Novel Plasmodium falciparum metabolic network reconstruction identifies shifts associated with clinical antimalarial resistance

Abstract

BackgroundMalaria remains a major public health burden and resistance has emerged to every antimalarial on the market, including the frontline drug, artemisinin. Our limited understanding of Plasmodium biology hinders the elucidation of resistance mechanisms. In this regard, systems biology approaches can facilitate the integration of existing experimental knowledge and further understanding of these mechanisms.ResultsHere, we developed a novel genome-scale metabolic network reconstruction, iPfal17, of the asexual blood-stage P. falciparum parasite to expand our understanding of metabolic changes that support resistance. We identified 11 metabolic tasks to evaluate iPfal17 performance. Flux balance analysis and simulation of gene knockouts and enzyme inhibition predict candidate drug targets unique to resistant parasites. Moreover, integration of clinical parasite transcriptomes into the iPfal17 reconstruction reveals patterns associated with antimalarial resistance. These results predict that artemisinin sensitive and resistant parasites differentially utilize scavenging and biosynthetic pathways for multiple essential metabolites, including folate and polyamines. Our findings are consistent with experimental literature, while generating novel hypotheses about artemisinin resistance and parasite biology. We detect evidence that resistant parasites maintain greater metabolic flexibility, perhaps representing an incomplete transition to the metabolic state most appropriate for nutrient-rich blood.ConclusionUsing this systems biology approach, we identify metabolic shifts that arise with or in support of the resistant phenotype. This perspective allows us to more productively analyze and interpret clinical expression data for the identification of candidate drug targets for the treatment of resistant parasites.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3905-1) contains supplementary material, which is available to authorized users.

Biochemical characterization and essentiality of fumarate hydratase

“…Genetic investigations on the role of TCA cycle enzymes in P. falciparum have revealed non-essentiality of all genes of the cycle except FH and malate-quinone oxidoreductase to asexual intra-erythrocytic stages (23). Recently, a metabolic network reconstruction of pathways in artemisinin resistant P. falciparum strains has identified FH reaction as uniquely essential to these parasites (24). Biochemical characterization of PfFH could throw light on unique features of the enzyme and also provide leads for the development of inhibitors.…”

Functional interrogation of Plasmodium genus metabolism identifies species- and stage-specific differences in nutrient essentiality and drug targeting

“…Under standard growth conditions, i AM-Pf480 correctly predicted 95% and 71% for the single gene knockouts and drug inhibition phenotypes, respectively ( Fig 2B , Table B and C in S1 Tables ). We also compared i AM-Pf480 gene essentiality predictions to i TH366[ 17 ], i Pfa[ 18 ] and i Pfal17[ 19 ], using our set of experimentally validated targets (Table B and C in S1 Tables ), which revealed that i AM-PF480 accounts for a larger scope of genomic content, a larger biochemical complement, and functionally outperformed previously published P . falciparum models (see supplementary material, Fig A and Table L in S1 Text , Table B and C in S1 Tables ).…”

Influential Parameters for the Analysis of Intracellular Parasite Metabolomics

“…This medium also contains high levels of other metabolites such as glutathione, hypoxanthine, glutamine, and many amino acids, which are correlated with phenol red and/or HEPES abundances. Third, we measured metabolites not expected to be produced or consumed by Plasmodium ( 2 ). For example, kynurenine is present in erythrocytes, derived from the amino acid l -tryptophan ( 29 , 30 ), and is not known to be involved in P. falciparum metabolism ( 31 ).…”

“…Omics approaches, such as genomics, transcriptomics, and proteomics, are widely used, but the limited annotation of the parasite’s genome makes these data sets challenging to interpret. One way to alleviate this lack of functional knowledge is to use network-based modeling to contextualize noisy or sparse data and facilitate the interpretation of complex data ( 2 ). Additionally, the measurement of direct mediators of the phenotype, such as signaling and biosynthetic metabolites, can improve the ability to characterize phenotypes mediated by proteins that are not yet annotated in the genome.…”