Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference. Issue 6 (April 2019)

• Record Type: Journal Article
• Authors: Dohlman, Anders B; Shen, Xiling
• Editors: von Recum, Horst
• Is Part Of: Experimental biology and medicine. Volume 244:Issue 6(2019)
• Page Start: 445
• Page End: 458
• Publication Date: 2019-04
• Subjects: Microbiota -- systems -- statistics -- experimental -- models -- gut; Physiology -- Periodicals; Biology, Experimental -- Periodicals; Medicine, Experimental -- Periodicals; 610.72
• DOI: 10.1177/1535370219836771
• Languages: English
• ISSNs: 1535-3702

Abstract

Advances in high-throughput sequencing have ushered in a new era of research into the gut microbiome and its role in human health and disease. However, due to the unique characteristics of microbiome survey data, their use for the detection of ecological interaction networks remains a considerable challenge, and a field of active methodological development. In this review, we discuss the landscape of existing statistical and experimental methods for detecting and characterizing microbial interactions, as well as the role that host and environmental metabolic signals play in mediating the behavior of these networks. Numerous statistical tools for microbiome network inference have been developed. Yet due to tool-specific biases, the networks identified by these methods are often discordant, motivating a need for the development of more general tools, the use of ensemble approaches, and the incorporation of prior knowledge into prediction. By elucidating the complex dynamics of the microbial interactome, we will enhance our understanding of the microbiome's role in disease, more precisely predict the microbiome's response to perturbation, and inform the development of future therapeutic strategies for microbiome-related disease. Impact statement: This review provides a comprehensive description of experimental and statistical tools used for network analyses of the human gut microbiome. Understanding the system dynamics of microbial interactions may lead to the improvement of therapeutic approaches for managing microbiome-associated diseases. Microbiome network inference tools have been developed and applied to both cross-sectional and longitudinal experimental designs, as well as to multi-omic datasets, with the goal of untangling the complex web of microbe-host, microbe-environmental, and metabolism-mediated microbial interactions. The characterization of these interaction networks may lead to a better understanding of the systems dynamics of the human gut microbiome, augmenting our knowledge of the microbiome's role in human health, and guiding the optimization of effective, precise, and rational therapeutic strategies for managing microbiome-associated disease.