Abstract
The human gut microbiome plays an important role in human health. In order to investigate changes in metabolic activity associated with dysbiotic microbiomes, we retrieved strain-level relative abundances from metagenomics data from a cohort of pediatric Crohn’s Disease patients with and without dysbiosis, and healthy control children to construct a personalized microbiome model for each sample using the AGORA resource of genome-scale gut microbial reconstructions. Subsequently, we systematically profiled each individual microbiome by predicting the quantitative biosynthesis potential for all secreted metabolites as well as the strain-level contributions to each metabolite in each individual microbiome. The predicted fecal metabolomes and strain-metabolite contributions of microbiomes from patients with dysbiosis were distinct from healthy controls and patients without dysbiosis. Finally, we validated the predicted amino acid production profiles against fecal metabolomic measurements. Taken together, we presented an efficient, scalable, tractable computational approach to systematically interrogate the metabolic potential of individual microbiomes.
