Interpreting the regulatory mechanisms underlying the predisposition to substance use disorders

摘要/摘要 物质使用障碍对人类的健康和福祉有深远的影响。的发展。 这些疾病的新治疗方法仍然很困难，因为神经回路和 潜在的遗传机制。最近的全基因组关联研究已经开始确定一些基因座 与成瘾倾向相关的基因座越多，自信心越低。这个 大多数与复杂大脑疾病有关的基因变异，包括物质使用，都可能 位于非编码调控区域，特别是增强子，而不是蛋白质编码基因。 尽管大脑中的增强子区域很重要，但研究的计算和实验工具 它们的功能仍处于初级阶段。 为了破译物质使用障碍背后的这一关键生物学机制，我们 寻求建立一个框架来研究活体中人类和小鼠大脑增强区的功能。第一, 我们将分析公开的遗传和表观遗传学数据集，以识别人类遗传变异，网址为 可能影响物质使用倾向的增强剂区域。接下来，我们将衡量 使用高通量报告分析的遗传变异，它有能力同时测试 在不同条件下，数千种增强剂在小鼠体内大脑中的活性。最后，我们会 使用CRISPR干扰小鼠中保守的同源增强子来验证这些预测。在一个 最初的测试案例，我们将应用我们的方法来解释吸烟行为的全基因组关联研究 使用特定细胞类型的人类表观基因组学。从计算分析中得出的关键候选者 将在尼古丁暴露的小鼠模型中进行筛选和验证。 我们研究工作的结果将是一张将高信心和低信心物质联系起来的地图 使用与神经增强器功能相关的遗传变异。此外，灵活的计算和 实验框架有可能研究候选增强剂和基因的任何组合 不同脑区、不同细胞类型和不同动物模型的变种。

Summary/Abstract Substance use disorders have a profound impact on human health and wellbeing. The development of new treatments for these disorders has remained difficult due to the complexity of the neural circuits and the underlying genetic mechanisms. Recent genome-wide association studies have begun to identify a few of loci associated with the predisposition to addiction, with many more loci are implicated with lower confidence. The majority of the genetic variants associated with complex brain disorders, including substance use, are likely to be located in non-coding regulatory regions, particularly enhancers, and not within protein-coding genes. Despite the importance of enhancer regions in the brain, the computational and experimental tools to study their function are still in their infancy. To work towards deciphering this critical biological mechanism underlying substance use disorders, we seek to build a framework to study the function of both human and mouse brain enhancer regions in vivo. First, we will analyze publically available genetic and epigenetic data sets to identify human genetic variation at enhancers regions that is likely to influence substance use predisposition. Next, we will measure the impact of that genetic variation using a high-throughput reporter assay, which has the ability to simultaneously test the activity of thousands of enhancers across the mouse brain in vivo under different conditions. Finally, we will validate those predictions using CRISPR interference on conserved orthologous enhancers in the mouse. In an initial test case, we will apply our methods to interpret a genome-wide association study of smoking behavior using cell type-specific human epigenomics. The key candidates that result from the computational analysis will be screened and validated in a mouse model of nicotine exposure. The result of our research effort will be a map that links high-confidence and low-confidence substance use-associated genetic variation to neural enhancer function. Furthermore, the flexible computational and experimental framework has the potential to study any combination of candidate enhancers and genetic variants in the context of different brain regions, different cell types, and different animal models.