Integrative Biology Approach to Complexity of Alzheimer's Disease

描述(申请人提供)：阿尔茨海默病(AD)影响美国一半的85岁以上的人口，并导致大脑中选定的网络和细胞群的破坏。AD最初表现为轻度认知能力下降，但逐渐恶化，总是致命的。尽管在全基因组关联和整个外显子组测序研究中确定AD的易感基因取得了重大进展，但到目前为止，仅凭DNA变异信息就能在个体基础上实现临床实用的AD的预测性风险评分仍然难以捉摸。这项建议旨在开发一种多尺度网络方法来阐明AD的复杂性。与AD因果关联的多尺度网络模型将基于现有的AD相关的大规模分子数据和通过该应用生成的高影响、高分辨率的互补数据集而开发。使用脑片培养、iPS细胞衍生的人类神经元、少突胶质细胞和星形胶质细胞系统的混合培养，以及AD的飞行模型，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，然后使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，而且验证我们在疾病中所涉及的分子网络。我们最初的多尺度研究表明，小胶质蛋白TYROBP是AD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，但我们将使用不同脑细胞类型的IPSC来源的混合培养物来进一步验证其他HIT，如小鼠脑 切片和AD飞行模型。我们将分析像TYROBP这样的网络衍生命中率对涉及A？和tau的标准AD病理进行调制的潜在能力，以及它以反映多尺度分析中发现的网络行为的方式改变这些相同系统中的网络的能力。重要的是，将反复进行模型构建和验证，以根据验证结果生成更新/改进的模型，然后挖掘这些更新的模型，以生成用于验证的优先目标的更新列表。这样，在授予过程中，随着外部新知识的积累和我们生成包括验证数据在内的更多数据量，我们的模型将考虑最新的信息，以生成最具预测性的AD模型。作为对AD研究社区的一项服务，我们将极大地改进对大规模、多维数据集的一般访问，以及对这些数据集的系统级分析。

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.