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Ji, B. W.

Publications and source records attributed to Ji, B. W..

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Macroecological relationships in the dynamics of gut microbiota

The gut microbiome is now widely recognized as a dynamic ecosystem that plays an important role in health and disease1. While current sequencing technologies make it possible to estimate relative abundances of host-associated bacteria over time2, 3, the biological processes governing their dynamics remain poorly understood. Therefore, as in other ecological systems4, 5, it is important to identify quantitative relationships describing global aspects of gut microbiota dynamics. Here we use multiple high-resolution time series data obtained from humans and mice6-8 to demonstrate that despite their inherent complexity, gut microbiota dynamics can be characterized by several robust scaling relationships. Interestingly, these patterns are highly similar to those previously observed across diverse ecological communities and economic systems, including the temporal fluctuations of animal and plant populations9-12 and the performance of publicly traded companies13. Specifically, we find power law relationships describing short- and long-term changes in gut microbiota abundances, species residence and return times, and the connection between the mean and variance of species abundances. The observed scaling relationships are altered in mice receiving different diets and affected by context-specific perturbations in humans. We use these macroecological relationships to reveal specific bacterial taxa whose dynamics are significantly affected by dietary and environmental changes. Overall, our results suggest that a quantitative macroecological framework will be important for characterizing and understanding complex dynamics of microbial communities.

microbiology

Quantification of human gut microbiota variability using replicate sampling and spike-in sequencing

Metagenomic sequencing has enabled detailed investigation of diverse microbial communities, but understanding their spatiotemporal dynamics remains an important challenge. Here we present DIVERS, a widely applicable method based on replicate sampling and spike-in sequencing that quantifies the contributions of temporal dynamics, spatial sampling variability and technical noise to the variances and covariances of absolute bacterial abundances. Using high resolution time series profiling, we apply DIVERS to the human gut microbiome. Our method reveals complex spatiotemporal dynamics of individual gut bacteria and unmasks key features of their behavior hidden from previous analyses.

microbiology