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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 sequencing data (e.g., 16S rDNA sequences) providing relative abundance of microbes instead of absolute cell count. In order to describe growtth dynamics of 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 based on 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 3 years of life. More microbial interactions are found in the gut flora of healthy children than that of T1D progressors. The inhibitory effects from  Actinobacteria  and  Bacilli  to  Bacteroidia , from  Bacteroidia  to  Clostridia , and the beneficial 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 3 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 3 years old.  Gammaproteobacteria  is found exerting an inhibition on  Bacteroidia  in the T1D progressors, which is not identified in the healthy controls.

Inference of Significant Microbial Interactions From Longitudinal Metagenomics Data