Biomarker Expression and Regulatory Haplotypes in Alzheimer's Disease

描述(申请人提供)：阿尔茨海默病(AD)死后脑(PMB)的特征表现是神经元变性以及大量的淀粉样沉积(A？42)(APP基因编码的淀粉样前体蛋白的切割产物)和tau(由MAPT基因编码)。脑脊液A42和tau水平可预测AD，但在鉴别AD与其他神经退行性疾病方面的可靠性有限。载脂蛋白(ApoE)？4等位基因和年龄是目前唯一与晚发性AD密切相关的因素。我们对阿尔茨海默病遗传方面的认识存在很大差距，可能包括影响迟发性阿尔茨海默病发病年龄和表型表达的其他遗传因素。与阿尔茨海默病等复杂疾病相关的个体遗传发现(非同源SNPs)不太可能完全解释遗传变异对疾病发病机制的实质性影响。多层次的致病因素可能是复杂疾病的基础，可能包括影响转录和转录后调控的基因内部和周围的多个基因位点，强调需要对大的遗传区域进行综合评估，并与蛋白质生物标记物水平的相关性作为预测疾病风险的手段。这一建议侧重于总体假设，即大基因区域周围和大基因区域内的多个遗传位点以AD特有的方式调节基因表达。在这项研究的指导阶段(K99)，第一个目标是寻找APOE、APP和MAPT基因周围和内部的多个SNP(单倍型)或组合，它们与脑脊液和PMB中的表达水平相关。候选遗传和蛋白质生物标记物将扩展到APOE、APP和MAPT基因之外，包括其他可能与神经退行性疾病生物学相关的基因。第二个目的是证明假定的调控单倍型通过利用基因组DNA来影响表达，基因组DNA包含特定的假定调控单倍型，作为报告和微基因分析中基因调控的活性部位。在独立阶段(R00)，最终目的是测试调控单倍型在区分不同的AD表型以及AD和其他神经退行性疾病之间的可靠性。总之，这些实验是独一无二的，因为它们超越了核心启动子座位和生物标记物表达水平之间的简单相关性，使用遗传、统计和功能技术相结合的方法来评估假定的远程调控元件内的多个座位对AD相关基因表达的影响，以找到预测AD的单倍型。这份K99/R00申请的研究和职业发展部分将为申请者提供必要的培训，使其成为一名成功的独立调查者，能够整合这些技术来提高我们对神经退行性疾病风险的理解。 公共卫生相关性：表征AD相关表达水平的单倍型调控(生物标志物的遗传预测指标)可能有助于为早期干预找到新的靶点，并转化为更准确的方法来预测和诊断AD的早期进展。

DESCRIPTION (provided by applicant): The characteristic findings in Alzheimer's disease (AD) post-mortem brain (PMB) are degeneration of neurons together with extensive amounts of amyloid deposits (A?42) (a cleavage product of the amyloid precursor protein encoded by the APP gene) and tau (encoded by the MAPT gene). Cerebrospinal fluid (CSF) A?42 and tau levels can predict AD but have a limited reliability in discriminating AD from other neurodegenerative diseases. The apolipoprotein (APOE) ?4 allele and age are currently the only factors strongly associated with late onset AD. The large gaps in our understanding of the genetic aspects of AD may include additional genetic factors that impact age of onset and phenotypic expression of late onset AD. Individual genetic findings (non- synonomous SNPs) associated with complex diseases, such as AD, are unlikely to fully explain the substantial impact of genetic variation on disease pathogenesis. Multilevel etiologic factors are likely to underlie complex diseases and may include multiple loci within and surrounding a gene that influence regulation of transcription and post-transcription, emphasizing the need for integrative evaluation of large genetic regions and correlations with protein biomarker levels as a means for predicting disease risk. This proposal focuses on the overall hypothesis that multiple genetic loci surrounding and within large gene regions act to regulate gene expression in an AD specific manner. During the mentored phase (K99) of this investigation the first aim is to find multiple loci or combinations of SNPs (haplotypes) surrounding and within the APOE, APP and MAPT genes that correlate with expression levels in CSF and PMB. Candidate genetic and protein biomarkers will expand beyond APOE, APP and MAPT genes to include other genes likely to be biologically relevant to neurodegenerative disease. The second aim is to demonstrate that putative regulatory haplotypes functionally impact expression by utilizing genomic DNA, containing a particular putative regulatory haplotype, as the active site of gene regulation in reporter and minigene assays. During the independent phase (R00), the final aim is to test regulatory haplotypes for their reliability in discerning between different AD phenotypes and between AD and other neurodegenerative diseases. Collectively, these proposed experiments are unique because they go beyond the simple correlation between core promoter loci and biomarker expression levels by using a combination of genetic, statistical and functional techniques to evaluate the influence of multiple loci within putative distant regulatory elements on AD relevant gene expression to find haplotypes that predict AD. The research and career development components of this K99/R00 application will provide the necessary training for the applicant to become a successful independent investigator who can integrate these techniques to improve our understanding of neurodegenerative disease risk. PUBLIC HEALTH RELEVANCE: Characterization of haplotype regulation of AD relevant expression levels (genetic predictors of biomarkers) may help find new targets for early intervention as well as translate into more accurate ways to predict and thus diagnose AD early in its progression.