Metabolic Dependencies Underlie Interaction Patterns of Gut Microbiota During Enteropathogenesis.

Die Dai,Wei-Hua Chen,Sicheng Wu,Na L Gao,Teng Wang

doi:10.3389/fmicb.2019.01205

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.

Highlights

In recent decades, increasing evidence has strongly suggested that gut bacteria play an important role in human health and disease (Selber-Hnatiw et al, 2017; Jackson et al, 2018)
The classification of the bacteria (Commensal, Pathogen, and Probiotic) is provided by the literature (Magnusdottir et al, 2017), which is shown in Supplementary Table S2
We showed that the alterations of the gut microbiota during enteropathogenesis can be explained by their immediate neighbors in the metabolic dependency network with reasonable accuracy

Summary

Introduction

In recent decades, increasing evidence has strongly suggested that gut bacteria play an important role in human health and disease (Selber-Hnatiw et al, 2017; Jackson et al, 2018). Changes in the composition of the gut microbiota have been proven to be associated with many diseases (Jackson et al, 2018) including inflammatory bowel disease (IBD; Joossens et al, 2011; Matsuoka and Kanai, 2015; Chu et al, 2016; Sartor and Wu, 2017; Zuo and Ng, 2018), type 2 diabetes (Delzenne et al, 2015), obesity (Moreno-Indias et al, 2014; Tai et al, 2015), atherosclerosis (Drosos et al, 2015; Yamashita et al, 2015) and colorectal cancer (CRC; Aarnoutse et al, 2017; Liang et al, 2017; Russo et al, 2018). IBD (Miyoshi and Chang, 2017; Sartor and Wu, 2017), including both Crohn’s Disease (CD) and ulcerative colitis (UC), is one of the most-studied imbalances between intestinal microflora and the immune system. CRC, one of the most common cancers with the highest mortality worldwide, has been reported to be associated with intestinal microflora (Zeller et al, 2014)

Methods

Results

Conclusion