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Open Access Highly Accessed Research article

Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis

Carola Huthmacher*, Andreas Hoppe, Sascha Bulik and Hermann-Georg Holzhütter

Author Affiliations

Institute of Biochemistry, Charité, Monbijoustraße 2, 10117 Berlin, Germany

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BMC Systems Biology 2010, 4:120  doi:10.1186/1752-0509-4-120

Published: 31 August 2010

Additional files

Additional file 1:

Assembled metabolic network of P. falciparum in SBML format.

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Additional file 2:

Assembled metabolic network of the human erythrocyte in SBML format.

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Additional file 3:

Gene expression samples. In order to calculate a stage-specific flux distributions our flux balance approach requires a gene expression profile of the stage of interest. We obtained gene expression samples from different publications, including Bozdech et al., Le Roch et al., Sacci et al., and Tarun et al.

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Additional file 4:

Normalized Hamming distance matrix for gene expression samples. In order to compare the gene expression profiles of the different time points normalized Hamming distances (see text for formula) have been calculated as described in the text for each pair of gene expression samples. The darker the color of a matrix entry, the lower is the corresponding Hamming distance. Sample labels are composed of the sample abbreviation and the number of expressed genes.

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Additional file 5:

Bozdech gene expression samples mapped onto metabolic pathways. Mapping gene expression data onto metabolic networks may uncover active pathways for each stage and metabolic differences between the individual life cycle stages. For this purpose, we calculated the ratio of expressed genes per KEGG pathway (# expressed genes/# genes with available expression data for pathway) for each Bozdech gene expression sample. The darker the color of a matrix entry, the lower is the ratio. Clusters of pathways with similar patterns of expressed genes during the individual life cycle time points were calculated with the built-in function hclust ('average' method) of the statistics software R (colored bars).

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Additional file 6:

Le Roch gene expression samples mapped onto metabolic pathways. See caption of Additional file 5.

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Additional file 7:

Tarun gene expression samples mapped onto metabolic pathways. See caption of Additional file 5.

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Additional file 8:

Daily gene expression samples mapped onto metabolic pathways. See caption of Additional file 5.

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Additional file 9:

Metabolites essential for parasite development. Metabolites listed in this table are assumed to be essential during those developmental stages of the parasite that are specified in the third column. Some entries represent pseudo-metabolites that do not correspond to actual metabolites. These have been added as products in the equations of certain important reactions such as glutathione reductase. Requiring the production of these pseudo-metabolite ensures that the respective reaction is active.

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Additional file 10:

Normalized Hamming distance matrix for calculated flux distributions. Flux distributions have been predicted with our flux balance approach (see Figure 1) for each time point of the parasite's life cycle for which a gene expression profile exists. Simulations were conducted considering only the metabolic network of the parasite without any further constraints reflecting the parasite's environment and without considering the expression status of genes during preceding time points. In order to compare the individual flux distributions normalized Hamming distances (see text for formula) have been determined for all pairs of flux distributions. The darker the color of a matrix entry, the lower is the corresponding Hamming distance.

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Additional file 11:

Predicted host parasite metabolite exchanges. Flux distributions have been predicted with our flux balance approach (see Figure 1) for each time point of the parasite's life cycle for which a gene expression profile exists. Simulations were conducted considering only the metabolic network of the parasite without any further constraints reflecting the parasite's environment and without considering the expression status of genes during preceding time points. Resulting metabolite exchanges between host and parasite are depicted in this figure. Red matrix entries represent metabolites that are predicted to be imported into the parasite while green matrix entries represent metabolites secreted into the host compartment.

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Additional file 12:

Normalized Hamming distance matrix for calculated flux distributions using improved approach. Flux distributions have been predicted with our improved flux balance approach (see Figure 1) for each time point of the intraerythrocytic developmental cycle for which a gene expression profile exists. Simulations were conducted on the basis of the combined metabolic network of parasite and host and additional constraints reflecting knowledge about the blood stage. Furthermore, the expression status of genes during preceding time points was considered for the flux calculations. In order to compare the individual flux distributions normalized Hamming distances (see text for formula) have been determined for all pairs of flux distributions. The darker the color of a matrix entry, the lower is the corresponding Hamming distance.

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Additional file 13:

Reaction distribution among stage-specific fluxes. Flux distributions have been predicted with our improved flux balance approach (see Figure 1) for each time point of the intraerythrocytic developmental cycle for which a gene expression profile exists. Simulations were conducted on the basis of the combined metabolic network of parasite and host and additional constraints reflecting knowledge about the blood stage. Furthermore, the expression status of genes during preceding time points was considered for the flux calculations. Subsequently, it was counted in how many of these stage-specific flux distributions a particular reaction was carrying a non-zero flux. The resulting histogram is shown here. The x-axis gives the number of flux distributions within which a reaction carries a non-zero flux and the y-axis indicates the frequency. In other words, the left most bar of the histogram represents the number of reactions that exclusively occur in a single flux distribution and are therefore very stage-specific, while the right most bar represents the number of reactions that are present in all (96) flux distributions related to the blood stage.

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Additional file 14:

Predicted metabolic fluxes consistent with Bozdech gene expression data mapped onto metabolic pathways. Flux distributions have been predicted with our improved flux balance approach (see Figure 1) for each time point of the intraerythrocytic developmental cycle for which a gene expression profile exists. Simulations were conducted on the basis of the combined metabolic network of parasite and host and additional constraints reflecting knowledge about the blood stage. Furthermore, the expression status of genes during preceding time points was considered for the flux calculations. In order to explore the predicted flux distributions on the level of metabolic pathways, we mapped the flux profiles onto KEGG pathways and counted active reactions, to assess whether a pathway is active or not and wether there are changes during the IDC. The darker the color of a matrix entry the fewer reactions of the corresponding pathway are active.

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Additional file 15:

Predicted metabolic fluxes consistent with Le Roch gene expression data mapped onto metabolic pathways. Same as Additional file 14 but fluxes were calculated using Le Roch gene expression data.

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Additional file 16:

Predicted metabolic fluxes consistent with Daily gene expression data mapped onto metabolic pathways. Same as Additional file 14 but fluxes were calculated using Daily gene expression data.

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Additional file 17:

Overview of pathway specific consensus reactions for different time points of intraerythrocytic cycle. Flux distributions have been predicted with our improved flux balance approach (see Figure 1) for each time point of the intraerythrocytic developmental cycle for which a gene expression profile exists. Simulations were conducted on the basis of the combined metabolic network of parasite and host and additional constraints reflecting knowledge about the blood stage. Furthermore, the expression status of genes during preceding time points was considered for the flux calculations. Consensus reactions, which are reactions that are predicted to be active for all gene expression samples covering the same stage, were determined. These reactions are more likely to actually occur during a certain stage, since they are derived from different data samples. In order to identify consensus reactions we grouped all calculated flux profiles corresponding to the blood stage into seven sets (eRing, lRing, eTropho, lTropho, eSchiz, lSchiz, Mero; see Additional file 3) with respect to represented stages and determined those reactions that carry a non-zero flux in all flux profiles of the same set. For each blood stage and each metabolic pathway the fraction of consensus reactions per total number of pathway reactions was computed to uncover the distribution of consensus reactions among pathways. To get an impression of how many consensus reactions are shared between the different stages, this fraction was also computed for those consensus reactions that two sets have in common. The darker the color of a matrix entry the lower is the percentage of consensus reactions.

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Additional file 18:

Ranked predicted essential reactions. Knock-outs were simulated with a simple FBA approach neglecting gene expression information. No assumptions were made about the cellular environment and nutrient uptake was not restricted, since such information is not available for all stages. Successively reactions were constrained to carry no flux, while the network was forced to produce all metabolites that are assumed to be essential during any developmental stage (all metabolites listed in Additional file 9). If no solution could be found to this problem the reaction was assumed to be essential. The resulting set of indispensable reactions, which are assigned to genes and not covered by our gold standard set of experimentally validated essential enzymes, is listed here. Reactions are ranked according to a score that is derived as follows: two points if corresponding genes are not homologous to human genes, an additional point if the reaction is targeted in any other organism (according to the SuperTarget database), and another additional point if the reaction is active during all parasitic life cycle stages, which makes respective drugs applicable for prophylaxis as well as disease treatment.

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