Inference of causal interaction networks of gut microbiota using transfer entropy

Understanding the complex dynamics of gut microbiota interactions is essential for unraveling their influence on human health. However, inferring causality from microbiome time-series data is challenging due to noise, sparsity, and high dimensionality. Constructing causal interaction networks can provide valuable insights into the regulatory mechanisms of the gut microbiome. In this study, we employed transfer entropy analysis to construct a causal interaction network among gut microbiota genera from time-series abundance data. Based on longitudinal microbiome data from two subjects, we found that the constructed gut microbiota regulatory networks exhibited a power-law degree distribution, intermediate modularity, and enrichment of feedback loops. Interestingly, the networks of the two subjects displayed differential enrichment of feedback loops, which may be associated with the differences in their recovery dynamics. The transfer entropy-based network construction approach offers valuable insights into the gut microbiota ecosystem and enables the identification of key microbial hubs that play pivotal roles in shaping microbial balance. This method provides a deeper understanding of microbial regulatory interactions and their potential implications for host health.