Integrative Biology Approach to Complexity of Alzheimer's Disease

描述（由申请人提供）：阿尔茨海默病（AD）影响了美国85岁以上人口的一半，并导致大脑内某些网络和细胞群的破坏。阿尔茨海默病最初表现为轻微的认知能力下降，但逐渐恶化，并且总是致命的。尽管在全基因组关联和全外显子组测序研究中，确定阿尔茨海默病的易感性位点取得了重大进展，但迄今为止，仅根据DNA变异信息，在个体基础上实现阿尔茨海默病的预测风险评分仍难以实现。本提案旨在发展一种多尺度网络方法来阐明AD的复杂性。与AD有因果关系的多尺度网络模型将基于现有AD相关的大规模分子数据和通过该应用程序生成的高影响、高分辨率补充数据集开发。利用脑切片培养、ips细胞衍生的人类神经元、少突胶质和星形胶质细胞系统混合培养以及AD的苍蝇模型，我们试图重建在这些生命系统的多尺度分析中发现的AD相关网络，然后采用高通量分子和细胞筛选分析，不仅验证单个基因在分子和细胞AD相关过程中的作用，同时也验证了与疾病有关的分子网络。我们最初的多尺度研究表明，小胶质蛋白TYROBP是阿尔茨海默病发病机制的一个关键驱动因素，我们已经部分证实了这一点，但我们将利用ipsc衍生的不同脑细胞类型的混合培养物和其他hit进一步验证

DESCRIPTION (provided by applicant): Alzheimer's disease (AD) affects half of the US population over the age of 85 and causes destruction of select networks and cell groups within the brain. AD manifests initially as mild cognitive decline, but gets progressively worse and is always fatal. Despite significant progress identifying susceptibility loci for AD in genome-wide association and whole exome sequencing studies, to date, a predictive risk score for AD that achieves clinical utility on an individual basis given DNA variation information alone has been elusive. This proposal aims to develop a multiscale-network approach to elucidating the complexity of AD. Multiscale network models causally linked to AD will be developed based on existing AD-related large scale molecular data and the high-impact, high-resolution complementary datasets generated through this application. Using brain slice cultures, iPS-cell-derived mixed cultures of human neuronal, oligodendroglial, and astrocytic cell systems, and fly models of AD, we seek to reconstitute the AD-related networks discovered in the multiscale analysis in these living systems and then employ high-throughput molecular and cellular screening assays to not only validate the actions of individual genes on molecular and cellular AD-associated processes, but also validate the molecular networks we implicated in the disease. Our initial multiscale studies have implicated the microglial protein TYROBP as one key driver of AD pathogenesis, a "hit" we have partially validated, but that we will further validae along with other hits using iPSC-derived mixed cultures of different brain cell types, murine brain slices and AD fly models. We will analyze the potential ability for network-derived hits like TYROBP to modulate standard AD pathology involving A� and tau as well as its ability to shift networks in those same systems in such a way as to reflect the behavior of networks discovered in the multi-scale analysis. Importantly, the model building and validation will be iterated to produce updated/refined models based on validation results that, in turn, will be mined to generate updated lists of prioritized targets for validation. In this way, through the course of th grant, as new knowledge accumulates externally and as we generate increased amounts of data including validation data, our models will take into account the most up to date information to produce the most predictive models of AD. As a service to the AD research community, we will provide dramatically improved general access to large-scale, multidimensional datasets, together with systems level analyses of these datasets.