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The use of non-thermal methods for food preservation is due to consumer demands for microbiological safe products, without changes in the sensory and nutritional qualities of the product. High hydrostatic pressure (HHP) has emerged as an alternative to traditional thermal processing methods for foods. Listeria monocytogenes CECT 5672 was treated under HHPs (350, 400 and 450 MPa) for 3, 16 and 23 min. The effects of pH (5, 6 and 7) and sodium chloride concentration (0, 0.5 and 1.0) of the recovery medium were studied on L. monocytogenes . The kinetic parameters were estimated by the method described by Metris, George, and Baranyi (2006) . From results obtained, histograms of the lag phase were generated and distributions were fitted. The duration of the lag phase of HHP damaged cells increased with the application of additional stresses. Histograms showed a shift to longer lag phases and an increase in variability with high stress levels in the recovery medium. Using a primary model together with Monte Carlo simulation, predictions of time to growth (100 cfu/g) of L. monocytogenes were established and they were compared with deterministic predictions. It was evidenced that deterministic predictions do not give a good indication of the probability of a certain level of growth.

The quantitative measurements of the growth of Salmonella can be traced back to the 1950s, when the Copenhagen School studied its cell cycle. Although predictive food microbiology has only been recognised as a discipline in its own right since the 1980s, the first predictive models on Salmonella, specifically on its thermal inactivation, were published in the 1960s. Today this is the foodborne pathogen for which the most D-values can be found in the literature. Being of concern in meat, growth models were developed mainly in poultry, other meats, egg products and culture media. With the advent of the internet, predictive modelling has become more advanced in terms of organising, analysing and visualising large amounts of data, and it has also become easier to disseminate the resultant predictive software packages. We anticipate that computational developments will generate further improvements, including complex scenario analysis, probabilistic and dynamic models.

We investigated if the transcriptional response of Salmonella Typhimurium to temperature and acid variations was hysteretic, i.e. whether the transcriptional regulation caused by environmental stimuli showed memory and remained after the stimuli ceased. The transcriptional activity of non-replicating stationary phase cells of S. Typhimurium caused by the exposure to 45 °C and to pH 5 for 30 min was monitored by microarray hybridizations at the end of the treatment period as well as immediately and 30 minutes after conditions were set back to their initial values, 25 °C and pH 7. One hundred and two out of 120 up-regulated genes during the heat shock remained up-regulated 30 minutes after the temperature was set back to 25 °C, while only 86 out of 293 down regulated genes remained down regulated 30 minutes after the heat shock ceased. Thus, the majority of the induced genes exhibited hysteresis, i.e., they remained up-regulated after the environmental stress ceased. At 25 °C the transcriptional regulation of genes encoding for heat shock proteins was determined by the previous environment. Gene networks constructed with up-regulated genes were significantly more modular than those of down-regulated genes, implying that down-regulation was significantly less synchronized than up-regulation. The hysteretic transcriptional response to heat shock was accompanied by higher resistance to inactivation at 50 °C as well as cross-resistance to inactivation at pH 3; however, growth rates and lag times at 43 °C and at pH 4.5 were not affected. The exposure to pH 5 only caused up-regulation of 12 genes and this response was neither hysteretic nor accompanied of increased resistance to inactivation conditions. Cellular memory at the transcriptional level may represent a mechanism of adaptation to the environment and a deterministic source of variability in gene regulation.

8 pages, 2 tables, 4 figures.-- Online Ahead of Print: October 8, 2010.-- This is a copy of an article published in the Foodborne Pathogens and Disease © 2011 [copyright Mary Ann Liebert, Inc.]; Foodborne Pathogens and Disease is available online at: http://www.liebertonline.com., Stochastic models are useful for estimating the risk of foodborne illness and they can be integrated, besides other sources of variability, into microbial risk assessment. A stochastic approach to evaluate growth of two strains of Listeria monocytogenes influenced by different factors affecting microbial growth (pH and storage temperature) was performed. An individual-based approach of growth through optical density measurements was used. From results obtained, histograms of the lag phase were generated and distributions were fitted. Histograms presented increased variation when the factors applied were suboptimal for L. monocytogenes and they were combined. The extreme value distribution was ranked as the best one in most cases, whereas normal was the poorest fitting distribution. To evaluate the influence of pH and storage temperature on L. monocytogenes CECT 5672 in real food, commercial samples of courgette and carrot soup were inoculated with this pathogen. It was able to grow in both soups at storage temperatures from 4°C to 20°C. Using the distributions adjusted, predictions of time to growth (102 cfu/g) of L. monocytogenes were established by Monte Carlo simulation and they were compared with deterministic predictions and observations in foods., Dr. Marina Muñoz thanks Fundación CAJAMURCIA for awarding her a fellowship. This project was funded by Ministerio de Educación y Ciencia Español (ref. AGL 2006-13320-C03-02=ALI)

The dynamic model for the growth of a bacterial population described by Baranyi and Roberts (J. Baranyi and T. A. Roberts, Int. J. Food Microbiol. 23: 277-294, 1994) was applied to model the lag period and exponential growth of L isteria monocytogenes under conditions of fluctuating temperature and water activity (a w ) values. To model the duration of the lag phase, the dependence of the parameter h 0 , which quantifies the amount of work done during the lag period, on the previous and current environmental conditions was determined experimentally. This parameter depended not only on the magnitude of the change between the previous and current environmental conditions but also on the current growth conditions. In an exponentially growing population, any change in the environment requiring a certain amount of work to adapt to the new conditions initiated a lag period that lasted until that work was finished. Observations for several scenarios in which exponential growth was halted by a sudden change in the temperature and/or a w were in good agreement with predictions. When a population already in a lag period was subjected to environmental fluctuations, the system was reset with a new lag phase. The work to be done during the new lag phase was estimated to be the workload due to the environmental change plus the unfinished workload from the uncompleted previous lag phase.

Background The aging process of bacteria in stationary phase is halted if cells are subcultured and enter lag phase and it is then followed by cellular division. Network science has been applied to analyse the transcriptional response, during lag phase, of bacterial cells starved previously in stationary phase for 1 day (young cells) and 16 days (old cells). Results A genome scale network was constructed for E. coli K-12 by connecting genes with operons, transcription and sigma factors, metabolic pathways and cell functional categories. Most of the transcriptional changes were detected immediately upon entering lag phase and were maintained throughout this period. The lag period was longer for older cells and the analysis of the transcriptome revealed different intracellular activity in young and old cells. The number of genes differentially expressed was smaller in old cells (186) than in young cells (467). Relatively, few genes (62) were up- or down-regulated in both cultures. Transcription of genes related to osmotolerance, acid resistance, oxidative stress and adaptation to other stresses was down-regulated in both young and old cells. Regarding carbohydrate metabolism, genes related to the citrate cycle were up-regulated in young cells while old cells up-regulated the Entner Doudoroff and gluconate pathways and down-regulated the pentose phosphate pathway. In both old and young cells, anaerobic respiration and fermentation pathways were down-regulated, but only young cells up-regulated aerobic respiration while there was no evidence of aerobic respiration in old cells. Numerous genes related to DNA maintenance and replication, translation, ribosomal biosynthesis and RNA processing as well as biosynthesis of the cell envelope and flagellum and several components of the chemotaxis signal transduction complex were up-regulated only in young cells. The genes for several transport proteins for iron compounds were up-regulated in both young and old cells. Numerous genes encoding transporters for carbohydrates and organic alcohols and acids were down-regulated in old cells only. Conclusion Network analysis revealed very different transcriptional activities during the lag period in old and young cells. Rejuvenation seems to take place during exponential growth by replicative dilution of old cellular components.