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An empirical test of the mutational landscape model of adaptation using a single-stranded DNA virus

Nature Genetics volume 37, pages 441–444 (2005)Cite this article

Abstract

The primary impediment to formulating a general theory for adaptive evolution has been the unknown distribution of fitness effects for new beneficial mutations1. By applying extreme value theory2, Gillespie circumvented this issue in his mutational landscape model for the adaptation of DNA sequences3,4,5, and Orr recently extended Gillespie's model1,6, generating testable predictions regarding the course of adaptive evolution. Here we provide the first empirical examination of this model, using a single-stranded DNA bacteriophage related to φX174, and find that our data are consistent with Orr's predictions, provided that the model is adjusted to incorporate mutation bias. Orr's work suggests that there may be generalities in adaptive molecular evolution that transcend the biological details of a system, but we show that for the model to be useful as a predictive or inferential tool, some adjustments for the biology of the system will be necessary.

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Figure 1: A schematic depiction of the extreme value theory predictions for a single step.
Figure 2: A comparison of the observed data with the expectations under Orr's model and the mutation-adjusted models.

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References

  1. Orr, H.A. The distribution of fitness effects among beneficial mutations. Genetics 163, 1519–1526 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Gumbel, E.J. Statistics of Extremes (Columbia University Press, New York, 1958).

    Google Scholar 

  3. Gillespie, J.H. A simple stochastic gene substitution model. Theor. Popul. Biol. 23, 202–215 (1983).

    Article  CAS  Google Scholar 

  4. Gillespie, J.H. Molecular evolution over the mutational landscape. Evolution 38, 1116–1129 (1984).

    Article  CAS  Google Scholar 

  5. Gillespie, J.H. The Causes of Molecular Evolution (Oxford University Press, New York, 1991).

    Google Scholar 

  6. Orr, H.A. The population genetics of adaptation: the adaptation of DNA sequences. Evolution 56, 1317–1330 (2002).

    Article  CAS  Google Scholar 

  7. Fisher, R.A. The Genetical Theory of Natural Selection (Oxford University Press, Oxford, UK, 1930).

    Book  Google Scholar 

  8. Orr, H.A. The population genetics of adaptation: the distribution of factors fixed during adaptive evolution. Evolution 52, 935–949 (1998).

    Article  Google Scholar 

  9. Orr, H.A. Adaptation and the cost of complexity. Evolution 54, 13–20 (2000).

    Article  CAS  Google Scholar 

  10. Hartl, D.L. & Taubes, C.H. Towards a theory of evolutionary adaptation. Genetica 102/103, 525–533 (1998).

    Article  Google Scholar 

  11. Haldane, J.B.S. A mathematical theory of natural and artificial selection. V. selection and mutation. Proc. Camb. Philos. Soc. 28, 838–844 (1927).

    Article  Google Scholar 

  12. Orr, H.A. A minimum on the mean number of steps taken in adaptive walks. J. Theor. Biol. 220, 241–247 (2003).

    Article  Google Scholar 

  13. Rokyta, D., Badgett, M.R., Molineux, I.J. & Bull, J.J. Experimental genomic evolution: extensive compensation for loss of DNA ligase activity in a virus. Mol. Biol. Evol. 19, 230–238 (2002).

    Article  CAS  Google Scholar 

  14. Wahl, L.M., Gerrish, P.J. & Saika-Voivod, I. Evaluating the impact of population bottlenecks in experimental evolution. Genetics 162, 961–971 (2002).

    PubMed  PubMed Central  Google Scholar 

  15. Wahl, L.M. & Gerrish, P.J. The probability that beneficial mutations are lost in populations with periodic bottlenecks. Evolution 55, 2606–2610 (2001).

    Article  CAS  Google Scholar 

  16. Swofford, D.L. Phylogenetic Analysis using Parsimony* (PAUP*) ver. 4.0. (Sinauer Associates, Sunderland, Massachusetts, 1998).

    Google Scholar 

  17. Thompson, J.D., Higgins, D.G. & Gibson, T.J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994).

    Article  CAS  Google Scholar 

  18. Minin, V., Abdo, Z., Joyce, P. & Sullivan, J. Performance-based selection of likelihood models for phylogeny estimation. Syst. Biol. 52, 1–10 (2003).

    Article  Google Scholar 

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Acknowledgements

We thank J.J. Bull, Z. Abdo, H.A. Orr and L. Wahl for discussions and comments. This work was supported by a grant from the US National Institutes of Health.

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Author notes

  1. S Brian Caudle

    Present address: Department of Biological Sciences, Section of Integrative Biology, University of Texas, Austin, Texas, 78712, USA

Authors and Affiliations

  1. Department of Biological Sciences, University of Idaho, Moscow, 83844, Idaho, USA

    Darin R Rokyta, S Brian Caudle & Holly A Wichman

  2. Program in Bioinformatics and Computational Biology, University of Idaho, Moscow, 83844, Idaho, USA

    Darin R Rokyta, Paul Joyce & Holly A Wichman

  3. Initiative for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, 83844, Idaho, USA

    Darin R Rokyta, Paul Joyce & Holly A Wichman

  4. Department of Mathematics, University of Idaho, Moscow, 83844, Idaho, USA

    Paul Joyce

  5. Department of Statistics, University of Idaho, Moscow, 83844, Idaho, USA

    Paul Joyce

Authors
  1. Darin R Rokyta
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  2. Paul Joyce
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  3. S Brian Caudle
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  4. Holly A Wichman
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Correspondence to Holly A Wichman.

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Rokyta, D., Joyce, P., Caudle, S. et al. An empirical test of the mutational landscape model of adaptation using a single-stranded DNA virus. Nat Genet 37, 441–444 (2005). https://doi.org/10.1038/ng1535

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