Functional prediction of environmental variables using metabolic networks

Abstract In this manuscript, we propose a novel approach to assess relationships between environment and metabolic networks. We used a comprehensive dataset of more than 5000 prokaryotic species from which we derived the metabolic networks. We compute the scope from the reconstructed graphs, which i...

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Autores principales:	Adèle Weber Zendrera, Nataliya Sokolovska, Hédi A. Soula
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8ce4fd5ad31844ed89ea7a0b4fce8c4e
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id	oai:doaj.org-article:8ce4fd5ad31844ed89ea7a0b4fce8c4e
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spelling	oai:doaj.org-article:8ce4fd5ad31844ed89ea7a0b4fce8c4e2021-12-02T17:30:53ZFunctional prediction of environmental variables using metabolic networks10.1038/s41598-021-91486-82045-2322https://doaj.org/article/8ce4fd5ad31844ed89ea7a0b4fce8c4e2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91486-8https://doaj.org/toc/2045-2322Abstract In this manuscript, we propose a novel approach to assess relationships between environment and metabolic networks. We used a comprehensive dataset of more than 5000 prokaryotic species from which we derived the metabolic networks. We compute the scope from the reconstructed graphs, which is the set of all metabolites and reactions that can potentially be synthesized when provided with external metabolites. We show using machine learning techniques that the scope is an excellent predictor of taxonomic and environmental variables, namely growth temperature, oxygen tolerance, and habitat. In the literature, metabolites and pathways are rarely used to discriminate species. We make use of the scope underlying structure—metabolites and pathways—to construct the predictive models, giving additional information on the important metabolic pathways needed to discriminate the species, which is often absent in other metabolic network properties. For example, in the particular case of growth temperature, glutathione biosynthesis pathways are specific to species growing in cold environments, whereas tungsten metabolism is specific to species in warm environments, as was hinted in current literature. From a machine learning perspective, the scope is able to reduce the dimension of our data, and can thus be considered as an interpretable graph embedding.Adèle Weber ZendreraNataliya SokolovskaHédi A. SoulaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Adèle Weber Zendrera Nataliya Sokolovska Hédi A. Soula Functional prediction of environmental variables using metabolic networks
description	Abstract In this manuscript, we propose a novel approach to assess relationships between environment and metabolic networks. We used a comprehensive dataset of more than 5000 prokaryotic species from which we derived the metabolic networks. We compute the scope from the reconstructed graphs, which is the set of all metabolites and reactions that can potentially be synthesized when provided with external metabolites. We show using machine learning techniques that the scope is an excellent predictor of taxonomic and environmental variables, namely growth temperature, oxygen tolerance, and habitat. In the literature, metabolites and pathways are rarely used to discriminate species. We make use of the scope underlying structure—metabolites and pathways—to construct the predictive models, giving additional information on the important metabolic pathways needed to discriminate the species, which is often absent in other metabolic network properties. For example, in the particular case of growth temperature, glutathione biosynthesis pathways are specific to species growing in cold environments, whereas tungsten metabolism is specific to species in warm environments, as was hinted in current literature. From a machine learning perspective, the scope is able to reduce the dimension of our data, and can thus be considered as an interpretable graph embedding.
format	article
author	Adèle Weber Zendrera Nataliya Sokolovska Hédi A. Soula
author_facet	Adèle Weber Zendrera Nataliya Sokolovska Hédi A. Soula
author_sort	Adèle Weber Zendrera
title	Functional prediction of environmental variables using metabolic networks
title_short	Functional prediction of environmental variables using metabolic networks
title_full	Functional prediction of environmental variables using metabolic networks
title_fullStr	Functional prediction of environmental variables using metabolic networks
title_full_unstemmed	Functional prediction of environmental variables using metabolic networks
title_sort	functional prediction of environmental variables using metabolic networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8ce4fd5ad31844ed89ea7a0b4fce8c4e
work_keys_str_mv	AT adeleweberzendrera functionalpredictionofenvironmentalvariablesusingmetabolicnetworks AT nataliyasokolovska functionalpredictionofenvironmentalvariablesusingmetabolicnetworks AT hediasoula functionalpredictionofenvironmentalvariablesusingmetabolicnetworks
_version_	1718380741728927744

Functional prediction of environmental variables using metabolic networks

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