Authors
Anil Kumar Baidya, Palok Aich
Published in
Omics : a journal of integrative biology. Pages 15578100261479266. Aug 14, 2026. Epub Aug 14, 2026.
Abstract
The gut microbiome shapes systemic physiology through metabolites that enter circulation, yet most computational approaches focus on predicting metabolite profiles from microbial features rather than inferring microbial composition from host metabolomes. Here, we investigate whether host-derived metabolomic profiles can be leveraged to predict gut microbial community structure and to determine how disease-associated dysbiosis reshapes metabolite-microbe interactions and gut-to-systemic metabolic communication. We developed an integrative multi-omics framework combining serum and cecal metabolomics with 16S rRNA-based microbiome profiling. Supervised learning models demonstrated that cecal metabolites carry predictive signals for microbial abundances across conditions. Regularized canonical correlation analysis (rCCA) revealed cross-compartment metabolite-microbe networks. These analyses showed both conserved and condition-specific interaction patterns, indicating substantial network reorganization under disease-associated dysbiosis. Pathway-level integration further identified metabolic pathways linking the gut microbiome, the cecal environment, and the systemic circulation, representing coordinated gut-to-systemic communication axes. Together, our results establish a multi-omics strategy for predictive inference of gut microbial composition from host metabolomes and provide a framework for identifying pathway-level mechanisms underlying host-microbe metabolic crosstalk.
PMID:
42598885
Bibliographic data and abstract were imported from PubMed on 14 Aug 2026.
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