Boar semen proteomics and sperm preservation
I. Parrilla, C. Perez-Patino, J. Li, I. Barranco, L. Padilla, HeribertoRodriguez-Martinez, E. A. Martinez and J. Roca
The self-archived postprint version of this journal article is available at Linköping University Institutional Repository (DiVA):
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159541
N.B.: When citing this work, cite the original publication.
Parrilla, I., Perez-Patino, C., Li, J., Barranco, I., Padilla, L., Rodriguez-Martinez, H., Martinez, E. A., Roca, J., (2019), Boar semen proteomics and sperm preservation, Theriogenology, 137, 23-29. https://doi.org/10.1016/j.theriogenology.2019.05.033
Original publication available at:
https://doi.org/10.1016/j.theriogenology.2019.05.033
Copyright: Elsevier (12 months)
Boar semen proteomics and sperm preservation
Parrilla I1*, Perez-Patiño C1, Li J1, Barranco I1, Padilla L1, Rodriguez-Martinez
H2, Martinez EA1, Roca J1
1 Faculty of Veterinary Medicine, International Excellence Campus for Higher
Education and Research “Campus Mare Nostrum”, University of Murcia, Murcia, Spain; Institute for Biomedical Research of Murcia (IMIB-Arrixaca), Murcia, Spain.
2 Department of Clinical and Experimental Medicine (IKE), Linköping University,
Sweden.
Abstract
Recently numerous proteomic approaches have been undertaken to identify sperm and seminal plasma (SP) proteins that can be used as potential biomarkers for sperm function including fertilization ability. This review aims firstly to briefly introduce the proteomic technologies and workflows that can be successfully applied for sperm and SP proteomic analysis. Secondly, we summarize the current knowledge about boar SP and sperm proteome focusing mainly in its relevance regarding sperm preservation procedures (liquid storage or cryopreservation) outcomes both at the level of sperm functionality and at the level of fertility rates.
1. Introduction
Effective fertilization requires a spermatozoon to be capable of accomplishing a series of sequential and essential processes that eventually result in a viable embryo: sperm capacitation, hyperactivation, penetration through the cumulus mass, adhesion to and penetration through the zona pellucida (ZP), sperm-oocyte membrane fusion and successful formation of interacting pronuclei (reviewed by [1]). These processes are intimately related to changes in the expression and/or configuration of proteins that surround the sperm membrane and interact with membrane structural proteins during epididymal maturation and ejaculation [2,3]. Since these changes are associated with fertility-related endpoints, proteomic analysis of seminal plasma (SP) and sperm has emerged as a very important tool for the identification of potential fertility biomarkers (reviewed by [4-6]).
Proteomics is the large-scale study of proteins, including quantitative expression, posttranslational modifications (PTMs) and protein interactions [7]. PTMs are modifications in the structure and functionality of a protein that occur after its synthesis and are considered key events for sperm function and potential fertility [8]. Studies in several animal species have demonstrated that SP and/or sperm proteins influence the response of ejaculates to sperm biotechnologies, from the simplest technologies, such as conventional artificial insemination (AI) with sperm subjected to long-term and liquid storage, to more sophisticated technologies such as freezing and/or sex-sorting, thus helping to identify presumable markers for sperm resilience (reviewed by [9]). Whether this influence is related to PTMs remains unclear. This restricted knowledge highlights a very interesting research area, the field of semen proteomics, that could help in the design of new diagnostic strategies related to male reproductive potential.
The present review summarizes the available research on the protein composition of sperm and SP in pigs, with a focus on the application of high-throughput proteomics. In particular, the described results, including those of our own research, are discussed in relation to the potential use of specific proteins as tools for improving boar sperm preservation.
2. Proteomic analysis of boar sperm and SP: Technologies and workflow
Seminal proteins are the main contributors to normal sperm functionality and fertilization ability [10]. Consequently, both sperm and SP proteomics are of paramount relevance for achieving a deeper understanding of the molecular mechanisms underlying reproductive functions [11] and, in the long term, for controlling and optimizing reproductive efficiency in swine [12].
The first stage in a proteomic study is the separation of extracted proteins, which is key to evaluating complex mixtures of proteins such as those present in SP. This separation can be performed at either the protein or peptide level. At the protein level, the process traditionally involves the use of sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) to separate proteins based on either molecular weight (one-dimensional electrophoresis; 1DE) or on both isoelectric charge and molecular weight (two-dimensional electrophoresis; 2DE). 2DE is more efficient for quantitative and qualitative protein studies on complex samples than 1DE and is especially useful for visualizing sperm protein PTMs (reviewed by [10 and 13]). However, 2DE has some relevant technical limitations, such as its inability to resolve proteins with very low or very high molecular weights (<10 kDa or >150 kDa, respectively) or proteins with high hydrophobicity or insolubility, thus reducing its usefulness for membrane protein studies [14]. A better alternative is to fragment proteins at the peptide level using liquid
chromatography (LC) after enzymatic protein digestion. LC separates peptides according to specific characteristics (such as hydrophobicity, size, charge or the presence of specific molecules) and substantially increases the number of proteins identified compared to gel-based methods. Consequently, LC is currently the most useful method for separating samples with complex protein compositions, such as SP, in which abundant proteins usually mask other less-abundant proteins; these less-abundant proteins are often the most important in biological processes [14].
Protein identification has been notably improved by the development of mass spectrometry (MS) technology for peptide sequencing. MS has proven to be effective, sensitive and accurate for identifying hydrophobic and low-abundance proteins in samples with complex protein compositions [15, 16]. At present, various workflows constructed from different combinations of separation and identification procedures can be used to process semen samples during proteomic studies (see Figure 1). One possible workflow involves 1DE or 2DE, excision and digestion of the proteins from the gel, and protein identification through matrix-assisted laser/desorption ionization-MS (MALDI-MS) or tandem MS (MS/(MALDI-MS). Another workflow option involves peptide generation via a combination of LC and tandem MS (LC-MS/MS). Since both approaches provide complimentary results, their combination has been proposed to be ideal for identification of differentially expressed proteins [17]. In addition to these label-free shotgun procedures, proteins can also be quantified by the incorporation of stable isotopes through chemical or metabolic labeling reactions, e.g., iTRAQ, a technique we have recently used to analyze the proteome of boar ejaculates [18].
Bioinformatics is the last essential step of proteomic analysis [13]. Some of the most commonly used databases for proteomics are the Dataset for Annotation, Visualization and Integrated Discovery (DAVID), the Protein ANalysis Through
Evolutionary Relationships database (PANTHER), the UniProt Knowledgebase (UniProt KB), Ensembl, and the National Center for Biotechnology Information-nonredundant database (NCBI-nr). In addition to providing the amino acid sequence and related gene for each protein for vertebrate species, these databases also provide useful information for comparative proteomic analysis, protein annotation, computation of multiple alignments, prediction of regulatory functions and assessment of biological or pathological processes in which proteins are involved. These databases are continuously updated, but information about domestic animals such as Sus scrofa is still quite limited [18, 19]. This lack of information highlights the need for continuous updating of databases to better disclose and manage proteomics-derived data.
Special attention should be paid to comparative proteomics, i.e., the identification and quantification of differentially expressed proteins through comparisons of protein profiles from different sources [20]. With regards to sperm, different populations or functional states of spermatozoa (e.g., mature vs immature, capacitated vs noncapacitated, or fresh vs cryopreserved) can be compared to search for differences in protein composition among individuals and samples and to identify suitable biomarkers of interest [5, 21]. The application of comparative proteomics has led to impressive studies identifying proteins involved in boar sperm capacitation as valuable predictive biomarkers of boar fertility [22-24]
For more information about the application of these methodologies with a special emphasis on reproductive biology, see the review by Wright et al. [7].
3. Sperm and SP proteomics and its importance for sperm preservation 3.1. Proteomics and liquid preservation of sperm
The proteomic profiles and functionality of ejaculated spermatozoa are tightly linked to the protein composition of the surrounding SP [10]. Thus, large-scale proteomic studies are needed to elucidate the biological pathways of the SP proteins involved in reproductive processes, a fundamental step towards the identification of effective biomarkers that could contribute to enhanced reproductive performance in swine [3, 25]. Under this rationale, our group recently performed studies on the boar SP proteome [18, 26-28]. First, SP obtained from the total ejaculate, or from selected ejaculate fractions from different boars, was processed by a combination of size exclusion chromatography (SEC), 1D SDS-PAGE, and LC-electrospray ionization (ESI)-MS/MS [26]. The resulting datasets were subjected to functional bioinformatics analysis, and the identified SP proteins were quantified by a Sequential Window Acquisition of all THeoretical Fragment Ion Spectra (SWATH; [29]) approach. This study included the first major characterization of boar SP so far, identifying more than 250 novel proteins. A total of 536 SP proteins were identified, 374 of which belonged to Sus scrofa. Notably, only 20 of the identified proteins were classified by bioinformatics analysis (Gene Ontology; GO) as directly related to reproductive functions (Figure 2). The most logical explanation for this low number of specific reproduction-related proteins is that a number of important identified boar SP proteins have not yet been associated with specific GO terms. However, even if many of the other identified proteins were related to immune responses; catalytic, binding and antioxidant activity; glycosylation; and ion- and calcium-binding properties; their concerted action could ultimately contribute to reproductive functions, including preservation of sperm functionality. Interestingly, the results also showed that the identified SP proteins were present in all ejaculate fractions but that some of them were differentially expressed in specific ejaculate fractions, implying that the variability in protein composition among
ejaculate fractions is more quantitative than qualitative. Sixteen proteins identified in Sus scrofa were differentially expressed among ejaculate fractions, many of which were directly implicated in sperm reproductive performance (see Table 1). Of these 16 proteins, eight were overexpressed and eight were under-expressed in the sperm-rich fraction (SRF; included the first 10 mL of the SRF as well with the rest of the SRF) compared with the post-SRF. The notion that SP protein composition, including types and relative amounts, influences boar sperm physiology is not new; several studies have demonstrated relationships between some SP proteins and the ability of sperm to withstand liquid storage and cooling and even between some SP proteins and in vivo fertility (reviewed by [30]). Our first study on the pig SP proteome partially confirmed these previous results and was followed by trials intended to increase understanding of the function of SP proteins in reproduction with a main goal of identifying reliable fertility and/or sperm quality biomarkers. Consequently, a detailed dataset including the proteins identified in SP and their putative reproductive functions has been provided for researchers interested in linking SP sperm proteins with fertilization success [27].
A subsequent study [28] compared the proteomes of SP from boars with different fertility rates to detect differences at the qualitative and/or quantitative levels using a novel proteomic methodology: combination of two prefractionation approaches [SCE and solid-phase extraction (SPE)] with 1D SDS-PAGE and LC-ESI-MS/MS. The total number of ultimately identified proteins was 872, of which 390 belonged to Sus scrofa, a much higher number than that in our first study [26]; these findings were clear evidence of the enhanced effectiveness of the new methodology. Furthermore, when the SP proteomes of boars differing in farrowing rate and litter size after AI were compared (10,526 sows inseminated), the results revealed differentially expressed proteins for both fertility parameters analyzed. Specifically, the differential expression of 11
proteins was related to differences in farrowing rate, and that of 4 other proteins was related to differences in litter size (see supplementary file 1). Surprisingly, only one of these 15 proteins, hyaluronidase sperm adhesion molecule 1 (SPAM1), was found to be related to reproduction in the GO analysis; overexpression of this protein in SP of boars was associated with high farrowing rates after AI. Since SPAM1 is a dispersive agent of the cumulus cell mass facilitating ZP-sperm binding [31], increased levels of this molecule could be related to increased fertility, as demonstrated in our study. The rest of the differentially expressed proteins were either unrelated or indirectly related to male or female reproductive processes (see supplementary files 1 and 2).
To the best of our knowledge, this study is still the most complete description of the boar SP proteome, and it also identified potential biomarker proteins of in vivo fertility. The results are very promising but provide only the foundation for more extensive studies on the potential effects of SP proteins on ejaculated sperm characteristics related to storage ability and/or fertility post-AI. Such studies would help to define the convenience of using only the SRF or the entire ejaculate for either AI dose preparation or the successful application of different sperm technologies [32]. Given the increasing application of semiautomatic systems to collect the total ejaculate at pig AI centers [33], for reasons related to practicality, efficacy and hygiene, the use of selected fractions (such as the SRF) versus the entire ejaculate is currently under consideration. Semiautomatic collection systems do not consider the relevance of protein differences among specific fractions, mainly the SRF, which is classically collected by the gloved hand method [34].
The available information indicates that sperm functional shaping is profoundly influenced by the composition of the SP, fraction-wise [19]. Therefore, the sperm proteome must be more thoroughly investigated to determine which proteins are
present, added and maintained in the cells at each stage of ejaculation and/or ex situ handling; such information is a prerequisite for determining the relevance of each protein to fertility and survivability during different procedures [5, 35]. Both our laboratories and those of others have recently performed studies on the complete proteome of boar sperm under physiological and capacitation conditions, providing valuable information to identify potentially usable biomarkers of sperm performance [16, 18, 19, 22-24]. Sequentially, Kwon et al. [16, 22-24] have shown that fertility-related proteins identified in capacitated spermatozoa are able to predict litter size more accurately than when these proteins are studied in non-capacitated spermatozoa (88% and 73% average accuracy for capacitated and non-capacitated spermatozoa, respectively; reviewed by [36]). These findings highlight the importance of knowing how sperm plasticity allows cells to adapt to different surroundings and how these sperm modifications define reproductive success. The ultimate goal of our investigation is to relate sperm protein composition to in vivo fertility is. However, we must link the specific functions of a large number of sperm proteins to specific reproductive outputs before the goal of providing the swine industry with reliable, identifiable markers can be achieved.
To advance towards this challenging aim, we performed experiments in which spermatozoa from the epididymis and from different ejaculate fractions (the first 10 mL of the SRF, the rest of the SRF, and the post-SRF) were subjected to iTRAQ-based 2D-LC-MS/MS to identify and quantify sperm proteins [18]. A total of 1,723 proteins were identified, 974 of which were encoded in Sus scrofa taxonomy and 960 of them were also quantified. While qualitative differences were not observed among ejaculate fractions, 43 proteins were differentially expressed with a fold change (FC) ≥ 1.5 between the sperm samples analyzed; 32 of them belonged to Sus scrofa. Three of these
proteins were overexpressed in cauda epididymal sperm vs the SRF, and 20 proteins were overexpressed in the post-SRF vs the rest of the sperm samples analyzed. It is well known that spermatozoa fortuitously present in the post-SRF are the most exposed to potential binding proteins, since they are bathed in a large amount of SP (post-SRF SP) which in addition is the SP fraction richest in proteins [30]. This would explain why spermatozoa from the post-SRF fraction contained a high number of differentially expressed proteins. Most of the overexpressed proteins in spermatozoa from the post-SRF, particularly spermadhesins (PSP-I, PSP-II, AWN, AQN1 and AQN3), have a negative effect on boar sperm performance either in vivo or in vitro [22, 28, 37, 38], which could contribute to the low resistance to cooling/cryopreservation shown by spermatozoa from this ejaculate fraction [33, 39, 40].
Our study [18] showed, for the first time, the high plasticity of the proteome of ejaculated (from fractions) and non-ejaculated (from the epididymis) boar spermatozoa. Whether this is a result of PTMs or of interactions between spermatozoa and the surrounding seminal fluids (cauda epididymal fluid or SP) has yet to be determined. More importantly, we further need to demonstrate whether differences in protein composition are the main reasons for the well-documented variations in responses of distinct sperm ejaculate fractions to certain sperm biotechnologies or even for the different fertility outcomes observed. Such relations need to be validated before the information and evidence provided by these large-scale sperm proteomic studies can be tested in the field and used for commercial breeding [36]. Meeting these research needs is essential to identify reliable fertility and sperm performance biomarkers and to develop additives to enhance sperm functionality after handling (liquid storage or cryopreservation).
In this context, Feugang et al. [19] analyzed ejaculated sperm from 8 boars using shotgun- and gel-based methodologies followed by functional bioinformatics and, most relevantly, subsequent validation of nine randomly selected proteins by 2D-gel identification, immunodetection (western blotting (WB) and immunofluorescence) and mRNA expression analysis. Over 2,000 proteins were identified, and special attention was paid by the authors to those proteins considered highly abundant (n=116). Bioinformatics revealed that these proteins appeared to be mainly associated with sperm structure and sperm-egg interactions, showing significant enrichment in different pathways including fertilization and reproduction. This study offered a comprehensive analysis of the boar sperm proteome and generated a valuable dataset that will be useful in improving our understanding of sperm biology, basic to enhancing fertility and developing adequate strategies for more effective semen handling in the swine industry.
3.2. Proteomics and cryopreservation
Proteins are potential key factors affecting sperm cryosurvival [41, 42]; thus, they have been studied as possible biomarkers for freezability [43, 44]. The levels of individual proteins such as acrosin, fibronectin, heat shock protein HSP90AA1 and voltage-dependent anion channel 2 are positively correlated with sperm cryotolerance, while those of N-acetyl-β-hexosaminidase and triosephosphate isomerase are negatively correlated, adding to the list of possible freezability markers (reviewed by [43]). However, complementary studies evaluating the influence of cryopreservation on the entire sperm proteome are also necessary to optimize the freezing process. Chen et al. [45], by using iTRAQ-coupled 2D LC-MS/MS, identified a panel of 41 proteins in boar SRF spermatozoa with specific expression changes during the cryopreservation process. Proteins regulate pivotal aspects of sperm functionality, such as oxidative stress, plasma
membrane integrity, sperm motility, energy metabolism, capacitation and sperm-oocyte fusion [45]. Notably, the great variability in sperm freezability among boar ejaculates, and even among ejaculate fractions/portions, has been attributed mainly to interactions between the sperm and the SP from different portions of the ejaculate [39, 40], but this variability has recently also been related to the protein composition of the sperm itself [34]. Sperm retrieved from the SRF withstand cryopreservation better than those exposed to the SP of the total ejaculate [34, 40]. Nevertheless, as noted above, pig AI enterprises now tend to collect the entire ejaculate to more easily (and more cost-effectively) prepare conventional doses for AI [33]. Such a practice does not replicate the in vivo situation (natural mating), in which spermatozoa are sequentially exposed to specific amounts of proteins contained in different ejaculate fractions.
To clarify whether differences in the abundance of specific proteins could explain why spermatozoa retrieved from different ejaculate fractions have different post-thaw functionality, we carried out a comparative study analyzing the proteomes of frozen-thawed (FT)-spermatozoa derived from semen sources with clearly different sperm freezability [46]. This study revealed a panel of up to 257 sperm proteins belonging to Sus scrofa that were differentially expressed among the FT-spermatozoa derived from three different ejaculate portions/sources: the first 10 mL of the SRF, the remaining SRF and the post-SRF. Many of these differentially expressed proteins are involved in sperm functions, such as capacitation and ZP-binding, or in activities related to sperm performance, e.g., fatty acid metabolism, cellular oxidoreductase activity, mitochondrial respiratory chain, ATP binding and glycolytic processes. The freely available software Search Tool for the Retrieval of Interacting Gens/Proteins (STRING; [47]) was used to construct a protein-protein interaction network of the differentially expressed proteins among FT-spermatozoa retrieved from the three different fractions
(Figure 3). The constructed network segregated the 257 differentially abundant proteins into clusters with specific functions, which could explain why spermatozoa ejaculated in different sperm portions respond differently to cryopreservation [34, 39]. Although these results provide preliminary information on the role of proteins in boar sperm freezability, further protein validation studies are needed to properly identify biomarkers in semen that will help us improve and predict the freezability of an ejaculate sample.
4. Concluding remarks
The best evidence of optimal sperm function is successful fertilization leading to a viable embryo. However, a better understanding of sperm function, beyond what is provided by conventional methods, is needed to accurately predict male reproductive potential. Extending our knowledge to the molecular basis of sperm functional regulation is essential to optimize sperm handling and maintain fertility. Most molecular mechanisms related to fertilization are protein-dependent, making proteomics the most powerful research tool in reproductive biology. The present review highlighted the potential impacts of proteomics on swine reproduction, mainly focusing on male aspects. From the studies reviewed herein, we can conclude that some sperm and SP proteins can be effectively used as biomarkers of semen performance, enabling accurate prediction of male fertility and the design of new strategies for improving semen preservation. However, before these findings can be applied in the field, a validation step is mandatory to rigorously confirm that the identified proteins can be reliable biomarkers. The use of specific antibodies for WB, ELISA and immunolocalization will strengthen the usefulness of specific proteins as biomarkers. In addition, it should not be forgotten that semen is a dynamic fluid whose protein composition can be influenced by
many factors; this dynamic nature should be taken into account when transferring our laboratory discoveries into practice. Finally, we must highlight the contribution by the different proteomic studies cited hereby to public repositories, for instance PRIDE, and to its continuous updating, mainly regarding protein functional roles. The currently available proteomics data on boar sperm and SP represent a starting point from which to develop new strategies to improve sperm performance in the assisted reproductive technologies used by the swine industry.
Acknowledgments
This study was supported by MINECO (Spain), FEDER (EU, AGL2015-69738-R) and Seneca Foundation Murcia (19892/GERM-15), Spain; and the Research Council FORMAS, (Project 2017-00946), Stockholm, Sweden.
References
[1] Gadella BM and Luna C. Cell biology and functional dynamics of the mammalian
sperm surface. Theriogenology. 2014, 81:74–84.
https://doi.org/10.1016/j.theriogenology.2013.09.005
[2] Dacheux JL, Belleannée C, Guyonet B, Labas V, Teixeira-Gomes AP, Ecroyd H, Druart X, Gatti JL, Dacheux F. The contribution of proteomics to understanding epididymal maturation of mammalian spermatozoa. Syst Biol Reprod Mec. 2012, 58:197-210. https://doi.org/10.3109/19396368.2012.663233
[3] Druart X and de Graaf S. Seminal plasma proteomes and sperm fertility. Anim Reprod Sci. 2018, 194:33-40. https://doi.org/10.1016/j.anireprosci.2018.04.061.
[4] Duncan MW and Thompson HS. Proteomic of semen and its constituents. Proteomics Clin Appl. 2007, 1:861-75. https://doi.org/10.1002/prca.200700228
[5] Rahman MdS, Lee JS, Kwon WS, Pang MG (2013). Sperm proteomics: road to male fertility and contraception. Int J Endocrinol. 2013, ID: 360986, 11 pages.
http://dx.doi.org/10.1155/2013/360986
[6] Li C, Wang D, Zhou X. Sperm proteome and reproductive technologies in
mammals. Anim Reprod Sci. 2016, 173:1-7.
https://doi.org/10.1016/j.anireprosci.2016.08.008
[7] Wright PC, Noriel J, Ow SY, Fazeli A. A review of current proteomic technologies with a survey on their widespread use in reproductive biology investigations. Theriogenology. 2012, 77:738-65. https://doi.org/10.1016/j.theriogenology.2011.11.012
[8] Brohi RD and Huo LJ. Posttranslational modifications in spermatozoa and effects on male fertility and sperm viability. Omics. 2017, 21:245-56.
[9] Caballero I, Parrilla I, Almiñana C, del Olmo D, Roca J, Martinez EA, Vázquez JM. Seminal plasma proteins as modulators of the sperm function and their application in sperm biotechnologies. Reprod Dom Anim. 2012, 47; 12-21.
https://doi.org/10.1111/j.1439-0531.2012.02028.x
[10] Baker MA, Nixon B, Naumovski N, Aitken RJ. Proteomic insights into the maturation and capacitation of mammalian spermatozoa. Syst Biol Reprod Mec. 2012, 58: 211-17. https://doi.org/10.3109/19396368.2011.639844
[11] Brewis IA. Proteomics in reproductive research. The potential importance of proteomics to research in reproduction. Hum Reprod. 1999, 14:2927-29. PMID:10601071
[12] De Almeida AM, Bendixen E. Pig proteomics: A review of a species in the crossroad between biomedical and food sciences. J Proteomics. 2012, 75:4296-314.
https://doi.org/10.1016/j.jprot.2012.04.010
[13] Selvam MKP and Agarwal A. Update on the proteomics of male infertility: A
systematic review. Arab J Urology. 2018, 103-112.
https://doi.org/10.1016/j.aju.2017.11.016
[14] Brewis IA and Gadella BM. Sperm surface proteomics: from protein list to biological function. Mol Hum Reprod. 2010, 16:68-79.
https://doi.org/10.1093/molehr/gap077
[15] Mirza SP and Olivier M. Methods and approaches for the comprehensive characterization and quantification of cellular proteomes using mass spectrometry. Physiol Genomics. 2008, 33; 3-11. https://doi.org/10.1152/physiolgenomics.00292.2007
[16] Kwon WS, Rahman MS, Lee JS, Kim J, Yoon SJ, Park YJ, You YA, Hwang S, Pan MG. A comprehensive proteomic approach to identifying capacitation related proteins in boar spermatozoa. BMC Genomics. 2014, 14; 897. http://www.biomedcentral.com/1471-2164/15/897
[17] Oliva R, de Mateo S, Estanyol JM. Sperm cell proteomics. Proteomics. 2009, 9:1004-17. https://doi.org/10.1002/pmic.200800588
[18] Perez-Patiño C, Parrilla I, Li J, Barranco I, Martinez EA, Rodriguez-Martinez H, Roca J. The proteome of pig spermatozoa is remodeled during ejaculation. Mol Cell
Proteomics. 2018, doi: 10.1074/mcp.RA118.000840.
https://doi.org/10.1074/mcp.RA118.000840.
[19] Feugang JM, Liao SF, Willard ST, Ryan LP. In-depth proteomic analysis of boar spermatozoa trough shotgun and gel-based methods. BMC Genomics. 2018, 19: 62.
https://doi.org/10.1186/s12864-018-4442-2
[20] Holland A, Ohlendieck K. Comparative profiling of the sperm proteome. Proteomics. 2015, 15:632–48. https://doi.org/10.1002/pmic.201400032
[21] Pini T, Leahy T, Soleilhavoup C, Tsikis G, Labas V, Combes-Soia L, Harichaux G, Rickard JP, Druart X, de Graaf SP. Proteomic investigation of ram spermatozoa and the proteins conferred by seminal plasma. J Proteome Res. 2016, 15: 3700-11. https://pubs.acs.org/doi/10.1021/acs.jproteome.6b00530
[22] Kwon WS, Rahman MdS, Lee JS, Yoon SJ, Park YJ, Pang MG. Discovery of predictive biomarker for litter size in boar spermatozoa. Mol Cell Proteomics. 2015, 14: 1230-40. https://doi.org/10.1074/mcp.M114.045369
[23] Kwon WS, Rahman MdS, Ryu DY, Park YJ, Pang MG. Increased male fertility using fertility-related biomarkers. Sci Rep. 2015, 5: 15654.
[24] Kwon WS, Oh SA, Kim YJ, Rahman MdS, Park YJ, Pang MG. Proteomic approaches for profiling negative fertility markers in inferior boar spermatozoa. Sci Rep. 2015, 5:13821. https://doi.org/10.1038/srep13821
[25] Gonzalez-Cadavid V, Martins JAM, Moreno FB, Andrade TS, Santos ACL, Monteiro-Moreira ACO, Moreira RA, Moura AA. Seminal plasma proteins of adult boars and correlations with sperm parameters. Theriogenology. 2014, 82:697-707.
https://doi.org/10.1016/j.theriogenology.2014.05.024
[26] Perez-Patiño C, Barranco I, Parrilla I, Valero ML, Martinez EA, Rodriguez-Martinez H, Roca J. Characterization of the porcine seminal plasma proteome comparing ejaculate portions. J Proteomics. 2016, 142:15-23.
https://doi.org/10.1016/j.jprot.2016.04.026
[27] Perez-Patiño C, Barranco I, Parrilla I, Martinez EA, Rodriguez-Martinez H, Roca J. Extensive dataset of boar seminal plasma proteome displaying putative reproductive functions of identified proteins. Data Brief. 2016, 29:1370-3. http://dx.doi.org/10.1016/j.dib.2016.07.037
[28] Pérez-Patiño C, Parrilla I, Barranco I, Vergara-Barberán M, Simó-Alfonso EF, Herrero-Martínez JM, Rodriguez-Martínez H, Martínez EA, Roca J. New In-Depth Analytical Approach of the Porcine Seminal Plasma Proteome Reveals Potential Fertility Biomarkers. J Proteome Res. 2018, 17:1065-76. https://pubs.acs.org/doi/10.1021/acs.jproteome.7b00728
[29] Gillet LC, Navarro P, Tate S, Röst H, Selevsek N, Reiter L, Bonner R, Aebersold R. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics. 2012, 11:O111.016717. https://doi.org/10.1074/mcp.O111.016717
[30] Rodriguez-Martinez H, Kvist U, Saravia F, Wallgren M, Johannisson A, Sanz L, Peña FJ, Martinez EA, Roca J, Vazquez JM, Calvete JJ. The physiological roles of the boar ejaculate. In: Rodriguez-Martinez H, Vallet JL and Ziecik AJ (eds.). Control of Pig Reproduction VIII. Nottingham University Press; 2009, 1-21. ISBN 978-1-904761-39-6.
[31] Yoon S, Chang KT, Cho H, Moon J, Kim JS, Min SH, Koo DB, Lee Sr, Kim SH, Park KE, Park YI, Kim E. Characterization of pig sperm hyaluronidase and improvement of the digestibility of cumulus cell mas by recombinant pSPAM1 hyaluronidase in vivo and in vitro fertilization assay. Anim Reprod Sci. 2014, 150: 107-14. https://doi.org/10.1016/j.anireprosci.2014.09.002
[32]Roca J, Broekhuijse MLWJ, Parrilla I, Rodriguez-Martinez H, Martinez EA, Bolarin A. Boar differences in artificial insemination outcomes: can they be minimized. Reprod Dom Anim. 2015, 50 (suppl 2);48-55.
https://doi.org/10.1111/rda.12530
[33]Barrabes S, Gary BG, Bouvier BP. Collectis® automated boar collection
technology. Theriogenology. 2008, 70:1368-1373.
https://doi.org/10.1016/j.theriogenology.2008.07.011
[34] Li J, Roca J, Perez-Patiño C, Barranco I, Martinez EA, Rodriguez-Martinez H, Parrilla I. Is boar sperm freezability more intrinsically linked to spermatozoa tan to the surrounding seminal plasma?. Anim Reprod Sci. 2018, 195:30-7.
https://doi.org/10.1016/j.anireprosci.2018.05.002
[35] Sharma R, Agarwal A, Mohanty G, Hamada AJ, Gopalan B, Willard B, Yadav S, du Plessis S. Proteomic analysis of human spermatozoa proteins with oxidative stress. Reprod Biol Endocrinol. 2013, 11:48. https://doi.org/10.1186/1477-7827-11-48
capacitation-https://doi.org/10.1002/mrd.22810
[37] Novak S, Ruiz-Sanchez A, Dixon WT, Foxcroft GR, Dyck MK. Seminal plasma proteins as potential markers of relative fertility in boars. J Androl. 2010, 31:188200.
https://doi.org/10.2164/jandrol.109.007583
[38] Dyck MK, Foxcroft GR, Novak S, Ruiz-Sanchez A, Patterson J, Dixon WT. Biological markers of boar fertility. Reprod Dom Anim. 2011, 46(suppl 2): 55-58. https://doi.org/10.1111/j.1439-0531.2011.01837.x
[39] Saravia F, Wallgren M, Johannisson A, Calvete JJ, Sanz L, Peña FJ, Roca J, Rodríguez-Martínez H. Exposure to the seminal plasma of different portions of the boar ejaculate modulates the survival of spermatozoa cryopreserved in MiniFlatPacks. Theriogenology. 2009, 71:662-75. https://doi.org/10.1016/j.theriogenology.2008.09.037
[40] Alkmin DV, Perez-Patiño C, Barranco I, Parrilla I, Vazquez JM, Martinez EA, Rodriguez-Martinez H, Roca J. Boar sperm cryosurvival is better after exposure to seminal plasma from selected fractions than to those from entire ejaculate. Cryobiology. 2014, 69:203-10. https://doi.org/10.1016/j.cryobiol.2014.07.004
[41] Yeste M. Recent advances in boar sperm cryopreservation: state of the art and current perspectives. Reprod Dom Anim. 2015, 50:71-9.
https://doi.org/10.1111/rda.12569
[42] Hezavehei M, Sharafi M, Kouchesfahani HM, Henkel R, Agarwal A, Esmaeili V, Shahverdi A. Sperm cryopreservation: A review on current molecular cryobiology and advanced approaches. Reprod Biomed Online. 2018, 37:327-339
https://doi.org/10.1016/j.rbmo.2018.05.012
[43] Yeste M. Sperm cryopreservation update: Cryodamage, markers, and factors affecting the sperm freezability in pigs. Theriogenology. 2016, 85:47-64.
https://doi.org/10.1016/j.theriogenology.2015.09.047
[44] Guimarães DB, Barros TB, van Tilburg MF, Martins JAM, Moura AA, Moreno FB, Monteiro-Moreira AC, Moreira RA, Toniolli R. Sperm membrane proteins associated with the boar semen cryopreservation. Anim Reprod Sci. 2017, 183:27-38.
https://doi.org/10.1016/j.anireprosci.2017.06.005
[45] Chen X, Zhu H, Hu C, Hao H, Zhang J, Li K, Zhao X, Qin T, Zhao K, Zhu H, Wang D. Identification of differentially expressed proteins in fresh and frozen-thawed boar spermatozoa by iTRAQ-coupled 2D LC-MS/MS. Reproduction. 2014, 147:321-30.
https://doi.org/10.1530/REP-13-0313
[46] Perez-Patiño C, Li J, Barranco I, Martinez EA, Rodriguez-Martinez H, Roca J, Parrilla I. The proteome of frozen-thawed pig spermatozoa is dependent on the ejaculate fraction source. Sci Reports. 2019, 24; 9:705. https://doi.org/10.1038/s41598-018-36624-5
[47] Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, Jensen LJ, von Mering C. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acid Res. 2017, 45: 362-368. https://doi.org/10.1093/nar/gkw937
FIGURE LEGENDS
Figure 1. Schematic diagram of a typical workflow for high-throughput proteomics for complex samples such as sperm or seminal plasma. The main steps include protein extraction, sample fractionation, mass spectrometry analysis and bioinformatics analysis.
Figure 2. List of the twenty proteins identified in boar seminal plasma specifically engaged in reproductive processes and their distribution in reproductive success groups according to the UniProt KB database (www.uniprot.org) in combination with PANTHER (www.pantherdb.org).
Figure 3. Network of protein-protein interactions among thirty-seven proteins identified in the boar seminal plasma proteome to be specifically engaged in reproductive processes. The network was created using STRING version 10.5 (www.string-db.org). The weight of each line represents the confidence of the predicted interaction. Minimum required interaction score: 0.150.
Table 1. List of proteins in boar seminal plasma that are differentially expressed between the sperm-rich ejaculate fraction (SRF) and the post-SRF and their putative reproductive roles (modified from [26]).
Protein Name Gene ID (log2)FC * P value (t-test) Taxonomy Putative Reproducti on-Related Function Overexpressed proteins in the SRF
Corticosteroid-binding globulin CBG 0.462 0.008 Sus scrofa
Hexosaminidase B HEXB 0.649 0.044 Sus scrofa capacitation Sperm Pancreatic secretory granule
membrane major glycoprotein
GP2 GP2 2.136 0.036 Sus scrofa
Acrosome reaction Epididymal-specific lipocalin-5 LCN5 0.632 0.009 Sus scrofa Fertilizing ability
Arylsulfatase A precursor ARSA 1.993 0.042 Sus scrofa Sperm-zona pellucida binding Galactosidase, beta 1-like 3 GLB1L3 0.826 0.032 Sus scrofa stability and Membrane
permeability Choline transporter-like protein 2 CTL2 0.242 0.028 Sus scrofa
Golgi apparatus protein 1 GLG1 1.025 0.007 Sus scrofa Heat shock cognate 71 kDa
protein HSPA8 0.645 < 0.001 Other
Putative phospholipase B-like 2 PLBD2 0.886 0.004 Other capacitation Sperm Guanine nucleotide-binding
protein subunit alpha-11 GNA11 1.984 0.047 Other Spermatogenesis Unnamed protein product PGK1 0.652 0.006 Other
Polypeptide
N-acetylgalactosaminyltransferase 2 GALNT2 0.762 0.014 Other maturation Sperm Fibronectin FN1 0.449 0.001 Other maturation Sperm Ezrin EZR 1.873 0.018 Other capacitation Sperm Fibronectin FN1 1.399 0.004 Other maturation Sperm
Under-expressed proteins in the SRF
Alpha-enolase ENO1 -0.057 0.009 Sus scrofa motility Sperm Alkaline phosphatase ALP -0.697 0.029 Sus scrofa motility Sperm Fibronectin FN1 -0.062 0.033 Sus scrofa maturation Sperm Nucleobindin-1 NUCB1 -1.226 0.001 Sus scrofa Calcium and
binding Sulfhydryl oxidase 1 QSOX1 -0.309 0.014 Sus scrofa maturation Sperm Angiotensin-converting enzyme
isoform 2 ACE -0.935 0.019 Sus scrofa maturation Sperm Epididymal secretory protein E1 NPC2 -0.658 0.001 Sus scrofa maturation Sperm Deoxyribonuclease-2-alpha DNASE2 -0.913 0.024 Sus scrofa integrity DNA EGF-like repeat and discoidin
I-like domain-containing protein 3 EDIL3 -0.582 0.012 Other capacitation Sperm Myelin protein zero-like protein 1 MPZL1 -1.819 0.009 Other Spermatogenesis
Plastin-3 isoform 1 PLS3 -0.834 0.018 Other Spermatogenesis Ectonucleotide
pyrophosphatase/phosphodiestera
se family member ENPP2 -1.266 0.044 Other
Alkaline phosphatase ALPL -1.112 0.031 Other motility Sperm Alkaline phosphatase ALPL -0.964 0.040 Other motility Sperm Beta-galactosidase-1-like protein
2-like GLB1L2 -0.002 0.020 Other maturation Sperm Pc21g16370 Pc21g16370 -1.155 0.019 Other
Syntaxin-binding protein 2 STXBP2 -2.021 < 0.001 Other capacitation Sperm Prominin-2 PROM2 -0.565 0.037 Other capacitation Sperm (*) Fold change. SRF: first 10 mL of the SRF and the rest of the SRF.
Table 2: List of seminal plasma proteins determined to be differentially expressed between boars with different fertility endpoints (FR: farrowing rate; LS: litter size) using Lasso regression (modified from [28]).
Protein Name Name UniProt KB ID Gene n//Fertility Correlatio
Parameter UniProt KB Functions* Furin FURIN H0YNB5_HUMAN 0.44//FR 2serine-type endopeptidase activity
Aldose reductase AKR1B1 A0A140TAK7_PIG 0.29//FR --- Ubiquitin-like
modifier UBA1 K7GRY0_PIG 0.22//FR
1cellular response to DNA damage stimulus
2ATP binding; ubiquitin-activating enzyme activity
Peptidyl-prolyl
cis-trans PIN1 Q307R2_RABIT 0.18//FR
1protein folding
2peptidyl-prolyl cis-trans isomerase activity
Sperm adhesion
molecule SPAM1 Q8MI02_PIG 0.70//FR
1carbohydrate metabolic process; fusion of sperm
to egg plasma membrane involved in single fertilization
2hyalurononglucosaminidase activity
Bleomycin
hydrolase BLMH L5JS14_PTEAL 0.16//FR
1regulation of cell growth
2insulin-like growth factor binding
Sphingomyelin SMPDL3A I3LV23_PIG 0.09//FR --- Keratin type I
cytoskeletal KRT17 H2QCZ8_PANTR 1.21//FR 2structural molecule activity Keratin type I
cytoskeletal KRT10 F7BV15_ORNAN -0.33//FR
1keratinocyte differentiation; peptide cross-linking;
protein heterotetramerization
2protein heterodimerization activity; structural
constituent of epidermis Tetratricopeptide
repeat TTC23 E9QKU9_MOUSE -0.95//FR ---
Angiotensin AGT U5L198_DELLE -0.43//FR 1renin-angiotensin regulation of systemic arterial blood pressure by Desmocollin-1 DSC1 Q9HB00_HUMAN 0.30//LS
1homophilic cell adhesion via plasma membrane
adhesion molecules
2calcium ion binding
Catalase CAT H2Q3E5_PANTR 0.05//LS
1aerobic respiration; cholesterol metabolic process;
hemoglobin metabolic process; hydrogen peroxide catabolic process; negative regulation of apoptotic process; negative regulation of NF-kappaB
transcription factor activity; positive regulation of NF-kappaB transcription factor activity; positive regulation of phosphatidylinositol 3-kinase signaling; protein homotetramerization; response to hydrogen peroxide; triglyceride metabolic process; UV protection
2aminoacylase activity; catalase activity; enzyme
binding; heme binding; metal ion binding; NADP binding; protein homodimerization activity;
(*) Functions obtained from the UniProt KB database. 1 Gene Ontology: biological processes; 2 Gene Ontology: molecular function Nexin-1 PN-1 Q8WNW8_PIG -0.02//LS Thrombospondin-1 THBS1 F1SS26_PIG -0.03x10 -3//LS
1activation of MAPK activity; cell adhesion; cell
cycle arrest; cell migration; chronic inflammatory response; engulfment of apoptotic cell; immune response; negative regulation of angiogenesis; negative regulation of antigen processing and presentation of peptide or polysaccharide antigen via MHC class II; negative regulation of blood vessel endothelial cell proliferation involved in sprouting angiogenesis; negative regulation of cell-matrix adhesion; negative regulation of cell
migration involved in sprouting angiogenesis; negative regulation of cGMP-mediated signaling; negative regulation of cysteine-type endopeptidase activity involved in apoptotic process; negative regulation of dendritic cell antigen processing and presentation; negative regulation of endothelial cell chemotaxis; negative regulation of fibrinolysis; negative regulation of fibroblast growth factor receptor signaling pathway; negative regulation of interleukin-12 production; negative regulation of nitric oxide-mediated signal transduction; negative regulation of plasma membrane long-chain fatty acid transport; negative regulation of plasminogen activation; peptide cross-linking; positive
regulation of angiogenesis; positive regulation of blood vessel endothelial cell migration; positive regulation of chemotaxis; positive regulation of endothelial cell apoptotic process; positive
regulation of extrinsic apoptotic signaling pathway via death domain receptors; positive regulation of fibroblast migration; positive regulation of
macrophage activation; positive regulation of protein kinase B signaling; positive regulation of reactive oxygen species metabolic process; positive regulation of smooth muscle cell proliferation; positive regulation of transforming growth factor beta receptor signaling pathway; positive
regulation of translation; positive regulation of tumor necrosis factor biosynthetic process;
response to calcium ion; response to drug; response to glucose; response to magnesium ion; sprouting angiogenesis
2binding activity (calcium ion, collagen V,
Supplementary File 1: List of proteins related to litter size in swine 44
Protein Name Name Gene Reference Correlation Litter Size UniProt KB Function Validation Protein source 60 kDa heat shock protein,
mitochondrial HSPD1 [24] Negative ATP binding; protein folding No Sperm Acrosin-binding protein
precursor ACRBP [24] Negative Sperm capacitation No Sperm
Actin-related protein T3 ACTRT3 [24] Negative Male germ cell nucleus No Sperm
Actin-related protein T2 ACTRT2 [24] Negative _ No Sperm
Arginine vasopressin
receptor 2 AVPR2 [24] Negative Response to cytokine Western blot Sperm ATP synthase subunit d,
mitochondrial ATP5H [24] Negative _ No Sperm
Beta-tubulin TUBB [24] Negative Microtubule cytoskeleton organization No Sperm Calmodulin CALM [22] Positive Calcium-mediated signaling blot/ELISA Western Sperm
Catalase CAT [28] Positive Response to oxidative stress No Seminal plasma Chain B, crystal structure
of bovine mitochondria [24] Negative _ No Sperm
Cytochrome b-c1 complex subunit 1 UQCRC1 [24] Negative Aerobic respiration; mitochondrial electron transport; mitochondrial respiratory
chain complex III
Western
blot Sperm
Cytochrome b-c1 complex
subunit 2 UQCRC2 [24] Positive Mitochondrial respiratory chain complex III Western blot Sperm Cytosolic 5′-nucleotidase
1B NT5C1B [22; 24] Positive _ No Sperm
et al., 2018
Equatorin EQTN [22; 24] Negative _ No Sperm
Glutathione peroxidase 4 GPx4 [24] Negative Response to oxidative stress Western blot Sperm Glutathione S-transferase
Mu3 GSTM3 [24] Negative process; response to estrogen Glutathione metabolic Western blot Sperm Homo sapiens CGI-104
protein mRNA CGI-104 [24] Negative _ No Sperm
L-amino acid oxidase LAAO [22] Positive _ No Sperm
mitochondrial malate
dehydrogenase 2 MDH2 [22] Positive Carbohydrate metabolic process blot/ELISA Western Sperm Lysozyme-like protein 4 LYZL4 [22] Positive Fertilization, defense response to
gram-negative/positive bacterium No Sperm Mutant beta-actin ACTB [24] Negative Cell motility, ATP binding No Sperm NADH dehydrogenase
[ubiquinone] iron-sulfur
protein 2 NDUFS2 [22] Negative
Mitochondrial ATP synthesis coupled electron transport; response to
oxidative stress
Western
blot/ELISA Sperm
Nexin-1 PN-1 [28] Negative _ No Seminal plasma
Pancreatic glycoprotein 2 GP2 [24] Negative _ No Sperm
Porin PORIN [24] Negative _ No Sperm
Prohibitin PHB [24] Negative cytokines; DNA biosynthetic Cellular response to
process No Sperm
Pyruvate dehydrogenase
subunit beta precursor PDHB [24] Negative
Tricarboxylic acid cycle, acetyl-CoA biosynthetic process from pyruvate;
glycolytic process
No Sperm
Seminal plasma
glycoprotein PSP-I [37] Negative Single fertilization Western blot Seminal plasma
Speriolin SPRN [24] Negative Protein import into nucleus No Sperm
Spermadhesin AQN-3 AQN-3 [22; 24] Negative Single fertilization No Sperm
Spermadhesin AWN AWN [22] Negative Single fertilization No Sperm
Trifunctional enzyme subunit alpha,
mitochondrial HADHA [24] Positive Fatty acid beta-oxidation No Sperm
Triosephosphate isomerase TPI [22] Negative Gluconeogenesis ELISA Sperm
Thrombospondin-1 THBS1 [28] Negative Immune response No Seminal plasma
