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CAREER: Enabling Immunomodulatory Treatment of Influenza Infection using Multiscale Modeling

职业：利用多尺度建模实现流感感染的免疫调节治疗

基本信息

批准号：
1943777
负责人：
Jason Shoemaker
金额：
$ 54.75万
依托单位：
University of Pittsburgh
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943777&HistoricalAwards=false
关键词：
CAREER Enabling Immunomodulatory Treatment Influenza

项目摘要

Respiratory virus infections are a constant threat to public health. Influenza infection results in up to 700,000 hospitalizations and 56,000 deaths each year in the US. Extreme lung inflammation due to an excessive immune response is a major factor of severe disease outcomes in influenza virus and coronavirus infection (including SARS, MERS, and in emerging evidence, the COVID-19 virus.) The immune system is a complex, interactive, dynamic system that must optimally clear the virus infection while minimizing collateral damage to the lungs and other organs initiated by immune-regulated processes. Engineering-based mathematical modeling approaches are ideally suited to create computational simulations of the immune system that can be used to estimate the system’s response to virus infection. The overall goal of this research project is to construct realistic, predictive, physiologically accurate computational models of the natural immune response during influenza virus infection. The computational models will form the foundation for simulation-based research to identify the immunologic conditions that allow for excessive immune responses to occur. Simulations will also identify the best way to suppress immune activity, using chemical inhibition to reduce tissue inflammation while ensuring rapid clearance of the infection. As excessive inflammation is a common feature of many respiratory virus infections, the insights on immune regulation generated are expected to impact a variety of respiratory virus diseases. In parallel with this research effort, the research team will develop virtual reality (VR) games to better engage the public on matters associated with respiratory infection and immune responses. The VR games will be distributed to user smartphones for free to ensure strong public engagement. The VR games will also be used during workshops at local high schools to promote student engagement in engineering and immunology. The Investigator’s long-term CAREER vision is to engineer computational modeling-based solutions to regulate and improve immune system responses. Toward this vision, the objectives of this CAREER project are to identify the molecular/cellular mechanisms that drive lung inflammation during influenza infection and to evaluate immunomodulatory treatment in silico using multiscale computational modeling. Studies show that selectively inhibiting the immune system can significantly improve infection outcomes by reducing inflammation without compromising virus clearance. Immunomodulation has also been shown to offer greater protection than administration of antiviral medicines (e.g. oseltamivir), but no comprehensive guidelines for administering immunomodulatory treatments, such as anti-inflammatory corticosteroids, exist. Such immunomodulatory strategies are inherently an engineering optimization challenge: modifying immune responses to limit inflammation while ensuring virus clearance. The predictive models designed can be used to evaluate specific hypotheses on the mechanisms regulating inflammation during influenza virus infection. The research program is organized under three synergistic objectives. The FIRST Objective is to construct and use an ODE (Ordinary Differential Equations) model to identify the molecular drivers of inflammation in influenza-infected human lung epithelial cells. A model of epithelial intracellular signaling will be developed and used to uncover the molecular drivers of inflammatory protein production, evaluate trade-offs between inflammation and suppressing virus replication, and to identify possible virus-specific inflammatory regulation when comparing highly pathogenic (H5N1) and milder virus infections. Completion of this objective will provide evidence of the molecular mechanisms regulating lung epithelial inflammation in general and in specific virus infections. The SECOND Objective is to construct and use an ODE model to identify the immunologic conditions that drive enhanced inflammation in influenza-infected mouse lungs. The model will link lung epithelial signaling with immune cell infiltration. The model will be used to identify the mechanisms that drive enhanced inflammation at the tissue level and explore treatment options. Comparisons between infection with different viruses and between male (less severe) vs female (more severe) infection may reveal virus and cohort-specific inflammation regulation. Completion of this objective will identify the key molecular and cellular drivers of influenza-induced inflammation, provide a novel computational model of the lung immune system that enables comparisons between important infection cohorts, and potentially identify virus-specific or sex-specific immune regulation that is driving differential inflammation. The THIRD Objective is to construct and use an ABM (Agent Based Model) of the lung immune system to quantify the impact of cell heterogeneity on tissue-level inflammation. Interferon production is stochastic and may factor into variable infection outcomes. Using customized code, an ABM in which ODEs define how agents (epithelial cells) interact with their environment will be constructed and used to interrogate how epithelial cellular heterogeneity impacts tissue-level inflammation during influenza infection. Completion of this objective will identify the components of the immune system responsible for maintaining a tightly regulated inflammatory response, provide knowledge of how intra-subject inflammation may vary due to differences in immune regulation, produce new code for the simulation community, and produce a novel ABM of the lung immune system during influenza infection.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

呼吸道病毒感染是对公共卫生的持续威胁。在美国，流感感染每年导致多达700，000人住院治疗和56，000人死亡。由于过度免疫反应导致的极端肺部炎症是流感病毒和冠状病毒感染（包括SARS，MERS和新出现的证据，COVID-19病毒）严重疾病后果的主要因素。免疫系统是一个复杂的、相互作用的、动态的系统，必须最佳地清除病毒感染，同时最大限度地减少由免疫调节过程引起的对肺部和其他器官的附带损害。基于工程的数学建模方法非常适合创建免疫系统的计算模拟，可用于估计系统对病毒感染的反应。该研究项目的总体目标是构建流感病毒感染期间自然免疫反应的现实，预测，生理准确的计算模型。计算模型将为基于模拟的研究奠定基础，以确定允许发生过度免疫反应的免疫条件。模拟还将确定抑制免疫活性的最佳方法，使用化学抑制来减少组织炎症，同时确保快速清除感染。由于过度炎症是许多呼吸道病毒感染的共同特征，因此对免疫调节的认识预计将影响各种呼吸道病毒疾病。在这项研究工作的同时，研究小组将开发虚拟现实（VR）游戏，以更好地让公众参与与呼吸道感染和免疫反应相关的问题。VR游戏将免费分发到用户智能手机上，以确保公众的强烈参与。VR游戏还将在当地高中的研讨会上使用，以促进学生参与工程和免疫学。研究者的长期职业愿景是设计基于计算建模的解决方案，以调节和改善免疫系统反应。为了实现这一愿景，该CAREER项目的目标是确定流感感染期间驱动肺部炎症的分子/细胞机制，并使用多尺度计算模型在计算机上评估免疫调节治疗。研究表明，选择性抑制免疫系统可以通过减少炎症而不影响病毒清除来显着改善感染结果。免疫调节也被证明比抗病毒药物（如奥司他韦）提供更大的保护，但目前还没有全面的免疫调节治疗（如抗炎皮质类固醇）指南。这种免疫调节策略本质上是一种工程优化挑战：修改免疫反应以限制炎症，同时确保病毒清除。所设计的预测模型可用于评估流感病毒感染期间炎症调节机制的特定假设。该研究计划是根据三个协同目标。第一个目标是构建和使用ODE（常微分方程）模型来识别流感感染的人肺上皮细胞中炎症的分子驱动因素。将开发上皮细胞内信号传导的模型，并用于揭示炎症蛋白产生的分子驱动因素，评估炎症和抑制病毒复制之间的权衡，并在比较高致病性（H5 N1）和轻度病毒感染时确定可能的病毒特异性炎症调节。这一目标的完成将提供证据的分子机制调节肺上皮炎症的一般和特定的病毒感染。第二个目标是构建并使用ODE模型来识别导致流感感染小鼠肺部炎症增强的免疫条件。该模型将肺上皮信号传导与免疫细胞浸润联系起来。该模型将用于确定在组织水平上驱动炎症增强的机制，并探索治疗方案。不同病毒感染之间的比较以及男性（不太严重）与女性（更严重）感染之间的比较可能揭示病毒和群体特异性炎症调节。这一目标的完成将确定流感诱导炎症的关键分子和细胞驱动因素，提供一种新的肺部免疫系统计算模型，使重要感染队列之间的比较成为可能，并可能确定驱动差异炎症的病毒特异性或性别特异性免疫调节。第三个目标是构建并使用肺部免疫系统的ABM（基于代理的模型）来量化细胞异质性对组织水平炎症的影响。干扰素的产生是随机的，并可能成为可变感染结果的因素。使用定制的代码，一个ABM，其中ODE定义如何代理（上皮细胞）与他们的环境相互作用将被构建和用于询问上皮细胞异质性如何影响组织水平的炎症在流感感染。这一目标的完成将确定负责维持严格调节的炎症反应的免疫系统的组成部分，提供受试者内炎症如何由于免疫调节的差异而变化的知识，为模拟社区产生新的代码，并在流感感染期间产生肺部免疫系统的新型ABM。该奖项反映了NSF的法定使命，并被认为值得支持通过使用基金会的知识价值和更广泛的影响审查标准进行评估。