Search Results (154)

Seasonal fluctuations in open solid-state fermentation drive batch-to-batch variability in Chinese Baijiu Daqu; however, how environmental shifts reshape microbial functional expression and non-volatile flavour precursors in medium-high-temperature Daqu remains poorly resolved. In this study, data-independent acquisition (DIA)-based quantitative metaproteomics and untargeted liquid chromatography–mass spectrometry (LC-MS) metabolomics were integrated to characterise winter and summer Daqu from Luzhou, Sichuan. Among 2904 annotated non-volatile metabolites, orthogonal partial least squares discriminant analysis (OPLS-DA) revealed clear seasonal separation; 1472 differential metabolites (560 up- and 912 downregulated in winter vs. summer; variable importance in projection [VIP] > 1, p < 0.05) were enriched in glycolysis/gluconeogenesis, the tricarboxylic acid (TCA) cycle, amino acid biosynthesis, and starch/sucrose metabolism. DIA-based quantitative metaproteomics further resolved season-specific enzyme expression: summer Daqu exhibited elevated saccharolytic, glycolytic and amino-acid-converting enzymes (β-glucosidase, 6-phosphofructokinase, pyruvate dehydrogenase), whereas winter Daqu was enriched in glucose oxidase, phosphoenolpyruvate carboxykinase and aldehyde dehydrogenase, consistent with a pattern suggestive of carbon-storage prioritisation. Proteome–metabolome integration established a coherent “enzyme protein abundance–inferred metabolic tendency–metabolite accumulation” correlative framework axis: higher hydrolytic and central-carbon enzyme abundance in summer corresponded to increased maltose, lactate, acetate, L-glutamate and L-aspartate. Therefore, production season reshapes Daqu quality chiefly by corresponding to distinct patterns of in situ enzyme protein abundance, providing a DIA quantitative metaproteome-anchored mechanistic framework for screening high-expression starters and stabilising seasonal Daqu quality. Full article

Background: Fresh frozen tissues are considered the gold standard for proteomic analyses due to their superior preservation of protein integrity; however, their use is limited by the logistical and financial requirements of long-term cold storage. Formaldehyde-fixed paraffin-embedded (FFPE) tissues provide a practical alternative, owing to their stability and widespread availability in clinical settings. A critical step in FFPE proteomics is deparaffinization, which traditionally relies on organic solvents such as xylene, along with the efficient reversal of formaldehyde-induced crosslinks. Methods: In this study, we evaluated multiple FFPE protein extraction and digestion workflows including chaotropic, surfactant-based, and detergent-free approaches in combination with xylene-free deparaffinization strategies, using label-free data-independent acquisition (DIA) LC-MS/MS. Results: Among the tested methods, a chaotropic, reductant, and surfactant-free in-solution digestion workflow demonstrated robust protein and peptide recovery. A modified version of this protocol further improved peptide coverage while maintaining comparable protein depth. The applicability of the optimized workflow was assessed using FFPE needle biopsy samples from control, hepatic steatosis, and liver fibrosis groups. Exploratory proteomic patterns were observed across conditions, with hepatic steatosis associated with early activation of stress-response pathways, while fibrosis showed evidence suggesting altered lipid metabolism. Conclusions: Overall, this study presents a simple, xylene-free, and MS-compatible workflow for FFPE proteomics that is suitable for low-input clinical samples and may support broader application of archival tissues in proteomic research. Full article

The root bark of Morus alba L. is commonly used as a natural antioxidant; however, its active constituents and underlying molecular mechanisms remain unclear. In this study, a bioactivity-guided isolation approach was employed to identify antioxidant substances from the root bark of Morus alba L. and to investigate their protective effects against oxidative damage in HaCaT cells. Using techniques such as silica gel column chromatography and semi-preparative HPLC, combined with NMR and HR-ESI-MS analysis, 22 compounds were isolated and identified from the dichloromethane extract of Morus alba L. root bark, including Diels–Alder adducts, flavonoids, and benzofurans. Among them, compounds 1 and 2 are new compounds, while compounds 12 and 16 were isolated from this plant for the first time. Bioactivity screening revealed that Kuwanon A (compound 17) exhibited significant cytoprotective effects in an H₂O₂-induced HaCaT cell injury model, effectively scavenging intracellular reactive oxygen species (ROS), restoring mitochondrial function, and enhancing the activities of antioxidant enzymes such as SOD and GSH. Further studies indicated that H₂O₂ induced ferroptosis in HaCaT cells, characterized by abnormal Fe²⁺ levels, lipid peroxidation, and elevated levels of pro-inflammatory cytokines (IL-1β, IL-6, TNF-α). Kuwanon A significantly ameliorated these pathological changes. Consistently, ELISA and Astral DIA quantitative proteomics analyses demonstrated that Kuwanon A specifically upregulates the expression of the sulfurtransferase NFS1, thereby promoting the expression of the core antioxidant enzyme GPX4 and the iron storage protein ferritin-H, collectively inhibiting ferroptosis. This study elucidates that Kuwanon A is a key active component responsible for the antioxidant and anti-inflammatory effects of Morus alba L. root bark, and its mechanism is closely associated with regulating the NFS1-mediated ferroptosis defense pathway. Full article

Introduction: SGLT2 inhibitors reduce renal composite endpoints and proteinuria, yet RCTs uniformly show an acute eGFR dip within 2 weeks to 2 months after initiation. However, demographic and clinical predictors of an acute eGFR dip demonstrate considerable heterogeneity across studies. This study aims to identify urinary protein biomarkers of this early eGFR dip and integrate them with routine variables to build a clinically actionable prediction model. Methods and analysis: This three-stage proteomics study includes retrospective discovery, prospective internal validation, and external validation cohorts (total n ≈ 600–700). DIA mass spectrometry will screen for urinary proteins associated with ≥10% eGFR decline at 1 month post-SGLT2i initiation in CKD stages 3–4. Top candidates (FDR < 10%, FC > 1.5, ion intensity > 1 × 10⁴, unique gene families) will be validated by ELISA. A LASSO-logistic regression model will integrate the top three proteins with seven routinely available clinical variables: age, BMI, diabetes status, heart failure, systolic blood pressure, baseline eGFR, and diuretic use. Model performance will be assessed using the C-statistic, NRI, IDI, and calibration metrics. Adaptive stopping rules are pre-specified. Ethics and dissemination: Approved by the Ethics Review Committee at Chinese PLA General Hospital (S2025-859-02, 2025KY126-KS002), all participants will provide written informed consent prior to enrollment, and the study will adhere to the Declaration of Helsinki. Data will be pseudonymized and stored securely according to institutional regulations. Findings will be published in peer-reviewed journals and presented at international nephrology conferences. Trial Registration: Registered Report Identifier: ChiCTR2600119772. Date of registration: 3 March 2026. Full article

Proteomics has become central to pharmacological research by providing quantitative readouts of protein abundance, post-translational modifications, interactions, and spatial context. However, proteomic datasets are high-dimensional, heterogeneous, and frequently affected by missingness, batch effects, and limited cohort size. Artificial intelligence (AI) and machine learning (ML) can help convert these complex data into decision-relevant outputs for target identification, biomarker discovery, pharmacodynamic monitoring, and drug repurposing. This review critically compares supervised learning, ensemble methods, dimensionality reduction, clustering, deep learning, graph learning, survival modeling, causal inference, and calibration approaches in proteomics-driven drug discovery. We also summarize major software ecosystems for mass-spectrometry processing, targeted assays, spectrum prediction, phosphoproteomics, structure modeling, and reproducible workflows. Emphasis is placed on model selection, benchmarking, missing-data handling, batch correction, interpretability, uncertainty, experimental validation, and translational readiness. Finally, we highlight emerging directions, including contrastive learning, diffusion models, graph-based integration, and federated analytics. Full article

To investigate the differences in reactive oxygen species (ROS) metabolism and signal transduction between the illuminated and non-illuminated surfaces of mangoes exposed to red light, this study used “Tainong No.1” mangoes as the test material, setting up three groups: mango exposed to red light, mango without red light and mango in darkness. The study measured maturity physiological indicators, ROS content, antioxidant enzyme activity, non-enzymatic substances, and combinations with DIA proteomics analysis. The results showed that red light exposure promoted the overall ripening of mangoes, and there was almost no difference in ripening between mango exposed to red light and mango without red light. Red light mainly induced rapid accumulation of hydrogen peroxide in the peel of the irradiated area and stimulated the synthesis of superoxide anion in the pulp. The antioxidant capacity of both the irradiated and non-irradiated areas was enhanced. Key proteins in the ROS signaling pathways such as Rab11, LRK-RLK, and PIN3 were significantly upregulated. In summary, red light promotes synchronous ripening of mango fruits by coordinately regulating the ROS homeostasis of the tissue, and provides new insights into the use of light signals for regulating fruit metabolism. Full article

Background: During maize anthesis, heat stress severely limits productivity—particularly under humid conditions where high humidity suppresses transpirational cooling, forcing tissues to endure direct thermal load. Methods: Using field enclosures to impose enclosure-imposed humid heat shock (EHS), we screened 135 maize inbred lines for flowering-stage yield resilience, using grain weight per ear at maturity under EHS relative to the corresponding control (CK) condition as the primary selection criterion. Based on this screen, we selected two tolerant (R025, R100) and two sensitive (R133, R135) genotypes for data-independent acquisition mass spectrometry (DIA-MS) profiling of the tassel-subtending leaf. Results: At baseline, the selected tolerant lines exhibited a constitutively distinct proteomic state, including lower abundance of light-harvesting complex components and higher abundance or detection frequency of several regulatory proteins, including SRK2E/OST1 and HSF-B2a. Under sustained EHS, the selected sensitive lines showed extensive proteomic disruption, including reduced abundance of photosynthesis-related proteins and oxidative phosphorylation, together with increased abundance of proteins associated with endoplasmic reticulum stress responses and protein turnover. In contrast, the selected tolerant lines displayed a more constrained acclimation response, characterized by relative maintenance of photosynthesis-related proteins together with selective increases in chaperone systems (HSP90/sHSPs) and benzoxazinoid biosynthesis-related proteins. Several proteins showed switch-like detection patterns between the selected tolerant and sensitive lines, including TMEM97-like and a peptidyl-prolyl isomerase, indicating potentially distinct regulatory states. Conclusions: These findings suggest that tolerant performance under enclosure-imposed heat stress is associated with a pre-conditioned proteomic state and enhanced protein homeostasis (proteostasis) buffering capacity that may help preserve photosynthetic function during flowering-stage stress. The identified proteins should be regarded as candidate markers requiring further functional validation before any application in breeding programs aimed at improving adaptation to increasingly frequent heat-stress events. Full article

Background: Epilepsy is a chronic neurological disorder characterized by recurrent seizures, complex neurochemical, and metabolic disturbances. Parishin B, a major bioactive component of Gastrodia elata, has shown neuroprotective potential, but its systemic mechanisms remain unclear. Methods: A pentylenetetrazol (PTZ)-induced seizure model in zebrafish larvae was developed and used to evaluate the anti-seizure effects of Parishin B. Behavioral analysis, ELISA-based biochemical assays, integrated untargeted metabolomics with DIA-based proteomics, and qPCR were performed to decipher underlying molecular mechanisms. Results: Parishin B (0.0625–0.25 mg/mL) significantly alleviated PTZ-induced hyperactivity without developmental toxicity. Parishin B restored neurotransmitter balance by increasing GABA, dopamine, and norepinephrine levels while reducing 5-HT. In addition, it suppressed neuroinflammation and enhanced antioxidant capacity. Integrated multi-omics analysis revealed that Parishin B modulated key metabolic pathways, particularly the TCA cycle and lipid metabolism, and reversed the downregulation of ATP-citrate lyase (ACLY). Parishin B was also associated with the regulation of ferroptosis-related pathways, supported by changes in acsl4a and fth1a expression. qPCR results further confirmed the regulation of aclya, unc13c, and GABAergic signaling genes. Conclusions: Parishin B exerts anti-seizure effects through coordinated regulation of neurotransmitter homeostasis, neuroinflammation, and ACLY-associated energy–lipid metabolism, with potential involvement in ferroptosis-related processes. These findings provide molecular insights supporting Parishin B as a promising candidate for epilepsy therapy. Full article

Conventional extraction of indigo, a vital natural dye, provides low yields and has a negative environmental impact. However, microbial synthesis has emerged as a sustainable alternative. In this study, we describe the optimization of indigo biosynthesis in an engineered Escherichia coli strain called E216. This strain carries, on a replicative plasmid, the styAB genes originating from Pseudomonas putida that constitute the monooxygenase biosynthetic pathway of indigo, as well as mdh, encoding malate dehydrogenase, which plays a role in reducing power generation. In this strain, the overexpression of mtr (a gene encoding a transporter of tryptophan (Trp), the precursor of indigo biosynthesis) and acrA (a gene encoding a protein involved in indigo efflux) was found to substantially enhance indigo yields. Consistently, knocking out these two genes using CRISPR-Cas9 significantly reduced indigo production, whereas it was restored through the complementation of these mutants. This study thus revealed that stimulating tryptophan uptake and indigo efflux, the latter of which limits indigo’s toxic intracellular accumulation, has a positive impact on indigo yields. Furthermore, a comparative mass spectrometry-based proteomic analysis of E216 grown in fermentation medium with or without tryptophan supplementation, integrated with data-independent acquisition (DIA), revealed the global impact of tryptophan supplementation on cellular metabolism. This analysis identified upregulation of key proteins and enriched metabolic pathways under conditions of tryptophan supplementation. Integrating the results of the genetic engineering and proteomic analysis establishes a strong scientific and practical basis for developing a highly efficient method for the green industrial production of indigo using engineered E. coli strains. Full article

Insulin resistance is a multifactorial cellular state involving coordinated alterations in protein homeostasis and organelle function; however, its proteome-wide organization and response to bioactive peptides remain incompletely defined. In this study, we employed DIA-based quantitative proteomics to characterize global protein abundance changes associated with insulin resistance in HepG2 cells and to examine proteomic remodeling following treatment with a camel milk-derived peptide (P2). Comparative proteomic profiling revealed that insulin-resistant cells exhibit extensive reorganization of protein networks linked to redox regulation, endoplasmic reticulum protein processing, mitochondrial metabolism, lysosomal function, and extracellular matrix-associated components. Gene Ontology, KEGG pathway, protein domain, and subcellular localization enrichment analyses consistently indicated disruption of organelle-associated proteomic architecture rather than isolated pathway perturbations. Peptide TYYPPQ treatment was associated with selective, rather than global, proteomic shifts, prominently affecting mitochondrial and peroxisome-associated protein groups as well as extracellular and secretory proteins. Enrichment and localization analyses suggest that peptide exposure reshapes organelle-linked protein representation patterns without implying direct activation of signaling pathways or physiological restoration. Collectively, these results define insulin resistance and peptide responsiveness at a systems-level proteomic resolution and establish an organelle-resolved framework for interpreting peptide-induced proteomic remodeling in insulin-resistant hepatocyte models. This dataset provides a foundation for future targeted functional validation of candidate pathways identified through proteomic association. Full article

Glycosaminoglycans (GAGs) and their degrading enzymes have extensive applications and biotechnology and medicine, and play a crucial role in the recycling of organic matter in oceans. In this study, a potential GAG utilization gene cluster was identified in the genome of a novel marine Bacteroidetes, Roseihalotalea indica gen. nov. sp. nov. TK19036^T, through sole carbon source cultivation and differential proteomic analysis. Multiple GAG-lyases within this locus were purified and characterized. RiPL8 comprises a functionally unknown N-terminal domain and a catalytic C-terminal domain, exhibiting specificity for degrading hyaluronic acid (HA). The activity of RiPL35 is sensitive to Ca²⁺ ion concentration with an optimum at 10 mM. RiPL38 is the first reported member of the PL38 family capable of degrading HA and chondroitin sulfate (CS). In summary, our study reveals Roseihalotalea indica gen. nov. sp. nov. TK19036^T harbors an unpredicted GAG degradation gene cluster, and the encoded GAG-lyases exhibit distinct substrate specificities compared to the host organism. Full article

Background/Objectives: Human milk composition is shaped by gestational age at delivery and stage of lactation; however, proteomic differences between milk from mothers of preterm and term infants and their temporal patterns remain incompletely characterised. Methods: This prospective study enrolled 40 lactating mothers: 20 who delivered preterm infants (<32 weeks’ gestation) and 20 who delivered at term (37–42 weeks). Each provided milk samples during early lactation (first 10 days postpartum) and during later lactation (week five postpartum). Milk serum was analysed using quantitative data-independent acquisition mass spectrometry. Differential protein abundance was assessed separately at each time point; functional annotation was performed using Gene Ontology biological process analysis. Results: Eighty samples were analysed. On average, a total of 662 proteins were identified per sample, of which 169 were consistently quantified across all samples (1% FDR). During early lactation, 10 proteins differed significantly, with bidirectional changes and moderate effect sizes. At week five, 19 proteins were differentially abundant, predominantly higher in preterm samples. Immune-related proteins constituted the largest functional category at both stages. Immunoglobulin heavy constant gamma 4 remained consistently downregulated in preterm milk (1.6-fold lower abundance). Ferritin heavy chain (1.5) and HLA class II histocompatibility antigen gamma chain (1.8) were elevated only early, whereas calprotectin subunits S100A8 (5.6) and S100A9 (5.2) were markedly upregulated later. Conclusions: Proteomic differences vary across lactation stages, highlighting lactation stage as an essential contextual variable in comparative milk proteomics. Full article

Establishing plasma biomarkers in the veterinary field has always been a challenge, due to a lack of significant understanding of pathophysiological attributes of disease. Advances in mass spectrometry-based proteomic techniques have improved plasma biomarker discovery in veterinary medicine. Feline hypertrophic cardiomyopathy is the most common cardiac disease in cats and has a complex and not fully elucidated pathophysiology. This study aimed to use SWATH-MS proteomics to identify novel plasma biomarkers for fHCM and to further elucidate disease pathogenesis. Plasma was collected from 20 cats, consisting of healthy controls (n = 10) and a HCM group (n = 10). Cats with fHCM, were diagnosed by echocardiography and disease statuses were determined by a veterinary cardiologist. Undepleted cat plasma samples were digested using FASP and quantitative analysis was performed using DIA-NN. A total of 40 plasma proteins were found to be dysregulated, primarily associated with innate and humoral responses, including complement C7 and C9 and properdin proteins. Other dysregulated proteins were involved in blood coagulation (fibrinogen, fibulin-1), lipid metabolism (apolipoproteins), and inflammation pathways (transthyretin and plasminogen). These findings provide possible biomarkers for fHCM, with the potential to detect disease before clinical signs become evident, which is a significant outcome for fHCM. These proteomic changes suggest critical pathways for earlier intervention and could potentially lead to more effective treatment outcomes. Furthermore, having significant similarity to human disease strengthens the case for using cats as a potential translational model for hHCM. Full article

Background: Prostate cancer (PCa) shows a marked biological heterogeneity that is closely associated with tumor aggressiveness. A substantial proportion of clinically significant tumors remain undetected by multiparametric magnetic resonance imaging (mpMRI). Elucidating the molecular basis of MRI visibility and identifying non-invasive biomarkers could improve the risk stratification and clinical management of patients. Accordingly, this study aimed to assess tissue and urine proteomic signatures associated with PCa aggressiveness and mpMRI visibility. Methods: In this exploratory study, we performed an integrated proteomic analysis of prostate tissue and preoperative urine samples from 24 patients stratified into four groups: benign prostatic hyperplasia (BPH), indolent PCa (Gleason 6), clinically significant PCa with MRI-visible lesions, and clinically significant PCa with MRI-non-visible lesions. Data-independent acquisition mass spectrometry (DIA workflows) was used to identify differentially expressed proteins associated with malignancy, tumor aggressiveness, and MRI visibility. Results: Pairwise proteomic analyses revealed significant molecular differences between BPH and all PCa groups, identifying 694 non-redundant proteins differentially expressed in tissue and 482 in preoperative urine, showing molecular features associated with both disease presence and progression. Comparative tissue and urine analyses identified 82 proteins, reflecting shared biological pathways in metabolism, cytoskeletal organization, immune processes, and extracellular matrix remodeling. Finally, a direct comparison of MRI-visible and MRI-non-visible clinically significant PCa identified a panel of differentially expressed proteins, including LCN2/NGAL, S100A9, and AOC1/DAO, that showed differential urinary abundance and prognostic relevance in the TCGA-PRAD cohort. Conclusions: Our results suggest that proteomic alterations in PCa are associated with disease progression and aggressiveness and capture biologically relevant differences between tissue and urinary proteomes. These differences are also observed between MRI-visible and MRI-non-visible clinically significant prostate cancers, supporting the potential of urinary proteomics as a non-invasive complement to imaging-based diagnostics. Full article

Type 2 diabetes (T2D) causes systemic metabolic and inflammatory changes that affect the oral cavity, but salivary molecular markers remain poorly characterized. A peptide-centric reanalysis of salivary proteomics data was performed using DIA-NN for peptide-level quantification, without collapsing peptide signals into protein-level summaries. Although the qualitative peptide repertoire was largely conserved between T2D and control samples (>96% overlap), T2D showed coordinated quantitative changes in specific peptide subsets. Differentially abundant peptides primarily originated from complement C3, alpha-2-macroglobulin, serotransferrin, mucins, apolipoproteins, and hemoglobin, with a significant enrichment of oxidized cysteine-containing peptides, indicating redox imbalance and low-grade inflammation. Structural analysis with AlphaFold showed that T2D-associated peptides are located in solvent-exposed and conformationally dynamic regions of proteins. These findings suggest that disease specificity in diabetic saliva occurs mainly at the peptide level, offering mechanistic insight into non-invasive biomarker identification and longitudinal disease monitoring. Full article