Abstract
Understanding how diet and gut microbiota interact in the context of human health is a key question in personalized nutrition. Genome-scale metabolic networks and constraint-based modeling approaches are promising to systematically address this complex problem. However, when applied to nutritional questions, a major issue in existing reconstructions is the limited information about compounds in the diet that are metabolized by the gut microbiota. Here, we present AGREDA, an extended reconstruction of diet metabolism in the human gut microbiota. AGREDA adds the degradation pathways of 209 compounds present in the human diet, mainly phenolic compounds, a family of metabolites highly relevant for human health and nutrition. We show that AGREDA outperforms existing reconstructions in predicting diet-specific output metabolites from the gut microbiota. Using 16S rRNA gene sequencing data of faecal samples from Spanish children representing different clinical conditions, we illustrate the potential of AGREDA to establish relevant metabolic interactions between diet and gut microbiota.
Highlights
Understanding how diet and gut microbiota interact in the context of human health is a key question in personalized nutrition
We present a new metabolic reconstruction of the human gut microbiota that is focused on covering significant gaps in the degradation pathways of dietary compounds into terminal downstream metabolites
We identified in AGREDA 151 output metabolites whose potential production is significantly affected by different recipes or clinical conditions, among which 49 metabolites depend on the clinical conditions, 87 metabolites depend on diet, and 15 metabolites depend on both factors
Summary
Understanding how diet and gut microbiota interact in the context of human health is a key question in personalized nutrition. AGORA constituted the first large effort in the literature, involving 818 species present in the human gut microbiota[11] These network-based community models, which integrate the metabolic capabilities of different bacterial species in the gut microbiome, can be analyzed via constraint-based modeling (CBM)[12,13,14]. Universal metabolic databases, such as the Model SEED7, on which reconstruction platforms rely for gap filling, are incomplete and include metabolic capabilities of species that are not present in the human gut Overall, these limitations restrict the scope of CBM approaches to predict the interaction between diet and gut microbiota
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