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.
Highlights
Malaria remains a major global health care concern, with almost half of the world population at risk of infection that results in over half a million deaths each year [1]
We identify metabolites that give rise to thermodynamic bottlenecks and suggest substrate channeling. These results provide novel insight into the metabolism of P. falciparum and may guide experimental studies to develop a better characterization of the parasite metabolism and the identification of antimalarial drug targets
We developed a Genome-scale metabolic models (GEMs) of P. falciparum and performed thermodynamically consistent studies using thermodynamics-based flux analysis (TFA) and integrating the metabolite concentration ranges previously measured in intraerythrocytic P. falciparum [19,20,21,22]
Summary
Malaria remains a major global health care concern, with almost half of the world population at risk of infection that results in over half a million deaths each year [1]. Of the five Plasmodium species capable of infecting humans, P. falciparum is responsible for most malaria-related deaths. The development of more efficient antimalarial treatments is, a highly pressing need. Because it is essential for cell development, metabolism represents a potential source for identifying novel targets. Computational methods can handle its complexity and facilitate the discovery of drug targets (as demonstrated for other pathogens [2, 3]) that are interesting for malaria research
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