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: 9922436
• 负责人: MICHELLE E EHRLICH
• 金额: $ 42.38万
• 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/9922436
• 关键词: 3-Dimensional; Age; Alzheimer' s Disease; Alzheimer' s disease model; Amyloid Fibrils; Amyloid beta-Protein; Amyloidosis; Astrocytes; Autopsy; Biological Assay; Biology; Brain; Brain Diseases; 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; Knock-out; Knowledge; Late Onset Alzheimer Disease; Light; Maps; Measures; Modeling; Molecular; Molecular Profiling; Mus; Neurites; Neurobiology; Neurofibrillary Tangles; Neuroglia; Neurons; Organoids; Pathology; Pathway Analysis; Patients; Penetrance; Performance; Phenotype; Population; 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; metabolomics; molecular imaging; molecular scale; molecular subtypes; mouse model; multidimensional data; network models; neuroimaging; novel; outcome forecast; 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 阿尔茨海默氏病（AD）病理学的特征在于在阿尔茨海默氏病（AD）中存在磷酸化的tau蛋白。 老年人神经纤维缠结（NFT）、营养不良的神经突和丰富的细胞外β-淀粉样蛋白 斑块然而，AD的病因学仍然难以捉摸，部分原因是广泛的临床和病理学特征。 AD患者的神经生物学/神经病理学特征因此，AD的异质性使 发现改善疾病的治疗方法并开发准确的体内指标的任务 诊断和临床预后。已经提出了不同的方法用于AD亚型，但是 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亚型的知识和理解将有助于 了解最近失败的临床试验，并提供定制治疗的潜力， 更同质的患者亚组。通过整合遗传、分子和神经影像学数据 为了更精确地定义AD亚型，我们可能能够更好地区分高度 重叠的临床表型。此外，这种亚型的鉴定可能 提高我们对其潜在病理机制的理解，预测其进程， 开发新的疾病改善治疗方法。在本申请中，我们提出系统地 通过开发和使用最先进技术来鉴定和表征AD的分子亚型 网络生物学方法，以多种现有的大规模遗传，基因表达，蛋白质组学和 功能性MRI数据集。我们将研究预测的潜在关键驱动因素的功能作用 AD亚型以及来自我们当前AMP-AD联盟的三个候选关键驱动因素， 对照和AD hiPSC衍生的神经共培养系统，然后通过筛选在复杂的类器官中 在单细胞和全细胞群体分析中预测的最关键驱动因子的转录影响。 将使用每种细胞类型的功能测定来建立与AD亚型相关性的证据 表型将生成单细胞RNA测序数据，以识别干扰特征， 选定的驱动程序，然后将被映射到子类型的特定网络，以建立全面的 每个驱动程序的信令映射。AD亚型的前三个最有希望的驱动因素和三个 现有的AMP-AD靶点将使用a）独立的尸检队列和B） 重组小鼠，包括淀粉样变性，tau蛋白病和新的“人源化”模型。