Alzheimer's MultiOme Data Repurposing: Artificial Intelligence, Network Medicine, and Therapeutics Discovery

阿尔茨海默氏症多组数据再利用：人工智能、网络医学和治疗方法发现

基本信息

批准号：
10684138
负责人：
Lynn Bekris
金额：
$ 79.65万
依托单位：
CLEVELAND CLINIC LERNER COM-CWRU
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10684138
关键词：
Adverse effects Aging Alzheimer&apos s Disease Alzheimer&apos s disease related dementia Artificial Intelligence Bayesian Modeling Binding Binding Proteins Brain Caring Cell Nucleus Cells Cerebrospinal Fluid Chromatin Clinic Clinical Clinical Data Code Complex Computers Data Databases Dementia Development Disease Disease Outcome Drug Combinations Drug Targeting Electronic Health Record Foundations Gene Expression Regulation Gene Targeting Genes Genetic Genome Genomics Genotype-Tissue Expression Project Germ-Line Mutation Goals Hi-C Histones Human Human Genome Intelligence Investments Link Medicine Methodology Molecular Multiomic Data Nerve Degeneration Network-based Neurodegenerative Disorders Nucleic Acid Regulatory Sequences Pathogenesis Patients Persons Pharmaceutical Preparations Pharmacoepidemiology Pharmacologic Substance Plasma Population Post-Translational Modification Site Predisposition Process Protein Conformation Proteins Publications Quantitative Trait Loci Regulator Genes Research Sample Size Structure Susceptibility Gene System Systems Biology Technology Testing Therapeutic Therapeutic Intervention Transgenic Mice United States United States National Institutes of Health Untranslated RNA Validation Variant Visualization biobank biomarker panel brain health candidate identification causal variant cell type cohort data resource deep learning model drug candidate drug discovery drug repurposing endophenotype ethnic diversity exome sequencing gene discovery gene network gene regulatory network genetic architecture genome wide association study genome-wide genomic data human genome sequencing improved outcome in silico innovation kernel methods mouse model multimodality multiple omics neglect neuroimaging new therapeutic target novel population based precision medicine protein expression protein protein interaction protein structure rare variant research and development risk variant single cell analysis statistics success targeted treatment therapeutic development tool transcription factor transcriptomics whole genome

项目摘要

PROJECT SUMMARY Predisposition to AD involves a complex, polygenic, and pleiotropic genetic architecture; furthermore, there are no disease modifying treatments that slow the neurodegenerative process for AD. Traditional reductionist paradigms overlook the inherent complexity of AD and have often led to treatments that are lack of clinical benefits or fraught with adverse effects. Existing multi-omics data resources, including genetics, genomics, transcriptomics, interactomics (protein-protein interactions and chromatin interactions), have not yet been fully utilized and integrated to explore the pathobiology and drug discovery for AD. Understanding AD genetics and genomics from the point-of-view of how cellular systems and molecular interactome perturbations underlie the disease (termed disease module) is the essence of network medicine. Systematic identification and characterization of novel underlying pathogenesis and disease module, will serve as a foundation for identifying and validating novel risk genes and drug targets in AD. Given our preliminary results, we posit that a genome- wide, multimodal artificial intelligence (AI) framework to identify new risk genes and networks from human genome/exome sequencing and multi-omics findings enable a more complete mechanistic understanding of AD pathogenesis and the rapid development of targeted therapeutic intervention for AD with great success. Aim 1 will determine whether rare coding and non-coding variants by whole-genome/exome sequencing (WGS/WES) are enriched in protein-functional and gene-regulatory regions using sequence and structure-based deep learning models. These analyses will assemble WGS/WES and clinical data from Alzheimer's Disease Sequencing Project (ADSP), publicly available protein structure (i.e., protein-protein interfaces, protein-ligand binding sites, post-translational modifications) and sequence (expression quantitative trait locus [eQTLs], histone-QTLs, and transcription factor binding-QTLs) information from the PDB database, GTEx, NIH RoadMap, FANTOM5, PsychENCODE, and NIH 4D Nucleome. Aim 2 will determine whether GWAS common variants linked to AD pathobiology and endophenotypes are enriched in gene regulatory networks in a cell-type specific manner using a Bayesian framework. We will validate risk gene and network findings using WGS/WES and protein panel expression data from our existing cohorts: The Cleveland Clinic Lou Ruvo Center for Brain Health Aging and Neurodegenerative Disease Biobank (CBH-Biobank) and the Cleveland Alzheimer's Disease Research Center (CADRC). Aim 3 will test the hypothesis that risk genes and networks can be modulated via in silico drug repurposing, population-based validation, and functional test, to identify candidate agents and drug combinations that will modify AD. The successful completion of this project will offer capable and intelligent computer-based toolboxes that enable searching, sharing, visualizing, querying, and analyzing genetics, genomics, and multi-omics profiling data for genome-informed therapeutic discoveries for AD and other neurodegenerative disease if broadly applied.

项目概要 AD 易感性涉及复杂、多基因和多效性的遗传结构；此外，还有没有任何疾病修饰疗法可以减缓 AD 的神经退行性过程。传统还原论这些范式忽视了 AD 固有的复杂性，并且常常导致缺乏临床依据的治疗方法。好处或充满不利影响。现有的多组学数据资源，包括遗传学、基因组学、转录组学、相互作用组学（蛋白质-蛋白质相互作用和染色质相互作用）尚未完全利用和整合来探索 AD 的病理生物学和药物发现。了解 AD 遗传学从细胞系统和分子相互作用组扰动背后的角度来看基因组学疾病（称为疾病模块）是网络医学的本质。系统识别和新的潜在发病机制和疾病模块的表征，将作为识别的基础验证 AD 中的新风险基因和药物靶点。根据我们的初步结果，我们假设基因组- 广泛的多模式人工智能 (AI) 框架，用于识别人类新的风险基因和网络基因组/外显子组测序和多组学研究结果使人们能够更全面地了解 AD 的机制 AD 的发病机制和靶向治疗干预的快速发展取得了巨大成功。目标1 将通过全基因组/外显子组测序 (WGS/WES) 确定是否存在罕见编码和非编码变异使用基于序列和结构的深度富集蛋白质功能和基因调控区域学习模型。这些分析将汇集来自阿尔茨海默病的 WGS/WES 和临床数据测序项目 (ADSP)，公开可用的蛋白质结构（即蛋白质-蛋白质界面、蛋白质-配体结合位点、翻译后修饰）和序列（表达数量性状基因座 [eQTL]、组蛋白 QTL 和转录因子结合 QTL）信息来自 PDB 数据库、GTEx、NIH RoadMap、 FANTOM5、PsychENCODE 和 NIH 4D 核组。目标 2 将确定 GWAS 是否存在常见变异与 AD 病理学和内表型相关的细胞类型特异性的基因调控网络中丰富使用贝叶斯框架的方式。我们将使用 WGS/WES 验证风险基因和网络发现，来自我们现有队列的蛋白质组表达数据：克利夫兰诊所 Lou Ruvo 脑健康中心衰老和神经退行性疾病生物库 (CBH-Biobank) 和克利夫兰阿尔茨海默病研究中心（CADRC）。目标 3 将检验风险基因和网络可以通过以下方式调节的假设：计算机模拟药物再利用、基于人群的验证和功能测试，以确定候选药物和药物将修改 AD 的组合。该项目的成功完成将为我们提供有能力、有智慧的人才基于计算机的工具箱，可实现搜索、共享、可视化、查询和分析遗传学，基因组学和多组学分析数据，用于 AD 和其他疾病的基因组学治疗发现如果广泛应用的话，神经退行性疾病。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Altered gene expression in excitatory neurons is associated with Alzheimer's disease and its higher incidence in women.

DOI：
10.1002/trc2.12373
发表时间：
2023-01
期刊：
ALZHEIMERS & DEMENTIA-TRANSLATIONAL RESEARCH & CLINICAL INTERVENTIONS
影响因子：
4.8
作者：
Garcia, A Xavier;Xu, Jielin;Cheng, Feixiong;Ruppin, Eytan;Schaffer, Alejandro A
通讯作者：
Schaffer, Alejandro A

PDE5 inhibitor drugs for use in dementia.

DOI：
10.1002/trc2.12412
发表时间：
2023-07
期刊：
ALZHEIMERS & DEMENTIA-TRANSLATIONAL RESEARCH & CLINICAL INTERVENTIONS
影响因子：
4.8
作者：
Hainsworth, Atticus H;Arancio, Ottavio;Elahi, Fanny M;Isaacs, Jeremy D;Cheng, Feixiong
通讯作者：
Cheng, Feixiong