使用全基因组测序和机器学习,对Listeria monocytogenes的消毒剂耐受性进行定量预测
Alexander Gmeiner1, Mirena Ivanova2, Patrick Murigu Kamau Njage2
1National Food Institute, Research Group for Genomic Epidemiology, Technical University of Denmark, Kgs. Lyngby, Denmark. algm@food.dtu.dk.
Scientific reports
|March 27, 2025
概括
这项研究预测了Listeria monocytogenes使用全基因组测序和机器学习的消毒剂耐受性. 这些发现使食品加工厂的卫生计划更好.
科学领域:
- 食品安全 食品安全
- 微生物学 微生物学
- 计算生物学 计算生物学
背景情况:
- 单细胞菌菌对公众健康构成重大风险,需要严格的食品安全法规.
- 在食品加工中,有效的卫生设施对于控制L. monocytogenes爆发至关重要.
- 预测L. monocytogenes中的消毒剂耐受性对于优化食品工业卫生协议至关重要.
研究的目的:
- 开发Listeria monocytogenes对消毒剂耐受性的预测模型.
- 探索使用全基因组测序 (WGS) 和机器学习 (ML) 来预测消毒剂耐受性和MIC值.
- 确定与消毒剂耐受性相关的新型遗传因素.
主要方法:
- 使用的WGS数据和来自1649 L. monocytogenes分离物的四级化合物 (QAC) 消毒剂的最小抑制度 (MIC) 值.
- 训练和评估各种ML分类器和回归器,以预测耐受性和MIC值.
- 对潜在的与消毒剂耐受性的关联进行了基因鉴定.
主要成果:
- 实现了高性能ML分类器,用于预测QAC消毒剂耐受性 (平衡精度高达0.97±0.02).
- 开发了ML回归器,用于预测MIC值,平均平方误差低 (0.07 ± 0.02).
- 确定了与细胞壁域,等离子体和菌体相关的新型基因,这些基因可能与消毒剂耐受性有关.
结论:
- 证明了WGS和ML在预测L. monocytogenes消毒剂耐受性的潜力.
- 这些发现代表了积极监测食品生产中消毒剂耐药性的重大进展.
- 未来的预测模型可以增强食品行业的卫生战略.
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