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. 2024 Jun 5;103(9):103928. doi: 10.1016/j.psj.2024.103928

Identification of potential candidate miRNAs related to semen quality in seminal plasma extracellular vesicles and sperms of male duck (Anas Platyrhynchos)

Xuliang Luo *, Liming Huang *, Yan Guo *, Yu Yang †, Ping Gong †, Shengqiang Ye †, Lixia Wang †, Yanping Feng *,1
PMCID: PMC11298939  PMID: 39003794

Abstract

Semen quality is an important indicator that can directly affect fertility. In mammals, miRNAs in seminal plasma extracellular vesicles (SPEVs) and sperms can regulate semen quality. However, relevant regulatory mechanism in duck sperms remains largely unclear. In this study, duck SPEVs were isolated and characterized by transmission electron microscopy (TEM), western blot (WB), and nanoparticle tracking analysis (NTA). To identify the important molecules affecting semen quality, we analysed the miRNA expression in sperms and SPEVs of male ducks in high semen quality group ((DHS, DHSE) and low semen quality group (DLS, DLSE). We identified 94 differentially expressed (DE) miRNAs in the comparison of DHS vs. DLS, and 21 DE miRNAs in DHSE vs. DLSE. Target genes of SPEVs DE miRNAs were enriched in ErbB signaling pathway, glycometabolism, and ECM-receptor interaction pathways (P < 0.05), while the target genes of sperm DE miRNAs were enriched in ribosome (P < 0.05). The miRNA-target-pathway interaction network analyses indicated that 5 DE miRNAs (miR-34c-5p, miR-34b-3p, miR-449a, miR-31-5p, and miR-128-1-5p) targeted the largest number of target genes enriched in MAPK, Wnt and calcium signaling pathways, of which FZD9 and ANAPC11 were involved in multiple biological processes related to sperm functions, indicating their regulatory effects on sperm quality. The comparison of DE miRNAs of SPEVs and sperms found that mir-31-5p and novel-273 could potentially serve as biomarkers for semen quality detection. Our findings enhance the insight into the crucial role of SPEV and sperm miRNAs in regulating semen quality and provide a new perspective for subsequent studies.

Key words: duck, extracellular vesicle, miRNA, sperm, semen quality

INTRODUCTION

China is the largest producer and consumer of ducks in the world, accounting for 75% of global duck production (Deng et al., 2019). Among meat-type ducks, the fast- growing white feathered ducks are the main breeding breeds, but their meat quality cannot satisfy the demand for high quality meat (Zhang et al., 2021). Wuqin-10 meat duck is a newly cultivated national meat duck breed in China, exhibiting multiple advantages such as small weight, good meat quality, and flavor. However, insufficient and poor semen quality of male ducks of this new breed affects the breeding efficiency, thus preventing its large-scale production. Therefore, exploring the molecular mechanisms of regulating semen quality and screening the molecular markers contributing to rapid detection of semen quality are essential for improving the efficiency of breeding selection in ducks.

Semen quality is influenced by many factors. For example, sperm motility and apoptosis have been reported to be affected by some molecules involved in mitochondrial function and sperm metabolism, such as human sperm miR-574 (Ma et al., 2020) and boar miR-26a (Wang et al., 2020). The seminal plasma (SP) serve as a microenvironment for sperm survival, which is closely related to sperm function. SP affects semen quality (Zou et al., 2019), sperm motility, sperm morphology, sperm metabolism, and survival (Druart and de Graaf, 2018), suggesting that there are interactions or material exchange between SP and sperm. Increasing evidence indicates that SP components exert multiple functions, including sperm capacitation, modulation of female immune response, gamete interaction, and fusion (Topfer-Petersen et al., 2005; Rodriguez-Martinez et al., 2011; Schjenken and Robertson, 2014). Therefore, it is necessary to identify the components maintaining sperm functions in duck SP.

Extracellular vesicles (EVs) are natural nanoparticles that contain bioactive molecules and are widely present in SP, blood plasma, urine, and other biological fluids. EVs are an important pathway for intercellular communication (Gupta et al., 2021; Van Niel et al., 2022). EVs with a diameter range of 30 nm to 250 nm are secreted by various cell populations, containing a variety of cellular products including proteins, lipids, and nucleic acids (DNA and RNA) (Thakur et al., 2014; Luo et al., 2022). In mammals, miRNAs of seminal plasma extracellular vesicles (SPEVs) and sperms are associated with semen quality and fertility (Ding et al., 2021; Werry et al., 2022). The miR-31-5p in human semen EVs is a highly sensitive biomarker for the identification of azoospermia (Barcelo et al., 2018). The effects of EV mRNAs isolated from primary cells of the rabbit prostate, epididymis, and testis on oocyte maturation have been confirmed by in vitro (Abumaghaid et al., 2022). In avians, SPEVs were more significantly enriched in the semen of fertile roosters than in subfertility roosters, and the transfer of SPEVs of fertile roosters to subfertility roosters obviously improves the sperm motility (Cordeiro L, 2021). At present, the studies on the effects of SPEVs on reproductive traits of poultry mainly concentrated in chickens, and there are few reports on EVs in ducks, only one study has reported that duck embryo fibroblast derived exosomes can mediate virus transmission in ducks (Xu et al., 2023). Other studies have focused on proteomics and the effects of antioxidants on duck semen quality (Fouda et al., 2022; Tang et al., 2022). The effects of SPEV and sperm miRNAs on semen quality in ducks have not been reported. Therefore, it is necessary to further explore the mechanism of SPEV and sperm miRNAs regulating reproductive traits in ducks.

In this study, we performed transcriptomic analysis of SPEV and sperm miRNAs from ducks with high and low semen quality. The miRNA-target-pathway interaction network analyses indicated that miR-34c-5p, miR-34b-3p, miR-449a, miR-31-5p, and miR-128-1-5p were involved in multiple biological processes related to sperm functions. The mir-31-5p and novel-273 exhibited the great potential to serve as biomarkers for semen quality detection. Our findings lay a foundation for further deciphering the regulatory molecular mechanism of duck semen quality.

MATERIALS AND METHODS

Experimental Animals

All animal experiments were carried out following standard procedures and approved by the Ethics Committee of Huazhong Agricultural University, China (ID Number: HZAUDU-2023-0005). Sexually mature 25-week-old male ducks of Wuqin-10 meat duck were individually housed in cages at the duck breeding farm of the Wuhan Academy of Agricultural Sciences with a standard diet and free drinking water (Wuhan, China).

Semen Collection and Quality Evaluation

The semen samples of 40 sexually mature ducks were collected by trained professionals. Semen volume was measured by a 1,000 µL pipettor. Sperm density was counted by a hemocytometer. Sperm viability (%) was assessed using trypan blue to determine the proportion of live and dead sperm. Sperm motility (%) was calculated as the percentage of linearly moving sperms in total sperm count under a light microscope, with 0 to 100% of the linearly moving sperms scored as 0 to 9. Specifically, 0 to 10% of linearly moving sperms received a score of 0, while those scoring between 10-20% were assigned a score of 2, and so on. The percentage of deformed sperms (with abnormal morphology) in total sperm count (%) was assessed using eosin. The semen quality test was completed within 15 min, with 3 replicates for the measurements of each indicator (Zhang et al., 2023). The original semen samples were stored at −80°C for subsequent analysis. Finally, three ducks with highest semen quality (Score ≥ 7) and three ducks with lowest semen quality (Score ≤ 1) were selected for subsequent experiments according to the above semen quality evaluation.

Sperm Samples Preparation and Isolation

Duck sperm samples derived from high and low semen quality group (DHS, DLS) were isolated by Earle's upstream method (Vasilescu et al., 2023). Briefly, 1.5 mL semen was slowly added to Earle's medium containing 10% fetal bovine serum (FBS) and stood for 60 min in an incubator at 37°C until interface formation. The supernatant was aspirated and centrifuged at 1,200 rpm for 10 min for sperm collection.

Preparation and Isolation of SPEV Samples

Six duck SPEV samples from high (DHSE) and low (DLSE) semen quality group were isolated separately by the ultrahigh-speed differential centrifugation and PEG6000 incubation method. There were 3 replicates for each group (DHSE and DLSE), respectively. The 1 mL semen sample from each duck was diluted (at 1:40) with phosphate buffer saline (PBS) and centrifuged at 1,000×g and 4°C for 10 min to remove sperm cells, and then centrifuged again at 12,000×g and 4°C for 30 min to remove residual cells and debris. The supernatant was filtered through 0.45 μm and 0.22 μm filters to remove larger vesicles, followed by mixing with PEG6000 at a ratio of 1:1 and overnight incubation at 4°C. The mixture was then ultracentrifuged at 100,000×g and 4°C for 90 min, and the resulting precipitate was resuspended in PBS solution and centrifuged at 100,000×g and 4°C for another 90 min. The SPEV sample precipitate was resuspended in 200 µL of PBS solution and stored at −80°C. Ultracentrifugation was performed using a Beckman Optima XE-90 ultracentrifuge with an SW-32Ti rotor (Beckman Coulter, Brea, CA), and total protein in the samples was quantified using a BCA protein assay kit (Biosharp, China).

Transmission Electron Microscopy

SPEV sample was dropped onto a copper mesh with a pore size of 2 nm and stood at room temperature for 2 min, and then the superfluous sample liquid on the mesh was absorbed from the side of the mesh using filter paper. Then SPEVs sample on the mesh was negatively stained with a 3% phosphotungstic acid solution at room temperature for 5 min and air-dried at room temperature. Finally, the isolated SPEVs were observed under an H-7650 kV analytical transmission electron microscope (TEM, Hitachi, Japan).

Nanoparticle Tracking Analysis

NTA (Particle Metrix, Germany) was performed to determine the particle size and concentration of SPEVs. The sample pool was washed with filtered molecular grade water and PBS buffer (Biological Industries, Israel). The instrument was calibrated using a polystyrene microsphere (110 nm), and the SPEV samples were diluted at 1:10,000 with filtered molecular grade water. Blank pool added with filtered molecular-grade water was used as a negative control. NTA analysis of each sample was conducted for 60 s with 3 replicates.

Western Blot

Western blot (WB) was performed to identify marker proteins of EVs. To obtain the SPEV proteins, radioimmunoprecipitation assay lysis buffer (RAPI, Vazyme, China) and phenylmethanesulfonyl fluoride (PMSF, Vazyme, China) were mixed at the ratio of 100:1 added into the SPEV suspension (at 1:1 v/v), incubated at 4°C for 15 min. Then the supernatant was collected after centrifuge at 12000 rpm and 4°C for 15 min, and centrifuged at 12,000 rpm and 4°C for 15 min to obtain supernatant. Subsequently, the concentration of SPEV proteins was measured using the BCA assay kit (Biosharp, China). SPEV proteins was mixed with protein loading dye (Biosharp, China) to reach a final concentration of into 2 µg/µL, denatured by heating at 98°C, and separated through 10% SDS polyacrylamide gel electrophoresis (Biosharp, China) at 200V for 60 min with protein loading volume of 10 µL. Then, the separated proteins were transferred to a polyvinylidene difluoride membrane at 200mA for 90 min and blocked with 5% bovine serum albumin (BioFroxx GmbH, China) at room temperature for 60 min. Then, the membranes were incubated with primary antibodies ALIX (abclonal, A2215), CD9 (abclonal, A1703), and β-actin (abcam, ab8226) proteins at 4°C overnight, respectively. Then the membranes were incubated with secondary antibody for 1 h, and detected using a chemiluminescent method kit (Abbkine, USA).

RNA Isolation, Small RNA Library Construction, and Sequencing

According to the semen quality, the experimental samples were divided into 4 groups, namely, DHSE, DLSE (duck SPEVs with high and low semen quality), DHS, DLS (duck sperms with high and low semen quality) groups. The RNA was extracted from SPEVs and sperms using the total RNA and protein isolation kit (Thermo Fisher, USA) according to the manufacturer's instructions. There were 3 biological replicates per group. The total RNA quantity and quality were assessed using the Agilent 2100 bioanalyzer with the RNA Nano 6000 assay kit and high sensitivity DNA assay (Agilent Technologies, Santa Clara, CA). The small RNA libraries were prepared from 2 µg total RNA isolated from each sample using NEBNext Multiplex small RNA library prep set for Illumina (NEB). Libraries were prepared using the TruSeq stranded mRNA library preparation kit (Illumina Inc, San Diego, CA). RNA sequencing was carried out on an Illumina HiSeq 4000 platform (Novaseq, China) and 50 bp single-end reads were generated, the small RNA ranging from 21 nt to 23 nt were used to identify miRNAs.

Identification of Differentially Expressed miRNAs and their Genes

After low-quality reads and adapter sequences were removed, miRNAs were identified based on the annotation information of the Anas platyrhynchos genome (https://www.ncbi.nlm.nih.gov/assembly/GCF015476345). Conserved miRNAs were predicted by the miRDeep2 and srna-tools-CLI aligning with known miRNAs of chicken (G.Gallus) present in the miRbase (https://www.miRBase.org). Novel miRNAs were predicted using exploring the secondary structure by the miRDeep2 (https://www.mdc-berlin.de/content/mirdeep2-documentation). Further, the differentially expressed (DE) miRNAs among different groups were identified with the thresholds of |log 2 fold change| ≥ 1 and P < 0.05 by the DESeq R package. The target genes of DE miRNAs were predicted by miRanda and RNAhybrid. Clusters of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichment analyses were performed to determine the functions of the target genes of DE miRNAs. KOBAS software was applied to determine the significant enrichment of the target genes in KEGG pathways. Cytoscape_3.6.1 software was used to construct miRNA-target-pathway interaction network.

Statistical Analysis

Student's t-test was performed by SPSS 22.0 software to determine the differences among groups. P < 0.05 was considered as statistically significant. Data were expressed as means ± SD, and diagrams and graphs were plotted using the GraphPad Prism8.

RESULTS

Quality Evaluation and Comparison of DHS and DLS

Semen quality assessment indicated that sperm motility, viability, and density of DHS ducks were higher than those of DLS ducks (P < 0.05). In contrast, the percentage of abnormal morphology in DHS ducks was lower than that in DLS (P < 0.01), while there was no significant difference in semen volume between DHS and DLS (Figure 1).

Figure 1.

Figure 1

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Semen quality parameters in duck semen with high semen quality group (DHS) and low semen quality group (DLS), including semen volume, sperm motility, sperm viability, sperm density, and abnormal morphology. *, P < 0.05, **, P < 0.01. Data are expressed as mean ± SD.

Characteristics of SPEVs

Duck SPEVs(DHSE and DLSE)were isolated,and their morphology and particle size were characterized by TEM and NTA. The TEM results indicated that DHSE and DLSE exhibited typical bilayer membrane structure (Figures 2A and 2B). The NTA revealed that the particle sizes of DHSE and DLSE ranged from 30 nm to 250 nm, with a main peak at 121.8 nm and 129.6 nm, respectively (Figures 2C and 2D), and the concentrations of DHSE and DLSE samples ranged from 2.9 ×1011 to 3.0 ×1011 particles/mL as measured by NTA. Marker proteins CD9 and ALIX of EVs were detected by WB (Figure 2E). All these results indicated successful isolation of SPEVs from the samples of 2 groups.

Figure 2.

Figure 2

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Morphological and molecular identifications of seminal plasma extracellular vesicles (SPEVs). (A–B) Morphological characteristics of duck SPEVs in high and low semen quality group (DHSE, DLSE). Black arrows point to SPEVs. (C–D) Particle size distribution of DHSE and DLSE. (E) Western Blot (WB) analysis of the common SPEV markers.

Differentially Expressed miRNAs in SPEVs and Sperms

To identify the miRNAs related to semen quality, RNA libraries were constructed for DHS, DLS, DHSE, and DLSE groups. An average of 13621277 raw reads were obtained. After low-quality reads and adapter sequences were removed, approximately 13055093 (95.8%) clean reads were obtained for each sample on average. These detailed data were presented in Table 1.

Table 1.

Reads of each sample obtained from small RNA sequencing.

Sample Total reads Clean reads Error rate Q20 (%) Q30 (%) GC content
DLS1 14640524(100.00%) 14121381(96.45%) 0.01% 98.89% 96.11% 53.87%
DLS2 15467779(100.00%) 14674722(94.87%) 0.01% 98.88% 96.42% 55.53%
DLS3 14911329(100.00%) 14496042(97.21%) 0.01% 99.02% 96.59% 55.26%
DLSE1 12277584(100.00%) 11274166(91.83%) 0.01% 98.39% 94.73% 54.21%
DLSE2 12260949(100.00%) 12085582(98.57%) 0.01% 98.67% 95.30% 53.51%
DLSE3 13293579(100.00%) 12615605(94.90%) 0.01% 98.71% 95.46% 53.91%
DHS1 12610296(100.00%) 11584963(91.87%) 0.01% 98.77% 95.90% 54.16%
DHS2 13204778(100.00%) 12768209(96.69%) 0.01% 98.80% 95.65% 54.49%
DHS3 15881559(100.00%) 15571055(98.04%) 0.01% 99.11% 96.75% 54.05%
DHSE1 11602056(100.00%) 11181421(96.37%) 0.01% 98.75% 95.66% 54.59%
DHSE2 11657167(100.00%) 10889008(93.41%) 0.01% 98.73% 95.71% 54.48%
DHSE3 15647733(100.00%) 15398963(98.41%) 0.01% 99.39% 97.45% 54.71%
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DHS, duck sperms in high semen quality group; DLS, duck sperms in low semen quality group; DHSE, duck SPEVs in high semen quality group; DLSE, duck SPEVs in high semen quality group. Q20 (%) and Q30 (%) are ratios of bases that have Phred quality score greater than or equal to 20 and 30, respectively.

By aligning to miRNAs in chicken miRbase, 411, 399, 345, and 395 miRNAs were identified from DHS, DLS, DHSE, and DLSE, respectively (Figure 3A and Table S1). The number of novel miRNAs was lower in DHSE group than in the other 3 groups. There was no significant difference in the number of known miRNAs among the 4 groups. A total of 258 (50.7%) miRNAs were shared by the 4 groups, with let-7, miR-34, and miR-146 families were highly expressed in all 4 groups (Figure 3B).

Figure 3.

Figure 3

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Identification and characterization of miRNAs in seminal plasma extracellular vesicles (SPEVs) and sperms from high and low semen quality groups. (A) Number of known miRNAs (gga miRNA) and novel miRNAs in SPEVs and sperms. (B) Venn diagram showing the number of identified miRNAs in SPEVs and sperms. (C) Volcanic diagrams showing the number of differentially expressed (DE) miRNAs in SPEVs and sperms between high semen quality group and low semen quality group(D) Heatmap of expression profiles of all DE miRNAs. DHS, duck sperm in high semen quality group; DLS, duck sperm in low semen quality group; DHSE, duck SPEVs in high semen quality group; DLSE, duck SPEVs in low semen quality group.

Further, the DE miRNAs of SPEVs and sperms were identified, and the details of DE miRNAs were presented in Table S2. A total of 94 DE miRNAs were identified from the sperms (33 up-regulated, 61 down-regulated) in the comparison of DHS vs. DLS, while 21 DE miRNAs were identified from the SPEVs (12 up-regulated and 9 down-regulated miRNAs) in the comparison of DHSE vs. DLSE. Volcano plot maps and heat maps of DE miRNAs among groups were plotted in Figures 3C and 3D.

Five shared DE miRNAs were identified by comparing the DE miRNAs in SPEVs (DHSE vs. DLSE) and sperms (DHS vs. DLS) (Figure 4A). Among them, miR-31-5p was up-regulated both in DHS and DHSE groups, and novel-273 showed a consistent down-regulation trend in DHS and DHSE groups (Figures 4B and 4C). However, miR-203a, miR-145-5p, and miR-499-5p exhibited opposite regulatory trends in SPEVs and sperms (Figures 4D–4F). The expression of miR-203a was significantly higher in DHS and lower in DHSE, while miR-145-5p and miR-499-5p displayed the highest expression levels in DLS group but were lower in DLSE group. These 5 miRNAs were expected to be used as marker miRNAs for semen quality detection.

Figure 4.

Figure 4

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Key miRNAs and their expression relationship in seminal plasma extracellular vesicles (SPEVs) and sperms. (A) Venn diagram of shared differentially expressed (DE) miRNAs in the comparison of DHSE vs. DLSE and DHS vs. DLS. (B–F) Differential expression relationship of the 5 shared miRNAs in DHSE vs. DLSE and DHS vs. DLS. The arrows point to miRNAs with higher expression. Red arrows point to miRNAs with a significantly higher expression (P < 0.05), and the blue arrow point to miRNAs with higher expression but with no significant difference (P > 0.05). DHS, duck sperm in high semen quality group; DLS, duck sperm in low semen quality group; DHSE, duck SPEVs in high semen quality group; DLSE, duck SPEVs in low semen quality group.

Prediction and Functional Annotation of Target Genes of DE miRNAs in SPEVs and Sperms

Further, we predicted the cis-regulated target genes of DE miRNAs. A total of 69,909 and 3,097 target genes were predicted for DE miRNAs in DHS vs. DLS and in DHSE vs. DLSE, respectively. GO enrichment analysis indicated that a total of 4,226 and 2,742 GO terms were enriched for SPEVs and sperms, respectively, of which 190 and 121 GO terms were significantly enriched (P < 0.05), respectively. Go terms related to sperm functions such as cargo receptor activity, autophagy, mitochondrial fusion, and organelle fusion were significantly enriched for SPEVs (P < 0.05, Figure 5A). For sperms, the GO terms of autophagy, intermediate filament cytoskeleton, metabolic process, and cargo receptor activity were significantly enriched (P < 0.05, Figure 5B). The KEGG enrichment analysis revealed that 154 and 134 pathways were enriched for the SPEVs and sperm, respectively. Focal adhesion, ErbB signaling pathway, Phagosome, glycometabolism, and ECM-receptor interaction were enriched in SPEVs (P < 0.05), while also being enriched in sperms. The top 20 GO terms and KEGG pathways for the SPEVs and sperms were shown in Figures 5C and 5D, respectively. Additionally, in both SPEVs and sperms, the pathways associated with sperm function including the MAPK signaling pathway, mTOR signaling pathway, TGF-beta signaling pathway, Wnt signaling pathway, and calcium signaling pathway were jointly enriched.

Figure 5.

Figure 5

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GO and KEGG analysis of the target genes of differential expressed (DE) miRNAs. (A) GO analysis of target genes in duck sperm in high semen quality group (DHS) and low semen quality group (DLS). (B) GO analysis of target genes in duck sperm seminal plasma extracellular vesicles in high semen quality group (DHSE) and low semen quality group (DLSE). (C) KEGG analysis of target genes in DHS and DLS. (D) KEGG analysis of target genes in DHSE and DLSE.

MiRNA-Target-Pathway Interaction Network Related to Sperm Function

Based on GO function and KEGG pathway analyses, 19 and 10 miRNAs related to sperm function were screened from 94 and 21 DE miRNAs of sperms and SPEVs, respectively (Table 2). Meanwhile, 5 main signaling pathways related to sperm function, namely, MAPK signaling pathway, mTOR signaling pathway, TGF-beta signaling pathway, Wnt signaling pathway, and calcium signaling pathway, were also screened. The miRNA-target-pathway interaction network consisting of 53 interactions in sperms and 30 interactions in SPEVs, was presented clearly in Figures 6A and 6B, respectively. The interaction network showed that a majority of target genes of miR-34c-5p, miR-34b-3p, miR-449a, miR-31-5p, miR-214, miR-145-5p, and miR-128-1-5p were enriched in MAPK signaling pathway, Wnt signaling pathway, and calcium signaling pathway. The target genes FZD9 and ANAPC11 were enriched several semen function-related signaling pathways, indicating that they played important role in regulating semen quality.

Table 2.

DE miRNAs related to with sperm functions in DHS vs DLS and DHSE vs. DLSE.

Combination miRNAs log2FC p-value Expression
DHS VS. DLS gga-let-7a-5p 0.9253 4.52E-02 up
gga-let-7f-5p 0.8836 3.33E-02 up
gga-let-7g-5p 0.8955 2.77E-02 up
gga-miR-449a 2.2146 9.25E-03 up
gga-miR-146b-5p 1.1863 3.58E-02 up
gga-miR-148b-3p 1.6757 1.15E-02 up
gga-miR-34c-5p 1.582 4.43E-02 up
gga-miR-19b-3p 3.3653 2.11E-04 up
gga-miR-34b-3p 1.6665 2.83E-02 up
gga-miR-203a 2.3677 6.91E-04 up
gga-miR-31-5p 3.0943 4.66E-03 up
gga-miR-27b-3p 1.5281 1.71E-03 up
gga-miR-221-3p 2.1355 6.94E-04 up
gga-miR-214 −4.1685 1.03E-03 down
gga-miR-206 −6.7663 1.94E-08 down
gga-miR-202-5p −1.2642 2.26E-02 down
gga-miR-182-5p −5.7642 9.44E-04 down
gga-miR-425-5p −1.3509 1.23E-02 down
gga-miR-10a-5p −1.118 1.04E-02 down
DHSE vs. DLSE gga-miR-7 1.0899 1.41E-02 up
gga-miR-128-1-5p 2.0042 2.00E-02 up
gga-miR-145-5p 2.461 6.53E-03 up
gga-miR-31-5p 1.6248 1.74E-02 up
gga-miR-199-5p 3.0731 1.53E-04 up
gga-miR-205a 1.9865 5.13E-03 up
gga-miR-204 0.8357 1.86E-02 up
gga-miR-122-5p 1.3588 2.19E-02 up
gga-miR-215-5p −1.4242 3.84E-03 down
gga-miR-16c-5p −0.8749 4.42E-02 down
gga-miR-203a −0.9426 3.13E-02 down
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DE, differentially expressed.

DHS, duck sperms in high semen quality group; DLS, duck sperms in low semen quality group; DHSE, duck SPEVs in high semen quality group; DLSE, duck SPEVs in high semen quality group.

Figure 6.

Figure 6

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MiRNA-target-pathway interaction network related to sperm function. (A) MiRNA-target-pathway interaction network involving the differential expression (DE) miRNAs between duck sperm in high semen quality group (DHS) and low semen quality group (DLS). (B) MiRNA-target-pathway interaction network involving the DE miRNAs between duck sperm seminal plasma extracellular vesicles in high semen quality group (DHSE) and low semen quality group (DLSE). Green diamonds represent DE miRNA, and blue circles represent the target genes of DE miRNA, and orange triangles represent signaling pathways. The larger graph size represents more interactions.

DISCUSSION

Semen quality is an important reproductive trait in poultry production and breeding (Tang et al., 2022), this trait is influenced by miRNAs in the SPEVs and sperms. In this study, several critical miRNAs in SPEVs and sperms related to semen quality were identified based on miRNA transcriptome data.

In this study, a total of 94 DE miRNAs were identified in sperms, of which 19 known DE miRNAs potentially played roles in the regulation of semen quality, including 13 miRNAs significantly up-regulated in the DHS group (such as let-7 families, miR-34 families, miR-449, and miR-19b-3p) and 6 miRNAs significantly highly expressed in DLS group (such as miR-10a-5p, miR-143-3p, miR-182-5p, and miR-202-5p). Our results indicated that the up-regulated DE miRNAs in DHS group might be related to semen quality, and thus these miRNAs could serve as key candidate genes for subsequent research. Actually, these functions of let-7a, let-7g-5p, miR-19b, miR-146b-5p, miR-34b/c and miR-449a have been widely reported in humans or mice (Comazzetto et al., 2014; Munoz et al., 2015; Ma et al., 2018; Mokanszki et al., 2020; Barbu et al., 2021). In humans, high expression of hsa-let-7a, hsa-miR-19b, and miR-146b-5p is negatively correlated with male infertility and azoospermia (Wang et al., 2011; Wu et al., 2012; Mokanszki et al., 2020). Overexpression of let-7g-5p enhances anti-apoptotic effects of spermatid cells, thus improving semen quality (Ma et al., 2018). The expression of miR-34c-5p and miR-449a was significantly up-regulated in the sperm and testes of healthy men than that in asthenozoospermia patients (Munoz et al., 2015; Barbu et al., 2021). In miR-34b/c and miR-449a knockout mice, a high incidence of apoptosis and a reduction in germ cell number after the pachytene phase result in sperm production decrease and infertility (Comazzetto et al., 2014). Additionally, the high expression of some miRNAs in DLS group are negatively correlated with semen quality. For example, the up-regulation of miR-10a-5p and miR-182-5p is correlated with low sperm motility in bulls (Gao et al., 2019) and in teratozoospermia in humans (Gholami et al., 2021). The overexpression of miR-10a-5p has been reported to lead to severe testicular atrophy and sterility in adulthood mice (Capra et al., 2017). The function of most DE miRNAs related to semen quality found in this study are consistent with those in the previous reports, but those of some DE miRNAs were inconsistent. For example miR-184, miR-125b-5p, and miR-27b-3p were found to be down-regulated in DLS group in this study, while miR-184 expression has been reported to be up-regulated in zebrafish sperm with low sperm motility (Domingues et al., 2021); miR-125b-5p expression is upregulated in azoospermia cases (Domingues et al., 2021; Nazmara et al., 2021); and miR-27b-3p expression is up-regulated in asthenozoospermia patients (Liu et al., 2020). Such a discrepancy in DE miRNA expression pattern may be due to the species differences, and this speculation remains to be further verified.

To understand the function of SPEVs in duck reproduction, we compared the characteristics of duck SPEVs with those of humans (Yue et al., 2022), boars (Dlamini et al., 2023), bulls (Lange-Consiglio et al., 2022; Yu et al., 2023), chickens (Chen et al., 2020; Cordeiro L, 2021), and ducks (Xu et al., 2023), and found significant differences in particle sizes of SPEVs between mammals and poultry. Most studies of mammals have shown that the peak size of SPEVs is greater than 140nm, while the peak size of poultry SPEVs is less than 130 nm. There is no significant difference in particle size of SPEVs between ducks and chickens. Our further comparison found that particle size of EVs from duck embryonic fibroblasts showed no significant differences from that of EVs from different duck tissues. Based on these findings, we speculated that the particle size of SPEVs in poultry might be smaller than that in mammals, which need to be confirmed by further studies.

We further investigated the miRNAs in SPEVs, and found that of 21 DE miRNAs identified in SPEVs, 10 miRNAs have been reported to potentially regulate sperm function. MiR-204-5p is one of the miRNAs that are preferentially expressed in the spermatogonial stem cell (Chen et al., 2017), and it is involved in regulating the proliferation and differentiation of goat spermatogonia stem cells (Niu et al., 2016). In addition, miR-31-5p expression in SPEVs is down-regulated in secretory azoospermia patients, compared to that in obstructive azoospermia patients, and this miRNA can serve as a biomarker to distinguish these 2 diseases (Barcelo et al., 2018). However, some discrepancies in miRNA expression are observed between our SPEV results and previous reports. In the diploid red crucian carp, miR-199-5p has been confirmed to be necessary for sperm flagellum assembly and spermatogenesis (Li et al., 2022). Furthermore, miR-16c-5 is down-regulated in transgenic zebrafish with low sperm motility (Domingues et al., 2021). In this study, miR-199-5p, miR-204, miR-214b-3p, and miR-31-5p were down-regulated in DLSE, whereas miR-16c-5 was down-regulated in DHSE. We speculated that the function of EVs could vary with tissues and cells, which might explain the inconsistent expression of some miRNAs in SPEVs from sperms or testes, and might also explain the opposite expression patterns of some miRNAs in EVs and in the EV producing cells (Foj et al., 2017).

The miRNAs are always used as biomarkers for disease diagnosis. For example, miR-31-5p in human semen EVs is a highly sensitive biomarker for the identification of azoospermia (Barcelo et al., 2018). Hsa-miR-19b and miR-146b-5p have been identified as non-invasive biomarkers for identifying sperm production obstacle (Wang et al., 2011; Wu et al., 2012). In the present study, to screen DE miRNAs serving as potential biomarkers in duck semen, 5 DE miRNAs shared by SPEVs and sperms were and identified. Among them, both miR-31-5p and novel-273 showed consistent up-or down-regulation trends in SPEVs and sperms, and thus these 2 miRNAs might be directly used as potential biomarkers for semen quality detection. However, miR-203a, miR-145-5p, and miR-499-5p showed an opposite expression trend in SPEVs and in sperms. In addition, the target genes of miR-203a and miR-145-5p are mainly enriched in Wnt, MAPK, and mTOR signaling pathways related to sperm function. Based on these results, it could be concluded that these miRNAs could serve as adjunct biomarkers for sperm quality detection. However, in this study we only investigated the miRNAs with the extreme values of miRNA expression from SPEVs and sperms, therefore, the cut-off value for high and low sperm motility remains unknown, which needs to be further production evaluation and theoretical research.

The KEGG pathway enrichment analysis showed that focal adhesion, ErbB signaling pathway, glycometabolism, and ECM-receptor interaction pathways were enriched with target genes of DE miRNAs from both SPEVs and sperms. The ECM-receptor interaction signaling pathway has been reported to promote the formation of chicken sperm stem cells (Hu et al., 2022), while glycometabolism pathway can affect boar sperm motility (Wang et al., 2020). These 2 pathways may also be crucial for regulating sperm motility in ducks. Furthermore, PI3K/AKT signaling pathway, MAPK signaling pathway, mTOR signaling pathway, TGF-beta signaling pathway, Wnt signaling pathway, and calcium signaling pathway associated with sperm function were jointly enriched with target genes of DE miRNAs from both SPEVs and sperms. It has been reported that the PI3K/AKT and mTOR signaling pathways are involved in regulating various male reproduction processes, including modulating the hypothalamus-pituitary gonad axis during spermatogenesis, promoting proliferation and differentiation of spermatogonia and somatic cells, and regulating sperm autophagy and testicular endocrine function (Ma et al., 2016; Huang et al., 2019). MAPK and TGF-β signaling pathways regulate the dynamics of tight junctions and adherens junctions, proliferation, and meiosis of germ cells, and proliferation of Sertoli cells (Ni et al., 2019). The Wnt signaling pathway can regulate the differentiation of chicken embryonic stem cells into spermatogonial stem cells (He et al., 2018), promote spermatogonia proliferation (Cheng et al., 2018), and facilitate mature sperm development in the epididymis (Li et al., 2021). In this study, most miRNA-target gene interactions were found to be enriched in MAPK signaling, Wnt signaling, and Calcium signaling pathways. These pathways might play a crucial role in regulating duck semen quality in SPEVs and sperms. Finally, the target genes FZD9 and ANAPC11 are involved in multiple biological processes, suggesting their important regulatory effects on sperm functions, which needs to be further investigated.

CONCLUSIONS

In summary, many DE miRNAs potentially related to semen quality were identified in the SPEVs and sperms of ducks. The miR-34c-5p, miR-34b-3p, miR-449a, miR-31-5p, and miR-128-1-5p regulated target genes involved in multiple biological processes, and thus they are essential for semen quality. Furthermore, miR-31-5p and novel-273 could be directly serve as potential biomarkers for semen quality detection. Our study demonstrates that both SPEV and sperm miRNAs play important regulatory roles in the reproductive system of poultry, providing a theoretical basis for further elucidating the functions of SPEVs.

DISCLOSURES

The authors declare no conflicts of interest.

ACKNOWLEDGMENTS

The authors thank all the co-authors for their contributions to this article. The authors also thank the Hubei Wuhan Academy of Agricultural Sciences for its strong support for this experiment.

This work was supported by Hubei Provincial Key Research and Development Program (Project No. 2020BBA034), National Key R&D Program of China (Grant No. 2021YFD1300100) and Hubei Provincial Technical Innovation Project (2022BEC037).

Author Contributions: Xuliang Luo, Liming Huang, Yan Guo, Yu Yang, Ping Gong, Shengqiang Ye, Lixia Wang: designed the experiments. Xuliang Luo, Liming Huang and Yan Guo: Conceptualization, Methodology, Investigation, Writing-original draft, Writing-review and editing. Yu Yang, Ping Gong, Shengqiang Ye, Lixia Wang: Animal feeding, sample collection. All authors read, contributed, and approved the final manuscript. Yanping Feng: Conceptualization, Supervision, Project administration, Writing—review and Editing, Funding acquisition. All authors have read and agreed to the published version of the manuscript.

Footnotes

Supplementary material associated with this article can be found in the online version at doi:10.1016/j.psj.2024.103928.

Appendix. Supplementary materials

mmc1.xlsx (82.2KB, xlsx)
mmc2.xlsx (18.8KB, xlsx)

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