The aim of this study was to identify and characterise Bacillus cereus from a unique national collection of 564 strains associated with 140 strong-evidence food-borne outbreaks (FBOs) occurring in France during 2007 to 2014. Starchy food and vegetables were the most frequent food vehicles identified; 747 of 911 human cases occurred in institutional catering contexts. Incubation period was significantly shorter for emetic strains compared with diarrhoeal strains A sub-panel of 149 strains strictly associated to 74 FBOs and selected on Coliphage M13-PCR pattern, was studied for detection of the genes encoding cereulide, diarrhoeic toxins (Nhe, Hbl, CytK1 and CytK2) and haemolysin (HlyII), as well as panC phylogenetic classification. This clustered the strains into 12 genetic signatures (GSs) highlighting the virulence potential of each strain. GS1 (nhe genes only) and GS2 (nhe, hbl and cytK2), were the most prevalent GS and may have a large impact on human health as they were present in 28% and 31% of FBOs, respectively. Our study provides a convenient molecular scheme for characterisation of B. cereus strains responsible for FBOs in order to improve the monitoring and investigation of B. cereus-induced FBOs, assess emerging clusters and diversity of strains.

Aims: To evaluate the performances of models predicting the growth rate or the growth probability of Listeria monocytogenes in food. Methods and Results: Cardinal and square root type models including or not interactions between environmental factors and probability models were evaluated for their ability to describe the behaviour of L. monocytogenes in liquid dairy products, cheese, meat and seafood products. Models excluding interactions seemed sufficient to predict the growth rate of L. monocytogenes. However, the accurate prediction of growth/no-growth limits needed to take interactions into account. A complete and a simplified form (preservatives deducted) of a new cardinal model including interactions and parameter values were suggested to predict confidence limits for the growth rate of L. monocytogenes in food. This model could also be used for the growth probability prediction. Conclusions: The new cardinal model including interactions was efficient to predict confidence limits for the growth rate of L. monocytogenes and its growth probability in liquid dairy products, meat and seafood products. In cheese, the model was efficient to predict the absence of growth of the pathogen. Significance and Impact of the Study: The suggested model can be used for risk assessment and risk management concerning L. monocytogenes in dairy, meat and seafood products.

Concerns about food contamination by Listeria monocytogenes are on the rise with increasing consumption of ready-to-eat foods. Biofilm production of L. monocytogenes is presumed to be one of the ways that confer its increased resistance and persistence in the food chain. In this study, a collection of isolates from foods and food processing environments (FPEs) representing persistent, prevalent, and rarely detected genotypes was evaluated for biofilm forming capacities including adhesion and sessile biomass production under diverse environmental conditions. The quantity of sessile biomass varied according to growth conditions, lineage, serotype as well as genotype but association of clonal complex (CC) 26 genotype with biofilm production was evidenced under cold temperature. In general, relative biofilm productivity of each strain varied inconsistently across growth conditions. Under our experimental conditions, there were no clear associations between biofilm formation efficiency and persistent or prevalent genotypes. Distinct extrinsic factors affected specific steps of biofilm formation. Sudden nutrient deprivation enhanced cellular adhesion while a prolonged nutrient deficiency impeded biofilm maturation. Salt addition increased biofilm production, moreover, nutrient limitation supplemented by salt significantly stimulated biofilm formation. Pan-genome-wide association study (Pan-GWAS) assessed genetic composition with regard to biofilm phenotypes for the first time. The number of reported genes differed depending on the growth conditions and the number of common genes was low. However, a broad overview of the ontology contents revealed similar patterns regardless of the conditions. Functional analysis showed that functions related to transformation/competence and surface proteins including Internalins were highly enriched.

We present the LiSEQ ( Listeria SEQuencing) project, funded by the European Food Safety Agency (EFSA) to compare Listeria monocytogenes isolates collected in the European Union from ready-to-eat foods, compartments along the food chain (e.g. food-producing animals, food-processing environments) and humans. In this article, we report the molecular characterization of a selection of this data set employing whole-genome sequencing analysis. We present an overview of the strain diversity observed in different sampled sources, and characterize the isolates based on their virulence and resistance profile. We integrate into our analysis the global L. monocytogenes genome collection described by Moura and colleagues in 2016 to assess the representativeness of the LiSEQ collection in the context of known L. monocytogenes strain diversity.

The effects of nine common food industry stresses on the times to the turbidity ( T _d ) distribution of Listeria monocytogenes were determined. It was established that the main source of the variability of T _d for stressed cells was the variability of individual lag times. The distributions of T _d revealed that there was a noticeable difference in response to the stresses encountered by the L. monocytogenes cells. The applied stresses led to significant changes of the shape, the mean, and the variance of the distributions. The variance of T _d of wells inoculated with single cells issued from a culture in the exponential growth phase was multiplied by at least 6 and up to 355 for wells inoculated with stressed cells. These results suggest stress-induced variability may be important in determining the reliability of predictive microbiological models.