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Functional comparison of metabolic networks across species
Metabolic phenotypes are pivotal for many areas, but disentangling how evolutionary history and environmental adaptation shape these phenotypes is an open problem. Especially for microbes, which are metabolically diverse and often interact in complex communities, few phenotypes can be determined dir...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10043025/ https://www.ncbi.nlm.nih.gov/pubmed/36973280 http://dx.doi.org/10.1038/s41467-023-37429-5 |
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author | Ramon, Charlotte Stelling, Jörg |
author_facet | Ramon, Charlotte Stelling, Jörg |
author_sort | Ramon, Charlotte |
collection | PubMed |
description | Metabolic phenotypes are pivotal for many areas, but disentangling how evolutionary history and environmental adaptation shape these phenotypes is an open problem. Especially for microbes, which are metabolically diverse and often interact in complex communities, few phenotypes can be determined directly. Instead, potential phenotypes are commonly inferred from genomic information, and rarely were model-predicted phenotypes employed beyond the species level. Here, we propose sensitivity correlations to quantify similarity of predicted metabolic network responses to perturbations, and thereby link genotype and environment to phenotype. We show that these correlations provide a consistent functional complement to genomic information by capturing how network context shapes gene function. This enables, for example, phylogenetic inference across all domains of life at the organism level. For 245 bacterial species, we identify conserved and variable metabolic functions, elucidate the quantitative impact of evolutionary history and ecological niche on these functions, and generate hypotheses on associated metabolic phenotypes. We expect our framework for the joint interpretation of metabolic phenotypes, evolution, and environment to help guide future empirical studies. |
format | Online Article Text |
id | pubmed-10043025 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100430252023-03-29 Functional comparison of metabolic networks across species Ramon, Charlotte Stelling, Jörg Nat Commun Article Metabolic phenotypes are pivotal for many areas, but disentangling how evolutionary history and environmental adaptation shape these phenotypes is an open problem. Especially for microbes, which are metabolically diverse and often interact in complex communities, few phenotypes can be determined directly. Instead, potential phenotypes are commonly inferred from genomic information, and rarely were model-predicted phenotypes employed beyond the species level. Here, we propose sensitivity correlations to quantify similarity of predicted metabolic network responses to perturbations, and thereby link genotype and environment to phenotype. We show that these correlations provide a consistent functional complement to genomic information by capturing how network context shapes gene function. This enables, for example, phylogenetic inference across all domains of life at the organism level. For 245 bacterial species, we identify conserved and variable metabolic functions, elucidate the quantitative impact of evolutionary history and ecological niche on these functions, and generate hypotheses on associated metabolic phenotypes. We expect our framework for the joint interpretation of metabolic phenotypes, evolution, and environment to help guide future empirical studies. Nature Publishing Group UK 2023-03-27 /pmc/articles/PMC10043025/ /pubmed/36973280 http://dx.doi.org/10.1038/s41467-023-37429-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ramon, Charlotte Stelling, Jörg Functional comparison of metabolic networks across species |
title | Functional comparison of metabolic networks across species |
title_full | Functional comparison of metabolic networks across species |
title_fullStr | Functional comparison of metabolic networks across species |
title_full_unstemmed | Functional comparison of metabolic networks across species |
title_short | Functional comparison of metabolic networks across species |
title_sort | functional comparison of metabolic networks across species |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10043025/ https://www.ncbi.nlm.nih.gov/pubmed/36973280 http://dx.doi.org/10.1038/s41467-023-37429-5 |
work_keys_str_mv | AT ramoncharlotte functionalcomparisonofmetabolicnetworksacrossspecies AT stellingjorg functionalcomparisonofmetabolicnetworksacrossspecies |