Behaviour of individual spores of non proteolytic Clostridium botulinum as an element in quantitative risk assessment

Suboptimal Bacillus licheniformis and Bacillus weihenstephanensis Spore Incubation Conditions Increase Heterogeneity of Spore Outgrowth Time

Changes with time of a population of Bacillus weihenstephanensis KBAB4 and Bacillus licheniformis AD978 dormant spores into germinated spores and vegetative cells were followed by flow cytometry, at pH ranges of 4.7 to 7.4 and temperatures of 10°C to 37°C for B. weihenstephanensis and 18°C to 59°C for B. licheniformis Incubation conditions lower than optimal temperatures or pH led to lower proportions of dormant spores able to germinate and extended time of germination, a lower proportion of germinated spores able to outgrow, an extension of their times of outgrowth, and an increase of the heterogeneity of spore outgrowth time. A model based on the strain growth limits was proposed to quantify the impact of incubation temperature and pH on the passage through each physiological stage. The heat treatment temperature or time acted independently on spore recovery. Indeed, a treatment at 85°C for 12 min or at 95°C for 2 min did not have the same impact on spore germination and outgrowth kinetics of B. weihenstephanensis despite the fact that they both led to a 10-fold reduction of the population. Moreover, acidic sporulation pH increased the time of outgrowth 1.2-fold and lowered the proportion of spores able to germinate and outgrow 1.4-fold. Interestingly, we showed by proteomic analysis that some proteins involved in germination and outgrowth were detected at a lower abundance in spores produced at pH 5.5 than in those produced at pH 7.0, maybe at the origin of germination and outgrowth behavior of spores produced at suboptimal pH.IMPORTANCE Sporulation and incubation conditions have an impact on the numbers of spores able to recover after exposure to sublethal heat treatment. Using flow cytometry, we were able to follow at a single-cell level the changes in the physiological states of heat-stressed spores of Bacillus spp. and to discriminate between dormant spores, germinated spores, and outgrowing vegetative cells. We developed original mathematical models that describe (i) the changes with time of the proportion of cells in their different states during germination and outgrowth and (ii) the influence of temperature and pH on the kinetics of spore recovery using the growth limits of the tested strains as model parameters. We think that these models better predict spore recovery after a sublethal heat treatment, a common situation in food processing and a concern for food preservation and safety.

Effect of storage temperature on the lag time of Geobacillus stearothermophilus individual spores

The lag times (λ) of Geobacillus stearothermophilus single spores were studied at different storage temperatures ranging from 45 to 59 °C using the Bioscreen C method. A significant variability of λ was observed among individual spores at all temperatures tested. The storage temperature affected both the position and the spread of the λ distributions. The minimum mean value of λ (i.e. 10.87 h) was observed at 55 °C, while moving away from this temperature resulted in an increase for both the mean and standard deviation of λ. A Cardinal Model with Inflection (CMI) was fitted to the reverse mean λ, and the estimated values for the cardinal parameters T_min, T_max, T_opt and the optimum mean λ of G. stearothermophilus were found to be 38.1, 64.2, 53.6 °C and 10.3 h, respectively. To interpret the observations, a probabilistic growth model for G. stearothermophilus individual spores, taking into account λ variability, was developed. The model describes the growth of a population, initially consisting of N₀ spores, over time as the sum of cells in each of the N₀ imminent subpopulations originating from a single spore. Growth simulations for different initial contamination levels showed that for low N₀ the number of cells in the population at any time is highly variable. An increase in N₀ to levels exceeding 100 spores results in a significant decrease of the above variability and a shorter λ of the population. Considering that the number of G. stearothermophilus surviving spores in the final product is usually very low, the data provided in this work can be used to evaluate the probability distribution of the time-to-spoilage and enable decision-making based on the "acceptable level of risk".

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.