Dissecting natural variation in transcription factor - DNA interactions

项目总结/摘要 在基因组中特定位置的转录因子结合的获得或丧失已经与广泛的基因突变相关联。 一系列人类疾病。尽管我们对转录因子-DNA的决定簇有一定的了解， 尽管存在相互作用，但准确预测由于遗传和基因突变引起的转录因子结合的变化仍然具有挑战性， 基因组中的表观遗传变异在我们对将《公约》纳入《联合国宪章》的理解方面， 内源基因组DNA上的序列和非序列信息，这些信息引起全基因组 转录因子的结合模式。我们的长期愿景是阐明基因组和表观基因组 变异，导致转录因子的全基因组靶点的变异，影响转录因子的调控。 细胞网络以及产生表型多样性的基因表达程序。 我们先前的研究表征了500多种转录因子的全基因组结合位置 在拟南芥的参考基因组上。我们的综合计算分析揭示了这些特征 内源基因组背景，包括序列基序，DNA形状和5-甲基胞嘧啶修饰 基因组DNA，在确定主要转录因子的结合景观中发挥作用， 结构家庭。为了进一步研究这些结合位点的变异性，由天然基因组和 表观基因组变异，我们产生了全基因组，5-甲基胞嘧啶，表观基因组的基础分辨率地图 在全世界1,000多个A. Thaliana， 补充了对这些种质中的基因组序列变异进行编目的努力。以多样性为导向 在基因组和表观基因组中，大量的表型数据和初步结果表明转录 在这些加入的因素约束变化，我们的目标是在未来五年内解决三个主要问题 理解转录因子结合的自然变异的挑战：1）确定全基因组 跨多个登录基因组的转录因子结合变异; 2）表征 转录因子编码变体对它们的全基因组结合特异性和靶基因的影响;和3） 研究蛋白质-蛋白质相互作用的自然变异如何改变靶基因和全基因组结合 相互作用的转录因子的特异性。这三个项目都将使用计算建模来评估 来自结合位点环境中的特征的贡献。 我们提出的实验和计算模型将通过表征 转录因子结合变异和理解序列和非序列所起的作用 内源性基因组DNA的特征。我们的研究结果将揭示基本原则， 基因组和表观基因组变异的调控功能，使发现和预测 调节变体及其分子机制。

PROJECT SUMMARY/ABSTRACT Gains or losses of transcription factor binding at specific locations in the genome have been linked to a wide range of human diseases. Despite our knowledge about the determinants of transcription factor-DNA interaction, it is still challenging to accurately predict changes in transcription factor binding due to genetic and epigenetic variants in the genome. Several critical gaps remain in our understanding of the integration of sequence and non-sequence information on endogenous genomic DNA that give rise to the genome-wide binding patterns of transcription factors. Our long-term vision is to shed light on how genome and epigenome variation, which leads to variation in the genome-wide targets of transcription factors, affects the regulatory networks of the cell, and the gene expression programs that give rise to phenotypic diversity. Our previous study characterized the genome-wide binding locations of more than 500 transcription factors in Arabidopsis thaliana on the reference genome. Our integrative computational analysis revealed the features of endogenous genome context, consisting of sequence motif, DNA shape, and 5-methylcytosine modification of genomic DNA, that play a role in determining the binding landscape of transcription factors of major structural families. To further study the variability of these binding sites, driven by native genome and epigenome variation, we generated genome-wide, base-resolution maps of 5-methylcytosine, an epigenomic mark on DNA, in a collection of over 1,000 world-wide, natural strains (accessions) of A. thaliana, complementing the efforts to catalog genome sequence variation in these accessions. Guided by the diversity in the genome and epigenome, a wealth of phenotypic data, and preliminary results suggesting transcription factor binding variation in these accessions, our goals for the next five years are to address three major challenges in understanding natural variation in transcription factor binding: 1) to determine the genome-wide transcription factor binding variation across multiple accession genomes; 2) to characterize the effect of transcription factor coding variants on their genome-wide binding specificities and target genes; and 3) to investigate how natural variation of protein-protein interactions alters target genes and genome-wide binding specificities for interacting transcription factors. All three projects will use computational modeling to evaluate the contributions from features in the binding site environment. Our proposed experiments and computational models will make a broad impact by characterizing transcription factor binding variation and understanding the role played by sequence and non-sequence features of endogenous genomic DNA. Our results will shed light on the fundamental principles underlying the regulatory functions of genome and epigenome variation, empowering the discovery and prediction of regulatory variants and their molecular mechanisms.