Journal
MOLECULAR BIOLOGY AND EVOLUTION
Volume 39, Issue 9, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/molbev/msac178
Keywords
computational biology; population genetics; molecular evolution; fitness landscapes
Funding
- Israel Science Foundation [1889/19]
- Hebrew University of Jerusalem
- Minerva Foundation
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This article investigates the fitness landscape of a yeast tRNA gene and finds that the wild type allele is sub-optimal. It also rules out the possibility that the wild type is the fittest on average or located on a local fitness maximum under four different conditions. The study further reveals that the wild type is mutationally robust, while more fit variants are typically mutationally fragile. Similar observations have been made in viral genomes.
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.
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