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高通量伪病毒中和绘制了流感A/H1N1病毒的抗原景观

Yimeng An¹, Xiao Ding², Yabin Tian³

¹State Key Laboratory of Respiratory Health and Multimorbidity, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China; Changping Laboratory, Beijing, 102206, China.

|December 3, 2025

概括

这项研究使用了伪病毒中和试验来绘制A/H1N1病毒演变的地图,识别了八个抗原集群,并验证了用于疫苗菌株选择的机器学习模型.

科学领域:

病毒学病毒学
免疫学免疫学免疫学
计算生物学计算生物学

背景情况:

由于HA基因突变,A/H1N1流感病毒经历持续的抗原漂移,需要经常更新疫苗.
伪病毒系统为研究病毒抗原性和进化提供了一个灵活的平台.

研究的目的:

从1918年到2023年分析人类A/H1N1病毒的抗原演变.
开发和验证用于抗原性评估的机器学习模型.
为了指导A/H1N1疫苗菌株的选择.

主要方法:

建立一个全面的伪病毒库 (123种代表性菌株,21种疫苗菌株).
伪病毒中和检测测试图绘制抗原集群.
开发PN-AgEvaH1机器学习模型用于抗原性预测.

主要成果:

在A/H1N1进化过程中识别了八个不同的抗原群.
疫苗菌株与流行的循环菌株一致的确认.
在抗原性评估中,PN-AgEvaH1模型的高精度 (ROC AUC 0.990).
确定影响pdm09抗原集群的关键氨基酸残留物.

结论:

伪病毒中和分析准确地识别了用于疫苗菌株选择的抗原匹配.
PN-AgEvaH1模型为表征A/H1N1抗原群提供了一个可靠的工具.
这项研究证明了伪病毒中和试验作为HI试验的补充或替代品的价值.

关键词:

抗原进化抗原进化抗原关系评估对抗原关系的评估.这种流感是A/H1N1流感.机器学习机器学习基于伪病毒的中和抗体测定.

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相关概念视频

Influenza

Influenza

Influenza is an acute, highly communicable viral disease that affects the respiratory tract and is responsible for seasonal epidemics worldwide. Influenza A is the most prevalent type associated with widespread outbreaks and is subtyped based on two surface glycoproteins: hemagglutinin (H) and neuraminidase (N), as in H1N1. These glycoproteins are essential for viral infectivity, transmission, and immune recognition. Transmission occurs primarily through respiratory droplets and contaminated...