Gender, Contraceptives and Individual Metabolic Predisposition Shape a Healthy Plasma Lipidome

Scientific Reports

Volumn 6, Issue , 2016, Pages

  Sales, Susanne ^a   Graessler, Juergen ^b   Ciucci, Sara ^c,d   Al Atrib, Rania ^b   Vihervaara, Terhi ^e   Schuhmann, Kai ^a   Kauhanen, Dimple ^e   Sysi Aho, Marko ^b,f   Bornstein, Stefan R ^b,f   Bickle, Marc ^a   Cannistraci, Carlo V ^c   Ekroos, Kim ^e   Shevchenko, Andrej ^a  

^a MAX PLANCK INSTITUTE OF MOLECULAR CELL BIOLOGY AND GENETICS   (Germany)

^b DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

^c DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

^d LIPOTYPE GMBH   (Germany)

^e Zora Biosciences Oy   (Finland)

^f KING'S COLLEGE HOSPITAL NHS FOUNDATION TRUST   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CONTRACEPTIVE AGENT; LIPID; SEX HORMONE BINDING GLOBULIN;

BLOOD; DISEASE PREDISPOSITION; DYSLIPIDEMIA; FEMALE; HUMAN; LIPID METABOLISM; MALE; METABOLIC SYNDROME X; METABOLISM; METABOLOME; MULTIVARIATE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY; SEXUAL CHARACTERISTICS;

CONTRACEPTIVE AGENTS; DISEASE SUSCEPTIBILITY; DYSLIPIDEMIAS; FEMALE; HUMANS; LIPID METABOLISM; LIPIDS; MALE; METABOLIC SYNDROME; METABOLOME; MULTIVARIATE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS; SEX CHARACTERISTICS; SEX HORMONE-BINDING GLOBULIN;

EID: 84974828469     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep27710     Document Type: Article

References (48)

1. Quehenberger, O. & Dennis, E. A. The human plasma lipidome. N Engl J Med 365, 1812-1823 (2011).
2. Quehenberger, O. et al. Lipidomics reveals a remarkable diversity of lipids in human plasma. J Lipid Res 51, 3299-3305 (2010).
3. Pietilainen, K. H. et al. Acquired obesity is associated with changes in the serum lipidomic profile independent of genetic effects-a monozygotic twin study. PLoS ONE 2, e218 (2007).
4. Oresic, M. et al. Dysregulation of lipid and amino acid metabolism precedes islet autoimmunity in children who later progress to type 1 diabetes. The Journal of experimental medicine 205, 2975-2984 (2008).
5. Rhee, E. P. et al. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J Clin Invest 121, 1402-1411 (2011).
6. Tagami, S. et al. Ganglioside GM3 participates in the pathological conditions of insulin resistance. J Biol Chem 277, 3085-3092 (2002).
7. Graessler, J. et al. Top-down lipidomics reveals ether lipid deficiency in blood plasma of hypertensive patients. PLoS One 4, e6261 (2009).
8. Fernandez, C. et al. Plasma lipid composition and risk of developing cardiovascular disease. PLoS One 8, e71846 (2013).
9. Tarasov, K. et al. Molecular lipids identify cardiovascular risk and are efficiently lowered by simvastatin and PCSK9 deficiency. J Clin Endocrinol Metab 99, E45-52 (2014).
10. Han, X. et al. Metabolomics in early Alzheimer's disease: identification of altered plasma sphingolipidome using shotgun lipidomics. PloS one 6, e21643 (2011).
11. Wood, P. L. et al. Lipidomics reveals dysfunctional glycosynapses in schizophrenia and the G72/G30 transgenic mouse. Schizophr Res 159, 365-369 (2014).
12. Oresic, M. et al. Phospholipids and insulin resistance in psychosis: a lipidomics study of twin pairs discordant for schizophrenia. Genome Med 4, 1 (2012).
13. De Leon, H. et al. Systems Biology Research into Cardiovascular Disease: Contributions of Lipidomics-based Approaches to Biomarker Discovery. Curr Drug Discov Technol 12, 129-154 (2015).
14. Smilowitz, J. T. et al. Nutritional lipidomics: molecular metabolism, analytics, and diagnostics. Mol Nutr Food Res 57, 1319-1335 (2013).
15. Gräßler, J. et al. Differential effects of lipoprotein apheresis by lipidfiltration or dextran sulfate adsorption on lipidomic profile. Atherosclerosis Supplements 14, 151-155 (2013).
16. Graessler, J. et al. Lipidomic profiling before and after Roux-en-Y gastric bypass in obese patients with diabetes. Pharmacogenomics J 14, 201-207 (2014).
17. Hyotylainen, T. & Oresic, M. Optimizing the lipidomics workflow for clinical studies-practical considerations. Anal Bioanal Chem (2015).
18. Zhao, Y. Y., Cheng, X. L. & Lin, R. C. Lipidomics applications for discovering biomarkers of diseases in clinical chemistry. Int Rev Cell Mol Biol 313, 1-26 (2014).
19. Jungnickel, H. & Luch, A. A personalized life: biomarker monitoring from cradle to grave. Exs 101, 471-498 (2012).
20. Wang, C., Wang, M. & Han, X. Applications of mass spectrometry for cellular lipid analysis. Mol Biosyst 11, 698-713 (2015).
21. Cajka, T. & Fiehn, O. Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry. Trends Analyt Chem 61, 192-206 (2014).
22. Brugger, B. Lipidomics: analysis of the lipid composition of cells and subcellular organelles by electrospray ionization mass spectrometry. Annu Rev Biochem 83, 79-98 (2014).
23. Chua, E. C. et al. Extensive diversity in circadian regulation of plasma lipids and evidence for different circadian metabolic phenotypes in humans. Proc Natl Acad Sci USA 110, 14468-14473 (2013).
24. Begum, H. et al. Discovering and validating between-subject variations in plasma lipids in healthy subjects. Sci Rep 6, 19139 (2016).
25. Varlamov, O., Bethea, C. L. & Roberts, C. T. Jr. Sex-specific differences in lipid and glucose metabolism. Front Endocrinol (Lausanne) 5, 241 (2014).
26. Reddy Kilim, S. & Chandala, S. R. A comparative study of lipid profile and oestradiol in pre-and post-menopausal women. J Clin Diagn Res 7, 1596-1598 (2013).
27. Hyotylainen, T. & Oresic, M. Systems biology strategies to study lipidomes in health and disease. Prog Lipid Res 55, 43-60 (2014).
28. Cannistraci, C. V., Alanis-Lobato, G. & Ravasi, T. Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding. Bioinformatics 29, 199-209 (2013).
29. Cannistraci, C. V. et al. Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes. Bioinformatics 26, i531-i539 (2010).
30. Alanis-Lobato, G. et al. Highlighting nonlinear patterns in population genetics datasets. Sci Rep 5, 8140 (2015).
31. Pynn, C. J. et al. Specificity and rate of human and mouse liver and plasma phosphatidylcholine synthesis analyzed in vivo. J Lipid Res 52, 399-407 (2011).
32. DeLong, C. J., Shen, Y. J., Thomas, M. J. & Cui, Z. Molecular distinction of phosphatidylcholine synthesis between the CDP-choline pathway and phosphatidylethanolamine methylation pathway. J Biol Chem 274, 29683-29688 (1999).
33. Sevastou, I., Kaffe, E., Mouratis, M. A. & Aidinis, V. Lysoglycerophospholipids in chronic inflammatory disorders: the PLA(2)/LPC and ATX/LPA axes. Biochim Biophys Acta 1831, 42-60 (2013).
34. Dreon, D. M., Slavin, J. L. & Phinney, S. D. Oral contraceptive use and increased plasma concentration of C-reactive protein. Life Sci 73, 1245-1252 (2003).
35. Rad, M. et al. Increased impact of a contraceptive vaginal ring with ethinyl estradiol and nestorone on C-reactive protein. Steroids 77, 1483-1486 (2012).
36. Simo, R. et al. Novel insights in SHBG regulation and clinical implications. Trends Endocrinol Metab 26, 376-383 (2015).
37. Bataille, V. et al. Sex hormone-binding globulin is a major determinant of the lipid profile: the PRIME study. Atherosclerosis 179, 369-373 (2005).
38. Yamashita, T. et al. Enhanced insulin sensitivity in mice lacking ganglioside GM3. Proc Natl Acad Sci USA 100, 3445-3449 (2003).
39. Sims, K. H. et al. Avanti lipid tools: connecting lipids, technology, and cell biology. Biochim Biophys Acta 1841, 1038-1048 (2014).
40. Durant, B. et al. Sex differences of urinary and kidney globotriaosylceramide and lyso-globotriaosylceramide in Fabry mice. J Lipid Res 52, 1742-1746 (2011).
41. Schuhmann, K. et al. Shotgun lipidomics on a LTQ Orbitrap mass spectrometer by successive switching between acquisition polarity modes. J Mass Spectrom 47, 96-104 (2012).
42. Herzog, R. et al. A novel informatics concept for high-throughput shotgun lipidomics based on the molecular fragmentation query language. Genome Biol 12, R8 (2011).
43. Heiskanen, L. A. et al. Long-term performance and stability of molecular shotgun lipidomic analysis of human plasma samples. Anal Chem 85, 8757-8763 (2013).
44. Fong, B., Norris, C., Lowe, E. & McJarrow, P. Liquid chromatography-high-resolution mass spectrometry for quantitative analysis of gangliosides. Lipids 44, 867-874 (2009).
45. Deems, R. et al. Detection and quantitation of eicosanoids via high performance liquid chromatography-electrospray ionizationmass spectrometry. Methods Enzymol 432, 59-82 (2007).
46. Sullards, M. C. et al. Structure-specific, quantitative methods for analysis of sphingolipids by liquid chromatography-tandem mass spectrometry: "inside-out" sphingolipidomics. Methods Enzymol 432, 83-115 (2007).
47. Bielawski, J. et al. Comprehensive quantitative analysis of bioactive sphingolipids by high-performance liquid chromatographytandem mass spectrometry. Methods Mol Biol 579, 443-467 (2009).
48. Ikeda, K., Shimizu, T. & Taguchi, R. Targeted analysis of ganglioside and sulfatide molecular species by LC/ESI-MS/MS with theoretically expanded multiple reaction monitoring. J Lipid Res 49, 2678-2689 (2008).