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: 10368920
• 负责人: Pankhuri Singhal
• 金额: $ 4.68万
• 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 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10368920
• 关键词: Affect; Aging; Alzheimer' s Disease; Alzheimer' s disease brain; Alzheimer' s disease patient; American; 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 Proteins; 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; base; 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 使用多组学数据集成研究 AD 亚型的分子基础 和机器学习 尽管对临床前阿尔茨海默氏病 (AD) 疾病模型进行了深入研究，但所有潜在疾病 - 修改药物在临床试验中失败了。大量的遗传学研究提出了许多生物学观点 然而，对于 AD 的遗传病因学尚未达成共识。这很可能是因为 将 AD 作为一种单一疾病的普遍观点过于简单化，并且没有考虑异质性致病因素 AD 遗传结构的变异。高通量研究表明 AD 是复杂、非线性的结果 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 关联研究成功揭示了与 AD 相关的基因，这些基因在一定程度上解释了疾病 患者群体所占比例很小，“遗传性缺失”的问题仍然存在。因此，在目标 1 中，我 建议在集成的多组学框架中使用线性和非线性方法与机器学习 确定 AD 中重要的途径。虽然几乎所有 AD 患者都表现出标志性的 b-淀粉样蛋白和 尽管神经原纤维缠结病理学不同，但它们在认知症状、行为和认知方面也表现出显着的变异性。 神经生理学。鉴于此，我假设 AD 相关通路和免疫通路存在个体间差异 在患者群体中驱动不同的疾病病因，最终形成共同的病理生理学。一 异质性的来源可能在于免疫通路在不同时期调节神经炎症反应 广告。在目标 2 中，我建议使用无监督分类方法来根据 患者在组学水平、成像数据和表型数据的通路变异方面的相似性。具体来说，我 假设先天免疫途径中的致病变异在驱动不同的 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 药物靶点优先排序的相互作用网络。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，通过精准医学方法来针对 AD 的特定组学亚型 而不是迄今为止失败的“一刀切”方法。通过研究 AD 的基因组异质性 这些目标有可能影响 AD 症状前个体的检测，并揭示更多信息 深入了解 AD 复杂的遗传结构。