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

基本信息

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
项目类别：
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&apos s disease patient Alzheimer&apos 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

项目摘要

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.

抽象的晚发性阿尔茨海默病 (LOAD) 全基因组关联研究 (GWAS) 发现了许多基因座。但将 GWAS 研究结果转化为疾病机制的需求仍然未得到满足。识别所涉及的特定基因、因果变异以及它们的分子机制发挥其致病作用。大多数 LOAD 相关的 SNP 位于指向基因的非编码区域调节作为重要的疾病机制。 LOAD 遗传学的另一个挑战是多样性，因为大多数研究是针对欧洲血统的受试者进行的，而其他人群的研究很大程度上还不够充分。我们的中心假设是 LOAD 特异性表观基因组特征以及非编码功能遗传变异导致在 LOAD 致病生物学途径中起关键作用的基因失调。虽然组学使用来自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 LOAD 基因座上的基因、来自不同人群的患者和对照大脑的单细胞组学数据将提供跨多个种族和民族的前所未有的脑细胞亚型精度的新知识组。我们将研究 LOAD 特异性基因表达、染色质可及性之间的关系通过单核多组学方法研究欧洲和非洲血统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）用于表征转录组中细胞亚型特异性变化和染色质可及性景观，分别在负载中与对照相比，是共享的和不同的跨越欧洲和非洲血统。目标 2 将整合这些数据集来识别开放/封闭染色质作为调节元件来影响 LOAD 状态下基因表达的位点，然后通过 CRISPR/Cas9 基因组编辑，使用同基因 hiPSC 衍生模型在相关细胞亚型中进行了验证。目标 3 将通过以下方式识别特定脑细胞亚型内的 LOAD 特定基因调控变异：综合单细胞基因组学。我们将通过细胞进行表达（e）QTL和染色质（c）QTL分析亚型特别关注先前发布的 GWAS 区域内的 QTL，以确定 GWAS 信号是否可以通过已确定的监管相互作用来解释。然后我们将编目确定为强且显着的 eQTL 和 cQTL 的 SNP，并优先考虑那些预测的 SNP 影响转录因子结合位点。最后，我们将验证基因组编辑中最优先的变异同基因 hiPSC 衍生模型。成功实现这些目标预计将产生重大影响，因为将增进对不同人群中 LOAD 的遗传复杂性的理解，并将破译调节 LOAD 风险的调控元件和相应基因。这些知识将会通过促进多基因风险评分的细化和新型治疗方法的开发来实现转化基于操纵失调基因的 LOAD 目标。