Modeling metabolic adjustment in Mycobacterium tuberculosis upon treatment with isoniazid

Syst Synth Biol. 2010 Dec;4(4):299-309. doi: 10.1007/s11693-011-9075-6. Epub 2011 Feb 26.


Complex biological systems exhibit a property of robustness at all levels of organization. Through different mechanisms, the system tries to sustain stress such as due to starvation or drug exposure. To explore whether reconfiguration of the metabolic networks is used as a means to achieve robustness, we have studied possible metabolic adjustments in Mtb upon exposure to isoniazid (INH), a front-line clinical drug. The redundancy in the genome of M. tuberculosis (Mtb) makes it an attractive system to explore if alternate routes of metabolism exist in the bacterium. While the mechanism of action of INH is well studied, its effect on the overall metabolism is not well characterized. Using flux balance analysis, inhibiting the fluxes flowing through the reactions catalyzed by Rv1484, the target of INH, significantly changes the overall flux profiles. At the pathway level, activation or inactivation of certain pathways distant from the target pathway, are seen. Metabolites such as NADPH are shown to reduce drastically, while fatty acids tend to accumulate. The overall biomass also decreases with increasing inhibition levels. Inhibition studies, pathway level clustering and comparison of the flux profiles with the gene expression data indicate the activation of folate metabolism, ubiquinone metabolism, and metabolism of certain amino acids. This analysis provides insights useful for target identification and designing strategies for combination therapy. Insights gained about the role of individual components of a system and their interactions will also provide a basis for reconstruction of whole systems through synthetic biology approaches.

Electronic supplementary material: The online version of this article (doi:10.1007/s11693-011-9075-6) contains supplementary material, which is available to authorized users.

Keywords: Applications of flux balance analysis; Flux profiles; Genome scale metabolic networks; Incorporating gene expression profiles; Robustness through metabolic adjustment.