Large-scale phenotypic and genomic characterization of<i>Listeria monocytogenes</i>susceptibility to quaternary ammonium compounds

Ivanova, Mirena; Kragh, Martin Laage; Szarvas, Judit; Tosun, Elif Seyda; Holmud, Natacha Friis; Gmeiner, Alexander; Amar, Corinne; Guldimann, Claudia; Huynh, TuAnh N.; Karpíšková, Renáta; García, Carmen Rota; Gomez, Diego; Aboagye, Eurydice; Etter, Andrea; Centorame, Patrizia; Torresi, Marina; De Angelis, Maria Elisabetta; Pomilio, Francesco; Okholm, Anders Hauge; Xiao, Yinghua; Kleta, Sylvia; Lueth, Stefanie; Pietzka, Ariane; Kovacevic, Jovana; Pagotto, Franco; Rychli, Kathrin; Zdovc, Irena; Papić, Bojan; Heir, Even; Langsrud, Solveig; Møretrø, Trond; Stephan, Roger; Brown, Phillip; Kathariou, Sophia; Tasara, Taurai; Aarestrup, Frank; Njage, Patrick Murigu Kamau; Fagerlund, Annette; Hansen, Lisbeth Truelstrup; Leekitcharoenphon, Pimlapas

doi:10.1101/2023.09.07.556668

Cited by 3 publications

(6 citation statements)

References 71 publications

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“…Recent studies have observed great concordance (up to 99%) between known QAC resistance genes and tolerant/sensitive phenotypes, raising the question of the benefit of a more complex ML solution [10]. In this dataset, 20 isolates showed tolerance to BC in laboratory experiments, but no QAC resistance genes were observed.…”

Section: Discussionmentioning

confidence: 96%

“…To be able to compare the classification predictions from our trained model with the validation set, we categorised the MIC values into tolerant and sensitive groups (i.e., phenotypes). This has been done as described by Ivanova et al [10]. In short, for the publications that do not report an MIC cut-off for tolerant/sensitive separation, we screened all isolates for the presence of one of four genes associated with QAC resistance (qacH (multiple variants), bcrABC (JX023284.1), emrE (NC_013766.2:c1850670-1850347), and emrC (MT912503.1:2384-2770)).…”

Section: Methodsmentioning

confidence: 99%

“…The data for this study was extracted from a large phenotypic and genomic characterisation study of Listeria monocytogenes by Ivanova et al [10]. Within that study, 1,671 L. monocytogenes isolates were tested for their sensitivity to QACs.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…The sensitivity of L. monocytogenes to QACs has been thoroughly studied, with several published reports of minimum inhibitory concentrations (MIC) phenotypes. However, the lack of standardised protocols and the broad variation of results due to different experimental setups make comparisons of MIC values between studies challenging [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Quantitative prediction of disinfectant tolerance inListeria monocytogenesusing whole genome sequencing and machine learning

Gmeiner,

Ivanova,

Kamau Njage

et al. 2023

Preprint

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Listeria monocytogenesis 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 ofL. monocytogenesoutbreaks. This emphasises that proper sanitation of food processing plants is of utmost importance. Consequently in recent years, there has been an increased interest inL. monocytogenestolerance to disinfectants used in the food industry. Even though many studies are focusing on laboratory quantification ofL. monocytogenestolerance, 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 1649L. monocytogenesisolates 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 inL. monocytogenes.The findings of this study are a first step towards prediction ofL. monocytogenestolerance 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.AUTHOR SUMMARYMicrobial contamination challenges food safety by potentially transmitting harmful microbes such as bacteria to consumers.Listeria monocytogenesis an example of such a bacteria, which is primarily transmitted through food and can cause severe diseases in at-risk groups. Fortunately, strict food safety regulations and stringent cleaning protocols are in place to prevent the transmission ofListeria monocytogenes. However, in recent years, there has been an increase in tolerance towards disinfectants used in the food industry, which can reduce their effectiveness. In this study, we used genome sequencing and phenotypic data to train machine learning models that can accurately predict whether individualListeria monocytogenesisolates are tolerant to selected disinfectants. We were able to train models that are not only able to distinguish sensitive/tolerant isolates but also can predict different degrees of tolerance to disinfectants. Further, we were able to report a set of genes that were important for the machine learning prediction and could give information about possible tolerance mechanisms. In the future, similar predictive models might be used to guide cleaning and disinfection protocols to facilitate maximum effectiveness.

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Section: Discussionmentioning

confidence: 96%

Section: Methodsmentioning

confidence: 99%

Section: Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quantitative prediction of disinfectant tolerance inListeria monocytogenesusing whole genome sequencing and machine learning

Gmeiner,

Ivanova,

Kamau Njage

et al. 2023

Preprint

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“…The L. monocytogenes isolates for the benchmarking data set 1 (n=97) were selected from Ivanova et al (2023) [28] and cover a broad range of geographical regions, isolation sources and isolate sub-types. The isolates' short-read WGS data was acquired from the European Nucleotide Archive (ENA).…”

Section: Benchmarking Data Setmentioning

confidence: 99%

ListPred: A predictive ML tool for virulence potential and disinfectant tolerance inListeria monocytogenes

Gmeiner,

Ivanova,

Kaas

et al. 2024

Preprint

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Despite current surveillance and sanitation strategies, foodborne pathogens continue to threaten the food industry and public health. Whole genome sequencing (WGS) has reached an unprecedented resolution to analyse and compare pathogenic bacterial isolates. The increased resolution significantly enhances the possibility of tracing transmission routes and contamination sources of foodborne pathogens. In addition, machine learning (ML) on WGS data has shown promising applications for predicting important microbial traits such as virulence, growth potential, and resistance to antimicrobials. Many regulatory agencies have already adapted WGS and ML methods. However, the food industry has not followed a similarly enthusiastic implementation. Some possible reasons for this might be the lack of computational resources and limited expertise to analyse WGS and ML data and interpret the results. Here, we present ListPred, a ML tool to analyse WGS data of Listeria monocytogenes, a very concerning foodborne pathogen. ListPred is able to predict two important bacterial traits, namely virulence potential and disinfectant tolerance, and only requires limited computational resources and practically no bioinformatic expertise, which is essential for a broad application in the food industry.

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Persistence of microbiological hazards in food and feed production and processing environments

Koutsoumanis,

Allende,

Bolton

et al. 2024

EFS2

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Listeria monocytogenes (in the meat, fish and seafood, dairy and fruit and vegetable sectors), Salmonella enterica (in the feed, meat, egg and low moisture food sectors) and Cronobacter sakazakii (in the low moisture food sector) were identified as the bacterial food safety hazards most relevant to public health that are associated with persistence in the food and feed processing environment (FFPE). There is a wide range of subtypes of these hazards involved in persistence in the FFPE. While some specific subtypes are more commonly reported as persistent, it is currently not possible to identify universal markers (i.e. genetic determinants) for this trait. Common risk factors for persistence in the FFPE are inadequate zoning and hygiene barriers; lack of hygienic design of equipment and machines; and inadequate cleaning and disinfection. A well‐designed environmental sampling and testing programme is the most effective strategy to identify contamination sources and detect potentially persistent hazards. The establishment of hygienic barriers and measures within the food safety management system, during implementation of hazard analysis and critical control points, is key to prevent and/or control bacterial persistence in the FFPE. Once persistence is suspected in a plant, a ‘seek‐and‐destroy’ approach is frequently recommended, including intensified monitoring, the introduction of control measures and the continuation of the intensified monitoring. Successful actions triggered by persistence of L. monocytogenes are described, as well as interventions with direct bactericidal activity. These interventions could be efficient if properly validated, correctly applied and verified under industrial conditions. Perspectives are provided for performing a risk assessment for relevant combinations of hazard and food sector to assess the relative public health risk that can be associated with persistence, based on bottom‐up and top‐down approaches. Knowledge gaps related to bacterial food safety hazards associated with persistence in the FFPE and priorities for future research are provided.

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Large-scale phenotypic and genomic characterization ofListeria monocytogenessusceptibility to quaternary ammonium compounds

Cited by 3 publications

References 71 publications

Quantitative prediction of disinfectant tolerance inListeria monocytogenesusing whole genome sequencing and machine learning

Quantitative prediction of disinfectant tolerance inListeria monocytogenesusing whole genome sequencing and machine learning

ListPred: A predictive ML tool for virulence potential and disinfectant tolerance inListeria monocytogenes

Persistence of microbiological hazards in food and feed production and processing environments

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