A systematic reconstruction and constraint-based analysis of Leishmania donovani metabolic network: identification of potential antileishmanial drug targets

Abstract

Visceral leishmaniasis, a lethal parasitic disease, is caused by the protozoan parasite Leishmania donovani. The absence of an effective vaccine, drug toxicity and parasite resistance necessitates the identification of novel drug targets. Reconstruction of genome-scale metabolic models and their simulation has been established as an important tool for systems-level understanding of a microorganism's metabolism. In this work, amalgamating the tools and techniques of computational systems biology with rigorous manual curation, a constraint-based metabolic model for Leishmania donovani BPK282A1 has been developed. New functional annotations for 18 formerly hypothetical or erroneously annotated genes (encountered during iterative refinement of the model) have been proposed. Further, to formulate an accurate biomass objective function, experimental determination of previously uncharacterized biomass constituents was performed. The developed model is a highly compartmentalized metabolic model, comprising 1159 reactions, 1135 metabolites and 604 genes. The model exhibited around 76% accuracy for the prediction of experimental phenotypes of gene knockout studies and drug inhibition assays. Employing in silico gene knockout studies, we identified 28 essential genes with negligible sequence identity to the human proteins. Moreover, by dissecting the functional interdependencies of metabolic pathways, 70 synthetic lethal pairs were identified. Finally, in order to delineate stage-specific metabolism, gene-expression data of the amastigote stage residing in human macrophages were integrated into the model. By comparing the flux distribution, we illustrated the stage-specific differences in metabolism and environmental conditions that are in good agreement with the experimental findings. The developed model can serve as a highly enriched knowledgebase of legacy data and an important tool for generating experimentally verifiable hypotheses.

Genome‐wide characterization of Phytophthora infestans metabolism: a systems biology approach

“…The size of our P. infestans model, in terms of integrated reactions and genes, is on the same order of magnitude as that of a recent GEM of Phaeodactylum tricornutum , a closely related diatom (Levering et al ., ), although our model involves more metabolites. The sizes of the GEMs of the malaria parasite Plasmodium falciparum (Plata et al ., ) and the leishmaniasis parasite Leishmania donovani (Sharma et al ., ) are much smaller, but the proportion of genes in the model is similar to that of the P. infestans model (∼7% of the total number of genes). Although these numbers might be smaller because of the genome annotation quality and the level of model curation, they might also be a result of the loss of primary metabolic pathways, for which these parasites rely on nutrient import from their hosts (Dean et al ., ; Gardner et al ., ).…”

“…There are numerous examples of the application of this method to suggest drug targets in pathogens (Hartman et al ., ; Kaltdorf et al ., ; Plata et al ., ; Sharma et al ., ; Yizhak et al ., ), thus suggesting that the identified enzymes in P. infestans represent interesting candidates for further study. To confirm that these enzymes are essential for viability, ideally the encoding genes must be deleted or targeted by site‐directed mutagenesis.…”

“…Such models have also already been applied to understand the metabolic interactions between pathogens and hosts (Duan et al ., ; Huthmacher et al ., ; Peyraud et al ., ). This can provide new hypotheses about a pathogen's infection strategy and may suggest novel control targets (Chavali et al ., ; Sharma et al ., ).…”

Efflux pumps and antimicrobial resistance: Paradoxical components in systems genomics

“…Application of GEM has already been applied to map the effect of chloroquine in Plasmodium falciparum (Tewari et al, 2017) for predicting dose-dependent inhibition of DNA replication for both drug-sensitive and drug-resistant P. falciparum strains. In silico gene knockout in GEMs of Leishmania infantum and Leishmania donovani (Sharma et al, 2017;Subramanian and Sarkar, 2017) has predicted Trypanothione reductase (TryR) and 28 other genes to be essential with negligible sequence identity to the human proteins. Further, comparison of stage specific flux distribution between the promastigote and amastigote stages has illustrated the differences in metabolism and environmental conditions.…”

RAVEN 2.0: a versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor

“…In addition to facilitating the analysis and visualization of existing GEMs, RAVEN particularly aimed to assist semi-automated draft model reconstruction, utilizing existing template GEMs and the KEGG database [10]. Since publication, RAVEN has been used in GEMs reconstruction for a wide variety of organisms, ranging from bacteria [11], archaea [12] to human gut microbiome [13], eukaryotic microalgae [14], parasites [15][16][17], and fungi [18], as well as various human tissues [19,20] and generic mammalians models with complex metabolism [21,22]. As such, the RAVEN toolbox has functioned as one of the two major MATLABbased packages for constraint-based metabolic modelling, together with the COBRA Toolbox [23][24][25].…”