The earliest biochemical indicators of ovarian follicle deviation in cattle include lower oestradiol and free IGF concentrations in subordinate compared with dominant follicles. We determined if decreases in FSH, IGF-I or insulin cause decreased P450 aromatase (P450arom) or P450 cholesterol side-chain cleavage (P450scc) mRNA expression in oestrogenic bovine granulosa cells in vitro. In the first experiment, cells obtained from small follicles (2-5 mm diameter) were cultured in serum-free medium supplemented with physiological concentrations of FSH, IGF-I and insulin for 4 days. A decrease in specific hormone concentration was produced by replacing 70% of spent medium with medium devoid of FSH, insulin, or insulin and IGF-I on day 4 and again on day 5 of culture. Cultures were terminated on day 7. A reduction in FSH concentrations during the last 3 days of culture decreased P450arom and P450scc mRNA levels. A reduction in insulin reduced P450arom but not P450scc mRNA levels, and a reduction of both insulin and IGF-I concentrations further decreased P450arom mRNA levels and decreased P450scc mRNA levels. In a second experiment, cells obtained from small follicles (2-5 mm diameter) were cultured with insulin (100 ng/ml) without FSH for 4 days, and then insulin was withdrawn from the culture and FSH added for a further 3 days. The withdrawal of insulin decreased (P<0.02) oestradiol accumulation and reduced P450arom mRNA to below detectable levels, but did not affect P450scc mRNA levels. The addition of FSH transiently increased oestradiol secretion and P450arom mRNA levels, but P450arom mRNA levels were undetectable at the end of the culture period. The addition of FSH significantly enhanced P450scc mRNA levels and progesterone accumulation. These data demonstrated that a reduction of insulin-like activity reduced aromatase gene expression in bovine follicles without necessarily affecting progesterone synthetic capability, and thus may initiate follicle regression in cattle at the time of follicle divergence.
Journal of Endocrinology is committed to supporting researchers in demonstrating the impact of their articles published in the journal.
The two types of article metrics we measure are (i) more traditional full-text views and pdf downloads, and (ii) Altmetric data, which shows the wider impact of articles in a range of non-traditional sources, such as social media.
More information is on the Reasons to publish page.
|Sept 2018 onwards||Past Year||Past 30 Days|
|Full Text Views||91||83||1|