Molecular Biology and EvolutionEvolutionary BiologyPeer-reviewed journal article
Epistatic impacts of cis- and trans-regulatory mutations on the distribution of mutational effects for gene expression in Saccharomyces cerevisiae
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Abstract
Epistasis can influence evolution by causing the distribution of phenotypic effects for new mutations to vary among genotypes. Here, we investigate how epistatic interactions between new mutations and an existing regulatory mutation might impact the evolution of gene expression using Saccharomyces cerevisiae. We do so by estimating the distribution of mutational effects for expression of a fluorescent reporter protein driven by the S. cerevisiae TDH3 promoter in a reference strain as well as in eight mutant strains. Each of the mutant strains differed from the reference strain by a single mutation affecting expression of the focal gene. We found that half of these regulatory mutations changed the variance and/or skewness of the distribution of mutational effects. A change in variance indicates a change in mutational robustness, and we found that one initial regulatory mutation increased mutational robustness while another decreased it. A change in skewness indicates a change in the relative frequency and/or effect size of mutations increasing or decreasing expression, and we found that the initial regulatory mutation in four strains had such an effect. Strikingly, in all four of these cases, the change in skewness increased the likelihood that new mutations would at least partially compensate for the effects of the initial regulatory mutation. If this form of epistatic impact on the distribution of mutational effects is common, it could provide a neutral mechanism reducing the divergence of gene expression and help explain the prevalence of alleles with compensatory effects in natural populations of S. cerevisiae.
- organism: yeast
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
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