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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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Annotation of reactions in the genome-scale metabolic model of P. falciparum. (A) Number of orphan (non-gene associated) reactions in P. falciparum grouped by metabolic processes. (B) Reactions grouped by Enzyme Commission (EC) classifications. (C) Reactions grouped by metabolic processes in P. falciparum and S. cerevisiae (Duarte et al, 2004).
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f1: Annotation of reactions in the genome-scale metabolic model of P. falciparum. (A) Number of orphan (non-gene associated) reactions in P. falciparum grouped by metabolic processes. (B) Reactions grouped by Enzyme Commission (EC) classifications. (C) Reactions grouped by metabolic processes in P. falciparum and S. cerevisiae (Duarte et al, 2004).

Mentions: Excluding metabolite-exchange reactions, 74% of the reactions in the model are directly associated with P. falciparum genes, which compares well to other models of eukaryotes such as the iND750 yeast model (70%) (Duarte et al, 2004) and the iAC560 model for Leishmania major (63%) (Chavali et al, 2008). The remaining reactions include spontaneous transformations that can proceed without enzymatic catalysis and reactions required for the proper functioning of the metabolic model. Intracellular and inter-compartmental transport reactions, most of which are not currently associated with any gene, account for about 6% and 15% of all reactions in the model, respectively (Figure 1A). Most of the transporter proteins in Plasmodium spp. are currently uncharacterized. However, it is well established that the parasite significantly modifies the permeability of the host cell membrane (Kirk et al, 1999; Martin et al, 2005) and several metabolic processes occur across different organelles. For instance, such metabolic pathways as heme biosynthesis and antioxidant defense have been shown to involve both host and parasite enzymes localized to multiple intracellular compartments (Bonday et al, 1997; Koncarevic et al, 2009). Given the importance of metabolite exchange, many transport reactions were included in the model, although the identities of the corresponding genes remain unknown (Figure 1A).


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)

Annotation of reactions in the genome-scale metabolic model of P. falciparum. (A) Number of orphan (non-gene associated) reactions in P. falciparum grouped by metabolic processes. (B) Reactions grouped by Enzyme Commission (EC) classifications. (C) Reactions grouped by metabolic processes in P. falciparum and S. cerevisiae (Duarte et al, 2004).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1: Annotation of reactions in the genome-scale metabolic model of P. falciparum. (A) Number of orphan (non-gene associated) reactions in P. falciparum grouped by metabolic processes. (B) Reactions grouped by Enzyme Commission (EC) classifications. (C) Reactions grouped by metabolic processes in P. falciparum and S. cerevisiae (Duarte et al, 2004).
Mentions: Excluding metabolite-exchange reactions, 74% of the reactions in the model are directly associated with P. falciparum genes, which compares well to other models of eukaryotes such as the iND750 yeast model (70%) (Duarte et al, 2004) and the iAC560 model for Leishmania major (63%) (Chavali et al, 2008). The remaining reactions include spontaneous transformations that can proceed without enzymatic catalysis and reactions required for the proper functioning of the metabolic model. Intracellular and inter-compartmental transport reactions, most of which are not currently associated with any gene, account for about 6% and 15% of all reactions in the model, respectively (Figure 1A). Most of the transporter proteins in Plasmodium spp. are currently uncharacterized. However, it is well established that the parasite significantly modifies the permeability of the host cell membrane (Kirk et al, 1999; Martin et al, 2005) and several metabolic processes occur across different organelles. For instance, such metabolic pathways as heme biosynthesis and antioxidant defense have been shown to involve both host and parasite enzymes localized to multiple intracellular compartments (Bonday et al, 1997; Koncarevic et al, 2009). Given the importance of metabolite exchange, many transport reactions were included in the model, although the identities of the corresponding genes remain unknown (Figure 1A).

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