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An epistatic ratchet constrains the direction of glucocorticoid receptor evolution

Author

Listed:
  • Jamie T. Bridgham

    (Center for Ecology and Evolutionary Biology, and,)

  • Eric A. Ortlund

    (Emory University School of Medicine, Atlanta, Georgia 30322, USA)

  • Joseph W. Thornton

    (Center for Ecology and Evolutionary Biology, and,
    Howard Hughes Medical Institute, University of Oregon, Eugene, Oregon 97403, USA)

Abstract

Evolution has no reverse Whether evolution can go back to an ancestral structure just by reversing the selection pressure on function has been a long-standing issue, but one hard to address based on just the history of forms. Bridgham et al. have now physically reconstituted ancient versions of a regulatory protein (the glucocorticoid receptor) and dissected the structural constraints imposed on the evolution of their function (which hormone they bind) at atomic resolution. They find that amino acids that were essential in an ancestral protein become neutral in a more recent form, where they are then subject to erosion by genetic drift. This loss deprives natural selection of the necessary raw material with which to reverse the historical substitutions — they are no longer 'adaptive' as they were in the other direction. Evolutionarily speaking, there is no turning back.

Suggested Citation

  • Jamie T. Bridgham & Eric A. Ortlund & Joseph W. Thornton, 2009. "An epistatic ratchet constrains the direction of glucocorticoid receptor evolution," Nature, Nature, vol. 461(7263), pages 515-519, September.
    • Handle: RePEc:nat:nature:v:461:y:2009:i:7263:d:10.1038_nature08249
    DOI: 10.1038/nature08249
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    Cited by:

    1. Karol Buda & Charlotte M. Miton & Nobuhiko Tokuriki, 2023. "Pervasive epistasis exposes intramolecular networks in adaptive enzyme evolution," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    2. C. Brandon Ogbunugafor, 2023. "Mutations that enhance evolvability may open doors to faster adaptation," Nature Communications, Nature, vol. 14(1), pages 1-3, December.
    3. Steve O'Hagan & Joshua Knowles & Douglas B Kell, 2012. "Exploiting Genomic Knowledge in Optimising Molecular Breeding Programmes: Algorithms from Evolutionary Computing," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-14, November.
    4. Lucile Vigué & Giancarlo Croce & Marie Petitjean & Etienne Ruppé & Olivier Tenaillon & Martin Weigt, 2022. "Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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