Skip to main content

Determining the Mode of Action of Antimalarial Drugs Using Time-Resolved LC-MS-Based Metabolite Profiling

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1859))

Abstract

Methods for assessing the mode of action of new antimalarial compounds identified in high throughput phenotypic screens are needed to triage and facilitate lead compound development and to anticipate potential resistance mechanisms that might emerge. Here we describe a mass spectrometry-based approach for detecting metabolic changes in asexual erythrocytic stages of Plasmodium falciparum induced by antimalarial compounds. Time-resolved or concentration-resolved measurements are used to discriminate between putative targets of the compound and nonspecific and/or downstream secondary metabolic effects. These protocols can also be coupled with 13C-stable-isotope tracing experiments under nonequilibrative (or nonstationary) conditions to measure metabolic dynamics following drug exposure. Time-resolved 13C-labeling studies greatly increase confidence in target assignment and provide a more comprehensive understanding of the metabolic perturbations induced by small molecule inhibitors. The protocol provides details on the experimental design, Plasmodium falciparum culture, sample preparation, analytical approaches, and data analysis used in either targeted (pathway focused) or untargeted (all detected metabolites) analysis of drug-induced metabolic perturbations.

This is a preview of subscription content, log in via an institution.

Buying options

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Gamo FJ et al (2010) Thousands of chemical starting points for antimalarial lead identification. Nature 465(7296):305–310

    Article  CAS  Google Scholar 

  2. Guiguemde WA et al (2010) Chemical genetics of Plasmodium falciparum. Nature 465(7296):311–315

    Article  CAS  Google Scholar 

  3. Meister S et al (2011) Imaging of Plasmodium liver stages to drive next-generation antimalarial drug discovery. Science 334(6061):1372–1377

    Article  CAS  Google Scholar 

  4. Spangenberg T et al (2013) The open access malaria box: a drug discovery catalyst for neglected diseases. PLoS One 8(6):e62906

    Article  CAS  Google Scholar 

  5. Van Voorhis WC et al (2016) Open source drug discovery with the malaria box compound collection for neglected diseases and beyond. PLoS Pathog 12(7):e1005763

    Article  Google Scholar 

  6. Herman JD et al (2014) A genomic and evolutionary approach reveals non-genetic drug resistance in malaria. Genome Biol 15(11):511

    Article  Google Scholar 

  7. McNamara CW et al (2013) Targeting Plasmodium PI(4)K to eliminate malaria. Nature 504(7479):248–253

    Article  CAS  Google Scholar 

  8. Rottmann M et al (2010) Spiroindolones, a potent compound class for the treatment of malaria. Science 329(5996):1175–1180

    Article  CAS  Google Scholar 

  9. Spillman NJ et al (2013) Na(+) regulation in the malaria parasite Plasmodium falciparum involves the cation ATPase PfATP4 and is a target of the spiroindolone antimalarials. Cell Host Microbe 13(2):227–237

    Article  CAS  Google Scholar 

  10. Lehane AM et al (2014) Diverse chemotypes disrupt ion homeostasis in the malaria parasite. Mol Microbiol 94(2):327–339

    Article  CAS  Google Scholar 

  11. Pretzel J et al (2016) Characterization and redox regulation of Plasmodium falciparum methionine adenosyltransferase. J Biochem 160(6):355–367

    Article  CAS  Google Scholar 

  12. Wu W et al (2015) A chemical rescue screen identifies a Plasmodium falciparum apicoplast inhibitor targeting MEP isoprenoid precursor biosynthesis. Antimicrob Agents Chemother 59(1):356–364

    Article  CAS  Google Scholar 

  13. Ferone R, Burchall JJ, Hitchings GH (1969) Plasmodium berghei dihydrofolate reductase. Isolation, properties, and inhibition by antifolates. Mol Pharmacol 5(1):49–59

    CAS  PubMed  Google Scholar 

  14. Foote SJ, Galatis D, Cowman AF (1990) Amino acids in the dihydrofolate reductase-thymidylate synthase gene of Plasmodium falciparum involved in cycloguanil resistance differ from those involved in pyrimethamine resistance. Proc Natl Acad Sci U S A 87(8):3014–3017

    Article  CAS  Google Scholar 

  15. Joet T et al (2003) Validation of the hexose transporter of Plasmodium falciparum as a novel drug target. Proc Natl Acad Sci U S A 100(13):7476–7479

    Article  CAS  Google Scholar 

  16. Biagini GA et al (2012) Generation of quinolone antimalarials targeting the Plasmodium falciparum mitochondrial respiratory chain for the treatment and prophylaxis of malaria. Proc Natl Acad Sci U S A 109(21):8298–8303

    Article  CAS  Google Scholar 

  17. van Brummelen AC et al (2009) Co-inhibition of Plasmodium falciparum S-adenosylmethionine decarboxylase/ornithine decarboxylase reveals perturbation-specific compensatory mechanisms by transcriptome, proteome, and metabolome analyses. J Biol Chem 284(7):4635–4646

    Article  Google Scholar 

  18. Zhang B et al (2011) A second target of the antimalarial and antibacterial agent fosmidomycin revealed by cellular metabolic profiling. Biochemistry 50(17):3570–3577

    Article  CAS  Google Scholar 

  19. Cobbold SA et al (2016) Metabolic dysregulation induced in Plasmodium falciparum by dihydroartemisinin and other front-line antimalarial drugs. J Infect Dis 213(2):276–286

    Article  CAS  Google Scholar 

  20. Allman EL et al (2016) Metabolomic profiling of the malaria box reveals antimalarial target pathways. Antimicrob Agents Chemother 60(11):6635–6649

    Article  CAS  Google Scholar 

  21. Creek DJ et al (2016) Metabolomics-based screening of the malaria box reveals both novel and established mechanisms of action. Antimicrob Agents Chemother 60(11):6650–6663

    Article  CAS  Google Scholar 

  22. Dickerman BK et al (2016) Identification of inhibitors that dually target the new permeability pathway and dihydroorotate dehydrogenase in the blood stage of Plasmodium falciparum. Sci Rep 6:37502

    Article  CAS  Google Scholar 

  23. Hapuarachchi SV et al (2017) The malaria parasite’s lactate transporter PfFNT is the target of antiplasmodial compounds identified in whole cell phenotypic screens. PLoS Pathog 13(2):e1006180

    Article  Google Scholar 

  24. Lambros C, Vanderberg JP (1979) Synchronization of Plasmodium falciparum erythrocytic stages in culture. J Parasitol 65(3):418–420

    Article  CAS  Google Scholar 

  25. Clasquin MF, Melamud E, Rabinowitz JD (2012) LC-MS data processing with MAVEN: a metabolomic analysis and visualization engine. Curr Protoc Bioinformatics 14:11

    PubMed  Google Scholar 

  26. Xia J, Wishart DS (2016) Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. Curr Protoc Bioinformatics 55:14-10-1–14-10-91

    Article  Google Scholar 

  27. Yuan J, Bennett BD, Rabinowitz JD (2008) Kinetic flux profiling for quantitation of cellular metabolic fluxes. Nat Protoc 3(8):1328–1340

    Article  CAS  Google Scholar 

  28. Gowda H et al (2014) Interactive XCMS online: simplifying advanced metabolomic data processing and subsequent statistical analyses. Anal Chem 86(14):6931–6939

    Article  CAS  Google Scholar 

  29. Li S et al (2013) Predicting network activity from high throughput metabolomics. PLoS Comput Biol 9(7):e1003123

    Article  CAS  Google Scholar 

  30. Fuhrer T et al (2017) Genomewide landscape of gene-metabolome associations in Escherichia coli. Mol Syst Biol 13(1):907

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by grants from the Australian National Health and Medical Research Council (NHMRC). M.J.M. is a NHMRC Principal Research Fellow, and S.A.C. is a University of Melbourne Early Career Research Fellow.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Malcolm J. McConville .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Cobbold, S.A., McConville, M.J. (2019). Determining the Mode of Action of Antimalarial Drugs Using Time-Resolved LC-MS-Based Metabolite Profiling. In: Baidoo, E. (eds) Microbial Metabolomics. Methods in Molecular Biology, vol 1859. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8757-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-8757-3_12

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8756-6

  • Online ISBN: 978-1-4939-8757-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics