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

项目概要 阿尔茨海默病（AD）是痴呆症的主要原因，也是最常见的神经退行性疾病， 影响美国超过 550 万人和全球 4700 万人。尽管社会规模巨大 与 AD 相关的经济成本，目前迟发性 AD 的遗传原因仍不清楚 AD 尚无治愈方法。 在这个名为“结合计算预测、网络分析和遗传筛选的项目中，C. 线虫揭示阿尔茨海默病（AD）的神经退行性原因”，我们提出了一种综合方法 系统生物学方法无缝结合新颖的计算遗传学预测、网络分析、 对秀丽隐杆线虫进行强有力的基因筛查，以揭示 AD 的神经退行性原因。首先，我们将开发 新颖的数据驱动网络系统方法通过以下方式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。其次，我们要鉴定遗传 神经变性的途径和分子网络；第三，我们将评估因果效应 在各种秀丽隐杆线虫模型中确定了候选基因、遗传修饰剂和分子网络 神经变性。我们的系统生物学方法充分利用并无缝集成大量 来自人类的知识和数据，然后指导我们对秀丽隐杆线虫进行确认性基因筛查。的 我们项目的输出将是候选因果基因、遗传修饰剂和分子网络的列表 神经变性，每种都与支持人类和表型证据相关 C.线虫。我们的研究将产生大量数据/知识/假设，可以作为起点 为其他人在不同的神经退行性变动物模型中进行假设驱动的测试提供了指导。我们会 建立神经退行性基因（NDGenes）综合知识库并开发互动网络 使所有数据公开可用的应用程序。 我们项目的独特而强大的优势是我们能够无缝结合新颖的计算 线虫基因筛查的预测。我们的项目可能会发现保守基因 和调节网络有助于神经退行性变的易感性或恢复力，并具有转化作用 对 AD 和其他神经退行性疾病治疗干预措施的发展具有重要意义。

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.