Information content of high-order associations of the human gut microbiota network

TL;DR

This paper quantifies the complexity of the infant gut microbiota ecosystem by analyzing high-order associations and information content in nonlinear relationships among microbial units, revealing ecological structure beyond pairwise interactions.

Contribution

It introduces a novel information-theoretic framework using maximum entropy models to characterize high-order associations in the gut microbiota, advancing understanding of microbial ecology complexity.

Findings

High-order associations encode significant ecological information.

The system's order correlates with the information content of microbial associations.

Nonlinear relationships reveal complex ecological interactions.

Abstract

The human gastrointestinal tract is an environment that hosts an ecosystem of microorganisms essential to human health. Vital biological processes emerge from fundamental inter- and intra-species molecular interactions that influence the assembly and composition of the gut microbiota ecology. Here we quantify the complexity of the ecological relationships within the human infant gut microbiota ecosystem as a function of the information contained in the nonlinear associations of a sequence of increasingly-specified maximum entropy representations of the system. Our paradigm frames the ecological state, in terms of the presence or absence of individual microbial ecological units that are identified by amplicon sequence variants (ASV) in the gut microenvironment, as a function of both the ecological states of its neighboring units and, in a departure from standard graphical model representations, the associations among the units within its neighborhood. We characterize the order of the system based on the relative quantity of statistical information encoded by high-order statistical associations of the infant gut microbiota.