Restoring effect of selenium on the molecular content, structure and fluidity of diabetic rat kidney brush border cell membrane


GURBANOV R., Bilgin M., Severcan F.

Biochimica et Biophysica Acta - Biomembranes, vol.1858, no.4, pp.845-854, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 1858 Issue: 4
  • Publication Date: 2016
  • Doi Number: 10.1016/j.bbamem.2016.02.001
  • Journal Name: Biochimica et Biophysica Acta - Biomembranes
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.845-854
  • Keywords: Artificial neural network (ANN), ATR-FTIR spectroscopy, Chemometrics, Curve-fitting, Diabetic kidney disease (DKD), Selenium
  • Bilecik Şeyh Edebali University Affiliated: No

Abstract

Diabetic kidney disease (DKD) is a dominant factor standing for kidney impairments during diabetes. In this study, attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy was used to disclose the diabetes-induced structural changes in the kidney and evaluate the effects of selenium on diabetes. The increase in the area of the olefinic band indicated increased amount of lipid peroxidation end products in diabetic kidney brush border cell membrane. Moreover, saturated lipid content of this cell membrane considerably diminished. DKD was found to disrupt lipid order and cause a decrease in membrane dynamics. However, the administration of selenium at low and medium doses was shown to improve these conditions by changing the lipid contents toward control values, restoring the ordered structure of the lipids and membrane dynamics. Curve-fitting and artificial neural network (ANN) analyses of secondary structures of proteins demonstrated a relative increase in α-helix and reduction in the β-sheet during diabetes in comparison to the control group, which were ameliorated following selenium treatment at low and medium doses. These findings were further confirmed by applying hierarchical cluster analysis (HCA) and principal component analysis (PCA). A clear separation of the experimental groups was obtained with high heterogeneity in the lipid and protein regions. These chemometric analyses showed that the low and medium doses of selenium-treated diabetic groups are successfully segregated from the diabetic group and clustered closer to the control. The study suggests that medium and, more predominantly, low-dose selenium treatment can be efficient in eliminating diabetes-induced structural alterations.