Metage2Metabo, microbiota-scale metabolic complementarity for the identication of key species
Abstract
To capture the functional diversity of microbiota, one must identify metabolic functions and species of interest within hundreds or thousands of microorganisms. We present Metage2Metabo (M2M) a resource that meets the need for de-novo functional screening of genome-scale metabolic networks (GSMNs) at the scale of a metagenome, and the identification of critical species with respect to metabolic cooperation. M2M comprises a flexible pipeline for the characterisation of individual metabolisms and collective metabolic complementarity. In addition, M2M identifies key species, that are meaningful members of the community for functions of interest. We demonstrate that M2M is applicable to collections of genomes as well as metagenome-assembled genomes, permits an efficient GSMN reconstruction with Pathway Tools, and assesses the cooperation potential between species. M2M identifies key organisms by reducing the complexity of a large-scale microbiota into minimal communities with equivalent properties, suitable for further analyses.
Data availability
Data for metabolic modelling is available in https://github.com/AuReMe/metage2metabo/tree/master/article_data .
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Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen.European Nucleotide Archive, PRJEB21624.
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Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota.European Nucleotide Archive PRJEB1786, PRJEB4336, PRJEB5224.
Article and author information
Author details
Funding
BBSRC (Gut Microbes and Health BB/r012490/1,and its constituent project BBS/e/F/000Pr1035)
- Clémence Frioux
- Falk Hildebrand
2 (IDEALG (ANR-10-BTBR-04) Investissements d'Avenir)
- Arnaud Belcour
- Clémence Frioux
- Méziane Aite
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2020, Belcour et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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