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Repeatability and Predictability in Experimental Evolution

  • Peter A. LindEmail author
Chapter

Abstract

Independent populations often use the same phenotypic and genetic solutions to adapt to a selective challenge, suggesting that evolution is surprisingly repeatable. This observation has inspired a shift in focus for evolutionary biology towards predictive studies, but progress is impeded by a lack of insight into the causes for repeatability, which prevents tests of forecasting models outside the original biological systems. Experimental evolution with microbes could provide a way to identify the causes of repeated evolution, directly test forecasting ability and develop methodology, but a range of difficulties limits successful prediction. This chapter discusses the limitations on forecasting of experimental evolution, what can and cannot be predicted on different biological levels and why predictions will often fail. Focusing on experimental populations of bacteria, the importance of selection, mutational biases and genotype-to-phenotype maps in determining evolutionary outcomes is discussed, as well as the potential for including these factors in forecasting models. The chapter concludes with a discussion on the desired properties of experimental evolution models suitable for testing forecasting models.

References

  1. Andersson DI, Hughes D (2009) Gene amplification and adaptive evolution in bacteria. Annu Rev Genet 43:167–195.  https://doi.org/10.1146/annurev-genet-102108-134805CrossRefPubMedGoogle Scholar
  2. Baek M, Park T, Heo L, Park C, Seok C (2017) GalaxyHomomer: a web server for protein homo-oligomer structure prediction from a monomer sequence or structure. Nucleic Acids Res 45:W320–W324.  https://doi.org/10.1093/nar/gkx246CrossRefGoogle Scholar
  3. Bailey SF, Bataillon T (2016) Can the experimental evolution programme help us elucidate the genetic basis of adaptation in nature? Mol Ecol 25:203–218.  https://doi.org/10.1111/mec.13378CrossRefPubMedGoogle Scholar
  4. Bailey SF, Blanquart F, Bataillon T, Kassen R (2017) What drives parallel evolution?: how population size and mutational variation contribute to repeated evolution. BioEssays 39:1–9.  https://doi.org/10.1002/bies.201600176CrossRefGoogle Scholar
  5. Barrick JE, Lenski RE (2013) Genome dynamics during experimental evolution. Nat Rev Genet 14:827–839.  https://doi.org/10.1038/nrg3564CrossRefPubMedPubMedCentralGoogle Scholar
  6. Bennett DE et al (2009) Drug resistance mutations for surveillance of transmitted HIV-1 drug-resistance: 2009 update. PLoS One 4:e4724.  https://doi.org/10.1371/journal.pone.0004724CrossRefGoogle Scholar
  7. Blank D, Wolf L, Ackermann M, Silander OK (2014) The predictability of molecular evolution during functional innovation. Proc Natl Acad Sci USA 111:3044–3049.  https://doi.org/10.1073/pnas.1318797111CrossRefPubMedGoogle Scholar
  8. Blattner FR et al (1997) The complete genome sequence of Escherichia coli K-12. Science 277:1453–1462CrossRefGoogle Scholar
  9. Bloom JD, Labthavikul ST, Otey CR, Arnold FH (2006) Protein stability promotes evolvability. Proc Natl Acad Sci USA 103:5869–5874.  https://doi.org/10.1073/pnas.0510098103CrossRefPubMedGoogle Scholar
  10. Blount ZD, Barrick JE, Davidson CJ, Lenski RE (2012) Genomic analysis of a key innovation in an experimental Escherichia coli population. Nature 489:513–518.  https://doi.org/10.1038/nature11514CrossRefPubMedPubMedCentralGoogle Scholar
  11. Blount ZD, Lenski RE, Losos JB (2018) Contingency and determinism in evolution: replaying life’s tape. Science 362.  https://doi.org/10.1126/science.aam5979CrossRefGoogle Scholar
  12. Bromberg Y, Rost B (2007) SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res 35:3823–3835.  https://doi.org/10.1093/nar/gkm238CrossRefGoogle Scholar
  13. Capriotti E, Calabrese R, Fariselli P, Martelli PL, Altman RB, Casadio R (2013) WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC Genomics 14(Suppl 3):S6.  https://doi.org/10.1186/1471-2164-14-s3-s6CrossRefGoogle Scholar
  14. Celniker G et al (2013) ConSurf: using evolutionary data to raise testable hypotheses about protein function. Isr J Chem 53:199–206.  https://doi.org/10.1002/ijch.201200096CrossRefGoogle Scholar
  15. Choi Y, Chan AP (2015) PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 31:2745–2747.  https://doi.org/10.1093/bioinformatics/btv195CrossRefPubMedPubMedCentralGoogle Scholar
  16. Cooper VS, Schneider D, Blot M, Lenski RE (2001) Mechanisms causing rapid and parallel losses of ribose catabolism in evolving populations of Escherichia coli B. J Bacteriol 183:2834–2841.  https://doi.org/10.1128/JB.183.9.2834-2841.2001CrossRefPubMedPubMedCentralGoogle Scholar
  17. Daegelen P, Studier FW, Lenski RE, Cure S, Kim JF (2009) Tracing ancestors and relatives of Escherichia coli B, and the derivation of B strains REL606 and BL21(DE3). J Mol Biol 394:634–643.  https://doi.org/10.1016/j.jmb.2009.09.022CrossRefGoogle Scholar
  18. de Visser JA, Krug J (2014) Empirical fitness landscapes and the predictability of evolution. Nat Rev Genet 15:480–490.  https://doi.org/10.1038/nrg3744CrossRefPubMedGoogle Scholar
  19. de Visser J, Elena SF, Fragata I, Matuszewski S (2018) The utility of fitness landscapes and big data for predicting evolution. Heredity (Edinb) 121:401–405.  https://doi.org/10.1038/s41437-018-0128-4CrossRefGoogle Scholar
  20. Deatherage DE, Kepner JL, Bennett AF, Lenski RE, Barrick JE (2017) Specificity of genome evolution in experimental populations of Escherichia coli evolved at different temperatures. Proc Natl Acad Sci USA 114:E1904–E1912.  https://doi.org/10.1073/pnas.1616132114CrossRefPubMedGoogle Scholar
  21. Dehouck Y, Kwasigroch JM, Gilis D, Rooman M (2011) PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality. BMC Bioinform 12:151.  https://doi.org/10.1186/1471-2105-12-151
  22. Eyre-Walker A, Keightley PD (2007) The distribution of fitness effects of new mutations. Nat Rev Genet 8:610–618.  https://doi.org/10.1038/nrg2146CrossRefPubMedGoogle Scholar
  23. Ferguson GC, Bertels F, Rainey PB (2013) Adaptive divergence in experimental populations of Pseudomonas fluorescens. V. Insight into the Niche specialist “Fuzzy Spreader” compels revision of the model Pseudomonas radiation genetics.  https://doi.org/10.1534/genetics.113.154948CrossRefGoogle Scholar
  24. Fischer A, Vazquez-Garcia I, Illingworth CJR, Mustonen V (2014) High-definition reconstruction of clonal composition in cancer. Cell Rep 7:1740–1752.  https://doi.org/10.1016/j.celrep.2014.04.055CrossRefPubMedPubMedCentralGoogle Scholar
  25. Flowers JM, Hanzawa Y, Hall MC, Moore RC, Purugganan MD (2009) Population genomics of the Arabidopsis thaliana flowering time gene network. Mol Biol Evol 26:2475–2486.  https://doi.org/10.1093/molbev/msp161CrossRefPubMedGoogle Scholar
  26. Gerrish PJ, Lenski RE (1998) The fate of competing beneficial mutations in an asexual population. Genetica 102–103:127–144CrossRefGoogle Scholar
  27. Gerstein AC, Lo DS, Otto SP (2012) Parallel genetic changes and nonparallel gene-environment interactions characterize the evolution of drug resistance in yeast. Genetics 192:241–252.  https://doi.org/10.1534/genetics.112.142620CrossRefPubMedPubMedCentralGoogle Scholar
  28. Goldstein BP (2014) Resistance to rifampicin: a review. J Antibiot (Tokyo) 67:625–630.  https://doi.org/10.1038/ja.2014.107CrossRefGoogle Scholar
  29. Good BH, Rouzine IM, Balick DJ, Hallatschek O, Desai MM (2012) Distribution of fixed beneficial mutations and the rate of adaptation in asexual populations. Proc Natl Acad Sci USA 109:4950–4955.  https://doi.org/10.1073/pnas.1119910109CrossRefPubMedGoogle Scholar
  30. Good BH, McDonald MJ, Barrick JE, Lenski RE, Desai MM (2017) The dynamics of molecular evolution over 60,000 generations. Nature 551:45–50.  https://doi.org/10.1038/nature24287CrossRefGoogle Scholar
  31. Gould SJ (1989) Wonderful life: the Burgess Shale and the nature of history, 1st edn. W.W. Norton, New YorkGoogle Scholar
  32. Gullberg E, Cao S, Berg OG, Ilback C, Sandegren L, Hughes D, Andersson DI (2011) Selection of resistant bacteria at very low antibiotic concentrations. PLoS Pathog 7:e1002158.  https://doi.org/10.1371/journal.ppat.1002158CrossRefPubMedPubMedCentralGoogle Scholar
  33. Harpak A, Bhaskar A, Pritchard JK (2016) Mutation rate variation is a primary determinant of the distribution of allele frequencies in humans. PLoS Genet 12:e1006489.  https://doi.org/10.1371/journal.pgen.1006489CrossRefPubMedPubMedCentralGoogle Scholar
  34. Herron MD, Doebeli M (2013) Parallel evolutionary dynamics of adaptive diversification in Escherichia coli. PLoS Biol 11:e1001490.  https://doi.org/10.1371/journal.pbio.1001490CrossRefPubMedPubMedCentralGoogle Scholar
  35. Hietpas RT, Jensen JD, Bolon DN (2011) Experimental illumination of a fitness landscape. Proc Natl Acad Sci USA 108:7896–7901.  https://doi.org/10.1073/pnas.1016024108CrossRefPubMedGoogle Scholar
  36. Hodgkinson A, Ladoukakis E, Eyre-Walker A (2009) Cryptic variation in the human mutation rate. PLoS Biol 7:e1000027.  https://doi.org/10.1371/journal.pbio.1000027CrossRefPubMedPubMedCentralGoogle Scholar
  37. Hudson RE, Bergthorsson U, Ochman H (2003) Transcription increases multiple spontaneous point mutations in Salmonella enterica. Nucleic Acids Res 31:4517–4522CrossRefGoogle Scholar
  38. Hughes D, Andersson DI (2017) Evolutionary trajectories to antibiotic resistance. Annu Rev Microbiol 71:579–596.  https://doi.org/10.1146/annurev-micro-090816-093813CrossRefPubMedGoogle Scholar
  39. Johnson PL, Hellmann I (2011) Mutation rate distribution inferred from coincident SNPs and coincident substitutions. Genome Biol Evol 3:842–850.  https://doi.org/10.1093/gbe/evr044CrossRefPubMedPubMedCentralGoogle Scholar
  40. Kassen R, Bataillon T (2006) Distribution of fitness effects among beneficial mutations before selection in experimental populations of bacteria. Nat Genet 38:484–488.  https://doi.org/10.1038/ng1751CrossRefPubMedGoogle Scholar
  41. Kawecki TJ, Lenski RE, Ebert D, Hollis B, Olivieri I, Whitlock MC (2012) Experimental evolution. Trends Ecol Evol 27:547–560.  https://doi.org/10.1016/j.tree.2012.06.001CrossRefPubMedGoogle Scholar
  42. Kaznatcheev A (2019) Computational complexity as an ultimate constraint on evolution. Genetics.  https://doi.org/10.1534/genetics.119.302000CrossRefPubMedGoogle Scholar
  43. Keightley PD, Eyre-Walker A (2010) What can we learn about the distribution of fitness effects of new mutations from DNA sequence data? Philos Trans R Soc Lond B Biol Sci 365:1187–1193.  https://doi.org/10.1098/rstb.2009.0266CrossRefPubMedPubMedCentralGoogle Scholar
  44. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10:845–858.  https://doi.org/10.1038/nprot.2015.053CrossRefGoogle Scholar
  45. Knoppel A, Knopp M, Albrecht LM, Lundin E, Lustig U, Nasvall J, Andersson DI (2018) Genetic adaptation to growth under laboratory conditions in Escherichia coli and Salmonella enterica. Front Microbiol 9:756.  https://doi.org/10.3389/fmicb.2018.00756CrossRefPubMedPubMedCentralGoogle Scholar
  46. Koskiniemi S, Andersson DI (2009) Translesion DNA polymerases are required for spontaneous deletion formation in Salmonella typhimurium. Proc Natl Acad Sci USA 106:10248–10253.  https://doi.org/10.1073/pnas.0904389106CrossRefPubMedGoogle Scholar
  47. Kovacs AT, Dragos A (2019) Evolved biofilm: review on the experimental evolution studies of Bacillus subtilis pellicles. J Mol Biol.  https://doi.org/10.1016/j.jmb.2019.02.005CrossRefPubMedGoogle Scholar
  48. Krasovec R et al (2017) Spontaneous mutation rate is a plastic trait associated with population density across domains of life. PLoS Biol 15:e2002731.  https://doi.org/10.1371/journal.pbio.2002731CrossRefPubMedPubMedCentralGoogle Scholar
  49. Kumar MD, Bava KA, Gromiha MM, Prabakaran P, Kitajima K, Uedaira H, Sarai A (2006) ProTherm and ProNIT: thermodynamic databases for proteins and protein-nucleic acid interactions. Nucleic Acids Res 34:D204–D206.  https://doi.org/10.1093/nar/gkj103CrossRefPubMedGoogle Scholar
  50. Lamrabet O, Plumbridge J, Martin M, Lenski RE, Schneider D, Hindre T (2019) Plasticity of promoter-core sequences allows bacteria to compensate for the loss of a key global regulatory gene. Mol Biol Evol.  https://doi.org/10.1093/molbev/msz042CrossRefPubMedGoogle Scholar
  51. Lang GI, Desai MM (2014) The spectrum of adaptive mutations in experimental evolution. Genomics 104:412–416.  https://doi.org/10.1016/j.ygeno.2014.09.011CrossRefPubMedPubMedCentralGoogle Scholar
  52. Lässig M, Mustonen V, Walczak AM (2017) Predicting evolution. Nat Ecol Evol 1:0077.  https://doi.org/10.1038/s41559-017-0077CrossRefGoogle Scholar
  53. Levinson G, Gutman GA (1987) Slipped-strand mispairing: a major mechanism for DNA sequence evolution. Mol Biol Evol 4:203–221.  https://doi.org/10.1093/oxfordjournals.molbev.a040442CrossRefPubMedGoogle Scholar
  54. Liao HX et al (2013) Co-evolution of a broadly neutralizing HIV-1 antibody and founder virus. Nature 496:469–476.  https://doi.org/10.1038/nature12053CrossRefGoogle Scholar
  55. Lind PA (2018) Evolutionary forecasting of phenotypic and genetic outcomes of experimental evolution in Pseudomonas. bioRxiv  https://doi.org/10.1101/342261
  56. Lind PA, Andersson DI (2008) Whole-genome mutational biases in bacteria. Proc Natl Acad Sci USA 105:17878–17883.  https://doi.org/10.1073/pnas.0804445105CrossRefPubMedGoogle Scholar
  57. Lind PA, Berg OG, Andersson DI (2010a) Mutational robustness of ribosomal protein genes. Science 330:825–827.  https://doi.org/10.1126/science.1194617CrossRefPubMedGoogle Scholar
  58. Lind PA, Tobin C, Berg OG, Kurland CG, Andersson DI (2010b) Compensatory gene amplification restores fitness after inter-species gene replacements. Mol Microbiol 75:1078–1089.  https://doi.org/10.1111/j.1365-2958.2009.07030.xCrossRefPubMedGoogle Scholar
  59. Lind PA, Farr AD, Rainey PB (2015) Experimental evolution reveals hidden diversity in evolutionary pathways. Elife 4.  https://doi.org/10.7554/elife.07074
  60. Lind PA, Arvidsson L, Berg OG, Andersson DI (2017a) Variation in mutational robustness between different proteins and the predictability of fitness effects. Mol Biol Evol 34:408–418.  https://doi.org/10.1093/molbev/msw239CrossRefPubMedGoogle Scholar
  61. Lind PA, Farr AD, Rainey PB (2017b) Evolutionary convergence in experimental Pseudomonas populations. ISME J 11:589–600.  https://doi.org/10.1038/ismej.2016.157CrossRefPubMedGoogle Scholar
  62. Lind PA, Libby E, Herzog J, Rainey PB (2019) Predicting mutational routes to new adaptive phenotypes. Elife 8.  https://doi.org/10.7554/elife.38822
  63. Long A, Liti G, Luptak A, Tenaillon O (2015) Elucidating the molecular architecture of adaptation via evolve and resequence experiments. Nat Rev Genet 16:567–582.  https://doi.org/10.1038/nrg3937CrossRefPubMedPubMedCentralGoogle Scholar
  64. Lovett ST (2004) Encoded errors: mutations and rearrangements mediated by misalignment at repetitive DNA sequences. Mol Microbiol 52:1243–1253.  https://doi.org/10.1111/j.1365-2958.2004.04076.xCrossRefPubMedGoogle Scholar
  65. Lovett ST, Hurley RL, Sutera VA Jr, Aubuchon RH, Lebedeva MA (2002) Crossing over between regions of limited homology in Escherichia coli. RecA-dependent and RecA-independent pathways. Genetics 160:851–859PubMedPubMedCentralGoogle Scholar
  66. Luksza M, Lassig M (2014) A predictive fitness model for influenza. Nature 507:57–61.  https://doi.org/10.1038/nature13087CrossRefPubMedGoogle Scholar
  67. Lundin E, Tang PC, Guy L, Nasvall J, Andersson DI (2017) Experimental determination and prediction of the fitness effects of random point mutations in the biosynthetic enzyme HisA. Mol Biol Evol.  https://doi.org/10.1093/molbev/msx325CrossRefPubMedPubMedCentralGoogle Scholar
  68. Lynch M, Ackerman MS, Gout JF, Long H, Sung W, Thomas WK, Foster PL (2016) Genetic drift, selection and the evolution of the mutation rate. Nat Rev Genet 17:704–714.  https://doi.org/10.1038/nrg.2016.104CrossRefGoogle Scholar
  69. MacLean RC, Buckling A (2009) The distribution of fitness effects of beneficial mutations in Pseudomonas aeruginosa. PLoS Genet 5:e1000406.  https://doi.org/10.1371/journal.pgen.1000406CrossRefPubMedPubMedCentralGoogle Scholar
  70. Maddamsetti R, Hatcher PJ, Green AG, Williams BL, Marks DS, Lenski RE (2017) Core genes evolve rapidly in the long-term evolution experiment with Escherichia coli. Genome Biol Evol.  https://doi.org/10.1093/gbe/evx064CrossRefPubMedPubMedCentralGoogle Scholar
  71. Maharjan RP, Ferenci T (2017) A shifting mutational landscape in 6 nutritional states: stress-induced mutagenesis as a series of distinct stress input-mutation output relationships. PLoS Biol 15:e2001477.  https://doi.org/10.1371/journal.pbio.2001477CrossRefGoogle Scholar
  72. Malone JG (2015) Role of small colony variants in persistence of Pseudomonas aeruginosa infections in cystic fibrosis lungs. Infect Drug Resist 8:237–247.  https://doi.org/10.2147/IDR.S68214CrossRefPubMedPubMedCentralGoogle Scholar
  73. McCandlish DM, Stoltzfus A (2014) Modeling evolution using the probability of fixation: history and implications. Q Rev Biol 89:225–252CrossRefGoogle Scholar
  74. McDonald MJ, Gehrig SM, Meintjes PL, Zhang XX, Rainey PB (2009) Adaptive divergence in experimental populations of Pseudomonas fluorescens. IV. Genetic constraints guide evolutionary trajectories in a parallel adaptive radiation. Genetics 183:1041–1053.  https://doi.org/10.1534/genetics.109.107110CrossRefPubMedPubMedCentralGoogle Scholar
  75. McDonald MJ, Cooper TF, Beaumont HJE, Rainey PB (2011) The distribution of fitness effects of new beneficial mutations in Pseudomonas fluorescens. Biol Letters 7:98–100.  https://doi.org/10.1098/Rsbl.2010.0547CrossRefGoogle Scholar
  76. Nasvall J, Sun L, Roth JR, Andersson DI (2012) Real-time evolution of new genes by innovation, amplification, and divergence. Science 338:384–387.  https://doi.org/10.1126/science.1226521CrossRefGoogle Scholar
  77. Neher RA, Russell CA, Shraiman BI (2014) Predicting evolution from the shape of genealogical trees. Elife 3.  https://doi.org/10.7554/elife.03568
  78. Ng PC, Henikoff S (2003) SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 31:3812–3814CrossRefGoogle Scholar
  79. O’Neill AJ, Huovinen T, Fishwick CW, Chopra I (2006) 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.  https://doi.org/10.1128/AAC.50.1.298-309.2006CrossRefPubMedPubMedCentralGoogle Scholar
  80. Orgogozo V (2015) Replaying the tape of life in the twenty-first century. Interface Focus 5:20150057.  https://doi.org/10.1098/rsfs.2015.0057CrossRefPubMedPubMedCentralGoogle Scholar
  81. Orr HA (2003) The distribution of fitness effects among beneficial mutations. Genetics 163:1519–1526PubMedPubMedCentralGoogle Scholar
  82. Orr HA (2010) The population genetics of beneficial mutations. Philos Trans R Soc Lond B Biol Sci 365:1195–1201.  https://doi.org/10.1098/rstb.2009.0282CrossRefPubMedPubMedCentralGoogle Scholar
  83. Otwinowski J (2018) Biophysical inference of epistasis and the effects of mutations on protein stability and function. Mol Biol Evol 35:2345–2354.  https://doi.org/10.1093/molbev/msy141CrossRefPubMedGoogle Scholar
  84. Perfeito L, Fernandes L, Mota C, Gordo I (2007) Adaptive mutations in bacteria: high rate and small effects. Science 317:813–815.  https://doi.org/10.1126/science.1142284CrossRefGoogle Scholar
  85. Rainey PB, Travisano M (1998) Adaptive radiation in a heterogeneous environment. Nature 394:69–72.  https://doi.org/10.1038/27900CrossRefPubMedGoogle Scholar
  86. Rainey PB, Remigi P, Farr AD, Lind PA (2017) Darwin was right: where now for experimental evolution? Curr Opin Genet Dev 47:102–109.  https://doi.org/10.1016/j.gde.2017.09.003CrossRefPubMedGoogle Scholar
  87. Reams AB, Roth JR (2015) Mechanisms of gene duplication and amplification. Cold Spring Harb Perspect Biol 7:a016592.  https://doi.org/10.1101/cshperspect.a016592CrossRefPubMedPubMedCentralGoogle Scholar
  88. Reams AB, Kofoid E, Duleba N, Roth JR (2014) Recombination and annealing pathways compete for substrates in making rrn duplications in Salmonella enterica. Genetics 196:119–135.  https://doi.org/10.1534/genetics.113.158519CrossRefPubMedGoogle Scholar
  89. Rokyta DR, Beisel CJ, Joyce P, Ferris MT, Burch CL, Wichman HA (2008) Beneficial fitness effects are not exponential for two viruses. J Mol Evol 67:368–376.  https://doi.org/10.1007/s00239-008-9153-xCrossRefPubMedPubMedCentralGoogle Scholar
  90. Romling U, Galperin MY, Gomelsky M (2013) Cyclic di-GMP: the first 25 years of a universal bacterial second messenger. Microbiol Mol Biol Rev 77:1–52.  https://doi.org/10.1128/mmbr.00043-12CrossRefGoogle Scholar
  91. Sanjuan R (2010) Mutational fitness effects in RNA and single-stranded DNA viruses: common patterns revealed by site-directed mutagenesis studies. Philos Trans R Soc Lond B Biol Sci 365:1975–1982.  https://doi.org/10.1098/rstb.2010.0063CrossRefPubMedPubMedCentralGoogle Scholar
  92. Sankar TS, Wastuwidyaningtyas BD, Dong Y, Lewis SA, Wang JD (2016) The nature of mutations induced by replication-transcription collisions. Nature.  https://doi.org/10.1038/nature18316CrossRefPubMedPubMedCentralGoogle Scholar
  93. Savageau MA, Fasani RA (2009) Qualitatively distinct phenotypes in the design space of biochemical systems. FEBS Lett 583:3914–3922.  https://doi.org/10.1016/j.febslet.2009.10.073CrossRefPubMedPubMedCentralGoogle Scholar
  94. Shewaramani S, Finn TJ, Leahy SC, Kassen R, Rainey PB, Moon CD (2017) Anaerobically grown Escherichia coli has an enhanced mutation rate and distinct mutational spectra. PLoS Genet 13:e1006570.  https://doi.org/10.1371/journal.pgen.1006570CrossRefPubMedPubMedCentralGoogle Scholar
  95. Sommer MOA, Munck C, Toft-Kehler RV, Andersson DI (2017) Prediction of antibiotic resistance: time for a new preclinical paradigm? Nat Rev Microbiol 15:689–696.  https://doi.org/10.1038/nrmicro.2017.75CrossRefPubMedGoogle Scholar
  96. Sousa A, Bourgard C, Wahl LM, Gordo I (2013) Rates of transposition in Escherichia coli. Biol Lett 9:20130838.  https://doi.org/10.1098/rsbl.2013.0838CrossRefPubMedPubMedCentralGoogle Scholar
  97. Spiers AJ, Kahn SG, Bohannon J, Travisano M, Rainey PB (2002) Adaptive divergence in experimental populations of Pseudomonas fluorescens. I. Genetic and phenotypic bases of wrinkly spreader fitness. Genetics 161:33–46PubMedPubMedCentralGoogle Scholar
  98. Spiers AJ, Bohannon J, Gehrig SM, Rainey PB (2003) Biofilm formation at the air-liquid interface by the Pseudomonas fluorescens SBW25 wrinkly spreader requires an acetylated form of cellulose. Mol Microbiol 50:15–27CrossRefGoogle Scholar
  99. Steenackers HP, Parijs I, Dubey A, Foster KR, Vanderleyden J (2016) Experimental evolution in biofilm populations. FEMS Microbiol Rev 40:373–397.  https://doi.org/10.1093/femsre/fuw002CrossRefPubMedPubMedCentralGoogle Scholar
  100. Stern DL (2013) The genetic causes of convergent evolution. Nat Rev Genet 14:751–764.  https://doi.org/10.1038/nrg3483CrossRefPubMedGoogle Scholar
  101. Stoltzfus A, McCandlish DM (2017) Mutational biases influence parallel adaptation. Mol Biol Evol 34:2163–2172.  https://doi.org/10.1093/molbev/msx180CrossRefPubMedPubMedCentralGoogle Scholar
  102. Sun S, Berg OG, Roth JR, Andersson DI (2009) Contribution of gene amplification to evolution of increased antibiotic resistance in Salmonella typhimurium. Genetics 182:1183–1195.  https://doi.org/10.1534/genetics.109.103028CrossRefPubMedPubMedCentralGoogle Scholar
  103. Tenaillon O, Rodriguez-Verdugo A, Gaut RL, McDonald P, Bennett AF, Long AD, Gaut BS (2012) The molecular diversity of adaptive convergence. Science 335:457–461.  https://doi.org/10.1126/science.1212986CrossRefPubMedPubMedCentralGoogle Scholar
  104. Thulin E, Sundqvist M, Andersson DI (2015) Amdinocillin (Mecillinam) resistance mutations in clinical isolates and laboratory-selected mutants of Escherichia coli. Antimicrob Agents Chemother 59:1718–1727.  https://doi.org/10.1128/aac.04819-14CrossRefGoogle Scholar
  105. Valderrama-Gomez MA, Parales RE, Savageau MA (2018) Phenotype-centric modeling for elucidation of biological design principles. J Theor Biol 455:281–292.  https://doi.org/10.1016/j.jtbi.2018.07.009CrossRefPubMedGoogle Scholar
  106. Van den Bergh B, Swings T, Fauvart M, Michiels J (2018) Experimental design, population dynamics, and diversity in microbial experimental evolution. Microbiol Mol Biol Rev 82.  https://doi.org/10.1128/mmbr.00008-18
  107. van Ditmarsch D et al (2013) Convergent evolution of hyperswarming leads to impaired biofilm formation in pathogenic bacteria. Cell Rep 4:697–708.  https://doi.org/10.1016/j.celrep.2013.07.026CrossRefPubMedPubMedCentralGoogle Scholar
  108. Viswanathan M, Lacirignola JJ, Hurley RL, Lovett ST (2000) A novel mutational hotspot in a natural quasipalindrome in Escherichia coli. J Mol Biol 302:553–564.  https://doi.org/10.1006/jmbi.2000.4088CrossRefPubMedGoogle Scholar
  109. Wang X, Zorraquino V, Kim M, Tsoukalas A, Tagkopoulos I (2018) Predicting the evolution of Escherichia coli by a data-driven approach. Nat Commun 9:3562.  https://doi.org/10.1038/s41467-018-05807-zCrossRefPubMedPubMedCentralGoogle Scholar
  110. Weinreich DM, Delaney NF, Depristo MA, Hartl DL (2006) Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312:111–114.  https://doi.org/10.1126/science.1123539CrossRefGoogle Scholar
  111. Wiser MJ, Ribeck N, Lenski RE (2013) Long-term dynamics of adaptation in asexual populations. Science 342:1364–1367.  https://doi.org/10.1126/science.1243357CrossRefGoogle Scholar
  112. Wong A, Rodrigue N, Kassen R (2012) Genomics of adaptation during experimental evolution of the opportunistic pathogen Pseudomonas aeruginosa. PLoS Genet 8:e1002928.  https://doi.org/10.1371/journal.pgen.1002928CrossRefPubMedPubMedCentralGoogle Scholar
  113. Yampolsky LY, Stoltzfus A (2001) Bias in the introduction of variation as an orienting factor in evolution. Evol Dev 3:73–83CrossRefGoogle Scholar
  114. Yona AH, Alm EJ, Gore J (2018) Random sequences rapidly evolve into de novo promoters. Nat Commun 9:1530.  https://doi.org/10.1038/s41467-018-04026-wCrossRefPubMedPubMedCentralGoogle Scholar
  115. Zhen Y, Aardema ML, Medina EM, Schumer M, Andolfatto P (2012) Parallel molecular evolution in an herbivore community. Science 337:1634–1637.  https://doi.org/10.1126/science.1226630CrossRefPubMedPubMedCentralGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Molecular BiologyUmeå UniversityUmeåSweden

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