Dynamic regulatory network models of human response to influenza virus

人类对流感病毒反应的动态调控网络模型

• 批准号: 10762225
• 负责人: Ivan Marazzi
• 金额: $ 30.4万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-17 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10762225
• 关键词: Dynamic; regulatory; network; models; human; Dynamic; regulatory; network; models; human

PROJECT SUMMARY The goal of this project is to build mathematical models of human innate immune responses to the global pathogen influenza virus A (IAV). To ensure successful replication, viral pathogens must simultaneously hijack several components of the host cell machinery while either evading or disabling innate cellular defenses. The host genetic background and subsequent viral and host signaling interactions dictate disease severity ranging from asymptomatic to mortality. Recent studies of IAV in genetically diverse murine models confirm the critical role of genotype in host response and outcome. Both molecular targets as well as key proteins involved in IAV pathogenesis could be therapeutically exploited to attenuate or prevent disease. Thus, we construct models of the molecular networks driving early innate IAV response that can be used to model genetic effects. Our experimental system is human lung epithelium, the first-line of defense against and target of IAV. Aim 1. Genetic predictions from the gene regulatory network (GRN) governing human epithelial IAV response. GRNs describe the control of gene expression by transcription factors (TFs). We showed that integrating ATAC-seq with RNA-seq improves GRN accuracy. To construct a dynamic GRN in our heterogeneous lung tissue model, we propose scRNA-seq and scATAC-seq measurements of IAV infection and IFNβ stimulation time courses. Our group recently discovered new mechanisms by which the IAV protein Ns1 drives promoter-independent transcriptional “read-through” and alters 3D-chromatin architecture. Thus, for modeling, we also measure genomic transcription initiation and promoter-capture Hi-C. Following experimental testing and GRN refinement, we will use a deep-learning model trained on DNA sequence and epigenetic data to provide inputs that enable dynamic GRN simulations for thousands of human genotypes. We will identify genetic risk loci and molecular mechanisms driving difference in gene expression responses across individuals. Aim 2. Model the protein-protein interactions (PPIs) and cellular signaling networks driving the innate immune response to IAV. We developed mutant influenza viruses, each encoding a FLAG-tagged viral protein, while maintaining virulence in vivo. We will use the mutant IAV to map host-virus PPIs in human lung epithelial cells and mouse lung in vivo. Integrating with diverse ‘omics datasets, we will construct a molecular network model connecting virus-host PPIs through cellular signaling pathways to IAV-dependent TFs. We will test pathway reconstruction with epistasis mapping. Completion of both aims will lead to a GRN spanning virus-host PPIs and cellular signaling to TF control of gene expression in an innate-immune cell type. Our experimental-computational design is widely applicable. This model, and its future adaptation to other cells, will help identify the genetic and molecular mechanisms driving diverse human IAV responses and the network vulnerabilities to be exploited for IAV therapy.

项目摘要 该项目的目的是建立人类先天免疫反应的数学模型 病原体影响病毒A（IAV）。为了确保成功复制，病毒病原体必须简单地劫持 在逃避或禁用先天细胞防御的同时，宿主细胞机械的几个组件。这 宿主遗传背景以及随后的病毒和宿主信号传导相互作用决定疾病严重程度 从无症状到死亡率。在一般多样化的鼠模型中对IAV的最新研究证实了关键 基因型在宿主反应和结果中的作用。分子靶标以及参与IAV的关键蛋白 可以热剥削发病机理以减弱或预防疾病。那就是我们构建的模型 驱动可用于建模遗传作用的早期先天IAV反应的分子网络。我们的 实验系统是人类肺上皮，这是IAV的防御和目标的第一线。 目的1。遗传预测来自人类上皮IAV的基因调节网络（GRN） 回复。 GRN描述了通过转录因子（TFS）对基因表达的控制。我们表明了这一点 与RNA-Seq集成ATAC-SEQ可提高GRN精度。在我们的 异质性肺组织模型，我们提出了IAV感染的SCRNA-SEQ和SCATAC-SEQ测量 IFNβ刺激时间课程。我们小组最近发现了IAV蛋白NS1的新机制 驱动与启动子无关的转录“读取”，并改变3D-染色质体系结构。那，是 建模，我们还测量了基因组转录计划和启动子捕获HI-C。实验 测试和GRN改进，我们将使用在DNA序列和表观遗传数据上训练的深学习模型 提供能够为数千种人类基因型的动态GRN模拟的输入。我们将确定 遗传风险基因座和分子机制跨个体基因表达反应的驱动差异。 目标2。建模蛋白质 - 蛋白质相互作用（PPI）和驱动先天的细胞信号网络 对IAV的免疫反应。我们开发了突变的影响，每个影响都编码标记标记的病毒蛋白， 同时在体内维持病毒。我们将使用突变体IAV绘制人肺上皮中的宿主病毒PPI 细胞和小鼠肺在体内。与潜水员的OMIC数据集集成，我们将构建一个分子网络 通过细胞信号通路将病毒宿主PPI连接到IAV依赖性TF的模型。我们将测试 途径重建，并具有上毒映射。 两个目标的完成将导致跨越病毒宿主PPI的GRN和细胞信号传导，以控制TF 先天性免疫细胞类型中的基因表达。我们的实验计算设计广泛适用。 该模型及其对其他细胞的未来适应将有助于确定遗传和分子机制 驱动潜水员的人类IAV反应和IAV治疗探索的网络漏洞。