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

• Published in: Nature communications Vol. 14; no. 1; pp. 5682 - 13
• Main Authors: Lim, Joe J., Diener, Christian, Wilson, James, Valenzuela, Jacob J., Baliga, Nitin S., Gibbons, Sean M.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14-09-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 45; 45/22; 45/23; 49; 631/158/1745; 631/158/855; 631/326/2565/2134; Animals; Bacteria; BASIC BIOLOGICAL SCIENCES; Bioreactors; Body Fluids; E coli; Ecosystem; Escherichia coli - genetics; Estimation; Feces; Human wastes; Humanities and Social Sciences; Humans; Intestinal microflora; Metabolism; Metagenome; Metagenomics; Microbiomes; Modelling; multidisciplinary; Population Density; Population dynamics; Population growth; Science; Science (multidisciplinary); Stochasticity; Time series
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-41424-1

Longitudinal sampling of the stool has yielded important insights into the ecological dynamics of the human gut microbiome. However, human stool samples are available approximately once per day, while commensal population doubling times are likely on the order of minutes-to-hours. Despite this mismatch in timescales, much of the prior work on human gut microbiome time series modeling has 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 stationary system, where population dynamics occur internally and the bacterial population sizes measured in a bolus of stool represent a steady-state endpoint of these dynamics. We formalize this idea as stochastic logistic growth. 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. 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.
Here, the authors present a novel approach for inferring in vivo growth phases of human gut bacteria from metagenomic time series data. These inferences can be used to better-constrain community scale metabolic modeling in the gut.