Effect of diet and intestinal AhR expression on fecal microbiome and metabolomic profiles

饮食和肠道 AhR 表达对粪便微生物组和代谢组学特征的影响

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作者:Fang Yang, Jennifer A A DeLuca, Rani Menon, Erika Garcia-Vilarato, Evelyn Callaway, Kerstin K Landrock, Kyongbum Lee, Stephen H Safe, Robert S Chapkin, Clinton D Allred, Arul Jayaraman

Background

Diet, loss of aryl hydrocarbon receptor (AhR) expression and their modification of the gut microbiota community composition and its metabolites affect the development of colorectal cancer (CRC). However, the concordance between fecal microbiota composition and the fecal metabolome is poorly understood. Mice with specific AhR deletion (AhRKO) in intestinal epithelial cell and their wild-type littermates were fed a low-fat diet or a high-fat diet. Shifts in the fecal microbiome and metabolome associated with diet and loss of AhR expression were assessed. Microbiome and metabolome data were integrated to identify specific microbial taxa that contributed to the observed metabolite shifts.

Conclusions

Akkermansia are likely to contribute to the synthesis and/or degradation of tryptophan metabolites. Our study highlights the use of multi-omic analysis to investigate the relationship between the microbiome and metabolome and identifies possible taxa that can be targeted to manipulate the microbiome for CRC treatment.

Results

Our analysis shows that diet has a more pronounced effect on mouse fecal microbiota composition than the impact of the loss of AhR. In contrast, metabolomic analysis showed that the loss of AhR in intestinal epithelial cells had a more pronounced effect on metabolite profile compared to diet. Integration analysis of microbiome and metabolome identified unclassified Clostridiales, unclassified Desulfovibrionaceae, and Akkermansia as key contributors to the synthesis and/or utilization of tryptophan metabolites. Conclusions: Akkermansia are likely to contribute to the synthesis and/or degradation of tryptophan metabolites. Our study highlights the use of multi-omic analysis to investigate the relationship between the microbiome and metabolome and identifies possible taxa that can be targeted to manipulate the microbiome for CRC treatment.

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