Abstract
We studied adaptive evolution of gene expression using long-term experimental evolution of Saccharomyces cerevisiae in ammonium-limited chemostats. We found repeated selection for non-synonymous variation in the DNA binding domain of the transcriptional activator, GAT1, which functions with the repressor, DAL80 in an incoherent type-1 feedforward loop (I1-FFL) to control expression of the high affinity ammonium transporter gene, MEP2. Missense mutations in the DNA binding domain of GAT1 reduce its binding to the GATAA consensus sequence. However, we show experimentally, and using mathematical modeling, that decreases in GAT1 binding result in increased expression of MEP2 as a consequence of properties of I1-FFLs. Our results show that I1-FFLs, one of the most commonly occurring network motifs in transcriptional networks, can facilitate adaptive tuning of gene expression through modulation of transcription factor binding affinities. Our findings highlight the importance of gene regulatory architectures in the evolution of gene expression.
Highlights
Gene expression evolution is a pervasive source of phenotypic diversity between and within species (Jacob and Monod, 1961; Jacob, 1977; Nocedal and Johnson, 2015)
We find that the beneficial effects of recurrently selected missense mutations in GAT1 are a result of its effect on increasing expression of the high affinity ammonium transporter gene, MEP2
Whole population sequencing of evolving populations, we identified recurrent selection for missense mutations in the DNA binding domain of GAT1 during the early stages of adaptive evolution in each population
Summary
Gene expression evolution is a pervasive source of phenotypic diversity between and within species (Jacob and Monod, 1961; Jacob, 1977; Nocedal and Johnson, 2015). Genetic variation causing evolutionary changes in gene expression impacts either the regulatory elements of a gene (cis-regulatory) or the factors that control its expression (trans-regulatory). A variety of approaches have been developed for identifying the genetic basis of intraspecific gene expression variation (Brem et al, 2002; Yvert et al, 2003) and defining the landscape of mutational changes that affect gene expression (Metzger et al, 2016). Comparative genomics using extant organisms is typically used for inferring the evolutionary processes and outcomes that underlie the evolution of gene expression (Wittkopp, 2010). These approaches face the challenge of distinguishing neutral from adaptive variation and cannot provide insights into the dynamics of gene expression evolution in evolving populations
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