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

转录因子-DNA相互作用基序中的自然选择:比较基因组学和群体基因组学视角

阅读:1

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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。