The global effect of follicle-stimulating hormone and tumour necrosis factor α on gene expression in cultured bovine ovarian granulosa cells
© Glister et al.; licensee BioMed Central Ltd. 2014
Received: 30 June 2013
Accepted: 22 January 2014
Published: 28 January 2014
Oocytes mature in ovarian follicles surrounded by granulosa cells. During follicle growth, granulosa cells replicate and secrete hormones, particularly steroids close to ovulation. However, most follicles cease growing and undergo atresia or regression instead of ovulating. To investigate the effects of stimulatory (follicle-stimulating hormone; FSH) and inhibitory (tumour necrosis factor alpha; TNFα) factors on the granulosa cell transcriptome, bovine ovaries were obtained from a local abattoir and pools of granulosa cells were cultured in vitro for six days under defined serum-free conditions with treatments present on days 3–6. Initially dose–response experiments (n = 4) were performed to determine the optimal concentrations of FSH (0.33 ng/ml) and TNFα (10 ng/ml) to be used for the microarray experiments. For array experiments cells were cultured under control conditions, with FSH, with TNFα, or with FSH plus TNFα (n = 4 per group) and RNA was harvested for microarray analyses.
Statistical analysis showed primary clustering of the arrays into two groups, control/FSH and TNFα/TNFα plus FSH. The effect of TNFα on gene expression dominated that of FSH, with substantially more genes differentially regulated, and the pathways and genes regulated by TNFα being similar to those of FSH plus TNFα treatment. TNFα treatment reduced the endocrine activity of granulosa cells with reductions in expression of FST, INHA, INBA and AMH. The top-ranked canonical pathways and GO biological terms for the TNFα treatments included antigen presentation, inflammatory response and other pathways indicative of innate immune function and fibrosis. The two most significant networks also reflect this, containing molecules which are present in the canonical pathways of hepatic fibrosis/hepatic stellate cell activation and transforming growth factor β signalling, and these were up regulated. Upstream regulator analyses also predicted TNF, interferons γ and β1 and interleukin 1β.
In vitro, the transcriptome of granulosa cells responded minimally to FSH compared with the response to TNFα. The response to TNFα indicated an active process akin to tissue remodelling as would occur upon atresia. Additionally there was reduction in endocrine function and induction of an inflammatory response to TNFα that displays features similar to immune cells.
KeywordsOvary Microarray analysis Bovine granulosa cells Follicles
An ovarian primordial follicle is composed of an inactive oocyte surrounded by granulosa cells all enclosed by a basal lamina. The granulosa cells of the ovarian follicle support and nurture the oocyte, and secrete oestrogens which are necessary for normal reproductive function. In mammals, the latter stage of follicle development can involve an approximate hundred fold increase in diameter, 21 doublings of granulosa cell numbers  and formation of a fluid-filled antrum . In cattle, the growth of follicles is tightly regulated, since two or three groups or waves of follicles emerge from a pool of follicles larger than 5 mm in diameter during each oestrous cycle [3, 4]. In these waves, follicles continue to enlarge over several days until one follicle grows faster and larger than the others and hence gains ‘dominance’ [5, 6]. This deviation in size occurs when the follicles are around 7–8 mm in diameter . These processes of follicular growth occur largely due to the stimulatory influence of FSH, through its receptor localised exclusively to the granulosa cells, though other factors produced locally, for example Growth Differentiation Factor (GDF)-9  and Bone Morphogenetic Protein (BMP)-15 from the oocyte , are also involved.
Instead of one primordial follicle growing to ovulatory size and then ovulating, many follicles commence growing during the course of the cycle. Most of these growing follicles become atretic, resulting in cows and humans, in only one or occasionally two follicles ovulating each cycle. The highest rates of atresia in follicular development occur around the time of antrum formation. It has been shown that the atretic process begins with cell death in the membrana granulosa initially by an apoptotic process . Generally, apoptosis may be instigated intracellularly by cytotoxic stress, possibly due to free radicals or calcium influx  which cause mitochondrial changes that eventually also lead to caspase activation. Apoptosis can be initiated externally to the cell by the binding of ‘death’ ligands such as Fas ligand, tumour necrosis factor α (TNFα) or TRAIL to specific receptors . In follicular atresia it is unlikely that cell death occurs on a cell-by-cell basis because numerous pyknotic nuclei are observed during atresia . Therefore it is probable that atresia is initiated by either the presence or absence of a particular external signal(s). TNFα can initiate apoptosis in granulosa cells [13, 14]. The expression of TNFα receptors on granulosa and theca cells has been shown to be increased in atretic follicles when compared with healthy small or preovulatory follicles . Studies on atretic follicles so far have shown that many of the genes/pathways involved are common to those stimulated by TNFα, as recently reviewed by Matsuda et al..
Investigation of the effects of various agents on granulosa in vitro is dependent on the follicle stage at which the cells were isolated and the composition of the culture medium. It has been demonstrated that granulosa from small antral follicles are more responsive to FSH in serum-free culture and are capable of increasing oestradiol production over a six day period [17, 18]. This is an important consideration for studying granulosa cells as they have a propensity to differentiate into granulosa-lutein cells in a process called luteinisation, if cultured in serum-supplemented medium . Such cells are completely unresponsive to FSH. A previous study showed that TNFα was able to block the effects of FSH in serum-free culture of rat granulosa cells . We were interested in the effect of FSH and TNFα on steroid production and global gene expression in bovine granulosa cells to help elucidate the mechanisms of action of these compounds at the transcriptional level. Interest in the action of pro-inflammatory signals like TNFα has been augmented by recent reports that ovarian granulosa cells of cattle and other species fulfil an innate immunity role, being capable of detecting and responding to bacterial pathogens . We therefore cultured granulosa cells from small bovine follicles with or without FSH and TNFα, alone and in combination, assayed steroid hormone production by immunoassay and conducted microarray analyses using Genechip bovine genome arrays.
Cell culture and hormone assays
Hierarchical clustering and principal component analyses (PCA)
Quantitation of gene expression by RT-PCR
Differential gene expression analyses
Comparison of all treatments against the control group by ANOVA with a three-fold change and a statistical cut-off of P < 0.05 produced a differentially-regulated list of 288 genes for the TNFα-treated arrays compared with the controls, and 232 genes were common to both TNFα datasets and regulated in the same direction and to approximately the same degree (Additional file 3: Figure S3 and Additional file 4: Table S1). These numbers reflect the results of the unsupervised statistical analyses.
The number of probe sets which were differentially regulated between control (± FSH) and TNFα (± FSH)-treated granulosa cell cultures
Genes which were >3-fold up regulated in TNFα (± FSH)-treated granulosa cells compared with control (± FSH) cells
(TNFα vs non TNFα treated)
(TNFα vs non TNFα treated)
(TNFα vs non TNFα treated)
Cytokines, hormones and receptors
Extracellular matrix and synthesis
Intercellular and cell to matrix adhesion
Proteolysis and inhibition
Genes which are >3-fold down regulated in TNF α (± FSH)-treated granulosa cells compared with control (± FSH) cells
(TNFα vs non TNFα treated)
(TNFα vs non TNFα treated)
(TNFα vs non TNFα treated)
Cytokines, hormones and receptors
Extracellular matrix and synthesis
Intercellular and cell to matrix adhesion
Proteolysis and inhibition
IPA and GO enrichment analysis of TNFα regulated dataset
Upstream regulators determined by IPA to be activated or inhibited by TNF α (± FSH) treatment of cultured granulosa cells†
Gene symbol or molecule
Full gene name
Actual fold change
P value of overlap
Tumour necrosis factor
Interleukin 1, beta
All-trans retinoic acid
Interferon beta 1
CD40 molecule, TNF receptor superfamily member 5
Estrogen Receptor 1
Interleukin 1 receptor antagonist
This study examined the effects of the treatment with FSH and TNFα, separately or combined, on granulosa cells cultured under serum-free conditions and maintained in a non-luteinised state. The effect of FSH alone at 0.33 ng/ml paralleled the result of a previous study by Glister et al., with similar increases in oestradiol production and expression of follistatin and inhibin A confirming the robustness of this physiologically relevant in vitro model used in the current experiments. Moreover, our qRT-PCR findings confirmed the ability of FSH to up regulate expression of its cognate receptor (FSHR) as reported previously [23, 24] although a statistically significant difference was not detected by microarray analysis. With the exception of FSHR, there was excellent agreement between microarray and qRT-PCR data with respect to treatment effects on the other eight transcripts used for validation purposes. The ability of TNFα to suppress the production of oestradiol in our experiment was also expected from results of previous studies in serum-free  and serum-supplemented  culture systems.
The unsupervised array analyses and the numbers of genes differentially regulated, show surprisingly perhaps, that in our experiment FSH alone had a minor effect on total gene expression, compared with TNFα, where many genes were differentially regulated. The effects of FSH were limited to stimulation of energy metabolism and steroidogenesis in overall terms, in comparison with TNFα which mainly influenced inflammatory pathways and molecules. Clearly the main specific effect of FSH treatment was to stimulate oestradiol production by up regulation of aromatase expression (CYP19A1). The production of oestradiol and the concomitant activation of the folliculogenesis regulating genes for inhibin A and follistatin occur through recognised FSH cascade signalling involving cyclic AMP and protein kinase A . The relatively low dose of FSH (0.33 ng/ml) used to treat the cells in our microarray experiment was selected as being optimal for promoting oestradiol secretion and was insufficient to induce an increase in progesterone synthesis or proliferation of the granulosa cells in culture. However, there was transcriptional activation of the cyclin B1 and B2 genes, which indicates an increase in mitotic activity, although a net increase in cell number was not observed under the culture conditions used. The endocrine functions of granulosa cells appeared to be down regulated upon TNFα treatments with reductions in FST (6.2 fold), INHA (5.8 fold), INBA (5.8 fold), AMH (3.4 fold).
CHST8 was also down regulated in the TNFα-responsive datasets. The encoded enzyme is a sulphur transferase that sulphates N-acetylgalactosamine β1,4 linked with N-acetylglucosamine (LacdiNAc) moieties on certain glycoproteins prior to secretion . Sulphation of these structures can modulate the activity of these molecules by affecting the kinetics of binding  and increases their rate of clearance from the body . Glycodelin, an immune mediator, is produced by the granulosa cells at antral stages and possesses these LacdiNAc moieties. Glycodelin is taken up by the cumulus cells, where it is deglycosylated, loses immunosuppressive function and acquires properties beneficial to the fertilisation process . It may be possible that sulphation may also play a role in determining the activity of glycodelin, but confirmation of this would require further investigation.
It is known that TNFα exerts its apoptotic effects through the Type I receptor (TNFR1), whereas other pro-inflammatory actions on growth and differentiation are mediated via the Type II receptor (TNFRII) as previously reviewed by Matsuda et al.. Since we did not observe any effect on viable cell number after 4 days exposure to TNFα, this might suggest that TNFα did not induce apoptosis in the granulosa cells and may act predominantly via TNFRII in our culture system to activate a pro-inflammatory cascade that modifies other aspects of cell function including matrix remodelling and up regulation of antigen presentation molecules. Many of the genes whose expression in granulosa cells was up regulated by TNFα are often associated with innate immune responses. This reinforces recent evidence that granulosa cells can act as immune sensors and play an active role in initiating protective inflammatory responses to bacterial pathogens, recognised via interaction of pathogen-associated molecular patterns (PAMPs) such as lipopolysaccharide, with toll-like receptor 4 (TLR4) on the cell surface [21, 29, 30]. Indeed, bovine granulosa cells were recently shown to express a full complement of TLRs. Moreover, functional inflammatory responses to PAMPs interacting with TLR2 and TLR4 were demonstrated .
The genes influenced by TNFα treatment were generally associated with endocrine function, apoptosis, inflammation, and degradation as were expected from previous studies. In our culture system, TNFα alone did not cause any net loss of cells. Some pro-apoptotic (XAF1, CASP4), but more anti-apoptotic genes (TGM2, BCL2A1, BIRC3, TNIP1), in fact, appeared to be stimulated by TNFα. It is likely that the genes which act to block the apoptotic process are responding as a cellular survival mechanism, although some may be directly activated via the TNFα signalling pathway. The blocking effect of TNFα on FSH-induced oestradiol production has been shown previously to be suppressed by treatment with peroxisome proliferator-activated receptor (PPAR) γ ligands , and in this study PPARG expression was inhibited by TNFα, indicating that this lipid metabolism pathway was also involved. HSD11B1 encodes a key enzyme in glucocorticoid metabolism and has been previously shown to be activated by TNFα via the regulatory gene CEBPB in cultured cells .
The genes identified as being most highly up regulated by TNFα include TGM2 (31-fold), GPR77 (62-fold), SLPI (59-fold) and TNC (52-fold), none of which have previously been noted in granulosa cells. TGM2 catalyses the cross-linking of proteins and the conjugation of polyamines to proteins. It is also implicated as a positive regulator of the inflammatory response, NF-κB signalling and cell adhesion . GPR77 (62-fold) is one of several receptors for the C5a molecule, a major chemotactic and pro-inflammatory product of the complement cascade activated during the innate immune response . SLPI (59-fold) was first characterised as a protease inhibitor but is now recognised as having additional properties including antimicrobial and immunomodulatory activities associated with the innate immune response. SLPI is up regulated by pro-inflammatory mediators and appears to have a tissue protective role [35, 36]. TNC (52-fold) is an extracellular matrix molecule that is highly expressed during embryonic development but is normally present in low amounts in adult tissues. However, TNC expression is up regulated in pathological situations involving tissue injury, wound healing, inflammation and cancer. TNC influences cell migration, proliferation and cell signalling pathways through a variety of mechanisms including induction of pro-inflammatory cytokines .
In vitro, the transcriptome of granulosa cells responded minimally to FSH compared with the response to TNFα. The response to TNFα indicated a reduction in endocrine function and an active process akin to tissue repair and remodelling as would occur upon atresia. Additionally there was an inflammatory response to TNFα that displays many features normally associated with immune cells.
Bovine ovaries and primary culture of granulosa cells
Bovine granulosa cells were isolated from adult bovine ovaries obtained from a local abattoir as described previously [17, 18, 38]. Contamination with theca cells was judged to be < 1% based on comparison of the relative expression of CYP17A1 and LHCGR in freshly isolated granulosa cells and theca cells as determined by qRT-PCR (data not shown). For each experiment cells were pooled from approximately 50 individual 4–6 mm follicles and seeded at 5×105 viable cells/ml into 24-well (microarray) or 75,000 cells/0.2 ml into 96 well plates (dose response) with four replicate wells per treatment. Cells were cultured for six days under defined serum-free conditions. The culture medium used consisted of McCoy’s 5A modified medium supplemented with 1% (v/v) antibiotic-antimycotic solution, 10 ng/ml bovine insulin, 2 mM L-glutamine, 10 mM HEPES, 5 μg/ml apo-transferrin, 5 ng/ml sodium selenite and 0.1% (w/v) BSA (all purchased from Sigma UK Ltd, Poole, Dorset, UK). The culture medium was supplemented with 10-7 mol/l androstenedione (Sigma UK Ltd, Poole, Dorset, UK) as a substrate for cytochrome P450 aromatase. Media were removed after 48 h and 96 h and replaced with fresh media containing treatments described below. Conditioned media were retained for hormone assays, and at the end of culture either viable cell number was determined (dose–response experiment) by neutral red uptake assay  or cell lysates were prepared (microarray experiment) using the lysis buffer component of the RiboPure RNA isolation kit (Ambion/ Life Technologies Ltd., Paisley, UK). Pooled lysates from replicate wells were stored at -80C until total RNA isolation. Each experiment was repeated four times using cells harvested from independent batches of ovaries.
Highly purified ovine FSH (NIADDK oFSH-19SIAPP) was supplied by NHPP, Torrance, CA, USA. Recombinant human TNFα was purchased from Sigma Aldrich, St Louis, MO, USA (Cat# T6674 with a stated endotoxin level <1 ng/ug). Treatments were dissolved in Hank’s balanced-salt solution containing 0.1% (w/v) BSA and stock solutions sterilized using 0.2 μm membrane filters before dilution in the culture medium. These treatments were applied on days 3 to 6 of culture for both the microarray and dose response experiments under the conditions specified above.
The concentrations of oestradiol in conditioned media were determined by radioimmunoassay . The detection limit of the assay was 2 pg/ml and mean intra- and inter-assay CVs were 6% and 9% respectively. Concentrations of progesterone in conditioned media were determined by competitive ELISA . The detection limit was 0.1 ng/ml and mean intra- and inter-assay CVs were 8% and 11% respectively.
Total RNA isolation, microarray analysis and quantitative RT-PCR
List of primers used for qRT-PCR
Genbank accession number
Forward primer 5′ to 3′
Reverse primer 5′ to 3′
Amplicon size (bp)
For qRT-PCR analyses, the ∆∆Ct method  was used for comparison of the relative abundance of each mRNA transcript. Ct values for each transcript in a given sample were first normalised to the β-actin Ct value (which was uniform across all experimental groups: ANOVA P > 0.1). Resultant ∆Ct values for individual replicates within each treatment group were then normalised to the average ∆Ct value of the respective vehicle-treated control group. These ∆∆Ct values were finally converted to fold differences using the formula: fold difference = 2(−∆∆Ct).
Results for hormone secretion (during final 96–144 h period of culture) were analysed using two-way ANOVA and are presented as means ± SEM based on four independent culture experiments. To reduce heterogeneity of variance, hormone data were log-transformed prior to statistical analysis. qRT-PCR data (from n = 4 independent granulosa cell batches) were statistically analysed (ANOVA and post-hoc Fisher’s LSD test) as ∆Ct values before conversion to fold-difference values for graphical presentation.
Following confirmation of the quality of the RNA and cDNA synthesis, hybridisations to GeneChip® Bovine Genome Arrays (Affymetrix, CA, USA) and scanning were performed according to Affymetrix protocols at the Almac Diagnostics Facility. All samples were analysed together as one lot using the same batch of arrays. First-strand cDNA synthesis was performed on two micrograms of RNA using a T7-linked oligo-dT primer, followed by second strand synthesis. In vitro transcription reactions were performed in batches to generate biotinylated cRNA targets, which were subsequently chemically fragmented at 95°C for 35 min. Ten μg of the fragmented, biotinylated cRNA was hybridized at 45°C for 16 h to Affymetrix GeneChip Bovine Genome Arrays, which contain 24,128 probe sets representing over 23,000 transcripts and variants, including 19,000 UniGene clusters. The arrays were then washed and stained with streptavidin-phycoerythrin (final concentration 10 μg/ml). Signal amplification was achieved by using a biotinylated anti-streptavidin antibody. The array was then scanned according to the manufacturer’s instructions. The scanned images were inspected for the presence of any defect (artefact or scratch) on the array.
Treatment and analysis of microarray data
Non-biological signal variation due to possible array differences or hybridisation treatments were minimised by normalisation of the raw data using the Robust Multi-array Average (RMA) method [42, 43] with adjustments as detailed previously [44, 45]. The normalisation and statistical analyses were performed in Partek Genomics Suite Software version 6.5 (Partek Incorporated, St Louis, MO, USA). Array quality controls were performed by spike-in analysis of standard amounts of bacterial specific cDNA against respective homologous probe sets on the chip. Statistical differences between treatment groups were determined by one-way ANOVA with FDR tests for multiple comparisons. The fold change in gene expression was determined from the non log-transformed signal data after correction and normalisation. The experimental details and array CEL data files have been deposited under series name GSE42535 in NCBI’s Gene Expression Omnibus (GEO) database.
Network and functional analysis
The groups of differentially expressed genes were uploaded into the Ingenuity Pathway Analysis (IPA) database for network and pathway determination (Ingenuity Systems, 2005). These datasets were also characterised according to their association with Gene Ontology (GO) terms listed under biological process using Gene Ontology Enrichment Analysis Software Toolkit (GOEAST) .
Transforming growth factor beta
Tumour necrosis factor alpha
Quantitative reverse transcription polymerase chain reaction
Principal component analysis
Benjamini-Hochberg false discovery rate
Gene ontology enrichment analysis software toolkit
Gene expression omnibus
Ingenuity pathway analysis
National centre for biotechnology information
Robust multi-array average
Least significant difference
Coefficient of variation.
This work was supported by the United Kingdom Biotechnology and Biological Sciences Research Council (grant no. BB/G017174/1 to PGK and CG and Underwood Fellowship award to RJR) and by the National Health and Medical Research Council of Australia and the Australian Research Council (NH, KH and RJR).
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