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.

摘要 阿尔茨海默病(AD)是一种进展缓慢的大脑疾病，其特征是认知能力下降，不可逆转 记忆力丧失、定向障碍和语言障碍。基因组技术的最新进展和研究进展 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 系统生物学识别新的预后和诊断生物标记物并开发创新的治疗方法 应对AD的策略。我们将建立一个全面的人类多腺苷酸化位点档案，通过结合 各种APA数据库。我们将训练一个可靠的深度神经网络(DNN)模型，通过同时考虑顺、反两个因素 反式因素，然后应用该DNN预测模型来表征AD样本中的APA事件 几个AD财团(目标1.1)。我们将开发基于深度的高效和准确的方法 学习识别apaQTL，以便最大限度地利用基因分型数据来了解功能 遗传变异在AD中的作用。我们将对其他公司生成的多组学数据进行综合分析 了解监管网络的项目，旨在为以下功能提供额外证据 阿尔茨海默病中apaQTL的解释(目标1.2)。我们将根据我们已建立的严谨标准进行综合分析 识别与AD特征相关的APA事件的计算方法，以便识别新的预后 和AD的诊断生物标志物(AIM 2.1)。方便广大公众利用大规模数据 生物医学界，我们将开发一个全面的数据资源，提供一个计算框架 这使得能够以用户友好的交互方式探索和可视化APA事件的生物医学意义 (目标2.2)。我们希望建立一个关键的基础，以证明APA事件代表了 生物标志物，并作为有希望的治疗靶点，以改善患者的预后。我们建议的研究 可以为助力精准医疗铺平创新之路，因为我们将发展高度创新的 基于深度学习的识别APA事件和执行apaQTL分析的计算框架 确定一类新的基于APA的生物标记物和治疗靶点。提出的研究具有较高的研究价值。 因为它将从根本上提高我们对阿尔茨海默病的分子基础和 有助于更广泛地了解AD的整体复杂性。