The 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.
Ziyu Liu, Haiqin Wu, S. Howe et al.· npj Biofilms and Microbiomes· 0 citations
Untargeted metabolomics often results in a significant portion of unannotated metabolites, or “metabolic dark matter,” which hinders biological interpretation. A two-step analytical approach was developed to systematically prioritize and interpret unannotated metabolites using plasma LC–MS/MS data from pregnant women with obesity as a biologically relevant test dataset. The first step involved clustering 1,021 known metabolites into ten structurally coherent groups based on the Tanimoto similarity, thus defining the biologically relevant chemical space of the dataset. These metabolites were further characterized by Absorption, Distribution, Metabolism, and Excretion (ADME) profiling, protein target prediction, molecular docking and Kyoto Encyclopedia of Genes and Genomes pathway mapping analysis, to establish biological plausibility and functional perspective. Candidate structures for 1,836 unannotated features were retrieved from PubChem using molecular formula and molecular weight matching within a ±0.5 Da tolerance. This search yielded 569,115 candidate structures, of which 368,197 unique structures were retained after curation. Tanimoto coefficient filtering reduced the candidate pool to 19,868 structurally plausible candidates, and retention time-based prioritization further refined this set to 418 high confidence candidate annotations, including 83 database-supported candidates identified through HMDB and LIPID MAPS structure database cross-referencing. RT-based prioritization effectively distinguished positional isomers sharing the same molecular formula by incorporating agreement between predicted and experimentally observed retention times. This improved discrimination among structurally similar candidates, expanded metabolite annotation confidence, and provided a scalable framework for prioritizing dark matter metabolites in untargeted metabolomics. Clustered-based workflow integrating chemical similarity and retention time to prioritize and annotate unknown metabolites
D. Bhandari, H. Paz, Keith Henderson et al.· Metabolomics· 0 citations
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