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Deciphering microbial interactions in synthetic human gut microbiome communities

View ORCID ProfileOphelia S. Venturelli, Alex C. Carr, Garth Fisher, Ryan H. Hsu, Rebecca Lau, Benjamin P. Bowen, Trent Northen, Adam P. Arkin
doi: https://doi.org/10.1101/228395
Ophelia S. Venturelli
1Department of Biochemistry, University of Wisconsin-Madison, Madison, WI
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  • ORCID record for Ophelia S. Venturelli
  • For correspondence: venturelli@wisc.edu
Alex C. Carr
5Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley CA
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Garth Fisher
5Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley CA
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Ryan H. Hsu
2California Institute for Quantitative Biosciences, University of California Berkeley, Berkeley CA
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Rebecca Lau
5Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley CA
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Benjamin P. Bowen
5Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley CA
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Trent Northen
5Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley CA
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Adam P. Arkin
2California Institute for Quantitative Biosciences, University of California Berkeley, Berkeley CA
3Department of Bioengineering, University of California Berkeley, Berkeley CA
4Energy Biosciences Institute, University of California Berkeley, Berkeley CA
5Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley CA
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ABSTRACT

The human gut microbiota comprises a dynamic ecological system that contributes significantly to human health and disease. The ecological forces that govern community assembly and stability in the gut microbiota remain unresolved. We developed a generalizable model-guided framework to predict higher-order consortia from time-resolved measurements of lower-order assemblages. This method was employed to decipher microbial interactions in a diverse 12-member human gut microbiome synthetic community. We show that microbial growth parameters and pairwise interactions are the major drivers of multi-species community dynamics, as opposed to context-dependent (conditional) interactions. The inferred microbial interaction network as well as a top-down approach to community assembly pinpointed both ecological driver and responsive species that were significantly modulated by microbial inter-relationships. Our model demonstrated that negative pairwise interactions could generate history-dependent responses of initial species proportions on physiological timescales that frequently does not originate from bistability. The model elucidated a topology for robust coexistence in pairwise assemblages consisting of a negative feedback loop that balances disparities in monospecies fitness levels. Bayesian statistical methods were used to evaluate the constraint of model parameters by the experimental data. Measurements of extracellular metabolites illuminated the metabolic capabilities of monospecies and potential molecular basis for competitive and cooperative interactions in the community. However, these data failed to predict influential organisms shaping community assembly. In sum, these methods defined the ecological roles of key species shaping community assembly and illuminated network design principles of microbial communities.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 03, 2017.
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Deciphering microbial interactions in synthetic human gut microbiome communities
Ophelia S. Venturelli, Alex C. Carr, Garth Fisher, Ryan H. Hsu, Rebecca Lau, Benjamin P. Bowen, Trent Northen, Adam P. Arkin
bioRxiv 228395; doi: https://doi.org/10.1101/228395
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Deciphering microbial interactions in synthetic human gut microbiome communities
Ophelia S. Venturelli, Alex C. Carr, Garth Fisher, Ryan H. Hsu, Rebecca Lau, Benjamin P. Bowen, Trent Northen, Adam P. Arkin
bioRxiv 228395; doi: https://doi.org/10.1101/228395

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