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Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major.

Chavali AK, Whittemore JD, Eddy JA, Williams KT, Papin JA - Mol. Syst. Biol. (2008)

Bottom Line: Using a systems-based approach, we hypothesized a comprehensive set of lethal single and double gene deletions, some of which were validated using published data with approximately 70% accuracy.Additionally, we generated hypothetical annotations to dozens of previously uncharacterized genes in the L. major genome and proposed a minimal medium for growth.It can serve as a tool for clarifying discrepancies between data sources, generating hypotheses that can be experimentally validated and identifying ideal therapeutic targets.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.

ABSTRACT
Systems analyses have facilitated the characterization of metabolic networks of several organisms. We have reconstructed the metabolic network of Leishmania major, a poorly characterized organism that causes cutaneous leishmaniasis in mammalian hosts. This network reconstruction accounts for 560 genes, 1112 reactions, 1101 metabolites and 8 unique subcellular localizations. Using a systems-based approach, we hypothesized a comprehensive set of lethal single and double gene deletions, some of which were validated using published data with approximately 70% accuracy. Additionally, we generated hypothetical annotations to dozens of previously uncharacterized genes in the L. major genome and proposed a minimal medium for growth. We further demonstrated the utility of a network reconstruction with two proof-of-concept examples that yielded insight into robustness of the network in the presence of enzymatic inhibitors and delineation of promastigote/amastigote stage-specific metabolism. This reconstruction and the associated network analyses of L. major is the first of its kind for a protozoan. It can serve as a tool for clarifying discrepancies between data sources, generating hypotheses that can be experimentally validated and identifying ideal therapeutic targets.

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Related in: MedlinePlus

F0F1-ATP synthase robustness analysis. The normalized growth rate corresponding to F0F1-ATP synthase flux is plotted. The white circles indicate single optimization simulations that are run at different ATP synthase flux values to measure flux through biomass (growth rate).
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f5: F0F1-ATP synthase robustness analysis. The normalized growth rate corresponding to F0F1-ATP synthase flux is plotted. The white circles indicate single optimization simulations that are run at different ATP synthase flux values to measure flux through biomass (growth rate).

Mentions: The robustness of the metabolic network can be evaluated by constraining the flux of any reaction from its wild-type value to zero and calculating the resultant effect on the growth rate. As an example, oligomycin is an inhibitor of mitochondrial F0F1-ATP synthase (Schnaufer et al, 2005). Therefore, the robustness of the metabolic network in response to varying the flux of mitochondrial F0F1-ATP synthase-catalyzed reaction was investigated. Figure 5 depicts the normalized growth rate response to changing the flux through mitochondrial F0F1-ATP synthase reaction. At different ATP synthase flux values, a single optimization was run and growth rate (i.e. the flux through the biomass reaction) was measured. The graph illustrates that growth rate is reduced to about 50% when the flux through ATP synthase is reduced to 8–9% of wild-type flux value. Reducing ATP synthase flux to 40% of its wild-type causes only a 3% change in growth rate. Robustness analysis provides an optimal flux value for a particular reaction that corresponds to the optimal growth rate. This is a proof-of-concept rather than a validation study of the in silico metabolic network. Particular enzymes with known inhibitors can be targeted to help reduce the proliferation of Leishmania spp. in mammalian hosts and subsequently slow the spread of infection.


Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major.

Chavali AK, Whittemore JD, Eddy JA, Williams KT, Papin JA - Mol. Syst. Biol. (2008)

F0F1-ATP synthase robustness analysis. The normalized growth rate corresponding to F0F1-ATP synthase flux is plotted. The white circles indicate single optimization simulations that are run at different ATP synthase flux values to measure flux through biomass (growth rate).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2290936&req=5

f5: F0F1-ATP synthase robustness analysis. The normalized growth rate corresponding to F0F1-ATP synthase flux is plotted. The white circles indicate single optimization simulations that are run at different ATP synthase flux values to measure flux through biomass (growth rate).
Mentions: The robustness of the metabolic network can be evaluated by constraining the flux of any reaction from its wild-type value to zero and calculating the resultant effect on the growth rate. As an example, oligomycin is an inhibitor of mitochondrial F0F1-ATP synthase (Schnaufer et al, 2005). Therefore, the robustness of the metabolic network in response to varying the flux of mitochondrial F0F1-ATP synthase-catalyzed reaction was investigated. Figure 5 depicts the normalized growth rate response to changing the flux through mitochondrial F0F1-ATP synthase reaction. At different ATP synthase flux values, a single optimization was run and growth rate (i.e. the flux through the biomass reaction) was measured. The graph illustrates that growth rate is reduced to about 50% when the flux through ATP synthase is reduced to 8–9% of wild-type flux value. Reducing ATP synthase flux to 40% of its wild-type causes only a 3% change in growth rate. Robustness analysis provides an optimal flux value for a particular reaction that corresponds to the optimal growth rate. This is a proof-of-concept rather than a validation study of the in silico metabolic network. Particular enzymes with known inhibitors can be targeted to help reduce the proliferation of Leishmania spp. in mammalian hosts and subsequently slow the spread of infection.

Bottom Line: Using a systems-based approach, we hypothesized a comprehensive set of lethal single and double gene deletions, some of which were validated using published data with approximately 70% accuracy.Additionally, we generated hypothetical annotations to dozens of previously uncharacterized genes in the L. major genome and proposed a minimal medium for growth.It can serve as a tool for clarifying discrepancies between data sources, generating hypotheses that can be experimentally validated and identifying ideal therapeutic targets.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.

ABSTRACT
Systems analyses have facilitated the characterization of metabolic networks of several organisms. We have reconstructed the metabolic network of Leishmania major, a poorly characterized organism that causes cutaneous leishmaniasis in mammalian hosts. This network reconstruction accounts for 560 genes, 1112 reactions, 1101 metabolites and 8 unique subcellular localizations. Using a systems-based approach, we hypothesized a comprehensive set of lethal single and double gene deletions, some of which were validated using published data with approximately 70% accuracy. Additionally, we generated hypothetical annotations to dozens of previously uncharacterized genes in the L. major genome and proposed a minimal medium for growth. We further demonstrated the utility of a network reconstruction with two proof-of-concept examples that yielded insight into robustness of the network in the presence of enzymatic inhibitors and delineation of promastigote/amastigote stage-specific metabolism. This reconstruction and the associated network analyses of L. major is the first of its kind for a protozoan. It can serve as a tool for clarifying discrepancies between data sources, generating hypotheses that can be experimentally validated and identifying ideal therapeutic targets.

Show MeSH
Related in: MedlinePlus