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297 records · Page 17Linked to original sources
PubMed2026

A Computational Workflow for Prioritizing Microbial Metabolite-Associated Host Genes in Constipation-Predominant Irritable Bowel Syndrome.

No standardized computational pipeline exists for systematically prioritizing microbial metabolite-associated host genes and protein-ligand complexes from publicly available chemical, genomic, and structural databases. This article describes an eight-stage workflow that accepts a user-defined set of gut microbiota-derived metabolites and produces a ranked shortlist of candidate metabolite-associated host genes, enriched biological pathways, and structurally prioritized protein-ligand complexes for experimental follow-up. The pipeline integrates (i) chemoinformatic metabolite profiling; (ii) multi-database candidate target prediction using protein-chemical interaction and ligand-based target-prediction tool and a molecular docking program; (iii) differential gene expression analysis of publicly available transcriptomic data; (iv) target-differentially expressed gene overlap; (v) protein-protein interaction network construction and pathway enrichment; (vi) molecular docking with a molecular docking program; (vii) 200 ns molecular dynamics simulation using a molecular dynamics engine with a protein force field used for molecular dynamics simulations; and (viii) MM-PBSA binding free-energy estimation. As a worked example, nine gut microbiota-derived or microbiota-modified metabolites representing short-chain fatty acids, bile acids, tryptophan-derived metabolites, and urolithin A were processed using the public IBS-C rectal mucosal transcriptomic dataset GSE36701. The workflow ranked 17 unique predicted metabolite-associated genes that were differentially expressed in this dataset. Docking, molecular dynamics simulation, and MM-PBSA analyses structurally prioritized five metabolite-protein complexes: lithocholic acid-VDR, lithocholic acid-NR1H4/FXR, ursodeoxycholic acid-NR1H4/FXR, tryptamine-HTR2A (simulated in an explicit 1-Palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid bilayer), and urolithin A-CASP3. The protocol is designed to be adaptable to other metabolite sets, disease transcriptomic datasets, and target classes; all outputs are hypothesis-generating computational predictions that require independent transcriptomic replication, protein-level validation, and functional ligand-response assays before causal or therapeutic conclusions can be drawn.

Irritable Bowel Syndrome↗
PubMed2024

Dissecting the anti-obesity components of ginseng: How ginseng polysaccharides and ginsenosides target gut microbiota to suppress high-fat diet-induced obesity.

INTRODUCTION: Ginseng demonstrates therapeutic potential in treating obesity, with both experimental and clinical studies suggesting its anti-obesity effects are mediated by gut microbiota. Nonetheless, the specific chemical components responsible for this effect remain largely unidentified. OBJECTIVES: This study aims to investigate the anti-obesity effects and mechanisms of ginseng polysaccharides (GP) and ginsenosides (GS), the primary chemical components of ginseng, with a focus on their impact on gut microbiota. METHODS: The impact of GP and GS on high-fat diet (HFD)-induced obesity was assessed using a mouse model. Molecular mechanisms were explored through a combination of chemical analysis, metagenomics, RT-qPCR, ELISA, and biochemical assays. RESULTS: GP or GS administration effectively prevented adiposity in HFD-fed mice, and both effects were mediated by gut microbiota. Chemical analysis revealed diverse glycosyl groups in GP and GS. Metagenomics data suggested that GP-enriched species, e.g., Bacteroides stercorirosoris and Clostridiales bacterium encoded carbohydrate-active enzymes GH35, GH43 and PL9_1, while GS-enriched Sulfurospirillum halorespirans encoded GH16_5. These enzymes facilitated the utilization of glycosyl groups in GP and GS, selectively stimulating bacterial growth and reshaping the gut microbiota. Furthermore, bacterial species enriched by GP or GS encoded specific functional genes involved in short-chain fatty acid (SCFA) synthesis (K00625 and K00925 for GP; K18118, K00100, and K18122 for GS) and intestinal gluconeogenesis (IGN) (K01678, K00024, and K01596 for GP; K18118 and K00278 for GS). Consequently, the SCFA-GLP-1/PYY signaling and IGN were activated by both GP and GS to ameliorate obesity phenotypes. CONCLUSION: GP and GS, containing diverse glycosyl groups, selectively stimulate specific gut bacteria, triggering mechanisms involved in SCFA-GLP-1/PYY signaling and IGN activation to reduce adiposity in HFD-fed mice. The study enhances understanding of the chemical components crucial for the gut microbiota-mediated anti-obesity effect of ginseng. The mechanistic understanding provides valuable insights for developing ginseng-based drugs or health products to combat obesity.

Gastrointestinal Microbiome↗
PubMed2026

Effects of Fecal Microbiota Transplantation on Intestinal Microbial Characteristics and Clinical Phenotypes in Patients with Parkinson's Disease.

Alterations in the gut microbiota have been associated with Parkinson's disease (PD), but longitudinal microbial changes after fecal microbiota transplantation (FMT) and their clinical associations remain poorly understood. This single-center retrospective observational study included 6 patients with PD, stratified into high- and low-severity subgroups based on disease duration (>6 years vs ≤6 years). Thirty-six fecal samples were collected before FMT and monthly for five months afterward. Microbial diversity, community structure, taxonomic composition, and predicted functional profiles were assessed using 16S ribosomal RNA gene sequencing. Analyses included alpha and beta diversity, taxonomic abundance, linear discriminant analysis effect size, Tax4Fun2-based functional prediction, and Spearman rank correlations between microbial features and clinical indicators. Descriptive analyses indicated differences in microbial richness, diversity, community structure, and predicted functions between severity subgroups and across post-FMT time points. At baseline, the low-severity subgroup had greater microbial richness and diversity than the high-severity subgroup, with relatively higher abundances of taxa including Bifidobacterium and Lactobacillus. One month after FMT, richness and diversity increased from baseline in the high-severity subgroup, accompanied by changes in taxonomic composition. Both subgroups showed time-associated variation in microbial diversity and predicted Kyoto Encyclopedia of Genes and Genomes pathway enrichment after FMT. Predicted functions included carbohydrate and amino acid metabolism, secondary metabolite biosynthesis, membrane transport, and signal transduction. Several operational taxonomic units correlated with indicators of motor impairment, constipation, sleep quality, functional status, and neuropsychiatric symptoms. FMT was therefore associated with longitudinal changes in gut microbial diversity, composition, and predicted functions, and specific microbial features were associated with motor and non-motor indicators. Given the small retrospective cohort, these findings are preliminary and warrant confirmation in larger controlled studies. Future studies should determine whether these microbial alterations are reproducible, persist beyond five months, reflect donor engraftment, and correspond to measurable clinical improvement after transplantation in PD.

Humans↗
PubMed2026

Trimethylamine-producing microbe Bacillus megaterium KCTC 3007 promotes antitumor immunity in endometrial cancer via type I interferon response pathways.

BACKGROUND: Endometrial cancer (ECa) is one of the most common gynecologic malignancies, with limited therapeutic responses in metastatic or recurrent cases. The bacterial microbiota has emerged as a key modulator of carcinogenesis and antitumor immunity. However, the role of endometrial microbiota in ECa pathogenesis and prognosis remains poorly understood. METHODS: We performed comprehensive multi-omics analysis integrating metatranscriptomics, transcriptomics, and targeted metabolomics from 60 ECa and 18 benign patients. RNA sequencing enabled simultaneous profiling of active tissue-resident microbiota and host gene expression. Serum metabolomics was conducted on all patients. Identified microbial-metabolite associations were validated through in vitro co-culture experiments using peripheral blood mononuclear cells (PBMCs), cancer cell lines, RNA sequencing, and live cell imaging. RESULTS: ECa patients exhibited significantly altered microbial diversity and composition compared to benign controls. Through integrated multi-omics analysis, we identified Bacillus megaterium (BM) KCTC 3007 as a beneficial microbe associated with prolonged recurrence-free survival. In an exploratory analysis of ECa subtypes, Cupriavidus taiwanensis and Marinomonas primoryensis showed potential links to poor prognosis, although these observations warrant caution due to the limited size of certain subgroups. Tissue BM abundance positively correlated with serum trimethylamine N-oxide (TMAO) levels, particularly in postmenopausal women. In vitro experiments demonstrated that BM KCTC 3007 enhanced antitumor immunity by promoting interleukin and type I interferon expression, expanding CD8 + T cell populations, and increasing immune cell-tumor cell interactions. RNA sequencing revealed activation of interferon alpha response and immune cell proliferation pathways, with IFNAR1 identified as a key upstream regulator. TMAO treatment recapitulated these immune-activating effects, enhancing CD8 + T cell responses and preferentially inducing pyroptotic cancer cell death. CONCLUSIONS: We provide the first evidence that tissue-resident BM KCTC 3007 promotes antitumor immunity in ECa through TMAO production and subsequent type I interferon-mediated immune activation. This integrated multi-omics approach establishes a complete microbe-metabolite-host mechanistic pathway and highlights the therapeutic potential of TMAO-producing probiotic strains for ECa treatment. Video Abstract.

Female↗
PubMed2025

SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing.

MOTIVATION: High-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. RESULTS: Addressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. AVAILABILITY AND IMPLEMENTATION: All source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna.

Deep Learning↗
PubMed2025

Urobiota analysis and genome-wide association study in pediatric recurrent urinary tract infections and vesicoureteral reflux.

Urinary tract infections (UTIs) are the most common severe bacterial infections in young children, often associated with vesicoureteral reflux (VUR). To explore host genetic-microbiota interactions and their clinical implications, we analyzed the urinary microbiota (urobiota) and conducted genome-wide association studies for bacterial abundance traits in pediatric patients with UTI and VUR from the Randomized Intervention for Children with Vesicoureteral Reflux and Careful Urinary Tract Infection Evaluation cohorts. We identified 4 urobiota community types based on relative abundance, characterized by the genera Enterococcus, Prevotella, Pseudomonas, and Escherichia/Shigella, and their associations with VUR, age, and toilet training. Children with VUR exhibited decreased microbial diversity and increased abundance of genera that included opportunistic pathogens, suggesting a disrupted urobiota. We detected genome-wide significant genetic associations with urinary bacterial relative abundances, in or near candidate genes including CXCL12, ABCC1, and ROBO1, which are implicated in urinary tract development and response to infection. We showed that Cxcl12 was induced 12 hours after uropathogenic bacterial infection in mouse bladder. The association with CXCL12 suggests a genetic link between UTI, VUR, and cardiovascular phenotypes later in life. These findings provide the first characterization to our knowledge of host genetic influences on the pediatric urobiota in UTI and VUR, offering insights into the interplay between disease, host genetics, and the urobiota composition.

Urinary Tract Infections↗
PubMed2026

Benchmarking DNA extraction protocols across use cases for culture-independent Nanopore metagenomics.

Oxford Nanopore Technologies (ONT) sequencing offers several advantages for metagenomics, including long reads, rapid turnaround, low upfront cost, scalability and portability. However, for ONT metagenomics, DNA yield, quality and integrity are important considerations when selecting an extraction method. Many metagenomic extraction methods use harsh lysis conditions to extract a wide range of species and provide an accurate community composition, but these conditions can compromise DNA fragment length. Therefore, extraction methods for ONT metagenomics must balance DNA shearing and recovery with representative community lysis. We systematically evaluated DNA extraction methods for ONT metagenomic sequencing using a use case-oriented framework. Among nearly 50 extraction methods screened, 7 were selected for detailed comparison based on suitability for metagenomics, variation in methodology, availability, cost and processing time: Norgen BioTek Corp's Stool DNA Isolation (NG), Zymo Research's ZymoBIOMICS Quick-DNA HMW MagBead (ZMG), Qiagen's DNeasy Blood and Tissue (QBT), Macherey-Nagel's NucleoMag DNA Microbiome (MN), Zymo Research's ZymoBIOMICS DNA Mini Prep (ZMI), Qiagen's DNeasy PowerSoil/QIAamp PowerFecal Pro (PS) and Qiagen's QIAamp Fast DNA Stool Mini (QIA). Methods were tested using Zymo Research's ZymoBIOMICS Microbial Community Standard (MCS), a matrix-free mock community with known composition. DNA extracts were sequenced on an ONT PromethION using the Rapid Barcoding Kit, except QIA due to insufficient DNA yield. Metrics for the method, DNA extracts, sequencing and genomes were evaluated, revealing trade-offs between methods. The two magnetic bead methods, MN and ZMG, produced the highest mean read length N50 values (13.9 and 16.5 kb, respectively) but showed apparent community compositions skewed towards Gram-negative bacteria. In contrast, ZMI and PS maintained a community composition close to expected, with reduced mean read length N50 values (4.5 vs. 7.5 kb). Performance across various metrics is presented in the context of the following use cases: maximizing genome coverage and assembly completeness, preserving composition accuracy, targeting specific species and limiting required resources (equipment, time or budget). The metrics and use case considerations presented offer practical guidance for informed selection of DNA extraction methods for ONT metagenomics. For accurate community composition, ZMI or PS are recommended, while PS and ZMG perform best at maximizing genome coverage and assembly completeness. NG and QBT may be the most economical options, though performance trade-offs were observed. Finally, PS may be the preferred method for time-sensitive diagnostic or field applications.

Metagenomics↗
PubMed2026

Microbial Interactions with Protein Intake and Preterm Infant Body Composition: Secondary Analysis of a Randomized Trial.

BACKGROUND: Enteral protein supplementation improves preterm infant growth and may impact body composition and the gut microbiota. OBJECTIVES: This study aimed to identify the effects of additional enteral protein supplementation on the gut microbiota and microbial and clinical drivers of body composition. METHODS: Secondary analysis of a masked randomized trial of additional enteral protein vs. standard fortification in preterm infants born at 25 to 28 weeks of gestation (NCT03586102) was conducted. Stool samples at weeks 4 and 8 underwent 16S rRNA sequencing; functional potential was predicted by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2). Body composition was measured by air-displacement plethysmography at 36 wk postmenstrual age (PMA). Least absolute shrinkage and selection operator (LASSO) regression with multivariable linear regression identified body composition predictors. RESULTS: Among 46 infants, gestational age (P = 0.16) and sex (P = 0.55) did not differ between groups. The protein group had higher week 4 Shannon diversity than standard fortification (median 1.2 vs. 0.87, P = 0.049). Week 4 Shannon diversity was positively correlated with fat-free mass z-score at 36 wk PMA (r2 = 0.34, P = 0.02). Adjusting for covariates, the protein group had higher Peptoniphilus (&#x3b2; = 1.6, Padj = 0.10) and lower Vibrio centered log-ratio abundance (&#x3b2; = -0.98, Padj = 0.10); 62 predicted metabolic pathways were lower in the protein group (false discovery rate < 0.20). In combined LASSO models, Bacillus abundance at week 4 was the strongest predictor of fat-free mass z-score (&#x3b2; = -0.17, P < 0.001; R2 = 0.80) and fat mass z-score (&#x3b2; = -0.31, P < 0.001; R2 = 0.66). CONCLUSIONS: Additional protein supplementation is associated with fat-free mass z-score and alterations to the gut microbiota. Clinical variables and microbial variables are key predictors of body composition, suggesting that nutrition, clinical factors, and the gut microbiota jointly contribute to body composition in extremely preterm infants. This study was registered at clinicaltrials.gov as NCT03586102 https://clinicaltrials.gov/study/NCT03586102 (registered in March 2020).

Humans↗
PubMed2026

Nonantibiotic-driven evolution reveals rare but predictable routes to broad antibiotic resistance.

Many medications not prescribed to treat infectious diseases have antibacterial activity at physiologically relevant concentrations, raising the risk that chronic administration of such nonantibiotics may inadvertently select for resistance in the host microbiome. However, how frequently such exposures select for adaptations that impact broad drug resistance, including to antibiotics, remains unclear. Here, we systematically evolved Escherichia coli under exposure to 40 antibiotics and nonantibiotics and profiled the cross-resistance of the drug-adapted strains to 21 antibiotics representing all major classes. Our measurements revealed that most drug-adapted strains did not become multidrug resistant. However, five nonantibiotics and three antibiotics emerged as exceptions and were repeatedly selected for broad antibiotic resistance. Whole-genome sequencing of all 168 evolved strains revealed that changes in the regulation of efflux pumps repeatedly underlay broad drug resistance and converged into two key regulatory genes, acrR and lon. Our work suggests that although inadvertent antibiotic cross-resistance is rare, specific nonantibiotics can still potentially pose a risk for the emergence of multidrug resistance.IMPORTANCEMany medications not typically prescribed to treat infectious diseases have potent antimicrobial activity at physiological concentrations. This anti-bacterial activity raises concern that long-term administration of such nonantibiotics might unintentionally select for multidrug resistance, including resistance to antibiotics. Using Escherichia coli, we show that in most cases, these nonantibiotics do not broadly select for resistance to antibiotics in vitro. However, we identified five nonantibiotics that repeatedly selected for resistance to multiple antibiotics through a shared mechanism of action-upregulation of the multidrug efflux pump AcrAB-TolC. These findings highlight that while the overall risk is low, certain nonantibiotics may still contribute to the emergence of multidrug resistance. Identifying these high-risk drugs can help guide safer prescribing practices and inform strategies to limit the spread of antibiotic resistance.

Escherichia coli↗