Assessment of the relationship between the MLST genetic diversity of Listeria monocytogenes and growth under selective and non-selective conditions.

Listeria monocytogenes can grow under stressful conditions and contaminate various food categories. Progresss in DNA sequencing-based identification methods, such as multi-locus sequence typing (MLST) now allow for more accurate characterization of pathogens. L. monocytogenes MLST genetic diversity is reflected by the different prevalence of the "clonal complexes" (CCs) in foods or infections. Better understanding of the growth potentials of L. monocytogenes is essential for quantitative risk assessment and efficient detection across CCs genetic diversity. Using optical density measurements taken with an automated spectrophotometer, we compared the maximal growth rate and lag phase of 39 strains from 13 different CCs and various food origins, in 3 broths mimicking stresful food conditions (8 °C, aw 0.95 and pH5) and in ISO Standard enrichment broths (Half Fraser and Fraser). This is important as growth could influence risk through pathogen multiplication in food. Besides, enrichment problems could lead to a lack of detection of some CCs. Despite small differences highlighting natural intraspecific variability, our results show that growth performances of L. monocytogenes strains under the conditions tested in selective and non-selective broth do not appear to be strongly correlated to CCs and cannot explain higher CC "virulence" or prevalence.

A microscopy-based approach for determining growth probability and lag time of individual bacterial cells.

The development of relevant predictive models for single-cell lag time and growth probability near growth limits is of critical importance for predicting pathogen behavior in foods. The classical methods for data acquisition in this field are based on turbidity measurements of culture media in microplate wells inoculated with approximately one bacterial cell per well. Yet, these methods are labour intensive and would benefit from higher throughput. In this study, we developed a quantitative experimental method using automated microscopy to determine the single-cell growth probability and lag time. The developed method consists of the use of direct cell observation with phase-contrast microscopy equipped with a 100× objective and a high-resolution device camera. The method is not a time-lapse method but is based on the observation of high numbers of colonies for a given time. Automation of image acquisition and image analysis was used to reach a high throughput. The single-cell growth probabilities and lag times of four strains of Listeria monocytogenes were determined at 4 °C. The microscopic method was shown to be a promising method for the determination of individual lag times and growth probability at the single-cell level.

Use of quantitative microbial risk assessment when investigating foodborne illness outbreaks: the example of a monophasic Salmonella Typhimurium 4,5,12:i:- outbreak implicating beef burgers.

A major community outbreak of salmonellosis occurred in France in October 2010. Classical epidemiological investigations led to the identification of beef burgers as the cause of the outbreak and the presence of the emerging monophasic Salmonella Typhimurium 4,5,12:i:-. The objective of this study was to understand the events that led to this large outbreak, that is to say, what are the contributing factors associated with consumer exposure to Salmonella. To this end, intensive microbiological investigations on several beef burgers were conducted and a risk assessment model was built. The microbiological results confirm the presence of Salmonella in all analysed frozen burgers at high levels of contamination above 1000 MPN/g. These results in frozen burgers combined with a model of thermal destruction were used to estimate the dose ingested by the exposed persons. Most people that consumed cooked beef burgers were exposed from 1.6 to 3.1 log10 (MPN). The number of sick people predicted with a dose-response relationship for Salmonella is consistent with the observed number of salmonellosis cases. The very high initial contamination level in frozen beef burgers is the primary cause of this large outbreak rather than bad cooking practices. Intensive investigations, modelling of the initial contamination and quantitative exposure and risk assessments are complementary to epidemiological investigation. They can be valuable elements for the assessment of missing information or the identification of the primary causes of outbreaks.

Design of challenge testing experiments to assess the variability of Listeria monocytogenes growth in foods.

The assessment of the evolution of micro-organisms naturally contaminating food must take into account the variability of biological factors, food characteristics and storage conditions. A research project involving eight French laboratories was conducted to quantify the variability of growth parameters of Listeria monocytogenes obtained by challenge testing in five food products. The residual variability corresponded to a coefficient of variation (CV) of approximately 20% for the growth rate (µ(max)) and 130% for the parameter K = µ(max) × lag. The between-batch and between-manufacturer variability of µ(max) was very dependent on the food tested and mean CV of approximately 20 and 35% were observed for these two sources of variability, respectively. The initial physiological state variability led to a CV of 100% for the parameter K. It appeared that repeating a limited number of three challenge tests with three different batches (or manufacturers) and with different initial physiological states seems often necessary and adequate to accurately assess the variability of the behavior of L. monocytogenes in a specific food produced by a given manufacturer (or for a more general food designation).

Validation of a stochastic modelling approach for Listeria monocytogenes growth in refrigerated foods.

A stochastic modelling approach was developed to describe the distribution of Listeria monocytogenes contamination in foods throughout their shelf life. This model was designed to include the main sources of variability leading to a scattering of natural contaminations observed in food portions: the variability of the initial contamination, the variability of the biological parameters such as cardinal values and growth parameters, the variability of individual cell behaviours, the variability of pH and water activity of food as well as portion size, and the variability of storage temperatures. Simulated distributions of contamination were compared to observed distributions obtained on 5 day-old and 11 day-old cheese curd surfaces artificially contaminated with between 10 and 80 stressed cells and stored at 14°C, to a distribution observed in cold smoked salmon artificially contaminated with approximately 13 stressed cells and stored at 8°C, and to contaminations observed in naturally contaminated batches of smoked salmon processed by 10 manufacturers and stored for 10 days a 4°C and then for 20 days at 8°C. The variability of simulated contaminations was close to that observed for artificially and naturally contaminated foods leading to simulated statistical distributions properly describing the observed distributions. This model seems relevant to take into consideration the natural variability of processes governing the microbial behaviour in foods and is an effective approach to assess, for instance, the probability to exceed a critical threshold during the storage of foods like the limit of 100 CFU/g in the case of L. monocytogenes.