Quantitative studies of influenza evolution

描述（由申请人提供）：巨大的突变率，加上病毒基因组RNA片段的重组（类似于染色体分离的过程）和自然人畜共患病，导致流感流行和人类大流行。据估计，季节性流感每年造成约500万例病例，造成25万至50万人死亡。每年，由大流行性流感导致的死亡人数可达数百万人，世界卫生组织（World Health Organization）预测哪一种病毒最有可能在接下来的一年传播。这种方法依赖年度全球监测数据来监测人类和牲畜的持续感染，并且容易出现重大错误。这些数据的一个主要缺点是，由于监测方面的限制，它们代表了循环病毒的部分情况，并且选择压力是在不受控制的环境中产生的，因此难以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能提供更多的保护。由于病毒的高突变率，我们建议通过整合夏皮拉和加西亚-萨斯特雷实验室的实验专业知识，在几年的时间内克服这些限制。Rabadan实验室的数学建模和计算框架，该实验室是基因组和细胞网络多尺度分析中心（MAGNet；该中心为应用系统生物学方法研究复杂生物系统（如流感进化）提供了核心框架）的成员。虽然已经确定来自不同宿主物种的流感分离株之间的重新组合可以产生具有大流行潜力的病毒，但重新组合，病毒突变率以及施加于该病毒的选择压力之间的关系仍然是流感生物学中的关键问题，也是全球公共卫生的主要问题。流感不仅是定量研究进化的理想实验室模型，在与MAGNet的合作中，我们建议使用现代基因组方法，结合遗传上可处理的哺乳动物系统，以前所未有的方式精确确定病毒在跨物种适应或与宿主先天和适应性免疫系统相互作用时所经历的进化变化，以计算单个病毒序列的进化轨迹。并确定施加在病毒上的选择压力。我们期望本提案中描述的实验和计算平台将促进对控制病毒进化的进化限制的新见解。加上正在进行的监测工作，即使对目前对流感适应度情况的了解略有改善，也将有助于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。

DESCRIPTION (provided by applicant): An enormous mutation rate, coupled with reassortment (a process analogous to chromosomal segregation) of RNA segments of the viral genome, and natural zoonosis, have led 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 effect 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.