4.8 Article

metaGEM: reconstruction of genome scale metabolic models directly from metagenomes

期刊

NUCLEIC ACIDS RESEARCH
卷 49, 期 21, 页码 -

出版社

OXFORD UNIV PRESS
DOI: 10.1093/nar/gkab815

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资金

  1. SciLifeLab fellows program
  2. Formas early-career research [2019-01403]
  3. Marius Jakulis Jason foundation
  4. UK Medical Research Council [MC UU 00025/11]
  5. European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [866028]
  6. DD-DeCaF consortium European Union's Horizon 2020 research and innovation programme [686070]
  7. Swedish Research Council [2018-05973]
  8. Swedish Research Council [2019-01403] Funding Source: Swedish Research Council
  9. Formas [2019-01403] Funding Source: Formas
  10. Forte [2019-01403] Funding Source: Forte
  11. European Research Council (ERC) [866028] Funding Source: European Research Council (ERC)
  12. MRC [MC_UU_00025/11] Funding Source: UKRI

向作者/读者索取更多资源

The metaGEM pipeline enables metabolic modeling of multi-species communities directly from metagenomes, showcasing fully represented metabolism in GEMs reconstructed from metagenomes and capturing intraspecies metabolic diversity. It provides a resource of metabolic models and showcases community-level modeling of microbiomes associated with disease conditions, allowing the generation of mechanistic hypotheses.
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.

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