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Characterizing Prebiotic Responders and Non-Responders Through Metagenomic Features of the Fecal Microbiome Using in Vitro and in Silico Approaches
Prebiotics have been widely studied as potential dietary strategies to modulate the gut microbiome. However, many studies have consistently reported non-universal effects across prebiotic interventions, resulting in observations of responders and non-responders. These phenotypes are thought to occur due to the highly individualized nature of the microbiome, emphasizing the need for a personalized approach towards prebiotic supplementation. Nonetheless, it remains a challenge to identify, a priori, personalized responses to specific prebiotics. To explore possible solutions, we firstly conducted a literature review to evaluate opportunities for personalized dietary fiber recommendations. From an ecological perspective, we hypothesized that the fecal metagenomes of prebiotic responders share common carbohydrate degradative systems required to compete for, degrade, and utilize a prebiotic, and ultimately produce measurable responses. In particular, short chain fatty acids (SCFA), known to have important beneficial effects on gut health, are the primary metabolic end-products of prebiotic fermentation. Accordingly, we developed an in vitro phenotyping method across three different prebiotics; xylooligosaccharides (XOS), fructooligosaccharide (FOS), and inulin using the SCFA, acetate and butyrate, as major response markers. All three prebiotics had an overall bifidogenic response in vitro. Through differential analyses of metagenomic sequences, we discovered prebiotic-specific biomarkers in the form of carbohydrate degradative genes and selected top ranking features using random forest classification. We identified 24 XOS, 12 FOS and 9 inulin-associated genes and designed primers to target and quantify these genes in baseline microbiomes. The expression of these genes in the presence of the prebiotic substrates was also confirmed by transcriptional analyses. Subsequently, we built support vector machine models for each prebiotic that could accurately (AUC > 0.9) discriminate between responders and non-responders using a defined set of genetic features. Moreover, we verified the prevalence of our biomarkers across 7,123 metagenomes and provided evidence for our models’ potential use in prebiotic feeding studies. In conclusion, using a rational approach, we successfully identified prebiotic-specific biomarkers that can reliably predict response status from baseline microbiomes.
Kok, Car Reen, "Characterizing Prebiotic Responders and Non-Responders Through Metagenomic Features of the Fecal Microbiome Using in Vitro and in Silico Approaches" (2021). ETD collection for University of Nebraska - Lincoln. AAI28865363.