Literature DB >> 36064611

Protein profiling of testicular tissue from boars with different levels of hyperactive sperm motility.

Maren van Son1, Dag Inge Våge2, Morten Skaugen3, Nina Hårdnes Tremoen2,4, Ann Helen Gaustad5,4, Teklu Tewoldebrhan Zeremichael4, Frøydis Deinboll Myromslien4, Eli Grindflek5.   

Abstract

Hyperactive sperm motility is important for successful fertilization. In the present study, a proteome profiling approach was performed to identify the differences between Landrace boars with different levels of hyperactive sperm motility in liquid extended semen. Two contrasts were studied: (i) high versus low levels of sperm hyperactivity at semen collection day and (ii) high versus low change in levels of sperm hyperactivity after 96 h semen storage. Testicular samples were analyzed on a Q Exactive mass spectrometer and more than 6000 proteins were identified in the 13 samples. The most significant differentially expressed proteins were mediator complex subunit 28 (MED28), cell division cycle 37 like 1 (CDC37L1), ubiquitin specific peptidase 10 (USP10), zinc finger FYVE-type containing 26 (ZFYVE26), protein kinase C delta (PRKCD), actinin alpha 4 (ACTN4), N(alpha)-acetyltransferase 30 (NAA30), C1q domain-containing (LOC110258309) and uncharacterized LOC100512926. Of the differentially expressed proteins, 11 have previously been identified as differentially expressed at the corresponding mRNA transcript level using the same samples and contrasts. These include sphingosine kinase 1 isoform 2 (SPHK1), serine and arginine rich splicing factor 1 (SRSF1), and tubulin gamma-1 (TUBG1) which are involved in the acrosome reaction and sperm motility. A mass spectrometry approach was applied to investigate the protein profiles of boars with different levels of hyperactive sperm motility. This study identified several proteins previously shown to be involved in sperm motility and quality, but also proteins with no known function for sperm motility. Candidates that are differentially expressed on both mRNA and protein levels are especially relevant as biological markers of semen quality.
© 2022. The Author(s).

Entities:  

Keywords:  Pig; Proteome; Sperm hyperactivity; Sperm motility

Mesh:

Substances:

Year:  2022        PMID: 36064611      PMCID: PMC9446748          DOI: 10.1186/s13028-022-00642-1

Source DB:  PubMed          Journal:  Acta Vet Scand        ISSN: 0044-605X            Impact factor:   2.048


Findings

The quality of semen in terms of fertilization rate is critical for efficient use of artificial insemination, and parameters that can predict the fertilization rate of semen samples are valuable. One important parameter is the level of hyperactive sperm motility, which is a movement pattern of spermatozoa after capacitation, normally taking place in the oviduct. The increased vigor in swimming movement makes the sperm more efficient in ascending the oviduct and penetrating the zona pellucida [1]. Computer-Assisted Semen Analysis (CASA) is an objective examination of semen characteristics [2] and we have previously shown that several CASA parameters defining hyperactive sperm motility was correlated to total number of piglets born [3]. Moreover, the Landrace pig breed developed a more hyperactive swimming pattern during storage compared to the Duroc breed, which is relevant as semen is usually stored before insemination. However, if hyperactive sperm motility is acquired too early, sperm cells may deplete their energy and die before reaching oocytes. Genetic factors related to fertility are interesting as they can be used to ensure good semen quality. We have previously identified differentially expressed (DE) genes in testis related to levels of hyperactive sperm motility [4]. The aim of the current study was to gain further insights into the molecular background causing sperm hyperactivity by performing protein profiling of the same testicular samples as previously used in mRNA expression. This allowed us to establish differentially expressed proteins related to hyperactive sperm motility as well as investigate the correlation of the proteome to the transcriptome. The animals, ejaculate samples, testicular tissue samples and CASA analysis used in this study have been described [4]. Data are also available in Additional file 1. From the CASA analysis, hyperactive motility for each single sperm cell track were defined as VCL > 97 µm/s, ALH > 3.5 µm, LIN < 32% and WOB < 71%. Testicular tissue was collected after slaughter and immediately frozen in liquid nitrogen and stored at -80 °C until protein extraction. Protein extraction was done using the Qproteome Mammalian Protein Prep kit [Qiagen, Germany] and the protocol “Purification of protein from animal tissues using the Qproteome Mammalian Protein Prep kit and the TissueRuptor”. Methods used for in-gel digestion, peptide clean-up and liquid chromatography-mass spectrometry are also described in detail in Additional file 1. Peak lists were generated from raw data files by the MaxQuant software and proteins were identified from the Uniprot porcine reference protein database. We performed protein DE analyses using the Bioconductor Differential Enrichment analysis of Proteomics data (DEP) package [5] and the Perseus software [6]. The DEP workflow started from MaxLFQ values obtained from MaxQuant, which are intensity values for each protein in each sample. Filtering removed proteins that were potential contaminants or originated from reverse sequences. Only proteins that were missing in maximum one sample were kept and normalization was done using the Variance Stabilizing Normalization approach [7]. Missing values were present in some samples due to low intensities, and these were imputed using random draws from a Gaussian distribution centered around a minimal value (option “MinProb”). Correction for multiple testing was done by the R package fdrtool [8] and DE was considered significant with an FDR of 0.05 and a log2 fold change of 0.6 (corresponding to a fold change of 1.5 on a linear scale). A workflow in Perseus v.1.6.7 was created starting from the same MaxLFQ values. Proteins that were only identified by a modification site, potential contaminants, or reverse sequences were removed. The dataset was log2 transformed and filtered so that each protein was present in at least 70% of the samples. Two-sample t-tests were performed for both contrasts. A p-value of 0.05 with log2 fold change of 0.6 was applied to indicate significance. Two different contrasts were examined in this study: (i) high versus low sperm hyperactivity at collection and (ii) high versus low change in levels of hyperactivity after 96 h storage (Table 1). Samples L2 and L11 are from the same boar and have extreme values in both contrasts, the same is the case for samples L7 and L14. Sample L2/L11 clustered as an outlier in both contrasts by hierarchical clustering and was excluded from DE analyses. The mean (± SD) hyperactivity values for the subsequent high and low groups were 14.5% (± 1.7) (n = 4) and 1.7% (± 0.2) (n = 3) for contrast (i) and 15.1% (± 3.0) (n = 3) and 2.8% (± 0.6) (n = 4) for contrast (ii). The data analysis obtained 66,366 unique peptides, which were assembled into 6,362 proteins (see Additional file 2).
Table 1

Sperm hyperactivity measurements for the different boars included in this study

GroupBoarNumber of ejaculatesMean %hyperactivitySD %hyperactivity
Landrace low at collectionL131.90.2
L241.20.7
L331.60.6
L431.60.2
Landrace high at collectionL5316.47.3
L6313.63.2
L7415.53.1
L8412.63.1
Landrace low change after 96 hL953.32.1
L1032.11.4
L1142.30.6
L1233.02.8
Landrace high change after 96 hL13311.77.0
L14417.39.0
L15316.23.7

The boars of contrast (i) (L1–L8) and (ii) (L9–L15) are presented with the number of ejaculates, mean and standard deviation (SD) for % sperm hyperactivity in the ejaculates

Sperm hyperactivity measurements for the different boars included in this study The boars of contrast (i) (L1–L8) and (ii) (L9–L15) are presented with the number of ejaculates, mean and standard deviation (SD) for % sperm hyperactivity in the ejaculates Using the DEP analysis, 32 and 16 proteins were found DE for contrasts (i) (see Additional file 3: Table X1) and (ii) (see Additional file 4: Table X1), respectively. The most significant DE proteins for contrast (i) were mediator complex (MED28), cell division cycle 37 like (CDC37L1), zinc finger FYVE-type containing 26 (ZFYVE26), ubiquitin carboxyl-terminal hydrolase (USP10) and uncharacterized LOC100512926 (all with q-value 1.76e−13). For contrast (ii), the most significant DE proteins were N(alpha)-acetyltransferase 30 (NAA30) and C1q domain-containing (LOC110258309) (both with q-value of 9.46e−13). Using the Perseus analysis procedure, 44 proteins were DE in contrast (i) (see Additional file 3: Table X2) and 87 proteins in contrast (ii) (see Additional file 4: Table X2). The most significant DE protein of contrast (i) was protein kinase C (PRKCB) (P-value = 0.0004), whereas for contrast (ii), the most significant protein was actinin alpha 4 (ACTN4; P-value = 0.0003). Results obtained by both DEP and Perseus workflows showed that nine and five proteins were significantly DE for contrast (i) and (ii), respectively, using both analysis methods (Table 2). Comparing results for the two different contrasts indicated that 10 proteins are relevant for sperm hyperactivity both at collection and after 96 h storage (Table 3). Of these, PRKCB, parvalbumin alpha (PVALB), sphingosine kinase 1 (SPHK1) (Table 2), ACTN4 and tubulin alpha chain (TUBA8) (Tables 2 and 3), have previously been associated with sperm quality. PRKCB is a protein kinase involved in sperm motility and acrosome reaction [9]. The ACTN4 gene was previously found up-regulated in Landrace boars with high sperm DNA fragmentation index [10], where the boars were from the same population as the current study. PVALB has previously been associated with sperm motility in carp [11] whereas TUBA8 levels were associated with reduced sperm motility in human [12]. SPHK1 was implicated to be involved in the acrosome reaction in mice [13].
Table 2

Differentially expressed proteins found in both analysis methods

ContrastUniProt IDProtein nameq-valueP-valueFC DFC PGene name
iK9J4L8Protein kinase C0.030.0004− 1.01− 1.26PRKCB
iF1SJ00LisH domain-containing protein0.030.0005− 1.97− 2.5DCAF1
iA0A286ZNB8Lysophospholipase-like protein 1 isoform a0.0050.010.870.89LYPLAL1
iF1SR79J domain-containing protein0.030.01− 0.78− 0.81DNAJB2
iF1RWP6Sphingosine kinase 1 isoform 20.010.020.860.78SPHK1
iF1SDG0Uncharacterized protein6.4e−050.02− 1.37− 1.12SF3B4
iF1SNT1Uncharacterized protein0.040.02− 0.69− 0.66ACAD11
iF1S5S7Inactive hydroxysteroid dehydrogenase-like protein 1 isoform X10.040.03− 1.06− 1.06HSDL1
iF1SKJ8Parvalbumin alpha0.030.03− 1.62− 0.95PVALB
iiA0A287BAZ5Uncharacterized protein0.050.0003− 0.7− 0.74ACTN4
iiI3LFS1Uncharacterized protein0.020.004− 0.9− 0.87USP39
iiF2Z5T8Uncharacterized protein7.0e−080.007− 2.72− 2.38MOBKL3
iiA0A287B5H5Tubulin alpha chain0.030.009− 0.94− 0.99TUBA8
iiF1SFI7Alpha-2-HS-glycoprotein0.050.011.010.93AHSG

Common proteins using the two analysis methods for contrast (i) and (ii) with UniProt ID, protein name, q-value for DEP, p-value for Perseus, fold change (FC) for DEP (D) and Perseus (P) and corresponding gene name

Table 3

Differentially expressed proteins found for both contrast (i) and (ii)

MethodUniProt IDProtein nameGene name
DEPA0A287BAZ5Uncharacterized proteinACTN4
I3LFS1Uncharacterized proteinUSP39
F2Z5T8Uncharacterized proteinMOBKL3
A0A287B5H5Tubulin alpha chainTUBA8
F1SFI7Alpha-2-HS-glycoproteinAHSG
PerseusK7GND3ATP-dependent RNA helicase ADHX9
A0A287BE52LEDGF domain-containing proteinPSIP1
F1S5S7Inactive hydroxysteroid dehydrogenase-like protein 1 isoform X1HSDL1
A0A287AIE8Uncharacterized proteinSUGP2
I3LDD5Pribosyltran domain-containing proteinPRTFDC1

Common proteins found for contrast (i) and (ii) using DEP or Perseus, presented with UniProt ID, Protein name and corresponding gene name

Differentially expressed proteins found in both analysis methods Common proteins using the two analysis methods for contrast (i) and (ii) with UniProt ID, protein name, q-value for DEP, p-value for Perseus, fold change (FC) for DEP (D) and Perseus (P) and corresponding gene name Differentially expressed proteins found for both contrast (i) and (ii) Common proteins found for contrast (i) and (ii) using DEP or Perseus, presented with UniProt ID, Protein name and corresponding gene name By comparing results of the proteome profiling to those previously obtained in transcriptome profiling of the same samples [4], 10 were in common for contrast (i) and one for contrast (ii) (Table 4). Some of these candidates have previously been shown to have a role in spermatogenesis and sperm motility, supporting a role also in hyperactive motility. Knock-out of the serine/arginine-rich splicing factor 1 (SRSF1) in mice showed that this protein is essential for sperm motility [14]. Tubulin gamma 1 (TUBG1) is suggested to have an important role in sperm motility in human samples [15]. Solute carrier family 12A7 (SLC12A7) is a NaCl cotransporter [16], making it an interesting candidate for sperm hyperactivity as Na ions are necessary for sperm motility, whereas WD repeat domain 13 (WDR13) is regulated by genes essential to normal spermatogenesis [17]. SPHK1 is already described above as it is one of the proteins identified by both analysis methods. The exact function of the other proteins listed in Table 4 needs to be further investigated, however based on their differential expression both on mRNA and protein level, we suggest an important role in hyperactive sperm motility.
Table 4

Complementary protein and transcriptome results for high versus low hyperactivity at collection (i) and after 96 h storage (ii)

Gene symbolProtein nameUniProt IDMethodContrast
SLC12A7Uncharacterized proteinI3L7H3DEPi
SPHK1Sphingosine kinase 1 isoform 2F1RWP6DEP, Perseusi
WDR13Uncharacterized proteinI3LPL0DEPi
NUCKS1Uncharacterized proteinA0A287AWH5DEPi
CDC37L1Hsp90 co-chaperone Cdc37-like 1F1SK44DEPi
FAM98CProtein FAM98CF1RI46Perseusi
OSBPL2Oxysterol-binding proteinA0A286ZQP4Perseusi
MSMO1Methylsterol monooxygenase 1D0G7F0Perseusi
TUBG1Tubulin gamma chainF2Z562Perseusi
SRSF1Serine/arginine-rich splicing factor 1Q3YLA6Perseusi
CREG1Uncharacterized proteinF1S268Perseusii

The correlating protein and transcriptome expression data is presented with gene symbol, protein name, UniProt ID, analysis method used to detect significance on protein level and contrast where differential expression was detected

Complementary protein and transcriptome results for high versus low hyperactivity at collection (i) and after 96 h storage (ii) The correlating protein and transcriptome expression data is presented with gene symbol, protein name, UniProt ID, analysis method used to detect significance on protein level and contrast where differential expression was detected In conclusion, we identified DE proteins in testicular samples from pigs related to levels of hyperactive sperm motility. Eleven candidates were significant both on protein and mRNA level and are highly relevant as potential biomarkers for pig fertility. Additional file 1. Methods used for in-gel digestion, peptide clean-up and liquid chromatography-mass spectrometry. Additional file 2. Peptides and proteins identified in the samples of this study. The data analysis revealed that 66,366 unique peptides (Table X1), which were assembled into 6362 proteins (Table X2), were identified. Additional file 3. Differentially expressed proteins for contrast (i) high versus low hyperactivity at collection. Results presented as analyzed by Perseus (Table X1) and as analyzed by DEP (Table X2) presented with UniProt ID, protein name, q-value, log2 fold change and corresponding gene name. Additional file 4. Differentially expressed proteins for contrast (ii) high versus low change in levels of hyperactivity after 96 h storage. Results as analyzed by Perseus (Table X1) and as analyzed by DEP (Table X2) presented with UniProt ID, protein name, q-value, log2 fold change and corresponding gene name.
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Journal:  Nat Methods       Date:  2016-06-27       Impact factor: 28.547

3.  Proteome-wide identification of ubiquitin interactions using UbIA-MS.

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4.  Relationship between sperm motility characteristics and ATP concentrations, and association with fertility in two different pig breeds.

Authors:  Nina Hårdnes Tremoen; Ann Helen Gaustad; Ina Andersen-Ranberg; Maren van Son; Teklu Tewoldebrhan Zeremichael; Kirsten Frydenlund; Eli Grindflek; Dag Inge Våge; Frøydis Deinboll Myromslien
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5.  Localization of sphingosine kinase-1 in mouse sperm acrosomes.

Authors:  Kenji Matsumoto; Yoshiko Banno; Takashi Murate; Yukihiro Akao; Yoshinori Nozawa
Journal:  J Histochem Cytochem       Date:  2005-02       Impact factor: 2.479

6.  Defining the human sperm microtubulome: an integrated genomics approach.

Authors:  Fanny Jumeau; Frédéric Chalmel; Francisco-Jose Fernandez-Gomez; Céline Carpentier; Hélène Obriot; Meryem Tardivel; Marie-Laure Caillet-Boudin; Jean-Marc Rigot; Nathalie Rives; Luc Buée; Nicolas Sergeant; Valérie Mitchell
Journal:  Biol Reprod       Date:  2017-01-01       Impact factor: 4.285

7.  Deficiency in the multicopy Sycp3-like X-linked genes Slx and Slxl1 causes major defects in spermatid differentiation.

Authors:  Julie Cocquet; Peter J I Ellis; Yasuhiro Yamauchi; Jonathan M Riel; Thomas P S Karacs; Aine Rattigan; Obah A Ojarikre; Nabeel A Affara; Monika A Ward; Paul S Burgoyne
Journal:  Mol Biol Cell       Date:  2010-08-25       Impact factor: 4.138

Review 8.  Mechanism of sperm capacitation and the acrosome reaction: role of protein kinases.

Authors:  Debby Ickowicz; Maya Finkelstein; Haim Breitbart
Journal:  Asian J Androl       Date:  2012-09-24       Impact factor: 3.285

9.  Induced hyperactivity in boar spermatozoa and its evaluation by computer-assisted sperm analysis.

Authors:  Harald Schmidt; Günter Kamp
Journal:  Reproduction       Date:  2004-08       Impact factor: 3.906

10.  Nucleo-cytoplasmic shuttling of splicing factor SRSF1 is required for development and cilia function.

Authors:  Fiona Haward; Magdalena M Maslon; Patricia L Yeyati; Nicolas Bellora; Jan N Hansen; Stuart Aitken; Jennifer Lawson; Alex von Kriegsheim; Dagmar Wachten; Pleasantine Mill; Ian R Adams; Javier F Caceres
Journal:  Elife       Date:  2021-08-02       Impact factor: 8.140

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