Disease Model Development and Phenotyping Project

项目摘要疾病建模项目(DMP) 印第安纳大学/杰克逊实验室阿尔茨海默病精确模型的总体目标 中心(IU/JAX ADPMC)将开发、表征和分发更精确的临床前模型 阿尔茨海默病(AD)。IU/JAX ADPMC疾病模型开发和表型项目将 使用CRISPR基因组编辑来生成携带不同人类风险等位基因组合的小鼠模型 适用于晚发性AD(负荷)。此外，一些最广泛使用的现有AD模型将完全 其特点是开发更多临床相关的表型分析平台。我们已经组建了一支专家团队 在人类和小鼠遗传学、小鼠AD模型、基因组编辑、基因组学方法的理解上 复杂疾病(包括测序和计算建模)和各种生物过程 与AD有关(包括APP处理、胆固醇运输、神经炎症和血管生物学)。 我们有三个具体目标。目标1是全面描述APP/PS1、5xFAD和hTau，这三个最重要的 广泛使用的AD小鼠模型。APP/PS1和5xFAD携带淀粉样前体基因突变组合 导致人类早发性AD(EoAD)的蛋白质(APP)和早老素1(PSEN1)。应用/PS1和5xFAD 已被广泛用于研究淀粉样变性和神经炎症。HTau携带人野生型微管 相关蛋白Tau(MAPT)在缺乏小鼠MAPT的情况下与年龄相关的MAPT 过度磷酸化，聚集，和一些神经变性。我们还将广泛地描述一个新的 我们创建的负荷模型携带了负荷的两个最大遗传风险因素，载脂蛋白Eε4和 TREM2R47H。我们将优先考虑临床相关的终点，包括活体成像、血液和组织 生物标志物和基因组学，并将它们与更传统使用的终点进行比较，如行为分析 在临床上被证明是不可靠的。在目标2中，我们将产生40只携带新的 通过生物信息学和数据管理核心识别等位基因变异并在体内使用有效的 筛选策略，以确定有希望的模式，以通过深入的表型。在早年 对于中心，我们将优先了解GWAS型(ABCA7、BIN1和CR1)以及 我们从AD测序计划(NANOS1)和AD神经成像分析中鉴定的两个基因 主动性(IL1RAP)。所有创建的模型将在第一时间通过JAX AD提供 小鼠突变资源(ADMMR)。在目标3中，我们将全面描述显示重要AD的模型- 相关表型包括淀粉样蛋白沉积、tau病理和深部神经变性 目标1中描述的表型策略。所有新发现都将在人体组织中得到验证。在整个过程中 在融资期间，我们预计将产生和分发40款新车型，最多8款新车型和4款现有车型 模型将被广泛地描述。我们的策略将人和鼠标数据紧密结合在一起，因此这些新的 AD模型将在新的治疗靶点的临床前测试中显示出高度的临床可转译性。

PROJECT SUMMARY DISEASE MODELING PROJECT (DMP) The overall goal of the Indiana University/The Jackson Laboratory Alzheimer's Disease Precision Models Center (IU/JAX ADPMC) is to develop, characterize and distribute more precise preclinical models for Alzheimer's disease (AD). The IU/JAX ADPMC Disease Modeling Development and Phenotyping Project will use CRISPR genome editing to generate mouse models that carry different combinations of human risk alleles for late-onset AD (LOAD). In addition, some of the most widely used existing models for AD will be fully characterized to develop more clinically relevant phenotyping platforms. We have assembled a team of experts in human and mouse genetics, mouse models of AD, genome editing, genomic approaches to understand complex diseases (including sequencing and computational modeling) and various biological processes implicated in AD (including APP processing, cholesterol trafficking, neuroinflammation and vascular biology). We have three specific aims. Aim 1 is to fully characterize APP/PS1, 5xFAD and hTau, three of the most widely used mouse models of AD. APP/PS1 and 5xFAD carry a combination of mutations in amyloid precursor protein (APP) and presenilin 1 (PSEN1) that cause early-onset AD (EOAD) in humans. APP/PS1 and 5xFAD have been widely used to study amyloidosis and neuroinflammation. hTau carries human wild type microtubule associated protein Tau (MAPT) in the absence of mouse Mapt and develops age-related MAPT hyperphosphorylation, aggregation, and some neurodegeneration. We will also extensively characterize a new model of LOAD that we have created that carries the two greatest genetic risk factors for LOAD, APOEε4 and TREM2R47H. We will prioritize clinically relevant endpoints including in vivo imaging, blood and tissue biomarkers and genomics, and compare these to more traditionally used endpoints such as behavioral assays that have not proven reliable when translated to the clinic. In Aim 2, we will generate mice carrying 40 new allelic variants identified through the Bioinformatics and Data Management Core and use an efficient in vivo screening strategy to determine the promising models to pass through to deep phenotyping. In the early years of the center we will prioritize understanding GWAS variants (ABCA7, BIN1 and CR1) as well as variations in two genes identified by us from analyses of the AD Sequencing Project (NANOS1) and AD Neuroimaging Initiative (IL1RAP). All models created will be made available at the earliest opportunity through the JAX AD Mouse Mutant Resource (ADMMR). In Aim 3, we will fully characterize models that show important AD- relevant phenotypes including amyloid deposition, tau pathology and neurodegeneration using the deep phenotyping strategy described in Aim 1. All new findings will be validated in human tissues. Throughout this funding period, we anticipate 40 new models will be generated and distributed, and up to 8 new and 4 existing models will be extensively characterized. Our strategy closely integrates human and mouse data, so these new AD models will show a high degree of clinical translatability for preclinical testing of new therapeutic targets.