Mostolizadeh, R.; Dräger, A. Computational Model Informs Effective Control Interventions against Y. enterocolitica Co-Infection. Biology2020, 9, 431.
Mostolizadeh, R.; Dräger, A. Computational Model Informs Effective Control Interventions against Y. enterocolitica Co-Infection. Biology 2020, 9, 431.
Mostolizadeh, R.; Dräger, A. Computational Model Informs Effective Control Interventions against Y. enterocolitica Co-Infection. Biology2020, 9, 431.
Mostolizadeh, R.; Dräger, A. Computational Model Informs Effective Control Interventions against Y. enterocolitica Co-Infection. Biology 2020, 9, 431.
Abstract
The complex interplay among pathogens, host factors, and the integrity and composition of the endogenous microbiome determine the course and outcome of gastrointestinal infections. The model organism Yersinia entercolitica (Ye) is one of the five top frequent causes of bacterial gastroenteritis based on the Epidemiological Bulletin of the Robert Koch Institute (RKI) published on September 10, 2020. A fundamental challenge in predicting the course of an infection is to understand whether co-infection with two Yersinia strains differing only in their capacity to resist killing by the host immune system may decrease the overall virulence by competitive exclusion or increase it by acting cooperatively. Herein, we study the primary interactions among Ye, the host immune system and the microbiota, and their influence on Yersinia population dynamics. The employed model considers two host compartments, the intestinal mucosa and lumen, commensal bacteria, the co-existence of wild-type and mutant Yersinia strains, as well the host immune responses. We determine four possible equilibria: the disease-free, wild-type-free, mutant-free, and co-existence of wild-type and mutant equilibrium. We also calculate the reproduction number for each strain as a threshold parameter to determine if the population may either be eradicated or persist within the host. We conclude that the infection should disappear if the reproduction numbers for each strain fall below one, and the commensal bacteria’s growth rate exceeds the pathogens’ growth rates. These findings will help inform public health control strategies. The supplement includes MATLAB source script, Maple workbook, and figures.
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
