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: 10549753
• 负责人: George ALLAN JOHNSON
• 金额: $ 123.13万
• 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/10549753
• 关键词: Age; Age Months; Age-associated memory impairment; Aging; Alzheimer' s Disease; Alzheimer' s disease model; Behavioral; Brain; Candidate Disease Gene; Cells; Cognition; Cognitive; Cognitive aging; Collaborations; Communities; Complex; DNA; Data; Data Set; Databases; Error Sources; Etiology; Exposure to; FAIR principles; Family; Female; Functional Magnetic Resonance Imaging; Functional disorder; Future; Genes; Genetic; Genetic Variation; Genome; Genomics; Genotype-Tissue Expression Project; Grain; Heritability; Hippocampus; 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; Neuroanatomy; Neurocognitive Deficit; Noise; Pathology; Pharmacotherapy; Phenotype; Population; Pre-Clinical Model; Preclinical Testing; Predisposition; 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; design; empowerment; endophenotype; familial Alzheimer disease; fear memory; functional decline; gene network; genetic testing; genetic variant; genome wide association study; high throughput analysis; imaging genetics; imaging program; improved; insight; male; memory process; mouse model; neocortical; 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.

阿尔茨海默病相关的认知功能下降（ARCD），阿尔茨海默病（AD）和迟发性AD相关的病理学是相关的。 大脑结构、细胞群、突触密度和连接、炎症、蛋白质 聚集和线粒体应激。然而，我们不了解复杂的因果网络， ARCD和AD的机制。在这个神经遗传学成像项目中，我们量化了人类家族性疾病的影响。 AD（FAD）基因变异对大脑结构和功能的影响，使用高度多样化的老龄小鼠杂交群体 将联合收割机人类基因变体与BXD家族结合起来。在目标1和2中，我们生成高分辨率的整体 40组性别匹配的转基因和老化对照杂交动物的脑MRI DTI数据和连接体 在约6个月和约14个月时使用最先进的分析工作流程。我们还生成匹配的行为数据， 作为光片免疫组织化学的整个大脑采取的情况下，最突出的子集 表型-对认知丧失高度敏感的品系和最有弹性的品系。光片， MRI-DTI和fMRI连接体与目标3中的MI-DTI合并。所有的工作都利用系统遗传学和绘图 我们开发并嵌入GeneNetwork Web服务的方法。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和CNS变化的广泛行为数据， 从AD-BXD和许多其他型号。这使我们能够定义位点、候选基因和机制 调节ARCD和AD，并系统地测试与年龄，性别和相关变化的相关性， 结构、连通性和细胞类型。最后，我们整合了BXD和其他基因组的组学数据， (e.g.,海马RNA-seq和蛋白质组）与全面的人类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。数据和工作流程将符合FAIR。主要成果是 (1)在遗传、分子、细胞和系统范围内， 与认知丧失和AD相关的过程。我们还提供因果分子和机械模型， 结合了高水平的遗传多样性--600万种DNA变异。这项工作使深入 对ARCD和AD中转化为人群的年龄相关功能下降的无偏倚分析。 成功将使ARCD干预和药物治疗的临床前测试更快，更强大 的AD。