Ex vivo interrogation of the genetic and biochemical networks controlling influe

项目摘要(见说明)；这一应用的基本假设是，宿主-病原体相互作用和反应的关键分子特征决定了病毒感染的致病结局。因此，全面了解病毒与宿主的相互作用、对病毒感染的先天反应和病毒逃避策略对于病毒致病机理的预测建模至关重要。在这里，我们将通过使用原代人类细胞在体外环境中调查宿主与病毒的相互作用，来全面概述在控制流感病毒感染方面发挥作用的遗传、化学和生化网络。将使用下一代测序技术来研究流感病毒复制对宿主的影响，以询问细胞RNA群体以确定转录组水平的变化(RNA-SEQ)，对RNA聚合酶参与的启动子进行全基因组调查(GRO-SEQ)，以及评估染色体格局中的表观遗传学变化(CHIP-SEQ)。此外，还将测量细胞内和细胞外代谢物水平、蛋白质丰度以及病毒感染引起的翻译后修饰的全球变化。将这些方法与全基因组功能基因组筛选和高通量蛋白质相互作用组分析相结合，将能够生成准确描述流感病毒与宿主之间相互作用等级的高分辨率网络。通过同时对三种驱动不同致病结果的病毒进行这些分析，对这些数据的计算建模将使我们能够识别病毒-宿主网络中预测病毒发病机制的关键节点。这些节点的体内和临床影响将分别在项目2和3中进行评估。

PROJECT SUMMARY (See instructions); The underlying hypothesis of this application is that critical molecular features of host-pathogen interactions and responses dictate the pathogenic outcome of viral infection. Thus, a comprehensive understanding of viral-host interactions, innate responses to viral infection, and viral evasion strategies is pivotal for predictive modeling of viral pathogenesis. Here, we will provide a comprehensive overview ofthe genetic, chemical, and biochemical networks that play a role in controlling influenza virus infection by investigating host-virus interactions in an ex vivo setting using primary human cells. The impact of influenza virus replication on the host will be studied using next generation sequencing technologies to interrogate cellular RNA populations to define transcriptome-level changes (RNA-seq), conduct genome-wide survey of promoters engaged by RNA polymerase (GRO-seq), as well as evaluate epigenetic alterations in the chromosomal landscape (CHiP-seq). Furthermore, global alterations in intracellular and extracellular metabolite levels, protein abundance, as well as post-translational modifications induced upon viral infection will be measured. Combining these approaches with genome-wide functional genomic screening and high-throughput protein interactome analysis will enable the generation of high-resolution networks that accurately depict the hierarchies of interactions between influenza virus and the host. By conducting these analyses simultaneously with three viruses that drive varying pathogenic outcomes, computational modeling of these data will enable us to identify critical nodes ofthe viral-host network that are predictive of viral pathogenesis. The in vivo and clinical impact of these nodes will be evaluated in Projects 2 and 3, respectively.