Multi-omics analysis of host-microbiome interactions in a mouse model of congenital hepatic fibrosis.

BACKGROUND

Congenital hepatic fibrosis (CHF) caused by mutations in the polycystic kidney and hepatic disease 1 (PKHD1) gene is a rare genetic disorder with poorly understood pathogenesis. We hypothesized that integrating gut microbiome and metabolomic analyses could uncover distinct host-microbiome interactions in CHF mice compared to wild-type controls.

METHODS

Pkhd1 mice were generated using CRISPR/Cas9 technology. Fecal samples were collected from 11 Pkhd1 mice and 10 littermate wild-type controls. We conducted a combined study using 16 S rDNA sequencing for microbiome analysis and untargeted metabolomics. The gut microbiome and metabolome data were integrated using Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO), which helped identify key microbial and metabolic features associated with CHF.

RESULTS

CHF mouse model was successfully established. Our analysis revealed that the genera Mucispirillum, Eisenbergiella, and Oscillibacter were core microbiota in CHF, exhibiting significantly higher abundance in Pkhd1 mice and strong positive correlations among them. Network analysis demonstrated robust associations between the gut microbiome and metabolome. Multi-omics dimension reduction analysis demonstrated that both the microbiome and metabolome could effectively distinguish CHF mice from controls, with area under the curve of 0.883 and 0.982, respectively. A significant positive correlation was observed between the gut microbiome and metabolome, highlighting the intricate relationship between these two components.

CONCLUSION

This study identifies distinct metabolic and microbiome profiles in Pkhd1 mice. Multi-omics analysis effectively differentiates CHF mice from controls and identified potential biomarkers. These findings indicate that gut microbiota and metabolites are integral to the pathogenesis of CHF, offering novel insights into the disease mechanism.