Using three-dimensional protein networks to uncover immuno-modulatory molecular phenotypes in infectious disease

利用三维蛋白质网络揭示传染病中的免疫调节分子表型

• 批准号: 10295268
• 负责人: Jishnu Das
• 金额: $ 47.01万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10295268
• 关键词: 3-Dimensional; Antiviral Response; Automobile Driving; Biological Markers; Communicable Diseases; Data; Data Set; Databases; Disease; Disease Progression; Functional disorder; Generations; Genetic; Genetic Diseases; Genetic Variation; Genomics; Goals; Guilt; HIV; HIV risk; HIV/TB; Homology Modeling; Human; Human Genetics; Immune System Diseases; Immune system; Immunity; Individual; Influenza; Intervention; Machine Learning; Malaria; Mediating; Mendelian disorder; Modernization; Molecular; Molecular Profiling; Mutation; Pathway Analysis; Penetrance; Phenotype; Population Genetics; Proteins; Resolution; Role; Sensitivity and Specificity; Structural Protein; Structure; System; Systems Biology; Technology; Vaccines; Validation; Variant; Viral Proteins; Work; base; comparative; disorder risk; experience; flu; frontier; genetic variant; genomic data; genomic locus; genomic variation; immunoregulation; molecular phenotype; novel; pathogen; predictive marker; protein data bank; structural genomics; therapy design; three dimensional structure; two-dimensional

Using three-dimensional protein networks to uncover immuno-modulatory molecular phenotypes in infectious disease CHALLENGE: Over the past decade, technologies for deep profiling of the human immune system, both in the context of natural and vaccine-mediated immunity, have become readily available. These approaches have generated a wide range of molecular profiles across infectious disease contexts. However, existing studies primarily focus on individual `omic datasets, and do not take into account the underlying molecular networks. Thus, the primary emphasis has been on uncovering predictive biomarkers, but these biomarkers may often be correlative surrogates and have little or no connection with the underlying molecular phenotypes driving disease pathophysiology. GOAL I propose to develop and use a novel framework to integrate genomic data with three-dimensional (3D) structurally-resolved protein networks to uncover immuno-modulatory molecular phenotypes in infectious disease. While protein networks are typically viewed as two-dimensional, with proteins as nodes and interactions between them as edges, this simplifying representation fails to take into account the 3D structures of the proteins themselves, and the corresponding interaction interfaces. My past work has demonstrated the critical importance of taking into account corresponding structural information in the integration of Mendelian mutations with protein networks, to elucidate molecular phenotypes underlying the corresponding genetic disorders, with high sensitivity and specificity. Here, I propose to develop a novel framework that integrates structural genomic data with host-pathogen protein interactome networks to generate 3D host-pathogen interactomes. These 3D interactome networks are then integrated with host (human) genetic data to uncover immuno-modulatory molecular phenotypes in HIV and influenza. INNOVATION AND IMPACT: The proposed work integrates both two orthogonal facets of my expertise in network systems biology and machine learning, and pushes the envelope on multiple key frontiers. First, it provides a novel framework for the integration of host genetic data with host-pathogen protein networks. Second, a key novelty is the incorporation of structural information corresponding to host-pathogen protein interaction interfaces to refine the traditional principle of “guilt-by-association”, and hone in on specific molecular phenotypes that modulate infectious disease risk. The identified molecular phenotypes will generate key mechanistic hypotheses regarding corresponding disease pathophysiology, and help design interventional strategies. Finally, while the focus here is to use this approach in HIV and influenza, the framework itself is generalizable and can be used across infectious disease contexts.

使用三维蛋白质网络揭示免疫调节分子表型， 传染病 挑战：在过去的十年中，用于人类免疫系统深度分析的技术，无论是在 天然免疫和疫苗介导的免疫的背景已经变得容易获得。这些方法 已经在传染病背景下产生了广泛的分子谱。但现有 研究主要集中在单个的“组学数据集”，而不考虑潜在的分子 网络.因此，主要的重点是发现预测性生物标志物，但这些生物标志物 通常可能是相关的替代物，与潜在的分子表型很少或没有联系 驱动疾病病理生理学。 目标我建议开发和使用一种新的框架，将基因组数据与三维（3D） 结构解析蛋白质网络揭示感染性疾病中免疫调节分子表型 疾病虽然蛋白质网络通常被视为二维的，蛋白质作为节点， 它们之间的相互作用作为边缘，这种简化表示未能考虑到3D结构 以及相应的相互作用界面。我过去的工作证明了 考虑到相应的结构信息在孟德尔整合的关键重要性 突变与蛋白质网络，以阐明相应的遗传基础的分子表型 具有较高的敏感性和特异性。在这里，我建议开发一个新颖的框架， 利用宿主-病原体蛋白质相互作用组网络的结构基因组数据生成3D宿主-病原体 相互作用体然后将这些3D相互作用组网络与宿主（人类）遗传数据集成以揭示 HIV和流感的免疫调节分子表型。 创新和影响：拟议的工作整合了我的专业知识的两个正交方面， 网络系统生物学和机器学习，并推动多个关键前沿的信封。一是 提供了一个新的框架整合宿主遗传数据与宿主-病原体蛋白质网络。 第二，一个关键的新奇是将对应于宿主-病原体蛋白质的结构信息并入 互动界面，以完善传统的原则，“有罪的协会”，并磨练在具体的 调节传染病风险的分子表型。鉴定的分子表型将产生 关于相应疾病病理生理学的关键机制假设，并帮助设计 干预策略。最后，虽然这里的重点是在艾滋病毒和流感中使用这种方法， 框架本身是可推广的，可以在传染病背景下使用。