Elucidating the Molecular Mechanisms of Neuropsychiatric Symptoms in Alzheimer's Disease

摘要 阿尔茨海默病(AD)是痴呆症最常见的原因，也是最常见的神经退行性变 世界范围内的疾病，影响到美国每8个65岁以上的人中就有一个。在AD内部，大约40%-60% 个人受到精神病症状(AD+P)的影响，这些症状与更快的认知能力有关 下降、更大的残疾、死亡率和照顾者负担，导致不成比例的大病 负担。最近的研究表明AD+P的风险存在遗传基础，但AD+P的分子基础在很大程度上仍然存在 没有特征，阻碍了寻找适当的治疗方法和新的治疗方法。在这项提案中，我们 试图通过系统的生成、整合和分析来系统地剖析AD+P的机制基础 两个脑区和四个细胞转录和表观基因组表型的实验解剖 类型。(1)我们对192具身体的单细胞RNA-seq和细胞类型特异的H3K27ac芯片-seq进行了分析 大脑样本，分别位于患有精神病的AD患者，非精神病的AD患者， 精神分裂症患者无阿尔茨海默病，并与对照组比较。(2)将所得到的数据集与遗传算法相结合 用来预测AD+P潜在的驱动基因、区域、变异和通路的信息和GWAS数据 用于因果关系、中介分析和遗传贝叶斯精细分析的最先进的机器学习方法 映射。(3)我们使用我们的计算预测来指导对分子的系统剖析 在IPSC系列中使用模块化和可编程的CRISPR-CAS9方法支持AD+P 调节调节元件、基因和等位基因，并测量由此产生的分子和细胞表型 在细胞自主和非自主表型中。如果成功，这项雄心勃勃的计划有可能 为了提供关于AD+P和/或P-AD中精神病症状发展的第一个机械论见解， 揭示可能影响临床的治疗干预的功能风险变异和靶基因 管理，以减轻与这些疾病相关的个人和社会负担。

Abstract Alzheimer's disease (AD) is the most common cause of dementia, and the most common neurodegenerative disease worldwide, affecting 1 in 8 individuals over 65 years old in the US. Within AD, approximately 40-60% individuals are affected by psychotic symptoms (AD+P), which are associated with more rapid cognitive decline, greater disability, mortality and caregiver burden, resulting in a disproportionately large disease burden. Recent studies indicate a genetic basis for AD+P risk, but the molecular basis of AD+P remains largely uncharacterized, hindering the search for appropriate treatments and novel therapeutics. In this proposal, we seek to systematically dissect the mechanistic basis of AD+P by systematic generation, integration, and experimental dissection of transcriptional and epigenomic phenotypes across two brain regions and four cell types. (1) We profile single-cell RNA-seq and cell-type specific H3K27ac ChIP-seq across 192 post-mortem brain samples, each in two regions across AD patients with psychosis, AD patients with no psychosis, schizophrenia patients with no AD, and control individuals. (2) We integrate the resulting datasets with genetic information and GWAS data to predict driver genes, regions, variants, and pathways underlying AD+P using state-of-the-art machine learning methods for causality, mediation analysis, and genetic Bayesian fine- mapping. (3) We use our computational predictions to guide a systematic dissection of the molecular underpinnings of AD+P using a modular and programmable CRISPR-Cas9 methodology in iPSC lines to modulate regulatory elements, genes and alleles, and measure the resulting molecular and cellular phenotypes in cell-autonomous and non-autonomous phenotypes. If successful, this ambitious proposal has the potential to provide the first mechanistic insights on the development of psychotic symptoms in AD+P and/or P-AD, reveal functional risk variants and target genes for therapeutic intervention that will likely influence clinical management in order to alleviate the personal and societal burden associated with these disorders.