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: 10418807
• 负责人: Ivan Marazzi
• 金额: $ 90.77万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 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 2022-10-03
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10418807
• 关键词: 3-Dimensional; ATAC-seq; Affinity Chromatography; Alleles; Architecture; Attenuated; Automobile Driving; Binding; Biological Models; Cells; Cessation of life; Chromatin; DNA Sequence; Data; Data Set; Differential Equation; Disease; Engineering; Ensure; Epigenetic Process; Epithelial; Epithelial Cells; Experimental Designs; Experimental Models; Flow Cytometry; Future; Gene Expression; Genes; Genetic; Genetic Epistasis; Genetic Models; Genetic Polymorphism; Genetic Risk; Genetic Transcription; Genome; Genomics; Genotype; Goals; Heterogeneity; Hi-C; Host Defense; Human; Immune; Immune response; Individual; Infection; Influenza A virus; Innate Immune Response; Interferon Type I; Interferon-beta; Joints; Knowledge; Learning; Lung; Maps; Mass Spectrum Analysis; Measurement; Measures; Mediating; Modeling; Molecular; Molecular Target; Mouse Strains; Mus; Outcome; Pathogenesis; Pathway interactions; Pattern recognition receptor; Population; Prize; Proteins; Reporter; Role; Severity of illness; Signal Pathway; Signal Transduction; Signaling Molecule; Small Interfering RNA; Structure of parenchyma of lung; System; Techniques; Testing; Therapeutic; Time; Tissue Model; Tracheal Epithelium; Training; Transcription Elongation; Transcription Initiation; Untranslated RNA; Validation; Viral; Viral Proteins; Virulence; Virus; adaptive immune response; antimicrobial; base; cell behavior; cell type; cytokine; deep learning; deep learning model; design; experimental study; flu; forest; gene regulatory network; global health; high dimensionality; human disease; human model; human pathogen; improved; in vivo; influenza infection; influenzavirus; mathematical model; molecular modeling; mortality; mouse model; mutant; network models; pathogen; pathogenic virus; predictive modeling; prevent; promoter; protein protein interaction; reconstruction; response; risk variant; simulation; single-cell RNA sequencing; tool; transcription factor; transcription factor USF; transcription termination; transcriptome sequencing; virus genetics

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 该项目的目标是建立人类先天免疫反应的数学模型， 病原体流感病毒A（IAV）。为了确保成功复制，病毒病原体必须同时劫持 宿主细胞的几个组成部分，同时逃避或禁用先天性细胞防御。的 宿主遗传背景和随后的病毒和宿主信号相互作用决定了疾病的严重程度 从无症状到死亡最近在遗传多样性小鼠模型中对IAV的研究证实了 基因型在宿主反应和结果中的作用。IAV的分子靶点和关键蛋白 可以在治疗上利用发病机制来减轻或预防疾病。因此，我们构建了 驱动早期先天IAV反应的分子网络，可用于模拟遗传效应。我们 实验系统是人肺上皮细胞，它是抗IAV的第一道防线和靶点。 目标1.人类上皮IAV基因调控网络的遗传预测 反应GRNs描述了转录因子（TF）对基因表达的控制。我们发现 将ATAC-seq与RNA-seq整合提高了GRN准确性。为了在我们的 在异质肺组织模型中，我们提出了IAV感染的scRNA-seq和scATAC-seq测量， IFNβ刺激时间进程。我们的研究小组最近发现了IAV蛋白Ns 1 驱动启动子非依赖性转录“通读”并改变3D染色质结构。因此 建模，我们还测量基因组转录起始和启动子捕获Hi-C。以下实验 测试和GRN细化，我们将使用一个在DNA序列和表观遗传数据上训练的深度学习模型 为数千种人类基因型的动态GRN模拟提供输入。我们将确定 遗传风险位点和分子机制驱动个体间基因表达反应的差异。 目标二。模拟蛋白质-蛋白质相互作用（PPI）和细胞信号网络驱动先天性 对IAV的免疫反应。我们开发了突变流感病毒，每种病毒都编码一种FLAG标记的病毒蛋白， 同时保持体内毒力。我们将使用突变的IAV在人肺上皮细胞中定位宿主病毒PPI， 细胞和小鼠肺。结合不同的组学数据集，我们将构建一个分子网络 通过细胞信号传导途径将病毒宿主PPI与IAV依赖性TF连接起来的模型。我们将测试 上位性作图的通路重建。 这两个目标的完成将导致GRN跨越病毒宿主PPI和TF控制的细胞信号传导。 基因在先天免疫细胞中的表达。我们的实验-计算设计具有广泛的适用性。 这种模型及其未来对其他细胞的适应性将有助于确定遗传和分子机制 驱动不同的人类IAV反应和网络漏洞被利用用于IAV治疗。