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 阿尔茨海默氏病（AD）是一种缓慢进行性脑部疾病，其特征是认知能力下降，不可逆转 记忆丧失，迷失方向和语言障碍。基因组技术的最新进展和 与疾病有关的爆炸性基因组信息已加速了发现科学与 临床医学。我们旨在利用计算生物学，RNA生物学和 系统生物学识别新颖的预后和诊断生物标志物并发展创新的治疗性 广告策略。我们将通过组合建立人类聚腺苷酸位点的全面档案 各种APA数据库。我们将通过考虑两个顺式广告来培训可靠的深神经网络（DNN）模型 反式因素，然后应用此DNN预测模型以表征跨广告样本中的APA事件 几个广告联盟（AIM 1.1）。我们将基于深度开发高效，准确的方法 学会识别APAQTL，以最大化基因分型数据的实用性以了解功能 AD中遗传变异的影响。我们将使用其他由其他的多摩斯数据进行集成分析 了解监管网络的项目，旨在为功能提供其他证据 AD中APAQTL的解释（AIM 1.2）。我们将对既定的严格进行综合分析 识别与AD特征相关的APA事件的计算方法，以识别新颖的预后 和AD的诊断生物标志物（AIM 2.1）。促进广泛使用大规模数据的利用 生物医学界，我们将开发一个全面的数据资源来提供计算框架 这样可以使用户友好的交互式探索和可视化APA事件的生物医学意义 （AIM 2.2）。我们希望建立一个关键的基础，以证明APA事件代表了新颖的类型 生物标志物并作为有前途的治疗靶标，以改善患者的预后。我们提出的研究 可以为有助于精密医学的创新方式铺平方法，因为我们将发展高度创新 基于深度学习的计算框架，以识别APA事件并执行APAQTL分析 确定一类新的基于APA的生物标志物和治疗靶标。拟议的研究很高 意义是因为它从根本上可以提高我们对AD分子基础的了解和 有助于更广泛地了解AD的整体复杂性。