Analysis of combinatorial cis-regulation in synthetic and genomic promoters

项目总结/摘要 大多数可遗传的致病变异存在于人类基因组的非编码部分， 基因组一个主要的假设是，大多数这种变异对细胞类型特异性顺式- 调节序列（CRS）。因此，解释这种变化将需要定量模型， “调节语法”控制CRS的细胞类型特异性活动。我们将监管定义为 一种细胞类型的语法是转录因子结合的独立和相互作用的贡献 位点（TFBS）的顺式调节活性。规范语法的模型还必须包括依赖关系 这些贡献的数量，方向，间距，和亲和力的TFBS。详细模型 监管语法仍处于起步阶段，部分原因是我们缺乏系统的培训数据， 在体内的不同细胞类型中表现。我们建议通过系统地衡量 完整哺乳动物组织内跨细胞类型的CRS活性。为了收集这些数据，我们将 单细胞大规模平行报告基因测定（scMPRA），测量细胞类型特异性 体内CRS活性。我们将使用正式的热力学模型对所得数据进行建模， 每个TF-DNA或TF-TF相互作用由其相互作用的自由能（ΔG）表示。通过比较 结果ΔG值的大小，我们将量化的独立和相互作用的贡献， 特异性TFBS，从而推导出捕获细胞之间差异的定量调节语法 哺乳动物视网膜（Aim 1）和哺乳动物大脑（Aim 2）内的类型。通过验证我们的模型 内源性CRS的序列变体，我们希望能够朝着一个准确的框架取得进展 预测非编码遗传变异对CRS功能的影响。

Project Summary/Abstract A majority of heritable disease-causing variation resides in the non-coding portions of the human genome. A leading hypothesis is that most of this variation exerts its effects on cell-type-specific cis- regulatory sequences (CRSs). Interpreting such variation will therefore require quantitative models of the ‘regulatory grammar’ that controls the cell-type-specific activities of CRSs. We define the regulatory grammar of a cell type to be the independent and interacting contributions of transcription factor binding sites (TFBSs) to cis-regulatory activity. Models of regulatory grammar must also include the dependencies of those contributions on the number, orientation, spacing, and affinity of TFBSs. Detailed models of regulatory grammars are still in their infancy, partly because we lack systematic training data for how CRSs behave across diverse cell types in vivo. We propose to address this gap by systematically measuring the activities of CRSs across cell types within intact mammalian tissues. To collect this data, we will introduce a single-cell massively parallel reporter gene assay (scMPRA) that measures the cell-type-specific activities of CRSs in vivo. We will model the resulting data using a formal thermodynamic model in which each TF-DNA or TF-TF interaction is represented by its free energy (ΔG) of interaction. By comparing the magnitudes of the resulting ΔG values, we will quantify the independent and interacting contributions of specific TFBSs, thus deriving quantitative regulatory grammars that capture the differences between cell types within the mammalian retina (Aim 1) and the mammalian brain (Aim 2). By validating our models on sequence variants of endogenous CRSs, we hope to make progress towards a framework for accurately predicting the effects of non-coding genetic variation on the function of CRSs.