Electrolyzed Saline Targets Biofilm Periodontal Pathogens In Vitro.

Preventing the development and recurrence of periodontal diseases often includes antimicrobial mouthrinses to control the growth of the periodontal pathogens. Most antimicrobials are nonselective, targeting the symbiotic oral species as well as the dysbiosis-inducing ones. This affects the overall microbial composition and metabolic activity and consequently the host-microbe interactions, which can be detrimental (associated with inflammation) or beneficial (health-associated). Consequently, guiding the antimicrobial effect for modulating the microbial composition to a health-associated one should be considered. For such an approach, this study investigated electrolyzed saline as a novel rinse. Electrolyzed saline was prepared from sterile saline using a portable electrolysis device. Multispecies oral homeostatic and dysbiotic biofilms were grown on hydroxyapatite discs and rinsed daily with electrolyzed saline (EOS). Corresponding positive (NaOCl) and negative (phosphate-buffered saline) controls were included. After 3 rinses, biofilms were analyzed with viability quantitative polymerase chain reaction and scanning electron microscopy. Supernatants of rinsed biofilms were used for metabolic activity analysis (high-performance liquid chromatography) through measuring organic acid content. In addition, human oral keratinocytes (HOKs) were exposed to EOS to test biocompatibility (cytotoxicity and inflammation induction) and also to rinsed biofilms to assess their immunogenicity after rinsing. Rinsing the dysbiotic biofilms with EOS could reduce the counts of the pathobionts (>3 log10 Geq/mm2 reduction) and avert biofilm dysbiosis (≤1% pathobiont abundance), leading to the dominance of commensal species (≥99%), which altered both biofilm metabolism and interleukin 8 (IL-8) induction in HOKs. EOS had no harmful effects on homeostatic biofilms. The scanning electron micrographs confirmed the same. In addition, tested concentrations of EOS did not have any cytotoxic effects and did not induce IL-8 production in HOKs. EOS showed promising results for diverting dysbiosis in in vitro rinsed biofilms and controlling key periopathogens, with no toxic effects on commensal species or human cells. This novel rinsing should be considered for clinical applications.

Oral Biofilm Cryotherapy as a Novel Ecological Modulation Approach.

Oral cryotherapy is used in dentistry as a safe, simple, and low-cost treatment for a variety of oral lesions. It is well known for its ability to aid in the healing process. However, its effect on oral biofilms is unknown. As a result, the purpose of this study was to assess the effects of cryotherapy on in vitro oral biofilms. In vitro multispecies oral biofilms were grown on the surface of hydroxyapatite discs in symbiotic or dysbiotic states. CryoPen X+ was used to treat the biofilms, whereas untreated biofilms served as control. One set of biofilms was collected for study immediately after cryotherapy, whereas another group was reincubated for 24 h to permit biofilm recovery. Changes in biofilm structure were analyzed with a confocal laser scanning microscope (CLSM) and a scanning electron microscope (SEM), while biofilm ecology and community compositional changes were analyzed with viability DNA extraction and quantitative polymerase chain reaction (v-qPCR) analysis. One cryo-cycle immediately reduced biofilm load by 0.2 to 0.4 log10 Geq/mL, which increased with additional treatment cycles. Although the bacterial load of the treated biofilms recovered to the same level as the control biofilms within 24 h, the CLSM detected structural alterations. Compositional alterations were also detected by SEM, corroborating the v-qPCR findings that showed ≈≤10% incidence of pathogenic species compared to nontreated biofilms that encompassed ≈45% and 13% pathogenic species in dysbiotic and symbiotic biofilms, respectively. Spray cryotherapy showed promising results in a novel conceptual approach to the control of oral biofilms. Acting selectively by targeting oral pathobionts and retaining commensals, spray cryotherapy could modify the ecology of in vitro oral biofilms to become more symbiotic and prevent the evolution of dysbiosis without the use of antiseptics/antimicrobials.

Clinical concentrations of peroxidases cause dysbiosis in in vitro oral biofilms.

BACKGROUND AND OBJECTIVE: Little is known about the initiation of dysbiosis in oral biofilms, a topic of prime importance for understanding the etiology of, and preventing, periodontitis. The aim of this study was to evaluate the effect of different concentrations of crevicular and salivary peroxidase and catalase on dysbiosis in multispecies biofilms in vitro. MATERIAL AND METHODS: The spotting technique was used to identify the effect of different concentrations of myeloperoxidase, lactoperoxidase, erythrocyte catalase, and horseradish peroxidase in salivary and crevicular fluid on the inhibitory effect of commensals on pathobiont growth. Vitality-quantitative real-time PCR was performed to quantify the dysbiotic effect of the peroxidases (adjusted to concentrations found in periodontal health, gingivitis, and periodontitis) on multispecies microbial communities. RESULTS: Agar plate and multispecies ecology experiments showed that production of hydrogen peroxide (H2 O2 ) by commensal bacteria decreases pathobiont growth and colonization. Peroxidases at concentrations found in crevicular fluid and saliva neutralized this inhibitory effect. In multispecies communities, myeloperoxidase, at the crevicular fluid concentrations found in periodontitis, resulted in a 1-3 Log increase in pathobionts when compared with the crevicular fluid concentrations found in periodontal health. The effect of salivary lactoperoxidase and salivary myeloperoxidase concentrations was, in general, similar to the effect of crevicular myeloperoxidase concentrations. CONCLUSIONS: Commensal species suppress pathobionts by producing H2 O2 . Catalase and peroxidases, at clinically relevant concentrations, can neutralize this effect and thereby can contribute to dysbiosis by allowing the outgrowth of pathobionts.

Dysbiotic Biofilms Deregulate the Periodontal Inflammatory Response.

Periodontal diseases originate from a dysbiosis within the oral microbiota, which is associated with a deregulation of the host immune response. Although little is known about the initiation of dysbiosis, it has been shown that H2O2 production is one of the main mechanisms by which some commensal bacteria suppress the outgrowth of pathobionts. Current models emphasize the critical nature of complex microbial biofilms that form unique microbial ecologies and of their change during transition from health (homeostatic) to disease (dysbiotic). However, very little is known on how this alters their virulence and host responses. The objective of this study was to determine differences in virulence gene expression by pathobionts and the inflammatory host response in homeostatic and dysbiotic biofilms originating from the same ecology. Quantitative polymerase chain reaction was performed to quantify the pathobiont outgrowth. Expression analysis of bacterial virulence and cellular inflammatory genes together with cytokine enzyme-linked immunosorbent assays were used to detect differences in bacterial virulence and to analyze potential differences in inflammatory response. An increase in pathobionts in induced dysbiotic biofilms was observed compared to homeostatic biofilms. The main virulence genes of all pathobionts were upregulated in dysbiotic biofilms. Exposure of these dysbiotic biofilms to epithelial and fibroblast cultures increased the expression of interleukin (IL)-6, IL-1ß, tumor necrosis factor-α, and matrix metalloprotease 8, but especially the chemokine CXCL8 (IL-8). Conversely, homeostatic and beneficial biofilms had a minor immune response at the messenger RNA and protein level. Overall, induced dysbiotic biofilms enriched in pathobionts and virulence factors significantly increased the inflammatory response compared to homeostatic and commensal biofilms.

Analysis of the lag phase to exponential growth transition by incorporating inoculum characteristics.

During the last decade, individual-based modelling (IbM) has proven to be a valuable tool for modelling and studying microbial dynamics. As each individual is considered as an independent entity with its own characteristics, IbM enables the study of microbial dynamics and the inherent variability and heterogeneity. IbM simulations and (single-cell) experimental research form the basis to unravel individual cell characteristics underlying population dynamics. In this study, the IbM framework MICRODIMS, i.e., MICRObial Dynamics Individual-based Model/Simulator, is used to investigate the system dynamics (with respect to the model and the system modelled). First, the impact of the time resolution on the simulation accuracy is discussed. Second, the effect of the inoculum state and size on emerging individual dynamics, such as individual mass, individual age and individual generation time distribution dynamics, is studied. The distributions of individual characteristics are more informative during the lag phase and the transition to the exponential growth phase than during the exponential phase. The first generation time distributions are strongly influenced by the inoculum state. All inocula with a pronounced heterogeneity, except the inocula starting from a uniform distribution, exhibit commonly observed microbial behaviour, like a more spread first generation time distribution compared to following generations and a fast stabilisation of biomass and age distributions.

Simultaneous versus sequential optimal experiment design for the identification of multi-parameter microbial growth kinetics as a function of temperature.

Optimal experiment design for parameter estimation (OED/PE) has become a popular tool for efficient and accurate estimation of kinetic model parameters. When the kinetic model under study encloses multiple parameters, different optimization strategies can be constructed. The most straightforward approach is to estimate all parameters simultaneously from one optimal experiment (single OED/PE strategy). However, due to the complexity of the optimization problem or the stringent limitations on the system's dynamics, the experimental information can be limited and parameter estimation convergence problems can arise. As an alternative, we propose to reduce the optimization problem to a series of two-parameter estimation problems, i.e., an optimal experiment is designed for a combination of two parameters while presuming the other parameters known. Two different approaches can be followed: (i) all two-parameter optimal experiments are designed based on identical initial parameter estimates and parameters are estimated simultaneously from all resulting experimental data (global OED/PE strategy), and (ii) optimal experiments are calculated and implemented sequentially whereby the parameter values are updated intermediately (sequential OED/PE strategy). This work exploits OED/PE for the identification of the Cardinal Temperature Model with Inflection (CTMI) (Rosso et al., 1993). This kinetic model describes the effect of temperature on the microbial growth rate and encloses four parameters. The three OED/PE strategies are considered and the impact of the OED/PE design strategy on the accuracy of the CTMI parameter estimation is evaluated. Based on a simulation study, it is observed that the parameter values derived from the sequential approach deviate more from the true parameters than the single and global strategy estimates. The single and global OED/PE strategies are further compared based on experimental data obtained from design implementation in a bioreactor. Comparable estimates are obtained, but global OED/PE estimates are, in general, more accurate and reliable.

Quantifying the heterogeneous heat response of Escherichia coli under dynamic temperatures.

AIMS: Non-sigmoid growth curves of Escherichia coli obtained at constant temperatures near the maximum growth temperature (T(max)) were previously explained by the coexistence of two subpopulations, i.e. a stress-sensitive and a stress-resistant subpopulation. Mathematical simulations with a heterogeneous model support this hypothesis for static experiments at 45 degrees C. In this article, the behaviour of E. coli, when subjected to a linearly increasing temperature crossing T(max), is studied. METHODS AND RESULTS: Subpopulation dynamics are studied by culturing E. coli K12 MG1655 in brain heart infusion broth in a bioreactor. The slowly increasing temperature (degrees C h(-1)) starting from 42 degrees C results in growth up to 60 degrees C, a temperature significantly higher than the known T(max). Given some additional presumptions, mathematical simulations with the heterogeneous model can describe the dynamic experiments rather well. CONCLUSIONS: This study further confirms the existence of a stress-resistant subpopulation and reveals the unexpected growth of E. coli at temperatures significantly higher than T(max). SIGNIFICANCE AND IMPACT OF THE STUDY: The growth of the small stress-resistant subpopulation at unexpectedly high temperatures asks for a revision of currently applied models in food safety and food quality strategies.

Unravelling Escherichia coli dynamics close to the maximum growth temperature through heterogeneous modelling.

AIMS: Previous work showed that the exponential phase of Escherichia coli K12 MG1655, grown in Brain Heart Infusion broth at temperatures close to its maximum growth temperature, is disturbed. Based on plate count data, microscopic images and literature, the existence of a heat-resistant subpopulation was hypothesized. Here, this hypothesis is mathematically explored via a heterogeneous model. METHODS AND RESULTS: A heat-sensitive and a heat-resistant subpopulation are considered. A large fraction of the population is inactivated, while the remaining smaller fraction is able to resist (or adapt to) the inimical temperature and grows. A heterogeneous model that encloses a growth model (resistant population) and an inactivation model (sensitive population) is used to describe the global population dynamics. Most experimental data can be predicted when taking parameter uncertainty via Monte Carlo simulation into account. CONCLUSIONS: The heterogeneous model accurately describes disturbed growth curves at superoptimal temperatures, except for high initial cell densities. SIGNIFICANCE AND IMPACT OF THE STUDY: This study strengthens the hypothesis of the existence of a (small) heat-resistant subpopulation in typical inoculum cultures of E. coli K12 MG1655.

Modelling the influence of the inoculation level on the growth/no growth interface of Listeria monocytogenes as a function of pH, aw and acetic acid.

This research is an extension of previous work reported in Gysemans et al. [Gysemans, K.P.M., Bernaerts, K., Geeraerd, A.H., Vermeulen, A., Debevere, J., Devlieghere, F., Van Impe, J.F., 2007. Exploring the performance of logistic regression model types on growth/no growth data of Listeria monocytogenes. International Journal of Food Microbiology 114, 316-331.] in which the growth/no growth interface of Listeria monocytogenes was modelled as a function of water activity (a(w)), pH and undissociated acetic acid percentage (UAc). The major difference with the previous work is that in the present research the influence of the cell density (N) is also considered during the modelling process. New experimental data were therefore collected as a function of a wide range of cell densities up until the level of the individual cell. Prior to the development of model that incorporates N, the expected inadequacy of the high cell density growth/no growth model developed in Gysemans et al. (2007) on the new cell density dependent data was illustrated. Inadequacy of the model at lower cell densities was expected since the data showed a significant reduction of the growth probability as N decreased. For the development of a model that incorporates the effect of N, a square-root type logistic regression model was proposed and evaluated. The model predicts a strong influence of the cell density with an increase in the growth probability if the cell count increased. The onset of this increase is dependent on the intrinsic factors of the medium (pH, a(w), and acetic acid concentration). The model also suggests that it is unlikely that a larger population has a higher chance to start growing just because the chance on a strong cell is higher in a larger population. It seems that the bacteria influence each other's growth.

Identification of non-linear microbial inactivation kinetics under dynamic conditions.

In this study dynamic microbial inactivation experiments are exploited for performing parameter identification of a non-linear microbial model. For that purpose microbial inactivation data are produced and a differential equation exhibiting a shoulder and a loglinear phase is employed. The derived parameter estimates from this method were used to perform predictions on an independent experimental set at fluctuating temperature. Joint confidence regions and asymptotic confidence intervals of the estimated parameters were compared with previous studies originating from parameter identification under isothermal conditions. The developed approach can provide more reliable estimates for realistic conditions compared to the usual or standard two step approach.

Accurate estimation of cardinal growth temperatures of Escherichia coli from optimal dynamic experiments.

Prediction of the microbial growth rate as a response to changing temperatures is an important aspect in the control of food safety and food spoilage. Accurate model predictions of the microbial evolution ask for correct model structures and reliable parameter values with good statistical quality. Given the widely accepted validity of the Cardinal Temperature Model with Inflection (CTMI) [Rosso, L., Lobry, J. R., Bajard, S. and Flandrois, J. P., 1995. Convenient model to describe the combined effects of temperature and pH on microbial growth, Applied and Environmental Microbiology, 61: 610-616], this paper focuses on the accurate estimation of its four parameters (T(min), T(opt), T(max) and micro(opt)) by applying the technique of optimal experiment design for parameter estimation (OED/PE). This secondary model describes the influence of temperature on the microbial specific growth rate from the minimum to the maximum temperature for growth. Dynamic temperature profiles are optimized within two temperature regions ([15 degrees C, 43 degrees C] and [15 degrees C, 45 degrees C]), focusing on the minimization of the parameter estimation (co)variance (D-optimal design). The optimal temperature profiles are implemented in a computer controlled bioreactor, and the CTMI parameters are identified from the resulting experimental data. Approximately equal CTMI parameter values were derived irrespective of the temperature region, except for T(max). The latter could only be estimated accurately from the optimal experiments within [15 degrees C, 45 degrees C]. This observation underlines the importance of selecting the upper temperature constraint for OED/PE as close as possible to the true T(max). Cardinal temperature estimates resulting from designs within [15 degrees C, 45 degrees C] correspond with values found in literature, are characterized by a small uncertainty error and yield a good result during validation. As compared to estimates from non-optimized dynamic experiments, more reliable CTMI parameter values were obtained from the optimal experiments within [15 degrees C, 45 degrees C].

Validation of a model for growth of Lactococcus lactis and Listeria innocua in a structured gel system: effect of monopotassium phosphate.

The effect of monopotassium phosphate (KH(2)PO(4)) on the chemical environment and on growth of Listeria innocua and Lactococcus lactis in coculture were investigated in a liquid and in a gelled microbiological medium at 12 degrees C and an initial pH of 6.2. As expected, addition of KH(2)PO(4) to both the liquid and gelled media resulted in an increase in buffering capacity. This effect on buffering capacity changed the profiles of lactic acid dissociation and pH evolution. At all gelatin concentrations studied, addition of KH(2)PO(4) increased the growth rate and the stationary cell concentration of L. lactis. In addition, the growth rate of L. innocua slightly increased but, in contrast, the stationary cell concentration remained unchanged. A new class of predictive models developed previously in our research team to quantify the effect of food model gel structure on microbial growth [Antwi, M., Bernaerts, K., Van Impe, J. F., Geeraerd, A. H., 2007. Modelling the combined effect of food model system and lactic acid on L. innocua and L. lactis growth in mono- and coculture. International Journal of Food Microbiology 120, 71-84] was applied. Our analysis indicate that KH(2)PO(4) influenced the parameters of the chemical and microbiological subprocesses of the model. Nonetheless, the growth model satisfactorily predicted the stationary cell concentration when (i) the undissociated lactic acid concentrations at which L. innocua and L. lactis growth cease were chosen as previously reported, and (ii) all other parameters of the chemical and microbiological subprocesses were computed for each medium. This confirms that the undissociated lactic acid concentrations at which growth ceases is a unique property of a bacterium and does not, within our case study, depend on growth medium. The study indicates that microbial growth depends on the interplay between the individual food components which affect the physicochemical properties of the food, such as the buffering capacity. Towards future research, it can be concluded that mathematical models which embody the effect of buffering capacity are needed for accurate predictions of microbial growth in food systems.

Modelling the unexpected effect of acetic and lactic acid in combination with pH and aw on the growth/no growth interface of Zygosaccharomyces bailii.

Microbial spoilage of shelf-stable acidified sauces is predominantly caused by lactic acid bacteria and yeasts. A specific spoilage yeast in these products is Zygosaccharomyces bailii, as this fructophilic, osmotolerant, and weak acid resistant yeast is difficult to control. A growth/no growth model was developed describing the influence of (i) pH in a range from pH 3.0 to pH 5.0 (5 levels), (ii) acetic acid in a range from 0 to 3.5% (w/v), and (iii) lactic acid in a range from 0 to 3.0% (w/v). aw was fixed at a level of 0.95 which is representative for acidified sauces with high sugar content. Modified Sabouraud medium was inoculated at +/- 10(4) CFU/ml, incubated at 30 degrees C and growth was assessed by optical density measurements. All combinations of environmental conditions were tested in at least twelve replicates, yielding precise values for the probability of growth. Results showed that replacing acetic acid by lactic acid, which has a milder taste, may imply some risks on food spoilage because, under some conditions, stimulation of growth by lactic acid was observed. This stimulation had also consequences on the model development: (i) only ordinary logistic regression models were able to describe this phenomenon due to their flexible behaviour, (ii) it was necessary to split up the data set into two subsets to have the best description of the obtained data. Two different ordinary logistic regression models were fitted on these data sets taking either the total acid concentration as one of the explanatory variables or differentiating between the undissociated and dissociated acid concentrations. The obtained models were compared with the CIMSCEE code [CIMSCEE, 1992. Code for the production of microbiologically safe and stable emulsified and non-emulsified sauces containing acetic acid. Comité des Industries des Mayonnaise et Sauces Condimentaires, de la Communauté Economique Européenne, Brussels, Belgium], a formula which is nowadays often used by the food industry to predict the stability of acidified products based on the undissociated acetic acid, NaCl and sugars concentration. Comparing this formula and the newly developed models showed that the CIMSCEE code made a slight underestimation of the growth probability. Advantages of the newly developed models are the description of the gradual transition zone between growth and no growth and the incorporation of the effect of lactic acid, alone or in combination with acetic acid.

Dynamics of Escherichia coli at elevated temperatures: effect of temperature history and medium.

AIMS: The dynamics of Escherichia coli near the maximum temperature for growth in a rich medium are analysed. The effects of temperature history, medium composition and physiological state of the inoculum are evaluated. METHODS AND RESULTS: Kinetics of E. coli K12 MG1655 is studied in 'brain-heart infusion' broth in a temperature controlled environment. Based on viable counts, 'smooth' growth curves are observed at 40, 41, 42 and 43 degrees C. The exponential growth phase at 44 and 45 degrees C is interrupted. At 46 degrees C, a period of exponential growth is followed by inactivation. Neither the physiological state of the inoculum nor medium enrichment alters the dynamics, whilst temperature pre-adaptation or chemical chaperones restore regular cell growth and division ('smooth' exponential growth). CONCLUSIONS: Atypical, nonexponential growth at 44, 45 and 46 degrees C seems related to protein destabilization and can (partly) be restored by an appropriate medium design (i.e. addition of chemical chaperones) or temperature history (i.e. selection of a more resistant subpopulation). SIGNIFICANCE AND IMPACT OF THE STUDY: This study indicates that the maximum temperature for growth is dependent on the temperature history and the chemical environment. These observations and the nonexponential kinetics have important implications for the development of predictive models for food safety and quality.

Growth/no growth models describing the influence of pH, lactic and acetic acid on lactic acid bacteria developed to determine the stability of acidified sauces.

Growth/no growth models were developed for two spoilage bacteria typical for acidified sauces, L. plantarum and L. fructivorans. Influencing factors embedded in the model are also those typically encountered in these acidified sauces. The pH was varied between 3.0 and 5.0 (5 levels), and the acetic and lactic acid concentration ranged from 0 to 3% (6 levels). Modified MRS broth was inoculated at a high inoculation level (10(6) CFU/ml), incubated at 30 degrees C and growth was assessed by optical density measurements. All combinations of environmental conditions were tested in twelvefold yielding precise values for the probability of growth. Data were modelled by means of ordinary logistic regression. A comparison was made between a model containing the total acid concentrations as explanatory variables, on the one hand, and a model differentiating between the dissociated and undissociated concentrations, on the other hand. Results showed that (i) L. plantarum and L. fructivorans behave differently, resulting in a clearly distinct growth/no growth interface, (ii) there was no great difference between the established models with different explanatory variables, (iii) in some cases, growth/no growth boundaries at very low probabilities (which are more practical in industry) show illogical behaviour. The results of this study were also compared with the CIMSCEE code, which is often used by food producers to determine the stability of their acidified food products.

Performance of a growth-no growth model for Listeria monocytogenes developed for mayonnaise-based salads: influence of strain variability, food matrix, inoculation level, and presence of sorbic and benzoic acid.

A previously developed growth-no growth model for Listeria monocytogenes, based on nutrient broth data and describing the influence of water activity (a(w)), pH, and acetic acid concentrations, was validated (i) for a variety of L. monocytogenes strains and (ii) in a laboratory-made, mayonnaise-based surimi salad (as an example of a mayonnaise-based salad). In these challenge tests, the influence of the inoculation level was tested as well. Also, the influence of chemical preservatives on the growth probability of L. monocytogenes in mayonnaise-based salads was determined. To evaluate the growth-no growth model performance on the validation data, four quantitative criteria are determined: concordance index, % correct predictions, % fail-dangerous, and % fail-safe. First, the growth probability of 11 L. monocytogenes strains, not used for model development, was assessed in nutrient broth under conditions within the interpolation region. Experimental results were compared with model predictions. Second, the growth-no growth model was assessed in a laboratory-made, sterile, mayonnaise-based surimi salad to identify a possible model completeness error related to the food matrix, making use of the above-mentioned validation criteria. Finally, the effect on L. monocytogenes of common chemical preservatives (sorbic and benzoic acid) at different concentrations under conditions typical of mayonnaise-based salads was determined. The study showed that the growth-no growth zone was properly predicted and consistent for all L. monocytogenes strains. A larger prediction error was observed under conditions within the transition zone between growth-no growth. However, in all cases, the classification between no growth (P = 0) and any growth (P > 0) occurred properly, which is most important for the food industry, where outgrowth needs to be prevented in all instances. The results in the sterile mayonnaise-based salad showed again that the growth-no growth zone was well predicted but that also, in real food systems, a transition zone between growth and no growth exists. This became even more obvious for lower inoculation levels. The maximum-allowed concentration of benzoic and sorbic acid in mayonnaise-based salads, according to the European Union legislation, eliminated the growth of L. monocytogenes. Concentrations of 600 and 300 ppm were already sufficient to inhibit growth at 7 and 4 degrees C, respectively, under conditions associated with mayonnaise-based salads (pH 5.6; a(w), 0.985).

Modelling the combined effects of structured food model system and lactic acid on Listeria innocua and Lactococcus lactis growth in mono- and coculture.

A new class of predictive model developed in a liquid system is extended in order to quantify gelatin gel matrix structure effects on the growth of Listeria innocua and Lactococcus lactis (both in mono- and coculture, and both producing mainly lactic acid). It was observed that gelatin does not only act as a structuring agent but also alters the buffering capacity of the medium. Model extension occurs in two stages, describing chemical and microbiological processes, respectively. Firstly, equations relating undissociated lactic acid concentration and total lactic acid concentration on the one hand, and undissociated lactic acid concentration and pH on the other hand, are extended to account for the effects of gelatin concentration. Secondly, these equations are incorporated into the growth model to describe the combined effect of gelatin concentration, (undissociated) lactic acid and pH on the growth of either microorganism. The description of the model is in good agreement with the experimental data acquired in monoculture conditions. In a subsequent model validation step, when gelatin concentration and total lactic acid profile of the coculture experiments are used as inputs, the developed growth model consisting of condensed knowledge extracted from the monoculture experiments, is able to predict accurately the interaction effect occurring in coculture. The study suggests that, on the one hand, the extent of the effects of undissociated lactic acid and pH on microbial growth in structured food systems can be modified by the increase in buffering capacity, which can protect microorganisms and eventually promote higher levels of cell growth in comparison with liquid culture conditions. On the other hand, food matrix structure, in casu the gelatin, reduces the rate of microbial multiplication. Both effects are incorporated in the growth model developed in this research.

Exploring the performance of logistic regression model types on growth/no growth data of Listeria monocytogenes.

Several model types have already been developed to describe the boundary between growth and no growth conditions. In this article two types were thoroughly studied and compared, namely (i) the ordinary (linear) logistic regression model, i.e., with a polynomial on the right-hand side of the model equation (type I) and (ii) the (nonlinear) logistic regression model derived from a square root-type kinetic model (type II). The examination was carried out on the basis of the data described in Vermeulen et al. [Vermeulen, A., Gysemans, K.P.M., Bernaerts, K., Geeraerd, A.H., Van Impe, J.F., Debevere, J., Devlieghere, F., 2006-this issue. Influence of pH, water activity and acetic acid concentration on Listeria monocytogenes at 7 degrees C: data collection for the development of a growth/no growth model. International Journal of Food Microbiology. .]. These data sets consist of growth/no growth data for Listeria monocytogenes as a function of water activity (0.960-0.990), pH (5.0-6.0) and acetic acid percentage (0-0.8% (w/w)), both for a monoculture and a mixed strain culture. Numerous replicates, namely twenty, were performed at closely spaced conditions. In this way detailed information was obtained about the position of the interface and the transition zone between growth and no growth. The main questions investigated were (i) which model type performs best on the monoculture and the mixed strain data, (ii) are there differences between the growth/no growth interfaces of monocultures and mixed strain cultures, (iii) which parameter estimation approach works best for the type II models, and (iv) how sensitive is the performance of these models to the values of their nonlinear-appearing parameters. The results showed that both type I and II models performed well on the monoculture data with respect to goodness-of-fit and predictive power. The type I models were, however, more sensitive to anomalous data points. The situation was different for the mixed strain culture. In that case, the type II models could not describe the curvature in the growth/no growth interface which was reversed to the typical curvatures found for monocultures. This unusual curvature may originate from the fact that (i) an interface of a mixed strain culture can result from the superposition of the interfaces of the individual strains, or that (ii) only a narrow range of the growth/no growth interface was studied (the local trend can be different from the trend over a wider range). It was also observed that the best type II models were obtained with the flexible nonlinear logistic regression, although reasonably good models were obtained with the less flexible linear logistic regression with the nonlinear-appearing parameters fixed at experimentally determined values. Finally, it was found that for some of the nonlinear-appearing parameters, deviations from their experimentally determined values did not influence the model fit. This was probably caused by the fact that only a limited part of the growth/no growth interface was studied.

Influence of pH, water activity and acetic acid concentration on Listeria monocytogenes at 7 degrees C: data collection for the development of a growth/no growth model.

Growth/no growth models can be used to determine the chance that microorganisms will grow in specific environmental conditions. As a consequence, these models are of interest in the assessment of the safety of foods which can be contaminated with food pathogens. In this paper, growth/no growth data for Listeria monocytogenes (in a monoculture and in a mixed strain culture) are presented. The data were gathered at 7 degrees C in Nutrient Broth with different combinations of environmental factors pH (5.0-6.0, six levels), water activity (0.960-0.990, six levels) and acetic acid concentration (0-0.8% (w/w), five levels). This combination of environmental factors for the development of a growth/no growth model was based on the characteristics of sauces and mayonnaise based salads. The strains used were chosen from screening experiments in which the pH, water activity and acetic acid resistance of 26 L. monocytogenes strains (LFMFP culture collection) was determined at 30 degrees C in Brain Heart Infusion broth. The screening showed that most L. monocytogenes strains were not able to grow at a(w)<0.930, pH<4.3 or a total acetic acid concentration >0.4% (w/w). Among these strains, the ones chosen were the most resistant to one of these factors in the hope that, if the resulting model predicted no growth at certain conditions for those more resistant strains, then these predictions would also be valid for the less resistant strains. A mixed strain culture was also examined to combine the strains that were most resistant to one of the factors. A full factorial design with the selected strains was tested. The experiments were performed in microtiter plates and the growth was followed by optical density measurements at 380 nm. The plates were inoculated with 6 log CFU/ml and twenty replicates were made for each treatment combination. These data were used (1) to determine the growth/no growth boundary and (2) to estimate the influence of the environmental conditions on the time to detection. From the monoculture and mixed strain data, the growth boundary of L. monocytogenes is shown not to be a straight cut-off but a rather narrow transition zone. The experiments also showed that in the studied region, a(w) did not have a pronounced influence on the position of the growth/no growth boundary while a low concentration of acetic acid (0.2% (w/w)) and a pH decrease from 6.0 to 5.8 was sufficient to significantly reduce the possibility of growth. The determination of the time to detection showed a significant increase at the combinations of environmental conditions near the 'no growth zone'. For example, at 0.2% (w/w) acetic acid, there was an increase from +/-10 days to 30 days by lowering pH from 5.8 to 5.6 at a(w) values of 0.985 and 0.979, while at pH 5.4 less than 50% growth occurred for all a(w) values.

Modelling the work to be done by Escherichia coli to adapt to sudden temperature upshifts.

AIMS: This paper studies and models the effect of the amplitude of a sudden temperature upshift DeltaT on the adaptation period of Escherichia coli, in terms of the work to be done by the cells during the subsequent lag phase (i.e., the product of growth rate mumax and lag phase duration lambda). METHODS AND RESULTS: Experimental data are obtained from bioreactor experiments with E. coli K12 MG1655. At a predetermined time instant during the exponential growth phase, a sudden temperature upshift is applied (no other environmental changes take place). The length of the (possibly) induced lag phase and the specific growth rate after the shift are quantified with the growth model of Baranyi and Roberts (Int J Food Microbiol 23, 1994, 277). Different models to describe the evolution of the product lambda x mumax as a function of the amplitude of the temperature shift are statistically compared. CONCLUSIONS: The evolution of lambda x mumax is influenced by the amplitude of the temperature shift DeltaT and by the normal physiological temperature range. As some cut-off is observed, the linear model with translation is preferred to describe this evolution. SIGNIFICANCE AND IMPACT OF THE STUDY: This work contributes to the characterization of microbial lag phenomena, in this case for E. coli K12 MG1655, in view of accurate predictive model building.