Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major

Arvind K Chavali¹, Jeffrey D Whittemore¹, James A Eddy¹, Kyle T Williams¹ & Jason A Papin¹

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Synopsis

Computational modeling and systems analysis techniques can facilitate the interrogation of biological systems on the genome scale. Using available genomic, proteomic and metabolomic data, models of biochemical networks with predictive capabilities can be constructed (Forst, 2006). Computational analysis of these reconstructed networks can give much insight into the biological system and yield a set of hypotheses that can be experimentally validated.

The metabolic networks of several organisms, including Escherichia coli, Helicobacter pylori, Saccharomyces cerevisiae and Homo sapiens have been reconstructed, and the reconstructions of many others are currently underway (Schilling et al, 2002; Forster et al, 2003; Reed et al, 2003, 2006a; Borodina and Nielsen, 2005; Duarte et al, 2007; Feist et al, 2007; Oberhardt et al, 2008). These network models have facilitated the reconciliation of discrepancies between heterogeneous data sets (Reed et al, 2006a). Such systematic compilation has enabled precise characterization of cellular networks and aided in efforts to refine genome annotation (Feist et al, 2006; Reed et al, 2006a, 2006b). The resultant in silico models have predicted growth rates and gene essentiality under different medium conditions and have characterized the use of alternative carbon sources by particular organisms (Schilling et al, 2002; Feist et al, 2006, 2007).

Here, we report the reconstruction of the metabolic network of the parasite Leishmania major Friedlin, the causative agent of cutaneous leishmaniasis. Closely related Leishmania spp. cause diffuse cutaneous, mucocutaneous and visceral forms of the disease. Overall, leishmaniasis has an annual incidence rate of two million cases and causes approximately 59 000 deaths worldwide each year (Davies et al, 2003). This reconstruction and the associated network analyses represent the first constraint-based model for a protozoan. Among unicellular eukaryotes, Leishmania spp. are unique in their capacity for polycistronic transcription (Campbell et al, 2003), the presence of glycosome, acidocalcisome and flagellar compartments (Hart and Opperdoes, 1984; Opperdoes and Michels, 1993; de Souza, 2002; Docampo et al, 2005), their use of trypanothione as an analog of glutathione (Fairlamb et al, 1985), the occurrence of novel fatty acid synthesis mechanisms (Lee et al, 2007) and the presence of kinetoplast DNA (Simpson and Da Silva, 1971; Stuart, 1983).

The metabolic network reconstruction presented here accounts for 560 genes, 1112 reactions and 1101 metabolites. The reconstruction is highly compartmentalized with eight unique subcellular localizations accounted for in the model. We identified 12% of single gene knockouts in the network as lethal and 10% as growth reducing, and proposed 56 non-trivial lethal double deletions. Approximately 83% of all lethal single knockout genes belonged to three metabolic processes: lipid, carbohydrate or amino-acid metabolism, highlighting how critical these are to general function. Each one of these deletions constitutes a promising drug target and an experimentally testable hypothesis.

We validated the metabolic network with experimental knockout data from related Leishmania and Trypanosoma species. Table I lists the complete set of lethal and non-lethal knockouts obtained from literature and the corresponding in silico predictions in four different medium conditions, namely: minimal medium; minimal medium and glucose; minimal medium, glucose and other amino acids; and an additional defined medium (see Merlen et al (1999); Schuster and Sullivan (2002)). The rows colored gray, yellow and white indicate agreement with literature, partial agreement with literature and disagreement between model predictions and experimental data, respectively. In silico predictions in minimal medium had the best overall compliance with experimental data (72.4%). Predictions in minimal medium–glucose, minimal medium–glucose–other amino acids and defined medium had overall compliances of 69.0, 69.0 and 65.5%, respectively.

Table 1: Validation of reaction knockout predictions

Full tableFigures & Tables index

Through the network reconstruction and associated analyses, we demonstrated systematic refinement of genome annotation. A total of 17 out of 25 new annotation refinements were proposed for 'hypothetical proteins.' The rest of the proposed annotations were for genes that had an incorrect annotation, localization or EC classification. In addition, we proposed a novel in silico minimal medium that supports growth of L. major and evaluated growth response to non-essential components in defined medium. The in silico medium consisted of arginine, cysteine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, hypoxanthine, phosphate and oxygen. The presence of hypoxanthine in the minimal medium highlighted the fact that L. major is unable to synthesize purines de novo (Berriman et al, 2005). If hypoxanthine is removed, AMP, dAMP, dGMP and GMP cannot be synthesized. Hypoxanthine, a purine derivative, fulfills the purine source requirement from the environment.

We provided two proof-of-concept examples yielding insight into the utility of network reconstruction and analysis. First, the robustness of the metabolic network was evaluated by varying the flux through mitochondrial F₀F₁-ATP synthase reaction (a target of oligomycin). By constraining the flux of the mitochondrial F₀F₁-ATP synthase reaction from its wild-type value to zero, the resulting effect on growth rate was calculated. Second, pathways differentially upregulated in the morphological stages of the parasite were identified by the use of protein expression data (see Figure 6). Differential protein expression data from Leishmania infantum suggested hexokinase was preferentially expressed in the amastigote stage, while alcohol dehydrogenase, enolase and ATP synthase were preferentially expressed in the promastigote stage (Leifso et al, 2007). Figure 6 depicts the reactions catalyzed by the differentially expressed enzymes. A red 'X' indicates that the flux through a particular reaction was reduced to 10% of its wild-type stage-independent value.

Figure 6

Reactions differentially expressed in amastigote or promastigote stages. The metabolic reactions that distinguish the morphological stages of L. major are highlighted. These include reactions catalyzed by hexokinase, ATP synthase, alcohol dehydrogenase and enolase. The red 'X' indicates that the flux through each reaction is reduced to 10% of its wild-type stage-independent flux value to characterize morphological stage-specific metabolism.

Full figure and legend (1,815K)Figures & Tables index

Finally, as an example of the model refinement process, we identified four gaps in the methionine salvage cycle and provided functional annotation to previously uncharacterized genes. Consequently, we improved our compliance of knockout predictions with experimental data from 51.7 to 65.5% agreement under defined medium conditions. The genome-scale metabolic reconstruction of L. major presented here provides a framework for the interrogation of human pathogens and a platform for integration of high-throughput data and generation of experimental hypotheses. These types of novel network analyses may be relevant to treating infectious diseases, particularly when diseases like leishmaniasis are considered emerging and uncontrolled, necessitating improvements in therapeutics and treatment strategies (Remme et al, 2002).

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