Metabolic Dependencies Underlie Interaction Patterns of Gut Microbiota During Enteropathogenesis

Abstract

In recent decades, increasing evidence has strongly suggested that gut microbiota play an important role in many intestinal diseases including inflammatory bowel disease (IBD) and colorectal cancer (CRC). The composition of gut microbiota is thought to be largely shaped by interspecies competition for available resources and also by cooperative interactions. However, to what extent the changes could be attributed to external factors such as diet of choice and internal factors including mutual relationships among gut microbiota, respectively, are yet to be elucidated. Due to the advances of high-throughput sequencing technologies, flood of (meta)-genome sequence information and high-throughput biological data are available for gut microbiota and their association with intestinal diseases, making it easier to gain understanding of microbial physiology at the systems level. In addition, the newly developed genome-scale metabolic models that cover significant proportion of known gut microbes enable researchers to analyze and simulate the system-level metabolic response in response to different stimuli in the gut, providing deeper biological insights. Using metabolic interaction network based on pair-wise metabolic dependencies, we found the same interaction pattern in two IBD datasets and one CRC datasets. We report here for the first time that the growth of significantly enriched bacteria in IBD and CRC patients could be boosted by other bacteria including other significantly increased ones. Conversely, the growth of probiotics could be strongly inhibited from other species, including other probiotics. Therefore, it is very important to take the mutual interaction of probiotics into consideration when developing probiotics or "microbial based therapies." Together, our metabolic interaction network analysis can predict majority of the changes in terms of the changed directions in the gut microbiota during enteropathogenesis. Our results thus revealed unappreciated interaction patterns between species could underlie alterations in gut microbiota during enteropathogenesis, and between probiotics and other microbes. Our methods provided a new framework for studying interactions in gut microbiome and their roles in health and disease.

Keywords: bacterial interaction patterns; enteropathogenesis; gut microbiota community; intestinal microbial ecology; metabolic interaction network; probiotics.

Figure 1

The bacteria interaction networks (weight > 4) obtained from healthy controls (A) and patients (B). ForceAtlas2 layout in Gephi (Bastian et al., 2009) was used for this representation. Nodes filled with gray, green, red, and pink represent commensal, probiotic, pathogenic, and opportunistic pathogenic bacteria, respectively. Two main subclusters were identified, one includes mostly probiotic bacteria (Probiotics Module), while the other consists mostly of species in the genus bacteroides (Bacteroides Module).

Figure 2

Most of the top 20 bacteria based on PageRanks are probiotics in health (A) and disease (B) states.

Figure 3

The growth of probiotics was strongly inhibited by other bacteria in both patients and healthy controls. (A,C) Proportions of inhibitory interactions in the four groups, calculated separately for patients (A) and healthy controls (C); Chi-square test was used to test pairwise differences between two groups. (B,D) Distribution of weight values in the four groups, calculated separately for patients (B) and healthy controls (D); Wilcoxon Rank Sum test was used for pairwise comparisons between two groups. Interaction data of the four groups are: background – interactions among bacteria excluding probiotics; within – interactions among probiotics; affecting – impacts of probiotics on others; affected – impacts of others on probiotics. Level of significance: NS – not significant; ^∗∗∗p < 0.01.

Figure 4

The growth of disease-enriched bacteria could be promoted by themselves and others. (A) An exemplary network of disease-enriched bacteria. (B) Proportion of promoting interactions in each group; chi-square test was used to perform pairwise (two-groups at a time) comparisons. (C) Distribution of weight values in the four groups; Wilcoxon Rank Sum test was used for pairwise comparisons. Interaction data of the four groups are: background – interactions among bacteria excluding probiotics and disease-enriched ones; within – interactions among disease-enriched bacteria; affecting – impacts of disease-enriched bacteria on others; affected – impacts of others on disease-enriched bacteria. Level of significance: NS – not significant; ^∗p < 0.05; ^∗∗0.01 < p < 0.05; ^∗∗∗p < 0.01.