可解释的AI多组学分析揭示了COVID-19和流感中共享和分离的宿主反应
Yan Zhang1, Lining Zhang2, Zehong Zhang3
1Medical Research Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China.
NPJ digital medicine
|January 27, 2026
概括
这项研究比较了宿主对COVID-19,流感和RSV的反应,确定了每个病毒的共享抗病毒途径和独特机制. 研究结果揭示了影响疾病严重程度的遗传因素和宿主蛋白,并为呼吸道病毒感染提供了针对性的治疗方法.
科学领域:
- * 呼吸道病毒感染的比较免疫学和宿主遗传学.
- * 综合性多主题分析,以了解疾病机制.
背景情况:
- *2019年新冠病毒病 (COVID-19),流感和呼吸道同胞性病毒 (RSV) 分享了一些宿主反应,但也表现出病毒特异性机制.
- *了解这些差异对于开发有效的治疗方法和广泛的抗病毒药物至关重要.
研究的目的:
- *使用多omics数据对COVID-19,流感和RSV的宿主感染机制进行比较分析.
- * 构建一个可解释的AI模型来区分宿主对这些病毒感染的反应.
- * 确定影响疾病严重程度的遗传和蛋白质组因素.
主要方法:
- *利用COVID-19宿主遗传倡议 (HGI) GWAS数据和公开可用的多omics数据集 (转录组,蛋白质组).
- *采用整合性分析和可解释的人工智能 (AI) 建模.
- * 利用门德尔的随机化 (MR) 来识别因果蛋白.
主要成果:
- *已确定共享的I型干扰素 (IFN) 信号传递和病毒特异性通路 (例如,SARS-CoV-2的凝血,流感的T细胞激活,RSV的中性细胞反应).
- *GWAS数据将COVID-19的严重程度与IFN和炎症途径的变异联系起来;流感遗传学指向受体使用.
- *MR确定了影响COVID-19结果的ABO和炎症调解剂等因果蛋白.
- *人工智能模型准确地将COVID-19与其他病毒感染区分开来,SHAP值突出了关键的致病机制.
结论:
- *这项交叉体质研究提供了主体反应的详细地图,区分COVID-19与流感和RSV.
- * 发现突出了宿主遗传,糖化学和免疫因素在疾病结果中的作用.
- * 洞察力支持开发向疗法 (例如IL-6/MAPK抑制剂) 和广泛的抗病毒药物 (例如IFN增强剂).
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