Integrative prediction of seasonal influenza evolution by genotype, phenotype, and geography

项目摘要/摘要 fl流感季节性的快速演变要求世界各国开发新的fl流感疫苗 世界卫生组织(WHO)每一至两年举行一次会议。这种进化是通过抗原漂移过程进行的。 血凝素(HA)表面蛋白中的氨基酸突变使目前正在传播的病毒 逃避对以前的疫苗病毒的适应性免疫。因此，在全球取得成功的季节在fluenza 病毒通常在抗原性上与以前的谱系截然不同。高质量的抗原性实验检测方法 漂移是费力和低吞吐量的，这导致研究人员开发出可以预测 仅从血凝素序列数据就成功地在fl中发现了流感病毒。自从这些原始序列公布以来- 仅在2014年的模型中，fi在fl的病毒学和计算方法方面就有了明显的进展 这可能有利于fit在fl流感预测模型中的应用。特别是fi，现在有了计算方法来衡量 通过准确推断HI试验、高通量突变试验中缺失的测量结果来实现抗原漂移 为了衡量对HA突变的功能约束，支持蛋白质重要性的研究 并详细分析了influenza的fi环流的变化地理环流。我提议创造一个 一种新的fl病毒进化预测模型，它集成了这些现代的、生物信息的fi强度度量 整合成一个单一的框架。这些新指标将建立在来自合作伙伴的密集、高质量HI分析的基础上， 美国疾病控制和预防中心对季节性fl流感病毒的深度突变扫描分析 来自杰西·布鲁姆博士实验室的合作者，一个为influenza策划的全基因组序列数据库，以及 对influenza全球移民率的经验估计。这种新的预测模型将提高预测的精度。 关于哪些病毒最有可能在fl流感季节取得成功的预测。这些改进 预测将成为贝德福德博士在年度疫苗设计会议上向世卫组织提出的建议的依据， 从而有效地改善了疫苗的fi疗效，并降低了人类fl流感相关的发病率和死亡率。 人口。

Project Summary/Abstract The rapid evolution of seasonal influenza requires the development of a new influenza vaccine by the World Health Organization (WHO) every one to two years. This evolution occurs through a process of antigenic drift where amino acid mutations in the hemagglutinin (HA) surface protein allow currently circulating viruses to evade adaptive immunity against previous vaccine viruses. Therefore, globally successful seasonal influenza viruses are often antigenically distinct from previous lineages. High-quality experimental assays for antigenic drift are laborious and low-throughput, leading researchers to develop computational models that can predict the success of influenza viruses from HA sequence data alone. Since the publication of these original sequence- only models in 2014, there have been significant advances in influenza virology and computational methods that could benefit influenza predictive models. Specifically, there are now computational methods to measure antigenic drift by accurately inferring missing measurements in HI assays, high-throughput mutagenesis assays to measure functional constraints on mutations in HA, research supporting the importance of proteins other than HA for influenza's fitness, and detailed analysis of influenza's variable geographic circulation. I propose to create a new predictive model of influenza evolution that integrates these modern, biologically-informed fitness metrics into a single framework. These new metrics will build on dense, high-quality HI assays from collaborators at the Centers for Disease Control and Prevention (CDC), deep mutational scanning assays of seasonal influenza from collaborators in Dr. Jesse Bloom's lab, a curated database of whole genome sequences for influenza, and empirical estimates of influenza's global migration rates. This new predictive model will improve the accuracy of predictions about which viruses are most likely to succeed in future influenza seasons. These improved predictions will inform recommendations by Dr. Bedford to the WHO at annual vaccine design meetings and, thereby, effect improvements in vaccine efficacy and reduce influenza-related morbidity and mortality in human populations.