Location: Produce Safety and Microbiology ResearchTitle: Predictive population dynamics of Escherichia coli O157:H7 and Salmonella enterica on plants: A mechanistic mathematical model based on weather parameters and bacterial state
|IVANEK, RENATA - Cornell University|
|ALLENDE, ANA - Centro De Edafologia Y Biologia Aplicada Del Segura (CEBAS)|
|MUNTHER, DANIEL - Cleveland State University|
Submitted to: Applied and Environmental Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/24/2023
Publication Date: 6/22/2023
Citation: Brandl, M., Ivanek, R., Allende, A., Munther, D. 2023. Predictive population dynamics of Escherichia coli O157:H7 and Salmonella enterica on plants: A mechanistic mathematical model based on weather parameters and bacterial state. Applied and Environmental Microbiology. 89(7). Article e00700-23. https://doi.org/10.1128/aem.00700-23.
Interpretive Summary: Outbreaks of produce-associated foodborne illness continue to pose a threat to human health and a challenge to the produce industry. Thus, the survival of foodborne pathogenic bacteria on plant surfaces is of great interest to public health. Weather affects key aspects of bacterial behavior on plants but has not been extensively investigated as a tool to assess risk of crop contamination with human foodborne pathogens. A novel mechanistic model informed by weather factors (temperature, radiation and dew point depression, which indicates presence of free water on the plant surface) and bacterial state (growing vs non-growing cells) was developed to predict population dynamics on leafy vegetables and then tested against published data tracking enteric pathogen population sizes on plants in the laboratory and in the field. The model successfully predicted the population sizes of EcO157 on young romaine lettuce lettuce plants in the field in Salinas, CA. Our results highlight the potential of a more comprehensive weather-based model in predicting contamination risk in the field.
Technical Abstract: Weather affects key aspects of bacterial behavior on plants but has not been extensively investigated as a tool to assess risk of crop contamination with human foodborne pathogens. A novel mechanistic model informed by weather factors and bacterial state was developed to predict population dynamics on leafy vegetables and then tested against published data tracking Escherichia coli O157:H7 (EcO157) and Salmonella enterica populations on lettuce and cilantro plants. The model utilizes temperature, radiation and dew point depression to characterize total pathogen growth and decay rates. In addition, the model incorporates the population level effect of bacterial physiological state dynamics in the phyllosphere in terms of the duration and frequency of specific weather parameters. The model predicted accurately EcO157 and S. enterica population sizes on lettuce and cilantro leaves in the laboratory under various conditions of temperature, relative humidity, and light intensity, and cycles of leaf wetness and dryness. Importantly, the model successfully predicted EcO157 population dynamics on four-week old romaine lettuce plants after night and morning inoculations under variable weather conditions in nearly all field trials. Prediction of initial EcO157 population decay rates after inoculation of six-week old romaine plants in the same field study was better than that of long-term survival, likely due to bacterial protection from weather stresses in the more heterogeneous micro-environment of the complex canopy of older plants. This suggest that future augmentation of the model should consider plant age and species morphology by including additional physical parameters. Although our model may be refined as more knowledge of EcO157 and S. enterica behavior across micro- and macroscales on plants becomes available, our results highlight the potential of a more comprehensive weather-based model than simple log-linear survival in predicting contamination risk in the field. Such a modeling approach would additionally be valuable for timing field sampling in quality control to ensure the microbial safety of produce.