Literature DB >> 21968442

A mathematical representation of the development of Mycobacterium tuberculosis active, latent and dormant stages.

Gesham Magombedze1, Nicola Mulder.   

Abstract

The majority of individuals infected with Mycobacterium tuberculosis (Mtb) bacilli develop latent infection. Mtb becomes dormant and phenotypically drug resistant when it encounters multiple stresses within the host, and expresses a set of genes, known as the dormancy regulon, in vivo. These genes are expressed in vitro in response to nitric oxide (NO), hypoxia (oxygen deprivation), and nutrient starvation. The occurrence and reactivation of latent tuberculosis (TB) is not clearly understood. The ability of the pathogen to enter and exit from different states is associated with its ability to cause persistent infection. During infection it is not known whether the organism is in a persistent slow replicating state or a dormant non-replicating state, with the latter ultimately causing a latent infection with the potential to reactivate to active disease. We collected gene expression data for Mtb bacilli under different stress conditions that simulate latency or dormancy. Time course experiments were selected and differentially expressed gene profiles were determined at each time point. A mathematical model was then developed to show the dynamics of Mtb latency based on the profile of differentially expressed genes. Analysis of the time course data show the dynamics of latency occurrence in vitro and the mathematical model reveals all possible scenarios of Mtb latency development with respect to the different conditions that may be produced by the immune response in vivo. The mathematical model provides a biological explanation of how Mtb latency occurs based on observed gene expression changes in in vitro latency models.
Copyright © 2011 Elsevier Ltd. All rights reserved.

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Year:  2011        PMID: 21968442     DOI: 10.1016/j.jtbi.2011.09.025

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  10 in total

1.  In silico models of M. tuberculosis infection provide a route to new therapies.

Authors:  Jennifer J Linderman; Denise E Kirschner
Journal:  Drug Discov Today Dis Models       Date:  2014-05-09

2.  A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment.

Authors:  Denise Kirschner; Elsje Pienaar; Simeone Marino; Jennifer J Linderman
Journal:  Curr Opin Syst Biol       Date:  2017-05-22

3.  A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes.

Authors:  Chang Gong; Jennifer J Linderman; Denise Kirschner
Journal:  Math Biosci Eng       Date:  2015-06       Impact factor: 2.080

Review 4.  In Vitro Granuloma Models of Tuberculosis: Potential and Challenges.

Authors:  Paul Elkington; Maria Lerm; Nidhi Kapoor; Robert Mahon; Elsje Pienaar; Dongeun Huh; Deepak Kaushal; Larry S Schlesinger
Journal:  J Infect Dis       Date:  2019-05-24       Impact factor: 5.226

5.  Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions.

Authors:  Elsje Pienaar; William M Matern; Jennifer J Linderman; Joel S Bader; Denise E Kirschner
Journal:  Infect Immun       Date:  2016-04-22       Impact factor: 3.441

Review 6.  The tuberculous granuloma: an unsuccessful host defence mechanism providing a safety shelter for the bacteria?

Authors:  Mayra Silva Miranda; Adrien Breiman; Sophie Allain; Florence Deknuydt; Frederic Altare
Journal:  Clin Dev Immunol       Date:  2012-07-03

Review 7.  Latent Tuberculosis: Models, Computational Efforts and the Pathogen's Regulatory Mechanisms during Dormancy.

Authors:  Gesham Magombedze; David Dowdy; Nicola Mulder
Journal:  Front Bioeng Biotechnol       Date:  2013-08-27

8.  Bacterial load slopes represent biomarkers of tuberculosis therapy success, failure, and relapse.

Authors:  Gesham Magombedze; Jotam G Pasipanodya; Tawanda Gumbo
Journal:  Commun Biol       Date:  2021-06-02

9.  Predicting the Role of IL-10 in the Regulation of the Adaptive Immune Responses in Mycobacterium avium Subsp. paratuberculosis Infections Using Mathematical Models.

Authors:  Gesham Magombedze; Shigetoshi Eda; Judy Stabel
Journal:  PLoS One       Date:  2015-11-30       Impact factor: 3.240

10.  A Long-term Co-perfused Disseminated Tuberculosis-3D Liver Hollow Fiber Model for Both Drug Efficacy and Hepatotoxicity in Babies.

Authors:  Shashikant Srivastava; Jotam G Pasipanodya; Geetha Ramachandran; Devyani Deshpande; Stephen Shuford; Howland E Crosswell; Kayle N Cirrincione; Carleton M Sherman; Soumya Swaminathan; Tawanda Gumbo
Journal:  EBioMedicine       Date:  2016-02-27       Impact factor: 8.143

  10 in total

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