Mural granulosa cell gene expression associated with oocyte developmental competence

All reagents were purchased from Sigma Chemical Company (St. Louis, MO) unless otherwise stated.

Three male rabbits (2.5 - 3.0 Kg body weight [BW]) were used to produce anti-eCG antisera as described previously [21]. Antibody titres were determined by ELISA. In the bioassay for the antiserum, immature female rats injected with 10 IU eCG were injected 24 h later with highest-titre antiserum or pre-immune serum (100 ul of 1:5 to 1:200 dilution in PBS, i.p.). The ovaries were removed 24 h after treatment and weighed, the ability of various concentrations of the antiserum to prevent eCG-induced ovarian weight gain was assessed. The above dilution (1:5 to 1:200 dilution) of anti-eCG serum significantly decreased ovarian weight in eCG-primed rats.

To assess the developmental competence of oocytes which were morphologically indistinguishable in both groups, COCs were inseminated in vitro and the fertilized oocytes were transferred into pseudo-pregnant rats as described previously [23]. Briefly, sperm suspensions (1 × 10⁶cells/ml) were pre-incubated in insemination media (400 μl of IVF-30 supplemented with 30 mM NaCl) for 5 to 7 h at 37°C in 5% CO₂ in air. COCs were then carefully transferred into the suspension drops and incubated for 12 h. The oocytes were transferred into 100 μl of culture medium and freed from surrounding cumulus cells. The denuded oocytes were considered fertilized if they exhibited the presence of pronuclei with sperm tail(s) in the vitellus.

To assess the developmental competence in vivo of embryos fertilized in vitro, nine to ten embryos at the 1-cell stage were transferred to the oviducts of each pseudo-pregnant recipient at Day 1. Vaginal smear of recipients was examined on days 1 and 4 as well as days 12-14 after transfer to confirm successful induction of pseudo-pregnancy and signs of pregnancy, respectively. All recipients were sacrificed by day 24 of pregnancy regardless of delivering offspring, and their uterine horns were examined for implantation sites. The number of young was counted on the day of parturition.

Total RNAs from mural granulosa cells collected from ovarian follicles were extracted using RNeasy Mini kit according to manufacturer's instructions and DNA contamination was removed by DNase I digestion (Qiagen Inc., Mississauga, ON, Canada). All total RNA specimens were quantified and checked for quality with a Bioanalyzer 2100 system (Agilent, Palo Alto, CA) before further manipulation.

A total of 4 NDC and 4 PDC samples were used, thus requiring a total of 8 GeneChips. The GeneChip hybridization and image acquisition were performed at the Ontario Genome Center. Briefly, two rounds of amplification were carried out to successfully generate sufficient labeled cRNA for microarray analysis from 100 ng of total RNA. For first round synthesis of double-stranded cDNA, total RNA was reverse transcribed using the Two-Cycle cDNA Synthesis kit (Affymetrix) and oligo (dT) 24-T7 (5'-GGCCAGTGAATTGTAATACGACTCACTATAGGGAGGCGG-3') primer followed by amplification with the MEGAscript T7 kit (Ambion, Inc., Austin, TX). After cleanup of the cRNA with a GeneChip Sample Cleanup Module IVT Column (Affymetrix), a second-round double-stranded cDNA was produced using the IVT Labeling kit (Affymetrix). A 15 μg-aliquot of labeled product was fragmented by heat and ion-mediated hydrolysis (94°C, 35 minutes) in 24 μL H₂O and 6 μL of 5× fragmentation buffer (Affymetrix). The fragmented cRNA was made into hybridization cocktail and was hybridized (16 h, 45°C) to an Affymetrix Rat 230.2 array. Washing and staining of the arrays with phycoerythrin-conjugated streptavidin (Molecular Probes, Eugene, OR) was completed in a Fluidics Station 450 (Affymetrix). The arrays were then scanned using a confocal laser GeneChip Scanner 3000 and GeneChip Operating Software (Affymetrix).

Gene expression patterns were determined using Affymetrix Genechip Arrays Rat 230.2. Prior to any statistical analysis, raw data were normalized and compared using RMA (robust multichip average) method from the BioConductor package http://​www.​bioconductor.​org, which uses a robust average of log₂-transformed background-corrected perfect match probe signal intensities combined with a quantile normalization method [24, 25]. The quality analysis of the slides was performed by checking the logarithmic scatter plots of probe set intensities in all the non-redundant pairs of replicated samples after the normalization procedure [26]. Normalized data were then filtered in three steps. First, probe sets called 'Absent' (A) over all conditions and replicates across the complete dataset were excluded. Second, a threshold as the 95^th percentile of all the absent call signals of the entire dataset was set. All the remaining probe sets whose expression values were consistently below this value were removed in each sample [26]. To extract significant genes between two independent groups, the two-sample t statistic was used for filtered genes. In addition, multiple testing corrections were performed by computing adjusted p values using the Bonferroni and Sidak algorithm which provides experiment-wise (or Family-wise) type I control. Genes with a fold change of 1.2 (increase or decrease) relative to the poor oocyte developmental competence were subsequently used. Hierarchical clustering of samples and gene expression values based on similarities of expression levels was performed using the average linkage method and Euclidean distance measurements as implemented in the TIGR Multiexperiment Viewer (MeV) program [27]. Gene Ontology (GO) analysis was performed with DAVID http://​david.​abcc.​ncifcrf.​gov/​[28].

Reproducibility between experiments was assessed by calculating the pairwise concordance of presence calls, which was 92.1-97.9%, and by computing the pairwise Adjusted Coefficient of Determination of log-transformed signal intensities (average of 0.952). High correlation of array signals (low intra-experimental group variation) was observed between rat samples within the groups with oocytes showing normal and poor developmental competence (Data not shown).

In order to validate the results of microarray, real time RT-PCR analysis was performed on all 8 samples. Briefly, 0.4 μg of total RNAs extracted from mural granulosa cells of each rat ovarian follicles were reverse transcribed in a final volume of 40 μl solution containing First-Strand Buffer, dNTPs, dithiothreitol (DTT), RevertAid Enzyme (Fermentas), and Random Decamer Primers (Ambion, Inc.). Ten representative genes whose expression levels were remarkably changed in microarray (see Table 1) were further validated, they are lysyl oxidase (Lox), glycoprotein-4-beta-galactosyltransferase 2 (Ggbt2; UDP-Gal), nerve growth factor receptor associated protein 1 (Ngfrap1), protein disulfide isomerase-associated 5 and 6 (Pdia5 and Pdia6), myeloid ecotropic viral integration site 1 homolog (Meis1), CD83 antigen, lysozyme (Lyz), trinucleotide repeat containing 6 (Tnrc6), interleukin 13 receptor alpha 1 (Il13ra1). Real-time quantitative PCR analyses for those genes were performed using a LightCycler 2.0 System (Roche Diagnostic Corporation) and a QuantiTect SYBR Green PCR kit (Qiagen, Mississauga, ON, Canada). The thermal cycling conditions were comprised of an initial denaturation step at 95°C (15 min) and 40 cycles at 95°C (15 sec), 58°C (20 sec) and 72°C (30 sec). The primer sequence for each gene, their PCR product size, primer location on rat chromosome, and GeneBank access numbers were shown in Table 1. 18S ribosomal RNA was used as control. Target gene expression level was calculated by relative expression ratio (RER) of Normal Developmental Competence (NDC) to Poor Developmental Competence (PDC), all normalized by 18S as described previously [29]. Briefly, the Livak Method (2^-ΔΔCt method) was performed by the following formula: 1) Calculate crossing point change of NDC relative to housekeeping gene 18S, ΔCt (NDC) = Ct (target gene, NDC)-Ct (18S, NDC); 2) Calculate crossing point change of PDC relative to housekeeping gene 18S, ΔCt (PDC) = Ct (target gene PDC) - Ct(18S, PDC); 3) Calculate the difference of these changes between NDC and PDC group, ΔΔCt = ΔCt(NDC)-ΔCt(PDC); 4) finally calculate RER = 2^-ΔΔCt. Fold changes by real-time qPCR in Table 1 were calculated by Mean of RER for NDC over PDC.

Data in Table 2 and real-time PCR results (Fig. 1) were analyzed by student's t-test tests using Graph Pad Prism 3 software. Differences with P < 0.05 were considered statistically significant.

Treatment of eCG-primed rats with low dose of anti-eCG antiserum (1:400 dilution) failed to significantly decrease paired ovarian weight (108.2 ± 7.9 mg versus 93.3 ± 4.7 mg; P > 0.05) and fertilization rates (93.5 ± 2.7% versus 95.8 ± 2.2%, P > 0.05) when compared with those in eCG plus pre-immune serum-treated group (Table 2). However, anti-eCG antiserum injection resulted in the production of oocytes with poor developmental competence. No embryos in this group could develop to term after embryo transfer. In contrast, as high as 30%-50% of oocytes from eCG-primed rats developed to offspring (P < 0.05). No significant differences in the number of implantation sites were observed between two groups (Table 2).

The global gene expression profiles in rat granulosa cell samples representing oocytes of poor and normal developmental competence were identified with microarray technique. Results in Fig. 2 (left panel) show that among the approximately 30,000 genes queried on Rat 230.2 array, there were more undetected genes than detected genes observed in all arrays. Mean expression intensities of detected genes were higher than those of undetected genes (Fig. 2, right panel). A log₂ signal intensity threshold of 98.3 was determined and only those genes with signal intensity smaller than 98.3 were filtered. 8985 genes were left for further analysis.

Of a total of about 30,000 probe sets, we observed that the expression of 701 genes (Table 3) were significantly different (P < 0.001) between oocytes with poor developmental competence compared to normal one, 43 of which were altered > 1.2-fold, and 13 of which > 1.5-fold. Both up- or down-requlated genes are shown in Table 3. A Euclidean clustering of these differential genes is shown in Fig. 3. All four samples from poor oocyte developmental competence (PDC) group had similar gene expression patterns and were included in the same PDC cluster. On the other hand, all other four samples from normal oocyte developmental competence (NDC) group had similar gene expression patterns and were included in the same NDC cluster. The gene expression patterns were very different between PDC and NDC clusters.

Gene ontology analysis showed that up-regulated genes in oocytes with normal developmental competence were linked to transcription regulation, protein phosphorylation and signal transduction, microtubule cytoskeleton organization and movement (Table 3). The genes participating in transcriptional regulation included nucleosome assembly protein 1-like 1, Necdin, Meis 1 and TAF9 RNA polymerase II and a transcribed locus homologous to polymerase I-transcript release factor (PTRF), while those involved in the control of protein phosphorylation and signal transduction were Lox, Pdia5 and Pdia6, golgi autoantigen and cell division cycle 2-like 5. The genes having a role in microtubule cytoskeleton organization and movement include CD83 antigen, Tnrc6, Goliath, vesicle-associated membrane protein 8 (Table 3).

Twelve genes were up-regulated, and one gene down-regulated, more than 1.5 folds in NDC group than those in PDC group. Gene ontology classification showed that the up-regulated genes included Lox and Ngfrap1. Lox is important in the regulation of copper ion binding [30]. Ngfrap1 plays an important role in apoptosis induction [31]. The down-regulated gene is Ggbt2 known to be involved in the regulation of extracellular matrix organization and biogenesis [32].

To determine the signaling pathways of up-regulated genes associated with normal oocyte developmental competence, all genes with more than 1.2-fold change were subjected to the pathway analysis by Pathwayexplorer https://​pathwayexplorer.​genome.​tugraz.​at/​. Although no directly related pathways were found, a potential signaling pathway of the highest-regulated gene, Lox, could be envisaged since oocyte-derived factors such as GDF-9 increases gene expression of Lox which induces differentiation of mural granulosa cells [33].

Ten representative genes, the expression levels of which were remarkably changed in microarray (Table 3), were selected for further validation by RT-PCR analyses. Of ten genes selected, Lox, Pdia5, and CD83 antigen mRNA abundance of mural granulosa cells in normal oocyte developmental competence group were higher (fold changes > 1.6) than that in poor oocyte developmental competence group, consistently in both gene microarray and quantitative RT-PCR analyses. The fold change from microarray and that from RT-PCR exhibit excellent concordance, with Pearson correlation equal to 0.94 (p < 0.0001). However, only Lox was statistically significantly different between the two groups (fold changes > 2.8, P < 0.05, Fig. 1 and Table 1). Our data suggested that the profile of Lox gene in mural granulosa cells could be a likely candidate for a potential biomarker for follicular maturity and oocyte quality.