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

阐明阿尔茨海默病神经精神症状的分子机制

基本信息

批准号：
10451516
负责人：
Manolis Kellis
金额：
$ 129.44万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-30 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10451516
关键词：
3-Dimensional Affect Alleles Alzheimer&apos s Disease Alzheimer&apos s disease patient Astrocytes Autopsy Bayesian Method Biological Biological Process Brain Brain region CRISPR/Cas technology Caregiver Burden Cells ChIP-seq Clinical Management Clustered Regularly Interspaced Short Palindromic Repeats Coculture Techniques Cognition Communities Computer Analysis Data Data Set Dementia Development Disease Dissection Distal EP300 gene Engineering Enhancers Etiology Gene Expression Gene Expression Profile Generations Genes Genetic Genetic Transcription Genetic Variation Hi-C Hippocampus (Brain)Impaired cognition Individual Knock-out Link Maps Measures Mediation Memory Methodology Microglia Molecular Neurodegenerative Disorders Neurons Nucleic Acid Regulatory Sequences Oligodendroglia Organoids Pathologic Pathway interactions Patients Personality Phenotype Prefrontal Cortex Process Psychoses RNA Regulatory Element Repression Risk Sampling Schizophrenia Severity of illness Social Behavior Sorting - Cell Movement Techniques Testing Therapeutic Intervention Variant associated symptom base burden of illness cell type data resource differential expression disability disorder control epigenomics genetic information genome wide association study genomic locus healthy aging human old age (65+)induced pluripotent stem cell insight machine learning method molecular scale mortality neuropsychiatric symptom novel novel therapeutics programs promoter psychotic symptoms risk variant single-cell RNA sequencing trait transcriptome transcriptomics

项目摘要

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.

抽象的阿尔茨海默病 (AD) 是痴呆症最常见的原因，也是最常见的神经退行性疾病该疾病在全球范围内流行，在美国，每 8 名 65 岁以上的老年人中就有 1 人受到影响。 AD 内，大约 40-60% 个体受到精神病症状（AD+P）的影响，这与更快的认知能力有关下降、更大的残疾、死亡率和护理人员负担，导致不成比例的大疾病负担。最近的研究表明 AD+P 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未表征，阻碍了对适当治疗方法和新疗法的寻找。在这个提案中，我们寻求通过系统生成、整合和分析来系统地剖析 AD+P 的机制基础对两个大脑区域和四个细胞的转录和表观基因组表型的实验剖析类型。 (1) 我们对 192 个死后样本的单细胞 RNA-seq 和细胞类型特异性 H3K27ac ChIP-seq 进行了分析大脑样本，分别位于患有精神病的 AD 患者和不患有精神病的 AD 患者的两个区域，没有 AD 的精神分裂症患者和对照个体。 (2) 我们将所得数据集与遗传数据相整合信息和 GWAS 数据来预测 AD+P 的驱动基因、区域、变异和通路用于因果关系、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) 我们使用计算预测来指导对分子的系统剖析 AD+P 的基础是在 iPSC 系中使用模块化和可编程的 CRISPR-Cas9 方法调节调控元件、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。如果成功的话，这个雄心勃勃的提议有潜力提供关于 AD+P 和/或 P-AD 精神病症状发展的第一个机制见解，揭示可能影响临床的功能性风险变异和治疗干预的目标基因管理，以减轻与这些疾病相关的个人和社会负担。