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: 10119596
• 负责人: Maria-Victoria Fernandez Hernandez
• 金额: $ 5.4万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10119596
• 关键词: Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease patient; Alzheimer' s disease risk; Amyloid beta-Protein; Astrocytes; Award; Big Data; Biological; Biology; Brain; Communities; Complement; Complex; Data; Deposition; Dimensions; Disease; Etiology; Genes; Genetic; Genotype; Goals; Grant; Human; Impairment; Individual; Investigation; Knowledge; Late Onset Alzheimer Disease; Light; Link; Metabolism; Molecular; Multiomic Data; Mutation; Pathogenicity; Pathologic; Pathology; Pathway Analysis; Pathway interactions; Phase; Phenotype; Prevention; Proteins; Proteomics; Quantitative Trait Loci; Research; Research Personnel; Role; Signal Transduction; Structure of molecular layer of cerebellar cortex; System; Systems Biology; Technology; Tissues; Training; Transcript; Variant; abeta deposition; biomarker identification; brain tissue; disease phenotype; early onset; induced pluripotent stem cell; insight; interest; machine learning method; metabolomics; molecular marker; multiple omics; new therapeutic target; novel; overexpression; protein aggregation; protein metabolite; tau Proteins; therapeutic target; 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）是一种多基因和多因素的疾病，具有共同的病理特征， Aβ和tau蛋白聚集体在脑中的沉积。一些基因直接参与了 蛋白质沉积代谢另外24个位点已被确定为AD的危险因素， 光进入其他受损的机制。我们对所有这些问题的理解存在着根本性的差距 途径相互关联，朝向相同的结束表型。Omic技术在以下方面发挥了重要作用： 补充了我们对特定基因座的破坏和最终的基因表达之间所涉及的途径的理解。 病理然而，这些研究中的每一项都只解释了AD病理学的一小部分， 复杂疾病涉及高度动态和相互作用的分子层系统。核心假设 不同的分子层在AD中是相互联系的，因此这些分子层中的任何一个的失调都会导致 最终的AD表型（Aβ和tau蛋白聚集体）。多组学分析可以提供一个洞察如何 不同的分子维度相互作用，这是单一的组学数据无法提供的。还有， 在同一组个体和组织上收集的多组学数据有限。的 该项目的目标是确定在分子层中一致的失调途径。在K99 在该奖项的第一阶段，我计划从转录组学，蛋白质组学和代谢组学中产生单组学概况。 脑组织的特征我还将利用这些人的现有GWA数据 进行成对整合分析，以确定作为遗传调节因子（QTL）的常见变异， 发现的失调分子标记。为了进行这些分析，我将接受网络培训， 路径分析，以及大数据和机器学习方法。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 使用转录组学数据已经鉴定出AGFG 2基因在AD病因学中过表达， 对照AGFG 2是星形胶质细胞表达的基因，似乎参与Aβ代谢。在K99 第一阶段将研究AGFG 2在来自AD患者的已知免疫缺陷病毒携带者的iPSC衍生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，我将探讨 AGFG 2的失调在ADAD中具有与在iPSC衍生的星形胶质细胞中相同的效果，从早期发病和晚期发病。 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 这将使我能够识别分子信号（转录本，蛋白质，代谢物），这些信号在整个过程中是一致的。 分子层如果成功，该项目有可能揭示AD生物学的新见解， 引起科学界的兴趣。此外，有了这个奖项，我将开发一个翻译的轮廓， 将是非常宝贵的建立我作为一个独立的调查员在不断增长的领域系统生物学。