DOI QR코드

DOI QR Code

Metabolite analysis in the type 1 diabetic mouse model

  • Park, Sung Jean (College of Pharmacy and Gachon Institute of Pharmaceutical Sciences, Gachon University)
  • 투고 : 2021.09.02
  • 심사 : 2021.09.14
  • 발행 : 2021.09.20

초록

Type 1 diabetes mellitus (T1DM) is caused by insufficient production of insulin, which is involved in carbohydrate metabolism. Type 2 diabetes mellitus (T2DM) has insulin resistance in which cells do not respond adequately to insulin. The purpose of this study was to estimate the characteristics of type 1 diabetes using streptozotocin-treated mice (STZ-mouse). The sera samples were collected from the models of hyperglycemic mouse and healthy mouse. Based on the pair-wise comparison, five metabolites were found to be noticeable: glucose, malonic acid, 3-hyroxybutyrate, methanol, and tryptophan. It was very natural glucose was upregulated in STZ-mouse. 3-hyroxybutyrate was also increased in the model. However, malonic acid, tryptophan, and methanol was downregulated in STZ-mouse. Several metabolites acetoacetate, acetone, alanine, arginine, asparagine, histidine, lysine, malate, methionine, ornithine, proline, propylene glycol, threonine, tyrosine, and urea tended to be varied in STZ-mouse while the statistical significance was not stratified for the variation. The multivariate model of PCA clearly showed the group separation between healthy control and STZ-mouse. The most significant metabolites that contributed the group separation included glucose, citrate, ascorbate, and lactate. Lactate did not show the statistical significance of change in t-test while it tends to down-regulated both in DNP and Diabetes.

키워드

과제정보

This work was supported by the Gachon University research fund of 2019 (GCU-2019-0707). This research was also supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (2018R1D1A1B07050426).

참고문헌

  1. J. Rui, S. Deng, A. L. Perdigoto, G. Ponath, R. Kursawe, N. Lawlor, T. Sumida, M. Levine-Ritterman, M. L. Stitzel and D. Pitt, Nat. Commun. 12, 5074 (2021) https://doi.org/10.1038/s41467-021-25367-z
  2. L. Wigger, M. Barovic, A.-D. Brunner, F. Marzetta, E. Schoniger, F. Mehl, N. Kipke, D. Friedland, F. Burdet and C. Kessler, Nat. Metab. 3, 1017 (2021) https://doi.org/10.1038/s42255-021-00420-9
  3. L. Ikelle, M. I. Naash and M. R. Al-Ubaidi, Adv. Exp. Med. Biol. 1185, 335 (2019) https://doi.org/10.1007/978-3-030-27378-1_55
  4. N. C. Holoman, J. J. Aiello, T. D. Trobenter, M. J. Tarchick, M. R. Kozlowski, E. R. Makowski, C. Darryl, C. Singh, J. E. Sears and I. S. Samuels, J. Neurosci. 41, 3275 (2021) https://doi.org/10.1523/JNEUROSCI.2010-20.2021
  5. J. H. Lee, M. Samsuzzaman, M. G. Park, S. J. Park and S. Y. Kim. Int. J. Biol. Macromol. 187, 409 (2021) https://doi.org/10.1016/j.ijbiomac.2021.07.058
  6. J. Danielsson, P. Kangastupa, T. Laatikainen, M. Aalto and O. Niemela, World J. Gastroenterol. 20, 11743 (2014) https://doi.org/10.3748/wjg.v20.i33.11743
  7. J. Blahova, M. Martiniakova, M. Babikova, V. Kovacova, V. Mondockova and R. Omelka, Pharmaceuticals (Basel) 14, 806 (2021) https://doi.org/10.3390/ph14080806
  8. K. V. Gantenbein and C. Kanaka-Gantenbein. Nutrients 13, 1951 (2021) https://doi.org/10.3390/nu13061951
  9. Y. Liu, H. Zhang, S. Wang, Y. Guo, X. Fang, B. Zheng, W. Gao, H. Yu, Z. Chen, R. J. Roman and F. Fan, Am. J. Physiol. Heart Circ. Physiol. 320, H549 (2021) https://doi.org/10.1152/ajpheart.00726.2020
  10. H. C. Park, Y. K. Lee, A. Cho, C. H. Han, J. W. Noh, Y. J. Shin, S. H. Bae and H. Kim. PLoS One 14, e0220506 (2019) https://doi.org/10.1371/journal.pone.0220506
  11. J.-S. Hyun, J. Yang, H.-H. Kim, Y.-B. Lee and S. J. Park, J. Kor. Magn. Reson. Soc. 22, 149 (2018) https://doi.org/10.6564/JKMRS.2018.22.4.149
  12. K. Sanchez-Alegria, C. E. Bastian-Eugenio, L. Vaca and C. Arias. FASEB J. 35, e21712 (2021)
  13. X Pan, Y Zhu, X Wu, L Liu, R Ying, L Wang, N Du, J Zhang, J Jin, X Meng, F Dai and Y Huang. Eur. J. Pharmacol. 893, 173816 (2021) https://doi.org/10.1016/j.ejphar.2020.173816
  14. K. Abramov-Harpaz, M. Pollock-Gagolashvili and Y. Miller. ACS Chem. Neurosci. 12, 3266 (2021) https://doi.org/10.1021/acschemneuro.1c00445
  15. S. J. Park and J. W. Choi, Arch. Pharm. Res. 43, 1017 (2020) https://doi.org/10.1007/s12272-020-01278-3
  16. C. A. Daykin, P. J. Foxall, S. C. Connor, J. C. Lindon and J. K. Nicholson. Anal. Biochem. 304, 220 (2002) https://doi.org/10.1006/abio.2002.5637
  17. A. Guleria, A. Pratap, D. Dubey, A. Rawat, S. Chaurasia, E. Sukesh, S. Phatak, S. Ajmani, U. Kumar and C. L. Khetrapal, Sci. Rep. 6, 35309 (2016) https://doi.org/10.1038/srep35309
  18. M. Bylesjo, M. Rantalainen, O. Cloarec, J. K. Nicholson, E. Holmes and J. Trygg, J. Chemom. 20, 341 (2006) https://doi.org/10.1002/cem.1006
  19. A. Craig, O. Cloarec, E. Holmes, J. K. Nicholson and J. C. Lindon, Anal. Chem. 78, 2262 (2006) https://doi.org/10.1021/ac0519312
  20. F. Dieterle, A. Ross, G. Schlotterbeck and H. Senn, Anal. Chem. 78, 4281 (2006) https://doi.org/10.1021/ac051632c
  21. J. Trygg, E. Holmes and T. Lundstedt, J. Proteome Res. 6, 469 (2007) https://doi.org/10.1021/pr060594q
  22. S. Wiklund, E. Johansson, L. Sjostrom, E. J. Mellerowicz, U. Edlund, J. P. Shockcor, J. Gottfries, T. Moritz and J. Trygg, Anal. Chem. 80, 115 (2008) https://doi.org/10.1021/ac0713510
  23. F. Pesarin, Multivariate permutation tests: with applications in biostatistics: Wiley Chichester; 2001
  24. B. Worley, S. Halouska and R. Powers, Analytical biochemistry 433, 102 (2013) https://doi.org/10.1016/j.ab.2012.10.011
  25. J. D. Spurrier, J. Nonparametric Stat. 15, 685 (2003) https://doi.org/10.1080/10485250310001634719
  26. S. Hur, H. Lee, A. Shin and S. J. Park, J. Kor. Mag. Reson. Soc. 18, 10 (2014) https://doi.org/10.6564/JKMRS.2014.18.1.010
  27. M. S. Bjune, C. Lindquist, M. H. Stafsnes, B. Bjorndal, P. Bruheim, T. A. Aloysius, O. Nygard, J. Skorve, L. Madsen and S. N. Dankel, Biochim. Biophys. Acta Mol. Cell. Biol. Lipids 1866, 158887 (2021) https://doi.org/10.1016/j.bbalip.2021.158887
  28. F. Zhang, R. Guo, W. Cui, L. Wang, J. Xiao, J. Shang and Z. Zhao, Ren. Fail. 43, 980 (2021) https://doi.org/10.1080/0886022X.2021.1937219