Introduction

Age is a crucial determinant of fertility in male animals. As age advances, various aspects, such as semen quality, embryo viability, sperm DNA integrity, conception rate, adverse pregnancy outcomes, and offspring health are subject to varying degrees of impairment1,2,3,4,5,6,7.

In contrast to human studies, animal breeding relies on the semen of male animals for artificial insemination, whereas studies on animal husbandry mainly focus on semen. With advancing research, the mechanism underlying the influence of age on spermatozoa has become better understood. Sperm quality and motility decline, and the proportion of abnormal spermatozoa increases in older animals8,9,10,11,12,13. The sperm morphology and activity of aged animals are influenced by various factors, including diminished antioxidant capacity, dysfunction in the epididymis and gonads, which can result in abnormal sperm motility and morphology as well as reduced fertility10,12,14.

In livestock production, the use of high-value semen is crucial for improving the genetic progress of species and the quality of offspring and is necessary to keep the semen-providing individuals within a certain age group15. Owing to the quiescent state of transcription and translation in spermatozoa, proteins are considered the main players in sperm functionality16. Sperm proteins and their interactions are closely related to sperm capacitation from in vitro to oocyte fertilization. Proteomic analysis of spermatozoa has been widely used to identify potential breeding markers17. Early studies on porcine semen proteomics were carried out based on 2-DE technology. Kwon et al. detected semen proteins before and after capacitation and found significant differences in proteins related to energy metabolism and active oxygen species, such as SUCLA2 and PRDX5, before and after capacitation, and used these as biomarkers to predict the quality of boar semen18. Due to their technical limitations, they detected only ten difference expressed proteins among 224 proteins. With the development of mass spectrometry, modern proteomics research has moved from 2D era to high throughput era19. An iTRAQ based proteomic analysis of different parts of pig spermatozoa showed that a total of 1723 proteins were identified, of which 32 proteins were different in different parts of semen20. Zhang et al. performed proteomic analysis of extracellular vesicles with different sperm motility and identified a total of 2576 proteins, of which 51 proteins were differentially expressed. Combined WGCNA analysis of these proteins and metabolites revealed significant correlations between sperm proteins and metabolites21. With the development of proteomic analysis techniques, more and more proteins in semen have been discovered, but there are still few studies on the proteome of semen of aged boars.

The Wannan black pig (WBP), a disease-resistant local variety, is predominantly found in the southern mountainous region of the Anhui Province, with a cultivation history spanning over 100 years22. It thrives in dark and humid environments where reproductive performance and adaptability play crucial roles. This breed has high fertility, superior meat quality, maternal stability, and excellent tolerance to roughage. The WBP has gained immense popularity among the residents of the Yangtze River Delta region and serves as the prime ingredient for renowned Huizhou cuisine delicacies such as “Huizhou ham” and “braised meat.” By 1982, there were 1702 purebred females within the WBP population23. The semen quality of WBP plays a crucial role in the development of the local pig industry and enhancement of pig breeds, directly affecting both the quantity and quality of offspring, as well as the economic benefits derived from pig farming24. The quality of boar semen exhibits significant variation, with a generally limited utilization time.

The objective of this study was to use proteomic methods to determine protein expression levels in the semen of boars of different ages, as previous research has primarily focused on the motility and morphological indicators of spermatozoa. A tandem mass tag (TMT) was employed for the quantification and identification of differentially expressed proteins in WBPs across various age groups with the aim of enhancing our understanding of the impact of age on semen at the molecular level.

Results

Overall, 4050 proteins belonging to 29,634 spectra were identified using LC-MS/MS. The pie chart depicts the coverage of the detected protein sequences, with proteins primarily concentrated between 0 and 10%, encompassing approximately 1940 proteins in this range, representing a total coverage of 47.9% (Fig. 1A). The distribution map of peptide lengths illustrates the length distribution of all identified peptides, revealing that most peptides fell within the range of 5–15 amino acids, with approximately 17,000 peptides falling into this category (Fig. 1B). As shown in Fig. 1C, spermatozoa from difference samples were well-separated in PCA score plot, indicating that there are significant different. Protein-unique peptides indicated the number of distinct peptides for each identified protein. Notably, there was a relatively high number (approximately 2700) of proteins containing 1–5 unique peptides (Fig. 1D).

Fig. 1
figure 1

Distribution of quality control results of mass spectrometry. (A) Map of protein coverage distribution illustrates the percentage of the detected peptide in the total length of the protein. (B) The peptide length profile shows the distribution of all identified peptide lengths. The abscissa represents the peptide length, and the ordinate represents the number of peptides detected. (C) Score plot of principal component analysis (PCA) of Old spermatozoa (O) and Young spermatozoa (Y). (D) Bar chart shows the number of unique peptides per protein.

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Functional enrichment analysis of these proteins was conducted using the GO and KEGG databases. GO analysis revealed that the sperm proteins were primarily involved in the biological processes of organelle organization, translation, small-molecule metabolic organization, and amide and peptide biosynthesis. In terms of cellular components, there was a significant enrichment in intracellular anatomical structures, organelles, intracellular organelles, and membrane-bound organelles. Molecular functions were predominantly enriched in catalytic activity, hydrolase activity, and small-molecule binding (Fig. 2A). The KEGG pathway enrichment analysis revealed that sperm proteins were primarily associated with lysosomal, ribosomal, and oxidative phosphorylation, the tricarboxylic acid cycle, and other pathways involved in energy metabolism (Fig. 2B).

Fig. 2
figure 2

Functional enrichment of all identified proteins. Figure 2 shows gene ontology (GO) (A) and Kyoto encyclopedia of genes and genomes (KEGG) (B) enrichment results for all the proteins. Number of unique peptides corresponding to the identified proteins. The size of the dots represent the number of proteins. The colors in A represent different GO terms: blue, biological processes; red, cellular components; green, molecular functions. The color in B indicates the value of the rich factor.

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We conducted differential expression analysis on these 4050 proteins, considering a fold change > 1.2 or < 0.83 and a p-value < 0.05. Of these, 130 exhibited significantly different expression levels. Compared to aged boars, 33 genes were upregulated and 97 proteins were downregulated in young boars. The expression patterns and abundance of these proteins are shown (Fig. 3A).

Fig. 3
figure 3

Overall profile of differential proteins. The expression levels and clustering results of all differentially expressed proteins (DEPs) between the two groups of samples are shown in (A). Red represents upregulation and blue represents downregulation. (B,C) Show the GO and KEGG enrichment results for all differential proteins; (B) is illustrated in (Fig. 2A), with red representing upregulation and blue representing downregulation in (C).

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We subsequently conducted functional enrichment analysis of the differentially expressed proteins. Unlike the total protein set, these differentially expressed proteins were enriched in reproductive processes, such as reproduction, fertilization, sexual reproduction, and spermatogenesis. This indicates a direct correlation between protein expression and reproductive function (Fig. 3B). The differentially expressed proteins (DEPs) were classified and annotated according to the KEGG database. The DEPs were primarily involved in 20 pathways, including the carbon pool formed by folate and steroid hormone biosynthesis. (Fig. 3C). All DEPs are listed in (Table S1).

To further explore the functions of the DEPs, functional enrichment analysis was performed for the upregulated and downregulated DEPs. GO biological process enrichment showed that ketone bodies, spermatid development and differentiation, and sulfide oxidation processes were uniquely enriched in the upregulated DEPs, whereas fertilization, reproduction, reproductive processes, sexual reproduction, and single fertilization were enriched in the downregulated DEPs (Fig. 4A, B). Cellular components of GO enrichment in downregulated DEPs were mainly enriched in acrosomal membrane and extracellular region, whereas those in upregulated DEPs were mainly in the intracellular region (Fig. 4C, D). Molecular function of GO enrichment also revealed the difference between up- and downregulated DEPs; the upregulated proteins were more related to energy metabolism enzyme activity, and the downregulated proteins were related to histone and DNA topoisomerase activity (Fig. 4E, F).

Fig. 4
figure 4

GO enrichment results of up- and downregulated DEPs. In GO enrichment results, the biological processes of up- and downregulated DEPs are shown in (A,B), cellular components are shows in (C,D), and molecular function is represented in in (E,F).

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The up- and downregulated proteins were subsequently subjected to KEGG and subcellular annotations, respectively. The results showed that 15% of the upregulated proteins were localized in the mitochondria (Fig. 5A) and were enriched in the tricarboxylic acid cycle (Fig. 5B). The proportion of downregulated proteins in the mitochondria was only 4.05%; however, 5.41% of the downregulated proteins were located in the Golgi apparatus (Fig. 5C) and enriched in the ribosomal pathway (Fig. 5D).

Fig. 5
figure 5

Subcellular localization and KEGG enrichment results of up- and downregulated DEPs. The results of subcellular localization and KEGG enrichment of the upregulated DEPs are shown in (A,B) and those of downregulated DEPs are shown in (C,D).

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A PPI network was constructed using STRING and Cytoscape to visually depict the interplay among these distinct proteins. These proteins were categorized into distinct interaction networks. Ribosomes (RPS5, RPLP0, and RPL7) and RNA polymerases (POLR2B and POLR2G) were centrally positioned and exhibited robust interconnections with other proteins (Fig. 6).

Fig. 6
figure 6

Protein-protein interaction network. Interaction network diagram of the DEPs between the two groups; nodes represent the differential proteins, and the connecting lines represent the direct interactions between the two linker proteins. Red and green nodes represent upregulated and downregulated protein expression, respectively. The size of the node represents the connectivity of the node, and the larger the node, the greater the interactions.

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The accuracy of the differential proteins between young and old boars was verified through PRM quantitative analysis, wherein nine proteins, including seminal plasma sperm motility inhibitor (SPMI), were selected for verification. Other proteins related to energy metabolism, such as aconitate hydratase (ACO2), Acetyl-coenzyme A synthetase (ACSS), and succinyl-CoA:3-ketoacid coenzyme A transferase 1 (OXCT1), were also included. The other five proteins Sulfide: quinone oxidoreductase (SQOR), Galectin-3-binding protein (LGALS3BP), MYC binding protein (MYCBP), Complement component 1 Q subcomponent-binding protein (C1QBP), and CUB domain-containing protein (AWN) were also chosen for PRM validation. The relative expression difference of the target protein’s corresponding peptides in the samples was calculated using a t-test based on their quantitative values (p < 0.05). The normalized results of relative protein expression are presented in Fig. 7 and Table S2 and are consistent with the proteomic analysis.

Fig. 7
figure 7

PRM protein expression quantities of the candidate proteins. The expression levels of the candidate proteins were verified using parallel reaction monitoring (PRM), and the differential expression levels in PRM were compared with the proteomic results. The abscis represents peptides belonging to different candidate proteins and the ordinate represents log2FC. Blue represents the level of difference in the PRM results, and orange represents the level of difference in the proteome results.

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Disscussion

Seminal fluid predominantly epitomizes the reproductive process of boars, with variations in their reproductive capabilities across different age cohorts, potentially attributed to individual sperm quality, semen vitality, health status, and oxidative stress25,26. Recently, proteomic analysis of semen from older adult human males has been reported, revealing a positive correlation between age and the DNA fragmentation index27. Furthermore, the DEPs were significantly enriched in pathways related to energy metabolism. To discern the disparities in protein levels among boars of varying ages, we conducted LC-MS-based proteomic analysis on juvenile (1-year-old) and mature (7-year-old) boars. A total of 4050 proteins were identified, including 130 proteins that were differentially expressed between the two groups.

FOLR2, an isoform of the folate receptor FR, is expressed in the placenta, hematopoietic cells, and macrophages and is equipped with a cellular glycosylphosphatidylinositol (GPI) anchor28,29. Therefore, FOLR2 expression may serve as a predictive marker of male fertility. Immunostaining results revealed that spermatozoa in semen exhibited a more robust immune response than spermatozoa in testicular tissue30. The presence of FOLR in spermatozoa enables the formation of folate complexes, thereby safeguarding the folate content within the sperm microenvironment and facilitating normal DNA replication post-fertilization through the transfer of folate carriers into the interior of the spermatozoa31. The role of energy metabolism in sperm flagellar motility has been extensively studied. Mammalian spermatozoa require sustained motility from ejaculation to fertilization, necessitating the generation of sufficient energy to meet their locomotion demands32. The tricarboxylic acid cycle serves as a crucial pathway for ATP production in sperm mitochondria. Aconitate hydratase (ACO2), an enzyme that regulates the tricarboxylic acid cycle, translocates from the cytoplasm to the nucleus during somatic cell reprogramming, thereby influencing cellular totipotency33. It also affects ATP-dependent sperm motility in spermatozoa34. The expression level of ACO2 was significantly lower in males with asthenospermia than in those with normal fertility. The addition of isocitrate results in a significant increase in sperm motility, which could be attributed to enhanced ATP production34. In our study, the expression level of ACO2 was higher in young boars than in old boars, suggesting that young boars exhibited greater semen motility, which is consistent with previously reported findings. Outside of ACO2, a larger proportion of the upregulated proteins was localized in the mitochondria (15%), indicating that young boars have a stronger energy source in the semen, which can enhance sperm motility.

Carboxypeptidase O (CPO) was identified as the most significant DEP, exhibiting over 11-fold higher expression in the Y group than in the O group. This protein belongs to the M14 family of metal carboxypeptidases and displays a preference for cleaving C-terminal acidic amino acids. In insects, seminal fluid proteins are synthesized in the male reproductive tract and transferred to females during mating, along with spermatozoa, inducing physiological and behavioral changes in females. CPB, another member of the carboxypeptidase family, possesses this functionality. Because they have the same domain, we hypothesized that CPO may also exhibit similar functions35. Mitochondria-associated cysteine-rich protein (SMCP) is a rapidly evolving protein that mainly localizes to the outer membrane of sperm mitochondria and enhances sperm motility36. In vitro fertilization experiments in SMCP-knockout mice showed that the fertilization success rate was three-fold lower than that in wild-type mice, indicating that sperm motility and oocyte penetration were reduced37. The findings of knockout experiments by Karim et al. further support this perspective38. Our results demonstrated a significantly higher expression of SMCP in the Y group, indicating enhanced motility in the semen of young boars.

Ribosomal proteins are the most highly expressed genes in virtually all cells, and their products play a pivotal role in ribosome biogenesis, thereby influencing protein folding39. Among these proteins, 40 S ribosomal protein S5 (RPS5) is a key RNA-binding component that contributes significantly to translation40. Our findings highlight the central involvement of RPS5 in PPIs and its pronounced expression in Group Y. In a study conducted by Sandeep et al., transcriptome sequencing was performed on 60 males with known fertility (n = 20), idiopathic infertility, and asthenospermia, revealing high expression of RPS5 in the normal group and low expression in the asthenospermia group41. Similarly, other members of the ribosomal protein family, such as RPLP0 and RPL7, interacted with RPS5, underscoring the crucial role played by ribosomal family members in sperm function, a finding consistent with that of Laxman’s transcriptome data analysis42. The downregulated proteins in our study were partially localized in the Golgi apparatus and were enriched in the ribosomal pathway, indicating that protein translation, processing, and folding are more important in the semen of old boars.

The AWN protein, a member of the sperm adhesin family, was initially discovered in boar seminal plasma and is found in the seminal plasma of most mammalian species43,44. Electron microscopy indicated that AWN predominantly localizes on the surface of sperm cells and exhibits a range of ligand-binding capabilities to interact with receptor molecules in the anterior region of ejaculated spermatozoa, thereby facilitating sperm capacitation45. This is consistent with the subcellular localization results of our proteomic analysis. Sajjad et al. conducted single-cell transcriptome sequencing of mouse spermatogonia and mesenchymal stem cells and identified histone acetyltransferase 2 A (KAT2A) as the predominant central regulator, based on centrality analysis46. KAT2A, an enzyme responsible for regulating various acylation modifications, potentially modulates its function by influencing post-translational modifications of both histone and non-histone proteins47,48. Histone modifications exert epigenetic effects on spermatogenesis by modulating gene transcription, either by activating or repressing transcription. Acetylation of histone H3K14 is associated with testosterone production and spermatogenesis49. Sodium arsenite exposure enhances the level of H3K14ac in rat testes by promoting the expression of KAT2A, which subsequently leads to the repression of steroidogenic-related genes, resulting in reduced testosterone production and impaired spermatogenesis50. Lower expression levels of KAT2A were also observed in Group Y, suggesting that Group Y may cause decreased acetylation and normal spermatogenesis. Protein modifications are closely associated with spermatogenesis. Protein acetylation serves as a protective mechanism against spontaneous acrosome reactions in spermatozoa, thereby enhancing fertilization rates51. Additionally, it plays a crucial role in regulating spermatozoa capacitation and influences semen quality post-thawing52,53. Histone crotonylation is a type of acylation that links metabolism and gene expression54. Histone crotonylation plays a pivotal role in promoter activation and male germ cell differentiation and exerts a significant influence during the late stages of spermatogenesis55. In addition to exerting an impact on gene expression in the testis, CDYL-mediated knockdown of histone crotonylation results in impaired fertility in mice, as evidenced by a decrease in the number of spermatozoa found in the epididymis and reduced motility of spermatids56.

In conclusion, the reproductive performance of breeding boars in practical production is influenced by age, and the variation in reproductive ability among boars of different ages may be associated with the protein composition of their semen. Comparative proteomic analysis of semen proteins from 1- and 7-year-old breeding boars identified 130 differentially expressed genes, including 33 upregulated and 97 downregulated genes. Functional enrichment analysis revealed that these differentially expressed genes were associated with energy metabolism, reproduction, and fertilization. Furthermore, protein post-translational modification may affect the quality of boar semen at different ages. This study provides insights into the age-related differences in boar semen at the protein level. In future studies, we will further explore the epigenetic effects on semen through post-translational modifications.

Materials and methods

Animals and semen collection

The animal study protocol was approved by the Animal Ethics Committee of the Anhui Agricultural University (approval no. AHAU20210826). The experimental animals were selected from the Wannan Black Pig National Breeder Farm of Guangde Sanxi Ecological Farming Co., Ltd. Twenty Wannan black pigs, each aged 1 (n = 10) and 7 (n = 10) years, with similar body weights and no physiological diseases were chosen.

Fresh ejaculations were continuously collected through an artificial vagina stimulated by a sow. Discard the anterior and caudal parts of the ejaculated semen and take the middle part into the semen collection cup. Semen was collected for each boar twice a week. Spermatozoa sample which is free from any foreign matter or odor, exhibiting a milk-white appearance and with a survival rate exceeding 90% is selected for subsequent testing. The spermatozoa were purified by centrifugation after mixing with semen in a percoll solution. Animal experiments in this study were performed in full accordance with the ARRIVE guideline reporting guidelines. All methods were carried out in accordance with relevant guidelines and regulations.

Sample preparation

Samples were suspended on ice in 200 µL lysis buffer (4% SDS, 100 mM DTT, 150 mM Tris-HCl pH 8.0). The cells and tissues were disrupted by agitation using a homogenizer and boiled for 5 min. The samples were then ultrasonicated and boiled for 5 min. Undissolved cellular debris were removed using centrifugation at 16,000 rpm for 15 min. The supernatant was collected and quantified using a BCA protein assay kit (Bio-Rad, USA).

Protein digestion

Digestion of protein (200 µg for each sample) was performed according to the FASP procedure described by Wisniewski, Zougman et al. Briefly, the detergent, DTT and other low-molecular-weight components were removed using 200 µL UA buffer (8 M Urea, 150 mM Tris-HCl pH 8.0) via repeated ultrafiltration (Microcon units, 30 kD) facilitated using centrifugation. Then, 100 µL 0.05 M iodoacetamide in UA buffer was added to block reduced cysteine residues and the samples were incubated for 20 min in the dark. The filter was washed with 100 µL UA buffer three times and then 100 µL 25 mM NH4HCO3 twice. Finally, the protein suspension was digested with 4 µg trypsin (Promega) in 40 µL 25 mM NH4HCO3 overnight at 37 °C, and the resulting peptides were collected as a filtrate. The peptide concentration was determined at OD280 using a Nanodrop device.

TMT labeling of peptides

The peptides were labeled with TMT reagent according to the manufacturer’s instructions (Thermo Fisher Scientific). Each aliquot (100 µg of peptide equivalent) was reacted with one tube of TMT reagent, respectively. After the sample was dissolved in 100 µL of 0.05 M TEAB solution, pH 8.5, the TMT reagent was dissolved in 41 µL of anhydrous acetonitrile. The mixture was then incubated at room temperature for 1 h. Then, 8 µL of 5% hydroxylamine was added to the sample and incubated for 15 min to quench the reaction. The multiplex-labeled samples were pooled and lyophilized.

High pH reverse phase fractionation (HPRP)

TMT-labeled peptides mixture was fractionated using a Waters XBridge BEH130 column (C18, 3.5 μm, 2.1 × 150 mm) on a Agilent 1290 HPLC operating at 0.3 mL/min. Buffer A consisted of 10 mM ammonium formate and buffer B consisted of 10 mM ammonium formate with 90% acetonitrile; both buffers were adjusted to pH 10 using ammonium hydroxide. A total of 30 fractions were collected from each peptide mixture and concatenated to 15 (pooling equal-interval RPLC fractions). The fractions were dried for nano-LC-MS/MS analysis.

LC-MS analysis (TMT10plex)

The LC-MS analysis was performed using a Q Exactive mass spectrometer coupled to an Easy nLC (Thermo Fisher Scientific). Peptide from each fraction was loaded onto a the C18-reversed phase column (12 cm long, 75 μm ID, 3 μm) in buffer A (2% acetonitrile and 0.1% Formic acid) and separated with a linear gradient of buffer B (90% acetonitrile and 0.1% Formic acid) at a flow rate of 300 nL/min over 90 min. The linear gradient was set as follows: 0–2 min, linear gradient from 2 to 5% buffer B; 2–62 min, linear gradient from 5 to 20% buffer B; 62–80 min, linear gradient from 20 to 35% buffer B; 80–83 min, linear gradient from 35 to 90% buffer B; and 83–90 min, buffer B maintained at 90%. MS data were acquired using a data-dependent top15 method dynamically choosing the most abundant precursor ions from the survey scan (300–1800 m/z) for HCD fragmentation. The target value was determined based on predictive Automatic Gain Control (pAGC). The AGC target values of 1e6 and a maximum injection time of 50 ms were used for full MS, and the target AGC value of 1e5 and a maximum injection time of 100 ms were used for MS2. The dynamic exclusion duration was 30 s. Survey scans were acquired at a resolution of 70,000 at m/z 200, and the resolution of the HCD spectra was set to 35,000 at m/z 200. The normalized collision energy was 30. The instrument was run in the peptide recognition mode.

Mass spectrometry proteomics data were deposited in the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository57,58 with the dataset identifier PXD050879.

Database searching and analysis

The resulting LC-MS/MS raw files were imported into the Proteome Discoverer 2.4 software (version 1.6.0.16) for data interpretation and protein identification against the Uniprot-Sus scrofa (Pig) [9823]-122175-220104.fasta) database. The initial search was performed using a precursor mass window of 10 ppm. The search followed the enzymatic cleavage rule of trypsin/phosphate and allowed two maximal missed cleavage sites and a mass tolerance of 20 ppm for fragment ions. The modification set was as follows: fixed modification, carbamidomethyl (C), TMT10plex(K), and TMT10plex(N-term); and variable modification, oxidation (M) and acetyl (Protein N-term). The minimum 6 amino acids for peptide, ≥ 1 unique peptides were required per protein. For peptide and protein identification, the false discovery rate (FDR) was set to 1%. The TMT reporter ion intensity was used for quantification.

Bioinformatic analysis

Analyses of the bioinformatics data were performed using Perseus software, Microsoft Excel, and R statistical computing software. Differentially expressed proteins were screened with a cutoff of a fold-change ratio of > 1.20 or < 0.83 and nominal p-value of < 0.05. Expression data were grouped together via hierarchical clustering according to the protein level. To annotate the sequences, information was extracted from the UniProtKB/Swiss-Prot, Kyoto encyclopedia of genes and genomes (KEGG), and gene ontology (GO). GO and KEGG enrichment analyses were performed using Fisher’s exact test, and FDR correction for multiple testing was performed. GO terms were grouped into three categories: biological processes (BP), molecular functions (MF), and cellular components (CC). The enriched GO and KEGG pathways were nominally statistically significant at nominal p < 0.05. Protein–protein interaction (PPI) networks were constructed using the STRING database and Cytoscape software.

Parallel reaction monitoring (PRM) analysis

To verify the protein expression levels obtained by TMT analysis, the expression levels of selected proteins were quantified using LC-PRM/MS [1]. Briefly, peptides were prepared according to the TMT protocol. Tryptic peptides were loaded onto C18 stage tips for desalting before reverse-phase chromatography using an Easy nLC-1200 system (Thermo Scientific). One-hour liquid chromatography gradients with acetonitrile ranging from 5 to 35% over 45 min were used. The PRM analysis was performed using a Q Exactive Plus mass spectrometer (Thermo Scientific). Methods optimized for the collision energy, charge state, and retention times of the most significantly regulated peptides were generated experimentally using unique peptides with high intensity and confidence for each target protein. The mass spectrometer was operated in positive ion mode with the following parameters: the full MS1 scan was acquired with a resolution of 70,000 (at 200 m/z), AGC target values of 3.0 × 106, and a 250 ms maximum ion injection time. Full MS scans were followed by 20 PRM scans at 35,000 resolution (at m/z 200) with AGC 3.0 × 106 and a maximum injection time of 200 ms. The targeted peptides were isolated using a 2Th window and fragmented at a normalized collision energy of 27 in a higher-energy dissociation (HCD) collision cell. Raw data were analyzed using Skyline (MacCoss Lab, University of Washington) [2] to get the signal intensities of the individual peptide sequences.

For the PRM MS data, the average base peak intensity of each sample was extracted from the full scan acquisition using RawMeat (version 2.1, VAST Scientific, www.vastscientific.com). The normalization factor for sample N was calculated as fN = the average base peak intensity of sample N/median of the average base peak intensities of all samples. The area under curve (AUC) for each transition from sample N was multiplied by this factor. After normalization, the AUC of each transition were summed to obtain the AUCs at the peptide level. The relative protein abundance was defined as the intensity of a specific peptide.

Conclusions

The reproductive performance of breeding boars in practical production is influenced by age, and the variation in reproductive ability among boars at different ages may be associated with the protein composition in their semen. Comparative proteomic analysis of semen proteins from 1-year-old and 7-year-old breeding boars identified a total of 130 differentially expressed genes, including 33 up-regulated genes and 97 down-regulated genes. Functional enrichment analysis revealed that these differentially expressed genes are related to energy metabolism, reproduction, and fertilization. Furthermore, it was observed that protein post-translational modification might impact the quality of boar semen at different ages. This study provides insights into the age-related differences in boar semen at the protein level. In future investigations, we will further explore the epigenetic effects on semen through post-translational modifications.