Integrative Network Modeling of Cognitive Resilience to Alzheimer's Disease

阿尔茨海默病认知弹性的综合网络建模

• 批准号: 10170187
• 负责人: MICHELLE E EHRLICH
• 金额: $ 121.2万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10170187
• 关键词: Academic Medical Centers; Activities of Daily Living; Affect; Aging; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease patient; Alzheimer' s disease risk; Autopsy; Biological Models; Biology; Brain; Caenorhabditis elegans; Categories; Cessation of life; Clinical; Clinical Data; Cognitive; DNA sequencing; Data; Data Set; Dementia; Deterioration; Development; Disease; Elderly; Gene Proteins; Genes; Genetic; Genotype-Tissue Expression Project; Goals; Human; Immunocompetence; Individual; Lead; Maps; Measurable; Memory; Memory Loss; Messenger RNA; Methods; Mitochondria; Modeling; Molecular; Molecular Profiling; Neurodegenerative Disorders; Neurofibrillary Tangles; Pathologic; Persons; Population; Prefrontal Cortex; Prevention strategy; Proteins; Proteomics; Research; Risk Factors; Scientist; Senile Plaques; Signal Pathway; Structure; System; Testing; Tissue Sample; Variant; aging brain; brain tissue; cohort; differential expression; epigenetic marker; genetic variant; high dimensionality; human very old age (85+); in vivo Model; medical schools; molecular scale; mouse model; network models; non-demented; novel; prevent; resilience; transcriptomics

Project Summary Alzheimer's disease (AD) is a debilitating neurodegenerative disorder. Pathologically, AD is characterized by amyloid plaques and neurofibrillary tangles. Clinically, AD patients present with progressive memory decline followed by deterioration of other cognitive domains and activities of daily living. Advanced age is the greatest risk factor. No effective method is available for preventing and/or treating this devastating disease. However, certain individuals of the elder population (≥ 85 years) remain cognitively intact, including some with substantial plaques and neurofibrillary tangle burdens, the two pathological hallmarks for fully symptomatic AD. The mechanisms of cognitive resilience and protection against AD in these elderly persons remain elusive. This proposal brings together scientists and postmortem human brain tissue samples from two major AD-research centers (the Icahn School of Medicine at Mount Sinai and the Rush University Medical Center) and aims to systematically identify and validate genetic variants, genes, proteins, and molecular networks underlying cognitive resilience to AD risk and proposes to build a comprehensive unbiased signaling pathway map underlying cognitive resilience to AD. Towards this end, we will develop an AD resilient cohort comprised of genetic, transcriptomic and proteomic data in the prefrontal cortex from a large number of brains in four categories: 1) very old (age of death (AoD) ≥ 85) AD-resilient, 2) young (AoD < 85) healthy, 3) very old (AoD ≥85) AD and 4) young (AoD < 85) AD. We will perform systems genetics and integrative network biology analyses on the large-scale high-dimensional molecular profiling data to identify genetic variants, genes, proteins, and molecular networks underlying cognitive resilience to AD risk. We will systematically validate key drivers of the molecular networks underlying the cognitive resilience to AD using two diverse (C. elegans and mouse) model systems. We will validate the structures of AD-resilient molecular networks for building a data- driven, comprehensive signaling pathway map underlying cognitive resilience to AD risk. In particular, we will test the hypotheses that enhanced mitochondrial function and immune competence as well as their underlying molecular networks confer cognitive resilience. Our study will not only present a global landscape of the interplays among genetic variants, mRNAs and proteins responsible for cognitive resilience to AD but also pinpoint critical network structures and key drivers that can potentially lead to development of novel prevention strategies in combating AD.

项目摘要 阿尔茨海默病(AD)是一种衰弱的神经退行性疾病。从病理上讲，AD的特征是 淀粉样斑块和神经原纤维缠结。临床上，阿尔茨海默病患者表现为进行性记忆衰退 其次是其他认知领域和日常生活能力的恶化。高龄是最伟大的 风险因素。没有有效的方法来预防和/或治疗这种毁灭性的疾病。然而， 老年人群中的某些个体(≥85岁)在认知上保持完好，包括一些有大量 斑块和神经原纤维缠绕负荷，这是完全症状性AD的两个病理特征。这个 这些老年人的认知弹性和对AD的保护机制仍然难以捉摸。这 Proposal汇集了科学家和两项主要AD研究的尸检人脑组织样本 中心(西奈山伊坎医学院和拉什大学医学中心)，目标是 系统地识别和验证遗传变异、基因、蛋白质和潜在的分子网络 对AD风险的认知弹性，并建议建立一个全面的、无偏见的信号通路图 潜在的AD认知复原力。为此，我们将发展一个具有AD弹性的队列，包括 前额叶皮质中的遗传、转录和蛋白质组学数据 类别：1)高龄(死亡年龄≥85)AD恢复力强；2)年轻(AOD&lt；85)健康；3)高龄(AOD) ≥85)AD和4)Young(Aod&lt；85)AD。我们将进行系统遗传学和综合网络生物学 对大规模高维分子图谱数据的分析，以识别遗传变异、基因、 蛋白质和分子网络是AD风险认知复原力的基础。我们将系统地验证密钥 使用两种不同的线虫(线虫和线虫)研究阿尔茨海默病认知弹性的分子网络驱动因素 鼠标)建立系统模型。我们将验证AD弹性分子网络的结构，以构建数据- 驱动的、全面的信号通路映射了潜在的对AD风险的认知弹性。特别是，我们将 测试增强线粒体功能和免疫能力的假说及其基础 分子网络赋予人们认知上的韧性。我们的研究不仅将呈现世界上 遗传变异、mRNAs和蛋白质之间的相互作用不仅与AD认知恢复有关，而且还 准确确定关键网络结构和关键驱动因素，这些结构和驱动因素可能会导致开发新的预防措施 防治阿尔茨海默病的策略。