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

Center for Computational Biology and Bioinformatics, Columbia University, New York City, NY 10032, USA.
Molecular Systems Biology (Impact Factor: 10.87). 09/2010; 6(1):408. DOI: 10.1038/msb.2010.60
Source: PubMed


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. © 2010 EMBO and Macmillan Publishers Limited All rights reserved.

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Available from: Germán Plata, Aug 05, 2014
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    • "After the manual correction, iJN746 was able to produce a non-zero biomass flux. Another five models, iNJ661m [61], iNJ661v [61], iSR432 [62], iTZ479 [13], and iTH366 [63] , initially failed to generate a non-zero biomass flux under FBA when loaded with the sbmlstrict option. A close examination of these non-viable models revealed additional inconsistencies in SBML syntax, where an attribute named " boundaryCondition " was missing. "
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    ABSTRACT: The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult to associate mathematical simulation results with the annotation and biological interpretation of metabolic models. In order to solve this problem, here we developed a Portable System for the Analysis of Metabolic Models (PSAMM), a new open-source software package that supports the integration of heterogeneous metadata in model annotations and provides a user-friendly interface for the analysis of metabolic models. PSAMM is independent of paid software environments like MATLAB, and all its dependencies are freely available for academic users. Compared to existing tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled flexible settings of simulation parameters using simple one-line commands. The integration of heterogeneous, model-specific annotation information in PSAMM is achieved with a novel format of YAML-based model representation, which has several advantages, such as providing a modular organization of model components and simulation settings, enabling model version tracking, and permitting the integration of multiple simulation problems. PSAMM also includes a number of quality checking procedures to examine stoichiometric balance and to identify blocked reactions. Applying PSAMM to 57 models collected from current literature, we demonstrated how the software can be used for managing and simulating metabolic models. We identified a number of common inconsistencies in existing models and constructed an updated model repository to document the resolution of these inconsistencies.
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    • "In FBA-div, however, inhibition diverts intermediate reaction flux to waste, yielding greater biomass reductions. The FBA-div approach can be extended to metabolic models of pathogens, such as tubercu- losis[40], plasmodium[41], and the ESKAPE pathogens[42]. As more FBA models become available for pathogens, combination effects within the current space of drugs and drug-like small molecules could be simulated, and new therapies could be rapidly tested against drugresistant strains. "
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    ABSTRACT: Flux balance analysis (FBA) is an increasingly useful approach for modeling the behavior of metabolic systems. However, standard FBA modeling of genetic knockouts cannot predict drug combination synergies observed between serial metabolic targets, even though such synergies give rise to some of the most widely used antibiotic treatments. Here we extend FBA modeling to simulate responses to chemical inhibitors at varying concentrations, by diverting enzymatic flux to a waste reaction. This flux diversion yields very similar qualitative predictions to prior methods for single target activity. However, we find very different predictions for combinations, where flux diversion, which mimics the kinetics of competitive metabolic inhibitors, can explain serial target synergies between metabolic enzyme inhibitors that we confirmed in Escherichia coli cultures. FBA flux diversion opens the possibility for more accurate genome-scale predictions of drug synergies, which can be used to suggest treatments for infections and other diseases.
    Full-text · Article · Jan 2016 · PLoS ONE
    • "Several bioinformatic approaches have previously been employed to help identify or prioritize drug targets for Plasmodium parasites. These include techniques based on automated identification of important steps in metabolic pathways (Yeh et al., 2004; Fatumo et al., 2009; Huthmacher et al., 2010; Plata et al., 2010), techniques that combine chemical starting points and proteinbased queries (Joubert et al., 2009), as well as the use of the TDRtargets web-resource ( (Magarinos et al., 2012) to prioritize drug targets through the combination of multiple data types relevant to drug development (Crowther et al., 2010). "
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    ABSTRACT: Aminoacyl-tRNA synthetases (aaRSs) are housekeeping enzymes that couple cognate tRNAs with an amino acid so as to transmit genomic information for protein translation. Plasmodium falciparum nuclear genome encodes two copies of methionyl-tRNA synthetases (PfMRS(cyt) and PfMRS(api)). Phylogenetic analyses reveal that both proteins are of primitive origin and related to heterokonts (PfMRS(cyt)) or proteo/primitive bacteria (PfMRS(api)). We show that PfMRS(cyt) localizes in parasite cytoplasm while PfMRS(api) localizes to apicoplast in asexual stages of malaria parasites. Two of the known bacterial MRS inhibitors, REP3123 and REP8839 hampered Plasmodium growth very effectively in early and late stages of parasite development. Small molecule drug-like libraries were screened against modeled PfMRS structures and several 'hits' showed significant effects on parasite growth. We then tested the effect of 'hit' compounds on protein translation by labeling nascent proteins with S(35) labeled cysteine and methionine. Three of the tested compounds reduced protein synthesis and also blocked parasite growth progression from ring to trophozoite stages. Drug docking studies suggest distinct modes of binding for three compounds when compared with the enzyme product methionyl adenylate. This study therefore provides new targets (PfMRSs) and hit compounds that together can be explored for development as anti-malarials. Copyright © 2015, American Society for Microbiology. All Rights Reserved.
    No preview · Article · Jan 2015 · Antimicrobial Agents and Chemotherapy
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