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，超过20个基因组区域和晚发之间的关联阿尔茨海默氏病（负载）。但是，精度靶基因，因果通用变体及其它们发挥致病作用的作用分子机制仍然很大未知。我们的长期目标是阐明因果通用工厂，以及导致风险的功能效应开发负载。我们的中心假设是，关键疾病基因表达水平的变化是载荷病因的重要分子机制和因果变异调节的表达这些疾病基因，从而有助于负载风险。载荷中基因表达的变化与健康我们的团队和其他人在脑组织中描述了对照，并以前的研究报告了顺便标记SNP具有附近负载风险基因表达的关联，提供了强大的科学前提对于拟议的研究。在这项研究中，我们将采用一种多方面的方法，在体外组合以及研究基因组中与载荷风险显着相关的基因组中区域的体内方法 GWA研究。在AIM 1中，我们将在显示差异的负载相关区域内确定目标基因沿载荷的神经病理学进展的表达。我们将确定这些区域内神经元，星形胶质细胞和从受影响和未受影响的小胶质细胞中的基因使用激光捕获微分解（LCM）与NCounter单细胞相结合的人体脑组织基因表达技术（纳米弦）。 AIM 2将发现负载中的调节非编码序列相关区域。首先，我们将使用生物信息学工具和人类优先考虑候选监管元素基因组数据库以及使用neun +/-核的ATAC序列实验未受影响的人脑组织以确定细胞类型和病理中的染色质可及性特征舞台 - 特定的举止。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，该系统将被编辑以携带预测调节的缺失序列。 AIM 3将侧重于简短的结构变体（SSV），并将研究功能效应和候选调节序列中SSV的因果关系。我们将使用SMRT测序与CAS9结合与对照相比受试者，并将使用编辑的ISEGENIC IPSC衍生的神经元和/或在SSV站点携带不同等位基因/单倍型的星形胶质细胞模型。我们的研究将推进鉴定因果遗传因素以及对有助于分子作用的理解增加负载的风险。这些知识将为开发的常规可行目标提供洞察力负载的新型疗法。此外，已确定的SSV将推动遗传的发展早期诊断的生物标志物和具有高风险受试者的临床试验的富集。