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.

AD患者的数量每年都在逐渐增加，经济负担越来越重