Gmeiner Alexander, Ivanova Mirena, Njage Patrick Murigu Kamau, Hansen Lisbeth Truelstrup, Chindelevitch Leonid, Leekitcharoenphon Pimlapas
National Food Institute, Research Group for Genomic Epidemiology, Technical University of Denmark, Kgs. Lyngby, Denmark.
National Food Institute, Research Group for Food Microbiology and Hygiene, Technical University of Denmark, Kgs. Lyngby, Denmark.
Sci Rep. 2025 Mar 26;15(1):10382. doi: 10.1038/s41598-025-94321-6.
Listeria monocytogenes is a potentially severe disease-causing bacteria mainly transmitted through food. This pathogen is of great concern for public health and the food industry in particular. Many countries have implemented thorough regulations, and some have even set 'zero-tolerance' thresholds for particular food products to minimise the risk of L. monocytogenes outbreaks. This emphasises that proper sanitation of food processing plants is of utmost importance. Consequently, in recent years, there has been an increased interest in L. monocytogenes tolerance to disinfectants used in the food industry. Even though many studies are focusing on laboratory quantification of L. monocytogenes tolerance, the possibility of predictive models remains poorly studied. Within this study, we explore the prediction of tolerance and minimum inhibitory concentrations (MIC) using whole genome sequencing (WGS) and machine learning (ML). We used WGS data and MIC values to quaternary ammonium compound (QAC) disinfectants from 1649 L. monocytogenes isolates to train different ML predictors. Our study shows promising results for predicting tolerance to QAC disinfectants using WGS and machine learning. We were able to train high-performing ML classifiers to predict tolerance with balanced accuracy scores up to 0.97 ± 0.02. For the prediction of MIC values, we were able to train ML regressors with mean squared error as low as 0.07 ± 0.02. We also identified several new genes related to cell wall anchor domains, plasmids, and phages, putatively associated with disinfectant tolerance in L. monocytogenes. The findings of this study are a first step towards prediction of L. monocytogenes tolerance to QAC disinfectants used in the food industry. In the future, predictive models might be used to monitor disinfectant tolerance in food production and might support the conceptualisation of more nuanced sanitation programs.
单核细胞增生李斯特菌是一种主要通过食物传播的、具有潜在严重致病性的细菌。这种病原体对公众健康，尤其是食品行业而言，是重大隐患。许多国家都实施了全面的法规，有些国家甚至为特定食品设定了“零容忍”阈值，以尽量降低单核细胞增生李斯特菌爆发的风险。这凸显了食品加工厂进行适当卫生处理的至关重要性。因此，近年来，人们对单核细胞增生李斯特菌对食品行业中使用的消毒剂的耐受性越来越感兴趣。尽管许多研究聚焦于实验室对单核细胞增生李斯特菌耐受性的量化，但预测模型的可能性仍研究不足。在本研究中，我们利用全基因组测序（WGS）和机器学习（ML）来探索耐受性和最低抑菌浓度（MIC）的预测。我们使用来自1649株单核细胞增生李斯特菌分离株的WGS数据和对季铵化合物（QAC）消毒剂的MIC值来训练不同的ML预测器。我们的研究显示，利用WGS和机器学习预测对QAC消毒剂的耐受性有令人期待的结果。我们能够训练出高性能的ML分类器，以高达0.97±0.02的平衡准确率分数来预测耐受性。对于MIC值的预测，我们能够训练出平均平方误差低至0.07±0.02的ML回归器。我们还鉴定出了几个与细胞壁锚定结构域、质粒和噬菌体相关的新基因，推测它们与单核细胞增生李斯特菌的消毒剂耐受性有关。本研究的结果是朝着预测单核细胞增生李斯特菌对食品行业中使用的QAC消毒剂的耐受性迈出的第一步。未来，预测模型可能会用于监测食品生产中的消毒剂耐受性，并可能有助于更细致入微的卫生计划的概念化。
