Author/Authors :
Komar، Carolyn M. نويسنده , , Jr، Thomas E. Curry, نويسنده ,
Abstract :
Messenger RNA for peroxisome proliferator-activated receptor (gamma) (PPAR(gamma)) has been found in granulosa cells, and its expression decreases after the LH surge. We determined which developmental stage of ovarian follicle expresses mRNA for PPAR(gamma) and evaluated the impact of PPAR(gamma) agonists on steroidogenesis. Ovaries were collected from immature eCG/hCG-treated rats at 0 (no eCG), 24, and 48 h post-eCG and 4 and 24 h post-hCG. Ovarian tissue was serially sectioned and processed for in situ hybridization to localize mRNA corresponding to PPAR(gamma), aromatase, and the LH receptor, and P450 side chain cleavage (P450SCC) and to determine whether apoptotic cells were present. During follicular development, there was no correlation between the expression of mRNAs for PPAR(gamma) and aromatase or the presence of apoptotic cells, but a general inverse correlation was observed between the expression of PPAR(gamma) mRNA and LH receptor mRNA. At 4 h posthCG, follicles expressing P450SCC mRNA had lost expression of PPAR(gamma) mRNA. This inverse pattern of expression between PPAR(gamma) and P450SCC mRNAs was also observed 24 h post-hCG, with developing luteal tissue expressing high levels of P450SCC mRNA but little or no PPAR(gamma) mRNA. To determine the impact of PPAR(gamma) on steroidogenesis, granulosa cells were collected from ovaries 24 h post-eCG and cultured alone, with FSH alone, or with FSH in combination with the PPAR(gamma) agonists ciglitazone or 15-deoxy-(delta)12,14prostaglandin J2 (PGJ2). Treatment of granulosa cells with PGJ2 stimulated basal progesterone secretion, whereas ciglitazone or PGJ2 had no significant effect on FSH-stimulated steroid production. These findings suggest that 1) PPAR(gamma) may regulate genes involved with follicular differentiation and 2) the decline in PPAR(gamma) in response to LH is important for ovulation and/or luteinization.
Keywords :
structure from motion , motion segmentation , dynamic scene reconstruction , computer vision