Systems-Based Approaches to Probing Metabolic Variation within the Mycobacterium tuberculosis Complex

Abstract

The Mycobacterium tuberculosis complex includes bovine and human strains of the tuberculosis bacillus, including Mycobacterium tuberculosis, Mycobacterium bovis and the Mycobacterium bovis BCG vaccine strain. M. bovis has evolved from a M. tuberculosis-like ancestor and is the ancestor of the BCG vaccine. The pathogens demonstrate distinct differences in virulence, host range and metabolism, but the role of metabolic differences in pathogenicity is poorly understood. Systems biology approaches have been used to investigate the metabolism of M. tuberculosis, but not to probe differences between tuberculosis strains. In this study genome scale metabolic networks of M. bovis and M. bovis BCG were constructed and interrogated, along with a M. tuberculosis network, to predict substrate utilisation, gene essentiality and growth rates. The models correctly predicted 87-88% of high-throughput phenotype data, 75-76% of gene essentiality data and in silico-predicted growth rates matched measured rates. However, analysis of the metabolic networks identified discrepancies between in silico predictions and in vitro data, highlighting areas of incomplete metabolic knowledge. Additional experimental studies carried out to probe these inconsistencies revealed novel insights into the metabolism of these strains. For instance, that the reduction in metabolic capability observed in bovine tuberculosis strains, as compared to M. tuberculosis, is not reflected by current genetic or enzymatic knowledge. Hence, the in silico networks not only successfully simulate many aspects of the growth and physiology of these mycobacteria, but also provide an invaluable tool for future metabolic studies.

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

“…Intracellular M. tuberculosis likely utilizes a range of host- Model that determines the possible set of behaviors, or the optimal behavior according to a predefined criterion, of a system within certain constraints, and assuming a steady state. CBMs use flux balance analysis to determine the possible set of fluxes through a metabolic network given constraints on enzymatic reactions and nutrient availability (45)(46)(47)(48).…”

“…In this work, we developed a dynamic CBM based on an existing CBM describing M. tuberculosis metabolism (45,46), using the "static-optimization approach" (50). The original model of M. tuberculosis metabolism is an updated version of GSMN-TB (45,46) kindly provided by the authors (Johnjoe McFadden, personal communication).…”

“…The original model of M. tuberculosis metabolism is an updated version of GSMN-TB (45,46) kindly provided by the authors (Johnjoe McFadden, personal communication). These updates to the published model include corrections of a few reaction imbalances, changes in the ATP penalties for the biomass function, and inclusion and exclusion of additional reactions based on new data.…”

“…Thus, we lower the biomass degradation rate (i.e., maintenance metabolism) in response to lower oxygen availability, allowing us to capture the reduced metabolic requirements for survival in the absence of replication. The original GSMN-TB model instead used a lower bound on ATP hydrolysis to incorporate biomass maintenance costs (45,46). Our form reflects the decreased maintenance cost in decreasing oxygen concentrations.…”

“…The parameters of these CBMs are generally selected to reproduce bacterial growth rates and known metabolic fluxes (44). CBMs of M. tuberculosis include GSMN-TB (45,46), iNJ661 (47), and, more recently, sMtb (48), which aimed to merge previous models.…”