Next Article in Journal
Digital PCR: What Relevance to Plant Studies?
Next Article in Special Issue
Multipath: An R Package to Generate Integrated Reproducible Pathway Models
Previous Article in Journal
Biochemical, Physiological, and Productive Response of Greenhouse Vegetables to Suboptimal Growth Environment Induced by Insect Nets
Previous Article in Special Issue
Fluid-Structure Interaction Simulation of an Intra-Atrial Fontan Connection
Article

Computational Model Informs Effective Control Interventions against Y. enterocolitica Co-Infection

by 1,2,3,4,* and 1,2,3,4
1
Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany
2
Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany
3
German Center for Infection Research (DZIF), Partner Site Tübingen, 72076 Tübingen, Germany
4
Cluster of Excellence ‘Controlling Microbes to Fight Infections’, University of Tübingen, 72076 Tübingen, Germany
*
Author to whom correspondence should be addressed.
Biology 2020, 9(12), 431; https://doi.org/10.3390/biology9120431
Received: 16 October 2020 / Revised: 19 November 2020 / Accepted: 23 November 2020 / Published: 30 November 2020
(This article belongs to the Special Issue Computational Biology)
Medical control strategies for infectious diseases remain enormously important. One germ that can cause gastrointestinal infections is Yersinia enterocolitica. This study investigates and analyzes a computational model to identify the occurrence of disease-free and co-infection states. Thereby, the reproduction number R 0 informs us about the germ’s ability to spread disease. Suppose this fundamental quantity takes a value between zero and one. In that case, every infectious strain will cause less than one secondary infection, so the strain will disappear. In contrast, if R 0 exceeds one, every infectious strain causes more than one secondary infection, and Yersinia infection strains will persist. A disease-free state occurs when the commensal bacteria’s growth rate exceeds the maximum immune action and the rate at which the intestines release the bacteria. With a large enough commensal bacteria growth rate, this state can be stable. Co-infection occurs when the maximum growth rates of the wild-type and mutant strains become unequal. Studying the immune system’s behavior can result in an infection’s disappearance from hosts with a healthy microbiota immune system. In this case, Yersinia strains do not spread in the lumen when the commensal bacteria’s growth rate exceeds the growth rate of wild-type and mutant Yersinia.
The complex interplay between 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), 10 September 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 commensal bacterial in two host compartments (the intestinal mucosa the and lumen), the co-existence of wt and mut Yersinia strains, and the host immune responses. We determine four possible equilibria: disease-free, wt-free, mut-free, and co-existence of wt and mut in equilibrium. We also calculate the reproduction number for each strain as a threshold parameter to determine if the population may 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 growth rate exceeds the pathogen’s growth rate. These findings will help inform medical control strategies. The supplement includes the MATLAB source script, Maple workbook, and figures. View Full-Text
Keywords: reproduction number; disease-free equilibrium; co-existence equilibrium; Yersinia; gastroenteritis reproduction number; disease-free equilibrium; co-existence equilibrium; Yersinia; gastroenteritis
Show Figures

Figure 1

MDPI and ACS Style

Mostolizadeh, R.; Dräger, A. Computational Model Informs Effective Control Interventions against Y. enterocolitica Co-Infection. Biology 2020, 9, 431. https://doi.org/10.3390/biology9120431

AMA Style

Mostolizadeh R, Dräger A. Computational Model Informs Effective Control Interventions against Y. enterocolitica Co-Infection. Biology. 2020; 9(12):431. https://doi.org/10.3390/biology9120431

Chicago/Turabian Style

Mostolizadeh, Reihaneh, and Andreas Dräger. 2020. "Computational Model Informs Effective Control Interventions against Y. enterocolitica Co-Infection" Biology 9, no. 12: 431. https://doi.org/10.3390/biology9120431

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop