Background: Exposure to arsenic and cadmium is common. Epidemiological and animal studies have suggested that exposure to these two heavy metals can cause metabolic health problems, including type 2 diabetes (T2DM). It has been hypothesized that T2DM could be mediated through the gut microbiome and the metabolites it produces. Although many studies have investigated the association between the gut microbiome and T2DM, few have focused on the connection to arsenic and cadmium. Results: We applied 16S rRNA gene amplicon sequencing and untargeted LC-MS/MS metabolomics to examine the changes in the gut microbiome and metabolite profiles of exposed mice to relevant levels of cadmium and arsenic in the drinking water over two weeks. Cadmium chloride (Cd) exposure significantly changed the mice gut microbiome and resulted in a significantly lower microbial diversity whereas sodium arsenite (As) caused a non-significant decrease in microbial diversity. For Cd and As treatment respectively, we identified 5 and 2 phyla with significant changes and 42 and 24 genera. Bacterial genera that were observed to decline upon both treatments, included several butyrate-producers. Both As and Cd treatment perturbed the metabolome significantly, with 50 ppm Cd compound exposure having the greatest effect when compared to 50 ppm As compound exposure. Two unidentified features were differentially abundant in the As group, while 33 features changed in the Cd group. Differential abundance analysis of all bile acid associated molecular components showed differences under both treatments. Finally, integrative network analysis via bipartite correlation networks suggested that several genera, including the metabolically important Blautia, Eisenbergiella, Clostridium_XlVa, etc. declined in numbers of metabolite interactions. Conclusions: These results demonstrated that As and Cd exposure caused significant changes to the gut microbiome and metabolome by affecting bile acids, amino acids and taxa associated with metabolic health.
- 16S rRNA microbiome
- LC-MS/MS metabolomics