Project 2 - Host-virus networks regulating flu replication and host responses ex vivo

项目2：宿主病毒网络调节流感复制和宿主反应离体。 Sumit Chanda，项目负责人; Megan Shaw，合作研究者; Ivan Marazzi，合作研究者，Nevan Krogan， 共同研究者。 在这个提议中，我们假设多重的、离散的分子通路决定流感疾病 严重性，并且这些途径引起可以通过基于网络的 系统级测量建模。在项目2中，我们建议利用领先的OMICS方法 以鉴定与临床疾病结果相关的离体分子标记。说明和 控制结果相关生物标志物特征的因素的表征提供了 开发新的抗病毒疗法。在目标1中，我们将使用系统生物学方法来阐明 与临床流感严重程度相关的网络签名。在这里，我们将分析对主机的更改 转录组、蛋白质组和代谢组，对不同致病性病毒感染的反应， 以及与临床结果相关的其他宿主干扰。与以下相关的离体分子特征 疾病严重程度将与项目1中生成的体内和临床数据进行整合和建模。在目标2中， 我们建议使用遗传工具来识别与结果相关的关键分子标记中的节点， 宿主反应途径和病毒复制的调节因子（“驱动基因”）。目标1和2都将依赖于 将离体特征与临床表型联系起来的重复实验和建模范例，以及 识别宿主蛋白和控制它们的途径。在目标3中，那些被发现调节通路的基因 并将进一步表征与疾病结果相关的标记（驱动基因）。我们建议 采用基于CRISPR的转录、表观遗传、蛋白质组和代谢谱分析， 深入了解这些因素在调节与疾病结果相关的反应中的作用（CRISPR-OMIC）。 将进行额外的分子、细胞、生物化学和体内研究以进一步表征这些化合物。 决定疾病结果的节点作为潜在的治疗靶点。

PROJECT 2: Host-virus networks regulating flu replication and host responses ex vivo. Sumit Chanda, Project Leader; Megan Shaw, Co-Investigator; Ivan Marazzi, Co-Investigator, Nevan Krogan , Co-Investigator. In this proposal, we hypothesize that multiple, discrete molecular pathways determine influenza disease severity, and that these pathways elicit biomarker signatures that can be identified through network-based modeling of system-level measurements. In Project 2, we propose to utilize leading edge OMICS approaches to identify ex vivo molecular signatures that correlate with clinical disease outcomes. The elucidation and characterization of factors that govern outcome-related biomarker signatures offer the potential for the development of novel antiviral therapies. In Aim 1, we will use a systems biology approach to elucidate network signatures associated with clinical influenza severity. Here, we will profile changes to the host transcriptome, proteome and metabolome, in response to infection using viruses of different pathogenicity, as well as additional host perturbations linked to clinical outcome. Ex vivo molecular signatures correlated with disease severity will be integrated and modeled with in vivo and clinical data generated in Project 1. In Aim 2, we propose to use genetic tools to identify nodes within outcome-related molecular signatures that are critical regulators of host response pathways and viral replication (`driver genes'). Both Aims 1 and 2 will rely on a paradigm of reiterative experimentation and modeling to link ex vivo signatures to clinical phenotypes, and identify the host proteins and pathways that govern them. In Aim 3, those genes found to regulate pathways and signatures associated with disease outcome (driver genes) will be further characterized. We propose to employ CRISPR-based analysis of transcriptional, epigenetic, proteomic, and metabolic profiles to provide insight into the role of these factors in regulating responses linked to disease outcomes (CRISPR-OMICs). Additional molecular, cellular, biochemical and in vivo studies will be conducted to further characterize those nodes that determine disease outcomes as potential therapeutic targets.