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

阐明阿尔茨海默病和药物再利用中的基因调控网络

• 批准号: 2742378
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2742378
• 关键词: Elucidating; Genetic; Regulatory; Networks; Alzheimer

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.

研究背景阿尔茨海默病（Alzheimer 'sDisease，AD）是一种进行性神经退行性疾病，是最常见的痴呆形式.目前，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一种有吸引力的方法来开发新策略的原理证据。AD进展以细胞类型特异性的方式发生。基因调控网络（GRNs）作为一种模型，将细胞类型特异性调控元件与其靶点连接起来。基因表达调节因子是化合物的理想靶点，因为它们控制着几个基因，这些基因在疾病发病机制中可以一起改变。该项目将揭示AD的机制见解，并产生一个可重复的和强大的药物再利用管道。AimsThe主要目标是建立一个强大的管道，识别受AD影响的细胞类型特异性GRNs。然后，我将使用这些GRN来确定目前批准的药物所针对的潜在疾病相关靶点。一旦确定在silico的结果可以使用iPSC模型在laborator.MethodsPublic AD snRNA-seq数据集将被整合，以提供足够的统计功率为新的基因发现后，标准化的预处理和QC。将对GRN进行系统评估。网络的稳定性、保存性和通路的重要性将通过统计检验和下采样进行评估。将探索计算机因果建模方法，以评估子网络中基因的相对重要性。此外，将考虑AD和非疾病控制网络之间的差异，以优先考虑疾病相关的子网络。可能“逆转”子网络失调的药物化合物将使用扰动数据库（如ConnectivityMap）在计算机上进行鉴定。将在体外iPSC模型中进行结果的初步验证。翻译机会这个项目有可能确定和提供初步的人类验证的新目标，可以评估治疗效果的AD的基础上药物再利用。