Mathematical modelling of polyamine metabolism in bloodstream-form trypanosoma brucei: An application to drug target identification

© 2013 Gu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedThis article has been made available through the Brunel Open Access Publishing Fund.We present the first computational kinetic model of polyamine metabolism in bloodstream-form Trypanosoma brucei, the causative agent of human African trypanosomiasis. We systematically extracted the polyamine pathway from the complete metabolic network while still maintaining the predictive capability of the pathway. The kinetic model is constructed on the basis of information gleaned from the experimental biology literature and defined as a set of ordinary differential equations. We applied Michaelis-Menten kinetics featuring regulatory factors to describe enzymatic activities that are well defined. Uncharacterised enzyme kinetics were approximated and justified with available physiological properties of the system. Optimisation-based dynamic simulations were performed to train the model with experimental data and inconsistent predictions prompted an iterative procedure of model refinement. Good agreement between simulation results and measured data reported in various experimental conditions shows that the model has good applicability in spite of there being gaps in the required data. With this kinetic model, the relative importance of the individual pathway enzymes was assessed. We observed that, at low-to-moderate levels of inhibition, enzymes catalysing reactions of de novo AdoMet (MAT) and ornithine production (OrnPt) have more efficient inhibitory effect on total trypanothione content in comparison to other enzymes in the pathway. In our model, prozyme and TSHSyn (the production catalyst of total trypanothione) were also found to exhibit potent control on total trypanothione content but only when they were strongly inhibited. Different chemotherapeutic strategies against T. brucei were investigated using this model and interruption of polyamine synthesis via joint inhibition of MAT or OrnPt together with other polyamine enzymes was identified as an optimal therapeutic strategy.The work was carried out under a PhD programme partly funded by Prof. Ray Welland, School of Computing Science, University of Glasgo

1+1 = 3: A Fusion of 2 Enzymes in the Methionine Salvage Pathway of Tetrahymena thermophila Creates a Trifunctional Enzyme That Catalyzes 3 Steps in the Pathway

The methionine salvage pathway is responsible for regenerating methionine from its derivative, methylthioadenosine. The complete set of enzymes of the methionine pathway has been previously described in bacteria. Despite its importance, the pathway has only been fully described in one eukaryotic organism, yeast. Here we use a computational approach to identify the enzymes of the methionine salvage pathway in another eukaryote, Tetrahymena thermophila. In this organism, the pathway has two fused genes, MTNAK and MTNBD. Each of these fusions involves two different genes whose products catalyze two different single steps of the pathway in other organisms. One of the fusion proteins, mtnBD, is formed by enzymes that catalyze non-consecutive steps in the pathway, mtnB and mtnD. Interestingly the gene that codes for the intervening enzyme in the pathway, mtnC, is missing from the genome of Tetrahymena. We used complementation tests in yeast to show that the fusion of mtnB and mtnD from Tetrahymena is able to do in one step what yeast does in three, since it can rescue yeast knockouts of mtnB, mtnC, or mtnD. Fusion genes have proved to be very useful in aiding phylogenetic reconstructions and in the functional characterization of genes. Our results highlight another characteristic of fusion proteins, namely that these proteins can serve as biochemical shortcuts, allowing organisms to completely bypass steps in biochemical pathways