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Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance


The evolution of antibiotic resistance can now be rapidly tracked with high-throughput technologies for bacterial genotyping and phenotyping. Combined with new approaches to evolve resistance in the laboratory and to characterize clinically evolved resistant pathogens, these methods are revealing the molecular basis and rate of evolution of antibiotic resistance under treatment regimens of single drugs or drug combinations. In this Progress article, we review these new tools for studying the evolution of antibiotic resistance and discuss how the genomic and evolutionary insights they provide could transform the diagnosis, treatment and predictability of antibiotic resistance in bacterial infections.

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Figure 1: Selection of antibiotic-resistant bacteria from experimental evolution.
Figure 2: Selection of antibiotic-resistant bacteria from clinical isolates.
Figure 3: Phylogenetic inference identifies parallel evolution.
Figure 4: Constrained evolutionary pathways to antibiotic resistance.


  1. World Health Organization. The evolving threat of antimicrobial resistance: options for action (World Health Organization, 2012).

  2. Paterson, D. L. Resistance in Gram-negative bacteria: Enterobacteriaceae. Am. J. Med. 119, S20–S28; discussion S62–S70 (2006).

    Article  CAS  Google Scholar 

  3. Didelot, X., Bowden, R., Wilson, D. J., Peto, T. E. & Crook, D. W. Transforming clinical microbiology with bacterial genome sequencing. Nature Rev. Genet. 13, 601–612 (2012).

    Article  CAS  Google Scholar 

  4. Morar, M. & Wright, G. D. The genomic enzymology of antibiotic resistance. Annu. Rev. Genet. 44, 25–51 (2010).

    Article  CAS  Google Scholar 

  5. Novais, A. et al. Evolutionary trajectories of β-lactamase CTX-M-1 cluster enzymes: predicting antibiotic resistance. PLoS Pathog. 6, e1000735 (2010).

    Article  Google Scholar 

  6. Elena, S. F. & Lenski, R. E. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nature Rev. Genet. 4, 457–469 (2003).

    Article  CAS  Google Scholar 

  7. Toprak, E. et al. Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nature Genet. 44, 101–105 (2012).

    Article  CAS  Google Scholar 

  8. 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 

  9. Zhang, Q. et al. Acceleration of emergence of bacterial antibiotic resistance in connected microenvironments. Science 333, 1764–1767 (2011).

    Article  CAS  Google Scholar 

  10. Torella, J. P., Chait, R. & Kishony, R. Optimal drug synergy in antimicrobial treatments. PLoS Comput. Biol. 6, e1000796 (2010).

    Article  Google Scholar 

  11. Bonhoeffer, S., Lipsitch, M. & Levin, B. R. Evaluating treatment protocols to prevent antibiotic resistance. Proc. Natl Acad. Sci. USA 94, 12106–12111 (1997).

    Article  CAS  Google Scholar 

  12. Michel, J. B., Yeh, P. J., Chait, R., Moellering, R. C. Jr & Kishony, R. Drug interactions modulate the potential for evolution of resistance. Proc. Natl Acad. Sci. USA 105, 14918–14923 (2008).

    Article  CAS  Google Scholar 

  13. Hegreness, M., Shoresh, N., Damian, D., Hartl, D. & Kishony, R. Accelerated evolution of resistance in multidrug environments. Proc. Natl Acad. Sci. USA 105, 13977–13981 (2008).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  15. Palmer, A. C., Angelino, E. & Kishony, R. Chemical decay of an antibiotic inverts selection for resistance. Nature Chem. Biol. 6, 105–107 (2010).

    Article  CAS  Google Scholar 

  16. Yeh, P. J., Hegreness, M. J., Aiden, A. P. & Kishony, R. Drug interactions and the evolution of antibiotic resistance. Nature Rev. Microbiol. 7, 460–466 (2009).

    Article  CAS  Google Scholar 

  17. Mwangi, M. M. et al. Tracking the in vivo evolution of multidrug resistance in Staphylococcus aureus by whole-genome sequencing. Proc. Natl Acad. Sci. USA 104, 9451–9456 (2007).

    Article  CAS  Google Scholar 

  18. Lieberman, T. D. et al. Parallel bacterial evolution within multiple patients identifies candidate pathogenicity genes. Nature Genet. 43, 1275–1280 (2011).

    Article  CAS  Google Scholar 

  19. Comas, I. et al. Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes. Nature Genet. 44, 106–110 (2012).

    Article  CAS  Google Scholar 

  20. Harris, S. R. et al. Evolution of MRSA during hospital transmission and intercontinental spread. Science 327, 469–474 (2010).

    Article  CAS  Google Scholar 

  21. Croucher, N. J. et al. Rapid pneumococcal evolution in response to clinical interventions. Science 331, 430–434 (2011).

    Article  CAS  Google Scholar 

  22. Baldauf, S. L. Phylogeny for the faint of heart: a tutorial. Trends Genet. 19, 345–351 (2003).

    Article  CAS  Google Scholar 

  23. Didelot, X. & Falush, D. Inference of bacterial microevolution using multilocus sequence data. Genetics 175, 1251–1266 (2007).

    Article  CAS  Google Scholar 

  24. Cohen, T. & Murray, M. Modeling epidemics of multidrug-resistant M. tuberculosis of heterogeneous fitness. Nature Med. 10, 1117–1121 (2004).

    Article  CAS  Google Scholar 

  25. 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 

  26. 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  CAS  Google Scholar 

  27. Chubiz, L. M., Lee, M. C., Delaney, N. F. & Marx, C. J. FREQ-seq: a rapid, cost-effective, sequencing-based method to determine allele frequencies directly from mixed populations. PLoS ONE 7, e47959 (2012).

    Article  CAS  Google Scholar 

  28. Levin-Reisman, I. et al. Automated imaging with ScanLag reveals previously undetectable bacterial growth phenotypes. Nature Methods 7, 737–739 (2010).

    Article  CAS  Google Scholar 

  29. Goodarzi, H., Hottes, A. K. & Tavazoie, S. Global discovery of adaptive mutations. Nature Methods 6, 581–583 (2009).

    Article  CAS  Google Scholar 

  30. Schenk, M. F., Szendro, I. G., Krug, J. & de Visser, J. A. Quantifying the adaptive potential of an antibiotic resistance enzyme. PLoS Genet. 8, e1002783 (2012).

    Article  CAS  Google Scholar 

  31. Salverda, M. L. et al. Initial mutations direct alternative pathways of protein evolution. PLoS Genet. 7, e1001321 (2011).

    Article  CAS  Google Scholar 

  32. van Opijnen, T., Bodi, K. L. & Camilli, A. Tn-seq: high-throughput parallel sequencing for fitness and genetic interaction studies in microorganisms. Nature Methods 6, 767–772 (2009).

    Article  CAS  Google Scholar 

  33. Girgis, H. S., Hottes, A. K. & Tavazoie, S. Genetic architecture of intrinsic antibiotic susceptibility. PLoS ONE 4, e5629 (2009).

    Article  Google Scholar 

  34. Poelwijk, F. J., Kiviet, D. J., Weinreich, D. M. & Tans, S. J. Empirical fitness landscapes reveal accessible evolutionary paths. Nature 445, 383–386 (2007).

    Article  CAS  Google Scholar 

  35. 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  CAS  Google Scholar 

  36. Lozovsky, E. R. et al. Stepwise acquisition of pyrimethamine resistance in the malaria parasite. Proc. Natl Acad. Sci. USA 106, 12025–12030 (2009).

    Article  CAS  Google Scholar 

  37. Tan, L., Serene, S., Chao, H. X. & Gore, J. Hidden randomness between fitness landscapes limits reverse evolution. Phys. Rev. Lett. 106, 198102 (2011).

    Article  Google Scholar 

  38. Trindade, S. et al. Positive epistasis drives the acquisition of multidrug resistance. PLoS Genet. 5, e1000578 (2009).

    Article  Google Scholar 

  39. Brown, K. M. et al. Compensatory mutations restore fitness during the evolution of dihydrofolate reductase. Mol. Biol. Evol. 27, 2682–2690 (2010).

    Article  CAS  Google Scholar 

  40. Hall, A. R. & MacLean, R. C. Epistasis buffers the fitness effects of rifampicin- resistance mutations in Pseudomonas aeruginosa. Evolution 65, 2370–2379 (2011).

    Article  Google Scholar 

  41. D'Costa, V. M., McGrann, K. M., Hughes, D. W. & Wright, G. D. Sampling the antibiotic resistome. Science 311, 374–377 (2006).

    Article  CAS  Google Scholar 

  42. Sommer, M. O., Church, G. M. & Dantas, G. The human microbiome harbors a diverse reservoir of antibiotic resistance genes. Virulence 1, 299–303 (2010).

    Article  Google Scholar 

  43. D'Costa, V. M. et al. Antibiotic resistance is ancient. Nature 477, 457–461 (2011).

    Article  CAS  Google Scholar 

  44. Riesenfeld, C. S., Goodman, R. M. & Handelsman, J. Uncultured soil bacteria are a reservoir of new antibiotic resistance genes. Environ. Microbiol. 6, 981–989 (2004).

    Article  CAS  Google Scholar 

  45. D'Costa, V. M. et al. Inactivation of the lipopeptide antibiotic daptomycin by hydrolytic mechanisms. Antimicrob. Agents Chemother. 56, 757–764 (2012).

    Article  CAS  Google Scholar 

  46. Chusri, S., Villanueva, I., Voravuthikunchai, S. P. & Davies, J. Enhancing antibiotic activity: a strategy to control Acinetobacter infections. J. Antimicrob. Chemother. 64, 1203–1211 (2009).

    Article  CAS  Google Scholar 

  47. Lewis, K. Antibiotics: recover the lost art of drug discovery. Nature 485, 439–440 (2012).

    Article  CAS  Google Scholar 

  48. Koser, C. U. et al. Rapid whole-genome sequencing for investigation of a neonatal MRSA outbreak. N. Engl. J. Med. 366, 2267–2275 (2012).

    Article  CAS  Google Scholar 

  49. Snitkin, E. S. et al. Tracking a hospital outbreak of carbapenem-resistant Klebsiella pneumoniae with whole-genome sequencing. Sci. Transl. Med. 4, 148ra116 (2012).

    Article  Google Scholar 

  50. Harris, S. R. et al. Whole-genome sequencing for analysis of an outbreak of meticillin-resistant Staphylococcus aureus: a descriptive study. Lancet Infect. Dis. 13, 130–136 (2012).

    Article  Google Scholar 

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We thank T. Lieberman for discussions on phylogeny and comments on the manuscript. This work was supported in part by US National Institutes of Health grants R01GM081617 and US National Institute of General Medical Science Center grant P50GM068763, and the Novartis Institutes for BioMedical Research.

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Correspondence to Roy Kishony.

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PowerPoint slides



An enzyme that can confer resistance to β-lactam antibiotics by catalysing their degradation.

Commensal microbes

Microbes living on or in a host without causing disease, although they typically include opportunistic pathogens.


The propensity of a genetic change that confers resistance to one drug also to affect resistance to a different drug (by either increasing or decreasing resistance).


The ratio of mutation rates at nonsynonymous (N) and synonymous (S) sites. dN/dS is increased by selection for amino acid changes (a signature of adaptive selection) and decreased by selection against amino acid changes (purifying selection).

Horizontal gene transfer

The acquisition of a gene by a means other than direct inheritance from a parent cell (vertical transfer). Common in many bacteria and archaea, mechanisms of horizontal gene transfer include transformation, conjugation and transduction.

Maximum likelihood and Bayesian approaches

This definition applies to the context of phylogenetics. Phylogenetic trees can be constructed by maximum parsimony, maximum likelihood and Bayesian inference. Maximum parsimony methods select from all possible trees the one containing the fewest mutations. Trees chosen by maximum likelihood and other Bayesian methods may contain more mutations, as they weigh the relative probabilities of different mutations according to various models.

Microfluidic device

Customized, microscopic chambers in which fluid flows can be precisely controlled. Applied to microbiology, these allow the study of bacterial behaviour in spatially and temporally controllable environments.


Chemical therapy by a single drug.

Parallel evolution

When the same mutations (or a range of mutations in the same gene) repeatedly occur in independent lineages; this provides an indication that these mutations may have been fixed by positive selection rather than by chance.

Proto-resistance genes

Evolutionary precursors to drug-resistance genes that do not yet contribute to drug resistance but may do so on mutation and selection by drug stress.

Resistance cassettes

A genetic element containing one or more drug resistance genes, often carried in transposable elements or plasmids that facilitate horizontal gene transfer.

Transposon mutagenesis

The insertion of transposons at random locations throughout a genome to generate a library of different gene disruptions. Transposons can be constructed with outward-facing promoters also to introduce gene overexpression into the library.


Devices that maintain constant cell density (turbidity) in a continuously growing microbial culture by routinely removing a small volume of culture and replacing it with fresh sterile media.

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Palmer, A., Kishony, R. Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nat Rev Genet 14, 243–248 (2013).

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