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  • Review Article
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New insights into bacterial adaptation through in vivo and in silico experimental evolution

Nature Reviews Microbiology volume 10, pages 352–365 (2012)Cite this article

Subjects

Key Points

  • Strategies for experimental evolution aim to reproduce Darwinian evolution in laboratory conditions in order to observe evolution in action. Bacteria are particularly well suited for such strategies, as they can provide frozen, revivable fossil records that allow direct comparisons of ancestors and evolved individuals over tens to tens of thousands of generations.

  • Various bacterial species can be used as ancestors to initiate replicate populations under different environmental conditions, providing information about the evolution of diverse phenotypes, including fitness, cell size, gene expression, metabolic traits, interactions with host cells, and altruistic and social behaviour. Evolved genetic changes can be rigorously identified and linked to phenotypic outcomes during evolution, providing new insights into the dynamics of cellular networks as a complement to traditional genetic studies.

  • Evolution experiments have also been designed with virtual 'organisms' that reproduce and mutate in silico. These experiments allow researchers to test the generic nature of evolutionary mechanisms identified in vivo and to increase the analytic and statistical power of in vivo experiments (using multiple repetitions, large parameter exploration and exhaustive records of evolutionary events).

  • Experimental evolution is associated with phenotypic innovations as well as frequent parallel (similar) phenotypic and genetic changes that occur in replicate populations evolving under similar conditions. Nevertheless, parallel evolution is not a general rule, and a high level of between-population allelic diversity is observed.

  • A high level of genetic diversity is generated within single populations, whatever the environment (homogeneous or heterogeneous). Stable polymorphisms, with several ecotypes coexisting, may eventually emerge, as well as cooperating groups of cells with altruistic and social behaviours that in turn select for the appearance of cheater cells.

  • Bacteria adapt through fine-tuning of their global regulatory networks rather than by local restructuring of more specific pathways. Adaptive mutations affecting global transcriptional regulators profoundly rewire regulatory networks through epistatic interactions and large pleiotropic effects.

  • Evolution has shaped bacterial cellular networks with tremendous plasticity that enables further adaptation to many perturbations. Bacterial and digital evolution experiments investigate the evolvability (the capacity for adaptive evolution) and robustness (the stability in the face of perturbations) of cellular networks.

Abstract

Microbiology research has recently undergone major developments that have led to great progress towards obtaining an integrated view of microbial cell function. Microbial genetics, high-throughput technologies and systems biology have all provided an improved understanding of the structure and function of bacterial genomes and cellular networks. However, integrated evolutionary perspectives are needed to relate the dynamics of adaptive changes to the phenotypic and genotypic landscapes of living organisms. Here, we review evolution experiments, carried out both in vivo with microorganisms and in silico with artificial organisms, that have provided insights into bacterial adaptation and emphasize the potential of bacterial regulatory networks to evolve.

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Figure 1: In vivo microbial and in silico evolution experiments.
Figure 2: Principle of in silico experimental evolution.
Figure 3: In vivo evolution of regulatory networks.
Figure 4: In silico evolution of regulatory networks.

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References

  1. Gould, S. J. The evolution of life on the earth. Sci. Am. 271, 84–91 (1994).

    Article  CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  3. Adami, C. Digital genetics: unravelling the genetic basis of evolution. Nature Rev. Genet. 7, 109–118 (2006). An introduction to the general principles of digital genetics and to the Avida framework, and a presentation of the main experiments that have been conducted using this framework.

    Article  CAS  PubMed  Google Scholar 

  4. Buckling, A., MacLean, R. C., Brockhurst, M. A. & Colegrave, N. The Beagle in a bottle. Nature 457, 824–829 (2009).

    Article  CAS  PubMed  Google Scholar 

  5. Philippe, N., Crozat, E., Lenski, R. E. & Schneider, D. Evolution of global regulatory networks during a long-term experiment with Escherichia coli. BioEssays 29, 846–860 (2007).

    Article  PubMed  Google Scholar 

  6. Brockhurst, M. A., Colegrave, N. & Rozen, D. E. Next-generation sequencing as a tool to study microbial evolution. Mol. Ecol. 20, 972–980 (2011).

    Article  PubMed  Google Scholar 

  7. Conrad, T. M., Lewis, N. E. & Palsson, B. O. Microbial laboratory evolution in the era of genome-scale science. Mol. Syst. Biol. 7, 509 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Bull, J. J. & Molineux, I. J. Predicting evolution from genomics: experimental evolution of bacteriophage T7. Heredity 100, 453–463 (2008).

    Article  CAS  PubMed  Google Scholar 

  9. Elena, S. F. et al. Experimental evolution of plant RNA viruses. Heredity 100, 478–483 (2008).

    Article  CAS  PubMed  Google Scholar 

  10. Burke, M. K. et al. Genome-wide analysis of a long-term evolution experiment with Drosophila. Nature 467, 587–590 (2010).

    Article  CAS  PubMed  Google Scholar 

  11. Darwin, C. On the Origin of Species by Means of Natural Selection (Murray, London, 1859).

    Google Scholar 

  12. Lenski, R. E. Phenotypic and genomic evolution during a 20,000-generation experiment with the bacterium Escherichia coli. Plant. Breed. Rev. 24, 225–265 (2004).

    Google Scholar 

  13. Lenski, R. E., Rose, M. R., Simpson, S. C. & Tadler, S. C. Long-term experimental evolution in Escherichia coli. I. Adaptation and divergence during 2,000 generations. Am. Nat. 138, 1315–1341 (1991).

    Article  Google Scholar 

  14. Helling, R. B., Vargas, C. N. & Adams, J. Evolution of Escherichia coli during growth in a constant environment. Genetics 116, 349–358 (1987).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Ferenci, T. Bacterial physiology, regulation and mutational adaptation in a chemostat environment. Adv. Microb. Physiol. 53, 169–229 (2008).

    Article  CAS  PubMed  Google Scholar 

  16. Gresham, D. et al. The repertoire and dynamics of evolutionary adaptations to controlled nutrient-limited environments in yeast. PLoS Genet. 4, e1000303 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Ferea, T. L., Botstein, D., Brown, P. O. & Rosenzweig, R. F. Systematic changes in gene expression patterns following adaptive evolution in yeast. Proc. Natl Acad. Sci. USA 96, 9721–9726 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Cooper, T. F. & Lenski, R. E. Experimental evolution with E. coli in diverse resource environments. I. Fluctuating environments promote divergence of replicate populations. BMC Evol. Biol. 10, 11 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Velicer, G. J. & Vos, M. Sociobiology of the myxobacteria. Annu. Rev. Microbiol. 63, 599–623 (2009). A review describing the ease of the evolution (back and forth) of social behavioural traits.

    Article  CAS  PubMed  Google Scholar 

  20. Velicer, G. J., Kroos, L. & Lenski, R. E. Loss of social behaviors by Myxococcus xanthus during evolution in an unstructured habitat. Proc. Natl Acad. Sci. USA 95, 12376–12380 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Velicer, G. J. & Yu, Y-T. N. Evolution of novel cooperative swarming in the bacterium Myxococcus xanthus. Nature 425, 75–78 (2003).

    Article  CAS  PubMed  Google Scholar 

  22. Fiegna, F., Yu, Y. T. N., Kadam, S. V. & Velicer, G. J. Evolution of an obligate social cheater to a superior cooperator. Nature 441, 310–314 (2006).

    Article  CAS  PubMed  Google Scholar 

  23. Rainey, P. B. & Travisano, M. Adaptive radiation in a heterogeneous environment. Nature 394, 69–72 (1998). An evolution experiment showing fast radiation involving phenotypic innovation to colonize a new ecological niche.

    Article  CAS  PubMed  Google Scholar 

  24. McDonald, M. J., Gehrig, S. M., Meintjes, P. L., Zhang, X.-X. & Rainey, P. B. Adaptive divergence in experimental populations of Pseudomonas fluorescens. IV. Genetic constraints guide evolutionary trajectories in a parallel adaptive radiation. Genetics 183, 1041–1053 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Griffin, A. S., West, S. A. & Buckling, A. Cooperation and competition in pathogenic bacteria. Nature 430, 1024–1027 (2004).

    Article  CAS  PubMed  Google Scholar 

  26. Marchetti, M. et al. Experimental evolution of a plant pathogen into a legume symbiont. PLoS Biol. 8, e1000280 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Zeyl, C. Experimental evolution with yeast. FEMS Yeast Res. 6, 685–691 (2006).

    Article  CAS  PubMed  Google Scholar 

  28. Nilsson, A. I. et al. Bacterial genome size reduction by experimental evolution. Proc. Natl Acad. Sci. USA 102, 12112–12116 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Lind, P. A. & Andersson, D. I. Whole-genome mutational biases in bacteria. Proc. Natl Acad. Sci. USA 105, 17878–17883 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. De Visser, J. A. G. M. et al. Perspective: evolution and detection of genetic robustness. Evolution 57, 1959–1972 (2003).

    PubMed  Google Scholar 

  31. Fong, S. S., Nanchen, A., Palsson, B. O. & Sauer, U. Latent pathway activation and increased pathway capacity enable Escherichia coli adaptation to loss of key metabolic enzymes. J. Biol. Chem. 281, 8024–8033 (2006).

    Article  CAS  PubMed  Google Scholar 

  32. Stoebel, D. M., Hokamp, K., Last, M. S. & Dorman, C. J. Compensatory evolution of gene regulation in response to stress by Escherichia coli lacking RpoS. PLoS Genet. 5, e1000671 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Charusanti, P. et al. Genetic basis of growth adaptation of Escherichia coli after deletion of pgi, a major metabolic gene. PLoS Genet. 6, e1001186 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Auriol, C., Bestel-Corre, G., Claude, J.-B., Soucaille, P. & Meynial-Salles, I. Stress-induced evolution of Escherichia coli points to original concepts in respiratory factor selectivity. Proc. Natl Acad. Sci. USA 108, 1278–1283 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Stern, S., Dror, T., Stolovicki, E., Brenner, N. & Braun, E. Genome-wide transcriptional plasticity underlies cellular adaptation to novel challenge. Mol. Syst. Biol. 3, 1–9 (2007).

    Article  CAS  Google Scholar 

  36. Floreano, D. & Mattiussi, C. Bio-Inspired Artificial Intelligence Theories, Methods, and Technologies (Massachusetts Institute of Technology Press, Cambridge, Massachusetts, 2008).

    Google Scholar 

  37. Smith, J. M. Byte-sized evolution. Nature 355, 772–773 (1992).

    Article  CAS  PubMed  Google Scholar 

  38. Ray, T. S. in Artificial Life II. Santa Fe Institute Studies in the Sciences of Complexity Vol. X (eds Langton, C. G., Taylor, C., Farmer, J. D. & Rasmussen, S.) 371–408 (Addison Wesley, Redwood City, California,1991). The seminal work that initiated the field of in silico evolution, describing the Tierra framework, in which complex host–parasite interactions emerge spontaneously.

    Google Scholar 

  39. Clarke, P. H. Experiments in microbial evolution: new enzymes, new metabolic activities. Proc. R. Soc. Lond. B 207, 385–404 (1980).

    Article  CAS  PubMed  Google Scholar 

  40. Mortlock, R. P. Microorganisms as Model Systems for Studying Evolution (Springer, New York, 1984).

    Google Scholar 

  41. Clarke, P. H. & Drew, R. An experiment in enzyme evolution. Studies with Pseudomonas aeruginosa amidase. Biosci. Rep. 8, 103–120 (1988).

    Article  CAS  PubMed  Google Scholar 

  42. Dykhuizen, D. E. Experimental studies of natural selection in bacteria. Annu. Rev. Ecol. Syst. 21, 373–398 (1990).

    Article  Google Scholar 

  43. Dykhuizen, D. E. & Dean, A. M. Enzyme activity and fitness: evolution in solution. Trends Ecol. Evol. 5, 257–262 (1990).

    Article  CAS  PubMed  Google Scholar 

  44. Philippe, N., Pelosi, L., Lenski, R. E. & Schneider, D. Evolution of penicillin-binding protein 2 concentration and cell shape during a long-term experiment with Escherichia coli. J. Bacteriol. 191, 909–921 (2009).

    Article  CAS  PubMed  Google Scholar 

  45. Fong, S. S., Joyce, A. R. & Palsson, B. O. Parallel adaptive evolution cultures of Escherichia coli lead to convergent growth phenotypes with different gene expression states. Genome Res. 15, 1365–1372 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Cooper, T. F., Remold, S. K., Lenski, R. E. & Schneider, D. Expression profiles reveal parallel evolution of epistatic interactions involving the CRP regulon in Escherichia coli. PLoS Genet. 4, e35 (2008). An example of evolution through changes in epistatic interactions inside regulatory networks.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Conrad, T. M. et al. RNA polymerase mutants found through adaptive evolution reprogram Escherichia coli for optimal growth in minimal media. Proc. Natl Acad. Sci. USA 107, 20500–20505 (2010). An example of evolution towards new kinetic properties of the transcriptional machinery to confer higher fitness in minimal media.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Finkel, S. E. & Kolter, R. Evolution of microbial diversity during prolonged starvation. Proc. Natl Acad. Sci. USA 96, 4023–4027 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hugues, B. S., Cullum, A. J. & Bennett, A. F. Evolutionary adaptation to environmental pH in experimental lineages of Escherichia coli. Evolution 61, 1725–1734 (2007).

    Article  Google Scholar 

  50. Harris, D. R. et al. Directed evolution of ionizing radiation resistance in Escherichia coli. J. Bacteriol. 191, 5240–5252 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ferenci, T. Maintaining a healthy SPANC balance through regulatory and mutational adaptation. Mol. Microbiol. 57, 1–8 (2005).

    Article  CAS  PubMed  Google Scholar 

  52. Toprak, E. et al. Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nature Genet. 44, 101–106 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  54. Spiers, A. J., Kahn, S. G., Bohannon, J., Travisano, M. & Rainey, P. B. Adaptive divergence in experimental populations of Pseudomonas fluorescens. I. Genetic and phenotypic bases of wrinkly spreader fitness. Genetics 161, 33–46 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. MacLean, R. C. & Bell, G. Experimental adaptive radiation in Pseudomonas. Am. Nat. 160, 569–581 (2002).

    Article  PubMed  Google Scholar 

  56. Lee, D. H. & Palsson, B. Ø. Adaptive evolution of Escherichia coli K-12 MG1655 during growth on a nonnative carbon source, L-1,2-propanediol. Appl. Environ. Microbiol. 76, 4158–4168 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Lenski, R. E. & Bennett, A. F. Evolutionary response of Escherichia coli to thermal stress. Am. Nat. 142, S47–S64 (1993).

    Article  PubMed  Google Scholar 

  58. Leroi, A. M., Bennett, A. F. & Lenski, R. E. Temperature acclimation and competitive fitness: an experimental test of the beneficial acclimation assumption. Proc. Natl Acad. Sci. USA 91, 1917–1921 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Blount, Z. D., Borland, C. Z. & Lenski, R. E. Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proc. Natl Acad. Sci. USA 105, 7899–7906 (2008). A seminal evolution replay experiment demonstrating the emergence of a phenotypic innovation that transcends the species boundaries through historical contingency.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Meyer, J. R. et al. Repeatability and contingency in the evolution of a key innovation in phage lambda. Science 335, 428–432 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Futuyama, D. J. Evolutionary Biology 2nd edn (Sinauer, Sunderland, Massachusetts,1986).

    Google Scholar 

  62. Tenaillon, O. et al. The molecular diversity of adaptive convergence. Science 335, 457–461 (2012).

    Article  CAS  PubMed  Google Scholar 

  63. Riehle, M. M., Bennett, A. F. & Long, A. D. Changes in gene expression following high-temperature adaptation in experimentally evolved populations of Escherichia coli. Physiol. Biochem. Zool. 78, 299–315 (2005).

    Article  CAS  PubMed  Google Scholar 

  64. Crozat, E. et al. Parallel genetic and phenotypic evolution of DNA superhelicity in experimental populations of Escherichia coli. Mol. Biol. Evol. 27, 2113–2128 (2010).

    Article  CAS  PubMed  Google Scholar 

  65. Treves, D. S., Manning, S. & Adams, J. Repeated evolution of an acetate-crossfeeding polymorphism in long-term populations of Escherichia coli. Mol. Biol. Evol. 15, 789–797 (1998).

    Article  CAS  PubMed  Google Scholar 

  66. Almahmoud, I., Kay, E., Schneider, D. & Maurin, M. Mutational paths towards increased fluoroquinolone resistance in Legionella pneumophila. J. Antimicrob. Chemother. 64, 284–293 (2009).

    Article  CAS  PubMed  Google Scholar 

  67. Huitric, E. et al. Rates and mechanisms of resistance development in Mycobacterium tuberculosis to a novel diarylquinoline ATP synthase inhibitor. Antimicrob. Agents Chemother. 54, 1022–1028 (2010).

    Article  CAS  PubMed  Google Scholar 

  68. Bantinaki, E. et al. Adaptive divergence in experimental populations of Pseudomonas fluorescens. III. Mutational origins of wrinkly spreader diversity. Genetics 176, 441–453 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Maharjan, R., Seeto, S., Notley-McRobb, L. & Ferenci, T. Clonal adaptive radiation in a constant environment. Science 313, 514–517 (2006).

    Article  CAS  PubMed  Google Scholar 

  70. Papadopoulos, D. et al. Genomic evolution during a 10,000-generation experiment with bacteria. Proc. Natl Acad. Sci. USA 96, 3807–3812 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  72. Le Gac, M. et al. Metabolic changes associated with adaptive diversification in Escherichia coli. Genetics 178, 1049–1060 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Spencer, C. C., Bertrand, M., Travisano, M. & Doebeli, M. Adaptive diversification in genes that regulate resource use in Escherichia coli. PLoS Genet. 3, e15 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Bohannan, B. J. M., Kerr, B., Jessup, C. M., Hughes, J. B. & Sandvik, G. Trade-offs and coexistence in microbial microcosms. Antonie Van Leeuwenhoek 81, 107–115 (2002).

    Article  CAS  PubMed  Google Scholar 

  75. Rozen, D. E., Philippe, N., de Visser, J. A., Lenski, R. E. & Schneider, D. Death and cannibalism in a seasonal environment facilitate bacterial coexistence. Ecol. Lett. 12, 34–44 (2009).

    Article  PubMed  Google Scholar 

  76. Laland, K. N., Odling-Smee, F. J. & Feldman, M. W. Evolutionary consequences of niche construction and their implications for ecology. Proc. Natl Acad. Sci. USA 96, 10242–10247 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Rosenzweig, F. R., Sharp, R. R., Treves, D. S. & Adams, J. Microbial evolution in a simple unstructured environment: genetic differentiation in Escherichia coli. Genetics 137, 903–917 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. West, S. A., Diggle, S. P., Buckling, A., Gardner, A. & Griffin, A. S. The social lives of microbes. Annu. Rev. Ecol. Evol. Syst. 38, 53–77 (2007).

    Article  Google Scholar 

  79. De Vos, D. et al. Study of pyroverdine type and production by Pseudomonas aeruginosa isolated from cystic fibrosis patients: prevalence of type II pyoverdine isolates and accumulation of pyoverdine-negative mutants. Arch. Microbiol. 175, 384–388 (2001).

    Article  CAS  PubMed  Google Scholar 

  80. West, S. A. & Buckling, A. Cooperation, virulence and siderophore production in bacterial parasites. Proc. R. Soc. Lond. B 270, 37–44 (2003).

    Article  Google Scholar 

  81. Rainey, P. B. & Rainey, K. Evolution of cooperation and conflict in experimental bacterial populations. Nature 425, 72–74 (2003).

    Article  CAS  PubMed  Google Scholar 

  82. Velicer, G. J., Kroos, L. & Lenski, R. E. Developmental cheating in the social bacterium Myxococcus xanthus. Nature 404, 598–601 (2000).

    Article  CAS  PubMed  Google Scholar 

  83. Manhes, P. & Velicer, G. J. Experimental evolution of selfish policing in social bacteria. Proc. Natl Acad. Sci. USA 108, 8357–8362 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Chow, S. S., Wilke, C. O., Ofria, C., Lenski, R. E. & Adami, C. Adaptive radiation from resource competition in digital organisms. Science 305, 84–86 (2004).

    Article  CAS  PubMed  Google Scholar 

  85. Crombach, A. & Hogeweg, P. Evolution of resource cycling in ecosystems and individuals. BMC Evol. Biol. 9, 122 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Hanage, W. P., Spratt, B. G., Turner, K. M. E. & Fraser, C. Modelling bacterial speciation. Phil. Trans. R. Soc. B 361, 2039–2044 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Lenski, R. E., Ofria, C., Pennock, R. T. & Adami, C. The evolutionary origin of complex features. Nature 423, 139–144 (2003).

    Article  CAS  PubMed  Google Scholar 

  88. Clune, J., Goldsby, H. J., Ofria, C. & Pennock, R. T. Selective pressures for accurate altruism targeting: evidence from digital evolution for difficult-to-test aspects of inclusive fitness theory. Proc. Biol. Sci. 278, 666–674 (2011).

    Article  PubMed  Google Scholar 

  89. Waibel, M., Floreano, D. & Keller, L. A. Quantitative test of Hamilton's rule for the evolution of altruism. PLoS Biol. 9, e1000615 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Pigliucci, M. Is evolvability evolvable? Nature Rev. Genet. 9, 75–82 (2008).

    Article  CAS  PubMed  Google Scholar 

  91. Cooper, V. S., Schneider, D., Blot, M. & Lenski, R. E. Mechanisms causing rapid and parallel losses of ribose catabolism in evolving populations of Escherichia coli B. J. Bacteriol. 183, 2834–2841 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Herring, C. D. et al. Comparative genome sequencing of Escherichia coli allows observation of bacterial evolution on a laboratory timescale. Nature Genet. 38, 1406–1412 (2006).

    Article  CAS  PubMed  Google Scholar 

  93. Pelosi, L. et al. Parallel changes in global protein profiles during long-term experimental evolution in Escherichia coli. Genetics 173, 1851–1869 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Barrick, J. E. et al. Genome evolution and adaptation in a long-term experiment with Escherichia coli. Nature 461, 1243–1247 (2009).

    Article  CAS  PubMed  Google Scholar 

  95. 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 

  96. Andersson, D. I. The biological cost of mutational antibiotic resistance: any practical conclusions? Curr. Opin. Microbiol. 9, 461–465 (2006).

    Article  CAS  PubMed  Google Scholar 

  97. Macvanin, M., Ballagi, A. & Hughes, D. Fusidic acid-resistant mutants of Salmonella enterica serovar Typhimurium have low levels of heme and a reduced rate of respiration and are sensitive to oxidative stress. Antimicrob. Agents Chemother. 48, 3877–3883 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Marcusson, L. L., Frimodt-Moller, N. & Hughes, D. Interplay in the selection of fluoroquinolones resistance and bacterial fitness. PLoS Pathog. 5, e1000541 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Macvanin, M. et al. Fusidic acid-resistant mutants of Salmonella enterica serovar Typhimurium with low fitness in vivo are defective in RpoS induction. Antimicrob. Agents Chemother. 47, 3743–3749 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Paulander, W., Maisnier-Patin, S. & Andersson, D. I. The fitness cost of streptomycin resistance depends on rpsL mutation, carbon source and RpoS (σS). Genetics 183, 539–546 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Kohanski, M. A., Dwyer, D. J. & Collins, J. J. How antibiotics kill bacteria: from targets to networks. Nature Rev. Microbiol. 8, 423–435 (2010).

    Article  CAS  Google Scholar 

  102. Cooper, T. F., Rozen, D. E. & Lenski, R. E. Parallel changes in gene expression after 20,000 generations of evolution in Escherichia coli. Proc. Natl Acad. Sci. USA 100, 1072–1077 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Knight, C. G. et al. Unraveling adaptive evolution: how a single point mutation affects the protein coregulation network. Nature Genet. 38, 1015–1022 (2006).

    Article  CAS  PubMed  Google Scholar 

  104. Kadam, S. V., Wegener-Feldbrugge, S., Sogaard-Andersen, L. & Velicer, G. J. Novel transcriptome patterns accompany evolutionary restoration of defective social development in the bacterium Myxococcus xanthus. Mol. Biol. Evol. 25, 1274–1281 (2008).

    Article  CAS  PubMed  Google Scholar 

  105. Velicer, G. J. et al. Comprehensive mutation identification in an evolved bacterial cooperator and its cheating ancestor. Proc. Natl Acad. Sci. USA 103, 8107–8112 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Notley-McRobb, L., King, T. & Ferenci, T. rpoS mutations and loss of general stress resistance in Escherichia coli populations as a consequence of conflict between competing stress responses. J. Bacteriol. 184, 806–811 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Wang, L. et al. Divergence involving global regulatory gene mutations in an Escherichia coli population evolving under phosphate limitation. Genome Biol. Evol. 2, 478–487 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  108. Applebee, M. K., Herrgård, M. J. & Palsson, B. O. Impact of individual mutations on increased fitness in adaptively evolved strains of Escherichia coli. J. Bacteriol. 190, 5087–5094 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Yu, Y. T., Yuan, X. & Velicer, G. J. Adaptive evolution of an sRNA that controls Myxococcus development. Science 328, 993 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Jacob, F. Evolution and tinkering. Science 196, 1161–1166 (1977).

    Article  CAS  PubMed  Google Scholar 

  111. McLean, R. C., Bell, G. & Rainey, P. B. The evolution of a pleiotropic fitness tradeoff in Pseudomonas fluorescens. Proc. Natl Acad. Sci. USA 101, 8072–8077 (2004).

    Article  Google Scholar 

  112. Khan, A. I., Dinh, D. M., Schneider, D., Lenski, R. E. & Cooper, T. F. Negative epistasis between beneficial mutations in an evolving bacterial population. Science 332, 1193–1196 (2011).

    Article  CAS  PubMed  Google Scholar 

  113. Woods, R. J. et al. Second-order selection for evolvability in a large Escherichia coli population. Science 331, 1433–1436 (2011). The demonstration that beneficial alleles can be selected because of their future evolvability.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Le Gac, M. & Doebeli, M. Epistasis and frequency dependence influence the fitness of an adaptive mutation in a diversifying lineage. Mol. Ecol. 19, 2430–2438 (2010).

    CAS  PubMed  Google Scholar 

  115. Bhagwat, A. A. et al. Functional heterogeneity of RpoS in stress tolerance of enterohemorrhagic Escherichia coli strains. Appl. Environ. Microbiol. 72, 4978–4986 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Ferenci, T., Galbiati, H. F., Betteridge, T., Phan, K. & Spira, B. The constancy of global regulation across a species: the concentrations of ppGpp and RpoS are strain-specific in Escherichia coli. BMC Microbiol. 11, 62 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Levert, M. et al. Molecular and evolutionary bases of within-patient genotypic and phenotypic diversity in Escherichia coli extraintestinal infections. PLoS Pathog. 6, e1001125 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  118. Yang, L. et al. Evolutionary dynamics of bacteria in a human host environment. Proc. Natl Acad. Sci. USA 108, 7481–7486 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Lieberman, T. D. et al. Parallel bacterial evolution within multiple patients identifies candidate pathogenicity genes. Nature Genet. 43, 1275–1281 (2011).

    Article  CAS  PubMed  Google Scholar 

  120. Rainey, P. B. & Cooper, T. F. Evolution of bacterial diversity and the origins of modularity. Res. Microbiol. 155, 370–375 (2004).

    Article  CAS  PubMed  Google Scholar 

  121. Wagner, G. P., Pavlicev, M. & Cheverud, J. M. The road to modularity. Nature Rev. Genet. 12, 921–931 (2007).

    Article  CAS  Google Scholar 

  122. Kashtan, N. & Alon, U. Spontaneous evolution of modularity and network motifs. Proc. Natl Acad. Sci. USA 102, 13773–13778 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Espinosa-Soto, C. & Wagner, A. Specialization can drive the evolution of modularity. PLoS Comp. Biol. 6, e1000719 (2010).

    Article  CAS  Google Scholar 

  124. François, P. & Hakim, V. Design of genetic networks with specified functions by evolution in silico. Proc. Natl Acad. Sci. USA 101, 580–585 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. 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  PubMed  Google Scholar 

  126. Kuo, P. D., Banzhaf, W. & Leier, A. Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence. BioSystems 85, 177–200 (2006).

    Article  CAS  Google Scholar 

  127. Beslon, G., Parsons, D. P., Sanchez-Dehesa, Y., Peña, J. M. & Knibbe, C. Scaling laws in bacterial genomes: a side-effect of selection of mutational robustness? BioSystems 102, 32–40 (2010).

    Article  CAS  PubMed  Google Scholar 

  128. Wielgoss, S. et al. Mutation rate inferred from synonymous substitutions in a long-term evolution experiment with Escherichia coli. G3 (Bethesda) 1, 183–186 (2011).

    Article  CAS  Google Scholar 

  129. Sniegowski, P. D., Gerrish, P. J. & Lenski, R. E. Evolution of high mutation rates in experimental populations of E. coli. Nature 387, 703–705 (1997).

    Article  CAS  PubMed  Google Scholar 

  130. Gaffé, J. et al. Insertion sequence-driven evolution of Escherichia coli in chemostats. J. Mol. Evol. 72, 398–412 (2011).

    Article  PubMed  CAS  Google Scholar 

  131. Oliver, A., Cantón, R., Campo, P., Baquero, F. & Blázquez, J. High frequency of hypermutable Pseudomonas aeruginosa in cystic fibrosis lung infection. Science 288, 1251–1254 (2000).

    Article  CAS  PubMed  Google Scholar 

  132. Cox, E. C. & Gibson, T. C. Selection for high mutation rates in chemostats. Genetics 77, 169–184 (1974).

    CAS  PubMed  PubMed Central  Google Scholar 

  133. Chao, L. & Cox, E. C. Competition between high and low mutating strains of Escherichia coli. Evolution 37, 125–134 (1983).

    Article  PubMed  Google Scholar 

  134. Racey, D., Inglis, R. F., Harrison, F., Oliver, A. & Buckling, A. The effect of elevated mutation rates on the evolution of cooperation and virulence of Pseudomonas aeruginosa. Evolution 64, 515–521 (2010).

    Article  PubMed  Google Scholar 

  135. Taddei, F. et al. Role of mutator alleles in adaptive evolution. Nature 387, 700–702 (1997).

    Article  CAS  PubMed  Google Scholar 

  136. Tenaillon, O., Toupance, B., Le Nagard, H., Taddei, F. & Godelle, B. Mutators, population size, adaptive landscape and the adaptation of asexual populations of bacteria. Genetics 152, 485–493 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. Bedau, M. A. & Packard, M. H. Evolution of evolvability via adaptation of mutation rates. BioSystems 69, 143–162 (2003).

    Article  PubMed  Google Scholar 

  138. Clune, J. et al. Natural selection fails to optimize mutation rates for long-term adaptation on rugged fitness landscapes. PLoS Comp. Biol. 4, e1000187 (2008).

    Article  CAS  Google Scholar 

  139. Wagner, G. P. & Altenberg, L. Complex adaptations and the evolution of evolvability. Evolution 50, 967–976 (1996).

    Article  PubMed  Google Scholar 

  140. Knibbe, C., Coulon, A., Mazet, O., Fayard, J. M. & Beslon, G. A long-term evolutionary pressure on the amount of noncoding DNA. Mol. Biol. Evol. 24, 2344–2353 (2007).

    Article  CAS  PubMed  Google Scholar 

  141. Crombach, A. & Hogeweg, P. Chromosome rearrangements and the evolution of genome structuring and adaptability. Mol. Biol. Evol. 24, 1130–1139 (2007).

    Article  CAS  PubMed  Google Scholar 

  142. Wilke, C. O., Wang, J. L., Ofria, C., Lenski, R. E. & Adami, C. Evolution of digital organisms at high mutation rates leads to survival of the flattest. Nature 412, 331–333 (2001). An in silico experiment that uses the Avida framework to investigate the complex interactions between fitness and robustness, and shows that selection for robustness can overcome selection for fitness at high mutation rates.

    Article  CAS  PubMed  Google Scholar 

  143. Crombach, A. & Hogeweg, P. Evolution of evolvability in gene regulatory networks. PLoS Comp. Biol. 4, e1000112 (2008). In silico experiments with the 'string-of-pearls' formalism, showing that evolvability may be selected in regulation networks.

    Article  CAS  Google Scholar 

  144. Lenski, R. E., Barrick, J. E. & Ofria, C. Balancing robustness and evolvability. PLoS Biol. 4, e428 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  145. Wagner, A. Neutralism and selectionism: a network-based reconciliation. Nature Rev. Genet. 9, 965–974 (2008).

    Article  CAS  PubMed  Google Scholar 

  146. McBride, R. C., Ogbunugafor, C. B. & Turner, P. E. Robustness promotes evolvability of thermotolerance in an RNA virus. BMC Evol. Biol. 8, 231 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  147. Elena, S. F. & Sanjuán, R. The effect of genetic robustness on evolvability in digital organisms. BMC Evol. Biol. 8, 284 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  148. Luria, S. E. & Delbrück, M. Mutations of bacteria from virus sensitivity to virus resistance. Genetics 28, 491–511 (1943).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. O'Neill, B. Digital evolution. PLoS Biol. 1, e18 (2003).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  150. Misevic, D., Ofria, C. & Lenski, R. E. Sexual reproduction reshapes the genetic architecture of digital organisms. Proc. Biol. Sci. 273, 457–464 (2006).

    Article  PubMed  Google Scholar 

  151. de Boer, F. K. & Hogeweg, P. Eco-evolutionary dynamics, coding structure and the information threshold. BMC Evol. Biol. 10, 361 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  152. Kashtan, N., Noor, E. & Alon, U. Varying environments can speed up evolution. Proc. Natl Acad. Sci. USA 104, 13711–13716 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Kashtan, N., Parter, M., Dekel, E., Mayo, A. E. & Alon, U. Extinctions in heterogeneous environments and the evolution of modularity. Evolution 63, 1964–1975 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  154. Kaneko, K. Proportionality between variances in gene expression induced by noise and mutation: consequence of evolutionary robustness. BMC Evol. Biol. 11, 27 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  155. Floreano, D., Mitri, S., Magnenat, S. & Keller, L. Evolutionary conditions for the emergence of communication in robots. Curr. Biol. 17, 514–519 (2007).

    Article  CAS  PubMed  Google Scholar 

  156. ten Tusscher, K. H. W. J. & Hogeweg, P. The role of genome and gene regulatory network canalization in the evolution of multi-trait polymorphisms and sympatric speciation. BMC Evol. Biol. 9, 159 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  157. Mattiussi, C. & Floreano, D. Analog genetic encoding for the evolution of circuits and networks. IEEE Trans. Evol. Comput. 11, 596–607 (2007).

    Article  Google Scholar 

  158. Marbach, D., Mattiussi, C. & Floreano, D. Replaying the evolutionary tape: biomimetic reverse engineering of gene networks. Ann. NY Acad. Sci. 1158, 234–245 (2009).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This research was funded by the Centre National de la Recherche Scientifique (CNRS; France), the Université Joseph Fourier (Grenoble, France), the Institut National des Sciences Appliquées (INSA) de Lyon (France), the Agence Nationale de la Recherche (ANR; France) programmes Blanc (ANR-08-BLAN-0283-01) and Génomique (ANR-08-GENM-023-001), the CNRS interdisciplinary programmes Projets Exploratoires/Premier Soutien (PEPS) and Projets Exploratoires Pluridisciplinaires Inter-Instituts (PEPII), and the Fondation Innovations en Infectiologie (FINOVI) foundation. The authors thank C. MacLean and the anonymous reviewers for their helpful comments.

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Authors and Affiliations

  1. Laboratoire Adaptation et Pathogénie des Microorganismes, Université Joseph Fourier, Institut Jean Roget, Grenoble, F-38041, France

    Thomas Hindré & Dominique Schneider

  2. Centre National de la Recherche Scientifique (CNRS) Unité Mixte de Recherche (UMR) 5163, Grenoble, F-38041, France

    Thomas Hindré & Dominique Schneider

  3. Université de Lyon, CNRS, Institut National de Recherche en Informatique et en Automatique (INRIA), Institut Rhône-Alpin des Systèmes Complexes (IXXI), France

    Carole Knibbe & Guillaume Beslon

  4. Laboratoire d'Informatique en Image et Sysètmes d'Information (LIRIS), Université Claude Bernard Lyon 1, UMR5205, Beagle Team, Lyon, F-69622, France

    Carole Knibbe

  5. LIRIS, Institut National des Sciences Appliquées (INSA) de Lyon, UMR5205, Beagle Team, Lyon, F-69621, France

    Guillaume Beslon

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Glossary

Variability

The potential or propensity of the phenotype to vary (whether or not it actually does in the present population or sample). This depends on the rates and patterns of mutation and recombination, and on the genotype–phenotype map.

Fitness

An integrated measure of the relative survival and reproductive rate of genotypes in a given environment.

Evolvability

The potential or propensity of the phenotype to vary in a possibly adaptive manner.

Digital organisms

Computational data structures that process resources, reproduce, mutate and therefore evolve. Such 'organisms' are used as tools to study Darwinian evolution.

Robustness

A measure of the invariance of a phenotype in the face of mutational or environmental perturbations. The mechanisms underlying robustness are diverse, ranging from thermodynamic stability at the RNA and protein levels to behaviour at the organismal level.

Parallelism

The independent evolution of similar traits in replicate lineages that are propagated in similar environments.

Niche exclusion

The idea that a single niche can sustain only a single genotype.

Niche construction

Environmental changes that are generated by the evolving organisms themselves.

Hamilton's rule

The theory that altruism can be selected for when rb–c>0 (in which c is the fitness cost to the altruist, b is the fitness benefit to the beneficiary and r is the genetic relatedness of the two organisms).

Genotype–phenotype map

A representation of how the genetic architecture of an organism produces its phenotype through developmental interactions with the environment.

Indirect selection

Selection acting on a property of the mutational processes, genetic architecture or developmental system that is not adaptive by itself but facilitates adaptive phenotypic evolution.

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Hindré, T., Knibbe, C., Beslon, G. et al. New insights into bacterial adaptation through in vivo and in silico experimental evolution. Nat Rev Microbiol 10, 352–365 (2012). https://doi.org/10.1038/nrmicro2750

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