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Bioaccumulation of therapeutic drugs by human gut bacteria

Abstract

Bacteria in the gut can modulate the availability and efficacy of therapeutic drugs. However, the systematic mapping of the interactions between drugs and bacteria has only started recently1 and the main underlying mechanism proposed is the chemical transformation of drugs by microorganisms (biotransformation). Here we investigated the depletion of 15 structurally diverse drugs by 25 representative strains of gut bacteria. This revealed 70 bacteria–drug interactions, 29 of which had not to our knowledge been reported before. Over half of the new interactions can be ascribed to bioaccumulation; that is, bacteria storing the drug intracellularly without chemically modifying it, and in most cases without the growth of the bacteria being affected. As a case in point, we studied the molecular basis of bioaccumulation of the widely used antidepressant duloxetine by using click chemistry, thermal proteome profiling and metabolomics. We find that duloxetine binds to several metabolic enzymes and changes the metabolite secretion of the respective bacteria. When tested in a defined microbial community of accumulators and non-accumulators, duloxetine markedly altered the composition of the community through metabolic cross-feeding. We further validated our findings in an animal model, showing that bioaccumulating bacteria attenuate the behavioural response of Caenorhabditis elegans to duloxetine. Together, our results show that bioaccumulation by gut bacteria may be a common mechanism that alters drug availability and bacterial metabolism, with implications for microbiota composition, pharmacokinetics, side effects and drug responses, probably in an individual manner.

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Fig. 1: Gut bacteria accumulate therapeutic drugs without altering them.
Fig. 2: Bioaccumulation of duloxetine affects bacterial physiology.
Fig. 3: Duloxetine bioaccumulation alters community assembly and host response.

Data availability

All data generated during this study are included in this published Article (and its Supplementary Information files). Supplementary Table 18 provides an overview of the different methods and data associated with all figures. UPLC and mass spectrometry data are deposited at the MetaboLights repository under the accession codes MTBLS1264, MTBLS1757, MTBLS1627, MTBLS1319, MTBLS1791, MTBLS1792, and MTBLS2885. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the dataset identifiers PXD016062 and PXD016064Source data are provided with this paper.

Code availability

The data analysis codes are available at https://github.com/sandrejev/drugs_bioaccumulation.

References

  1. 1.

    Zimmermann, M., Zimmermann-Kogadeeva, M., Wegmann, R. & Goodman, A. L. Mapping human microbiome drug metabolism by gut bacteria and their genes. Nature 570, 462–467 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Forslund, K., Hildebrand, F., Nielsen, T. & Falony, G. A. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).

    ADS  CAS  PubMed  Article  Google Scholar 

  4. 4.

    Maier, L. & Typas, A. Systematically investigating the impact of medication on the gut microbiome. Curr. Opin. Microbiol. 39, 128–135 (2017).

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Jackson, M. A. et al. Gut microbiota associations with common diseases and prescription medications in a population-based cohort. Nat. Commun. 9, 2655 (2018).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  6. 6.

    Maier, L. et al. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 555, 623–628 (2018).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Spanogiannopoulos, P., Bess, E. N., Carmody, R. N. & Turnbaugh, P. J. The microbial pharmacists within us: a metagenomic view of xenobiotic metabolism. Nat. Rev. Microbiol. 14, 273–287 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Alexander, J. L. et al. Gut microbiota modulation of chemotherapy efficacy and toxicity. Nat. Rev. Gastroenterol. Hepatol. 14, 356–365 (2017).

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Fuller, A. T. Is p-aminobenzenesulphonamide the active agent in prontosil therapy? Lancet 229, 194–198 (1937).

    Article  Google Scholar 

  10. 10.

    Goldman, P., Peppercorn, M. A. & Goldin, B. R. Metabolism of drugs by microorganisms in the intestine. Am. J. Clin. Nutr. 27, 1348–1355 (1974).

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Chhabra, R. S. Intestinal absorption and metabolism of xenobiotics. Environ. Health Perspect. 33, 61–69 (1979).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Koppel, N., Maini Rekdal, V. & Balskus, E. P. Chemical transformation of xenobiotics by the human gut microbiota. Science 356, eaag2770 (2017).

    PubMed  Article  CAS  Google Scholar 

  13. 13.

    Sousa, T. et al. The gastrointestinal microbiota as a site for the biotransformation of drugs. Int. J. Pharm. 363, 1–25 (2008).

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Klaassen, C. D. & Cui, J. Y. Review: mechanisms of how the intestinal microbiota alters the effects of drugs and bile acids. Drug Metab. Dispos. 43, 1505–1521 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Haiser, H. J., Seim, K. L., Balskus, E. P. & Turnbaugh, P. J. Mechanistic insight into digoxin inactivation by Eggerthella lenta augments our understanding of its pharmacokinetics. Gut Microbes 5, 233–238 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Koppel, N., Bisanz, J. E., Pandelia, M.-E., Turnbaugh, P. J. & Balskus, E. P. Discovery and characterization of a prevalent human gut bacterial enzyme sufficient for the inactivation of a family of plant toxins. eLife 7, e33953 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Wallace, B. D., Wang, H., Lane, K. T. & Scott, J. E. Alleviating cancer drug toxicity by inhibiting a bacterial enzyme. Science 330, 831–835 (2010).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Tramontano, M. et al. Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies. Nat. Microbiol. 3, 514–522 (2018).

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Chrystal, E. J. T., Koch, R. L., McLafferty, M. A. & Goldman, P. Relationship between metronidazole metabolism and bactericidal activity. Antimicrob. Agents Chemother. 18, 566–573 (1980).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Mahmood, S., Khalid, A., Arshad, M., Mahmood, T. & Crowley, D. E. Detoxification of azo dyes by bacterial oxidoreductase enzymes. Crit. Rev. Biotechnol. 36, 639–651 (2016).

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Khan, A. K. A., Guthrie, G., Johnston, H. H., Truelove, S. C. & Williamson, D. H. Tissue and bacterial splitting of sulphasalazine. Clin. Sci. 64, 349–354 (1983).

    CAS  Article  Google Scholar 

  22. 22.

    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).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Shu, Y. Z. & Kingston, D. G. A. Metabolism of levamisole, an anti-colon cancer drug, by human intestinal bacteria. Xenobiotica 21, 737–750 (1991).

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Schloissnig, S. et al. Genomic variation landscape of the human gut microbiome. Nature 493, 45–50 (2013).

    ADS  PubMed  Article  CAS  Google Scholar 

  25. 25.

    Fenner, K., Canonica, S., Wackett, L. P. & Elsner, M. Evaluating pesticide degradation in the environment: blind spots and emerging opportunities. Science 341, 752–758 (2013).

    ADS  CAS  PubMed  Article  Google Scholar 

  26. 26.

    Gulde, R., Anliker, S., Kohler, H. E. & Fenner, K. Ion trapping of amines in protozoa: a novel removal mechanism for micropollutants in activated sludge. Environ. Sci. Technol. 52, 52–60 (2018).

    ADS  CAS  PubMed  Article  Google Scholar 

  27. 27.

    Congeevaram, S., Dhanarani, S., Park, J., Dexilin, M. & Thamaraiselvi, K. Biosorption of chromium and nickel by heavy metal resistant fungal and bacterial isolates. J. Hazard. Mater. 146, 270–277 (2007).

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Bae, W., Chen, W., Mulchandani, A. & Mehra, R. K. Enhanced bioaccumulation of heavy metals by bacterial cells displaying synthetic phytochelatins. Biotechnol. Bioeng. 70, 518–524 (2000).

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    Becher, I. et al. Thermal profiling reveals phenylalanine hydroxylase as an off-target of panobinostat. Nat. Chem. Biol. 12, 908–910 (2016).

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Franken, H. et al. Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry. Nat. Protoc. 10, 1567–1593 (2015).

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Brochado, A. R. et al. Species-specific activity of antibacterial drug combinations. Nature 559, 259–263 (2018).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Rakoff-Nahoum, S., Coyne, M. J. & Comstock, L. E. An ecological network of polysaccharide utilization among human intestinal symbionts. Curr. Biol. 24, 40–49 (2014).

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    Hooper, L. V., Littman, D. R. & Macpherson, A. J. Interactions between the microbiota and the immune system. Science 336, 1268–1273 (2012).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Zhang, F. et al. Caenorhabditis elegans as a model for microbiome research. Front. Microbiol. 8, 485 (2017).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Vetizou, M. et al. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science 350, 1079–107 (2015).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    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).

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    Macedo, D. et al. Antidepressants, antimicrobials or both? Gut microbiota dysbiosis in depression and possible implications of the antimicrobial effects of antidepressant drugs for antidepressant effectiveness. J. Affect. Disord. 208, 22–32 (2017).

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Sharon, G., Sampson, T. R., Geschwind, D. H. & Mazmanian, S. K. The central nervous system and the gut microbiome. Cell 167, 915–932 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Dent, R. et al. Changes in body weight and psychotropic drugs: a systematic synthesis of the literature. PLoS ONE 7, e36889 (2012).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794–1805 (2011).

    ADS  CAS  PubMed  Article  Google Scholar 

  42. 42.

    Elias, J. E. & Gygi, S. P. Target–decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).

    PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    MathSciNet  MATH  Google Scholar 

  45. 45.

    Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Porollo, A. EC2KEGG: a command line tool for comparison of metabolic pathways. Source Code Biol. Med. 9, 19 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  47. 47.

    Mateus, A. et al. Thermal proteome profiling in bacteria: probing protein state in vivo. Mol. Syst. Biol. 14, e8242 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  48. 48.

    Hughes, C. S. et al. Ultrasensitive proteome analysis using paramagnetic bead technology. Mol. Syst. Biol. 10, 757 (2014).

    PubMed  Article  CAS  Google Scholar 

  49. 49.

    Hughes, C. S. et al. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat. Protoc. 14, 68–85 (2019).

    CAS  PubMed  Article  Google Scholar 

  50. 50.

    Ortmayr, K., Charwat, V., Kasper, C., Hann, S. & Koellensperger, G. Uncertainty budgeting in fold change determination and implications for non-targeted metabolomics studies in model systems. Analyst, 142, 80–90 (2017).

    ADS  CAS  Article  Google Scholar 

  51. 51.

    He, L., Diedrich, J., Chu, Y. Y. & Yates, J. R. 3rd Extracting accurate precursor information for tandem mass spectra by RawConverter. Anal. Chem. 87, 11361–11367 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Mahieu, N. G., Genenbacher, J. L. & Patti, G. J. A roadmap for the XCMS family of software solutions in metabolomics. Curr. Opin. Chem. Biol. 30, 87–93 (2016).

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).

    CAS  PubMed  Article  Google Scholar 

  54. 54.

    Vinaixa, M. et al. A guideline to univariate statistical analysis for lc/ms-based untargeted metabolomics-derived data. Metabolites 2, 775–795 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Smith, C. A. et al. METLIN: a metabolite mass spectral database. Ther. Drug Monit. 27, 747–751 (2005).

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Tanabe, M. & Kanehisa, M. Using the KEGG database resource. Curr. Protoc. Bioinfomatics 38, 1.12.1–1.12.43 (2012).

    Google Scholar 

  57. 57.

    Fuhrer, T., Heer, D., Begemann, B. & Zamboni, N. High-throughput, accurate mass metabolome profiling of cellular extracts by flow injection–time-of-flight mass spectrometry. Anal. Chem. 83, 7074–7080 (2011).

    CAS  PubMed  Article  Google Scholar 

  58. 58.

    Ponomarova, O. et al. yeast creates a niche for symbiotic lactic acid bacteria through nitrogen overflow. Cell Syst. 5, 345–357 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Wishart, D. S. et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 46, D608–D617 (2018).

    CAS  PubMed  Article  Google Scholar 

  60. 60.

    Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3, 211–221 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. USA 108, 4516–4522 (2011).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. 62.

    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    CAS  PubMed  Article  Google Scholar 

  63. 63.

    Brenner, S. The genetics of Caenorhabditis elegans. Genetics 77, 71–94 (1974).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Kanehisa, M. et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199–D205 (2014).

    CAS  PubMed  Article  Google Scholar 

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Acknowledgements

This project was supported by the European Union’s Horizon 2020 research and innovation programme under the grant agreement number 686070, and by the UK Medical Research Council (project number MC_UU_00025/11). A.M., L.M., M.T. and V.P. were supported by the EMBL interdisciplinary postdoctoral program. We thank EMBL Genomics, Metabolomics and Proteomics core facilities for their support in respective analyses.

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Authors

Contributions

M. Klünemann, P.B., A.T. and K.R.P. conceived the study. M. Klünemann, S.A., A.T. and K.R.P. planned the overall experiments. S.A. performed the overall data analysis. K.Z. and V.P. performed the drug clustering. M. Klünemann carried out the interaction screen, large-volume validation and UPLC data analysis. A.R.B., L.M., M.T. and M. Banzhaf assisted with the screen set-up. F.H. and C.S. designed and synthesized the clickable drug. M. Klünemann, M.-T.M. and T.B. performed the click chemistry proteomics experiments. M. Beck designed and supervised the click chemistry proteomics analysis. A.M. and M.M.S. planned the TPP experiments. S.B. and A.M. performed the TPP experiments. A.M., M.M.S. and S.A. analysed the TPP data. J.V. and D.C.S. performed the FIA–MS experiments and data analysis. S.B. performed the bacterial culturing experiments and P.P. performed the LC–MS analysis for drug measurements. M. Klünemann and P.P. performed the secreted metabolite LC–MS analysis in buffer. M. Klünemann and S.B. prepared the samples for, and B.S., L.N. and J.H. performed, the NMR analysis. M. Klünemann and D.K. performed growth assays and sample preparation for cross-fed metabolite analysis. S.B. performed growth assays and sample preparation for secreted metabolite analysis in GMM. S.D., E.M., E.K. and M.Z. performed the HILIC–MS/MS analysis. K.Z. analysed the cross-feeding metabolomics data. M. Kumar performed the motif analysis. M. Klünemann performed the community assembly experiments and Y.K. analysed the 16S data. T.A.S. and F.C. performed the C. elegans experiments and data analysis, D.K. measured drug concentrations. M. Klünemann and K.R.P. wrote the paper.

Corresponding authors

Correspondence to Peer Bork, Athanasios Typas or Kiran R. Patil.

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Competing interests

M. Klünemann, S.A., L.M., M.T., Y.K., P.B., A.T. and K.R.P. are inventors in a patent application based on the findings reported in this study (US patent application number 16966322). S.B., A.M., P.P., S.D., J.V., B.S., T.A.S., E.K., D.K., K.Z., E.M., M. Banzhaf, M.-T.M., F.H., L.N., A.R.B., T.B., V.P., M. Kumar, C.S., M. Beck, J.H., M.Z., D.C.S., F.C. and M.M.S. declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Bacteria and drug selection.

a, Distribution of the selected 25 bacterial strains by their phylogenetic class, and their cumulative metabolic diversity measured as the coverage of annotated enzymes as per the KEGG database64. b, We started with approximately 1,000 annotated drugs from the SIDER side effect database (Kuhn et al. 2016), which were filtered for their gut related side effects. Drug selection was enriched from another database (Saad et al. 2012) for known or suspected interactions with the gut microbiome, before filtered for oral administration and manually curated for overall interest. Final selection was filtered for availability from vendors and establishment of UPLC methods. c, The drugs used in this study span a broad range of structural diversity. Shown is the spread of the selected drugs in a principle coordinate analysis, covering >2,000 drugs from the DrugBank database. Maximum common sub-structure was used to calculate the distances between drug pairs. d, Selected drugs cover several therapeutic classes / indication areas. e, Chemical structures of the 15 drugs used in this study.

Extended Data Fig. 2 Correlation between the screen and validation in higher-volume cultures.

For screen, n≥4 independent replicates (median number of replicates = 17). For validation, n = 3. Error bars = S.E.M. For screening, multiple independent batches were performed as indicated in Supplementary Table 3. Shown R (correlation coefficient) and p-value based on Pearson correlation test.

Extended Data Fig. 3 NMR measurements showing duloxetine depletion by bacterial cells.

a, B. uniformis, b, E. coli ED1A, c, E. coli IAI1, and d, C. saccharolyticum. e, NMR spectrum from C. saccharolyticum cell pellet extract showing that the recovered drug is unmodified duloxetine. Resonances appearing to be out of phase and strong baseline distortions are due to the presence of large solvent signals outside the displayed chemical shift range.

Extended Data Fig. 4 NMR measurements showing unmodified duloxetine recovered from bacterial pellet.

Bacterial cells were incubated with the drug for 4 h in PBS buffer prior to recovery. a, Illustration of the experimental procedure marking the sample collection points. b, NMR spectra of recovered duloxetine from different fractions of E. coli IAI1 and C. saccharolyticum preincubated in PBS. The reference spectrum was scaled to the amount present in the sample to assess the relative amount of free duloxetine present in respective samples. Resonances appearing to be out of phase and strong baseline distortions are due to the presence of large solvent signals outside the displayed chemical shift range.

Extended Data Fig. 5 Duloxetine bioaccumulation by E. coli IAI1 in GMM and recovery from pellet.

a, Procedure and collected samples: S0-S4. b, Recovered duloxetine from different samples (S0-S4) collected as described in a. Different starting duloxetine concentrations, between 0-70 µM, were used. S0 = medium without bacteria (drug only control), S1 = total culture (medium plus bacteria), S2 = supernatant, S3 = wash (pellet was washed with PBS, no drug was found therein supporting intracellular accumulation), S4 = washed pellet. n = 3, error bars = SD, central squares mark the mean. c, MS/MS spectra of duloxetine standard (bottom) and duloxetine detected in a S1 sample (top).

Extended Data Fig. 6 Molecular effects of duloxetine bioaccumulation.

a, Alkynated duloxetine made for the biotin-pull down assay. b, Fold change of proteins detected in the duloxetine pull down assay in C. saccharolyticum lysate using alkynated duloxetine. Four replicates were used in both test and control sets. Significantly enriched (hypergeometric test, FDR corrected p < 0.1, log2(Fc)>2) proteins are shown in red. c, d, Bioaccumulating E. coli strain features larger change in protein abundance in response to drug treatment. Shown are the number of proteins with altered abundance in E. coli ED1A (c, non-bioaccumulating), and E. coli IAI1 (d, bioaccumulating) strains in response to duloxetine exposure at different concentrations. eh, Comparison of MS/MS spectra of four nucleotide-pathway metabolites from the supernatant of duloxetine-treated C. saccharolyticum with MS/MS spectra of analytical standards. (CE = 10 eV; further details in Methods) Related to Fig. 2d and Supplementary Table 11.

Extended Data Fig. 7 Duloxetine induces a shift in metabolite secretion.

a, Effect of duloxetine treatment on the exo-metabolome of six gut bacterial strains. Shown is the distribution of individual samples over the first two principle components. Principle Component Analysis (PCA) was performed on untargeted FIA–MS data (Methods). The numbers in parentheses of PC1 and PC2 mark the corresponding explained variance for the first and the second principle component, respectively. The dotted block arrow marks the duloxetine induced shift in exo-metabolome of C. saccharolyticum. b, Duloxetine concentration dependent changes in the C. saccharolyticum exo-metabolome. The ion mapping to the deprotonated duloxetine was removed from the PCA analysis shown in a and b. c, The signal for the closest matching ion for deprotonated duloxetine [M-H]- from the exometabolomics data (m/z 296.110079) plotted against initial duloxetine concentration. Data from all six species are pooled together (n = 24 for each initial duloxetine concentration). Overlaid box plots show the interquartile range (IQR), the median value and whiskers extending to include all the values less than 1.5 × IQR away from the 1st or 3rd quartile, respectively. d, Duloxetine signal in the FIA–MS data stratified by species. The signal for the closest matching ion for deprotonated duloxetine [M-H]- from the exometabolomics data (FIA–MS) (m/z 296.110079) plotted against initial duloxetine concentration. Thick transparent line traces medians of replicates (n = 4) at each initial concentration. The dotted lines show linear regression fit.

Extended Data Fig. 8 Duloxetine-induced exo-metabolome changes.

a, Change in C. saccharolyticum exo-metabolome (HILIC-MS data) in response to non-bioaccumulated roflumilast. b, Same as in Fig. 2g, but based on 69 metabolites, whose chemical identity was putatively assigned, and confirmed for 2 metabolites using chemical standards (Supplementary Fig. 3, Supplementary Table 17), using HILIC-MS/MS analysis.

Extended Data Fig. 9 Duloxetine bioaccumulation, community assembly and host response.

a, E. rectale relative abundance in transfer assays based on 16-S amplicon reads. b, E. rectale relative abundance as in a but normalized with respect to equal abundance of each of the five species in the inoculum mixture. Mean values from biological triplicates are shown. c, Duloxetine depletion in community assembly assay. Dashed line indicates mean of control. n = 6 (3 biological replicates, 2 measurements per sample); overlaid box plots show the interquartile range (IQR), the median value and whiskers extending to include all the values less than 1.5 × IQR away from the 1st or 3rd quartile, respectively. d, Metabolic cross-feeding between S. salivarius and E. rectale. Shown are the results of untargeted metabolomics analysis (FIA–MS) of supernatants collected during the growth of S. salivarius in GMM with duloxetine and the subsequent growth of E. rectale in the cell-free conditioned medium. Shown are the profiles of the ions that increased during S. salivarius growth and decreased during E. rectale growth, implying cross-feeding. Ions showing similar pattern in the drug-free solvent (DMSO) control were filtered out. Mean intensities from three biological replicates are shown. e, Dose dependent effects of duloxetine on muscular function in wild type C. elegans animals. Larval stage four (L4) worms were incubated in LB medium in the presence of duloxetine at the indicated concentrations. Each bar represents the mean of six independent experiments, each performed with two technical replicates, ± SD. P values mark difference to the no-drug control, estimated using one-way ANOVA followed by correction for multiple pair-wise comparisons (Tukey’s test). f, Duloxetine concentration in the C. elegans behaviour assay (n = 6; 3 biological replicates, 2 measurements per sample). Overlaid box plots show the interquartile range (IQR), the median value and whiskers extending to include all the values less than 1.5 × IQR away from the 1st or 3rd quartile, respectively

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Klünemann, M., Andrejev, S., Blasche, S. et al. Bioaccumulation of therapeutic drugs by human gut bacteria. Nature 597, 533–538 (2021). https://doi.org/10.1038/s41586-021-03891-8

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