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：
10177837
负责人：
Manolis Kellis
金额：
$ 129.5万
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/10177837
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.

抽象的阿尔茨海默氏病（AD）是痴呆症的最常见原因，也是最常见的神经退行性原因全球疾病，在美国影响65岁以上的8个人中有1个人。在广告中，大约40-60％个人受精神病症状（AD+P）的影响，这些症状与更快的认知有关下降，残疾，死亡率和照顾者负担，导致不成比例的大疾病负担。最近的研究表明AD+P风险的遗传基础，但是AD+P的分子基础在很大程度上仍然是未表征，阻碍寻找适当的治疗和新型治疗剂。在这个建议中，我们寻求通过系统的产生，集成和跨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解剖类型。（1）我们介绍了192年验尸的单细胞RNA-seq和细胞类型的特异性H3K27AC芯片seq 大脑样本，各个在AD精神病患者中的两个区域，无精神病患者，没有AD的精神分裂症患者和对照个体。（2）我们将结果数据集整合到遗传信息和GWAS数据可预测AD+P的驱动基因，区域，变体和途径最先进的机器学习方法，用于因果关系，中介分析和遗传贝叶斯精细 - 映射。（3）我们使用计算预测来指导分子的系统解剖 AD+P的基础使用IPSC行中的模块化和可编程CRISPR-CAS9方法调节调节元件，基因和等位基因，并测量所得的分子和细胞表型在细胞自主和非自治表型中。如果成功，这个雄心勃勃的提议有潜力为了提供有关AD+P和/或P-AD中精神病症状发展的第一个机械见解，揭示了可能影响临床的治疗干预措施的功能风险变异和靶基因为了减轻与这些疾病相关的个人和社会负担。