High-throughput experiments to guide influenza vaccine strain selection

高通量实验指导流感疫苗株选择

• 批准号: 9757688
• 负责人: Jesse D Bloom
• 金额: $ 42.36万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-26 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9757688
• 关键词: Affect; Amino Acids; Big Data; Biological Assay; Cell Culture Techniques; Cessation of life; Codon Nucleotides; Computational Technique; Computer Simulation; Data; Development; Effectiveness; Epidemic; Epitopes; Erythrocytes; Evolution; Ferrets; Frequencies; Genes; Growth; Hemagglutinin; Homologous Gene; Human; Immune Sera; Immunity; Influenza; Influenza A Virus, H3N2 Subtype; Internet; Intuition; Link; Measurement; Measures; Membrane Proteins; Methods; Modeling; Mutation; Nucleoproteins; Phylogeny; Point Mutation; Population; Process; Property; Proteins; Public Health; Research Personnel; Seasons; Serum; Shapes; Source; Techniques; Testing; Time; Vaccination; Vaccines; Viral; Virus; Work; computer code; computerized tools; cost; deep sequencing; experimental study; fitness; homologous recombination; improved; influenza virus vaccine; innovation; mutant; mutation screening; predictive modeling; public health intervention; seasonal influenza; time use; vaccine effectiveness; vaccine efficacy

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.

项目摘要 每年，季节性影响5-15％的人口，导致超过25万 全球死亡。年度影响是反对的主要公共卫生干预措施 这些情节。疫苗中的菌株必须在影响力季节之前选择。 不幸的是，选定的菌株有时无法结束匹配那些实际上最终的匹配 在人口中循环；这种菌株不匹配降低了疫苗效率。方法 因此，为了更好地选择疫苗，菌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 应变选择。两个关键特性决定了哪种影响力菌株主导了一个季节： 成功的菌株具有很高的继承健身度（由于有害突变的负荷而表现出来） 以及人类免疫识别的表位中的大量抗原突变。我们 将衡量这些特性中的每一种都受到所有可能的氨基酸突变的影响 病毒表面蛋白血凝素。为了进行这些高通量测量，我们将 生成携带所有可能的密码子突变的病毒池，然后 在存在和不存在人血清的情况下，这些突变病毒通过。然后我们将使用 超精确的深度测序，以计算每个突变前后的频率 选择，使我们能够量化每个突变如何影响有害负荷和抗原 血清从人口的横截面中识别。 为了改善疫苗 - 应变的选择，我们将使用实时的Web平台覆盖我们的 测量有害突变负荷和抗原变化对影响的测量 系统发育。该平台将使决策者能够直观地可视化“大数据” 通过我们的实验生成，因为它们在应变选择过程中加权所有证据来源 过程。此外，我们将使我们的数据和计算机代码随时提供，以便其他 可以利用我们的工作做自己的努力，以更好地预测影响应变动态的影响。 这项工作与公共卫生有直接的相关性，因为它将有助于指导更好的疫苗应变 以当前方法成本的一小部分选择，从而改善了季节性影响力 疫苗有效性。