Personalized predictions of probiotic and prebiotic therapy success by computer models

A new study demonstrated that computer models of gut metabolism can predict which probiotics will successfully establish themselves in a person’s gut and how different prebiotics affect production of health-promoting short-chain fatty acids.

The findings were published February 19th in the open-access journal PLOS Biology by Sean Gibbons of the Institute for Systems Biology, US, and colleagues. Dr Gibbons is a member of Applied Microbiology International’s One Health Advisory Group.

Probiotic and prebiotic supplements show highly variable results across individuals, making it difficult to predict who will benefit from these interventions. This variability comes from complex interactions between introduced probiotics, each person’s existing gut microbiota, and their diet.

In the new work, researchers first tested a metabolic model on data from two previous studies in which participants diagnosed with type 2 diabetes were given a placebo or probiotic/prebiotic mixture designed to improve glucose control and healthy participants were given a placebo or a probiotic treatment designed to teat recurrent Clostridioides difficile infections, respectively.

The model predicted with 75%-80% accuracy which probiotic species would successfully colonize each person’s gut in the two cohorts. It also revealed associations between the engraftment success of one bacterial strain and people’s blood glucose levels, suggesting a mechanism for treatment efficacy in diabetes.

Dietary shift

Then, the team tested the ability of the model to make predictions in a third group of 1,786 generally healthy individuals who were shifting their diets from low- to high-fiber. It was able to predict responses to increasing dietary fiber on both molecules in the gut and on cardiometabolic markers.

“Taken together, these findings demonstrate the utility of [metabolic models] as a predictive framework for assessing prebiotic, probiotic, and dietary interventions at the individual and population levels,” the authors say. “Ultimately, leveraging [metabolic models] in a clinical setting could enable precision microbiome therapeutics, optimizing probiotic, prebiotic, and dietary intake to more effectively treat a wide range of acute and chronic diseases.”

READ MORE: Disarming a hidden killer: Predicting – and preventing – C. diff before it strikes

READ MORE: The Gibbons Lab

First author Nick Quinn-Bohmann states, “Here, we bridge the gap between probiotic design and real-world application, using deep mechanistic insight to identify the right intervention for each individual.” 

Sean Gibbons adds, “This work further demonstrates the potential of microbial community-scale metabolic models (MCMMs) as tools for designing and optimizing personalized probiotic and prebiotic interventions.”