Application of computational data modeling to a large-scale population cohort assists the discovery of specific nutrients that influence beneficial human gut bacteria Faecalibacterium prausnitzii

Abstract

Faecalibacterium prausnitzii (F. prausnitzii) is a bacterial taxon of the human gut with anti-inflammatory properties and negative associations with chronic inflammatory conditions. F. prausnitzii may be one of key species contributing to the effects of healthy eating habits, and yet little is known about the nutrients that enhance the growth of F. prausnitzii other than simple sugars and fibers. Here we combined dietary and microbiome data from the American Gut Project (AGP) to identify nutrients that may be linked to the relative abundance of F. prausnitzii. Using a machine learning approach in combination with univariate analyses, we identified that sugar alcohols, carbocyclic sugar and vitamins may contribute to F. prausnitzii growth. We next explored the effects of these nutrients on the growth of two F. prausnitzii strains in vitro and observed strain dependent growth patterns on the nutrient tested. In the context of a complex community using in vitro fermentation, none of the tested nutrients and nutrient combinations exerted a significant growth-promoting effect on F. prausnitzii due to high variability in batch responses. A positive association between F. prausnitzii and butyrate concentrations was observed. Future nutritional studies aiming to increase relative abundance of F. prausnitzii should explore a personalized approach accounting for strain-level genetic variations and community-level microbiome composition.