ena-STUDY-Institute for Medical Microbiology and Hygiene, University Hospital Tuebingen-02-04-2020-21:54:12:876-8
Model-based prediction of bacterial population dynamics in gastrointestinal infection
The complex interplay of a pathogen with its virulence and fitness factors, the host immune response, and the endogenous microbiome determine the course and outcome of gastrointestinal infection (GI). A pathogen expansion within the gastrointestinal tract implies an increased risk for the development of severe systemic infection. Antibiotic treatment strategies of GI cause microbiome disruption and may even impose severe sequelae. By means of computational modelling we aimed to calculate bacterial population dynamics in GI in order to predict infection course and outcome. For the implementation and parameterization of the model, oral mouse infection experiments with enteric Yersinia enterocolitica (Ye) were used. Our model accounts for specific pathogen characteristics and can consider disrupted microbial colonization resistance or immune responses. We were able to approve the model calculations for these scenarios by experimental mouse infections and show that in a genuinely interdisciplinary approach, it is possible to predict the infection course using computational methods. Future clinical application of computational modelling of infections may pave the way to personalized treatment and prevention strategies of GI.
The complex interplay of a pathogen with its virulence and fitness factors, the host immune response, and the endogenous microbiome determine the course and outcome of gastrointestinal infection (GI). A pathogen expansion within the gastrointestinal tract implies an increased risk for the development of severe systemic infection. Antibiotic treatment strategies of GI cause microbiome disruption and may even impose severe sequelae. By means of computational modelling we aimed to calculate bacterial population dynamics in GI in order to predict infection course and outcome. For the implementation and parameterization of the model, oral mouse infection experiments with enteric Yersinia enterocolitica (Ye) were used. Our model accounts for specific pathogen characteristics and can consider disrupted microbial colonization resistance or immune responses. We were able to approve the model calculations for these scenarios by experimental mouse infections and show that in a genuinely interdisciplinary approach, it is possible to predict the infection course using computational methods. Future clinical application of computational modelling of infections may pave the way to personalized treatment and prevention strategies of GI.