Towards an Ecological and Functional Framework for Modeling the Structure and Dynamics of the Human Gut Microbiome

Abstract

The human gut microbiota is composed of complex communities of microorganisms, which are modulated by various factors including diet. It plays a fundamental role in human health and nutrition by metabolizing compounds that are not digestible by the human intestine. Understanding the mechanisms that regulate and modify the microbiota's structure under specific conditions is therefore essential. In this study, we used ecological dynamic modeling grounded on the generalized Lotka-Volterra (gLV) model, and comparative and functional genomics with time-course compositional and transcriptomic data of a representative human gut microbial community inoculated in two groups of gnotobiotic mice subjected to different dietary schemes; with the aim of determining the ecological interactions of the community species, the metabolic functions that mediate these interactions, and how diet influences these interactions. We found that in circumstances where bacteria are growing optimally, the community is enriched with negative interactions, particularly interspecies competition; whereas in circumstances where the community is not growing optimally, fewer interactions can be seen overall. Additionally, the order in which the bacteria are exposed to different diets influence the impact that the diet switch has over the species. Furthermore, bacteria belonging to the Bacteroidetes phylum, which in general dominated the system, present a generalist metabolism, showing a wide repertoire of mechanisms to digest both carbohydrates and amino acid from the diet; while bacteria belonging to other phyla, proved to be specialists, with more reduced and specific metabolic activities to metabolize certain components of the diet, in particular, amino acids. Overall, we present an ecological and functional modeling approach that elucidates relevant mechanisms of gut microbiota structure and dynamics that are hardly detectable using traditional methods of metagenomic analysis.