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Frequency of antibiotic application drives rapid evolutionary adaptation of Escherichia coli persistence

Abstract

The evolution of antibiotic resistance is a major threat to society and has been predicted to lead to 10 million casualties annually by 20501. Further aggravating the problem, multidrug tolerance in bacteria not only relies on the build-up of resistance mutations, but also on some cells epigenetically switching to a non–growing antibiotic-tolerant ‘persister’ state26. Yet, despite its importance, we know little of how persistence evolves in the face of antibiotic treatment7. Our evolution experiments in Escherichia coli demonstrate that extremely high levels of multidrug tolerance (20–100%) are achieved by single point mutations in one of several genes and readily emerge under conditions approximating clinical, once-daily dosing schemes. In contrast, reversion to low persistence in the absence of antibiotic treatment is relatively slow and only partially effective. Moreover, and in support of previous mathematical models810, we show that bacterial persistence quickly adapts to drug treatment frequency and that the observed rates of switching to the persister state can be understood in the context of ‘bet-hedging’ theory. We conclude that persistence is a major component of the evolutionary response to antibiotics that urgently needs to be considered in both diagnostic testing and treatment design in the battle against multidrug tolerance.

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Figure 1: Frequent antibiotic exposure leads to the fast emergence of high survival levels.
Figure 2: Single mutations in evolved clones cause multidrug tolerance, not by increased antibiotic resistance but by increased persistence.
Figure 3: Costs and benefits of persistence under contrasting levels of antibiotic exposure.
Figure 4: Persister levels quickly adapt in response to the frequency of antibiotic treatment.

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References

  1. Review on Antimicrobial Resistance. Tackling a Crisis for the Health and Wealth of Nations (2015); http://amr-review.org/sites/default/files/Report-52.15.pdf

  2. Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L. & Leibler, S. Bacterial persistence as a phenotypic switch. Science 305, 1622–1625 (2004).

    Article  CAS  Google Scholar 

  3. Lewis, K. Persister cells. Annu. Rev. Microbiol. 64, 357–372 (2010).

    Article  CAS  Google Scholar 

  4. Fauvart, M., De Groote, V. N. & Michiels, J. Role of persister cells in chronic infections: clinical relevance and perspectives on anti-persister therapies. J. Med. Microbiol. 60, 699–709 (2011).

    Article  Google Scholar 

  5. Maisonneuve, E. & Gerdes, K. Molecular mechanisms underlying bacterial persisters. Cell 157, 539–548 (2014).

    Article  CAS  Google Scholar 

  6. Verstraeten, N. et al. Obg and membrane depolarization are part of a microbial bet-hedging strategy that leads to antibiotic tolerance. Mol. Cell 59, 9–21 (2015).

    Article  CAS  Google Scholar 

  7. Balaban, N. Q., Gerdes, K., Lewis, K. & McKinney, J. D. A problem of persistence: still more questions than answers? Nature Rev. Microbiol. 11, 587–591 (2013).

    CAS  Google Scholar 

  8. Kussell, E., Kishony, R., Balaban, N. Q. & Leibler, S. Bacterial persistence: a model of survival in changing environments. Genetics 169, 1807–1814 (2005).

    Article  Google Scholar 

  9. Gardner, A., West, S. A. & Griffin, A. S. Is bacterial persistence a social trait? PLoS ONE 2, e752 (2007).

    Article  Google Scholar 

  10. Patra, P. & Klumpp, S. Population dynamics of bacterial persistence. PLoS ONE 8, e62814 (2013).

    Article  CAS  Google Scholar 

  11. Balázsi, G., Van Oudenaarden, A. & Collins, J. J. Cellular decision making and biological noise: from microbes to mammals. Cell 144, 910–925 (2011).

    Article  Google Scholar 

  12. Veening, J.-W., Smits, W. K. & Kuipers, O. P. Bistability, epigenetics, and bet-hedging in bacteria. Annu. Rev. Microbiol. 62, 193–210 (2008).

    Article  CAS  Google Scholar 

  13. Kint, C. I., Verstraeten, N., Fauvart, M. & Michiels, J. New-found fundamentals of bacterial persistence. Trends Microbiol. 20, 577–585 (2012).

    Article  CAS  Google Scholar 

  14. Stepanyan, K. et al. Fitness trade-offs explain low levels of persister cells in the opportunistic pathogen Pseudomonas aeruginosa. Mol. Ecol. 24, 1572–1583 (2015).

    Article  Google Scholar 

  15. Mulcahy, L. R., Burns, J. L., Lory, S. & Lewis, K. Emergence of Pseudomonas aeruginosa strains producing high levels of persister cells in patients with cystic fibrosis. J. Bacteriol. 192, 6191–6199 (2010).

    Article  CAS  Google Scholar 

  16. Fridman, O., Goldberg, A., Ronin, I., Shoresh, N. & Balaban, N. Q. Optimization of lag time underlies antibiotic tolerance in evolved bacterial populations. Nature 513, 418–421 (2014).

    Article  CAS  Google Scholar 

  17. Lee, H. H., Molla, M. N., Cantor, C. R. & Collins, J. J. Bacterial charity work leads to population-wide resistance. Nature 467, 82–85 (2010).

    Article  CAS  Google Scholar 

  18. Allison, K. R., Brynildsen, M. P. & Collins, J. J. Metabolite-enabled eradication of bacterial persisters by aminoglycosides. Nature 473, 216–220 (2011).

    Article  CAS  Google Scholar 

  19. Wakamoto, Y. et al. Dynamic persistence of antibiotic-stressed mycobacteria. Science 339, 91–95 (2013).

    Article  CAS  Google Scholar 

  20. Orman, M. A. & Brynildsen, M. P. Dormancy is not necessary or sufficient for bacterial persistence. Antimicrob. Agents Chemother. 57, 3230–3239 (2013).

    Article  CAS  Google Scholar 

  21. Acar, M., Mettetal, J. T. & van Oudenaarden, A. Stochastic switching as a survival strategy in fluctuating environments. Nature Genet. 40, 471–475 (2008).

    Article  CAS  Google Scholar 

  22. Philippi, T. & Seger, J. Hedging one's evolutionary bets, revisited. Trends Ecol. Evol. 4, 41–44 (1989).

    Article  CAS  Google Scholar 

  23. Baskin, C. C. & Baskin, J. M. Seeds: ecology, biogeography, and evolution of dormancy and germination (Elsevier, 2014).

    Google Scholar 

  24. Beaumont, H. J. E., Gallie, J., Kost, C., Ferguson, G. C. & Rainey, P. B. Experimental evolution of bet hedging. Nature 462, 90–93 (2009).

    Article  CAS  Google Scholar 

  25. Kussell, E. & Leibler, S. Phenotypic diversity, population growth, and information in fluctuating environments. Science 309, 2075–2078 (2005).

    Article  CAS  Google Scholar 

  26. McKenzie, C. Antibiotic dosing in critical illness. J. Antimicrob. Chemother. 66, ii25–ii31 (2011).

    Article  CAS  Google Scholar 

  27. ABC Project Team Ascertaining Barriers for Compliance: Policies for Safe, Effective and Cost-effective use of Medicines in Europe (2012); http://abcproject.eu/img/ABCFinal.pdf

  28. Oz, T. et al. Strength of selection pressure is an important parameter contributing to the complexity of antibiotic resistance evolution. Mol. Biol. Evol. 31, 2387–2401 (2014).

    Article  CAS  Google Scholar 

  29. Andersson, D. I. & Hughes, D. Antibiotic resistance and its cost: is it possible to reverse resistance? Nature Rev. Microbiol. 8, 260–271 (2010).

    Article  CAS  Google Scholar 

  30. Cohen, N. R., Lobritz, M. A. & Collins, J. J. Microbial persistence and the road to drug resistance. Cell Host Microbe 13, 632–642 (2013).

    Article  CAS  Google Scholar 

  31. Yu, J., Xiao, J., Ren, X., Lao, K. & Xie, X. S. Probing gene expression in live cells, one protein molecule at a time. Science 311, 1600–1603 (2006).

    Article  CAS  Google Scholar 

  32. Cherepanov, P. P. & Wackernagel, W. Gene disruption in Escherichia coli: TcR and KmR cassettes with the option of Flp-catalyzed excision of the antibiotic-resistance determinant. Gene 158, 9–14 (1995).

    Article  CAS  Google Scholar 

  33. Fux, C. A., Costerton, J. W., Stewart, P. S. & Stoodley, P. Survival strategies of infectious biofilms. Trends Microbiol. 13, 34–40 (2005).

    Article  CAS  Google Scholar 

  34. Eng, R. H. K., Padberg, F. T., Smith, S. M., Tan, E. N. & Cherubin, C. E. Bactericidal effects of antibiotics on slowly growing and nongrowing bacteria. Antimicrob. Agents Chemother. 35, 1824–1828 (1991).

    Article  CAS  Google Scholar 

  35. Wu, M.-L., Tan, J. & Dick, T. Eagle effect in non-replicating persister Mycobacteria. Antimicrob. Agents Chemother. 59, 7786–7789 (2015).

    Article  CAS  Google Scholar 

  36. Gocke, E. Mechanism of quinolone mutagenicity in bacteria. Mutat. Res. 248, 135–143 (1991).

    Article  CAS  Google Scholar 

  37. Rodríguez-Rojas, A., Rodríguez-Beltrán, J., Couce, A. & Blázquez, J. Antibiotics and antibiotic resistance: A bitter fight against evolution. Int. J. Med. Microbiol. 303, 293–297 (2013).

    Article  Google Scholar 

  38. Lázár, V. et al. Genome-wide analysis captures the determinants of the antibiotic cross-resistance interaction network. Nature Commun. 5, 4352 (2014).

    Article  Google Scholar 

  39. Ruiz, J. Mechanisms of resistance to quinolones: target alterations, decreased accumulation and DNA gyrase protection. J. Antimicrob. Chemother. 51, 1109–1117 (2003).

    Article  CAS  Google Scholar 

  40. Kim, S., Lieberman, T. D. & Kishony, R. Alternating antibiotic treatments constrain evolutionary paths to multidrug resistance. Proc. Natl Acad. Sci. 111, 14494–14499 (2014).

    Article  CAS  Google Scholar 

  41. Wiser, M. J., Ribeck, N. & Lenski, R. E. Long-term dynamics of adaptation in asexual populations. Science 1364, 1364–1367 (2013).

    Article  Google Scholar 

  42. Wiegand, I., Hilpert, K. & Hancock, R. E. W. Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances. Nature Protoc. 3, 163–175 (2008).

    Article  CAS  Google Scholar 

  43. Liebens, V. et al. Identification and characterization of an anti-pseudomonal dichlorocarbazol derivative displaying anti-biofilm activity. Bioorg. Med. Chem. Lett. 24, 5404–5408 (2014).

    Article  CAS  Google Scholar 

  44. Thierauf, A., Perez, G. & Maloy, A. S. Generalized transduction. Methods Mol. Biol. 501, 267–286 (2009).

    Article  CAS  Google Scholar 

  45. Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 2006.0008 (2006).

    Article  Google Scholar 

  46. Otto, S. P. & Day, T. A Biologist's Guide to Mathematical Modeling in Ecology and Evolution (Princeton University Press, 2007).

    Google Scholar 

  47. Helaine, S. et al. Dynamics of intracellular bacterial replication at the single cell level. Proc. Natl Acad. Sci. 107, 3746–3751 (2010).

    Article  CAS  Google Scholar 

  48. Francois, K. et al. Modelling the individual cell lag phase. Isolating single cells: protocol development . Lett. Appl. Microbiol. 37, 26–30 (2003).

    Article  CAS  Google Scholar 

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Acknowledgements

B.V.D.B. is a Research Foundation - Flanders (FWO)-fellow and J.E.M. is a fellow of the Agency for Innovation by Science and Technology (IWT). The research was supported by the KU Leuven Research Council (PF/10/010; PF/10/07; IDO/09/010; IDO/13/008; CREA/13/019; DBOF/12/035; DBOF/14/049), Interuniversity Attraction Poles–Belgian Science Policy Office (IAP-BELSPO) (IAP P7/28), ERC (241426), Human Frontier Science Program (HFSP) (RGP0050/2013), FWO (G047112N, KAN2014 1.5.222.14), Flanders Institute for Biotechnology(VIB) and the European Molecular Biology organisation (EMBO). We thank S. Xie for providing the ancestor strains and E. Toprak and S. Diggle for their comments on this manuscript.

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Authors

Contributions

B.V.D.B. designed and performed the experiments, analysed the data and wrote the manuscript. J.E.M. helped in performing the experiments and analysing the data and edited the manuscript. T.W. made the evolutionary model, helped in analysing the data and edited the manuscript. E.M.W. helped in analysing the evolutionary model. P.V.B. and D.K. helped in performing the experiments. L.D.M. and K.J.V. edited the manuscript. N.V., M.F. and J.M. designed the experiments and edited the manuscript. J.E.M. and T.W. contributed equally. M.F. and J.M. contributed equally as senior authors.

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Correspondence to Jan Michiels.

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The authors declare no competing financial interests.

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Supplementary information

Supplementary Figures 1-8, Tables 1 and 2, Methods and References. (PDF 1311 kb)

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Van den Bergh, B., Michiels, J., Wenseleers, T. et al. Frequency of antibiotic application drives rapid evolutionary adaptation of Escherichia coli persistence. Nat Microbiol 1, 16020 (2016). https://doi.org/10.1038/nmicrobiol.2016.20

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