Untangling the diversity in the genetic architecture of late-onset Alzheimer's disease using single cell multi-omics

利用单细胞多组学揭示迟发性阿尔茨海默病遗传结构的多样性

• 批准号: 10452296
• 负责人: Ornit Chiba-Falek
• 金额: $ 233.39万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10452296
• 关键词: ATAC-seq; Affect; African ancestry; Alzheimer' s disease patient; Alzheimer' s disease risk; Architecture; Astrocytes; Autopsy; Binding Sites; Biological; Brain; CRISPR/Cas technology; Catalogs; Cell Nucleus; Cells; Chromatin; Chromatin Structure; Clinical; Data; Data Set; Development; Disease; Ethnic group; European; Event; Freezing; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Diseases; Genetic Transcription; Genomics; Genotype; Goals; Hi-C; Individual; Intervention; Knowledge; Late Onset Alzheimer Disease; Maps; Mediating; Mediation; Medical; Microglia; Modeling; Molecular; Neurofibrillary Tangles; Neurons; Outcome; Pathogenicity; Pathway Analysis; Pathway interactions; Patients; Population; Population Group; Population Heterogeneity; Positioning Attribute; Preventive therapy; Process; Publishing; Quantitative Trait Loci; Race; Regulator Genes; Regulatory Element; Risk; Sampling; Signal Transduction; Site; Subgroup; Symptoms; Testing; Tissues; Translating; Untranslated RNA; Variant; XCL1 gene; base; brain cell; brain tissue; causal variant; cell type; differential expression; epigenome; epigenomics; falls; functional genomics; gene function; genetic architecture; genetic association; genetic variant; genome editing; genome wide association study; genomic platform; molecular targeted therapies; multiple omics; neuropathology; new therapeutic target; polygenic risk score; pre-clinical; programs; risk variant; transcription factor; transcriptome; transcriptome sequencing; transcriptomics; whole genome

ABSTRACT Late Onset Alzheimer's Disease (LOAD) genome wide association studies (GWAS) discovered numerous loci. But there remains an unmet need to translate the GWAS findings to disease mechanisms through the identification of the specific genes involved, the causal variants, and the molecular mechanisms by which they exert their pathogenic effects. Most LOAD-associated SNPs are in noncoding regions pointing to gene regulation as an important disease mechanism. Another challenge in LOAD genetics is diversity, as most studies were conducted in subjects from European ancestry, while other populations are largely understudied. Our central hypothesis is that LOAD-specific epigenomic signatures, as well as noncoding functional genetic variants result in dysregulation of genes with key roles in LOAD pathogenic biological pathways. While omics studies using bulk brain tissue from European ancestry donors have produced informative data for a few genes at LOAD loci, single-cell omics data from brains of patients and controls from diverse populations will provide new knowledge in unprecedented brain cell-subtype precision across multiple racial and ethnical groups. We will investigate the relationships between LOAD-specific gene expression, chromatin accessibility and genetic variability in European and African ancestries by single-nuclei multi-omics approaches following three specific aims. Aim 1 will generate matched single-nuclei (sn)RNA-seq and ATAC-seq datasets using the 10X Genomics platform (Single Cell Multiome) to characterize cell-subtype specific changes in transcriptomic and chromatin accessibility landscape, respectively, in LOAD compared to control, that are shared and distinct across European and African ancestries. Aim 2 will integrate these datasets to identify open/closed chromatin sites that function as regulatory elements to impact gene expression in LOAD state, which will be then validated in the relevant cell-subtype using isogenic hiPSC-derived models by CRISPR/Cas9 genome editing. Aim 3 will identify LOAD specific gene regulatory variants within specific brain cell-subtypes through integrative single-cell genomics. We will perform expression(e)QTL and chromatin(c)QTL analyses by cell- subtype focusing specifically on the QTLs that fall within previously published GWAS regions to determine whether GWAS signals can be explained by the identified regulatory interactions. We will then catalogue the SNPs that identified as both strong and significant eQTL and cQTL and prioritize those that predicted to affect transcription factor binding sites. Last, we will validate the top prioritized variants in genome edited isogenic hiPSC-derived models. Successful accomplishment of these aims is expected to be high impact as it will advance the understanding of the genetic complexity underpinning LOAD in diverse populations and will decipher the regulatory elements and the corresponding genes mediating LOAD risk. This knowledge will be translational by promoting the refinement of Polygenic Risk Scores, and the development of novel therapeutic targets for LOAD based on manipulation of dysregulated genes.

摘要 晚发性阿尔茨海默病(LOAD)全基因组关联研究(GWAS)发现了大量的基因座。 但是，仍然有一个尚未得到满足的需求，即通过 鉴定涉及的特定基因、因果变异以及它们的分子机制 发挥其致病作用。大多数与负载相关的SNPs位于指向基因的非编码区 调控作为一种重要的发病机制。负载遗传学的另一个挑战是多样性，就像大多数 研究是在欧洲血统的受试者中进行的，而其他人群在很大程度上没有得到充分的研究。 我们的中心假设是特定于负载的表观基因组特征，以及非编码的功能性遗传 变异导致在LOAD致病生物学途径中起关键作用的基因的失调。而组学 使用欧洲血统捐赠者的大量脑组织进行的研究已经为一些人提供了信息数据 来自不同人群的患者和对照组的脑部单细胞组学数据 提供前所未有的跨种族和民族脑细胞亚型精确度的新知识 组。我们将研究负载特异性基因表达与染色质可及性之间的关系 用单核多组学方法研究欧洲和非洲祖先的遗传变异 三个具体目标。目标1将使用以下方法生成匹配的单核(SN)RNA-seq和atac-seq数据集 10x基因组学平台(单细胞多组体)在转录中表征细胞亚型的特异性变化 和染色质可及性景观，分别与对照相比，这是共享的和不同的 横跨欧洲和非洲的祖先。AIM 2将整合这些数据集以识别开放/闭合染色质 在负荷状态下作为调节元件影响基因表达的位点，这将是 通过CRISPR/Cas9基因组编辑，使用等基因的HiPSC衍生模型在相关细胞亚型中进行验证。 AIM 3将通过以下方式识别特定脑细胞亚型中的负载特定基因调控变体 整合的单细胞基因组学。我们将进行表达(E)QTL和染色质(C)QTL分析 将重点放在以前发表的GWAS区域内的QTL上，以确定 Gwas信号是否可以通过已识别的调控相互作用来解释。然后，我们将对 确定强而显著的eQTL和cQTL的SNPs，并优先处理那些预测 影响转录因子结合位点。最后，我们将验证基因组编辑中优先级最高的变体 等基因hPSC衍生模型。这些目标的成功实现预计将产生很大的影响，因为 将促进对支撑不同人群中的负荷的遗传复杂性的理解 破译调节元件和相应的调节负荷风险的基因。这一知识将是 通过促进多基因风险评分的精细化和开发新的治疗方法进行翻译 基于对失调基因的操纵而产生的负荷靶标。