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.

Figure 1. GSMN-MB in silico flux prediction when glucose is a sole carbon source.

The in silico prediction of flux from glucose to the TCA cycle when glucose is a sole carbon source for M. bovis. Acon: aconitase, Cit: citrate synthase, Eno: enolase, Fba: fructose-bisphosphate aldolase, Gck: glucokinase, Gdh: glycine dehydrogenase, GlcB: malate synthase, GlyA: glycine hydromethytransferase, Gpm: phosphoglycerate mutase, Icl: isocitrate lyase, Mdh: malate dehydrogenase, Pepck:, phosphoenolpyruvate carboxykinase, Pfk: 6-phosphofructokinase, Pgi: glucose-6-phosphate isomerase, SerA: phosphoglycerate dehydrogenase, SerB: phosphoserine phosphatase, SerC: phosphoserine transaminase, TpiA: triose-phosphate isomerase.

Figure 2. The metabolic pathways that converge around L-serine in Mycobacterium species.

Blue: present in M. tuberculosis, M. bovis and M. bovis BCG, Red: present in M. tuberculosis and M. bovis [33], Green: present in M. tuberculosis and M. bovis BCG [3,18,19], Purple: present in M. tuberculosis [44].. Acon: aconitase, Cgl: cystathionine gamma-lyase Cit: citrate synthase, CysE, CysK1, CysK2: cysteine synthase, CysM: cystathionine beta-synthase, Eno: enolase, Gdh: glycine dehydrogenase, GlcB: malate synthase, GlyA: glycine hydromethytransferase, Gpm: phosphoglycerate mutase, Icl: isocitrate lyase, Mdh: malate dehydrogenase, Pdh: pyruvate dehydrogenase, PykA: pyruvate kinase, Ppdk: pyruvate phosphate dikinase, SerA: phosphoglycerate dehydrogenase, SerB: phosphoserine phosphatase, SerC: phosphoserine transaminase, SdaA: serine deaminase.

Figure 3. The GSMN-MB ROC curve for TraSH thresholds.

The plot shows ROC curves for different transposon site hybridisation (TraSH) ratio thresholds for the determination of essential genes in experimental data [30]. Five ROCs are plotted with 4 different TraSH thresholds as shown in the legend box. Each ROC curve shows the points corresponding to True positive rate (sensitivity) and false positive rate (1-specificity) of the model predictions obtained for all growth rate thresholds. For all ROC curves see Figures S1-S5.