Project 2 - Ex Vivo Analysis of Coronavirus Tropism, Adaptation, Replication, and Host Response

项目 2 - 冠状病毒趋向性、适应、复制和宿主反应的体外分析

• 批准号: 10551586
• 负责人: Adolfo Garcia-Sastre
• 金额: $ 79.57万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-20 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10551586
• 关键词: 2019-nCoV; A549; ATAC-seq; Affect; Antiviral Therapy; Appearance; Biochemical; Biological; Biological Models; COVID-19; COVID-19 pandemic; COVID-19 severity; CRISPR/Cas technology; Candidate Disease Gene; Cell Line; Cells; Cessation of life; ChIP-seq; Chemicals; Chiroptera; Clinical; Clinical Data; Clinical Research; Clustered Regularly Interspaced Short Palindromic Repeats; Coronavirus; Data; Development; Disease; Disease Outcome; Disease Progression; Epigenetic Process; Epithelial Cells; Generations; Genes; Genetic; Genetic Transcription; Hi-C; Human; Immune; Immune response; Infection; Inflammatory; Life Cycle Stages; Link; Lung; Maps; Measurement; Mediating; Modeling; Molecular; Molecular Profiling; Mus; Mutation; Network-based; Outcome; Pathogenicity; Pathway interactions; Post-Translational Protein Processing; Production; Proteins; Proteomics; Role; SARS coronavirus; SARS-CoV-2 variant; Sampling; Sarbecovirus; Severity of illness; Systems Biology; Tertiary Protein Structure; Testing; Tropism; Validation; Variant; Viral; Viral Proteins; Virus; Virus Diseases; Virus Replication; Zoonoses; candidate marker; cell type; clinical biomarkers; cross-species transmission; cytokine; data integration; data modeling; design; epigenomics; experimental study; improved; in silico; in vivo; in vivo Model; induced pluripotent stem cell; insight; knockout gene; lens; loss of function; machine learning algorithm; mutant; novel; novel therapeutics; pandemic disease; pathogen; pneumocyte; post SARS-CoV-2 infection; predictive marker; predictive modeling; protein expression; proteomic signature; receptor; response; single-cell RNA sequencing; small molecule; therapeutic target; transcriptome sequencing; transcriptomics; transmission process; virus host interaction

PROJECT 2: Ex Vivo Analysis of Coronavirus Tropism, Adaptation, Replication, and Host Response In this proposal, we hypothesize that multiple and discrete virus-host interactions and host responses to infection dictate the pathogenic outcome and host tropism of SARS-CoV-2. We further hypothesize that these critical circuits can be dissected through network-based modeling of system-level measurements offering the potential for the development of novel antiviral therapies. In Project 2, we propose to utilize leading edge OMICs- based measurements to define virus-host interactions and networks that control viral replication, viral tropism/adaptation and host responses across bat coronaviruses, SARS-CoV-1, and SARS-CoV-2 variants. In Aim 1, we will conduct proteomics (AP-MS, protein abundance and post-translational modifications), transcriptomics (RNA-seq and scRNA-seq), epigenomics (ATAC-seq, Hi-C and ChiP-seq) and genetic and chemical compound screens using ex vivo primary epithelial cells and iPSC-derived pneumocytes derived both from human and bats and a lung epithelial cell line (A549). In Aim 2, these data will be integrated and modeled with in vivo and clinical data collected in Project 1 using network-based predictive modeling approaches to identify host proteins, networks, and pathways that correlate with disease severity and host adaptation (‘driver genes’). Reiterative modeling of data generated in Aim 1 and Project 1 will be employed to refine these predictions. In Aim 3, CRISPR-editing approaches will be used in concert with targeted OMICs measurements (CRISPR-OMICs), including transcriptional, epigenetic, proteomic analyses, to validate the impact of these ‘driver genes’ on disease outcome and host tropism. Additional molecular, cellular, biochemical, and in vivo studies will be conducted to further characterize nodes that determine disease outcome as potential therapeutic targets. Overall, these studies will enable us to correlate in vivo and clinical biomarkers predictive of COVID-19 severity (Project 1) to specific genes and networks that impact viral infection, cellular host responses, and interspecies transmission.

项目2：冠状病毒嗜性、适应性、复制和宿主反应的离体分析 在这个建议中，我们假设多重和离散的病毒-宿主相互作用和宿主对 感染决定了SARS-CoV-2的致病结果和宿主嗜性。我们进一步假设， 关键电路可以通过基于网络的系统级测量建模来剖析， 开发新型抗病毒疗法的潜力。在项目2中，我们建议利用领先的OMIC- 基于测量来定义病毒-宿主相互作用和控制病毒复制的网络， 蝙蝠冠状病毒、SARS-CoV-1和SARS-CoV-2变异体的嗜性/适应性和宿主反应。在 目标1，我们将进行蛋白质组学（AP-MS，蛋白质丰度和翻译后修饰）， 转录组学（RNA-seq和scRNA-seq）、表观基因组学（ATAC-seq、Hi-C和ChiP-seq）和遗传学， 使用离体原代上皮细胞和iPSC衍生的肺细胞进行化合物筛选，两者均衍生 来自人类和蝙蝠以及肺上皮细胞系（A549）。在目标2中，这些数据将被整合和建模 利用项目1中收集的体内和临床数据，使用基于网络的预测建模方法， 确定与疾病严重程度和宿主适应性相关的宿主蛋白质、网络和途径（“驱动因素 genes '）。目标1和项目1中生成的数据的重复建模将用于细化这些 预测。在目标3中，CRISPR编辑方法将与目标OMIC测量相结合 （CRISPR-OMIC），包括转录，表观遗传，蛋白质组学分析，以验证这些影响 “驱动基因”对疾病结果和宿主向性的影响。其他分子、细胞、生物化学和体内 将进行研究，以进一步表征确定疾病结局的淋巴结，作为潜在的治疗方法， 目标的总的来说，这些研究将使我们能够将体内和临床生物标志物预测COVID-19相关联 严重性（项目1）对影响病毒感染、细胞宿主反应和 种间传播