Adaptive landscapes of transcription factors and their in vivo binding sites in 1135 Arabidopsis thaliana genomes

基因表达是决定生物体表型的关键因素，因此基因表达调控的变化是适应和进化的主要力量。基因的表达受几个分子过程的控制，包括转录因子（TF）与DNA上特定结合位点（TFBSs）的结合。基因表达变异的一个来源是顺式调控序列的点突变，导致TF结合亲和力的改变。为了了解点突变如何影响TF的结合亲和力，开发了由所有可能的长度为10个碱基对的双链DNA片段组成的蛋白质结合微阵列（PBM）。这样的阵列允许将tf的结合亲和力分配给所有连续的和间隙的8-mers。这种组合完整数据集的一个优点是，它们揭示了所有遗传背景中每个个体突变的影响。因此，利用结合亲和力作为适应性的代表，PBM实验的结果是将表型映射到基因型和构建适应性景观的理想选择。适应性景观非常适合研究生物学中的一个核心问题：种群遵循（不遵循）哪些突变路径以及原因。本研究将首次阐明拟南芥实际种群基因组数据中转录因子结合位点的进化。这样的研究现在是可能的，因为拟议的工作将采用最近发表的数据集。其中包括从全球拟南芥中获得的1135个基因组序列，在PBM实验中确定的313个TF的结合亲和力，在体内确定的529个TF结合位点和1203种植物的转录组。具体来说，该项目主要集中在两个方面：在TFBSs的进化过程中，选择、中立和上位性的作用是什么？进化的可重复性和约束性如何？为了解决这些问题，基因型网络将被采用。这些网络的节点代表基因组中一个TFBS的序列。如果两个节点的基础序列恰好相差一个核苷酸，则它们是相连的。通过考虑每个结合位点的结合亲和力，可以重建适应性景观。这种方法可以利用丰富的工具集进行网络分析，并成功地用于调查经验数据。总之，该项目将促进我们对自然种群中TFBSs进化的理解。

Gene expression is a crucial factor for determining organismal phenotypes and changes in the regulation of gene expression are consequently major forces in adaptation and evolution. The expression of a gene is controlled by several molecular processes, including the binding of a transcription factor (TF) to specific binding sites (TFBSs) on DNA. One source of variation in gene expression are point mutations in cis-regulatory sequences that lead to altered TF binding affinities. To understand how point mutations affect the binding affinity of a TF, protein binding microarrays (PBM) consisting of all possible double stranded DNA fragments with a length of ten base pairs were developed. Such arrays allow to assign binding affinities of TFs to all contiguous and gapped 8-mers. One beauty of such combinatorically complete data sets is that they uncover the effect of each individual mutation in all genetic backgrounds. Using binding affinities as a proxy for adaptation, the results of PBM experiments are therefore ideal for mapping phenotypes to genotypes and for building adaptive landscapes. Adaptive landscapes are well suited to investigate a central question in biology: which mutational paths are (not) followed by a population and why. The proposed study will for the first time elucidate the evolution of transcription factor binding sites in actual population genomics data of Arabidopsis thaliana. Such studies are now possible, because the proposed work will employ recently published data sets. These include 1,135 genome sequences obtained from a worldwide collection of A. thaliana, binding affinities of 313 TFs determined in PBM experiments, a collection of 529 in vivo determined TF binding sites and transcriptomes of 1,203 plants. Specifically, the project is centered on two main aspects: what are the roles of selection, neutrality and epistasis during the evolution of TFBSs? How repeatable and constraint is the evolution? To address these questions, genotype networks will be employed. These are networks where nodes represent the sequence of one TFBS in the genome. Two nodes are connected if their underlying sequences differ by exactly one nucleotide. By considering binding affinities for each binding site, adaptive landscapes can be reconstructed. This approach allows to take advantage of the rich tool set available for network analysis and was successfully used for investigating empirical data. In summary, the project will advance our understanding of the evolution of TFBSs in natural populations.