Contribution of omics to biopreservation: Toward food microbiome engineering.

Biopreservation is a sustainable approach to improve food safety and maintain or extend food shelf life by using beneficial microorganisms or their metabolites. Over the past 20 years, omics techniques have revolutionised food microbiology including biopreservation. A range of methods including genomics, transcriptomics, proteomics, metabolomics and meta-omics derivatives have highlighted the potential of biopreservation to improve the microbial safety of various foods. This review shows how these approaches have contributed to the selection of biopreservation agents, to a better understanding of the mechanisms of action and of their efficiency and impact within the food ecosystem. It also presents the potential of combining omics with complementary approaches to take into account better the complexity of food microbiomes at multiple scales, from the cell to the community levels, and their spatial, physicochemical and microbiological heterogeneity. The latest advances in biopreservation through omics have emphasised the importance of considering food as a complex and dynamic microbiome that requires integrated engineering strategies to increase the rate of innovation production in order to meet the safety, environmental and economic challenges of the agri-food sector.

Genetic, physiological, and cellular heterogeneities of bacterial pathogens in food matrices: Consequences for food safety.

In complex food systems, bacteria live in heterogeneous microstructures, and the population displays phenotypic heterogeneities at the single-cell level. This review provides an overview of spatiotemporal drivers of phenotypic heterogeneity of bacterial pathogens in food matrices at three levels. The first level is the genotypic heterogeneity due to the possibility for various strains of a given species to contaminate food, each of them having specific genetic features. Then, physiological heterogeneities are induced within the same strain, due to specific microenvironments and heterogeneous adaptative responses to the food microstructure. The third level of phenotypic heterogeneity is related to cellular heterogeneity of the same strain in a specific microenvironment. Finally, we consider how these phenotypic heterogeneities at the single-cell level could be implemented in mathematical models to predict bacterial behavior and help ensure microbiological food safety.

Potential of multiparametric characterization of foodstuffs by nuclear magnetic resonance to better predict microbial behavior.

Numerous predictive microbiology models have been proposed to describe bacterial population behaviors in foodstuffs. These models depict the growth kinetics of particular bacterial strains based on key physico-chemical parameters of food matrices and their storage temperature. In this context, there is a prominent issue to accurately characterize these parameters, notably pH, water activity (aw ), and NaCl and organic acid concentrations. Usually, all these product features are determined using one destructive analysis per parameter at macroscale (>5 g). Such approach prevents an overall view of these characteristics on a single sample. Besides, it does not take into account the intra-product microlocal variability of these parameters within foods. Nuclear magnetic resonance (NMR) is a versatile non-invasive spectroscopic technique. Experiments can be recorded successively on a same collected sample without damaging it. In this work, we designed a dedicated NMR approach to characterize the microenvironment of foods using 10-mg samples. The multiparametric mesoscopic-scale approach was validated on four food matrices: a smear soft cheese, cooked peeled shrimps, cold-smoked salmon, and smoked ham. Its implementation in situ on salmon fillets enabled to observe the intra-product heterogeneity and to highlight the impact of process on the spatial distribution of pH, NaCl, and organic acids. This analytical development and its successful application can help address the shortcomings of monoparametric methods traditionally used for predictive microbiology purposes.

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.

Modeling carbon dioxide effect in a controlled atmosphere and its interactions with temperature and pH on the growth of L. monocytogenes and P. fluorescens.

The effect of carbon dioxide, temperature, and pH on growth of Listeria monocytogenes and Pseudomonas fluorescens was studied, following a protocol to monitor microbial growth under a constant gas composition. In this way, the CO2 dissolution didn't modify the partial pressures in the gas phase. Growth curves were acquired at different temperatures (8, 12, 22 and 37 °C), pH (5.5 and 7) and CO2 concentration in the gas phase (0, 20, 40, 60, 80, 100% of the atmospheric pressure, and over 1 bar). These three factors greatly influenced the growth rate of L. monocytogenes and P. fluorescens, and significant interactions have been observed between the carbon dioxide and the temperature effects. Results showed no significant effect of the CO2 concentration at 37 °C, which may be attributed to low CO2 solubility at high temperature. An inhibitory effect of CO2 appeared at lower temperatures (8 and 12 °C). Regardless of the temperature, the gaseous CO2 is sparingly soluble at acid pH. However, the CO2 inhibition was not significantly different between pH 5.5 and pH 7. Considering the pKa of the carbonic acid, these results showed the dissolved carbon under HCO3- form didn't affect the bacterial inhibition. Finally, a global model was proposed to estimate the growth rate vs. CO2 concentration in the aqueous phase. This dissolved concentration is calculated according to the physical equations related to the CO2 equilibriums, involving temperature and pH interactions. This developed model is a new tool available to manage the food safety of MAP.

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.

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.

Diversity and assessment of potential risk factors of Gram-negative isolates associated with French cheeses.

The goal of this study was to identify at the species level a large collection of Gram-negative dairy bacteria isolated from milks or semi-hard and soft, smear-ripened cheeses (cheese core or surface samples) from different regions of France. The isolates were then assessed for two risk factors, antibiotic resistance and volatile and non-volatile biogenic amine production in vitro. In total, 173 Gram-negative isolates were identified by rrs and/or rpoB gene sequencing. A large biodiversity was observed with nearly half of all Gram-negative isolates belonging to the Enterobacteriaceae family. Overall, 26 different genera represented by 68 species including potential new species were identified among the studied Gram-negative isolates for both surface and milk or cheese core samples. The most frequently isolated genera corresponded to Pseudomonas, Proteus, Psychrobacter, Halomonas and Serratia and represented almost 54% of the dairy collection. After Pseudomonas, Chryseobacterium, Enterobacter and Stenotrophomonas were the most frequently isolated genera found in cheese core and milk samples while Proteus, Psychrobacter, Halomonas and Serratia were the most frequently isolated genera among surface samples. Antibiotic resistance profiles indicated that resistances to the aminosid, imipemen and quinolon were relatively low while more than half of all tested isolates were resistant to antibiotics belonging to the monobactam, cephem, fosfomycin, colistin, phenicol, sulfamid and some from the penam families. Thirty-six% of isolates were negative for in vitro biogenic amine production. Among biogenic amine-producers, cadaverine was the most frequently produced followed by isoamylamine, histamine and putrescine. Only low levels (<75 mg/l) of tyramine were detected in vitro.

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.

Development of a hierarchical Bayesian model to estimate the growth parameters of Listeria monocytogenes in minimally processed fresh leafy salads.

The optimal growth rate mu(opt) of Listeria monocytogenes in minimally processed (MP) fresh leafy salads was estimated with a hierarchical Bayesian model at (mean+/-standard deviation) 0.33+/-0.16 h(-1). This mu(opt) value was much lower on average than that in nutrient broth, liquid dairy, meat and seafood products (0.7-1.3 h(-1)), and of the same order of magnitude as in cheese. Cardinal temperatures T(min), T(opt) and T(max) were determined at -4.5+/-1.3 degrees C, 37.1+/-1.3 degrees C and 45.4+/-1.2 degrees C respectively. These parameters were determined from 206 growth curves of L. monocytogenes in MP fresh leafy salads (lettuce including iceberg lettuce, broad leaf endive, curly leaf endive, lamb's lettuce, and mixtures of them) selected in the scientific literature and in technical reports. The adequacy of the model was evaluated by comparing observed data (bacterial concentrations at each experimental time for the completion of the 206 growth curves, mean log(10) increase at selected times and temperatures, L. monocytogenes concentrations in naturally contaminated MP iceberg lettuce) with the distribution of the predicted data generated by the model. The sensitivity of the model to assumptions about the prior values also was tested. The observed values mostly fell into the 95% credible interval of the distribution of predicted values. The mu(opt) and its uncertainty determined in this work could be used in quantitative microbial risk assessment for L. monocytogenes in minimally processed fresh leafy salads.

Development and validation of experimental protocols for use of cardinal models for prediction of microorganism growth in food products.

An experimental protocol to validate secondary-model application to foods was suggested. Escherichia coli, Listeria monocytogenes, Bacillus cereus, Clostridium perfringens, and Salmonella were observed in various food categories, such as meat, dairy, egg, or seafood products. The secondary model validated in this study was based on the gamma concept, in which the environmental factors temperature, pH, and water activity (aw) were introduced as individual terms with microbe-dependent parameters, and the effect of foodstuffs on the growth rates of these species was described with a food- and microbe-dependent parameter. This food-oriented approach was carried out by challenge testing, generally at 15 and 10 degrees C for L. monocytogenes, E. coli, B. cereus, and Salmonella and at 25 and 20 degrees C for C. perfringens. About 222 kinetics in foods were generated. The results were compared to simulations generated by existing software, such as PMP. The bias factor was also calculated. The methodology to obtain a food-dependent parameter (fitting step) and therefore to compare results given by models with new independent data (validation step) is discussed in regard to its food safety application. The proposed methods were used within the French national program of predictive microbiology, Sym'Previus, to include challenge test results in the database and to obtain predictive models designed for microbial growth in food products.

Survival of Listeria monocytogenes After Irradiation Treatment of Camembert Cheeses Made from Raw Milk.

Raw milk camembert cheeses have been inoculated with a pathogenic strain of Listeria monocytogenes (V7 serotype ½ a) and irradiated with gamma or X-rays. The D10 of L. monocytogenes irradiated in cheese was 0.50 ± 0.05 kGy as compared to 0.35 in pure culture. A treatment dose of 2.6 kGy which does not alter the organoleptic properties of camembert, allows a complete destruction of 104 L. monocytogenes /g; When 105 bacteria/g are inoculated some of them are still viable after the irradiation and at the end of 45 days; however, the surviving bacteria have lost their ability to multiply.