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在a型流感病毒中预测宿主热带性:从多段核酸签名的见解

Weijie Chen^1,2, Tingting Pei¹, Zhan Zhang¹

¹Shanghai Changning District Center for Disease Control and Prevention (Shanghai Changning District Health Inspection Institute), Shanghai, 200335, China.

Journal of translational medicine

|December 19, 2025

概括

机器学习使用核酸序列准确地预测流感A病毒宿主热带. 确定了关键部位及其相互作用,有助于理解病毒适应和动物感染风险.

科学领域:

病毒学病毒学
基因组学就是基因组学.
生物信息学是一种生物信息学.
机器学习机器学习

背景情况:

由于其跨物种传播和复杂的宿主适应,A型流感病毒 (IAV) 构成了严重的公共卫生威胁.
了解IAV宿主热带性对于预测和预防动物传染病溢出事件至关重要.

研究的目的:

开发和基准机器学习模型,以使用全基因组核酸数据预测IAV宿主热带.
为了确定关键的核酸位和它们的相互作用,驱动IAV宿主适应.
加强IAV监测,并为应对新出现的病毒威胁的战略提供信息.

主要方法:

从鸟类,人类,猪和牛宿主中分析了64,000个IAV序列.
开发一个四类分类框架,使用所有八个基因组段的核酸位特征.
八个机器学习算法的基准测试,使用准确度,精度,回忆,F1分数,AUPRC和AUC来评估性能.
使用SHAP分析对关键核酸位点的优先确定,并通过双变异关联测试识别协同/对抗位点相互作用.

主要成果:

在区分人类衍生的IAV序列方面,XGBoost算法获得了最高的性能 (AUC > 0.95).
SHAP分析确定了每个基因段的前20个关键核酸位,包括NS段的特定位点.
核酸组成分析揭示了宿主特定的相似性 (例如,人-猪,禽-牛),并突出了区分某些宿主菌株 (例如,人与猪HA细分) 的挑战.
双变位点关联测试揭示了基因段内的关键核酸位之间复杂的协同和对抗网络.

结论:

这项研究提供了IAV宿主适应的机制理解,并确定了动物性传染病风险分层的分子决定因素.
建立了一个可扩展的机器学习框架,以基于核酸签名来预测病毒宿主热带.
这些发现增强了IAV监控策略,并为防止新出现的病毒威胁提供了预防措施的信息.

关键词:

跨物种的传播方式.主体热带主义主体热带主义流感 A 病毒病毒.核酸签名核酸签名预测建模的预测建模.

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