Comparison of growth limits of Listeria monocytogenes in milk, broth and cheese

Modelling the Adhesion and Biofilm Formation Boundary of Listeria monocytogenes ST9

Listeria monocytogenes is a major foodborne pathogen that can adhere to or form a biofilm on food contact surfaces, depending on the environmental conditions. The purpose of this work is to determine the adhesion and biofilm formation boundaries for L. monocytogenes ST9 under the combination environments of temperature (5, 15, and 25 °C), NaCl concentration (0%, 3%, 6%, and 9% (w/v)) and pH (5.0, 6.0, 7.0, and 8.0). The probability models of adhesion and biofilm formation were built using the logistic regression. For adhesion, only the terms of linear T and NaCl are significant for L. monocytogenes ST9 (p < 0.05), whereas the terms of linear T, NaCl, and pH, and the interaction between T and pH were significant for biofilm formation (p < 0.05). By analyzing contour maps and their surface plots for two different states, we discovered that high temperature promoted adhesion and biofilm formation, whereas excessive NaCl concentration inhibited both of them. With a stringent threshold of 0.1667, the accuracy rate for identifying both adhesion/no-adhesion and biofilm formation/no-biofilm formation events were 0.929, indicating that the probability models are reasonably accurate in predicting the adhesion and biofilm formation boundary of L. monocytogenes ST9. The boundary model may provide a useful way for determining and further controlling L. monocytogenes adhesion and biofilm formation in various food processing environments.

The occurrence, growth, and biocontrol of Listeria monocytogenes in fresh and surface-ripened soft and semisoft cheeses

Listeria monocytogenes continues to pose a food safety risk in ready-to-eat foods, including fresh and soft/semisoft cheeses. Despite L. monocytogenes being detected regularly along the cheese production continuum, variations in cheese style and intrinsic/extrinsic factors throughout the production process (e.g., pH, water activity, and temperature) affect the potential for L. monocytogenes survival and growth. As novel preservation strategies against the growth of L. monocytogenes in susceptible cheeses, researchers have investigated the use of various biocontrol strategies, including bacteriocins and bacteriocin-producing cultures, bacteriophages, and competition with native microbiota. Bacteriocins produced by lactic acid bacteria (LAB) are of particular interest to the dairy industry since they are often effective against Gram-positive organisms such as L. monocytogenes, and because many LAB are granted Generally Regarded as Safe (GRAS) status by global food safety authorities. Similarly, bacteriophages are also considered a safe form of biocontrol since they have high specificity for their target bacterium. Both bacteriocins and bacteriophages have shown success in reducing L. monocytogenes populations in cheeses in the short term, but regrowth of surviving cells can commonly occur in the finished cheeses. Competition with native microbiota, not mediated by bacteriocin production, has also shown potential to inhibit the growth of L. monocytogenes in cheeses, but the mechanisms are still unclear. Here, we have reviewed the current knowledge on the growth of L. monocytogenes in fresh and surface-ripened soft and semisoft cheeses, as well as the various methods used for biocontrol of this common foodborne pathogen.

Listeria monocytogenes contamination of ready-to-eat foods and the risk for human health in the EU

Food safety criteria for Listeria monocytogenes in ready-to-eat (RTE) foods have been applied from 2006 onwards (Commission Regulation (EC) 2073/2005). Still, human invasive listeriosis was reported to increase over the period 2009-2013 in the European Union and European Economic Area (EU/EEA). Time series analysis for the 2008-2015 period in the EU/EEA indicated an increasing trend of the monthly notified incidence rate of confirmed human invasive listeriosis of the over 75 age groups and female age group between 25 and 44 years old (probably related to pregnancies). A conceptual model was used to identify factors in the food chain as potential drivers for L. monocytogenes contamination of RTE foods and listeriosis. Factors were related to the host (i. population size of the elderly and/or susceptible people; ii. underlying condition rate), the food (iii. L. monocytogenes prevalence in RTE food at retail; iv. L. monocytogenes concentration in RTE food at retail; v. storage conditions after retail; vi. consumption), the national surveillance systems (vii. improved surveillance), and/or the bacterium (viii. virulence). Factors considered likely to be responsible for the increasing trend in cases are the increased population size of the elderly and susceptible population except for the 25-44 female age group. For the increased incidence rates and cases, the likely factor is the increased proportion of susceptible persons in the age groups over 45 years old for both genders. Quantitative modelling suggests that more than 90% of invasive listeriosis is caused by ingestion of RTE food containing > 2,000 colony forming units (CFU)/g, and that one-third of cases are due to growth in the consumer phase. Awareness should be increased among stakeholders, especially in relation to susceptible risk groups. Innovative methodologies including whole genome sequencing (WGS) for strain identification and monitoring of trends are recommended.

Diffusible substances from lactic acid bacterial cultures exert strong inhibitory effects on Listeria monocytogenes and Salmonella enterica serovar enteritidis in a co-culture model

Background

Food-borne infections cause huge economic and human life losses. Listeria monocytogenes and Salmonella enterica serovar Enteritidis are among the top ranking pathogens causing such losses. Control of such infections is hampered by persistent contamination of foods and food-processing environments, resistance of pathogens to sanitizing agents, existence of heterogeneous populations of pathogens (including culturable and viable but non-culturable cells) within the same food items, and inability to detect all such pathogens by culture-based methods. Modern methods such as flow cytometry allow analyses of cells at the single cell level within a short time and enable better and faster detection of such pathogens and distinctions between live and dead cells. Such methods should be complemented by control strategies including the use of beneficial bacteria that produce metabolites capable of inhibiting food-borne pathogens. In this study, broth cultures of lactic acid bacteria (LAB) isolated from fermented milk were tested for production of substances capable of inhibiting L. monocytogenes and S. Enteritidis in co-culture with LAB by assessment of colony-forming units (CFU) and live:dead cell populations by flow cytometry.

Results

The LAB isolates belonged to the species Lactococcus lactis, Enterococcus faecalis and Enterococcus faecium. Some LAB were effective in inhibition. Plating indicated up to 99% reduction in CFU from co-cultures compared to control cultures. Most of the bacteria in both cultures were in the viable but non-culturable state. The flow data showed that there were significantly higher dead cell numbers in co-cultures than in control cultures, indicating that such killing was caused by diffusible substances produced by the LAB cultures.

Conclusion

This study showed that metabolites from selected local LAB species can be used to significantly reduce pathogen load. However, conditions of use and application need to be further investigated and optimized for large-scale utilization.

Electronic supplementary material

The online version of this article (doi:10.1186/s12866-017-0944-3) contains supplementary material, which is available to authorized users.

Colonial vs. planktonic type of growth: mathematical modeling of microbial dynamics on surfaces and in liquid, semi-liquid and solid foods

Predictive models are mathematical expressions that describe the growth, survival, inactivation, or biochemical processes of foodborne bacteria. During processing of contaminated raw materials and food preparation, bacteria are entrapped into the food residues, potentially transferred to the equipment surfaces (abiotic or inert surfaces) or cross-contaminate other foods (biotic surfaces). Growth of bacterial cells can either occur planktonically in liquid or immobilized as colonies. Colonies are on the surface or confined in the interior (submerged colonies) of structured foods. For low initial levels of bacterial population leading to large colonies, the immobilized growth differs from planktonic growth due to physical constrains and to diffusion limitations within the structured foods. Indeed, cells in colonies experience substrate starvation and/or stresses from the accumulation of toxic metabolites such as lactic acid. Furthermore, the micro-architecture of foods also influences the rate and extent of growth. The micro-architecture is determined by (i) the non-aqueous phase with the distribution and size of oil particles and the pore size of the network when proteins or gelling agent are solidified, and by (ii) the available aqueous phase within which bacteria may swarm or swim. As a consequence, the micro-environment of bacterial cells when they grow in colonies might greatly differs from that when they grow planktonically. The broth-based data used for modeling (lag time and generation time, the growth rate, and population level) are poorly transferable to solid foods. It may lead to an over-estimation or under-estimation of the predicted population compared to the observed population in food. If the growth prediction concerns pathogen bacteria, it is a major importance for the safety of foods to improve the knowledge on immobilized growth. In this review, the different types of models are presented taking into account the stochastic behavior of single cells in the growth of a bacterial population. Finally, the recent advances in the rules controlling different modes of growth, as well as the methodological approaches for monitoring and modeling such growth are detailed.

Modeling the growth of Listeria monocytogenes on the surface of smear- or mold-ripened cheese

Surface-ripened cheeses are matured by means of manual or mechanical technologies posing a risk of cross-contamination, if any cheeses are contaminated with Listeria monocytogenes. In predictive microbiology, primary models are used to describe microbial responses, such as growth rate over time and secondary models explain how those responses change with environmental factors. In this way, primary models were used to assess the growth rate of L. monocytogenes during ripening of the cheeses and the secondary models to test how much the growth rate was affected by either the pH and/or the water activity (aw) of the cheeses. The two models combined can be used to predict outcomes. The purpose of these experiments was to test three primary (the modified Gompertz equation, the Baranyi and Roberts model, and the Logistic model) and three secondary (the Cardinal model, the Ratowski model, and the Presser model) mathematical models in order to define which combination of models would best predict the growth of L. monocytogenes on the surface of artificially contaminated surface-ripened cheeses. Growth on the surface of the cheese was assessed and modeled. The primary models were firstly fitted to the data and the effects of pH and aw on the growth rate (μmax) were incorporated and assessed one by one with the secondary models. The Logistic primary model by itself did not show a better fit of the data among the other primary models tested, but the inclusion of the Cardinal secondary model improved the final fit. The aw was not related to the growth of Listeria. This study suggests that surface-ripened cheese should be separately regulated within EU microbiological food legislation and results expressed as counts per surface area rather than per gram.

Modeling the growth of Listeria monocytogenes in soft blue-white cheese

The aim of this study was to develop a predictive model simulating growth over time of the pathogenic bacterium Listeria monocytogenes in a soft blue-white cheese. The physicochemical properties in a matrix such as cheese are essential controlling factors influencing the growth of L. monocytogenes. We developed a predictive tertiary model of the bacterial growth of L. monocytogenes as a function of temperature, pH, NaCl, and lactic acid. We measured the variations over time of the physicochemical properties in the cheese. Our predictive model was developed based on broth data produced in previous studies. New growth data sets were produced to independently calibrate and validate the developed model. A characteristic of this tertiary model is that it handles dynamic growth conditions described in time series of temperature, pH, NaCl, and lactic acid. Supplying the model with realistic production and retail conditions showed that the number of L. monocytogenes cells increases 3 to 3.5 log within the shelf life of the cheese.