Abstract
Poor ovarian response is a significant problem encountered during in vitro fertilization and embryo transfer procedures. Many infertile women may suffer from poor ovarian response and its incidence tends to be increasing in young patients nowadays. It is a major cause of maternal infertility because it is associated with low pregnancy and live birth rates. However, the cause of poor ovarian response is not clear. In this study, we extracted microRNAs from human follicular fluid and performed miRNA sequencing to investigate a potential posttranscriptional mechanism underlying poor ovarian response. The results showed that many miRNAs were obviously different between the poor ovarian response and non-poor ovarian response groups. We then performed quantitative polymerase chain reaction, Western blot analysis and used an in vitro culture system to verify the sequencing results and to study the mechanism. Notably, we found that miRNA-15a-5p was significantly elevated in the young poor ovarian response group. Furthermore, we demonstrated that high levels of miR-15a-5p in the young poor ovarian response group repressed granulosa cell proliferation by regulating the PI3K-AKT-mTOR signaling pathway and promoted apoptosis through BCL2 and BAD. This could explain the reduced oocyte retrieval number seen in poor ovarian response patients.
Introduction
Poor ovarian response (POR) is a condition characterized by decreased follicular numbers and low estradiol levels following controlled ovarian hyperstimulation during in vitro fertilization and embryo transfer (IVF-ET) procedures. It has been reported that the incidence of POR is about 9%–24% among patients undergoing IVF (Tarlatzis et al. 2003). Advanced age is one of the most significant risk factors for POR. The prevalence of POR increases with age, and in women over 40 years of age, it is over 50% (Ferraretti et al. 2011). In POR patients undergoing IVF, the success rate is extremely low and the total cancelled cycles are quite high due to the loss of follicles from the ovary and to decreased oocyte quality.
Nevertheless, little is known about the molecular mechanisms underlying POR. Previous investigations have demonstrated that polymorphisms in the FSHR gene (Asn680Ser) and defects in the estrogen receptor beta may be associated with an altered ovarian response (Drummond & Fuller 2010, Sheikhha et al. 2011). Estrogen receptor beta is critical for granulosa cell differentiation and for the ovulatory response to gonadotropins, and it acts by affecting aromatase activity, estradiol synthesis and the expression of the LH receptor (Couse et al. 2005). It has been reported that defects in estrogen receptor beta lead to a partial arrest of follicle genesis and ovulatory dysfunction in estrogen receptor beta knockout mice (Drummond & Fuller 2010). In other reports, some genes that may contribute to premature ovarian failure and be associated with POR have been identified, for example, Fmr1, Foxl2, Foxo3a and Gdf9 (Skillern & Rajkovic 2008).
Recently, it was reported that posttranscriptional alterations occurring in the germline were linked to defects in oocyte maturation (Espey & Richards 2002, Pangas & Rajkovic 2006, Murchison et al. 2007). In this regard, miRNAs execute key posttranscriptional functions by degrading target mRNAs or repressing their translation by binding to 3′UTR sequences and form RNA-induced silencing complexes to regulate gene expression (Benfey 2003). Through these mechanisms, miRNAs regulate many cellular processes including cell proliferation, differentiation and apoptosis (Bartel 2004).
We hypothesized that changes in miRNA patterns in follicular fluid could explain the pathological defects in oocyte maturation and the reduced numbers of retrieved oocytes in POR patients. We recovered follicular fluid samples from POR and non-POR women to perform miRNA sequencing. The goals of this study are (i) to investigate possible changes in miRNA patterns in the follicular fluid of POR patients and (ii) to identify miRNAs and related biological processes that could explain the pathological defects in POR.
We divided POR patients into old and young groups. Based on the miRNA-sequencing results, we found that expressions of miR-15a-5p and miR-483-5p were clearly different. The expression of miRNA-15a-5p was significantly increased while miRNA-483-5p was significantly decreased in young POR patients when compared with non-POR in follicular fluid. It means miRNA-15a-5p and miRNA-483-5p might be associated with young POR. We then validated the sequencing results with additional samples by means of quantitative polymerase chain reaction (qPCR) and Western blot and studied the possible mechanisms through which these miRNAs exert their functions.
Materials and methods
Patients
In this study, we collected follicular fluid samples from 45 patients undergoing IVF and intracytoplasmic sperm injection. All patients were recruited from the Center for Reproductive Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, between April 2015 and April 2016. POR patients were diagnosed according to the Bologna Criteria (Ferraretti et al. 2011). Fifteen of them were young (about 25 to 35 years old) and fifteen were old (over 40 years of age). In order to define the presence of POR during IVF, at least two of the following three features had to be present: (i) advanced maternal age or any other risk factor for POR; (ii) previous POR or (iii) an abnormal ovarian reserve test. Two POR episodes after maximal stimulation were sufficient to define a patient as a poor responder in the absence of advanced maternal age or an abnormal ovarian reserve test. The other fifteen patients were non-POR with normal antral follicle counts whose infertility was caused by tubal or male infertility factors. All the patients recruited had regular menstrual cycles (26–35 days).
We excluded patients suffering from other related disorders, such as polycystic ovary syndrome, Turner syndrome, thyrotoxicosis, hyperprolactinemia or recurrent spontaneous abortion, as well as patients who had undergone ovarian surgery or chemotherapy. All patients underwent controlled ovarian hyperstimulation protocols with different follicle-stimulating hormones (FSH)/human menopausal gonadotropin starting doses depending on their age (Ferraretti et al. 2011). The Ethics Committee of the Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine approved the study and written consent was obtained from each patient included in the study.
Collection of human follicular fluid and granulosa cells
During IVF, patients received human chorionic gonadotrophin when the diameter of their follicles was >18 mm. Oocytes were collected 36 h after human chorionic gonadotrophin injection by transvaginal ultrasound-guided puncture and aspiration of follicles with a diameter of 18–20 mm. The follicular fluid (2 mL) from the first aspirated follicle, not contaminated by blood, was carefully collected from each donor, centrifuged at 2000 g for 20 min and stored at −80°C until miRNA isolation. At the same time, granulosa cells from the same patients undergoing IVF cycles were collected using the isolation protocol described previously (Matsubara et al. 2000). The granulosa cells were also stored at −80°C until RNA isolation.
MicroRNA extraction and purification
Total RNA from follicular fluid was extracted with the TRIzol LS Reagent (Invitrogen) according to the manufacturer’s instructions. Briefly, 0.75 mL of TRIzol reagent was added to 0.25 mL of follicular fluid. After incubating for 5 min, 0.2 mL of chloroform was added and the samples were mixed vigorously for 15 s. Following an additional 10-min incubation, samples were centrifuged at 12000 g for 15 min at 4°C. The upper aqueous phase was carefully transferred to a new tube and 0.5 mL of 100% isopropanol was added. After incubating at −20°C for 3 h, the samples were centrifuged at 12000 g for 20 min at 4°C. The RNA pellets were then washed with 1 mL of 75% ethanol, centrifuged at 12000 g for 5 min at 4°C and resuspended in 20 µL of RNase-free water. The RNA quality was checked by means of a Bioanalyzer 2200 instrument (Aligent). Then, the RNA (RIN > 8.0) was purified with a miRNeasy Mini Kit (Qiagen) for miRNA and the purification was validated by gel electrophoresis. The microRNAs extracted from the follicular fluid of three young and three old POR patients (POR-Y, POR-O), as well as three non-POR patients, were sent for miRNA sequencing. The nine samples for miRNA sequencing were picked up randomly according to the Bologna Criteria.
Library construction and miRNA sequencing
Complementary DNA (cDNA) libraries for single-end sequencing were prepared using the Ion Total RNA-Seq Kit, v2.0 (Life Technologies) according to the manufacturer’s instructions. The cDNA library for miRNA sequencing was size-selected by means of PAGE gel electrophoresis. After enrichment, the mixed template-positive Ion PITM Ion Sphere particles were applied to the P1v2 Proton Chip (Life Technologies) and sequenced on Proton Sequencers. The EB-Seq algorithm was applied to identify differentially expressed genes after significance and false discovery rate (FDR) analysis, applying the following criteria: FDR < 0.05 (Benjamini et al. 2001).
Gene ontology (GO) and pathway analysis
Targetscan was utilized for predicting miRNA targets (Chiang et al. 2010, Agarwal et al. 2015). GO analysis was performed to elucidate the biological implications of unique genes present in the significant or representative profiles of differentially expressed miRNA target genes. GO annotations were downloaded from NCBI (http://www.ncbi.nlm.nih.gov/), UniProt (http://www.uniprot.org/) and GO (http://www.geneontology.org/). Fisher’s exact test was applied to identify the significant GO categories and FDR was used to correct the P values. Pathway analysis was used to identify significant pathways for the differentially expressed genes according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Fisher’s exact test was used to identify the significant pathways, and the threshold for significance was defined by the P value and the FDR (Draghici et al. 2007). Additionally, integrative miRNA–mRNA network analysis was carried out according to the significant GO categories and pathways.
Verification of miRNA and mRNA expression
Additional follicular fluid and granulosa cells from twelve POR-Y, twelve POR-O and twelve non-POR patients were used to validate the miRNA-sequencing results and to quantify mRNA expression. Quantitative PCR was performed after MicroRNA Reverse Transcription with the Mir-X miRNA qRT-PCR SYBR Kit (Cat. No. 638315, Takara Bio USA), following the manufacturer’s instructions. The specific primers for miRNAs were as follows: hsa-miR-15a-5p: TAG CAG CAC ATA ATG GTT TGT G, hsa-miR-483-5p: AAG ACG GGA GGA AAG AAG GGA G, hsa-miR-199a-5p: CCC AGT GTT CAG ACT ACC TGT TC, hsa-miR-885-5p: TCC ATT ACA CTA CCC TGC CTC T. Expression of the miRNAs was normalized to U6 levels. As for mRNA detection and quantification, qPCR of AKT, mTOR, S6K1, IGF2, BCL2 and BAD mRNA was carried out using the SYBR GREEN PCR Master Mix (Toyobo, Osaka, Japan). Expression of these mRNAs was normalized to GAPDH levels. The primer sequences are listed in Table 1.
Primer list.
| Gene | Sequences | Fragment size (bp) | |
|---|---|---|---|
| AKT | ForwardReverse | 5′-GTCATCGAACGCACCTTCCAT-3′5′- AGCTTCAGGTACTCAAACTCGT-3′ | 218 |
| mTOR | ForwardReverse | 5′-TTGCTTGAGGTGCTACTGAA-3′5′-CTGACTTGACTTGGATTCTGAC-3′ | 183 |
| S6K1 | ForwardReverse | 5′-TTTGAGCTACTTCGGGTACTTGG-3′5′-CGATGAAGGGATGCTTTACTTCC-3′ | 196 |
| IGF2 | ForwardReverse | 5′-CGGCTTCTACTTCAGCAGGC-3′5′-TGGCGGGGGTAGCACAGT-3′ | 122 |
| BCL2 | ForwardReverse | 5′-GGTGAACTGGGGGAGGATTG-3′5′-GTGCCGGTTCAGGTACTCAG-3′ | 130 |
| BAD | ForwardReverse | 5′-CGGAGGATGAGTGACGAGTT -3′5′-CCAAGTTCCGATCCCACCAG -3′ | 136 |
| GAPDH | ForwardReverse | 5′-GGAGCGAGATCCCTCCAAAAT-3′5′-GGCTGTTGTCATACTTCTCATGG-3′ | 197 |
Amplification was carried out in an ABI PRISMH7700 sequence detector system (Life Technologies, Inc.) using the following program: 5 min at 95°C, followed by 40 cycles of 10 s at 95°C, 20 s at 59°C and 20 s at 72°C, ending with a 5-min extension at 72°C. All reactions were run in triplicate. The relative miRNA and mRNA levels were determined using the ΔΔCt method (Pfaffl 2001).
Cell culture and transfection
The human granulosa-like tumor cell line KGN was maintained in phenol red-free DMEM/F12 (Gibco, Grand Island, NY) supplemented with 10% charcoal-stripped FBS (Blood Bioind Stem Origin: Israel) and incubated in 6-well dishes at a density of 1 × 105 cells/well at 37°C and 5% CO2 for 24 h. The miR-15a-5p mimic, miR-15a-5p inhibitor and their scrambled controls were purchased from Ribobio (Guangzhou, China). The miRNA mimics are double-stranded, chemically synthesized RNAs that resemble mature endogenous miRNAs, and the miRNA inhibitors are chemically modified antisense RNA oligonucleotides optimized to specifically target miRNA molecules in cells. Either miRNA mimics (50 nM/well) or inhibitors (100 nM/well) and their scrambled controls were transfected into KGN cells grown at 60% confluence by means of LipofectamineTM3000 (Invitrogen, Carlsbad, CA) (7.5 µL/well) diluted in Opti-MEM Medium (Gibco) according to the manufacturer’s protocol. The medium was replaced by fresh medium 6 h after transfection, and cells were cultured for another 48 h.
Western blot analysis
Aliquots of the cells harvested after transfection were mixed with sample buffer, consisting of 0.01% (w/v) bromophenol blue, 2% (w/v) SDS, 5% (w/v) β-mercaptoethanol, 25% (v/v) glycerol and 62.5 mM Tris–HCl (pH 6.8) and boiled for 10 min. Then, 10 µg of protein from the cell samples were loaded onto SDS polyacrylamide gels, electrophoresed and transferred to polyvinylidene fluoride membranes. The membranes were blocked with 5% non-fat milk for 60 min at room temperature and then incubated overnight at 4°C with the primary antibodies. The primary antibodies were as follows: rabbit anti-Phospho-mTOR (1:1000 dilution; Ser2448, Cell Signaling), rabbit anti-mTOR (1:1000 dilution; Cell Signaling), rabbit anti-Phospho-p70 S6 Kinase (1:1000 dilution; Ser371, Cell Signaling), rabbit anti-Phospho-4E-BP1 (1:1000 dilution; Cell Signaling), rabbit anti-BCL2 (1:2000 dilution; Proteintech) and mouse monoclonal IgG1 antibody specific for β-actin (1:5000 dilution; Santa Cruz Biotechnology). After incubation with the primary antibodies, the membranes were washed four times with Tris-buffered saline containing 0.1% Tween-20 and incubated with the secondary antibodies (1:2000 dilution; Cell Signaling) for 90 min at room temperature. The ECL chemiluminescence kit (Millipore) was used to detect the protein bands.
Statistical analysis
All the results were expressed as mean ± standard error of the mean (s.e.m.), and all the experiments were repeated at least three times except for miRNA mimic experiment, which was repeated 6 times starting from cell inoculation. To determine the differences between groups, we used the unpaired Student’s t-test or one-way ANOVA test followed by the Newman–Keuls multiple comparison test. All analyses were conducted using SPSS 21.0 for Windows (IBM). Significance was defined as P < 0.05.
Results
Clinical characteristics
The clinical characteristics of the POR-Y, POR-O and non-POR patients are shown in Table 2. After comparing the POR-Y with the non-POR group, no statistical differences were found in terms of age, body mass index (BMI), baseline FSH or estradiol (E2) levels. Except for age (P = 0.00), no statistical differences were found between the BMI, baseline FSH and baseline E2 levels of the POR-O and non-POR groups. However, anti-Mullerian hormone (AMH) levels were much lower in the POR-Y (P = 0.004) and POR-O (P = 0.001) groups than those in the non-POR group. The number of retrieved oocytes was significantly different between the POR-Y and non-POR groups (P = 0.001) and between the POR-O and non-POR groups (P = 0.001). The average number of retrieved oocytes in the POR-Y and POR-O groups was 1.00, whereas for the non-POR group, the value was 10.67. In addition, the high-quality embryo rate was significantly lower for both the POR-Y (P = 0.0015) and POR-O (P = 0.0011) groups than that for the non-POR group. Furthermore, patients from both the POR-Y and POR-O groups underwent approximately 3–4 IVF cycles, significantly more than those in the non-POR group (P = 0.026 for POR-Y vs non-POR, P = 0.039 for POR-O vs non-POR). Nevertheless, the duration of infertility (years spent trying to get pregnant) between the three groups was not statistically different.
Clinical characteristics of women with POR and Non-POR group.
| Group | Age (year) | BMI | AMH (ng/mL) | FSH (baseline) (IU/L) | E2 (baseline)(pg/mL) | Retrieved oocytes (number) | Infertility duration (year) | High-embryoquality rate (%) | IVF cycles (number) |
|---|---|---|---|---|---|---|---|---|---|
| POR-Y | 33.00 ± 0.57 | 20.12 ± 0.99 | 0.9477 ± 0.06* | 11.16 ± 1.67 | 26.87 ± 18.58 | 1.00 ± 0.58* | 3.00 ± 0.00 | 48.09 ± 6.58* | 3.00 ± 0.58* |
| POR-O | 41.33 ± 0.68* | 21.50 ± 0.96 | 0.4743 ± 0.21* | 9.207 ± 0.99 | 46.62 ± 9.720 | 1.00 ± 0.0* | 4.33 ± 0.88 | 41.38 ± 7.72* | 4.00 ± 1.00* |
| Non-POR | 30.67 ± 0.67 | 20.12 ± 0.69 | 3.508 ± 0.27 | 7.390 ± 0.68 | 48.48 ± 15.44 | 10.67 ± 0.88 | 3.00 ± 1.00 | 85.7 ± 4.47 | 1.00 ± 0.00 |
Values are presented as mean ± s.e.m.
Significant statistical difference (P < 0.05).
AMH, anti-mullerian hormone; BMI, body mass index; E2, estradiol; FSH, follicle-stimulating hormone; IVF, in vitro fertilization; Non-POR, non-poor ovarian response group; POR-O, old poor ovarian response group; POR-Y, young poor ovarian response group.
Differential microRNA expression between the three groups
MicroRNA sequencing was performed on the follicular fluid samples collected from nine women. After sequencing, approximately 5,900,000–23,700,000 clean reads were generated per sample. In order to control for sequencing quality, the Fast-QC software (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) was used, and the percentage of reads after filtering was above 70%. The length distribution for clean reads in each sample oscillated between 20 and 24 bp.
The EB-Seq algorithm was applied to identify differentially expressed genes after FDR analysis (log2FC>1 or <−1, FDR<0.05) (Benjamini et al. 2001). In total, 1258 miRNAs were identified. Seven upregulated miRNAs, including miRNA-15a-5p, miRNA-199a-5p, miR-374c-3p and miR-127-5p, as well as 32 downregulated miRNAs, including miR-483-5p, miR-483-3p, hsa-miR-4286 and miR-1180-3p were found when comparing the POR-Y group with the non-POR group (Table 3, FDR < 0.05). In addition, 24 upregulated miRNAs (including miR-885-5p, miR-199a-5p, miR-3591-3p, miR-122-5p and miR-199a-3p) and 21 downregulated miRNAs (such as miR-513a-5p, miR-509-3p, miR-4433b-5p and miR-4433a-3p) were identified when comparing the POR-O group with the non-POR group (Table 4, FDR < 0.05). We then analyzed and compared POR-Y with non-POR as well as POR-O with non-POR and presented our findings as Venn diagram (Fig. 1). The Venn diagram was showing the overlap of differentially expressed miRNAs among the groups. It has showed that there were 9 common miRNAs that were differentially expressed.
The Venn diagram showing the three cohorts analyzed. We compared POR-Y with Non-POR and compared POR-O with Non-POR.
Citation: Reproduction 154, 4; 10.1530/REP-17-0157
Differential expressed miRNAs comparing the POR-Y group with non-POR group (FDR < 0.05).
| miRNA | POR-Y | NC | Log2FC | FDR | Style |
|---|---|---|---|---|---|
| hsa-miR-15a-5p | 3388.614892 | 1411.330492 | 1.263639823 | 1.09756E-11 | up |
| hsa-miR-199a-5p | 1284.752417 | 593.7848962 | 1.113478064 | 0.002285319 | up |
| hsa-miR-374c-3p | 369.5640955 | 124.2907119 | 1.572106111 | 2.33455E-06 | up |
| hsa-miR-127-5p | 121.0437509 | 46.22530741 | 1.388773781 | 1.55371E-05 | up |
| hsa-miR-374b-5p | 357.9029607 | 158.1541622 | 1.178236954 | 0.000846028 | up |
| hsa-miR-337-5p | 372.8248122 | 102.7475428 | 1.859393985 | 0.036948449 | up |
| hsa-miR-376a-5p | 102.7468717 | 40.49478286 | 1.343286514 | 7.76809E-10 | up |
| hsa-miR-483-3p | 4743.035481 | 40192.97755 | −3.083060892 | 4.40025E-08 | down |
| hsa-miR-483-5p | 3928.394976 | 11803.96589 | −1.587259761 | 0.020350909 | down |
| hsa-miR-4286 | 1385.546002 | 3004.741992 | −1.116786505 | 5.74632E-05 | down |
| hsa-miR-1180-3p | 397.8031602 | 1287.185295 | −1.694093108 | 0 | down |
| hsa-miR-122-5p | 309.97814 | 1045.754901 | −1.754306375 | 0.000111351 | down |
| hsa-miR-3591-3p | 309.7438028 | 1021.647894 | −1.721750741 | 0.002405069 | down |
| hsa-miR-326 | 313.6875269 | 650.3972124 | −1.05199291 | 0.007763576 | down |
| hsa-miR-296-5p | 278.0368329 | 646.9533542 | −1.21838568 | 0.000109978 | down |
| hsa-miR-328-3p | 251.4509887 | 559.4710418 | −1.153786233 | 0.000493141 | down |
| hsa-miR-6087 | 148.1539961 | 463.4171951 | −1.645214037 | 0 | down |
| hsa-miR-23b-5p | 85.24016624 | 177.4453114 | −1.057769143 | 2.3338E-12 | down |
| hsa-miR-1306-5p | 73.81051089 | 181.0069675 | −1.294147051 | 0.00168457 | down |
| hsa-miR-532-3p | 73.33754978 | 164.5637574 | −1.166022668 | 8.4504E-08 | down |
| hsa-miR-4433b-5p | 32.76720295 | 85.63912071 | −1.386017455 | 7.61625E-05 | down |
| hsa-miR-4433a-3p | 30.77533653 | 91.73689773 | −1.575727488 | 2.21012E-08 | down |
| hsa-miR-4448 | 27.2431315 | 99.1027352 | −1.863032331 | 1.39888E-14 | down |
| hsa-miR-1247-5p | 26.28720708 | 135.3637206 | −2.364408354 | 0.005499031 | down |
| hsa-miR-4279 | 16.726534 | 126.7506106 | −2.921782264 | 0 | down |
| hsa-miR-885-5p | 22.79072419 | 113.2523496 | −2.313022309 | 3.17635E-12 | down |
| hsa-miR-450b-3p | 0.474389996 | 132.6266502 | −8.127081402 | 3.18117E-07 | down |
| hsa-miR-1468-5p | 20.43449195 | 52.46859956 | −1.360447909 | 1.88797E-05 | down |
| hsa-miR-1469 | 14.31886189 | 40.4601364 | −1.498584357 | 8.4044E-05 | down |
| hsa-miR-139-3p | 5.028248177 | 23.87834048 | −2.247574811 | 0.000414013 | down |
| hsa-miR-586 | 1.90470441 | 88.88895869 | −5.544365197 | 0.000668823 | down |
| hsa-miR-133b | 1.291712518 | 16.36698787 | −3.663431911 | 0.000684218 | down |
| hsa-miR-3664-3p | 1.423169987 | 36.9677732 | −4.699088247 | 0.014616087 | down |
| hsa-miR-636 | 6.852934997 | 22.09751242 | −1.689090062 | 0.001609992 | down |
| hsa-miR-1226-3p | 5.232578807 | 27.68945112 | −2.403742416 | 0.006986897 | down |
| hsa-miR-4751 | 1.90470441 | 16.90747265 | −3.150021991 | 0.012175441 | down |
| hsa-miR-4422 | 0.474389996 | 9.472342261 | −4.31957572 | 0.025041832 | down |
| hsa-miR-6510-3p | 0.408661261 | 15.09892742 | −5.207396768 | 0.037682512 | down |
| hsa-miR-8079 | 0 | 31.25110724 | −20 | 1.18936E-09 | down |
FDR, false discovery rate; non-POR, non-poor ovarian response group; POR-Y, young poor ovarian response group.
Differential expressed miRNAs comparing the POR-O group with non-POR group (FDR < 0.05).
| miRNA | POR-O | NC | Log2FC | FDR | Style |
|---|---|---|---|---|---|
| hsa-miR-885-5p | 835.9918256 | 113.2523496 | 2.883947854 | 0 | up |
| hsa-miR-199a-5p | 2437.124942 | 593.7848962 | 2.037167913 | 0 | up |
| hsa-miR-3591-3p | 3854.994982 | 1021.647894 | 1.915830918 | 0 | up |
| hsa-miR-122-5p | 3854.934322 | 1045.754901 | 1.882161522 | 0 | up |
| hsa-miR-199a-3p | 4452.745819 | 1531.380477 | 1.539862489 | 0 | up |
| hsa-miR-199b-3p | 2234.804608 | 777.480429 | 1.523270435 | 0 | up |
| hsa-miR-26a-5p | 12862.51624 | 4998.534916 | 1.363595695 | 0 | up |
| hsa-miR-193a-5p | 1271.245424 | 525.4759349 | 1.274545981 | 0 | up |
| hsa-miR-378a-3p | 879.3534043 | 396.0575469 | 1.15073302 | 0 | up |
| hsa-miR-3120-3p | 184.3751714 | 72.25172843 | 1.351540385 | 1.92069E-14 | up |
| hsa-miR-194-5p | 174.3865401 | 54.60975081 | 1.675058212 | 3.6997E-10 | up |
| hsa-miR-6795-5p | 105.7298647 | 28.88821865 | 1.871829791 | 1.83924E-08 | up |
| hsa-miR-214-5p | 196.3352431 | 76.38851579 | 1.361891501 | 1.16585E-07 | up |
| hsa-miR-497-3p | 62.5401552 | 12.37571564 | 2.337270846 | 1.56511E-05 | up |
| hsa-miR-20b-5p | 190.400702 | 51.64401381 | 1.882365761 | 7.92338E-05 | up |
| hsa-miR-342-3p | 7577.246311 | 2685.426354 | 1.496522488 | 0.000257417 | up |
| hsa-miR-3960 | 910.2494137 | 346.5408258 | 1.393236581 | 0.000348215 | up |
| hsa-miR-3141 | 153.1657535 | 20.79480551 | 2.880798663 | 0.00039502 | up |
| hsa-miR-26b-5p | 1859.179499 | 758.6050374 | 1.293245209 | 0.00170782 | up |
| hsa-miR-3120-5p | 8621.563781 | 2120.238647 | 2.023722911 | 0.006482311 | up |
| hsa-miR-214-3p | 8570.912928 | 2113.392506 | 2.019888147 | 0.008459883 | up |
| hsa-miR-452-5p | 72.10416858 | 23.04682543 | 1.645514628 | 0.011818169 | up |
| hsa-miR-106b-5p | 1428.07098 | 673.2846641 | 1.084779179 | 0.011818241 | up |
| hsa-miR-6722-3p | 21.24100648 | 2.286666384 | 3.215534326 | 0.024235108 | up |
| hsa-miR-513a-5p | 171.737733 | 469.8060024 | −1.451858093 | 0 | down |
| hsa-miR-509-3p | 359.4087483 | 1671.275955 | −2.217252534 | 0 | down |
| hsa-miR-4433b-5p | 10.19083033 | 85.63912071 | −3.070998378 | 0 | down |
| hsa-miR-4433a-3p | 8.644007872 | 91.73689773 | −3.40772983 | 4.52971E-14 | down |
| hsa-miR-508-5p | 99.01685742 | 304.8542047 | −1.622373377 | 4.85079E-12 | down |
| hsa-miR-513c-5p | 18.7741785 | 197.0967837 | −3.392082548 | 2.28404E-10 | down |
| hsa-let-7f-2-3p | 98.78432855 | 353.331532 | −1.838668412 | 1.14204E-09 | down |
| hsa-miR-135b-5p | 24.00102303 | 102.9484923 | −2.100754896 | 3.00507E-09 | down |
| hsa-miR-655-3p | 47.14270024 | 131.8644281 | −1.483949133 | 3.76168E-08 | down |
| hsa-miR-4448 | 33.57514636 | 99.1027352 | −1.561531187 | 6.69595E-07 | down |
| hsa-miR-506-3p | 39.44902774 | 248.4359086 | −2.654812065 | 1.92201E-05 | down |
| hsa-miR-450a-2-3p | 11.59611348 | 54.02769028 | −2.22005765 | 0.000665489 | down |
| hsa-miR-129-5p | 7.218504819 | 39.95429808 | −2.468578764 | 0.000714556 | down |
| hsa-miR-4700-5p | 70.2540476 | 354.5995925 | −2.335537626 | 0.001691417 | down |
| hsa-miR-32-3p | 3.700241966 | 25.2572696 | −2.771007168 | 0.002931079 | down |
| hsa-miR-513b-5p | 14.31569023 | 70.86587001 | −2.307493741 | 0.005352759 | down |
| hsa-miR-376c-5p | 4.195629552 | 22.56870428 | −2.427364385 | 0.005739847 | down |
| hsa-miR-508-3p | 109.4604365 | 453.5083075 | −2.050719464 | 0.012242875 | down |
| hsa-miR-493-5p | 38.21561375 | 114.0769354 | −1.577773024 | 0.019458091 | down |
| hsa-miR-4751 | 1.172754284 | 16.90747265 | −3.849688344 | 0.031295761 | down |
| hsa-miR-326 | 273.8381254 | 650.3972124 | −1.247997753 | 0.04162212 | down |
FDR, false discovery rate; non-POR, non-poor ovarian response group; POR-O, old poor ovarian response group.
GO and pathway analyses
GO and pathway enrichment analyses were performed to identify significantly enriched biological processes. As shown in Supplementary Fig. 1 (see section on supplementary data given at the end of this article), when the POR-Y group was compared with the non-POR group, many biological processes were found to be significantly enriched, including the epidermal growth factor receptor signaling pathway, negative regulation of cell migration and the Wnt signaling pathway (Supplementary Fig. 1A). This indicates that the differential miRNA expression found in POR-Y patients may be associated with cell differentiation, proliferation and apoptotic processes that occur during follicular development and oocyte maturation. In contrast, the main biological processes identified in the POR-O group with respect to the non-POR group were regulation of transcription, DNA-templated, nervous system development, etc (Supplementary Fig. 1B).
Pathway analysis was performed using the KEGG database. Enriched KEGG pathways are shown in Supplementary Fig. 2. These differentially regulated pathways were consistent with the results of the GO analysis. When the POR-Y group was compared with the non-POR group (Supplementary Fig. 2A), many of these signaling pathways, such as the Hippo signaling pathway, Wnt signaling pathway, pathways in cancer, Ras signaling pathway, PI3K-Akt signaling pathway, MAPK signaling pathway, mTOR signaling pathway and insulin signaling pathway, were all significantly enriched. The same phenomenon occurred in the POR-O group, as shown in Supplementary Fig. 2B, where pathways in cancer, the Hippo signaling pathway, MAPK signaling pathway and insulin signaling pathway were significantly different. However, the axon guidance pathway, related to the nervous system, was enriched the most. In addition, other previously unreported signaling pathways also seem to play a role in POR-O, such as the neurotrophin signaling pathway, focal adhesion and endocytosis pathways.
Network analysis
Figure 2 shows the results of the miRNA–mRNA network analysis, with the red box nodes representing overexpressed miRNAs and the green box nodes representing underexpressed miRNAs. The blue cycle nodes represent mRNAs. Their relationship is represented by one edge. One can see that the microRNAs in the network regulated some common target genes, such as FGF9, IPO7, TAOK1 and KPNA4. Among them, miRNA-15a-5p was the one that regulated the most genes, including BCL2. To further explore the miR-15-5p-mRNA network, functional enrichment analysis of these predicted targets was performed (Fig. 3). This analysis shows that these genes correlate tightly with oocyte development, cell–cell signaling, apoptotic process, negative regulation of cell proliferation, phosphatidylinositol 3-kinase signaling, etc.
miRNA-mRNA network analysis according to the significant GO categories and pathways. The red box nodes represent overexpressed miRNAs and the green box nodes represent underexpressed miRNAs. The blue cycle nodes represent mRNAs.
Citation: Reproduction 154, 4; 10.1530/REP-17-0157
GO and pathway analysis of miR-15a-5p. The purple cycle nodes represent genes identified in the pathway analysis and the red cycle nodes represent genes identified in the GO analysis.
Citation: Reproduction 154, 4; 10.1530/REP-17-0157
Verification of miRNA expression
Based on GO and pathway analysis of the miRNA sequencing, it can be concluded that the expressions of miR-15a-5p, miR-483-5p, miR-199a-5p and miR-885-5p were clearly different between the POR and non-POR groups (Fig. 4A). In order to validate the miRNA sequencing results, we collected additional follicular fluid and granulosa cells samples from another twelve POR-Y, twelve POR-O and twelve non-POR patients. The results from this analysis shows that the expression levels of miRNA-15a-5p and miRNA-483-5p in the follicular fluid were consistent with our high-throughput sequencing results: miRNA-15a-5p was significantly elevated in the POR-Y group (P < 0.001), but not in the POR-O group (Fig. 4B), whereas miRNA-483-5p was significantly downregulated in the POR-Y group (P < 0.05) but not in the POR-O group (Figure 4C). As for miR-199a-5p and miR-885-5p, there were no significant differences between the three groups (Fig. 4D and E). Regarding the expression levels in granulosa cells, the tendency was the same as that seen in the follicular fluid: miRNA-15a-5p was significantly upregulated (P < 0.01) and miRNA-483-5p was significantly downregulated (P < 0.01) in the POR-Y group but not in the POR-O group, as shown in Fig. 4 (F, G, H and I).
Verification of the miRNA sequencing results by qPCR in additional follicular fluid and granulosa cell samples. (A) Significantly expressed miRNAs, including miR-15a-5p, miR-483-5p, miR-199a-5p and miR-885-5p, based on the miRNA sequencing results; *FDR < 0.05. (B, C, D and E) Differential expression of the four miRNAs by qPCR in additional follicular fluid samples (Non-POR: n = 12; POR-Y: n = 12; POR-O: n = 12). (F, G, H and I) Differential expression of the four miRNAs in granulosa cells (Non-POR: n = 12; POR-Y: n = 12; POR-O: n = 12). One-way ANOVA test was used and all data are reported as mean ± s.e.m. *P < 0.05; **P < 0.01; ***P < 0.001. Non-POR, non-poor ovarian response group; POR-O, old poor ovarian response group; POR-Y, young poor ovarian response group.
Citation: Reproduction 154, 4; 10.1530/REP-17-0157
Target mRNA expression in GCs
After combining the qPCR results and the published studies (Cimmino et al. 2005, Guo et al. 2014, Kwon et al. 2014, Li et al. 2015, Long et al. 2016), we decided to focus our attention on the possible biological effects of miRNA-15a-5p in POR-Y patients since it had been demonstrated to play critical roles in cell proliferation. We investigated the transcription of AKT, mTOR, S6K1, BCL2, BAD, IGF2 in the granulosa cells of POR-Y patients. The results are shown in Fig. 5 (A, B, C, D, E and F). When compared with the non-POR group, several genes promoting cell proliferation, such as AKT, mTOR and S6K1, were all greatly downregulated in POR-Y patients; interestingly, both AKT and S6K1 have binding sites for miRNA-15a-5p. The expression of BCL2, the target gene of miRNA-15a-5p (Cimmino et al. 2005, Li et al. 2015), was significantly downregulated, while BAD was highly upregulated in POR-Y patients (P < 0.01). IGF2, which is the host gene of miRNA-483-5p and is co-expressed together with miRNA-483-5p (Shi et al. 2015), was shown to be downregulated in POR-Y patients, and this finding is consistent with our sequencing results.
Quantification of mRNA from AKT, mTOR, S6K1, BCL2, BAD and IGF2 by qPCR, comparing POR-Y with Non-POR in granulosa cells. (A, B, C, D, F) Expression of AKT, mTOR, S6K1, BCL2 and IGF2 mRNA is significantly lower in the POR-Y group. (E) BAD mRNA expression is significantly higher in the POR-Y group. Student’s t-test was used and all data are reported as mean ± s.e.m. *P < 0.05; **P < 0.01. Non-POR, non-poor ovarian response group; POR-Y, young poor ovarian response group.
Citation: Reproduction 154, 4; 10.1530/REP-17-0157
miR-15a-5p regulates proliferation and apoptosis in the KGN cell line
The miR-15a-5p mimic, miR-15a-5p inhibitor and their scrambled controls were transfected into KGN cells for 48 h, and qPCR and Western blot were performed to analyze the expression of related genes. The qPCR results, shown in Fig. 6, were entirely consistent with our previous results obtained with the granulosa cells of patients. Thus, after the miR-15a-5p mimic was transfected, we observed significantly decreased transcription of AKT, mTOR, S6K1 and BCL2 (P < 0.001). In contrast, the expression of BAD was significantly increased after transfection (P < 0.001). Importantly, following transfection of the miR-15a-5p inhibitor, the outcome was the opposite, with AKT, mTOR (P < 0.05), S6K1 and BCL2 (P < 0.01) showing significant upregulation when compared with the scrambled control. However, BAD expression levels were not significantly different from those of the scrambled control.
Transcription of miRNA-related genes in the KGN cell line after miR-15a-5p mimic and miR-15a-5p inhibitor transfection. (A) miR-15a-5p mimic and miR-15a-5p inhibitor transfection efficiencies. (B–F) Significant differences in expression of AKT, mTOR, S6K1, BCL2 and BAD at the RNA level. One-way ANOVA test was used and all data are reported as mean ± s.e.m. *P < 0.05; **P < 0.01; ***P < 0.001.
Citation: Reproduction 154, 4; 10.1530/REP-17-0157
Regarding protein expression, following transfection of the miR-15a-5p mimic, BCL2 protein levels were markedly decreased (P < 0.01), as were the levels of p-mTOR, p-S6K1 and p-4EBP1. Furthermore, the p-mTOR/ mTOR ratio was greatly decreased (P < 0.001) (Fig. 7A and B). In contrast, after miR-15a-5p inhibitor transfection, only the levels of p-mTOR were significantly increased (P < 0.05). The miR-15a-5p inhibitor had very limited effects on the KGN cell line (Fig. 7C and D).
Western blot quantification of protein products of miRNA-related genes in the KGN cell line after miR-15a-5p mimic and miR-15a-5p inhibitor transfection. (A and B) Western blot bands; p-mTOR, mTOR, p-S6K1, p-4EBP1 and BCL2 protein levels after normalization to β-actin and p-mTOR/ mTOR ratio after miR-15a-5p mimic transfection. (C and D) Western blot bands; p-mTOR, mTOR, p-S6K1, p-4EBP1 and BCL2 proteins levels after normalization to β-actin and p-mTOR/ mTOR ratio after miR-15a-5p inhibitor transfection. Student’s t-test was used and all data are reported as mean ± s.e.m. *P < 0.05; **P < 0.01; ***P < 0.001.
Citation: Reproduction 154, 4; 10.1530/REP-17-0157
Discussion
In this study, we performed miRNA sequencing to determine the miRNA profile of POR patients, explored the roles of miRNA-15a-5p and its possible relationship with the pathogenesis of POR in young patients. We demonstrated that miRNA-15a-5p was significantly increased in both the follicular fluid and granulosa cells of POR patients when compared with non-POR. Related genes, such as AKT, mTOR, S6K1, BCL2, BAD and IGF2 were significantly altered at the mRNA level in the granulosa cells of POR patients. In vitro culture experiments also showed similar results, with transfection of the miR-15a-5p mimic into KGN cells producing an outcome that was extremely consistent with that seen in the granulosa cells of POR-Y patients. In contrast, the miR-15a-5p inhibitor showed the opposite results. Furthermore, the Western blot results showed that the protein levels of p-mTOR, p-S6K1, p-4EBP1 and BCL2, as well as the p-mTOR/ mTOR ratio, were significantly decreased following miR-15a-5p mimic transfection. These findings strongly suggest that miRNA-15a-5p participates in the pathogenesis of POR in young patients.
POR is a significant challenge for both clinicians and patients. It is widely accepted that POR may be an early sign of ovarian aging and reduced ovarian reserve (Ferraretti et al. 2011). These patients show low responses to ovarian stimulation during IVF, produce low-quality embryos and are associated with high cancellation rates. So they undergo more IVF cycles. In our study, both the POR-Y and POR-O groups underwent approximately 3–4 IVF cycles, significantly more than that of non-POR group. Their AMH levels were significantly lower in both POR groups. AMH has the best sensitivity and specificity for predicting ovarian response (Ferraretti et al. 2011).This significantly lower levels of AMH in POR patients demonstrated their reduced ovarian reserve. Furthermore, the number of retrieved oocytes was significantly lower in the POR groups because POR patients have decreased follicular numbers even though following maximal stimulation. Many strategies have been attempted to try to improve this outcome, including high doses of gonadotrophins (Karande et al. 1990), a combination of clomiphene citrate and human menopausal gonadotropin (Saadat et al. 2003), human gonadotropin-releasing hormone antagonist and growth hormone co-treatment (Akman et al. 2001), minimal stimulation (Weghofer et al. 2004), etc. However, the results have been controversial and the improvement in pregnancy rates has been limited, in part because we know so little about the molecular mechanisms underlying POR.
In our study, we divided POR patients into old and young groups, and found very few miRNAs were commonly expressed in POR-Y and POR-O groups, including miR-199-5p and miR-885-5p. However, further experiment by qPCR using additional samples found no significant differences when they were compared together (P = 0.4118 and P = 0.6216, respectively). That is maybe because the pathogenesis of the two groups is totally different. As we all know, the ovarian reserve decline in POR-O group is a normal physiological phenomenon mainly due to aging, POR-O women have poor ovarian response to FSH because their antral follicle has been used out, while it is pathological and abnormal for POR-Y patients, these females have abnormal antral follicle pool because of some unknown factors. We explored the reasons from the side of miRNA function, and further studies are needed to explore the mechanism underlying the disease.
The follicular fluid provides a life-sustaining microenvironment for oocytes and the surrounding granulosa cells. It contains a significant variety of molecules, including miRNAs, proteins, lipids and vitamins, which are vital for the maturation and quality of oocytes (Hanrieder et al. 2008, Revelli et al. 2009). It has been reported that the presence of miRNAs is in the supernatant, microvesicles or exosomes from cell-free human follicular fluid (Sang et al. 2013). Microvesicles and exosomes play an important role in cell communication by transferring proteins, RNA and/or miRNA molecules to target cells (Valadi et al. 2007, Yuan et al. 2009). These cell-secreted vesicles containing miRNAs and proteins were took up by surrounding granulosa cells (da Silveira et al. 2012).
miRNAs regulate gene expression post-transcriptionally (Cimmino et al. 2005, Wienholds & Plasterk 2005). Alterations in miRNA expression have been found to play a role in tumorigenesis and development (Ganepola et al. 2014, Guo et al. 2014, Song et al. 2015, Huan et al. 2016, Long et al. 2016). Moreover, miRNAs have been linked to female mouse infertility (Murchison et al. 2007, Nagaraja et al. 2008). MicroRNA-mediated gene regulation is critical for the normal development and function of reproductive somatic tissues and female fertility (Hong et al. 2008). MicroRNAs have also been shown to accelerate transcription turnover during oocyte maturation, which is essential for oocyte meiotic maturation and later ovulation (Murchison et al. 2007). Female mice in which Dicer, an essential factor for miRNA biogenesis, has been knocked out, become infertile (Murchison et al. 2007). It has been demonstrated that altered miRNA expression in cumulus cells is associated with POR, specifically with elevated expression of miR-21-5p, which is an anti-apoptotic factor (Ma et al. 2011, Karakaya et al. 2015). In our study, we found many significant changes in miRNAs through miRNA sequencing, especially in miR-15a-5p, strongly suggesting that it is involved in the pathogenesis of POR by regulating oocytes, granulosa cells proliferation and apoptosis.
In recent years, a number of reports have demonstrated that miR-15a-5p is associated with tumor proliferation and apoptosis (Cimmino et al. 2005, Guo et al. 2014, Li et al. 2015). Overexpression of miR-15a-5p reduced proliferation and induced cell cycle arrest in human hepatocellular carcinoma (Long et al. 2016). Furthermore, other studies indicate that miR-15a regulates biological processes through the insulin/PI3K-AKT-mTOR signaling pathway (Kwon et al. 2014). In our study, by GO pathway and network enrichment analysis, we found that miR-15a-5p was closely linked to the PI3K-AKT-mTOR signaling pathway, apoptosis, GnRH signaling pathway and the estrogen signaling pathway. All these biological processes are closely associated with female oocyte proliferation and maturation. Other investigations have also demonstrated that the oocyte PTEN-PI3K-AKT pathway controls the initiation of oocyte growth and primordial follicle activation (Reddy et al. 2008, Adhikari et al. 2010, Zhang & Liu 2015). Furthermore, in a study on chronic lymphocytic leukemia, the mir15 family was shown to negatively regulate BCL2 expression and promote apoptosis (Cimmino et al. 2005), and BCL2 is one of the important targets of mir-15a-5p (Cimmino et al. 2005, Li et al. 2015).
Our results demonstrate that the levels of expression at the mRNA level of the related genes AKT, mTOR, S6K1 and BCL2 significantly decrease and of BAD significantly increase in the granulosa cells of POR-Y group compared to the non-POR group, and the in vitro culture experiments show similar results. In addition, the protein levels of p-mTOR, p-S6K1, p-4EBP1, BCL2 as well as the p-mTOR/ mTOR ratio significantly decrease after miR-15a-5p mimic transfection, p-mTOR, p-S6K1, p-4EBP1, as well as the p-mTOR/mTOR reflects the active status of PI3K-AKT-mTOR pathway, which is responsible for cell proliferation, their decrease indicates this signaling is repressed. Then how does miR-15a-5p influence the phosphorylation levels of these molecules? We hypothesized that miR-15a-5p might target some genes upstream and thus regulate the phosphorylation status of AKT-mTOR pathway indirectly by the affected genes as Bonci and his colleagues have reported. They have demonstrated that both miR-15a and miR-16 have direct interaction with the 3′UTRs of WNT3A (Bonci et al. 2008). WNT3A signaling has been demonstrated to promote increased β-catenin protein abundance and the activation of other survival and proliferation pathways through phosphorylation of ERK and AKT (Almeida et al. 2005, Yun et al. 2005), when the levels of WNT3A decreased in the presence of miR-15a and miR-16, the expression of β-catenin and the phosphorylated AKT and ERK reduced (Bonci et al. 2008). Though the exact genes targeted by miR-15a-5p in our study needs to be further investigated.
In our results, we noticed the discrepancy between mRNA level and protein level of mTOR. This might be because gene expression is a complex process that is controlled at multiple steps such as transcriptional regulation, posttranscriptional regulation (RNA processing, RNA stability), translational regulation, post-translational regulation and protein stability. Due to these multiple regulatory check points, RNA and protein levels are not always well correlated (Gupta et al. 2015). Abreu and coworkers also reported the correlation coefficients between mRNA and protein concentrations vary widely across organisms and are often surprisingly low. That is while there is a clear and significant correspondence between the protein and mRNA concentrations in protein extracts from various organisms, more than half of the variation in protein concentrations cannot be explained by variation in mRNA concentrations. The protein-per-mRNA ratio is different for different genes, but it may also change for a given gene under different cellular conditions (de Sousa Abreu et al. 2009). The translation ‘rate’ of a protein is a composite of the rate of peptide bond formation between amino acids (Fraser et al. 2004), ribosome occupancy and density as well as tRNA availability (Arava et al. 2003). Therefore, the decrease of mRNA level does not necessarily mean the decrease of protein level at the same time.
Additionally, miR-483-5p was found to be significantly decreased in our sequencing and verification experiments. miR-483-5p is an intronic miRNA encoded within the second intron of the IGF2 gene (Landgraf et al. 2007, Ma et al. 2011). In a differential gene expression analysis of women with diminished ovarian reserve (closely associated with POR), it was shown that downregulation of IGF1 and IGF2 ligands and their receptors may be associated with DOR pathogenesis. In our study, we also identified changes in the expression of IGF2 in the granulosa cells of POR-Y. We found that IGF2 is significantly downregulated in the granulosa cells of POR-Y patients, which is consistent with decreased expression of miR-483-5p in the follicular fluid of POR-Y patients. However, additional studies need to be performed to explore the possible mechanisms of miR-483-5p involvement in POR pathogenesis.
In conclusion, we found several differentially expressed miRNAs in human follicular fluid by means of miRNA sequencing. We demonstrate that high levels of miR-15a-5p in follicular fluid are associated with oocytes, granulosa cells proliferation and apoptosis. This is likely associated with the mechanisms that underlie POR pathogenesis, especially in the case of POR-Y patients. Furthermore, miR-15a-5p modulates the PI3K-AKT-mTOR signaling pathway and apoptosis, so it might be an important clinical parameter to predict POR. However, further studies are needed to explore its possible application.
Supplementary data
This is linked to the online version of the paper at http://dx.doi.org/10.1530/REP-17-0157.
Declaration of interest
We declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.
Funding
This work was supported by grants from the National Natural Science Foundation of China (NSFC: 31172040, 31471399 and 31671199) and National Key R&D Program of China (2017YFC1001403) to C Z, by a grant from the Major Program of the National Natural Science Foundation of China (81490743) to Z-J C and by the Shanghai Commission of Science and Technology (17DZ2271100).
Acknowledgements
The authors would like to thank all the patients for their participation and help in this study. They also thank all the professors and clinicians for guiding our work.
References
Adhikari D, Zheng W, Shen Y, Gorre N, Hamalainen T, Cooney AJ, Huhtaniemi I, Lan ZJ & Liu K 2010 Tsc/mTORC1 signaling in oocytes governs the quiescence and activation of primordial follicles. Human Molecular Genetics 19 397–410. (doi:10.1093/hmg/ddp483)
Agarwal V, Bell GW, Nam JW & Bartel DP 2015 Predicting effective microRNA target sites in mammalian mRNAs. Elife 4 e05005. (doi:10.7554/elife.05005)
Akman MA, Erden HF, Tosun SB, Bayazit N, Aksoy E & Bahceci M 2001 Comparison of agonistic flare-up-protocol and antagonistic multiple dose protocol in ovarian stimulation of poor responders: results of a prospective randomized trial. Human Reproduction 16 868–870. (doi:10.1093/humrep/16.5.868)
Almeida M, Han L, Bellido T, Manolagas SC & Kousteni S 2005 Wnt proteins prevent apoptosis of both uncommitted osteoblast progenitors and differentiated osteoblasts by beta-catenin-dependent and -independent signaling cascades involving Src/ERK and phosphatidylinositol 3-kinase/AKT. Journal of Biological Chemistry 280 41342–41351. (doi:10.1074/jbc.M502168200)
Arava Y, Wang Y, Storey JD, Liu CL, Brown PO & Herschlag D 2003 Genome-wide analysis of mRNA translation profiles in Saccharomyces cerevisiae. PNAS 100 3889–3894. (doi:10.1073/pnas.0635171100)
Bartel DP 2004 MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116 281–297. (doi:10.1016/S0092-8674(04)00045-5)
Benfey PN 2003 Molecular biology: microRNA is here to stay. Nature 425 244–245. (doi:10.1038/425244a)
Benjamini Y, Drai D, Elmer G, Kafkafi N & Golani I 2001 Controlling the false discovery rate in behavior genetics research. Behavioural Brain Research 125 279–284. (doi:10.1016/S0166-4328(01)00297-2)
Bonci D, Coppola V, Musumeci M, Addario A, Giuffrida R, Memeo L, D’Urso L, Pagliuca A, Biffoni M, Labbaye C & Bartucci M et al. 2008 The miR-15a-miR-16-1 cluster controls prostate cancer by targeting multiple oncogenic activities. Nature Medicine 14 1271–1277. (doi:10.1038/nm.1880)
Chiang HR, Schoenfeld LW, Ruby JG, Auyeung VC, Spies N, Baek D, Johnston WK, Russ C, Luo S & Babiarz JE et al. 2010 Mammalian microRNAs: experimental evaluation of novel and previously annotated genes. Genes and Development 24 992–1009. (doi:10.1101/gad.1884710)
Cimmino A, Calin GA, Fabbri M, Iorio MV, Ferracin M, Shimizu M, Wojcik SE, Aqeilan RI, Zupo S, Dono M & Rassenti L et al. 2005 miR-15 and miR-16 induce apoptosis by targeting BCL2. PNAS 102 13944–13949. (doi:10.1073/pnas.0506654102)
Couse JF, Yates MM, Deroo BJ & Korach KS 2005 Estrogen receptor-beta is critical to granulosa cell differentiation and the ovulatory response to gonadotropins. Endocrinology 146 3247–3262. (doi:10.1210/en.2005-0213)
da Silveira JC, Veeramachaneni DN, Winger QA, Carnevale EM & Bouma GJ 2012 Cell-secreted vesicles in equine ovarian follicular fluid contain miRNAs and proteins: a possible new form of cell communication within the ovarian follicle. Biology of Reproduction 86 71. (doi:10.1095/biolreprod.111.093252)
de Sousa Abreu R, Penalva LO, Marcotte EM & Vogel C 2009 Global signatures of protein and mRNA expression levels. Molecular BioSystems 5 1512–1526. (doi:10.1039/b908315d)
Draghici S, Khatri P, Tarca AL, Amin K, Done A, Voichita C, Georgescu C & Romero R 2007 A systems biology approach for pathway level analysis. Genome Research 17 1537–1545. (doi:10.1101/gr.6202607)
Drummond AE & Fuller PJ 2010 The importance of ERbeta signalling in the ovary. Journal of Endocrinology 205 15–23. (doi:10.1677/JOE-09-0379)
Espey LL & Richards JS 2002 Temporal and spatial patterns of ovarian gene transcription following an ovulatory dose of gonadotropin in the rat. Biology of Reproduction 67 1662–1670. (doi:10.1095/biolreprod.102.005173)
Ferraretti AP, La Marca A, Fauser BC, Tarlatzis B, Nargund G, Gianaroli L & Definition EwgoPOR 2011 ESHRE consensus on the definition of ‘poor response’ to ovarian stimulation for in vitro fertilization: the Bologna criteria. Human Reproduction 26 1616–1624. (doi:10.1093/humrep/der092)
Fraser HB, Hirsh AE, Giaever G, Kumm J & Eisen MB 2004 Noise minimization in eukaryotic gene expression. PLoS Biology 2 e137. (doi:10.1371/journal.pbio.0020137)
Ganepola GA, Rutledge JR, Suman P, Yiengpruksawan A & Chang DH 2014 Novel blood-based microRNA biomarker panel for early diagnosis of pancreatic cancer. World Journal of Gastrointestinal Oncology 6 22–33. (doi:10.4251/wjgo.v6.i1.22)
Guo S, Xu X, Tang Y, Zhang C, Li J, Ouyang Y, Ju J, Bie P & Wang H 2014 miR-15a inhibits cell proliferation and epithelial to mesenchymal transition in pancreatic ductal adenocarcinoma by down-regulating Bmi-1 expression. Cancer Letters 344 40–46. (doi:10.1016/j.canlet.2013.10.009)
Gupta S, Verma S, Mantri S, Berman NE & Sandhir R 2015 Targeting MicroRNAs in prevention and treatment of neurodegenerative disorders. Drug Development Research 76 397–418. (doi:10.1002/ddr.21277)
Hanrieder J, Nyakas A, Naessen T & Bergquist J 2008 Proteomic analysis of human follicular fluid using an alternative bottom-up approach. Journal of Proteome Research 7 443–449. (doi:10.1021/pr070277z)
Hong X, Luense LJ, McGinnis LK, Nothnick WB & Christenson LK 2008 Dicer1 is essential for female fertility and normal development of the female reproductive system. Endocrinology 149 6207–6212. (doi:10.1210/en.2008-0294)
Huan L, Bao C, Chen D, Li Y, Lian J, Ding J, Huang S, Liang L & He X 2016 MicroRNA-127-5p targets the biliverdin reductase B/nuclear factor-kappaB pathway to suppress cell growth in hepatocellular carcinoma cells. Cancer Science 107 258–266. (doi:10.1111/cas.12869)
Karakaya C, Guzeloglu-Kayisli O, Uyar A, Kallen AN, Babayev E, Bozkurt N, Unsal E, Karabacak O & Seli E 2015 Poor ovarian response in women undergoing in vitro fertilization is associated with altered microRNA expression in cumulus cells. Fertility and Sterility 103 1469.e1461–1476.e1463. (doi:10.1016/j.fertnstert.2015.02.035)
Karande VC, Jones GS, Veeck LL & Muasher SJ 1990 High-dose follicle-stimulating hormone stimulation at the onset of the menstrual cycle does not improve the in vitro fertilization outcome in low-responder patients. Fertility and Sterility 53 486–489. (doi:10.1016/S0015-0282(16)53345-7)
Kwon DN, Chang BS & Kim JH 2014 MicroRNA dysregulation in liver and pancreas of CMP-Neu5Ac hydroxylase null mice disrupts insulin/PI3K-AKT signaling. BioMed Research International 2014 Article ID: 236385. (doi:10.1155/2014/236385)
Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, Aravin A, Pfeffer S, Rice A, Kamphorst AO & Landthaler M et al. 2007 A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129 1401–1414. (doi:10.1016/j.cell.2007.04.040)
Li Y, Liu S, Zhang F, Jiang P, Wu X & Liang Y 2015 Expression of the microRNAs hsa-miR-15a and hsa-miR-16-1 in lens epithelial cells of patients with age-related cataract. International Journal of Clinical and Experimental Medicine 8 2405–2410.
Long J, Jiang C, Liu B, Fang S & Kuang M 2016 MicroRNA-15a-5p suppresses cancer proliferation and division in human hepatocellular carcinoma by targeting BDNF. Tumor Biology 37 5821–5828. (doi:10.1007/s13277-015-4427-6)
Ma N, Wang X, Qiao Y, Li F, Hui Y, Zou C, Jin J, Lv G, Peng Y & Wang L et al. 2011 Coexpression of an intronic microRNA and its host gene reveals a potential role for miR-483-5p as an IGF2 partner. Molecular and Cellular Endocrinology 333 96–101. (doi:10.1016/j.mce.2010.11.027)
Matsubara H, Ikuta K, Ozaki Y, Suzuki Y, Suzuki N, Sato T & Suzumori K 2000 Gonadotropins and cytokines affect luteal function through control of apoptosis in human luteinized granulosa cells. Journal of Clinical Endocrinology and Metabolism 85 1620–1626. (doi:10.1210/jcem.85.4.6509)
Murchison EP, Stein P, Xuan Z, Pan H, Zhang MQ, Schultz RM & Hannon GJ 2007 Critical roles for Dicer in the female germline. Genes and Development 21 682–693. (doi:10.1101/gad.1521307)
Nagaraja AK, Andreu-Vieyra C, Franco HL, Ma L, Chen R, Han DY, Zhu H, Agno JE, Gunaratne PH & DeMayo FJ et al. 2008 Deletion of Dicer in somatic cells of the female reproductive tract causes sterility. Molecular Endocrinology 22 2336–2352. (doi:10.1210/me.2008-0142)
Pangas SA & Rajkovic A 2006 Transcriptional regulation of early oogenesis: in search of masters. Human Reproduction Update 12 65–76. (doi:10.1093/humupd/dmbib33)
Pfaffl MW 2001 A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Research 29 e45. (doi:10.1093/nar/29.9.e45)
Reddy P, Liu L, Adhikari D, Jagarlamudi K, Rajareddy S, Shen Y, Du C, Tang W, Hamalainen T & Peng SL et al. 2008 Oocyte-specific deletion of Pten causes premature activation of the primordial follicle pool. Science 319 611–613. (doi:10.1126/science.1152257)
Revelli A, Delle Piane L, Casano S, Molinari E, Massobrio M & Rinaudo P 2009 Follicular fluid content and oocyte quality: from single biochemical markers to metabolomics. Reproductive Biology and Endocrinology 7 40. (doi:10.1186/1477-7827-7-40)
Saadat P, Slater CC, Jain JK, Tourgeman DE, Stanczyk FZ & Paulson RJ 2003 Treatment-associated serum FSH levels in very poor responders to ovarian stimulation. Journal of Assisted Reproduction and Genetics 20 395–399. (doi:10.1023/A:1026280508821)
Sang Q, Yao Z, Wang H, Feng R, Wang H, Zhao X, Xing Q, Jin L, He L & Wu L et al. 2013 Identification of microRNAs in human follicular fluid: characterization of microRNAs that govern steroidogenesis in vitro and are associated with polycystic ovary syndrome in vivo. Journal of Clinical Endocrinology and Metabolism 98 3068–3079. (doi:10.1210/jc.2013-1715)
Sheikhha MH, Eftekhar M & Kalantar SM 2011 Investigating the association between polymorphism of follicle-stimulating hormone receptor gene and ovarian response in controlled ovarian hyperstimulation. Journal of Human Reproductive Sciences 4 86–90. (doi:10.4103/0974-1208.86089)
Shi L, Liu S, Zhao W & Shi J 2015 miR-483-5p and miR-486-5p are down-regulated in cumulus cells of metaphase II oocytes from women with polycystic ovary syndrome. Reproductive Biomed Online 31 565–572. (doi:10.1016/j.rbmo.2015.06.023)
Skillern A & Rajkovic A 2008 Recent developments in identifying genetic determinants of premature ovarian failure. Sexual Development 2 228–243. (doi:10.1159/000152039)
Song T, Zhang X, Yang G, Song Y & Cai W 2015 Decrement of miR-199a-5p contributes to the tumorigenesis of bladder urothelial carcinoma by regulating MLK3/NF-kappaB pathway. American Journal of Translational Research 7 2786–2794.
Tarlatzis BC, Zepiridis L, Grimbizis G & Bontis J 2003 Clinical management of low ovarian response to stimulation for IVF: a systematic review. Human Reproduction Update 9 61–76. (doi:10.1093/humupd/dmg007)
Valadi H, Ekstrom K, Bossios A, Sjostrand M, Lee JJ & Lotvall JO 2007 Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nature Cell Biology 9 654–659. (doi:10.1038/ncb1596)
Weghofer A, Margreiter M, Bassim S, Sevelda U, Beilhack E & Feichtinger W 2004 Minimal stimulation using recombinant follicle-stimulating hormone and a gonadotropin-releasing hormone antagonist in women of advanced age. Fertility and Sterility 81 1002–1006. (doi:10.1016/j.fertnstert.2003.09.050)
Wienholds E & Plasterk RH 2005 MicroRNA function in animal development. FEBS Letters 579 5911–5922. (doi:10.1016/j.febslet.2005.07.070)
Yuan A, Farber EL, Rapoport AL, Tejada D, Deniskin R, Akhmedov NB & Farber DB 2009 Transfer of microRNAs by embryonic stem cell microvesicles. PLoS ONE 4 e4722. (doi:10.1371/journal.pone.0004722)
Yun MS, Kim SE, Jeon SH, Lee JS & Choi KY 2005 Both ERK and Wnt/beta-catenin pathways are involved in Wnt3a-induced proliferation. Journal of Cell Science 118 313–322. (doi:10.1242/jcs.01601)
Zhang H & Liu K 2015 Cellular and molecular regulation of the activation of mammalian primordial follicles: somatic cells initiate follicle activation in adulthood. Human Reproduction Update 21 779–786. (doi:10.1093/humupd/dmv037)
