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: 10363157
• 负责人: Leng Han
• 金额: $ 15.15万
• 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 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10363157
• 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; Modeling; 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; 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