P2-257 A Stochastic Single Cell Survival Model of Escherichia coli and Salmonella enterica in a Desiccation Environment

Monday, July 27, 2015
Exhibit Hall (Oregon Convention Center)
Kento Koyama, Hokkaido University, Sapporo, Japan
Hidekazu Hokunan, Hokkaido University, Sapporo, Japan
Mayumi Hasegawa, Hokkaido University, Sapporo, Japan
Shuso Kawamura, Hokkaido University, Sapporo, Japan
Shige Koseki, Hokkaido University, Sapporo, Japan
Introduction:  A very small number of viable Salmonella enterica and pathogenic Escherichia coli cells are required for these pathogens to infect humans. The behavior and/or survival probability of a small number (in particular a single cell) of these pathogenic bacteria can be used to assess their risk of causing foodborne illness. We focused on low water activity (aw) and/or dried foods as critical pathogen carriers. 

Purpose:  We developed a predictive model that can be used to estimate the probability of the survival of a single cell as well as between serotypes.

Methods:  The survival of the Salmonella enterica serotypes Stanley, Typhimurium, Chester and Oranienburg as well as E. coli O111, O26 and O157 were assessed. Single cells of each type were placed in microdroplets (2 µl) of distilled water on a 96-well microplate and exposed to temperatures ranging from 5°C to 25°C. The single cells of each pathogenic bacteria were prepared via a 10-fold dilution of the bacterial culture. The prepared microplates were then dried in a drying chamber (ca. 9% RH). We confirmed the survival of the cells in each well by adding nutrient broth (100 µl) at arbitrary intervals to promote cell recovery.

Results:  The number of cells in each well followed a Poisson distribution. The mean number of cells was two. Survival/death data obtained using logistic regression as a function of time and temperature indicated that a higher drying temperature reduced cell survival. The changes in survival probability showed that more than 90% of the cells died during the drying process; however, a small fraction survived.

Significance:  This predictive model for estimating the survival probability of a single cell after drying could play an important role in assessing the risk of low aw and/or dried foods.