Figure 1.
Standardization of experimental animal model for characterization of periovulatory events during the LH surge.
(A) Schematic representation of the experimental protocol employed for collection of follicular contents before and at different time points after peak LH surge. Follicular fluid, granulosa cells and follicle wall of the ovulatory follicle were harvested from the ovaries collected at the indicated time points for carrying out various analyses. (B) Circulating concentration of P4 during different days of estrous cycle for collection of follicular contents before and at different time points post peak LH surge. Serum P4 level was determined in blood samples collected daily beginning on day 1 of estrous cycle and at 12 h intervals after administration of PGF2α and GnRH treatments. For different days of estrous cycle, values from all animals recruited for this experiment (n = 23) were pooled and represented as mean ± SEM. (C) Mean ± SEM serum concentration of LH and FSH during the preovulatory period. (D) Follicular fluid concentration (mean ± SEM) of E2 and P4 at different time points before and after peak LH surge (n = >5 animals/time point). For each hormone, bars with different superscripts are significantly different (p<0.05).
Figure 2.
Schematic representation of temporal changes in differentially expressed genes at 1 h and 22 h post peak LH surge.
(A) Venn diagram representing the details of differentially expressed genes identified after microarray analysis of granulosa cells collected from the ovulatory follicle at −2, 1 and 22 h post peak LH surge. Data analyzed with ≥2 fold change cut-off and statistics. The number of differentially expressed genes found common between −2 vs. 1 h (red circle) and −2 vs. 22 h (blue circle) post peak LH surge, as well as comparison of differentially expressed genes between 1 vs. 22 h (green circle) post peak LH surge are presented. (B) Schematic representation of differentially expressed genes at different time points post peak LH surge. Red hexagon represents genes that were up regulated both at 1 and 22 h as well as those genes that were down regulated at 1 h but were up regulated at 22 h time point. Green hexagon represents differentially expressed genes that were down regulated both at 1 and 22 h time points as well as those genes that were up regulated at 1 h but were down regulated at 22 h time point. Also provided is the number of genes (represented as open hexagon) that were differentially expressed at 1 h time point but not at 22 h. (C) The number of differentially expressed genes listed as alphabetical groups in Fig. 2B are further represented in tabular form providing details on the number of genes in each group, pattern of expression change at both time points post peak LH surge as well as classification based on their ontological distribution within each ‘biological processes’ terms having high scores indicative of processes associated with each group.
Figure 3.
Classification of differentially expressed genes into various canonical pathways by Ingenuity pathway analysis (IPA).
The pathway analysis indicates that a large number of differentially expressed genes belonged to canonical pathways such as PI3K/AKT signaling; IGF-I signaling and coagulation system. The orange line represents score for the likelihood [−log (B–H P<0.05)] that genes belonging to a specific signaling pathway category affected at 1 and 22 h post peak LH surge. The stacked bars indicate the percentage of genes distributed according to regulation i.e. green (down), red (up) and open bars (No overlap with dataset) in each canonical pathway.
Figure 4.
Network 1; Ingenuity pathway analysis of the differentially regulated genes at 1 h post peak LH surge shows a network of 22 focus molecules with a score of 40, with top biological functions of cell-to-cell signaling and interaction, cell cycle and tissue development and morphology.
The network is displayed graphically as nodes (genes/gene products) and edges (biological relationship between nodes). The node colour intensity indicates the fold change expression of genes; with red representing up-regulation, and green down-regulation of genes between −2 vs. 1 h post peak LH surge in granulosa cells. The fold change value for individual gene is indicated under each node. The shapes of nodes indicate the functional class of the gene product and the lines indicate the type of interaction.
Figure 5.
Network 2; Ingenuity pathway analysis of the differentially regulated gene at 22 h post peak LH surge shows a network of 20 focus molecules with a score of 35, with top biological functions of cell-to-cell signaling and interaction, cell death, and endocrine system disorders.
The network is displayed graphically as nodes (genes/gene products) and edges (biological relationship between nodes). The node color intensity indicates the fold change expression of genes; with red representing up-regulation, and green down-regulation of genes between −2 vs. 22 h post peak LH surge in granulosa cells. The fold value for individual gene is indicated under each node. The shapes of nodes indicate the functional class of the gene product and the lines indicate the type of interaction.
Figure 6.
Expression of genes associated with steroidogenesis, cell adhesion and proteolysis in the periovulatory follicle.
(A) Microarray fold expression changes at 1 and 22 h post peak LH surge in granulosa cells for few of the selected up and down regulated genes associated with specific biological process. (B) The genes selected by microarray analysis were also subjected to qPCR analysis and fold expression changes for granulosa cells and follicular wall collected at different time points are represented as bar graphs. Bar for each gene represents mean ± SEM fold expression change value at each time point (n = 3 animals). For each gene, bars with different alphabets above them are significantly different (p<0.05). More details on the analysis are provided in the results section.
Figure 7.
Expression of genes associated with cell survival, apoptosis and blood coagulation in periovulatory follicle.
(A) Microarray fold expression changes at 1 and 22 h post peak LH surge in granulosa cells for few of the selected up and down regulated genes associated with specific biological process like cell survival, apoptosis and blood coagulation are represented as bar graph. (B) The genes selected by microarray analysis were also subjected to qPCR analysis and the fold expression changes for granulosa cells and follicular wall collected at different time points are represented in bar graphs. Bar for each gene represents the mean ± SEM fold expression change values at each time point (n = 3 animals). Bars with different alphabets above them are significantly different (p<0.05). More details on the analysis are provided in the results section.
Figure 8.
Effects of bGH treatment on IGFBP expression and various hormone concentration.
(A) Circulating plasma concentration (mean ±SEM) of IGF-I in blood samples collected at different time intervals in Veh and bGH treated animals. Bars with different letters above them are significantly different (p<0.05). (B) Serum concentration of LH before and after GnRH administration to induce ovulation in buffalo cows treated with Veh (solid triangle) or bGH (solid circle). Concentration of LH was determined in blood samples collected at various time points before and after administration of GnRH to elicit endogenous LH surge. (C) Concentration of IGF-I (ng/ml) measured in the follicular fluid collected from follicles before (−2 h) and 22 h post peak LH surge of Veh or bGH treated animals. Bars with different letters above them are significantly different (p<0.05). (D) Mean ± SEM concentrations of E2 and P4 in follicular fluid collected from follicles before and at 22 h post peak LH surge of Veh or bGH treated animals. Bars with different alphabets within each hormone indicate significantly different concentration (p<0.05). (E) Protein expression of IGFBP2, IGFBP3 and Cleaved IGFBP3 in the ovarian follicular fluid. Protein lysates (100 µg) prepared from follicular fluid collected from the preovulatory follicle before (−2 h) and 22 h post peak LH surge of Veh or bGH treated animals were resolved on 10% SDS PAGE, transferred onto PVDF membrane and immunoblot analysis was performed using anti-IGFBP2 or anti-IGFBP3 antibody. The positions of 36 kDa IGFBP2 and 44–40 kDa IGFBP3 doublet bands are indicated by arrows and 30 kDa bands of IGFBP3 proteolytic fragment is highlighted in the boxed area. Densitometric values were determined and are indicated as mean ± SEM below the bands. The image of Ponceau-S stained membrane is depicted to indicate that equal amount of protein was loaded in each lane. (F) Densitometric analysis of immunoblots for 30 kDa IGFBP3 proteolytic fragment: mean ± SEM of relative amount of 30 kDa IGFBP3 proteolytic fragment expressed as intensity of Ponceau-S stained bands in each group (n = 3 animals/group). Bars with different letters are significantly different (p<0.05).
Figure 9.
Gene expression changes in the preovulatory follicle of animals receiving bGH treatment.
The qPCR fold change in expression pattern of selected genes associated with of IGF-I signaling (IGF-IR and PAPPA), luteinization (Oxytocin and StAR), cell survival, proliferation and differentiation (BCL-2, FKHR, PAWR, RASA1 and SRF) in granulosa cells of follicles retrieved before (green) and 22 h post peak LH surge of Veh (blue) or bGH (orange) treated animals. Bars with different alphabets above them are significantly different (p<0.05).