Quantitative studies of influenza evolution

巨大的突变率，加上重组(类似于染色体分离的过程) 病毒基因组的RNA片段和自然人畜共患病导致了流感流行和在 人类。据估计，季节性流感每年造成约500万例病例，导致 25万至50万人死亡。大流行性流感造成的死亡人数可达数百万人， 每年， 世界卫生组织预测哪些病毒株最有可能在以下地区传播 年。这种方法依赖于年度全球监测数据来监测人类和 并且容易出现重大错误。 这些数据的一个主要缺点是它们代表了部分 由于监测的局限性和选择性压力而产生的传播病毒情况 一种不受控制的环境，很难评估复杂进化过程的影响。 然而， 即使这样的预测是准确的，一种特定的流感疫苗通常也不会提供更多的保护 我们建议通过以下方式克服这些限制 将夏皮拉和加西亚-萨斯特雷实验室的实验专业知识与 不到几年，由于病毒的高变异率。 拉巴丹实验室的数学建模和计算框架 基因组和细胞网络的多尺度分析(MAGNET；它为 应用系统生物学方法研究复杂的生物系统，如流感的进化)。 虽然已经确定不同宿主物种的流感分离株之间的重新分类可以 产生具有大流行潜力的病毒，重新分类之间的关系，以及病毒变异率 由于施加在这种病毒上的选择性压力仍然是流感生物学中的关键问题，并且是主要问题 为全球公共卫生服务。 不仅是因为 流感是定量研究的理想实验室模型 进化论，在 与Magic合作，我们建议使用现代基因组方法，结合遗传学 易驯服的哺乳动物系统，以前所未有的方式确定到底是什么进化 当病毒跨物种适应时，或当它与宿主先天和适应性相互作用时，病毒经历的变化 免疫系统，计算单个病毒序列的进化轨迹，并识别 对病毒施加了选择压力。我们预计实验和计算平台 这项提案中所描述的将促进对支配病毒进化的进化约束的新见解。 再加上正在进行的监测工作，即使是对目前对 流感健康状况将允许更好地评估流行毒株的大流行风险潜力。 了解影响抗原漂移和病毒适应的变量是至关重要的。

An enormous mutation rate, coupled with reassortment (a process analogous to chromosomal segregation) of RNA segments of the viral genome, and natural zoonosis, have lead to influenza epidemics and pandemics in humans. It has been estimated seasonal influenza causes approximately 5 million cases annually, resulting in 250,000 to 500,000 deaths. Deaths resulting from pandemic influenza can reach millions and each year, the World Health Organization predicts which strains of the virus are most likely to be circulating in the following year. This approach relies on yearly global surveillance data to monitor ongoing infections in humans and livestock and is prone to significant error. A major shortcoming of these data is that they represent a partial landscape of circulating viruses due to limitations in surveillance and the selective pressures are generated in an uncontrolled setting making it difficult to assess the affect of complex evolutionary processes. However, even when such predictions are accurate, a particular influenza vaccine usually confers protection for no more We propose to overcome these limitations by integrating the experimental expertise of the Shapira and Garcia-Sastre laboratories together with than a few years due to the high mutation rate of the virus. mathematical modeling and computational framework of the Rabadan laboratory, a member of the Center for the Multiscale Analysis of Genomic and Cellular Networks (MAGNet; which provides a core framework for applying systems biology approaches to studying complex biological systems such as influenza evolution). While it is well established that reassortment between influenza isolates from different host species can generate viruses with pandemic potential, the relationship between reassortment, viral mutation rates, as well as the selective pressures imposed on this virus remain key questions in influenza biology and are major issues for global public health. Not only does influenza represent an ideal laboratory model for quantitative studies of evolution, In collaboration with MAGNet, we propose to use modern genomic approaches, coupled with genetically tractable mammalian systems, to determine, in an unprecedented fashion, precisely what evolutionary changes the virus undergoes as it adapts across species, or as it interacts with the host innate and adaptive immune systems, to compute the evolutionary trajectory of individual viral sequences, and to identify selection pressures exerted on the virus. We expect that the experimental and computational platform described in this proposal will foster new insights into the evolutionary constrains that govern viral evolution. Coupled with ongoing surveillance efforts, even modest improvements on the current understanding of the influenza fitness landscape will allow for better assessment of pandemic risk potential of circulating strains. understanding the variables that influence antigenic drift, and viral adaptation is paramount.