From metagenomic data to personalized computational microbiotas: Predicting dietary supplements for Crohn's disease

TL;DR

This study develops personalized computational microbiota models from metagenomic data to predict dietary interventions that could improve gut health in Crohn's disease patients, aligning with experimental results.

Contribution

It introduces a novel integration of metagenomic data with genome-scale metabolic models to personalize dietary treatment predictions for Crohn's disease.

Findings

Predicted SCFA levels matched experimental data.

Unique SCFA signatures identified for each patient.

Personalized dietary suggestions could enhance treatment outcomes.

Abstract

Crohn's disease (CD) is associated with an ecological imbalance of the intestinal microbiota, consisting of hundreds of species. The underlying complexity as well as individual differences between patients contributes to the difficulty to define a standardized treatment. Computational modeling can systematically investigate metabolic interactions between gut microbes to unravel novel mechanistic insights. In this study, we integrated metagenomic data of CD patients and healthy controls with genome-scale metabolic models into personalized in silico microbiotas. We predicted short chain fatty acid (SFCA) levels for patients and controls, which were overall congruent with experimental findings. As an emergent property, low concentrations of SCFA were predicted for CD patients and the SCFA signatures were unique to each patient. Consequently, we suggest personalized dietary treatments that could improve each patient's SCFA levels. The underlying modeling approach could aid clinical practice to find novel dietary treatment and guide recovery by rationally proposing food aliments.