Quantifying host-microbiota interactions | Science

The human microbiota is a complex microbial community living on and in our bodies. Its impact on a host’s health is immense, affecting digestion (1), the immune system (2), behavior (3), metabolic diseases (4), and responses to drugs (5–7). Rapid advances in experimental and computational methods have moved the human microbiome field from identifying associations between microbiota composition and host health to unraveling the underlying molecular mechanisms (8–10). However, exactly how much the microbiota contributes to host health is a very difficult question to answer.

By focusing on mechanistic and quantitative questions about the microbiome’s contributions to host metabolism, I leverage my background in applied mathematics and systems biology to develop computational models describing host-microbiota interactions. Good models require good data from controlled experiments—a challenging proposition in complex host-microbiota systems. As a postdoc, I joined Andy Goodman’s lab at Yale University and found myself in a perfect position to collect such data.

By combining bacterial genetics with gnotobiotic mouse models, I learned how to modify the microbiome of germ-free, sterile mice. In the Goodman lab, we used these mice to study the contribution of microbiota to host metabolism of a number of pharmaceutical drugs. We found that this was also a good system to quantify host-microbiome interactions in vivo, because the compounds we used can be introduced into the system in a controlled way.

We first focused on brivudine, an antiviral compound that can be converted into a potentially toxic metabolite, bromovinyluracil (BVU), by either a host or its microbiome (11). To identify bacteria capable of converting brivudine to BVU, we incubated individual bacterial species with the drug in vitro. One of the most potent brivudine metabolizers was Bacteroides thetaiotaomicron, a common gut bacterium with a genetic deletion library readily available. By incubating this library with the drug, we identified one bacterial mutant that had lost the capacity to convert brivudine to BVU. We then colonized germ-free mice with either the wild-type or mutant B. thetaiotaomicron, which provided us with a controllable host-microbiome system and two mouse groups that were identical, save for a single bacterial gene.

When we administered brivudine to these two groups, the observed outcome was somewhat puzzling. Although drug levels in the intestine were much higher in mice colonized with the mutant bacterium, serum levels were comparable between the two mouse groups. The metabolite levels showed the opposite pattern: no difference (and very low levels) in the intestine but much higher metabolite levels in the sera of mice colonized with the wild-type bacterium (see the figure). These data could potentially be explained by bacterial conversion of the drug in the intestine and the rapid metabolite absorption into the serum.

To test this explanation, we started with a simple kinetic model with two equations describing host drug metabolism in the liver and bacterial drug metabolism in the intestine. Once solved, this equation system showed that the difference between the amounts of metabolite absorbed into the sera of each of the two mouse groups was determined by the amount of BVU produced by microbes in the gut. This controlled experimental setup allowed us to quantify that the bacterial contribution to the toxic drug metabolite in vivo was about 70% (12) (see the figure).

We expanded the model to describe drug metabolism processes in eight different tissues and in enterohepatic circulation (when the drug metabolized in the liver is secreted back into the small intestine via bile). We then demonstrated that our approach can be generalized to estimate the bacterial contribution to drug metabolism even if the metabolizing species remain unknown by using data from germ-free mice and mice harboring a complex microbial community. We also showed that microbial contribution to the drug metabolite far exceeds the host for sorivudine, an antiviral drug with different host and microbiome metabolism rates, and for clonazepam, an anxiolytic and anticonvulsant drug converted to multiple metabolites (12).