Use of Clinical Samples to Identify Influenza Virus Antigenic Variants

使用临床样本鉴定流感病毒抗原变异体 总结 甲型流感病毒（IAV）引起大流行和季节性爆发，导致数千人死亡， 数百万人的生命接种疫苗是预防流感爆发和尽量减少 方面的影响.了解流感病毒的抗原进化和快速选择良好的- 匹配的流感疫苗株是制定有效疫苗接种计划的关键。然而，抗原 流感病毒的表征提出了两个巨大的挑战：1）病毒繁殖， 常规血清学检测可导致培养适应性突变并扭曲病毒的抗原特性 在临床样品中，和2）在常规血清学测定中使用的参考血清在流感病毒中产生， 病毒血清阴性雪貂，并不代表人血清的免疫学特征，人血清通常具有 既往通过接种疫苗、自然感染或两者同时接触流感病毒。的理想平台 确定流感病毒的抗原特性和选择流感疫苗株应直接 使用临床样本。 本项目的目标是：1）开发和应用一种新的高通量技术， 通过使用未经病毒分离的人类临床样本来表征流感病毒的抗原特性 2）直接利用临床标本了解IAV的抗原进化。的 抗原性表征将包括流感病毒阳性临床样品， 被培养。了解临床标本中流感病毒准种及培养适应的效果 突变的抗原表征，我们将进行下一代基因组测序的临床 样品和相应的分离物。然后我们将研究序列多样性对抗原性的影响， 流感病毒的变异。我们还将确定先前暴露于流感病毒的影响 在流感疫苗株选择过程中的抗原特性。 本项目将为流感病毒抗原性的研究提供基础技术 临床样品中的病毒，而不繁殖病毒。由此产生的抗原表征平台将 克服常规血清学测定中病毒繁殖引起的偏倚。此外，这是一个高- 通量方法，并将显著减少血清学表征所需的人力， 减少抗原鉴定所需的时间，增加抗原鉴定中的样品数量， 特征化因此，该项目将导致流感疫苗株的重大技术进步 选择和促进流感预防和控制。此外，该项目还将提供有关 流感病毒准种和基因组抗原变异的分子机制 关于人类先前暴露于流感病毒的多样性和知识，这将有助于我们优化抗原性， 在疫苗株选择和了解流感病毒的抗原进化方面的特性。

Use of Clinical Samples to Identify Influenza Virus Antigenic Variants Summary Influenza A viruses (IAVs) cause pandemic and seasonal outbreaks that lead to the loss of thousands to millions of human lives. Vaccination is the best option for preventing influenza outbreaks and minimizing their effects. An understanding of the antigenic evolution of influenza viruses and the rapid selection of a well- matched influenza vaccine strain is the key to developing an effective vaccination program. However, antigenic characterization for influenza viruses presents two great challenges: 1) virus propagation, which is required in conventional serologic assays, can cause culture-adapted mutations and skew antigenic properties of viruses in clinical samples, and 2) reference sera used in conventional serologic assays are produced in influenza virus–seronegative ferrets and do not represent the immunologic profiles of human serum, which often has had prior exposures to influenza viruses through vaccination, natural infection, or both. An ideal platform for determining antigenic properties of influenza viruses and for selecting influenza vaccine strain should directly use clinical samples. The objectives of this project are 1) to develop and apply a novel high-throughput technology to directly characterize antigenic properties of influenza viruses by using human clinical samples without virus isolation and propagation and 2) to understand antigenic evolution of IAVs by using clinical samples directly. The antigenic characterization will include influenza virus–positive clinical samples from which virus can or cannot be cultivated. To understand influenza virus quasispecies in clinical samples and the effect of culture-adapted mutations on antigenic characterization, we will perform next-generation genomic sequencing on the clinical samples and corresponding isolates. We will then study the effects of the sequence diversity on antigenic variations of influenza viruses. We will also determine the effect that prior exposure to influenza virus(es) has on antigenic characterization during influenza vaccine strain selection. This project will help us provide fundamental technology for characterizing the antigenicity of influenza viruses in clinical samples without propagating virus. The resulting platform for antigenic characterization will overcome biases arising from virus propagation in conventional serologic assays. In addition, this is a high- throughput method and will significantly reduce the human labor needed for serologic characterization, decrease the time required for antigenic characterization, and increase the number of samples in antigenic characterization. Thus, this project will lead to significant technologic advances in influenza vaccine strain selection and facilitate influenza prevention and control. In addition, this project will provide knowledge about molecular mechanisms in antigenic variations associated with influenza virus quasispecies and genomic diversity and knowledge about prior human exposure to influenza viruses, which will help us optimize antigenic characterization in vaccine strain selection and understand antigenic evolution of influenza viruses.