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Metabolomics Data Analysis Improvement by Use of the Filter Diagonalization Method

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Abstract

The filter diagonalization method (FDM) was implemented and used instead of fast Fourier transform (FFT) to obtain the nuclear magnetic resonance (NMR) spectra from the free induction decay (FID) signals. The areas obtained by the FDM, from selected absorption lines, were used as input for a multidimensional method of data analysis. This procedure was applied in a NMR-based metabolomics investigation. In FDM, instead of spectra, the absorption peaks’ specification, such as central frequency, line width, amplitude and relative phases, are estimated and the spectra are built using this information. Therefore, one can select the lines by width and intensity to exclude the broad lines such as baseline, solvent line and albumin peak. Also lines with small amplitude such as noise can be excluded from the spectra. Moreover, the spectra do not suffer from aliasing or baseline problems. These characteristics are fundamental in the metabolomics investigations. To show the superiority of our method over the standard FFT to obtain the spectra, we reconstructed the spectra from simulated FID by both methods. As an example, this new approach is used to analyze the non-small cell lung cancer A549 exposed to different treatments and principal component analysis is used to compare the performance of both methods.

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Notes

  1. The defined Hamiltonian is not hermitian, but, is symmetrical, in spite of this fact, it is possible to adapt the theory of quantum mechanics for this symmetric Hamiltonian.

  2. Laboratório Nacional de Biociências.

References

  1. E.D. Lawe, J. Skiling, J. Staunton, S. Sibisi, R. Brereton, J. Magn. Reson. 62, 437–452 (1985)

    ADS  Google Scholar 

  2. H. Barkhuijsen, R. Debeer, W.M.M.J. Bovee, J.H.N. Creyghton, D. Vanormondt, Magn. Reson. Med. 2(1), 86–89 (1985). https://doi.org/10.1002/mrm.1910020111

    Article  Google Scholar 

  3. R.H. Cervantes, S.R. Rabbani, Solid State Commun. 110(4), 215–220 (1999)

    Article  ADS  Google Scholar 

  4. S. Haykin (ed.), Nonlinear Methods of Spectral Analysis (Springer, Berlin New York, 1979)

  5. V.A. Mandelshtam, H.S. Taylor, J. Chem. Phys. 107(17), 6756–6769 (1997). https://doi.org/10.1063/1.475324

    Article  ADS  Google Scholar 

  6. J.H. Chen, V.A. Mandelshtam, A.J. Shaka, J. Magn. Reson. 146(2), 363–368 (2000). https://doi.org/10.1006/jmre.2000.2155

    Article  ADS  Google Scholar 

  7. C. Magon, Livre docencia, Instituto de Física da USP de São Carlos (2007)

  8. B. Dai, C.D. Eads, Magn. Reson. Chem. 48(3), 230–234 (2010). https://doi.org/10.1002/mrc.2550

    Article  Google Scholar 

  9. A. Zhang, H. Sun, P. Wang, Y. Han, X. Wang, Analyst 137(2), 293–300 (2012). https://doi.org/10.1039/C1AN15605E

    Article  ADS  Google Scholar 

  10. M. Mamas, W.B. Dunn, L. Neyses, R. Goodacre, Arch. Toxicol. 85(1), 5–17 (2011). https://doi.org/10.1007/s00204-010-0609-6

    Article  Google Scholar 

  11. V.N. Kristensen, O.C. Lingjoerde, H.G. Russnes, H.K.M. Vollan, A. Frigessi, A.-L. Borresen-Dale, Nat. Rev. Cancer 14(5), 299–313 (2014). https://doi.org/10.1038/nrc3721

    Article  Google Scholar 

  12. L. Fernandez, A. Rodriguez, P. Garcia, ISME J. 12(5), 1171–1179 (2018). https://doi.org/10.1038/s41396-018-0049-5

    Article  Google Scholar 

  13. T. Young, A.C. Alfaro, Rev. Aquac. 10(1), 26–56 (2018). https://doi.org/10.1111/raq.12146

    Article  Google Scholar 

  14. R. Alcazar, T. Altabella, F. Marco, C. Bortolotti, M. Reymond, C. Koncz, P. Carrasco, A.F. Tiburcio, Planta 231(6), 1237–1249 (2010). https://doi.org/10.1007/s00425-010-1130-0

    Article  Google Scholar 

  15. J.M. Cevallos-Cevallos, J.I. Reyes-De-Corcuera, E. Etxeberria, M.D. Danyluk, G.E. Rodrick, Trends Food Sci. Technol. 20(11–12), 557–566 (2009). https://doi.org/10.1016/j.tifs.2009.07.002

    Article  Google Scholar 

  16. J.G. Bundy, M.P. Davey, M.R. Viant, Metabolomics 5(1), 3–21 (2009). https://doi.org/10.1007/s11306-008-0152-0

    Article  Google Scholar 

  17. R. Bracewell, The Fourier Transform and its Applications. McGraw-Hill Series in Electrical Engineering Circuits and Systems (McGraw-Hill, New York, 1986)

  18. I. Karaman, D.L.S. Ferreira, C.L. Boulangé, M.R. Kaluarachchi, D. Herrington, A.C. Dona, R. Castagné, A. Moayyeri, B. Lehne, M. Loh, P.S. de Vries, A. Dehghan, O.H. Franco, A. Hofman, E. Evangelou, I. Tzoulaki, P. Elliott, J.C. Lindon, T.M.D. Ebbels, J. Proteome Res. 15(12), 4188–4194 (2016). https://doi.org/10.1021/acs.jproteome.6b00125

    Article  Google Scholar 

  19. R. Vettukattil, in Methods in Molecular Biology (Springer Nature, 2015), pp. 123–136. https://doi.org/10.1007/978-1-4939-2377-9_10

    Google Scholar 

  20. D. Neuhauser, J. Chem. Phys. 93(4), 2611–2616 (1990). https://doi.org/10.1063/1.458900

    Article  ADS  Google Scholar 

  21. M. Goldman, Quantum Description of High-Resolution NMR in Liquids, International Series of Monographs on Chemistry (Oxford University Press, Clarendon, 1991)

  22. Chenomx Inc., Chenomx nmr suite 7.7 (2013). https://www.chenomx.com/

  23. P.S. Bacchi, A.C. Bloise, S.O. Bustos, L. Zimmermann, R. Chammas, S.R. Rabbani, Springerplus 3, 470 (2014). https://doi.org/10.1186/2193-1801-3-470

    Article  Google Scholar 

  24. A.B. Martins-Bach, A.C. Bloise, M. Vainzof, S.R. Rabbani, Magn. Reson. Imaging 30(8), 1167–1176 (2012). https://doi.org/10.1016/j.mri.2012.04.003

    Article  Google Scholar 

  25. A.B. Martins-Bach, A.C. Bloise, S.R. Rabbani, M. Vainzof, Neuromuscul. Disord. 21(9–10), 655–655 (2011). https://doi.org/10.1016/j.nmd.2011.06.805

    Article  Google Scholar 

  26. F. Savorani, G. Tomasi, S.B. Engelsen, J. Magn. Reson. 202(2), 190–202 (2010). https://doi.org/10.1016/j.jmr.2009.11.012

    Article  ADS  Google Scholar 

  27. M. Hollander, D.A. Wolfe, E. Chicken, The One-Way Layout (Wiley, New York, 2015), Ch. 6, pp. 202–288. https://doi.org/10.1002/9781119196037.ch6

  28. P.H. Kvam, B. Vidakovic, Designed Experiments (Wiley, New Jersey, 2007), Ch. 8, pp. 141–152. https://doi.org/10.1002/9780470168707.ch8

  29. I. Jolliffe, Principal Component Analysis (Springer, New York, 2002)

    MATH  Google Scholar 

  30. B.B. Misra, S. Mohapatra, Electrophoresis 40(2), 227–246 (2019). https://doi.org/10.1002/elps.201800428

    Article  Google Scholar 

  31. H.U. Zacharias, M. Altenbuchinger, W. Gronwald, Metabolites 8(3), 47 (2018). https://doi.org/10.3390/metabo8030047

    Article  Google Scholar 

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Acknowledgements

The author HJC wants to thank Dr. Claudio Jose Magon for the useful discussions about the FDM. The NMR experiments were executed at the Laboratório Nacional de Biociências (LNBIO), part of the Centro Nacional de Pesquisa em Energia e Materiais (CNPEM), project RMN-20606. The authors desire to express gratitude to Sílvia Rocco and Maurício Luís Sforça of the LNBIO for their assistance in NMR measurements. The non-small cell lung cancer, A549 cells line, were cultivated with collaboration of Dr. Roger Chammas group in Cancer Institute of the São Paulo State (ICESP).

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Cervantes, H.J., Kopel, F.M. & Rabbani, S.R. Metabolomics Data Analysis Improvement by Use of the Filter Diagonalization Method. Appl Magn Reson 50, 1369–1380 (2019). https://doi.org/10.1007/s00723-019-01158-0

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