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Intrinsic instability of the dysbiotic microbiome revealed through dynamical systems inference at scale

View ORCID ProfileTravis E. Gibson, Younhun Kim, Sawal Acharya, David E. Kaplan, Nicholas DiBenedetto, Richard Lavin, Bonnie Berger, Jessica R. Allegretti, Lynn Bry, View ORCID ProfileGeorg K. Gerber
doi: https://doi.org/10.1101/2021.12.14.469105
Travis E. Gibson
1Division of Computational Pathology, Brigham and Women’s Hospital, Boston MA, USA
3Harvard Medical School, Boston MA, USA
6Computer Science and Artificial Intelligence Lab, MIT, Cambridge, Boston MA, USA
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  • ORCID record for Travis E. Gibson
Younhun Kim
5Mathematics Department, MIT, Cambridge MA, USA
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Sawal Acharya
1Division of Computational Pathology, Brigham and Women’s Hospital, Boston MA, USA
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David E. Kaplan
1Division of Computational Pathology, Brigham and Women’s Hospital, Boston MA, USA
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Nicholas DiBenedetto
2Massachusetts Host Microbiome Center, Brigham and Women’s Hospital, Boston MA, USA
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Richard Lavin
2Massachusetts Host Microbiome Center, Brigham and Women’s Hospital, Boston MA, USA
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Bonnie Berger
5Mathematics Department, MIT, Cambridge MA, USA
6Computer Science and Artificial Intelligence Lab, MIT, Cambridge, Boston MA, USA
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Jessica R. Allegretti
3Harvard Medical School, Boston MA, USA
7Division of Gastroenterology, Brigham and Women’s Hospital, Boston MA, USA
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Lynn Bry
2Massachusetts Host Microbiome Center, Brigham and Women’s Hospital, Boston MA, USA
3Harvard Medical School, Boston MA, USA
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Georg K. Gerber
1Division of Computational Pathology, Brigham and Women’s Hospital, Boston MA, USA
2Massachusetts Host Microbiome Center, Brigham and Women’s Hospital, Boston MA, USA
3Harvard Medical School, Boston MA, USA
4MIT-Harvard Health Sciences and Technology, Cambridge MA, USA
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  • ORCID record for Georg K. Gerber
  • For correspondence: ggerber@bwh.harvard.edu
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Abstract

Despite the importance of microbial dysbiosis in human disease, the phenomenon remains poorly understood. We provide the first comprehensive and predictive model of dysbiosis at ecosystem-scale, leveraging our new machine learning method for efficiently inferring compact and interpretable dynamical systems models. Coupling this approach with the most densely temporally sampled interventional study of the microbiome to date, using microbiota from healthy and dysbiotic human donors that we transplanted into mice subjected to antibiotic and dietary interventions, we demonstrate superior predictive performance of our method over state-of-the-art techniques. Moreover, we demonstrate that our approach uncovers intrinsic dynamical properties of dysbiosis driven by destabilizing competitive cycles, in contrast to stabilizing interaction chains in the healthy microbiome, which have implications for restoration of the microbiome to treat disease.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/gerberlab/MDSINE2

  • https://github.com/gerberlab/MDSINE2_Paper

  • https://doi.org/10.5281/zenodo.5527256

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted December 16, 2021.
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Intrinsic instability of the dysbiotic microbiome revealed through dynamical systems inference at scale
Travis E. Gibson, Younhun Kim, Sawal Acharya, David E. Kaplan, Nicholas DiBenedetto, Richard Lavin, Bonnie Berger, Jessica R. Allegretti, Lynn Bry, Georg K. Gerber
bioRxiv 2021.12.14.469105; doi: https://doi.org/10.1101/2021.12.14.469105
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Intrinsic instability of the dysbiotic microbiome revealed through dynamical systems inference at scale
Travis E. Gibson, Younhun Kim, Sawal Acharya, David E. Kaplan, Nicholas DiBenedetto, Richard Lavin, Bonnie Berger, Jessica R. Allegretti, Lynn Bry, Georg K. Gerber
bioRxiv 2021.12.14.469105; doi: https://doi.org/10.1101/2021.12.14.469105

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