All experiments were performed in accordance with the relevant guidelines and regulations and adhered to the ARRIVE guidelines for reporting animal experiments. This study was conducted according to the principles of the Basel Declaration and recommendations of the Guide for the Care and Use of Laboratory Animals. The protocol was approved by the Ethics Committee of the Anhui Agricultural University under permit no. AHAU20201025.

Fresh porcine ovaries (from Landrace, ~1 year old) were collected at local abattoir (Hefei, Anhui, China) for the experiments. The ovaries were collected immediately after slaughter, the fatty tissue around the ovaries was removed, and the ovaries were stored in sterile saline solution at 37°C containing 1% penicillin/streptomycin mixture, and transported to the laboratory in a thermos flask within 2 h. After washing the ovaries with phosphate-buffered saline (PBS) at 37°C, healthy small follicles with a pink and well-vascularized follicular wall between 3 and 5 mm in diameter were punctured with a disposable syringe; clear follicular fluid was collected from a number of ovaries (ovaries number >50) to negate any individual animal effects. The follicle suspensions were pooled, centrifuged at 1,000 r/min for 5 min, resuspended and centrifuged again, and the POGCs were harvested immediately.

Briefly, the POGCs were cultured (1 × 10⁶ viable cells/well in 6-wells and 1 × 10⁴ viable cells/well in 96-wells) in a humidified atmosphere (5% CO₂, 95% air, and 37°C) in the Dulbecco's Modified Eagle Medium (DMEM) medium (Gibco, USA) containing 10% fetal bovine serum (FBS) (Gibco, USA) with 1% penicillin/streptomycin mixture (Gibco, USA) until the POGCs confluence reached up to 80%. Then the medium was removed to establish a model of POGCs treated with different concentrations of resveratrol, then, the treatments received various solutions of RES and were cultured for 24 and 48 h, as followed: 0, 25, 50, 75, and 100 μM. For each treatment, POGCs were allocated to five RES-treated groups with different concentration, each with three replicates. The media and POGCs were collected for subsequent measurements after the 24 and 48 h treatment period.

Methyl thiazolyl tetrazolium (MTT) assay was performed using an MTT cell proliferation kit (Bestbio, Shanghai, China) according to the manufacturer's instructions. Briefly, POGCs were seeded in 96-well plates, treated with resveratrol (10, 25, 50, 75, and 100 μM), and control were harvested for 24 and 48 h. Absorbance was measured using an Infinite M1000 PRO Microplate Reader (Tecan, Swiss) by optical density at a wavelength of 570 nm. All experiments were repeated at least three times.

POGCs apoptosis was measured by Annexin V-EGFP Apoptosis Detection Kit (Bestbio, Shanghai, China) via flow cytometry. The procedure was performed as follows: 1 × l0⁶ cells were collected and washed twice with cold PBS, then resuspended in 500 μL of binding buffer containing 5 μL of propidium iodide (PI) and 5 μL of Annexin V-FITC at 4°C for 30 min. The mixture was incubated for 10 min in the dark at room temperature and then measured by flow cytometry. The FITC and PI fluorescence positive cells were considered apoptotic. Data were analyzed using the FlowJo v7.6 software (Stanford University, CA). All experiments were repeated at least three times.

POGCs subjected to control (n = 3, 0 μM RES groups), low groups (n = 3, 50 μM RES groups), and high groups (n = 3, 100 μM RES groups) were used for RNA-seq. Total RNA was isolated and purified from each sample using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's protocol. Then, the possible contamination and degradation of the RNA sample were detected with 1% agarose gel electrophoresis. RNA purity and concentration were examined using the NanoPhotometer^® spectrometer (IMPLEN, CA, USA). RNA integrity and quantity were finally measured using the RNA Nano 6,000 assay kit of the Bioanalyzer 2,100 system (Agilent Technologies, CA, USA). RNA with an OD260/D280 absorbance of between 1.8 and 2.0 and RNA integrity number (RIN) equal to or higher than 7.0 was RNA used for further experiments.

For lncRNA sequencing, ~3 μg of RNA of each RNA sample was used for library preparations. Total RNA removed ribosomal RNA (rRNA) to construct chain-specific libraries. Briefly, the ribosomal RNA was depleted from total RNA by the rRNA Removal Kit following the manufacturer's instruction, then, the rRNA-depleted RNA was used to generate sequencing libraries using the EBNext^® Ultra™ Directional RNA Library Prep Kit for Illumina^® (NEB, USA) following the manufacturer's recommendations. Sequencing was carried out using a 2 × 150 paired-end (PE) configuration on Illumina HiSeq 4,000 platform (Illumina, San Diego, CA, USA).

Clean reads were filtered from raw reads, which were first processed using in-house Perl scripts. In the step, raw 150 bp paired-end reads were filtered by removing reads that were adaptor-containing, low-quality, and ploy N-containing. Clean reads for each sample were aligned to the swine reference genome sus scrofa(pig)-11.1 using the software HISTA. The transcriptome reads alignment results were transferred to the program StringTie for transcript assembly. For miRNA data, raw reads of FASTQ format were first containing poly-N (with 5' adapter contaminants and ploy A or T or G or C, without 3' adapters or the insert tag) and low-quality reads from raw data. The Q20, Q30, and GC content of the clean data were calculated for all RNAs, then, chose the 18- to 35-nt fragments from clean reads to do all the downstream analysis.

All the transcripts were merged using Cuffmerge software, and the unknown transcripts were screened as putative lncRNAs or protein-coding RNAs according to length ≥200 bp, exon number ≥2, and FPKM ≥0.5. Removal of the transcripts with protein-coding capability using CNCI, Pfam, and CPC2 databases.

The transcripts were aligned with lncRNA databases (lncRNAdb and NONCODE) to identify known lncRNAs, novel lncRNAs were named following rules of HGNC (The HUGO Gene Nomenclature Committee) and the characteristics of novel lncRNA were compared with known lncRNA and mRNA. The co-expression relationship between lncRNA and mRNA was described by Trans. The basic principle of the transaction is that the function of lncRNA is not related to the location of the coding gene, but to the protein-coding genes that they co-expressed. According to the expression correlation coefficient cor was > 0.9 between lncRNA and mRNA.

The expression level of mRNAs and lncRNAs were quantified based on fragments per kilobase of transcript per million fragments mapped (FPKM) using the StringTie software, the edgeR software package was used to detect differentially expressed lncRNAs (DELs) with criteria of log₂|fold change|≥1 and P-adjust value < 0.05, differentially expressed mRNAs (DEMs) were similarly screened out.

Gene Ontology (GO) enrichment analysis was performed on the target gene candidates of the DELs by GOseq (22). Pathway analysis for genes was provided, based on the KEGG (Kyoto Encyclopedia of Genes and Genomes; http://www.genome.jp/kegg) database. KOBAS software was used to test the statistical significance of the target gene candidates of the DELs enrichment in KEGG pathways.

To test the accuracy of the sequencing results, we selected four DELs and DEMs for RT-qPCR verification. We used a cDNA synthesis kit to reverse transcribe the extracted total RNA into cDNA. Primers of mRNAs and lncRNAs were designed and synthesized by TsingkeBio (Beijing, China). Supplementary Table 1 lists the primers used in this study. Real-time quantitative PCR reactions were performed on a Bio-Rad CFX96 Real-Time Detection System (BioRad, CA, United States) using an iTaq Universal SYBR Green Supermix Kit and ran each sample in triplicate to ensure the accuracy of the quantitative results. The 2^−ΔΔCt method was used to calculate the relative expression of the target genes, GAPDH and β-actin were used as internal controls for mRNAs and lncRNAs, respectively.

To construct lnc-GAM overexpression plasmids, the full lengths of lncR-GAM gene were amplified by PCR using Tks GflexTM DNA polymerase (Takara, Japan) and cloned into pcDNA3.1 vector. And the recombinant vectors were named pcDNA3.1-lncGAM and pcDNA3.1-NC. Transfection of POGCs with plasmids was conducted with Lipofectamine 3,000 reagent (Invitrogen, USA) in accordance with the manufacturer's protocol.

After plasmids transfection, cell viability was determined using the CCK-8 assay according to the manufacturer's protocol and measured at 450 nm. Also, EdU Cell Proliferation Assay Kit (Bestbio, Shanghai, China) was used following the manual book. Cell proliferation was analyzed under a fluorescent microscope.

ELISA was used to detect the levels of estrogen (E2) and progesterone (P4) in GCs with different treatments according to the protocol of the E2 ELISA Kit and the PROG ELISA Kit (Enzyme-linked Biotechnology Co., Ltd., Shanghai, China). Briefly, the POGCs were treated for 48 h in 6-well plates, and 1 mL supernatants were collected from the POGCs cultures. Then, the Standards were diluted serially and test samples were incubated in an ELISA plate. Finally, the absorbance at 450 nm was determined by using a Fluorescence/Multi-Detection Microplate Reader (Bio Rad, USA).

Data are presented as means ± standard deviation (SD) for at least triplicates. The GraphPad Prism (version 5.0) software (San, Diego, CA, United States) was used to analyze the results of RT-qPCR and for graphing.

Differences between different groups were analyzed by a two-tailed Student's t-test. A p-value < 0.05 was considered to be statistically significant. The significance was marked as ^*P < 0.05 and ^**P < 0.01.

For the above experiments, we carried out a workflow flow chart (Figure 1).

The flow cytometry and MTT assay were first applied to determine the POGCs apoptosis and proliferation in resveratrol (RES) concentration groups (0, 10, 25, 50, 75, and 100 μM). Results showed that the lowest POGCs apoptosis occurred in 0 μM group, and the highest rate of apoptosis was observed in 100 μM group (Figures 2A, B), followed by 50 μM group. And the proliferation rate of POGCs gradually increased with increasing RES concentration at 48 h (Figure 2C). The highest proliferation rate was achieved when the concentration reached 50 μM. However, the apoptosis rate of POGCs is increasing all the time compared with 0 μM group. These results suggest that RES regulates POGCs differentiation and apoptosis. Therefore, we selected these three groups of POGCs for RNA-seq: control (RES concentration = 0 μM), low group (RES concentration = 50 μM), and high group (RES concentration = 100 μM).

After low-quality reads were removed, 78.8–104.7 M paired-end reads (2 × 150 bp in the length) for the mRNAs and lncRNAs, were acquired from the three groups (CON, LOW and HIGH) by Illumina Hiseq 2,500 platform (Supplementary Table 2). Subsequently, we identified 18,172 novel lncRNAs based on analysis by the Coding Potential Calculator (CPC), Coding-Non-Coding Index (CNCI), and Protein Families database (PFAM) (Figure 3A), which included 4,123 lncRNAs (55.11%), 1,826 antisense-lncRNA (24.41%), and 1,532 sense-overlapping (20.48%) (Figure 3B). The expression volume box plots of each sample were constructed based on FPKM. The results showed that the distribution of each box plot was flat (Figure 3C), indicating that the expression symmetry and distribution of lncRNAs in each sample are correct. Meanwhile, although small differences in lncRNA expression levels could be observed in the samples between groups, the distribution of lncRNA was similar. To investigate the characteristic differences between lncRNAs and mRNAs in POGCs, we compared the number of overlapping mRNAs in the LOW, HIGH, and CON groups and found that lncRNAs and mRNAs had essentially the same range of length distributions, but compared to mRNAs, annotated and novel lncRNAs were smaller in size, fewer exons, and fewer open reading frames (Figures 3D–F).

Considering there were three different treatment concentrations, we performed the analysis in the three comparisons (LOW vs. CON, HIGH vs. CON, and HIGH vs. LOW), respectively. Differentially expressed lncRNAs (DELs) and differentially expressed mRNAs (DEMs) in the samples were shown using volcano plots and heatmaps. And there were 68 (34 upregulated and 34 downregulated), 118 (65 upregulated and 53 downregulated), and 54 (31 upregulated and 23 downregulated) DELs identified in the LOW vs. CON (Figure 4A, Supplementary Table 3A), HIGH vs. CON (Figure 4B, Supplementary Table 3B), and HIGH vs. LOW groups (Figure 4C, Supplementary Table 3C), respectively. In addition, there were 278 (153 upregulated and 125 downregulated), 905 (491 upregulated and 414 downregulated), and 373 (115 upregulated and 258 downregulated) DEMs identified in the LOW vs. CON (Figure 4D, Supplementary Table 4A), HIGH vs. CON (Figure 4E, Supplementary Table 4B), and HIGH vs. LOW groups (Figure 4F, Supplementary Table 4C), respectively. Hierarchical clustering of the DELs (Figure 4G) and DEMs (Figure 4H) revealed the expression patterns of the individuals for the same three comparisons. Moreover, among the three groups, a total of 26, 72 and 30 specific DELs were observed in LOW vs. CON, HIGH vs. CON, and HIGH vs. LOW groups by Venn diagram analyses, respectively (Figure 4I). It is noteworthy that the specific expression of lncRNAs in Venn diagrams, we found lncRNA TCONS_00201518 was highly expressed and significantly upregulated [log2 (Fold Change) = 15.56] in LOW vs. CON group. Meanwhile, lncRNA TCONS_00043772 was significantly upregulated [log2 (Fold Change) = 14.69] in HIGH vs. CON group. The results indicated that these DELs may play important roles in the regulation of POGCs apoptosis induced by RES.

To verify the sequencing reliability, nine lncRNAs were randomly selected from the three comparison groups and subjected to RT-qPCR testing. The qPCR results showed that the expression levels of TCONS_00154615 were the lowest in CON group and the highest in HIGH group. TCONS_00171282, TCONS_00171983, and TCONS_00028069 were all higher in the LOW group, and the expression levels of these lncRNAs were relatively lower in the CON group and the HIGH group. These results were consistent with our RNA-seq. In addition, the expression levels of COL8A1 and SFRP4 genes in CON, LOW, and HIGH groups decreased in turn, which was consistent with the sequencing results. The mRNA expression levels of FTL and MGST1 were also shown to be consistent in RNA-seq (Figure 5).

To further explore the physiological and molecular functions of DELs on apoptosis in POGCs induced by RES. For 68 DELs in the LOW vs. CON, target gene prediction based on the correlation between lncRNA and mRNA expression, with a Pearson correlation coefficient >0.95 as the threshold (Supplementary Table 5A), we predicted 520 target genes. Moreover, we predicted 1,404 and 362 target genes of DELs in HIGH vs. CON (Supplementary Table 5B) and HIGH vs. LOW groups (Supplementary Table 5C), respectively. Meanwhile, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted using the identified potential target genes of DELs (Figure 6, Supplementary Tables 6, 7). The results of GO enrichment showed that these target genes were involved in the regulation of various biological processes such as negative regulation of biological process, metabolic process, and apoptotic process (Figures 6A, C, E, Supplementary Table 6). Hence, it can be speculated that these DELs may participate in the apoptosis response by regulating their target genes during RES-induced POGCs apoptosis. It is noteworthy that metabolic process was enriched among the three comparison groups, especially in HIGH vs. CON and HIGH vs. LOW groups, suggesting that metabolic process might play a critical role in RES-induced POGCs apoptosis. In KEGG analysis, some interesting signaling pathways are enriched, which are related to cell development, including Wnt signaling pathway, PI3K-Akt signaling pathway, FoxO signaling pathway, and DNA replication signaling pathway (Figures 6B, D, F, Supplementary Table 7). Notably, the Metabolic signaling pathway was enriched in HIGH vs. CON (Figure 6D), this finding is consistent with the GO term enrichment results. Therefore, we constructed a lncRNA-mRNA networks for “Metabolic” based on DELs and DEMs (Figure 7A, Supplementary Table 8A). The 20 lncRNAs involved in this function target 21 genes, of which lncRNA TCONS_00154615 has 4 targeted genes and is identified as a key-lncRNA for this function. Furthermore, we screened the GO terms and KEGG signaling pathways associated with “Cell Apoptosis”, and constructed a lncRNA-mRNA network with 22 lncRNAs involved in this function target 34 genes (Figure 7B, Supplementary Table 8B). In the Apoptosis network, we found lncRNA TCONS_00154615, which is also involved in the Metabolic network. The result shows that lncRNA TCONS_00154615 plays a significant role in the Apoptosis network. In addition, lncRNA TCONS_00187410, lncRNA TCONS_00099960, lncRNA TCONS_00049818, and lncRNA TCONS_00000119 may also play key roles in the apoptosis.

To investigate the role of lnc-GAM in the POGCs, lnc-GAM overexpression and control vectors were constructed. And the overexpression efficiency of lnc-GAM was verified by RT-qPCR in two groups: a positive control group (pcDNA3.1), an overexpression group (pcDNA3.1-lncGAM) (Figure 8A). We measured cell proliferation after transfection. EdU and CCK8 assays consistently showed that upregulated lnc-GAM inhibited cell proliferation (Figure 8B) and reduced the proportion of proliferating cells in total cells (Figure 8D). These findings suggest that upregulated lnc-GAM may inhibit POGCs proliferation. Furthermore, we found lnc-GAM overexpression significantly promoted the expression of apoptosis-related mRNAs (Bax, BAD and AIF) and significantly inhibited the mRNA expression of the anti-apoptosis-related gene (Bcl-2) (Figure 8C). Taken together, the data suggested that lnc-GAM can inhibited POGCs proliferation through affecting mRNA expression of apoptosis- (Bax, BAD and AIF) related makers.

The levels of E2 and P4 in cell-free supernatant were determined by ELISA after transfection of pcDNA 3.1-lncGAM in POGCs for 24 h and 48 h to explore the effect of lnc-GAM on steroid hormone synthesis. lnc-GAM overexpressing cells showed enhanced secretion of E2 and P4 (Figure 9A) compared with pcDNA3.1-NC. In addition, the expression levels of steroidogenesis-related genes were assessed by qRT-PCR, and the results are displayed in Figure 9B. When lnc-GAM was overexpressed, the expression levels of steroidogenic acute regulatory (StAR) protein and 3β-hydroxysteroid dehydrogenase (3βHSD) were significantly increased in POGCs. These results suggested that lnc-GAM affected steroid hormone synthesis and its related-gene expression in POGCs.