Project 2: Identify and enhance LOAD-related signatures in outbred and genetically-engineered marmosets

项目 2：识别并增强近交系和基因工程狨猴中与 LOAD 相关的特征

• 批准号: 10494776
• 负责人: Gregory W Carter
• 金额: $ 39.66万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10494776
• 关键词: Aging; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease risk; Animal Model; Autopsy; Behavioral; Bioinformatics; Biological; Biological Markers; Biological Models; Callithrix; Cell model; Clinical; Clinical Research; Cognitive; Cohort Studies; Communities; Complement; Data; Data Aggregation; Dementia; Dimensions; Disease; Disease Outcome; Disease Progression; Disease model; Drug Targeting; Engineering; Evaluation; Event; Foundations; Future; Generations; Genetic; Genetic Engineering; Genetic Variation; Genome; Genomics; Genotype; Goals; Human; Human Genetics; Image; Impaired cognition; Knowledge Portal; Laboratories; Late Onset Alzheimer Disease; Link; Longevity; Methods; Modeling; Molecular; Outcome; Outcome Study; Pathology; Pathway interactions; Phenotype; Population; Primates; Process; Proteins; Research; Rodent Model; Serum; Statistical Models; Structure; Study models; Testing; Tissues; Validation; Variant; Work; analytical method; clinically relevant; data integration; data modeling; data-driven model; efficacy testing; functional genomics; genetic analysis; genetic association; genetic variant; genome-wide; genomic data; human data; human study; in vivo imaging; molecular marker; molecular scale; mouse model; multi-scale modeling; multimodality; multiple data types; multiple omics; neuropathology; nonhuman primate; phenotypic data; pre-clinical research; preclinical efficacy; preclinical study; presenilin-1; risk variant; targeted treatment; whole genome

PROJECT SUMMARY PROJECT 2 Determining the early molecular and cellular events in the origins and progression of late-onset Alzheimer’s disease (LOAD) will require an analytical approach that integrates genetic, molecular, in vivo imaging, and behavioral data. Many clinical studies with this goal are currently underway, which increasingly complement genetic data with genome-scale molecular data from biofluids and post-mortem tissues, in vivo imaging data of structure and neuropathology, and detailed cognitive data collected over disease progression. Transforming the outcomes of these studies into targeted therapeutic strategies requires translatable animal model systems, both for understanding the biological underpinnings of disease outcomes and preclinical efficacy testing of candidate treatments. The marmoset is potentially the most promising non-human primate model of LOAD, providing an analytical bridge between human studies and high-capacity cell and rodent model systems. Laboratory marmosets with outbred genetics can potentially provide a range of genotypic and phenotypic variation in relevant clinical outcomes. This standing variation can be augmented by genetically engineering variants at specific risk loci, as we have demonstrated with PSEN1. Phenotypic changes in multi-omic, imaging, cognitive, and cellular outcomes can be rigorously studied in an aging primate with an intermediate lifespan. However, to date there have not been systematic studies of aging marmosets at scale. In this project, we will initiate these systematic studies through integrated analyses of genetics and LOAD-related phenotypes in aging marmosets. We will then rigorously test correspondences between human and marmosets at all biological levels, from genetic to multi-scale models. Our goal is to develop the marmoset into a mature platform for preclinical research, which we will pursue with the following three aims: (1) assess natural genetic variation in outbred marmosets as a model Alzheimer’s disease risk in humans; (2) integrate genetic, genomic, and phenotype data to establish robust statistical models of disease in marmosets; and (3) evaluate disease relevance of models by aligning molecular markers of Alzheimer’s disease in marmosets with human study cohorts. Through this work, we expect to lay the foundations for LOAD-related functional genomics in marmosets, provide an expanded view of the impact of natural genetic variation in laboratory marmosets, prioritize genetic variants to engineer in marmosets, and create the first models of LOAD-related marmoset pathology at multiple scales.

项目总结项目2 确定迟发性阿尔茨海默病发生和发展的早期分子和细胞事件 疾病(LOAD)将需要一种分析方法，将遗传、分子、活体成像和 行为数据。许多以此为目标的临床研究目前正在进行中，这些研究日益补充 遗传数据与来自生物体液和死后组织的基因组规模的分子数据，活体成像数据 结构和神经病理学，以及关于疾病进展收集的详细认知数据。转变 这些靶向治疗策略的研究结果需要可翻译的动物模型系统，两者 为了了解疾病结局的生物学基础和候选患者的临床前疗效测试 治疗。 绒猴可能是最有希望的非人类灵长类负载模型，提供了一种分析 人类研究与大容量细胞和啮齿动物模型系统之间的桥梁。实验室的绒猴和 近亲交配遗传学可能在相关的临床中提供一系列的基因和表型变异 结果。这种长期的变异可以通过在特定风险基因座上进行基因工程变异来扩大，例如 我们已经用PSEN1进行了演示。多染色体、成像、认知和细胞的表型变化 可以严格研究中等寿命的老化灵长类动物的结局。然而，到目前为止，那里 还没有对大规模老化的绒猴进行系统的研究。 在这个项目中，我们将通过遗传学和负荷相关的综合分析来启动这些系统研究 老化绒猴的表型。然后，我们将严格测试人类和绒猴之间的对应关系 在所有生物层面，从基因到多尺度模型。我们的目标是将绒猴培育成成熟的 临床前研究平台，我们将追求以下三个目标：(1)评估自然遗传 作为人类阿尔茨海默病风险模型的近交绒猴的变异；(2)整合遗传、基因组和 和表型数据，以建立可靠的绒猴疾病统计模型；以及(3)评估疾病 通过匹配绒猴阿尔茨海默病分子标志物建立的模型与人类研究的相关性 一群人。通过这项工作，我们希望为与Load相关的功能基因组学奠定基础 绒猴，提供了实验室绒猴自然遗传变异的影响的扩展视图， 优先在绒猴中设计基因变异，并创建与负荷相关的第一个模型 多尺度的病理学。