Up-regulation of miR-21 and 146a expression and increased DNA damage frequency in a mouse model of polycystic ovary syndrome (PCOS).

Salimi-Asl, Mohammad; Mozdarani, Hossein; Kadivar, Mehdi

Introduction: Polycystic ovary syndrome (PCOS), a multigenic endocrine disorder, is highly associated with low-grade chronic inflammation, however its etiology remains unclear. In this study, we employed dehydroepiandrosterone (DHEA)-treated mice to reveal the molecular mechanism of inflammation and its correlation with oxidative stress in PCOS patients. Methods: miR-21 and miR-146a expression levels were measured using quantitative real-time polymerase chain reaction (qRT-PCR). DNA strand breakage frequency was measured using the single cell gel electrophoresis (SCGE) assay (comet assay) and micronucleus test (MN). CRP levels were measured by ELISA method and ESR values were measured by means of Micro-Dispette (Fisher No: 02-675-256) tubes according to the manufacturer's instructions. Data were analyzed using one-way ANOVA in SPSS 21.0 software. Results: Our results showed that miR-21 and miR-146a as inflammation markers were upregulated in the sample group in comparison with control group. Erythrocyte sedimentation rate (ESR) and C- reactive protein (CRP) levels were also increased in mouse models of PCOS (p < 0.000). Micronucleated polychromatic erythrocyte (MNPCE) rates per 1000 polychromatic erythrocyte (PCE) significantly increased in DHEA treated mice (6.22 ± 3.28) in comparison with the controls (2.33 ± 2.23, p < 0.000). Moreover, mean arbitrary unit in DHEA treated animals (277 ± 92) was significantly higher than that in controls (184 ± 76, p = 0.005). Conclusion: To conclude, increased DNA strand breakage frequency and increased expression levels of miR-21 and miR-146a in DHEA administrated animals suggest that low grade chronic inflammation and oxidative stress can act as the main etiologies of PCOS.

POLYCYSTIC ovary syndrome treatment; POLYCYSTIC ovary syndrome; MICRORNA; GENE expression; DNA damage; POLYMERASE chain reaction; DATA analysis; LABORATORY mice; GENETICS

BioImpacts, 2016, Vol 6, Issue 2, p85

2228-5652

Academic Journal

10.15171/bi.2016.12