Deciphering the regulation of gene expression in the etiology of LOAD

解读 LOAD 病因中基因表达的调控

基本信息

批准号：
10200620
负责人：
Ornit Chiba-Falek
金额：
$ 72.2万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-15 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10200620
关键词：
ATAC-seq Advanced Development Affect Age Aliquot Alleles Alzheimer&apos s disease patient Alzheimer&apos s disease risk Astrocytes Autopsy Bioinformatics Biological Models Biological Process CRISPR/Cas technology Catalogs Cell Nucleus Cells Chromatin Clinical Clinical Trials Complex Coupled DNA amplification Data Set Development Disease Disease susceptibility Early Diagnosis Elements Etiology Evaluation Event Gene Expression Gene Expression Regulation Genes Genetic Genetic Determinism Genetic Markers Genetic Transcription Genetic Variation Genome Genomic Segment Genotype Goals Haplotypes Human Human Genome In Vitro Induced pluripotent stem cell derived neurons Knowledge Late Onset Alzheimer Disease Matched Case-Control Study Medical Methods Microglia Modeling Molecular Molecular Mechanisms of Action Neurofibrillary Tangles Neuroglia Neurons Outcome Pathogenicity Pathologic Phase Play Preventive therapy Process Regulator Genes Regulatory Element Reporting Risk Role Short Tandem Repeat Site Sorting - Cell Movement Spliced Genes Stretching Structure Symptoms System Technology Testing United States National Institutes of Health Untranslated RNA Validation Variant XCL1 gene base bioinformatics tool brain tissue causal variant cell type differential expression effective therapy epigenome experimental study genetic variant genome database genome editing genome wide association study genomic locus high risk in silico in vivo induced pluripotent stem cell innovation insertion/deletion mutation insight laser capture microdissection molecular targeted therapies nano-string neuropathology novel therapeutics risk variant sex single molecule real time sequencing stem trait

项目摘要

ABSTRACT Large multi-center GWA studies have found associations between over 20 genomic loci and late-onset Alzheimer’s disease (LOAD). However, the precise target genes, the causal genetic variants and their molecular mechanisms of action through which they exert their pathogenic effects remain largely unknown. Our long-term goal is to elucidate causal genetic factors and their functional effects that contribute to the risk of developing LOAD. Our central hypothesis is that changes in expression levels of critical disease genes is an important molecular mechanism underlying LOAD etiology and that causal variants modulate expression of these disease genes, and by that contribute to LOAD risk. Changes in gene expression in LOAD vs. healthy controls were described in brain tissues by our team and others and previous studies reported the cis- associations of tagging SNPs with expression of nearby LOAD-risk genes, providing a strong scientific premise for the proposed study. In this study, we will employ a multifaceted approach that combines in silico, in vitro and in vivo methods to investigate regions in the genome that were significantly associated with LOAD-risk in GWA studies. In Aim 1 we will identify target genes within LOAD-associated regions that show differential expression along the neuropathological progression of LOAD. We will determine the expression profile of genes within these regions in neurons, astrocytes and microglia isolated from affected and unaffected rapidly autopsied human brain tissues using laser capture microdissection (LCM) coupled with nCounter single cell gene expression technology (NanoString). Aim 2 will discover regulatory noncoding sequences within LOAD- associated regions. First, we will prioritize candidate regulatory elements using bioinformatics tools and human genome databases, as well as ATAC-sequencing experiments using NeuN+/- nuclei from affected and unaffected human brain tissues to determine chromatin accessibility profiles in cell type- and pathological stage- specific manners. The functionality of the candidate noncoding sequences will be then characterized using iPSC-derived model systems that will be genome edited to carry deletions of the predicted regulatory sequences. Aim 3 will focus on Short Structural Variants (SSVs) and will investigate the functional effects and causality of SSVs in the candidate regulatory sequences. We will use SMRT sequencing combined with Cas9 system (PacBio) to accurately determine the SSVs genotype and haplotypes in LOAD compared to control subjects, and will examine their regulatory effects using genome edited isogenic iPSC-derived neurons and/or astrocytes models that carry different alleles/haplotypes at the SSV site. Our study will advance the identification of causal genetic factors and the understanding of their molecular effects that contribute to the risk of developing LOAD. This knowledge will provide insight regarding actionable targets for development of novel therapies for LOAD. Furthermore, the identified SSVs will advance the development of genetic biomarkers for early diagnosis and for enrichment of clinical trials with subjects at high risk.

抽象的大型多中心 GWA 研究发现 20 多个基因组位点与晚发型发病之间存在关联然而，阿尔茨海默病 (LOAD) 的精确靶基因、致病基因变异及其相关信息。它们发挥致病作用的分子作用机制仍然很大程度上未知。我们的长期目标是阐明导致以下风险的遗传因素及其功能影响：我们的中心假设是，关键疾病基因表达水平的变化是一个关键因素。负载病因学的重要分子机制以及因果变异调节表达这些疾病基因，从而导致 LOAD 与健康人的基因表达变化。我们的团队和其他人在脑组织中描述了对照，之前的研究报告了顺式- 标记 SNP 与附近 LOAD 风险基因表达的关联，提供了强有力的科学前提在本研究中，我们将采用计算机模拟和体外相结合的多方面方法。以及体内方法来研究基因组中与 LOAD 风险显着相关的区域在 GWA 研究中，我们将识别 LOAD 相关区域内显示差异的靶基因。我们将确定 LOAD 沿神经病理学进展的表达谱。快速从受影响和未受影响的神经元、星形胶质细胞和小胶质细胞中分离出这些区域内的基因使用激光捕获显微切割 (LCM) 结合 nCounter 单细胞对人体脑组织进行尸检基因表达技术（NanoString）将发现 LOAD- 内的调控非编码序列。首先，我们将使用生物信息学工具和人类对候选调控元件进行优先排序。基因组数据库，以及使用来自受影响和受影响的 NeuN+/- 细胞核的 ATAC 测序实验未受影响的人脑组织，以确定细胞类型和病理学中的染色质可及性概况然后将表征候选非编码序列的功能。使用 iPSC 衍生的模型系统，该系统将进行基因组编辑以删除预测的监管目标 3 将重点关注短结构变体 (SSV)，并研究功能效应和我们将使用 SMRT 测序结合 Cas9 来分析 SSV 的因果关系。系统 (PacBio) 可准确确定 LOAD 中与对照相比的 SSV 基因型和单倍型受试者，并将使用基因组编辑的同基因 iPSC 衍生的神经元检查其调节作用和/或在 SSV 位点携带不同等位基因/单倍型的星形胶质细胞模型。我们的研究将推进识别因果遗传因素并了解其分子效应开发 LOAD 的风险。这些知识将为开发 LOAD 的可行目标提供见解。 LOAD 的新疗法。此外，已确定的 SSV 将促进基因疗法的发展用于早期诊断和丰富高风险受试者临床试验的生物标志物。