Inferring microbial interactions with their environment from genomic and metagenomic data

Brunner, James D.; Gallegos-Graves, Laverne A.; Kroeger, Marie E.; Laxman, ed., Sunil

doi:10.1371/journal.pcbi.1011661

Inferring microbial interactions with their environment from genomic and metagenomic data

Journal Article · Mon Nov 13 00:00:00 EST 2023 · PLoS Computational Biology (Online)

DOI:https://doi.org/10.1371/journal.pcbi.1011661· OSTI ID:2221915

; Gallegos-Graves, Laverne A.; ; Laxman, ed., Sunil

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time.

Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized , and so name our tool MetConSIN: Met abolically Con textualized S pecies I nteraction N etworks.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 2221915

Alternate ID(s):: OSTI ID: 2205570
OSTI ID: 2282538

Report Number(s):: LA-UR--23-25888; 10.1371/journal.pcbi.1011661

Journal Information:: PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Journal Issue: 11 Vol. 19; ISSN 1553-7358

Publisher:: Public Library of Science (PLoS)Copyright Statement

Country of Publication:: United States

Language:: English

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