Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks.
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.
Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland.
Anush
A
Chiappino-Pepe
Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland.|Department of Microbiology and Molecular Medicine, Faculty of Medicine, University of Geneva, CMU, Geneva, Switzerland.
Stepan
0000-0002-7364-7054
S
Tymoshenko
Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland.
Meriç
M
Ataman
Department of Microbiology and Molecular Medicine, Faculty of Medicine, University of Geneva, CMU, Geneva, Switzerland.
Dominique
0000-0003-4156-2109
D
Soldati-Favre
Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland.
Vassily
0000-0001-6432-4694
V
Hatzimanikatis
D003198:Computer Simulation; D004734:Energy Metabolism / Q000502:physiology*; D020043:Genes, Essential / Q000502:physiology*; D064688:Metabolic Flux Analysis / Q000379:methods; D055442:Metabolome / Q000502:physiology; D008954:Models, Biological; D009751:Nutritional Requirements / Q000502:physiology*; D010963:Plasmodium falciparum / Q000502:physiology*; D015800:Protozoan Proteins / Q000378:metabolism*; D013816:Thermodynamics
D015800:Protozoan Proteins