-Omics driven network analysis in Lewy Body dementia

项目摘要--项目1 项目1将侧重于使用组学派生的数据集来阐明遗传因素和转录本来预测 路易体痴呆(LBD)的病因学基础路径和网络。目标1将建立在 调查人员之前的研究。我们的工作表明，已建立的常见遗传易感变种 不要推动α-SYN或Aβ的病理负担或路易斯安那州观察到的神经细胞丢失的程度 身体失调。这些数据表明，驱动疾病进展和负担的基因和变异 病理可能不同于那些增加疾病风险的变异。为了检验这一假设， 我们将分析全基因组序列数据与α-SYN或Aβ病理负担和神经元的关系 黑质中的细胞丢失和其他神经病理措施(通过病理学核心产生 [CORE B])用于我们的路易体疾病系列中的500多个病例。为了补充这些研究，目标2将执行 在所有具有不同水平α-SYN或Aβ负担的病理病例中 不偏不倚的方法，评估与突触核蛋白负荷相关的转录本水平。此外，一个子集 将使用单核RNAseq来分析案例，以检查转录水平之间的关系 和α-SYN或A-β病理负担按核心B PI Dickson博士分类。这些目标将 帮助描述驱动LBD异质性的基因组架构。最终目标3将使用这些数据来建模 根据不同的LBD病理机制建立网络，并预测疾病驱动因素。AIM 3还将纳入-组学 在项目2中产生的数据：候选基因、转录本和通路的功能特征 基因及其与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 将与项目4的绩效指标合作执行。

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.