Integrated analysis of differential m6A methylation and transcriptome profiles in ovaries of young and aged Qira black sheep

Abstract

Ovarian aging leads to follicular atresia and a decline in both the quantity and quality of oocytes, yet its molecular regulatory mechanisms remain incompletely understood. N6-methyladenosine (m6A), one of the most prevalent RNA modifications in mammals, is thought to play a key role in ovarian function and aging. In this study, ovarian tissues from 1–2-year-old and 5–6-year-old Qira black sheep were collected, and an m6A methylation transcriptomic atlas was constructed based on MeRIP-seq and RNA-seq. A total of 660 differential methylation peaks (DMPs) and 2,018 differentially expressed genes (DEGs) were identified between the two groups, most of which were enriched in pathways associated with ovarian development, cellular senescence, and immune responses. GO and KEGG enrichment analyses showed that DMPs were significantly enriched in the positive regulation of the BMP signaling pathway, in utero embryonic development, and VEGF and Wnt signaling pathways. DEGs were mainly enriched in processes related to cellular senescence, NF-κB/MAPK signaling pathways, RNA binding, and transcriptional regulation. Integrated analysis revealed 109 overlapping differential genes between DMPs and DEGs, several of which (such as CEBPB, COL6A3, and PLCG1) were associated with aging, inflammatory responses, and the maintenance of ovarian function. This study provides the first m6A methylation transcriptomic profile of the ovaries of Qira black sheep and offers new insights into the regulatory mechanisms of m6A in ovarian aging.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-026-12604-2.

Introduction

Qira Black Sheep, a valuable indigenous breed from Xinjiang, is characterized by high fecundity and prolificacy: its average annual lambing rate reaches approximately 215.46%, gestation lasts 148 ~ 149 days, and the productive lifespan of ewes can extend to seven years with up to eight lambing events over a lifetime [1, 2]. In recent years, however, the breed has shown increasing incidences of estrus disruption, ovarian aging and degeneration, which undermine ewe health and reproductive performance and thereby threaten population size and genetic stability. These emerging problems underscore the urgent need for molecular-level research to inform conservation and sustainable utilization strategies. Previous RNA-seq studies of ovine ovaries have revealed extensive N6-methyladenosine (m6A) methylation, suggesting that RNA methylation may play an important regulatory role in ovarian physiology [3].

m6A can affect RNA stability, splicing and translation, and is an important mechanism of post-transcriptional regulation [4, 5]. m6A modification is reversible and is regulated by methyltransferases (“writers”), demethylases (“erasers”) and m6A-binding proteins (“readers”) [6, 7]. Among the various RNA modifications, m6A methylation has been extensively studied and shown to be closely associated with numerous cellular processes and physiological functions [8]. An increasing number of studies indicate that m6A modification plays an important role in the regulation of ovarian function. Related studies have reported that methyltransferases such as METTL14 can promote cellular senescence and inhibit cell proliferation by modulating m6A levels, thereby contributing to ovarian ageing [9]. MeRIP-seq analyses have shown abundant m6A modifications in ovaries during oestrus and pregnancy, and expression levels of multiple methyltransferases differ significantly across physiological stages, suggesting stage-specific regulation by m6A [10]. In addition, methyltransferases play critical roles in oogenesis and embryonic development, and alterations in demethylases can disrupt mRNA splicing or expression, leading to reproductive dysfunction or infertility [11–13]. Studies have reported elevated m6A levels during ovarian ageing, increased m6A expression is associated with reduced cell proliferation and with significant increases in markers of apoptosis, cellular senescence and autophagy [14]. These findings suggest that RNA methylation may participate in the molecular mechanisms of ovarian ageing. Ovarian ageing is typically manifested by declines in oocyte number and quality and by reduced secretion of reproductive hormones, which in turn profoundly affect individual reproductive performance and population genetic fitness [15, 16].

Despite the insights gained from the above studies regarding the potential role of m6A in ovarian ageing, research on m6A modification in sheep ovaries remains limited. Therefore, in this study, we used MeRIP-seq and RNA-seq to construct an m6A methylation transcriptome profile of the ovine ovary and to identify ageing-related genes and regulatory pathways, with the aim of providing a theoretical basis and research framework for elucidating the molecular mechanisms of m6A in ovarian ageing.

Materials and methods

All animal experimental protocols were carried out in strict accordance with the relevant guidelines of the Scientific Ethics Committee of Tarim University (Approval No. PA20250312055). All procedures were performed in accordance with the applicable guidelines and regulations issued by the Ministry of Agriculture of the People’s Republic of China.

Experimental animals and sample preparation

The sheep used in this study were obtained from the Xinjiang Jinken Animal Husbandry Meat Sheep Research Institute. All experimental animals were maintained under identical husbandry conditions. The experimental animals were anesthetized by intramuscular injection of Xylazine at a dose of 0.4 mg/kg. Subsequently, euthanasia was performed by intravenous injection of pentobarbital at a dose three times the anesthetic dose. Bilateral thoracotomy was conducted to confirm the death of the experimental animals. Forty Qira Black ewes were selected and divided into two age groups of 20 ewes each: a young group (Group D, 1–2 years old) and an aged group (Group H, 5–6 years old). Ovarian tissues were collected from each ewe, and the number of follicles with diameters of 2 mm or greater was recorded. The collected ovarian samples were then placed in 5 mL cryogenic vials and immediately immersed in liquid nitrogen for preservation. Subsequently, three ewes from Group D and three from Group H were selected for MeRIP-seq and RNA-seq analyses.

Total RNA isolation and quality control

Total RNA was extracted from ovarian tissues using TRIzol reagent (Invitrogen, CA, USA). Genomic DNA was removed by treatment with RNase-free DNase I (TaKaRa). RNA purity and integrity were assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) and a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Samples selected for library construction had an A260/A280 ratio ≥ 1.8, RIN ≥ 8, and a visibly stronger 28 S rRNA band than 18 S rRNA, the RNA concentration of all samples was 200 ng/µL. cDNA was synthesized according to the manufacturer’s instructions (Reverse Transcription Kit, Takara Bio Inc., Dalian, China) and stored at − 80 °C for subsequent experiments.

cDNA library construction and sequencing

Quality-qualified RNA was used for library construction with the TruSeq Stranded Total RNA Kit (Illumina, San Diego, CA, USA). High-throughput sequencing was performed on an Illumina NovaSeq 6000 platform (OE Biotech Co., Ltd., Shanghai, China). The raw sequencing data have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE287189.

MeRIP-seq and RNA-seq data analysis

To ensure the reproducibility of this study, three biological replicates per group were sequenced, yielding a total of 12 libraries, including both input and RIP samples. Raw reads were subjected to quality control and filtering using FASTP [17], and clean reads were then aligned to the sheep reference genome (GCF_016772045.1) with HISAT2 [18]. m6A methylation peaks were identified in each m6A IP sample using MeTDiff [19], with the corresponding input samples serving as controls to detect DMPs according to the criteria p < 0.05 and fold change (FC) > 1.5, these peaks were subsequently annotated with ChIPseeker [20]. DEGs were identified using DESeq2 [21], with thresholds of q < 0.05 and FC > 2 or < 0.5. GO and KEGG enrichment analyses of DMPs and DEGs were performed using a hypergeometric distribution test. GO analysis was conducted with tools provided by the Gene Ontology Consortium (http://www.geneontology.org), and KEGG analysis was performed using tools provided by the Kyoto Encyclopedia of Genes and Genomes (https://www.genome.jp/kegg/). Correlation analyses were carried out in Python (v.2.7.12). The protein–protein interaction (PPI) network was constructed using the STRING online database (https://string-db.org/), with the minimum required interaction score set to medium confidence (confidence score > 0.4).

qRT-PCR

Primers for five differentially expressed genes (TDRD3, COL6A3, FKBP3, TTC12, RNF145) were designed based on the Ovis aries gene sequences available in NCBI and synthesized by Shenggong Bioengineering (Shanghai) Co., Ltd., primer sequences are listed in Table 1. qRT-PCR reactions (total volume 15 µL) contained 7.5 µL of 2×Universal Blue SYBR Green qPCR Master Mix, 2 µL template cDNA, 1.5 µL each of forward and reverse primers, and 4 µL Water Nuclease-Free. The qPCR reaction conditions were as follows: 95 °C for 30 s, followed by 40 cycles of 95 °C for 15 s and 60 °C for 30 s. A melting-curve analysis was then performed from 65 °C to 95 °C, with fluorescence signals collected at 0.5 °C increments. β-actin was used as the internal reference gene, and the relative expression levels of target genes were calculated using the 2^−ΔΔCT method.

Table 1.

Primer sequences for qRT-PCR

Gene	Primer sequences(5′- 3′)	Length (bp)
TDRD3	F: AGTGCTGCTGGTAACCGAAA R: GGTGGACCCGTAACAGGTTT	239
COL6A3	F: GACGGCTTTGCTCTACCCTC R: GATGAATGCACACAGGCCAC	194
FKBP3	F: GCGTTTCAAGGGTACTGAAAGT R: CCCGTCTTGTAGTGTTCCTGT	217
TTC12	F: AGTGCTGTACACCAACCGAG R: GCTAGGTGGGCTTTTCCCAT	138
RNF145	F: GTACTCGAGATCAGCCTGCC R: CCTTCTGTCATGCCCCGATT	211
β-actin	F: GAAGATCAAGATCATCGCGCC R: TAACGCAGCTAACAGTCCGC	174

MeRIP-qPCR

Five genes (CENPB, PLCG1, IGF2, FAM193A, and MATN2) were selected, and the primer sequences are listed in Table 2. RNA samples were fragmented, and the fragment size was checked using a 1% agarose gel. A 2 µL aliquot of fragmented RNA was reserved as the input sample, and 20 µL RNA was used for the m6A-IP and IgG-IP samples, respectively. For immunoprecipitation, 4 µg anti-m6A antibody and 4 µg IgG antibody (Abcam, Cambridge, UK) were added to the m6A and IgG groups, followed by overnight incubation at 4 °C on a vertical rotator (Kylin-Bell Lab Instruments Co., Ltd., Haimen, China). Protein A/G magnetic beads were then added to both groups, and the immune complexes were captured using a magnetic rack (Thermo Fisher Scientific, Waltham, MA, USA). After RNA purification and recovery, reverse transcription was performed using an RNA methylation immunoprecipitation kit (MeRIP kit) (Bio.ruqi, China). qPCR was carried out in a 10 µL reaction volume containing 5 µL Hieff qPCR SYBR^® Green Master Mix (Low Rox), 2 µL cDNA, 0.2 µL each of forward and reverse primers, and 2.6 µL RNase-free water. The cycling conditions were 95 °C for 5 min, 40 cycles of 95 °C for 10 s and 60 °C for 30 s, followed by melt curve analysis from 60 °C to 95 °C.

Table 2.

Primer sequences for MeRIP-qPCR

Gene	Primer sequences(5′- 3′)	Length (bp)
CENPB	F: GAGCGCAAGTACGGTGTAGC R: GGATTTGCTGGAACCAGGCG	100
PLCG1	F: TCCTATTCTGGGCCTCCACG R: TAGTCCCAAGACGGAAGGCT	119
IGF2	F: TCTGCCAAGTGACACCATCT R: CAGACAGGACGGTACAGGGA	92
FAM193A	F: TGGCATGAATCATAGAACACCAC R: CGCGGTCTTCACGCCAC	106
MATN2	F: CCCTGAGAAACTTCAGCTCGG R: TCCGTCCGTGAAGACGATG	144

Statistical analysis

Statistical analyses were performed using SPSS version 26.0. All data are presented as the Mean ± SD(n = 3). Comparisons between two groups were conducted using an independent samples t-test, while comparisons among three or more groups were conducted using one-way analysis of variance (ANOVA). * indicates a significant difference (p < 0.05), ** indicates a highly significant difference (p < 0.01), and *** indicates an extremely significant difference (p < 0.001).

Results

Ovarian morphology and follicular characteristics of Qira black sheep

The results revealed significant age-related differences in ovarian morphology between the two groups (Fig. 1A). Both the number and diameter of follicles in group H were significantly lower than those in group D, indicating that ewes in group D possessed superior reproductive performance (Table 3). These findings support the physiological trend that follicle numbers decline, follicular development potential diminishes, and ovarian function deteriorates with advancing age in sheep.

Fig. 1.

Ovarian morphology and m6A expression profiles in Qira Black sheep. A Morphological characteristics of ovaries from Qira Black sheep. B Violin plot showing the enrichment fold changes of m6A peaks. C Genomic distribution of m6A peaks across functional regions. D Pie chart illustrating the overall distribution of m6A peaks

Table 3.

Numbers and diameters of follicles in group D and H

Group	Number of follicles	Follicle diameter (mm)
Group D	10.75 ± 2.47	5.92 ± 1.33
Group H	4.6 ± 1.50**	3.73 ± 0.48**

Note: ** indicates a highly significant difference (p < 0.01)

Summary of sequencing data

A total of 27.63 Gb of clean data were generated, with the proportion of bases with a quality score ≥ Q30 ranging from 92.24% to 95.93% and an average GC content of 51.92% (Supplementary Table S1). The genome mapping rates for individual samples ranged from 81.08% to 97.78% (Supplementary Table S2). The alignment results were classified into three categories: unaligned reads, uniquely mapped reads, and multiple mapped reads. Only uniquely mapped reads were retained for downstream analyses, with unique mapping rates ranging from 72.18% to 91.40% (Supplementary Figure S1A).

Analysis of MeRIP-seq sequencing results

As summarized in the Table 4, a total of 4,378 m6A peaks were detected in group D, covering 0.03% of the genome, whereas 3,656 m6A peaks were identified in group H, accounting for 0.04% of the genome. Both groups thus displayed extensive m6A methylation (Fig. 1B). We found that the majority of peaks were annotated in the 3′UTR region, with 47.33% of peaks in group D and 66.38% in group H located in 3′UTRs (Fig. 1C–D). Differential methylation analysis between the group D and H identified a total of 660 DMPs, of which 407 were significantly hypermethylated and 253 were significantly hypomethylated (Fig. 2A–B).

Table 4.

Peaks detection statistics

Sample Name	Group D	Group H
Number of Peaks	4378	3656
Total Length of Peaks (bp)	890940	941557
Average Length of Peaks (bp)	203.5	257.54
Median Length of Peaks (bp)	195	201
Percentage of Genome (%)	0.03	0.04

Fig. 2.

Analysis of DMPs in the ovaries of Qira Black sheep. A Summary statistics of DMPs. B Volcano plot showing the genomic distribution of DMPs. C Chord diagram of GO enrichment for DMPs. D GO enrichment analysis of DMPs. E KEGG enrichment analysis of DMPs. F KEGG pathway analysis of DMPs

GO enrichment analysis of DMPs

GO enrichment analysis of DMPs was performed, and the results showed that genes associated with DMPs were significantly enriched in several biological process (BP) terms, including positive regulation of BMP signaling pathway, cell–cell adhesion and in utero embryonic development. In the cellular component (CC) category, they were significantly enriched in terms such as nucleus, Golgi stack and collagen-containing extracellular matrix. In the molecular function (MF) category, significant enrichment was observed in integrin binding, cadherin binding and histone H4K12 acetyltransferase activity (Fig. 2D). The genes identified from the GO enrichment analysis were further visualized to better illustrate the regulatory pathways in which they are involved (Fig. 2C). For example, S1PR1 is associated with lamellipodium assembly, ARHGEF28 and CEBPZ are involved in RNA binding, and NCK1 is expressed in multiple regulatory pathways, including cytosol, lamellipodium assembly and cadherin binding.

KEGG enrichment analysis of DMPs

KEGG enrichment analysis showed that DMPs were annotated to 279 signaling pathways. These KEGG pathways were grouped into five major categories: Cellular Processes, Environmental Information Processing, Human Diseases, Metabolism and Organismal Systems. The DMPs were involved in multiple pathways, including regulation of actin cytoskeleton, VEGF signaling pathway, Wnt signaling pathway, endometrial cancer, N-glycan biosynthesis, leukocyte transendothelial migration and thyroid hormone signaling pathway. DMPs were closely associated with processes such as signal transduction, cancer, infectious diseases (particularly bacterial and viral infections) and cell motility, suggesting that they may play important roles in various physiological and pathological contexts (Fig. 2E–F).

RNA-seq data analysis

RNA-seq identified a total of 22,021 genes. The FPKM values of genes in each sample are shown in Supplementary Figure S1B, overall, FPKM values in group D were lower than those in group H, indicating higher gene expression levels in group H. Based on the distribution of FPKM values across samples (Supplementary Figure S1C), most genes showed moderate to high expression levels (FPKM > 0.5). Differential expression analysis between the group D and H identified 2,018 DEGs, of which 707 DEGs were upregulated and 1,311 DEGs were downregulated (Fig. 3A–B).

Fig. 3.

Analysis of DEGs in the ovaries of Qira Black sheep. A Summary statistics of DEGs. B Volcano plot showing the distribution of DEGs. C Circular plot of GO enrichment for DEGs. D GO enrichment analysis of DEGs. E KEGG enrichment analysis of DEGs. F KEGG pathway analysis of DEGs

GO enrichment analysis of DEGs

GO enrichment analysis of DEGs revealed significant enrichment in several BP terms, including cell adhesion, negative regulation of I-kappaB kinase/NF-kappaB signaling, and positive regulation of leukocyte adhesion to vascular endothelial cells. In the CC category, DEGs were significantly enriched in the extracellular matrix, early endosome membrane and nucleolus. In the MF category, significant enrichment was observed in mitogen-activated protein kinase binding, histone methyltransferase binding and transcription corepressor activity (Fig. 3D). As shown in Fig. 3C, genes associated with RNA binding were the most numerous and showed a high degree of enrichment. Notably, all DEGs involved in negative regulation of mRNA splicing, via spliceosome were downregulated, indicating that this process was markedly suppressed. Overall, the DEGs were involved in a wide range of biological processes, including immune responses, cell differentiation, signal transduction and protein synthesis, suggesting that they may have potential relevance to the pathogenesis of inflammatory and metabolic diseases.

KEGG enrichment analysis of DEGs

KEGG enrichment analysis indicated that the DEGs were predominantly annotated to disease-related signaling pathways and were significantly enriched in pathways such as Cellular senescence, MAPK signaling pathway, Transcriptional misregulation in cancer, MicroRNAs in cancer, and Antigen processing and presentation (Fig. 3E). These DEGs may affect a broad range of biological functions, including Transcription and Translation, Endocrine and metabolic disease, Energy metabolism, Aging, Development and regeneration, and Immune system (Fig. 3F).

Integrated analysis of MeRIP-seq and RNA-Seq data

An integrative analysis of m6A methylation and transcriptional changes was performed to investigate the potential relationship between m6A and mRNA expression, and a total of 109 differentially m6A-methylated genes (DMGs) were identified. Among these DMGs, 7 showed concomitant increases in both m6A methylation and mRNA expression, 55 exhibited increased m6A methylation but decreased mRNA expression, 33 displayed concordant decreases in both m6A methylation and mRNA expression, and 14 showed decreased m6A methylation but increased mRNA expression (Fig. 4A). These patterns indicate that, at the single-gene level, changes in m6A methylation can be associated with either concordant or inverse changes in mRNA expression, with inverse regulatory relationships being more common. To further assess DMG expression changes at the global level, DMGs were grouped into a hypomethylated group (Hypo), a hypermethylated group (Hyper) and a non-significant group (No Sig.) according to the differential status of their m6A peaks, and the mRNA expression levels in the Hypo and Hyper groups were significantly different from those in the No Sig. group, whereas no significant difference was observed between the Hypo and Hyper groups (Supplementary Figure S1D). This finding suggests that m6A remodeling is closely linked to transcriptional reprogramming during ovarian aging and that genes undergoing altered m6A methylation are more likely to be downregulated at the mRNA level. To identify DMGs with potentially key roles in ovarian aging, 30 DMGs associated with aging and inflammatory responses were selected to construct a PPI network (Fig. 4B), in which genes such as INSR, CEBPB, PLCG1, IGF2, COL6A3 and SETDB2 showed high connectivity and formed dense interaction clusters, implying that they may serve as important regulators in inflammation-related signaling pathways during ovarian aging.

Fig. 4.

Integrated analysis of MeRIP-seq and RNA-seq data. A Integrated analysis of DMPs and DEGs. B Interaction network of genes associated with aging and inflammatory responses. C qRT-PCR validation of selected targets. D MeRIP-qPCR validation of selected targets

qRT-PCR

As shown in Fig. 4C, TTC12 expression was upregulated in the ovaries of group H, whereas TDRD3, COL6A3, FKBP3 and RNF145 were downregulated. These expression patterns were consistent with the RNA-Seq results, supporting the reliability of the sequencing data.

MeRIP-qPCR

As shown in Fig. 4D, the expression levels of CENPB, PLCG1 and IGF2 were upregulated in the ovaries of group H, whereas FAM193A and MATN2 were downregulated. These results were consistent with the MeRIP-seq data.

Discussion

Ovarian development and aging constitute a continuous and complex process that is closely associated with follicular reserve, oocyte quality, and overall reproductive capacity [22–24]. In this study, morphological assessment first showed that both follicle number and follicle diameter were significantly lower in Qira black sheep in group H than in group D, indicating a typical age-related decline in ovarian function, consistent with previous reports [25]. Furthermore, by integrating MeRIP-seq and RNA-seq, we constructed an m6A methylome–transcriptome landscape of the Qira black sheep ovary, systematically characterizing age-associated remodeling of m6A modification and accompanying transcriptional changes, and providing new evidence for the epigenetic regulatory mechanisms underlying ovarian aging [26, 27].

Abundant m6A peaks were detected in ovaries from both group D and group H, with the majority enriched in the 3′UTR region, further supporting m6A as an important post-transcriptional regulatory mechanism in the ovary. Previous studies have shown that ovarian development and aging are coordinately regulated by multiple pathways, including insulin signaling, angiogenesis, reproductive hormone secretion and synthesis, and extracellular matrix remodeling [28–31]. In our study, GO and KEGG enrichment analyses indicated that DMPs were mainly associated with processes related to development and angiogenesis, such as cell–cell adhesion, in utero embryonic development, and the VEGF and Wnt signaling pathways, whereas DEGs were significantly enriched in negative regulation of the I-kappaB kinase/NF-kappaB signaling pathway, cellular senescence, and MAPK-related signaling pathways. Notably, WNT4 has been reported to play a pivotal role in follicular development, and dysregulation of WNT4 can lead to oocyte damage [32]. In addition, chronic inflammation has been implicated in accelerating ovarian aging, with inflammatory factors highly expressed in granulosa cells during ovarian aging [33]. Moreover, activation of the MAPK pathway can inhibit granulosa cell proliferation and accelerate cellular senescence [34, 35]. Collectively, these findings suggest that ovarian aging is accompanied not only by reduced follicle number and functional decline, but also by enhanced inflammatory signaling and activation of senescence-associated pathways, m6A modification may participate in the fine-tuned regulation of these key processes [36–39].

In this study, integrative analysis identified a total of 109 DMGs, and a PPI network was further constructed to prioritize potential target genes associated with ovarian aging. Notably, downregulation of COL6A3 has been reported to induce cellular senescence [40]. INSR and SETDB2 can mediate inflammatory responses through the NF-κB pathway, suggesting that insulin signaling and histone methyltransferase activity are tightly linked to inflammation, which is consistent with the enriched pathways observed in our ovarian aging samples [41, 42]. In addition, PLCG1 was enriched across multiple pathways, and its aberrant accumulation and impaired degradation have been shown to trigger cellular senescence [43, 44]. CEBPB serves as a key transcription factor regulating a broad set of inflammation-related genes [45], and SENP6 is closely associated with neuroinflammation [46]. Moreover, alterations in the expression and modification of extracellular matrix–related genes, such as COL6A3 and MATN2, may contribute to ovarian stromal fibrosis and thereby compromise ovarian function [47, 48]. Collectively, these findings further highlight immune-inflammatory responses and extracellular matrix remodeling as potential critical nodes in the progression of ovarian aging [49–51].

In summary, m6A modification may contribute to ovarian aging by regulating a set of key genes, including INSR, CEBPB, PLCG1, COL6A3, and SETDB2, thereby modulating inflammation-related and cellular senescence–associated pathways. In this study, we preliminarily established a regulatory network linking “m6A–inflammation–ovarian aging,” providing further insight into the molecular mechanisms through which m6A remodeling may drive ovarian aging, and offering potential candidate genetic targets for delaying ovarian aging and improving reproductive performance in livestock.

Conclusion

This study performed sequencing and analysis of ovarian tissues from Qira black sheep of different ages using MeRIP-seq and RNA-seq, and for the first time constructed an m6A transcriptome analysis map of the Qira black sheep ovary. The results showed that a total of 109 DMGs were identified, and multiple target genes were significantly enriched in pathways such as intrauterine embryonic development and the VEGF, NF-κB, and Wnt signaling pathways. However, at present there are relatively few related studies, and the specific mechanisms by which m6A functions in ovarian physiology still need further in-depth investigation. In the future, functional validation and mechanistic analyses should be carried out for key DMGs.

Authors’ contributions

All authors have made substantial contributions to this paper and are therefore eligible for authorship. L.P., responsible for the conduct of the entire experiment and the writing of the manuscript. W.W. and P.G., experimental execution was performed and data were analyzed. A.Q. and X.D., participated in sample collection for experimental validation. X.X., provide financial support, review and editing. C.L., provides guidance on experimental design and manuscript writing.

Funding

This research was sponsored by the Natural Science Support Program of XPCC through the project “Epitranscriptomics reveals the molecular mechanisms underlying variation between ovarian aging and estrus traits in local sheep breeds in Xinjiang” (Grant No. 2024TSYCCX0119). This work was also supported by the project “Study on transcriptomic methylation for selection of sheep reproductive performance and application of timed artificial insemination technology” (Grant No. 2023A02011-2-3) and the project “Precise genome editing in sheep using the PE system” (Grant No. TDZKBS202417).

Data availability

The raw data for this study are stored in the Gene Expression Omnibus (GEO) database (Accession number: GSE287189).

Declarations

Ethics approval and consent to participate

All experimental protocols involving animals strictly adhered to the relevant guidelines established by the Science and Technology Ethics Committee of Tarim University (Approval No. PA20250312055). All the experiments were performed in accordance with the relevant guidelines and regulations set by the Ministry of Agriculture of the People’s Republic of China.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.