Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning
Abstract 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’ thr...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-94321-6 |
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| author | Alexander Gmeiner Mirena Ivanova Patrick Murigu Kamau Njage Lisbeth Truelstrup Hansen Leonid Chindelevitch Pimlapas Leekitcharoenphon |
| author_facet | Alexander Gmeiner Mirena Ivanova Patrick Murigu Kamau Njage Lisbeth Truelstrup Hansen Leonid Chindelevitch Pimlapas Leekitcharoenphon |
| author_sort | Alexander Gmeiner |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-c8c2aa66a5614dc8b835ed44ff231522 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c8c2aa66a5614dc8b835ed44ff2315222025-08-20T02:49:32ZengNature PortfolioScientific Reports2045-23222025-03-0115111110.1038/s41598-025-94321-6Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learningAlexander Gmeiner0Mirena Ivanova1Patrick Murigu Kamau Njage2Lisbeth Truelstrup Hansen3Leonid Chindelevitch4Pimlapas Leekitcharoenphon5National Food Institute, Research Group for Genomic Epidemiology, Technical University of DenmarkNational Food Institute, Research Group for Genomic Epidemiology, Technical University of DenmarkNational Food Institute, Research Group for Genomic Epidemiology, Technical University of DenmarkNational Food Institute, Research Group for Food Microbiology and Hygiene, Technical University of DenmarkMRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College LondonNational Food Institute, Research Group for Genomic Epidemiology, Technical University of DenmarkAbstract 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.https://doi.org/10.1038/s41598-025-94321-6 |
| spellingShingle | Alexander Gmeiner Mirena Ivanova Patrick Murigu Kamau Njage Lisbeth Truelstrup Hansen Leonid Chindelevitch Pimlapas Leekitcharoenphon Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning Scientific Reports |
| title | Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning |
| title_full | Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning |
| title_fullStr | Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning |
| title_full_unstemmed | Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning |
| title_short | Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning |
| title_sort | quantitative prediction of disinfectant tolerance in listeria monocytogenes using whole genome sequencing and machine learning |
| url | https://doi.org/10.1038/s41598-025-94321-6 |
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