一种机器学习方法来破译沙门氏菌在肯塔基州肯塔基州的宿主特异性和地理起源的基因组基础
Lauren M McAllister1, Seon Woo Kim1, Jo Ann Van Kessel1
1Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, 307 Center Rd., Beltsville, Maryland 20705, USA.
Journal of food protection
|January 23, 2026
概括
机器学习模型可以准确预测沙门氏菌,肯塔基州和肯塔基州.
科学领域:
- 微生物学和基因组学 微生物学和基因组学
- 计算生物学和生物信息学
- 食品安全与公共卫生问题
背景情况:
- 肯塔基州的沙门氏菌是美国重要的食物传播病原体,经常在食用动物中发现.
- 肯塔基州的人类沙门氏菌感染通常与国际旅行有关,这表明复杂的传播途径.
- 了解沙门氏菌肯塔基州的宿主特异性和地理起源对于有效的食品安全干预至关重要.
研究的目的:
- 开发和验证机器学习模型,以预测沙门氏菌肯塔基州分离的动物宿主 (牛或家禽) 和地理来源 (北美或非北美).
- 为了识别特定的基因组特征,如单核酸多态 (SNP) 和基因,与沙门氏菌肯塔基州的宿主特异性相关.
- 将这些预测模型应用于美国人体临床隔离物,以推断它们的起源和潜在来源.
主要方法:
- 利用核心基因组单核酸多态 (SNP),基因存在和基因间区域来训练各种机器学习算法.
- 采用XGBoost在核心基因组SNP上训练的模型,在预测宿主和起源方面表现出卓越的性能.
- 分析了特征重要性得分,以精确确定与沙门氏菌肯塔基州宿主特异性相关的基因组标记.
主要成果:
- 机器学习模型在预测宿主动物 (家禽F1得分高达0.943,牛F1得分高达0.891) 和地理来源 (F1得分高达0.982) 方面取得了高准确性.
- 确定了关键的基因组特征,包括药物排放基因和毒性因素,如牛相关菌株中的rhuM,以及家禽相关菌株中具有耐药性基因的移动遗传元素.
- 预测,美国普遍存在的人类临床分离物 (ST198) 可能来自北美以外的家禽,而ST152菌株主要来自家禽.
结论:
- 基于核心基因组SNP的机器学习模型在区分沙门氏菌肯塔基州宿主和来源方面非常有效.
- 基因组特征分析揭示了对宿主特异性适应和沙门氏菌肯塔基生态的潜在驱动因素的见解.
- 这些预测工具增强了对食源性病原体生态学的研究,并有助于为食用动物制定有针对性的缓解策略.
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