Inferring dynamic signatures of microbes in complex host ecosystems

• Published in: PLoS computational biology Vol. 8; no. 8; p. e1002624
• Main Authors: Gerber, Georg K, Onderdonk, Andrew B, Bry, Lynn
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01-08-2012; Public Library of Science (PLoS)
• Subjects: Algorithms; Antibiotics; Automation; Behavior; Binomial distribution; Biology; Computer Science; Disease; Ecosystem; Ecosystems; Experiments; Host-parasite relationships; Humans; Mathematics; Mean square errors; Medicine; Metagenome; Methods; Microbial colonies; Microbiology; Phylogeny; Physiological aspects; Population biology; RNA, Ribosomal, 16S - genetics; Studies; Taxonomy; United States; Automation; Algorithms; Ecosystem; Humans; Phylogeny; RNA, Ribosomal, 16S; Metagenome
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1002624

The human gut microbiota comprise a complex and dynamic ecosystem that profoundly affects host development and physiology. Standard approaches for analyzing time-series data of the microbiota involve computation of measures of ecological community diversity at each time-point, or measures of dissimilarity between pairs of time-points. Although these approaches, which treat data as static snapshots of microbial communities, can identify shifts in overall community structure, they fail to capture the dynamic properties of individual members of the microbiota and their contributions to the underlying time-varying behavior of host ecosystems. To address the limitations of current methods, we present a computational framework that uses continuous-time dynamical models coupled with Bayesian dimensionality adaptation methods to identify time-dependent signatures of individual microbial taxa within a host as well as across multiple hosts. We apply our framework to a publicly available dataset of 16S rRNA gene sequences from stool samples collected over ten months from multiple human subjects, each of whom received repeated courses of oral antibiotics. Using new diversity measures enabled by our framework, we discover groups of both phylogenetically close and distant bacterial taxa that exhibit consensus responses to antibiotic exposure across multiple human subjects. These consensus responses reveal a timeline for equilibration of sub-communities of micro-organisms with distinct physiologies, yielding insights into the successive changes that occur in microbial populations in the human gut after antibiotic treatments. Additionally, our framework leverages microbial signatures shared among human subjects to automatically design optimal experiments to interrogate dynamic properties of the microbiota in new studies. Overall, our approach provides a powerful, general-purpose framework for understanding the dynamic behaviors of complex microbial ecosystems, which we believe will prove instrumental for future studies in this field.