Quantifying and Validating Immune Response Dynamics for Influenza and Viral-Bacterial Pneumonias

项目摘要 甲型流感病毒（IAV）和继发性细菌感染（SBI）是导致大量疾病的原因 每年的死亡人数。这些疾病的管理是困难的，部分原因是缺乏对复杂疾病的了解。 宿主-病原体相互作用的相互作用。为了推进开发有效治疗方法的目标，新的微生物 可以评估宿主免疫反应如何限制病毒负荷并增强细菌入侵的工具 量化细节至关重要。该补助金旨在通过以下方式解决IAV和SBI生物学知识的差距 利用预测数学模型，这些模型经过校准并随后通过定量 实验数据拟议的研究将严格的动力学建模与有针对性的实验研究相结合，以： (1)量化病毒感染细胞的杀伤，以解释平台状病毒峰值和快速病毒下降， 确定杀伤是否是密度依赖性的，以及CD 8 + T细胞如何促进病毒衰变;（2）定量 在流感期间上皮细胞和免疫细胞产生IFN-α/β，以解释IFN-β和IFN-γ的双峰。 IFN-α的持续平台，并确定它们如何发挥作用以限制病毒感染;（3）确定AM如何成为 通过量化它们的衰变，并确定病毒载量和SBI如何因损失而改变， 这些细胞。在这些研究中，我们将开发和分析机械数学模型， 定量感染数据，实验测试特定模型预测，并使用生成的数据来完善和 扩展模型。这种迭代模型驱动的实验方法将导致详细的和定量的 了解对流感的免疫反应以及这些反应如何导致病毒-细菌合并感染 致病性这种研究方法是理解免疫反应中复杂反馈的关键， 揭示了治疗和预防流感及相关细菌感染的新靶点 感染.

Project Summary Influenza A virus (IAV) and secondary bacterial infections (SBI) are responsible for a significant number of illnesses and deaths each year. Management of these diseases is difficult, in part due to a lack of understanding of complex interplay of host-pathogen interactions. To advance the goal of developing effective therapeutics, new microbiologic tools that can assess how host immune responses work to limit viral burden and enhance bacterial invasion quantitative detail is essential. This grant aims to address the gap in biological knowledge of IAV and SBIs by exploiting predictive mathematical models that are calibrated and subsequently validated with quantitative experimental data. The proposed studies integrate rigorous kinetic modeling with targeted experimental studies to: (1) quantify the killing of virus-infected cells to explain a plateau-shaped viral peak and a rapid viral decline, and determine if the killing is density dependent and how CD8+ T cells contribute to viral decay; (2) quantify the production of IFN-α/βs from epithelial cells and immune cells during influenza to explain a double peak in IFN-β and a sustained plateau of IFN-α, and determine how they function to limit virus infection; (3) identify how AMs become depleted during influenza by quantifying their decay, and determine how viral loads and SBIs are altered by the loss of these cells. In each of these studies, we will develop and analyze mechanistic mathematical models together with quantitative infection data, test specific model predictions experimentally, and use the generated data to refine and extend the models. This iterative model-driven experimental approach will result in a detailed and quantitative understanding of the immune responses to influenza and how these contribute to viral-bacterial coinfection pathogenicity. This investigative approach is key to understanding the complex feedbacks in immune responses and in viral-bacterial interactions and reveal new targets for treatment and prevention of influenza and related bacterial infections.