Background: Up 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 tool for evaluating genetic and nutritional
perturbations on Mtb metabolic reprogramming. Nonetheless, few in silico assessments of the effect of nutritional
conditions on Mtb’s vulnerability and metabolic adaptation have been carried out.
Results: A genome-scale model (GEM) of Mtb, modified from the H37Rv iOSDD890, was used to explore the
metabolic reprogramming of two Mtb knockout mutants (pfkA- and icl-mutants), lacking key enzymes of central
carbon metabolism, while exposed to changing nutritional conditions (oxygen, and carbon and nitrogen sources).
A combination of shadow pricing, sensitivity analysis, and flux distributions patterns allowed us to identify
metabolic behaviors that are in agreement with phenotypes reported in the literature. During hypoxia, at high
glucose consumption, the Mtb pfkA-mutant showed a detrimental growth effect derived from the accumulation of
toxic sugar phosphate intermediates (glucose-6-phosphate and fructose-6-phosphate) along with an increment of
carbon fluxes towards the reductive direction of the tricarboxylic acid cycle (TCA). Furthermore, metabolic
reprogramming of the icl-mutant (icl1&icl2) showed the importance of the methylmalonyl pathway for the
detoxification of propionyl-CoA, during growth at high fatty acid consumption rates and aerobic conditions. At
elevated levels of fatty acid uptake and hypoxia, we found a drop in TCA cycle intermediate accumulation that
might create redox imbalance. Finally, findings regarding Mtb-mutant metabolic adaptation associated with
asparagine consumption and acetate, succinate and alanine production, were in agreement with literature reports.
Conclusions: This study demonstrates the potential application of genome-scale modeling, flux balance analysis
(FBA), phenotypic phase plane (PhPP) analysis and shadow pricing to generate valuable insights about Mtb
metabolic reprogramming in the context of human granulomas.