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Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine

A. Heinken, J. Hertel, G. Acharya, DA. Ravcheev, M. Nyga, OE. Okpala, M. Hogan, S. Magnúsdóttir, F. Martinelli, B. Nap, G. Preciat, JN. Edirisinghe, CS. Henry, RMT. Fleming, I. Thiele

. 2023 ; 41 (9) : 1320-1331. [pub] 20230119

Language English Country United States

Document type Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't

Persistent link   https://www.medvik.cz/link/bmc23016462

Grant support
U19 AG063744 NIA NIH HHS - United States
757922 European Research Council - International
RF1 AG058942 NIA NIH HHS - United States

E-resources Online Full text

NLK ProQuest Central from 2000-01-01 to 1 year ago
Health & Medicine (ProQuest) from 2000-01-01 to 1 year ago

Links

PubMed 36658342
DOI 10.1038/s41587-022-01628-0
Knihovny.cz E-resources

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strain- and molecule-resolved, scalable computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.72 to 0.84, surpassing other reconstruction resources and predicted known microbial drug transformations with an accuracy of 0.81. We demonstrate that AGORA2 enables personalized, strain-resolved modeling by predicting the drug conversion potential of the gut microbiomes from 616 patients with colorectal cancer and controls, which greatly varied between individuals and correlated with age, sex, body mass index and disease stages. AGORA2 serves as a knowledge base for the human microbiome and paves the way to personalized, predictive analysis of host-microbiome metabolic interactions.

References provided by Crossref.org

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