Metabolomics

, 13:44 | Cite as

Metabolite profiling: development and application of an UHR-QTOF-MS(/MS) method approach for the assessment of metabolic changes in high fat diet fed mice

  • Philipp Werner
  • Ernst Meiss
  • Ludger Scheja
  • Joerg Heeren
  • Markus Fischer
Original Article
  • 271 Downloads

Abstract

Introduction

The metabolic alterations accompanying the development of insulin resistance and type 2 diabetes mellitus (T2DM) are complex, not coherently understood and only partially represented by conventional clinical tests like the oral glucose tolerance test. Changes in plasma metabolite concentrations preceding insulin resistance or overt T2DM may help understand the etiology of metabolic disorders and they are potential predictive risk markers.

Objectives

Here, we describe a non-targeted metabolomics platform based on UPLC-UHR-QToF-MS(/MS) for the assessment of plasma non-polar metabolites.

Methods

This method was applied to a longitudinal mouse obesity study comparing mice on control and high fat diet (HFD), respectively. Plasma metabolites were assessed 2, 4, 8 and 16 weeks after initiation of feeding. Multivariate analysis of the metabolite dataset showed clear differentiation of the feeding groups after 8 weeks when the HFD-fed mice exhibited clear signs of insulin resistance.

Results

The discrimination of the groups was due to changes in various metabolic pathways including, among others, glycerophospholipid, sphingolipid and cholesterol metabolism.

Conclusion

From 81 compounds with a p-value lower than 0.05, a total of 19 metabolites could be putatively identified due to their accurate mass, isotope and fragmentation pattern. Thirteen of these observed metabolites are known key metabolites to diabetes or its secondary diseases like diabetic nephropathy and neuropathy (Meiss, Werner, John, Scheja, Herbach, Heeren, Fischer 2015). The compounds putatively identified here may provide valuable starting points for further investigations and developments of clinical diagnostics and prediagnostics for T2DM and related diseases.

Keywords

UPLC-UHR-QTOF-MS(/MS) High fat diet mice Diabetes and the metabolic syndrome Non-targeted analysis 

Supplementary material

11306_2017_1181_MOESM1_ESM.xlsx (457 kb)
Supplementary material 1 (XLSX 457 KB)

References

  1. Amrutkar, M., et al. (2015). Genetic disruption of protein kinase stk25 ameliorates metabolic defects in a diet-induced type 2 diabetes model. Diabetes, 64, 2791–2804. doi:10.2337/db15-0060.CrossRefPubMedPubMedCentralGoogle Scholar
  2. An, Y., et al. (2013). High-fat diet induces dynamic metabolic alterations in multiple biological matrices of rats. Journal of Proteome Research, 12, 3755–3768. doi:10.1021/pr400398b.CrossRefPubMedGoogle Scholar
  3. Bartelt, A., et al. (2013). Effects of adipocyte lipoprotein lipase on de novo lipogenesis and white adipose tissue browning. Biochimica et biophysica acta, 1831, 934–942. doi:10.1016/j.bbalip.2012.11.011.CrossRefPubMedGoogle Scholar
  4. Cao, H., Gerhold, K., Mayers, J. R., Wiest, M. M., Watkins, S. M., & Hotamisligil, G. S. (2008). Identification of a lipokine, a lipid hormone linking adipose tissue to systemic metabolism. Cell, 134, 933–944. doi:10.1016/j.cell.2008.07.048.CrossRefPubMedPubMedCentralGoogle Scholar
  5. Dutta, T., et al. (2012). Concordance of changes in metabolic pathways based on plasma metabolomics and skeletal muscle transcriptomics in type 1 diabetes. Diabetes, 61, 1004–1016. doi:10.2337/db11-0874.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Eisinger, K., Liebisch, G., Schmitz, G., Aslanidis, C., Krautbauer, S., & Buechler, C. (2014). Lipidomic analysis of serum from high fat diet induced obese mice. International Journal of Molecular Sciences, 15, 2991–3002. doi:10.3390/ijms15022991.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Godzien, J., et al. (2011). Metabolomic approach with LC-QTOF to study the effect of a nutraceutical treatment on urine of diabetic rats. Journal of Proteome Research, 10, 837–844. doi:10.1021/pr100993x.CrossRefPubMedGoogle Scholar
  8. Guo, X., et al. (2012). Palmitoleate induces hepatic steatosis but suppresses liver inflammatory response in mice. PLoS One, 7, e39286. doi:10.1371/journal.pone.0039286.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Ha, C. Y., et al. (2011). The association of specific metabolites of lipid metabolism with markers of oxidative stress, inflammation and arterial stiffness in men with newly diagnosed type 2 Diabetes. Clinical Endocrinology (Oxf). doi:10.1111/j.1365-2265.2011.04244.x.Google Scholar
  10. Hotamisligil, G. S. (2006). Inflammation and metabolic disorders. Nature, 444, 860–867. doi:10.1038/nature05485.CrossRefPubMedGoogle Scholar
  11. Huang, Q., et al. (2011). Method for liver tissue metabolic profiling study and its application in type 2 diabetic rats based on ultra performance liquid chromatography-mass spectrometry. Journal of Chromatography B, 879, 961–967. doi:10.1016/j.jchromb.2011.03.009.CrossRefGoogle Scholar
  12. IDF (2015). IDF Diabetes Atlas. 7th Edition.Google Scholar
  13. Kim, H. J., et al. (2011). Metabolomic analysis of livers and serum from high-fat diet induced obese mice. Journal of Proteome Research, 10, 722–731.CrossRefPubMedGoogle Scholar
  14. Kleemann, R., et al. (2010). Time-resolved and tissue-specific systems analysis of the pathogenesis of insulin resistance. PLoS One, 5, e8817. doi:10.1371/journal.pone.0008817.CrossRefPubMedPubMedCentralGoogle Scholar
  15. Laguzzi, F., et al. (2016). Cross-sectional relationships between dietary fat intake and serum cholesterol fatty acids in a Swedish cohort of 60-year-old men and women. Journal of human nutrition and dietetics, 29, 325–337. doi:10.1111/jhn.12336.CrossRefPubMedGoogle Scholar
  16. Lappas, M., et al. (2015). The prediction of type 2 diabetes in women with previous gestational diabetes mellitus using lipidomics. Diabetologia, 58, 1436–1442. doi:10.1007/s00125-015-3587-7.CrossRefPubMedGoogle Scholar
  17. Li, Y., Li, J. J., Wen, X. D., Pan, R., He, Y. S., & Yang, J. (2014). Metabonomic analysis of the therapeutic effect of Potentilla discolor in the treatment of type 2 diabetes mellitus. Molecular BioSystems, 10, 2898–2906. doi:10.1039/c4mb00278d.CrossRefPubMedGoogle Scholar
  18. Loftus, N., Miseki, M., Iida, J., Gika, H. G., Theodoridis, T., & Wilson, I. D. (2008). Profiling and biomarker identification in plasma from different Zucker rat strains via high mass accuracy multistage mass spectrometric analysis using liquid chromatography/mass spectrometry with a quadrupole ion trap-time of flight mass spectrometer. Rapid Communications in Mass Spectrometry, 22, 2547–2554.CrossRefPubMedGoogle Scholar
  19. Meiss, E., et al. (2016). Metabolite targeting: Development of a comprehensive targeted metabolomics platform for the assessment of diabetes and its complications. Metabolomics, 12, 52. doi:10.1007/s11306-016-0958-0. CrossRefGoogle Scholar
  20. Oresic, M., et al. (2008). 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. doi:10.1084/jem.20081800.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Pallares-Mendez, R., Aguilar-Salinas, C. A., Cruz-Bautista, I., & Del Bosque-Plata, L. (2016). Metabolomics in diabetes, a review. Annals of Medicine, 48, 89–102. doi:10.3109/07853890.2015.1137630.CrossRefPubMedGoogle Scholar
  22. Pereira, T. J., et al. (2015). Maternal obesity characterized by gestational diabetes increases the susceptibility of rat offspring to hepatic steatosis via a disrupted liver metabolome. The Journal of Physiology, 593, 3181–3197. doi:10.1113/JP270429.CrossRefPubMedPubMedCentralGoogle Scholar
  23. Psychogios, N., et al. (2011). The human serum metabolome. PLoS One, 6, doi:10.1371/journal.pone.0016957.
  24. Renner, S., et al. (2012). Changing metabolic signatures of amino acids and lipids during the prediabetic period in a pig model with impaired incretin function and reduced beta-cell mass. Diabetes, 61, 2166–2175. doi:10.2337/db11-1133.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Rhee, E. P., et al. (2011). Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. Journal of Clinical Investigation. doi:10.1172/JCI44442.Google Scholar
  26. Rubio-Aliaga, I., et al. (2011). Alterations in hepatic one-carbon metabolism and related pathways following a high-fat dietary intervention. Physiological Genomics, 43, 408–416.CrossRefPubMedGoogle Scholar
  27. Scheja, L., et al. (2008). Liver TAG transiently decreases while PL n-3 and n-6 fatty acids are persistently elevated in insulin resistant mice. Lipids, 43, 1039–1051. doi:10.1007/s11745-008-3220-3.CrossRefPubMedGoogle Scholar
  28. Schmelzer, K., Fahy, E., Subramaniam, S., & Dennis, E. A. (2007). The lipid maps initiative in lipidomics. Methods in Enzymology, 432, 171–183.CrossRefPubMedGoogle Scholar
  29. Stahlman, M., et al. (2013). Dyslipidemia, but not hyperglycemia and insulin resistance, is associated with marked alterations in the HDL lipidome in type 2 diabetic subjects in the DIWA cohort: Impact on small HDL particles. Biochimica et Biophysica acta, 1831, 1609–1617. doi:10.1016/j.bbalip.2013.07.009.CrossRefPubMedGoogle Scholar
  30. Tchernof, A., & Despres, J. P. (2013). Pathophysiology of human visceral obesity: An update. Physiological Reviews, 93, 359–404. doi:10.1152/physrev.00033.2011.CrossRefPubMedGoogle Scholar
  31. Tsutsui, H., et al. (2011). Biomarker discovery in biological specimens (plasma, hair, liver and kidney) of diabetic mice based upon metabolite profiling using ultra-performance liquid chromatography with electrospray ionization time-of-flight mass spectrometry. Clinica Chimica Acta, 412, 861–872. doi:10.1016/j.cca.2010.12.023.CrossRefGoogle Scholar
  32. Ugarte, M., M. Brown, K. A. Hollywood, G. J. Cooper, P. N. Bishop, W. B. Dunn (2012). Metabolomic analysis of rat serum in streptozotocin-induced diabetes and after treatment with oral triethylenetetramine (TETA). Genome Medicine, 4, 35. doi:10.1186/gm334.CrossRefPubMedPubMedCentralGoogle Scholar
  33. Wahl, S., et al. (2012). Childhood obesity is associated with changes in the serum metabolite profile. Obesity Facts, 5, 660–670. doi:10.1159/000343204.CrossRefPubMedGoogle Scholar
  34. Yang, J., et al. (2004). Discrimination of type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles. Journal of Chromatography B, 813, 53–58.CrossRefGoogle Scholar
  35. Zhao, X., et al. (2010). Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits. Metabolomics, 6, 362–374. doi:10.1007/s11306-010-0203-1.CrossRefPubMedPubMedCentralGoogle Scholar
  36. Zhu, Y., et al. (2013). Effect of metformin on the urinary metabolites of diet-induced-obese mice studied by ultra performance liquid chromatography coupled to time-of-flight mass spectrometry (UPLC-TOF/MS). Journal of chromatography, 925, 110–116. doi:10.1016/j.jchromb.2013.02.040.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Philipp Werner
    • 1
  • Ernst Meiss
    • 1
  • Ludger Scheja
    • 2
  • Joerg Heeren
    • 2
  • Markus Fischer
    • 1
  1. 1.Institute of Food ChemistryHamburg School of Food Science, University of HamburgHamburgGermany
  2. 2.Department of Biochemistry and Molecular Cell BiologyUniversity Medical Center Hamburg-EppendorfHamburgGermany

Personalised recommendations