Forecasting influenza evolution on a heterogeneous immune landscape

预测异质免疫环境中流感的演变

• 批准号: 10573200
• 负责人: Trevor BC Bedford
• 金额: $ 74.44万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10573200
• 关键词: Accounting; Adult; Affect; Amino Acids; Antibody-mediated protection; Antigens; Biological Assay; Child; Communities; Computer Models; Computing Methodologies; Data; Evolution; Ferrets; Frequencies; Genomics; Genotype; Hemagglutination; Heterogeneity; Human; Immune; Immunity; Individual; Infection; Influenza; Influenza Hemagglutinin; Maps; Measurement; Measures; Membrane Proteins; Methods; Modeling; Mutation; Persons; Phenotype; Population; Recording of previous events; Scientist; Seasons; Specificity; System; Update; Vaccination; Vaccines; Variant; Viral; Virus; Visualization; Work; combat; experimental study; fitness; improved; influenza surveillance; influenza virus vaccine; influenzavirus; innovation; meetings; mutant; mutation screening; neutralizing antibody; novel strategies; protein function; response; seasonal influenza

PROJECT SUMMARY Influenza virus evolves rapidly to escape immunity elicited by prior infections and vaccinations. To combat this evolution, the strains in the influenza vaccine are updated every season to keep pace with viral evolution. However, it is currently difficult to accurately forecast which viral strain will dominate the coming season, and vaccines are less effective when the wrong strain is chosen for the vaccine. Here we propose a combined experimental / computational approach that will overcome some of the factors that currently make vaccine strain selection so difficult. First, we will use a new deep mutational scanning approach to directly map the genotypes of the viral surface proteins to their antigenic phenotype with respect to human antibody immunity. Second, we will use this new experimental approach to reconstruct the heterogeneous human immune landscape over which influenza virus evolves. Finally, we will build computational models that forecast how influenza evolves year-to- year in response to human immunity. All of our data and forecasts will be integrated into easy-to-use interactive visualizations. Overall, this work will improve the capacity to accurately identify which viral strains should be selected for the seasonal influenza vaccine.

项目总结 流感病毒迅速进化，以逃避先前感染和接种疫苗引起的免疫。要与此作斗争 在进化过程中，流感疫苗中的毒株每个季节都会更新，以跟上病毒进化的步伐。 然而，目前很难准确预测哪种病毒株将在下一季占据主导地位，以及 当疫苗选择了错误的毒株时，疫苗的效果就会降低。在这里，我们提出了一个合并的 一种实验/计算方法，将克服目前使疫苗毒株产生的一些因素 选择太难了。首先，我们将使用一种新的深度突变扫描方法来直接定位基因类型 病毒表面蛋白对其抗原性的表型与人类抗体免疫的关系。第二，我们 将使用这种新的实验方法来重建不同种类的人类免疫格局 流感病毒会进化。最后，我们将建立预测流感每年如何演变的计算模型 作为对人类豁免权的回应。我们的所有数据和预测都将集成到易于使用的交互功能中 视觉化。总体而言，这项工作将提高准确识别哪些病毒株应该是 被选为季节性流感疫苗。