-Omics driven network analysis in Lewy Body dementia

项目摘要-项目1 项目1将集中于使用组学衍生的数据集来阐明遗传因子和转录本，以预测 路易体痴呆（Lewy body dementia，LBD）的病因学基础的通路和网络。目标1将建立在 研究人员以前的研究。我们的工作表明，已建立的常见遗传易感性变异 并不驱动α-syn或Aβ病理负荷或Lewy中观察到的神经元细胞丢失程度 身体失调这些数据表明，驱动疾病进展和疾病负担的基因和变异， 病理学可能不同于那些增加疾病风险的变异。为了验证这个假设， 我们将分析全基因组序列数据与α-syn或Aβ病理负荷和神经元 黑质中的细胞损失，以及额外的神经病理学测量（如通过病理学核心生成的 [Core B]），我们的路易体病系列超过500例。为了补充这些研究，Aim 2将执行 在具有不同水平的α-syn或Aβ负荷的所有病理病例中进行批量RNA-Seq， 无偏的方法，评估与突触核蛋白负荷相关的转录水平。此外，一个子集 的病例将使用单核RNAseq进行分析，以检查转录水平与转录水平之间的关系。 和α-syn或Aβ病理负荷，由核心B PI Dr. Dickson分类为不同的组。这些目标将 有助于描述驱动LBD异质性的基因组结构。最终的Aim 3将使用这些数据来建模 网络由于不同的LBD病理和预测疾病的驱动因素。Aim 3还将纳入-omics 项目2中生成的数据。候选基因、转录本和通路的功能表征 基因以及与α-syn或Aβ的相互作用和细胞读数（例如自噬，溶酶体功能障碍）将 与项目4的PI合作执行。

PROJECT SUMMARY – PROJECT 1 Project 1 will focus on using –omics derived datasets to elucidate genetic factors and transcripts to predict the pathways and networks underlying the etiology of Lewy body dementia (LBD). Aim 1 will build upon the investigators previous studies. Our work has shown that the established common genetic susceptibility variants do not drive either the α-syn or Aβ pathologic burden or the degree of neuronal cell loss observed in Lewy body disorders. These data suggest that the genes and variants that drive disease progression and burden of pathology may be different from those variants that increase the risk of disease. To examine this hypothesis, we will analyze whole-genome sequence data for association with α-syn or Aβ pathologic burden and neuronal cell loss in the nigra, and additional neuropathologic measures (as generated through the Pathology Core [Core B]) for over 500 cases of our Lewy Body disease series. To complement these studies Aim 2 will perform bulk RNA-Seq across all of the pathologic cases with varying levels of α-syn or Aβ burden, in an unbiased approach, assess the levels of transcripts that are related to synuclein burden. In addition, a subset of cases will be analyzed using single-nucleus RNAseq to examine the relationship between transcript level and α-syn or Aβ pathologic burden categorized into distinct groups by Core B PI Dr. Dickson. These Aims will help describe the genomic architecture driving LBD heterogeneity. The final Aim 3 will use these data to model networks due to different LBD pathologies and to predict disease drivers. Aim 3 will also incorporate –omics data generated within Project 2. Functional characterization of candidate genes, transcript and pathways genes and the interaction with α-syn or Aβ and cellular readouts (e.g. autophagy, lysosomal dysfunction) will be performed in collaboration with the PIs of Project 4.