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Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss
BACKGROUND: Metabolic dependencies between microbial species have a significant impact on the assembly and activity of microbial communities. However, the evolutionary origins of such dependencies and the impact of metabolic and genomic architecture on their emergence are not clear. RESULTS: To addr...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6003207/ https://www.ncbi.nlm.nih.gov/pubmed/29907104 http://dx.doi.org/10.1186/s12918-018-0588-4 |
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author | McNally, Colin P. Borenstein, Elhanan |
author_facet | McNally, Colin P. Borenstein, Elhanan |
author_sort | McNally, Colin P. |
collection | PubMed |
description | BACKGROUND: Metabolic dependencies between microbial species have a significant impact on the assembly and activity of microbial communities. However, the evolutionary origins of such dependencies and the impact of metabolic and genomic architecture on their emergence are not clear. RESULTS: To address these questions, we developed a novel framework, coupling a reductive evolution model with a multi-species genome-scale metabolic model to simulate the evolution of two-species microbial communities. Simulating thousands of independent evolutionary trajectories, we surprisingly found that under certain environmental and evolutionary settings metabolic dependencies emerged frequently even though our model does not include explicit selection for cooperation. Evolved dependencies involved cross-feeding of a diverse set of metabolites, reflecting constraints imposed by metabolic network architecture. We additionally found metabolic ‘missed opportunities’, wherein species failed to capitalize on metabolites made available by their partners. Examining the genes deleted in each evolutionary trajectory and the deletion timing further revealed both genome-wide properties and specific metabolic mechanisms associated with species interaction. CONCLUSION: Our findings provide insight into the evolution of cooperative interaction among microbial species and a unique view into the way such relationships emerge. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12918-018-0588-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6003207 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-60032072018-06-26 Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss McNally, Colin P. Borenstein, Elhanan BMC Syst Biol Research Article BACKGROUND: Metabolic dependencies between microbial species have a significant impact on the assembly and activity of microbial communities. However, the evolutionary origins of such dependencies and the impact of metabolic and genomic architecture on their emergence are not clear. RESULTS: To address these questions, we developed a novel framework, coupling a reductive evolution model with a multi-species genome-scale metabolic model to simulate the evolution of two-species microbial communities. Simulating thousands of independent evolutionary trajectories, we surprisingly found that under certain environmental and evolutionary settings metabolic dependencies emerged frequently even though our model does not include explicit selection for cooperation. Evolved dependencies involved cross-feeding of a diverse set of metabolites, reflecting constraints imposed by metabolic network architecture. We additionally found metabolic ‘missed opportunities’, wherein species failed to capitalize on metabolites made available by their partners. Examining the genes deleted in each evolutionary trajectory and the deletion timing further revealed both genome-wide properties and specific metabolic mechanisms associated with species interaction. CONCLUSION: Our findings provide insight into the evolution of cooperative interaction among microbial species and a unique view into the way such relationships emerge. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12918-018-0588-4) contains supplementary material, which is available to authorized users. BioMed Central 2018-06-15 /pmc/articles/PMC6003207/ /pubmed/29907104 http://dx.doi.org/10.1186/s12918-018-0588-4 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article McNally, Colin P. Borenstein, Elhanan Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss |
title | Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss |
title_full | Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss |
title_fullStr | Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss |
title_full_unstemmed | Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss |
title_short | Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss |
title_sort | metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6003207/ https://www.ncbi.nlm.nih.gov/pubmed/29907104 http://dx.doi.org/10.1186/s12918-018-0588-4 |
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