The human intestinal microbiome starts to form immediately after birth, and can greatly influence the health of the infant. During the first days facultative anaerobic species generally dominate,... Show moreThe human intestinal microbiome starts to form immediately after birth, and can greatly influence the health of the infant. During the first days facultative anaerobic species generally dominate, followed by a dominance of strictly anaerobic species, particularly Bifidobacterium species. An early transition to Bifidobacterium is associated with health benefits. To study the mechanisms of this transition and its hypothesised relation to oxygen, we introduce a multiscale mathematical model that considers metabolism, spatial bacterial population dynamics and resource sharing. Based on publicly available metabolic network data, the model predicts that differences in oxygen availability explain some of the observed individual variation in succession to anaerobic species. The model also predicts that anaerobic Bifidobacterium species become dominant through metabolizing lactose with a suboptimal yield, but a higher anaerobic growth rate than its competitors. The current work is the first step towards a more comprehensive understanding of the formation of a steady state adult colonic microbiota. Show less
The human intestinal microbiota starts to form immediately after birth and is important for the health of the host. During the first days, facultatively anaerobic bacterial species generally... Show moreThe human intestinal microbiota starts to form immediately after birth and is important for the health of the host. During the first days, facultatively anaerobic bacterial species generally dominate, such as Enterobacteriaceae. These are succeeded by strictly anaerobic species, particularly Bifidobacterium species. An early transition to Bifidobacterium species is associated with health benefits; for example, Bifidobacterium species repress growth of pathogenic competitors and modulate the immune response. Succession to Bifidobacterium is thought to be due to consumption of intracolonic oxygen present in newborns by facultative anaerobes, including Enterobacteriaceae. To study if oxygen depletion suffices for the transition to Bifidobacterium species, here we introduced a multiscale mathematical model that considers metabolism, spatial bacterial population dynamics, and cross-feeding. Using publicly available metabolic network data from the AGORA collection, the model simulates ab initio the competition of strictly and facultatively anaerobic species in a gut-like environment under the influence of lactose and oxygen. The model predicts that individual differences in intracolonic oxygen in newborn infants can explain the observed individual variation in succession to anaerobic species, in particular Bifidobacterium species. Bifidobacterium species became dominant in the model by their use of the bifid shunt, which allows Bifidobacterium to switch to suboptimal yield metabolism with fast growth at high lactose concentrations, as predicted here using flux balance analysis. The computational model thus allows us to test the internal plausibility of hypotheses for bacterial colonization and succession in the infant colon.IMPORTANCE The composition of the infant microbiota has a great impact on infant health, but its controlling factors are still incompletely understood. The frequently dominant anaerobic Bifidobacterium species benefit health, e.g., they can keep harmful competitors under control and modulate the intestinal immune response. Controlling factors could include nutritional composition and intestinal mucus composition, as well as environmental factors, such as antibiotics. We introduce a modeling framework of a metabolically realistic intestinal microbial ecology in which hypothetical scenarios can be tested and compared. We present simulations that suggest that greater levels of intraintestinal oxygenation more strongly delay the dominance of Bifidobacterium species, explaining the observed variety of microbial composition and demonstrating the use of the model for hypothesis generation. The framework allowed us to test a variety of controlling factors, including intestinal mixing and transit time. Future versions will also include detailed modeling of oligosaccharide and mucin metabolism. Show less
