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  • Review Article
  • Published:

Evolutionary consequences of drug resistance: shared principles across diverse targets and organisms

Key Points

  • Across the diverse biological systems discussed in this Review, the underlying principles concerning the mechanisms and dynamics of resistance development are similar.

  • Drug resistance has emerged in all biological systems in which drugs are used as a standard therapeutic strategy to control infections or cancer. There is an urgent need not only to develop new drugs to support effective therapy but also to develop a better understanding of the underlying mechanisms and forces that drive resistance development.

  • Large population sizes and/or high mutation rates ensure that the emergence of drug resistance is not limited by mutation supply in HIV, in many bacterial infections or in human cancers. Mutation supply may be a limiting factor for fungal and parasitic infections.

  • Horizontal gene transfer (HGT) from a very broad gene pool substantially contributes to the emergence of drug resistance in bacteria but is absent as a source of genetic variation in the other systems discussed. We currently know very little about the dynamics and trajectories of HGT events and have a very poor ability to make predictions.

  • The study and understanding of the dynamics of growth and competition within complex populations subjected to drug therapy are being advanced by the increasing application of next-generation sequencing technologies.

  • In biological systems in which resistance emergence has long been acknowledged to be a problem (particularly HIV infection and human cancer), therapy with combinations of drugs is standard of care. The systematic use of drug combinations in the treatment of bacterial, fungal and parasitic infections might be the most effective short-term means to slow resistance emergence.

Abstract

Drug therapy has a crucial role in the treatment of viral, bacterial, fungal and protozoan infections, as well as the control of human cancer. The success of therapy is being threatened by the increasing prevalence of resistance. We examine and compare mechanisms of drug resistance in these diverse biological systems (using HIV and Plasmodium falciparum as examples of viral and protozoan pathogens, respectively) and discuss how factors — such as mutation rates, fitness effects of resistance, epistasis and clonal interference — influence the evolutionary trajectories of drug-resistant clones. We describe commonalities and differences related to resistance development that could guide strategies to improve therapeutic effectiveness and the development of a new generation of drugs.

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Figure 1: The nature of fitness costs in different systems.
Figure 2: Common fitness-compensatory mechanisms.
Figure 3: The relationship between fitness, degree of resistance and rate of formation of drug-resistant mutants.
Figure 4: Evolutionary trajectories of drug-resistance development.
Figure 5: Epistasis and relative fitness.

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References

  1. World Health Organization. World Health Statistics 2014 (WHO Press, 2014).

  2. Abram, M. E., Ferris, A. L., Shao, W., Alvord, W. G. & Hughes, S. H. Nature, position, and frequency of mutations made in a single cycle of HIV-1 replication. J. Virol. 84, 9864–9878 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Mansky, L. M. & Temin, H. M. Lower in vivo mutation rate of human immunodeficiency virus type 1 than that predicted from the fidelity of purified reverse transcriptase. J. Virol. 69, 5087–5094 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Chan, J. M., Carlsson, G. & Rabadan, R. Topology of viral evolution. Proc. Natl Acad. Sci. USA 110, 18566–18571 (2013).

    CAS  PubMed  Google Scholar 

  5. Sprenger, H. G., Bierman, W. F., van der Werf, T. S., Gisolf, E. H. & Richter, C. A systematic review of a single-class maintenance strategy with nucleoside/nucleotide reverse transcriptase inhibitors in HIV/AIDS. Antivir Ther. 19, 625–636 (2014).

    CAS  PubMed  Google Scholar 

  6. Sharma, M. & Saravolatz, L. D. Rilpivirine: a new non-nucleoside reverse transcriptase inhibitor. J. Antimicrob. Chemother. 68, 250–256 (2013).

    CAS  PubMed  Google Scholar 

  7. De Clercq, E. The nucleoside reverse transcriptase inhibitors, nonnucleoside reverse transcriptase inhibitors, and protease inhibitors in the treatment of HIV infections (AIDS). Adv. Pharmacol. 67, 317–358 (2013).

    CAS  PubMed  Google Scholar 

  8. Michaud, V. et al. The dual role of pharmacogenetics in HIV treatment: mutations and polymorphisms regulating antiretroviral drug resistance and disposition. Pharmacol. Rev. 64, 803–833 (2012).

    CAS  PubMed  Google Scholar 

  9. Ridky, T. & Leis, J. Development of drug resistance to HIV-1 protease inhibitors. J. Biol. Chem. 270, 29621–29623 (1995).

    CAS  PubMed  Google Scholar 

  10. Thompson, M. A. et al. Antiretroviral treatment of adult HIV infection: 2012 recommendations of the International Antiviral Society-USA panel. JAMA 308, 387–402 (2012).

    CAS  PubMed  Google Scholar 

  11. Drake, J. W., Charlesworth, B., Charlesworth, D. & Crow, J. F. Rates of spontaneous mutation. Genetics 148, 1667–1686 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Komp Lindgren, P., Karlsson, Å. & Hughes, D. Mutation rate and evolution of fluoroquinolone resistance in Escherichia coli isolates from patients with urinary tract infections. Antimicrob. Agents Chemother. 47, 3222–3232 (2003).

    PubMed  PubMed Central  Google Scholar 

  13. Andersson, D. I. & Hughes, D. Gene amplification and adaptive evolution in bacteria. Annu. Rev. Genet. 43, 167–195 (2009).

    CAS  PubMed  Google Scholar 

  14. Sun, S., Berg, O. G., Roth, J. R. & Andersson, D. I. Contribution of gene amplification to evolution of increased antibiotic resistance in Salmonella typhimurium. Genetics 182, 1183–1195 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Anderson, J. B. Evolution of antifungal-drug resistance: mechanisms and pathogen fitness. Nat. Rev. Microbiol. 3, 547–556 (2005).

    CAS  PubMed  Google Scholar 

  16. Wright, G. D. The antibiotic resistome: the nexus of chemical and genetic diversity. Nat. Rev. Microbiol. 5, 175–186 (2007).

    CAS  PubMed  Google Scholar 

  17. Davies, J. & Davies, D. Origins and evolution of antibiotic resistance. Microbiol. Mol. Biol. Rev. 74, 417–433 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Dye, C. Doomsday postponed? Preventing and reversing epidemics of drug-resistant tuberculosis. Nat. Rev. Microbiol. 7, 81–87 (2009).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  20. Hoiby, N. et al. The clinical impact of bacterial biofilms. Int. J. Oral Sci. 3, 55–65 (2011).

    PubMed  PubMed Central  Google Scholar 

  21. Mackinnon, M. J. & Read, A. F. Virulence in malaria: an evolutionary viewpoint. Phil. Trans. R. Soc. Lond. B 359, 965–986 (2004).

    Google Scholar 

  22. Muller, I. B. & Hyde, J. E. Antimalarial drugs: modes of action and mechanisms of parasite resistance. Future Microbiol. 5, 1857–1873 (2010).

    PubMed  Google Scholar 

  23. Shandilya, A., Chacko, S., Jayaram, B. & Ghosh, I. A plausible mechanism for the antimalarial activity of artemisinin: a computational approach. Sci. Rep. 3, 2513 (2013).

    PubMed  PubMed Central  Google Scholar 

  24. Visser, B. J., van Vugt, M. & Grobusch, M. P. Malaria: an update on current chemotherapy. Expert Opin. Pharmacother. 15, 2219–2254 (2014).

    CAS  PubMed  Google Scholar 

  25. Dondorp, A. M. et al. The threat of artemisinin-resistant malaria. N. Engl. J. Med. 365, 1073–1075 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Noedl, H. et al. Evidence of artemisinin-resistant malaria in western Cambodia. N. Engl. J. Med. 359, 2619–2620 (2008). First report of the worrying emergence of resistance to the most effective antimalarial drug therapy.

    CAS  PubMed  Google Scholar 

  27. Takala-Harrison, S. et al. Genetic loci associated with delayed clearance of Plasmodium falciparum following artemisinin treatment in Southeast Asia. Proc. Natl Acad. Sci. USA 110, 240–245 (2013).

    CAS  PubMed  Google Scholar 

  28. Ariey, F. et al. A molecular marker of artemisinin-resistant Plasmodium falciparum malaria. Nature 505, 50–55 (2014). Identification of mutations in the K13 propellor as important determinants of artemisinin resistance and a molecular marker for surveillance.

    PubMed  Google Scholar 

  29. Holohan, C., Van Schaeybroeck, S., Longley, D. B. & Johnston, P. G. Cancer drug resistance: an evolving paradigm. Nat. Rev. Cancer 13, 714–726 (2013).

    CAS  PubMed  Google Scholar 

  30. Duesberg, P., Stindl, R. & Hehlmann, R. Explaining the high mutation rates of cancer cells to drug and multidrug resistance by chromosome reassortments that are catalyzed by aneuploidy. Proc. Natl Acad. Sci. USA 97, 14295–14300 (2000).

    CAS  PubMed  Google Scholar 

  31. Swanton, C. et al. Chromosomal instability determines taxane response. Proc. Natl Acad. Sci. USA 106, 8671–8676 (2009).

    CAS  PubMed  Google Scholar 

  32. Housman, G. et al. Drug resistance in cancer: an overview. Cancers (Basel) 6, 1769–1792 (2014).

    CAS  Google Scholar 

  33. McMillin, D. W., Negri, J. M. & Mitsiades, C. S. The role of tumour-stromal interactions in modifying drug response: challenges and opportunities. Nat. Rev. Drug Discov. 12, 217–228 (2013).

    CAS  PubMed  Google Scholar 

  34. Timp, W. & Feinberg, A. P. Cancer as a dysregulated epigenome allowing cellular growth advantage at the expense of the host. Nat. Rev. Cancer 13, 497–510 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Easwaran, H., Tsai, H. C. & Baylin, S. B. Cancer epigenetics: tumor heterogeneity, plasticity of stem-like states, and drug resistance. Mol. Cell 54, 716–727 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Boutwell, C. L., Rowley, C. F. & Essex, M. Reduced viral replication capacity of human immunodeficiency virus type 1 subtype C caused by cytotoxic-T-lymphocyte escape mutations in HLA-B57 epitopes of capsid protein. J. Virol. 83, 2460–2468 (2009).

    CAS  PubMed  Google Scholar 

  37. Armstrong, K. L., Lee, T. H. & Essex, M. Replicative fitness costs of nonnucleoside reverse transcriptase inhibitor drug resistance mutations on HIV subtype C. Antimicrob. Agents Chemother. 55, 2146–2153 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Cong, M. E., Heneine, W. & García-Lerma, J. G. The fitness cost of mutations associated with human immunodeficiency virus type 1 drug resistance is modulated by mutational interactions. J. Virol. 81, 3037–3041 (2007).

    CAS  PubMed  Google Scholar 

  39. Andersson, D. I. & Hughes, D. Persistence of antibiotic resistance in bacterial populations. FEMS Microbiol. Rev. 35, 901–911 (2011).

    CAS  PubMed  Google Scholar 

  40. Vogwill, T. & MacLean, R. C. The genetic basis of the fitness costs of antimicrobial resistance: a meta-analysis approach. Evol. Appl. 8, 284–295 (2015).

    PubMed  Google Scholar 

  41. Sander, P. et al. Fitness cost of chromosomal drug resistance-conferring mutations. Antimicrob. Agents Chemother. 46, 1204–1211 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Bottger, E. C., Springer, B., Pletschette, M. & Sander, P. Fitness of antibiotic-resistant microorganisms and compensatory mutations. Nat. Med. 4, 1343–1344 (1998). Provided the first clear evidence for the selection of low-cost antibiotic resistance mutations in a clinical environment.

    CAS  PubMed  Google Scholar 

  43. Vincent, B. M., Lancaster, A. K., Scherz-Shouval, R., Whitesell, L. & Lindquist, S. Fitness trade-offs restrict the evolution of resistance to amphotericin B. PLoS Biol. 11, e1001692 (2013). Experimental work showing how conflicting selective pressures shape evolutionary trajectories and suggesting a strategy for limiting the rapid emergence of drug resistance.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Rosenthal, P. J. The interplay between drug resistance and fitness in malaria parasites. Mol. Microbiol. 89, 1025–1038 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Gatenby, R. A. A change of strategy in the war on cancer. Nature 459, 508–509 (2009).

    CAS  PubMed  Google Scholar 

  46. Schock, H. B., Garsky, V. M. & Kuo, L. C. Mutational anatomy of an HIV-1 protease variant conferring cross-resistance to protease inhibitors in clinical trials. Compensatory modulations of binding and activity. J. Biol. Chem. 271, 31957–31963 (1996).

    CAS  PubMed  Google Scholar 

  47. Zhang, Y. M. et al. Drug resistance during indinavir therapy is caused by mutations in the protease gene and in its Gag substrate cleavage sites. J. Virol. 71, 6662–6670 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Bleiber, G., Munoz, M., Ciuffi, A., Meylan, P. & Telenti, A. Individual contributions of mutant protease and reverse transcriptase to viral infectivity, replication, and protein maturation of antiretroviral drug-resistant human immunodeficiency virus type 1. J. Virol. 75, 3291–3300 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Kožišek, M. et al. Mutations in HIV-1 gag and pol compensate for the loss of viral fitness caused by a highly mutated protease. Antimicrob. Agents Chemother. 56, 4320–4330 (2012).

    PubMed  PubMed Central  Google Scholar 

  50. Koval, C. E., Dykes, C., Wang, J. & Demeter, L. M. Relative replication fitness of efavirenz-resistant mutants of HIV-1: correlation with frequency during clinical therapy and evidence of compensation for the reduced fitness of K103N + L100I by the nucleoside resistance mutation L74V. Virology 353, 184–192 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Demeter, L. M. et al. Association of efavirenz hypersusceptibility with virologic response in ACTG 368, a randomized trial of abacavir (ABC) in combination with efavirenz (EFV) and indinavir (IDV) in HIV-infected subjects with prior nucleoside analog experience. HIV Clin. Trials 9, 11–25 (2008).

    PubMed  PubMed Central  Google Scholar 

  52. Schulz zur Wiesch, P., Engelstädter, J. & Bonhoeffer, S. Compensation of fitness costs and reversibility of antibiotic resistance mutations. Antimicrob. Agents Chemother. 54, 2085–2095 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Soskine, M. & Tawfik, D. S. Mutational effects and the evolution of new protein functions. Nat. Rev. Genet. 11, 572–582 (2010).

    CAS  PubMed  Google Scholar 

  54. Schrag, S. J., Perrot, V. & Levin, B. R. Adaptation to the fitness costs of antibiotic resistance in Escherichia coli. Proc. Biol. Sci. 264, 1287–1291 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Bjorkman, J., Samuelsson, P., Andersson, D. I. & Hughes, D. Novel ribosomal mutations affecting translational accuracy, antibiotic resistance and virulence of Salmonella typhimurium. Mol. Microbiol. 31, 53–58 (1999).

    CAS  PubMed  Google Scholar 

  56. Kumpornsin, K. et al. Origin of robustness in generating drug-resistant malaria parasites. Mol. Biol. Evol. 31, 1649–1660 (2014).

    PubMed  PubMed Central  Google Scholar 

  57. McFarland, C. D., Mirny, L. A. & Korolev, K. S. Tug-of-war between driver and passenger mutations in cancer and other adaptive processes. Proc. Natl Acad. Sci. USA 111, 15138–15143 (2014).

    CAS  PubMed  Google Scholar 

  58. Takebe, N. et al. Targeting Notch, Hedgehog, and Wnt pathways in cancer stem cells: clinical update. Nat. Rev. Clin. Oncol. http://dx.doi.org/10.1038/nrclinonc.2015.61 (2015).

  59. Worby, C. J., Lipsitch, M. & Hanage, W. P. Within-host bacterial diversity hinders accurate reconstruction of transmission networks from genomic distance data. PLoS Comput. Biol. 10, e1003549 (2014).

    PubMed  PubMed Central  Google Scholar 

  60. Lynch, M. Evolution of the mutation rate. Trends Genet. 26, 345–352 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Foster, P. L. Methods for determining spontaneous mutation rates. Methods Enzymol. 409, 195–213 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Duffy, S., Shackelton, L. A. & Holmes, E. C. Rates of evolutionary change in viruses: patterns and determinants. Nat. Rev. Genet. 9, 267–276 (2008).

    CAS  PubMed  Google Scholar 

  63. Abdulkarim, F. & Hughes, D. Homologous recombination between the tuf genes of Salmonella typhimurium. J. Mol. Biol. 260, 506–522 (1996).

    CAS  PubMed  Google Scholar 

  64. Cabezón, E., Ripoll-Rozada, J., Peña, A., de la Cruz, F. & Arechaga, I. Towards an integrated model of bacterial conjugation. FEMS Microbiol. Rev. 39, 81–95 (2014).

    PubMed  Google Scholar 

  65. Shaw, G. M. & Hunter, E. HIV transmission. Cold Spring Harb. Perspect. Med. 2, a006965 (2012).

    PubMed  PubMed Central  Google Scholar 

  66. Smith, M. R. & Wood, W. B. Jr. An experimental analysis of the curative action of penicillin in acute bacterial infections. III. The effect of suppuration upon the antibacterial action of the drug. J. Exp. Med. 103, 509–522 (1956).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Del Monte, U. Does the cell number 109 still really fit one gram of tumor tissue? Cell Cycle 8, 505–506 (2009).

    CAS  PubMed  Google Scholar 

  68. Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Andersson, D. I. & Levin, B. R. The biological cost of antibiotic resistance. Curr. Opin. Microbiol. 2, 489–493 (1999).

    CAS  PubMed  Google Scholar 

  70. Wiser, M. J., Ribeck, N. & Lenski, R. E. Long-term dynamics of adaptation in asexual populations. Science 342, 1364–1367 (2013).

    CAS  PubMed  Google Scholar 

  71. Lipsitch, M., Bergstrom, C. T. & Levin, B. R. The epidemiology of antibiotic resistance in hospitals: paradoxes and prescriptions. Proc. Natl Acad. Sci. USA 97, 1938–1943 (2000).

    CAS  PubMed  Google Scholar 

  72. Iyidogan, P. & Anderson, K. S. Current perspectives on HIV-1 antiretroviral drug resistance. Viruses 6, 4095–4139 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Gotte, M. The distinct contributions of fitness and genetic barrier to the development of antiviral drug resistance. Curr. Opin. Virol. 2, 644–650 (2012).

    CAS  PubMed  Google Scholar 

  74. Bastarache, S. M., Mesplede, T., Donahue, D. A., Sloan, R. D. & Wainberg, M. A. Fitness impaired drug resistant HIV-1 is not compromised in cell-to-cell transmission or establishment of and reactivation from latency. Viruses 6, 3487–3499 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Butler, J. et al. Estimating the fitness advantage conferred by permissive neuraminidase mutations in recent oseltamivir-resistant A(H1N1)pdm09 influenza viruses. PLoS Pathog. 10, e1004065 (2014).

    PubMed  PubMed Central  Google Scholar 

  76. Shcherbakov, D. et al. Directed mutagenesis of Mycobacterium smegmatis 16S rRNA to reconstruct the in vivo evolution of aminoglycoside resistance in Mycobacterium tuberculosis. Mol. Microbiol. 77, 830–840 (2010). Reconstructed the resistance genotypes of clinical strains in an isogenic background, revealing a pathway for resistance development that suggests that compensatory evolution contributes to drug-resistant TB.

    CAS  PubMed  Google Scholar 

  77. O'Neill, A. J., Huovinen, T., Fishwick, C. W. & Chopra, I. Molecular genetic and structural modeling studies of Staphylococcus aureus RNA polymerase and the fitness of rifampin resistance genotypes in relation to clinical prevalence. Antimicrob. Agents Chemother. 50, 298–309 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Brandis, G., Pietsch, F., Alemayehu, R. & Hughes, D. Comprehensive phenotypic characterization of rifampicin resistance mutations in Salmonella provides insight into the evolution of resistance in Mycobacterium tuberculosis. J. Antimicrob. Chemother. 70, 680–685 (2015).

    CAS  PubMed  Google Scholar 

  79. Mesplede, T. et al. Viral fitness cost prevents HIV-1 from evading dolutegravir drug pressure. Retrovirology 10, 22 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Gullberg, E. et al. Selection of resistant bacteria at very low antibiotic concentrations. PLoS Pathog. 7, e1002158 (2011). Provided experimental evidence that sub-MIC levels of antibiotics select and enrich for resistant mutants.

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Hughes, D. & Andersson, D. I. Selection of resistance at lethal and non-lethal antibiotic concentrations. Curr. Opin. Microbiol. 15, 555–560 (2012).

    CAS  PubMed  Google Scholar 

  82. Gullberg, E., Albrecht, L. M., Karlsson, C., Sandegren, L. & Andersson, D. I. Selection of a multidrug resistance plasmid by sublethal levels of antibiotics and heavy metals. mBio 5, e01918-14 (2014).

    PubMed  PubMed Central  Google Scholar 

  83. Andersson, D. I. & Hughes, D. Microbiological effects of sublethal levels of antibiotics. Nat. Rev. Microbiol. 12, 465–478 (2014).

    CAS  PubMed  Google Scholar 

  84. Menéndez-Arias, L., Martinez, M. A., Quñones-Mateu, M. E. & Martinez-Picado, J. Fitness variations and their impact on the evolution of antiretroviral drug resistance. Curr. Drug Targets Infect. Disord. 3, 355–371 (2003).

    PubMed  Google Scholar 

  85. Nijhuis, M., van Maarseveen, N. M. & Boucher, C. A. Antiviral resistance and impact on viral replication capacity: evolution of viruses under antiviral pressure occurs in three phases. Handb. Exp. Pharmacol. 189, 299–320 (2009).

    CAS  Google Scholar 

  86. Mariam, S. H., Werngren, J., Aronsson, J., Hoffner, S. & Andersson, D. I. Dynamics of antibiotic resistant Mycobacterium tuberculosis during long-term infection and antibiotic treatment. PLoS ONE 6, e21147 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Suerbaum, S. & Josenhans, C. Helicobacter pylori evolution and phenotypic diversification in a changing host. Nat. Rev. Microbiol. 5, 441–452 (2007).

    CAS  PubMed  Google Scholar 

  88. Markussen, T. et al. Environmental heterogeneity drives within-host diversification and evolution of Pseudomonas aeruginosa. mBio 5, e01592-14 (2014).

    PubMed  PubMed Central  Google Scholar 

  89. Gerrish, P. J. & Lenski, R. E. The fate of competing beneficial mutations in an asexual population. Genetica 102–103, 127–144 (1998).

  90. Hughes, J. M. et al. The role of clonal interference in the evolutionary dynamics of plasmid–host adaptation. mBio 3, e00077-12 (2012).

    PubMed  PubMed Central  Google Scholar 

  91. Gifford, D. R. & MacLean, R. C. Evolutionary reversals of antibiotic resistance in experimental populations of Pseudomonas aeruginosa. Evolution 67, 2973–2981 (2013).

    PubMed  Google Scholar 

  92. Miralles, R., Gerrish, P. J., Moya, A. & Elena, S. F. Clonal interference and the evolution of RNA viruses. Science 285, 1745–1747 (1999). Measured the effects of clonal interference in an asexual RNA virus and quantified the rates and effects of beneficial mutations.

    CAS  PubMed  Google Scholar 

  93. Arjan, J. A. et al. Diminishing returns from mutation supply rate in asexual populations. Science 283, 404–406 (1999). Showed that the rate of evolutionary adaptation is proportional to mutation supply rate only in particular circumstances of small or well-adapted populations.

    CAS  PubMed  Google Scholar 

  94. Kao, K. C. & Sherlock, G. Molecular characterization of clonal interference during adaptive evolution in asexual populations of Saccharomyces cerevisiae. Nat. Genet. 40, 1499–1504 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Lang, G. I. et al. Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature 500, 571–574 (2013). A detailed analysis of the dynamics of evolutionary adaptation evaluating how this determines which mutations fix in a population and the reproducibility of evolution.

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Maisnier-Patin, S., Berg, O. G., Liljas, L. & Andersson, D. I. Compensatory adaptation to the deleterious effect of antibiotic resistance in Salmonella typhimurium. Mol. Microbiol. 46, 355–366 (2002).

    CAS  PubMed  Google Scholar 

  97. Brandis, G., Wrande, M., Liljas, L. & Hughes, D. Fitness-compensatory mutations in rifampicin-resistant RNA polymerase. Mol. Microbiol. 85, 142–151 (2012).

    CAS  PubMed  Google Scholar 

  98. Brandis, G. & Hughes, D. Genetic characterization of compensatory evolution in strains carrying rpoB Ser531Leu, the rifampicin resistance mutation most frequently found in clinical isolates. J. Antimicrob. Chemother. 68, 2493–2497 (2013).

    CAS  PubMed  Google Scholar 

  99. Nagaev, I., Bjorkman, J., Andersson, D. I. & Hughes, D. Biological cost and compensatory evolution in fusidic acid-resistant Staphylococcus aureus. Mol. Microbiol. 40, 433–439 (2001).

    CAS  PubMed  Google Scholar 

  100. Reynolds, M. G. Compensatory evolution in rifampin-resistant Escherichia coli. Genetics 156, 1471–1481 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Bjorkman, J., Hughes, D. & Andersson, D. I. Virulence of antibiotic-resistant Salmonella typhimurium. Proc. Natl Acad. Sci. USA 95, 3949–3953 (1998).

    CAS  PubMed  Google Scholar 

  102. Levin, B. R., Perrot, V. & Walker, N. Compensatory mutations, antibiotic resistance and the population genetics of adaptive evolution in bacteria. Genetics 154, 985–997 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Casali, N. et al. Evolution and transmission of drug-resistant tuberculosis in a Russian population. Nat. Genet. 46, 279–286 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Pingen, M., Nijhuis, M., de Bruijn, J. A., Boucher, C. A. & Wensing, A. M. Evolutionary pathways of transmitted drug-resistant HIV-1. J. Antimicrob. Chemother. 66, 1467–1480 (2011).

    CAS  PubMed  Google Scholar 

  105. Martinez-Picado, J. & Martínez, M. A. HIV-1 reverse transcriptase inhibitor resistance mutations and fitness: a view from the clinic and ex vivo. Virus Res. 134, 104–123 (2008).

    CAS  PubMed  Google Scholar 

  106. Wang, Y. et al. Co-evolution of compensatory mutation K43E with mutation M41L in long-term HIV antiretroviral treatment. Curr. HIV Res. 12, 22–31 (2014).

    CAS  PubMed  Google Scholar 

  107. de Visser, J. A. & Krug, J. Empirical fitness landscapes and the predictability of evolution. Nat. Rev. Genet. 15, 480–490 (2014).

    CAS  PubMed  Google Scholar 

  108. Bedhomme, S., Hillung, J. & Elena, S. F. Emerging viruses: why they are not jack of all trades? Curr. Opin. Virol. 10, 1–6 (2015).

    PubMed  Google Scholar 

  109. Kondrashov, D. A. & Kondrashov, F. A. Topological features of rugged fitness landscapes in sequence space. Trends Genet. 31, 24–33 (2015).

    CAS  PubMed  Google Scholar 

  110. Komp Lindgren, P., Marcusson, L. L., Sandvang, D., Frimodt-Moller, N. & Hughes, D. Biological cost of single and multiple norfloxacin resistance mutations in Escherichia coli implicated in urinary tract infections. Antimicrob. Agents Chemother. 49, 2343–2351 (2005).

    PubMed  Google Scholar 

  111. Marcusson, L. L., Frimodt-Moller, N. & Hughes, D. Interplay in the selection of fluoroquinolone resistance and bacterial fitness. PLoS Pathog. 5, e1000541 (2009). Illustrated how fitness costs incurred during resistance evolution can act as a driver for further resistance evolution.

    PubMed  PubMed Central  Google Scholar 

  112. Trindade, S. et al. Positive epistasis drives the acquisition of multidrug resistance. PLoS Genet. 5, e1000578 (2009). Examined epistasis between antibiotic resistance mutations that led to insights that suggested reasons why multidrug-resistant bacteria are so prevalent.

    PubMed  PubMed Central  Google Scholar 

  113. Angst, D. C. & Hall, A. R. The cost of antibiotic resistance depends on evolutionary history in Escherichia coli. BMC Evol. Biol. 13, 163 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  116. 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). Analysis of the multistep evolution of high-level resistance to β-lactamases reveals that only a few evolutionary trajectories are accessible and consequently that much of evolution may be reproducible and even predictable.

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  118. Szybalski, W. & Bryson, V. Genetic studies on microbial cross resistance to toxic agents. J. Bacteriol. 64, 489–499 (1952).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Alekshun, M. N. & Levy, S. B. Molecular mechanisms of antibacterial multidrug resistance. Cell 128, 1037–1050 (2007).

    CAS  PubMed  Google Scholar 

  120. Stephan, J., Mailaender, C., Etienne, G., Daffé, M. & Niederweis, M. Multidrug resistance of a porin deletion mutant of Mycobacterium smegmatis. Antimicrob. Agents Chemother. 48, 4163–4170 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Garcia, L. G. et al. Antibiotic activity against small-colony variants of Staphylococcus aureus: review of in vitro, animal and clinical data. J. Antimicrob. Chemother. 68, 1455–1464 (2013).

    CAS  PubMed  Google Scholar 

  122. Perichon, B. & Courvalin, P. Synergism between β-lactams and glycopeptides against VanA-type methicillin-resistant Staphylococcus aureus and heterologous expression of the vanA operon. Antimicrob. Agents Chemother. 50, 3622–3630 (2006). Revealed and explained the basis for an unexpected and potentially useful synergy between unrelated antibiotics against MRSA.

    CAS  PubMed  PubMed Central  Google Scholar 

  123. Macvanin, M. & Hughes, D. Hyper-susceptibility of a fusidic acid-resistant mutant of Salmonella to different classes of antibiotics. FEMS Microbiol. Lett. 247, 215–220 (2005).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  125. Imamovic, L. & Sommer, M. O. Use of collateral sensitivity networks to design drug cycling protocols that avoid resistance development. Sci. Transl Med. 5, 204ra132 (2013). Combined experimental evolution and genome sequencing to map cross-resistance interactions between antibiotics in E. coli and derive common evolutionary principles.

    PubMed  Google Scholar 

  126. Lazar, V. et al. Genome-wide analysis captures the determinants of the antibiotic cross-resistance interaction network. Nat. Commun. 5, 4352 (2014). Experimentally showed the prevalence of collateral sensitivity, a potentially novel therapeutic paradigm for the cyclic use of drugs to treat infectious diseases and cancer.

    CAS  PubMed  PubMed Central  Google Scholar 

  127. Pena-Miller, R. et al. When the most potent combination of antibiotics selects for the greatest bacterial load: the smile–frown transition. PLoS Biol. 11, e1001540 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  128. Chait, R., Craney, A. & Kishony, R. Antibiotic interactions that select against resistance. Nature 446, 668–671 (2007). A groundbreaking paper exploring the fitness landscape for the evolution of resistance to multidrug combinations and revealing the trade-offs between drug potency and the selection that the drugs impose on emerging resistant populations.

    CAS  PubMed  Google Scholar 

  129. 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).

    CAS  PubMed  Google Scholar 

  130. Oliveira, M., Mesplede, T., Quashie, P. K., Moisi, D. & Wainberg, M. A. Resistance mutations against dolutegravir in HIV integrase impair the emergence of resistance against reverse transcriptase inhibitors. AIDS 28, 813–819 (2014).

    CAS  PubMed  Google Scholar 

  131. Anderson, J. P., Daifuku, R. & Loeb, L. A. Viral error catastrophe by mutagenic nucleosides. Annu. Rev. Microbiol. 58, 183–205 (2004).

    CAS  PubMed  Google Scholar 

  132. Clementi, M. Perspectives and opportunities for novel antiviral treatments targeting virus fitness. Clin. Microbiol. Infect. 14, 629–631 (2008).

    CAS  PubMed  Google Scholar 

  133. Crotty, S., Cameron, C. E. & Andino, R. RNA virus error catastrophe: direct molecular test by using ribavirin. Proc. Natl Acad. Sci. USA 98, 6895–6900 (2001). Show experimentally that RNA virus mutagens can effectively cause a loss of viral viability and may represent a promising class of antiviral drugs.

    CAS  PubMed  Google Scholar 

  134. Crotty, S. et al. The broad-spectrum antiviral ribonucleoside ribavirin is an RNA virus mutagen. Nat. Med. 6, 1375–1379 (2000).

    CAS  PubMed  Google Scholar 

  135. Airaksinen, A., Pariente, N., Menéndez-Arias, L. & Domingo, E. Curing of foot-and-mouth disease virus from persistently infected cells by ribavirin involves enhanced mutagenesis. Virology 311, 339–349 (2003).

    CAS  PubMed  Google Scholar 

  136. Clementi, M. & Lazzarin, A. Human immunodeficiency virus type 1 fitness and tropism: concept, quantification, and clinical relevance. Clin. Microbiol. Infect. 16, 1532–1538 (2010).

    CAS  PubMed  Google Scholar 

  137. Smith, R. A., Loeb, L. A. & Preston, B. D. Lethal mutagenesis of HIV. Virus Res. 107, 215–228 (2005).

    CAS  PubMed  Google Scholar 

  138. Pfeiffer, J. K. & Kirkegaard, K. A single mutation in poliovirus RNA-dependent RNA polymerase confers resistance to mutagenic nucleotide analogs via increased fidelity. Proc. Natl Acad. Sci. USA 100, 7289–7294 (2003).

    CAS  PubMed  Google Scholar 

  139. Pfeiffer, J. K. & Kirkegaard, K. Ribavirin resistance in hepatitis C virus replicon-containing cell lines conferred by changes in the cell line or mutations in the replicon RNA. J. Virol. 79, 2346–2355 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  140. Sierra, M. et al. Foot-and-mouth disease virus mutant with decreased sensitivity to ribavirin: implications for error catastrophe. J. Virol. 81, 2012–2024 (2007).

    CAS  PubMed  Google Scholar 

  141. Pfeiffer, J. K. & Kirkegaard, K. Increased fidelity reduces poliovirus fitness and virulence under selective pressure in mice. PLoS Pathog. 1, e11 (2005).

    PubMed  PubMed Central  Google Scholar 

  142. Perales, C., Agudo, R. & Domingo, E. Counteracting quasispecies adaptability: extinction of a ribavirin-resistant virus mutant by an alternative mutagenic treatment. PLoS ONE 4, e5554 (2009).

    PubMed  PubMed Central  Google Scholar 

  143. Goldberg, D. E., Siliciano, R. F. & Jacobs, W. R. Jr. Outwitting evolution: fighting drug-resistant TB, malaria, and HIV. Cell 148, 1271–1283 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  144. Fischbach, M. A. Combination therapies for combating antimicrobial resistance. Curr. Opin. Microbiol. 14, 519–523 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  145. Piccolo, M. T., Menale, C. & Crispi, S. Combined anticancer therapies: an overview of the latest applications. Anticancer Agents Med. Chem. 15, 408–422 (2015).

    CAS  PubMed  Google Scholar 

  146. Hill, J. A., O'Meara, T. R. & Cowen, L. E. Fitness trade-offs associated with the evolution of resistance to antifungal drug combinations. Cell Rep. 10, 809–819 (2015). Identified evolutionary constraints that may minimize the evolution of resistance to combinations of antifungal drugs.

    CAS  PubMed  Google Scholar 

  147. Lazar, V. et al. Bacterial evolution of antibiotic hypersensitivity. Mol. Syst. Biol. 9, 700 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  Google Scholar 

  149. Silver, L. L. Challenges of antibacterial discovery. Clin. Microbiol. Rev. 24, 71–109 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  150. Rossolini, G. M., Mantengoli, E., Montagnani, F. & Pollini, S. Epidemiology and clinical relevance of microbial resistance determinants versus anti- Gram-positive agents. Curr. Opin. Microbiol. 13, 582–588 (2010).

    CAS  PubMed  Google Scholar 

  151. Woodman, Z. & Williamson, C. HIV molecular epidemiology: transmission and adaptation to human populations. Curr. Opin. HIV AIDS 4, 247–252 (2009).

    PubMed  PubMed Central  Google Scholar 

  152. Anastassopoulou, C. G. et al. Escape of HIV-1 from a small molecule CCR5 inhibitor is not associated with a fitness loss. PLoS Pathog. 3, e79 (2007).

    PubMed  PubMed Central  Google Scholar 

  153. Andersson, D. I., Hughes, D. & Roth, J. R. EcoSal-Escherichia coli and Salmonella: Cellular and Molecular Biology (ASM, 2011).

    Google Scholar 

  154. Burch, C. L. & Chao, L. Evolvability of an RNA virus is determined by its mutational neighbourhood. Nature 406, 625–628 (2000).

    CAS  PubMed  Google Scholar 

  155. Domingo, E., Sheldon, J. & Perales, C. Viral quasispecies evolution. Microbiol. Mol. Biol. Rev. 76, 159–216 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  156. Jabara, C. B., Jones, C. D., Roach, J., Anderson, J. A. & Swanstrom, R. Accurate sampling and deep sequencing of the HIV-1 protease gene using a Primer ID. Proc. Natl Acad. Sci. USA 108, 20166–20171 (2011).

    CAS  PubMed  Google Scholar 

  157. Kinde, I., Wu, J., Papadopoulos, N., Kinzler, K. W. & Vogelstein, B. Detection and quantification of rare mutations with massively parallel sequencing. Proc. Natl Acad. Sci. USA 108, 9530–9535 (2011).

    PubMed  Google Scholar 

  158. Schmitt, M. W. et al. Detection of ultra-rare mutations by next-generation sequencing. Proc. Natl Acad. Sci. USA 109, 14508–14513 (2012).

    CAS  PubMed  Google Scholar 

  159. Levy, S. F. et al. Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519, 181–186 (2015). Developed a sequence-based system to track 500,000 yeast lineages, thus simultaneously revealing significant differences in early and later evolutionary events.

    CAS  PubMed  PubMed Central  Google Scholar 

  160. Qi, H. et al. A quantitative high-resolution genetic profile rapidly identifies sequence determinants of hepatitis C viral fitness and drug sensitivity. PLoS Pathog. 10, e1004064 (2014).

    PubMed  PubMed Central  Google Scholar 

  161. Ding, L., Raphael, B. J., Chen, F. & Wendl, M. C. Advances for studying clonal evolution in cancer. Cancer Lett. 340, 212–219 (2013).

    CAS  PubMed  Google Scholar 

  162. Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  163. Zhu, Y. O., Siegal, M. L., Hall, D. W. & Petrov, D. A. Precise estimates of mutation rate and spectrum in yeast. Proc. Natl Acad. Sci. USA 111, E2310–E2318 (2014).

    CAS  PubMed  Google Scholar 

  164. Bopp, S. E. et al. Mitotic evolution of Plasmodium falciparum shows a stable core genome but recombination in antigen families. PLoS Genet. 9, e1003293 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  165. Haase, A. T. et al. Quantitative image analysis of HIV-1 infection in lymphoid tissue. Science 274, 985–989 (1996).

    CAS  PubMed  Google Scholar 

  166. Sandegren, L., Lindqvist, A., Kahlmeter, G. & Andersson, D. I. Nitrofurantoin resistance mechanism and fitness cost in Escherichia coli. J. Antimicrob. Chemother. 62, 495–503 (2008).

    CAS  PubMed  Google Scholar 

  167. White, S. J. et al. Self-regulation of Candida albicans population size during GI colonization. PLoS Pathog. 3, e184 (2007).

    PubMed  PubMed Central  Google Scholar 

  168. Miller, L. H., Ackerman, H. C., Su, X. Z. & Wellems, T. E. Malaria biology and disease pathogenesis: insights for new treatments. Nat. Med. 19, 156–167 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  169. Alix-Panabieres, C. & Pantel, K. Challenges in circulating tumour cell research. Nat. Rev. Cancer 14, 623–631 (2014).

    CAS  PubMed  Google Scholar 

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Acknowledgements

D.H. and D.I.A. acknowledge funding from the Innovative Medicines Initiative Joint Undertaking under grant agreement number 115583, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies' in kind contribution. D.H. and D.I.A. also acknowledge support from Vetenskapsrådet (Swedish Science Council), SSF (Swedish Strategic Science Foundation), Vinnova (Swedish Innovation Science), and the Knut and Alice Wallenberg Foundation (RiboCore Project). D.I.A. acknowledges support from the EU (EvoTar project).

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PowerPoint slides

Glossary

Isogenic variants

Genetic variants that are derived from a single cell or genotype.

Epistatic

Refers to the phenomenon of epistasis, which involves interactions (genetic, regulatory, and physiological) between or within genes and results in non-additive effects with regard to phenotype.

Population bottlenecks

The concept that only a limited number of individuals (and thus genotypes) act as founders of the next generation of cells or organisms.

Fixation

When there are at least two variants of a gene in a population, fixation refers to the situation when, owing to selection or chance fluctuations, only one allele remains.

Deleterious

A mutation that reduces relative fitness under a particular condition.

Sweep

A selective sweep is the reduction or elimination of variation within a population as a result of an increase in the proportion of one 'successful' variant.

Minimal inhibitory concentration

(MIC). The lowest concentration of an antimicrobial drug that, under a set of agreed conditions, inhibits the visible growth of a microorganism after overnight incubation.

Pleiotropic

When one gene influences multiple phenotypic traits.

Resistome

The collection of all of the antibiotic resistance genes and their precursors in both pathogenic and non-pathogenic bacteria.

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Hughes, D., Andersson, D. Evolutionary consequences of drug resistance: shared principles across diverse targets and organisms. Nat Rev Genet 16, 459–471 (2015). https://doi.org/10.1038/nrg3922

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