Abstract
The human gut microbiome plays an influential role in maintaining human health, and it is a potential target for prevention and treatment of disease. Genome-scale metabolic models (GEMs) can provide an increased understanding of the mechanisms behind the effects of diet, the genotype-phenotype relationship and microbial robustness. Here we reconstructed GEMs for three key species, (Bacteroides thetaiotamicron, Eubacterium rectale and Methanobrevibacter smithii) as relevant representatives of three main phyla in the human gut (Bacteroidetes, Firmicutes and Euryarchaeota). We simulated the interactions between these three bacteria in different combinations of gut ecosystems and compared the predictions with the experimental results obtained from colonization of germ free mice. Furthermore, we used our GEMs for analyzing the contribution of each species to the overall metabolism of the gut microbiota based on transcriptome data and demonstrated that these models can be used as a scaffold for understanding bacterial interactions in the gut.
Highlights
The human gut microbiome plays an influential role in maintaining human health, and it is a potential target for prevention and treatment of disease
In order to understand the function of bacteria in the human gut ecosystem and its interaction with the host, we reconstructed three Genome-scale metabolic models (GEMs), iBth1201 (B. thetaiotaomicron), iEre[400] (E. rectale) and iMsi[385] (M. smithii), which are relevant representatives of three main phyla in the human gut (Bacteroidetes, Firmicutes and Euryarchaeota)
We simulated that the production of butyrate increased when B. thetaiotaomicron was co-colonized with E. rectale
Summary
The human gut microbiome plays an influential role in maintaining human health, and it is a potential target for prevention and treatment of disease. Metagenomic studies have shown that the gut microbiome is associated with human diseases such as obesity[3], type 2 diabetes[4,5] and atherosclerosis[6], and the composition of the gut microbiota has been shown to be influenced by the diet, environment and age[7]. Better understanding of the interactions between these phyla as well as with the host may provide valuable insights into the underlying mechanisms of the different disorders. These interactions can be mediated by the production of short chain fatty acids (SCFAs) (acetate, propionate and butyrate), hydrogen and methane which are of potential interests in our study. A statistical method was developed for prediction of the bacterial abundance and identification of important factors in each diet[23]
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