Background & Summary

Sheep (Ovis aries) are the fundamental source of meat and wool for humans, and are one of the most economically significant livestock. Reproductive traits have important economic value for sheep production and are regulated by genetics1, hormones2, environment3, and management factors. Previous studies have demonstrated the crucial role of genetic factors in determining sheep reproductive performance, namely the influence of major genes and polygenic combinations on litter size in sheep. At present, multiple major genes that affect fertility have been reported in sheep4, but the key FecB homozygous mutation (c.746 A > G) genotype (BB) has been reported to be positively correlated with litter size in multiple sheep breeds5,6,7. Compared with wild type (++) ewes, BB type ewes have higher concentrations of FSH and E2 (P ≤ 0.05) after estrus, as well as a shorter estrus cycle6. Due to the varying distribution of FecB genotypes between breeds and strains8,9, analyzing the differential gene expression in the ovaries of sheep with different FecB genotypes can help reveal the genetic mechanism of sheep multiparity and promote the reproduction of Chinese sheep breeds.

Extracellular vesicles are membrane vesicles with a diameter of 30–150 nm, produced and secreted by living cells, carrying various proteins, lipids, and nucleic acids10. Once released into the extracellular environment, extracellular vesicles can transmit signals and molecules to other cells, playing a crucial role in intercellular communication11. There are approximately 1014 extracellular vesicles in the human body, and each cell produces an average of 1000–10000 extracellular vesicles12. The nucleic acids it carries include miRNA, lncRNAs, circRNAs, and mRNA, which participate in intracellular signal transduction and help regulate cellular biological processes13. Studies have shown that using extracellular vesicles derived from bone marrow mesenchymal stem cells (BMSCs) to load RNA targeting p53 can improve follicular morphology and inhibit apoptosis in mice14. In addition, injecting extracellular vesicles derived from human umbilical cord mesenchymal stem cells can improve ovarian function by regulating the Hippo signaling pathway. Given the important role of extracellular vesicle follicle development and cell communication, we hope to compare the expression characteristics of lncRNA and mRNA in ovarian extracellular vesicles of sheep with different FecB genotypes, and analyze potential biological functions, providing a new perspective for the mechanism of high reproductive capacity in sheep.

Methods

Animals and ethics statement

Select 10 1-year-old young ewes each from Jiangsu Qianbao Animal Husbandry Co., Ltd. (Yancheng, China) and Inner Mongolia Academy of Agricultural and Animal Husbandry Science Research Base (Siziwang Banner, Inner Mongolia). Three Hu sheep with genotype FecBBB and three Mongolian sheep with genotype FecB++were selected using Sanger sequencing (Fig. 1A,B). All ewes underwent estrus synchronization via the insertion of a progesterone-impregnated vaginal sponge for 11 days, combined with an intramuscular injection of 0.2 mg cloprostenol sodium at the time of sponge removal. The onset of estrus was detected by exposure to a teaser ram three times daily (at 9:00, 12:00, and 18:00), with standing estrus (standing still to accept mounting) used as the criterion for confirmation15. Animals were slaughtered and samples were collected within 12 hours after confirmation of estrus. This sampling time point corresponds to the period after the luteinizing hormone (LH) surge but before ovulation (Fig. 1C). Upon gross examination, the collected ovaries showed the presence of mature preovulatory follicles on the surface and the absence of fresh corpora lutea. Ewes are slaughtered in approved slaughterhouses according to standards and ethical procedures. All experimental procedures, including animal care, have been approved by the Institutional Animal Care and Use Committee of Nanjing Agricultural University (SYXK2022-0031).

Fig. 1
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Two sheep breeds with different reproductive abilities and genotyping. (A) Mongolian sheep and corresponding genotype. (B) Hu sheep and corresponding genotype. (C) LH concentrations of two sheep breeds at different time points.

Isolation and identification of extracellular vesicles from ovarian tissue

After collecting live samples, the tissue is dried and placed in a pre-designated low-temperature storage tube. Transfer the low-temperature storage tube to a liquid nitrogen container for rapid freezing, with a freezing time of at least 30 min, and store at −80 °C. Obtain extracellular vesicles using the method described by Lin et al., with slight modifications16. Tissues were sliced into thin sections on dry ice and then placed on ice (4 °C) for slow thawing for 10–15 min. After thawing, the tissues were immediately transferred into pre-chilled Dulbecco’s Phosphate-Buffered Saline (DPBS). This buffer contained 1 × protease inhibitor (EDTA included), 1 × phosphatase inhibitor, and 20 U/mL ribonuclease (RNase) inhibitor. The tissue blocks were gently yet thoroughly rinsed three times in the ice-cold DPBS supplemented with the aforementioned inhibitors, with fresh buffer replaced each time to wash away free nucleic acids and cytoplasmic proteins released by the rupture of a small number of cells during freeze-thawing.After washing, we adopted a mild enzymatic dissociation protocol to release exosomes in the tissue interstitium while maximizing the preservation of cell integrity. Specifically, the tissues were placed in pre-chilled DMEM (Gibco) containing 75 U/mL Collagenase Type III (Worthington) for dissociation, with the enzymatic digestion buffer used at a volume of 800 μL per 100 mg of tissue. The dissociation was performed in a shaking water bath at 37 °C for a total duration of 10–15 min. To facilitate uniform dissociation and avoid mechanical damage, we performed extremely gentle mixing during incubation: at 5 min post-incubation, the tubes were gently inverted once, and the tissue suspension was gently pipetted twice using a wide-bore pipette. Upon completion of incubation, the samples were immediately placed on ice, and a sufficient amount of protease inhibitors and phosphatase inhibitors was added to achieve a final concentration of 1×, so as to completely terminate the enzymatic activity. Then, residual tissue was removed using a 70 μm filter. After rotating at 300 × g and 4 °C for 10 min, the supernatant was collected in a new tube and rotated again at 2000 × g and 4 °C for 10 min to deplete cell debris. Centrifuge at 10,000 × g for 20 min at 4 °C. Filter the supernatant with a 0.22 μm filter. At 4 °C, centrifuge at 150,000 × g for 2 h. Resuspend the particles in 1 ml PBS. Extracellular vesicles using 100 kDa MWCO Amicon ® Concentrate to 200 μL using an Ultra filter (Merck, Germany). Extracellular vesicles were identified by transmission electron microscopy (TEM), and protein markers were analyzed by western blot. Measure particle size distribution and concentration using a Flow NanoAnalyzer (NanoFCM Inc., China).

ELISA

Sheep serum samples were allowed to coagulate naturally at room temperature for 10–20 min, centrifuged at 2000–3000 rpm for approximately 20 min to collect the supernatant, and stored at −80°C. The concentration of LH in peripheral blood was detected using an ELISA kit (Catalog No.: DRE-S9371c) following the manufacturer’s instructions2, and the average optical density (OD) values of each standard and sample were measured with a standard microplate reader (SPARK, Tecan, Austria).

Western blotting

A portion of the exosomes was lysed using BeyoExo™ Lysis Buffer for Exosome [0.5% deoxycholate, 0.5% Triton X-100, 150 mM NaCl, and 50 mM Tris (pH 7.5)] followed by sonication for 20 min. After centrifugation at 13,000 × g for 5 min at 4 °C, the supernatant was collected. The protein concentration was determined using the BCA assay, and then 5 × SDS buffer (without reducing agent) was added for denaturation. The proteins were first separated by electrophoresis on 4–20% SDS-polyacrylamide gels using a Mini-PROTEAN system (Bio-Rad) and then transferred onto 0.22 μm PVDF membranes via the wet transfer method at 100 V for 90 min at 4 °C. The membranes were blocked with 5% non-fat milk in TBST for 1 hour at room temperature and subsequently incubated overnight at 4 °C with primary antibodies—TSG101 (ABclonal, 1:1000 dilution) and the negative control calnexin (ABclonal, 1:500 dilution)—diluted in 5% BSA/TBST. After washing with TBST, membranes were incubated with HRP-conjugated secondary antibodies for 1 hour at room temperature. Protein bands were visualized using enhanced chemiluminescence (ECL) substrate and imaged with a ChemiDoc MP system (Bio-Rad). The results are shown in Fig. 2A.

Fig. 2
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Identification of exosomes of ovarian tissue. (A) Western blot analysis (WB) showed that the positive protein (TSG101) was expressed in the extracellular vesicles, while the negative protein (calnexin) was not expressed. (B) The result of transmission electron microscopy (TEM) of exosomes of plasma. (C) The result of Nanoparticle tracking analysis (NTA) of exosomes.

TEM

Add 10 μL of extracellular vesicle suspension dropwise onto a carbon coated copper mesh and let it stand at room temperature for 1 min. After rinsing with distilled water, add uranyl acetate solution dropwise for staining for 1 min, and filter paper to remove excess liquid. After drying for 2 mins, the sample was observed using a transmission electron microscope (HT-7700, Hitachi Ltd.)17. The morphology of the exosomes is shown in Fig. 2B.

Nanoparticle tracking analysis (NTA)

NTA was performed using a ZetaView PMX 110 instrument (Particle Metrix, Meerbusch, Germany) equipped with a 405-nm laser. Frozen exosome samples were thawed in 25 °C water and kept on ice. Samples were diluted in 1 × PBS to concentrations between 1 × 10⁷/mL and 1 × 10⁹/mL prior to analysis. Particle size and concentration were determined using NTA software (ZetaView 8.02.28)17. The average diameter of the exosomes was 93.5 nm, with a gated range of diameters from 53.75 to 149.75 accounting for 97.43%. The concentration was 4.80E + 10 particles/mL (Fig. 2C).

ExoRNA isolation and RNA analyses

Exosomal RNA (ExoRNA) was isolated with the Exosome RNA Purification Kit (Simgen, China; Cat#5202050-50 T). RNA quality (concentration/purity) from tissue samples was assessed using an Agilent Bioanalyzer 2100 System with the RNA Nano 6000 assay kit (Agilent Technologies, USA).

Library preparation and sequencing

Prior to library construction, ribosomal RNA (accounting for ~90% of total RNA) was depleted. Using at least 4.5 ng of RNA per sample, libraries were prepared according to the manufacturer’s protocol for the SMARTer stranded total RNA-Seq Kit V2 (Takara Bio USA, Inc.). Quality control involved assessment with an Agilent Bioanalyzer 2100, quantitative PCR, and clustering of index-coded samples on a cBot Cluster Generation System (TruSeq PE Cluster Kit v3-cBot-HS; Illumina, San Diego, CA, USA). Finally, paired-end sequencing was performed on the Illumina NovaSeq. 6000 platform.

Quality validation of the raw data and library

Clean reads were generated from raw data by removing adapter-containing, poly-N, and low-quality sequences using Perl scripts. Data quality was assessed via Q30, GC-content, and duplication levels. All samples exceeded 90.32% Q30, confirming high-quality raw data. Post-QC, ≥ 17.93 Gb clean data were retained per sample (Table 1).

Table 1 Quality control of transcriptome data.
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Library quality was evaluated by insert size dispersion. The peak near 200 bp (Fig. 3A) indicated minimal size deviation and proper fragment selection. mRNA degradation was assessed via randomness distribution, revealing 3′ bias attributable to exosomal RNA characteristics, not degradation (Fig. 3B). Gene saturation analysis confirmed sufficient sequencing depth (Fig. 4). Base error rates per sequencing reaction reflected run-specific quality.

Fig. 3
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Quality validation of library. (A) Dispersion degree of estimated insert size; (B) Randomness distribution map.

Fig. 4
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The saturation testing of the library.

Read alignment

Sequence alignment and subsequent analyses used the Ovis aries genome (Ensembl release 111; https://ftp.ensembl.org/pub/release-111/fasta/ovis_aries/dna/) as reference. HISAT2 (v2.2.1.0) aligned clean reads for both lncRNAs and mRNAs, followed by assembly and quantification with StringTie (v2.1.3). The results of read mapping of the lncRNAs and mRNAs are shown in Table 2.

Table 2 mRNA and lncRNA seq read statistics.
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Prediction and identification of novel LncRNA and mRNA

Transcriptomes were assembled with StringTie using reads mapped to the reference genome, followed by annotation via gffcompare (Cufflinks package). Putative lncRNAs were identified from unannotated transcripts using a four-method screening approach: CPC, CNCI, Pfam, and CPAT. Meanwhile, potential protein-coding RNAs were filtered using minimum length and exon number thresholds. Transcripts meeting lncRNA criteria ( ≥ 200 nt length, ≥ 2 exons, FPKM ≥ 0.1) were further validated with CPC/CNCI/Pfam/CPAT. A total of 2455 lncRNAs were detected, including 170 known lncRNAs and 2285 new lncRNAs; 14480 genes, including 13358 known genes and 1122 new genes (Table S1, S2). The box plot showed that the distribution of transcripts and the gene expression levels of different samples in the two groups appeared to be similar (Fig. 5).

Fig. 5
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Box plots of expression distribution show similar levels of gene expression in different samples. (A) mRNA expression distribution. (B) LncRNA expression distribution.

Differential expression of mRNA and lncRNA

Differential expression analysis was conducted using edgeR. The obtained raw P-values were corrected for false discovery rate using the Benjamini-Hochberg method. mRNAs and lncRNAs with corrected FDR values < 0.05 and |log₂ fold change| > 1.5 were defined as significantly differentially expressed. A total of 180 DEGs of mRNA were identified in the heatmap, of which 49 were upregulated and 131 were downregulated (Fig. 6A and Table S3). 15 DEGs of lncRNA, of which 2 were upregulated and 13 were downregulated (Fig. 6B and Table S4).

Fig. 6
Fig. 6The alternative text for this image may have been generated using AI.
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The expression profiles of two groups of mRNAs and lncRNAs. (A) Heat map of differentially expressed mRNAs Red represents high relative expression level, blue represents low relative expression level. (B) Heat map of differentially expressed lncRNAs. Red represents high relative expression level blue represents low relative expression level.

Gene ontology and pathway enrichment analysis

Gene Ontology (GO; http://geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/) enrichment analyses were performed to characterize DEGs of mRNA. GO categorizes functions into biological process (BP), cellular component (CC), and molecular function (MF) domains, while KEGG identifies associated biological pathways. Statistical significance was assessed using hypergeometric distribution (P-value) with Benjamini-Hochberg correction (q-value). The GO and KEGG pathways were shown in Fig. 7A,B,C,D and Tables S5, S6.

Fig. 7
Fig. 7The alternative text for this image may have been generated using AI.
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Gene Ontology (GO) Enrichment and KEGG Annotation Analysis of DEGs of mRNA. (A) The bar chart of enriched Biological Processes. (B) The bar chart of enriched Cellular Components. (C) The bar chart of enriched Molecular Functions. (D) The bar chart of KEGG annotation.

LncRNA-mRNA correlation analysis

Co-expression analysis was performed to investigate direct regulatory relationships between lncRNAs and target mRNAs. LncRNA-mRNA pairs with |Pearson correlation| > 0.99 and P < 0.05 were selected. The interactions were visualized as a coding-non-coding co-expression network using Cytoscape. The Fig. 8 and Table S7 shows the co-expression network of lncRNA and mRNA.

Fig. 8
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Co-expression network of differentially expressed lncRNAs and mRNAs. There are 34 connections between 9 lncRNAs and 16 mRNAs in the network. Rectangle and circle represent mRNAs and lncRNAs respectively. The thickness of the lines represents the degree of correlation.

Data Record

The deposition of FASTQ files containing raw data has been completed in the NCBI Sequence Read Archive (SRA) accession of SRP59351918. The Bioprojects number are PRJNA1279339. https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1279339. The deposit of files of RNA prediction, expression abundance quantification and DEGs analysis has also been completed in figshare19. The transcript abundance has been deposited in the NCBI Gene Expression Omnibus (GEO) under accession number of GSE31406420. The dataset is available at https://dataview.ncbi.nlm.nih.gov/object/PRJNA1279339?reviewer=5mb36e76fo0v1e31eu96b7p5th.

Technical Validation

Assessment of RNA integrity

The RNA extracted from exosomes was analyzed using an Agilent 2100 Bioanalyzer, revealing the absence of distinct 18S and 28S ribosomal RNA peaks. Instead, it exhibited a concentrated distribution in the region of < 200 nt (Supplementary Fig. 1), serving as a crucial quality control standard to confirm that the RNA originated from exosomes rather than cellular debris.

Quality assessment of RNA data

In order to obtain high-quality sequences (i.e. Clean Reads), the quality control standards for the raw sequences measured in the ribosome library are: (1) removing reads containing adapters; (2) Remove low-quality reads (including reads with a removal ratio of N greater than 10%); Remove reads with a quality value Q ≤ 10 that account for more than 50% of the entire read.