Constructing A Transcriptomic Atlas of Retrotransposon in Alzheimer's Disease

构建阿尔茨海默病逆转录转座子转录组图谱

• 批准号: 10431366
• 负责人: Zhongming Zhao
• 金额: $ 31.2万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10431366
• 关键词: Aducanumab; Aging; Algorithms; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease patient; Antiviral Agents; Archives; Atlases; Autopsy; Benchmarking; Brain; Brain region; Cell Nucleus; Cells; Characteristics; Chromatin; Clinical; Clinical Trials; Communities; Computing Methodologies; DNA; DNA Methylation; DNA Sequence; Data; Data Set; Databases; Disease; Drosophila genus; Economic Burden; Elements; Endogenous Retroviruses; Epigenetic Process; Gene Expression; Gene Proteins; Genetic Diseases; Genetic Transcription; Genome; Genomics; Healthcare; Human; Human Genome; Innate Immune Response; Investigation; Knowledge; Mediating; Medical center; Memory; Methods; Methylation; MicroRNAs; Mining; Molecular; Multiomic Data; Nerve Degeneration; Neurodegenerative Disorders; Nuclear; Other Genetics; Outcome; Pathogenesis; Pathogenicity; Pathologic Processes; Patients; Pharmacotherapy; Play; Prevention; Proteins; Public Health; Publishing; RNA-Directed DNA Polymerase; Regulation; Relaxation; Repression; Research; Research Personnel; Resolution; Resources; Retrotransposon; Reverse Transcription; Role; Signal Pathway; Small Nuclear RNA; Specificity; Time; Tissue Sample; Tissues; Transcript; United States; Update; Visualization; base; brain tissue; cell type; complex data; data resource; design; drug efficacy; efficacy evaluation; epigenetic silencing; epigenome; epigenomics; expectation; experience; graph neural network; histone modification; human disease; improved; inhibitor; innovation; interest; machine learning method; novel; religious order study; sex; statistical and machine learning; tau Proteins; tau aggregation; tool; trait; transcriptome; transcriptome sequencing; transcriptomics; transposon/insertion element; treatment research; user-friendly; web portal; web site

Project Summary The number of AD patients is gradually increasing every year, and the economic burden of health care of AD patients, estimated at $335 billion in 2021, is predicted to triple by 2050. In the interest of public health and the economy, understanding AD genetics and finding effective AD prevention and treatment are important. Numerous studies have suggested that AD is a complicated genetic disorder, often involving genomic structural changes and regulation. Thus, there is a strong need to investigate not only regular genes, proteins, and their regulations but also the other genetic components in AD. Retrotransposons (RTEs) are DNA sequences that copy themselves and insert their copies into the genome. There has been some interest in studying retrotransposons in AD research. For example, it is known that chromatin relaxation mediated by Tau protein accumulation may overly activate the retrotransposons. This massive activation may provoke an innate immune response and damage the genome, which can result in neurodegeneration. Moreover, a study showed that antiviral drugs could suppress activation of RTEs in AD by inhibiting their reverse transcriptase, and the suppression results in the prevention of neurodegeneration. These studies suggested that investigating the features and roles of retrotransposons in AD will provide an additional and important way to understand the regulations of RTEs in AD pathogenesis. However, molecular characteristics of the RTEs in AD, such as cell type-/sex-specificity, are still unknown. Characterizing RTEs requires generating large-scale RTE expression datasets. Such data has not been available publicly, although the AD research community has made tremendous efforts to generate large-scale postmortem AD transcriptome data, including bulk RNA-seq of ~2,000 subjects and single-cell nuclei RNA-seq of ~260,000 cells. Therefore, we propose two specific aims to perform the first systematic study of RTE by constructing an RTE atlas resource for AD study: Aim 1. To generate large-scale RTE expression datasets by mining and processing public AD transcriptome datasets. We will extend our SalmonTE algorithm to mine RTE expressions from AD transcriptome datasets at both tissue-level and single- cell resolution. Aim 2. To generate AD RTE atlas resources by characterizing RTEs and AD patients using statistical and machine learning methods. We will expand our in-house computational methods to calculate context-specificity (e.g., brain region, cell type, and sex) of each RTE in human AD brains. We will also develop an unsupervised graph neural network using RTE expression and multi-omics data to characterize AD patients. In the end, we will create an atlas website to share our findings with the AD research community. Successful completion of this project will provide 1) novel computational methods to rigorously characterize RTEs in AD, 2) identification of context-specific RTEs in AD and characterization of AD patients using RTE expression, and 3) a well-annotated AD RTE atlas to deepen our knowledge in the molecular basis of AD.

项目概要 AD患者数量逐年增加，AD患者的经济负担 AD 患者的医疗保健费用预计到 2021 年将达到 3,350 亿美元，预计到 2050 年将增加两倍。为了公众利益 健康和经济、了解 AD 遗传学并找到有效的 AD 预防和治疗是 重要的。大量研究表明 AD 是一种复杂的遗传性疾病，通常涉及基因组 结构变化和监管。因此，不仅迫切需要研究常规基因、蛋白质， 及其调控，以及 AD 中的其他遗传成分。逆转录转座子 (RTE) 是 DNA 自我复制并将其副本插入基因组的序列。有人对此感兴趣 研究 AD 研究中的逆转录转座子。例如，已知 Tau 介导的染色质松弛 蛋白质积累可能会过度激活逆转录转座子。这种大规模的激活可能会激发一种与生俱来的 免疫反应并损害基因组，这可能导致神经退行性变。此外，一项研究表明 抗病毒药物可以通过抑制逆转录酶来抑制 AD 中 RTE 的激活，并且 抑制可预防神经变性。这些研究表明，调查 逆转录转座子在 AD 中的特征和作用将为理解 AD 提供一种额外且重要的方法 RTE 在 AD 发病机制中的调节。然而，AD 中 RTE 的分子特征，例如细胞 类型/性别特异性仍然未知。表征 RTE 需要生成大规模 RTE 表达式 数据集。尽管 AD 研究界已经取得了巨大的成果，但此类数据尚未公开。 努力生成大规模死后 AD 转录组数据，包括约 2,000 名受试者的大量 RNA 测序数据 以及约 260,000 个细胞的单细胞核 RNA-seq。因此，我们提出两个具体目标来实现第一个目标 通过构建用于 AD 研究的 RTE 图集资源来系统地研究 RTE：目标 1. 生成大规模 通过挖掘和处理公共 AD 转录组数据集来获得 RTE 表达数据集。我们将延长我们的 SalmonTE 算法从组织水平和单水平的 AD 转录组数据集中挖掘 RTE 表达 细胞分辨率。目标 2. 通过使用 RTE 和 AD 患者的特征来生成 AD RTE 图集资源 统计和机器学习方法。我们将扩展我们的内部计算方法来计算 人类 AD 大脑中每个 RTE 的上下文特异性（例如，大脑区域、细胞类型和性别）。我们还将开发 使用 RTE 表达和多组学数据来表征 AD 患者的无监督图神经网络。 最后，我们将创建一个图集网站，与 AD 研究界分享我们的发现。成功的 该项目的完成将提供 1) 新颖的计算方法来严格表征 AD 中的 RTE，2) 识别 AD 中特定背景的 RTE 并使用 RTE 表达来表征 AD 患者，以及 3) 注释完善的 AD RTE 图集，以加深我们对 AD 分子基础的了解。