PT - JOURNAL ARTICLE AU - Maureen A. Carey AU - Jason A. Papin AU - Jennifer L. Guler TI - Novel <em>Plasmodium falciparum</em> metabolic network reconstruction identifies shifts associated with clinical antimalarial resistance AID - 10.1101/119941 DP - 2017 Jan 01 TA - bioRxiv PG - 119941 4099 - http://biorxiv.org/content/early/2017/03/23/119941.short 4100 - http://biorxiv.org/content/early/2017/03/23/119941.full AB - BACKGROUND Malaria 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.RESULTS Here, 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, and others within the mitochondria. Our findings are consistent with experimental literature, while generating novel hypotheses about artemisinin resistance and parasite biology. We detect evidence that resistance parasites maintain greater metabolic flexibility, perhaps representing an incomplete transition to the metabolic state most appropriate for nutrient-rich blood.CONCLUSION Using 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.MADEMetabolic Adjustment for Differential Expression algorithmSHMTserine hydroxymethyltransferaseTCAtricarboxylic acidFVAcycle Flux variability analysisFBAFlux balance analysis