Investigating a molecular basis for Alzheimer's disease subtypes using multiomic data integration and machine-learning

使用多组数据集成和机器学习研究阿尔茨海默病亚型的分子基础

• 批准号: 10524780
• 负责人: Pankhuri Singhal
• 金额: $ 1.93万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10524780
• 关键词: Affect; Aging; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease patient; American; Amyloid; Amyloid beta-Protein; Automobile Driving; Behavior; Biochemical; Biological; Biological Process; Biology; Catalogs; Classification; Clinical Trials; Complex; Consensus; DNA; Data; Detection; Disease; Disease model; Drug Targeting; Early Diagnosis; Etiology; Failure; Functional disorder; Gene Expression Regulation; Gene Targeting; Genes; Genetic; Genetic Heterogeneity; Genetic Predisposition to Disease; Genetic study; Genome; Genomics; Genotype; Heritability; Heterogeneity; Image; Immune; Individual; Investigation; Knowledge; Late Onset Alzheimer Disease; Light; Machine Learning; Malignant Neoplasms; Medicine; Memory; Methods; Methylation; Molecular; Molecular Biology; Molecular Profiling; Multiomic Data; Natural Immunity; Neurobehavioral Manifestations; Neurodegenerative Disorders; Neurofibrillary Tangles; Non-linear Models; Online Mendelian Inheritance In Man; Pathogenicity; Pathology; Pathway Analysis; Pathway interactions; Patients; Pharmaceutical Preparations; Physiological; Play; Precision Medicine Initiative; Property; Proteins; Proteome; RNA; Role; Senile Plaques; Source; Structure; Symptoms; Validation; Variant; abeta accumulation; clinical phenotype; data integration; detection method; diagnostic strategy; disorder risk; disorder subtype; drug development; endophenotype; epigenome; gene network; genetic architecture; genetic risk factor; genome sequencing; genome wide association study; innate immune pathways; insight; inter-individual variation; knowledgebase; molecular subtypes; multiple omics; neural network; neuroimaging; neuroinflammation; neurophysiology; patient population; personalized diagnostics; phenomics; phenotypic data; pleiotropism; pre-clinical; precision medicine; profiles in patients; prognostic; protein expression; protein protein interaction; quantitative imaging; religious order study; response; tau Proteins; tau aggregation; therapeutic candidate; therapeutic gene; therapeutic target; transcriptome; transcriptome sequencing; unsupervised learning; whole genome

PROJECT ABSTRACT Investigating a molecular basis for AD subtypes using multiomic data integration and machine-learning Despite intense investigation into preclinical Alzheimer’s Disease (AD) disease models, all potential disease- modifying drugs have failed in clinical trials. Numerous genetic studies have proposed a number of biological mechanisms, however there has been no consensus on the genetic etiology of AD. This is likely because the prevailing view of AD as a singular disease is oversimplified and does not consider heterogeneous pathogenic variation in AD genetic architecture. High-throughput studies indicate that AD is a result of complex, nonlinear interactions within and between the genome, transcriptome, epigenome, and proteome. While genome-wide association studies have successfully revealed genes associated with AD, these genes explain disease in a small proportion of the patient population, and the question of “missing heritability” remains. Thus, in Aim 1, I propose using linear and nonlinear methods in an integrated multiomics framework with machine learning to identify pathways significant in AD. While almost all AD patients present the hallmark b-amyloid and neurofibrillary tangle pathology, they also present significant variability in cognitive symptoms, behaviors, and neurophysiology. Given this, I hypothesize that inter-individual variation in AD-associated and immune pathways drives different disease etiologies across the patient population culminating in a common pathophysiology. One source of heterogeneity may be in immune pathways differentially regulating neuroinflammatory response during AD. In Aim 2, I propose using an unsupervised classification approach to determine subtypes of AD based on patient similarity in pathway variation across omic levels, imaging data, and phenotypic data. Specifically, I hypothesize that pathogenic variation within innate immunity pathways plays a critical role in driving different disease etiologies between patients. In aim 3, I propose characterizing each omic subtype by generating protein interaction networks for drug target prioritization. Knowledge from these aims will inform a shift in the current AD drug development paradigm by informing a precision medicine approach to target specific omic subtypes of AD instead of a “one size fits all” approach that has failed to date. Investigating genomic heterogeneity in AD through these aims has the potential to impact detection of pre-symptomatic AD individuals as well as reveal more insights into the complex genetic architecture of AD.

项目摘要 利用多组学数据整合研究AD亚型的分子基础 和机器学习 尽管对临床前阿尔茨海默病（AD）疾病模型进行了深入研究，但所有潜在的疾病- 修饰药物在临床试验中失败了。许多遗传学研究已经提出了一些生物学上的 机制，但一直没有达成共识的遗传病因学的AD。这可能是因为 AD作为单一疾病的流行观点过于简单化，没有考虑异质性致病性 AD遗传结构的变异。高通量研究表明，AD是复杂的、非线性的 基因组、转录组、表观基因组和蛋白质组内部和之间的相互作用。虽然全基因组 关联研究已经成功地揭示了与AD相关的基因，这些基因以一种 这是一个小比例的患者群体，“缺失遗传性”的问题仍然存在。因此，在目标1中， 建议在机器学习的集成多组学框架中使用线性和非线性方法， 识别AD中重要的通路。虽然几乎所有的AD患者都表现出标志性的b-淀粉样蛋白， 神经系统缠结病理，他们也提出了显着的变化，认知症状，行为， 神经生理学鉴于此，我假设AD相关和免疫途径的个体间差异 在患者群体中驱动不同的疾病病因，最终导致共同的病理生理学。一 异质性的来源可能是在免疫途径中差异调节神经炎症反应， AD.在目标2中，我建议使用无监督分类方法来确定AD的亚型， 不同组学水平、成像数据和表型数据之间通路变异的患者相似性。我特别 假设先天免疫途径内的病原变异在驱动不同免疫途径方面发挥着关键作用 患者之间的疾病病因。在目标3中，我建议通过生成蛋白质来表征每个组学亚型， 药物靶向优先化的相互作用网络。从这些目标中获得的知识将为当前AD的转变提供信息 药物开发范例，通过告知精确医学方法来靶向AD的特定组学亚型 而不是迄今为止失败的“一刀切”的方法。研究AD的基因组异质性， 这些目标有可能影响症状前AD个体的检测， 深入了解AD的复杂遗传结构。

项目成果

Session Introduction: SALUD: Scalable Applications of cLinical risk Utility and preDiction.

会议简介：SALUD：临床风险效用和预测的可扩展应用。

• 发表时间: 2023
• 期刊: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
• 作者: Singhal,Pankhuri;Veturi,Yogasudha;Judy,Renae;Park,Yoson;Vujkovic,Marijana;Veatch,Olivia;Kember,Rachel;Verma,ShefaliSetia
• 通讯作者: Verma,ShefaliSetia

Gene Interactions in Human Disease Studies-Evidence Is Mounting.

人类疾病研究中的基因相互作用——证据正在不断增加。

• DOI: 10.1146/annurev-biodatasci-102022-120818
• 发表时间: 2023
• 期刊: Annual review of biomedical data science
• 作者: Singhal,Pankhuri;Verma,ShefaliSetia;Ritchie,MarylynD
• 通讯作者: Ritchie,MarylynD