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Unravelling the collateral damage of antibiotics on gut bacteria

Abstract

Antibiotics are used to fight pathogens but also target commensal bacteria, disturbing the composition of gut microbiota and causing dysbiosis and disease1. Despite this well-known collateral damage, the activity spectrum of different antibiotic classes on gut bacteria remains poorly characterized. Here we characterize further 144 antibiotics from a previous screen of more than 1,000 drugs on 38 representative human gut microbiome species2. Antibiotic classes exhibited distinct inhibition spectra, including generation dependence for quinolones and phylogeny independence for β-lactams. Macrolides and tetracyclines, both prototypic bacteriostatic protein synthesis inhibitors, inhibited nearly all commensals tested but also killed several species. Killed bacteria were more readily eliminated from in vitro communities than those inhibited. This species-specific killing activity challenges the long-standing distinction between bactericidal and bacteriostatic antibiotic classes and provides a possible explanation for the strong effect of macrolides on animal3,4,5 and human6,7 gut microbiomes. To mitigate this collateral damage of macrolides and tetracyclines, we screened for drugs that specifically antagonized the antibiotic activity against abundant Bacteroides species but not against relevant pathogens. Such antidotes selectively protected Bacteroides species from erythromycin treatment in human-stool-derived communities and gnotobiotic mice. These findings illluminate the activity spectra of antibiotics in commensal bacteria and suggest strategies to circumvent their adverse effects on the gut microbiota.

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Fig 1: Activity spectrum of antibiotic classes on human gut commensals.
Fig 2: Macrolides and tetracyclines kill some human gut commensal species.
Fig 3: Dicumarol selectively protects Bacteroides species from erythromycin in microbial communities.

Data availability

All primary data generated in this study are in the Article and its Supplementary Information and are available from Zenodo: (https://doi.org/10.5281/zenodo.3527540). Clinical breakpoints (Fig. 1c) were retrieved from the EUCAST database: https://eucast.org/clinical_breakpoints/Source data are provided with this paper.

Code availability

Code for analysing data and generating the figures (except Fig. 2 and Extended Data Figs. 5, 6) is available at https://git.embl.de/maier/abxbug.

References

  1. 1.

    Blaser, M. J. Antibiotic use and its consequences for the normal microbiome. Science 352, 544–545 (2016).

    ADS  CAS  PubMed Central  Article  PubMed  Google Scholar 

  2. 2.

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

    ADS  CAS  PubMed Central  Article  PubMed  Google Scholar 

  3. 3.

    Cho, I. et al. Antibiotics in early life alter the murine colonic microbiome and adiposity. Nature 488, 621–626 (2012).

    ADS  CAS  PubMed Central  Article  PubMed  Google Scholar 

  4. 4.

    Cox, L. M. et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell 158, 705–721 (2014).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  5. 5.

    Ruiz, V. E. et al. A single early-in-life macrolide course has lasting effects on murine microbial network topology and immunity. Nat. Commun. 8, 518 (2017).

    ADS  PubMed Central  Article  CAS  PubMed  Google Scholar 

  6. 6.

    Korpela, K. et al. Intestinal microbiome is related to lifetime antibiotic use in Finnish pre-school children. Nat. Commun. 7, 10410 (2016).

    ADS  CAS  PubMed Central  Article  PubMed  Google Scholar 

  7. 7.

    Parker, E. P. K. et al. Changes in the intestinal microbiota following the administration of azithromycin in a randomised placebo-controlled trial among infants in south India. Sci. Rep. 7, 9168 (2017).

    ADS  PubMed Central  Article  CAS  PubMed  Google Scholar 

  8. 8.

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

    ADS  CAS  Article  Google Scholar 

  9. 9.

    Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).

    ADS  CAS  Article  Google Scholar 

  10. 10.

    Zimmermann, M., Patil, K. R., Typas, A. & Maier, L. Towards a mechanistic understanding of reciprocal drug–microbiome interactions. Mol. Syst. Biol. 17, e10116 (2021).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  11. 11.

    Vich Vila, A. et al. Impact of commonly used drugs on the composition and metabolic function of the gut microbiota. Nat. Commun. 11, 362 (2020).

    ADS  CAS  PubMed Central  Article  PubMed  Google Scholar 

  12. 12.

    Kuhn, M., Letunic, I., Jensen, L. J. & Bork, P. The SIDER database of drugs and side effects. Nucleic Acids Res. 44, D1075–D1079 (2016).

    CAS  Article  Google Scholar 

  13. 13.

    Dethlefsen, L. & Relman, D. A. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc. Natl Acad. Sci. USA 108, 4554–4561 (2011).

    ADS  CAS  Article  Google Scholar 

  14. 14.

    Uzan-Yulzari, A. et al. Neonatal antibiotic exposure impairs child growth during the first six years of life by perturbing intestinal microbial colonization. Nat. Commun. 12, 443 (2021).

    ADS  CAS  PubMed Central  Article  PubMed  Google Scholar 

  15. 15.

    Nagy, E., Boyanova, L., Justesen, U. S. & ESCMID Study Group of Anaerobic Infections. How to isolate, identify and determine antimicrobial susceptibility of anaerobic bacteria in routine laboratories. Clin. Microbiol. Infect. 24, 1139–1148 (2018).

    CAS  Article  Google Scholar 

  16. 16.

    European Committee on Antimicrobial Susceptibility Testing. Breakpoint tables for interpretation of MICs and zone diameters. v.; http://www.eucast.org/clinical_breakpoints/ (2019).

  17. 17.

    Bullman, S. et al. Analysis of Fusobacterium persistence and antibiotic response in colorectal cancer. Science 358, 1443–1448 (2017).

    ADS  CAS  PubMed Central  Article  PubMed  Google Scholar 

  18. 18.

    Manfredo Vieira, S. et al. Translocation of a gut pathobiont drives autoimmunity in mice and humans. Science 359, 1156–1161 (2018).

    ADS  CAS  Article  Google Scholar 

  19. 19.

    Gaulton, A. et al. The ChEMBL database in 2017. Nucleic Acids Res. 45, D945–D954 (2017).

    CAS  Article  Google Scholar 

  20. 20.

    Slimings, C. & Riley, T. V. Antibiotics and hospital-acquired Clostridium difficile infection: update of systematic review and meta-analysis. J. Antimicrob. Chemother. 69, 881–891 (2014).

    CAS  Article  Google Scholar 

  21. 21.

    Baron, S., Diene, S. & Rolain, J.-M. Human microbiomes and antibiotic resistance. Hum. Microb. J. 10, 43–52 (2018).

    Article  Google Scholar 

  22. 22.

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

    CAS  Article  Google Scholar 

  23. 23.

    Habib, G. et al. 2015 ESC Guidelines for the management of infective endocarditis: the Task Force for the Management of Infective Endocarditis of the European Society of Cardiology (ESC). Endorsed by: European Association for Cardio-Thoracic Surgery (EACTS), the European Association of Nuclear Medicine (EANM). Eur. Heart J. 36, 3075–3128 (2015).

    Article  Google Scholar 

  24. 24.

    Kasper, D.L., F. A., Hauser S. L. & Longo D. L. Harrison’s Principles of Internal Medicine (McGraw-Hill, 2012).

  25. 25.

    Lobritz, M. A. et al. Antibiotic efficacy is linked to bacterial cellular respiration. Proc. Natl Acad. Sci. USA 112, 8173–8180 (2015).

    ADS  CAS  PubMed Central  Article  PubMed  Google Scholar 

  26. 26.

    French, G. L. Bactericidal agents in the treatment of MRSA infections—the potential role of daptomycin. J. Antimicrob. Chemother. 58, 1107–111 (2006).

    CAS  Article  Google Scholar 

  27. 27.

    Jelic, D. & Antolovic, R. From erythromycin to azithromycin and new potential ribosome-binding antimicrobials. Antibiotics (Basel) 5, 29 (2016).

    Article  Google Scholar 

  28. 28.

    Nemeth, J., Oesch, G. & Kuster, S. P. Bacteriostatic versus bactericidal antibiotics for patients with serious bacterial infections: systematic review and meta-analysis. J. Antimicrob. Chemother. 70, 382–395 (2015).

    CAS  Article  Google Scholar 

  29. 29.

    Wald-Dickler, N., Holtom, P. & Spellberg, B. Busting the myth of “static vs cidal” a systemic literature review. Clin. Infect. Dis. 66, 1470–1474 (2018).

    CAS  Article  Google Scholar 

  30. 30.

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

    ADS  CAS  PubMed Central  Article  PubMed  Google Scholar 

  31. 31.

    Brugiroux, S. et al. Genome-guided design of a defined mouse microbiota that confers colonization resistance against Salmonella enterica serovar Typhimurium. Nat. Microbiol. 2, 16215 (2016).

    CAS  Article  Google Scholar 

  32. 32.

    Palleja, A. et al. Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat. Microbiol. 3, 1255–1265 (2018).

    CAS  Article  Google Scholar 

  33. 33.

    Schmidt, T. S. B., Raes, J. & Bork, P. The human gut microbiome: from association to modulation. Cell 172, 1198–1215 (2018).

    CAS  Article  Google Scholar 

  34. 34.

    Milanese, A. et al. Microbial abundance, activity and population genomic profiling with mOTUs2. Nat. Commun. 10, 1014 (2019).

    ADS  PubMed Central  Article  CAS  PubMed  Google Scholar 

  35. 35.

    Feng, Q. et al. Gut microbiome development along the colorectal adenoma-carcinoma sequence. Nat. Commun. 6, 6528 (2015).

    ADS  CAS  Article  Google Scholar 

  36. 36.

    Vogtmann, E. et al. Colorectal cancer and the human gut microbiome: reproducibility with whole-genome shotgun sequencing. PLoS ONE 11 (2016).

  37. 37.

    Wirbel, J. et al. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nat. Med. 25, 679–689 (2019).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  38. 38.

    Yu, J. et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut 66, 70–78 (2017).

    CAS  Article  Google Scholar 

  39. 39.

    Zeller, G. et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol. Syst. Biol. 10, 766 (2014).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  40. 40.

    Kultima, J. R. et al. MOCAT2: a metagenomic assembly, annotation and profiling framework. Bioinformatics 32, 2520–2523 (2016).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  41. 41.

    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  42. 42.

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  43. 43.

    Frostegård, A. et al. Quantification of bias related to the extraction of DNA directly from soils. Appl. Environ. Microbiol. 65, 5409–5420 (1999).

    ADS  PubMed Central  Article  PubMed  Google Scholar 

  44. 44.

    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).

    Article  Google Scholar 

  45. 45.

    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  46. 46.

    Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  47. 47.

    Matias Rodrigues, J. F., Schmidt, T. S. B., Tackmann, J. & von Mering, C. MAPseq: highly efficient k-mer search with confidence estimates, for rRNA sequence analysis. Bioinformatics 33, 3808–3810 (2017).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  48. 48.

    Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935 (2013).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  49. 49.

    Matias Rodrigues, J. F. & von Mering, C. HPC-CLUST: distributed hierarchical clustering for large sets of nucleotide sequences. Bioinformatics 30, 287–288 (2014).

    CAS  Article  Google Scholar 

  50. 50.

    Schmidt, T. S. B., Matias Rodrigues, J. F. & von Mering, C. Limits to robustness and reproducibility in the demarcation of operational taxonomic units. Environ. Microbiol. 17, 1689–1706 (2015).

    CAS  Article  Google Scholar 

  51. 51.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  52. 52.

    McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).

    ADS  PubMed Central  Article  CAS  PubMed  Google Scholar 

  53. 53.

    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 

  54. 54.

    Chen, M. et al. Inhibition of renal NQO1 activity by dicoumarol suppresses nitroreduction of aristolochic acid I and attenuates its nephrotoxicity. Toxicol. Sci. 122, 288–296 (2011).

    CAS  Article  Google Scholar 

  55. 55.

    Cai, H. Y. et al. Benzbromarone, an old uricosuric drug, inhibits human fatty acid binding protein 4 in vitro and lowers the blood glucose level in db/db mice. Acta Pharmacol. Sin. 34, 1397–1402 (2013).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  56. 56.

    Herp, S. et al. Mucispirillum schaedleri antagonizes Salmonella virulence to protect mice against colitis. Cell Host Microbe 25, 681–694 (2019).

    CAS  Article  Google Scholar 

  57. 57.

    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 Central  Article  PubMed  Google Scholar 

  58. 58.

    Sunagawa, S. et al. Metagenomic species profiling using universal phylogenetic marker genes. Nat. Methods 10, 1196–1199 (2013).

    CAS  Article  Google Scholar 

  59. 59.

    Huerta-Cepas, J., Serra, F. & Bork, P. ETE 3: reconstruction, analysis, and visualization of phylogenomic data. Mol. Biol. Evol. 33, 1635–1638 (2016).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  60. 60.

    Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).

    PubMed Central  Article  PubMed  Google Scholar 

  61. 61.

    Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  62. 62.

    Cani, P. D. & de Vos, W. M. Next-generation beneficial microbes: the case of Akkermansia muciniphila. Front. Microbiol. 8, 1765 (2017).

    PubMed Central  Article  PubMed  Google Scholar 

  63. 63.

    Routy, B. et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018).

    ADS  CAS  Article  Google Scholar 

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Acknowledgements

We thank S. Göttig and members of the Typas lab for feedback on the manuscript; A. R. Brochado for help with experimental design; and EMBL GeneCore and Flow Cytometry Core Facilities for services and experimental advice. We acknowledge EMBL, JPIAMR grant combinatorials and ERC grant uCARE (ID 819454) for funding. L.M., S.G.-S. and M.P. were supported by the EMBL Interdisciplinary Postdoc programme under the Marie Sklodowska Curie Actions COFUND (grant numbers 291772 and 664726). L.M. is supported by the DFG (CMFI Cluster of Excellence EXC 2124 and Emmy Noether Program). C.V.G. is the recipient of an EMBO long-term postdoctoral fellowship and an add-on fellowship from the Christiane Nüsslein-Volhard-Stiftung. U.L. is supported by JPIAMR grant EMBARK. K.R.P. is supported by the UK Medical Research Council (MC_UU_00025/11). B.S. is supported by DFG CRC1371, ERC grant EVOGUTHEALTH (ID 865615), DZIF and CEGIMIR.

Author information

Affiliations

Authors

Contributions

This study was conceived by K.R.P., P.B. and A.T.; designed by L.M., C.V.G., M.P. and A.T.; and supervised by L.M., M.Z., B.S., G.Z., P.B. and A.T. L.M., M.P., T.B. and E.E.A. conducted MIC measurements. C.V.G. performed the bactericidal/bacteriostatic experiments. L.M., C.V.G., C.E., P.M., S.G.S., E.C., B.Z. and C.G. performed the antidote experiments (L.M., P.M., E.C. and C.G. the screen and in vitro validation; C.V.G., P.M. and S.G.-S. the community experiments; and L.M., C.E. and B.Z. the mouse experiments). Data pre-processing, curation and comparisons to databases were performed by J.W., M.K., A.M., U.L. and S.K.F. Data interpretation was performed by L.M., C.V.G., J.W., M.Z., B.S., G.Z. and A.T. L.M., C.V.G. and A.T. wrote the manuscript with feedback from all authors. L.M., C.V.G., J.W. and M.K. designed the figures with input from G.Z. and A.T. All authors approved the final version for publication.

Corresponding authors

Correspondence to Lisa Maier or Athanasios Typas.

Ethics declarations

Competing interests

EMBL has filed a patent on using the antidotes identified in this study for prevention and/or treatment of dysbiosis and for microbiome protection (European patent application no. EP19216548.8). L.M., C.V.G., E.C. and A.T. are listed as inventors.

Additional information

Peer review information Nature thanks Gerry Wright and the other, anonymous, reviewer(s) for their contributions to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Effects of 144 antibiotics on 40 human gut commensals.

Heat map according to sensitivity or resistance of each strain to the respective antibiotic at a concentration of 20 µM. Antibiotics are grouped according to drug classes and species are clustered according to their responses across the 144 antibiotics tested. Data is replotted from2. Of note, Akkermansia muciniphila, a species associated with protection against different diseases and dysbiotic states62, and even positive responses to immunotherapy63, is resistant to nearly all quinolone antibiotics (red box). We consolidated this finding by MIC determination for Ciprofloxacin (>32 µg/ml), Gatifloxacin (>32 µg/ml), Moxifloxacin (>32 µg/ml), Norfloxacin (>256 µg/ml) and Ofloxacin (>32 µg/ml).

Source data

Extended Data Fig. 2 MICs for 20 species/27 strains on 35 antimicrobials.

Heat map depicts MICs for each drug-strain pair in µg/ml. Heat map color gradient is adjusted to the MICs concentration range tested on the respective MIC test strip. Black depicts sensitivity and light grey resistance. Mean values across two biological replicates are shown (Suppl. Table 4). The species/strains from the screen are shown in black, additional strains to investigate intraspecies and intragenus variation within the Bacteroides genus are shown in blue. The grey background indicates that several strains per species were tested. Of note, C. difficile is particularly resistant to all tested macrolides and clindamycin (red box).

Source data

Extended Data Fig. 3 MIC dataset validates antibiotic sensitivity profiles from the screen and is consistent with  publicly available MICs.

a. Receiver operating characteristic (ROC) curve analysis was performed to evaluate sensitivity and specificity of the screen2 using the MIC dataset. Results from the screen were considered as validated if MICs were below/above the 20 µM antibiotic concentration that was tested in the screen (allowing a twofold error margin). N is the number of antibiotics that we tested both in the screen and determined MICs for; AUROC is the area under the characteristic ROC. TN denotes true negatives, FP false positives, TP true positives, FN false negatives. b. Comparison including Spearman correlation coefficients of the MICs from this study to MICs from the ChEMBL19 and EUCAST16 databases. Panels in the upper row: comparison between all MICs that are shared between the two indicated datasets. Panels in the lower row: comparison of the 69 MICs that are shared across all three datasets. Despite experimental differences, our MICs correlate well with available EUCAST/ ChEMBL data. c. Number of the sum of new (this study) and already available MICs (EUCAST/ ChEMBL) per drug according to antibiotic class and prevalence/virulence of the bacterial species. The new dataset expands MICs across the board and specifically fills the knowledge gap on non-pathogenic species.

Source data

Extended Data Fig. 4 Antibiotic classes exhibit distinct behaviours in gut bacterial species.

a. Number of inhibited strains per antibiotic class (number of tested drugs per class in brackets). In total 40 strains were tested at a 20 µM antibiotic concentration. Boxes span the IQR and whiskers extend to the most extreme data points up to a max of 1.5 times the IQR. b. Number of inhibited strains per (fluoro-)quinolone drug generation. Number of tested drugs per generation is indicated in brackets - boxplots as in a. c–d. Overview of the number of drugs tested per β-lactam subclasses on Bacteroides species (spp) in screen (c) and for MICs (d). e. Heat map of phylogenetic relationship between Bacteroides spp (upper triangular matrix) ordered by phylogeny and their resistance profiles across β-lactam antibiotics (lower triangular matrix). Colors represent the pairwise phylogenetic distance and the Euclidean distance on the log2 transformed MICs for β-lactams. Examples of strains from the same species (B. fragilis / B. uniformis) that respond differently to β-lactam antibiotics, are highlighted.

Source data

Extended Data Fig. 5 Selective killing of macrolides and tetracyclines.

a. Time-kill curves. The survival of 12 abundant gut microorganisms was assessed over a 5 h-treatment with either erythromycin, azithromycin or doxycycline. The graph shows the mean±SD of 3 independent experiments. b. Live/dead staining of macrolide or tetracycline-treated E. coli ED1a and B. vulgatus. The left panel shows an overlay of phase contrast and fluorescence microscopy images of propidium iodide (PI)-stained E. coli ED1a or B. vulgatus before and 5 h after erythromycin, azithromycin or doxycycline treatment. Cultures were concentrated before imaging; the scale bar is 10 µm. The right panel shows the corresponding quantification of live/dead-stained cells by flow cytometry with Syto9 on the x-axis (live cells) and PI on the y-axis (dead cells). As E. coli ED1a cells stain poorly with Syto9, we only quantified PI stained cells in this case. Both the total number of measured events (n) and the percentage of cells found in each region of the graph are indicated.

Source data

Extended Data Fig. 6 Assessing potential confounding factors for the killing capacities of erythromycin, azithromycin and doxycycline.

a. Scatter plot of individual bacterial specific growth rates (μ - hr−1) and percentage survival after a 5-hour treatment with 5-fold MIC of erythromycin, azithromycin or doxycycline. r is the Spearman correlation coefficient. Tested species are color-coded here and, in all panels thereafter as indicated at the bottom of this figure. b. B. fragilis (blue), F. nucleatum (beige), P. copri (pink) and E. coli ED1a (grey) survival was assessed after a 5h erythromycin and azithromycin treatment (5-fold MIC) at 30 °C (slow growth) and 37 °C (fast growth) - mean ±SD of three independent experiments. No monotonic trend was observed. c. Scatter plot of MICs and % survival after a 5h treatment with 5-fold MIC of erythromycin, azithromycin or doxycycline. r is the Spearman correlation coefficient. Doxycycline exhibited a significant ( P value = 0.0015) anti-correlation, i.e. more sensitive species to doxycycline (lower MIC) survived better when treated with antibiotic. Therefore, we tested further whether increasing the drug concentration in sensitive strains increased killing (panel d). d. B. fragilis (blue) and F. nucleatum (beige) survival after a 5-hour treatment as function of increasing doxycycline concentrations (mean ± SD of three independent experiments). No significant differences observed. In all cases doxycycline remained bacteriostatic. Significance calculated by unpaired two-sided t-test here and in all panels thereafter. e. To evaluate whether outgrowth from stationary phase affected our results, we selected two slow-growing strains, E. rectale (green) and R. intestinalis (orange) and grew them for 2 or 3h after diluting from an overnight culture to an of OD578 0.01. Both strains were then treated for 5h with 5-fold MIC of erythromycin, azithromycin or doxycycline and their survival was assessed (mean ± SD of three independent experiments). Although 3h grown cultures were killed slightly more effectively (difference is not statistically significant due to low number of replicates), this did not change the bactericidal or bacteriostatic characteristic of antibiotics. If anything, this means that we underestimate the killing for slow-growers, since all other experiments were performed with 2 h outgrowth. Nd: not detected (detection limit: 1 CFU/ml.). f–g. The survival of 8 selected gut microorganisms was measured after treating cells in exponential phase (E – 2h after dilution from an overnight culture) or in stationary phase (S – overnight growth) with 5-fold MIC of erythromycin (f) or doxycycline (g) for 5h (mean ± SD of three independent experiments). Consistent with the knowledge that antibiotic killing requires active growth, survival is higher in stationary phase for most strains (but not all – see F. nucleatum) that erythromycin or doxycycline kills. ns = non-significant; *, ** and *** denote P value <0.05, <0.01 and <0.001, respectively. nd as in e. h. E. coli ED1a survival was assessed after 5h treatment with 5-fold MIC of doxycycline in the presence or absence of oxygen. Killing was similar in both conditions.

Source data

Extended Data Fig. 7 Identification and validation of macrolide and tetracycline antagonists (antidotes) in B. vulgatus and B. uniformis.

a. Schematic illustration of combinatorial screen concept: searching for antidote compounds that antagonize the antibacterial effect of erythromycin or doxycycline on commensal but not on pathogenic bacteria. b. Z-scores on bacterial growth for combinatorial drug exposure with antibiotic and 1197 FDA-approved drugs of Prestwick library (2 replicates). Compounds that successfully protected B. vulgatus and/or B. uniformis in the presence of antibiotic (z-score > 3) are indicated in gray. The strongest hits (circles) were validated in concentration-dependent assays (c–d). Box plots as in Fig. 1c. c. Validation of the strongest antagonistic interactions in independent experiments. Erythromycin and doxycycline concentrations were kept constant for each species and concentration ranges were tested for antagonists. Asterisks indicate that at least 25% of the bacterial growth (compared to no drug controls) could be rescued by the antagonist at a given concentration. Heat map depicts median growth across triplicates. d. For 10 of the validated antagonists, 8 × 8 checkerboard assays were performed to define better the range of the antagonistic interaction. Heat maps depict bacterial growth based on normalized median of AUCs of 3–4 replicates. Antagonistic interactions are framed in red (all). e. Percentage of surviving B. vulgatus cells were determined after 5h incubation with either erythromycin (3.25 µM) or doxycycline (0.4 µM) alone or in presence of benzbromarone (40 µM), dicumarol (20 µM), tolfenamic acid (40 µM) or diflunisal (80 µM). Data is based on three independent experiments. Boxplots are plotted as in Fig. 1c.

Source data

Extended Data Fig. 8 Schematic overview of screen for microbiome-protective antibiotic antidotes.

Workflow with decision process on which erythromycin and doxycycline antagonists to move to next evaluation step.

Extended Data Fig. 9 Antidotes work on further gut commensals, but do not compromise antibiotic efficacy on relevant pathogens.

a. 8 × 8 checkerboard assays to investigate if antidote is also protective for additional gut commensals. All combinations were tested in MGAM medium under anaerobic conditions. Heat map depicts bacterial growth based on median AUCs from 2–3 independent replicates. Concentrations are stated in µM. b. 8 × 8 checkerboard assays to evaluate antidote effects on the activity of erythromycin and doxycycline in relevant pathogenic species. The gastrointestinal pathogens E. faecalis and E. faecium were tested under anaerobic conditions. S. aureus, a cause of extra-intestinal infections, such as bacteremia and infective endocarditis, was tested under aerobic conditions. Heatmaps depict mean normalized AUCs of three biological replicates. Antidotes exhibit either neutral or even slight synergistic effects with antibiotics. c. Dicumarol rescues commensal growth (n = 2, anaerobic conditions) in a concentration-dependent manner. Erythromycin still retains its activity against pertinent pathogens such as E. faecium, E. faecalis (n = 3, anaerobic conditions) and S. aureus (n = 3, aerobic conditions) - see Suppl. Table 1 for strains used. 0.65 µM (~0.5 µg/ml) erythromycin is within range of the MIC breakpoints for Staphylococcus (1 µg/ml) and Streptococci groups A, B, C & G (0.25 µg/ml). Error bars depict standard deviation.

Source data

Extended Data Fig. 10 The antidote benzbromarone selectively protects Bacteroides species from erythromycin in microbial communities.

a. The same 7-member synthetic gut microbial community as in Fig. 3a can be protected from erythromycin by the antidote benzbromarone. Heatmaps depict median bacterial growth based on normalized AUCs of the community of three replicates. b. Community compositions in selected erythromycin-benzbromarone concentration combinations (1–4 referring to checkerboard tiles in a) demonstrate that benzbromarone alone does not alter the community structure, but rescues some Bacteroides species and largely the community composition from erythromycin treatment. Depicted as in Fig. 3b - control and erythromycin alone experiments same as in Fig. 3b. c. When the Bacteroidales community contains the pathogen E. faecalis, benzbromarone rescues community growth upon erythromycin treatment, but enhances the ability of erythromycin to target E. faecalis. Plotted as in Fig. 3c. d–f. In complex human-stool derived communities from nine healthy donors (column #1 – 9), benzbromarone protects 65% of Bacteroidales OTUs from erythromycin, and at least one sensitive Bacteroidales OTU per individual (2 biological × 2 technical replicates). Plotted as in Fig. 3d. The fractions of rescued OTUs per order (e) and for Bacteroidales OTUs per genus (f) across all nine donors indicate that primarily Bacteroides species are rescued. g. In gnotobiotic mice colonized with a defined 12-member mouse microbiome31 and B. vulgatus, administration of benzbromarone slightly (albeit not significantly, two-sided Mann-Whitney U test) mitigates the temporal decrease in fecal B. vulgatus counts that erythromycin causes. Mice received a single oral dose of erythromycin (N = 9) or erythromycin + benzbromarone (N = 9) in two independent experiments. Data of the erythromycin-treated group is partially overlapping with data shown in Fig. 3g as experiments were conducted in parallel. Boxes are plotted as in Fig. 1c. h. Both groups of mice show similar faecal erythromycin concentrations over the course of the experiment shown in g.

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Extended Data Fig. 11 The antidote tolfenamic acid protects Bacteroides species from erythromycin in microbial communities.

a. Tolfenamic acid rescues commensal growth (based on median AUCs, N = 2) at clinical relevant erythromycin concentrations in a concentration-dependent manner (anaerobic conditions). Erythromycin still retains its activity against pertinent pathogens such as E. faecium, E. faecalis (based on median AUCs, N = 3, anaerobic conditions) and S. aureus ([erythromycin] = 0.14 µM, N = 3, aerobic conditions). Error bars depict standard deviation. b. The same 7-member synthetic gut microbial community as in Fig. 3a can be protected from erythromycin by the tolfenamic acid. Heat maps depict median bacterial growth based on normalized AUCs of the community of 3 replicates. c. Community compositions in selected erythromycin-tolfenamic acid concentration combinations (1–4 referring to checkerboard tiles in b) demonstrate that tolfenamic acid alone does not alter the community structure, but rescues some Bacteroides species and largely the community composition from erythromycin treatment. Depicted as in Fig. 3b – control and erythromycin alone experiments same as in Fig. 3b. d–f. In complex human-stool derived communities from 9 healthy donors (column #1 – 9), tolfenamic acid can rescue 42% of the erythromycin-sensitive Bacteroidales OTUs (2 biological × 2 technical replicates). Data is plotted as in Fig. 3d. Bars depict the absolute numbers of erythromycin-sensitive OTUs and the percentage of rescued OTUs per order (e) or genus (f) across all nine individuals.

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Supplementary information

Supplementary Fig. 1

This file contains an illustration of the flow cytometry gating strategy.

Reporting Summary

Supplementary Tables 1–6

This file contains Supplementary Tables 1–6.

Supplementary Video 1

Time-lapse of B. vulgatus growing on mGAM-agarose 1% pad.

Supplementary Video 2

Time-lapse of B. vulgatus growing on mGAM-agarose 1% pad containing fivefold MIC of erythromycin.

Supplementary Video 3

Time-lapse of B. uniformis growing on mGAM-agarose 1% pad.

Supplementary Video 4

Time-lapse of B. uniformis growing on mGAM-agarose 1% pad containing fivefold MIC of erythromycin.

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Maier, L., Goemans, C.V., Wirbel, J. et al. Unravelling the collateral damage of antibiotics on gut bacteria. Nature (2021). https://doi.org/10.1038/s41586-021-03986-2

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