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. 2012 Sep 24;153(11):5535–5545. doi: 10.1210/en.2012-1733

Rhesus Monkey Cumulus Cells Revert to a Mural Granulosa Cell State After an Ovulatory Stimulus

Charles L Chaffin 1,*, Young S Lee 1,*, Catherine A VandeVoort 1,*,✉, Bela G Patel 1, Keith E Latham 1,*
PMCID: PMC3473200  PMID: 23008515

Abstract

Follicular somatic cells (mural granulosa cells and cumulus cells) and the oocyte communicate through paracrine interactions and through direct gap junctions between oocyte and cumulus cells. Considering that mural and cumulus cells arise through a common developmental pathway and that their differentiation is essential to reproductive success, understanding how these cells differ is a key aspect to understanding their critical functions. Changes in global gene expression before and after an ovulatory stimulus were compared between cumulus and mural granulosa cells to test the hypothesis that mural and cumulus cells are highly differentiated at the time of an ovulatory stimulus and further differentiate during the periovulatory interval. The transcriptomes of the two cell types were markedly different (>1500 genes) before an ovulatory hCG bolus but converged after ovulation to become completely overlapping. The predominant transition was for the cumulus cells to become more like mural cells after hCG. This indicates that the differentiated phenotype of the cumulus cell is not stable and irreversibly established but may rather be an ongoing physiological response to the oocyte.

Antrum formation in growing ovarian follicles promotes the differentiation of granulosa cells into mural cells that line the inner face of the follicle and cumulus cells that surround the oocyte. These cell types have different roles during ovulation and corpus luteum formation. Mural cells promote ovulation through the secretion of proteases and then differentiate into progesterone producing luteal cells. Cumulus cells provide energetic substrates to the oocyte through cellular processes that cross the zona pellucida (1). Follicular cells and oocytes coordinate their functions through paracrine-mediated mechanisms, including mural-derived growth factors and oocyte-derived factors acting on the cumulus cells (2, 3). This is especially relevant after an ovulatory stimulus, when gap junctional communication between mural granulosa cells is lost and the transzonal processes that connect the cumulus cells and oocyte recede. The ovulatory stimulus permits the physical restructuring of the follicle into a corpus luteum and promotes the resumption of oocyte meiosis (4–6). Understanding these events is important for understanding later events related to luteinization and for understanding how cumulus cells influence oocyte health after ovulation.

Although our understanding of intrafollicular organization continues to increase, there remain few data comparing mural and cumulus cells during ovulation and corpus luteum formation. Hernandez-Gonzalez et al. (7) showed that after an ovulatory stimulus, mouse cumulus cells express genes related to innate immunity, suggesting a degree of subspecialization and plasticity. Preovulatory mural and cumulus cells also differ in proliferation, with higher basal rates of proliferation seen in cumulus cells in rats, and a differential response to IGF-I (8, 9). However, although rodent models offer exquisite experimental control, the profound species differences at the level of the ovary may limit extrapolating these observations to other mammalian taxa. The nonhuman primate is an excellent model for human ovarian physiology: a single dominant follicle is selected for ovulation, they have a periovulatory interval of equal length to women and a functional luteal phase, and nonhuman primates are more amenable to clinically relevant experimental protocols than humans.

Mural and cumulus cells arise through a common developmental pathway, and their unique roles are essential to reproductive success. It is important to understand how these cell types arise, and how their specialized states are maintained. It is unknown whether the two cell types are irreversibly specialized, or if their phenotypes must be maintained through continuous intercellular communication. It is not known whether disruption in the physiologic relationship between mural, cumulus, and oocyte, such as occurs during in vitro oocyte maturation (IVM) (10), affects oocyte quality negatively. Oocyte maturation in vivo entails the loss of transzonal processes, which diminishes direct communication between the oocyte and cumulus cells. Transzonal processes persist during IVM, and cumulus cell gene expression is abnormal, and both abnormalities are associated with poor oocyte quality (11, 12). These data suggest that the oocyte itself may direct the gene expression profile of those cells close to, or physically connected to it, and that the follicular environment may regulate the specific functional state of the cumulus cells as well as the oocyte. The goal of this study was to compare gene expression profiles of mural granulosa and cumulus cells before and after ovulation in rhesus macaques undergoing controlled ovarian stimulation cycles.

Materials and Methods

Collection and lysis of rhesus granulosa cells

Mural granulosa cells were collected as described (Fig. 1) (11). Adult female rhesus macaques were housed at the California National Primate Research Center. All procedures employed to obtain mural granulosa cells were conducted in accordance with recommendations of the Guide for the Care and Use of Laboratory Animals and the Animal Welfare Act and its amendments. Animal handling procedures were reviewed and approved in advance by the Institutional Animal Use and Care Administrative Advisory Committee at the University of California at Davis. Animals were caged individually with a 0600–1800 light cycle and maintained at a temperature of 25–27 C. Animals were allowed to socialize in pairs during the day from approximately 0800 to 1400. Animals were fed Purina Monkey Chow (Purina, Gray Summit, MO) and water ad libitum, and seasonal produce, seeds, and cereal were offered as supplements for environmental enrichment. Only females with a history of normal menstrual cycles were selected for this study.

Fig. 1.

Fig. 1.

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Schematic summary of experimental design of study.

Females were observed daily for signs of vaginal bleeding, and the first day of menstruation was assigned as cycle d 1. Beginning on cycle d 1–4, recombinant human FSH (r-hFSH) (Organon, West Orange, NJ) was administered (37.5 IU im, twice daily) for 7 d and follicular contents aspirated by ultrasound-guided aspiration (13, 14). As part of a study involving oocyte IVM, these samples have been previously referred to as “pre-IVM” but will herein be termed “prehuman chorionic gonadotropin (pre-hCG).” To obtain luteinized mural granulosa cells, females were given recombinant hCG (1000 IU im Ovidrel; Serono, Rockland, MA) on d 8 after the initiation of the r-hFSH treatment outlined above and follicles aspirated from follicles 28–30 h later. Mural cells obtained after an hCG bolus will be termed “post-hCG.” Aspirated cells were collected into Tyrode lactate-HEPES medium (37 C) containing 0.1 mg/ml polyvinyl alcohol and 5 ng/ml r-hFSH. Aspirates were placed immediately in a heated isolette (37 C), and oocyte-cumulus-complexes were retrieved from aspirates and used for another study (11). Small sheets of mural granulosa cells were selected manually from the aspirate and rinsed in fresh Tyrode lactate-HEPES medium before being placed in PicoPure RNA extraction buffer.

RNA isolation, amplification, and array hybridization

Transcriptome profiles were generated for rhesus monkey mural granulosa cells (current) or cumulus cells (11) using the Affymetrix Rhesus Genome arrays (Affymetrix, Santa Clara, CA). Each array sample represented cells obtained from multiple follicles of a single monkey. Arrays were obtained for pre- and post-hCG mural granulosa cell samples (four each), and these were compared with pre- and post-hCG cumulus cells from the same cycles reported in an earlier study (11). Total RNA was isolated from cells using the PicoPure RNA isolation kit (Life Technologies, Grand Island, NY) according to the manufacturer's instruction. Fifty nanograms of total RNA from each array sample were subjected to two rounds of cDNA synthesis and in vitro transcription labeling to achieve a linear amplification (Eukaryotic Small Sample Target Labeling Assay, Affymetrix GeneChip Expression Analysis Technical Manual) with minor modifications (initial 5-μl volume for annealing and RT for 30 min at 42 C followed by 30 min at 45 C). The biotin-labeled cRNA samples were fragmented, and 10 μg were hybridized to Affymetrix Rhesus Genome GeneChip arrays. Posthybridization washing, staining, and scanning were performed as described in the Affymetrix GeneChip Expression Analysis Technical Manual.

Microarray data analysis

Probe hybridization intensity data were imported into the Affymetrix Expression Console Software and summarized using the robust multichip analysis algorithm with a global background correction and a quantile normalization (15). To minimize false detections, expression data were filtered based on present/absent calls determined by Affymetrix Microarray Suite 5.0 algorithm at the default settings (detection P < 0.05 and τ = 0.015). Only those probe sets called “present” in all biological replicates of one of the groups in pairwise comparisons were selected for the analysis. To minimize the chance of false positives in subsequent analyses, probe sets with maximum raw intensity values less than 100 were omitted from the dataset. To identify differentially expressed genes, the significance analysis of microarray (SAM) (16) was performed. Differentially expressed probe sets were identified at a false discovery rate (FDR) less than 1%, and the Student's t test was used to select further for the genes with statistical significance (P < 0.01). After SAM analysis to identify differentially expressed genes, some genes displayed inconsistent directions of change among multiple probe sets, and these were excluded from further analysis.

Full datasets have been deposited in NCBI's Gene Expression Omnibus (17) and are accessible through GEO Series accession no. GSE38387. Data are also available at www.preger.org.

The mural granulosa cell array expression data presented here have not been previously published. An earlier report provided the results of analyses of cumulus cell transcriptome analyses comparing gene expression profiles before (pre-hCG) and after (post-hCG) in vivo maturation or IVM (11). For comparative purposes, some tables, figures, and analyses incorporated data from that previously published set of array data from cumulus cells (Geo Series accession no. GSE25288). This allowed us to compare transitions in granulosa cell state before and after an ovulatory stimulus with transitions in cumulus cells and to compare cell states between the two cell types. The granulosa samples employed here and the cumulus cell samples employed previously were isolated in the same studies using the same animals, and raw expression data for the two sets of arrays were normalized and processed together. Thus, the expression values shown here for granulosa cells can be compared directly to expression values obtained for cumulus cells.

Pathway analysis and biological functional analysis were performed using the rhesus array annotations provided in Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, Redwood City, CA). For biological functional analysis, Fisher's exact test was performed with the P value threshold of 0.05 to identify molecular and cellular functional categories with statistical significance.

Real-time quantitative RT-PCR (qRT-PCR) analysis

Each sample used for the qRT-PCR assay corresponded to total RNA pooled from granulosa cells obtained from multiple follicles of one monkey. For greater statistical power, the total number of samples was expanded for qRT-PCR. For mural granulosa cells, five pre-hCG and seven post-hCG samples were analyzed (including four of each used for arrays). For cumulus cells, three pre-hCG and nine post-hCG were used (four post-hCG cumulus cell samples were used for arrays); due to limited material available for pre-hCG cumulus cell samples, all three samples were from females assayed by array. Total RNA was isolated using the PicoPure RNA isolation kit (Molecular Devices, Sunnyvale, CA) and subjected to a whole transcriptome amplification using the QuantiTect whole transcriptome kit (QIAGEN, Valencia, CA). Custom TaqMan gene expression assays were designed based on rhesus cDNA sequences using Primer Express software version 3.0 (Applied Biosystems, Foster City, CA). The primer and probe sequences of the custom-designed TaqMan assays are listed in Supplemental Table 1, published on The Endocrine Society's Journals Online web site at http://endo.endojournals.org. Quantitative real-time PCR was performed on approximately 100 ng of cDNA for each assay using ABI StepOne Plus instrument according to the manufacturer's recommendations (Applied Biosystems). The mRNA abundance of a target gene was normalized to the endogenous mitochondrial ribosomal protein S18C mRNA (MRPS18C) for sample to sample comparisons, and the relative expression was calculated by the comparative CT (threshold cycle) method (18). Statistical analysis was performed between groups using relative expression software tool for the genes assayed by qRT-PCR method (19).

Results

Overview of the array analysis

All quality control parameters were within acceptable ranges for all array samples (Supplemental Table 2). Average detection rates ranged from 35.7 to 48.1%. Hierarchical clustering analysis revealed that the biological replicates of pre-hCG cumulus and granulosa cells clustered separately without any apparent outliers (Supplemental Fig. 1). The post-hCG cumulus and granulosa cell replicates intermixed but segregated from the pre-hCG samples, indicating a surprising similarity in profiles after hCG. Cumulus cell samples resulting from IVM (denoted IVM-CC in data reported in Ref. 11) represent a unique and nonoverlapping cluster. Three-dimensional principal component analysis (PCA) confirmed clustering of the replicates in this manner (Fig. 2).

Fig. 2.

Fig. 2.

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PCA. Before hCG, cumulus cells (CC; blue) and granulosa cells (GC; brown) display distinct phenotypes. After hCG, the cumulus cell (gray) and granulosa cell (green) phenotypes become indistinguishable. In contrast, after IVM, the cumulus cells transition to a distinct, abnormal cell state (red). This figure combines the granulosa data presented in this study with previously published cumulus cell data (11).

Convergence in gene expression profiles of granulosa and cumulus cells during in vivo maturation

One objective of this study was to determine the extent to which gene expression profiles differ between mural granulosa (Supplemental Tables 3 and 4) and cumulus cells (11) and if these differences become more or less pronounced as a result of an ovulatory stimulus. Comparisons of pre- and post-hCG samples revealed that cumulus cells underwent a larger change [4431 significant differences in probe sets (1147 increased, 3284 decreased after hCG)] than did granulosa cells [2493 significant differences in probe sets (1406 increased, 1087 decreased)] (Table 1). Before hCG, cumulus and mural granulosa cells displayed 3306 significant differences in probe sets (2106 genes), 2918 (88%) of which were more highly expressed in cumulus cells. Surprisingly, after hCG treatment, comparison of cumulus and granulosa cell profiles yielded zero significant differences (FDR 1%). This convergence in gene expression pattern mirrored the PCA and hierarchical clustering analysis results (Fig. 2 and Supplemental Fig. 1).

Table 1.

Number of probe sets differentially expressed in rhesus cumulus and mural granulosa cells based on SAM

No. detected probe setsa No. differentially expressed probe setsc
Total Fold change ≥2 Fold change ≥10
Pre-hCG-CC vs. post-hCG-CCb Higher in post-hCG-CC Higher in pre-hCG-CC Higher in post-hCG-CC Higher in pre-hCG-CC Higher in post-hCG-CC Higher in pre-hCG-CC
14,521 1147 3284 762 2078 93 61
Pre-hCG-GC vs. post-hCG-GC Higher in post-hCG-GC Higher in pre-hCG-GC Higher in post-hCG-GC Higher in pre-hCG-GC Higher in post-hCG-GC Higher in pre-hCG-GC
13,296 1406 1087 975 634 137 24
Pre-hCG-CC vs. Pr e-hCG-GC Higher in pre-hCG-CC Higher in pre-hCG-GC Higher in pre-hCG-CC Higher in pre-hCG-GC Higher in pre-hCG-CC Higher in pre-hCG-GC
14,144 2918 388 1646 186 34 4
Post-hCG-CC vs. post-hCG-GC Higher in post-hCG-CC Higher in post-hCG-GC Higher in post-hCG-CC Higher in post-hCG-GC Higher in post-hCG-CC Higher in post-hCG-GC
12,566 0 0 0 0 0 0
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a

Filtered for present calls in all replicates of one group and the raw array intensity value more than or equal to 100.

b

Reproduced from Lee et al. (11).

c

FDR < 1% and P < 0.01.

To evaluate the overall directionality of changes in gene expression by which this convergence occurred, we divided the 2106 genes displaying significant differences between cumulus and mural granulosa cells before hCG into 10 categories based on whether expression increased or decreased in just one cell type or converged to an intermediate value (Fig. 3, A–I, and Supplemental Table 5). The largest category was A [1452 (69%) of the initial differences], in which genes were initially expressed more highly in cumulus cells and decreased to a level similar to mural granulosa cells after hCG. Genes in category B (95 genes) showed the reciprocal pattern, initially lower expression in cumulus cells and then rising to resemble granulosa cell values. The second largest category C contained 206 genes that were initially expressed at lower levels in granulosa cells and then increased in expression to resemble cumulus cell expression. The next largest category G contained 189 genes that were initially more highly expressed in cumulus cells but declined in both cell types after hCG to a similar, reduced level of expression. An additional 754 genes displayed similar levels of expression between cumulus and granulosa cells both before and after hCG, decreasing (group K) or increasing (group L) in expression during the transition. The predominant pattern displayed for categories A–J was one of cumulus cells transitioning to a more granulosa-like state (1547 genes, 73%) (Fig. 3 and Supplemental Table 5).

Fig. 3.

Fig. 3.

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Expression categories for changes in gene expression in granulosa cells (GC) and cumulus cells (CC) before and after hCG. Each arrow indicates whether mRNAs in this category increased or decreased significantly in expression or did not change significantly, based on array data comparisons. Numbers next to each category indicate the number of genes displaying the illustrated expression pattern. Categories A–J display the relative changes in the two cell types (GC and CC) for mRNAs that were significantly different between granulosa and cumulus cells before or after hCG stimulation. Categories K and L display changes for mRNAs that were not significantly different between the two cell types either before or after hCG but that increased or decreased significantly during the maturation period.

Biofunction analysis for differentially expressed genes

To ascertain the functions most highly affected by the convergence in cumulus and mural granulosa cell gene expression profiles, we applied the IPA program to analyze the largest categories (A–C, G, K, and L) (Table 2 and Supplemental Table 6). For the largest category (A), cell death/apoptosis, gene transcription, cell growth/proliferation, posttranslational modification, cellular assembly and organization, and DNA replication/repair were prominent affected functions. Several disease processes were also highlighted, including reproductive system disease (93 genes) (Supplemental Table 6), and this was predominantly related to cell proliferation and cancer-related genes. Most of these functions were echoed in the groups B and G. The most prominent function in group C was cellular assembly and organization. Cell death and cell growth/proliferation, cell cycle and DNA replication, recombination, and repair were the most prominent functions for group K.

Table 2.

Major IPA biofunctions and pathways overrepresented in six major categories

Annotation Expression pattern
A (1452)
 B (95)
 C (206)
 G (189)
 K (373)
 L (381)
n % n % n % n % n % n %
Molecular and cellular functions
    Cell death 294 20.2 21 22.1 45 21.8 46 24.3 70 18.8 108 28.3
    Apoptosis 229 15.8 20 21.1 32 15.5 37 19.6 0 0.0 90 23.6
    Gene transcription 223 15.4 0 0.0 31 15.0 34 18.0 22 5.9 53 13.9
    Cell growth and proliferation 200 13.8 25 26.3 14 6.8 64 33.9 61 16.4 105 27.6
    Cell cycle 180 12.4 7 7.4 9 4.4 50 26.5 57 15.3 46 12.1
    Posttranslational modification 138 9.5 8 8.4 25 12.1 8 4.2 11 2.9 22 5.8
    Cellular assembly and organization 131 9.0 9 9.5 50 24.3 42 22.2 26 7.0 47 12.3
    DNA replication, recombination, and repair 117 8.1 6 6.3 0 0.0 54 28.6 53 14.2 21 5.5
    Cell morphology 94 6.5 7 7.4 33 16.0 11 5.8 13 3.5 48 12.6
    Cell development 91 6.3 10 10.5 36 17.5 28 14.8 11 2.9 98 25.7
    Protein synthesis 79 5.4 16 16.8 15 7.3 5 2.6 7 1.9 10 2.6
    Small molecule biochemistry 76 5.2 11 11.6 14 6.8 13 6.9 31 8.3 68 17.8
    RNA posttranscriptional modification 71 4.9 10 10.5 0 0.0 11 5.8 9 2.4 0 0.0
    Molecular transport 69 4.8 6 6.3 20 9.7 0 0.0 9 2.4 52 13.6
    Cellular function and maintenance 55 3.8 6 6.3 33 16.0 14 7.4 6 1.6 32 8.4
    Carbohydrate metabolism 45 3.1 0 0.0 12 5.8 0 0.0 13 3.5 37 9.7
    Protein trafficking 34 2.3 0 0.0 15 7.3 0 0.0 0 0.0 10 2.6
    Amino acid metabolism 31 2.1 0 0.0 0 0.0 0 0.0 0 0.0 11 2.9
    Cell signaling 31 2.1 0 0.0 24 11.7 5 2.6 0 0.0 23 6.0
    Cellular compromise 31 2.1 8 8.4 13 6.3 12 6.3 7 1.9 5 1.3
    Lipid metabolism 30 2.1 10 10.5 8 3.9 5 2.6 8 2.1 54 14.2
    Cell movement 25 1.7 7 7.4 31 15.0 10 5.3 0 0.0 65 17.1
    Nucleic acid metabolism 16 1.1 0 0.0 0 0.0 0 0.0 16 4.3 0 0.0
    Canonical pathways
        Molecular mechanisms of cancer 46 3.2 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0
        Protein ubiquitination pathway 37 2.5 4 4.2 0 0.0 0 0.0 0 0.0 0 0.0
        Glucocorticoid receptor signaling 32 2.2 0 0.0 0 0.0 0 0.0 0 0.0 14 3.7
        Huntington's disease signaling 30 2.1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0
        Protein kinase A signaling 29 2.0 0 0.0 7 3.4 0 0.0 0 0.0 0 0.0
        Actin cytoskeleton signaling 0 0.0 0 0.0 10 4.9 0 0.0 0 0.0 0 0.0
        Integrin signaling 0 0.0 0 0.0 7 3.4 0 0.0 0 0.0 0 0.0
        Inositol phosphate signaling 0 0.0 0 0.0 6 2.9 0 0.0 0 0.0 0 0.0
        Pyrimidine metabolism 0 0.0 0 0.0 0 0.0 6 3.2 9 2.4 0 0.0
        Purine metabolism 0 0.0 0 0.0 0 0.0 6 3.2 12 3.2 0 0.0
        One carbon pool by folate 0 0.0 0 0.0 0 0.0 5 2.6 0 0.0 0 0.0
        Aryl hydrocarbon receptor signaling 0 0.0 0 0.0 0 0.0 4 2.1 0 0.0 0 0.0
        Mitotic roles of PLK 0 0.0 0 0.0 0 0.0 0 0.0 8 2.1 0 0.0
        Cell cycle control of chromosomal replication 0 0.0 0 0.0 0 0.0 0 0.0 6 1.6 0 0.0
        Cyclins and cell cycle regulation 0 0.0 0 0.0 0 0.0 0 0.0 6 1.6 0 0.0
        Hereditary breast cancer signaling 0 0.0 0 0.0 0 0.0 0 0.0 6 1.6 0 0.0
        Ovarian cancer signaling 0 0.0 0 0.0 0 0.0 0 0.0 6 1.6 0 0.0
        ATM signaling 0 0.0 0 0.0 0 0.0 0 0.0 5 1.3 0 0.0
        Cell cycle: G1/S checkpoint regulation 0 0.0 0 0.0 0 0.0 0 0.0 5 1.3 0 0.0
        Pancreatic adenocarcinoma signaling 0 0.0 0 0.0 0 0.0 0 0.0 5 1.3 0 0.0
        Axonal guidance signaling 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 20 5.2
        Acute phase response signaling 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 14 3.7
        Role of NFAT in cardiac hypertrophy 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 14 3.7
        Role of macrophages, fibroblasts, and endothelial cells in rheumatoid arthritis 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 14 3.7
        Molecular mechanisms of cancer 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 14 3.7
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Numbers in parentheses indicate total number of molecules. Note: sum of values in % columns can exceed 100 due to overlap between functional categories. Categories A–C and G–L are defined in the legend of Fig. 1.

Comparing the fraction of gene entities among the categories in Table 2 revealed some notable relationships between patterns of regulation and hCG exposure. Two functions (DNA replication, recombination, and repair and cell cycle) were most prominent for categories G and K, both of which displayed an overall down-regulation of the affected genes. Apoptosis and cell death were prominent for all six of the expression categories analyzed. Cell morphology and cell movement genes were most prominent in groups C and L, displaying a net upward trend in regulation in mural granulosa cells. RNA posttranscriptional modification was most prominent for group B. There was an overall increased expression of genes related to lipid metabolism in categories B and L.

Protein ubiquitination was shared as a pathway for categories A and B, and glucocorticoid receptor signaling was shared between categories A and L, thus changing from their cumulus cell states to the mural granulosa cell state. Pyrimidine and purine metabolism pathways were down-regulated after hCG, being associated with both groups G and K. Aryl hydrocarbon signaling was uniquely associated with group G. Group C was associated with multiple canonical signaling pathways, including protein kinase A signaling, actin cytoskeleton signaling, integrin signaling, and inositol phosphate signaling, indicating that genes related to these pathways increase in expression to levels more similar to those seen in cumulus cells. Group K yielded the largest number of associated canonical pathways, and these were generally related to cell proliferation, encompassing cell cycle checkpoint and cancer-related pathways.

Individual mRNA expression analysis

We next compared the expression of 44 individual mRNAs between the cumulus and mural granulosa cells before and after hCG using qRT-PCR (Fig. 4). These mRNAs were chosen based on the magnitudes of their raw intensity values, fold change evident in the array data, and known functions. Thirty-seven of the mRNAs were members of categories A–L. Eight of these mRNAs plus an additional five mRNAs not in categories A–L comprise cumulus cell-expressed mRNAs predictive of oocyte quality (20). Two additional mRNAs (AREG and CDKN1B) were chosen because of potential roles in folliculogenesis (21–24).

Fig. 4.

Fig. 4.

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Quantitative analysis of selected mRNAs by qRT-PCR. The 44 mRNAs were selected for analysis based on magnitude of raw intensity values on arrays, array fold changes, and biological functions. Array and qRT-PCR fold change values are indicated for cumulus cells (CC; white and stippled bars, respectively, left pair of bars for each gene) and granulosa cells (GC; hatched and black bars, respectively, right pair of bars for each gene). Those marked with “a” are the 19 mRNAs for which qRT-PCR analysis revealed relative differences in expression before and after hCG statistically consistent with the array data for both cumulus and mural granulosa in cells. Those marked with “b” are the 13 mRNAs for which results by qRT-PCR were consistent with array data with respect to directionality but deviated slightly from strict adherence to the assignments to categories based on array values. Thirteen mRNAs were previously found to display expression in cumulus cells predictive of oocyte quality and are marked with an asterisk.

Nineteen of the 37 category A–L mRNAs displayed differences in expression before and after hCG, statistically consistent with the array data for both cumulus and mural granulosa in cells. The allocation of genes to categories A–L was based on statistical discrimination based on the array values (for example, categories A, G, and J delineate very similar patterns, differing in the initial relative abundance of mRNA in cumulus vs. mural granulosa cells, and in the magnitude of change in mural granulosa cells). Thus, deviation in conformity to the patterns dictated for categories A–L was anticipated comparing qRT-PCR and array measurements for some of the mRNAs. Thirteen of the mRNAs displayed results by qRT-PCR that were consistent with array data with respect to directionality but deviated slightly from strict adherence to the assignments to categories based on array values. Overall, 32 of the 37 category A–L mRNAs that displayed qualitatively similar patterns of expression by the two methods. Of eight mRNAs related to steroidogenesis, three (STAR, HSD11B1, and LDLR) were up-regulated in both cell types after hCG, two (HSD17B1 and CYP19A1) were down-regulated in both cell types, and SCARB1 was down-regulated more highly in cumulus vs. mural cells. CYP11A1 mRNA was up-regulated in cumulus but down-regulated in granulosa. Another mRNA related to lipid metabolism (PON2) was strongly up-regulated in cumulus cells. Of seven mRNAs related to cell cycle, proliferation, or DNA replication, CCNB2 was down-regulated more highly in granulosa cells, and CDKN1B, GREM1, CDC20, and ESR1 were down-regulated in both cell types. There were eight mRNAs examined that related to metabolism and showed qRT-PCR results consistent with array values; IGFBP5 was up-regulated in both cell types, HK2 was down-regulated in both cell types, PYGB was up-regulated only in cumulus cells, SLC2A3 was up-regulated in mural granulosa cells only, three mRNAs (MGLL, ACAT1, and PFKM) were down-regulated in mural granulosa cells only, and one other mRNA (HK1) was slightly reduced in mural granulosa cells. There was little change in IRS1 mRNA expression in granulosa cells after hCG. The RUNX2 mRNA was up-regulated in both cell types. The PPM1A mRNA was down-regulated in both cell types.

Thirteen mRNAs that display expression in cumulus cells predictive of oocyte quality were examined to determine whether the change in expression after hCG in mural granulosa cells resembled the change in expression in cumulus cells (11, 20). Most of these mRNAs were regulated similarly between the two cell types. Six (AQP11, CYP19A1, GMNN, HSD11B2, HSD17B1, and IGFBP5) were down-regulated and five (FN1, FOSL2, IGF1, NEK6, and CLU) were up-regulated in both cell types, although CLU up-regulation was more pronounced in cumulus than granulosa cells. Thus, most (11/13) of these mRNAs followed similar patterns of regulation in granulosa and cumulus cells after hCG.

Discussion

The unique relationship between cumulus cells and the oocyte is remarkable. During antrum formation, the oocyte drives nearby granulosa cells to assume a cumulus cell phenotype (25). Although often viewed as a terminal differentiation event, the stability and reversibility of this conversion has not been examined in detail. Our data reveal for the first time that the conversion from the mural to cumulus cell state is reversible. This reversion occurs as the cumulus cells are released from oocyte control with retraction of the transzonal processes. A parallel response to endocrine factors may also contribute. This discovery provides new insight into the pre- and postovulatory role of cumulus cells in oocyte biology.

Although cumulus and mural granulosa cells appear to differentiate along distinct paths during antrum formation, little is known about the control of their respective phenotypes. The transcriptomes of mural and cumulus cells are very different before an ovulatory stimulus, suggesting unique roles and regulation during follicle growth. The convergence of mural and cumulus cell transcriptomes after hCG strongly implicates the oocyte in directing the specialized cumulus cell gene expression pattern. In mice, oocyte-derived TGFβ signals, such as growth differentiation factor 9 (GDF9), repress Lhcgr and Cyp11a1 mRNA expression while promoting cumulus-specific genes, such as Has2 and Tnfaip6 (26). Both cumulus and mural cells can respond to hCG stimulation with changes in mRNA expression. Of 10 genes examined in detail (Syndecans 1–4, Betaglycan, and Glypicans 1–4 and 6), six displayed similar initial changes in expression in the two cell types but then diverged in the two cell types by 16-h post-hCG, two showed cell type-specific changes, and two displayed parallel changes in expression but with quantitatively different endpoints (26). These data and others confirm that paracrine signals such as GDF9 from the oocyte modulate gene expression in both cell types even during the periovulatory interval (26–28). Endocrine factors, such as the FSH surge may also modulate cumulus cell gene expression. By examining the entire transcriptomes of these two cell types during ovulation, our data reveal that the effects of ovulation on cumulus cell gene expression are extensive. Thus, oocyte-derived factors may be required to maintain the cumulus cell phenotype, a finding that could not be derived from examining a small number of highly selected marker genes. It is important to note that upon aspiration, cells were pooled from multiple follicles that are heterogenous in terms of size, health, and maturity; thus whether these results accurately reflect the natural cycle requires further study.

The reversion of cumulus cells to a mural state together with our previous studies of IVM effects on oocytes and cumulus cells (see Ref. 11, 20 and Fig. 2) highlights the crucial role played by the follicular environment, and particularly the intimate association between cumulus cell and oocyte, in maintaining the correct phenotypes of all of both cell types. Transzonal processes linking cumulus cells directly to the oocyte are lost approximately 24 h after an ovulatory stimulus in vivo (12). If the direct link between cumulus cells and the oocyte is important for maintaining the cumulus phenotype, then disruption in the transzonal processes and release of the cumulus cells from the oocyte influence would allow cumulus cells to revert to a mural granulosa-like state. In contrast to oocyte maturation in vivo, transzonal processes are maintained during IVM, and IVM cumulus cells have a gene expression profile quite distinct from that seen during in vivo maturation (11, 12). Moreover, oocytes derived from IVM are of lesser quality (11, 20). Whether a mechanistic link exists between a cumulus cell gene profile during IVM that is radically different that seen during in vivo maturation, persistent transzonal processes, and poor oocyte quality remains to be determined.

As an alternative to signaling through transzonal processes, diffusible products could influence cells closest to the oocyte. In essence, a gradation in phenotypes from mural cells at the base of the cumulus stalk, up the stalk, and through the corona radiata could exist before an ovulatory stimulus. Consistent with this idea, GDF9 expression is graded across the cumulus stalk in primates, with higher levels in cells at the base of the stalk and less around the oocyte (29). A similar effect is seen in luteinizing rat follicles, where proliferation occurs in cumulus cells but fades down the cumulus stalk and is absent in mural cells after hCG (30). Thus, both proximity and biophysical connection of granulosa cells to the oocyte may contribute to maintaining the cumulus cell gene expression pattern. Our cumulus cell data were derived from cumulus-oocyte complexes aspirated before or after hCG and represent cells in close proximity to the oocyte, i.e. not the basal portion of the cumulus stalk. Further research is necessary to distinguish that the effects of proximity vs. direct oocyte contact in defining mural and cumulus cell states.

One implication of our data is that cumulus cells may assume new roles after ovulation, such as a local source of progesterone. The oocyte may direct the cumulus cells to provide one set of support functions during oocyte growth and maturation and until cumulus expansion and ovulation have occurred, but that release from this control may allow a different set of cumulus cell functions after ovulation. We propose that the cumulus cell state is regulated dynamically by the oocyte and that flexible specialization allows conversion back to a granulosa cell state to take over essential paracrine interactions as granulosa cell factors are lost upon follicle rupture (25). Most of the convergence in mural and cumulus cell transcriptomes arises by a shift in cumulus cells toward the mural cell state. Although the oocyte directs the cumulus gene expression profile, LH/hCG directs the mural granulosa cell profile. A widely accepted major role of mural granulosa-lutein cells is to synthesize progesterone to maintain early pregnancy (31). Because the cumulus transcriptome shifts toward that of mural granulosa cells after hCG, peri- and postovulatory cumulus cells may produce progesterone. Mural granulosa and cumulus cells increased the expression of steroidogenic acute regulatory protein and low density lipoprotein receptor in response to hCG, as well as cytochrome b5 type B. Cytochrome b5 type B is essential for cytochrome P450 activity, including cytochrome P450, family 11, subfamily A, polypeptide 1 (CYP11A1), and is an important component of steroidogenesis (32). Reduced CYP11A1 mRNA expression in cumulus cells does not necessarily suggest a block at that point in the steroidogenic pathway, because CYP11A1 expression also initially decreases in primate mural granulosa cells after an hCG bolus and recovers near the time of ovulation (33). Preovulatory mural granulosa cells are primed to synthesize progesterone by expressing CYP11A1 and 3β-hydryoxysteroid dehydrogenase before ovulation, primarily lacking cholesterol and pregnenolone substrates necessary to synthesize progesterone (34, 35). A similar situation likely exists in cumulus cells, so postovulatory cumulus cells can provide an important source of progesterone. In fact, cumulus cells may have a higher progesterone synthetic capacity than mural granulosa cells (36). Progesterone is both a powerful antiapoptotic and a sperm chemotactic factor (37–39). Cumulus cells associated with the ovulated oocyte may produce progesterone to provide these important functions.

Documented LH/hCG-induced changes in whole follicle (40) and mural granulosa cell gene expression (41) are consistent with our array and qRT-PCR results. For example, the epidermal growth factor-like ligand amphiregulin increases in response to an ovulatory stimulus in primates (21), and this is consistent with both the microarray and qRT-PCR data. Importantly, hCG also increase AREG mRNA levels in cumulus cells of mice (2), and this was confirmed in primate cumulus cells. An ovulatory stimulus also decreases the expression of specific genes in mural cells, for example cyclin D2 (CCND2) (7, 23, 24). Data from both the microarray and qRT-PCR data confirm the reduction in CCND2 mRNA in both mural and cumulus cells after hCG.

Changes in genes associated with cell cycle in group A (declining expression in cumulus, constant expression in mural) are prevalent, consistent with the physiology of the follicle, where mural granulosa cells exit the cell cycle before an ovulatory stimulus (42, 43). Relatively few changes in mural cell cycle machinery would be expected. In contrast, cumulus cell proliferation continues until an ovulatory stimulus or even for a short time thereafter (7, 30), arresting before ovulation. Group K (declining expression in both mural and cumulus cells) includes mRNAs encoding cyclin-dependent kinase 6, proliferating cell nuclear antigen, and cell division cycle 25 homolog, each of which are important in the transition of cells across the G1-S boundary. There are very few cell cycle genes that change in group C (constant in cumulus, increasing in mural), supporting the hypothesis that the cell cycle is arrested in mural cells before hCG and soon after in cumulus cells. Group L (increasing expression in both mural and cumulus cells) shows increases in levels of the mRNAs encoding cell cycle inhibitors cyclin-dependent kinase inhibitor 1A (p21), runt-related transcription factor 2, and growth arrest and DNA damage 45 A and B. Thus in both cell types, changes in gene expression are consistent with either the maintenance (mural) or initiation (cumulus) of cell cycle arrest.

The search for molecular markers of oocyte quality is an active area of research, and much of this field is pursuing the identification of cumulus cell markers that predict oocyte quality. The data here reinforce the notion that normal regulation of oocyte-cumulus cell interactions could have positive effects on the cumulus-cell gene expression profile and that abnormal interactions are expected to compromise oocyte quality. More importantly, our data provide a conceptual framework within which to interpret the biological meaning of abnormal shifts in cumulus cell phenotype, such as seen during IVM (11). The current data in conjunction with previous studies on cumulus gene expression should be useful as a means to search for markers of oocyte quality.

In summary, our data indicate that gene expression profiles in cumulus and mural granulosa cells are markedly different before an ovulatory stimulus but converge to become completely overlapping within 24 h of hCG administration. It is important to note that the final 12 h leading up to follicle rupture remain a very dynamic period, and changes to the gene expression profile of either or both mural granulosa and cumulus cells during this time period have been reported (40). Importantly, the bulk of the convergence represents cumulus cells becoming more similar to mural granulosa cells, although the reverse is also true but to a lesser extent. These data suggest that the oocyte maintains or enforces a specific gene expression profile in cumulus, such that the loss of direct communication between cumulus and oocytes promotes to revision of cumulus cells to a mural-like state. After ovulation, cumulus cells may replace beneficial paracrine functions of the granulosa cells in primates, specifically to synthesize progesterone. These data change the paradigm in which cumulus granulosa cells differentiate irreversibly from mural granulosa cells and must now be considered to remain a traditional granulosa cell but under the reversible and temporary control of the oocyte.

Supplementary Material

Supplemental Data
supp_153_11_5535__index.html (1.5KB, html)

Acknowledgments

We thank the technical assistance Dana Hill in collecting material for this study.

This work was supported by grants from the National Institutes of Health, Office of Research Infrastructure Programs Division of Comparative Medicine and National Center for Research Resources Grants R24 OD-012221/R24RR015253 (to K.E.L.) and OD011107/RR00169 (California National Primate Research Center) and OD010967/RR025880 (to C.A.V.).

Disclosure Summary: The authors have nothing to disclose.

Footnotes

Abbreviations:
CYP11A1
Cytochrome P450, family 11, subfamily A, polypeptide 1
GDF9
growth differentiation factor 9
FDR
false discovery rate
hCG
human chorionic gonadotropin
IPA
Ingenuity Pathway Analysis
IVM
in vitro oocyte maturation
PCA
principal component analysis
qRT-PCR
quantitative RT-PCR
r-hFSH
recombinant human FSH
SAM
significance analysis of microarray.

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Associated Data

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Supplementary Materials

Supplemental Data
supp_153_11_5535__index.html (1.5KB, html)
supp_en.2012-1733_En-12-1733_fig_1.pdf (1.1MB, pdf)
supp_en.2012-1733_Supplemental_Table_EN-12-1733.xlsx (12.9KB, xlsx)
supp_en.2012-1733_Supplemental_Table2_EN-12-1733.xlsx (10.2KB, xlsx)
supp_en.2012-1733_Supplemental_Table3_EN-12-1733.xlsx (144.4KB, xlsx)
supp_en.2012-1733_Supplemental_Table4_EN-12-1733.xlsx (155.8KB, xlsx)
supp_en.2012-1733_Supplemental_Table5_EN-12-1733.xlsx (392.7KB, xlsx)
supp_en.2012-1733_Supplemental_Table6_EN-12-1733.xlsx (105.7KB, xlsx)

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