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

Short-chain fatty acids (SCFAs) are beneficial molecules created by bacteria living 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 consuming the same diet, and we do not currently have the tools to estimate this interindividual variation.

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

In other words, ISB scientists can create 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 the pattern specific to each individual. They detailed their results in a paper published in 2014. 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 would represent a major advance in translating microbiome science to the clinic.”

Dr. Sean Gibbons, ISB associate professor and co-senior author

Unlike black-box machine learning approaches to prediction, MCMMs are transparent and mechanistic; Tens of thousands of metabolites and enzymes in dozens of organisms provide a high degree of information about the specific microbes, dietary components, and metabolic pathways that contribute 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 measurements to model predictions of the same species. However, SCFAs are difficult to measure in the wild because the body quickly depletes them once they are created. To overcome this challenge, the authors measured SCFA production rates. in a laboratory environment (i.e., test tube) communities consisting of random mixtures of human gut bacterial isolates and ex vivo Fecal homogenates from different people incubated in an anaerobic chamber with various dietary fibers (i.e., outside the body).

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

During in vivo Because measurements of butyrate and propionate production (i.e., in the body) were not possible, the authors were able to use indirect relationships between SCFA production rates and blood-based health markers to confirm the physiological effects of interindividual production differences. First, they showed that MCMM estimates could discriminate between individuals showing different immune responses in a high-fiber diet study: most individuals showed reductions in markers of systemic inflammation, but a group of people showed increases in inflammation on the high-fiber diet. diet. According to MCMM estimates, individuals in the high inflammation response group showed a significantly reduced capacity to produce propionate. The authors then showed that butyrate estimates were significantly associated with blood markers of cardiometabolic and immune health in a population of more than 2,000 individuals. Specifically, higher butyrate production estimated by MCMM was significantly associated with lower LDL cholesterol, lower triglycerides, better insulin sensitivity, lower systemic inflammation, and lower blood pressure.

“Prediction accuracy of MCMMs in a laboratory environment“Combined with the significant relationships between SCFA estimates and health markers in human cohorts, it gives us confidence in the usefulness of these models for precision nutrition,” said lead author Dr. Nick Quinn-Bohmann, a University of Washington graduate student at the ISB who recently defended his thesis.

After validating MCMM predictions in various ways, the authors demonstrated the potential of this approach for designing personalized prebiotic, probiotic, and dietary interventions that optimize SCFA production profiles. They simulated butyrate production rates of two different diets—the standard Austrian diet (i.e., standard European diet) and the vegan high-fiber diet—in a cohort of more than 2,000 individuals from the Western Pacific region of the United States. They found that a small subgroup of individuals showed almost no increase in butyrate production when they switched to a high-fiber diet (called “non-responders”), and another subgroup saw a small decrease in butyrate production when switched to a high-fiber diet (called “non-responders”). “decliners”). They then simulated three simple common interventions on both background diets to try and increase butyrate production in non-responders and regressors: adding the prebiotic fiber inulin, adding the prebiotic fiber pectin, or adding a butyrate-producing probiotic.fecalibacteria). The results showed that no single combinatorial intervention was optimal for all individuals: some benefited most from adding prebiotic fiber, while others required the addition of a butyrate-producing probiotic to their microbiota.

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