Molecular Networks Underlying Resilience to Alzheimer's Disease Among APOE E4 Carriers

项目摘要/摘要 载脂蛋白E(ApoE)基因的e4等位基因被认为是最强的遗传基因之一 迟发性阿尔茨海默病(AD)的决定因素。一般说来，e4等位基因与减少 神经保护和修复，增加携带者在其一生中积累的伤害脆弱性。 然而，尽管载脂蛋白E e4的外显率相对较高，但相当大比例的e4携带者永远不会 开发AD。该项目的总体目标是对跨多个组学网络的交互进行建模，以确定 涉及散发性阿尔茨海默病易感性与高危个体复原力的生物学途径。 AD的多因素性质表明，它可能是跨 基因组、表观基因组、转录组和蛋白质组。识别参与AD发病的中枢网络 将需要综合系统生物学方法。这项拟议的研究提供了一种新的创新方式 整合跨越多个组织和组学平台的网络--生物学的一个主要特征--以便 在高危人群(e4+)中，确定AD复原力的先天和动态先兆。朝向这个方向 目标，我们将：(1)使用新开发的基于GWAS的网络分析，以确定改变 载脂蛋白E e4与认知功能减退/痴呆的关系；(2)产生DNA甲基化和RNA序列 来自大脑样本的数据，我们将使用加权基因相关网络分析(WGCNA)来分析 在ApoE e4+中确定与AD神经病理和认知能力下降相关的网络；(3)生成 来自大脑和脑脊液样本的蛋白质组数据，我们将使用WGCNA分析这些数据以确定相关网络 ApoE e4+与AD的神经病理和认知功能下降；(4)使用先进的积分网络分析 确定参与AD复原力的多组学和多组织途径和生物系统。 利用系统生物学整合多个“组学”数据将是解开这些联系和 参与复杂疾病的不同功能水平之间的相互作用，如阿尔茨海默病。总体而言，我们的建议 研究将提高对阿尔茨海默病易感性背后复杂生物学的理解。这些研究具有 发现新的治疗靶点的可能性，这可能会为未来药理学的发展提供信息 旨在预防或减缓AD发病的干预措施。

PROJECT SUMMARY/ABSTRACT The e4 allele of the Apolipoprotein E (ApoE) gene has been identified as one of the strongest genetic determinants of late-onset Alzheimer’s disease (AD). In general, the e4 allele is associated with reductions in neural protection and repair, increasing a carrier’s vulnerability to damage accumulated over his/her lifetime. Nevertheless, while the penetrance of ApoE e4 is relatively high, a significant proportion of e4 carriers will never develop AD. The overall goal of this project is to model interactions across multiple omics networks to identify the biological pathways involved in sporadic AD susceptibility versus resilience among high-risk individuals. The multifactorial nature of AD suggests that it may manifest as a result of complex interactions across the genome, epigenome, transcriptome, and proteome. Identifying the central networks involved in AD pathogenesis will require integrative Systems Biology approaches. The proposed research offers a new and innovative way to integrate networks—a dominant feature in biology—across multiple tissues and omics platforms, in order to identify innate and dynamic precursors of resilience to AD, among a high-risk population (e4+). Towards this goal, we will: (1) employ newly developed GWAS-based network analysis, to identify SNP networks that alter the association between ApoE e4 and cognitive decline/dementia; (2) generate DNA methylation and RNA-seq data from brain samples that we will analyze using weighted gene correlation network analysis (WGCNA) to identify networks associated with AD neuropathology and cognitive decline among ApoE e4+; (3) generate proteomic data from brain and CSF samples that we will analyze using WGCNA to identify networks associated with AD neuropathology and cognitive decline among ApoE e4+; (4) use advanced integromic network analysis to identify multi-omics and multi-tissue pathways and biological systems involved in AD resilience. The integration of multiple 'omics' data using systems biology will be crucial for unraveling the connections and interactions between various functional levels involved in complex diseases, such as AD. Overall, our proposed research will improve understanding of the complex biology underlying AD susceptibility. These studies have the potential to identify novel therapeutic targets that could inform the development of future pharmacologic interventions aimed at preventing or slowing AD pathogenesis.