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

通过系统生物学视角（SYBIL）对 SARS-CoV 进行适应性改造

• 批准号: 10551578
• 负责人: Adolfo Garcia-Sastre
• 金额: $ 265.15万
• 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/10551578
• 关键词: 2019-nCoV; Animal Model; Animals; Antiviral Response; Appearance; Bioinformatics; Biological; Biological Markers; Blood; COVID-19; COVID-19 pandemic; COVID-19 severity; Cells; Cessation of life; Chiroptera; Clinical; Clinical Trials; Communities; Coronavirus Infections; Data; Data Set; Diabetes Mellitus; Disease; Disease Outcome; Disease Progression; Disease model; Epigenetic Process; Experimental Models; Generations; Genes; Genetic Screening; Human; Immune response; Infection; Influenza; Influenza A virus; Integration Host Factors; Intervention; Lighting; Link; Measurement; Modeling; Molecular; Molecular Profiling; Mus; Obesity; Outcome; Pathogenesis; Pathogenicity; Pathway interactions; Patients; Pharmaceutical Preparations; Process; Progress Reports; Proteome; Proteomics; Research; Risk Assessment; SARS coronavirus; SARS-CoV-2 infection; Sampling; Severity of illness; Source; Specific qualifier value; System; Systems Biology; Technology; Testing; Therapeutic Intervention; Tissues; Tropism; Vaccination; Validation; Variant; Viral; Viral Proteins; Virus; Virus Diseases; Virus Replication; Zoonoses; biomarker identification; clinically relevant; cohort; companion diagnostics; cross-species transmission; data integration; data management; epigenome; functional genomics; immunological status; improved; in vivo; induced pluripotent stem cell; influenza infection; influenzavirus; insight; lens; machine learning algorithm; metabolome; mouse model; multidisciplinary; multiple omics; mutant; network models; new therapeutic target; next generation; novel; novel therapeutics; novel virus; pandemic disease; pathogen; personalized medicine; predictive marker; predictive modeling; programs; protein expression; respiratory; response; targeted treatment; therapeutic target; transcriptome; transcriptomics; virus host interaction

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.

在COVID-19大流行之前，我们在同一个U19机制下， 团队获得多个基于OMICS的系统级测量，并使用建模将其集成 方法和机器学习算法来识别和验证主机病毒网络， 甲型流感病毒疾病的严重程度，血液中反映这些网络激活状态的生物标志物， 治疗干预的新宿主靶点。因此，我们确定了重要的宿主因素， 参与抗病毒反应和病毒复制和疾病的途径，导致潜在的治疗。的 2020年COVID-19大流行的突然爆发促使我们将系统生物学的工作转向研究 SARS-CoV-2感染。我们现在建议根据U19续期申请继续进行这些研究， 通过系统生物学透镜（SYBIL）的SARS-CoV适应。我们的基本假设是， 参与SARS-CoV复制和早期宿主反应的宿主网络调节疾病结果， 代表治疗干预的目标。由于这些病毒在蝙蝠中具有动物源性，我们还将 探索参与人类适应的病毒宿主网络，并负责改变 蝙蝠的原始宿主为了确定与SARS-CoV复制有关的临床相关网络， 我们建议将致病机理和宿主向性整合到预测和综合模型中， 1）来自人类队列的人类血液和呼吸道样本， 记录的SARS-CoV感染和不同的临床结果（项目1）; 2）来自 在各种条件和干扰下实验感染动物，导致各种疾病 结果（项目1）和3）人类和蝙蝠细胞进行病毒蛋白表达，遗传筛选， 感染不同的SARS-CoV，包括来源于iPSC前体的原代细胞（项目2）。样品 将被处理并发送到全球转录组学，表观遗传学，蛋白质组学和 功能基因组学分析OMICS数据集将由建模核心进行整合和比较， 生成疾病的特定假设和网络模型，发现血液生物标志物并识别宿主， 病毒复制、宿主反应、疾病结果和宿主嗜性的关键驱动因素。预测网络 调节器将被用作后续迭代扰动的来源，以改进现有的， 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。 生物信息学核心我们希望这些研究将揭示新的病毒-宿主致病网络， 与之相关的血液生物标志物、人类适应的决定因素和特定的治疗靶点。在 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 我们定义了与SARS-CoV蝙蝠-人类宿主跳跃COVID-19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离体特征。