Towards a better understanding of genetic architecture of Alzheimer's disease with human iPSC models

利用人类 iPSC 模型更好地了解阿尔茨海默病的遗传结构

• 批准号: 10231253
• 负责人: BINGSHAN LI
• 金额: $ 76.71万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10231253
• 关键词: ATAC-seq; Abeta synthesis; Address; Affect; Alzheimer like pathology; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease risk; Astrocytes; Biological Process; Brain; Cell Death; Cell physiology; Cells; Chromatin; Chromatin Conformation Capture and Sequencing; Clustered Regularly Interspaced Short Palindromic Repeats; Coculture Techniques; Complex; DNA; DNA Methylation; Data; Dementia; Dendrites; Development; Disease; Dissection; Distal; Epigenetic Process; Ethnic Origin; Etiology; Gene Expression; Genes; Genetic; Genetic Heterogeneity; Genetic Predisposition to Disease; Genetic Risk; Genetic Transcription; Genetic study; Genome; Genomics; Goals; Heritability; Heterogeneity; Human; Human Genome; Human body; Induced pluripotent stem cell derived neurons; Investigation; Lead; Link; Linkage Disequilibrium Mapping; Machine Learning; Maps; Microglia; Modeling; Morphology; Nature; Neurodegenerative Disorders; Neurons; Oxidative Stress; Pathogenesis; Pathway interactions; Play; Regulation; Reporting; Research; Role; Synapses; Testing; Therapeutic; United States; Untranslated RNA; Variant; base; biological systems; brain tissue; causal variant; cell type; cellular pathology; design; disease heterogeneity; disorder risk; epigenomics; genetic architecture; genetic variant; genome wide association study; histone modification; indexing; induced pluripotent stem cell; insight; machine learning method; molecular pathology; multiple omics; neuroinflammation; novel therapeutic intervention; risk variant; synaptic function; tau aggregation; tau phosphorylation; trait; transcriptome sequencing; transcriptomics; virtual

PROJECT SUMMARY Alzheimer's disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia with high heritability (~70%). It is increasingly clear that AD is highly polygenic, and for most of AD cases it is the polygenicity of the risk variants across the genome that predisposes the disease risk. In contrast to the rapid identification of risk loci associated with AD by recent genome-wide association studies (GWAS), identifying the potential causal variants/genes at the reported risk loci and decoding these variants/genes into molecular and cellular pathology have lagged far behind. Since disease variants, mostly locating in noncoding regions of the human genome, have been shown to affect cellular function through multi-level regulations such as DNA accessibility and histone modifications, DNA methylation and RNA expression in a cell type-specific manner, comprehensive and unbiased investigating the cell type-specific influence of generic risk variants on AD risk at multiple levels, including epigenomic, transcriptomic, and cellular levels, in an isogenic background is crucial to understand the genetic basis of AD pathogenesis. In the current application, by combining human induced pluripotent stem cells (hiPSCs) with gene editing and comprehensive multi-omics and cellular analyses, we will dissect the AD genetic risk variants into cell type-specific molecular and cellular pathology. Given the polygenic nature of AD, and the heterogeneity of AD risk genes on the cellular level, we hypothesize that multiple genetic risk variants act synergistically among different compartments (e.g. cell types) to contribute to pathogenesis of AD. First, we will identify AD risk variants and genes with comprehensive analyses of AD genetic architecture using machine learning approaches including DVAR, eVAR and iRIGS (Aim 1). Second, we will delineate the cell type-specific epigenetic and transcriptomic signatures associated with AD candidate risk variants using human iPSC-derived neurons/microglia/astrocytes (Aim 2). Last, we will determine the functional impact of AD candidate risk variants on AD-like cellular pathology in neurons, microglia, astrocytes, and their co-cultures (Aim 3). Our proposal may advance our understanding of the complex genetic architecture of AD, leading to a better understanding of AD pathogenesis and facilitating the development of novel therapeutic strategies.

项目总结 阿尔茨海默病(AD)是一种进行性神经退行性疾病，是痴呆的主要原因 遗传力高(~70%)。越来越清楚的是，AD是高度多基因的，对于大多数AD病例来说，它是 易患疾病风险的全基因组风险变异体的多基因。与快速的 通过最近的全基因组关联研究(GWAS)识别与AD相关的风险基因座，识别 并将这些变异/基因解码为分子和基因 细胞病理学已经远远落后了。由于疾病变体，大多位于非编码区 人类基因组，已被证明通过多水平的调控，如DNA，影响细胞功能 可及性和组蛋白修饰，DNA甲基化和特定细胞类型的RNA表达， 全面和公正地研究通用风险变量对AD风险的特定细胞类型的影响 多个水平，包括表观基因组、转录和细胞水平，在相同的基因背景下是至关重要的 了解AD发病的遗传学基础。在当前的应用中，通过结合人类诱导的 多能干细胞(HiPSCs)通过基因编辑和全面的多组学和细胞分析，我们将 将阿尔茨海默病的遗传风险变量分解为特定细胞类型的分子和细胞病理学。考虑到多基因 AD的本质，以及AD危险基因在细胞水平上的异质性，我们假设多基因 风险变异体在不同的隔室(例如细胞类型)之间协同作用，从而促进了 广告。首先，我们将通过对AD遗传结构的综合分析来识别AD风险变体和基因 使用机器学习方法，包括DVAR、EVAR和iRIGS(目标1)。第二，我们将划定 与AD候选风险变异相关的细胞类型特定的表观遗传和转录特征 人IPSC来源的神经元/小胶质细胞/星形胶质细胞(目标2)。最后，我们将确定AD的功能影响 神经元、小胶质细胞、星形胶质细胞及其混合培养中AD样细胞病理的候选风险变体(AIM 3)。我们的建议可能会增进我们对阿尔茨海默病复杂遗传结构的理解，导致更好的 了解阿尔茨海默病的发病机制，促进新的治疗策略的发展。