Energy-dependent fitness: A quantitative model for the evolution of yeast transcription factor binding sites

Abstract

We present a genomewide cross-species analysis of regulation for broad-acting transcription factors in yeast. Our model for binding site evolution is founded on biophysics: the binding energy between transcription factor and site is a quantitative phenotype of regulatory function, and selection is given by a fitness landscape that depends on this phenotype. The model quantifies conservation, as well as loss and gain, of functional binding sites in a coherent way. Its predictions are supported by direct cross-species comparison between four yeast species. We find ubiquitous compensatory mutations within functional sites, such that the energy phenotype and the function of a site evolve in a significantly more constrained way than does its sequence. We also find evidence for substantial evolution of regulatory function involving point mutations as well as sequence insertions and deletions within binding sites. Genes lose their regulatory link to a given transcription factor at a rate similar to the neutral point mutation rate, from which we infer a moderate average fitness advantage of functional over nonfunctional sites. In a wider context, this study provides an example of inference of selection acting on a quantitative molecular trait.binding energy ͉ transcriptional regulation ͉ quantitative molecular trait R egulatory elements can often be distinguished from background sequence by their evolutionary conservation. At the same time, it has become clear that many regulatory functions are not widely conserved, but are specific to certain species or clades (1). Thus, it seems likely that the evolution of regulatory function and, in particular, of cis-regulatory elements is a key component in evolutionary innovation and the differentiation between species (2-7). However, functional changes in regulation are difficult to gauge from sequence divergence alone. For example, many different sequence states of a promoter may lead to similar binding of transcription factors and thus have similar effects on the transcription of a regulated gene. Thus, discerning function from sequence requires a phenotype for regulatory elements and an evolutionary model to quantify natural selection acting on this phenotype.In this article we address regulatory evolution from a biophysical perspective. We show that the binding energy of a transcription factor (TF) provides a quantitative phenotype for its target sites, and we develop a predictive model for binding site evolution based on this phenotype. A key ingredient of this model is the mapping from genotype to phenotype, that is, the sequence dependence of the binding energy for a given TF. Direct energy measurements are available for a few (mostly prokaryotic) transcription factors (8, 9), but low-throughput experiments generally do not provide enough data to fully constrain the energy function. By contrast, high-throughput binding assays (10, 11) provide copious, if indirect, data on TF binding to promoter regions, and we use recently developed methods to infer binding energies from ...

Evolutionary meandering of intermolecular interactions along the drift barrier

“…In the preceding analyses, it was assumed that selection favors maximum binding strength. Such a fitness function is justified by analyses of TFBSs in E. coli and S. cerevisiae that consistently infer a monotonic increase of fitness with increasing binding strength (15,30,31). Nonetheless, situations likely exist in which an intermediate phenotype is favored.…”

Hybrid Incompatibility Despite Pleiotropic Constraint in a Sequence-Based Bioenergetic Model of Transcription Factor Binding

“…We obtain a simple quantitative model of compensatory evolution by applying a bioenergetic, information-based model of transcriptional regulation (Von Hippel and Berg 1986;Gerland et al 2002;Mustonen et al 2008;Tulchinsky et al 2014). We give an overview here and refer the reader to Tulchinsky et al (2014) for details.…”

“…Our shorthand follows a distinction made by evolutionary geneticists when offering alternative explanations, pleiotropy vs. linkage disequilibrium, for correlations between evolving traits (e.g., Hartl and Clark 2007). We characterize the one-domain TF as being mechanistically pleiotropic, while the two-domain TF serves as a control for the potential effects of maximal linkage disequilibrium between otherwise mechanistically independent regulatory sites.We obtain a simple quantitative model of compensatory evolution by applying a bioenergetic, information-based model of transcriptional regulation (Von Hippel and Berg 1986;Gerland et al 2002;Mustonen et al 2008;Tulchinsky et al 2014). We give an overview here and refer the reader to Tulchinsky et al (2014) occupancy-the probability that a TF is associated with its cis-regulatory site at any given moment.…”

Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions

“…This model assumes the positions within the binding site are independent, and the contribution at one position of the binding site to the overall affinity does not depend on the identity of nucleotides in other positions of the site. Despite the restrictions imposed by this strong independence assumption, the PWM model has been successfully used to identify TF binding sites (TFBS) in sets of coexpressed genes (Stormo and Hartzell 1989;Roth et al 1998;Tavazoie et al 1999;Bussemaker et al 2001) as well as model TF binding site evolution (Doniger and Fay 2007;Mustonen et al 2008;Bradley et al 2010). Quantitative analysis of high-throughput binding data has also shown that PWMs are a good quantitative model for most TFs (Zhao et al 2009;Zhao and Stormo 2011).…”