权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
9894500
负责人：
CATHERINE COOK KACZOROWSKI
金额：
$ 244.96万
依托单位：
JACKSON LABORATORY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-16 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9894500
关键词：
Algorithmic Analysis Alzheimer&apos s Disease Alzheimer&apos s disease model Alzheimer&apos s disease related dementia Amyloid Animal Model Atlases Basic Science Behavioral Biological Blood Vessels Brain CRISPR/Cas technology Cells Cerebral Amyloid Angiopathy Clinical Trials Clustered Regularly Interspaced Short Palindromic Repeats Cognitive Cognitive deficits Communities Comorbidity Complex Computer software Coupled Data Databases Dementia Diagnosis Diet Disease Disease Progression Elderly Ensure Environment Environmental Exposure Environmental Risk Factor Exhibits Fertility Frontotemporal Dementia Funding Mechanisms Gene Expression Profile Genes 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 base behavior test cognitive testing cohort combinatorial data resource disease-causing mutation disorder risk drug discovery familial Alzheimer disease feature detection functional genomics gene conservation genome editing genomic data hippocampal sclerosis human data human disease human model image processing improved insight mimetics mixed dementia model development molecular phenotype 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

项目摘要

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病是老年痴呆症最常见的原因，但也有一些其他的原因。在行为、认知和神经病理方面表现出大量重叠的相关痴呆疾病的表现。事实上，大多数痴呆症病例可能是由一种痴呆症同时发生引起的这些AD和AD相关的病理中的一种或多种，很少有人表现出“纯粹的”阿尔茨海默氏症病理学（例如，仅淀粉样斑块）。这种复杂性使得诊断和治疗的发展具有挑战性的是，由于缺乏准确的ADRD动物模型，概括了这些疾病的分子、细胞、认知和行为病理学的全谱，痴呆症为了响应PAR-19-167，我们将创建一组遗传多样性敲入小鼠，使用精确的基因组编辑来确保与AD和几种相关痴呆症相关的已知突变生物相关基因表达模式和水平。在目标1中，我们将使用CRISPR/Cas9来创建小鼠携带App、Psen 1、Mapt、Tardbp和Snca中致病突变的组合，以产生一组我们希望通过“核心”菌株的筛选能够更好地捕捉ADRD的复杂性。为了抓住遗传背景的作用在疾病风险中，我们将这些“核心”小鼠与四种已知的基因背景杂交， ADRD的易感性或恢复性（DBA/2 J、FVB/NJ、WSB/EiJ和C57 B1/6 J）。然后我们将利用我们的在高通量小鼠神经行为表型方面的专业知识，以筛选16种新的ADRD菌株，最能模拟ADRD的线路。在目标2中，我们将使用我们的深度表型分析管道来充分表征我们的在整个ADRD相关症状谱中，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，神经病理学的程度，以及对基因网络和途径的影响与疾病有关。最后，在目标3中，我们将验证我们的新模型用于基础科学，通过确定小鼠和人类数据之间的一致性并使用网络建模进行临床前研究确定疾病早期驱动因素的方法，这些方法可预测人类的晚期结果。该项目将为AD和相关痴呆症生产急需的新模型，这将大大提高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通过JAX Repository分发给社区。我们还将公开所有的表型数据可使用诸如小鼠表型数据库、GeneWeaver和Synapse的资源获得。