title: Establishment and Validation of RNA-Based Predictive Models for Understanding Survival of Vibrio parahaemolyticus in Oysters Stored at Low Temperatures.
abstract: This study developed RNA-based predictive models describing the survival ofVibrio parahaemolyticusin Eastern oysters (Crassostrea virginica) during storage at 0, 4, and 10°C. Postharvested oysters were inoculated with a cocktail of fiveV. parahaemolyticusstrains 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 thetlhgene was used to evaluate the number of survivingV. parahaemolyticuscells, which was then used to establish primary molecular models (MMs). Before construction of the MMs, consistent expression levels of thetlhgene at 0, 4, and 10°C were confirmed, and this gene was used to monitor the survival of the totalV. parahaemolyticuscells. In addition, thetdhandtrhgenes were used for monitoring the survival of virulentV. parahaemolyticusTraditional models (TMs) were built based on data collected using a plate counting method. From the MMs,V. parahaemolyticuspopulations had decreased 0.493, 0.362, and 0.238 log10CFU/g by the end of storage at 0, 4, and 10°C, respectively. Rates of reduction ofV. parahaemolyticusshown in the TMs were 2.109, 1.579, and 0.894 log10CFU/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 log10CFU/day, respectively) were higher than those estimated with the MMs (-0.134, -0.0887, and -0.0732 log10CFU/day, respectively) for storage at 0, 4, and 10°C. Higher viableV. parahaemolyticusnumbers 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.IMPORTANCEOne important method for validating postharvest techniques and for monitoring the behavior ofV. parahaemolyticusis 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 ofV. parahaemolyticusin 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 thetlhgene and pathogenesis-associated genes (tdhandtrh), real-time RT-PCR can evaluate the total survivingV. parahaemolyticuspopulation 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.
authors.affiliation: Food Microbiology and Safety Lab, Department of Animal Sciences, Auburn University, Auburn, Alabama, USA.
authors.forename: Chao
authors.initials: C
authors.lastname: Liao
authors.affiliation: Laboratory of Quality & Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture, Shanghai Ocean University, Shanghai, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: Food Microbiology and Safety Lab, Department of Animal Sciences, Auburn University, Auburn, Alabama, USA lzw0022@auburn.edu.
authors.forename: Luxin
authors.initials: L
authors.lastname: Wang
mesh_terms: D000818:Animals; D015169:Colony Count, Microbial; D049896:Crassostrea / Q000382:microbiology*; D005511:Food Handling / Q000379:methods*; D005516:Food Microbiology; D015964:Gene Expression Regulation, Bacterial / Q000235:genetics; D008954:Models, Biological; D012313:RNA / Q000032:analysis / Q000235:genetics; D018570:Risk Assessment / Q000379:methods*; D012758:Shellfish / Q000382:microbiology*; D014736:Vibrio parahaemolyticus / Q000254:growth & development*
chemical_list: D012313:RNA
keywords: Eastern oyster; RNA; Vibrio parahaemolyticus; molecular predictive model; predictive models; real-time RT-PCR