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: 10621928
• 负责人: BINGSHAN LI
• 金额: $ 75.16万
• 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/10621928
• 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; 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; Pluripotent Stem Cells; Regulation; Reporting; Research; Role; Synapses; Testing; Therapeutic; United States; Untranslated RNA; Variant; biological systems; brain tissue; causal variant; cell type; cellular pathology; design; disorder risk; epigenomics; genetic architecture; genome wide association study; histone modification; indexing; induced pluripotent stem cell; insight; machine learning method; molecular pathology; multi-ethnic; multiple omics; neuroinflammation; novel therapeutic intervention; risk variant; synaptic function; tau aggregation; tau-1; 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.

项目摘要 阿尔茨海默氏病（AD）是一种进行性神经退行性疾病，是痴呆症的主要原因 高遗传力（〜70％）。越来越明显的是，AD是高度多基因的，在大多数AD情况下 整个基因组风险变异的多基因，使疾病风险容易患。与快速相反 通过最近全基因组关联研究（GWAS）确定与AD相关的风险基因座的鉴定，确定 在报告的风险基因座的潜在因果变异/基因将这些变体/基因解码为分子和 细胞病理远远落后。由于疾病变异，主要位于 人类基因组已被证明通过多级法规（例如DNA）影响细胞功能 可访问性和组蛋白修饰，以细胞类型特异性方式的DNA甲基化和RNA表达， 全面且公正地调查了通用风险变体对AD风险的特定细胞类型特定影响 多个水平，包括表观基因组，转录组和细胞水平，在等源背景中对 了解AD发病机理的遗传基础。在当前的应用中，通过将人类诱导的 具有基因编辑和全面的多词和细胞分析的多能干细胞（HIPSC），我们将 将AD遗传风险变体剖分为细胞类型特异性分子和细胞病理学。给定多基因 AD的性质以及在细胞水平上AD风险基因的异质性，我们假设多个遗传 风险变体在不同的隔室（例如细胞类型）之间协同作用，以促进 广告。首先，我们将通过对AD遗传结构进行全面分析来确定AD风险变体和基因 使用包括DVAR，EVAR和IRIGS在内的机器学习方法（AIM 1）。第二，我们将描绘 使用与AD候选风险变体相关的细胞类型特异性表观遗传和转录组特征 人IPSC衍生的神经元/小胶质细胞/星形胶质细胞（AIM 2）。最后，我们将确定AD的功能影响 神经元，小胶质细胞，星形胶质细胞及其共培养的AD样细胞病理的候选风险变异（AIM 3）。我们的建议可能会促进我们对AD复杂遗传结构的理解，从而使 了解AD发病机理并促进新型治疗策略的发展。