A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism
Fig 5
a) Systems modeling framework used in this study. Inputs used for constraint-based and kinetic modeling are derived from molecular modeling calculations and experimental data when available. In order to understand how small-scale changes from enzyme variants affect the entire system, we look at the internal system changes (in reaction flux and metabolite concentration), differences in metabolite import & export, and how the cell handles an increase in oxidative or energy loads. Oxidative load is defined as the conversion of NADPH to NADP+, whose rate of reaction is increased under states of oxidative stress. Energy load is defined as the use of ATP. For all panels, the change in metabolic flux is colored by a difference from the wild-type flux state, red being a decreased flux in the mutant state and blue being an increased flux. b) Constraint-based modeling for the mutant COMT enzyme. The SNP is predicted to decrease the binding affinity of the enzyme in norepinephrine and dopamine metabolism. Increasing the Km (predicted) of COMT for the respective reactions leads to decreased flux and as a result decreased export of their methylated counterparts. Inhibitors tolcapone (TCW) and entacapone (ENT) are also predicted to have a lowered binding affinity to COMT, leading to similar effects. c) Kinetic modeling for the mutant G6PD enzyme. Decreases of the Km (predicted and experimental) and of the Kcat (experimental) lead to major systemic changes of the pentose phosphate pathway and glycolysis. The ratio of NADPH to NADP+ greatly decreases and subsequently the oxidative load able to be handled also decreases. d) Kinetic modeling for the mutant GAPDH enzyme. The cell is unable to handle the predicted increase in Km (predicted) and results in an infeasible state of the model, corresponding to cell lysis.