UC Irvine MODEL-AD

加州大学欧文分校模型-AD

• 批准号: 10592219
• 负责人: Kim Green
• 金额: $ 906.77万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10592219
• 关键词: ATAC-seq; Academia; Affect; Aging; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease risk; Alzheimer' s disease therapy; Amyloid Beta A4 Precursor Protein; Amyloid beta-Protein; Animal Model; Animals; Apolipoprotein E; Award; Behavioral; Biochemical; Biochemistry; Bioinformatics; Biological Markers; Brain; Brain imaging; California; Cell Nucleus; Cells; Clustered Regularly Interspaced Short Palindromic Repeats; Communities; Data; Data Set; Databases; Deposition; Development; Diet; Disease; Disease Progression; Disease model; Electrophysiology (science); Ensure; Evaluation; Funding; Gene Expression; Generations; Genes; Genetic; Genetic Risk; Genome; Goals; Histology; Human; Image; Individual; Industry; Infrastructure; International; Investigation; Knowledge Portal; Late Onset Alzheimer Disease; Liquid substance; Longevity; Medical; Methods; Modeling; Mouse Strains; Mus; Mutation; Names; Nomenclature; Outcome; Pathology; Pathway interactions; Persons; Phase; Phenocopy; Phenotype; Physiology; Plasma; Population; Proteomics; Protocols documentation; Reproducibility; Research; Research Personnel; Resolution; Resources; Rodent; Sampling; Series; Societies; Synapses; Technology; Testing; The Jackson Laboratory; Therapeutic Intervention; Time; Tissues; United States National Institutes of Health; Universities; Validation; Variant; Vision; Work; base; biomarker validation; cognitive testing; combat; data management; data modeling; design; disorder subtype; effective therapy; efficacious treatment; environmental stressor; gene replacement; genome wide association study; human disease; human model; imaging modality; improved; insight; lipidomics; member; metabolome; metabolomics; microbiome; mouse model; neuroimaging; neurovascular; novel; open source; programs; response; risk variant; screening; single-cell RNA sequencing; socioeconomics; tau Proteins; therapeutic evaluation; therapeutically effective; transcriptome sequencing; transcriptomics; web site

ABSTRACT The goal of the University of California, Irvine MODEL-AD U54 Center is to develop novel mouse models of late- onset Alzheimer’s disease (LOAD), to deeply phenotype these and to make all data and mouse strains available to enable researchers to select the optimal mouse model and timepoints for therapeutic and intervention testing, as well as testing of hypotheses concerning mechanisms of LOAD. During the past five years, we have generated and deeply phenotyped mice with one component of our base genetic platform in which the Aß region of the App gene was converted from the rodent to the human sequence, and we have recently introduced the second component, a humanized MAPT (TAU) locus produced via gene-replacement. We have also used CRISPR and genome replacement to model and validate nine GWAS identified LOAD risk loci and have characterized mice with each of these both on a wild-type and 5xFAD background to determine their effects on plaque generation and damage exerted on the brain in response to pathology. In this continuation, we will use the results of these analyses to identify the combinations of LOAD risk variants most likely to phenocopy LOAD and introduce them on two complementary hAb-KI, hTAU, hAPOE4 platform lines, designed to mimic sub-types of AD that have been recently defined. To ensure translationability, we have an expanded focus on biomarkers and alignment with human phenotypes. To that end we have established a new Core – the Neuroimaging and neurovascular core (NIVC), which will provide brain imaging modalities currently employed in human AD subjects to align phenotypes in our mice with human disease progression. We have also expanded our fluid biomarker analysis efforts to include CSF, as well as plasma lipidomics and metabolomics to be compared to human AD plasma signatures. Similarly, our bioinformatics and data management efforts have been expanded to include single cell and nucleus RNA-seq and ATAC-seq, as well as spatial transcriptomics to enable alignment of data from our models with human AD signatures, but also to understand the mechanisms underlying disease progression in our mice. We are utilizing a comprehensive approach to evaluate our mice across their lifespans, which includes behavioral/cognitive assessment, electrophysiological analysis, super-resolution synaptic imaging, neuroimaging, bulk and single-cell RNA-seq, single cell level spatial transcriptomic analysis, unbiased proteomics, and microbiome and metabolome investigations. The UCI MODEL-AD Center will leverage the resources of our NIA-funded Alzheimer’s Disease Research Center combined with AMP-AD and other human AD datasets to facilitate comparisons to the human condition to identify the best mouse models to evaluate further. All data and models will be made available without restrictions, via The Jackson Labs, and data will be explorable via the modeladexplorer.org website, and raw data freely available for download via the AD Knowledge Portal.

摘要 加州大学欧文分校模型AD U 54中心的目标是开发新的小鼠模型， 阿尔茨海默病（LOAD），深入表型，使所有数据和小鼠品系可用 为了使研究人员能够选择最佳的小鼠模型和时间点进行治疗和干预试验， 以及关于LOAD机制的假设的测试。在过去的五年里，我们已经产生了 和深度表型小鼠与我们的基础遗传平台的一个组成部分，其中的App的April区域， 基因从啮齿动物转化为人类序列，我们最近引入了第二个 在一个实施方案中，所述基因组组分是通过基因置换产生的人源化MAPT（TAU）基因座。我们还使用了CRISPR， 基因组替换，以模拟和验证九个GWAS确定的LOAD风险位点， 以确定它们对噬斑产生的影响 以及病理学对大脑造成的损伤。在本续中，我们将使用这些结果 分析，以确定最有可能复制LOAD的LOAD风险变体的组合，并将其引入 在两个互补的hAb-KI、hTAU、hAPOE 4平台系上，设计用于模拟AD亚型， 最近被定义。为了确保可重复性，我们扩大了对生物标志物和比对的关注 人类的表型。为此，我们建立了一个新的核心-神经影像和神经血管 核心（NIVC），这将提供目前在人类AD受试者中采用的脑成像模式， 表型与人类疾病进展。我们还扩大了我们的流体生物标志物分析 努力纳入CSF以及血浆脂质组学和代谢组学，以与人AD血浆进行比较 签名.同样，我们的生物信息学和数据管理工作已经扩大到包括单细胞 和核RNA-seq和ATAC-seq，以及空间转录组学，使我们的数据对齐， 模型与人类AD签名，但也了解疾病进展的机制， 我们的老鼠我们正在利用一种全面的方法来评估我们的小鼠的寿命，其中包括 行为/认知评估，电生理分析，超分辨率突触成像， 神经影像学，批量和单细胞RNA-seq，单细胞水平空间转录组学分析，无偏 蛋白质组学、微生物组学和代谢组学研究。UCI模型-AD中心将利用 我们的NIA资助的阿尔茨海默病研究中心的资源与AMP-AD和其他人类 AD数据集有助于与人类状况进行比较，以确定最佳小鼠模型进行评估 进一步.所有数据和模型将通过杰克逊实验室无限制地提供，数据将 可通过modeladexplorer.org网站进行探索，原始数据可通过AD免费下载 知识门户。