Integrative Network Biology Approaches to Identify, Characterize and Validate Molecular Subtypes in Alzheimer's Disease

识别、表征和验证阿尔茨海默病分子亚型的综合网络生物学方法

• 批准号: 10251248
• 负责人: MICHELLE E EHRLICH
• 金额: $ 184.8万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10251248
• 关键词: 3-Dimensional; AD transgenic mice; Age; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease model; Alzheimer' s disease pathology; Alzheimer' s disease patient; Amyloid Fibrils; Amyloid beta-Protein; Amyloidosis; Astrocytes; Autopsy; Biological Assay; Biology; Brain; Brain region; CRISPR/Cas technology; Cell Culture Techniques; Cells; Characteristics; Clinical; Clinical Trials; Coculture Techniques; Cognition; Complex; Data; Data Set; Development; Diagnosis; Diffusion Magnetic Resonance Imaging; Disease; Epigenetic Process; Etiology; Functional Magnetic Resonance Imaging; Functional disorder; Gene Expression; Genes; Genetic; Genetic Diseases; Genetic Transcription; Genomics; Heterogeneity; Human; In Vitro; Individual; Induced pluripotent stem cell derived neurons; Knock-out; Knowledge; Late Onset Alzheimer Disease; Light; Maps; Measures; Modeling; Molecular; Molecular Profiling; Mus; Neurites; Neurobiology; Neurofibrillary Tangles; Neuroglia; Neurons; Organoids; Pathway Analysis; Penetrance; Performance; Phenotype; Population; Prognosis; Proteomics; Protocols documentation; Quality Control; Recombinants; Role; Sampling; Senile Plaques; Signal Transduction; Specificity; Structure; System; Tauopathies; Testing; Thick; Transgenic Mice; Validation; Work; brain cell; cell type; clinical phenotype; cohort; course development; disorder subtype; experimental study; extracellular; high dimensionality; improved; in vivo; indexing; individualized medicine; induced pluripotent stem cell; insight; knock-down; large scale data; metabolomics; molecular imaging; molecular scale; molecular subtypes; mouse model; multidimensional data; network models; neuroimaging; novel; overexpression; patient subsets; precision medicine; relating to nervous system; screening; single cell analysis; single-cell RNA sequencing; tau-1; transcriptome sequencing; transcriptomics

Project Summary Alzheimer's disease (AD) pathology is characterized by the presence of phosphorylated tau in neurofibrillary tangles (NFTs), dystrophic neurites and abundant extracellular β-amyloid in senile plaques. However, the etiology of AD remains elusive, partly due to the wide spectrum of clinical and neurobiological/neuropathological features in AD patients. Thus, heterogeneity in AD has complicated the task of discovering disease-modifying treatments and developing accurate in vivo indices for diagnosis and clinical prognosis. Different approaches have been proposed for AD subtyping, but they are generally neither suitable for high-dimensional data nor actionable due to the lack of mechanistic insights. Increased knowledge and understanding of different AD subtypes would shed light on recently failed clinical trials and provide for the potential to tailor treatments with specificity to more homogeneous subgroups of patients. By integrating genetic, molecular and neuroimaging data to more precisely define AD subtypes, we may be able to better discriminate between highly overlapping clinical phenotypes. Furthermore, the identification of such subtypes may potentially improve our understanding of its underlying pathomechanisms, prediction of its course, and the development of novel disease-modifying treatments. In this application, we propose to systematically identify and characterize molecular subtypes of AD by developing and employing cutting-edge network biology approaches to multiple existing large-scale genetic, gene expression, proteomic and functional MRI datasets. We will investigate the functional roles of key drivers underlying predicted AD subtypes as well as three candidate key drivers from our current AMP-AD consortia work in control and AD hiPSC-derived neural co-culture systems and then in complex organoids by screening the predicted transcriptional impact of top key drivers in single cell and cell-population-wide analyses. Functional assays in each cell type will be used to build evidence for relevance to AD-subtype phenotypes. Single cell RNA sequencing data will be generated to identify perturbation signatures in selected drivers that will then be mapped to subtype specific networks to build comprehensive signaling maps for each driver. The top three most promising drivers of AD subtypes and the three existing AMP-AD targets will be further validated using a) an independent postmortem cohort, and b) recombinant mice, including amyloidosis, tauopathy and new “humanized” models.

项目摘要 阿尔茨海默病(AD)的病理特征是存在磷酸化的tau 老年人神经原纤维缠结、营养不良轴突和丰富的细胞外β-淀粉样蛋白 斑块。然而，阿尔茨海默病的病因仍然难以捉摸，部分原因是临床和 阿尔茨海默病患者的神经生物学/神经病理学特征因此，AD中的异质性变得复杂 发现疾病治疗方法和开发准确的体内指标的任务 诊断和临床预后。对于AD亚型已经提出了不同的方法，但 它们通常既不适合高维数据，也不可行，因为缺乏 机械的洞察力。增加对不同AD亚型的知识和理解 阐明最近失败的临床试验，并提供量身定做具有特异性的治疗方法的可能性 患者亚群的同质性更高。通过整合遗传、分子和神经成像数据 为了更准确地定义AD亚型，我们可能能够更好地区分高度 临床表型重叠。此外，这种亚型的识别可能潜在地 提高我们对其潜在的发病机制，其进程的预测，以及 开发新的治疗疾病的方法。在本申请中，我们建议系统地 开发和利用前沿技术识别和表征AD的分子亚型 网络生物学方法用于多种现有的大规模遗传、基因表达、蛋白质组和 功能磁共振数据集。我们将调查预测背后的关键驱动因素的功能角色 AD子类型以及来自我们当前AMP-AD联盟的三个候选关键驱动因素在 对照和AD-HiPSC来源的神经共培养系统，然后通过筛选在复杂的有机体内 单细胞和全细胞分析中主要关键驱动因素的转录影响预测。 将使用每种细胞类型的功能分析来建立与AD亚型相关的证据 表型。将生成单细胞RNA测序数据以识别 选定的驱动因素，然后将映射到子类型特定网络，以构建全面的 每个司机的信号地图。AD亚型最有希望的三大驱动因素和三大 将使用a)独立的尸检队列和b)进一步验证现有的AMP-AD目标 重组小鼠，包括淀粉样变性、牛磺酸和新的“人性化”模型。