miR-17-5p-Mediated RNA Activation Upregulates KPNA2 Expression and Inhibits High-Glucose-Induced Apoptosis of Sheep Granulosa Cells.

Details of granulosa cells collection, isolation, and culture have been described previously [ 6 ]. Briefly, ewe ovaries were removed from Thin-tailed Han sheep (ages ranged from 1 to 1.5 years) at a local slaughterhouse (Tang County, Baoding, China) and returned to the laboratory within 2 h in buffered saline solution at 37 °C. Ovaries were rinsed at least three times with saline and 1× PBS, respectively. To eliminate any individual effects, follicular fluid was collected from at least 60 follicles 1–3 mm in diameter using a 20 mL disposable syringe. GCs were harvested after the pooled follicle suspensions were centrifuged at 1000× g for 10 min. Then, the GCs were seeded in cell culture plates (ThermoFisher Scientific, Waltham, MA, USA) at a density of 2 × 10 5 /well. Granulosa cells were cultured in DMEM F12 medium (Gibco, Waltham, MA, USA) supplemented with 10% fetal bovine serum and 1% streptomycin/penicillin mixture, and the cells were placed in a humidified atmosphere of 37 °C and 5% CO 2 for 48 h. The original medium was removed when the cells reached 70% of the medium. Cells were cultured in DMEM without serum, pyruvate, glucose, or phenol red (Solarbio, Beijing, China) for 8 h. The GCs were divided into three groups with three replicates per group and cultured with glucose at concentrations of 8.4 mM and 33.6 mM. The 8.4 mM group was the low-concentration group (Low), representing the optimal glucose concentration for proliferation and differentiation of ewe GCs in vitro [ 6 ]. The 33.6 mM concentration group was the high-concentration group (High), representing a high glucose concentration that far exceeds the physiological concentration of follicle [ 6 , 37 , 38 ]. Two groups of granulosa cells were harvested after 24 h and used for subsequent RNA sequencing. The sRNA sequencing was performed on GCs in the low-glucose and high-glucose groups. Total RNA of the two groups were isolated using TRIzol Reagent (Invitrogen, Carlsbad, CA, USA), and the purity (OD 260/280) and concentration of extracted RNA were evaluated by Nanodrop 2000 [ 39 ]. For miRNA, total RNA was used as the starting sample, and the two ends of sRNA were directly connected with the linker, and then synthesis of complementary DNA (cDNA) by reverse transcription. To obtain cDNA library after PCR amplification, the target DNA fragments were separated by PAGE gel electrophoresis. According to the requirement of specified concentration and target data volume, different libraries were combined and sequenced by HiSeq (Illumina HiSeqTM2500, San Diego, CA, USA). To ensure the quality of sequencing data, raw data quality must be evaluated, filtered, and screened. The GC content, Q20, and Q30 percentages of clean data were evaluated, and the clean reads of each sample were screened for sRNAs within a certain length range for subsequent analysis. The distribution of sRNAs on the reference sequence was analyzed by positioning the length-screened sRNAs to the reference sequence using Bowtie (Irvine, CA, USA) [ 40 , 41 ]. The expression amount of known and novel miRNAs in each sample was counted and Transcripts Per Kilobase of exon model per Million mapped reads (TPM) conversion was performed to obtain the expression level of the transcript. The data of differentially expressed miRNAs were analyzed using DESeq2 (version 1.34.0) based on negative binomial distribution [ 42 ]. Differential miRNAs were screened from two aspects: fold change and corrected significance level ( p -adj/q-value). The miRanda 3.3a ( http://www.microrna.org , accessed on 12 July 2024) [ 43 ], miRDB ( https://mirdb.org/ , accessed on 12 July 2024) and TargetScan ( http://www.targetscan.org/ , accessed on 20 July 2024) software packages [ 44 ] were adopted to predict potential target mRNAs. Gene Ontology (GO, http://www.geneontology.org/ ) and Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/ , accessed on 26 July 2024) databases were used to annotate candidate target genes to better understand the function of target genes of DE miRNAs and their corresponding metabolic networks. The KPNA2 overexpression construct was generated by amplifying the KPNA2 coding sequence, which was subsequently integrated into the HindIII/KpnI restriction sites of the pcDNA3.1 overexpression plasmid (named pcDNA3.1- KPNA2 ). The siRNA target against the KPNA2 gene (si- KPNA2 ), siRNA nonspecific vector (vect.), non-specific control (ctl.), mimic negative control (miR-NC), and miR-17-5p mimics were synthesized by RiboBio (Guangzhou, China). miR-17-5p binding sites in KPNA2 3′UTR was amplified by PCR using a cDNA template synthesized from total RNA. Then, the PCR products were subcloned into XhoI/XbaI restriction sites in the pmirGLO dual-luciferase reporter vector to generate the pmirGLO-Luc- KPNA2 reporter. The GCs were seeded in 96-well (5 × 10 3 cells/well), 12-well (1 × 10 5 cells/well), or six-well (1 × 10 6 cells/well) cell culture plates until the cell density reached 60–70% (RiboBio, Guangzhou, China). All transfections were detected using Lipofectamine TM 2000 (Invitrogen, Carlsbad, CA, USA), and synthetic liposomes were performed according to the manufacturer’s directions. Cells from each group were collected 24 h after transfection. The oligonucleotide sequences information used in study were shown in Supplementary Table S4 . After transfection, GCs proliferation was detected using the Cell Counting Kit-8 (TransGen Biotech, Beijing, China) by adding 10 µL of CCK-8 solution to each well and incubating for 1–4 h in a cell incubator. Subsequently, the absorbance at a wavelength of 450 nm was determined using a microplate reader (BioTek, Shoreline, WA, USA). Cells were seeded into 12-well plates and then transfected with miRNA mimics. After that, the proliferation assay (EDU, Beyotime, Shanghai, China) was added to the GCs following the manufacturer’s protocol. Finally, images were randomly captured and collected using fluorescence microscopy (DMi8; Leica, Wetzlar, Germany). After transfection, the cells were fixed with a final concentration of 70% ethanol for more than 4 h. The fixed cells were incubated with 100 µL of RNase in the dark at 37 °C for 30 min. The cells were then labelled with 400 µL of propidium iodide (PI) for 30 min and processed in flow cytometer (BD Biosciences, San Jose, CA, USA). After transfection for 24 h, the GCs were digested into centrifuge tubes. The cells were stained with FITC Annexin V (AnV) (FITC Annexin V Apoptosis Detection Kit I, BD Biosciences, NJ, USA) and propidium iodide (PI), the data of cell apoptosis was calculated using FlowJo software (version 7.6.5). Primary GCs were seeded in 96-well plates. After transfection for 24 h, GCs cells were subjected to the caspase3/7 activity assay by Caspase-Glo_3/7 Assay Systems (Promega, #G8091, Madison, WI, USA) according to the manufacturer’s instructions. The assay was conducted in triplicate and repeated independently three times, which was represented as a fold increase in fluorescence calculated by comparing cells with untreated control cells. After transfection, total RNA was extracted from cultured cells using TRIzol reagent (Invitrogen, Life Technologies, Carlsbad, CA, USA) according to the manufacturer’s instructions. The OD260, OD280, and OD260/OD280 values were determined by microspectrophotometer, and the purity and concentration of the RNA were calculated. The cDNA synthesis of the mRNAs was carried out using the PrimeScript RT Reagent Kit With gDNA Eraser (Takala, Beijing, China), and the ReverTra Ace qPCR RT Kit (Toyobo, Osaka, Japan) was used for synthesizing the cDNA of miRNA. The RT–qPCR analysis of mRNAs and miRNA were performed in an Applied Biosystem ® StepOnePlus™ system (Thermo Fisher Scientific Inc., Carlsbad, CA, USA). Primers of miRNA and mRNAs were designed using RiboBio (RiboBio, Guangzhou, China), and U6 and GAPDH were regarded as endogenous controls for miRNA and mRNAs, respectively. Information of primers was listed in Table S5 . Data analyses were performed using the 2 −ΔΔCt method. Total proteins from tissues and cells were lysed conforming to the user’s guidebook of RIPA lysis buffer (Beyotime, Shanghai, China); this was followed by separation with 10% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) and transfer with polyvinylidene difluoride (PVDF) membranes (Bio-Rad, Hercules, CA, USA). After incubation with the indicated primary and secondary antibodies, signals were visualized by ECL. Membrane was then ready for scanning by Image studio system. Protein quantification was conducted by ImageJ software (version 1.8.0). The antibody used included the Bcl-2, Bax, Caspase-3, Caspase-9, KPNA2, anti-Argonaute-2, and anti-β-actin were purchased from abcam group (Cambridge, UK). The goat anti-rabbit IgG (H + L)-HRP (1:5000; catalogue no. ab6721, Abcam, Cambridge, UK) was used as a secondary antibody. Approximately 3 × 10 4 GCs were plated onto 24-well tissue culture plates 24 h before transfection. Cells were transfected with a mixture of Renilla luciferase and indicated luciferase reporters using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA). Forty-eight hours after transfection, the cells were harvested and subjected to an assay by using the Dual Luciferase Reporter Assay system (Promega, Madison, WI, USA). The luciferase activity was detected by the Fluorescence/MultiDetection Microplate Reader (BioTek, Winooski, VT, USA). The relative luciferase activities were normalized with the Renilla luciferase activities. Each experiment was performed at least three times. All data were normally distributed continuous variables and reported as the mean ± standard error of the mean (SEM). The statistics of all trials except sequencing data were analyzed by SPSS version 22.0 (SPSS Inc., Chicago, IL, USA). One-way analysis of variance (ANOVA) and Tukey’s test were used to assess statistical significance. A p < 0.05 was considered statistically significant.

Follicular development of ruminants is nutrition sensitive. Long-term production practice and research have found that reasonable increase of dietary energy can not only increase the glucose concentration in follicular fluid but can also promote follicular maturation and ovulation [ 1 ]. An excessive energy level can lead to abnormal glucose and lipid metabolism, ovarian dysfunction, and apoptosis of granulosa cells (GCs) [ 2 , 3 , 4 ]. Therefore, glucose metabolism homeostasis in the blood circulation and follicular fluid microenvironment plays an important role in regulating follicle maturation and ovulation, which directly affects the subsequent lambing number, reproductive efficiency, and breeding economic benefits of maternal animals. Granulosa cells are the main site of glucose metabolism in the follicle, which can generate energy substrates such as lactate and pyruvate through the glycolytic pathway to provide energy for oocyte maturation and follicle development [ 5 ]. Our previous study showed that high glucose concentration (33.6 mM) induced apoptosis and inhibited steroidogenesis in GCs [ 6 ]. In both human and mouse in vivo studies, it has been confirmed that high concentrations of glucose induce GCs apoptosis, triggering follicular degeneration or atresia [ 7 , 8 ]. However, the molecular mechanism by which glucose regulates follicular development is very complex, involving precise spatiotemporal expression of multiple regulatory factors within germ cells. Exploring the key genes and regulatory mechanisms of high-glucose induced granulosa cell apoptosis is of great significance for understanding the dynamic process of follicle development in ruminants under nutrient regulation. microRNA (miRNA) is a class of endogenous non-coding single-stranded RNA molecules with a length of 18–24 nt, which can regulate gene expression at the level of chromatin structure, RNA editing, RNA stability, transcription and translation, and participate in a variety of key biological and cellular processes [ 9 , 10 ]. It is known that the vast majority of miRNAs silence target gene expression by directing specific degradation of target mRNA sequences or inhibiting protein translation [ 11 , 12 ]. However, recent studies have found that some miRNAs can also activate gene expression and play a positive regulatory role by targeting the non-coding regions of gene promoters [ 13 ]. For example, miR-744 and miR-1186 upregulate the expression of Cyclin by activating the Cyclin B1 promoter region and affecting the growth of mouse prostate cancer cells in vitro [ 14 ]. In female reproduction, miR-551b-3p binds to the STAT3 promoter complementary sequence, recruits RNA polymerase II and TWIST1 transcription factors, activates STAT3 transcription, and upregulates STAT3 expression in ovarian cancer cells [ 15 ]. Thus, miRNAs regulate gene expression in complex and diverse ways. The miRNA-17–92 gene cluster is one of the most widely studied miRNA gene clusters, including miRNA-17 , miRNA-18 , miRNA19a , miRNA-19b , miRNA-20 , and miRNA-92 [ 16 ]. It has functions such as regulating cell proliferation [ 17 ] and apoptosis [ 18 ] and plays an important role in glucose and lipid metabolism [ 19 ]. As a member of the miRNA-17–92 cluster, miR-17-5p has been reported to be involved in the regulation of porcine granulosa cell differentiation [ 20 ]. However, its function and mechanism in ovine follicular GC apoptosis are still unclear. In this study, we first focused on miR-17-5p , a miRNA related to GCs apoptosis, and found that high concentrations of glucose significantly downregulated the expression of miR-17-5p and that miR-17-5p had the function of inhibiting GCs apoptosis. Mechanism studies have shown that miR-17-5p targets the KPNA2 promoter region, upregulates KPNA2 expression, and thus inhibits granulosa cell apoptosis. Our results provide reliable genetic resources and theoretical support for understanding the dynamic process of follicle development under glucose regulation.

The GC samples were divided into Low and High groups for Small RNA sequencing (sRNA-seq), and the raw data quality situation showed that the GC content was higher than 46% and the Q30 was higher than 98% ( Table S1 ). There were 796 (417 upregulated and 379 downregulated) DE mRNAs and 94 (47 upregulated and 47 downregulated) DE miRNAs identied in the high and low glucose-induced GCs groups ( Figures S1 and S2 ). Hierarchical clustering of DE mRNAs ( Figure 1 A) and DE miRNAs ( Figure 1 B) revealed the expression patterns of the individuals in the high and low group comparisons. To study the function of observed changes in DE miRNAs in the high and low comparison groups, a Gene Ontology (GO) term enrichment analysis of the predicted target genes of DE miRNAs was performed. As shown in Figure 1 C, the activation of MAPKKK activity, regulation of cell size, and tRNA 2-phosphotransferase activity were significantly enriched in the two groups, suggesting that concentrations of glucose may regulate GC function by regulating cell proliferation, apoptosis, and differentiation-related signaling pathway activity, as well as participating in RNA transcription and translation. Subsequently, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis in miRNA target genes was performed. The KEGG analysis revealed several significantly enriched pathways ( Figure 1 D), including PI3K-AKT signaling pathway, Ras signaling pathway, TNF signaling pathway, and NF-Kappa B signaling pathway. This indicates that DE miRNAs are involved in the regulation of granulosa cell apoptosis and growth by glucose. Based on the functional enrichment analysis of DE miRNAs, we hypothesized that DE miRNAs may affect granulosa cell apoptosis by regulating apoptosis-related signaling pathways ( Table S2 ). We focused on one differential miRNA: miR-17-5p . The expression of miR-17-5p was significantly downregulated in the high-glucose group compared with the low-glucose concentration group, and the analysis of quantitative Real-time PCR (qRT-PCR) also verified this result ( Figure 2 A). Therefore, an miR-17-5p -target genes-signaling pathway interaction network was constructed in GCs, including 28 target genes and eight pathways ( Figure 2 B, Table S3 ). To examine the potential function of miR-17-5p in GCs apoptosis, we detected the proliferation and apoptosis of cells after miR-17-5p transfection. The CCK-8 and EDU staining showed that the proliferation rate of GCs transfected with miR-17-5p mimics was significantly increased compared with GCs transfected with miR-NC ( Figure 3 A,B). We found that the proportion of GCs cells entering G1 and G2 phase of the cell cycle decreased and the proportion of cells entering S phase increased after miR-17-5p transfection ( Figure 3 C,D). These results reveal that miR-17-5p could promote GCs proliferation. Meanwhile, the cell apoptosis assay showed that overexpression of miR-17-5p significantly reduced the GC apoptosis rate ( Figure 3 E,F) and caspase3/7 activity ( Figure 3 G) compared with that of the GCs transfected with miR-NC. We found overexpression of miR-17-5p significantly downregulated the expression of pro-apoptotic-related genes ( caspase-3 , caspase-9 , and bax ), and significantly upregulated the expression of anti-apoptotic gene bcl-2 ( Figure 4 A–D). Consistently, the bcl-2/bax ratio was significantly higher in the miR-17-5p group than in the miR-NC group ( Figure 4 E). Furthermore, our study demonstrated that miR-17-5p significantly upregulated the expression of genes associated with steroid hormone synthesis, specifically CYP11A1 and CYP19A1 ( Figure 4 F,G). We predicted that Karyopherin-alpha2 ( KPNA2 ) was a target gene of miR-17-5p , and further validation found that overexpression of miR-17-5p significantly promoted the expression of KPNA2 ( Figure 4 H). The Western blot assay also showed that overexpression of miR-17-5p decreased the protein levels of bax, caspase-3, and caspase-9 but increased the protein levels of bcl-2 and KPNA2 ( Figure 4 I). Based on the above miR-17-5p – KPNA2 -pathway network, several genes (e.g., KPNA2 , PIK3R1 , and STAT3 ) were predicted to be miR-17-5p target genes ( Figure 2 B). Interestingly/significantly, RNA-seq analysis showed that the expression of both miR-17-5p and its target gene KPNA2 was significantly downregulated under high glucose concentration ( Figure 1 A,B), and overexpression of miR-17-5p significantly promoted the mRNA expression of KPNA2 ( Figure 5 A). In addition, co-transfection of miR-17-5p mimics plasmid and KPNA2 overexpression plasmid (pcDNA3.1– KPNA2 ) in GCs showed that KPNA2 overexpression further upregulated the miR-17-5p -induced KPNA2 expression ( Figure 5 B). Conversely, we added KPNA2 inhibitor to the miR-17-5p mimic group and found that KPNA2 inhibitor can rescue miR-17-5p -induced upregulation of the KPNA2 expression level ( Figure 5 C). These results implied that miR-17-5p may directly target KPNA2 and promote KPNA2 expression. To further investigate the binding relationship between miR-17-5p and KPNA2 , dual luciferase reporter constructs containing miRNA response elements (MRE; Wild-type (WT)), mutant 1 (MT1) plasmid, and mutant (MT2) plasmid were co-transfected into GCs with miR-17-5p mimics ( Figure 4 G). These results indicated that miR-17-5p had a direct binding relationship with KPNA2 , and the targeted binding site of miR-17-5p was within 1000 bp of the 3′ end of KPNA2 . On the basis of these results, we constructed an order short plasmid in the KPNA2 promoter region by software prediction and further analyzed the specific binding site of miR-17-5p to the KPNA2 promoter region by dual luciferase reporter assay. The sequence “GUUUCACG” (5→3′) was demonstrated as miR-17-5p -targeted motif in the KPNA2 gene. To further explore whether miR-17-5p inhibits cell apoptosis via promoting KPNA2 expression, we co-transfected miR-17-5p mimics plasmid and KPNA2 overexpression vector (pcDNA3.1– KPNA2 ) into GCs. We confirmed that co-transfection of miR-17-5p mimics and KPNA2 overexpression plasmid significantly increased cell viability and reduced caspase3/7 activity compared with that of the miR-17-5p mimics transfected cells ( Figure 6 A,B). Meanwhile, we found KPNA2 overexpression further promoted the miR-17-5p -induced upregulation of anti-apoptosis-related mRNA ( bcl-2 ) expression and downregulation of apoptosis-related mRNAs ( bax , caspase-9 and caspase-3 ) expression ( Figure 6 C–F). This result was also verified at the protein level by western blot assay ( Figure 6 M). Conversely, we co-transfected miR-17-5p and KPNA2 small interfering RNA (si- KPNA2 ) into GCs. Results showed that KPNA2 knockdown significantly reversed the increased proliferation of GCs expression of senescence markers, as revealed by CCK-8 analysis, and mitigated the decreased cellular apoptosis induced by miR-17-5p overexpression, as indicated by caspase3/7 Activity assay ( Figure 6 G,H). The qRT–PCR and western blot indicated decreased apoptosis-related genes and proteins expression in the miR-17-5p mimics group, which enhanced adding si- KPNA2 treatment, while the si- KPNA2 treatment significantly inhibited anti-apoptosis-related genes upregulation by miR-17-5p mimics ( Figure 6 I–L,N).

Glucose, as the most basic energy supply substance in biology, is the primary source of cell metabolism and body energy. In recent years, studies in female reproduction have found that the occurrence and development of endometriosis [ 21 ], polycystic ovary syndrome [ 22 ], and other diseases are often accompanied by abnormal glucose metabolism in ovary GCs. With the development of bioinformatics and experimental technology, more and more miRNAs have been confirmed to play important functions in reproduction. In this study, we identified a novel inhibitor of GC apoptosis— miR-17-5p —and found that miR-17-5p was involved in the regulation of cell apoptosis under high glucose. In other cell types such as brain endothelial cells [ 23 ], thyroid cancer cells [ 24 ], and hepatocellular carcinoma cells [ 25 ], miR-17-5p has been shown to function as an apoptotic modulator. This study presents the identification of a potential small molecule that can mitigate fertility decline in females caused by glucose metabolism disorders. This discovery enhances our understanding of the mechanisms underlying nutrient regulation during the dynamic development of ruminant follicles. With the occurrence of follicular waves, mammalian ovarian and follicular development is a cyclical and dynamic process [ 26 ]. Given the dynamic nature of mammalian follicle development, miRNAs have been predicted to play important regulatory roles in multiple aspects, such as follicle function [ 27 ] and corpus luteal development [ 28 ], as well as ovarian diseases [ 29 ]. Studies on mouse cumulus cells have found that miRNAs play a critical role in glucose metabolism of ovarian GCs, with miR-23b-3p , let-7b-5p , 34b-5p , and 145a-5p involved in the regulation of glycolysis, while miR-24-3p , 3078-3p , 183-5p , and 7001-5p inhibit the pentose phosphate pathway of CCs [ 30 ]. Our previous study found that high concentrations of glucose (33.6) could inhibit GCs proliferation, glycolytic metabolism, and steroid hormone production, and 8.4 mM glucose represents an optimum concentration for glycolysis and steroid hormone secretion [ 6 ]. In the present study, we discovered a new signaling pathway, the miR-17-5p / KPNA2 pathway, that contributes to high glucose-induced GCs apoptosis and enriches the molecular function of miR-17-5p in GCs. KPNA2, as an adaptor protein for nuclear transport, has been shown to be involved in the regulation of reproduction [ 31 ]. KPNA2 deficiency results in defective zygotic genome activation and arrested embryo development [ 32 ]. Therefore, identification of the functional characteristics of miR-17-5p during follicular development can provide a valuable reference for in-depth understanding of the dynamic development process of ruminant follicles. Bioinformatics analysis software can predict the binding sites between miRNA and mRNA through specific computational algorithms [ 33 ]. Therefore, the important functions and mechanisms of miRNA during follicular development have been further revealed. Most studies in ovine follicles have shown that miRNAs silence post-transcriptional gene expression by targeting the 3′-untranslated and/or coding regions of mRNA or inhibit protein translation of target genes by binding to the 5′-untranslated regions of mRNA. For example, miR-346 is involved in regulating the proliferation of Husheep ovarian GCs by targeting the LIF / STAT3 signaling pathway [ 34 ], while miR-27a-3p inhibits CYP19A1 expression in granulosa cells, thereby inhibiting estrogen synthesis [ 35 ]. Recent studies have found that miRNA can also play a positive regulatory role by activating the non-coding region of the promoter to induce the expression of target genes [ 13 ]. This regulatory mode of inducing gene-transcription activation (RNAa) was first reported in human cell lines. Researchers designed and synthesized Small Double-stranded RNAs (dsRNAs) targeting 21 nt of human gene promoters. The dsRNAs were found to activate the expression of target genes specifically and permanently [ 36 ]. Similarly, our study first discovered that miR-17-5p binds to the KPNA2 promoter region in sheep GCs, inducing KPNA2 transcriptional activation, promoting GCs proliferation, and inhibiting apoptosis. The non-classical regulatory mechanism of miRNA-mediated RNA activation promoting gene expression is enriched in sheep germ cells, which provides new clues for the study of miRNA in ruminants.

In summary, we evaluated the miRNA expression profile of sheep ovarian GCs treated with different doses of glucose and found that miR-17-5p is involved in the regulation of high-glucose-induced GCs apoptosis. In addition, it was further found that miR-17-5p promoted GCs proliferation and inhibited apoptosis by upregulating the expression of its target gene KPNA2 , which provided reliable genetic resources and theoretical support for understanding the dynamic process of follicle development under the regulation of nutrients.