High-throughput experiments to guide influenza vaccine strain selection

项目总结 每年，季节性流感感染了5%-15%的人口，导致超过25万人 世界各地的死亡人数。每年接种流感疫苗是预防流感的主要公共卫生干预措施。 这些流行病。疫苗中的毒株必须在流感季节之前选择。 不幸的是，所选择的菌株有时与最终实际出现的菌株并不十分匹配。 在人群中传播；这种毒株错配会降低疫苗效力。方法 因此，更好地选择疫苗毒株对公共卫生至关重要。 我们将使用创新的新实验和计算技术来指导更好的疫苗- 菌种选育。两个关键特性决定了哪种流感病毒株在一个季节占据主导地位： 成功的菌株具有很高的内在适合度(表现为有害突变的负荷较低)。 以及人类免疫识别表位中的大量抗原突变。我们 将测量每一种可能的氨基酸突变是如何影响这些特性的 病毒表面蛋白血凝素。为了进行这些高吞吐量的测量，我们将 产生携带所有可能的血凝素密码子突变的病毒库，然后 在人血清存在和不存在的情况下传代这些突变病毒。然后我们将使用 超精确的深度测序，计算每一个突变前后的频率 选择，使我们能够量化每个突变如何影响有害负载和抗原 通过来自人类人口横截面的血清识别。 为了改进疫苗株的选择，我们将使用一个实时网络平台来覆盖我们的 流感病毒有害突变载量和抗原性变化的检测 系统发育学。这一平台将使决策者能够直观地直观地呈现出 由我们的实验产生，因为他们在菌株选择过程中权衡了所有证据来源 进程。此外，我们将使我们的数据和计算机代码随时可用，以便其他人 可以利用我们的工作为自己的努力更好地预测流感毒株的动态。 这项工作与公共卫生有直接关系，因为它将有助于指导更好的疫苗株 选择成本仅为目前方法的一小部分，从而改善季节性流感 疫苗的有效性。

PROJECT SUMMARY Every year, seasonal influenza infects 5-15% of the human population, resulting in over 250,000 deaths worldwide. The annual influenza vaccine is the primary public-health intervention against these epidemics. The strains in the vaccine must be selected before the influenza season. Unfortunately, the selected strains sometimes fail to closely match those that end up actually circulating in the human population; such strain mismatches reduce vaccine efficacy. Methods for better selecting vaccine strains are therefore of paramount importance to public health. We will use innovative new experimental and computational techniques to guide better vaccine- strain selection. Two key properties determine which influenza strains dominate a season: successful strains have high inherent fitness (manifested by a low load of deleterious mutations) and an abundance of antigenic mutations in the epitopes recognized by human immunity. We will measure how each of these properties is affected by every possible amino-acid mutation to the viral surface protein hemagglutinin. To make these high-throughput measurements, we will generate pools of viruses carrying all possible codon mutations to hemagglutinin, and then passage these mutant viruses in the presence and absence of human serum. We will then use ultra-accurate deep sequencing to count the frequency of every mutation pre- and post- selection, enabling us to quantify how each mutation affects both deleterious load and antigenic recognition by serum from a cross-section of the human population. To improve vaccine-strain selection, we will use a real-time web platform to overlay our measurements of deleterious mutational load and the antigenic change onto an influenza phylogeny. This platform will enable decision makers to intuitively visualize the “Big Data” generated by our experiments as they weigh all sources of evidence during the strain-selection process. In addition, we will make our data and computer code readily available, so that others can leverage our work for their own efforts to better predict influenza strain dynamics. This work has direct relevance to public health in that it will help guide better vaccine-strain selection at a fraction of the cost of current approaches, and thereby improve seasonal influenza vaccine effectiveness.