Analysis of combinatorial cis-regulation in synthetic and genomic promoters

项目概要/摘要 大多数可遗传的致病变异存在于人类的非编码部分 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发挥作用。 调控序列（CRS）。因此，解释这种变化需要定量模型 控制 CRS 的细胞类型特异性活动的“调节语法”。我们定义监管 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 顺式调节活性位点（TFBS）。监管语法模型还必须包括依赖关系 这些对 TFBS 的数量、方向、间距和亲和力的贡献。详细型号 监管语法仍处于起步阶段，部分原因是我们缺乏关于 CRS 的系统培训数据 体内不同细胞类型的行为。我们建议通过系统地衡量这一差距来解决这一差距 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，我们将介绍 单细胞大规模平行报告基因检测 (scMPRA)，可测量细胞类型特异性 CRS在体内的活性。我们将使用正式的热力学模型对结果数据进行建模，其中 每个 TF-DNA 或 TF-TF 相互作用都由其相互作用的自由能 (ΔG) 表示。通过比较 产生的 ΔG 值的大小，我们将量化独立和相互作用的贡献 特定的 TFBS，从而导出捕获细胞之间差异的定量调控语法 哺乳动物视网膜（目标 1）和哺乳动物大脑（目标 2）内的类型。通过验证我们的模型 内源性 CRS 的序列变异，我们希望在准确识别内源性 CRS 的框架方面取得进展 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.