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Cholesterol Metabolism by Uncultured Human Gut Bacteria Influences Host Cholesterol Level.


ABSTRACT: The human microbiome encodes extensive metabolic capabilities, but our understanding of the mechanisms linking gut microbes to human metabolism remains limited. Here, we focus on the conversion of cholesterol to the poorly absorbed sterol coprostanol by the gut microbiota to develop a framework for the identification of functional enzymes and microbes. By integrating paired metagenomics and metabolomics data from existing cohorts with biochemical knowledge and experimentation, we predict and validate a group of microbial cholesterol dehydrogenases that contribute to coprostanol formation. These enzymes are encoded by ismA genes in a clade of uncultured microorganisms, which are prevalent in geographically diverse human cohorts. Individuals harboring coprostanol-forming microbes have significantly lower fecal cholesterol levels and lower serum total cholesterol with effects comparable to those attributed to variations in lipid homeostasis genes. Thus, cholesterol metabolism by these microbes may play important roles in reducing intestinal and serum cholesterol concentrations, directly impacting human health.

SUBMITTER: Kenny DJ 

PROVIDER: S-EPMC7435688 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Cholesterol Metabolism by Uncultured Human Gut Bacteria Influences Host Cholesterol Level.

Kenny Douglas J DJ   Plichta Damian R DR   Shungin Dmitry D   Koppel Nitzan N   Hall A Brantley AB   Fu Beverly B   Vasan Ramachandran S RS   Shaw Stanley Y SY   Vlamakis Hera H   Balskus Emily P EP   Xavier Ramnik J RJ  

Cell host & microbe 20200615 2


The human microbiome encodes extensive metabolic capabilities, but our understanding of the mechanisms linking gut microbes to human metabolism remains limited. Here, we focus on the conversion of cholesterol to the poorly absorbed sterol coprostanol by the gut microbiota to develop a framework for the identification of functional enzymes and microbes. By integrating paired metagenomics and metabolomics data from existing cohorts with biochemical knowledge and experimentation, we predict and val  ...[more]

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