Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
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 inter-individual 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.
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 inter-individual 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.
Background The gut microbiome is a salient contributor to the development of obesity, and diet is the greatest modifier of the gut microbiome, which highlights the need to better understand how specific diets alter the gut microbiota to impact metabolic disease. Increased dietary fiber intake shifts the gut microbiome and improves energy and glucose homeostasis. Dietary fibers are found in various plant-based flours which vary in fiber composition. However, the comparative efficacy of specific plant-based flours to improve energy homeostasis and the mechanism by which this occurs is not well characterized. Methods In experiment 1, obese rats were fed a high fat diet (HFD) supplemented with four different plant-based flours for 12 weeks. Barley flour (BF), oat bran (OB), wheat bran (WB), and Hi-maize amylose (HMA) were incorporated into the HFD at 5% or 10% total fiber content and were compared to a HFD control. For experiment 2, lean, chow-fed rats were switched to HFD supplemented with 10% WB or BF to determine the preventative efficacy of flour supplementation. Results In experiment 1, 10% BF and 10% WB reduced body weight and adiposity gain and increased cecal butyrate. Gut microbiota analysis of WB and BF treated rats revealed increases in relative abundance of SCFA-producing bacteria. 10% WB and BF were also efficacious in preventing HFD-induced obesity; 10% WB and BF decreased body weight and adiposity, improved glucose tolerance, and reduced inflammatory markers and lipogenic enzyme expression in liver and adipose tissue. These effects were accompanied by alterations in the gut microbiota including increased relative abundance of Lactobacillus and LachnospiraceaeUCG001, along with increased portal taurodeoxycholic acid (TDCA) in 10% WB and BF rats compared to HFD rats. Conclusions Therapeutic and preventative supplementation with 10%, but not 5%, WB or BF improves metabolic homeostasis, which is possibly due to gut microbiome-induced alterations. Specifically, these effects are proposed to be due to increased concentrations of intestinal butyrate and circulating TDCA.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.