Inferring Dynamic Signatures of Microbes in Complex Host Ecosystems

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. Author Summary: Microbes colonize the human body soon after birth and propagate to form rich ecosystems. These ecosystems play essential roles in health and disease. Recent advances in DNA sequencing technologies make possible comprehensive studies of the time-dependent behavior of microbes throughout the body. Sophisticated computer-based methods are essential for the analysis and interpretation of these complex datasets. We present a computational method that models how human microbial ecosystems respond over time to perturbations, such as when subjects in a study are treated with a drug. When applied to a large publicly available dataset, our method yields new insights into the diversity of dynamic responses to antibiotics among microbes in the human body. We find that within an individual, sub-populations of microbes that share certain physiological roles also share coordinated responses. Moreover, we find that these responses are similar across different people. We use this information to improve the experimental design of the previously conducted study, and to develop strategies for optimal design of future studies. Our work provides an integrated computer-based method for automatically discovering patterns of change over time in the microbiota, and for designing future experiments to identify changes that impact human health and disease.