Genome-scale reconstruction of the Streptococcus pyogenes M49 metabolic network reveals growth requirements and indicates potential drug targets.

Genome-scale metabolic models comprise stoichiometric relations between metabolites, as well as associations between genes and metabolic reactions and facilitate the analysis of metabolism. We computationally reconstructed the metabolic network of the lactic acid bacterium Streptococcus pyogenes M49. Initially, we based the reconstruction on genome annotations and already existing and curated metabolic networks of Bacillus subtilis, Escherichia coli, Lactobacillus plantarum and Lactococcus lactis. This initial draft was manually curated with the final reconstruction accounting for 480 genes associated with 576 reactions and 558 metabolites. In order to constrain the model further, we performed growth experiments of wild type and arcA deletion strains of S. pyogenes M49 in a chemically defined medium and calculated nutrient uptake and production fluxes. We additionally performed amino acid auxotrophy experiments to test the consistency of the model. The established genome-scale model can be used to understand the growth requirements of the human pathogen S. pyogenes and define optimal and suboptimal conditions, but also to describe differences and similarities between S. pyogenes and related lactic acid bacteria such as L. lactis in order to find strategies to reduce the growth of the pathogen and propose drug targets.

Kinetic characterization of arginine deiminase and carbamate kinase from Streptococcus pyogenes M49.

Streptococcus pyogenes (group A Streptococcus, GAS) is an important human pathogen causing mild superficial infections of skin and mucous membranes, but also life-threatening systemic diseases. S. pyogenes and other prokaryotic organisms use the arginine deiminase system (ADS) for survival in acidic environments. In this study, the arginine deiminase (AD), and carbamate kinase (CK) from S. pyogenes M49 strain 591 were heterologously expressed in Escherichia coli DH5α, purified, and kinetically characterized. AD and CK from S. pyogenes M49 share high amino acid sequence similarity with the respective enzymes from Lactococcus lactis subsp. lactis IL1403 (45.6% and 53.5% identical amino acids) and Enterococcus faecalis V583 (66.8% and 66.8% identical amino acids). We found that the arginine deiminase of S. pyogenes is not allosterically regulated by the intermediates and products of the arginine degradation (e.g., ATP, citrulline, carbamoyl phosphate). The Km and Vmax values for arginine were 1.13±0.12mM (mean±SD) and 1.51±0.07µmol/min/mg protein. The carbamate kinase is inhibited by ATP but unaffected by arginine and citrulline. The Km and Vmax values for ADP were 0.72±0.08mM and 1.10±0.10µmol/min/mg protein and the Km for carbamoyl phosphate was 0.65±0.07mM. The optimum pH and temperature for both enzymes were 6.5 and 37°C, respectively.

Regulation of the activity of lactate dehydrogenases from four lactic acid bacteria.

Despite high similarity in sequence and catalytic properties, the l-lactate dehydrogenases (LDHs) in lactic acid bacteria (LAB) display differences in their regulation that may arise from their adaptation to different habitats. We combined experimental and computational approaches to investigate the effects of fructose 1,6-bisphosphate (FBP), phosphate (Pi), and ionic strength (NaCl concentration) on six LDHs from four LABs studied at pH 6 and pH 7. We found that 1) the extent of activation by FBP (Kact) differs. Lactobacillus plantarum LDH is not regulated by FBP, but the other LDHs are activated with increasing sensitivity in the following order: Enterococcus faecalis LDH2 ≤ Lactococcus lactis LDH2 < E. faecalis LDH1 < L. lactis LDH1 ≤ Streptococcus pyogenes LDH. This trend reflects the electrostatic properties in the allosteric binding site of the LDH enzymes. 2) For L. plantarum, S. pyogenes, and E. faecalis, the effects of Pi are distinguishable from the effect of changing ionic strength by adding NaCl. 3) Addition of Pi inhibits E. faecalis LDH2, whereas in the absence of FBP, Pi is an activator of S. pyogenes LDH, E. faecalis LDH1, and L. lactis LDH1 and LDH2 at pH 6. These effects can be interpreted by considering the computed binding affinities of Pi to the catalytic and allosteric binding sites of the enzymes modeled in protonation states corresponding to pH 6 and pH 7. Overall, the results show a subtle interplay among the effects of Pi, FBP, and pH that results in different regulatory effects on the LDHs of different LABs.