Identification and Characterization of Common Pathways across Alzheimer Disease Genotypes using a Multiomic Approach

使用多组学方法鉴定和表征跨阿尔茨海默病基因型的常见途径

• 批准号: 10397216
• 负责人: Maria-Victoria Fernandez Hernandez
• 金额: $ 7.68万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10397216
• 关键词: Abeta synthesis; Address; Age; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease pathology; Alzheimer' s disease patient; Alzheimer' s disease risk; Amyloid beta-Protein; Astrocytes; Autopsy; Award; Big Data; Biological; Biology; Brain; Categories; Cells; Clinical; Communities; Complement; Complex; Computational Technique; Data; Deposition; Development; Dimensions; Disease; Etiology; Evaluation; Event; Family; Gene Expression; Genes; Genetic; Genomics; Genotype; Goals; Grant; Impairment; Individual; Knowledge; Late Onset Alzheimer Disease; Lead; Light; Literature; Measures; Mentors; Metabolism; Molecular; Multiomic Data; Mutation; Parietal; Pathogenicity; Pathologic; Pathology; Pathway Analysis; Pathway interactions; Phase; Phenotype; Play; Presenile Alzheimer Dementia; Proteins; Proteomics; Quantitative Trait Loci; Research Personnel; Risk; Role; Signal Transduction; Structure of molecular layer of cerebellar cortex; Symptoms; System; Systems Biology; Technology; Time; Tissues; Training; Transcript; Variant; abeta deposition; base; brain tissue; design; differential expression; disease phenotype; early onset; functional genomics; genome editing; genome wide association study; genomic locus; induced pluripotent stem cell; insight; interest; machine learning method; metabolomics; molecular marker; molecular phenotype; multiple omics; new therapeutic target; novel; overexpression; presenilin-1; presenilin-2; protein aggregation; protein metabolite; tau Proteins; tau-1; transcriptomics

Alzheimer disease (AD) is a multigenic and multifactorial condition with a common pathological hallmark, deposition of Aβ and tau proteins aggregates in the brain. A few genes have been directly involved in the protein deposition metabolism. Another 24 loci have been identified as risk factors for AD which have shed light into other impaired mechanisms. There is a fundamental gap in our understanding of how all these pathways are interrelated towards a same ending phenotype. Omic technologies have been instrumental in complementing our understanding of the pathways involved between disruption of particular loci and final pathology. However, each one of these studies only explains a modest portion of the pathology of AD, whilst complex diseases involve a highly dynamic and interactive system of molecular layers. The central hypothesis is that different molecular layers are interconnected in AD so that the dysregulation of any of these causes the ultimate AD phenotype (Aβ and tau proteins aggregates). Multi-omic analysis can provide an insight into how different molecular dimensions interact with each other, an insight that single omic data cannot provide. Also, there is limited availability of multi-omic data collected on the same group of individuals and tissue. The objective of this project is to identify dysregulated pathways consistent across molecilar layers. In the K99 phase of the award, I plan to generate single-omic profiles (transcriptomic, proteomic and metabolomic) from brain tissue from highly characterized individuals. I will also leverage existing GWAs data for these individuals to conduct pair-wise integrative analysis to identify common variants that act as genetic regulators (QTL) for the identified dysregulated molecular markers. To conduct these analyses, I will gain training in network and pathway analysis, but also in big data and machine learning methods. During this period, I will also receive training in handling of induced pluripotent stem cells (iPSc) and in functional analysis. Preliminary analysis using transcriptomic data have identified AGFG2 gene to be overexpressed across AD etiologies compared to controls. AGFG2 is an astrocyte expressed gene that seems to be involved in Aβ metabolism. During the K99 phase I will examine the role of AGFG2 in iPSC-derived astrocytes from AD patients' carriers of known pathogenic mutations (ADAD). Having acquired this knowledge, during the R00 phase I will explore whether dysregulation of AGFG2 has the same effect in ADAD as in iPSC-derived astrocytes from early onset and late onset AD patients. Finally, I will elevate the pair-wise integration of omic data to a meta-dimensional level. This will allow me to identify molecular signals (transcripts, proteins, metabolites) that are consistent across molecular layers. If successful, this project has the potential to reveal novel insights of AD biology, which will be of interest to the scientific community. In addition, with this award I will develop a translational profile which will be invaluable for establishing me as an independent investigator in the growing field of systems biology.

阿尔茨海默病(AD)是一种多基因、多因素的疾病，具有共同的病理特征。 Aβ和tau蛋白在脑内聚集沉积。有几个基因直接参与了 蛋白质沉积代谢。另有24个基因座已被确定为AD的危险因素 光线进入其他受损的机制。我们对所有这些问题的理解存在一个根本性的差距 通向相同的结束表型的途径是相互关联的。基因技术一直在帮助 补充了我们对特定基因座中断和最终结果之间的途径的理解 病理学。然而，这些研究中的每一项都只解释了AD病理的一小部分，而 复杂的疾病涉及一个高度动态和相互作用的分子层系统。中心假说 在AD中不同的分子层是相互连接的，所以其中任何一个的失调都会导致 最终的AD表型(Aβ和tau蛋白聚集)。多组学分析可以提供对如何 不同的分子维度相互作用，这是单一的基因组数据无法提供的洞察力。另外， 在同一组个人和组织中收集的多组数据的可用性有限。这个 这个项目的目标是识别跨分子层一致的失调通路。在K99公路上 在颁奖阶段，我计划从以下方面生成单体组图谱(转录组、蛋白质组和代谢组) 来自具有高度特征的个体的脑组织。我还将利用这些个人的现有Gwas数据 进行配对综合分析，以确定作为基因调控(QTL)的常见变异 鉴定出的调控异常的分子标记。为了进行这些分析，我将接受网络和 路径分析，也是在大数据和机器学习方法中。在此期间，我还将收到 在处理诱导多能干细胞(IPSC)和功能分析方面的培训。初步分析 使用转录数据发现AGFG2基因在AD病因中过度表达 控制。AGFG2是星形胶质细胞表达的基因，可能与Aβ代谢有关。在K99期间 第一阶段将研究AGFG2在AD患者已知携带者的iPSC来源的星形胶质细胞中的作用 致病突变(ADAD)。掌握了这些知识后，在R00阶段，我将探索 AGFG2的失调在ADAD中的作用与IPSC来源的星形胶质细胞在早期和晚期的影响相同 起病的AD患者。最后，我将把组学数据的成对集成提升到元维度的水平。这 将使我能够识别分子信号(转录本、蛋白质、代谢物) 分子层。如果成功，这个项目有可能揭示AD生物学的新见解，这将 引起科学界的兴趣。此外，有了这个奖项，我将开发一个翻译简介， 对于在不断增长的系统生物学领域确立我作为独立研究员的地位将是无价的。