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Natural Selection in Transcription Factor–DNA Interaction Motifs: A Comparative and Population Genomics Perspective

Manas Joshi,Pablo Duchen,A. Kapopoulou,Stefan Laurent

Published 2025 in Genome Biology and Evolution

ABSTRACT

Abstract Natural selection heavily influences the evolutionary trajectories of species by impacting their genotype-to-phenotype transitions. On the molecular level, these transitions are shaped by the regulatory sequences. In this study, we employed a combination of population and comparative genomics to investigate how natural selection affects specific regulatory sequence classes involved in the regulatory transcription factor–DNA interactions. These interactions consist of two motifs, namely: transcription factor-binding domains and transcription factor-binding sites. Using publicly available annotation data for Homo sapiens, Arabidopsis thaliana, and Drosophila melanogaster, we first constructed the species-specific lists of the transcription factor-binding domain regions. On applying some of the commonly used summary statistics, we found signals of purifying selection acting on transcription factor-binding domains, consistent with their functional importance. Next, using the biochemical assay-based annotations, we identified potential transcription factor-binding site regions and used variants within them as nonsynonymous equivalents. Interestingly, we also observed that noncoding transcription factor-binding site regions showed similar levels of constraint to that of coding regions for populations with large Ne. Signals of positive selection were limited. Nevertheless, McDonald–Kreitman estimates revealed that, in both fruit-fly and thale-cress, α for transcription factor-binding domains was consistently higher than for adjacent nonbinding domains, whereas no such difference was apparent in humans. Taken together, our comparative analysis shows that the efficiency of negative—and to a lesser extent positive—selection on transcription factor–DNA interface elements scales with effective population size. The dataset and analysis pipeline provide a baseline for future studies of regulatory evolution across coding and noncoding regions.

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