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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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 PXD016064. Source data are provided with this paper.
The data analysis codes are available at https://github.com/sandrejev/drugs_bioaccumulation.
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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.
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.
Peer review information Nature thanks Kim Lewis, Michael Shapira and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data figures and tables
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.
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.
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.
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).
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. e–h, 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.
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.
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.
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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