Individuals vary widely in their responses to medicinal drugs, which can be dangerous and expensive owing to treatment delays and adverse effects. Although increasing evidence implicates the gut microbiome in this variability, the molecular mechanisms involved remain largely unknown. Here we show, by measuring the ability of 76 human gut bacteria from diverse clades to metabolize 271 orally administered drugs, that many drugs are chemically modified by microorganisms. We combined high-throughput genetic analyses with mass spectrometry to systematically identify microbial gene products that metabolize drugs. These microbiome-encoded enzymes can directly and substantially affect intestinal and systemic drug metabolism in mice, and can explain the drug-metabolizing activities of human gut bacteria and communities on the basis of their genomic contents. These causal links between the gene content and metabolic activities of the microbiota connect interpersonal variability in microbiomes to interpersonal differences in drug metabolism, which has implications for medical therapy and drug development across multiple disease indications.
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All data generated during this study are included in the published Article and its Supplementary Tables. Data are available from figshare, at https://doi.org/10.6084/m9.figshare.8119058. Raw sequencing data have been deposited on the ENA server, with accession number PRJEB31790. Raw metabolomics data have been deposited in the MetaboLights repository, with accession number MTBLS896.
Analysis pipeline schemes and input files are available from figshare (https://doi.org/10.6084/m9.figshare.8119058); scripts for analysing data and generating figures are available on GitHub (https://github.com/mszimmermann/drug-bacteria-gene_mapping) and archived at Zenodo (https://doi.org/10.5281/zenodo.2827640).
Obach, R. S. Pharmacologically active drug metabolites: impact on drug discovery and pharmacotherapy. Pharmacol. Rev. 65, 578–640 (2013).
Sousa, T. et al. On the colonic bacterial metabolism of azo-bonded prodrugs of 5-aminosalicylic acid. J. Pharm. Sci. 103, 3171–3175 (2014).
Haiser, H. J. et al. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science 341, 295–298 (2013).
Okuda, H., Ogura, K., Kato, A., Takubo, H. & Watabe, T. A possible mechanism of eighteen patient deaths caused by interactions of sorivudine, a new antiviral drug, with oral 5-fluorouracil prodrugs. J. Pharmacol. Exp. Ther. 287, 791–799 (1998).
Zimmermann, M., Zimmermann-Kogadeeva, M., Wegmann, R. & Goodman, A. L. Separating host and microbiome contributions to drug pharmacokinetics and toxicity. Science 363, eaat9931 (2019).
Wallace, B. D. et al. Alleviating cancer drug toxicity by inhibiting a bacterial enzyme. Science 330, 831–835 (2010).
Klatt, N. R. et al. Vaginal bacteria modify HIV tenofovir microbicide efficacy in African women. Science 356, 938–945 (2017).
Aziz, R. K., Hegazy, S. M., Yasser, R., Rizkallah, M. R. & ElRakaiby, M. T. Drug pharmacomicrobiomics and toxicomicrobiomics: from scattered reports to systematic studies of drug–microbiome interactions. Expert Opin. Drug Metab. Toxicol. 14, 1043–1055 (2018).
Goodman, A. L. et al. Identifying genetic determinants needed to establish a human gut symbiont in its habitat. Cell Host Microbe 6, 279–289 (2009).
Wilson, I. D. & Nicholson, J. K. Gut microbiome interactions with drug metabolism, efficacy, and toxicity. Transl. Res. 179, 204–222 (2017).
Winter, J. et al. Mode of action of steroid desmolase and reductases synthesized by Clostridium “scindens” (formerly Clostridium strain 19). J. Lipid Res. 25, 1124–1131 (1984).
Morris, G. N., Winter, J., Cato, E. P., Ritchie, A. E. & Bokkenheuser, V. D. Clostridium scindens sp. nov., a human intestinal bacterium with desmolytic activity on corticoids. Int. J. Syst. Evol. Microbiol. 35, 478–481 (1985).
Ridlon, J. M. et al. Clostridium scindens: a human gut microbe with a high potential to convert glucocorticoids into androgens. J. Lipid Res. 54, 2437–2449 (2013).
Boyd, R. A. et al. The pharmacokinetics and pharmacodynamics of diltiazem and its metabolites in healthy adults after a single oral dose. Clin. Pharmacol. Ther. 46, 408–419 (1989).
Molden, E., Åsberg, A. & Christensen, H. Desacetyl-diltiazem displays severalfold higher affinity to CYP2D6 compared with CYP3A4. Drug Metab. Dispos. 30, 1–3 (2002).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Maier, L. et al. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 555, 623–628 (2018).
Wu, H. et al. Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat. Med. 23, 850–858 (2017).
Clayton, T. A., Baker, D., Lindon, J. C., Everett, J. R. & Nicholson, J. K. Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism. Proc. Natl Acad. Sci. USA 106, 14728–14733 (2009).
Björkholm, B. et al. Intestinal microbiota regulate xenobiotic metabolism in the liver. PLoS ONE 4, e6958 (2009).
Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2018).
Goodman, A. L. et al. Extensive personal human gut microbiota culture collections characterized and manipulated in gnotobiotic mice. Proc. Natl Acad. Sci. USA 108, 6252–6257 (2011).
Holdeman, L. V., Moore, W. E. C. & Cato, E. P. Anaerobe Laboratory Manual (Virginia Polytechnic Institute and State Univ., 1977).
Forsberg, K. J. et al. The shared antibiotic resistome of soil bacteria and human pathogens. Science 337, 1107–1111 (2012).
Goodman, A. L., Wu, M. & Gordon, J. I. Identifying microbial fitness determinants by insertion sequencing using genome-wide transposon mutant libraries. Nat. Protoc. 6, 1969–1980 (2011).
Koropatkin, N. M., Martens, E. C., Gordon, J. I. & Smith, T. J. Starch catabolism by a prominent human gut symbiont is directed by the recognition of amylose helices. Structure 16, 1105–1115 (2008).
Warrens, A. N., Jones, M. D. & Lechler, R. I. Splicing by overlap extension by PCR using asymmetric amplification: an improved technique for the generation of hybrid proteins of immunological interest. Gene 186, 29–35 (1997).
Whitaker, W. R., Shepherd, E. S. & Sonnenburg, J. L. Tunable expression tools enable single-cell strain distinction in the gut microbiome. Cell 169, 538–546.e12 (2017).
Petersen, T. N., Brunak, S., von Heijne, G. & Nielsen, H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat. Methods 8, 785–786 (2011).
Lim, B., Zimmermann, M., Barry, N. A. & Goodman, A. L. Engineered regulatory systems modulate gene expression of human commensals in the gut. Cell 169, 547–558.e15 (2017).
Cullen, T. W. et al. Antimicrobial peptide resistance mediates resilience of prominent gut commensals during inflammation. Science 347, 170–175 (2015).
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
McIver, L. J. et al. bioBakery: a meta’omic analysis environment. Bioinformatics 34, 1235–1237 (2018).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
Kaminski, J. et al. High-specificity targeted functional profiling in microbial communities with ShortBRED. PLOS Comput. Biol. 11, e1004557 (2015).
Suzek, B. E., Wang, Y., Huang, H., McGarvey, P. B. & Wu, C. H. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31, 926–932 (2015).
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
Yeung, P. K. et al. Pharmacokinetics and metabolism of diltiazem in healthy males and females following a single oral dose. Eur. J. Drug Metab. Pharmacokinet. 18, 199–206 (1993).
We thank the Goodman laboratory for helpful discussions and N. A. Barry, L. Valle, D. Lazo, W. B. Schofield, P. H. Degnan, T. Wu and the Yale Center for Molecular Discovery for technical assistance. This work was supported by NIH grants GM118159, GM105456, AI124275, DK114793, the Center for Microbiome Informatics and Therapeutics, the Burroughs Wellcome Fund, the Yale Cancer Center, and the HHMI Faculty and Pew Scholars Programs to A.L.G. M.Z. received Early and Advanced Postdoc Mobility Fellowships from the Swiss National Science Foundation (P2EZP3_162256 and P300PA_177915, respectively) and a Long-Term Fellowship (ALTF 670-2016) from the European Molecular Biology Organization. M.Z.-K. received an Early Postdoc Mobility Fellowship from the Swiss National Science Foundation (P2EZP3_178482).
M.Z., M.Z.-K. and A.L.G. have filed a patent application based on these studies with the US Patent and Trademark Office (62/693,741). The specific aspects included in the patent application include methods for measuring microbial drug metabolism and for identifying microbial taxa and gene products associated with drug metabolism.
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Extended data figures and tables
Extended Data Fig. 1 Setup of drug assay, characterization of tested drugs and summary of metabolic bacteria–drug interactions.
a, Schematic of combinatorial pooling scheme using 21 drug pools (A–U) and 3 non-drug controls (V–X). Each of the 271 drugs is tested in quadruplicate (present in 4 pools) and any 2 drugs are tested in the same pool twice at most (Supplementary Table 2). b, c, Molecular mass (b) and log P value (c) distribution of 271 tested drugs (red) in comparison with 2,099 clinically approved drugs (from DrugBank21). d, Distribution of predicted drug concentration in the colon for 58 of the 271 drugs tested (data from a previous study17). The predicted median and mean concentration in the large intestine for these compounds is 103 μM and 362 μM, respectively, when each drug is administered at its standard oral dose. e, Number of drugs metabolized as a function of the selection threshold (metabolized fraction). f, Number of gut bacteria that metabolize a given drug. g, Number of drugs metabolized by each bacterial strain.
Extended Data Fig. 2 Metabolism of drugs previously reported to be transformed by bacteria, and functional chemical group distribution.
a, Per cent of consumption between 0 h and 12 h for each drug after incubation with each gut bacterial species or strain are shown. Bars and error bars depict the mean and s.d. of n = 4 assay replicates. b, Distribution of functional chemical groups in drugs that are metabolized or not metabolized across the 76 bacterial strains we tested. The abundance of each chemical group among the 271 selected drugs and 2,099 clinical drugs (from DrugBank21) is indicated.
Extended Data Fig. 4 Structural drug features targeted for biotransformation and microbiome metabolism of dexamethasone.
a, Examples of drugs that are associated with a particular mass shift between the parent drug and its metabolite. Functional groups that are enriched in drugs that undergo specific mass shift (Fig. 2d) are highlighted. b, Dexamethasone metabolism by each of the 76 bacterial strains we tested. Bar plots and error bars represent mean and s.d. of n = 4 assay replicates. c, Validation of C. scindens desmolase activity by mass comparison of metabolites produced from either dexamethasone or d5-dexamethasone and their corresponding LC–MS/MS spectra. Shaded areas correspond to mean ± s.d., n = 6 independent cultures. Representative ion fragments are highlighted in red, to illustrate the loss of the dexamethasone side chain (labelled with two deuterium atoms, compared to the steroid backbone labelled with three deuterium atoms).
a, Dexamethasone serum profile in conventional mice after a single oral dose of dexamethasone. Line depicts the fit of first-order drug elimination kinetics. n = 4 mice in total (of either sex) were used for each time point. Data are provided in Supplementary Table 7. b, Dexamethasone and dexamethasone metabolite levels across tissues of germ-free and C. scindens mono-colonized gnotobiotic mice (GNC. scindens) after 7 h of drug exposure. Horizontal lines show mean values of n = 6 mice. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001 (unpaired two-sided Student’s t-test). Data and P values are provided in Supplementary Table 8. c, C. scindens desmolase activity for different corticosteroids. Shaded areas correspond to mean ± s.d., n = 6 independent cultures. d, Bacterial density of human gut communities. CFU denotes colony-forming units measured by anaerobic culturing. Horizontal bars represent mean of n = 4 independent cultures. e, Ex vivo dexamethasone metabolism of gut communities isolated from 28 humans (each colour represents a human donor, lines depict the mean of n = 4 replicate assays). C. scindens species abundance (quantified by species-specific quantitative PCR) is not sufficient to explain the dexamethasone-metabolizing activity of these human gut communities. Data are provided in Supplementary Table 9. f, Correlation between community CFU ml−1 values and dexamethasone (left) or androgen dexamethasone metabolite (right) consumption and production slopes after 12 h of incubation with each of the 28 human gut communities. P values were calculated for the null hypothesis that there is zero correlation, against the two-sided alternative that there is non-zero correlation (see Methods).
Extended Data Fig. 6 Gain-of-function approach to identifying microbial drug-metabolizing gene products, using diltiazem metabolism by B. thetaiotaomicron as an example.
a, Drugs metabolized by B. thetaiotaomicron and candidate drug metabolites identified by untargeted metabolomics. b, Identification of active 384-well library plates that include clones with diltiazem deacetylation activity. c, Mapping of diltiazem-converting activity within active plates to identify active clones. d, Four independent E. coli clones that demonstrate gain of diltiazem-metabolizing activity carry inserts that all map to the same region in the B. thetaiotaomicron genome. e, Validation of BT_4096 activity by targeted expression of the open reading frame in E. coli. Shaded areas depict the mean and s.d. of independent cultures or assays (n = 4). f, Bacterial load of gnotobiotic mice mono-colonized with either B. thetaiotaomicron wild type or the bt4096-mutant strain. Horizontal bars represent mean of n = 35 independent mice per group. P value was calculated with unpaired two-sided Student’s t-test. g, In vitro enzyme assay with N-desmethyldiltiazem as substrate to demonstrate that BT_4096 also deacetylates N-desmethyldiltiazem, which is the major metabolic product of diltiazem metabolism in the mouse. Lines and shaded areas depict the mean and s.d. of n = 4 assay replicates, respectively.
a, Diltiazem and diltiazem metabolite kinetics in tissues following a single oral dose of diltiazem in gnotobiotic mice mono-colonized with B. thetaiotaomicron wild-type or the bt4096-mutant strain. b, Intestinal diltiazem and diltiazem metabolite levels following multiple oral doses of diltiazem in mice mono-colonized with B. thetaiotaomicron wild-type or the bt4096-mutant strain. Five oral doses were administrated to mice in six-hour intervals. Tissues were collected 12 h after the final oral dose of diltiazem. For all mouse experiments, horizontal lines show the mean of n = 5 mice and times reflect hours after oral administration of diltiazem. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001 (unpaired two-sided Student’s t-test with FDR correction for multiple hypotheses testing). Data and P values are provided in Supplementary Tables 10, 11.
a, B. thetaiotaomicron genomic DNA fragments identified in norethindrone-acetate- and pericyazine-metabolizing E. coli clones. b, All drug-metabolizing gain-of-function hits were validated by assays with E. coli expression constructs that carried PCR-amplified gene sequences, and their combinations in the case of operons (for example, BT_2068–BT_2066, metabolizing norethindrone). c, d, Levonorgestrel- and progesterone-metabolizing activity of BT_2068, shown by E. coli that expresses bt2068 (c) and B. thetaiotaomicron wild-type, bt2068-mutant and bt2068-complemented strains (d). Gene complementation at different expression levels: P1E6 > P4E5 > P2E5 > P5E4 > P1E4 > P2E3. e, Example of LC–MS/MS validation of O-acetyl-periciazine. The experiment was performed n = 3 times, with comparable results. f, Enzymatic validation of O-acetyl- and O-propionyl-transferase activity using purified BT_2367 and pericyazine as substrate. g, Enzymatic validation of O-acyl-transferase activity of purified BT_2367 using substrates structurally similar to pericyazine. Although no acetyl-transferase activity could be measured for cyamemazine, aminopropylpiperidinol is converted to O-acetyl-aminopropylpiperidinol by BT_2367. In a–d, f, g, shaded areas depict the mean and s.d. of independent cultures or assays (n = 4).
Extended Data Fig. 10 Identified drug-metabolizing gene products explain the observed drug metabolism of microbial strains and species.
a, Genome coverage and fragment size distribution in E. coli gain-of-function libraries specific for B. dorei (based on 78 sequenced clones) and C. aerofaciens (based on 81 sequenced clones). Both libraries contained about 37,000 clones. b, Network of enzyme–substrate–product drug metabolic interactions for B. dorei and C. aerofaciens. Each node represents an enzyme (rectangles), a drug substrate (hexagons) or a metabolite product (circles), and each edge represents a validated metabolic interaction (targeted cloning of the gene into E. coli results in metabolism of a given drug or production of a specific drug metabolite). c, Comparison between maximal BD_03091 and CA_01707 (BD refers to the BACDOR gene locus tag abbreviation for B. dorei; CA refers to the COLAER gene locus tag abbreviation for C. aerofaciens) identity of a given bacterial strain and its metabolism of norethindrone acetate and tinidazole, respectively. d, e, Reciprocal BLAST analysis of identified drug-metabolizing proteins. Line width depicts the percentage of length (d) and identity (e) of mutual protein sequence alignment. e, Specific drug metabolism rates of 67 genome-sequenced gut bacteria, and the presence of homologues to respective drug-metabolizing gene products. Notably, roxatidine acetate, famciclovir, diacetamate and diltiazem (Fig. 5b) all undergo the same chemical transformation (deacetylation), but distinct sets of gene products explain their microbial metabolism. Bars and error bars represent mean and s.d. of n = 4 assay replicates.
Extended Data Fig. 11 Identified drug-metabolizing gene products explain the observed drug metabolism of bacterial gut communities.
a, b, Diltiazem conversion to desacetyldiltiazem by 28 human gut communities (each colour represents a human donor, lines depict the mean of n = 4 assay replicates). Diltiazem-metabolizing activity of the microbiota does not correlate with total bacterial culture densities or with microbiota abundance of B. thetaiotaomicron (quantified by species-specific 16S RNA quantitative PCR). P values were calculated for the null hypothesis that there is zero correlation, against the two-sided alternative that there is non-zero correlation (see Methods). c, Composition and diversity of the 28 bacterial communities based on metagenomic sequencing. d, Correlation analysis between diltiazem-metabolizing activity of the microbiota and community CFU or metagenomic abundance of BT_4096 homologues, and diltiazem-metabolizing bacterial species, genera and phyla identified in this study. e, f, Correlation analysis identical to that shown in d, but for the metabolism of norethindrone acetate and famciclovir by the 28 bacterial communities (each colour represents a human donor, lines depict the mean of n = 4 replicate assays). P values were calculated for the null hypothesis that there is zero correlation, against the two-sided alternative that there is non-zero correlation (see Methods). Data are available in Supplementary Tables 16–19.
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Zimmermann, M., Zimmermann-Kogadeeva, M., Wegmann, R. et al. Mapping human microbiome drug metabolism by gut bacteria and their genes. Nature 570, 462–467 (2019). https://doi.org/10.1038/s41586-019-1291-3
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