Boar semen proteomics and sperm preservation I. Parrilla, C. Perez-Patino, J. Li, I. Barranco, L. Padilla, Heriberto Rodriguez-

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) http://www.elsevier.com/

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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.

*Corresponding author: parrilla@um.es (I Parrilla)

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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.

Keywords: boar; spermatozoa; proteomics; preservation

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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.

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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

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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/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

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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

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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

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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

9

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

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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

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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).

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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

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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

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(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

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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.

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[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 [36] Rahman MS, Kwon WS, Pang MG. Prediction of male fertility using capacitation-associated proteins in spermatozoa. Mol Reprod Dev. 2017, 84:749-759.

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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

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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.

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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 FC

(log2)*

P value (t-test)

Taxonomy

Putative Reproduction-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 Sperm capacitation

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 Membrane

stability and 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 Sperm capacitation

Guanine nucleotide-binding protein subunit alpha-11 GNA11 1.984 0.047 Other Spermatoge

nesis Unnamed protein product PGK1 0.652 0.006 Other

Polypeptide N-acetylgalactosaminyltransferase 2 GALNT2 0.762 0.014 Other Sperm

maturation

Fibronectin FN1 0.449 0.001 Other Sperm maturation

Ezrin EZR 1.873 0.018 Other Sperm capacitation

Fibronectin FN1 1.399 0.004 Other Sperm maturation

Under-expressed proteins in the SRF

Alpha-enolase ENO1 -0.057 0.009 Sus scrofa Sperm motility

Alkaline phosphatase ALP -0.697 0.029 Sus scrofa Sperm motility

Fibronectin FN1 -0.062 0.033 Sus scrofa Sperm maturation

Nucleobindin-1 NUCB1 -1.226 0.001 Sus scrofa Calcium and DNA

21

binding

Sulfhydryl oxidase 1 QSOX1 -0.309 0.014 Sus scrofa Sperm maturation

Angiotensin-converting enzyme isoform 2 ACE -0.935 0.019 Sus scrofa Sperm

maturation

Epididymal secretory protein E1 NPC2 -0.658 0.001 Sus scrofa Sperm maturation

Deoxyribonuclease-2-alpha DNASE2 -0.913 0.024 Sus scrofa DNA integrity

EGF-like repeat and discoidin I-like domain-containing protein 3 EDIL3 -0.582 0.012 Other Sperm

capacitation

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 Sperm motility

Alkaline phosphatase ALPL -0.964 0.040 Other Sperm motility

Beta-galactosidase-1-like protein 2-like GLB1L2 -0.002 0.020 Other Sperm

maturation

Pc21g16370 Pc21g16370 -1.155 0.019 Other

Syntaxin-binding protein 2 STXBP2 -2.021 < 0.001 Other Sperm capacitation

Prominin-2 PROM2 -0.565 0.037 Other Sperm capacitation

(*) 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 Gene Name UniProt KB ID

Correlation//Fertility 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_RABI

T 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_PAN

TR 1.21//FR 2structural molecule activity

Keratin type I cytoskeletal KRT10 F7BV15_ORNA

N -0.33//FR

1keratinocyte differentiation; peptide cross-linking; protein heterotetramerization 2protein heterodimerization activity; structural constituent of epidermis

Tetratricopeptide repeat TTC23 E9QKU9_MOU

SE -0.95//FR ---

Angiotensin AGT U5L198_DELLE -0.43//FR

1regulation of systemic arterial blood pressure by renin-angiotensin

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; signaling receptor binding

(*) 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, fibrinogen, fibroblast growth factor, fibronectin…)

1

Supplementary File 1: List of proteins related to litter size in swine 44

Protein Name Gene Name Reference Litter Size

Correlation 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 Western blot/ELISA 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

Desmocollin-1 DSC1 Pérez-Patiño Positive Calcium ion binding No Seminal plasma

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 Glutathione metabolic

process; response to estrogen 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

Western blot/ELISA 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 Cellular response to

cytokines; DNA biosynthetic process

No Sperm

Pyruvate dehydrogenase subunit beta precursor PDHB [24] Negative

Tricarboxylic acid cycle, acetyl-CoA biosynthetic process from pyruvate;

glycolytic process

No Sperm

Ras-related protein Rab-2A RAB2A [22; 24] Negative GTPase activity ELISA 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

4