Dataset Information

Bazzani2012 - Genome scale networks of P.falciparum and human hepatocyte

ABSTRACT: Bazzani2012 - Genome scale networks of P.falciparum and human hepatocyte This model is described in the article: Network-based assessment of the selectivity of metabolic drug targets in Plasmodium falciparum with respect to human liver metabolism. Bazzani S, Hoppe A, Holzhütter HG. BMC Syst Biol. 2012 Aug 31;6(1):118. PMID: 2937810 Abstract: ABSTRACT: BACKGROUND: The search for new drug targets for antibiotics against Plasmodium falciparum, a major cause of human deaths, is a pressing scientific issue, as multiple resistance strains spread rapidly. Metabolic network-based analyses may help to identify those parasite's essential enzymes whose homologous counterparts in the human host cells are either absent, non-essential or relatively less essential. RESULTS: Using the well-curated metabolic networks PlasmoNet of the parasite Plasmodium falciparum and HepatoNet1 of the human hepatocyte, the selectivity of 48 experimental antimalarial drug targets was analyzed. Applying in silico gene deletions, 24 of these drug targets were found to be perfectly selective, in that they were essential for the parasite but non-essential for the human cell. The selectivity of a subset of enzymes, that were essential in both models, was evaluated with the reduced fitness concept. It was, then, possible to quantify the reduction in functional fitness of the two networks under the progressive inhibition of the same enzymatic activity. Overall, this in silico analysis provided a selectivity ranking that was in line with numerous in vivo and in vitro observations. CONCLUSIONS: Genome-scale models can be useful to depict and quantify the effects of enzymatic inhibitions on the impaired production of biomass components. From the perspective of a host-pathogen metabolic interaction, an estimation of the drug targets-induced consequences can be beneficial for the development of a selective anti-parasitic drug. This model is hosted on BioModels Database and identified by: MODEL1206070000 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. PMID: 20587024 . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to [CC0 Public Domain Dedication>http://creativecommons.org/publicdomain/zero/1.0/] for more information.

SUBMITTER: Susanna Bazzani

PROVIDER: MODEL1206070000 | BioModels | 2005-01-01

REPOSITORIES: BioModels

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Network-based assessment of the selectivity of metabolic drug targets in Plasmodium falciparum with respect to human liver metabolism.

Bazzani Susanna S Hoppe Andreas A Holzhütter Hermann-Georg HG

BMC systems biology 20120831

<h4>Background</h4>The search for new drug targets for antibiotics against Plasmodium falciparum, a major cause of human deaths, is a pressing scientific issue, as multiple resistance strains spread rapidly. Metabolic network-based analyses may help to identify those parasite's essential enzymes whose homologous counterparts in the human host cells are either absent, non-essential or relatively less essential.<h4>Results</h4>Using the well-curated metabolic networks PlasmoNet of the parasite Pla ...[more]

PMID: 22937810

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Project description:This model is from the article: Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis. Huthmacher C, Hoppe A, Bulik S, Holzhütter HG. BMC Syst Biol. 2010 Aug 31;4:120. 20807400 , Abstract: BACKGROUND: Despite enormous efforts to combat malaria the disease still afflicts up to half a billion people each year of which more than one million die. Currently no approved vaccine is available and resistances to antimalarials are widely spread. Hence, new antimalarial drugs are urgently needed. RESULTS: Here, we present a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. We assembled a compartmentalized metabolic model and predicted life cycle stage specific metabolism with the help of a flux balance approach that integrates gene expression data. Predicted metabolite exchanges between parasite and host were found to be in good accordance with experimental findings when the parasite's metabolic network was embedded into that of its host (erythrocyte). Knock-out simulations identified 307 indispensable metabolic reactions within the parasite. 35 out of 57 experimentally demonstrated essential enzymes were recovered and another 16 enzymes, if additionally the assumption was made that nutrient uptake from the host cell is limited and all reactions catalyzed by the inhibited enzyme are blocked. This predicted set of putative drug targets, shown to be enriched with true targets by a factor of at least 2.75, was further analyzed with respect to homology to human enzymes, functional similarity to therapeutic targets in other organisms and their predicted potency for prophylaxis and disease treatment. CONCLUSIONS: The results suggest that the set of essential enzymes predicted by our flux balance approach represents a promising starting point for further drug development. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Project description:This is the genome-scale metabolic network of Plasmodium falciparum described in the article: 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 Sep 7;6:408.; PmID: 20823846 , DOI: 10.1038/msb.2010.60 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. To make this model SBML compliant, the unit for the FLUX_VALUE parameters had to be changed from mmole per gramm dry weight per hour to mmole per hr. To change the model back to its original form, either alter the unit definition of the unit mmol_per_hr accordingly or replace mmol_per_hr by mmol_per_gDW_per_hr in all reactions. This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Dataset Information

Bazzani2012 - Genome scale networks of P.falciparum and human hepatocyte

Publications

Network-based assessment of the selectivity of metabolic drug targets in Plasmodium falciparum with respect to human liver metabolism.

Similar Datasets

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