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:10276964
- 负责人:
- 金额:$ 79.65万
- 依托单位:
- 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:
- 关键词:Adverse effectsAgingAlzheimer&aposs DiseaseAlzheimer&aposs disease related dementiaArtificial IntelligenceBayesian ModelingBindingBinding ProteinsBinding SitesBiologicalBrainCaringCell NucleusCellsCerebrospinal FluidChromatinClinicClinicalClinical DataCodeComplexComputersDataDatabasesDevelopmentDiseaseDisease OutcomeDrug CombinationsDrug TargetingElectronic Health RecordFoundationsGene Expression RegulationGene TargetingGenesGeneticGenomeGenomicsGenotype-Tissue Expression ProjectGoalsHi-CHistonesHumanHuman GenomeIntelligenceInvestmentsLinkMedicineMethodologyModelingMolecularMultiomic DataNerve DegenerationNetwork-basedNeurodegenerative DisordersNucleic Acid Regulatory SequencesPathogenesisPatientsPharmaceutical PreparationsPharmacoepidemiologyPharmacologic SubstancePlasmaPopulationPost-Translational Protein ProcessingPredispositionProcessProtein ConformationProteinsPublicationsQuantitative Trait LociRegulator GenesResearchSample SizeStructureSusceptibility GeneSystemSystems BiologyTechnologyTestingTherapeuticTherapeutic InterventionTransgenic MiceUnited StatesUnited States National Institutes of HealthUntranslated RNAValidationVariantbasebiobankbiomarker panelbrain healthcausal variantcell typecohortdata resourcedeep learningdrug candidatedrug discoverydrug repurposingendophenotypeethnic diversityexome sequencinggene discoverygenetic analysisgenetic architecturegenome wide association studygenome-widegenomic datahuman genome sequencingimproved outcomein silicoinnovationkernel methodsmouse modelmultimodalitymultiple omicsneglectneuroimagingnew therapeutic targetnovelpopulation basedprecision medicineprotein expressionprotein protein interactionprotein structurerare variantresearch and developmentrisk variantsingle cell analysisstatisticssuccesstargeted treatmenttherapeutic developmenttooltranscription factortranscriptomicswhole 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.
项目摘要
AD的易感性涉及复杂的、多基因的和多效性的遗传结构;此外,
没有减缓AD神经退行性过程的疾病修饰治疗。传统还原论者
但是,现有的治疗模式忽视了AD的固有复杂性,并且常常导致缺乏临床疗效的治疗。
有好处,也有负面影响。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的角度来看,
疾病(称为疾病模块)是网络医疗的本质。系统地查明和
新的潜在发病机制和疾病模块的表征,将作为识别的基础
以及验证AD中的新风险基因和药物靶点。根据我们的初步结果,我们认为基因组-
广泛的多模式人工智能(AI)框架,以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
发病机制和针对AD的靶向治疗干预的快速发展取得了巨大成功。要求1
将通过全基因组/外显子组测序(WGS/WES)
使用基于序列和结构的深度测序技术,
学习模式这些分析将汇集WGS/WES和阿尔茨海默病的临床数据
测序计划(ADSP),公开可获得的蛋白质结构(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,
FANTOM 5、PsychENCODE和NIH 4D Nucleome。目标2将确定GWAS的常见变体
与AD病理生物学和内表型相关的基因在细胞类型特异性的基因调控网络中富集,
使用贝叶斯框架。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:克利夫兰诊所卢鲁沃脑健康中心
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
这些组合将改变AD。该项目的成功完成将提供有能力和智能
基于计算机的工具箱,使搜索,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信息治疗发现
神经退行性疾病,如果广泛应用。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Lynn Bekris其他文献
Lynn Bekris的其他文献
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{{ truncateString('Lynn Bekris', 18)}}的其他基金
Multimodal single-cell genomic and epigenomic analyses elucidate Alzheimer’s sexual dimorphism in human immune systems aging
多模式单细胞基因组和表观基因组分析阐明了人类免疫系统衰老中的阿尔茨海默氏症性别二态性
- 批准号:
10467465 - 财政年份:2021
- 资助金额:
$ 79.65万 - 项目类别:
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
- 批准号:
10684138 - 财政年份:2021
- 资助金额:
$ 79.65万 - 项目类别:
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
- 批准号:
10475133 - 财政年份:2021
- 资助金额:
$ 79.65万 - 项目类别:
Biomarker Expression and Regulatory Haplotypes in Alzheimer's Disease
阿尔茨海默氏病的生物标志物表达和调节单元型
- 批准号:
8849625 - 财政年份:2014
- 资助金额:
$ 79.65万 - 项目类别:
Biomarker Expression and Regulatory Haplotypes in Alzheimer's Disease
阿尔茨海默氏病的生物标志物表达和调节单元型
- 批准号:
8700271 - 财政年份:2014
- 资助金额:
$ 79.65万 - 项目类别:
Biomarker Expression and Regulatory Haplotypes in Alzheimer's Disease
阿尔茨海默氏病的生物标志物表达和调节单元型
- 批准号:
8527655 - 财政年份:2012
- 资助金额:
$ 79.65万 - 项目类别:
Biomarker Expression and Regulatory Haplotypes in Alzheimer's Disease
阿尔茨海默氏病的生物标志物表达和调节单元型
- 批准号:
8442059 - 财政年份:2012
- 资助金额:
$ 79.65万 - 项目类别:
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