Fig 1.
The workflow for manually curated, data-driven multi-species reconstructions of a representative fraction of the plasmodium genus.
Step 1. The Plasmodium falciparum reconstruction was built using the genome annotation, lists of biomolecules, the literature, and organism-specific databases. The reconstruction was refined through iterations of manual curation, hypothesis generation, validation against experimental data and incorporation of new knowledge[15]. Step 2. The reconstruction was converted into a model by specifying inputs, outputs and relevant parameters, and by representing the network mathematically. The model was validated against omic and physiological data. Step 3(i). Once networks were accurately reconstructed and converted into stage-specific in silico models, using high throughput data, the stage-specific models were then used to predict genes critical for growth in different life-cycle stages and to detect stage-specific redirection of flux in the parasite’s central carbon metabolism. Step 3(ii). Comparative genomics and manual curation were used to build reconstructions for Plasmodium species commonly used in experimental animal models to test how predicted drug targets differ from predictions in the human-infecting system. Multi-species metabolic models were also used to characterize pan and core metabolic capacities of the different species.
Fig 2.
Content description and performance evaluation of iAM-Pf480 (P. falciparum).
(A) Reaction and gene content of iAM-Pf480 (see details in Table A in S1 Text). (b) Comparison of iAM-Pf480 gene essentiality predictions (simulating standard in vitro growth conditions, Table B and C in S1 Tables) showed 95% and 71% accuracy when compared to single gene deletion and drug inhibition experiments, respectively. In silico gene essentiality was graded according to the percentage of reduction in growth rate compared to wild type. Fisher exact test as well as Mathew correlation coefficient (MCC) were used to compute significance of overlapping consistent predictions for iAM-Pf480. (C-D) Validation of iAM-Pf480 predicted glycolytic flux rates against experimentally measured fluxomic data[20] in (c) wild-type and (d) PDH-knock out P. falciparum while simulating standard in vitro growth conditions. Flux rates are in nmol/ 1x108 cells/min.
Fig 3.
Life cycle stage specific models of P. falciparum predict gene targets that are essential for asexual, sexual and mosquito stages.
(a) The core and pan metabolic content of the genome-scale life cycle stage specific models of P. falciparum are 720 and 1002 reactions, respectively. (b-c) Parameters and AUC plots for performance evaluation of stage-specific pairwise differential gene expression comparisons following a similar approach to Nam, et al.[52] (S2 Fig). AUROC: area under the receiver operator curve (ROC), DEG: differential expression analysis, T: trophozoite, GII: early gametocyte stage, GV: late gametocyte stage, Ook: ookinete.
Fig 4.
Stage-specific central metabolic flux patterns in malaria.
(a) Correlated reaction sets for iAM-Pf480 were used to define stage and model specific pathways, which were analyzed and compared across different stages. Modularity indices (see methods) were 0.023, 0.024, 0.026, 0.145, 0.022 for the T, Schizont, GII, GV, and Ook stages, respectively. In proliferative versus non-proliferative stages of malaria, there were changes in the patterns of central carbon metabolism, notably the non-oxidative PPP and glycolysis. (b) The direction of flux in the non-oxidative branch of PPP goes towards production of glycolytic intermediates in the Trophozoite, Schizont, GII, and GV stages but not the Ookinete stage. Reversal of non-oxidative pentose phosphate pathway fluxes in the Ook enables provision of ribose 5 phosphate (r5p) needed for the synthesis of nucleotide precursors of DNA. The non-oxidative branch in the schizont is colored in red indicating its coupling to growth rate in this stage (Table G in S1 Tables). Both the oxidative and non-oxidative PPP branches were correlated in GII. Glycolysis was split into upper and lower branches in all stages except GV where the non-oxidative PPP branch was correlated with inositol metabolism. Arrows are omitted from the schizont pathway map to account for reduced flux values relative the other 4 stages. (c) Predicted sampled flux distribution are shown in the non-oxidative branch of PPP (Transketolase; TKT1) and inositol metabolism (myo-inositol-3-phosphate lyase; MI3PS) across all the stages showing increased involvement of inositol metabolism in the GV stage (see supplementary material for discussion and S1 Fig for the high resolution version of the figure).
Fig 5.
Stage-specific essentiality predictions of experimentally validated druggable targets and single-gene deletion experiments.
Comprehensive map of experimentally tested treatment targets for P. falciparum with stage-specific model predictions projected (in color) projected on top of the map. Colored reaction pathways correspond to drug inhibition studies (Table C in S1 Tables) and colored reaction names in rectangles correspond to single gene deletion experiments (Table B in S1 Tables). The color legend inset corresponds to iAM-Pf480 predictions. Validated drug targets that are also predictive to reduce growth in proliferative as well as late gametocyte stages are of particular interest. This comprehensive assessment of the model and experimental results enables stratification of existing drugs, new drugs to target, as well as new areas of metabolism warranting further investigation. See S2 Fig for the high resolution version of the figure.
Fig 6.
Species-specific models provide mechanistic explanation for differences in drug response between human- and rodent-infecting malaria species.
(a) The core and pan metabolic content of 5 malaria species was identified based on the respective species-specific reconstructions. The core content, illustrated by the intersection of the Venn diagram, is shared by all species. The pan content represents the union of the content across all of the multi-species reconstructions. (b) 14 metabolic reactions differed in their presence across the 5 reconstructed Plasmodium species. (c) Thiamine pyrophosphokinase (TPK) and (d) Choline kinase (CK) were predicted by the models to be essential for the growth of the rodent-infecting species (P. berghei) while their deletion had no effect on the growth of human and non-human primate species. Differential essentiality of TPK is due to absence of phosphomethylpyrimidine kinase and thiamine-phosphate pyrophosphorylase the rodent-infecting species. In the case of CK, the differential essentiality is due to the absence of phosphoethanolamine N-methyltransferase. (See Table A in S1 Tables for reactions abbreviations and gene-protein-reaction associations). (e) Pantothenate metabolism showed differences in essentiality between stage- and species-specific models. Tables indicate percentage in growth reduction compared to the WT upon deletion of the respective gene. ‘X’ indicates absence of a reaction from the respective reconstruction, ‘—‘ indicates no effect on growth upon deletion of the corresponding reaction and ‘%’ indicates the growth reduction percentage resulting from deletion of the corresponding gene. T: trophozoite, GII: early gametocyte stage, GV: late gametocyte stage, Ook: ookinete. See S3 Fig for the high resolution version of the figure.