Characterization of Phosphofructokinase Activity in Mycobacterium tuberculosis Reveals That a Functional Glycolytic Carbon Flow Is Necessary to Limit the Accumulation of Toxic Metabolic Intermediates under Hypoxia

10.1371/journal.pone.0056037PLoS ONE82

Molecular Insights into Factors Affecting the Generation Behaviors, Dynamic Properties, and Interfacial Structures of Methane Gas Bubbles

Molecular dynamics simulations were performed to study the effects of temperatures, pressures, and methane mole fractions on the generation behaviors, dynamic properties, and interfacial structures of methane gas bubbles. Methane gas bubbling can be promoted by high temperatures and high mole fractions of methane, which come from the generation of larger methane clusters in solution. Bubbles were found to be highly dynamic, with more methane molecules exchanging between bubbles and the surrounding solution at high pressures and in systems with high mole fractions of methane. The interfacial structures between bubbles and the surrounding solution were rough at a molecular level, and the roughness of the outermost methane and water molecules was high at high temperatures, low pressures, and in systems with high methane mole fractions. The dissolution of methane molecules depended on the interactions between the outermost methane and water molecules, which would become stronger with decreasing temperatures, increasing pressures, and decreasing methane mole fractions. The results obtained can help in understanding both the generation behaviors of bubbles when gas hydrates decompose and the re-nucleation behaviors of gas hydrates in the presence of bubbles

Characterization of Phosphofructokinase Activity in Mycobacterium tuberculosis Reveals That a Functional Glycolytic Carbon Flow Is Necessary to Limit the Accumulation of Toxic Metabolic Intermediates under Hypoxia

Mycobacterium tuberculosis metabolic versatility has been increasingly recognized as a major virulence mechanism enabling this pathogen to persist in many microenvironments encountered in its host. Glucose is one of the most abundant source of carbon that is exploited by many pathogenic bacteria in the human host. M. tuberculosis has an intact glycolytic pathway that is highly conserved in all clinical isolates sequenced to date suggesting that, in addition to lipids, glucose may represent a non-negligible source of carbon and energy for this pathogen in vivo. Fructose-6-phosphate phosphorylation represents the key-committing step in glycolysis and is catalyzed by phosphofructokinase (PFK). Two genes, pfkA and pfkB, have been annotated to encode putative PFK in M. tuberculosis. Here, we show that PFKA is the sole PFK enzyme in M. tuberculosis with no functional redundancy with PFKB. PFKA is required for growth on glucose as sole carbon source. Furthermore, in co-metabolism experiments, a disrupted glycolytic pathway resulted in decreased survival due to the accumulation of glucose-derived toxic intermediate metabolites. Coincidentally we found that glucose metabolism is highly toxic for the long term survival of hypoxic non-replicating M. tuberculosis. Indeed, M. tuberculosis survived several order of magnitudes better in a glucose-depleted culture medium, compared to what is traditionally achieved in the original glucose-supplemented medium. This novel finding improves the potential and relevance of the Wayne model for the study of the mechanisms of persistence in M. tuberculosis. In conclusion, although a functional glycolytic pathway is not required for infection and persistence in the mouse model, we propose that glycolysis is required for regulating the pool of sugar phosphate that may be otherwise toxic for hypoxic mycobacteria

A comparative analysis of clustering algorithms: O2 migration in truncated hemoglobin I from transition networks

The ligand migration network for O2-diffusion in truncated Hemoglobin N is analyzed based on three different clustering schemes. For coordinate-based clustering, the conventional k-means and the kinetics-based Markov Clustering (MCL) methods are employed, whereas the locally scaled diffusion map (LSDMap) method is a collective-variable-based approach. It is found that all three methods agree well in their geometrical definition of the most important docking site, and all experimentally known docking sites are recovered by all three methods. Also, for most of the states, their population coincides quite favourably, whereas the kinetics of and between the states differs. One of the major differences between k-means and MCL clustering on the one hand and LSDMap on the other is that the latter finds one large primary cluster containing the Xe1a, IS1, and ENT states. This is related to the fact that the motion within the state occurs on similar time scales, whereas structurally the state is found to be quite diverse. In agreement with previous explicit atomistic simulations, the Xe3 pocket is found to be a highly dynamical site which points to its potential role as a hub in the network. This is also highlighted in the fact that LSDMap cannot identify this state. First passage time distributions from MCL clusterings using a one- (ligand-position) and two-dimensional (ligand-position and protein-structure) descriptor suggest that ligand- and protein-motions are coupled. The benefits and drawbacks of the three methods are discussed in a comparative fashion and highlight that depending on the questions at hand the best-performing method for a particular data set may differ