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

通过基因型、表型和地理综合预测季节性流感演变

• 批准号: 9760537
• 负责人: JOHN HUDDLESTON
• 金额: $ 4.07万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9760537
• 关键词: Amino Acids; Biological; Biological Assay; Blood Circulation; Centers for Disease Control and Prevention (U.S.); Cessation of life; Characteristics; Collaborations; Complement; Computer Simulation; Computing Methodologies; Data; Databases; Development; Dissection; Epitopes; Evolution; Frequencies; Future; Genome; Genotype; Geographic Distribution; Geography; Health; Hemagglutination; Hemagglutinin; Human; Immunity; Influenza; Influenza Hemagglutinin; Mathematics; Measurement; Measures; Membrane Proteins; Modeling; Modernization; Morbidity - disease rate; Mutagenesis; Mutation; Neuraminidase; Pattern; Persons; Phenotype; Phylogenetic Analysis; Population; Population Genetics; Process; Proteins; Public Health; Publications; Recommendation; Reporting; Research Personnel; Research Support; Seasons; Source; Structure; Training; Vaccination; Vaccine Design; Vaccines; Viral; Virus; World Health Organization; adaptive immunity; cross reactivity; epidemiology study; experimental study; fitness; genome sequencing; geographic population; improved; influenza virus vaccine; influenzavirus; meetings; migration; mortality; mutation screening; novel; predictive modeling; response; seasonal influenza; success; vaccine development; vaccine efficacy; viral fitness; virology; whole genome

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.

项目概要/摘要 季节性流感的快速演变需要世界开发新的流感疫苗 卫生组织 (WHO) 每一到两年一次。这种进化是通过抗原漂移的过程发生的 血凝素 (HA) 表面蛋白的氨基酸突变使得当前流行的病毒能够 逃避针对先前疫苗病毒的适应性免疫。因此，全球范围内成功的季节性流感 病毒通常在抗原上与以前的谱系不同。高质量的抗原实验测定 漂移是费力且低通量的，导致研究人员开发可以预测漂移的计算模型 仅从 HA 序列数据就可以看出流感病毒的成功。自从这些原始序列出版以来—— 仅在 2014 年的模型中，流感病毒学和计算方法就取得了重大进展 这可能有利于流感预测模型。具体来说，现在有计算方法来测量 通过准确推断 HI 测定、高通量诱变测定中缺失的测量值来进行抗原漂移 为了测量 HA 突变的功能限制，研究支持除 流感适应性的HA，以及流感可变地理循环的详细分析。我建议创建 一种新的流感进化预测模型，整合了这些现代的、生物信息丰富的健康指标 到一个单一的框架中。这些新指标将建立在来自合作者的密集、高质量的 HI 检测的基础上。 疾病控制和预防中心 (CDC)，季节性流感的深度突变扫描分析 来自 Jesse Bloom 博士实验室的合作者，这是一个精心策划的流感全基因组序列数据库，以及 流感全球迁移率的经验估计。这种新的预测模型将提高准确性 预测哪些病毒最有可能在未来的流感季节取得成功。这些改进了 预测将为贝德福德博士在年度疫苗设计会议上向世界卫生组织提出的建议提供依据， 从而提高疫苗功效并降低人类与流感相关的发病率和死亡率 人口。