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.

APC Microbiome Ireland Cork Ireland

Center for Molecular Medicine University Medical Center Utrecht Utrecht the Netherlands

Computation Institute University of Chicago Chicago IL USA

Czech University of Life Sciences Prague Prague Czech Republic

Department of Psychiatry and Psychotherapy University Medicine Greifswald Greifswald Germany

Division of Microbiology University of Galway Galway Ireland

INSERM UMRS 1256 Nutrition Genetics and Environmental Risk Exposure University of Lorraine Nancy France

Integrated BioBank of Luxembourg Dudelange Luxembourg

Leiden Academic Centre for Drug Research Leiden University Leiden the Netherlands

Mathematics and Computer Science Division Argonne National Laboratory Argonne IL USA

Ryan Institute University of Galway Galway Ireland

School of Medicine University of Galway Galway Ireland

University of Luxembourg Esch sur Alzette Luxembourg

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Lynch SV, Pedersen O. The human intestinal microbiome in health and disease. N. Engl. J. Med. 2016;375:2369–2379. doi: 10.1056/NEJMra1600266. PubMed DOI

Nebert DW, Zhang G, Vesell ES. From human genetics and genomics to pharmacogenetics and pharmacogenomics: past lessons, future directions. Drug Metab. Rev. 2008;40:187–224. doi: 10.1080/03602530801952864. PubMed DOI PMC

Tralau T, Sowada J, Luch A. Insights on the human microbiome and its xenobiotic metabolism: what is known about its effects on human physiology? Expert Opin. Drug Metab. Toxicol. 2015;11:411–425. doi: 10.1517/17425255.2015.990437. PubMed DOI

Spanogiannopoulos P, Bess EN, Carmody RN, Turnbaugh PJ. The microbial pharmacists within us: a metagenomic view of xenobiotic metabolism. Nat. Rev. Microbiol. 2016;14:273–287. doi: 10.1038/nrmicro.2016.17. PubMed DOI PMC

Zimmermann M, Zimmermann-Kogadeeva M, Wegmann R, Goodman AL. Mapping human microbiome drug metabolism by gut bacteria and their genes. Nature. 2019;570:462–467. doi: 10.1038/s41586-019-1291-3. PubMed DOI PMC

Javdan B, et al. Personalized mapping of drug metabolism by the human gut microbiome. Cell. 2020;181:1661–1679 e1622. doi: 10.1016/j.cell.2020.05.001. PubMed DOI PMC

Guthrie L, Kelly L. Bringing microbiome-drug interaction research into the clinic. EBioMedicine. 2019;44:708–715. doi: 10.1016/j.ebiom.2019.05.009. PubMed DOI PMC

Palsson, B. Systems Biology: Properties of Reconstructed Networks (Cambridge Univ. Press, 2006).

Thiele I, Palsson BØ. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc. 2010;5:93–121. doi: 10.1038/nprot.2009.203. PubMed DOI PMC

Baldini F, et al. The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities. Bioinformatics. 2019;35:2332–2334. doi: 10.1093/bioinformatics/bty941. PubMed DOI PMC

Diener C, Gibbons SM, Resendis-Antonio O. MICOM: metagenome-scale modeling to infer metabolic interactions in the gut microbiota. mSystems. 2020;5:e00606–e00619. doi: 10.1128/mSystems.00606-19. PubMed DOI PMC

Magnusdottir S, Thiele I. Modeling metabolism of the human gut microbiome. Curr. Opin. Biotechnol. 2018;51:90–96. doi: 10.1016/j.copbio.2017.12.005. PubMed DOI

van der Ark KCH, van Heck RGA, Martins Dos Santos VAP, Belzer C, de Vos WM. More than just a gut feeling: constraint-based genome-scale metabolic models for predicting functions of human intestinal microbes. Microbiome. 2017;5:78. doi: 10.1186/s40168-017-0299-x. PubMed DOI PMC

Lagier JC, et al. Many more microbes in humans: enlarging the microbiome repertoire. Clin. Infect. Dis. 2017;65:S20–S29. doi: 10.1093/cid/cix404. PubMed DOI

Machado D, Andrejev S, Tramontano M, Patil KR. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 2018;46:7542–7553. doi: 10.1093/nar/gky537. PubMed DOI PMC

Zorrilla, F., Buric, F., Patil, K. R. & Zelezniak, A. metaGEM: reconstruction of genome scale metabolic models directly from metagenomes. Nucleic Acids Res.49 (2021). 10.1093/nar/gkab 815 PubMed PMC

Bidkhori, G. et al. The reactobiome unravels a new paradigm in human gut microbiome metabolism. Preprint at 10.1101/2021.02.01.428114 (2021).

Zimmermann J, Kaleta C, Waschina S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol. 2021;22:81. doi: 10.1186/s13059-021-02295-1. PubMed DOI PMC

Heinken A, Magnusdottir S, Fleming RMT, Thiele I. DEMETER: efficient simultaneous curation of genome-scale reconstructions guided by experimental data and refined gene annotations. Bioinformatics. 2021;37:3974–3975. doi: 10.1093/bioinformatics/btab622. PubMed DOI PMC

Magnusdottir S, et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 2017;35:81–89. doi: 10.1038/nbt.3703. PubMed DOI

Brunk E, et al. Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat. Biotechnol. 2018;36:272–281. doi: 10.1038/nbt.4072. PubMed DOI PMC

Thiele I, et al. Personalized whole-body models integrate metabolism, physiology, and the gut microbiome. Mol. Syst. Biol. 2020;16:e8982. doi: 10.15252/msb.20198982. PubMed DOI PMC

Noronha A, et al. The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease. Nucleic Acids Res. 2019;47:D614–D624. doi: 10.1093/nar/gky992. PubMed DOI PMC

Arkin AP, et al. KBase: the United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 2018;36:566–569. doi: 10.1038/nbt.4163. PubMed DOI PMC

Bernstein DB, Sulheim S, Almaas E, Segre D. Addressing uncertainty in genome-scale metabolic model reconstruction and analysis. Genome Biol. 2021;22:64. doi: 10.1186/s13059-021-02289-z. PubMed DOI PMC

Aziz RK, et al. SEED servers: high-performance access to the SEED genomes, annotations, and metabolic models. PLoS ONE. 2012;7:e48053. doi: 10.1371/journal.pone.0048053. PubMed DOI PMC

Henry CS, et al. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat. Biotechnol. 2010;28:977–982. doi: 10.1038/nbt.1672. PubMed DOI

Norsigian CJ, et al. BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree. Nucleic Acids Res. 2020;48:D402–D406. PubMed PMC

Fleming RM, Vlassis N, Thiele I, Saunders MA. Conditions for duality between fluxes and concentrations in biochemical networks. J. Theor. Biol. 2016;409:1–10. doi: 10.1016/j.jtbi.2016.06.033. PubMed DOI PMC

Lim R, et al. Large-scale metabolic interaction network of the mouse and human gut microbiota. Sci. Data. 2020;7:204. doi: 10.1038/s41597-020-0516-5. PubMed DOI PMC

Sung J, et al. Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis. Nat. Commun. 2017;8:15393. doi: 10.1038/ncomms15393. PubMed DOI PMC

Madin JS, et al. A synthesis of bacterial and archaeal phenotypic trait data. Sci. Data. 2020;7:170. doi: 10.1038/s41597-020-0497-4. PubMed DOI PMC

Reimer LC, et al. BacDive in 2019: bacterial phenotypic data for high-throughput biodiversity analysis. Nucleic Acids Res. 2019;47:D631–D636. doi: 10.1093/nar/gky879. PubMed DOI PMC

Orth JD, Thiele I, Palsson BO. What is flux balance analysis? Nat. Biotechnol. 2010;28:245–248. doi: 10.1038/nbt.1614. PubMed DOI PMC

Zimmermann M, Zimmermann-Kogadeeva M, Wegmann R, Goodman AL. Separating host and microbiome contributions to drug pharmacokinetics and toxicity. Science. 2019;363:eaat9931. doi: 10.1126/science.aat9931. PubMed DOI PMC

Pollet RM, et al. An atlas of β-glucuronidases in the human intestinal microbiome. Structure. 2017;25:967–977.e5. doi: 10.1016/j.str.2017.05.003. PubMed DOI PMC

Heinken A, Hertel J, Thiele I. Metabolic modelling reveals broad changes in gut microbial metabolism in inflammatory bowel disease patients with dysbiosis. Syst. Biol. Appl. 2021;7:19. doi: 10.1038/s41540-021-00178-6. PubMed DOI PMC

Yachida S, et al. Metagenomic and metabolomic analyses reveal distinct stage-specific phenotypes of the gut microbiota in colorectal cancer. Nat. Med. 2019;25:968–976. doi: 10.1038/s41591-019-0458-7. PubMed DOI

Maini Rekdal V, Bess EN, Bisanz JE, Turnbaugh PJ, Balskus EP. Discovery and inhibition of an interspecies gut bacterial pathway for Levodopa metabolism. Science. 2019;364:eaau6323. doi: 10.1126/science.aau6323. PubMed DOI PMC

Wirbel J, et al. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nat. Med. 2019;25:679–689. doi: 10.1038/s41591-019-0406-6. PubMed DOI PMC

Hertel J, Heinken A, Martinelli F, Thiele I. Integration of constraint-based modeling with fecal metabolomics reveals large deleterious effects of Fusobacterium spp. on community butyrate production. Gut Microbes. 2021;13:1–23. doi: 10.1080/19490976.2021.1915673. PubMed DOI PMC

Lieven C, et al. MEMOTE for standardized genome-scale metabolic model testing. Nat. Biotechnol. 2020;38:272–276. doi: 10.1038/s41587-020-0446-y. PubMed DOI PMC

Heinken A, Thiele I. Microbiome Modelling Toolbox 2.0: efficient, tractable modelling of microbiome communities. Bioinformatics. 2022;38:2367–2368. doi: 10.1093/bioinformatics/btac082. PubMed DOI PMC

Sen P, Oresic M. Metabolic modeling of human gut microbiota on a genome scale: an overview. Metabolites. 2019;9:22. doi: 10.3390/metabo9020022. PubMed DOI PMC

Monk JM, et al. iML1515, a knowledgebase that computes Escherichia coli traits. Nat. Biotechnol. 2017;35:904–908. doi: 10.1038/nbt.3956. PubMed DOI PMC

Heinken, A., Basile, A. & Thiele, I. Advances in constraint-based modelling of microbial communities. Curr. Opin. Syst. Biol.27 (2021). 10.1016/j.coisb.2021.05.007

Heirendt L, et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat. Protoc. 2019;14:639–702. doi: 10.1038/s41596-018-0098-2. PubMed DOI PMC

Bebb JR, Scott BB. How effective are the usual treatments for ulcerative colitis? Aliment. Pharm. Ther. 2004;20:143–149. doi: 10.1111/j.1365-2036.2004.02018.x. PubMed DOI

Thiele I, Clancy CM, Heinken A, Fleming RMT. Quantitative systems pharmacology and the personalized drug-microbiota-diet axis. Curr. Opin. Syst. Biol. 2017;4:43–52. doi: 10.1016/j.coisb.2017.06.001. PubMed DOI PMC

Krauss M, et al. Integrating cellular metabolism into a multiscale whole-body model. PLoS Comput. Biol. 2012;8:e1002750. doi: 10.1371/journal.pcbi.1002750. PubMed DOI PMC

Heinken A, Basile A, Hertel J, Thinnes C, Thiele I. Genome-scale metabolic modeling of the human microbiome in the era of personalized medicine. Annu. Rev. Microbiol. 2021;75:199–222. doi: 10.1146/annurev-micro-060221-012134. PubMed DOI

van der Maaten L, Hinton G. Viualizing data using t-SNE. J. Mach. Learn. Res. 2008;9:2579–2605.

Overbeek R, et al. The subsystems approach to genome annotation and its use in the Project to Annotate 1000 Genomes. Nucleic Acids Res. 2005;33:5691–5702. doi: 10.1093/nar/gki866. PubMed DOI PMC

Bisanz JE, et al. A genomic toolkit for the mechanistic dissection of intractable human gut bacteria. Cell Host Microbe. 2020;27:1001–1013.e9. doi: 10.1016/j.chom.2020.04.006. PubMed DOI PMC

Forster SC, et al. A human gut bacterial genome and culture collection for improved metagenomic analyses. Nat. Biotechnol. 2019;37:186–192. doi: 10.1038/s41587-018-0009-7. PubMed DOI PMC

Disz T, et al. Accessing the SEED genome databases via Web services API: tools for programmers. BMC Bioinformatics. 2010;11:319. doi: 10.1186/1471-2105-11-319. PubMed DOI PMC

Ravcheev DA, Thiele I. Systematic genomic analysis reveals the complementary aerobic and anaerobic respiration capacities of the human gut microbiota. Front. Microbiol. 2014;5:674. doi: 10.3389/fmicb.2014.00674. PubMed DOI PMC

Magnusdottir S, Ravcheev D, de Crecy-Lagard V, Thiele I. Systematic genome assessment of B-vitamin biosynthesis suggests co-operation among gut microbes. Front. Genet. 2015;6:148. doi: 10.3389/fgene.2015.00148. PubMed DOI PMC

Ravcheev DA, Thiele I. Genomic analysis of the human gut microbiome suggests novel enzymes involved in quinone biosynthesis. Front. Microbiol. 2016;7:128. doi: 10.3389/fmicb.2016.00128. PubMed DOI PMC

Heinken A, et al. Personalized modeling of the human gut microbiome reveals distinct bile acid deconjugation and biotransformation potential in healthy and IBD individuals. Microbiome. 2019;7:75. doi: 10.1186/s40168-019-0689-3. PubMed DOI PMC

Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45:D353–D361. doi: 10.1093/nar/gkw1092. PubMed DOI PMC

Wolf YI, Koonin EV. A tight link between orthologs and bidirectional best hits in bacterial and archaeal genomes. Genome Biol. Evol. 2012;4:1286–1294. doi: 10.1093/gbe/evs100. PubMed DOI PMC

Altschul SF, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389–3402. doi: 10.1093/nar/25.17.3389. PubMed DOI PMC

Marchler-Bauer A, et al. CDD: conserved domains and protein three-dimensional structure. Nucleic Acids Res. 2013;41:D348–D352. doi: 10.1093/nar/gks1243. PubMed DOI PMC

Yu CS, Chen YC, Lu CH, Hwang JK. Prediction of protein subcellular localization. Proteins. 2006;64:643–651. doi: 10.1002/prot.21018. PubMed DOI

Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–1797. doi: 10.1093/nar/gkh340. PubMed DOI PMC

Larkin MA, et al. Clustal W and Clustal X version 2.0. Bioinformatics. 2007;23:2947–2948. doi: 10.1093/bioinformatics/btm404. PubMed DOI

Thompson JD, Gibson TJ, Plewniak F, Jeanmougin F, Higgins DG. The CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Res. 1997;25:4876–4882. doi: 10.1093/nar/25.24.4876. PubMed DOI PMC

Guindon S, et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 2010;59:307–321. doi: 10.1093/sysbio/syq010. PubMed DOI

Huson DH, et al. Dendroscope: an interactive viewer for large phylogenetic trees. BMC Bioinformatics. 2007;8:460. doi: 10.1186/1471-2105-8-460. PubMed DOI PMC

Krieg, N. et al. Bergey’s Manual® of Systematic Bacteriology (Springer, New York, 2010).

Chen IA, et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 2019;47:D666–D677. doi: 10.1093/nar/gky901. PubMed DOI PMC

Aziz RK, et al. The RAST Server: rapid annotations using subsystems technology. BMC Genomics. 2008;9:75. doi: 10.1186/1471-2164-9-75. PubMed DOI PMC

Kanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M. New approach for understanding genome variations in KEGG. Nucleic Acids Res. 2019;47:D590–D595. doi: 10.1093/nar/gky962. PubMed DOI PMC

Thorleifsson SG, Thiele I. rBioNet: a COBRA toolbox extension for reconstructing high-quality biochemical networks. Bioinformatics. 2011;27:2009–2010. doi: 10.1093/bioinformatics/btr308. PubMed DOI

Osterman A, Overbeek R. Missing genes in metabolic pathways: a comparative genomics approach. Curr. Opin. Chem. Biol. 2003;7:238–251. doi: 10.1016/S1367-5931(03)00027-9. PubMed DOI

Zou L, et al. Bacterial metabolism rescues the inhibition of intestinal drug absorption by food and drug additives. Proc. Natl Acad. Sci. USA. 2020;117:16009–16018. doi: 10.1073/pnas.1920483117. PubMed DOI PMC

Koppel N, Bisanz JE, Pandelia ME, Turnbaugh PJ, Balskus EP. Discovery and characterization of a prevalent human gut bacterial enzyme sufficient for the inactivation of a family of plant toxins. eLife. 2018;7:e33953. doi: 10.7554/eLife.33953. PubMed DOI PMC

Wishart DS, et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2018;46:D608–D617. doi: 10.1093/nar/gkx1089. PubMed DOI PMC

Hoffmann MF, et al. The Transformer database: biotransformation of xenobiotics. Nucleic Acids Res. 2014;42:D1113–D1117. doi: 10.1093/nar/gkt1246. PubMed DOI PMC

Wallace BD, et al. Alleviating cancer drug toxicity by inhibiting a bacterial enzyme. Science. 2010;330:831–835. doi: 10.1126/science.1191175. PubMed DOI PMC

Saitta KS, et al. Bacterial β-glucuronidase inhibition protects mice against enteropathy induced by indomethacin, ketoprofen or diclofenac: mode of action and pharmacokinetics. Xenobiotica. 2014;44:28–35. doi: 10.3109/00498254.2013.811314. PubMed DOI PMC

Sahoo S, Haraldsdottir H, Fleming RM, Thiele I. Modeling the effects of commonly used drugs on human metabolism. FEBS J. 2015;282:297–317. doi: 10.1111/febs.13128. PubMed DOI

Kim S, et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2019;47:D1102–D1109. doi: 10.1093/nar/gky1033. PubMed DOI PMC

Hastings J, et al. The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res. 2013;41:D456–D463. doi: 10.1093/nar/gks1146. PubMed DOI PMC

Heller SR, McNaught A, Pletnev I, Stein S, Tchekhovskoi D. InChI, the IUPAC international chemical identifier. J. Cheminform. 2015;7:23. doi: 10.1186/s13321-015-0068-4. PubMed DOI PMC

Rahman SA, et al. Reaction Decoder Tool (RDT): extracting features from chemical reactions. Bioinformatics. 2016;32:2065–2066. doi: 10.1093/bioinformatics/btw096. PubMed DOI PMC

Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat. Methods. 2015;12:59–60. doi: 10.1038/nmeth.3176. PubMed DOI

R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).

Truong DT, et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods. 2015;12:902–903. doi: 10.1038/nmeth.3589. PubMed DOI

Harrell, F. E. Regression Modeling Strategies: with Applications to Linear Models, Logistic Regression, and Survival Analysis (Springer, 2001).

Gudmundsson S, Thiele I. Computationally efficient flux variability analysis. BMC Bioinformatics. 2010;11:489. doi: 10.1186/1471-2105-11-489. PubMed DOI PMC

Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:W256–W259. doi: 10.1093/nar/gkz239. PubMed DOI PMC

Krzywinski M, et al. Circos: an information aesthetic for comparative genomics. Genome Res. 2009;19:1639–1645. doi: 10.1101/gr.092759.109. PubMed DOI PMC

Leveraging genome-scale metabolic models to understand aerobic methanotrophs