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BMC Syst Biol. 2018 Mar 2;12(1):25. doi: 10.1186/s12918-018-0557-y.

Updated and standardized genome-scale reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, simulates flux states indicative of physiological conditions.

Author information

1
Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
2
Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, USA.
3
Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA. palsson@ucsd.edu.
4
Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, USA. palsson@ucsd.edu.
5
Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA. palsson@ucsd.edu.
6
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800, Kongens Lyngby, Denmark. palsson@ucsd.edu.
7
Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA. jmonk@ucsd.edu.

Abstract

BACKGROUND:

The efficacy of antibiotics against M. tuberculosis has been shown to be influenced by experimental media conditions. Investigations of M. tuberculosis growth in physiological conditions have described an environment that is different from common in vitro media. Thus, elucidating the interplay between available nutrient sources and antibiotic efficacy has clear medical relevance. While genome-scale reconstructions of M. tuberculosis have enabled the ability to interrogate media differences for the past 10 years, recent reconstructions have diverged from each other without standardization. A unified reconstruction of M. tuberculosis H37Rv would elucidate the impact of different nutrient conditions on antibiotic efficacy and provide new insights for therapeutic intervention.

RESULTS:

We present a new genome-scale model of M. tuberculosis H37Rv, named iEK1011, that unifies and updates previous M. tuberculosis H37Rv genome-scale reconstructions. We functionally assess iEK1011 against previous models and show that the model increases correct gene essentiality predictions on two different experimental datasets by 6% (53% to 60%) and 18% (60% to 71%), respectively. We compared simulations between in vitro and approximated in vivo media conditions to examine the predictive capabilities of iEK1011. The simulated differences recapitulated literature defined characteristics in the rewiring of TCA metabolism including succinate secretion, gluconeogenesis, and activation of both the glyoxylate shunt and the methylcitrate cycle. To assist efforts to elucidate mechanisms of antibiotic resistance development, we curated 16 metabolic genes related to antimicrobial resistance and approximated evolutionary drivers of resistance. Comparing simulations of these antibiotic resistance features between in vivo and in vitro media highlighted condition-dependent differences that may influence the efficacy of antibiotics.

CONCLUSIONS:

iEK1011 provides a computational knowledge base for exploring the impact of different environmental conditions on the metabolic state of M. tuberculosis H37Rv. As more experimental data and knowledge of M. tuberculosis H37Rv become available, a unified and standardized M. tuberculosis model will prove to be a valuable resource to the research community studying the systems biology of M. tuberculosis.

KEYWORDS:

Antibiotic resistance; Environmental condition; Genome-scale reconstruction; M. tuberculosis

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