The Development of Rapid Growth Potential Analysis Method of Foodborne Pathogens by Real-Time PCR

概要

Foodborne illness is widely distributed globally in both developed and developing countries at an unprecedented rate while global food demand is projected to increase sharply. In order to secure microbial safety of food, the practice of an intelligent hurdle’ combination is widely applied in food manufacturing practice. In relation to microbial hurdle technology, the boundary limit of growth or no-growth of bacteria in such stress conditions can be predicted by the approach of predictive microbiology that can be used for specific types of microorganisms, physio-chemical properties of food, and the used hurdles. However, most of predictive models are utilizing the application of conventional bacterial enumeration methods that has been recognized for having several limitations. A highly sensitive and specific real-time PCR quantification method that provide the ability to yield high throughput quantification results in a short period of time should be considered as an alternative data collection tool of bacterial growth in food materials for the purpose of predictive model construction.

　The author evaluated the real-time PCR quantification results to the conventional method by agar plate (both selective and non-selective medium) that considered as a golden standard for bacterial enumeration, as well as to the most probable number (MPN) method. The sensitivity and specificity of this assay were examined in pasteurized product and in food materials with naturally occurring microbial background flora, then evaluated the accuracy of the developed model constructed by real-time PCR data to the existed models generated from ComBase and MRV database. The author also evaluated the performance of four primary models in predicting the growth parameters of foodborne pathogens from various tested conditions. The inhibitory effect of pH, water activity, and temperature on the growth of target pathogens was evaluated and the growth prediction
under combined conditions was tested.

　The initial inoculum level of artificially inoculated samples was set as 104 to 105 CFU/ml or CFU/g. Water activity level of sample was adjusted by glucose, sucrose, lactose, maltose, galactose, and NaCl in Staphylococcus aureus study, while the adjustment of aw in Listeria monocytogenes study was using only NaCl. The pH was adjusted by HCl and NaOH to achieve the desired pH conditions. The incubation temperatures in all experiments were set at 4°C to 35°C and fluctuating temperature scenario performed for Salmonella Enteritidis in chicken juice was set at 5°C and 30°C, while for L. monocytogenes study in pasteurized milk was set at 2°C, 8°C, 12°C, 15°C, and 30°C. Sampling steps were performed following the designated incubation period in each condition. Samples for agar method were adequately diluted in phosphate buffered saline and spread on selective and/or non-selective medium using spiral plater, then incubated at 35°C or 37°C for 24-48 h. The MPN samples were appropriately diluted and transferred to 10 mL Fraser broth prior to incubation at 35°C for 24-48 h. The samples for real-time PCR quantification were immediately stored at -20˚C until all samples were collected for DNA extraction. The primers used for S. Enteritidis targeted the invA gene fragment [1], the hlyA gene fragment for L. monocytogenes [2], and the nuc gene fragment for S. aureus [3].

　The initiation of a rapid quantification by real-time PCR was successfully developed for S. Enteritidis in chicken juice samples. All growth data from this experiment showed goodness of fit with the model’ prediction, indicating that the model reflected S. Enteritidis growth in chicken juice by real-time PCR quantification. The results of this study were also reflecting the previous data from MRV and ComBase. S. Enteritidis growth prediction model in chicken juice samples with naturally occurring background microflora developed in this study provide useful knowledge for the further development of growth prediction studies from many other kinds of foodborne pathogens in various food materials [4]. Real-time PCR has a powerful advantage considering its ability to measure the number of target genes in a large number of samples.

　Growth monitoring of L. monocytogenes in pasteurized milk samples under constant and fluctuating temperature conditions was successfully performed by real-time PCR. A high correlation was obtained between bacterial growth rate and incubation temperature, where the R2 of the slope of the square root model was calculated to be 0.993 and 0.996 for real-time PCR and conventional culture method, respectively. Moreover, the obtained maximum specific growth rate (µmax) data plots were correlated with 188 L. monocytogenes µmax data points from the existing model according to the ComBase database, with an R2 of 0.961 for real-time PCR and of 0.931 for the conventional culture method. The prediction results fell within ± 20% of the relative error zone, showing that real-time PCR quantification could be used for fast, sensitive, and specific bacterial growth monitoring with high throughput results. Real-time PCR should be considered a promising option and a powerful tool for the construction of a bacterial growth prediction model for safety risk analysis in the dairy industry.

　The growth behavior of S. aureus under various aw conditions adjusted by sugars and salt was successfully evaluated by real-time PCR quantification method with satisfactory results. The inhibitory capacity of each compound was investigated, where galactose performed higher inhibitory capability compared to other compounds. This indicates that although the materials set under the same aw level, the inhibitory effect is specific to the kind of additive used in the product. Therefore, the food industry should consider the effect of various components for the construction of bacterial growth predictive modeling and not rely solely on the final adjusted aw value. In this study, the performance of four kinds of primary models was also evaluated. Baranyi and Roberts’ model was showing better goodness of fit since it has the lowest mean squared error (MSE) with better accuracy and bias factors (Af and Bf), while the three-phase Buchanan model was the least fit model compared to Baranyi and Roberts’, Huang and modified Gompertz models.

　Real-time PCR was also showing the ability to construct growth prediction model of L. monocytogenes in ground pork sample as a function of temperature, aw, and pH. Though, the selective agar medium was underestimating L. monocytogenes cell count from ground pork sample. On the other hand, the data from MPN method was showing similarity to the obtained data from real-time PCR quantification. The obtained growth parameters from L. monocytogenes in ground pork samples were used for growth rate prediction of L. monocytogenes under combined temperature, aw, and pH conditions by secondary Cardinal model. Great agreement between the actual growth rate under combined effects quantified by real-time PCR to those of predicted by secondary Cardinal model was obtained, since the proportion of relative error (pRE) was obtained as 1, Af of 1.0951 and Bf of 0.9283, root mean square error (RMSE) of 0.0029 and R2 of 0.9928. The predicted µmax values from Cardinal model were showing well agreement to 263 ComBase data with the accuracy factor (Af) and the bias factor (Bf) were calculated as 1.063 and 1.213, respectively. The results of this study indicate that a novel real-time PCR quantification technique combined with mathematical prediction model is highly potential to determine food formulation factors in food hurdle approach. Real-time PCR application for the construction of prediction model provides an insight for the development of a future potential mathematical prediction model that can cover all conditions of food formulation factors.