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.

项目概要 阿尔茨海默病 (AD) 病理学的特征是磷酸化 tau 蛋白的存在 老年神经原纤维缠结（NFT）、营养不良的神经突和丰富的细胞外 β-淀粉样蛋白 牌匾。然而，AD 的病因仍然难以捉摸，部分原因是临床和疾病范围广泛。 AD 患者的神经生物学/神经病理学特征。因此，AD 的异质性变得复杂 发现疾病缓解疗法并开发准确的体内指数的任务 诊断和临床预后。针对 AD 子类型提出了不同的方法，但是 它们通常既不适合高维数据，也由于缺乏可操作性 机制的见解。增加对不同 AD 亚型的了解和理解将有助于 回顾最近失败的临床试验，并提供针对特定情况定制治疗的可能性 更同质的患者亚组。通过整合遗传、分子和神经影像数据 为了更准确地定义 AD 亚型，我们也许能够更好地区分高度不同的 AD 亚型。 重叠的临床表型。此外，此类亚型的识别可能会 提高我们对其潜在病理机制的理解、对其病程的预测以及 开发新的疾病缓解疗法。在此应用中，我们建议系统地 通过开发和采用尖端技术来识别和表征 AD 的分子亚型 网络生物学方法可用于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。我们将研究预测背后的关键驱动因素的功能作用 AD 子类型以及我们当前 AMP-AD 联盟的三个候选关键驱动因素 控制和 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 单细胞和细胞群分析中最关键驱动因素的预测转录影响。 每种细胞类型的功能测定将用于建立与 AD 亚型相关的证据 表型。将生成单细胞 RNA 测序数据来识别扰动特征 然后将选定的驱动程序映射到子类型特定网络以构建全面的 每个驾驶员的信号图。 AD 亚型最有希望的三个驱动因素以及三个 现有的 AMP-AD 目标将使用 a) 独立的尸检队列进一步验证，以及 b) 重组小鼠，包括淀粉样变性、tau蛋白病和新的“人源化”模型。