metaGEM: reconstruction of genome scale metabolic models directly from metagenomes
Journal article, 2021

Metagenomic analyses of microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts mainly rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here, we present metaGEM (https://github.com/franciscozo rrilla/metaGEM), an end-to-end pipeline enabling metabolic modeling of multi-species communities directly from metagenomes. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from MAGs to community level flux balance analysis (FBA) simulations. To demonstrate the capabilities of metaGEM, we analyzed 483 samples spanning lab culture, human gut, plant-associated, soil, and ocean metagenomes, reconstructing over 14,000 GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to isolated genomes. We demonstrate that metagenomic GEMs capture intraspecies metabolic diversity and identify potential differences in the progression of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, metaGEM enables FBA-ready metabolic model reconstruction directly from metagenomes, provides a resource of metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.

Author

Francisco Zorrilla

University of Cambridge

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

European Molecular Biology Laboratory

Filip Buric

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

K. R. Patil

University of Cambridge

European Molecular Biology Laboratory

Aleksej Zelezniak

Vilnius University

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Nucleic Acids Research

0305-1048 (ISSN) 1362-4962 (eISSN)

Vol. 49 21 e126

Subject Categories

Microbiology

Bioinformatics (Computational Biology)

Physical Geography

DOI

10.1093/nar/gkab815

PubMed

34614189

More information

Latest update

1/11/2022