通过机器学习整合呼吸道微生物群和宿主免疫反应,用于呼吸道感染诊断
Hongbin Chen1, Tianqi Qi2, Siyu Guo3
1Department of Clinical Laboratory, Peking University People's Hospital, Beijing, P. R. China. chenhongbin_pkuph@163.com.
NPJ biofilms and microbiomes
|September 12, 2024
概括
诊断下呼吸道感染 (LRTI) 是一个挑战. 将下呼吸道微生物组 (LRTM) 和宿主基因数据与机器学习相结合,为LRTIs提供了一个有前途的新诊断工具.
科学领域:
- 微生物学 微生物学
- 免疫学 免疫学 免疫学
- 生物信息学是一种生物信息学.
背景情况:
- 下呼吸道感染 (LRTI) 存在诊断挑战,需要改进方法.
- 下呼吸道微生物组 (LRTM) 与宿主免疫反应在LRTIs之间的相互作用尚未完全理解.
研究的目的:
- 在LRTIs期间调查LRTM和宿主免疫反应的变化.
- 开发和验证LRTIs的诊断模型,整合LRTM和宿主转录组数据.
主要方法:
- 对136名患者的支气管洗液 (BALF) 的转录分析.
- 差异基因表达分析以确定宿主免疫反应.
- 使用随机森林分类器开发机器学习模型.
主要成果:
- 慢性转录病毒感染与减少LRTM多样性,减少共生细菌和增加机会性病原体 (如Klebsiella pneumoniae) 相关.
- 主体基因表达揭示了感染和免疫反应途径的丰富,K. pneumoniae和炎症基因之间存在特定的相关性.
- 整合LRTM和宿主转录组数据的机器学习模型在区分LRTIs方面实现了高性能 (ROC AUC:0.937).
结论:
- 该研究强调了LRTM和宿主免疫反应在LRTIs期间的显著变化.
- 结合微生物组和宿主转录组数据的综合诊断模型显示了作为LRTIs诊断的有效工具的潜力.
- 该模型的进一步验证可以改善LRTIs的临床诊断和管理.
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