Systems Modeling of Alzheimers Disease in C. elegans

项目摘要 阿尔茨海默氏病（AD）是一种主要的与衰老有关的神经系统疾病，约有530万人患有这种疾病 美国人，美国每年的总成本约为2260亿美元。尽管人们努力将该法规 AD的病理过程，特别是β-淀粉样蛋白斑块的产生，目前的干预措施旨在 阻断β-淀粉样蛋白聚集对临床症状只有适度的影响，这表明新的方法 为了找到独立于β-淀粉样蛋白聚集的危险因素，应进行研究，特别是确定近端 疾病的原因。模式生物的遗传学研究表明， 通路调节衰老，针对这些通路的干预可以显著延长哺乳动物的寿命。 健康和寿命。C.线虫一直处于模式生物寿命研究的前沿，揭示了 新的基因，途径和分子机制，调节衰老和年龄相关的下降速度， 包括认知能力下降与此同时，全基因组关联（GWAS）研究表明， 基因调控变化是AD的危险因素。基因调控区中的AD风险变异可能失调 背景特异性转录输出，有助于独立于调节和 β-淀粉样斑块的产生。我们的假设是，通过定义细胞的基因调控网络， 在衰老和遗传易感AD神经病理状态下，将有可能推断出 细胞接收环境信号，并解释其基因表达的结果程序。这些年龄- 依赖性转录变化可能在神经元的进化时间尺度上是保守的， 导致模型系统C中的认知下降。Elegans也是。我们将利用这一优势， 模型系统（简单的遗传学，寿命短，快速老化，学习和记忆的功能测定， 分离的神经元的转录分析），结合来自AD的基因组和遗传研究的数据， 从人类神经元细胞的实验结果，以确定共享的基因调控网络，可能有助于 对AD的易感性。这些基因和基因网络将为药物治疗提供重要的新靶点 阿尔茨海默病的发病和进展的干预措施。

PROJECT SUMMARY Alzheimer’s Disease (AD) is a major aging-related neurological disorder that afflicts roughly 5.3 million Americans, with total US annual costs of ~ $226 billion. Despite intensive efforts to characterize the regulation of pathological processes of AD, particularly the generation of -amyloid plaques, current interventions aimed at blocking -amyloid aggregation have only modest effects on clinical symptoms, suggesting that new approaches to find risk factors independent of -amyloid aggregation should be investigated, particularly to identify proximal causes of the disease. Genetic studies in model organisms have demonstrated that evolutionarily conserved pathways modulate aging, and interventions that target these pathways can dramatically extend mammalian healthspan and lifespan. C. elegans has been at the forefront of model organism longevity studies, revealing new genes, pathways, and molecular mechanisms that regulate the rate of aging and age-related declines, including cognitive decline. In parallel, Genome-wide association (GWAS) studies have implicated a role for gene regulatory changes as a risk factor for AD. AD risk variants in gene regulatory regions may dysregulate context-specific transcriptional outputs, contributing to susceptibility to AD independent of the regulation and generation of -amyloid plaques. Our hypothesis is that by defining a cell’s gene regulatory networks during aging and in genetically predisposed AD neuropathological states, it will be possible to both infer the environmental signals the cell receives and explain its resulting program of gene expression. These age- dependent transcriptional changes may be conserved in neurons across evolutionary time scales, and may contribute to cognitive decline in the model system C. elegans, as well. We will leverage the strengths of this model system (simple genetics, short lifespan, rapid aging, functional assays of learning and memory, and transcriptional analysis of isolated neurons), combined with data from genomic and genetic studies of AD and experimental results from human neuronal cells, to identify shared gene regulatory networks that may contribute to the susceptibility to AD. These genes and gene networks will provide important new targets for pharmaceutical interventions for the onset and progression of Alzheimer’s Disease.