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Antibiotic interactions that select against resistance


Multidrug combinations are increasingly important in combating the spread of antibiotic-resistance in bacterial pathogens1,2,3. On a broader scale, such combinations are also important in understanding microbial ecology and evolution4,5. Although the effects of multidrug combinations on bacterial growth have been studied extensively, relatively little is known about their impact on the differential selection between sensitive and resistant bacterial populations1,6,7. Normally, the presence of a drug confers an advantage on its resistant mutants in competition with the sensitive wild-type population1. Here we show, by using a direct competition assay between doxycycline-resistant and doxycycline-sensitive Escherichia coli, that this differential selection can be inverted in a hyper-antagonistic class of drug combinations. Used in such a combination, a drug can render the combined treatment selective against the drug’s own resistance allele. Further, this inversion of selection seems largely insensitive to the underlying resistance mechanism and occurs, at sublethal concentrations, while maintaining inhibition of the wild type. These seemingly paradoxical results can be rationalized in terms of a simple geometric argument. Our findings demonstrate a previously unappreciated feature of the fitness landscape for the evolution of resistance and point to a trade-off between the effect of drug interactions on absolute potency and the relative competitive selection that they impose on emerging resistant populations.

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Figure 1: Schematic representation of synergistic, additive, antagonistic and suppressive drug pairs.
Figure 2: Rescaling of effective drug concentrations by resistance generates a region exclusive to growth of sensitive bacteria in a suppressive drug combination.
Figure 3: Competitive selection against resistance in a suppressive drug combination.

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  1. Levy, S. B. & Marshall, B. Antibacterial resistance worldwide: causes, challenges and responses. Nature Med. 10, S122–S129 (2004)

    Article  CAS  Google Scholar 

  2. Golan, D. E. et al. (eds) Principles of Pharmacology: The Pathophysiologic Basis of Drug Therapy. (Lippincott Williams & Wilkins, Philadelphia, 2005)

    Google Scholar 

  3. Pillai, S. K., Moellering, R. C. & Eliopoulos, G. M. in Antibiotics in Laboratory Medicine (ed. Lorian, V.) 365–440 (Lippincott Williams & Wilkins, Philadelphia, 2005)

    Google Scholar 

  4. Yim, G., Wang, H. H. M. & Davies, J. The truth about antibiotics. Int. J. Med. Microbiol. 296, 163–170 (2006)

    Article  CAS  Google Scholar 

  5. Czaran, T. L., Hoekstra, R. F. & Pagie, L. Chemical warfare between microbes promotes biodiversity. Proc. Natl Acad. Sci. USA 99, 786–790 (2002)

    Article  ADS  CAS  Google Scholar 

  6. Klein, M. & Schorr, S. E. The role of bacterial resistance in antibiotic synergism and antagonism. J. Bacteriol. 65, 454–465 (1953)

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Lipsitch, M. & Levin, B. R. The population dynamics of antimicrobial chemotherapy. Antimicrob. Agents Chemother. 41, 363–373 (1997)

    Article  CAS  Google Scholar 

  8. Keith, C. T., Borisy, A. A. & Stockwell, B. R. Multicomponent therapeutics for networked systems. Nature Rev. Drug Discov. 4, 71–78 (2005)

    Article  CAS  Google Scholar 

  9. Loewe, S. The problem of synergism and antagonism of combined drugs. Arzneimittelforschung 3, 285–290 (1953)

    CAS  PubMed  Google Scholar 

  10. Yeh, P., Tschumi, A. I. & Kishony, R. Functional classification of drugs by properties of their pairwise interactions. Nature Genet. 38, 489–494 (2006)

    Article  CAS  Google Scholar 

  11. Fraser, T. R. The antagonism between the actions of active substances. BMJ 2, 485–487 (1872)

    Article  CAS  Google Scholar 

  12. Chopra, I. & Roberts, M. Tetracycline antibiotics: Mode of action, applications, molecular biology, and epidemiology of bacterial resistance. Microbiol. Mol. Biol. Rev. 65, 232–260 (2001)

    Article  CAS  Google Scholar 

  13. Walsh, C. Antibiotics: Actions, Origins, Resistance 335 (American Society for Microbiology Press, Washington DC, 2003)

    Book  Google Scholar 

  14. Poole, K. Efflux-mediated antimicrobial resistance. J. Antimicrob. Chemother. 56, 20–51 (2005)

    Article  CAS  Google Scholar 

  15. Hillen, W. & Berens, C. Mechanisms underlying expression of Tn10 encoded tetracycline resistance. Annu. Rev. Microbiol. 48, 345–369 (1994)

    Article  CAS  Google Scholar 

  16. Singer, M. et al. A collection of strains containing genetically linked alternating antibiotic-resistance elements for genetic-mapping of Escherichia coli. Microbiol. Rev. 53, 1–24 (1989)

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Bjarnason, J., Southward, C. M. & Surette, M. G. Genomic profiling of iron-responsive genes in Salmonella enterica serovar typhimurium by high-throughput screening of a random promoter library. J. Bacteriol. 185, 4973–4982 (2003)

    Article  CAS  Google Scholar 

  18. Kishony, R. & Leibler, S. Environmental stresses can alleviate the average deleterious effect of mutations. J. Biol. 2, 14.1–14.10 (2003)

    Article  Google Scholar 

  19. Lenski, R. E., Simpson, S. C. & Nguyen, T. T. Genetic analysis of a plasmid-encoded, host genotype-specific enhancement of bacterial fitness. J. Bacteriol. 176, 3140–3147 (1994)

    Article  CAS  Google Scholar 

  20. Lenski, R. E. et al. Epistatic effects of promoter and repressor functions of the Tn10 tetracycline-resistance operon on the fitness of Escherichia coli. Mol. Ecol. 3, 127–135 (1994)

    Article  CAS  Google Scholar 

  21. Yang, W. R. et al. TetX is a flavin-dependent monooxygenase conferring resistance to tetracycline antibiotics. J. Biol. Chem. 279, 52346–52352 (2004)

    Article  CAS  Google Scholar 

  22. Whittle, G. et al. Identification of a new ribosomal protection type of tetracycline resistance gene, tet(36), from swine manure pits. Appl. Environ. Microbiol. 69, 4151–4158 (2003)

    Article  CAS  Google Scholar 

  23. Hegreness, M., Shoresh, N., Hartl, D. & Kishony, R. An equivalence principle for the incorporation of favorable mutations in asexual populations. Science 311, 1615–1617 (2006)

    Article  ADS  CAS  Google Scholar 

  24. Chao, L. Unusual interaction between the target of nalidixic acid and novobiocin. Nature 271, 385–386 (1978)

    Article  ADS  CAS  Google Scholar 

  25. Deeks, S. G. Treatment of anti retroviral-drug-resistant HIV-1 infection. Lancet 362, 2002–2011 (2003)

    Article  CAS  Google Scholar 

  26. Tsui, W. H. W. et al. Dual effects of MLS antibiotics: Transcriptional modulation and interactions on the ribosome. Chem. Biol. 11, 1307–1316 (2004)

    Article  CAS  Google Scholar 

  27. Drlica, K. & Hooper, D. C. in Quinolone Antimicrobial Agents (eds Hooper, D.C. & Rubinstein, E.) 19–40 (ASM Press, Washington DC, 2003)

    Google Scholar 

  28. Weinreich, D. M., Delaney, N. F., DePristo, M. A. & Hartl, D. L. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312, 111–114 (2006)

    Article  ADS  CAS  Google Scholar 

  29. Lutz, R. & Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Res. 25, 1203–1210 (1997)

    Article  CAS  Google Scholar 

  30. Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002)

    Article  ADS  CAS  Google Scholar 

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We thank U. Alon, N. Q. Balaban, B. Chait, J. Davies, M. Elowitz, Y. Fink, L. Garwin, D. Hartl, M. Hegreness, D. Kahne, M. Kirschner, S. Leibler, R. Lenski, R. Milo, T. Mitchison, R. Moellering, A. Murray, D. Segre', N. Shoresh, S. Walker, C. Walsh, R. Ward and P. Yeh for comments and valuable discussions; and G. Jacoby, N. Shoemaker and G. Wright for their gifts of plasmids. This work was supported partly by a grant from the Human Frontiers Science Program.

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Correspondence to Roy Kishony.

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Reprints and permissions information is available at The authors declare no competing financial interests.

Supplementary information

Supplementary Information

This file includes Supplementary Figures S1-S7 with Legends, Supplementary Tables S1-S3 and additional references. Supplementary Table S1 shows doxycycline-erythromycin competition data, Supplementary Table S2 shows doxycycline-ciprofloxacin competition data, Supplementary Table S3 shows strains and plasmids. (PDF 4677 kb)

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Chait, R., Craney, A. & Kishony, R. Antibiotic interactions that select against resistance. Nature 446, 668–671 (2007).

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