PubMed

Indian J Pediatr. 2026 Jul 13. doi: 10.1007/s12098-026-06300-y. Online ahead of print.ABSTRACTNecrotizing enterocolitis (NEC) is one of the most catastrophic gastrointestinal emergency occurring predominantly in preterm neonates. It contributes to substantial neonatal morbidity and mortality. Disturbances in the intestinal microbiome are crucial to disease pathogenesis. In preterm infants, an immature intestinal barrier, dysregulated immune responses, and environmental exposures altogether predispose to alteration in microbial colonization and intestinal inflammation. This review was done to present the current evidence on gut microbiome alterations associated with NEC in preterm infants. A systematic search of the MEDLINE and EMBASE databases was performed using search strategy related to prematurity, intestinal microbiota, and necrotizing enterocolitis. A total of 42 studies assessing microbial composition, microbial progression, or microbial functional patterns in relation to NEC were included. Across the included studies, NEC was commonly preceded by reduced microbial diversity, delayed maturation of anerobic communities, and expansion of Proteobacteria, particularly Enterobacteriaceae family such as Klebsiella and Escherichia. Longitudinal studies further showed that these microbial changes may become evident days to weeks before clinical disease, suggesting a potential window for early risk identification. Functional analyses also showed alterations in microbial metabolic pathways, including short-chain fatty acids, tricarboxylic acid intermediates, volatile compounds, and viral signatures that may lead to epithelial injury and inflammatory signaling. Clinical and environmental factors including antibiotic exposure, mode of delivery, feeding practices, and NICU microbial ecosystem are important determinants of neonatal gut microbiome development. Thus, the current evidence supports a reproducible pattern of intestinal dysbiosis preceding NEC. Better understanding of microbiome dynamics may aid early risk stratification and support microbiome-targeted preventive strategies in vulnerable preterm populations.PMID:42437837 | DOI:10.1007/s12098-026-06300-y

J Ethnopharmacol. 2026 Jul 12:122184. doi: 10.1016/j.jep.2026.122184. Online ahead of print.ABSTRACTETHNOPHARMACOLOGICAL RELEVANCE: Amomi Fructus (Amomum villosum Lour.)-processed Rehmanniae Radix (Rehmannia glutinosa Libosch.) is a traditional Chinese herbal preparation used to tonify blood and improve digestive tolerance. It is considered a rational processing method to enhance the efficacy of Rehmanniae Radix (RR) in blood deficiency syndrome (BDS).AIM OF THE STUDY: This study investigated the therapeutic effects of Amomi Fructus -processed Rehmanniae Radix (AR) on BDS and explored its underlying mechanisms using a multi-omics approach.MATERIALS AND METHODS: A rat model of BDS was established using cyclophosphamide and acetylphenylhydrazine. Rats were treated with AR or RR. Hematological parameters, spleen histopathology, TUNEL staining, bone marrow apoptosis by flow cytometry, transcriptomics, spleen metabolomics, and gut microbiota profiling were performed.RESULTS: AR significantly improved RBC, WBC, HGB, and HCT levels and alleviated spleen pathological injury in BDS rats. Transcriptomic analysis showed that the PI3K/Akt, and MAPK signaling pathway was significantly enriched among differentially expressed genes. AR reduced phosphorylation of PI3K, Akt, ERK, JNK, and p38, and markedly suppressed apoptosis in the spleen and bone marrow. Metabolomic profiling indicated that AR restored BDS-induced metabolic disturbances, while gut microbiota analysis showed that AR modulated microbial composition. Overall, AR produced more pronounced effects than RR.CONCLUSIONS: AR alleviated BDS in rats, potentially by inhibiting PI3K/Akt/MAPK-mediated apoptosis and restoring metabolic and microbial homeostasis. These findings support the traditional use of AR as a blood-tonifying herbal preparation.PMID:42437650 | DOI:10.1016/j.jep.2026.122184

Brain Res Bull. 2026 Jul 12:112037. doi: 10.1016/j.brainresbull.2026.112037. Online ahead of print.ABSTRACTCircadian rhythm disruption (CRD) exacerbates epileptic seizures, yet the underlying molecular mechanisms remain unclear. Using a pentylenetetrazol (PTZ) kindling rat model combined with continuous light exposure, we found that CRD significantly shortened seizure latency and increased seizure frequency. 16S rRNA sequencing revealed that CRD substantially increased the abundance of Ruminococcus gnavus (R. gnavus) in the gut microbiota. Antibiotic-mediated microbiota depletion, fecal microbiota transplantation (FMT), and R. gnavus monocolonization experiments confirmed that R. gnavus enrichment is a critical factor driving seizure aggravation. Metabolomic analysis demonstrated that elevated R. gnavus suppressed the expression of argininosuccinate synthase 1 (ASS1), a key enzyme for arginine biosynthesis in the kidney, leading to impaired L-citrulline-to-L-arginine conversion and consequently decreased L-arginine levels in serum and hippocampal tissues. Arginine deficiency subsequently activated NADPH oxidase 4 (NOX4) upregulation in the hippocampus, triggering enhanced oxidative stress (elevated malondialdehyde (MDA) and reduced superoxide dismutase (SOD) activity), which ultimately induced ferroptosis (characterized by mitochondrial cristae reduction, acyl-CoA synthetase long-chain family member 4 (ACSL4) upregulation, and glutathione peroxidase 4 (GPX4) downregulation). Pharmacological intervention with the NOX4-specific inhibitor GLX351322 ameliorated oxidative stress, suppressed ferroptosis, and alleviated seizure severity. Importantly, L-arginine supplementation significantly prolonged seizure latency and reduced seizure frequency by reversing ferroptosis activation through downregulating NOX4-ACSL4 expression and upregulating GPX4 expression. This study unveils a complete signaling axis whereby CRD promotes epileptic seizures through the R. gnavus-ASS1-arginine-NOX4-ferroptosis cascade, providing novel therapeutic targets for clinical intervention.PMID:42437603 | DOI:10.1016/j.brainresbull.2026.112037

Mol Ther Nucleic Acids. 2026 Jun 24;37(3):102992. doi: 10.1016/j.omtn.2026.102992. eCollection 2026 Sep 8.ABSTRACTPreeclampsia (PE) is a leading cause of maternal and fetal morbidity and mortality worldwide, with placental inflammation recognized as a central pathogenic feature, yet the upstream triggers and inflammatory mechanisms remain incompletely understood. Here, we combined placental single-cell transcriptomics with gut metagenomic and metabolomic profiling to characterize inflammatory signatures in PE. Stratified analyses across clinical subgroups-defined by fetal number, onset timing, and fetal sex-revealed that placental single-cell transcriptomics coupled with multi-view machine learning consistently prioritized bacteria-associated inflammatory features across all subgroups. Superimposed on this shared foundation, we identified subgroup-specific trajectories: twin PE exhibited IL-1-dominant inflammation with compensatory antioxidant metabolic shifts, while singleton PE showed IFN-II-associated immune activation. Early-onset PE displayed sexual dimorphism-male fetuses featured bacterial defense pathways, lipid metabolic programs, and trophoblast-confined glycolysis, while female fetuses exhibited angiogenesis, chemotaxis, nitric oxide signaling pathways, and glycolytic reprogramming in immune cells, whereas late-onset PE exhibited comparatively attenuated inflammatory activity. Gut metagenomic profiling revealed enrichment of lipopolysaccharide (LPS)-producing taxa and depletion of beneficial commensals in PE, accompanied by metabolomic alterations that aligned with inflammatory pathways also highlighted in placental analyses. Collectively, these findings reveal a conserved bacteria-associated inflammatory program in PE that is modulated by clinical context and linked to gut microbial dysbiosis.PMID:42437307 | PMC:PMC13355745 | DOI:10.1016/j.omtn.2026.102992

PeerJ. 2026 Jul 8;14:e21504. doi: 10.7717/peerj.21504. eCollection 2026.ABSTRACTBACKGROUND: Non-small cell lung cancer (NSCLC) remains the leading cause of cancer mortality, largely due to the lack of reliable non-invasive tools for detection and risk stratification. Lipid metabolic reprogramming is a hallmark of cancer and may serve as a promising source of diagnostic biomarkers.METHODS: Serum from 40 NSCLC patients and 30 controls was profiled by high-performance liquid chromatography-high-resolution mass spectrometry (HPLC-HRMS), quantifying 331 annotated lipids. Differential and pathway analyses were performed. Least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), Extreme Gradient Boosting (XGBoost), and Light Gradient-Boosting Machine (LightGBM) models were evaluated using stratified 10-fold cross-validation; feature prioritization used recursive feature elimination and Shapley additive explanations (SHAP). A combined clinical-lipid model incorporating selected lipids and clinical covariates was assessed with discrimination, calibration, and decision-curve analysis.RESULTS: NSCLC exhibited broad decreases in glycerophospholipids, sphingolipids, and triacylglycerols, consistent with membrane-lipid remodeling. LightGBM showed the best discrimination in internal validation. Key discriminant lipids included lysophosphatidylcholine (LPC(O-18:1)), decanoylcarnitine, and sulfatide (SL) (SL 38:5). The integrated lipid-clinical model achieved good discrimination (area under the receiver operating characteristic curve (AUC) = 0.946) and acceptable calibration. A nomogram was constructed for individualized risk estimation.CONCLUSIONS: This study nominates candidate serum lipid markers and an interpretable modeling workflow for NSCLC classification in an exploratory case-control cohort. External validation and targeted quantification in larger, multicenter and screening-relevant populations are required before clinical implementation.PMID:42437038 | PMC:PMC13355613 | DOI:10.7717/peerj.21504

Food Chem X. 2026 Jul 2;37:104166. doi: 10.1016/j.fochx.2026.104166. eCollection 2026 Jul.ABSTRACTThis study investigated the effects of thermal treatment (30, 55, and 85 °C) under mild acidic conditions (pH 4.5) during protein precipitation on structural characteristics, emulsifying properties, metabolite profile and protein composition of Moringa leaf protein (MoLP) from two cultivars (local Thai and PKM-1). Moderate heating at 55 °C resulted in the highest protein recovery, reaching 4.59% and 2.47% for Thai and PKM-1 cultivars, respectively. In contrast, treatment at 85 °C reduced protein content to 54.8% and 50.1%. Structural analyses indicated heat-induced unfolding, thiol-disulfide rearrangement, decreased thermal stability, and increased β-sheet formation, indicating protein aggregation. SDS-PAGE further confirmed protein alterations, particularly affecting RuBisCO-associated proteins. LC-MS/MS analysis demonstrated selective proteomic remodeling, with retention of metabolic enzymes and enrichment of stress-related proteins, whereas thermolabile photosynthetic proteins declined after heating. Metabolomic analysis showed cultivar-dependent responses, with PKM-1 exhibiting greater thermal resilience. Heat treatment at 55 °C substantially improved emulsion stability, increasing from 20 to 86 min in local Thai cultivar and from 119 to 208 min in PKM-1 cultivar. Overall, moderate thermal treatment at 55 °C provided the balance between protein recovery, structural integrity, and emulsifying functionality of MoLP, demonstrating the potential of MoLP as plant-based emulsifier.PMID:42436747 | PMC:PMC13355659 | DOI:10.1016/j.fochx.2026.104166

Biomark Res. 2026 Jul 11. doi: 10.1186/s40364-026-00970-8. Online ahead of print.ABSTRACTBACKGROUND: Transthyretin (TTR) amyloidosis is a progressive, life-threatening disorder caused by extracellular deposition of amyloid fibrils derived from the plasma protein TTR. Inherited forms are associated with destabilizing TTR mutations; however, recent findings indicate that amyloid formation in vivo may be promoted by disulfide bond formation between TTR subunits, suggesting oxidative stress as a potential contributor to protein misfolding and disease progression. Glutathione (GSH) is a central component of the antioxidant defense system, and disruption of GSH homeostasis can lead to the accumulation of pyroglutamate (PGA), which is detectable in plasma. Moreover, oxidative stress is frequently linked to inflammation, which may be reflected by increased indoleamine 2,3-dioxygenase 1 (IDO1) activity, observed as an elevated plasma kynurenine/tryptophan ratio.METHODS: Plasma levels of PGA, kynurenine, and tryptophan were quantified by liquid chromatography-mass spectrometry in cohorts comprising healthy TTR wild-type controls, asymptomatic carriers of the TTR-V30M mutation, and symptomatic patients with TTR-V30M amyloidosis.RESULTS: Symptomatic individuals had significantly elevated plasma PGA levels compared with both asymptomatic carriers and age-matched healthy controls, consistent with impaired GSH homeostasis. In parallel, the kynurenine/tryptophan ratio was increased in symptomatic TTR-V30M carriers, supporting inflammatory activation in manifest disease.CONCLUSIONS: These findings identify disrupted GSH homeostasis and inflammatory activation as metabolic features associated with symptomatic TTR-V30M amyloidosis in vivo, supporting a link between redox imbalance and disease manifestation. Strategies aimed at restoring antioxidant homeostasis and limiting inflammatory oxidative stress may therefore warrant further investigation as approaches to delay onset or slow disease progression.PMID:42436580 | DOI:10.1186/s40364-026-00970-8

Anim Microbiome. 2026 Jul 11. doi: 10.1186/s42523-026-00597-y. Online ahead of print.ABSTRACTEarly postnatal growth is a critical determinant of meat production efficiency and long-term genetic improvement in goats; however, the molecular mechanisms underlying individual variation in growth performance remain poorly understood. In this study, a total of 123 Hechuan white goats were included. First, a genome-wide association study (GWAS) for average daily gain (ADG) was performed using all 123 individuals. Subsequently, based on the coefficient of variation of ADG (CV = 65.6%), an extreme phenotype sampling (EPS) strategy was applied to select 39 individuals with extreme growth phenotypes for subsequent metabolomic, microbiome, and integrated mGWAS analyses.The results showed that ADG approximately followed a normal distribution across the 123 goats. GWAS identified 22 loci significantly associated with ADG, mapping to genes including DLK1, NCAPG2, LCORL, CNTNAP2, and SLC8A1, which are involved in pathways related to skeletal muscle development, cell cycle regulation, ion transport, and immune function. Metabolomic profiling detected 1,589 putative metabolites, revealing differential enrichment of lipid, amino acid, and bile acid metabolic pathways between fast- and slow-growing goats. Gut microbiome analysis demonstrated that Christensenellaceae_R-7_group and Monoglobus were significantly enriched in fast-growing individuals, whereas Desulfovibrio was more abundant in slow-growing goats.Integrated mGWAS analysis further revealed extensive effects of host genetic variation on gut microbiota and fecal metabolites. Specifically, 11 bacterial genera were significantly associated with host genomic variants, among which Desulfovibrio exhibited the highest number of associated loci. Integration of multiple variant types consistently linked Desulfovibrio, Eubacterium_hallii_group, and Candidatus_Saccharimonas with genes such as ARHGAP24 and IGF2BP2. In addition, 14 metabolites were significantly associated with host genetic variants, with Lysopc(14:1(9Z)/0:0) and glycocholic acid showing the strongest associations. Notably, the peak signal for Lysopc was located within HMGA2.Collectively, these findings define a coordinated host genome-gut microbiota-metabolite network underlying early growth variation in goats and provide a mechanistic foundation for precision breeding and targeted nutritional strategies in goat production systems.PMID:42436575 | DOI:10.1186/s42523-026-00597-y

J Exp Clin Cancer Res. 2026 Jul 11. doi: 10.1186/s13046-026-03748-w. Online ahead of print.ABSTRACTBACKGROUND: To explore mesenchymal stem cell (MSC)-driven metabolic reprogramming of EGFR-wild-type (EGFR-wt) tumor cells contributing to tyrosine kinase inhibitor (TKI) resistance in EGFR-mutant (EGFR-mt) non-small cell lung cancer.METHODS: Isolate MSCs from paired tumor and non-tumor lungs of EGFR-mt and EGFR-wt patients. Integrate transcriptional RNA sequencing, targeted free fatty acid and energy metabolomics to characterize signaling pathways promoting MSC-EGFR-wt tumor cell interaction within EGFR-mt tumors. Assess spatial correlation between these cells in TKI-resistant and TKI-sensitive patients using immunohistochemistry and multiplex immunofluorescence.RESULTS: EGFR-mt lung cancer-derived MSCs (mtLC-MSCs) promoted EGFR-wt tumor proliferation, and reduced TKI efficacy in vivo. Mechanistically, mtLC-MSCs increased fatty acid levels, supporting stem-like features in EGFR-wt tumor cells. These effects were mediated by IL-6 and IL-1α secreted from mtLC-MSCs, which enhanced S100A9 expression in EGFR-wt tumor cells and activated downstream c-Myc/β-catenin-FASN signaling, driving fatty acid synthesis. TKI plus IL-6 pathway blockade reversed resistance in mice. In EGFR-mt NSCLC patient, the proportions mtLC-MSCs was comparable with mtTF-MSCs. TKI-resistant samples exhibited greater mtLC-MSCs wrapping density around tumor masses than sensitive samples. EGFR-mt tumor cells showed closer spatial proximity to mtLC-MSCs than did EGFR-wt cells.CONCLUSION: These findings firstly reveal the spatial heterogeneity and metabolic reprograming of EGFR-wt tumor cells driven by mtLC-MSCs in EGFR-mt tumors, providing novel insight into therapeutic resistance and potential combination strategies for TKI-resistant patients.PMID:42436555 | DOI:10.1186/s13046-026-03748-w

J Transl Med. 2026 Jul 11. doi: 10.1186/s12967-026-08551-x. Online ahead of print.ABSTRACTBACKGROUND: Glucose deprivation is a prevalent stressor within the tumor microenvironment. Nonetheless, the fundamental mechanism through which non-small cell lung cancer (NSCLC) cells orchestrate survival and progression through specific metabolic hubs under such conditions remains poorly understood.METHODS: This study utilized integrated multiomics analyses, encompassing transcriptomics and metabolomics, to identify pivotal targets. Functional validation was performed via in vitro assays, including CCK-8, colony formation, Transwell, and EdU assays, as well as in vivo models, such as subcutaneous xenografts and tail-vein lung metastasis models. The function and mechanism of the MAPK/ATF3/ASNS signaling axis were comprehensively investigated using gene knockdown/overexpression techniques, Western blotting, immunohistochemistry, and metabolomic analysis.RESULTS: Combined transcriptomic and metabolomic analyses revealed that glucose deprivation markedly upregulates ASNS expression in NSCLC cells, correlating with unfavorable patient outcomes. Functionally, both in vitro and in vivo experiments confirmed that ASNS significantly promotes the malignant behaviors of NSCLC cells under glucose-deprived conditions. Metabolomic analysis revealed that ASNS supports tumor cell survival during energetic stress by maintaining a dynamic balance of multiple amino acids. Mechanistically, glucose deprivation activates the MAPK signaling pathway, leading to the upregulation of the transcription factor ATF3, which binds directly to the ASNS promoter and transcriptionally activates its expression, thereby promoting malignant progression in NSCLC. Finally, the combined targeting of glucose metabolism using 2-DG, along with ASNS inhibition, demonstrated additive antitumor efficacy in vivo.CONCLUSION: The results of this study revealed that, under glucose deprivation stress, the MAPK/ATF3/ASNS axis functions as a critical signaling-metabolic hub, promoting NSCLC progression by driving amino acid metabolic reprogramming. Targeting this axis offers a novel strategy for intervening in tumor metabolic adaptation and developing innovative combination therapies.PMID:42436529 | DOI:10.1186/s12967-026-08551-x

Diabetol Metab Syndr. 2026 Jul 11. doi: 10.1186/s13098-026-02237-6. Online ahead of print.ABSTRACTBACKGROUND: Cardiometabolic heart failure with preserved ejection fraction (HFpEF) is a high-risk phenotype primarily driven by metabolic syndrome, with a significantly increased incidence and risk of adverse outcomes. A fundamental reason for this is the lack of early clinical diagnosis. As a tool capable of accurately capturing pathophysiological states, metabolomics provides a critical entry point for addressing this issue; however, studies focusing on the metabolic characteristics of this population remain limited.METHODS: This study integrated a clinical cohort and untargeted metabolomics to compare serum metabolic profiles between patients with cardiometabolic HFpEF and those with metabolic syndrome (MetS). Baseline characteristics were balanced using propensity score matching (PSM). Differential metabolites were identified by untargeted metabolomics, followed by KEGG pathway enrichment analysis. Machine-learning approaches were further applied to screen candidate metabolites with potential diagnostic efficacy, and weighted gene co-expression network analysis (WGCNA) together with SHapley Additive exPlanations (SHAP) were used to evaluate phenotype association and feature contribution. In an independent clinical cohort, total sphingomyelin (SM) levels were assessed by ELISA as an external evaluation strategy based on clinical applicability.RESULTS: Differential metabolites between the two groups were mainly enriched in sphingolipid metabolism and glycerophospholipid metabolism pathways. Through multi-method screening, C24:1 Sphingomyelin was identified as a candidate metabolite with potential diagnostic efficacy. The co-expression module containing C24:1 Sphingomyelin was significantly correlated with NT-proBNP, a key biomarker of heart failure, and SHAP analysis indicated that C24:1 Sphingomyelin contributed substantially to the classification model. In the external cohort, total SM levels were associated with disease status, suggesting the potential clinical association of sphingolipid-related signals.CONCLUSION: This study preliminarily characterized the metabolic features distinguishing cardiometabolic HFpEF from MetS alone, suggesting that sphingolipid dysregulation is associated with the development and progression of this phenotype. Among the identified metabolites, C24:1 Sphingomyelin was identified as a candidate metabolite with potential diagnostic performance, and SM showed potential clinical applicability. These findings provide new clues for biomarker discovery and preliminary clinical translational exploration in cardiometabolic HFpEF.PMID:42436519 | DOI:10.1186/s13098-026-02237-6

BMC Cardiovasc Disord. 2026 Jul 11. doi: 10.1186/s12872-026-06265-y. Online ahead of print.ABSTRACTINTRODUCTION: Hypertrophic cardiomyopathy is an inherited cardiovascular disease with heterogeneous presentation. However, the metabolic changes resulting from mutations and their relationship to the phenotype remain unclear.OBJECTIVES: To investigate the association between TNNI3 and MYBPC3 variants and both clinical phenotype and metabolic disorders in HCM patients.METHODS: 34 newly diagnosed HCM patients, 51 healthy individuals, and 23 unaffected family members were included. Clinical information and plasma samples were collected and analyzed. Whole-exome and Sanger sequencing were used for variant identification. Non-targeted metabolomics was performed using ultra-high-performance liquid chromatography-high-resolution mass spectrometry.RESULTS: TNNI3 and MYBPC3 variants were identified in familial HCM cases, which exhibited earlier onset and increased interventricular septum thickness. Metabolomics revealed lower L-valine and higher free fatty acid levels in HCM patients. Patients with TNNI3 variants showed dysregulation of lyso-phosphatidylcholines and lyso-phosphatidylethanolamines, along with disturbances in glutamic acid-related pathways. MYBPC3 variants were linked to dysregulation in energy metabolism. Correlation analysis highlighted associations between specific lipid metabolites and cardiac structure and function.CONCLUSION: Significant metabolic alterations, particularly in amino acid and lipid metabolism, are prevalent in HCM. These findings enhance our understanding of HCM pathogenesis and suggest potential biomarkers and therapeutic targets for this genetic heart disease.PMID:42436400 | DOI:10.1186/s12872-026-06265-y

NPJ Biofilms Microbiomes. 2026 Jul 11. doi: 10.1038/s41522-026-01085-6. Online ahead of print.ABSTRACTThe swine intestinal microbiota dynamically remodels during development and supports gut homeostasis. However, whether stage-specific microbial shifts, are associated with epithelial development remains poorly understood. Here, longitudinal metagenomic profiling of the swine gut microbiome identified Lactiplantibacillus plantarum as a transiently enriched nursery-stage bacterium positively associated with goblet cell numbers. Dietary supplementation with L. plantarum validated this association, showing increased goblet cell numbers and MUC2 expression in the ileum of nursery piglets. Co-culture with porcine ileum organoids further demonstrated that L. plantarum cell-free supernatant promoted ileal organoid growth and goblet cell differentiation. Integrated untargeted metabolomic analyses of ileal samples and bacterial culture supernatants identified indole-3-lactic acid (ILA) as a potential key microbial metabolite from L. plantarum. Mechanistically, ILA promoted intestinal stem cell proliferation and MUC2 expression, accompanied by increased expression of aryl hydrocarbon receptor (AHR) and its downstream target CYP1A1 in ileal organoids. Consistently, activation of AHR using FICZ increased MUC2 expression, whereas inhibition with CH-223191 suppressed MUC2 expression in ileal organoids. Collectively, these findings uncover a L. plantarum-ILA-AHR signaling axis that promotes intestinal goblet cell differentiation, providing mechanistic insight into microbial metabolite-mediated regulation of epithelial homeostasis during post-weaning period in pigs.PMID:42436183 | DOI:10.1038/s41522-026-01085-6

J Proteomics. 2026 Jul 11:105708. doi: 10.1016/j.jprot.2026.105708. Online ahead of print.ABSTRACTAdvances 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.PMID:42436009 | DOI:10.1016/j.jprot.2026.105708

Biochim Biophys Acta Mol Basis Dis. 2026 Jul 11:168363. doi: 10.1016/j.bbadis.2026.168363. Online ahead of print.ABSTRACTBACKGROUND: Hyperglycemia is an independent risk factor for the accelerated progression of metabolic dysfunction-associated steatohepatitis (MASH) to liver fibrosis, with the underlying mechanisms not yet fully elucidated.METHODS: A diabetic metabolic dysfunction-associated steatohepatitis (T2DM-MASH) mouse model was successfully established, and metabolomics screening identified indole-3-carbaldehyde (3-IAld) as a potential therapeutic candidate. The efficacy of 3-IAld (25 and 50 mg/kg) was evaluated in vivo, and its anti-fibrotic mechanism was further validated in human hepatic stellate cells (LX-2) exposed to pro-fibrotic conditions.RESULTS: The T2DM-MASH model was validated by metabolic disturbances (hyperglycemia, dyslipidemia), hepatic injury, and histopathological steatohepatitis. Fecal 3-IAld levels were significantly reduced in T2DM-MASH mice and inversely correlated with metabolic and fibrotic markers. In vivo, 3-IAld supplementation significantly ameliorated metabolic parameters, liver enzymes (ALT/AST), and pathological fibrosis in a dose-dependent manner (P < 0.05). Ultrastructural analysis revealed that 3-IAld restored intestinal barrier integrity by upregulating tight junction proteins (ZO-1, occludin) and reducing plasma LPS (P < 0.05). In vitro, 3-IAld significantly inhibited HSC activation, characterized by suppressed cell proliferation and downregulated fibro-genic markers (α-SMA, Col1a1, TGF-β1, TIMP-1). This protective effect was reversed by the SIRT1 inhibitor EX-527, confirming the reliance on SIRT1 signaling. Mechanistically, 3-IAld alleviated hepatic fibrosis, at least in part, by suppressing the TGF-β/SMAD signaling pathway, as evidenced by reduced phosphorylation of Smad2 and Smad3.CONCLUSIONS: Our findings, underscore the potential of the microbial metabolite 3-IAld to attenuate fibrosis progression in T2DM-MASH, at least in part, by regulating the SIRT1/TGF-β/SMAD pathway. However, definitive confirmation of this specific signaling axis in vivo remains to be established with genetic or pharmacological models.PMID:42435945 | DOI:10.1016/j.bbadis.2026.168363

J Genet Genomics. 2026 Jul 11:S1673-8527(26)00236-5. doi: 10.1016/j.jgg.2026.07.003. Online ahead of print.ABSTRACTCancer remains the leading cause of death worldwide, presenting substantial challenges to precision medicine due to its complex heterogeneity. Radiogenomics, as a method combining quantitative radiologic data with genomic information, provides a robust analysis framework to assess tumor heterogeneity and cancer progression. Here, we summarize the application of radiogenomics into two key fusion methods: feature-level and decision-level fusion. Feature-level fusion combines multimodal data into a rich feature set to improve the predictive power of models, while decision-level fusion integrates decision results from multiple independent models to improve robustness and reliability. Furthermore, we explore the integration of radiomics with various omics technologies, including transcriptomics, metabolomics, and proteomics. This integration enables a deeper understanding of the dynamic tumor microenvironment, metabolic dysregulation, and cancer progression mechanisms. Finally, we provide a detailed overview of publicly available datasets relevant to radiogenomics research, such as The Cancer Imaging Archive, cBioPortal, UK Biobank and Human Connectome Project; and further describe multiple types of omics data and sample characteristics for each resource for the benefit to readers. In summary, this review charts a path beyond radiogenomics by advancing radiomics and multi-omics horizons to transform precision medicine in cancer.PMID:42435838 | DOI:10.1016/j.jgg.2026.07.003

Chem Biol Interact. 2026 Jul 11:112254. doi: 10.1016/j.cbi.2026.112254. Online ahead of print.ABSTRACTBACKGROUND: Clear cell Renal cell carcinoma (ccRCC) represents the most common malignant tumor of the kidney. Metallothionein 2A (MT2A) is abnormally expressed in various cancers, but its functional role and underlying mechanism in RCC remain to be elucidated.METHODS: The expression of MT2A in RCC and its adjacent tissues was analyzed using the public TCGA and GEO databases. The effects of MT2A on cell proliferation, migration and invasion were investigated by using CCK-8, Transwell, and wound healing assays. Potential downstream targets and pathways were screened by transcriptomic and metabolomic sequencing. The regulatory mechanisms were further validated via qRT-PCR, Western blotting, ChIP, immunohistochemistry, luciferase reporter assays, and CUT&Tag. Moreover, the in vivo function of MT2A was investigated using a nude mouse xenograft tumor model.RESULTS: MT2A was significantly upregulated in ccRCC and correlated with clinical stage and overall survival. MT2A enhanced ccRCC metastatic potential and epithelial-mesenchymal transition, with a moderate, late-onset effect on cell proliferation, via the TFAP2A/YAP axis. MT2A was shown to enhance lactic acid accumulation and H3K18 lactylation. Further studies confirmed that MT2A contributed to TFAP2A transcription via HBO1-mediated H3K18 lactylation. Consistently, MT2A knockdown in vivo enhanced the tumor-suppressing effect of sorafenib.CONCLUSIONS: MT2A could promote the malignant progression of ccRCC through the TFAP2A/YAP axis, which is controlled by HBO1-mediated H3K18 lactylation. Targeting MT2A effectively inhibits tumor growth and enhances the efficacy of sorafenib, providing a novel potential therapeutic target for ccRCC.PMID:42435834 | DOI:10.1016/j.cbi.2026.112254

J Nutr. 2026 Jul 11:101724. doi: 10.1016/j.tjnut.2026.101724. Online ahead of print.ABSTRACTBACKGROUND: Donor human milk (DHM) is widely used for preterm infants when mother's own milk (MOM) is unavailable, yet its metabolome is poorly described.OBJECTIVE: To profile the metabolome of pooled, pasteurized DHM obtained from a single milk bank over a one-year period and compare it to human milk (HM) from reference cohorts.METHODS: Pooled DHM (n=47) was collected weekly from the Mother's Milk Bank of Florida over 47 consecutive weeks from July 2022 to June 2023. Using 1H-NMR spectroscopy, we quantified 59 polar metabolites in DHM and compared the metabolomic profile with previously published data on HM from mothers of preterm (n=29) and term (n=97) infants collected across the first 3 months postpartum and analyzed using identical methods. Variability and group differences were assessed with regression, linear mixed-effects models, PCA, and PERMANOVA; p-values were FDR-adjusted.RESULTS: DHM showed limited variability, as the coefficient of variation for nearly half of the metabolites was 20-30%. Compared with both preterm and term colostrum and transitional milk, DHM had lower acetylcarnitine, myo-inositol, 3'-sialyllactose, and 6'-sialyllactose (FDR-adjusted p-value < 0.1). PCA separated HM by lactation stage and delivery type, with DHM clustering closest to term HM at 3 months postpartum and farthest from preterm colostrum. PERMANOVA and dispersion testing indicated that DHM differed significantly from all HM groups (FDR-adjusted p-value < 0.1), reflecting compositional differences related to lactation stage and reduced heterogeneity from pooling.CONCLUSIONS: While DHM is the preferred alternative to MOM for preterm infants, it contains lower concentrations of several potentially important metabolites than preterm HM, which may be further diluted by fortification. Future studies should evaluate how these differences and fortification may affect infant growth, development, and long-term outcomes.PMID:42435809 | DOI:10.1016/j.tjnut.2026.101724

Food Chem. 2026 Jul 6;525(Pt 1):150323. doi: 10.1016/j.foodchem.2026.150323. Online ahead of print.ABSTRACTSeaweed polyphenols possess hypoglycemic biological functions, but the specific mechanisms remain unclear, which limits their further application. In this study, the area under the blood glucose curve (AUC) of the fourth-period Porphyra haitanensis polyphenols extract (FPPE)-fed mice was significantly reduced by 17.12 ± 0.87%. Moreover, FPPE exhibited α-glucosidase inhibitory activity at 64.39 ± 4.05%. In vitro fermentation experiments showed that FPPE modulated the composition of the gut microbiota, thereby enhancing the production of short-chain fatty acids (SCFAs). Analysis of intestinal flora and metabolites in mice revealed that FPPE selectively enriched hypoglycemic-functional bacteria, such as Bacteroides and Alloprevotella. These bacteria feedback-regulated the production of hypoglycemic polyphenolic substances such as isovitexin and isoquercetin, which showed a strong positive correlation. Further mechanistic studies revealed that FPPE's digest could suppress cellular glucose transport by inhibiting the expression of Sodium Glucose Cotransporter 1 (SGLT1) and glucose transporter 2 (GLUT2), thereby reducing blood glucose levels.PMID:42435668 | DOI:10.1016/j.foodchem.2026.150323

Poult Sci. 2026 Jun 29;105(10):107356. doi: 10.1016/j.psj.2026.107356. Online ahead of print.ABSTRACTThe present study aimed to evaluate the influence of egg geometry, weight loss, and specific density (SD) on hatchability in older white layer breeder hens. In addition, based on the positive correlation observed between fertility and egg SD, we hypothesized that the mineral and metabolomic composition of the yolk may be associated with specific density. A total of 8,874 eggs from five Lohmann LSL Lite breeder flocks (52-64 weeks) were analyzed. We found that digital image analysis is an adequate method for evaluating the egg shape index, whereas Archimedes' principle is suitable for determining SD. Although we observed an influence of shape index and weight loss on hatchability, the clearest findings were related to SD. Eggs within the upper 50% SD range (1075-1110g/L) exhibited higher hatchability than those with lower SD, primarily due to reduced infertility and lower early embryonic mortality. Low-SD eggs showed increased contamination rates. Yolk mineral concentrations did not differ significantly between SD groups, except for the Na:K ratio. However, the N;Ka ratio showed minimal predictive value, indicating that mineral composition alone has limited explanatory power for eggshell density. Untargeted metabolomics identified 310 metabolites in yolk, but only melatonin glucuronide and dihydroxytetradecanoic acid met the significance criteria (p<0.05; fold change > 2). This study provides a precise way to evaluate egg geometry and SD. Furthermore, using a large dataset, it demonstrates that eggs with low SD have reduced hatchability due to infertility and early mortality, likely linked to contamination. Although the Na:K ratio, melatonin glucuronide, and dihydroxytetradecanoic acid showed limited predictive performance when evaluated individually, their identification highlights potential biochemical differences associated with SD and supports their further investigation as candidate markers.PMID:42435608 | DOI:10.1016/j.psj.2026.107356