Establishment and Validation of RNA-Based Predictive Models for Understanding Survival of Vibrio parahaemolyticus in Oysters Stored at Low Temperatures.

This study developed RNA-based predictive models describing the survival of in Eastern oysters () during storage at 0, 4, and 10°C. Postharvested oysters were inoculated with a cocktail of five strains and were then stored at 0, 4, and 10°C for 21 or 11 days. A real-time reverse transcription-PCR (RT-PCR) assay targeting expression of the gene was used to evaluate the number of surviving cells, which was then used to establish primary molecular models (MMs). Before construction of the MMs, consistent expression levels of the gene at 0, 4, and 10°C were confirmed, and this gene was used to monitor the survival of the total cells. In addition, the and genes were used for monitoring the survival of virulent Traditional models (TMs) were built based on data collected using a plate counting method. From the MMs, populations had decreased 0.493, 0.362, and 0.238 log CFU/g by the end of storage at 0, 4, and 10°C, respectively. Rates of reduction of shown in the TMs were 2.109, 1.579, and 0.894 log CFU/g for storage at 0, 4, and 10°C, respectively. Bacterial inactivation rates (IRs) estimated with the TMs (-0.245, -0.152, and -0.121 log CFU/day, respectively) were higher than those estimated with the MMs (-0.134, -0.0887, and -0.0732 log CFU/day, respectively) for storage at 0, 4, and 10°C. Higher viable numbers were predicted using the MMs than using the TMs. On the basis of this study, RNA-based predictive MMs are the more accurate and reliable models and can prevent false-negative results compared to TMs. One important method for validating postharvest techniques and for monitoring the behavior of is to establish predictive models. Unfortunately, previous predictive models established based on plate counting methods or on DNA-based PCR can underestimate or overestimate the number of surviving cells. This study developed and validated RNA-based molecular predictive models to describe the survival of in oysters during low-temperature storage (0, 4, and 10°C). The RNA-based predictive models show the advantage of being able to count all of the culturable, nonculturable, and stressed cells. By using primers targeting the gene and pathogenesis-associated genes ( and ), real-time RT-PCR can evaluate the total surviving population as well as differentiate the pathogenic ones from the total population. Reliable and accurate predictive models are very important for conducting risk assessment and management of pathogens in food.