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

Short-chain fatty acids (SCFAs) are beneficial molecules produced by the bacteria 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 widely between individuals eating the exact same diet and we currently lack tools to predict this inter-individual variation.

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

In other words, ISB scientists can build a “digital twin” of gut microbiome metabolism that can simulate personalized responses to food, using gut microbiome sequencing data and dietary intake information to narrow down each individual specific model. They detailed their results in an article published in Natural microbiology.

Primarily, the gut microbiome is a bioreactor that converts dietary fiber into these SCFAs. Understanding how to quantitatively map gut ecology and dietary intake to SCFA outputs will go a long way 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, with tens of thousands of metabolites and enzymes in dozens of organisms providing a high degree of knowledge about the specific microbes, dietary components, and metabolic pathways that contribute to SCFA production . . Despite this transparency, the complexity of these models makes it difficult to validate them experimentally.

One approach is to measure SCFA production rates for an entire ecosystem, and then compare these ecosystem-scale measurements with their related model predictions. However, measuring SCFAs in the wild is difficult because the body consumes them quickly after they are produced. To address this challenge, the authors measured SCFA production rates in vitro (i.e., test tube) communities of random mixtures of human gut bacteria isolates and out ex vivo (i.e. outside the body) fecal homogenates 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 demonstrate that MCMM predictions were significantly correlated with measured production rates across a range of fibers for both butyrate and propionate, two of the most abundant and physiologically potent SCFAs.

While in vivo (i.e. in the body) measurements of butyrate and propionate production were not feasible, 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 production differences. First, they showed that MCMM predictions could distinguish between individuals from a high-fiber diet study who showed divergent immune responses: most individuals showed a reduction in systemic markers of inflammation, but a subset of people showed an increase in inflammation on a high-fiber diet. eating pattern. According to the MCMM predictions, individuals in the high inflammatory response group showed a significantly reduced ability to produce propionate. The authors then showed that butyrate predictions were significantly associated with blood markers of cardiometabolic and immune health in a population of more than 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.

“The predictive accuracy of MCMMs in vitrocoupled with the significant associations between SCFA predictions and health markers in human cohorts, gives us confidence in the utility of these models for precision nutrition,” said lead author Dr. Nick Quinn-Bohmann, a graduate student at the University of Washington at ISB who recently defended his dissertation.

After validating MCMM predictions in several ways, the authors then demonstrated the potential of this approach for designing personalized prebiotic, probiotic, and dietary interventions that optimize SCFA production profiles. They simulated butyrate production rates for two different diets – the standard Austrian diet (i.e. the standard European diet) and a vegan high-fiber diet – for a cohort of more than 2,000 individuals from the Pacific West of the US. They found that a small subset of individuals showed virtually no increase in butyrate production when switching to the high-fiber diet (referred to as ‘non-responders’) and another subgroup actually saw a small decrease in butyrate production on the high-fiber diet (the so-called ‘non-responders’). “regressors”). They then simulated three simple co-interventions on both 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 butyrate-producing probiotic .Faecalibacterium). The results showed that no single combinatorial intervention was optimal for all individuals: some benefited most from adding a prebiotic fiber, while others appeared to require the addition of a butyrate-producing probiotic to their microbiota.

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


Institute for Systems Biology (ISB)

Magazine reference:

Quinn-Bohmann, N., et al. (2024). Community-scale microbial metabolic modeling predicts personalized short-chain fatty acid production profiles in the human gut. Natural microbiology.

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