Initial Analysis of Lipid Metabolomic Profile Reveals Differential Expression Features in Myeloid Malignancies

Adriana Ramos de Oliveira¹, Ismael Dale Cotrim Guerreiro Da Silva², Edson G. Lo Turco², Helio Alves Martins Júnior², Maria de Lourdes L. Ferrari Chauffaille Chauffaille^1*

¹Department of Clinical and Experimental Oncology, Federal University of Sao Paulo, Sao Paulo, Brazil.
²Gynecologic Department, Federal University of Sao Paulo, Sao Paulo, Brazil.

DOI: 10.4236/jct.2015.615138                

Abstract

The purpose of this preliminary study was to determine the comparative lipid profile of blood plasma samples of healthy individuals and patients with Myeloproliferative Neoplasms. Methods: Untargeted Shotgun MS/MS Analysis was performed to evaluate plasma samples from 153 participants, being 90 of the Control Group, 43 Myeloproliferative Neoplasms (MPN), 11 Myelodysplastic Syndromes (MDS) and 9 Acute Myeloid Leukemias (AML). Lipids were extracted from plasma using the Bligh-Dyer protocol. Data were acquired using the AB-Sciex Analyst TF, processed using the AB-Sciex LipidView^TM and the web-based analytical pipeline MetaboAnalyst 2.0 (www.metaboanalyst.ca). Results: Untargeted analysis identified in negative and positive-modes a total of 658 features at 2 ppm resolution. PCA and PLS-DA analysis revealed clear discrimination among groups, in particular for AML patients. Main lipid groups differentially expressed were: Monoacylglycerols (MAG), Glucosylceramide E (GlcdE), Ethyl Esters (EE), Lysophosphatidic acid (LPA), Sulfoquinovosil diacylglycerols (SQDG), Monoglycerols (MG), Methyl Ethanolamines (ME), Lysophosphatidylcholines (LPC), Dimethyl Phosfatidyletanolamines (DMPE), Monometylphosphatidiletanolamines (MMPE), Ceramide-1-phosphate (CerP), Glicerophosphoglycerols (GP), Lysomonomethyl-Phosphatidylethanolamines (LMMPE), Phosphatidic Acids (PA), Ergosterols (ERG), Glycerophosphoserine (PS), Diacylglycerols (DAG), Hexocylceramides (HexCer) and Lanosterol (Lan). ROC Curve Analysis revealed Total LMMPE as the strongest discriminating marker between Controls from Patients. In addition, these lipids were also able to differentiate MDS and AML from NPM. Conclusions: The Myeloproliferative Neoplasms from the point of view of global plasma lipidomics are accompanied by several modifications. In particular, the Lysomonomethyl-Phosphatidylethanolamines (LMMPE) seems to play important differentiating roles among them.

Keywords

Lipids, Lipidomics, Myeloproliferative Neoplasms, Mass Spectrometry

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Vardiman, J.W., Thiele, J., Arber, D.A., Brunning, R.D., Borowitz, M.J., Porwit, A., Harris, N.L., Le Beau, M.M., Hellstrom-Lindberg, E., Tefferi, A. and Bloomfield, C.D. (2009) The 2008 Revision of the World Health Organization (WHO) Classification of Myeloid Neoplasms and Acute Leukemia: Rationale and Important Changes. Blood, 114, 937-951. http://dx.doi.org/10.1182/blood-2009-03-209262
[2]	National Cancer Institute (2013) National Cancer Institute SEER Cancer Statistics Review 1975-2010. National Cancer Institute, 1992-2010.
[3]	Dennis, E.A. (2009) Lipidomics Joins the Omics Evolution. Proceedings of the National Academy of Sciences of the United States of America, 106, 2089-2090. http://dx.doi.org/10.1073/pnas.0812636106
[4]	Han, X. and Gross, R.W. (2005) Shotgun Lipidomics: Electrospray Ionization Mass Spectrometric Analysis and Quantitation of Cellular Lipidomes Directly from Crude Extracts of Biological Samples. Mass Spectrometry Reviews, 24, 367-412. http://dx.doi.org/10.1002/mas.20023
[5]	Wenk, M.R. (2010) Lipidomics: New Tools and Applications. Cell, 143, 888-895. http://dx.doi.org/10.1016/j.cell.2010.11.033
[6]	Roberts, L.D., McCombie, G., Titman, C.M. and Griffin, J.L. (2008) A Matter of Fat: An Introduction to Lipidomic Profiling Methods. Journal of Chromatography B, 871, 174-181. http://dx.doi.org/10.1016/j.jchromb.2008.04.002
[7]	Wymann, M.P. and Schneiter, R. (2008) Lipid Signalling in Disease. Nature Reviews Molecular Cell Biology, 9, 162-176. http://dx.doi.org/10.1038/nrm2335
[8]	Murphy, R.C., Raetz, C.R.H., Reynolds, C.M. and Barkley, R.M. (2005) Mass Spectrometry Advances in Lipidomica: Collision-Induced Decomposition of Kdo2-Lipid A. Prostaglandins & Other Lipid Mediators, 77, 131-140. http://dx.doi.org/10.1016/j.prostaglandins.2004.09.004
[9]	Gross, R.W. and Han, X. (2011) Lipidomics at the Interface of Structure and Function in Systems Biology. Chemistry & Biology, 18, 284-291. http://dx.doi.org/10.1016/j.chembiol.2011.01.014
[10]	Karpova, M.A., Moshkovskii, S.A., Toropygin, I.Y. and Archakov, A.I. (2010) Cancer-Specific MALDI-TOF Profiles of Blood Serum and Plasma: Biological Meaning and Perspectives. Journal of Proteomics, 73, 537-551. http://dx.doi.org/10.1016/j.jprot.2009.09.011
[11]	Rübsaamen, K. (2010) Lipidomic Analysis of Circulating Human Blood Cells. Doctoral Thesis, University of Regensburg, Regensburg. http://epub.uni-regensburg.de/13246/1/Dissertation_Katharina_Ruebsaamen.pdf
[12]	Xia, J., Broadhurst, D.I., Wilson, M. and Wishart, D.S. (2013) Translational Biomarker Discovery in Clinical Metabolomics: An Introductory Tutorial. Metabolomics, 9, 280-299. http://dx.doi.org/10.1007/s11306-012-0482-9
[13]	Lahiri, S. and Futerman, A.H. (2007) The Metabolism and Function of Sphingolipids and Glycosphingolipids. Cellular and Molecular Life Sciences, 64, 2270-2284. http://dx.doi.org/10.1007/s00018-007-7076-0
[14]	Ryland, L.K., Fox, T.E., Liu, X., Loughran, T.P. and Kester, M. (2011) Dysregulation of Sphingolipid Metabolism in Cancer. Cancer Biology & Therapy, 11, 138-149. http://dx.doi.org/10.4161/cbt.11.2.14624
[15]	Lv, W. and Yang, T. (2012) Identification of Possible Biomarkers for Breast Cancer from Free Fatty Acid Profiles Determined by GC-MS and Multivariate Statistical Analysis. Clinical Biochemistry, 45, 127-133. http://dx.doi.org/10.1016/j.clinbiochem.2011.10.011