Perspective

Predicting evolution

  • Nature Ecology & Evolution 1, Article number: 0077 (2017)
  • doi:10.1038/s41559-017-0077
  • Download Citation
Received:
Accepted:
Published online:

Abstract

The face of evolutionary biology is changing: from reconstructing and analysing the past to predicting future evolutionary processes. Recent developments include prediction of reproducible patterns in parallel evolution experiments, forecasting the future of individual populations using data from their past, and controlled manipulation of evolutionary dynamics. Here we undertake a synthesis of central concepts for evolutionary predictions, based on examples of microbial and viral systems, cancer cell populations, and immune receptor repertoires. These systems have strikingly similar evolutionary dynamics driven by the competition of clades within a population. These dynamics are the basis for models that predict the evolution of clade frequencies, as well as broad genetic and phenotypic changes. Moreover, there are strong links between prediction and control, which are important for interventions such as vaccine or therapy design. All of these are key elements of what may become a predictive theory of evolution.

  • Subscribe to Nature Ecology & Evolution for full access:

    $99

    Subscribe

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

References

  1. 1.

    Wonderful Life: The Burgess Shale and the Nature of History (1989).

  2. 2.

    Replaying the tape of life in the twenty-first century. Interface Focus 5, 20150057 (2015).

  3. 3.

    & Replaying the tape of life: quantification of the predictability of evolution. Front. Genet. 3, 246 (2012).

  4. 4.

    , , & The reproducibility of adaptation in the light of experimental evolution with whole genome sequencing. Adv. Exp. Med. Biol. 781, 211–231 (2014).

  5. 5.

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

  6. 6.

    et al. Antibody landscapes after influenza virus infection or vaccination. Science 346, 996–1000 (2014).

  7. 7.

    et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).

  8. 8.

    et al. Cancer evolution and the limits of predictability in precision cancer medicine. Trends Cancer 2, 49–63 (2016).

  9. 9.

    Evolutionary determinants of cancer. Cancer Discov. 5, 806–820 (2015).

  10. 10.

    & Parallel bacterial evolution within multiple patients identifies candidate pathogenicity genes. Nat. Genet. 43, 1275–1280 (2011).

  11. 11.

    , & Bias in the αβ T-cell repertoire: implications for disease pathogenesis and vaccination. Immunol. Cell Biol. 89, 375–387 (2011).

  12. 12.

    & Predicting evolution from genomics: experimental evolution of bacteriophage T7. Heredity (Edinb.) 100, 453–463 (2008).

  13. 13.

    & A predictive fitness model for influenza. Nature 507, 57–61 (2014).

  14. 14.

    , , , & Prediction of resistance development against drug combinations by collateral responses to component drugs. Sci. Transl. Med. 6, 262ra156 (2014).

  15. 15.

    , & Predicting evolution from the shape of genealogical trees. eLife 3, e03568 (2014).

  16. 16.

    et al. Relative rate and location of intra-host HIV evolution to evade cellular immunity are predictable. Nat. Commun. 7, 11660 (2016).

  17. 17.

    Genetic draft, selective interference, and population genetics of rapid adaptation. Annu. Rev. Ecol. Evol. Syst. 44, 195–215 (2013).

  18. 18.

    & The fate of competing beneficial mutations in an asexual population. Genetica 102–103, 127–144 (1998).

  19. 19.

    , , & Clonal interference and the evolution of RNA viruses. Science 285, 1745–1747 (1999).

  20. 20.

    , & Genetic variation and the fate of beneficial mutations in asexual populations. Genetics 188, 647–661 (2011).

  21. 21.

    & Clonal interference in the evolution of influenza. Genetics 192, 671–682 (2012).

  22. 22.

    et al. Human effector and memory CD8+ T cell responses to smallpox and yellow fever vaccines. Immunity 28, 710–722 (2008).

  23. 23.

    , , & Monitoring chronic lymphocytic leukemia progression by whole genome sequencing reveals heterogeneous clonal evolution patterns. Blood 120, 4191–4197 (2012).

  24. 24.

    et al. Mutations driving CLL and their evolution in progression and relapse. Nature 526, 525–530 (2015).

  25. 25.

    et al. Dynamics of immunoglobulin sequence diversity in HIV-1 infected individuals. Philos. Trans. R. Soc. B 370, 20140241 (2015).

  26. 26.

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

  27. 27.

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

  28. 28.

    , , & Elucidating the molecular architecture of adaptation via evolve and resequence experiments. Nat. Rev. Genet. 16, 567–582 (2015).

  29. 29.

    & The spectrum of adaptive mutations in experimental evolution. Genomics 104, 412–416 (2014).

  30. 30.

    , & The cancer genome. Nature 458, 719–724 (2009).

  31. 31.

    et al. Genome sequencing of normal cells reveals developmental lineages and mutational processes. Nature 513, 422–425 (2014).

  32. 32.

    , , & The molecular basis for public T-cell responses? Nat. Rev. Immunol. 8, 231–238 (2008).

  33. 33.

    & Quantifying lymphocyte receptor diversity. Preprint at bioRxiv (2016).

  34. 34.

    et al. Inferring processes underlying B-cell repertoire diversity. Philos. Trans. R. Soc. B 370, 20140243 (2015).

  35. 35.

    et al. Tracking global changes induced in the CD4 T-cell receptor repertoire by immunization with a complex antigen using short stretches of CDR3 protein sequence. Bioinformatics 30, 3181–3188 (2014).

  36. 36.

    & Whole genome, whole population sequencing reveals that loss of signaling networks is the major adaptive strategy in a constant environment. PLoS Genet. 9, e1003972 (2013).

  37. 37.

    & Genome dynamics during experimental evolution. Nat. Rev. Genet. 14, 827–839 (2013).

  38. 38.

    , , & Microbial evolution. Global epistasis makes adaptation predictable despite sequence-level stochasticity. Science 344, 1519–1522 (2014).

  39. 39.

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

  40. 40.

    , & Adaptive immunity increases the pace and predictability of evolutionary change in commensal gut bacteria. Nat. Commun. 6, 8945 (2015).

  41. 41.

    , & Causes and evolutionary significance of genetic convergence. Trends Genet. 26, 400–405 (2010).

  42. 42.

    , & Macrophage adaptation leads to parallel evolution of genetically diverse Escherichia coli small-colony variants with increased fitness in vivo and antibiotic collateral sensitivity. Evol. Appl. 9, 994–1004 (2016).

  43. 43.

    & Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

  44. 44.

    et al. Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes. Nat. Commun. 6, 5901 (2014).

  45. 45.

    et al. Counting antigen-specific CD8 T cells: a reevaluation of bystander activation during viral infection. Immunity 8, 177–187 (1998).

  46. 46.

    , & Quorum sensing allows T cells to discriminate between self and nonself. Proc. Natl Acad. Sci. USA 110, 11833–11838 (2013).

  47. 47.

    , , & Emergent neutrality in adaptive asexual evolution. Genetics 189, 1361–75 (2011).

  48. 48.

    , , , & Distribution of fixed beneficial mutations and the rate of adaptation in asexual populations. Proc. Natl Acad. Sci. USA 109, 4950–4955 (2012).

  49. 49.

    & Microbial genetics: evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat. Rev. Genet. 4, 457–469 (2003).

  50. 50.

    Genomewide patterns of substitution in adaptively evolving populations of the RNA bacteriophage MS2. Genetics 181, 1535–1544 (2009).

  51. 51.

    , , , & Mutators, population size, adaptive landscape and the adaptation of asexual populations of bacteria. Genetics 152, 485–493 (1999).

  52. 52.

    & Components of selection in the evolution of the influenza virus: linkage effects beat inherent selection. PLoS Pathog. 8, e1003091 (2012).

  53. 53.

    , & Universality and predictability in molecular quantitative genetics. Curr. Opin. Genet. Dev. 23, 684–693 (2013).

  54. 54.

    , & Protein stability imposes limits on organism complexity and speed of molecular evolution. Proc. Natl Acad. Sci. USA 104, 16152–16157 (2007).

  55. 55.

    , & Interplay in the selection of fluoroquinolone resistance and bacterial fitness. PLoS Pathog. 5, e1000541 (2009).

  56. 56.

    , , & The population genetics of antibiotic resistance: integrating molecular mechanisms and treatment contexts. Nat. Rev. Genet. 11, 405–414 (2010).

  57. 57.

    & Antibiotic resistance and its cost: is it possible to reverse resistance? Nat. Rev. Microbiol. 8, 260–71 (2010).

  58. 58.

    & A biophysical protein folding model accounts for most mutational fitness effects in viruses. Proc. Natl Acad. Sci. USA 108, 9916–9921 (2011).

  59. 59.

    , & Stability-mediated epistasis constrains the evolution of an influenza protein. eLife 2, e00631 (2013).

  60. 60.

    et al. Biophysical principles predict fitness landscapes of drug resistance. Proc. Natl Acad. Sci. USA 113, E1470–E1478 (2016).

  61. 61.

    & Evolutionary consequences of drug resistance: shared principles across diverse targets and organisms. Nat. Rev. Genet. 16, 459–471 (2015).

  62. 62.

    et al. Capturing the mutational landscape of the beta-lactamase TEM-1. Proc. Natl Acad. Sci. USA 110, 13067–13072 (2013).

  63. 63.

    et al. Passenger DNA alterations reduce cancer fitness in cell culture and mouse models. Preprint at bioRxiv (2015).

  64. 64.

    , & Tug-of-war between driver and passenger mutations in cancer and other adaptive processes. Proc. Natl Acad. Sci. USA 111, 15138–15143 (2014).

  65. 65.

    & The effects of a deleterious mutation load on patterns of influenza A/H3N2's antigenic evolution in humans. eLife 4, e07361 (2015).

  66. 66.

    , , & Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312, 111–114 (2006).

  67. 67.

    et al. Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes. Nat. Genet. 44, 106–110 (2011).

  68. 68.

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

  69. 69.

    , & Evolution of Escherichia coli rifampicin resistance in an antibiotic-free environment during thermal stress. BMC Evol. Biol. 13, 50 (2013).

  70. 70.

    , , & Should evolutionary geneticists worry about higher-order epistasis? Curr. Opin. Genet. Dev. 23, 700–707 (2013).

  71. 71.

    & Predicting the evolution of antibiotic resistance. BMC Biol. 11, 14 (2013).

  72. 72.

    , , & Empirical fitness landscapes reveal accessible evolutionary paths. Nature 445, 383–386 (2007).

  73. 73.

    et al. Evolution of DNA Specificity in a transcription factor family produced a new gene regulatory module. Cell 159, 58–68 (2014).

  74. 74.

    & The rule of declining adaptability in microbial evolution experiments. Front. Genet. 6, 99 (2015).

  75. 75.

    , & Evolutionary assembly patterns of prokaryotic genomes. Genome Res. 26, 826–833 (2016).

  76. 76.

    et al. Predicting C4 photosynthesis evolution: modular, individually adaptive steps on a Mount Fuji fitness landscape. Cell 153, 1579–1588 (2013).

  77. 77.

    & Empirical fitness landscapes and the predictability of evolution. Nat. Rev. Genet. 15, 480–490 (2014).

  78. 78.

    , , & On the (un)predictability of a large intragenic fitness landscape. Proc. Natl Acad. Sci. USA 113, 14085–14090 (2016).

  79. 79.

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

  80. 80.

    , & How good are statistical models at approximating complex fitness landscapes. Mol. Biol. Evol. 33, 2454–2468 (2016).

  81. 81.

    , & Compensatory mutations, antibiotic resistance and the population genetics of adaptive evolution in bacteria. Genetics 154, 985–997 (2000).

  82. 82.

    , , , & The epidemiological fitness cost of drug resistance in Mycobacterium tuberculosis. Proc. Natl Acad. Sci. USA 106, 14711–14715 (2009).

  83. 83.

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

  84. 84.

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

  85. 85.

    , , & Temporal constraints on the incorporation of regulatory mutants in evolutionary pathways. Mol. Biol. Evol. 26, 2455–2462 (2009).

  86. 86.

    & The rate of compensatory mutation in the DNA bacteriophage X174. Genetics 170, 989–999 (2005).

  87. 87.

    , , & Compensatory adaptation to the deleterious effect of antibiotic resistance in Salmonella typhimurium. Mol. Microbiol. 46, 355–366 (2002).

  88. 88.

    , & Cost of antibiotic resistance and the geometry of adaptation. Mol. Biol. Evol. 29, 1417–1428 (2012).

  89. 89.

    et al. Evolving new protein-protein interaction specificity through promiscuous intermediates. Cell 163, 594–606 (2015).

  90. 90.

    , & Standing genetic variation drives repeatable experimental evolution in outcrossing populations of Saccharomyces cerevisiae. Mol. Biol. Evol. 31, 3228–3239 (2014).

  91. 91.

    et al. Background-dependent effects of selection on subclonal heterogeneity. Preprint at bioRxiv (2016).

  92. 92.

    et al. The first steps of adaptation of Escherichia coli to the gut are dominated by soft sweeps. PLoS Genet. 10, e1004182 (2014).

  93. 93.

    et al. Lineage structure of the human antibody repertoire in response to influenza vaccination. Sci. Transl. Med. 5, 171ra19 (2013).

  94. 94.

    & From fitness landscapes to seascapes: non-equilibrium dynamics of selection and adaptation. Trends Genet. 25, 111–119 (2009).

  95. 95.

    , , & Genetic drift at expanding frontiers promotes gene segregation. Proc. Natl Acad. Sci. USA 104, 19926–19930 (2007).

  96. 96.

    et al. Tempo and mode of genome evolution in a 50,000-generation experiment. Nature 536, 165–170 (2016).

  97. 97.

    et al. Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519, 181–186 (2015).

  98. 98.

    et al. Development of a comprehensive genotype-to-fitness map of adaptation-driving mutations in yeast. Cell 166, 1585–1589.e22 (2016).

  99. 99.

    , , & Measuring the sequence-affinity landscape of antibodies with massively parallel titration curves. eLife (2016).

  100. 100.

    et al. Deconstructing the peptide-MHC specificity of T cell recognition. Cell 157, 1073–1087 (2014).

  101. 101.

    & A systems approach to measuring the binding energy landscapes of transcription factors. Science 315, 233–237 (2007).

  102. 102.

    DNA microarray technologies for measuring protein–dna interactions. Curr. Opin. Biotechnol. 17, 422–430 (2006).

  103. 103.

    , , & Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. Proc. Natl Acad. Sci. USA 107, 9158–9163 (2010).

  104. 104.

    & The inherent mutational tolerance and antigenic evolvability of influenza hemagglutinin. eLife 3, e03300 (2014).

  105. 105.

    & Evolutionary distances for protein-coding sequences: modeling site- specific residue frequencies. Mol. Biol. Evol. 15, 910–917 (1962).

  106. 106.

    , & Adaptive evolution of transcription factor binding sites. BMC Evol. Biol. 4, 42 (2004).

  107. 107.

    et al. Exploring the complexity of the HIV-1 fitness landscape. PLoS Genet. 8, e1002551 (2012).

  108. 108.

    From biophysics to evolutionary genetics: statistical aspects of gene regulation. BMC Bioinform. 8(suppl. 6), S7 (2007).

  109. 109.

    , , & Energy-dependent fitness: a quantitative model for the evolution of yeast transcription factor binding sites. Proc. Natl Acad. Sci. USA 105, 12376–12381 (2008).

  110. 110.

    & Merging molecular mechanism and evolution: theory and computation at the interface of biophysics and evolutionary population genetics. Curr. Opin. Struct. Biol. 26, 84–91 (2014).

  111. 111.

    , , , & MONKEY: identifying conserved transcription-factor binding sites in multiple alignments using a binding site-specific evolutionary model. Genome 5, R98 (2004).

  112. 112.

    et al. The interface of protein structure, protein biophysics, and molecular evolution. Protein Sci. 21, 769–785 (2012).

  113. 113.

    & On the selection and evolution of regulatory DNA motifs. J. Mol. Evol. 55, 386–400 (2002).

  114. 114.

    & Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015).

  115. 115.

    The Genetical Theory of Natural Selection. (Clarendon, 1930).

  116. 116.

    , & Antibiotic resistance and stress in the light of Fisher's model. Evolution 66, 3815–3824 (2012).

  117. 117.

    , & On the rapidity of antibiotic resistance evolution facilitated by a concentration gradient. Proc. Natl Acad. Sci. USA 109, 10775–10780 (2012).

  118. 118.

    , & Mutational pathway determines whether drug gradients accelerate evolution of drug-resistant cells. Phys. Rev. Lett. 109, 88101 (2012).

  119. 119.

    , & Mapping the antigenic and genetic evolution of influenza virus. Science 305, 371–377 (2004).

  120. 120.

    et al. Integrating influenza antigenic dynamics with molecular evolution. eLife 3, e01914 (2014).

  121. 121.

    , , , & Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses. Proc. Natl Acad. Sci. USA 113, E1701–E1709 (2016).

  122. 122.

    , & Systems-biology approaches for predicting genomic evolution. Nat. Rev. Genet. 12, 591–602 (2011).

  123. 123.

    et al. Manipulating the selection forces during affinity maturation to generate cross-reactive HIV antibodies. Cell 160, 785–797 (2015).

  124. 124.

    , & Host-pathogen coevolution and the emergence of broadly neutralizing antibodies in chronic infections. PLoS Genet. 12, e1006171 (2016).

  125. 125.

    , , & Forecasting epidemiological and evolutionary dynamics of infectious diseases. Trends Ecol. Evol. 31, 1–13 (2016).

  126. 126.

    & Evolutionary rationale for phages as complements of antibiotics. Trends Microbiol. 24, 249–56 (2016).

  127. 127.

    et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).

  128. 128.

    , & Predictability, complexity, and learning. Neural Comput. 13, 2409–2463 (2001).

  129. 129.

    & Fitness flux and ubiquity of adaptive evolution. Proc. Natl Acad. Sci. USA 107, 4248–4253 (2010).

  130. 130.

    , & The evolution of drug resistance and the curious orthodoxy of aggressive chemotherapy. Proc. Natl Acad. Sci. USA 108, 10871–10877 (2011).

  131. 131.

    & Exploring the collaboration between antibiotics and the immune response in the treatment of acute, self-limiting infections. Proc. Natl Acad. Sci. USA 111, 8331–8338 (2014).

  132. 132.

    et al. Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance. Nature 494, 251–255 (2013).

  133. 133.

    , , & M. Diversity of immune strategies explained by adaptation to pathogen statistics. Proc. Natl Acad. Sci. USA 113, 8630–8635 (2016).

  134. 134.

    , & The value of monitoring to control evolving populations. Proc. Natl Acad. Sci. USA 112, 1007–1012 (2015).

  135. 135.

    , , , & HIV-1 dynamics in vivo: virion clearance rate, infected cell life-span, and viral generation time. Science 271, 1582–1586 (1996).

  136. 136.

    et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N. Engl. J. Med. 364, 2507–2516 (2011).

  137. 137.

    et al. Viraemia suppressed in HIV-1-infected humans by broadly neutralizing antibody 3BNC117. Nature 522, 487–491 (2015).

  138. 138.

    , , , & Nonlinear fitness landscape of a molecular pathway. PLoS Genet. 7, e1002160 (2011).

  139. 139.

    et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015).

  140. 140.

    , , , & Population biological principles of drug-resistance evolution in infectious diseases. Lancet Infect. Dis. 11, 236–247 (2011).

  141. 141.

    , , & High-definition reconstruction of clonal composition in cancer. Cell Rep. 7, 1740–1752 (2014).

  142. 142.

    et al. Co-evolution of a broadly neutralizing HIV-1 antibody and founder virus. Nature 496, 469–476 (2013).

Download references

Acknowledgements

We thank M. Desai, I. Gordo, M. Łuksza, T. Mora and A. Nourmohammad for comments on the manuscript. M. Desai, M. Łuksza and A. Nourmohammad also provided important input to illustrations. This work has been partially supported by Deutsche Forschungs-gemeinschaft grant SFB 680 (M.L.), Wellcome Trust grant 098051 (V.M.), and European Research Council ERCStG 306312 (A.M.W.).

Author information

Author notes

    • Ville Mustonen

    Present address: Department of Biosciences, University of Helsinki, PO Box 65, 00014, Finland.

Affiliations

  1. Institute of Theoretical Physics, University of Cologne, 50937 Cologne, Germany.

    • Michael Lässig
  2. Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK.

    • Ville Mustonen
  3. Laboratoire de Physique Théorique CNRS, Ecole Normale Supérieure, 75005 Paris, France.

    • Aleksandra M. Walczak

Authors

  1. Search for Michael Lässig in:

  2. Search for Ville Mustonen in:

  3. Search for Aleksandra M. Walczak in:

Contributions

All authors developed concepts and wrote the paper. Authors are listed in alphabetical order.

Competing interests

The author declares no competing financial interests.

Corresponding authors

Correspondence to Michael Lässig or Ville Mustonen or Aleksandra M. Walczak.