Inference of significant microbial interactions from longitudinal metagenomics sequencing data

Abstract

Data of next-generation sequencing (NGS) and their analysis have been facilitating advances in our understanding of microbial ecosystems such as human gut microbiota. However, inference of microbial interactions occurring within an ecosystem is still a challenge mainly due to metagenomics sequencing (e.g., 16S rDNA sequences) providing relative abundance of microbes instead of absolute cell count. In order to describe the population dynamics in microbial communities and estimate the involved microbial interactions, we introduce a procedure by integrating generalized Lotka-Volterra equations, forward stepwise regression and bootstrap aggregation. First, we successfully identify experimentally confirmed microbial interactions with relative abundance data of a cheese microbial community. Then, we apply the procedure to time-series of 16S rDNA sequences of gut microbiomes of children who were progressing to Type 1 diabetes (T1D progressors), and compare their gut microbial interactions to a healthy control group. Our results suggest that the number of inferred microbial interactions increased over time during the first three years of life. More microbial interactions are found in the gut flora of healthy children than the T1D progressors. The inhibitory effects from Actinobacteria and Bacilli to Bacteroidia, from Bacteroidia to Clostridia, and the benifit effect from Clostridia to Bacteroidia are shared between healthy children and T1D progressors. An inhibition of Clostridia by Gammaproteobacteria is found in healthy children that maintains through their first three years of life. This suppression appears in T1D progressors during the first year of life, which transforms to a commensalism relationship at the age of three years old. Gammaproteobacteria is found exerting an inhibition on Bacteroidia in the T1D progressors, which is not identified in the healthy controls.