metaGEM: reconstruction of genome scale metabolic models directly from metagenomes
Artikel i vetenskaplig tidskrift, 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 ( 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.


Francisco Zorrilla

University of Cambridge

Chalmers, Biologi och bioteknik, Systembiologi

European Molecular Biology Laboratory

Filip Buric

Chalmers, Biologi och bioteknik, Systembiologi

K. R. Patil

University of Cambridge

European Molecular Biology Laboratory

Aleksej Zelezniak

Vilniaus universitetas

Chalmers, Biologi och bioteknik, Systembiologi

Nucleic Acids Research

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

Vol. 49 21 e126



Bioinformatik (beräkningsbiologi)






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