Imaging Genetics of Brain Structure and Cognitive Aging in Murine Models of Alzheimer's Disease

阿尔茨海默病小鼠模型中大脑结构和认知衰老的影像遗传学

• 批准号: 10331876
• 负责人: George ALLAN JOHNSON
• 金额: $ 124.35万
• 依托单位: UNIVERSITY OF TENNESSEE HEALTH SCI CTR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10331876
• 关键词: Age; Age-Months; Age-associated memory impairment; Aging; Alzheimer' s Disease; Alzheimer' s disease model; Behavioral; Brain; Candidate Disease Gene; Cells; Chromosome Mapping; Cognition; Cognitive; Cognitive aging; Communities; Complex; DNA; Data; Data Discovery; Data Set; Databases; Error Sources; Etiology; Exposure to; FAIR principles; Family; Female; Functional Magnetic Resonance Imaging; Functional disorder; Future; Genetic; Genetic Variation; Genets; Genome; Genomics; Genotype-Tissue Expression Project; Grain; Heritability; Hippocampus (Brain); Human; Hybrids; Image; Immunohistochemistry; Impaired cognition; Inflammation; Informatics; Internet; Intervention; Knowledge Portal; Late Onset Alzheimer Disease; Left; Light; Link; Magnetic Resonance Imaging; Maps; Measures; Memory; Memory Loss; Methods; Microscopy; Mitochondria; Modality; Modeling; Molecular; Mus; Neocortex; Neurocognitive Deficit; Noise; Pathology; Pharmacotherapy; Phenotype; Population; Pre-Clinical Model; Preclinical Testing; Prevention; Process; Proteome; Protocols documentation; Research; Resolution; Rest; Services; Severities; Severity of illness; Stress; Structure; Synapses; System; Techniques; Testing; Time; Transgenes; Transgenic Mice; Transgenic Organisms; Translating; Variant; Work; age related; aging brain; biobank; brain magnetic resonance imaging; cell type; cohort; conditioned fear; connectome; data integration; density; design; endophenotype; familial Alzheimer disease; fear memory; functional decline; genetic variant; genome wide association study; high throughput analysis; imaging genetics; imaging program; improved; insight; male; memory process; mouse model; neurogenetics; neuron loss; normal aging; phenome; protein aggregation; resilience; sex; success; therapeutic target; tool; transcriptome sequencing; web services; whole genome

Age-related cognitive decline (ARCD), Alzheimer disease (AD), and late-onset AD-related pathologies are linked to changes in brain structure, cell populations, synapse densities and connections, inflammation, protein aggregation and mitochondrial stress. However, we do not understand the complex causal networks and mechanisms of ARCD and AD. In this neurogenetics imaging program we quantify the impact of human familial AD (FAD) gene variants on brain structure and function using a highly diverse cohort of aging mouse hybrids that combine human genes variants with the BXD family. In Aims 1 and 2 we generate high resolution whole brain MRI DTI data and connectomes for each of 40 sex-matched sets of transgenic and aging control hybrids at ~6 and ~14 months using state-of-the art analysis workflows. We generate matched behavioral data, as well as light-sheet immunohistochemistry for entire brains taken from subsets of cases with the most outstanding phenotypes—lines that are highly susceptible to cognitive loss and those that are most resilient. Light-sheet, MRI-DTI and fMRI connectomes is merged with MI-DTI in Aim 3. All work exploits systems genetics and mapping methods we have developed and embedded in the GeneNetwork web service. A crucial facet of Aim 3 is integrating extensive behavioral data on age-related cognitive and other behavioral and CNS changes generated from AD-BXD and many other models. This allows us to define loci, candidate genes, and mechanisms modulating ARCD and AD, and to systematically test for associations with age, sex, and linked changes in structure, connectivity, and cell types. Finally, we integrate omics data we have for BXD and other genomes (e.g., hippocampal RNA-seq and proteomes) with comprehensive human AD GWAS, imaging, and omics data. All results are shared openly using robust internet services—GeneWeaver, CIVM server, NIF, Mouse Phenome Database, and the AMP-AD Knowledge Portal. Data and workflows will be FAIR-compliant. Key deliverables are (1) far more quantitative, unbiased, global, and replicable data on genetic, molecular, cellular, and system-wide processes linked to cognitive loss and AD. We also deliver causal molecular and mechanistic models of that incorporate realistically high levels of genetic diversity—6 million DNA variants. This work empowers in-depth unbiased analyses of age-related functional decline in ARCD and AD that translate to human populations. Success will enable faster and more robust preclinical testing of interventions and drug treatments for ARCD and AD.

年龄相关性认知功能减退(ARCD)、阿尔茨海默病(AD)和迟发性AD相关的病理是相关的 脑结构、细胞群、突触密度和连接、炎症、蛋白质的变化 聚集和线粒体应激。然而，我们并不了解复杂的因果网络和 ARCD和AD的发病机制。在这个神经遗传学成像程序中，我们量化了人类家族的影响 AD(FAD)基因变异对脑结构和功能的影响 将人类基因变异与BXD家族相结合。在目标1和目标2中，我们生成高分辨率整体 40组性别匹配的转基因和衰老对照杂交种的脑MRI DTI数据和连接 在~6个月和~14个月使用最先进的分析工作流。我们也会生成匹配的行为数据 作为整个大脑的薄片免疫组织化学，取自最突出的病例的子集 表型--高度易受认知障碍影响的品系，也是最具弹性的品系。灯片， 在AIM 3中，MRI-DTI和fMRI连接与MI-DTI合并。所有工作都利用了系统遗传学和作图 我们开发的方法并嵌入到GeneNetwork Web服务中。目标3的一个关键方面是 整合与年龄相关的认知和其他行为及中枢神经系统变化的大量行为数据 来自AD-BXD和许多其他型号。这使我们能够定义基因座、候选基因和机制 调节ARCD和AD，并系统地测试与年龄、性别和相关变化之间的关系 结构、连接性和单元类型。最后，我们整合了BXD和其他基因组的组学数据 (例如，海马体RNA-seq和蛋白质组)，具有全面的人类AD GWAS、成像和组学数据。 所有结果都使用强大的互联网服务公开共享--GeneWeaver、CIVM服务器、NIF、Mouse Phenome 数据库和AMP-AD知识门户网站。数据和工作流程将是公平的。主要交付成果有 (1)更多关于遗传、分子、细胞和系统范围的量化、无偏见、全球化和可复制的数据 与认知丧失和阿尔茨海默病相关的过程。我们还提供了因果分子和机理模型。 结合实际的高水平的遗传多样性-600万个DNA变种。这项工作赋予了深入 对转化为人类的ARCD和AD中与年龄相关的功能衰退的无偏见分析。 成功将使ARCD干预和药物治疗的临床前测试更快、更有力 和AD。