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
- 344 Downloads
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.
Here, we describe a non-targeted metabolomics platform based on UPLC-UHR-QToF-MS(/MS) for the assessment of plasma non-polar metabolites.
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.
The discrimination of the groups was due to changes in various metabolic pathways including, among others, glycerophospholipid, sphingolipid and cholesterol metabolism.
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.
KeywordsUPLC-UHR-QTOF-MS(/MS) High fat diet mice Diabetes and the metabolic syndrome Non-targeted analysis
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.
- IDF (2015). IDF Diabetes Atlas. 7th Edition.Google Scholar
- 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
- Psychogios, N., et al. (2011). The human serum metabolome. PLoS One, 6, doi: 10.1371/journal.pone.0016957.
- 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
- 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
- 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