Metabolic model-based ecological modeling for probiotic design

  1. James D Brunner  Is a corresponding author
  2. Nicholas Chia  Is a corresponding author
  1. Los Alamos National Laboratory, United States
  2. Argonne National Laboratory, United States

Abstract

The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter 'probiotic' treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual's microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.

Data availability

Data from the study that we used to evaluate our method can be found at the following source:https://www.ncbi.nlm.nih.gov/bioproject/PRJNA324129/Our method is implemented as the \emph{friendlyNets} package, available for download at \url{https://github.com/lanl/friendlyNets} along with re-formatted data and python scripts for the analysis found in this paper.

The following previously published data sets were used

Article and author information

Author details

  1. James D Brunner

    Biosciences Division, Los Alamos National Laboratory, Los Alamos, United States
    For correspondence
    jdbrunner@lanl.gov
    Competing interests
    James D Brunner, is an employee of Triad National Security, LLC.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8147-2522
  2. Nicholas Chia

    Data Science and Learning, Argonne National Laboratory, Lemont, United States
    For correspondence
    chia@anl.gov
    Competing interests
    Nicholas Chia, is an employee of UChicago Argonne, LLC.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9652-691X

Funding

U.S. Department of Energy (255LANL2018)

  • James D Brunner

Mayo Clinic

  • Nicholas Chia

Los Alamos National Laboratory (Center For Nonlinear Studies)

  • James D Brunner

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2024, Brunner & Chia

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. James D Brunner
  2. Nicholas Chia
(2024)
Metabolic model-based ecological modeling for probiotic design
eLife 13:e83690.
https://doi.org/10.7554/eLife.83690

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https://doi.org/10.7554/eLife.83690

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