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: 7208003
• 负责人: Brett McKinney
• 金额: $ 10.38万
• 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/7208003
• 关键词: Address; Adverse event; Algorithms; Behavior; Biochemical; Biomedical Research; Bioterrorism; Cells; Collaborations; Communities; Complex; Computer software; Coupled; Coupling; Cytokine Network Pathway; Cytokine Signaling; Data; Engineering; Equation; Event; Feedback; Foundations; Future; Goals; Immune response; Immune system; Immunization; Immunologics; Immunology; Individual; Infectious Disease Immunology; Kinetics; Knowledge; Machine Learning; Mentors; Methods; Military Personnel; Modeling; Molecular; Nature; Organism; Pathogenesis; Patients; Pattern; Population; Proteins; Proteomics; Public Health; Research; Research Personnel; Risk; Role; Series; Serum; Signal Transduction; Signaling Protein; Simulate; Smallpox; Smallpox Vaccine; Software Tools; Systems Biology; Testing; Time; Today; Training Programs; Vaccination; Vaccine Research; Vaccines; Vent; Work; base; chemical reaction; cytokine; design; experience; interest; network models; novel; programs; protein expression; research study; response; simulation; software development; tool; volunteer

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（由申请方提供）：在接种天花疫苗的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意义。考虑到保护美国人民和军队免受潜在生物恐怖主义制剂侵害的努力，今天尤其如此。我们最近已经表明，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的作用。进一步描述不良事件的免疫学机制的挑战是细胞因子很少单独起作用以诱导免疫应答，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性的小信号蛋白。虽然通常很好地理解它们如何单独起作用，但细胞因子作为信号网络的一部分的行为不太为人所知，并且可能取决于感染生物体的性质。我们建议开发和评估一种全面的策略，以确定详细的动力学细胞因子网络模型与天花疫苗接种后的不良事件。该策略将使用来自103名志愿者的蛋白质组学时间序列数据进行开发和评价，这些志愿者是正在进行的NIAID/NIH申办的评价Aventis Pasteur天花疫苗（APSV）试验的一部分。我们将使用机器学习算法开发软件工具，从观察到的时间序列细胞因子表达水平中自动发现细胞因子信号网络模型。一旦基本的细胞因子网络模型已经估计，我们的目标是使用动态模型作为模拟工具，以建议如何创建疫苗，最大限度地减少与疫苗接种相关的不良事件的风险。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物化学相互作用，因此，将为疫苗研究界提供一个有用和新颖的软件包。在免疫学这一重要研究问题的合作过程中获得的经验和知识，加上培训计划的教学和指导部分，将为我开发和测试传染病免疫学未来研究的重要假设奠定坚实的基础。