2017
DOI: 10.1186/s12918-017-0496-z
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Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection

Abstract: BackgroundUp to date, Mycobacterium tuberculosis (Mtb) remains as the worst intracellular killer pathogen. To establish infection, inside the granuloma, Mtb reprograms its metabolism to support both growth and survival, keeping a balance between catabolism, anabolism and energy supply. Mtb knockouts with the faculty of being essential on a wide range of nutritional conditions are deemed as target candidates for tuberculosis (TB) treatment. Constraint-based genome-scale modeling is considered as a promising too… Show more

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Cited by 16 publications
(11 citation statements)
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“…It is recognized that M . tuberculosis responds to environmental cues by reprogramming its metabolism and halting growth [ 68 , 69 ]. Host storage lipids may trigger or favor the pathogen’s metabolic reprogramming that is associated with mycobacterial persistence [ 7 , 70 ].…”
Section: Discussionmentioning
confidence: 99%
“…It is recognized that M . tuberculosis responds to environmental cues by reprogramming its metabolism and halting growth [ 68 , 69 ]. Host storage lipids may trigger or favor the pathogen’s metabolic reprogramming that is associated with mycobacterial persistence [ 7 , 70 ].…”
Section: Discussionmentioning
confidence: 99%
“…Subsequently, the experimentally observed metabolic scenarios were simulated using the constructed and validated genome-scale model of S. clavuligerus in order to identify putative connections between the central carbon and amino acid metabolism, nutrient limiting conditions and CA production. A common approach for studying the effect of nutritional, genetic and environmental perturbations on metabolism is the genome-scale metabolic modeling using flux balance analysis (FBA) [21]. In the case of S. clavuligerus, metabolic network models have been reconstructed and/or updated for this purpose [10,[22][23][24], providing insights about the metabolic features of the species and possible genetic targets for further strain improvement.…”
Section: Introductionmentioning
confidence: 99%
“…Metabolic fluxes were quantified by means of a two-stage optimization approach, which is a combination of the maximization of the objective function and minimization of the overall flux [10] , [15] , [16] . The mathematical problem can be represented as follows:…”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%