Elucidating Genetic Regulatory Networks in Alzheimer's Disease and Drug Repurposing

阿尔茨海默病(AD)是一种进行性神经退行性疾病，是痴呆的最常见形式。目前，还没有直接影响疾病进展的治疗方法。重新利用已建立的化合物是为新策略开发原理证据的一种有吸引力的方法。AD进展以细胞类型特定的方式发生。基因调控网络(GRN)是一种将细胞类型的特定调控元件与其靶标联系起来的模型。基因表达调节剂是化合物的理想靶标，因为它们控制着几个基因，这些基因可以在疾病发病机制中一起改变。该项目将揭示对AD的机械性见解，并产生一条可重复性和健壮的药物再利用流水线。目的主要目标是建立一条健壮的管道，识别受AD影响的细胞类型的特定GRN。然后，我将使用这些GRN来确定当前批准的药物针对的潜在疾病相关靶点。一旦在计算机上确认，结果可以用在实验室建立的IPSC模型来验证。方法公共AD SnRNA-seq数据集将被整合，在标准化的预处理和质量控制之后，为新的基因发现提供足够的统计能力。将对GRN进行系统评估。将通过统计测试和下采样来评估网络的稳定性、保存性和路径重要性。在电子因果模型中，将探索评估基因在子网络中的相对重要性的方法。此外，将考虑AD和非疾病控制网络之间的差异，以确定与疾病相关的子网络的优先顺序。利用像ConnectivityMap这样的微扰数据库，可以在电子计算机中识别可能“逆转”子网络失调的药物化合物。研究结果的初步验证将在IPSC模型的体外进行。翻译机会该项目有可能确定并提供对新靶点的初步人类验证，这些靶点可以基于药物再利用来评估AD的治疗效果。

BackgroundAlzheimer's Disease (AD) is a progressive neurodegenerative disease and is the most prevalent form of dementia. Currently, there are no therapies that directly affect disease progression. Re-purposing established compounds is an attractive approach to developing proofs of principle for new strategies.AD progression occurs in a cell-type specific manner. Gene regulatory networks (GRNs) serve as a model linking cell-type specific regulatory elements to their targets. Regulators of gene expression are ideal targets for compounds because they control several genes that can become altered together in disease pathogenesis. This project will uncover mechanistic insights into AD as well as produce a reproducible and robust drug repurposing pipeline.AimsThe main objective is to build a robust pipeline that identifies cell-type specific GRNs affected by AD. Then, I will use these GRNs to identify potential disease-associated targets against which currently approved drugs are directed. Once identified in silico results can be validated using iPSC models established in the laboratory.MethodsPublic AD snRNA-seq datasets will be integrated to provide sufficient statistical power for new gene discovery after standardized pre-processing and QC. Systematic assessment of GRNs will be performed. Network stability, preservation and pathway significance will be assessed through statistical tests and down-sampling. In silico causal modeling approaches will be explored to assess the relative importance of genes within the sub-networks. Also, differences between AD and non-diseased control networks will be considered to prioritise disease-associated sub-networks. Drug compounds that potentially "reverse" dysregulation of sub-networks will be identified in silico using perturbation databases like ConnectivityMap. Initial validation of findings will be performed in vitro in iPSC models. Translational OpportunitiesThis project has the potential to identify and provide preliminary human validation of novel targets that could be assessed for therapeutic efficacy in AD based on drug repurposing.