Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks

Abstract

Novel antimalarial therapies are urgently needed for the fight against drug-resistant parasites. The metabolism of malaria parasites in infected cells is an attractive source of drug targets but is rather complex. Computational methods can handle this complexity and allow integrative analyses of cell metabolism. In this study, we present a genome-scale metabolic model (iPfa) of the deadliest malaria parasite, Plasmodium falciparum, and its thermodynamics-based flux analysis (TFA). Using previous absolute concentration data of the intraerythrocytic parasite, we applied TFA to iPfa and predicted up to 63 essential genes and 26 essential pairs of genes. Of the 63 genes, 35 have been experimentally validated and reported in the literature, and 28 have not been experimentally tested and include previously hypothesized or novel predictions of essential metabolic capabilities. Without metabolomics data, four of the genes would have been incorrectly predicted to be non-essential. TFA also indicated that substrate channeling should exist in two metabolic pathways to ensure the thermodynamic feasibility of the flux. Finally, analysis of the metabolic capabilities of P. falciparum led to the identification of both the minimal nutritional requirements and the genes that can become indispensable upon substrate inaccessibility. This model provides novel insight into the metabolic needs and capabilities of the malaria parasite and highlights metabolites and pathways that should be measured and characterized to identify potential thermodynamic bottlenecks and substrate channeling. The hypotheses presented seek to guide experimental studies to facilitate a better understanding of the parasite metabolism and the identification of targets for more efficient intervention.

Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models

“…Pb = P. berghei , Pc = P. cynomolgi , Pf = P. falciparum , Pk = P. knowlesi , Pv = P. vivax . Longer names indicate previously published reconstructions (iPfal19, from Untaroiu et al (2019) and Carey, Papin, and Guler (2017), iPfa2017 from Chiappino-Pepe et al (2017), all others from Abdel-Haleem et al (2018)). (D): Prediction accuracy.…”

“…Semi-curated reconstructions recapitulate the biology of experimentally-facile parasites as well as published, manually-curated reconstructions. We tested accuracy of model predictions from the de novo reconstruction, the orthology-translated reconstruction, and the final semi-curated reconstruction for P. bergehi and compared these summary statistics to the prediction accuracy generated by our well-curated iPfal19 and other published reconstructions (Chiappino-Pepe et al 2017; Abdel-Haleem et al 2018; Tymoshenko et al 2015). This comparison was used to motivate our approach over de novo reconstruction building as our pipeline generates a reconstruction with greater predictive accuracy than de novo reconstruction and comparable to a well-curated reconstruction.…”

“…Lastly, all draft and semi-curated reconstructions were gapfilled using parsimonious flux balance analysis (pFBA)-based gapfilling (Biggs and Papin 2017; Medlock and Papin 2018) to complete biochemical requirements identified in the experimental literature ( Figure 2A , step 5 ) and to produce biomass (see the Supplemental Methods ). As a result, when compared to manually-curated parasite reconstructions (Carey, Papin, and Guler 2017; Abdel-Haleem et al 2018; Chiappino-Pepe et al 2017; Tymoshenko et al 2015), semi-curated reconstructions are larger in scope than de novo reconstructions and generate predictions with comparable accuracy ( Figure 2C-D ). These reconstructions are also more compliant with community standards (Lieven et al 2018; Carey et al 2019) than previous reconstructions for parasites (representative examples shown in Supplemental Material: Supplemental Documents ).…”

Large-scale kinetic metabolic models ofPseudomonas putidafor a consistent design of metabolic engineering strategies

“…Methods that use thermodynamics data such as the thermodynamics-based flux analysis TFA [35][36][37][38][39] allow us to integrate the metabolomics data together with the fluxomics data, to eliminate in silico designed biosynthetic pathways not obeying the second law of thermodynamics [40,41], to eliminate infeasible thermodynamic cycles [42][43][44], and to identify how far reactions operate from thermodynamic equilibrium [45,46]. Despite the fact that usefulness of thermodynamics has been demonstrated in many applications, only a few reconstructed GEMs are curated for this important property [45,[47][48][49][50].…”

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].…”