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

• Published in: Nature biotechnology Vol. 41; no. 9; pp. 1320 - 1331
• Main Authors: Heinken, Almut, Hertel, Johannes, Acharya, Geeta, Ravcheev, Dmitry A., Nyga, Malgorzata, Okpala, Onyedika Emmanuel, Hogan, Marcus, Magnúsdóttir, Stefanía, Martinelli, Filippo, Nap, Bram, Preciat, German, Edirisinghe, Janaka N., Henry, Christopher S., Fleming, Ronan M. T., Thiele, Ines
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01-09-2023; Nature Publishing Group; Springer Nature
• Subjects: 631/326/2565/2134; 631/553/2710; Accuracy; Agriculture; BASIC BIOLOGICAL SCIENCES; biochemical networks; Bioinformatics; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Biotransformation; Body mass index; Body size; Colorectal carcinoma; Customization; Drug metabolism; Drugs; Gastrointestinal Microbiome - genetics; Genome; Genomes; Genomics; Humans; Intestinal microflora; Knowledge bases (artificial intelligence); Life Sciences; Literature reviews; Metabolism; microbiome; Microbiomes; Microbiota; Microorganisms; Precision Medicine; Reconstruction
• ISSN: 1087-0156; 1546-1696; 1546-1696
• DOI: 10.1038/s41587-022-01628-0

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.
A resource of microbial metabolic models reveals how an individual’s gut microbiome influences drug metabolism.