Characterization of Alternative Polyadenylation in Alzheimer's Disease

阿尔茨海默病中替代多腺苷酸化的表征

• 批准号: 10321676
• 负责人: Leng Han
• 金额: $ 8.11万
• 依托单位: TEXAS A&M UNIVERSITY HEALTH SCIENCE CTR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10321676
• 关键词: Address; Algorithms; Alzheimer' s Disease; Alzheimer’s disease biomarker; Archives; Binding Sites; Biological Markers; Biology; Biomedical Research; Brain Diseases; Clinical; Clinical Medicine; Communities; Computational Biology; Data; Databases; Dementia; Dermal; Development; Disease; Disorientation; Drug Targeting; Equilibrium; Event; Foundations; Genomics; Genotype; Glutamates; Goals; Human; Impaired cognition; Individual; Investigation; Knowledge; Lung; Malignant Neoplasms; Medical; Memory Loss; Metabolic Diseases; Methylation; Molecular; Multiomic Data; Neural Network Simulation; Patient-Focused Outcomes; Pharmaceutical Preparations; Phase; Polyadenylation; Process; Prognostic Marker; Quantitative Trait Loci; RNA; RNA Splicing; Research; Role; Sampling; Science; Senile Plaques; Systems Biology; Techniques; Technology; Therapeutic; Training; Variant; Visualization; base; cholinergic; clinically relevant; cognitive function; computer framework; data resource; data-driven model; deep learning; deep neural network; diagnostic biomarker; effective therapy; genetic variant; genome editing; genome-wide; human disease; improved; innovation; insight; language impairment; large scale data; neuroinflammation; novel; novel therapeutic intervention; potential biomarker; precision medicine; predictive modeling; tau Proteins; therapeutic target; therapy development; trait; transcriptome; transcriptome sequencing; transcriptomics; user-friendly

Abstract Alzheimer's disease (AD) is a slowly progressive brain disorder characterized by cognitive decline, irreversible memory loss, disorientation, and language impairment. Recent advances in genomic technologies and the explosive genomic information related to disease have accelerated the convergence of discovery science with clinical medicine. We aim to utilize cutting-edge techniques in computational biology, RNA biology, and systems biology to identify novel prognostic and diagnostic biomarkers and to develop innovative therapeutic strategies for AD. We will establish a comprehensive archive of human polyadenylation sites by combining various APA databases. We will train a reliable deep neural network (DNN) model by considering both cis ad trans factors, and then apply this DNN prediction model to characterize APA events in AD samples across several AD consortia (Aim 1.1). We will develop highly efficient and accurate approaches based on deep learning to identify apaQTLs in order to maximize the utility of genotyping data to understand the functional effects of genetic variants in AD. We will perform integrative analysis with multi-omics data generated by other projects to understand the regulatory network, aiming to provide additional evidence for functional interpretation of apaQTLs in AD (Aim 1.2). We will perform integrative analysis with our established rigorous computational approaches to identify APA events associated with AD traits, in order to identify novel prognostic and diagnostic biomarkers for AD (Aim 2.1). To facilitate the utilization of large-scale data by the broad biomedical community, we will develop a comprehensive data resource to provide a computational framework that enables user-friendly interactive exploration and visualization of the biomedical significance of APA events (Aim 2.2). We expect to build a critical foundation to demonstrate that APA events represent novel types of biomarkers and serve as promising therapeutic targets to improve patient outcomes. Our proposed research could pave the innovative way for aiding precision medicine because we will develop highly innovative computational framework based on deep learning to identify APA events and perform apaQTL analysis to identify a novel class of APA-based biomarkers and therapeutic targets. The proposed research is of high significance because it will fundamentally advance our knowledge about the molecular basis of AD and contribute to a broader understanding of the overall complexity of AD.

抽象的 阿尔茨海默病 (AD) 是一种缓慢进展的脑部疾病，其特征是不可逆转的认知能力下降 记忆力减退、定向障碍和语言障碍。基因组技术的最新进展和 与疾病相关的爆炸性基因组信息加速了发现科学与 临床医学。我们的目标是利用计算生物学、RNA 生物学和 系统生物学，以确定新的预后和诊断生物标志物并开发创新的治疗方法 AD 策略。我们将结合人类多聚腺苷酸化位点建立全面的档案 各种 APA 数据库。我们将通过考虑顺式和逆式来训练可靠的深度神经网络（DNN）模型 反式因子，然后应用此 DNN 预测模型来表征 AD 样本中的 APA 事件 几个 AD 联盟（目标 1.1）。我们将基于深度开发高效、准确的方法 学习识别 apaQTL，以最大限度地利用基因分型数据来了解功能 遗传变异对 AD 的影响。我们将与其他机构生成的多组学数据进行综合分析 了解监管网络的项目，旨在为功能性提供更多证据 AD 中 apaQTL 的解释（目标 1.2）。我们将按照我们既定的严格标准进行综合分析 识别与 AD 特征相关的 APA 事件的计算方法，以确定新的预后 AD 的诊断生物标志物（目标 2.1）。方便广大群众利用海量数据 生物医学界，我们将开发全面的数据资源以提供计算框架 实现 APA 事件的生物医学意义的用户友好的交互式探索和可视化 （目标 2.2）。我们希望建立一个关键的基础来证明 APA 事件代表了新型的 生物标志物并作为有希望的治疗靶点来改善患者的治疗结果。我们提出的研究 可以为辅助精准医疗铺平创新之路，因为我们将开发高度创新的产品 基于深度学习的计算框架来识别 APA 事件并进行 apaQTL 分析 确定一类新型的基于 APA 的生物标志物和治疗靶点。所提出的研究具有较高的 意义重大，因为它将从根本上增进我们对 AD 分子基础的认识， 有助于更广泛地理解 AD 的整体复杂性。