bioRxivEvolutionary BiologyPreprint
Adaptive gene duplication can exceed optimal dosage
Frédéric Bédard, Alexandre K. DubéiD, Isabelle Gagnon‐ArsenaultiD, Christian R. LandryiD
Abstract
Gene duplication is an important evolutionary mechanism contributing to genomic innovation and adaptation. While the long term fate of duplicated genes has been extensively studied, immediate effects such as those resulting from increase in gene dosage remain understudied. Of particular interest is the question of how expression level relates to the optimum after a duplication, as it could fall short, exceed or directly reach the optimal expression. In this study, we investigated the relationship between expression and fitness for 11 genes previously reported to increase fitness when duplicated in Saccharomyces cerevisiae to compare the expression level achieved by duplication to the one maximizing fitness. While most of the studied genes show little fitness variation across the expression levels tested, we found that ESP1, which encodes a separase essential to anaphase initiation, exhibits a clear fitness optimum at an expression level above the native single-copy state. However, the expression level associated with gene duplication exceeds the optimum. We find that ESP1 overexpression is associated with a reduced cell size and a lower proportion of the cell cycle spent in G2 phase, consistent with its role in cell cycle progression. We propose that the duplication of ESP1 accelerates the cell cycle, resulting in smaller, faster-dividing cells. When measured in different environments, the fitness functions also exhibit similar shapes, with slight variation in the optimal expression. Our findings highlight how gene duplication can confer an immediate selective advantage through dosage increase while being non-optimal, reversing the directionality of natural selection on expression level after duplication.
- organism: yeast
- assay: DNA methylation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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