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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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Minimal medium prediction and effects of non-essential substrates in defined medium on growth. (A) Minimal medium and non-essential substrates in defined medium are delineated. The defined medium was obtained from Merlen et al (1999); Schuster and Sullivan (2002). (B) The effects of in silico minimal medium on production of biomass constituents are summarized. Gray- and red-colored boxes are indicative of particular biomass constituents that are and are not produced, respectively, when the corresponding substrate in the minimal medium is prevented from entering the metabolic system. The dotted ‘X' indicates biomass constituents that cannot be produced when oxygen and another substrate in minimal medium are restricted from entering the metabolic system. (C) The effects of non-essential substrates in defined medium on growth rate are shown. The vertical bars represent normalized growth rate when each substrate is removed from the environment. †See Supplementary Information VII for all metabolite abbreviations. ‡Non-essential substrates in defined medium belonging to the ‘other' category include a-D-glucose, b-D-glucose, ascb, asn-L, asp-L, btn, ca2, chsterol, co2, fol, gln-L, glu-L, gly, gthrd, gua, h, h2o, inost, nac, ncam, nh3, nh4, pnto-R, pydx, pydxn, rib-D, so4, thym, tyr-L and xan.
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f4: Minimal medium prediction and effects of non-essential substrates in defined medium on growth. (A) Minimal medium and non-essential substrates in defined medium are delineated. The defined medium was obtained from Merlen et al (1999); Schuster and Sullivan (2002). (B) The effects of in silico minimal medium on production of biomass constituents are summarized. Gray- and red-colored boxes are indicative of particular biomass constituents that are and are not produced, respectively, when the corresponding substrate in the minimal medium is prevented from entering the metabolic system. The dotted ‘X' indicates biomass constituents that cannot be produced when oxygen and another substrate in minimal medium are restricted from entering the metabolic system. (C) The effects of non-essential substrates in defined medium on growth rate are shown. The vertical bars represent normalized growth rate when each substrate is removed from the environment. †See Supplementary Information VII for all metabolite abbreviations. ‡Non-essential substrates in defined medium belonging to the ‘other' category include a-D-glucose, b-D-glucose, ascb, asn-L, asp-L, btn, ca2, chsterol, co2, fol, gln-L, glu-L, gly, gthrd, gua, h, h2o, inost, nac, ncam, nh3, nh4, pnto-R, pydx, pydxn, rib-D, so4, thym, tyr-L and xan.

Mentions: An in silico minimal medium composed of arginine, cysteine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, hypoxanthine, phosphate and oxygen was predicted (see Figure 4A). In addition, Figure 4A delineates non-essential and essential substrates in the defined medium.


Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major.

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

Minimal medium prediction and effects of non-essential substrates in defined medium on growth. (A) Minimal medium and non-essential substrates in defined medium are delineated. The defined medium was obtained from Merlen et al (1999); Schuster and Sullivan (2002). (B) The effects of in silico minimal medium on production of biomass constituents are summarized. Gray- and red-colored boxes are indicative of particular biomass constituents that are and are not produced, respectively, when the corresponding substrate in the minimal medium is prevented from entering the metabolic system. The dotted ‘X' indicates biomass constituents that cannot be produced when oxygen and another substrate in minimal medium are restricted from entering the metabolic system. (C) The effects of non-essential substrates in defined medium on growth rate are shown. The vertical bars represent normalized growth rate when each substrate is removed from the environment. †See Supplementary Information VII for all metabolite abbreviations. ‡Non-essential substrates in defined medium belonging to the ‘other' category include a-D-glucose, b-D-glucose, ascb, asn-L, asp-L, btn, ca2, chsterol, co2, fol, gln-L, glu-L, gly, gthrd, gua, h, h2o, inost, nac, ncam, nh3, nh4, pnto-R, pydx, pydxn, rib-D, so4, thym, tyr-L and xan.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Minimal medium prediction and effects of non-essential substrates in defined medium on growth. (A) Minimal medium and non-essential substrates in defined medium are delineated. The defined medium was obtained from Merlen et al (1999); Schuster and Sullivan (2002). (B) The effects of in silico minimal medium on production of biomass constituents are summarized. Gray- and red-colored boxes are indicative of particular biomass constituents that are and are not produced, respectively, when the corresponding substrate in the minimal medium is prevented from entering the metabolic system. The dotted ‘X' indicates biomass constituents that cannot be produced when oxygen and another substrate in minimal medium are restricted from entering the metabolic system. (C) The effects of non-essential substrates in defined medium on growth rate are shown. The vertical bars represent normalized growth rate when each substrate is removed from the environment. †See Supplementary Information VII for all metabolite abbreviations. ‡Non-essential substrates in defined medium belonging to the ‘other' category include a-D-glucose, b-D-glucose, ascb, asn-L, asp-L, btn, ca2, chsterol, co2, fol, gln-L, glu-L, gly, gthrd, gua, h, h2o, inost, nac, ncam, nh3, nh4, pnto-R, pydx, pydxn, rib-D, so4, thym, tyr-L and xan.
Mentions: An in silico minimal medium composed of arginine, cysteine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, hypoxanthine, phosphate and oxygen was predicted (see Figure 4A). In addition, Figure 4A delineates non-essential and essential substrates in the defined medium.

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