Inorganic arsenic (iAs) exposure is associated with disrupted glucose homeostasis and increased risk of type 2 diabetes in human and animal studies, but reported effects vary in direction and magnitude across investigations. The gut microbiome is both a target of arsenic toxicity and a potential mediator of systemic metabolic responses. The authors hypothesized that baseline differences in microbial community composition between animal housing facilities could modulate how identical iAs exposures influence metabolic phenotypes.
The investigation performed parallel exposures of male C57BL/6J mice to 50 ppm inorganic arsenic in drinking water at two separate animal housing facilities. Outcomes compared across facilities included metabolic phenotypes, hepatic arsenic concentrations, targeted and untargeted metabolomics (including bile acids and polar metabolites), and shotgun metagenomic sequencing of the gut microbiome. Statistical ordinations, random forest classification, and functional metagenomic analyses were applied to assess variance attributable to housing facility versus iAs treatment.
Hepatic arsenic levels confirmed comparable exposure between the two sites, indicating that the delivered dose and internal arsenic burden did not differ appreciably across facilities. Despite equivalent exposure, metabolic responses diverged: iAs exposure impaired glucose clearance at one facility while showing a trend toward improved glucose clearance at the other. Thus, facility-specific baseline factors altered the direction of the metabolic effect of identical arsenic treatment.
Across multiple data layers—microbiome composition, bile acid profiles, polar metabolites, and untargeted metabolomics—the housing facility explained more variance than the iAs treatment group. Facility membership accounted for 19–26% of variance in ordinations of these datasets, while iAs treatment did not achieve significance in those ordinations. Baseline microbial communities and metabolic phenotypes at each institution differed, and those baseline differences propagated into the observed treatment effects.
A supervised classification approach (random forest) was used to determine whether microbial signatures could predict facility or treatment. A classifier built from 22 microbial species identified the housing facility with 96% cross-validated accuracy. In contrast, classification by iAs treatment did not exceed 67% accuracy. These results emphasize that facility-associated microbial differences were a stronger and more consistent signal than the effect of arsenic treatment under the conditions studied.
Functional metagenomic analysis quantified gene-level differences attributable to facility and treatment. Approximately 11,733 genes—representing 63% of detected genes—were differentially abundant between the two facilities. By comparison, only 139 genes were differentially abundant with iAs treatment. This large disparity suggests that facility-driven functional potential of the gut microbiome substantially outweighs the detectable functional shifts induced by the arsenic exposure used here.
The study demonstrates that identical host genetics and arsenic exposure can yield divergent metabolic outcomes when the baseline gut microbiome and metabolome differ by housing facility. For toxicology and metabolic research, these findings highlight the importance of characterizing baseline microbial and metabolic states to interpret exposure effects accurately and to improve reproducibility across sites. Clinically and translationally, the results imply that pre-exposure gut microbiome and metabolome profiles could help identify individuals more susceptible to arsenic-associated metabolic dysfunction. Moreover, modulating the gut microbiome might represent a potential avenue to reduce metabolic risk from arsenic exposure, although specific interventions were not tested in this study.
The authors report that housing facility explained more variance across data layers than iAs treatment and that baseline microbial and metabolic differences altered the direction of glucose-related outcomes. Exact sample sizes, detailed metabolite identities, and additional statistical metrics were not reported in the provided source text. Overall, the data indicate that environment-driven heterogeneity in the gut microbiome and metabolome can confound or modify the metabolic effects of arsenic exposure, underscoring the need to account for baseline microbial context in studies of environmental toxicants and metabolic health.