Fitness Landscape Analysis of a tRNA Gene Reveals that the Wild Type Allele is Sub-optimal, Yet Mutationally Robust

Tzahi Gabzi, Yitzhak Pilpel*, Tamar Friedlander*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Fitness landscape mapping and the prediction of evolutionary trajectories on these landscapes are major tasks in evolutionary biology research. Evolutionary dynamics is tightly linked to the landscape topography, but this relation is not straightforward. Here, we analyze a fitness landscape of a yeast tRNA gene, previously measured under four different conditions. We find that the wild type allele is sub-optimal, and 8-10% of its variants are fitter. We rule out the possibilities that the wild type is fittest on average on these four conditions or located on a local fitness maximum. Notwithstanding, we cannot exclude the possibility that the wild type might be fittest in some of the many conditions in the complex ecology that yeast lives at. Instead, we find that the wild type is mutationally robust ("flat"), while more fit variants are typically mutationally fragile. Similar observations of mutational robustness or flatness have been so far made in very few cases, predominantly in viral genomes.

Original languageEnglish
Article numbermsac178
JournalMolecular Biology and Evolution
Volume39
Issue number9
DOIs
StatePublished - 1 Sep 2022

Bibliographical note

Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.

Keywords

  • computational biology
  • fitness landscapes
  • molecular evolution
  • population genetics

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