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Effects of bisphenol A and bisphenol S on microRNA expression during bovine (Bos taurus) oocyte maturation and early embryo development.

Reproductive toxicology (Elmsford, N.Y.) | 2021

Bisphenol A (BPA) and its alternative, bisphenol S (BPS), are widespread endocrine disrupting compounds linked in several studies to poor female fertility. Sufficient oocyte competence and subsequent embryo development are highly dependent on oocyte maturation, an intricate process that is vulnerable to BPA. These effects as well as the effects of its analog, BPS, have not been fully elucidated. Although the harmful consequences of bisphenols on the reproductive system are largely due to interferences with canonical gene expression, more recent evidence implicates noncoding RNAs, including microRNAs (miRNA), as significant contributors. The aim of this work was to test the hypothesis that abnormal expression of key miRNAs during oocyte maturation and embryo development occurs following BPA and BPS exposure during maturation. Using qPCR, primary and mature forms of miR-21, -155, -34c, -29a, -10b, -146a were quantified in an in vitro bovine model of matured cumulus-oocyte complexes, fertilized embryos, and cultured cumulus cells after exposure to BPA or BPS at the LOAEL dose (0.05 mg/mL). Expression of miR-21, miR -155, and miR-29a were markedly increased (P = 0.02, 0.04, <0.0001) while miR-34c and miR-10b were decreased (P = 0.01, 0.01), after BPA treatment. miR-146a expression remained stable. BPS had no effects, suggesting may not exert its actions through these six miRNAs examined. Overall, this study indicates that BPA effects are likely miRNA specific rather than a global effect on miRNA synthesis and processing mechanisms and that its analog, BPS, may not possess the same properties required to interfere with these miRNAs during bovine oocyte maturation.

Pubmed ID: 33285269 RIS Download

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geNORM (tool)

RRID:SCR_006763

Software to determine most stable reference (housekeeping) genes from set of tested candidate reference genes in given sample panel. From this, gene expression normalization factor can be calculated for each sample based geometric mean of user-defined number of reference genes.

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