Advertisement

Profiling of Yeast Lipids by Shotgun Lipidomics

  • Christian KloseEmail author
  • Kirill TarasovEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1361)

Abstract

Lipidomics is a rapidly growing technology for identification and quantification of a variety of cellular lipid molecules. Following the successful development and application of functional genomic technologies in yeast Saccharomyces cerevisiae, we witness a recent expansion of lipidomics applications in this model organism. The applications include detailed characterization of the yeast lipidome as well as screening for perturbed lipid phenotypes across hundreds of yeast gene deletion mutants. In this chapter, we describe sample handling, mass spectrometry, and bioinformatics methods developed for yeast lipidomics studies.

Key words

Yeast Lipidomics Mass spectrometry Lipids 

Notes

Acknowledgements

The authors would like to thank Kai Simons for critically reading the manuscript. C.K. acknowledges fruitful discussions with Julio L. Sampaio and Michal A. Surma.

References

  1. 1.
    Breslow DK, Collins SR, Bodenmiller B et al (2010) Orm family proteins mediate sphingolipid homeostasis. Nature 463(7284):1048–1053PubMedCentralCrossRefPubMedGoogle Scholar
  2. 2.
    De Smet CH, Vittone E, Scherer M et al (2012) The yeast acyltransferase Sct1p regulates fatty acid desaturation by competing with the desaturase Ole1p. Mol Biol Cell 23(7):1146–1156PubMedCentralCrossRefPubMedGoogle Scholar
  3. 3.
    Guan XL, Souza CM, Pichler H et al (2009) Functional interactions between sphingolipids and sterols in biological membranes regulating cell physiology. Mol Biol Cell 20(7):2083–2095PubMedCentralCrossRefPubMedGoogle Scholar
  4. 4.
    Kohlwein SD (2010) Triacylglycerol homeostasis: insights from yeast. J Biol Chem 285(21):15663–15667PubMedCentralCrossRefPubMedGoogle Scholar
  5. 5.
    Kurat CF, Wolinski H, Petschnigg J et al (2009) Cdk1/Cdc28-dependent activation of the major triacylglycerol lipase Tgl4 in yeast links lipolysis to cell-cycle progression. Mol Cell 33(1):53–63CrossRefPubMedGoogle Scholar
  6. 6.
    Surma MA, Klose C, Peng D et al (2013) A lipid E-MAP identifies Ubx2 as a critical regulator of lipid saturation and lipid bilayer stress. Mol Cell 51(4):519–530PubMedCentralCrossRefPubMedGoogle Scholar
  7. 7.
    Daum G, Lees ND, Bard M et al (1998) Biochemistry, cell biology and molecular biology of lipids of Saccharomyces cerevisiae. Yeast 14(16):1471–1510CrossRefPubMedGoogle Scholar
  8. 8.
    Han X, Yang K, Gross RW (2012) Multi-dimensional mass spectrometry-based shotgun lipidomics and novel strategies for lipidomic analyses. Mass Spectrom Rev 31(1):134–178PubMedCentralCrossRefPubMedGoogle Scholar
  9. 9.
    Ejsing CS, Sampaio JL, Surendranath V et al (2009) Global analysis of the yeast lipidome by quantitative shotgun mass spectrometry. Proc Natl Acad Sci U S A 106(7):2136–2141PubMedCentralCrossRefPubMedGoogle Scholar
  10. 10.
    Klemm RW, Ejsing CS, Surma MA et al (2009) Segregation of sphingolipids and sterols during formation of secretory vesicles at the trans-Golgi network. J Cell Biol 185(4):601–612PubMedCentralCrossRefPubMedGoogle Scholar
  11. 11.
    Klose C, Surma MA, Gerl MJ et al (2012) Flexibility of a eukaryotic lipidome—insights from yeast lipidomics. PLoS One 7(4), e35063PubMedCentralCrossRefPubMedGoogle Scholar
  12. 12.
    Surma MA, Klose C, Klemm RW et al (2011) Generic sorting of raft lipids into secretory vesicles in yeast. Traffic 12(9):1139–1147CrossRefPubMedGoogle Scholar
  13. 13.
    Tarasov K, Stefanko A, Casanovas A et al (2014) High-content screening of yeast mutant libraries by shotgun lipidomics. Mol Biosyst 10(6):1364–1376CrossRefPubMedGoogle Scholar
  14. 14.
    Shevchenko A, Simons K (2010) Lipidomics: coming to grips with lipid diversity. Nat Rev Mol Cell Biol 11(8):593–598CrossRefPubMedGoogle Scholar
  15. 15.
    Wang M, Han X (2014) Multidimensional mass spectrometry-based shotgun lipidomics. Methods Mol Biol 1198:203–220PubMedCentralCrossRefPubMedGoogle Scholar
  16. 16.
    Ejsing CS, Moehring T, Bahr U et al (2006) Collision-induced dissociation pathways of yeast sphingolipids and their molecular profiling in total lipid extracts: a study by quadrupole TOF and linear ion trap-orbitrap mass spectrometry. J Mass Spectrom 41(3):372–389CrossRefPubMedGoogle Scholar
  17. 17.
    Klose C, Ejsing CS, Garcia-Saez AJ et al (2010) Yeast lipids can phase-separate into micrometer-scale membrane domains. J Biol Chem 285(39):30224–30232PubMedCentralCrossRefPubMedGoogle Scholar
  18. 18.
    Sandhoff R, Brugger B, Jeckel D et al (1999) Determination of cholesterol at the low picomole level by nano-electrospray ionization tandem mass spectrometry. J Lipid Res 40(1):126–132PubMedGoogle Scholar
  19. 19.
    Liebisch G, Binder M, Schifferer R et al (2006) High throughput quantification of cholesterol and cholesteryl ester by electrospray ionization tandem mass spectrometry (ESI-MS/MS). Biochim Biophys Acta 1761(1):121–128CrossRefPubMedGoogle Scholar
  20. 20.
    Schuhmann K, Almeida R, Baumert M et al (2012) Shotgun lipidomics on a LTQ Orbitrap mass spectrometer by successive switching between acquisition polarity modes. J Mass Spectrom 47(1):96–104CrossRefPubMedGoogle Scholar
  21. 21.
    Fahy E, Subramaniam S, Brown HA et al (2005) A comprehensive classification system for lipids. J Lipid Res 46(5):839–861CrossRefPubMedGoogle Scholar
  22. 22.
    Foster JM, Moreno P, Fabregat A et al (2013) LipidHome: a database of theoretical lipids optimized for high throughput mass spectrometry lipidomics. PLoS One 8(5), e61951PubMedCentralCrossRefPubMedGoogle Scholar
  23. 23.
    Husen P, Tarasov K, Katafiasz M et al (2013) Analysis of lipid experiments (ALEX): a software framework for analysis of high-resolution shotgun lipidomics data. PLoS One 8(11), e79736PubMedCentralCrossRefPubMedGoogle Scholar
  24. 24.
    Herzog R, Schwudke D, Schuhmann K et al (2011) A novel informatics concept for high-throughput shotgun lipidomics based on the molecular fragmentation query language. Genome Biol 12(1):R8PubMedCentralCrossRefPubMedGoogle Scholar
  25. 25.
    Ejsing CS, Husen P, Tarasov K (2012) Lipid informatics: from a mass spectrum to interactomics. Lipidomics. Wiley, Weinheim, pp 147–174. doi: 10.1002/9783527655946.ch8 Google Scholar
  26. 26.
    Fahy E, Cotter D, Byrnes R et al (2007) Bioinformatics for lipidomics. Methods Enzymol 432:247–273CrossRefPubMedGoogle Scholar
  27. 27.
    Kamleh MA, Ebbels TM, Spagou K et al (2012) Optimizing the use of quality control samples for signal drift correction in large-scale urine metabolic profiling studies. Anal Chem 84(6):2670–2677CrossRefPubMedGoogle Scholar
  28. 28.
    Wang SY, Kuo CH, Tseng YJ (2013) Batch Normalizer: a fast total abundance regression calibration method to simultaneously adjust batch and injection order effects in liquid chromatography/time-of-flight mass spectrometry-based metabolomics data and comparison with current calibration methods. Anal Chem 85(2):1037–1046CrossRefPubMedGoogle Scholar
  29. 29.
    Demšar J, Curk T, Erjavec A (2013) Orange: data mining toolbox in python. J Mach Learn Res 14:4Google Scholar
  30. 30.
    Bartlett GR (1959) Phosphorus assay in column chromatography. J Biol Chem 234(3):466–468PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Lipotype GmbHDresdenGermany
  2. 2.Department of Biochemistry and Molecular MedicineUniversité de MontréalMontréalCanada

Personalised recommendations