Combine computational prediction, network analysis and genetic screening in C elegans to uncover neurodegenerative causes in Alzheimer's Disease

项目摘要 阿尔茨海默病（AD）是痴呆的主要原因和最常见的神经退行性疾病， 影响了美国550万人和全球4700万人。尽管有巨大的社会 与AD相关的经济成本，目前晚发性AD的遗传原因仍不清楚 并且不存在治疗AD的方法。 在这个名为“将计算预测、网络分析和遗传筛选结合在C。 elegans揭示阿尔茨海默病（AD）的神经退行性原因"，我们提出了一个综合的 系统生物学方法，无缝结合了新的计算遗传学预测，网络分析， 并在C. elegans揭示AD的神经退行性疾病原因。首先，我们将发展 一种新的数据驱动的基于网络的系统方法来识别神经退行性候选基因， 利用来自人类的大量表型、遗传和基因组数据。第二，我们将识别基因 神经退行性变的潜在途径和分子网络;第三，我们将评估 在多种C. elegans模型 神经变性我们的系统生物学方法充分利用并无缝集成了大量的 知识和数据，然后指导我们在C.优雅的 我们的项目的输出将是候选致病基因，遗传修饰和分子网络的列表， 神经变性，每一个都与来自人类和 C.优雅我们的研究将产生大量的数据/知识/假设，可以作为一个开始。 其他人在不同的神经退行性疾病动物模型中进行假设驱动的测试。我们将 建立一个全面的神经变性基因（NDGenes）知识库，并开发交互式网站 应用程序，使所有数据公开。 我们的项目的独特和强大的力量是我们能够无缝地将新的计算联合收割机 C.基因筛查的预测。优雅我们的项目可能会导致保守基因的发现 和调节网络，有助于对神经变性的易感性或恢复力， 对AD和其他神经退行性疾病的治疗干预的发展的影响。

PROJECT SUMMARY Alzheimer's disease (AD) is the leading cause of dementia and the most common neurodegenerative disorder, affecting over 5.5 million people in United States and 47 million people worldwide. Despite the enormous social economical cost associated with AD, currently the genetic causes for late-onset AD remains poorly characterized and there exist no cures for AD. In this project titled “Combine computational prediction, network analysis, and genetic screening in C. elegans to uncover neurodegenerative causes in Alzheimer’s disease (AD)”, we propose an integrated systems biology approach that seamlessly combines novel computational genetics prediction, network analysis, and robust genetic screening in C. elegans to uncover neurodegenerative causes in AD. First, we will develop novel data-driven network-based systems approach to identify neurodegenerative candidate genes by leveraging large amounts of phenotypic, genetic and genomic data from humans. Second, we will identify genetic pathways and molecular networks underlying neurodegeneration; Third, we will evaluate the causal effects of identified candidate genes, genetic modifiers and molecular networks in a variety of C. elegans models of neurodegeneration. Our systems biology approach fully utilizes and seamlessly integrate vast amounts of knowledge and data from humans, which then guides our confirmatory genetic screening in C. elegans. The output of our project will be lists of candidate causal genes, genetic modifiers and molecular networks for neurodegeneration, each associated with supporting genetic and phenotypic evidence from both humans and C. elegans. Our study will generate large amounts of data/knowledge/hypotheses that could serve as a starting point for others to conduct hypothesis-driven testing in different animal models of neurodegeneration. We will build a comprehensive knowledge base of Neurodegeneration Genes (NDGenes) and develop interactive web applications to make all the data publicly available. The unique and powerful strength of our project is our ability to seamlessly combine novel computational predictions with genetic screening in C. elegans. Our project will likely lead to discovery of the conserved genes and regulatory networks that contribute to susceptibility or resilience to neurodegeneration, with translational implications for the development of therapeutic interventions for AD and other neurodegenerative disorders.