A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites

Genome-scale modeling of microbiome metabolism enables the simulation of diet-host-microbiome-disease interactions. However, current genome-scale reconstruction resources are limited in scope by computational challenges. We developed an optimized and highly parallelized reconstruction and analysis pipeline to build a resource of 247,092 microbial genome-scale metabolic reconstructions, deemed APOLLO. APOLLO spans 19 phyla, contains >60% of uncharacterized strains, and accounts for strains from 34 countries, all age groups, and multiple body sites. Using machine learning, we predicted with high accuracy the taxonomic assignment of strains based on the computed metabolic features. We then built 14,451 metagenomic sample-specific microbiome community models to systematically interrogate their community-level metabolic capabilities. We show that sample-specific metabolic pathways accurately stratify microbiomes by body site, age, and disease state. APOLLO is freely available, enables the systematic interrogation of the metabolic capabilities of largely still uncultured and unclassified species, and provides unprecedented opportunities for systems-level modeling of personalized host-microbiome co-metabolism.

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Publication Details

Journal
Cell Systems
Published
2025-02-01
DOI
https://doi.org/10.1016/j.cels.2025.101196
Citations
14
Primary Topic
Microbial Metabolic Engineering and Bioproduction
Type
article
Field-Weighted Citation Impact
11.28

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article

A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites

Phoebe Evans, Celine Gallagher, Francesca McDonagh, Bram Nap et al.
14 citations
Cell Systems
Microbial Metabolic Engineering and Bioproduction
11.28
article

A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites

Phoebe Evans, Celine Gallagher, Francesca McDonagh, Bram Nap, Ronan Farrell, Cyrille C. Thinnes, Neil Francis O’Sullivan, Ronan M. T. Fleming, Almut Heinken, Ines Thiele, Arianna Basile, Rachel Daly, Filippo Martinelli Boneschi, Ian Lalor, Trevor Kilgannon, Amy O’Brolchain, Maeve Bergin, Saoirse Hill, Saoirse Roisin McAuliffe, E.L. Magee, Robert Delaney, Timothy Otto Hulshof
article en
14 citations

Abstract

Genome-scale modeling of microbiome metabolism enables the simulation of diet-host-microbiome-disease interactions. However, current genome-scale reconstruction resources are limited in scope by computational challenges. We developed an optimized and highly parallelized reconstruction and analysis pipeline to build a resource of 247,092 microbial genome-scale metabolic reconstructions, deemed APOLLO. APOLLO spans 19 phyla, contains >60% of uncharacterized strains, and accounts for strains from 34 countries, all age groups, and multiple body sites. Using machine learning, we predicted with high accuracy the taxonomic assignment of strains based on the computed metabolic features. We then built 14,451 metagenomic sample-specific microbiome community models to systematically interrogate their community-level metabolic capabilities. We show that sample-specific metabolic pathways accurately stratify microbiomes by body site, age, and disease state. APOLLO is freely available, enables the systematic interrogation of the metabolic capabilities of largely still uncultured and unclassified species, and provides unprecedented opportunities for systems-level modeling of personalized host-microbiome co-metabolism.

Cell SystemsVol. 16(2)
University College Dublin (IE), University of Padua (IT), Inserm (FR), Ollscoil na Gaillimhe – University of Galway (IE), University College Cork (IE), APC Microbiome Institute (IE), Nutrition-Génétique et Exposition aux Risques Environnementaux (FR), Université de Lorraine (FR)
European Commission, Science Foundation Ireland, Agence Nationale de la Recherche, National Institutes of Health, H2020 European Research Council
Openalex Percentile: Top 1%
Microbial Metabolic Engineering and Bioproduction
11.28
DOIOpen Access PDFOpenAlex
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