SARS-CoV adaptations through a Systems Biology Lens (SYBIL)

在新冠肺炎大流行之前，我们在相同的U19机制下聚集了一个多学科 团队获取多个基于OMICS的系统级别测量并使用建模将其集成 识别和验证主机病毒网络的方法和机器学习算法 甲型流感病毒疾病严重性，血液中反映这些网络和 治疗干预的新宿主靶点。因此，我们确定了重要的宿主因素和 参与抗病毒反应、病毒复制和疾病的途径，导致潜在的治疗方法。这个 2020年突如其来的新冠肺炎大流行促使我们将系统生物学的努力转向研究 SARS-CoV-2感染。我们现在建议在这份U19续期申请下继续进行这些研究 通过系统生物学透镜(SYBIL)适应SARS冠状病毒。我们潜在的主要假设是 参与SARS-CoV复制和早期宿主反应的宿主网络调节疾病结局和 代表治疗干预的目标。由于这些病毒起源于蝙蝠的人畜共患病，我们也将 探索参与人类适应并负责改变取向的病毒-宿主网络 原始蝙蝠是人类的宿主。为了识别与SARS-CoV复制有关的临床相关网络， 我们建议将致病机制和宿主取向整合到全球预测和综合模型中 三个系统在感染期间的反应1)来自人类队列的血液和呼吸道样本 记录的SARS冠状病毒感染和不同的临床结果(项目1)；2)来自 在各种条件和扰动下实验感染动物，导致各种疾病 结果(项目1)和3)人类和蝙蝠细胞受到病毒蛋白表达、基因筛选和 感染各种SARS冠状病毒，包括来自IPSC前体的原代细胞(项目2)。样本 将被处理并发送到技术核心进行全球转录、表观遗传学、蛋白质组学和 功能基因组学分析。组学数据集将由建模核心进行整合和比较，以 生成疾病的特定假说和网络模型，发现血液生物标记物，识别宿主和 病毒复制、宿主反应、疾病结局和宿主取向的关键驱动因素。预测网络 监管机构将被用作后续几轮迭代扰动的来源，以完善现有的和 确定新的网络模型。数据和模型将由数据管理部门管理和传播 和生物信息学核心。我们预计这些研究将发现新的病毒-宿主致病网络， 与它们相关的血液生物标记物、人类适应的决定因素和特定的治疗靶点。在……里面 总之，我们的建模方法将发现不同实验系统之间的关联，这将有助于 美国定义与SARS-CoV蝙蝠-人类宿主跳新冠肺炎严重程度相关的决定因素，并将它们联系起来 用于风险评估、伴随诊断和个性化治疗的体内和体外签名。

Previous to the COVID-19 pandemic, we assembled, under the same U19 mechanism, a multidisciplinary team to obtain multiple OMICS-based systems level measurements and integrate them using modeling approaches and machine learning algorithms to identify and validate host-virus networks that modulate influenza A virus disease severity, biomarkers in blood that reflect the activation states of these networks and novel host targets for therapeutic interventions. As a result, we have identified important host factors and pathways involved in antiviral response and virus replication and disease, leading to potential treatments. The sudden onset of the COVID-19 pandemic in 2020 prompted us to redirect our systems biology efforts to study SARS-CoV-2 infections. We now propose to continue these studies under this U19 renewal application entitled SARS-CoV adaptations through a Systems Biology Lens (SYBIL). Our underlying main hypothesis is that host networks involved in SARS-CoV replication and early host responses regulate disease outcomes and represent targets for therapeutic intervention. As these viruses have a zoonotic origin in bats, we will also explore virus-host networks involved in human adaptation and responsible for changing the tropism from the original bat host to humans. In order to identify clinically relevant networks involved in SARS-CoV replication, pathogenesis and host tropism we propose to integrate into predictive and comprehensive models global responses during infection in three systems 1) human blood and respiratory samples from human cohorts with documented SARS-CoV infection and diverse clinical outcomes (Project 1); 2) blood and tissues from experimentally infected animals under a variety of conditions and perturbations resulting in diverse disease outcomes (Project 1) and 3) human and bat cells subjected to viral protein expression, genetic screens and infection with diverse SARS-CoVs, including primary cells derived from iPSC precursors (Project 2). Samples will be processed and send to the Technology Core for global transcriptomics, epigenetics, proteomics and functional genomics analysis. OMICS data sets will be integrated and compared by the Modeling Core to generate specific hypothesis and network models of disease, uncover blood biomarkers and identify host and virus key drivers of viral replication, host response, disease outcome and host tropism. Predicted network regulators will be used as a source for subsequent iterative rounds of perturbations to refine existing and to identify new network models. Data and models will be managed and disseminated by the Data Management and Bioinformatics Core. We expect that these studies will uncover novel virus-host pathogenic networks, blood biomarkers associated with them, determinants of human adaptation and specific therapeutic targets. In summary, our modeling approaches will find associations between diverse experimental systems that will help us define determinants associated with SARS-CoV bat-human host jumping COVID-19 severity, and link them to in vivo and ex vivo signatures for risk assessment, companion diagnostics and personalized therapies.