Abstract
Simple SummaryComputational modeling of bacterial infection is an attractive way to simulate infection scenarios. In the long-term, such models could be used to identify factors that make individuals more susceptible to infection, or how interference with bacterial growth influences the course of bacterial infection. This study used different mouse infection models (immunocompetent, lacking a microbiota, and immunodeficient models) to develop a basic mathematical model of a Yersinia enterocolitica gastrointestinal infection. We showed that our model can reflect our findings derived from mouse infections, and we demonstrated how crucial the exact knowledge about parameters influencing the population dynamics is. Still, we think that computational models will be of great value in the future; however, to foster the development of more complex models, we propose the broad implementation of the interdisciplinary training of mathematicians and biologists.The complex interplay of a pathogen with its virulence and fitness factors, the host’s immune response, and the endogenous microbiome determine the course and outcome of gastrointestinal infection. The expansion of a pathogen within the gastrointestinal tract implies an increased risk of developing severe systemic infections, especially in dysbiotic or immunocompromised individuals. We developed a mechanistic computational model that calculates and simulates such scenarios, based on an ordinary differential equation system, to explain the bacterial population dynamics during gastrointestinal infection. For implementing the model and estimating its parameters, oral mouse infection experiments with the enteropathogen, Yersinia enterocolitica (Ye), were carried out. Our model accounts for specific pathogen characteristics and is intended to reflect scenarios where colonization resistance, mediated by the endogenous microbiome, is lacking, or where the immune response is partially impaired. Fitting our data from experimental mouse infections, we can justify our model setup and deduce cues for further model improvement. The model is freely available, in SBML format, from the BioModels Database under the accession number MODEL2002070001.
Highlights
The gastrointestinal microbiome provides resistance to pathogen colonization and infection by contributing to the development of the host immune system [1,2] and conferring colonization resistance (CR) [3], as well as the direct competition of a pathogen with members or compounds produced by the microbiota [4]
We first conducted laboratory experiments to generate experimental datasets and we summarized the current knowledge of infection scenarios with Yersinia enterocolitica (Ye) in (i) immunocompetent hosts with complex microbiomes, (ii) immunocompetent hosts with no microbiome, and (iii) immunodeficient hosts with the complex microbiome
The absolute values we obtained for the growth rates of the Ye strains were moderately different between the Ye wt/YadA0 and Ye wt/T3S0 coinfection scenarios, e.g., in specific pathogen-free (SPF) animals, which aggravated an easy comparison of values
Summary
The gastrointestinal microbiome provides resistance to pathogen colonization and infection by contributing to the development of the host immune system [1,2] and conferring colonization resistance (CR) [3], as well as the direct competition of a pathogen with members or compounds produced by the microbiota [4]. Several measures of the immune system contribute to this involved process; amongst them are the production of secretory IgA, the release of antimicrobial peptides (AMP), the expression of pro-inflammatory cytokines, and the recruitment of, e.g., neutrophils [10,11]. Gastrointestinal infection is a frequent disease that causes significant morbidity and a high economic burden [12,13]. Gastrointestinal tract (GIT) infection can cause high morbidity, and even fatal diseases, in healthcare settings and specific populations, such as newborns, the elderly, and immunocompromised individuals. Understanding the underlying mechanisms and identifying the crucial factors for a mild or severe course of infection is highly desirable
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