The interaction effects of genetic variants, age, diet, sex and mitochondrial copy number on Alzheimer's disease, aging-phenotypes and longevity

遗传变异、年龄、饮食、性别和线粒体拷贝数对阿尔茨海默病、衰老表型和寿命的相互作用

• 批准号: 10551316
• 负责人: David George Ashbrook
• 金额: $ 46.2万
• 依托单位: UNIVERSITY OF TENNESSEE HEALTH SCI CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10551316
• 关键词: Address; Age; Age Months; Aging; Alzheimer' s Disease; Alzheimer' s disease risk; Behavioral; Bioinformatics; Biological Assay; Biological Markers; Biological Models; Biology; Blood; Candidate Disease Gene; Cell physiology; Cognition; Cognitive; Cognitive aging; Collection; Complex; DNA; DNA copy number; Data; Data Set; Databases; Diet; Disease; Environment; Etiology; Family; Fatty acid glycerol esters; Future; Generations; Genes; Genetic; Genetic Engineering; Genetic Variation; Genome; Genomics; Health; High Fat Diet; Hippocampus; Human; Human Genome; Impaired cognition; Inbreeding; Individual; Internet; Intervention; Kidney; Learning; Link; Liver; Liver Mitochondria; Longevity; Measures; Mediating; Memory; Metabolic; Methamphetamine; Mitochondria; Mitochondrial DNA; Modeling; Molecular; Motor; Mouse Strains; Mus; Muscle; Neuroanatomy; Outcome; Outcome Measure; Outcome Study; Pathogenicity; Performance; Peripheral; Phenotype; Population; Process; Proteome; Proxy; QTL Genes; Quantitative Trait Loci; Reactive Oxygen Species; Recombinants; Reproducibility; Research Proposals; Sampling; Services; Severity of illness; Sex Differences; Skin; System; Testing; Tissues; Transgenes; Transgenic Organisms; Translating; Variant; Whole Organism; Work; age effect; age related; biobank; cell type; clinical application; cognitive performance; cohort; data integration; empowerment; endophenotype; familial Alzheimer disease; functional decline; gene environment interaction; gene network; genetic analysis; genetic resource; genetic variant; genome wide association study; genome-wide; genomic locus; healthy aging; improved; metabolome; mouse model; novel; novel therapeutic intervention; phenome; precision medicine; segregation; sex; success; trait; transcriptome sequencing; web site

As the average age of the population increases, understanding the biology of longevity and diseases of aging is increasingly important. The key role of mitochondria in Alzheimer’s disease (AD) and pathogenic aging has been established in studies across species and mechanistically validated using genetically engineered models. Mitochondrial DNA copy number (mtDNAcn) changes with age and diet, in various tissues, and across species. Higher mtDNAcn is associated with better health outcomes in aging and with increased longevity, while decreased mtDNAcn is linked to disorders of aging including AD. However, we do not understand the mechanistic interaction between genetic variants, mtDNAcn, diet, sex, aging and AD. Here we propose to identify gene-by-environment interactions (GxE) that link mtDNAcn to AD- and aging- relevant phenotypes already collected in the recombinant inbred BXD and transgenic AD-BXD mouse lines, including longevity, memory, learning, motor, and neuroanatomical phenotypes. In Aims 1 and 2, we will test GxE, and identify loci underlying these interactions in three “peripheral” (skin, blood, muscle) and three “central” (liver, kidney, hippocampus) tissues. We will use previously gathered tissue from 45 BXD strains between 6- and 24-months old that had been fed either standard chow or high-fat diet, and quantify mtDNAcn. In Aim 3, we will identify relationships between mtDNAcn, age, sex and the familial AD transgenes (5XFAD), using tissue already collected from the AD-BXD. As part of Aims 2 and 3, we will re-produce a subset of the above strains and carry out analysis of mitochondrial function and reactive oxygen species generation to determine the link between mtDNAcn and mitochondrial function across tissues. In Aim 4, we will integrate our generated data with extensive behavioral data on age-related cognitive and other behavioral and CNS changes generated from BXD and AD-BXD. This will allow us to define loci, candidate genes, and mechanisms of AD and longevity and to systematically test for associations with age, sex, diet, and linked changes in mitochondrial DNAcn or function. Finally, we will integrate previously generated -omics data that we have for BXD and other genomes (e.g., RNA-seq, meth-seq, metabolomes and proteomes) with data from large human AD and mtDNAcn GWASs, and other existing -omics data. All results will be shared openly using robust internet services—Mouse Phenome Database, GeneNetwork, etc. Data and workflows will be FAIR-compliant. Key deliverables are far more quantitative, unbiased, global, and replicable data on genetic, molecular, and environmental processes that act with mitochondria to mediate cognitive loss, AD and longevity. We will also deliver causal molecular and mechanistic models that incorporate realistically high levels of genetic diversity— 6 million DNA variants. This work empowers in-depth, unbiased analyses of age-related functional decline that translates to human populations. Success will provide a platform in which to test novel interventions in this genomically- and environmentally- replicable population — so called “experimental precision medicine”.

随着人口平均年龄的增加，了解长寿的生物学和衰老疾病 越来越重要。线粒体在阿尔茨海默病（AD）和致病性衰老中的关键作用 已在跨物种研究中建立，并使用基因工程进行机械验证 模型。各种组织中的线粒体 DNA 拷贝数 (mtDNAcn) 会随着年龄和饮食而变化，并且 跨物种。较高的 mtDNAcn 与衰老过程中更好的健康结果相关，并且与增加的 长寿，而 mtDNAcn 减少与包括 AD 在内的衰老疾病有关。然而，我们不 了解遗传变异、mtDNAcn、饮食、性别、衰老和 AD 之间的机制相互作用。这里 我们建议识别将 mtDNAcn 与 AD 和衰老相关的基因与环境相互作用 (GxE) 已在重组近交 BXD 和转基因 AD-BXD 小鼠系中收集的表型，包括 长寿、记忆力、学习力、运动力和神经解剖学表型。在目标 1 和 2 中，我们将测试 GxE，并且 确定三个“外周”（皮肤、血液、肌肉）和三个“中枢”（肝脏、 肾、海马）组织。我们将使用之前从 6 到 6 之间的 45 个 BXD 菌株收集的组织。 喂食标准食物或高脂肪饮食的 24 个月大的动物，并量化 mtDNAcn。在目标 3 中，我们 将使用组织识别 mtDNAcn、年龄、性别和家族性 AD 转基因 (5XFAD) 之间的关系 已从 AD-BXD 收集。作为目标 2 和 3 的一部分，我们将重新生产上述菌株的子集 并进行线粒体功能和活性氧生成的分析以确定其中的联系 mtDNAcn 和跨组织线粒体功能之间的关系。在目标 4 中，我们将整合生成的数据 产生与年龄相关的认知和其他行为和中枢神经系统变化的广泛行为数据 来自 BXD 和 AD-BXD。这将使我们能够定义 AD 和 AD 的基因座、候选基因和机制。 长寿并系统地测试与年龄、性别、饮食和线粒体相关变化的关系 DNAcn 或功能。最后，我们将整合之前生成的 BXD 和其他组学数据 基因组（例如 RNA-seq、meth-seq、代谢组和蛋白质组）以及来自大型人类 AD 和 mtDNAcn GWAS 和其他现有的组学数据。所有结果将通过强大的互联网公开共享 服务——小鼠表型组数据库、基因网络等。数据和工作流程将符合 FAIR 标准。钥匙 可交付成果是关于遗传、分子和生物多样性的更加定量、公正、全球性和可复制的数据。 与线粒体作用介导认知丧失、AD 和长寿的环境过程。我们还将 提供包含实际高水平遗传多样性的因果分子和机制模型 - 600 万个 DNA 变体。这项工作能够对与年龄相关的功能衰退进行深入、公正的分析 这转化为人口。成功将为测试新的干预措施提供一个平台 这种基因组和环境上可复制的群体——所谓的“实验精准医学”。