Effect of exogenous gonadotropin on the transcriptome of human granulosa cells and follicular fluid hormone profiles

The study was conducted at the Reproductive Medical Center of the Third Hospital Peking University (Beijing, China). All the recruited patients were under 36 years old and had a normal BMI range from 18.8 to 26.6 kg/m². These patients received either the Gn stimulation protocol (the same COS protocol) or natural cycle for their IVF treatment, as explained below. The exclusion criteria were patients with the ovulatory disorder or follicular dysplasia. Four women who received the COS protocol for IVF treatment had a regular menstrual cycle because of male factor or tubal factor. For these patients, a combination of recombinant follicle stimulating hormone (FSH) (Gonal-F, 150 IU/day, Merck Serono SA Aubonne Branch) and human menopausal gonadotrophin (Menotropins for injection FSH 75 IU: LH 75 IU, Livzon Pharmaceutical Group Inc.) in a fixed-dose was started on Day 2 of the menstrual cycle with the option to adjust dose according to response after 4 days of stimulation (Day 6 of menstrual cycle). GnRH antagonist (Cetrorelix 250 μg/day, Merck Serono, Darmstadt, Germany) was started when a leading follicle of 12 mm was achieved. Moreover, when one or two leading follicles reached an average diameter of 18 mm, the recombinant human chorionic gonadotrophin (hCG) (Ovitrelle, 250 μg; Merck Serono S.p.A, Rome, Italy) was administered followed by follicular aspiration 34–36 h later. These patients were assigned as the Gn-stimulated group (n = 4). In the unstimulated-cycle group (assigned as the control group, n = 3), three patients were arranged for IVF treatment with their natural cycles without Gn intervention and with no hCG triggering. These infertile women were due to tubal factors, including hydrosalpinx and proximal tubal obstruction. In both groups, the follicular fluid samplesobtained from the largest pre-ovulatory follicle of each patient were collected for analysis. The technique of follicular aspiration was performed with a new 17-gauge single-lumen aspiration needle (K-OPS-7035-REH-ET; Cook, Queensland, Australia) and a suction pressure of 120 mmHg, under the guidance with transvaginal ultrasonography. Immediately after the aspirates were collected, we centrifuged these aspirates at 2000Xg for 10 min, and the supernatant was transferred to a 2-ml cryotube for cryopreservation at − 80 °C until later analysis. The sediment samples were collected for granulosa cell isolation, which were further washed in phosphate-buffered saline (PBS) and centrifuged over Ficoll (GE Healthcare Corp., USA) to remove the red blood cells. The aggregates that contain granulosa cells were isolated from the follicular fluid by a pipette, flushed twice in phosphate-buffered saline (PBS), and transferred to a tube placed in ice water. Granulosa cells were washed again with PBS, and the cell deposits were flash frozen in liquid nitrogen within 30 min and stored at − 80 °C until RNA extraction.

The experimental protocol for cDNA preparation and PCR pre-amplification was as previously described [10]. Briefly, deposits of a small amount of granulosa cells were transferred into lysis buffer and reversely transcribed into the first-strand cDNA. The cDNA was then amplified by a PCR machine for 20 cycles.

PCR was purified and ultimately eluted. Five nanograms of cDNA were then used for the tagmentation reaction, purified, and then used for second PCR amplification. The PCR products were purified using an AMPure XP kit (Beckman Coulter, Brea, CA, USA), and samples were loaded onto an Agilent Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA). Quantification of the library was performed using the Qubit 3.0 Fluorometer (Thermo Fisher Scientific, Singapore). Libraries were diluted to a final concentration of 2 nM, and a total of 10 pmol of each library was sequenced using an Illumina HiSeq 2000.

Clean data (clean reads) were obtained by removing artificial adapters, poly-A, and low-quality bases from raw data. Clean data were aligned to human (hg19) genome using Tophat2 (v2.1.0) with default settings and filtered for uniquely mapped reads. Gene expression values were calculated as FPKM using Cufflinks (v2.2.1) [11].

Differential expression analysis of the two groups was performed using the DEGSeq R package (v1.18.0). DEGSeq analysis was used to provide statistical routines for determining differentially expressed genes (DEGs) using a model based on the negative binomial distribution. P-values were adjusted using the Benjamini–Hochberg method to control the false discovery rate. Genes with adjusted P-value < 0.05 were defined as differentially expressed.

Gene Ontology (GO) enrichment analysis was performed using the GOseq R package with correction for gene length bias. GO terms with corrected P-values less than 0.05 were considered to be significantly enriched. Enrichment of DEGs in the Kyoto Encyclopedia of Genes and Genomes (KEGG) was analyzed using the KOBAS software [12].

All hormonal assays were performed at the endocrine laboratory of the Peking University Third Hospital Reproductive Centre using commercially available ELISA kits (IMMULITE 2000, SIEMENS, USA). The lower detection limits of the six assays used in this study were as follows: 0.1 mIU/ml for FSH, 0.1 mIU/ml for luteinizing hormone (LH), 73.4 pmol/l for estradiol (E2), 0.64 nmol/l for progesterone (P4), 0.69 nmol/l for testosterone (T), and 1.05 nmol/l for androstenedione (AND), respectively. The inter-assay coefficients of variation (CVs) for the six hormone levels were 5% (FSH), 6% (LH), 5% (E2), 6% (P), 6% (T), and 5% (AND), respectively.

Hormonal concentration valuesare expressed as means ± SD (standard deviation). Differences in hormone concentration between the two groups were analyzed by student’s t test.

The characteristics of the patients of the control group (n = 3) and Gn-stimulated group (n = 4) were as the previous described in the Materials and Methods section. Transcriptomes of the individual luteinized granulosa cells from every single pre-ovulatory follicle obtained from these seven women were sequenced.

RNA transcriptomes were sequenced using the Illumina HiSeq platform. We obtained 69–90 million 150 bp reads for each sample, and 76 Gb of raw data in total were obtained for all of the samples. Clean data were ~ 10 Gb per sample (Additional file 2: Table S1). All the downstream analyses were conducted based on clean, high-quality data.

We calculated the Pearson correlation coefficient of every two samples by using the gene expression in the samples. As shown in Additional file 1: Figure S1A, the expression patterns were generally homogeneous among the three controls (R² > 0.9 for both comparisons). In contrast, the expression patterns were relatively heterogeneous in the Gn-stimulated group, with Gns1 and Gns3 (R² = 0.917), and Gns2 and Gns4 (R² = 0.841), are more similar to each other.

We then examined the total number of transcripts that were expressed in each group. The results obtained from the comparison analyses revealed that 11,923 genes were commonly expressed in both groups in which 2437 genes were expressed uniquely in the Gn-stimulated group and 920 genes were uniquely expressed in the control group (Additional file 1: Figure S1B).

We then analyzed the DEGs between the two groups. As shown in Fig. 1a, we came up with the unsupervised hierarchical clustering of DEGs. Compared with the control group, 715 genes were up-regulated, whereas 287 genes were down-regulated in the Gn-stimulated group (P < 0.05; FC of log₂-transformed FPKM > 1) (Fig. 1b). The detailed regarding genes that were up-regulated and down-regulated in each group are presented in Additional file 3: Table S2 and Additional file 4: Table S3.

To investigate the possible biological functions of DEGs between the two groups, we performed GO analysis of DEGs and identified enriched biological processes in each group (the detailed information is presented in Additional file 5: Table S4 and Additional file 6: Table S5). The top 15 GO enrichment results are shown in Fig. 2a (the down-regulated DEGs) and Fig. 2b (the up-regulated DEGs). The DEGs down-regulated in the Gn-stimulated group were significantly enriched and related to the cell cycle and chromosome segregation. In contrast, the up-regulated DEGs were enriched in functions that are related to immune response or processes.

Next, we performed the KEGG analysis to investigate the differences between the two groups. Detailed pathway enrichment analyses of down-regulated and up-regulated DEG data are presented in Additional file 7: Table S6 and Additional file 8: Table S7, respectively. The representative top 10 pathways (P < 0.05) in DEGs that were down-regulated in the Gn-stimulated group are shown in Fig. 3a. Amongthem, only five pathways (cell cycle, oocyte meiosis, steroid hormone biosynthesis, P-mediated oocyte maturation, and ovarian steroidogenesis) were significantly enriched (adjusted P < 0.05). The names of the DEGs in each pathway are annotated in Table 1. On the other hand, among the up-regulated genes in the Gn-stimulated group, 38 pathways were enriched (adjusted P < 0.05). The top 10 terms are shown in Fig. 3b, including three representative terms (cytokine-cytokine receptor interaction, chemokine signaling pathway, and NF-kappa B signaling pathway), with the correlated input genes annotating in Table 1.

To further investigate whether oocyte meiosis was affected by Gn stimulation, we clustered some mitosis–associated genes and examined their expression among the seven samples (Fig. 4). This analysis revealed that some cell cycle– or mitosis-associated genes were expressed at low levels in the Gn-stimulated group, including CCND2, CCNB1, CDC23, CDK1,and SMAD3. Moreover, BUB3 (a mitotic checkpoint gene) was also expressed at low levels in the Gn-stimulated group. Intriguingly, HDAC2 (a member of the histone deacetylase family) was also down-regulated in the Gn-stimulated group, which forms transcriptional repressor complexes by associating with many different proteins, and thus plays an important role in transcriptional regulation, cell cycle progression, and developmental events.

Next, we verified different hormone concentrations in the FF samples of every single pre-ovulatory follicle used for the transcriptome analysis. The sizes of the pre-ovulatory follicles were similar between the two groups. Table 2 shows the different follicular hormonal concentrations. The LH and E2 concentrations in FF were lower in the Gn-stimulated group than in the control group (P = 0.006 and P = 0.002, respectively). However, the P4 concentration was higher in the Gn-stimulated group (P = 0.003). The levels of the other four hormones tested were not significantly different between the two groups.