Proteomic analysis reveals the difference between the sperm of young and old Sus Scrofa

doi:10.21203/rs.3.rs-4798285/v1

Download PDF

Article

Proteomic analysis reveals the difference between the sperm of young and old Sus Scrofa

https://doi.org/10.21203/rs.3.rs-4798285/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: The Wannan black pig is a superior local breed in Anhui province, renowned for its exceptional meat quality and remarkable adaptability to various environmental conditions. Semen, being a crucial indicator of male sexual maturity and fertility, significantly influences the performance of breeding boars. The molecular basis for comprehending the fecundity of boars in practical production lies in understanding the disparities in sperm proteins among boars of varying ages. In this investigation, sperm from three one-year-old and three seven-year-old Wannan black pigs were individually chosen.

Results: Employing a Tandem Mass Tag (TMT)-based quantitative proteomics approach, a total of 4050 proteins were identified, out of which 130 proteins exhibited significant differences between the two groups. GO enrichment analysis revealed that these proteins primarily participated in energy metabolism, spermatogenesis, fertilization, and reproduction. KEGG enrichment analysis demonstrated that the differential proteins predominantly resided within the ribosome pathway. A protein-protein interaction (PPI) network was constructed to identify core proteins such as RPS5. Ultimately, parallel reaction monitoring (PRM) was conducted on the selected differential proteins to validate result accuracy.

Conclusions: The findings of this study establish a foundation for elucidating the molecular mechanism underlying variations in sperm protein levels among Wannan Black Pig with different age.

Biological sciences/Biochemistry/Proteomics

Biological sciences/Developmental biology/Germline development

Wannan black pig

proteomics

sperm

parallel reaction monitoring(PRM)

KAT2A

Age is a crucial determinant of fertility 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 impairment [1–7].

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. Sperm quality and motility decline, and the proportion of abnormal sperm increases in older animals [8–13]. With advancing research, the mechanism underlying the influence of age on sperm has become better understood. The reduction in antioxidant capacity in the semen of aged animals affects sperm viability [10, 12], and dysfunction of the epididymis and gonads affects sperm motility [14], all of which lead to abnormal sperm morphogenesis in aged animals.

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 group [15]. Owing to the quiescent state of transcription and translation in sperm, proteins are considered the main players in sperm functionality [16]. Sperm proteins and their interactions are closely related to sperm capacitation from in vitro to oocyte fertilization. Proteomic analysis of sperms has been widely used to identify potential residual markers [17].

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 years [18]. 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 population [19]. 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 farming[20]. 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 sperm. 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.

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, sperm from different 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).

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(Figure 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).

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 genes were downregulated in young boars. The expression patterns and abundance of these proteins are shown (Fig. 3A).

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 12 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).

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).

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).

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 (A0A0B8RTA5), Acetyl-coenzyme A synthetase (A0A481B8E5), and succinyl-CoA:3-ketoacid coenzyme A transferase 1 (Q29551), were also included. 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.

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 stress [21, 22]. Recently, proteomic analysis of semen from older adult human males has been reported, revealing a positive correlation between age and the DNA fragmentation index [23]. 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) anchor [24, 25]. Therefore, FOLR2 expression may serve as a predictive marker of male fertility. Immunostaining results revealed that sperm in semen exhibited a more robust immune response than sperm in testicular tissue [26]. The presence of FOLR in sperm 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 sperm[27]. The role of energy metabolism in sperm flagellar motility has been extensively studied. Mammalian sperms require sustained motility from ejaculation to fertilization, necessitating the generation of sufficient energy to meet their locomotion demands [28]. 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 totipotency [29]. It also affects ATP-dependent sperm motility in sperm [30]. 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 production [30]. 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 sperm, inducing physiological and behavioral changes in females. CPB, another member of the carboxypeptidase family, possesses this functionality. Because of they have the same domain, we hypothesized that CPO may also exhibit similar functions [31]. Mitochondria-associated cysteine-rich protein (SMCP) is a rapidly evolving protein that mainly localizes to the outer membrane of sperm mitochondria and enhances sperm motility [32]. 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 reduced [33]. The findings of knockout experiments by Karim et al. further support this perspective [34]. 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 folding [35]. Among these proteins, 40S ribosomal protein S5 (RPS5) is a key RNA-binding component that contributes significantly to translation [36]. 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 group [37]. 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 analysis [38]. 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 species [39, 40]. 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 sperm, thereby facilitating sperm capacitation [41]. 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 KAT2A as the predominant central regulator, based on centrality analysis [42]. KAT2A, an enzyme responsible for regulating various acylation modifications, potentially modulates its function by influencing post-translational modifications of both histone and non-histone proteins [43, 44]. 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 spermatogenesis[45]. 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 spermatogenesis [46]. Lower expression levels of KAT2A were also observed in Group Y, suggesting that Group Y may cause to decreased acetylation and normal spermatogenesis. Protein modifications are closely associated with spermatogenesis. Protein acetylation serves as a protective mechanism against spontaneous acrosome reactions in sperm, thereby enhancing fertilization rates [47]. Additionally, it plays a crucial role in regulating sperm capacitation and influences semen quality post-thawing [48, 49]. Histone crotonylation is a type of acylation that links metabolism and gene expression[50]. Histone crotonylation plays a pivotal role in promoter activation and male germ cell differentiation and exerts a significant influence during the late stages of spermatogenesis [51]. 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 sperms found in the epididymis and reduced motility of spermatids [52].

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.

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. Sperm 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 sperm 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 16000 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 0.05M 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, 3μm) 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 repository [53,54] 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 70000 (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 35000 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.

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.

Conflicts of interest:

The authors declare no conflicts of interest.

Funding:

This study received financial support from the Joint Research Project on Breeding and Innovative Utilization of New Local Pig Breeds (Lines) in Anhui Province(340000211260001000431), National Key R&D Program of China༈2021YFD1301200༉, Major special science and technology project of Anhui Province༈202103a06020013༉, The Anhui Provincial Modern Industrial Technology System of Swine.

Author Contribution

Conceptualization, X.Z.; methodology, F.X.; software, S.F.; formal analysis, S.F.; investigation, Q.W. and H.Y.; resources, Y.T.; data curation, H.Z.; writing—original draft preparation, S.F.; writing—review and editing, X.Z. and Z.Y.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.

Data Availability

The mass spectrometry proteomics data have been deposited to the iProX database (https://www.iprox.cn/page/PSV023.html;?url=1724907390614V6t8) with password T7Wd.

Auger J, Kunstmann JM, Czyglik F, Jouannet P. Decline in semen quality among fertile men in Paris during the past 20 years. N Engl J Med 1995;332:281-5.
Ahmad E, Ahmad N, Naseer Z, et al. Relationship of age to body weight, scrotal circumference, testicular ultrasonograms, and semen quality in Sahiwal bulls. Trop Anim Health Prod 2011;43:159-64. http://doi.org/10.1007/s11250-010-9668-1
Halvaei I, Litzky J, Esfandiari N. Advanced paternal age: effects on sperm parameters, assisted reproduction outcomes and offspring health. Reproductive Biology and Endocrinology : RB&E 2020;18:110. http://doi.org/10.1186/s12958-020-00668-y
Johnson SL, Dunleavy J, Gemmell NJ, Nakagawa S. Consistent age-dependent declines in human semen quality: a systematic review and meta-analysis. Ageing Res Rev 2015;19:22-33. http://doi.org/10.1016/j.arr.2014.10.007
Das M, Al-Hathal N, San-Gabriel M, et al. High prevalence of isolated sperm DNA damage in infertile men with advanced paternal age. J Assist Reprod Genet 2013;30:843-8. http://doi.org/10.1007/s10815-013-0015-0
Winkle T, Rosenbusch B, Gagsteiger F, Paiss T, Zoller N. The correlation between male age, sperm quality and sperm DNA fragmentation in 320 men attending a fertility center. J Assist Reprod Genet 2009;26:41-6. http://doi.org/10.1007/s10815-008-9277-3
Levitas E, Lunenfeld E, Weisz N, Friger M, Potashnik G. Relationship between age and semen parameters in men with normal sperm concentration: analysis of 6022 semen samples. Andrologia 2007;39:45-50.
Rijsselaere T, Maes D, Hoflack G, de Kruif A, Van Soom A. Effect of body weight, age and breeding history on canine sperm quality parameters measured by the Hamilton-Thorne analyser. Reprod Domest Anim 2007;42:143-8.
Long JA, Bongalhardo DC, Pelaéz J, et al. Rooster semen cryopreservation: effect of pedigree line and male age on postthaw sperm function. Poult Sci 2010;89:966-73. http://doi.org/10.3382/ps.2009-00227
Carreira JT, Trevizan JT, Carvalho IR, et al. Does sperm quality and DNA integrity differ in cryopreserved semen samples from young, adult, and aged Nellore bulls? Basic Clin Androl 2017;27:12. http://doi.org/10.1186/s12610-017-0056-9
Goericke-Pesch S, Failing K. Retrospective analysis of canine semen evaluations with special emphasis on the use of the hypoosmotic swelling (HOS) test and acrosomal evaluation using Spermac(®). Reprod Domest Anim 2013;48:213-7. http://doi.org/10.1111/j.1439-0531.2012.02134.x
Kelso KA, Redpath A, Noble RC, Speake BK. Lipid and antioxidant changes in spermatozoa and seminal plasma throughout the reproductive period of bulls. J Reprod Fertil 1997;109:1-6.
Hallap T, Jaakma U, Rodriguez-Martinez H. Changes in semen quality in Estonian Holstein AI bulls at 3, 5 and 7 years of age. Reprod Domest Anim 2006;41:214-8.
Dowsett KF, Knott LM. The influence of age and breed on stallion semen. Theriogenology 1996;46:397-412.
Huang YH, Lo LL, Liu SH, Yang TS. Age-related changes in semen quality characteristics and expectations of reproductive longevity in Duroc boars. Anim Sci J 2010;81:432-7. http://doi.org/10.1111/j.1740-0929.2010.00753.x
Li C, Ren C, Chen Y, et al. Changes on proteomic and metabolomic profiling of cryopreserved sperm effected by melatonin. J Proteomics 2023;273:104791. http://doi.org/10.1016/j.jprot.2022.104791
De Lazari FL, Sontag ER, Schneider A, et al. Seminal plasma proteins and their relationship with sperm motility and morphology in boars. Andrologia 2019;51:e13222. http://doi.org/10.1111/and.13222
Wu X, Zhou R, Zhang W, et al. Genome-wide scan for runs of homozygosity identifies candidate genes in Wannan Black pigs. Anim Biosci 2021;34:1895-902. http://doi.org/10.5713/ab.20.0679
Zhang W, Zhou M, Liu L, et al. Population Structure and Selection Signatures Underlying Domestication Inferred from Genome-Wide Copy Number Variations in Chinese Indigenous Pigs. Genes (Basel) 2022;13. http://doi.org/10.3390/genes13112026
Xu C, Yang X, Sui H, et al. Effects of different ages on frozen semen quality and in vitro fertilization efficiency in Wannan black pigs. Front Vet Sci 2024;11:1395718. http://doi.org/10.3389/fvets.2024.1395718
Vega‐Trejo R, Fox RJ, Iglesias‐Carrasco M, et al. The effects of male age, sperm age and mating history on ejaculate senescence. Functional Ecology 2019;33:1267-79. http://doi.org/10.1111/1365-2435.13305
Aitken RJ, Curry BJ. Redox regulation of human sperm function: from the physiological control of sperm capacitation to the etiology of infertility and DNA damage in the germ line. Antioxid Redox Signal 2011;14:367-81. http://doi.org/10.1089/ars.2010.3186
Guo Y, Li J, Hao F, et al. A new perspective on semen quality of aged male: The characteristics of metabolomics and proteomics. Front Endocrinol (Lausanne) 2022;13:1058250. http://doi.org/10.3389/fendo.2022.1058250
Antony AC. Folate receptors. Annu Rev Nutr 1996;16:501-21.
Sabharanjak S, Mayor S. Folate receptor endocytosis and trafficking. Adv Drug Deliv Rev 2004;56:1099-109.
Malm J, Birn H, Frohm B, et al. A minor fraction of a high-affinity folate binding protein from the epididymis is associated with membranous vesicles and spermatozoa in human semen. Int J Androl 2005;28:267-74.
Holm J, Hansen SI. Characterization of soluble folate receptors (folate binding proteins) in humans. Biological roles and clinical potentials in infection and malignancy. Biochim Biophys Acta Proteins Proteom 2020;1868:140466. http://doi.org/10.1016/j.bbapap.2020.140466
Takei GL, Miyashiro D, Mukai C, Okuno M. Glycolysis plays an important role in energy transfer from the base to the distal end of the flagellum in mouse sperm. J Exp Biol 2014;217:1876-86. http://doi.org/10.1242/jeb.090985
Li W, Long Q, Wu H, et al. Nuclear localization of mitochondrial TCA cycle enzymes modulates pluripotency via histone acetylation. Nat Commun 2022;13:7414. http://doi.org/10.1038/s41467-022-35199-0
Tang M, Liu B-J, Wang S-Q, et al. The role of mitochondrial aconitate (ACO2) in human sperm motility. Syst Biol Reprod Med 2014;60:251-6. http://doi.org/10.3109/19396368.2014.915360
Chen G, Gao X, Zhang Y, et al. The carboxypeptidase B and carbonic anhydrase genes play a reproductive regulatory role during multiple matings in Ophraella communa. Front Mol Biosci 2023;10:1095645. http://doi.org/10.3389/fmolb.2023.1095645
Hawthorne SK, Goodarzi G, Bagarova J, et al. Comparative genomics of the sperm mitochondria-associated cysteine-rich protein gene. Genomics 2006;87:382-91.
Nayernia K, Adham IM, Burkhardt-Göttges E, et al. Asthenozoospermia in mice with targeted deletion of the sperm mitochondrion-associated cysteine-rich protein (Smcp) gene. Mol Cell Biol 2002;22:3046-52.
Nayernia K, Drabent B, Meinhardt A, et al. Triple knockouts reveal gene interactions affecting fertility of male mice. Mol Reprod Dev 2005;70:406-16.
Petibon C, Malik Ghulam M, Catala M, Abou Elela S. Regulation of ribosomal protein genes: An ordered anarchy. Wiley Interdiscip Rev RNA 2021;12:e1632. http://doi.org/10.1002/wrna.1632
Qiu L, Chao W, Zhong S, Ren A-J. Eukaryotic Ribosomal Protein S5 of the 40S Subunit: Structure and Function. Int J Mol Sci 2023;24. http://doi.org/10.3390/ijms24043386
Bansal SK, Gupta N, Sankhwar SN, Rajender S. Differential Genes Expression between Fertile and Infertile Spermatozoa Revealed by Transcriptome Analysis. PLoS One 2015;10:e0127007. http://doi.org/10.1371/journal.pone.0127007
Lukkani LK, Naorem LD, Muthaiyan M, Venkatesan A. Identification of potential key genes related to idiopathic male infertility using RNA-sequencing data: an in-silico approach. Hum Fertil (Camb) 2023;26:1149-63. http://doi.org/10.1080/14647273.2022.2144771
Sanz L, Calvete JJ, Jonáková V, Töpfer-Petersen E. Boar spermadhesins AQN-1 and AWN are sperm-associated acrosin inhibitor acceptor proteins. FEBS Lett 1992;300:63-6.
Töpfer-Petersen E, Romero A, Varela PF, et al. Spermadhesins: a new protein family. Facts, hypotheses and perspectives. Andrologia 1998;30:217-24.
Kroh PD, Braun BC, Liu F, Müller P, Müller K. Boar spermadhesin AWN: novel insights in its binding behavior and localization on sperm. Biol Reprod 2022;106:775-91. http://doi.org/10.1093/biolre/ioab244
Sisakhtnezhad S, Heshmati P. Comparative analysis of single-cell RNA sequencing data from mouse spermatogonial and mesenchymal stem cells to identify differentially expressed genes and transcriptional regulators of germline cells. J Cell Physiol 2018;233:5231-42. http://doi.org/10.1002/jcp.26303
Xu Y, Wan W. Acetylation in the regulation of autophagy. Autophagy 2023;19:379-87. http://doi.org/10.1080/15548627.2022.2062112
Wang Y, Guo YR, Liu K, et al. KAT2A coupled with the α-KGDH complex acts as a histone H3 succinyltransferase. Nature 2017;552:273-7. http://doi.org/10.1038/nature25003
Liang J, Zhu H, Li C, et al. Neonatal exposure to benzo[a]pyrene decreases the levels of serum testosterone and histone H3K14 acetylation of the StAR promoter in the testes of SD rats. Toxicology 2012;302:285-91. http://doi.org/10.1016/j.tox.2012.08.010
Li X, Shen K, Yuan D, et al. Sodium arsenite exposure enhances H3K14 acetylation and impairs male spermatogenesis in rat testes. Reprod Toxicol 2023;122:108474. http://doi.org/10.1016/j.reprotox.2023.108474
Bowker Z, Goldstein S, Breitbart H. Protein acetylation protects sperm from spontaneous acrosome reaction. Theriogenology 2022;191:231-8. http://doi.org/10.1016/j.theriogenology.2022.08.005
Ritagliati C, Luque GM, Stival C, et al. Lysine acetylation modulates mouse sperm capacitation. Sci Rep 2018;8:13334. http://doi.org/10.1038/s41598-018-31557-5
Ali MA, Qin Z, Dou S, et al. Cryopreservation Induces Acetylation of Metabolism-Related Proteins in Boar Sperm. Int J Mol Sci 2023;24. http://doi.org/10.3390/ijms241310983
Sabari BR, Zhang D, Allis CD, Zhao Y. Metabolic regulation of gene expression through histone acylations. Nat Rev Mol Cell Biol 2017;18. http://doi.org/10.1038/nrm.2016.140
Tan M, Luo H, Lee S, et al. Identification of 67 histone marks and histone lysine crotonylation as a new type of histone modification. Cell 2011;146:1016-28. http://doi.org/10.1016/j.cell.2011.08.008
Liu S, Yu H, Liu Y, et al. Chromodomain Protein CDYL Acts as a Crotonyl-CoA Hydratase to Regulate Histone Crotonylation and Spermatogenesis. Mol Cell 2017;67. http://doi.org/10.1016/j.molcel.2017.07.011
Ma J, Chen T, Wu S, et al. iProX: an integrated proteome resource. Nucleic Acids Res 2019;47:D1211-D7. http://doi.org/10.1093/nar/gky869
Chen T, Ma J, Liu Y, et al. iProX in 2021: connecting proteomics data sharing with big data. Nucleic Acids Res 2022;50:D1522-D7. http://doi.org/10.1093/nar/gkab1081

No competing interests reported.

SupplementaryMaterial.docx

Download PDF

Editorial decision: Revision requested
23 Oct, 2024
Reviews received at journal
23 Sep, 2024
Reviews received at journal
19 Sep, 2024
Reviewers agreed at journal
06 Sep, 2024
Reviewers agreed at journal
04 Sep, 2024
Reviewers invited by journal
04 Sep, 2024
Editor assigned by journal
04 Sep, 2024
Editor invited by journal
02 Sep, 2024
Submission checks completed at journal
02 Sep, 2024
First submitted to journal
24 Jul, 2024

You are reading this latest preprint version

Proteomic analysis reveals the difference between the sperm of young and old Sus Scrofa

Status:

Version 1

Abstract

Figures

INTRODUCTION

RESULTS

DISSCUSSION

MATARIALS AND METHODS

Animals and semen collection

Sample preparation

Protein digestion

TMT Labeling of peptides

High pH Reverse Phase Fractionation (HPRP)

LC-MS Analysis (TMT10plex)

Database Searching and Analysis

Bioinformatic analysis

Parallel reaction monitoring (PRM) analysis

CONCLUSIONS

Declarations

Conflicts of interest:

Funding:

Author Contribution

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1