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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：
10475089
负责人：
MICHELLE E EHRLICH
金额：
$ 227.24万
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 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10475089
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 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 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 clinical prognosis 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

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.

项目总结