Publication

Quantification of Salmonella Survival and Infection in an In vitro Model of the Human Intestinal Tract as Proxy for Foodborne Pathogens

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Last modified
  • 03/03/2025
Type of Material
Authors
    Lucas M. Wijnands, National Institute of Public Health and the EnvironmentPeter Teunis, Emory UniversityAngelina F. A. Kuijpers, National Institute of Public Health and the EnvironmentEllen H. M. Delfgou-Van Asch, National Institute of Public Health and the EnvironmentAnnemarie Pielaat, National Institute of Public Health and the Environment
Language
  • English
Date
  • 2017-06-30
Publisher
  • Frontiers Media
Publication Version
Copyright Statement
  • © 2017 Wijnands, Teunis, Kuijpers, Delfgou-Van Asch and Pielaat.
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Final Published Version (URL)
Title of Journal or Parent Work
ISSN
  • 1664-302X
Volume
  • 8
Grant/Funding Information
  • This research was funded by the Netherlands Food and Consumer Products Safety Authority research program.
Supplemental Material (URL)
Abstract
  • Different techniques are available for assessing differences in virulence of bacterial foodborne pathogens. The use of animal models or human volunteers is not expedient for various reasons; the use of epidemiological data is often hampered by lack of crucial data. In this paper, we describe a static, sequential gastrointestinal tract (GIT) model system in which foodborne pathogens are exposed to simulated gastric and intestinal contents of the human digestive tract, including the interaction of pathogens with the intestinal epithelium. The system can be employed with any foodborne bacterial pathogens. Five strains of Salmonella Heidelberg and one strain of Salmonella Typhimurium were used to assess the robustness of the system. Four S. Heidelberg strains originated from an outbreak, the fifth S. Heidelberg strain and the S. Typhimurium strain originated from routine meat inspections. Data from plate counts, collected for determining the numbers of surviving bacteria in each stage, were used to quantify both the experimental uncertainty and biological variability of pathogen survival throughout the system. For this, a hierarchical Bayesian framework using Markov chain Monte Carlo (MCMC) was employed. The model system is able to distinguish serovars/strains for in vitro infectivity when accounting for within strain biological variability and experimental uncertainty.
Author Notes
Keywords
Research Categories
  • Biology, Microbiology
  • Health Sciences, Public Health

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