Advertisement

Metabolomics

, 14:106 | Cite as

Characterization of trotter horses urine metabolome by means of proton nuclear magnetic resonance spectroscopy

  • Chenglin Zhu
  • Vanessa Faillace
  • Fulvio Laus
  • Marilena Bazzano
  • Luca Laghi
Original Article

Abstract

Background

Metabolomics has been recognized as a powerful approach for disease screening. In order to highlight potential health issues in subjects, a key factor is the possibility to compare quantitatively the metabolome of their biofluids with reference values from healthy individuals. Such efforts towards the systematic characterization of the metabolome of biofluids in perfect health conditions, far from concluded for humans, have barely begun on horses.

Objectives

The present work attempts, for the first time, to give reference quantitative values for the molecules mostly represented in the urine metabolome of horses at rest and under light training, as observable by 1H-NMR.

Methods

The metabolome of ten trotter horses, four male and six female, ranging from 3 to 8 years of age, has been observed by 1H-NMR spectroscopy before and after three training sessions.

Results

We could characterize and quantify 54 molecules in trotter horse urine, originated from diet, protein digestion, energy generation or gut-microbial co-metabolism.

Conclusion

We were able to describe how gender, age and exercise affected their concentration, by means of a two steps protocol based on univariate and robust principal component analysis.

Keywords

Trotter horse Urine Metabolomics 1H-NMR 

Notes

Acknowledgements

Chenglin Zhu gratefully acknowledges financial support from Chinese Scholarship Council (Grant No. 201606910076).

Author contributions

VF, FL, MB and LL conceived and designed research. VF, FL and MB collected the samples. CZ and LL performed metabolomics analysis. CZ, LL and FL wrote the manuscript. All authors read and approved the manuscript.

Compliance with ethical standards

Conflict of interest

Authors declare that they have no conflict of interest.

Supplementary material

11306_2018_1403_MOESM1_ESM.docx (4.7 mb)
Supplementary material 1 (DOCX 4822 KB)

References

  1. Artioli, G. G., Gualano, B., Smith, A., Stout, J., & Lancha, A. H. (2010). Role of β-alanine supplementation on muscle carnosine and exercise performance. Medicine and Science in Sports and Exercise, 42(6), 1162–1173.  https://doi.org/10.1249/MSS.0b013e3181c74e38.PubMedCrossRefGoogle Scholar
  2. Assfalg, M., Bertini, I., Colangiuli, D., Luchinat, C., Schäfer, H., Schütz, B., & Spraul, M. (2008). Evidence of different metabolic phenotypes in humans. Proceedings of the National Academy of Sciences of the United States of America, 105(5), 1420–1424.  https://doi.org/10.1073/pnas.0705685105.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Barrilero, R., Ramírez, N., Vallvé, J. C., Taverner, D., Fuertes, R., Amigó, N., & Correig, X. (2017). Unravelling and quantifying the “nMR-Invisible” metabolites interacting with human serum albumin by binding competition and T2 relaxation-based decomposition analysis. Journal of Proteome Research, 16(5), 1847–1856.  https://doi.org/10.1021/acs.jproteome.6b00814.CrossRefPubMedGoogle Scholar
  4. Bollard, M. E., Stanley, E. G., Lindon, J. C., Nicholson, J. K., & Holmes, E. (2005). NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition. NMR in Biomedicine.  https://doi.org/10.1002/nbm.935.PubMedCrossRefGoogle Scholar
  5. Buhl, R., & Ersbøll, A. K. (2012). Echocardiographic evaluation of changes in left ventricular size and valvular regurgitation associated with physical training during and after maturity in Standardbred trotters. Journal of the American Veterinary Medical Association, 240(2), 205–212.  https://doi.org/10.2460/javma.240.2.205.CrossRefPubMedGoogle Scholar
  6. Carroll, C. L., & Huntington, P. J. (1988). Body condition scoring and weight estimation of horses. Equine Veterinary Journal, 20(1), 41–45.  https://doi.org/10.1111/j.2042-3306.1988.tb01451.x.CrossRefPubMedGoogle Scholar
  7. Chambers, J. M., Freeny, A., & Heiberger, R. M. (1992). Analysis of variance; Designed experiments. Statistical models in S, 5, 145–193.Google Scholar
  8. Conover, W. J., & Iman, R. L. (1981). Rank transformations as a bridge between parametric and nonparametric statistics. American Statistician, 35(3), 124–128.  https://doi.org/10.1080/00031305.1981.10479327.CrossRefGoogle Scholar
  9. De Luca, A., Pierno, S., & Camerino, D. C. (2015). Taurine: The appeal of a safe amino acid for skeletal muscle disorders. Journal of Translational Medicine, 13(1), 243.  https://doi.org/10.1186/s12967-015-0610-1.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Dieterle, F., Ross, A., Schlotterbeck, G., & Senn, H. (2006). Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Analytical Chemistry, 78(13), 4281–4290.  https://doi.org/10.1021/ac051632c.CrossRefPubMedGoogle Scholar
  11. Dougal, K., De La Fuente, G., Harris, P. A., Girdwood, S. E., Pinloche, E., Geor, R. J., et al. (2014). Characterisation of the faecal bacterial community in adult and elderly horses fed a high fibre, high oil or high starch diet using 454 pyrosequencing. PLoS ONE, 9(2), e87424  https://doi.org/10.1371/journal.pone.0087424.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Escalona, E. E., Leng, J., Dona, A. C., Merrifield, C. A., Holmes, E., Proudman, C. J., & Swann, J. R. (2015). Dominant components of the thoroughbred metabolome characterised by 1H-nuclear magnetic resonance spectroscopy: A metabolite atlas of common biofluids. Equine Veterinary Journal, 47(6), 721–730.  https://doi.org/10.1111/evj.12333.CrossRefPubMedGoogle Scholar
  13. Foschi, C., Laghi, L., D’Antuono, A., Gaspari, V., Zhu, C., Dellarosa, N., et al. (2018). Urine metabolome in women with Chlamydia trachomatis infection. PLoS ONE, 13(3), e0194827.  https://doi.org/10.1371/journal.pone.0194827.CrossRefPubMedPubMedCentralGoogle Scholar
  14. Fujii, H., Nakai, K., & Fukagawa, M. (2011). Role of oxidative stress and indoxyl sulfate in progression of cardiovascular disease in chronic kidney disease. Therapeutic Apheresis and Dialysis.  https://doi.org/10.1111/j.1744-9987.2010.00883.x.CrossRefPubMedGoogle Scholar
  15. Hermansen, L., & Osnes, J. B. (1972). Blood and muscle pH after maximal exercise in man. Journal of Applied Physiology, 32(3), 304–308.CrossRefPubMedGoogle Scholar
  16. Hubert, M., Rousseeuw, P. J., & Vanden Branden, K. (2005). ROBPCA: A new approach to robust principal component analysis. Technometrics, 47(1), 64–79.  https://doi.org/10.1098/004017004000000563.CrossRefGoogle Scholar
  17. Joncquel-Chevalier Curt, M., Voicu, P. M., Fontaine, M., Dessein, A. F., Porchet, N., Mention-Mulliez, K., et al. (2015). Creatine biosynthesis and transport in health and disease. Biochimie.  https://doi.org/10.1016/j.biochi.2015.10.022.PubMedCrossRefGoogle Scholar
  18. Joré, C., Loup, B., Garcia, P., Paris, A. C., Popot, M. A., Audran, M., et al. (2017). Liquid chromatography: High resolution mass spectrometry-based metabolomic approach for the detection of Continuous Erythropoiesis Receptor Activator effects in horse doping control. Journal of Chromatography A, 1521, 90–99.  https://doi.org/10.1016/j.chroma.2017.09.029.CrossRefPubMedGoogle Scholar
  19. Kneen, M. A., & Annegarn, H. J. (1996). Algorithm for fitting XRF, SEM and PIXE X-ray spectra backgrounds. Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms, 109–110, 209–213.  https://doi.org/10.1016/0168-583X(95)00908-6.CrossRefGoogle Scholar
  20. Kochhar, S., Jacobs, D. M., Ramadan, Z., Berruex, F., Fuerholz, A., & Fay, L. B. (2006). Probing gender-specific metabolism differences in humans by nuclear magnetic resonance-based metabonomics. Analytical Biochemistry, 352(2), 274–281.  https://doi.org/10.1016/j.ab.2006.02.033.CrossRefPubMedGoogle Scholar
  21. Laghi, L., Picone, G., & Capozzi, F. (2014). Nuclear magnetic resonance for foodomics beyond food analysis. TrAC - Trends in Analytical Chemistry.  https://doi.org/10.1016/j.trac.2014.04.009.CrossRefGoogle Scholar
  22. Laghi, L., Zhu, C., Campagna, G., Rossi, G., Bazzano, M., & Laus, F. (2018). Probiotic supplementation in trained trotter horses: Effect on blood clinical pathology data and urine metabolomic assessed in field. Journal of Applied Physiology.  https://doi.org/10.1152/japplphysiol.01131.2017.PubMedCrossRefPubMedCentralGoogle Scholar
  23. Liland, K. H., Almøy, T., & Mevik, B. H. (2010). Optimal choice of baseline correction for multivariate calibration of spectra. Applied Spectroscopy, 64(9), 1007–1016.  https://doi.org/10.1366/000370210792434350.CrossRefPubMedGoogle Scholar
  24. Lostroh, A. J. (1968). Regulation by testosterone and insulin of citrate secretion and protein synthesis in explanted mouse prostates. Proceedings of the National Academy of Sciences, 60(4), 1312–1318.CrossRefGoogle Scholar
  25. Marcolini, E., Babini, E., Bordoni, A., Di Nunzio, M., Laghi, L., Maczó, A., et al. (2015). Bioaccessibility of the bioactive peptide carnosine during in vitro digestion of cured beef meat. Journal of Agricultural and Food Chemistry, 63(20), 4973–4978  https://doi.org/10.1021/acs.jafc.5b01157.CrossRefGoogle Scholar
  26. Mayneris-Perxachs, J., Bolick, D. T., Leng, J., Medlock, G. L., Kolling, G. L., Papin, J. A., et al. (2016). Protein-and zinc-deficient diets modulate the murine microbiome and metabolic phenotype. American Journal of Clinical Nutrition, 104(5), 1253–1262.  https://doi.org/10.3945/ajcn.116.131797.CrossRefPubMedGoogle Scholar
  27. Merrifield, C. A., Lewis, M., Claus, S. P., Beckonert, O. P., Dumas, M.-E., Duncker, S., et al. (2011). A metabolic system-wide characterisation of the pig: A model for human physiology. Molecular BioSystems, 7(9), 2577.  https://doi.org/10.1039/c1mb05023k.CrossRefPubMedGoogle Scholar
  28. Mikami, T., Kita, K., Tomita, S., Qu, G. J., Tasaki, Y., & Ito, a (2000). Is allantoin in serum and urine a useful indicator of exercise-induced oxidative stress in humans? Free Radical Research, 32(3), 235–244.  https://doi.org/10.1080/10715760000300241.CrossRefPubMedGoogle Scholar
  29. Müller, W., Nocke, T., & Schumann, H. (2006). Enhancing the visualization process with principal component analysis to support the exploration of trends. In Conferences in Research and Practice in Information Technology Series.Google Scholar
  30. Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999). “Metabonomics”: Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 29(11), 1181–1189.  https://doi.org/10.1080/004982599238047.CrossRefPubMedGoogle Scholar
  31. Pappalardo, L., Pelczer, I., & Ralston, S. L. (2013). Metabolic differences between draft-cross and mustang horses detected by metabonomic analyses. Journal of Equine Veterinary Science, 33(12), 1044–1049.  https://doi.org/10.1016/j.jevs.2013.03.182.CrossRefGoogle Scholar
  32. Poli, D., Carbognani, P., Corradi, M., Goldoni, M., Acampa, O., Balbi, B., et al. (2005). Exhaled volatile organic compounds in patients with non-small cell lung cancer: Cross sectional and nested short-term follow-up study. Respiratory Research.  https://doi.org/10.1186/1465-9921-6-71.PubMedPubMedCentralCrossRefGoogle Scholar
  33. Psihogios, N. G., Gazi, I. F., Elisaf, M. S., Seferiadis, K. I., & Bairaktari, E. T. (2008). Gender-related and age-related urinalysis of healthy subjects by NMR-based metabonomics. NMR in Biomedicine, 21(3), 195–207.  https://doi.org/10.1002/nbm.1176.CrossRefPubMedGoogle Scholar
  34. R Development Core Team. (2011). R: A language and environment for statistical computing (Vol. 1). Vienna, Austria: R foundation for statistical computing.  https://doi.org/10.1007/978-3-540-74686-7.CrossRefGoogle Scholar
  35. Slupsky, C. M., Rankin, K. N., Wagner, J., Fu, H., Chang, D., Weljie, A. M., et al. (2007). Investigations of the effects of gender, diurnal variation, and age in human urinary metabolomic profiles. Analytical Chemistry, 79(18), 6995–7004.  https://doi.org/10.1021/ac0708588.CrossRefPubMedGoogle Scholar
  36. Soupart, P. (1959). Urinary excretion of free amino acids in normal adult men and women. Clinica Chimica Acta, 4(2), 265–271.CrossRefGoogle Scholar
  37. Wishart, D. S., Tzur, D., Knox, C., Eisner, R., Guo, A. C., Young, N., et al. (2007). HMDB: The human metabolome database. Nucleic Acids Research, 35(SUPPL. 1), D521–D526.  https://doi.org/10.1093/nar/gkl923.CrossRefGoogle Scholar
  38. Zhang, S., Liu, L., Steffen, D., Ye, T., & Raftery, D. (2012). Metabolic profiling of gender: Headspace-SPME/GC–MS and 1H NMR analysis of urine. Metabolomics, 8(2), 323–334.CrossRefGoogle Scholar
  39. Zylber-Katz, E., Granit, L., & Levy, M. (1984). Relationship between caffeine concentrations in plasma and saliva. Clinical Pharmacology and Therapeutics, 36(1), 133–137.  https://doi.org/10.1038/clpt.1984.151.CrossRefPubMedGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Agro-Food Science and Technology, Centre of FoodomicsUniversity of BolognaCesenaItaly
  2. 2.School of Biosciences and Veterinary MedicineUniversity of CamerinoMatelicaItaly

Personalised recommendations