Cytokine Signaling Network Response to Smallpox Vaccine

描述(由申请人提供)：对接种天花疫苗的患者观察到的不良事件的分子和细胞基础的鉴定具有重大的公共卫生利益。考虑到保护美国民众和军队免受潜在生物恐怖主义毒剂的伤害，这一点在今天尤其如此。我们最近发现，接种天花疫苗后的不良反应与全身性细胞因子模式相关，提示细胞因子在不良事件的发病机制中发挥作用。进一步描述不良事件的免疫学机制的一个挑战是，细胞因子很少单独作用于诱导免疫反应，而是在免疫系统细胞对其做出反应的复杂网络中发挥作用。细胞因子是整合免疫系统细胞活动的小信号蛋白。虽然人们经常很好地了解它们是如何单独发挥作用的，但细胞因子作为信号网络的一部分的行为却不太为人所知，可能取决于感染机体的性质。我们建议开发和评估一种综合策略，以确定与天花疫苗接种后不良事件相关的详细动态细胞因子网络模型。这一策略将使用来自103名志愿者的蛋白质组时间序列数据进行开发和评估，这些志愿者是NIAID/NIH赞助的正在进行的评估安万特巴斯德天花疫苗(APSV)试验的一部分。我们将开发使用机器学习算法的软件工具，从观察到的时间序列细胞因子表达水平自动发现细胞因子信号网络模型。一旦估计了潜在的细胞因子网络模型，我们的目标是使用动态模型作为模拟工具来建议创建疫苗的方法，将与疫苗接种相关的不良事件的风险降至最低。该提案中开发的软件工具将普遍适用于生物医学研究，以了解时间序列数据中的生化相互作用，从而为疫苗研究界提供一个有用的、新颖的软件包。在免疫学这一重要研究问题上的合作中获得的经验和知识，加上培训计划的授课和指导部分，将为我为未来传染病免疫学研究开发和测试重要的假设奠定坚实的基础。

DESCRIPTION (provided by applicant): The identification of the molecular and cellular basis for adverse events observed in patients immunized against smallpox is of great public health interest. This is especially true today given efforts to defend the U.S. population and military against potential bioterrorism agents. We have shown recently that adverse vents following smallpox vaccination correlate with systemic cytokine patterns, suggesting a role for cytokines in the pathogenesis of adverse events. A challenge to further delineating the immunological mechanisms of adverse events is that cytokines rarely act in isolation to induce an immune response, but rather they work in a complex network to which immune system cells respond. Cytokines are small signaling proteins that integrate the activities of immune system cells. While it is often well understood how they act individually, the behavior of cytokines as part of a signaling network is less well known and likely depends on the nature of the infecting organism. We propose to develop and evaluate a comprehensive strategy to identify detailed kinetic cytokine network models associated with adverse events following smallpox vaccination. This strategy will be developed and evaluated using proteomic time-series data available from 103 volunteers that are part of an ongoing NIAID/NIH-sponsored trial to evaluate the Aventis Pasteur Smallpox Vaccine (APSV). We will develop software tools using machine learning algorithms to automatically discover cytokine signaling network models from observed time-series cytokine expression levels. Once the underlying cytokine network model has been estimated, our goal is to use the dynamic model as a simulation tool to suggest ways to create vaccines that minimize the risk of adverse events associated with vaccination. The software tools developed in this proposal will be generally applicable for biomedical research to understand the biochemical interactions in time-series data, and, thus, a useful and novel software package will be made available to the vaccine research community. The experience and knowledge gained during the collaboration on this important research problem in immunology coupled with the didactic and mentoring portions of the training program will create a firm foundation upon which I can develop and test significant hypotheses for future studies on the immunology of infectious diseases.