with subtype-specific expression patterns. Integrated analysis revealed seven significantly enriched pathways, including glycolysis/gluconeogenesis, central carbon metabolism in cancer, and the pentose phosphate pathway. Increased LDHA abundance in metastatic AN3CA-derived EVs was confirmed by Western blot (p = 0.047). EC-derived EVs display subtype- and metastatic-status-specific metabolo-proteomic signatures, with glycolysis, TCA cycle remodeling, and central carbon metabolism as convergent pathway signatures of molecular reprogramming. These findings establish a multi-omics framework for characterizing EV cargo in EC and identify candidate enzyme-metabolite nodes for future biomarker validation in patient-derived specimens.

Female↗

PubMed2026

Temporal DIA-MS proteomics reveals coordinated metabolic reprogramming associated with oil accumulation in oil palm mesocarp.

Oil palm (Elaeis guineensis Jacq.) is the most productive oil-bearing crop globally, yet the molecular basis of mesocarp development and lipid accumulation remains poorly understood. Ultra-deep data-independent acquisition mass spectrometry (DIA-MS) was applied to characterize proteome dynamics in two contrasting genotypes, seedless (KS) and thin-shelled (TS), across five developmental stages (P1-P5) spanning fruit development to mature oil accumulation. Phenotypic analysis revealed higher mesocarp proportion and oil content in KS during late maturation. A total of 137,615 peptides corresponding to 12,163 protein groups were identified, providing a temporal proteomic landscape of mesocarp development. Multivariate analysis indicated that developmental progression was the primary contributor to proteomic variation, whereas genotype-associated differences increased during lipid accumulation. Differentially abundant proteins were mainly associated with carbohydrate metabolism, photosynthesis, proteolysis, antioxidant responses, and lipid biosynthesis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and KOG analyses suggested extensive remodeling of metabolic networks, including developmental changes in photosynthesis-associated proteins and increased representation of lipid-associated pathways during maturation. Weighted protein co-expression network analysis identified 17 modules associated with developmental progression and lipid accumulation, highlighting candidate proteins involved in carbon metabolism, energy production, and cellular protection. Genes encoding selected hub protein candidates were further examined by RT-qPCR. Biochemical analyses supported these proteomic patterns, showing increased acetyl-CoA availability, enhanced antioxidant enzyme activities (SOD, CAT, APX, and GR), improved GSH/GSSG balance, and reduced oxidative damage in KS. Together, these findings provide a temporal proteomic and biochemical framework for understanding genotype-associated differences in oil accumulation and identify candidate metabolic networks for functional studies.

Carbon metabolism↗

PubMed2025

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics.

Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we developed Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models. To make Carafe more accessible to the community, we have integrated Carafe into the widely used Skyline tool.

Journal Article↗