Speaker
Description
From the first hours of life until the last, the human body is colonized by microbes that consume and produce a wide array of metabolites, predominantly within the gastrointestinal tract. These microbial metabolites are intimately linked to inflammation and immune signaling. Molecules such as short-chain fatty acids (SCFAs) and indoles have been shown to attenuate host inflammatory responses, making the gut microbiome a promising target for clinical interventions. However, metabolic output within microbial communities results from a complex interplay between hundreds of species, host factors, and dietary intake. Consequently, metabolic function cannot be accurately predicted from microbiome composition alone. Microbial Community Metabolic Models (MCMMs) offer a mechanistic solution to this challenge, yet their utility has been hindered by poorly defined optimization objectives and a lack of robust validation. Here, we demonstrate that ecological two-step objectives significantly enhance the predictive accuracy of MCMMs, rendering them suitable for clinical intervention modeling. We validate our flux predictions using large-scale ex vivo microbiome cultivation and engraftment studies. Our results establish MCMMs as a potent, mechanism-based strategy for predicting personalized intervention effects within the human gut microbiome.
