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.

Contribution of Foods and Poor Food-Handling Practices to the Burden of Foodborne Infectious Diseases in France.

The foodborne disease burden (FBDB) related to 26 major biological hazards in France was attributed to foods and poor food-handling practices at the final food preparation step, in order to develop effective intervention strategies, especially food safety campaigns. Campylobacter spp. and non-typhoidal Salmonella accounted for more than 60% of the FBDB. Approximately 30% of the FBDB were attributed to 11 other hazards including bacteria, viruses and parasites. Meats were estimated as the main contributing food category causing (50-69%) (CI90) of the FBDB with (33-44%), (9-21%), (4-20%) (CI90) of the FBDB for poultry, pork and beef, respectively. Dairy products, eggs, raw produce and complex foods caused each approximately (5-20%) (CI90) of the FBDB. When foods are contaminated before the final preparation step, we estimated that inadequate cooking, cross-contamination and inadequate storage contribute for (19-49%), (7-34%) and (9-23%) (CI90) of the FBDB, respectively; (15-33%) (CI90) of the FBDB were attributed to the initial contamination of ready-to-eat foods-without any contribution from final food handlers. The thorough implementation of good hygienic practices (GHPs) at the final food preparation step could potentially reduce the FBDB by (67-85%) (CI90) (mainly with the prevention of cross-contamination and adequate cooking and storage).

Critical Orientation in the Jungle of Currently Available Methods and Types of Data for Source Attribution of Foodborne Diseases.

With increased interest in source attribution of foodborne pathogens, there is a need to sort and assess the applicability of currently available methods. Herewith we reviewed the most frequently applied methods for source attribution of foodborne diseases, discussing their main strengths and weaknesses to be considered when choosing the most appropriate methods based on the type, quality, and quantity of data available, the research questions to be addressed, and the (epidemiological and microbiological) characteristics of the pathogens in question. A variety of source attribution approaches have been applied in recent years. These methods can be defined as top-down, bottom-up, or combined. Top-down approaches assign the human cases back to their sources of infection based on epidemiological (e.g., outbreak data analysis, case-control/cohort studies, etc.), microbiological (i.e., microbial subtyping), or combined (e.g., the so-called 'source-assigned case-control study' design) methods. Methods based on microbial subtyping are further differentiable according to the modeling framework adopted as frequency-matching (e.g., the Dutch and Danish models) or population genetics (e.g., Asymmetric Island Models and STRUCTURE) models, relying on the modeling of either phenotyping or genotyping data of pathogen strains from human cases and putative sources. Conversely, bottom-up approaches like comparative exposure assessment start from the level of contamination (prevalence and concentration) of a given pathogen in each source, and then go upwards in the transmission chain incorporating factors related to human exposure to these sources and dose-response relationships. Other approaches are intervention studies, including 'natural experiments,' and expert elicitations. A number of methodological challenges concerning all these approaches are discussed. In absence of an universally agreed upon 'gold' standard, i.e., a single method that satisfies all situations and needs for all pathogens, combining different approaches or applying them in a comparative fashion seems to be a promising way forward.

Insights from genome-wide approaches to identify variants associated to phenotypes at pan-genome scale: Application to L. monocytogenes' ability to grow in cold conditions.

Intraspecific variability of the behavior of most foodborne pathogens is well described and taken into account in Quantitative Microbial Risk Assessment (QMRA), but factors (strain origin, serotype, …) explaining these differences are scarce or contradictory between studies. Nowadays, Whole Genome Sequencing (WGS) offers new opportunities to explain intraspecific variability of food pathogens, based on various recently published bioinformatics tools. The objective of this study is to get a better insight into different existing bioinformatics approaches to associate bacterial phenotype(s) and genotype(s). Therefore, a dataset of 51 L. monocytogenes strains, isolated from multiple sources (i.e. different food matrices and environments) and belonging to 17 clonal complexes (CC), were selected to represent large population diversity. Furthermore, the phenotypic variability of growth at low temperature was determined (i.e. qualitative phenotype), and the whole genomes of selected strains were sequenced. The almost exhaustive gene content, as well as the core genome SNPs based phylogenetic reconstruction, were derived from the whole sequenced genomes. A Bayesian inference method was applied to identify the branches on which the phenotype distribution evolves within sub-lineages. Two different Genome Wide Association Studies (i.e. gene- and SNP-based GWAS) were independently performed in order to link genetic mutations to the phenotype of interest. The genomic analyses presented in this study were successfully applied on the selected dataset. The Bayesian phylogenetic approach emphasized an association with "slow" growth ability at 2 °C of the lineage I, as well as CC9 of the lineage II. Moreover, both gene- and SNP-GWAS approaches displayed significant statistical associations with the tested phenotype. A list of 114 significantly associated genes, including genes already known to be involved in the cold adaption mechanism of L. monocytogenes and genes associated to mobile genetic elements (MGE), resulted from the gene-GWAS. On the other hand, a group of 184 highly associated SNPs were highlighted by SNP-GWAS, including SNPs detected in genes which were already likely involved in cold adaption; hypothetical proteins; and intergenic regions where for example promotors and regulators can be located. The successful application of combined bioinformatics approaches associating WGS-genotypes and specific phenotypes, could contribute to improve prediction of microbial behaviors in food. The implementation of this information in hazard identification and exposure assessment processes will open new possibilities to feed QMRA-models.

Distribution of Escherichia coli O157:H7 in ground beef: Assessing the clustering intensity for an industrial-scale grinder and a low and localized initial contamination.

Undercooked ground beef is regularly implicated in food-borne outbreaks involving pathogenic Shiga toxin-producing Escherichia coli. The dispersion of bacteria during mixing processes is of major concern for quantitative microbiological risk assessment since clustering will influence the number of bacteria the consumers might get exposed to as well as the performance of sampling plans used to detect contaminated ground beef batches. In this study, batches of 25kg of ground beef were manufactured according to a process mimicking an industrial-scale grinding with three successive steps: primary grinding, mixing and final grinding. The ground beef batches were made with 100% of chilled trims or with 2/3 of chilled trims and 1/3 of frozen trims. Prior grinding, one beef trim was contaminated with approximately 106-107CFU of E. coli O157:H7 on a surface of 0.5cm2 to reach a concentration of 10-100cells/g in ground beef. The E. coli O157:H7 distribution in ground beef was characterized by enumerating 60 samples (20 samples of 5g, 20 samples of 25g and 20 samples of 100g) and fitting a Poisson-gamma model to describe the variability of bacterial counts. The shape parameter of the gamma distribution, also known as the dispersion parameter reflecting the amount of clustering, was estimated between 1.0 and 1.6. This k-value of approximately 1 expresses a moderate level of clustering of bacterial cells in the ground beef. The impact of this clustering on the performance of sampling strategies was relatively limited in comparison to the classical hypothesis of a random repartition of pathogenic cells in mixed materials (purely Poisson distribution instead of Poisson-gamma distribution).

Modeling the behavior of Listeria monocytogenes during enrichment in half Fraser broth; impact of pooling and the duration of enrichment on the detection of L. monocytogenes in food.

A stochastic model describing the growth of Listeria monocytogenes during enrichment in half Fraser was developed for the purpose of estimating the effects of modifications to the first enrichment step of the EN ISO 11290-1 detection method. Information pertaining to the variability of growth rates, physiological state of the cell, and the behavior of individual cells contaminating the food were obtained from previously published studies. We used this model to investigate the impact of pooling enrichment broths (wet pooling) on the performance of the standard method. For validation of the model, the numbers of L. monocytogenes occurring in 88 naturally contaminated foods following pre-enrichment were compared to model-simulated microbial counts. The model was then used to perform simulations representative of the natural contamination observed for smoked salmon in the European baseline survey of 2010-2011. The model-estimated L. monocytogenes levels following individual enrichment or following the pooling of five broths where only one would be contaminated were compared. The model indicated a 10% loss of method sensitivity resulting from wet pooling. The model also predicted a 5% decrease in the sensitivity of the method when the duration of the enrichment was reduced from 24 to 22 h.

Comparison of individual-based modeling and population approaches for prediction of foodborne pathogens growth.

Individual-based modeling (IBM) approach combined with the microenvironment modeling of vacuum-packed cold-smoked salmon was more effective to describe the variability of the growth of a few Listeria monocytogenes cells contaminating irradiated salmon slices than the traditional population models. The IBM approach was particularly relevant to predict the absence of growth in 25% (5 among 20) of artificially contaminated cold-smoked salmon samples stored at 8 °C. These results confirmed similar observations obtained with smear soft cheese (Ferrier et al., 2013). These two different food models were used to compare the IBM/microscale and population/macroscale modeling approaches in more global exposure and risk assessment frameworks taking into account the variability and/or the uncertainty of the factors influencing the growth of L. monocytogenes. We observed that the traditional population models significantly overestimate exposure and risk estimates in comparison to IBM approach when contamination of foods occurs with a low number of cells (<100 per serving). Moreover, the exposure estimates obtained with the population model were characterized by a great uncertainty. The overestimation was mainly linked to the ability of IBM to predict no growth situations rather than the consideration of microscale environment. On the other hand, when the aim of quantitative risk assessment studies is only to assess the relative impact of changes in control measures affecting the growth of foodborne bacteria, the two modeling approach gave similar results and the simplest population approach was suitable.

[New approach for managing microbial risks in food]. / Nouvelle approche de la gestion des risques microbiologiques dans les aliments.

The aim of the food legislation is to ensure the protection of human health. Traditionally, the food legislation requires food business operators to apply good hygiene practices and specific procedures to control foodborne pathogens. These regulations allowed reaching a high level of health protection. The improvement of the system will require risk-based approaches. Firstly, risk assessment should allow the identification of high-risk situations where resources should be allocated for a better targeting of risk management. Then, management measures should be adapted to the health objective. In this approach, the appropriate level of protection is converted intofood safety and performance objectives on the food chain, i.e., maximum microbial contamination to fulfil the acceptable risk level. When objectives are defined, the food business operators and competent authorities can identify control options to comply with the objectives and establish microbiological criteria to verify compliance with these objectives. This approach, described for approximately 10 years, operative thanks to the development of quantitative risk assessment techniques, is still difficult to use in practical terms since it requires a commitment of competent authorities to define the acceptable risk and needs also the implementation of sometimes complex risk models.

Combining individual-based modeling and food microenvironment descriptions to predict the growth of Listeria monocytogenes on smear soft cheese.

An individual-based modeling (IBM) approach was developed to describe the behavior of a few Listeria monocytogenes cells contaminating smear soft cheese surface. The IBM approach consisted of assessing the stochastic individual behaviors of cells on cheese surfaces and knowing the characteristics of their surrounding microenvironments. We used a microelectrode for pH measurements and micro-osmolality to assess the water activity of cheese microsamples. These measurements revealed a high variability of microscale pH compared to that of macroscale pH. A model describing the increase in pH from approximately 5.0 to more than 7.0 during ripening was developed. The spatial variability of the cheese surface characterized by an increasing pH with radius and higher pH on crests compared to that of hollows on cheese rind was also modeled. The microscale water activity ranged from approximately 0.96 to 0.98 and was stable during ripening. The spatial variability on cheese surfaces was low compared to between-cheese variability. Models describing the microscale variability of cheese characteristics were combined with the IBM approach to simulate the stochastic growth of L. monocytogenes on cheese, and these simulations were compared to bacterial counts obtained from irradiated cheeses artificially contaminated at different ripening stages. The simulated variability of L. monocytogenes counts with the IBM/microenvironmental approach was consistent with the observed one. Contrasting situations corresponding to no growth or highly contaminated foods could be deduced from these models. Moreover, the IBM approach was more effective than the traditional population/macroenvironmental approach to describe the actual bacterial behavior variability.

Single-Cell Growth Probability of Listeria monocytogenes at Suboptimal Temperature, pH, and Water Activity.

The single-cell growth probability of Listeria monocytogenes was characterized in tryptone soy broth supplemented with 0.6% yeast extract at temperature ranging from 5 to 25°C, pH ranging from 4.4 to 6.5, and water activity ranging from 0.919 to 0.989. Growth was monitored visually in 96-microwell plates and the growth probability was deduced from concentrations estimated with the MPN calculation. Models were proposed to describe the increase of the probability from 0 to 1 with increasing values of environmental factors. An exponential model was used for the temperature and the individual minimal temperature for growth ranged from -3.6°C [-4.5°C, -2.7°C] to 17.3°C [16.0°C, 18.6°C]. An inverse exponential model was convenient for the pH and the minimal pH for growth of individual cells ranged from 4.34 [3.93, 4.75] to 5.93 [4.85, 7.01]. A linear model was used for the water activity and the minimal value for growth of individual cells ranged from 0.917 [0.909, 0.925] to 0.988 [0.966, 1]. In spite of the great between-experiment variability, the minimal values estimated for the growth limits of individual cells were in accordance with values usually proposed for L. monocytogenes populations. This study provides models allowing the prediction of the growth probability of a few cells contaminating chilled foods with suboptimal pH and water activity improving thus the assessment of the behavior of L. monocytogenes cells naturally contaminating foods.

Relationship between the culturability of stressed Listeria monocytogenes cells in non-selective and selective culture media and the cellular esterase activity measured by solid phase cytometry.

Solid phase cytometry in conjunction with fluorescent probe was applied to rapidly quantify cellular esterase activity of Listeria monocytogenes cells. Viability of cells stressed by several treatments (starvation, NaCl, lactic acid and peracetic acid) was assessed simultaneously by their esterase activity estimated by fluorescence intensity and by their ability to multiply in liquid and solid, non-selective and selective, culture media. It was determined that cell physiological state has a significant impact on the cellular fluorescence intensity which was very dependent on the stress suffered by cells. No general relationship was observed between the bacterial populations observed by cytometry and the populations able to grow on culture media. The link between the cell culturability in non-selective and selective media and the esterase activity was always dependent on the stress suffered. Nevertheless, it was also established that solid phase cytometry is an efficient, sensitive and accurate tool to characterize the ability of non-selective and selective enrichment broths to allow the repair of stressed L. monocytogenes cells by examining the increase in the fraction of the most esterase active cells during the course of resuscitation.

Modeling the occurrence of Mycobacterium avium subsp. paratuberculosis in bulk raw milk and the impact of management options for exposure mitigation.

Mycobacterium avium subsp. paratuberculosis (MAP) is the causal agent of paratuberculosis (Johne's disease) in cattle and other farm ruminants. The potential role of MAP in Crohn's disease in humans and the contribution of dairy products to human exposure to MAP continue to be the subject of scientific debate. The occurrence of MAP in bulk raw milk from dairy herds was assessed using a stochastic modeling approach. Raw milk samples were collected from bulk tanks in dairy plants and tested for the presence of MAP. Results from this analytical screening were used in a Bayesian network to update the model prediction. Of the 83 raw milk samples tested, 4 were positive for MAP by culture and PCR. We estimated that the level of MAP in bulk tanks ranged from 0 CFU/ml for the 2.5th percentile to 65 CFU/ml for the 97.5th percentile, with 95% credibility intervals of [0, 0] and [16, 326], respectively. The model was used to evaluate the effect of measures aimed at reducing the occurrence of MAP in raw milk. Reducing the prevalence of paratuberculosis has less of an effect on the occurrence of MAP in bulk raw milk than does managing clinically infected animals through good farming practices.

Individual cell lag time distributions of Cronobacter (Enterobacter sakazakii) and impact of pooling samples on its detection in powdered infant formula.

Cells of six strains of Cronobacter were subjected to dry stress and stored for 2.5 months at ambient temperature. The individual cell lag time distributions of recovered cells were characterized at 25 °C and 37 °C in non-selective broth. The individual cell lag times were deduced from the times taken by cultures from individual cells to reach an optical density threshold. In parallel, growth curves for each strain at high contamination levels were determined in the same growth conditions. In general, the extreme value type II distribution with a shape parameter fixed to 5 (EVIIb) was the most effective at describing the 12 observed distributions of individual cell lag times. Recently, a model for characterizing individual cell lag time distribution from population growth parameters was developed for other food-borne pathogenic bacteria such as Listeria monocytogenes. We confirmed this model's applicability to Cronobacter by comparing the mean and the standard deviation of individual cell lag times to populational lag times observed with high initial concentration experiments. We also validated the model in realistic conditions by studying growth in powdered infant formula decimally diluted in Buffered Peptone Water, which represents the first enrichment step of the standard detection method for Cronobacter. Individual lag times and the pooling of samples significantly affect detection performances.

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).

Challenges in risk assessment and predictive microbiology of foodborne spore-forming bacteria.

Mathematical description of the behavior of bacterial foodborne pathogens and concepts of risk assessment were first applied to spore-forming bacteria and specially to Clostridium botulinum with numerous works dealing with spores heat destruction to ensure the safety of canned foods or with their germination and growth probability in foods. This paper discusses two aspects which appear specific to pathogenic sporeformers in comparison to vegetative microorganisms, that is, firstly, the extreme intra-species biodiversity of spore-forming bacteria and its consequences for risk assessment and, secondly, the modeling of spore germination and outgrowth processes. The intra-species biodiversity of spore-forming bacteria has a great impact on hazard identification, exposure assessment and hazard characterization leading thus to an extremely variable individual poisoning risk for consumers. The germination and outgrowth processes were shown independent at the single cell level and although numerous studies were performed to study the effect of spores treatments and growth conditions on these two events, the mathematical modeling and the prediction of these processes is still challenging today. The difficulties to accurately assess the biodiversity and the germination and outgrowth processes of spore-forming bacteria lead to a substantial uncertainty in risk estimates related to the exposure to these microorganisms. Nevertheless, significant progress have been made these last years improving the relevance of quantitative risk assessments for spore-forming bacteria and decreasing the risk uncertainty. Despite these difficulties, risk assessment still constitutes a valuable tool to justify the implementation of management options.

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.

Global sensitivity analysis applied to a contamination assessment model of listeria monocytogenes in cold smoked salmon at consumption.

In this study, a variance-based global sensitivity analysis method was first applied to a contamination assessment model of Listeria monocytogenes in cold smoked vacuum packed salmon at consumption. The impact of the choice of the modeling approach (populational or cellular) of the primary and secondary models as well as the effect of their associated input factors on the final contamination level was investigated. Results provided a subset of important factors, including the food water activity, its storage temperature, and duration in the domestic refrigerator. A refined sensitivity analysis was then performed to rank the important factors, tested over narrower ranges of variation corresponding to their current distributions, using three techniques: ANOVA, Spearman correlation coefficient, and partial least squares regression. Finally, the refined sensitivity analysis was used to rank the important factors.

Influence of stress on single-cell lag time and growth probability for Listeria monocytogenes in half Fraser broth.

The impacts of 12 common food industry stresses on the single-cell growth probability and single-cell lag time distribution of Listeria monocytogenes were determined in half Fraser broth, the primary enrichment broth of the International Organization for Standardization detection method. First, it was determined that the ability of a cell to multiply in half Fraser broth is conditioned by its history (the probability for a cell to multiply can be decreased to 0.05), meaning that, depending on the stress in question, the risk of false-negative samples can be very high. Second, it was established that when cells are injured, the single-cell lag times increase in mean and in variability and that this increase represents a true risk of not reaching the detection threshold of the method in the enrichment broth. No relationship was observed between the impact on single-cell lag times and that on growth probabilities. These results emphasize the importance of taking into account the physiological state of the cells when evaluating the performance of methods to detect pathogens in food.