A systems biology approach in Drosophila to identify novel factors that influence AD pathogenicity

阿尔茨海默病（AD）是一种常见的神经退行性疾病，影响超过10%的人口 65岁以上的人，85岁以上的人几乎占50%。早发性家族性AD通常由以下基因突变引起： 三个常染色体基因之一相比之下，更常见的迟发性散发性AD（LOAD）， 与淀粉样蛋白β积累、tau蛋白过度磷酸化和线粒体功能障碍相关， 受大量基因的影响，其中大多数是未知的。全基因组关联 LOAD的研究指出，全基因组上位性和基因×环境相互作用有助于整体 风险AD的遗传基础和下游效应都是系统性的和复杂的。要解析这个 在AD的原因和后果的复杂性，在这里，我们创建了一个新的系统生物学管道， 将其应用于一个强大的AD遗传模型，使用果蝇，果蝇。具有三 互补的目标，我们的管道使我们能够创建一个全面的AD基因型-表型图谱。我们 第一个目标是使用完全测序的果蝇遗传参考小组（DGRP）来鉴定果蝇品系， 放大或改善Aβ和tau对年龄相关性神经退行性变的影响，并表征 代谢组学和单细胞脑转录组学网络与这种扩增和保护相关。 第二个目标是将果蝇开发为下游系统范围表型的强大模型， 从大脑中特定单细胞类型的电生理测量，到步行的机器视觉分析 步态动力学帮助我们理解基因和分子网络中的上游变异（Aim 1） 翻译为下游细胞和行为表型（目标2），目标3检查蛋白质动力学 Aβ和tau蛋白，包括其周转、聚集和丰度，以及Aβ和tau蛋白对 线粒体完整性，包括线粒体形态，运输和周转。目标3将比较 这些元件在菌株中对Aβ和tau的有害作用最敏感， 最耐药，描绘影响Aβ和tau毒性的分子和细胞机制。到 为了实现这些目标，我们已经建立了一个优秀的研究团队，具有高度互补性 skills.我们在此发表的和初步的数据建立了我们测量基因型变异的能力， 果蝇AD风险，并构建和分析与该风险相关的大规模分子网络， 研究AD的表型后果，从单个神经元中的电脉冲到神经元中的复杂行为， 整个苍蝇，并发现潜在的生化机制，连接基因型表型。先前 果蝇的研究工作在我们理解基础生物学和疾病方面发挥了重要作用 机制，包括神经退行性疾病。这里提出的研究有可能带来新的成果 深入了解影响自然人群中AD发病机制的复杂相互作用途径， 从而为AD的治疗干预带来新的生物标志物和新的机会。

Alzheimer's disease (AD) is a common neurodegenerative disorder affecting more than 10% of the population over age 65, and almost 50% of those over age 85. Early onset familial AD is typically caused by mutations in one of just three autosomal genes. In contrast, the more common, late-onset sporadic AD (LOAD), which is associated with amyloid β accumulation, tau hyperphosphorylation, and mitochondrial dysfunction, appears to be influenced by a very large number of genes, most of which are unidentified. Genome-wide association studies of LOAD point to genome-wide epistasis and gene-×-environment interactions contributing to overall risk. Both the genetic basis and the downstream effects of AD are system-wide and complex. To parse this complexity in both causes and consequences of AD, here we create a novel Systems Biology Pipeline and apply it to a powerful genetic model of AD using the fruit fly, Drosophila melanogaster. With three complementary aims, our pipeline enables us to create a comprehensive genotype-phenotype map of AD. Our first aim uses the fully sequenced Drosophila Genetic Reference Panel (DGRP) to identify fly strains that amplify or ameliorate the effects of Aβ and tau on age-related neurodegeneration, and characterizes the metabolomic and single-cell brain transcriptomic networks associated with this amplification and protection. The second aim develops the fly as a powerful model for downstream systems-wide phenotypes, from electrophysiological measures of specific single cell types in the brain, to machine-vision analysis of walking gait dynamics. To help us understand how upstream variation in genes and molecular networks (Aim 1) translates to downstream cellular and behavioral phenotypes (Aim 2), Aim 3 examines the protein dynamics of Aβ and tau, including their turnover, aggregation and abundance, and the influence of Aβ and tau on mitochondrial integrity, including mitochondrial morphology, trafficking and turnover. Aim 3 will then compare these elements in strains that are most sensitive to the deleterious effects of Aβ and tau with those that are most resistant, delineating molecular and cellular mechanisms that influence Aβ and tau toxicity. To accomplish these aims, we have constructed an outstanding team of researchers with highly complementary skills. Our published and preliminary data presented here establish our ability to measure genotypic variation in AD risk in Drosophila and construct and analyze large-scale molecular networks associated with that risk, to study the phenotypic consequences of AD from electrical pulses in a single neuron to intricate behaviors in a whole fly, and to discover the underlying biochemical mechanisms that link genotype to phenotype. Previous work with Drosophila has played an important role in our understanding of both basic biology and of disease mechanism, including neurodegenerative diseases. The studies proposed here have the potential to shed new insight on the complex, interacting pathways that influence AD pathogenesis in natural populations, potentially leading to new biomarkers and new opportunities for therapeutic intervention in AD.