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

基本信息

批准号：
10005927
负责人：
MICHELLE E EHRLICH
金额：
$ 311.56万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
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/10005927
关键词：
3-Dimensional AD transgenic mice Age Alzheimer&apos s Disease Alzheimer&apos s disease brain Alzheimer&apos s disease model Alzheimer&apos s disease pathology Alzheimer&apos 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 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 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 outcome forecast overexpression patient subsets precision medicine relating to nervous system screening single cell analysis single-cell RNA sequencing tau-1 transcriptome sequencing transcriptomics

项目摘要

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）病理的特征是存在磷酸化的tau 神经纤维缠结（NFTS），营养不良的神经运动和老年的大量细胞外β-淀粉样蛋白斑块。但是，AD的病因仍然难以捉摸，部分是由于临床和 AD患者的神经生物学/神经病理学特征。那样，广告中的异质性变得复杂发现疾病改良治疗和开发精确的体内指数的任务诊断和临床进展。已经提出了用于广告子类型的不同方法，但是它们通常不适合高维数据，也不适合由于缺乏机械见解。对不同广告子类型的知识和理解会增加关于最近失败的临床试验的启示，并规定了特异性调整治疗的潜力患者的更多均匀亚组。通过整合遗传，分子和神经成像数据更精确地定义AD子类型，我们可能能够更好地区分高度重叠的临床表型。此外，这种亚型的识别可能有可能提高我们对其潜在的病理机理的理解，对课程的预测以及开发新型疾病改良治疗。在此应用程序中，我们建议系统地提出通过开发和使用尖端来识别和表征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，蛋白质组学和功能性MRI数据集。我们将研究预测的基本关键驱动因素的功能作用广告子类型以及我们当前AMP-AD Consortia工作的三个候选主要驱动因素控制和AD HIPSC衍生的神经共培养系统，然后通过筛选在复杂的器官中单个细胞和整个细胞群分析中，顶级主要驱动因素的转录影响预测。每种单元格中的功能分析将用于建立与AD-SUBTYPE相关的证据表型。将生成单细胞RNA测序数据，以识别在然后将选定的驱动程序映射到亚型特定网络以构建综合每个驱动程序的信号图。广告子类型的前三名最有前途的驱动因素和三个现有的AMP-AD目标将使用a）独立的验尸队列进一步验证，b）重组小鼠，包括淀粉样变性，陶氏病和新的“人性化”模型。