Alzheimer's Disease-Related Dementia Models by Precision Editing and Relevant Genetic x Environmental Exposures

通过精确编辑和相关基因 x 环境暴露建立与阿尔茨海默病相关的痴呆模型

• 批准号: 10618758
• 负责人: Amy Dunn
• 金额: $ 84.99万
• 依托单位: JACKSON LABORATORY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-16 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10618758
• 关键词: Algorithmic Analysis; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease pathology; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Amyloid; Animal Model; Atlases; Basic Science; Behavioral; Blood Vessels; Brain; CRISPR/Cas technology; Cells; Cerebral Amyloid Angiopathy; Clinical Trials; Clustered Regularly Interspaced Short Palindromic Repeats; Cognitive; Cognitive deficits; Communities; Complex; Computer software; Coupled; Data; Databases; Dementia; Diagnosis; Diet; Dimensions; Disease; Disease Progression; Early identification; Elderly; Ensure; Environment; Environmental Exposure; Environmental Risk Factor; Exhibits; Fertility; Frontotemporal Dementia; Funding Mechanisms; Gene Expression Profile; Genetic; Genetic Variation; Health; Heterogeneity; Human; Image; Impaired cognition; Individual; Informatics; Knock-in; Knock-in Mouse; Late Onset Alzheimer Disease; Lewy Body Dementia; Longevity; Magnetic Resonance Imaging; Measurement; Memory Loss; Modeling; Molecular; Mus; Mutation; Neurodegenerative Disorders; Neurologic; Outcome; Pathogenesis; Pathologic; Pathology; Pathway Analysis; Pathway interactions; Patients; Pattern; Peripheral; Phase; Phenotype; Physiological; Population; Positioning Attribute; Preclinical Testing; Predisposition; Process; RNA; Reproducibility; Resources; Rest; Risk; Role; Route; Selection Criteria; Senile Plaques; Severity of illness; Staging; Structure; Symptoms; Synapses; Time; Tissues; Translating; Translations; United States National Institutes of Health; Variant; Vascular Dementia; Viral; alpha synuclein; amyloid pathology; behavior test; cognitive testing; cohort; combinatorial; comorbidity; data resource; disease-causing mutation; disorder risk; drug discovery; familial Alzheimer disease; feature detection; functional genomics; gene conservation; gene network; genome editing; genomic data; hippocampal sclerosis; human data; human disease; human model; image processing; improved; insight; mimetics; mixed dementia; model development; molecular phenotype; mouse genetics; mouse model; network models; neurobehavioral; neuroimaging; neuropathology; neurotoxic; new therapeutic target; next generation; novel; phenome; phenotypic data; preclinical study; programs; protein TDP-43; repository; resilience; response; risk variant; screening; sex; synucleinopathy; tau Proteins; therapeutic development; transcriptome sequencing; western diet

PROJECT SUMMARY Alzheimer’s disease is the most common cause of dementia in the elderly, but there are a number of other related dementias that exhibit substantial overlap in the behavioral, cognitive, and neuropathological manifestations of the disease. In fact, the majority of dementia cases likely arise from the co-occurrence of one or more of these AD and AD-related pathologies, with very few individuals exhibiting ‘pure’ Alzheimer’s pathology (e.g., only amyloid plaques). This complexity makes diagnosis and therapeutic development challenging, a problem exacerbated by a paucity of accurate animal models for ADRD that faithfully recapitulate the full spectrum of the molecular, cellular, cognitive, and behavioral pathologies of these dementias. In response to PAR-19-167, we will create a panel of genetically diverse knock-in mice harboring known mutations associated with AD and several related dementias using precise genomic editing to ensure biologically-relevant gene expression patterns and levels. In Aim 1, we will use CRISPR/Cas9 to create mice carrying combinations of disease-causing mutations in App, Psen1, Mapt, Tardbp, and Snca to produce a set of ‘core’ strains we expect to better capture the complexity of ADRD. To capture the role of genetic background in disease risk, we will then cross these ‘core’ mice to four genetic backgrounds known to promote susceptibility or resilience of ADRD (DBA/2J, FVB/NJ, WSB/EiJ, and C57Bl/6J). We will then leverage our expertise in high-throughput mouse neurobehavioral phenotyping to screen 16 new ADRD strains to identify the lines that best model ADRD. In Aim 2, we will use our deep phenotyping pipeline to fully characterize our top strains across the entire spectrum of ADRD-related symptoms, including both cognitive and non-cognitive domains. We will also use high-field MRI, histopathological measurements, and molecular phenotypes to assess effects on brain structure, extent of neuropathologies, and impact on gene networks and pathways associated with disease. Finally, in Aim 3, we will validate our new models for use in basic science and preclinical studies by determining concordance between mouse and human data and use network modeling approaches to identify early drivers of disease that predict late-stage outcomes in humans. This project will produce much-needed new models for AD and related dementias that will greatly enhance our understanding of the pathological mechanisms underlying these diseases. Finally, all of the models produced here will be distributed to the community via the JAX Repository. We will also make all of the phenotyping data publicly available using resources such as Mouse Phenome Database, GeneWeaver, and Synapse.

项目摘要 阿尔茨海默氏病是古老的痴呆症最常见的原因，但还有许多其他 相关痴呆症暴露于行为，认知和神经病理学的重叠 疾病的表现。实际上，大多数痴呆症病例可能是由一个同时发生的 或更多这些广告和与广告相关的病理，很少有人表现出“纯”阿尔茨海默氏症 病理学（例如，仅淀粉样斑块）。这种复杂性使诊断和治疗性发展 具有挑战性，这个问题加剧了准确的动物模型的忠实动物模型 概括了这些分子，细胞，认知和行为病理的全部光谱 痴呆症。为了响应19套167年，我们将创建一个藏有多元化的敲门老鼠的小组 使用精确的基因组编辑与AD和几个相关痴呆相关的已知突变，以确保 与生物学相关的基因表达模式和水平。在AIM 1中，我们将使用CRISPR/CAS9创建老鼠 在APP，PSEN1，MAPT，TARDBP和SNCA中携带引起疾病的突变的组合以产生集合 我们期望更好地捕获ADRD的复杂性的“核心”菌株。捕获遗传背景的作用 在疾病的风险中，我们将将这些“核心”小鼠越过已知促进的四种遗传背景 ADRD的敏感性或弹性（DBA/2J，FVB/NJ，WSB/EIJ和C57BL/6J）。然后，我们将利用我们的 高通量小鼠神经行为表型的专业知识屏幕16新的ADRD菌株以识别 最佳型号ADRD的线。在AIM 2中，我们将使用深层表型管道来充分表征我们的 全部与ADRD相关症状的全部菌株，包括认知和非认知症状 域。我们还将使用高场MRI，组织病理学测量和分子表型 评估对大脑结构的影响，神经病理的程度以及对基因网络和途径的影响 与疾病有关。最后，在AIM 3中，我们将验证我们的新模型用于基础科学和 临床前研究通过确定鼠标和人类数据之间的一致性并使用网络建模 鉴定预测人类晚期结局的疾病早期驱动因素的方法。这个项目将 为广告和相关痴呆症制作了急需的新模型，这将大大增强我们的理解 这些疾病背后的病理机制。最后，这里生产的所有模型都将是 通过JAX存储库分发给社区。我们还将公开制作所有表型数据 使用鼠标现象数据库，GeneWeaver和Synapse等资源可用。