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Reconstruction and flux-balance analysis of the Plasmodium falciparum metabolic network.

Plata G, Hsiao TL, Olszewski KL, Llinás M, Vitkup D - Mol. Syst. Biol. (2010)

Bottom Line: Moreover, using constraints based on gene-expression data, the model was able to predict the direction of concentration changes for external metabolites with 70% accuracy.Using FBA of the reconstructed network, we identified 40 enzymatic drug targets (i.e. in silico essential genes), with no or very low sequence identity to human proteins.To demonstrate that the model can be used to make clinically relevant predictions, we experimentally tested one of the identified drug targets, nicotinate mononucleotide adenylyltransferase, using a recently discovered small-molecule inhibitor.

View Article: PubMed Central - PubMed

Affiliation: Center for Computational Biology and Bioinformatics, Columbia University, New York City, NY 10032, USA.

ABSTRACT
Genome-scale metabolic reconstructions can serve as important tools for hypothesis generation and high-throughput data integration. Here, we present a metabolic network reconstruction and flux-balance analysis (FBA) of Plasmodium falciparum, the primary agent of malaria. The compartmentalized metabolic network accounts for 1001 reactions and 616 metabolites. Enzyme-gene associations were established for 366 genes and 75% of all enzymatic reactions. Compared with other microbes, the P. falciparum metabolic network contains a relatively high number of essential genes, suggesting little redundancy of the parasite metabolism. The model was able to reproduce phenotypes of experimental gene knockout and drug inhibition assays with up to 90% accuracy. Moreover, using constraints based on gene-expression data, the model was able to predict the direction of concentration changes for external metabolites with 70% accuracy. Using FBA of the reconstructed network, we identified 40 enzymatic drug targets (i.e. in silico essential genes), with no or very low sequence identity to human proteins. To demonstrate that the model can be used to make clinically relevant predictions, we experimentally tested one of the identified drug targets, nicotinate mononucleotide adenylyltransferase, using a recently discovered small-molecule inhibitor.

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Comparison between the predicted and experimentally measured shifts in metabolite concentrations in infected red blood cells. UP/DOWN indicates direction of experimentally measured changes in metabolic concentrations in infected versus uninfected cells. Blue color indicates agreement between experiment and predictions, whereas yellow indicates disagreement. In most cases (70%, P-value=9 × 10−4), the shifts in metabolic concentrations from one stage to the next can be predicted based on changes in the P. falciparum metabolic exchange fluxes. The in silico predictions of exchange fluxes were made based on the expression-constrained flux-balance analysis (Colijn et al, 2009). Briefly, for genes with available mRNA-expression data, the maximum flux through the associated metabolic reactions was constrained proportionally to their expression level; with the highest expression value corresponding to the maximum allowed flux.
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f3: Comparison between the predicted and experimentally measured shifts in metabolite concentrations in infected red blood cells. UP/DOWN indicates direction of experimentally measured changes in metabolic concentrations in infected versus uninfected cells. Blue color indicates agreement between experiment and predictions, whereas yellow indicates disagreement. In most cases (70%, P-value=9 × 10−4), the shifts in metabolic concentrations from one stage to the next can be predicted based on changes in the P. falciparum metabolic exchange fluxes. The in silico predictions of exchange fluxes were made based on the expression-constrained flux-balance analysis (Colijn et al, 2009). Briefly, for genes with available mRNA-expression data, the maximum flux through the associated metabolic reactions was constrained proportionally to their expression level; with the highest expression value corresponding to the maximum allowed flux.

Mentions: Importantly, the metabolic model of the parasite can be also used to integrate various genomic data, such as gene expression (Oberhardt et al, 2009). To illustrate these possibilities, we applied gene-expression data as constraints for the flux-balance model (Colijn et al, 2009) in order to predict changes in metabolic exchange fluxes. We found that the model was able to correctly predict the changes in external metabolite concentrations (Olszewski et al, 2009) with about 70% accuracy (Figure 3). The availability of a human metabolic network reconstruction (Duarte et al, 2007) would allow, in the future, to analyze the combined parasite–host network, which would deepen understanding of the P. falciparum metabolic vulnerabilities.


Reconstruction and flux-balance analysis of the Plasmodium falciparum metabolic network.

Plata G, Hsiao TL, Olszewski KL, Llinás M, Vitkup D - Mol. Syst. Biol. (2010)

Comparison between the predicted and experimentally measured shifts in metabolite concentrations in infected red blood cells. UP/DOWN indicates direction of experimentally measured changes in metabolic concentrations in infected versus uninfected cells. Blue color indicates agreement between experiment and predictions, whereas yellow indicates disagreement. In most cases (70%, P-value=9 × 10−4), the shifts in metabolic concentrations from one stage to the next can be predicted based on changes in the P. falciparum metabolic exchange fluxes. The in silico predictions of exchange fluxes were made based on the expression-constrained flux-balance analysis (Colijn et al, 2009). Briefly, for genes with available mRNA-expression data, the maximum flux through the associated metabolic reactions was constrained proportionally to their expression level; with the highest expression value corresponding to the maximum allowed flux.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2964117&req=5

f3: Comparison between the predicted and experimentally measured shifts in metabolite concentrations in infected red blood cells. UP/DOWN indicates direction of experimentally measured changes in metabolic concentrations in infected versus uninfected cells. Blue color indicates agreement between experiment and predictions, whereas yellow indicates disagreement. In most cases (70%, P-value=9 × 10−4), the shifts in metabolic concentrations from one stage to the next can be predicted based on changes in the P. falciparum metabolic exchange fluxes. The in silico predictions of exchange fluxes were made based on the expression-constrained flux-balance analysis (Colijn et al, 2009). Briefly, for genes with available mRNA-expression data, the maximum flux through the associated metabolic reactions was constrained proportionally to their expression level; with the highest expression value corresponding to the maximum allowed flux.
Mentions: Importantly, the metabolic model of the parasite can be also used to integrate various genomic data, such as gene expression (Oberhardt et al, 2009). To illustrate these possibilities, we applied gene-expression data as constraints for the flux-balance model (Colijn et al, 2009) in order to predict changes in metabolic exchange fluxes. We found that the model was able to correctly predict the changes in external metabolite concentrations (Olszewski et al, 2009) with about 70% accuracy (Figure 3). The availability of a human metabolic network reconstruction (Duarte et al, 2007) would allow, in the future, to analyze the combined parasite–host network, which would deepen understanding of the P. falciparum metabolic vulnerabilities.

Bottom Line: Moreover, using constraints based on gene-expression data, the model was able to predict the direction of concentration changes for external metabolites with 70% accuracy.Using FBA of the reconstructed network, we identified 40 enzymatic drug targets (i.e. in silico essential genes), with no or very low sequence identity to human proteins.To demonstrate that the model can be used to make clinically relevant predictions, we experimentally tested one of the identified drug targets, nicotinate mononucleotide adenylyltransferase, using a recently discovered small-molecule inhibitor.

View Article: PubMed Central - PubMed

Affiliation: Center for Computational Biology and Bioinformatics, Columbia University, New York City, NY 10032, USA.

ABSTRACT
Genome-scale metabolic reconstructions can serve as important tools for hypothesis generation and high-throughput data integration. Here, we present a metabolic network reconstruction and flux-balance analysis (FBA) of Plasmodium falciparum, the primary agent of malaria. The compartmentalized metabolic network accounts for 1001 reactions and 616 metabolites. Enzyme-gene associations were established for 366 genes and 75% of all enzymatic reactions. Compared with other microbes, the P. falciparum metabolic network contains a relatively high number of essential genes, suggesting little redundancy of the parasite metabolism. The model was able to reproduce phenotypes of experimental gene knockout and drug inhibition assays with up to 90% accuracy. Moreover, using constraints based on gene-expression data, the model was able to predict the direction of concentration changes for external metabolites with 70% accuracy. Using FBA of the reconstructed network, we identified 40 enzymatic drug targets (i.e. in silico essential genes), with no or very low sequence identity to human proteins. To demonstrate that the model can be used to make clinically relevant predictions, we experimentally tested one of the identified drug targets, nicotinate mononucleotide adenylyltransferase, using a recently discovered small-molecule inhibitor.

Show MeSH
Related in: MedlinePlus