University of Illinois at Chicago
Browse
Bauschard Michael.pdf (2.63 MB)

Conditional Knockdown of the Insulin Receptor in Ovarian Granulosa and Theca Cells and Female Fertility

Download (2.63 MB)
thesis
posted on 2012-12-13, 00:00 authored by Michael J. Bauschard
Insulin is a pleiotropic hormone involved in the controls of metabolism, growth, and survival in a wide range of tissues. In the ovary, insulin has been shown to synergize with the primary gonadotropins, FSH and LH, on the regulation of granulosa and theca cells. In vivo knockout of downstream signaling proteins of the insulin receptor leads to ovarian dysfunction. We hypothesize that insulin actions are crucial for normal ovarian function. To examine this hypothesis, we bred CYP19-Cre and CYP17-Cre mice with Insulin Receptor flox/flox mice in order to knockdown the receptor in granulosa and theca cells, respectively. We also sought to investigate the mechanism by which insulin synergizes with FSH through the use of cultured granulosa cells. PCOS is linked with obesity and hyperinsulemia in women. It is characterized in part by higher levels of androgens, and it has been shown that increased levels of circulating insulin correlate with excessive androgen production. In rodents, a high-fat diet feeding leads to hyperinsulinemia and ovarian dysfunction, suggesting that elevated levels of insulin may also have a negative effect on ovarian function. We hypothesize that knockdown of the insulin receptor using our cre-lox system will ablate the detrimental effects of hyperinsulemia in these mice. We found that inhibition of either PI3K or ERK in cultured granulosa cells is able to ablate FSH and insulin synergism on CYP19 aromatase expression, suggesting a role for both of these pathways. Our cre-lox system used to knockdown insulin receptor in ovarian cells showed a 75% reduction in INSR expression in granulosa cells. However, downstream signaling of the INSR, as measured by phosphorylation of AKT, showed no reduction in double knockout mice. This suggests that insulin action in the ovary was not affected in our knockdown animals. Accordingly, no reproductive defects in these mice were observed. HFD feeding of mice led to ovarian dysfunction characterized by abnormal estrous cycling and aberrant ovarian morphology. However, a decrease of insulin receptor expression in the ovary did not prevent the detrimental effects of the HFD. As the HFD increased body weight mostly through adipose tissue, this tissue may be responsible for secreting factors that affect ovarian function. Future studies will use an alternative cre lox system which will include further knockdown of the insulin receptor and well as a similar receptor, the IGF-1 receptor. We will also attempt to knockdown signaling of adipose secreted factors in the ovary, as well as assess HFD feeding of mice with an alternate knockdown of the insulin and the IGF-1 receptor. In summary, while studies have shown a significant effect on insulin in ovarian cells in vitro, research has yet to isolate insulin actions in the ovary in vivo. Ablating insulin action in vivo in granulosa and theca cells is an important step towards understanding the role of this hormone in ovarian dysfunction. We have provided new information regarding the intracellular signaling pathways that mediate the effect of insulin on aromatase expression in granulosa cells. Thus, our findings demonstrated that both Akt and EKR1/2 activation is needed for full activation of the aromatase gene by FSH and insulin. We have verified the detrimental effect of a HFD on ovarian function in mice, and shown this defect is not remediated by a reduction in the insulin receptor.

History

Advisor

Gibori, Geula

Department

Physiology and Biophysics

Degree Grantor

University of Illinois at Chicago

Degree Level

  • Masters

Committee Member

Carlos Stocco Terry Unterman

Submitted date

2012-08

Language

  • en

Issue date

2012-12-13

Usage metrics

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC