Cytokine Signaling Network Response to Smallpox Vaccine

细胞因子信号网络对天花疫苗的反应

• 批准号: 7389130
• 负责人: Brett McKinney
• 金额: $ 7.38万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7389130
• 关键词: Cytokine; Signaling; Network; Response; Smallpox

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.

描述（由申请人提供）：在天花免疫患者中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制剂的侵害，今天尤其如此。我们最近表明，天花疫苗接种后的不良事件与全身细胞因子模式相关，表明细胞因子在不良事件的发病机制中起作用。进一步描述不良事件的免疫机制的一个挑战是，细胞因子很少单独作用以诱导免疫反应，而是在免疫系统细胞应答的复杂网络中起作用。细胞因子是整合免疫系统细胞活动的小信号蛋白。虽然人们通常很清楚它们是如何单独起作用的，但细胞因子作为信号网络的一部分的行为却鲜为人知，可能取决于感染生物体的性质。我们建议制定和评估一个全面的策略，以确定与天花疫苗接种后不良事件相关的详细动力学细胞因子网络模型。该策略的制定和评估将使用103名志愿者提供的蛋白质组学时序数据，这些志愿者是正在进行的NIAID/ nih资助的安万特巴斯德天花疫苗（APSV）评估试验的一部分。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。一旦潜在的细胞因子网络模型被估计出来，我们的目标是使用动态模型作为模拟工具，提出方法来创建疫苗，将疫苗接种相关不良事件的风险降到最低。本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，因此，将向疫苗研究界提供一个有用和新颖的软件包。在这个重要的免疫学研究问题上的合作中获得的经验和知识，加上培训计划的教学和指导部分，将为我建立一个坚实的基础，在这个基础上，我可以为未来的传染病免疫学研究发展和检验重要的假设。