Using a microbial community-scale metabolic modeling approach for precision nutrition

Short-chain fatty acids (SCFAs) are beneficial molecules created by the bacteria that live in our gut that are closely linked to improved host metabolism, lower systemic inflammation, better cardiovascular health, lower cancer risk, and more. However, SCFA profiles can vary greatly between individuals eating the exact same diet and we currently lack tools to predict this inter-individual variation.

Researchers at the Institute of Systems Biology (ISB) have developed a new way to simulate personalized, microbiome responses to diet. They use a microbial community-scale metabolic modeling (MCMM) approach to predict individual-specific SCFA production rates in response to various dietary, prebiotic and probiotic inputs.

In other words, ISB scientists can build a “digital twin” of gut microbiome metabolism that can simulate personalized responses to diet, using gut microbiome sequencing data and information on dietary intake to constrain each model to specific to individuals. They detailed their findings in a paper published in Nature Microbiology.

To a first approximation, the gut microbiome is a bioreactor that converts dietary fibers into these SCFAs. Understanding how gut ecology and dietary intake can be quantitatively mapped to SCFA outputs will be a major step forward in translating microbiome science into the clinic.”

Sean Gibbons, ISB associate professor and co-senior author

Unlike black-box machine learning approaches to prediction, MCMMs are transparent and mechanistic, with thousands of metabolites and enzymes across dozens of organisms providing a high level of knowledge about the specific microbes, nutrients, and pathways a metabolite that contributes to SCFA production. . Despite this transparency, the complexity of these models makes them difficult to validate experimentally.

One approach is to measure SCFA production rates for an entire ecosystem, and then compare these ecosystem-scale measures to their associated hypothetical predictions. However, it is difficult to measure SCFAs in the wild because the body consumes them quickly after they are created. To overcome this challenge, the authors measured SCFA production rates from in vitro (ie, test tube) populations of random mixtures of human gut bacterial isolates and from ex vivo (ie, outside the body) homogeneous stool from different people incubated in an anaerobic chamber with a variety of dietary fibers.

By isolating microbiota-driven SCFA production from host absorption, ISB scientists were able to show that MCMM predictions were significantly correlated with measured production rates across a range of fibers for butyrate and propionate, two of the most abundant and potent SCFAs physiologically.

even though in vivo (ie, in the body) measurements of butyrate and propionate production were not possible, the authors were able to use indirect associations between SCFA production rates and blood-based health markers to validate the physiological effects of inter-individual differences in production. First, they showed that MCMM predictions could distinguish individuals from a high-fiber feeding study who showed different immune responses: most individuals showed a decrease in systemic markers of inflammation, but a subset of individuals showed an increase in inflammation on high fiber. diet. Individuals in the high-inflammatory response group showed a significantly reduced ability to produce propionate, as predicted by MCMM. Subsequently, the authors showed that butyrate predictors were significantly associated with blood markers of cardio-metabolic and immune health in a population of over 2,000 individuals. Specifically, higher MCMM-predicted butyrate production was significantly associated with lower LDL cholesterol, lower triglycerides, improved insulin sensitivity, lower systemic inflammation, and lower blood pressure.

“Predictive accuracy of MCMMs in vitrocombined with the significant links between SCFA predictions and health markers in human cohorts, we have confidence in the utility of these models for precision nutrition,” said lead author Dr. Nick Quinn-Bohmann, a University graduate student. Washington at ISB recently defended his thesis.

After validating MCMM predictions in several ways, the authors then demonstrated the potential of this approach to design personalized prebiotic, probiotic and nutritional interventions that would overcome SCFA production profiles. They simulated butyrate production rates for two different diets – a typical Austrian diet (ie a standard European diet) and a high-fibre vegan diet – across a cohort of over 2,000 individuals from the Pacific West of the USA. ​​​​​​They found that a small subset of people showed little increase in butyrate production when switched to the high-fiber diet (known as “non-responders”) and another subset saw a small decrease in production butyrate on the high-fiber diet (called “regressors”). Afterwards, they simulated a simple joint intervention on the two background diets to try to increase butyrate production in the non-responders and the regressors: adding the prebiotic fiber inulin, adding the prebiotic fiber pectin, or adding a probiotic produces butyrate (Facalibacterium). The results showed that no single combined intervention was optimal across all individuals: some benefited most from adding prebiotic fiber, while others appeared to need the addition of a butyrate-producing probiotic to their microbiota.

“Together, these results are an important proof of concept for a new path forward in microbiome-mediated precision nutrition,” said Dr. Christian Diener, co-senior author and assistant professor at the Medical University of Graz in Austria. “But, of course, more work needs to be done to validate the predictive ability of these models in prospective human trials before they can enter clinical practice.”