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.

项目摘要项目2 确定晚期阿尔茨海默氏症的起源和进展中的早期分子和细胞事件 疾病（负载）将需要一种分析方法，以整合遗传，分子，体内成像和 行为数据。目前正在进行许多有关此目标的临床研究，这越来越完整 带有基因组规模的分子数据的遗传数据，来自生物流体和验尸组织，体内成像数据 结构和神经病理学，以及有关疾病进展的详细认知数据。改变 这些研究对有针对性的治疗策略的结果需要可翻译的动物模型系统，这既需要 了解疾病结局的生物基础和候选者的临床前效率测试 治疗。 摩尔摩斯奶酪可能是最有前途的非人类私人负载模型，提供了分析 人类研究与高容量细胞和啮齿动物模型系统之间的桥梁。实验室摩托车 概念遗传学可以潜在地提供相关临床的一系列基因型和表型变异 结果。在特定风险基因座的基因工程变体中，可以增强这种常规变异，因为 我们已经证明了PSEN1。多运动，成像，认知和细胞的表型变化 结局可以在具有中间寿命的衰老灵长类动物中进行严格研究。但是，到目前为止 尚未对衰老的摩尔果会进行系统的研究。 在这个项目中，我们将通过遗传学和与负载相关的综合分析来启动这些系统研究 衰老果mos虫的表型。然后，我们将严格测试人和果酱之间的对应关系 从遗传到多尺度模型，在所有生物学水平上。我们的目标是将果酱发展成成熟 临床前研究平台，我们将以以下三个目的进行追求：（1）评估自然遗传 作为人类的阿尔茨海默氏病风险，杂种果mos虫的变化； （2）综合遗传，基因组， 和表型数据，以建立马尔莫斯特疾病的强大统计模型； （3）评估疾病 通过对人类研究中阿尔茨海默氏病的分子标记对齐模型的相关性 同伙。通过这项工作，我们期望在与负载相关的功能基因组中奠定基础 摩尔果会扩大对实验室摩尔群岛自然遗传变异的影响的扩展，， 将遗传变体优先为Marmoset的工程师，并创建与负载相关的Marmoset的第一个模型 多个尺度的病理。