Functional interrogation of Plasmodium genus metabolism identifies species- and stage-specific differences in nutrient essentiality and drug targeting
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Type
ArticleAuthors
Abdel-Haleem, Alyaa M.
Hefzi, Hooman
Mineta, Katsuhiko

Gao, Xin

Gojobori, Takashi

Palsson, Bernhard O.
Lewis, Nathan E.
Jamshidi, Neema
KAUST Department
Biological and Environmental Sciences and Engineering (BESE) DivisionBioscience Program
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
KAUST Grant Number
URF/1/1976-03Date
2018-01-04Permanent link to this record
http://hdl.handle.net/10754/626862
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Show full item recordAbstract
Several antimalarial drugs exist, but differences between life cycle stages among malaria species pose challenges for developing more effective therapies. To understand the diversity among stages and species, we reconstructed genome-scale models (GEMs) of metabolism for five life cycle stages and five species of Plasmodium spanning the blood, transmission, and mosquito stages. The stage-specific models of Plasmodium falciparum uncovered stage-dependent changes in central carbon metabolism and predicted potential targets that could affect several life cycle stages. The species-specific models further highlight differences between experimental animal models and the human-infecting species. Comparisons between human- and rodent-infecting species revealed differences in thiamine (vitamin B1), choline, and pantothenate (vitamin B5) metabolism. Thus, we show that genome-scale analysis of multiple stages and species of Plasmodium can prioritize potential drug targets that could be both anti-malarials and transmission blocking agents, in addition to guiding translation from non-human experimental disease models.Citation
Abdel-Haleem AM, Hefzi H, Mineta K, Gao X, Gojobori T, et al. (2018) Functional interrogation of Plasmodium genus metabolism identifies species- and stage-specific differences in nutrient essentiality and drug targeting. PLOS Computational Biology 14: e1005895. Available: http://dx.doi.org/10.1371/journal.pcbi.1005895.Sponsors
AMAH, XG, KM, and TG acknowledge funding from King Abdullah University of Science and Technology (KAUST) and the Office of Sponsored Research (OSR) under Award No. URF/1/1976-03. NEL acknowledges generous support from the Novo Nordisk Foundation provided to the Center for Biosustainability at the Technical University of Denmark and funding from NIH (R35 GM119850-01 and R01 AI090141-05). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Publisher
Public Library of Science (PLoS)Journal
PLOS Computational BiologyPubMed ID
29300748ae974a485f413a2113503eed53cd6c53
10.1371/journal.pcbi.1005895
Scopus Count
Except where otherwise noted, this item's license is described as This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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