Anoxic Conditions Promote Species-Specific Mutualism between Gut Microbes In Silico

Abstract

bThe human gut is inhabited by thousands of microbial species, most of which are still uncharacterized. Gut microbes have adapted to each other's presence as well as to the host and engage in complex cross feeding. Constraint-based modeling has been successfully applied to predicting microbe-microbe interactions, such as commensalism, mutualism, and competition. Here, we apply a constraint-based approach to model pairwise interactions between 11 representative gut microbes. Microbe-microbe interactions were computationally modeled in conjunction with human small intestinal enterocytes, and the microbe pairs were subjected to three diets with various levels of carbohydrate, fat, and protein in normoxic or anoxic environments. Each microbe engaged in species-specific commensal, parasitic, mutualistic, or competitive interactions. For instance, Streptococcus thermophilus efficiently outcompeted microbes with which it was paired, in agreement with the domination of streptococci in the small intestinal microbiota. Under anoxic conditions, the probiotic organism Lactobacillus plantarum displayed mutualistic behavior toward six other species, which, surprisingly, were almost entirely abolished under normoxic conditions. This finding suggests that the anoxic conditions in the large intestine drive mutualistic cross feeding, leading to the evolvement of an ecosystem more complex than that of the small intestinal microbiota. Moreover, we predict that the presence of the small intestinal enterocyte induces competition over host-derived nutrients. The presented framework can readily be expanded to a larger gut microbial community. This modeling approach will be of great value for subsequent studies aiming to predict conditions favoring desirable microbes or suppressing pathogens.T he human intestine is inhabited by a complex ecosystem consisting of thousands of microbial species. Its collective genome (the microbiome) contains more than 150 times as many genes as our own genome (1). The community of gut microbes has coevolved with humans and has adapted to the competitive conditions in the intestine. Gut microbes differ in their metabolic potential to exploit various environmental conditions and to persist in the intestine (2). They respond differently to the availability of dietary nutrients (3). Moreover, the gut microbiota has to contend with a steep oxygen gradient, which ranges from a partial O 2 pressure of approximately 80 mm Hg to nearly anoxic conditions (4). The diverse physiology of the small intestine creates a wide range of local oxygen microenvironments that favor particular groups of bacteria (4). Furthermore, metabolic interaction patterns between microbes affect microbial growth. Generally, six types of speciesspecies interactions can be distinguished (5) (Fig. 1a). In the case of neutralism, two organisms do not depend on each other for growth. If shared resources become scarce, this condition leads to competition (5). In the gut, competition over fermentable carbohydrates typically arises (6). If only one...

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

“…5a). Consistent with one previous in silico study 33 , the presence of oxygen resulted in a decrease in commensalism and mutualism, especially for Gammaproteobacteria (Supplementary Fig. 6).…”

“…Pairwise simulations were performed on every possible pair of the 773 AGORA metabolic reconstructions (298,378 pairs). Microbial models were paired by introducing a common lumen compartment, as described elsewhere 33 , in which each model could secrete or from which it could take up metabolites. Dietary compounds were added to the lumen and byproducts were removed.…”

Metabolic interactions in microbial communities: untangling the Gordian knot

“…Community models so far have been largely devoted to understanding general principles of community structure [24,83,[87][88][89], but also have accurately captured experimentally observed metabolic dependencies [7 , 18,89]. The next frontier for the models will be to provide hypotheses verifiable with the experimental approaches discussed above.…”

“…For identifying potential exchanged metabolites, steady-state models are of particular interest as these can be applied with as little information as the identity of community members and their genome sequences [7 ]. These can be further extended, albeit for small communities and with additional information on the metabolic physiology of the community and its members, to address more complex problems such as community dynamics [86].Community models so far have been largely devoted to understanding general principles of community structure [24,83,[87][88][89], but also have accurately captured experimentally observed metabolic dependencies [7 ,18,89]. The next frontier for the models will be to provide hypotheses verifiable with the experimental approaches discussed above.…”

Community metabolic modeling approaches to understanding the gut microbiome: Bridging biochemistry and ecology

“…This approach generated testable predictions about the metabolites exchanged between microbe and host, the ability of B. thetaiotaomicron to rescue certain lethal enzymopathies in the host, and the ability of the host to do the same for the bacterium. In a more complex study, the same research group modeled pairwise interactions between 11 representative gut microbes in conjunction with human small intestinal enterocytes, under three different diets [70]. They found that the anoxic conditions of the large intestine appear to drive mutualistic crossfeeding, resulting in a more complex ecosystem than is present in the less anoxic small intestine.…”