Advertisement

Metabolomics

, 14:38 | Cite as

Detection of potential new biomarkers of atherosclerosis by probe electrospray ionization mass spectrometry

  • Hisashi Johno
  • Kentaro Yoshimura
  • Yuki Mori
  • Tokuhide Kimura
  • Manabu Niimi
  • Masaki Yamada
  • Tetsuo Tanigawa
  • Jianglin Fan
  • Sen Takeda
Original Article

Abstract

Introduction

Atherosclerotic diseases are the leading cause of death worldwide. Biomarkers of atherosclerosis are required to monitor and prevent disease progression. While mass spectrometry is a promising technique to search for such biomarkers, its clinical application is hampered by the laborious processes for sample preparation and analysis.

Methods

We developed a rapid method to detect plasma metabolites by probe electrospray ionization mass spectrometry (PESI-MS), which employs an ambient ionization technique enabling atmospheric pressure rapid mass spectrometry. To create an automatic diagnosis system of atherosclerotic disorders, we applied machine learning techniques to the obtained spectra.

Results

Using our system, we successfully discriminated between rabbits with and without dyslipidemia. The causes of dyslipidemia (genetic lipoprotein receptor deficiency or dietary cholesterol overload) were also distinguishable by this method. Furthermore, after induction of atherosclerosis in rabbits with a cholesterol-rich diet, we were able to detect dynamic changes in plasma metabolites. The major metabolites detected by PESI-MS included cholesterol sulfate and a phospholipid (PE18:0/20:4), which are promising new biomarkers of atherosclerosis.

Conclusion

We developed a remarkably fast and easy method to detect potential new biomarkers of atherosclerosis in plasma using PESI-MS.

Keywords

Probe electrospray ionization mass spectrometry Atherosclerosis Dyslipidemia Blood plasma Machine learning 

Notes

Acknowledgements

We thank Ayumi Iizuka for technical assistance with the PESI-MS analyses. This work was partially supported by JSPS KAKENHI Grant Number 16K08964 (Grant-in-Aid for Scientific Research (C) to K. Y.).

Availability of data

The datasets generated during and/or analyzed during the current study are available in the Figshare repository, [ https://doi.org/10.6084/m9.figshare.5783205].

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

Animal experiments were performed with the approval of the Animal Care Committee of the University of Yamanashi and complied with the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health.

Supplementary material

11306_2018_1334_MOESM1_ESM.pdf (8.9 mb)
Supplementary material 1 (PDF 9063 KB)

References

  1. Aoyagi, R., Ikeda, K., Isobe, Y., & Arita, M. (2017). Comprehensive analyses of oxidized phospholipids using a measured MS/MS spectra library. Journal of Lipid Research, 58(11), 2229–2237.CrossRefPubMedGoogle Scholar
  2. Badimon, L., Vilahur, G., & Padro, T. (2010). Nutraceuticals and atherosclerosis: Human trials. Cardiovascular Therapeutics, 28(4), 202–215.CrossRefPubMedGoogle Scholar
  3. Berliner, J. A., & Watson, A. D. (2005). A role for oxidized phospholipids in atherosclerosis. The New England Journal of Medicine, 353(1), 9–11.CrossRefPubMedGoogle Scholar
  4. Bousquet, O., & Elisseeff, A. (2002). Stability and Generalization. Journal of Machine Learning Research, 2, 499–526.Google Scholar
  5. Dang, V. T., Huang, A., Zhong, L. H., Shi, Y., & Werstuck, G. H. (2016). Comprehensive plasma metabolomic analyses of atherosclerotic progression reveal alterations in glycerophospholipid and sphingolipid metabolism in apolipoprotein E-deficient mice. Scientific Reports, 6, 35037.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Dang, V. T., & Werstuck, G. H. (2016). Metabolomics-based biomarkers of the pathogenesis of atherosclerosis. Biomarkers Journal, 2, 10.Google Scholar
  7. Djekic, D., Nicoll, R., Novo, M., & Henein, M. Y. (2015). Metabolomics in atherosclerosis. IJC. Metabolic & Endocrine, 8, 26–30.CrossRefGoogle Scholar
  8. Eberlin, L. S., Dill, A. L., Costa, A. B., Ifa, D. R., Cheng, L., Masterson, T., et al. (2010). Cholesterol sulfate imaging in human prostate cancer tissue by desorption electrospray ionization mass spectrometry. Analytical Chemistry, 82(9), 3430–3434.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Fruhwirth, G. O., Loidl, A., & Hermetter, A. (2007). Oxidized phospholipids: From molecular properties to disease. Biochimica et Biophysica Acta, 1772(7), 718–736.CrossRefPubMedGoogle Scholar
  10. Grundy, S. M., Pasternak, R., Greenland, P., Smith, S. Jr., & Fuster, V. (1999). AHA/ACC scientific statement. Assessment of cardiovascular risk by use of multiple-risk-factor assessment equations: A statement for healthcare professionals from the American Heart Association and the American College of Cardiology. Journal of the American College of Cardiology, 34(4), 1348–1359.CrossRefPubMedGoogle Scholar
  11. Hoefer, I. E., Steffens, S., Ala-Korpela, M., Bäck, M., Badimon, L., Bochaton-Piallat, M. L., et al. (2015). Novel methodologies for biomarker discovery in atherosclerosis. European Heart Journal, 36(39), 2635–2642.CrossRefPubMedGoogle Scholar
  12. Huang, M. Z., Cheng, S. C., Cho, Y. T., & Shiea, J. (2011). Ambient ionization mass spectrometry: A tutorial. Analytica Chimica Acta, 702(1), 1–15.CrossRefPubMedGoogle Scholar
  13. Kolodgie, F. D., Katocs, A. S. Jr., Largis, E. E., Wrenn, S. M., Cornhill, J. F., Herderick, et al. (1996). Hypercholesterolemia in the rabbit induced by feeding graded amounts of low-level cholesterol. Methodological considerations regarding individual variability in response to dietary cholesterol and development of lesion type. Arteriosclerosis, Thrombosis, and Vascular Biology, 16(12), 1454–1464.CrossRefPubMedGoogle Scholar
  14. Lee, S., Birukov, K. G., Romanoski, C. E., Springstead, J. R., Lusis, A. J., & Berliner, J. A. (2012). Role of phospholipid oxidation products in atherosclerosis. Circulation Research, 111(6), 778–799.CrossRefPubMedPubMedCentralGoogle Scholar
  15. Lorenz, M. W., Markus, H. S., Bots, M. L., Rosvall, M., & Sitzer, M. (2007). Prediction of clinical cardiovascular events with carotid intima-media thickness: A systematic review and meta-analysis. Circulation, 115(4), 459–467.CrossRefPubMedGoogle Scholar
  16. Mandal, M. K., Yoshimura, K., Chen, L. C., Yu, Z., Nakazawa, T., Katoh, R., et al. (2012). Application of probe electrospray ionization mass spectrometry (PESI-MS) to clinical diagnosis: Solvent effect on lipid analysis. Journal of The American Society for Mass Spectrometry, 23(11), 2043–2047.CrossRefPubMedGoogle Scholar
  17. Navab, M., Ananthramaiah, G. M., Reddy, S. T., Van Lenten, B. J., Ansell, B. J., Fonarow, G. C., et al. (2004). The oxidation hypothesis of atherogenesis: The role of oxidized phospholipids and HDL. Journal of Lipid Research, 45(6), 993–1007.CrossRefPubMedGoogle Scholar
  18. Nicoll, R., & Henein, M. Y. (2013). Arterial calcification: Friend or foe? International Journal of Cardiology, 167(2), 322–327.CrossRefPubMedGoogle Scholar
  19. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.Google Scholar
  20. Ravandi, A., Babaei, S., Leung, R., Monge, J. C., Hoppe, G., Hoff, H., et al. (2004). Phospholipids and oxophospholipids in atherosclerotic plaques at different stages of plaque development. Lipids, 39(2), 97–109.CrossRefPubMedGoogle Scholar
  21. Sampson, U. K., Fazio, S., & Linton, M. F. (2012). Residual cardiovascular risk despite optimal LDL cholesterol reduction with statins: The evidence, etiology, and therapeutic challenges. Current Atherosclerosis Reports, 14(1), 1–10.CrossRefPubMedPubMedCentralGoogle Scholar
  22. Seneff, S., Davidson, R. M., Lauritzen, A., Samsel, A., & Wainwright, G. (2015). A novel hypothesis for atherosclerosis as a cholesterol sulfate deficiency syndrome. Theoretical Biology and Medical Modelling, 12, 9.CrossRefPubMedPubMedCentralGoogle Scholar
  23. Strott, C. A., & Higashi, Y. (2003). Cholesterol sulfate in human physiology: What’s it all about? Journal of Lipid Research, 44(7), 1268–1278.CrossRefPubMedGoogle Scholar
  24. Takeda, S., Yoshimura, K., & Hiraoka, K. (2012). Innovations in Analytical oncology—Status quo of mass spectrometry-based diagnostics for malignant tumor. Journal of Analytical Oncology, 1(1), 74–80.Google Scholar
  25. Vogeser, M., & Kirchhoff, F. (2011). Progress in automation of LC-MS in laboratory medicine. Clinical Biochemistry, 44(1), 4–13.CrossRefPubMedGoogle Scholar
  26. Wold, S., Sjostrom, M., & Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130.CrossRefGoogle Scholar
  27. Yoshimura, K., Chen, L. C., Mandal, M. K., Nakazawa, T., Yu, Z., Uchiyama, T., et al. (2012). Analysis of renal cell carcinoma as a first step for developing mass spectrometry-based diagnostics. Journal of The American Society for Mass Spectrometry, 23(10), 1741–1749.CrossRefPubMedGoogle Scholar
  28. Yoshimura, K., Mandal, M. K., Hara, M., Fujii, H., Chen, L. C., Tanabe, K., et al. (2013). Real-time diagnosis of chemically induced hepatocellular carcinoma using a novel mass spectrometry-based technique. Analytical Biochemistry, 441(1), 32–37.CrossRefPubMedGoogle Scholar
  29. Yu, H. F., Huang, F. L., & Lin, C. J. (2011). Dual coordinate descent methods for logistic regression and maximum entropy models. Machine Learning, 85(1–2), 41–75.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Hisashi Johno
    • 1
  • Kentaro Yoshimura
    • 1
  • Yuki Mori
    • 1
  • Tokuhide Kimura
    • 2
  • Manabu Niimi
    • 2
  • Masaki Yamada
    • 3
  • Tetsuo Tanigawa
    • 3
  • Jianglin Fan
    • 2
  • Sen Takeda
    • 1
  1. 1.Department of Anatomy and Cell Biology, Interdisciplinary Graduate School of MedicineUniversity of YamanashiChuoJapan
  2. 2.Department of Molecular Pathology, Interdisciplinary Graduate School of MedicineUniversity of YamanashiChuoJapan
  3. 3.Analytical and Measuring Instruments Division, Global Application Development CenterShimadzu CorporationKyotoJapan

Personalised recommendations