Systems Genetics Analysis of Resilience to Alzheimer’s disease

项目总结 阿尔茨海默病(AD)的认知韧性是一种个体对其大部分疾病产生抵抗力的现象 对认知的破坏性影响，尽管存在已知的家族性阿尔茨海默病(FAD)突变或晚期 神经病理学。因此，促进认知韧性的遗传因素可能为治疗和治疗提供关键靶点 预防老年痴呆症。我们的总体目标是通过使用网络方法来确定认知弹性的驱动因素 将从老鼠FAD模型收集的数据与人类AD数据相结合。为此，我们将在目标1中使用 在分离的遗传背景上整合高危人类FAD突变的新型小鼠面板 多样性(BXD小组)，以确定有助于“人性化”小鼠群体中AD复原力的修饰物。 来自这些小鼠的高维分子、认知和病理数据将被整合起来预测 使用因果推理分析的弹性因素和网络。在目标2中，我们将测试两组基因 与无症状阿尔茨海默病患者的恢复力有关：1)先前确认的基因列表 通过蛋白质组学和行为分析与小鼠的特殊认知寿命相关，以及2) 我们在目标1中的分析所涉及的新基因和网络。 在功能强大的AD小鼠模型中确定它们对记忆相关脑网络的影响，测试两种新的 目标1和目标2中确定的候选人和先验的候选人(例如，TRPC3、Adamts17和HP1BP3)。这个项目 将提供新的、经过验证的目标，以促进健康的大脑老化和应对AD的弹性。此外，我们还将 提供对AD弹性的机械洞察，具体支持或驳斥我们的假设，即修饰者 FAD中的认知功能障碍同样通过保留记忆的功能连接性来影响迟发性AD 相关网络。我们将对这些数据进行注释、整理，并迅速向广大科学界传播 在通过NIA支持的AMP-AD知识门户发布之前，以最大限度地提高这些数据的可用性 荟萃分析和系统生物学研究。

PROJECT SUMMARY Cognitive resilience to Alzheimer's Disease (AD) is a phenomenon whereby individuals are resistant to its most damaging effects on cognition, despite the presence of known familial AD (FAD) mutations or advanced neuropathology. Genetic factors promoting cognitive resilience may thus provide key targets for treatment and prevention of AD. Our overall objective is to identify drivers of cognitive resilience by using network approaches to integrate data collected from mouse FAD models with human AD data. To this end, we will in Aim 1 use a novel mouse panel that incorporates high-risk human FAD mutations on a segregated background of genetic diversity (BXD panel) to identify modifiers that contribute to AD resilience in a `humanized' mouse population. High-dimensional molecular, cognitive and pathologic data from these mice will be integrated to predict resilience factors and networks using causal inference analyses. In Aim 2, we will test two set of genes for association with resilience in humans with asymptomatic AD: 1) a previously validated list of genes identified by proteomics and behavioral analyses to be associated with exceptional cognitive longevity in mice and 2) novel genes and networks implicated by our analyses in Aim 1. In Aim 3, we will validate resilience factors and determine their effects on memory-relevant brain networks in powerful AD mouse models, testing both novel candidates identified in Aims 1 and 2 and a priori candidates (e.g., Trpc3, Adamts17 and Hp1bp3). This project will deliver novel, validated targets for promoting healthy brain aging and resilience to AD. Moreover, we will provide mechanistic insight into AD resilience, specifically supporting or refuting our hypothesis that modifiers of cognition in FAD similarly influence late-onset AD by preserving the functional connectivity of memory relevant networks. We will annotate, curate, and rapidly disseminate the data to the broad scientific community prior to publication via the NIA-supported AMP-AD Knowledge Portal to maximize the usability of these data for meta-analysis and systems biology research.