Core C - Modeling Core

项目总结/摘要 SARS-CoV-2感染导致不同的临床结果，从轻微症状到 住院治疗，有时甚至死亡。然而，预测COVID-10疾病结局的生物标志物 (i.e.,症状严重程度，病毒复制），这是预防医学的关键，尚未被 鉴定重要的是，阐明了控制预测特征的分子组分， 临床结果将提供重要的线索，指导新的治疗方法的开发。 战略布局该U19将建立在前一代FluOMICS下一代收集的大量数据的基础上 科学家和追求两个趋同的假设1）宿主先天免疫反应途径触发 由SARS-CoV-2感染决定COVID-19疾病的致病结果2）串扰 在SARS-CoV-2变异体和这些宿主先天途径之间， 翻译后签名有助于疾病严重程度和宿主嗜性的差异， 病毒建模核心的主要目标是使用基于网络的建模方法 整合项目1和项目2产生的实验数据，确定先天生物学 可以预测COVID-19疾病结果并在功能上进行验证的流程 项目1和项目2。在目标1中，建模核心将集成在 项目1来自人体内和小鼠体内研究的数据，并由 技术核心以及数据管理和生物信息学核心。建模核心将构建主机 反应网络模型，描述在COVID-19早期事件中发挥作用的病毒-宿主相互作用 感染和疾病，即，病毒复制，逃避宿主天然免疫反应， 感染和接种疫苗后以及触发有害的促炎性免疫应答。在 目标2，建模核心将应用启发式方法和项目1的迭代过程， 项目2.这种方法将有助于确定宿主-病原体相互作用下游的新途径。 重要的是，它将指导项目2中这些基因和途径的基因编辑的实施 以验证这些模型，并通过实验提高其准确性;这将有助于了解SARS- CoV-2变体影响Aim 1内置的病毒宿主网络。在目标3中，建模核心将使用 通过实验确定了病毒-宿主相互作用的3D结构和人工智能模型， AlphaFold，用于识别蝙蝠（冠状病毒的天然宿主）中的病毒-宿主相互作用， 将它们与人类的相互作用进行比较，以研究病毒的宿主嗜性并推断这些变化是如何发生的 可能会影响感染的致病结果。总体而言，建模核心将提供 该平台将指导识别和实验验证新的可药用靶点， 阻止病毒在受感染的宿主和全球人群中传播。

PROJECT SUMMARY/ABSTRACT SARS-CoV-2 infection leads to different clinical outcomes that range from mild symptoms to hospitalization and sometimes death. However, biomarkers predictive of COVID-10 disease outcomes (i.e., symptoms severity, viral replication), which are key for preventive medicine, have not yet been identified. Importantly, elucidation of the molecular components that govern predictive signatures of clinical outcomes will provide important clues that will guide the development of novel therapeutic strategies. This U19 will build on a wealth of data gathered by the previous FluOMICS next-generation scientist and pursue two converging hypotheses 1) host innate immune response pathways triggered by SARS-CoV-2 infection determine the pathogenic outcome of COVID-19 disease 2) the crosstalk between SARS-CoV-2 variants and these host innate pathways results in different transcriptional and post-translational signatures contributing to the difference in disease severity and host-tropism of the viruses. The primary objective of the Modeling Core will be to use network-based modeling approaches to integrate the experimental data generated by Project 1 and Project 2, identify innate biological processes that can predict COVID-19 disease outcomes and validate them functionally in collaboration with Project 1 and Project 2. In Aim 1, the Modeling Core will integrate OMIC datasets generated in Project 1 from data from Human in vivo and mice in vivo studies and processed and analyzed by the Technology Core and the Data Management and Bioinformatics Core. The Modeling core will build host response network models that depict the viral-host interaction at play in early events of COVID-19 infection and disease, i.e., viral replication, escape from the host innate immune response in natural infection and upon vaccination and triggering of the deleterious pro-inflammatory immune response. In Aim 2, the Modeling Core will apply heuristic approaches and an iterative process with Project 1 and Project 2. This approach will help identify novel pathways downstream of host-pathogen interactions. Importantly, it will guide the implementation of gene editing of these genes and pathways in Project 2 to validate these models and improve their accuracy experimentally; It will help understand how SARS- CoV-2 variants affect the viral-host networks built-in Aim 1. In Aim 3, the Modeling Core will use experimentally identified 3D structures of viral-host interactions and an artificial intelligence model, AlphaFold, to identify virus-host interaction in the bat, the natural host of the coronaviruses, and compare them to interactions in humans to study host-tropism of the virus and infer how these changes could impact on the pathogenic outcome of the infection. Altogether the Modeling core will provide a platform that will guide the identification and experimental validation of novel druggable targets that can stop the spread of the virus in infected hosts and worldwide populations.