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

基本信息

批准号：
10402828
负责人：
BINGSHAN LI
金额：
$ 75.86万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
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/10402828
关键词：
ATAC-seq Abeta synthesis Address Affect Alzheimer like pathology Alzheimer&apos s Disease Alzheimer&apos s disease pathology Alzheimer&apos 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

项目摘要

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.

项目概要阿尔茨海默病（AD）是一种进行性神经退行性疾病，也是导致痴呆的主要原因高遗传力（~70%）。越来越明显的是，AD 具有高度多基因性，对于大多数 AD 病例来说，这是基因组中易患疾病风险的风险变异的多基因性。与快速相比通过最近的全基因组关联研究 (GWAS) 识别与 AD 相关的风险位点，确定报告的风险位点处的潜在因果变异/基因，并将这些变异/基因解码为分子和细胞病理学已经远远落后。由于疾病变异，大多位于非编码区域人类基因组已被证明可以通过 DNA 等多级调控影响细胞功能可及性和组蛋白修饰、DNA 甲基化和 RNA 以细胞类型特异性方式表达，全面、公正地调查一般风险变异对 AD 风险的细胞类型特异性影响同基因背景下的多个水平，包括表观基因组、转录组和细胞水平，对于了解 AD 发病机制的遗传基础。在目前的应用中，通过结合人体感应通过基因编辑和全面的多组学和细胞分析的多能干细胞（hiPSC），我们将将 AD 遗传风险变异剖析为细胞类型特异性分子和细胞病理学。鉴于多基因 AD 的性质以及 AD 风险基因在细胞水平上的异质性，我们假设多种遗传因素风险变异在不同区室（例如细胞类型）之间协同作用，促进疾病的发病机制广告。首先，我们将通过全面分析 AD 遗传结构来识别 AD 风险变异和基因使用机器学习方法，包括 DVAR、eVAR 和 iRIGS（目标 1）。其次，我们将划定使用与 AD 候选风险变异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（目标 2）。最后，我们将确定 AD 的功能影响神经元、小胶质细胞、星形胶质细胞及其共培养物中 AD 样细胞病理学的候选风险变异（目标 3）。我们的建议可能会增进我们对 AD 复杂遗传结构的理解，从而更好地了解 AD 的遗传结构。了解 AD 发病机制并促进新治疗策略的开发。