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Growth phase estimation for abundant bacterial populations sampled longitudinally from human stool metagenomes

Joe J. Lim, View ORCID ProfileChristian Diener, James Wilson, View ORCID ProfileJacob J. Valenzuela, View ORCID ProfileNitin S. Baliga, View ORCID ProfileSean M. Gibbons
doi: https://doi.org/10.1101/2022.04.23.489288
Joe J. Lim
1Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA 98105
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Christian Diener
2Institute for Systems Biology, Seattle, WA 98109
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James Wilson
2Institute for Systems Biology, Seattle, WA 98109
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Jacob J. Valenzuela
2Institute for Systems Biology, Seattle, WA 98109
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Nitin S. Baliga
2Institute for Systems Biology, Seattle, WA 98109
3Department of Microbiology, University of Washington, Seattle, WA 98105
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Sean M. Gibbons
2Institute for Systems Biology, Seattle, WA 98109
4Department of Bioengineering, University of Washington, Seattle, WA 98105
5Department of Genome Sciences, University of Washington, Seattle, WA 98105
6eScience Institute, University of Washington, Seattle, WA 98105
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  • For correspondence: sgibbons@isbscience.org
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ABSTRACT

Longitudinal sampling of the stool has yielded important insights into the ecological dynamics of the human gut microbiome. However, due to practical limitations, the most densely sampled time series from the human gut are collected at a frequency of about once per day, while the population doubling times for gut commensals are on the order of minutes-to-hours. Despite this, much of the prior work on human gut microbiome time series modeling has, implicitly or explicitly, assumed that day-to-day fluctuations in taxon abundances are related to population growth or death rates, which is likely not the case. Here, we propose an alternative model of the human gut as a flow-through ecosystem at a dynamical steady state, where population dynamics occur internally and the bacterial population sizes measured in a bolus of stool represent an endpoint of these internal dynamics. We formalize this idea as stochastic logistic growth of a population in a system held at a semi-constant flow rate. We show how this model provides a path toward estimating the growth phases of gut bacterial populations in situ. We validate our model predictions using an in vitro Escherichia coli growth experiment. Finally, we show how this method can be applied to densely-sampled human stool metagenomic time series data. Consistent with our model, stool donors with slower defecation rates tended to harbor a larger proportion of taxa in later growth phases, while faster defecation rates were associated with more taxa in earlier growth phases. We discuss how these growth phase estimates may be used to better inform metabolic modeling in flow-through ecosystems, like animal guts or industrial bioreactors.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • We have added an in vitro validation of our phase inference model, along with data on longitudinal dietary variation, and simulation results on how fluctuations in carrying capacities do not impact growth phase inferences.

  • https://github.com/Gibbons-Lab/human-microbiome-time-series-growth-phase-estimation

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 09, 2023.
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Growth phase estimation for abundant bacterial populations sampled longitudinally from human stool metagenomes
Joe J. Lim, Christian Diener, James Wilson, Jacob J. Valenzuela, Nitin S. Baliga, Sean M. Gibbons
bioRxiv 2022.04.23.489288; doi: https://doi.org/10.1101/2022.04.23.489288
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Growth phase estimation for abundant bacterial populations sampled longitudinally from human stool metagenomes
Joe J. Lim, Christian Diener, James Wilson, Jacob J. Valenzuela, Nitin S. Baliga, Sean M. Gibbons
bioRxiv 2022.04.23.489288; doi: https://doi.org/10.1101/2022.04.23.489288

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