Superovulatory response and embryo quality in Boer does following dietary supplementation with different sources of omega-3 and omega-6 fatty acids during the breeding season

The purpose of this study was to determine effects of various sources of omega-3 and omega-6 fatty acids on ovarian response and embryo quality in Boer does when there was a superovulation treatment regimen imposed. Pluriparous does were randomly assigned to be treated with 300 g of one of four experimental supplements containing linseed oil (LO), soybean oil (SO), palm oil (PO), or a control supplement without fatty acids (CO), for 15 days. Does were fitted with a controlled internal drug release (CIDR) device containing 0.3 g progesterone for 7 days. At 48 h before CIDR withdrawal, does were treated with 80 mg follicle-stimulating hormone (FSH) administered at 12 h intervals. Embryos were collected 7 days after the last natural mating. Estrous response and interval between CIDR withdrawals to estrous onset were similar between treatments (P > 0.05). Number of ovulations was similar for does in the different groups (10.0, 9.2, 7.0, and 7.0, in LO, SO, PO, and CO, respectively; P > 0.05). There was premature luteal regression in does of the SO, PO, and CO groups, except in LO group. The LO-treated does had a larger (P < 0.05) mean number of ova/embryos recovered than does of SO, PO, and CO groups (7.2, 2.0, 0.2, 0.2, respectively) and transferable embryos (5.1, 1.4, 0.2, 0.2, respectively). These results indicate that including LO in supplements may be a feasible strategy for preventing premature luteal regression and improving embryo quality in goats treated to induce follicular super-stimulation for induction of superovulation.

In vitro progesterone production by luteinized human mural granulosa cells is modulated by activation of AMPK and cause of infertility

BACKGROUND

Mural granulosa cells from IVF patients were provided by the West Virginia University Center for Reproductive Medicine in Morgantown, WV. The effect of adenosine monophosphate activated protein kinase (AMPK) activation, primary cause of infertility, age, BMI, and pregnancy outcome on production of progesterone were examined separately.

METHODS

Isolated mural sheets from IVF patients (n = 26) were centrifuged, supernatant discarded, and the pellet re-suspended in 500 μl of DMEM/F12. Mural granulosa cells were plated at 10,000 cells/well in triplicate per treatment group with 300 μl DMEM/F12 media at 37 °C and 5% CO2 in a humidified incubator to permit luteinization. Four days after initial plating, cells were treated with either an AMPK inhibitor, DM; an AMPK activator, AICAR; or hCG. Cells were cultured for 24 h after treatment when medium was collected and frozen at -20 °C until assayed for P4 by radioimmunoassay.

RESULTS

The AMPK activator, AICAR, inhibited P4 production (P < 0.001), whereas the AMPK inhibitor, DM, did not affect basal P4 (P < 0.05). Progesterone production increased when cells from patients whose primary cause of infertility was a partner having male infertility were treated with hCG compared to control (P = 0.0045), but not in patients with other primary infertility factors (P > 0.05). Additionally, hCG increased P4 production in patients between the ages 30-35 (P = 0.008) and 36-39 (P = 0.04), but not in patients ages 25-29 (P = 0.73). Patients with normal BMI had increased P4 production when treated with hCG (P < 0.0001), however there was no change in P4 production from cells of patients who were overweight or obese (P > 0.05). Cells from patients who became pregnant to IVF had greater P4 production when stimulated with hCG than those who did not become pregnant when compared to controls (P > 0.05).

CONCLUSIONS

Understanding how AMPK activation is regulated in ovarian cells could lead to alternative or novel infertility treatments. Human mural granulosa cells can serve as a valuable model for understanding how AMPK affects P4 production in steroidogenic cells. Additionally, when stimulated with hCG, P4 production by mural granulosa cells differed among infertility type, age, BMI, and pregnancy outcome.