Integrative Analysis of the Mouse Cecal Microbiome Across Diet, Age, and Metabolic State in the Diverse BXD Population
Abstract
Background
The gut microbiota both adapts to, and shapes, the host’s metabolic state through metabolites and gene regulatory networks, influencing multiple organs via axes such as the gut–liver connection. While diet, age, and host genetics each modulate gut microbial composition, their combined effects and mechanistic links to host molecular phenotypes remain poorly understood. Integrated multi-omics approaches in genetically diverse populations offer an opportunity to dissect these interactions and identify predictive microbial and host features.
Results
We profiled the cecal metagenome, metatranscriptome, and host transcriptome from 232 mice spanning two diets (chow, high-fat), four adult ages (6–24 months), and 43 BXD strains. Diet exerted the strongest influence on microbiota composition, followed by genetic background and age, with high-fat feeding reducing diversity and altering >300 species. Machine learning models based on microbial profiles accurately predicted body weight (AUC = 0.92) and chronological age (AUC = 0.84), with accuracy rising to 0.95 when integrating top microbial features with liver proteomics. Network analyses revealed specific host–microbe links, including a negative association between cecal Ido1 expression and short-chain fatty acid (SCFA)-producing Lachnospiraceae, suggesting dietary fat may modulate host tryptophan metabolism through microbiota shifts.
Conclusions
This multi-omics study maps how diet, age, and genotype jointly shape the cecal microbiome and host molecular responses, identifying robust microbial biomarkers of aging and metabolism. Using inbred mice sampled across time, we defined signature taxonomic sets with strong predictive value for future metabolic outcomes, driven by microbial–host metabolic networks linking the cecum and liver. By advancing understanding of the gut–liver axis, our findings offer mechanistic hypotheses and compact biomarker panels for earlier, personalized diagnosis of metabolic disease, tailored to an individual’s genetic background and environmental context.
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