Analysis of combinatorial cis-regulation in synthetic and genomic promoters

项目摘要/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。一种主要的假说是，这种变异的大部分作用于特定细胞类型的顺式调节因子。 调控序列(CRSS)。因此，解释这种变化将需要对 控制CRSS特定细胞类型活动的“调控语法”。我们定义了监管 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 站点(TFBs)到顺式调控活动。规则语法的模型还必须包括依存关系 在TFBs的数量、取向、间距和亲和力方面的贡献。详细的模型 监管语法仍处于初级阶段，部分原因是我们缺乏系统的培训数据来说明CRSS如何 在体内跨越不同类型的细胞的行为。我们建议通过系统地衡量 在完整的哺乳动物组织中，CRSS在不同细胞类型中的活性。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分析(ScMPRA)测量细胞类型特异性 CRSS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 每一种Tf-DNA或Tf-Tf相互作用均由其相互作用自由能(ΔG)表示。通过比较 产生的ΔG值的大小，我们将量化 特定的TFBs，从而推导出捕捉细胞之间差异的定量调控语法 哺乳动物视网膜的类型(目标1)和哺乳动物大脑的类型(目标2)。通过验证我们的模型 内源性CRSS的序列变异，我们希望朝着准确的框架取得进展 预测非编码遗传变异对CRSS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.