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: 10350150
• 负责人: Trevor BC Bedford
• 金额: $ 3.52万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH 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 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10350150
• 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; base; 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.

项目摘要 流感病毒迅速发展，以避免先前感染和疫苗接种引起的免疫力。对抗这一点 进化，每个季节都会更新流感疫苗的菌株，以保持病毒进化。 但是，目前很难准确地预测哪些病毒株将在即将到来的季节中占主导地位，以及 选择疫苗的错误应变时，疫苗的有效性较小。在这里，我们提出了一个组合 实验 /计算方法将克服当前使疫苗应变的某些因素 选择如此困难。首先，我们将使用一种新的深突变扫描方法直接映射基因型 与人类抗体免疫相对于其抗原表型的病毒表面蛋白的蛋白质。第二，我们 将使用这种新的实验方法来重建异质的人类免疫景观 流感病毒进化。最后，我们将建立计算模型，以预测流感如何逐年发展 一年对人类免疫力的回应。我们的所有数据和预测都将集成到易于使用的交互式中 可视化。总体而言，这项工作将提高准确识别哪些病毒株的能力 选择用于季节性流感疫苗。