Identification of carbohydrate gene clusters obtained from in vitro fermentations as predictive biomarkers of prebiotic responses

Authors

Car Reen Kok, University of Nebraska-Lincoln, Lawrence Livermore National Laboratory
Devin J. Rose, University of Nebraska-LincolnFollow
Juan Cui, University of Nebraska-LincolnFollow
lisa Whisenhunt, University of Nebraska-LincolnFollow
Robert Hutkins, University of Nebraska-Lincoln

Document Type

Article

Date of this Version

5-25-2024

Citation

Kok et al. BMC Microbiology (2024) 24:183 https://doi.org/10.1186/s12866-024-03344-y

Comments

Open access.

Abstract

Background Prebiotic fibers are non-digestible substrates that modulate the gut microbiome by promoting expansion of microbes having the genetic and physiological potential to utilize those molecules. Although several prebiotic substrates have been consistently shown to provide health benefits in human clinical trials, responder and non-responder phenotypes are often reported. These observations had led to interest in identifying, a priori, prebiotic responders and non-responders as a basis for personalized nutrition. In this study, we conducted in vitro fecal enrichments and applied shotgun metagenomics and machine learning tools to identify microbial gene signatures from adult subjects that could be used to predict prebiotic responders and non-responders.

Results Using short chain fatty acids as a targeted response, we identified genetic features, consisting of carbohydrate active enzymes, transcription factors and sugar transporters, from metagenomic sequencing of in vitro fermentations for three prebiotic substrates: xylooligosacharides, fructooligosacharides, and inulin. A machine learning approach was then used to select substrate-specific gene signatures as predictive features. These features were found to be predictive for XOS responders with respect to SCFA production in an in vivo trial.

Conclusions Our results confirm the bifidogenic effect of commonly used prebiotic substrates along with interindividual microbial responses towards these substrates. We successfully trained classifiers for the prediction of prebiotic responders towards XOS and inulin with robust accuracy (≥ AUC 0.9) and demonstrated its utility in a human feeding trial. Overall, the findings from this study highlight the practical implementation of pre-intervention targeted profiling of individual microbiomes to stratify responders and non-responders.