微生物生态签名预测病原体的出现和多药物耐药性在囊性纤维化航空公司提前多达一年
medRxiv : the preprint server for health sciences
|January 8, 2026
概括
这项研究引入了一种深度学习框架,用于早期预测囊性纤维化肺部感染. 该模型准确地识别了Pseudomonas aeruginosa和Aspergillus fumigatus等病原体,从而实现了主动治疗.
科学领域:
- 微生物学 微生物学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 囊性纤维化 (CF) 中的慢性感染是由微生物组的转变驱动的,但缺乏早期检测标志物.
- 分析气道微生物组的现有方法在捕捉疾病进展方面存在局限性.
- 了解CF感染的生态动态对于及时干预至关重要.
研究的目的:
- 开发一种先进的计算框架,用于早期预测囊性纤维化患者的慢性感染.
- 整合分类学和功能微生物组数据,以提高预测准确度.
- 识别微生物特征,表明病原体殖民,抗生素耐药性和即将感染.
主要方法:
- 开发了一个基于自编码器的框架,将分类学和功能性元基因组数据集成到分类学和功能集群 (COPF) 中.
- 利用带有COPF的渐变增强随机森林来预测Pseudomonas aeruginosa殖民和多药物耐药性.
- 嵌入的真核标记物来检测Aspergillus fumigatus的存在.
- 在广泛的全球CF元基因组数据集上验证了模型.
主要成果:
- 在分析微生物群转变方面,COPFs框架显著超过了传统方法.
- 该模型准确地预测了Pseudomonas aeruginosa殖民和多药物耐药性,在临床检测前多达一年.
- 持续的Aspergillus fumigatus签名甚至在培养负的时期也被确定.
- 在全球多种CF数据集中预测P. aeruginosa状态时达到94%的准确性.
结论:
- 深度学习与元基因组学的整合为CF慢性感染提供了强大的预测框架.
- 该框架揭示了保存的微生物生态和代谢机制,推动了疾病的进展.
- 这种方法将元基因组学转化为一种主动管理慢性CF感染的工具.
- 该模型证明了跨不同平台和地理位置的通用实用性.
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