(726d) A Coordinated Modeling-Experimental Design Reveals the Controlling Effect of Substrate Diversity on Microbial Populations and Interactions Among Human Gut Microbiota

The human gut microbiome, the collection of microorganisms in the gastrointestinal tract, is a rich collection of life, and like any other ecosystems, their composition and functions are susceptible to biotic and abiotic factors. Sustaining an ecological balance of interacting populations in the human microbiome is essential as dysbiosis can be associated with disease. Despite rapid advancements in the field, we still lack fundamental principles that dictate the adjustment in microbial populations and interactions in response to environmental variations. For example, we have a limited understanding of how substrate properties (such as diversity and evenness) drive microbial diversity and interactions, critical information required for rational microbiome engineering (1). In this work, therefore, we co-designed culture experiments and computational modeling study to investigate how microbial populations and interactions in human fecal bacterial communities shift across growth cultures with varying substrate diversity. We cultured fecal microbiota from different healthy humans in media of varying carbohydrate composition in anaerobic continuous-flow bioreactors and analyzed changes in community composition through 16S rRNA gene amplicon sequencing (2). Based on the resulting time-series population data, we inferred media-specific microbial interaction networks using a regression-based approach termed LIMITS. As the accuracy of network inference using LIMITS deteriorates if population variation is stochastic and/or the total community biomass significantly fluctuates over time as often found in general fecal samples, we used carefully designed continuous-flow bioreactors to minimize those effects (3, 4). As a result, we not only achieved significant improvement in the quality of fit of the inferred descriptive model of the measured data, but also obtained new findings on the controlling role of substrate diversity in media composition in connectivity-diversity stability relationship and nutrient niche partitioning. Our coordinated modeling and experimental approach proposed and demonstrated in this work is readily translatable to other complex microbial communities to understand and reveal key ecological rules that govern their compositional and functional dynamics.

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3.Fisher CK, Mehta P. 2014. Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression. PLoS One 9:1–10.

4.Faust K, Bauchinger F, Laroche B, de Buyl S, Lahti L, Washburne AD, Gonze D, Widder S. 2018. Signatures of ecological processes in microbial community time series. Microbiome 6:1–13.