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. 2017 Aug;7(2):138-146.
doi: 10.1016/j.ijpddr.2017.03.004. Epub 2017 Mar 22.

Using a Genome-Scale Metabolic Network Model to Elucidate the Mechanism of Chloroquine Action in Plasmodium Falciparum

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Free PMC article

Using a Genome-Scale Metabolic Network Model to Elucidate the Mechanism of Chloroquine Action in Plasmodium Falciparum

Shivendra G Tewari et al. Int J Parasitol Drugs Drug Resist. .
Free PMC article

Abstract

Chloroquine, long the default first-line treatment against malaria, is now abandoned in large parts of the world because of widespread drug-resistance in Plasmodium falciparum. In spite of its importance as a cost-effective and efficient drug, a coherent understanding of the cellular mechanisms affected by chloroquine and how they influence the fitness and survival of the parasite remains elusive. Here, we used a systems biology approach to integrate genome-scale transcriptomics to map out the effects of chloroquine, identify targeted metabolic pathways, and translate these findings into mechanistic insights. Specifically, we first developed a method that integrates transcriptomic and metabolomic data, which we independently validated against a recently published set of such data for Krebs-cycle mutants of P. falciparum. We then used the method to calculate the effect of chloroquine treatment on the metabolic flux profiles of P. falciparum during the intraerythrocytic developmental cycle. The model predicted dose-dependent inhibition of DNA replication, in agreement with earlier experimental results for both drug-sensitive and drug-resistant P. falciparum strains. Our simulations also corroborated experimental findings that suggest differences in chloroquine sensitivity between ring- and schizont-stage P. falciparum. Our analysis also suggests that metabolic fluxes that govern reduced thioredoxin and phosphoenolpyruvate synthesis are significantly decreased and are pivotal to chloroquine-based inhibition of P. falciparum DNA replication. The consequences of impaired phosphoenolpyruvate synthesis and redox metabolism are reduced carbon fixation and increased oxidative stress, respectively, both of which eventually facilitate killing of the parasite. Our analysis suggests that a combination of chloroquine (or an analogue) and another drug, which inhibits carbon fixation and/or increases oxidative stress, should increase the clearance of P. falciparum from the host system.

Keywords: Carbon fixation; Chloroquine; Metabolic network modeling; Plasmodium; Redox metabolism.

Figures

Image 1
Fig. 1
Fig. 1
Schematic representation of the method developed to simulate the effect of chloroquine treatment on metabolic flux profiles of P. falciparum during the intraerythrocytic development cycle. The method shown above with ‘α=1’ is identical to a previously proposed method (Fang et al., 2014) and is used to estimate reaction fluxes under control conditions (black curves in the bottom panel). We modified the previous approach (Fang et al., 2014) to integrate the effect of stress (such as chloroquine treatment). In the metabolic network shown above, v1,v2,andv3 are intracellular fluxes; vi is the input; ve is the output; and vg is the flux representing the growth rate. We simulated the effect of external stress by scaling the baseline flux of a reaction (vref), using the relative change in the transcriptome data (α) of that reaction in response to stress. The objective was to find a new intracellular flux distribution (red curves in the bottom panel) based on the modified vref. These new intracellular fluxes were sufficient to capture the phenotypic alterations that occur in response to external stress. In the dotted box, w represents the weight of individual reactions, S represents the stoichiometry of reactions, and lb and ub represent lower and upper bounds of reactions, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2
Fig. 2
Reaction fluxes based on changes in gene transcription obtained in response to a certain stress condition are sufficient to mimic the metabolic phenotype corresponding to that condition. A) Schematic representation of the thermodynamically balanced computer model of mitochondrial oxidative phosphorylation used to predict relative changes in metabolites on the basis of relative changes in estimated reaction fluxes under wild-type (WT) and knockout (KO) conditions; figure modified and reproduced from (Wu et al., 2007). The two red circles represent the two genes that were deleted in a previous study (Ke et al., 2015). B) Alterations in the concentrations of TCA cycle metabolites after the deletion of two TCA cycle enzymes. Open bars show experimentally observed values and closed bars the computationally predicted values. Metabolite concentrations were computed by using the model shown in A and reaction flux ratios shown in C. C) Median relative changes in TCA cycle enzyme fluxes, estimated by using the approach shown in Fig. 1, during the intraerythrocytic development cycle under two conditions (WT and KO). The effect of the stress condition, i.e., knockout of two TCA cycle enzymes, is captured by the transcriptome data obtained under WT and KO conditions (Ke et al., 2015). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 3
Fig. 3
Simulated effect of chloroquine on rates of DNA synthesis by the parasite during the intraerythrocytic development cycle (IDC). In the legend, CQN denotes chloroquine. A) Dose-dependent decrease in the rate of DNA synthesis for Pf3D7. The DNA synthesis rate at a CQN dose of 0 nM was estimated from transcriptome data of Pf3D7 (Llinas et al., 2006). The effect of CQN dose on the DNA synthesis rate was simulated by the approach described in the Materials and methods Section, using transcriptome data of Pf3D7 (Hu et al., 2010). B) The DNA synthesis rate at a CQN dose of 0 nM was estimated from transcriptome data of PfDd2 (Llinas et al., 2006). The effect of CQN dose on the DNA synthesis rate was estimated by the same approach as that in A, using transcriptome data of PfDd2 (Hu et al., 2010). For both strains, the decrease in DNA synthesis occurring later in the IDC (beyond 30 h after infection) was more pronounced than that occurring early in the IDC (the first 18 h after infection) in response to CQN treatment. To account for variability of gene expression in our simulations, we added 10% Gaussian noise to the expression level of each gene. The results were averaged across twenty independent simulations. Error bars represent the standard error associated with each prediction.
Fig. 4
Fig. 4
Simulated effect of chloroquine on net synthesized DNA, obtained by integrating the rate of DNA synthesis (Fig. 3) over the intraerythrocytic development cycle. The ordinate shows values of net synthesized DNA (•) relative to control values as a function of chloroquine dose. The net amount of synthesized DNA decreases as the dose increases (red symbols). The amount of synthesized DNA returns back to control levels (green symbols) when α is set to 1 for the core genes in Table S3, while simulating the effect of chloroquine on metabolic fluxes. Control values of net synthesized DNA (•) were obtained from transcriptome data of Pf3D7 or PfDd2 (Llinas et al., 2006) (Table S2). In the legend, CTL denotes DNA synthesized under control conditions after chloroquine treatment, whereas -CG denotes that after chloroquine treatment with α set to 1 for core genes. The results were averaged across twenty independent simulations. Error bars represent the standard error associated with each prediction. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 5
Fig. 5
Predicted mechanism by which chloroquine kills the malaria parasite. Chloroquine (CQN) leads to the accumulation of heme. Although the downstream consequences of this effect are unknown, our analysis suggests that the accumulated heme leads to inhibition of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and thioredoxin reductase (TrxR), and that the downstream effects of these inhibitory events are the major determinants of chloroquine efficacy. This hypothesis also provides a mechanistic explanation of how chloroquine inhibits DNA synthesis (Cohen and Yielding, 1965, Polet and Barr, 1968a). Abbreviations: CDP, cytidine diphosphate; CO2, carbon dioxide; dCDP, deoxycytidine diphosphate; dCTP, deoxycytidine triphosphate; DHODH, dihydroorotate dehydrogenase; dTDP, deoxythymidine diphosphate; dTTP, deoxythymidine triphosphate; G3P, glyceraldehyde 3-phosphate; H2O, water; H2O2, hydrogen peroxide; Hb, hemoglobin; Hz, hemozoin; NADPH, reduced nicotinamide adenine dinucleotide phosphate; NADP+, nicotinamide adenine dinucleotide phosphate; NDPK, nucleoside diphosphokinase; PEP, phosphoenolpyruvate; PGK, phosphoglycerate kinase; PGM, phosphoglycerate mutase; PPP, pentose phosphate pathway; RNR, ribonucleotide reductase; TCA, tricarboxylic acid; Tpx, thioredoxin peroxidase; Trx (S2), oxidized thioredoxin; Trx (SH2), reduced thioredoxin; UDP, uridine diphosphate.

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