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免疫调节的分子表型 传染病 挑战：在过去的十年里，深入分析人类免疫系统的技术，无论是在 自然免疫和疫苗介导免疫的背景已经变得容易获得。这些方法 在传染病背景下产生了广泛的分子图谱。然而，现有的 研究主要集中在个体基因组数据集，而没有考虑潜在的分子 网络。因此，主要的重点一直是发现预测生物标记物，但这些生物标记物 可能经常是相关的代用品，与潜在的分子表型几乎没有联系 驾驶疾病病理生理学。 目标I建议开发和使用一种新的框架来集成基因组数据和三维(3D) 结构分辨蛋白质网络揭示感染性疾病免疫调节分子表型 疾病。而蛋白质网络通常被视为二维的，以蛋白质为节点和 它们之间的相互作用作为边，这种简化表示没有考虑到3D结构 蛋白质本身以及相应的相互作用界面。我过去的工作证明了 孟德尔积分中考虑相应结构信息的重要意义 突变与蛋白质网络，以阐明潜在的相应基因的分子表型 疾病，具有很高的敏感性和特异性。在这里，我建议开发一个新的框架，它集成了 利用宿主-病原体蛋白相互作用组网络生成3D宿主-病原体的结构基因组数据 互动。然后，将这些3D交互作用组网络与宿主(人类)基因数据相结合，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在以下两个方面的专业知识 网络系统生物学和机器学习，并在多个关键前沿推动边界。首先，它 为宿主基因数据与宿主-病原体蛋白网络的集成提供了一个新的框架。 其次，一个关键的创新是结合了与宿主-病原体蛋白相对应的结构信息 交互界面，提炼传统的关联内疚原则，并在具体细节上磨练 调节传染病风险的分子表型。识别出的分子表型将产生 关于相应疾病病理生理学的关键机械假说，并帮助设计 干预性策略。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，但 框架本身是可推广的，可以跨传染病环境使用。

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.