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Microbial community-scale metabolic modeling predicts personalized short-chain-fatty-acid production profiles in the human gut

Nick Bohmann, View ORCID ProfileTomasz Wilmanski, Lisa Levy, View ORCID ProfileJohanna W. Lampe, View ORCID ProfileThomas Gurry, View ORCID ProfileNoa Rappaport, View ORCID ProfileChristian Diener, View ORCID ProfileSean M. Gibbons
doi: https://doi.org/10.1101/2023.02.28.530516
Nick Bohmann
1Institute for Systems Biology, Seattle, WA 98109, USA
2Molecular Engineering Graduate Program, University of Washington, Seattle, WA 98195, USA
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Tomasz Wilmanski
1Institute for Systems Biology, Seattle, WA 98109, USA
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Lisa Levy
3Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
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Johanna W. Lampe
3Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
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Thomas Gurry
4Pharmaceutical Biochemistry Group, School of Pharmaceutical Sciences, University of Geneva, Switzerland
5Myota GmbH, Berlin, Germany
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Noa Rappaport
1Institute for Systems Biology, Seattle, WA 98109, USA
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Christian Diener
1Institute for Systems Biology, Seattle, WA 98109, USA
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  • For correspondence: cdiener@isbscience.org sgibbons@isbscience.org
Sean M. Gibbons
1Institute for Systems Biology, Seattle, WA 98109, USA
2Molecular Engineering Graduate Program, University of Washington, Seattle, WA 98195, USA
6Departments of Bioengineering and Genome Sciences, University of Washington, Seattle, WA 98195, USA
7eScience Institute, University of Washington, Seattle, WA 98195, USA
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  • ORCID record for Sean M. Gibbons
  • For correspondence: cdiener@isbscience.org sgibbons@isbscience.org
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Abstract

Microbially-derived short-chain fatty acids (SCFAs) in the human gut are tightly coupled to host metabolism, immune regulation, and integrity of the intestinal epithelium. However, the production of SCFAs can vary widely between individuals consuming the same diet, with lower levels often associated with disease. A mechanistic understanding of this heterogeneity is lacking. We present a microbial community-scale metabolic modeling (MCMM) approach to predict individual-specific SCFA production profiles. We assess the quantitative accuracy of our MCMMs using in vitro, ex vivo, and in vivo data. Next, we identify associations between MCMM SCFA predictions and a panel of blood-based clinical chemistries in a large human cohort. Finally, we demonstrate how MCMMs can be leveraged to design personalized dietary, prebiotic, and probiotic interventions that optimize SCFA production in the gut. Our results represent an important advance in engineering gut microbiome functional outputs for precision health and nutrition.

Competing Interest Statement

One of our coauthors, Dr. Thomas Gurry, works for a commercial prebiotics company (Myota GmbH, Inc.). Myota was not involved in the funding or conduct of this research. The authors report no other conflicts of interest.

Footnotes

  • https://github.com/Gibbons-Lab/scfa_predictions

  • https://doi.org/10.5281/zenodo.4642238

  • https://github.com/RyanLincolnClark/DesignSyntheticGutMicrobiomeAssemblyFunction

  • https://github.com/ThaisaJungles/fiber_specificity

Copyright 
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 4.0 International license.
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Posted March 01, 2023.
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Microbial community-scale metabolic modeling predicts personalized short-chain-fatty-acid production profiles in the human gut
Nick Bohmann, Tomasz Wilmanski, Lisa Levy, Johanna W. Lampe, Thomas Gurry, Noa Rappaport, Christian Diener, Sean M. Gibbons
bioRxiv 2023.02.28.530516; doi: https://doi.org/10.1101/2023.02.28.530516
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Microbial community-scale metabolic modeling predicts personalized short-chain-fatty-acid production profiles in the human gut
Nick Bohmann, Tomasz Wilmanski, Lisa Levy, Johanna W. Lampe, Thomas Gurry, Noa Rappaport, Christian Diener, Sean M. Gibbons
bioRxiv 2023.02.28.530516; doi: https://doi.org/10.1101/2023.02.28.530516

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