Application of a log-logistic model to describe the survival of Yersinia enterocolitica at sub-optimal pH and temperature

Stringent Response Factor DksA Contributes to Fatty Acid Degradation Function to Influence Cell Membrane Stability and Polymyxin B Resistance of Yersinia enterocolitica

DksA is a proteobacterial regulator that binds directly to the secondary channel of RNA polymerase with (p)ppGpp and is responsible for various bacterial physiological activities. While (p)ppGpp is known to be involved in the regulation and response of fatty acid metabolism pathways in many foodborne pathogens, the role of DksA in this process has yet to be clarified. This study aimed to characterize the function of DksA on fatty acid metabolism and cell membrane structure in Yersinia enterocolitica. Therefore, comparison analysis of gene expression, growth conditions, and membrane permeabilization among the wide-type (WT), DksA-deficient mutant (YEND), and the complemented strain was carried out. It confirmed that deletion of DksA led to a more than four-fold decrease in the expression of fatty acid degradation genes, including fadADEIJ. Additionally, YEND exhibited a smaller growth gap compared to the WT strain at low temperatures, indicating that DksA is not required for the growth of Y. enterocolitica in cold environments. Given that polymyxin B is a cationic antimicrobial peptide that targets the cell membrane, the roles of DksA under polymyxin B exposure were also characterized. It was found that DksA positively regulates the integrity of the inner and outer membranes of Y. enterocolitica under polymyxin B, preventing the leakage of intracellular nucleic acids and proteins and ultimately reducing the sensitivity of Y. enterocolitica to polymyxin B. Taken together, this study provides insights into the functions of DksA and paves the way for novel fungicide development.

Modeling the impact of high temperatures on microalgal viability and photosynthetic activity

BACKGROUND

Culture collapse due to high temperatures can significantly impact the profitability of outdoor algal cultivation systems. The objective of this study was to model for the first time the impact of high temperatures on algal activity and viability.

RESULTS

Viability measurements on Dunaliella salina cultures were based on cytometry with two fluorescent markers (erythrosine and fluorescein di-acetate), and photosynthetic activity was measured by Pulse Amplitude Modulation (PAM) fluorometry. Kinetic studies revealed that viability and activity losses during exposure to high temperatures could be described by a Weibull model. Both mortality and activity were shown to be functions of the thermal dose received by the algae, defined as the product of duration of exposure to high temperatures and an exponential function of temperature. Simulations at five climatic locations revealed that culture collapse due to high temperatures could impact productivity of D. salina in non-temperature-controlled outdoor photobioreactors by 35 and 40% in arid and Mediterranean climates, respectively.

CONCLUSIONS

The model developed in this study can be used to forecast the impact of high temperatures on algal biofuel productivity. When coupled with models predicting the temperature of outdoor cultivation systems, this model can also be used to select the best combination of location, system geometry, and algal species to minimize the risks of culture collapse and therefore maximize biofuel productivity.

Note: Use of a Distribution of Frequencies Model to Interpret the Tailed Heat Inactivation Curves of Prions

Inactivation of prions responsible of bovine spongiform encephalytis (BSE) by heat is a major concern for the canned food industry in Europe. Experimental data of heat inactivation of prions obtained from the scientific literature have been modelled. Two models were applied to analyse the inactivation curves, the Weibull distribution model and a two-parameter empirical model. Statistical analysis of available data indicated that the Weibull frequency distribution provided the best description of non-linear survival curves. The effect of the temperature on Weibull model parameters ( a and b) was also studied. The shape parameter, b, indicates deviations from linearity in the inactivation curves, and the scale parameter, a, can be considered as a kinetic constant rate for the inactivation. The values of the b parameter were on the order of 0.15-0.2 which meant that these curves had a strong upper concavity. The a values obtained ranged from 1.8 × 10−3 to 2.3 × 10−6 for temperatures between 100 and 160°C. These results indicate that temperature has a relevant effect on the inactivation of prions, although it remains a considerable fraction of prions with infective capacity. The Weibull frequency distribution model appears as a useful and convenient model because the a parameter could allow a quantitative comparison of the inactivation of prions at different temperatures or under different conditions.

Use of linear, Weibull, and log-logistic functions to model pressure inactivation of seven foodborne pathogens in milk

Survival curves of six foodborne pathogens suspended in ultra high-temperature (UHT) whole milk and exposed to high hydrostatic pressure at 21.5 degrees C were obtained. Vibrio parahaemolyticus was treated at 300 MPa and other pathogens, Listeria monocytogenes, Escherichia coli O157:H7, Salmonella enterica serovar Enteritidis, Salmonella enterica serovar Typhimurium, and Staphylococcus aureus were treated at 600 MPa. All the survival curves showed a rapid initial drop in bacterial counts followed by tailing caused by a diminishing inactivation rate. A linear model and two nonlinear models were fitted to these data and the performances of these models were compared using mean square error (MSE) values. The log-logistic and Weibull models consistently produced better fits to the inactivation data than the linear model. The mean MSE value of the linear model was 6.1, while the mean MSE values were 0.7 for the Weibull model and 0.3 for the log-logistic model. There was no correlation between pressure resistance and the taxonomic group the bacteria belong to. The order, most to least pressure-sensitive, of the single strains tested was: V. parahaemolyticus (gram negative)<L. monocytogenes (gram positive)<Salmonella Typhimurium (gram negative) approximately = Salmonella Enteritidis (gram negative)<E. coli O157:H7 approximately = Staphylocollus aureus (gram positive)<Shigella flexneri (gram negative). The most pressure-resistant gram-negative bacterium, Shigella flexneri, and most pressure resistant gram-positive bacterium, Staphylocollus aureus, were pressurized at 50 degrees C. Staphylocollus aureus was treated at 500 MPa and Shigella flexneri at 600 MPa. Elevated temperature considerably enhanced pressure inactivation of these two pathogens, but did not affect the overall shape of the survival curves. Pressure level (250 MPa) and substrate (1% peptone water plus 3% NaCl) in which V. parahaemolyticus was suspended affected the shape of survival curves of V. parahaemolyticus.

General model, based on two mixed weibull distributions of bacterial resistance, for describing various shapes of inactivation curves

Cells of Listeria monocytogenes or Salmonella enterica serovar Typhimurium taken from six characteristic stages of growth were subjected to an acidic stress (pH 3.3). As expected, the bacterial resistance increased from the end of the exponential phase to the late stationary phase. Moreover, the shapes of the survival curves gradually evolved as the physiological states of the cells changed. A new primary model, based on two mixed Weibull distributions of cell resistance, is proposed to describe the survival curves and the change in the pattern with the modifications of resistance of two assumed subpopulations. This model resulted from simplification of the first model proposed. These models were compared to the Whiting's model. The parameters of the proposed model were stable and showed consistent evolution according to the initial physiological state of the bacterial population. Compared to the Whiting's model, the proposed model allowed a better fit and more accurate estimation of the parameters. Finally, the parameters of the simplified model had biological significance, which facilitated their interpretation.

Inactivation kinetics of Yersinia enterocolitica by citric and lactic acid at different temperatures

Inactivation of Yersinia enterocolitica by citric (1--20% w/v) and lactic (0.3--4.0% v/v) acids at different temperatures (4, 20, 40 degrees C) has been investigated. Inactivation effect of citric and lactic acids was dependent on time and temperature of exposure and acid concentration. Survival curves of Y. enterocolitica suspended in citric acid solutions at 4 and 20 degrees C displayed a shoulder followed by an exponential inactivation, but at 40 degrees C a shoulder was not observed. At all temperatures investigated, survival curves of Y. enterocolitica suspended in lactic acid solutions were linear or slightly concave upwards. A mathematical model based on the Weibull distribution accurately described the kinetics of inactivation of Y. enterocolitica by both acids. The influence of the citric acid concentration on Y. enterocolitica resistance was independent of the treatment temperature. However for lactic acid, the influence of the acid concentration on microbial inactivation depended on the temperature. At any temperature investigated, lactic acid was significantly more effective than citric acid.

Pressure inactivation kinetics of Yersinia enterocolitica ATCC 35669

The survival curves of Yersinia enterocolitica ATCC 35669 inactivated by high hydrostatic pressure were obtained at four pressure levels (300, 350, 400, and 450 MPa) in sodium phosphate buffer (0.1 M, pH 7.0) and four pressure levels (350, 400, 450, 500 MPa) in UHT whole milk. Tailing was observed in all the survival curves. A linear model and three nonlinear models were fitted to these data and the performances of these models were compared. The linear regression model for survival curves at four pressure levels had regression coefficients (R2) values of 0.785-0.962 and mean square error (MSE) of 0.265-0.893. A residual plot strongly suggested that a linear regression function was not appropriate as there was strong curvature in the plotted data. The nonlinear regression model using the log-logistic had R2 values of 0.946-0.982 and MSE values of 0.110-0.320. The Weibull model had R2 values of 0.944-0.975 and MSE values of 0.153-0.349. These results indicated that both were better models to describe the pressure inactivation kinetics of Y. enterocolitica in milk and buffer. Among the three nonlinear models studied, the modified Gompertz model produced the poorest fit to data. The number of parameters of the log-logistic model was reduced from four to two so that the model was greatly simplified. The reduced log-logistic model still produced a fit comparable to the full model. Since pressure had no significant effect on the shape factors of the Weibull model at the pressure levels of 300-400 MPa for buffer and 400-500 MPa for milk, models were developed to predict survival curves of Y. enterocolitica at pressures different from the experimental pressures.

Microbiological safety of mayonnaise, salad dressings, and sauces produced in the United States: a review

The literature on the death and survival of foodborne pathogens in commercial mayonnaise, dressing, and sauces was reviewed and statistically analyzed with emphasis on Salmonella, Escherichia coli O157:H7, and Listeria monocytogenes. The absence of reports of foodborne illness associated directly with the consumption of commercially prepared acidic dressings and sauces is evidence of their safety. Salmonella, E. coli O157:H7, E. coli, L. monocytogenes, Staphylococcus aureus, and Yersinia enterocolitica die when inoculated into mayonnaise and dressings. Historically, mayonnaise and dressings have been exempt from the acidified food regulations and have justly deserved this status due primarily to the toxic effect of acetic and to a lesser extent lactic and citric acids. These organic acids are inimical to pathogenic bacteria and are effective natural preservatives with acetic being the most effective in killing pathogenic bacteria at the pH values encountered in these products. Statistical analysis on data reported in the literature shows that the most important and significant factor in destroying pathogenic bacteria is pH as adjusted with acetic acid followed by the concentration of acetic acid in the water phase. The reported highest manufacturing target pH for dressings and sauces is 4.4, which is below the 4.75 pKa of acetic acid and below the reported inhibitory pH of 4.5 for foodborne pathogens in the presence of acetic acid. The overall conclusion is that these products are very safe. They should remain exempt from the acidified food regulations providing adequate research has been done to validate their safety, and the predominant acid is acetic and reasonable manufacturing precautions are taken.

Development and evaluation of a model predicting the survival of Escherichia coli O157:H7 NCTC 12900 in homemade eggplant salad at various temperatures, pHs, and oregano essential oil concentrations

Homemade eggplant salad, a traditional Greek appetizer, was inoculated with Escherichia coli O157:H7 NCTC 12900 supplemented with different concentrations of oregano essential oil (0.0, 0.7, 1. 4, and 2.1% [vol/wt]) and stored at different temperatures (0, 5, 10, and 15 degrees C). The product's pH was adjusted to 4.0, 4.5, or 5. 0 with lemon juice. For each combination of the environmental factors, the bacterial counts were modeled, using the Baranyi model, as a function of time to estimate the kinetic parameters of the pathogen. A reduction of more than 1 log unit in E. coli O157:H7 counts was observed in all cases, and the death rate depended on the pH, the storage temperature, and the essential oil concentration. Separate quadratic models were developed with natural logarithms of the shoulder period and death rate as estimated by the growth model, as a function of temperature, pH, and oregano essential oil concentrations. These were further used to predict the population of E. coli O157:H7 NCTC 12900 from other inoculated eggplant salads at random conditions of temperature, pH, and oregano oil concentration. The predicted values were compared with viable-count measurements for validation.

A predictive model for the non-thermal inactivation of Salmonella enteritidis in a food model system supplemented with a natural antimicrobial

Home-made taramasalad, a traditional Greek appetizer, was inoculated with Salmonella enteritidis supplemented with different concentrations of oregano essential oil (0.0, 0.5, 1.0, 2.0% v/w) and stored at different temperatures (5, 10, 15, 20 degrees C). The product's pH was adjusted from 4.3 to 5.3 with lemon juice. At each combination of the environmental factors, the bacterial counts were modelled as a function of time in order to estimate the kinetic parameters of the pathogen. For comparison, two different models were used. A reduction of Salmonella enteritidis was observed in all cases and its death rate depended on the pH, the storage temperature and the essential oil concentration. Death responses as a function of pH, storage temperature and concentration of oregano essential oil were described using a quadratic function which was then used to predict the death of Salmonella enteritidis in home-made taramasalad of different compositions.

A mathematical model for bacterial inactivation

The first order kinetic model, the Buchanan model and Cerf's model, can model a linear survival curve, a survival curve with a shoulder and a survival curve with a tailing, respectively. However, they are not suitable for fitting a sigmoidal survival curve. The three models were integrated into a new model that was capable of fitting the four most commonly observed survival curves: linear curves, curves with a shoulder, curves with a tailing (biphasic curves) and sigmoidal curves. The new model was compared with the Whiting-Buchanan model using the survival curves of Staphylococcus aureus. The goodness-of-fit of the proposed model is practically as good as that of the Whiting-Buchanan model. Compared with the Whiting-Buchanan model, the proposed model has a more mechanistic background. Since for non-linear survival curves, such as biphasic and sigmoidal curves, the t(m-D) value (the time required for an m-log-cycle reduction of microorganisms under a given condition) cannot be estimated accurately by the existing or traditional method, a new method is also proposed to predict accurately the t(m-D) value for non-linear survival curves.

Model of the influence of time and mild temperature on Listeria monocytogenes nonlinear survival curves

Heat treatment has long been regarded as one of the most widely used and most effective means of destroying pathogens in food. Up to now the linear relationship between the death rate and the temperature has been used when choosing the best heat treatment to apply. However, the information given by this linear relationship is no longer sufficient when nonlinear survival curves are observed. Consequently, the agri-food industry needs a tool to choose the best mild heat treatment to apply in the case of nonlinear survival curves. This study deals with the temperature-induced death of Listeria monocytogenes CIP 7831 in the stationary phase of growth. Eleven temperatures were tested. With the proposed primary and secondary models good fits of our data were obtained. A model describing both the effect of the duration of treatment and the temperature on the logarithm of the number of survivors was then built. A clear increase in the precision of the estimation of the parameters was obtained with this model. Moreover, with this model a new graphical strategy to choose a mild heat increase regarding a maximal survivor number has been proposed.

Development of thermal inactivation models for Salmonella enteritidis and Escherichia coli O157:H7 with temperature, pH and NaCl as controlling factors

The thermal inactivation of Salmonella enteritidis phage type 4 and Escherichia coli O157:H7 as affected by temperature (54.5-64.5 degrees C), pH (4.2-9.6 with HCl or NaOH) and NaCl concentration (0.5-8.5% w/w) was studied. Cell suspensions in modified tryptone soya broth were heated in a submerged-coil heating apparatus and survivors were enumerated on tryptone soya agar incubated aerobically. For most thermal inactivation data there was a logarithmic decrease in the viable cell concentration over the initial 4-6 log10 reduction and D-values were fitted. In some cases, tailing of the survivor curves was observed with cells surviving longer than the D-values predicted. Models describing the effect of temperature, pH and NaCl concentration on the thermal inactivation of S. enteritidis and E. coli O157:H7 were produced. For both organisms, predicted z-values of 4.6-7.0 C degrees were obtained depending on conditions, with larger z-values at higher levels of NaCl. Optimum survival occurred between pH 5 and pH 7 and increasing acidity or alkalinity caused a decrease in the predicted D-values. At equivalent pH, acetic acid and lactic acid (at 0.5, 1 and 2% w/w) generally had a similar, or increased, lethal effect compared with HCl, whereas in most cases citric acid had a less lethal effect. For E. coli O157:H7, increasing NaCl concentration had a protective effect up to the maximum tested (8.5% w/w), while for S. enteritidis optimal survival at a NaCl concentration of 5-7% w/w was predicted. The models were validated in foods by comparing predictions with published data. Most (80%) of the predicted D-values from the S. enteritidis model were within the 95% confidence interval (within 2.45-fold of the published data) for different Salmonella serotypes in whole egg, egg albumen, egg yolk, beef and milk. Most (93%) of the predicted D-values from the E. coli O157:H7 model were larger than the limited published data for this organism in meat, poultry, milk and apple juice with 42% within the 95% confidence interval (within 2.05-fold of the published data). The D-value models were incorporated into Version 1, and subsequent versions, of the predictive microbiology software program, Food MicroModel.

The application of a log-logistic model to describe the thermal inactivation of Clostridium botulinum 213B at temperatures below 121.1 degrees C

In this work, the death of Clostridium botulinum 213B was measured at temperatures between 101 degrees C and 121 degrees C. It was found that at all temperatures tested, survivor curves deviated from log-linearity which prevented their description using traditional first order kinetics. The survivor curves were better described using a vitalistic approach and the log-logistic transformation proposed by Cole et al. (1993). A single equation was derived to describe all survivor curves over the temperature range tested and a comparison of predicted and measured data showed good correlation. The implications of the use of the vitalistic approach to the validity of the 'minimum botulinum cook' is discussed.