Metabolic model-based ecological modeling for probiotic design

Abstract
Data availability
Article and author information
Metrics

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

(2016) Human fecal microbiome before and after consumption of AH1206 - Raw sequence reads
SRA: PRJNA324129.

https://www.ncbi.nlm.nih.gov/bioproject/PRJNA324129

Article and author information

Author details

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.

"This ORCID iD identifies the author of this article:" 0000-0002-8147-2522
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.

"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

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.