A gap-filling algorithm for prediction of metabolic interactions in microbial communities.

The study of microbial communities and their interactions has attracted the interest of the scientific community, because of their potential for applications in biotechnology, ecology and medicine. The complexity of interspecies interactions, which are key for the macroscopic behavior of microbial c...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Dafni Giannari, Cleo Hanchen Ho, Radhakrishnan Mahadevan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/852e18f9d68b484382082892c6d38c9a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:852e18f9d68b484382082892c6d38c9a
record_format	dspace
spelling	oai:doaj.org-article:852e18f9d68b484382082892c6d38c9a2021-12-02T19:57:40ZA gap-filling algorithm for prediction of metabolic interactions in microbial communities.1553-734X1553-735810.1371/journal.pcbi.1009060https://doaj.org/article/852e18f9d68b484382082892c6d38c9a2021-11-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009060https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The study of microbial communities and their interactions has attracted the interest of the scientific community, because of their potential for applications in biotechnology, ecology and medicine. The complexity of interspecies interactions, which are key for the macroscopic behavior of microbial communities, cannot be studied easily experimentally. For this reason, the modeling of microbial communities has begun to leverage the knowledge of established constraint-based methods, which have long been used for studying and analyzing the microbial metabolism of individual species based on genome-scale metabolic reconstructions of microorganisms. A main problem of genome-scale metabolic reconstructions is that they usually contain metabolic gaps due to genome misannotations and unknown enzyme functions. This problem is traditionally solved by using gap-filling algorithms that add biochemical reactions from external databases to the metabolic reconstruction, in order to restore model growth. However, gap-filling algorithms could evolve by taking into account metabolic interactions among species that coexist in microbial communities. In this work, a gap-filling method that resolves metabolic gaps at the community level was developed. The efficacy of the algorithm was tested by analyzing its ability to resolve metabolic gaps on a synthetic community of auxotrophic Escherichia coli strains. Subsequently, the algorithm was applied to resolve metabolic gaps and predict metabolic interactions in a community of Bifidobacterium adolescentis and Faecalibacterium prausnitzii, two species present in the human gut microbiota, and in an experimentally studied community of Dehalobacter and Bacteroidales species of the ACT-3 community. The community gap-filling method can facilitate the improvement of metabolic models and the identification of metabolic interactions that are difficult to identify experimentally in microbial communities.Dafni GiannariCleo Hanchen HoRadhakrishnan MahadevanPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 11, p e1009060 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Dafni Giannari Cleo Hanchen Ho Radhakrishnan Mahadevan A gap-filling algorithm for prediction of metabolic interactions in microbial communities.
description	The study of microbial communities and their interactions has attracted the interest of the scientific community, because of their potential for applications in biotechnology, ecology and medicine. The complexity of interspecies interactions, which are key for the macroscopic behavior of microbial communities, cannot be studied easily experimentally. For this reason, the modeling of microbial communities has begun to leverage the knowledge of established constraint-based methods, which have long been used for studying and analyzing the microbial metabolism of individual species based on genome-scale metabolic reconstructions of microorganisms. A main problem of genome-scale metabolic reconstructions is that they usually contain metabolic gaps due to genome misannotations and unknown enzyme functions. This problem is traditionally solved by using gap-filling algorithms that add biochemical reactions from external databases to the metabolic reconstruction, in order to restore model growth. However, gap-filling algorithms could evolve by taking into account metabolic interactions among species that coexist in microbial communities. In this work, a gap-filling method that resolves metabolic gaps at the community level was developed. The efficacy of the algorithm was tested by analyzing its ability to resolve metabolic gaps on a synthetic community of auxotrophic Escherichia coli strains. Subsequently, the algorithm was applied to resolve metabolic gaps and predict metabolic interactions in a community of Bifidobacterium adolescentis and Faecalibacterium prausnitzii, two species present in the human gut microbiota, and in an experimentally studied community of Dehalobacter and Bacteroidales species of the ACT-3 community. The community gap-filling method can facilitate the improvement of metabolic models and the identification of metabolic interactions that are difficult to identify experimentally in microbial communities.
format	article
author	Dafni Giannari Cleo Hanchen Ho Radhakrishnan Mahadevan
author_facet	Dafni Giannari Cleo Hanchen Ho Radhakrishnan Mahadevan
author_sort	Dafni Giannari
title	A gap-filling algorithm for prediction of metabolic interactions in microbial communities.
title_short	A gap-filling algorithm for prediction of metabolic interactions in microbial communities.
title_full	A gap-filling algorithm for prediction of metabolic interactions in microbial communities.
title_fullStr	A gap-filling algorithm for prediction of metabolic interactions in microbial communities.
title_full_unstemmed	A gap-filling algorithm for prediction of metabolic interactions in microbial communities.
title_sort	gap-filling algorithm for prediction of metabolic interactions in microbial communities.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/852e18f9d68b484382082892c6d38c9a
work_keys_str_mv	AT dafnigiannari agapfillingalgorithmforpredictionofmetabolicinteractionsinmicrobialcommunities AT cleohanchenho agapfillingalgorithmforpredictionofmetabolicinteractionsinmicrobialcommunities AT radhakrishnanmahadevan agapfillingalgorithmforpredictionofmetabolicinteractionsinmicrobialcommunities AT dafnigiannari gapfillingalgorithmforpredictionofmetabolicinteractionsinmicrobialcommunities AT cleohanchenho gapfillingalgorithmforpredictionofmetabolicinteractionsinmicrobialcommunities AT radhakrishnanmahadevan gapfillingalgorithmforpredictionofmetabolicinteractionsinmicrobialcommunities
_version_	1718375788213960704

A gap-filling algorithm for prediction of metabolic interactions in microbial communities.

Ejemplares similares