Gut metabolites are more predictive of disease- and cohoused- states than gut bacterial features in a mouse model of polycystic ovary syndrome
Polycystic ovary syndrome (PCOS) impacts [~]10% of reproductive-aged women worldwide. In addition to infertility, women with PCOS suffer from metabolic dysregulation which increases their risk of developing type 2 diabetes, cardiovascular disease and non-alcoholic fatty liver disease. Studies have shown differences in the gut microbiome of women with PCOS compared to controls, a pattern replicated in mouse models. Recently, using a letrozole-induced mouse model of PCOS, we demonstrated that cohousing was protective against development of metabolic and reproductive phenotypes and showed via 16S amplicon sequencing that this protection correlated with time-dependent shifts in gut bacteria. Here, we applied untargeted metabolomics and shotgun metagenomics approaches to further analyze the longitudinal samples from the cohousing experiment. Analysis of beta diversity found that untargeted metabolites had the strongest correlation to both disease and cohoused states and that shifts in metabolite diversity were detected prior to shifts in bacterial diversity. In addition, log2-fold analyses found numerous metabolite features, particularly bile acids (BA), to be highly differentiated between placebo (P) and letrozole (LET), as well as cohoused LET versus LET. Our results indicate that changes in gut metabolites, particularly BAs, are associated with a PCOS-like phenotype in the LET mouse model as well as the protective effect of cohousing. Our results also suggest that transfer of metabolites via coprophagy occurs rapidly and may precipitate changes in bacterial diversity. This study joins a growing body of research highlighting changes in primary and secondary bile acids that may provide a link between host metabolism and gut microbes relevant to the pathology of PCOS. IMPORTANCEUsing a combination of untargeted metabolomics and metagenomics, we performed a comparative longitudinal analysis of the feces collected in a cohousing study with a PCOS mouse model. Our results showed that gut metabolite composition experienced earlier and more pronounced differentiation in both the disease model and cohoused mice compared with the microbial composition. Notably, statistical and machine learning approaches identified shifts in the relative abundance of primary and secondary BA, which have been implicated as modifiers of gut microbial growth and diversity. Network correlation analysis showed strong associations between particular BA and bacterial species, particularly members of Lactobacillus, and that these correlations were time and treatment dependent. Our results provide novel insights into host/microbe relationships related to hyperandrogenism in females and indicate that focused research into small molecule control of gut microbial diversity and host physiology may provide new therapeutic options for the treatment of PCOS.