Systems Genetics Analysis of Resilience to Alzheimer’s disease

项目摘要 阿尔茨海默病（AD）的认知弹性是一种现象，个体对它的最大抵抗力 尽管存在已知的家族性AD（FAD）突变或晚期AD， 神经病理学因此，促进认知恢复力的遗传因素可能为治疗和治疗提供关键靶点。 预防AD。我们的总体目标是通过使用网络方法来识别认知弹性的驱动因素 整合从小鼠FAD模型收集的数据与人类AD数据。为此，我们将在目标1中使用 在分离的遗传背景上合并高风险人类FAD突变的新型小鼠组 多样性（BXD小组）来鉴定有助于"人源化"小鼠群体中AD恢复力的修饰剂。 这些小鼠的高维分子、认知和病理数据将被整合，以预测 弹性因素和网络使用因果推理分析。在目标2中，我们将测试两组基因， 与无症状AD患者的恢复力相关：1）先前验证的基因列表 通过蛋白质组学和行为分析，与小鼠的异常认知寿命有关，2） 我们在Aim 1中的分析所涉及的新基因和网络。在目标3中，我们将验证弹性因素， 在强大的AD小鼠模型中确定它们对记忆相关大脑网络的影响，测试这两种新的 在目标1和2中识别的候选者和先验候选者（例如，Trpc3、Adamts17和Hp1bp3）。这个项目 将为促进健康的大脑衰老和对AD的适应力提供新的，经过验证的目标。而且还要 提供对AD弹性的机械见解，特别支持或反驳我们的假设，即修饰符 FAD的认知功能通过保持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，并迅速传播给广大的科学界。 在通过NIA支持的AMP-AD知识门户发布之前， 元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.