Selection of endogenous reference microRNA genes for quantitative reverse transcription polymerase chain reaction studies of boar spermatozoa cryopreservation

Theriogenology xxx (2014) 1–8

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Selection of endogenous reference microRNA genes for quantitative reverse transcription polymerase chain reaction studies of boar spermatozoa cryopreservation Yan Zhang, Chang-Jun Zeng*, Lian He, Li Ding, Ke-Yi Tang, Wen-Pei Peng Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Ya’an, Sichuan Province, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 29 May 2014 Received in revised form 28 October 2014 Accepted 28 October 2014

It is important to select high-quality reference genes for the accurate interpretation of quantitative reverse transcription polymerase chain reaction data, in particular for certain miRNAs that may demonstrate unstable expression. Although several studies have attempted to validate reference miRNA genes in the porcine testis, spermatozoa, and other tissues, no validation studies have been carried out on cryopreserved boar spermatozoa. In this study, 15 commonly used reference miRNA genes (5S, let-7c-5p, ssc-miR-16-5p, ssc-miR17-5p, ssc-miR-20a, ssc-miR-23a, ssc-miR-24-3p, ssc-miR-26a, ssc-miR-27a-3p, ssc-miR-92a, ssc-miR-103-3p, ssc-miR-106a, ssc-miR-107-3p, ssc-miR-186, and ssc-miR-221-3p) were selected to evaluate the expression stability of target miRNAs in boar spermatozoa under different experimental conditions and concentrations. The stability of the expression of these reference miRNAs across each sample was evaluated using geNorm, NormFinder, and BestKeeper software. The results showed that ssc-miR-186 (mean rank value ¼ 5.00), sscmiR-23a (5.33), and ssc-miR-27a (5.33) were the most suitable reference genes using three different statistical algorithms and comprehensive ranking. The identification of these reference miRNAs will allow for more accurate quantification of the changes in miRNA expression during cryopreservation of boar spermatozoa. Ó 2014 Elsevier Inc. All rights reserved.

Keywords: Boar spermatozoa cryopreservation miRNA geNorm NormFinder BestKeeper

1. Introduction The technique of artificial insemination is widely used in the modern pig production industry. More than 99% of inseminations are conducted with liquid-stored semen, which has been stored at 15  C to 25  C for 0 to 5 days. However, artificial insemination with frozen/thawed (FT) semen has been limited to no more than 1% of the total inseminations because of low farrowing rates and litter sizes [1,2]. Although substantial progress has been made toward optimization of the freezing procedures used and screening different cryoprotectants (CPAs) to limit damage to boar spermatozoa during cryopreservation, the cryopreservation efficiency is still * Corresponding author. Tel./fax: þ86 835 2886080. E-mail address: [email protected] (C.-J. Zeng). 0093-691X/$ – see front matter Ó 2014 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.theriogenology.2014.10.027

considered to be unsatisfactory. Furthermore, the mechanisms underlying the significant cryoinjury to boar spermatozoa remain unknown. Recently, studies have demonstrated that microRNAs (miRNAs) are enriched in mammalian testis, spermatozoa, and seminal plasma. Functionally, these miRNAs are widely involved in spermatogenesis and structure integrity, motility, and metabolism of boar spermatozoa [3–6]. Additionally, miRNAs may play a critical role in adjusting messenger RNA translation and regulating metabolism in response to cold or exposure to freezing conditions [7,8]. Therefore, comprehensive analysis of the expression patterns of different miRNAs may enhance our understanding of the mechanism of cryoinjury or damage from thawing during boar spermatozoa cryopreservation. MicroRNAs are a class of regulatory, small, noncoding RNAs (typically 20–24 nucleotides) that are involved in a

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variety of biological processes [9–14]. In mammals, a single miRNA may potentially target hundreds of mRNAs, and its expression in different tissues is correlated with transcription factors or RNA-binding proteins [15–17]. Many miRNAs have specialized expression patterns, with expression limited to specific tissues or developmental stages, leading to highly regulated protein synthesis and the generation of cell-type specificity [18,19]. Testicular spermatogenesis is a complex and precisely regulated process, which is tightly controlled by miRNA-mediated pathways [20–23]. Quantitative reverse transcription polymerase chain reactions (RT-qPCR) are fast, sensitive, and specific for messenger RNA detection and quantification of gene expression [24]. To produce reliable gene expression data using RTqPCR, reference genes are widely used to control for variations in samples [25–27]. An ideal reference gene is expected to be expressed at a constant level in different tissues at all developmental stages and within all experimental treatments [28]. However, numerous studies have demonstrated that reference gene expression across multiple types of tissues and divergent experimental conditions is not always consistent [29–31]. In addition, miRNAs comprise only 0.01% of the total RNAs, and this total amount of this fraction can vary significantly across different samples because of changes in extraction efficiency [29,32,33]. In one study, Gu et al. demonstrated that the three most stable endogenous control miRNA genes (ssc-miR-17, ssc-miR-103, and sscmiR-107) were suitable for comparison in 47 different porcine tissues including the epididymis and testis. However, previous studies also have shown that the reference miRNA gene, miR-16, is abundantly expressed in all tissues and could be used experimentally for RT-qPCR normalization [5,33,34]. Identification of valid, sperm-specific, reference miRNAs is necessary to quantify miRNA expression during boar spermatozoa cryopreservation. In this study, we performed an extensive evaluation of 15 commonly used reference miRNA genes, including 5S, let-7c-5p, ssc-miR-16-5p, ssc-miR-17-5p, ssc-miR-20a, sscmiR-23a, ssc-miR-24-3p, ssc-miR-26a, ssc-miR-27a-3p, ssc-miR-92a, ssc-miR-103-3p, ssc-miR-106a, ssc-miR-1073p, ssc-miR-186, and ssc-miR-221-3p, to evaluate the stability of expression levels of target miRNA genes in boar spermatozoa under different conditions. Furthermore, geNorm, NormFinder, and BestKeeper software programs were used to determine the stability of these 15 miRNA reference genes. 2. Materials and methods 2.1. Spermatozoa collection and cryopreservation Three healthy and sexually mature Landrace boars were humanely sacrificed as necessary to ameliorate suffering according to the Regulations for the Administration of Affairs Concerning Experimental Animals (Ministry of Science and Technology, China, revised in June 2004) and approved by the Institutional Animal Care and Use Committee in the College of Animal Science and Technology, Sichuan Agricultural University, Sichuan, China, under permit No. DKYB20081003. The spermatozoa in the cauda epididymis were collected immediately according to the procedures described

by Saenz et al. [35] with some modifications. Briefly, two small incisions were made at the medial section of the cauda epididymis, and RNase-free PBS was used to flush the lumen of the cauda epididymis. The cauda epididymal spermatozoa were frozen directly in liquid nitrogen and stored at 80  C before RNA extraction. In addition, the spermatozoa-rich fractions of ejaculates were collected from three sexually mature Landrace boars using a manual collection method [36]. The quality parameters of spermatozoa were measured using SQA-V (MES, Israel). Only the spermatozoa-rich fractions of the ejaculates that exhibited a spermatozoa motility of greater than 0.8, normal morphology, and spermatozoa concentration higher than 1  108 mL1 were used. Additionally, some fresh spermatozoa were used directly to isolate miRNA. It has been reported that freezing extender containing 3% glycerol and 10% egg yolk was beneficial for the cryopreservation of boar spermatozoa [37–41]. Cryopreservation of boar spermatozoa was performed on the basis of a previous protocol [30]. Briefly, the sperm was diluted (1:1 v:v), washed with Beltsville thawing solution (3.7 g of glucose, 0.3 g of Na3 citrate, 0.125 g of NaHCO3, 0.125 g of Na2-EDTA, 0.075 g of KCl, 0.6 g/L penicillin G sodium, and 1.0 g/L dihydrostreptomycin; all diluted to 100 mL), and cooled slowly to 15  C for 2 hours. Then, the spermatozoa pellet was diluted to a concentration of 2  109 mL1 using lactose–egg yolk extender (80 mL [80%, v:v], 310-mM b-lactose, and 20 mL of hen’s egg yolk) and cooled to 4  C at a rate of approximately 0.2  C minute1. At 4  C, the spermatozoa were further diluted with a second freezing extender (lactose–egg yolk supplemented with glycerol) to yield final concentration of 3% glycerol. The mixtures were packaged into 0.25-mL straws (FHK, Japan) and frozen using a controlled-rate freezing instrument (CryoMed ControlledRate Freezer, Thermo Fisher, USA). The straws remained in the liquid nitrogen tank for at least 2 weeks before use. 2.2. MicroRNA extraction and complementary DNA (cDNA) synthesis The spermatozoa were divided into three treatment groups: fresh, cryopreserved, and cauda epididymal spermatozoa. The small RNA was extracted from boar spermatozoa using the mirVana miRNA Isolation Kit (Ambion, USA) with some modifications. Briefly, to avoid somatic cell contamination, the spermatozoa pellets were resuspended in 1 mL of hypotonic solution with 0.5% Triton X-100 (Roche, Germany). Next, 600-mL lysis buffer was added, and the mixture was incubated at 60  C for 30 minutes. MicroRNA (60 mL) of homogenate additive was added to the homogenate and incubated on ice for 20 minutes. Then, 600 mL of acid phenol chloroform was added equal to the lysate, followed by a 10-minute centrifugation at 10,000 g. The supernatant containing the miRNA was removed, and one-third of the volume of 100% ethanol was added. The solution was passed through a filter cartridge by centrifugation at 10,000 g for 15 seconds. Finally, the miRNA was recovered with 40-mL elution solution, preheated to 95  C. The concentration and quality of the miRNA were determined using a NanoDrop ND1000 spectrophotometer (NanoDrop Technologies, USA).

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The miRNA first-strand cDNA was synthesized from 0.06 mg of miRNA from each sample using the PrimeScript miRNA qPCR Starter Kit version 2.0 (Takara Biotech, China) according to the manufacturer’s instructions. 2.3. Selection of reference miRNA genes and RT-qPCR analysis Multiple studies have been conducted to validate the stable expression of reference miRNA genes across different tissues and organs of porcine, mouse, and human for specific purposes [5,29,33,34]. However, no studies have been carried out to validate reference genes in frozen boar spermatozoa. On the basis of the literature, 15 reference miRNA genes, including 5S, let-7c-5p, ssc-miR-16-5P, sscmiR-17-5p, ssc-miR-20a, ssc-miR-23a, ssc-miR-24-3p, sscmiR-26a, ssc-miR-27a-3p, ssc-miR-92a, ssc-miR-103-3p, ssc-miR-106a, ssc-miR-107-3p, ssc-miR-186, and ssc-miR221-3p, were selected (Table 1). Primer specificity and the formation of primer dimers were examined using RT-PCR amplification with cDNA from FT boar spermatozoa. The size of the amplicons was confirmed using 2.0% agarose gel electrophoresis. The PCR products were then cloned into the pMD19-T vector (Takara Biotech, China). The plasmid DNA was extracted with a plasmid mini kit I (Omega, USA), and the concentration was determined using a NanoDrop ND1000 spectrophotometer (NanoDrop Technologies, USA). To generate standard curves for each reference gene, serial dilutions were performed on the basis of copies of the plasmid DNA templates. All the points on the standard curve and all the samples were run in triplicate as technical replicates.

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0.5 mL of miRNA-specific forward primer; then, 3-mL RNasefree water was added for a total volume of 10 mL. The thermal cycling profile was the following: dwell temperature of 95  C for 3 minutes followed by 40 cycles of 94  C for 10 seconds and the primer-specific annealing temperature for 30 seconds, and a final step at which the fluorescence was acquired. A melt curve then was generated by increasing the temperature from 65  C to 95  C at increments of 0.5  C and acquiring the fluorescence after each step. 2.5. Determination of reference gene expression stability The raw RT-qPCR data from all the samples were exported from the StepOnePlus software (Applied BioSystems) to Microsoft Excel 2003. The mean quantification cycle (Cq) value was converted to relative expression level using the 2DDCq method for miRNA expression stability analysis. To compare the miRNA genes expression stability and rank, geNorm (http://www.biogazelle.com), NormFinder (http://moma.dk/ normfinder-software), and BestKeeper (http://www.genequantification.de/bestkeeper.html) were used. The averages of the Cq values run in triplicate were used for the comparison of the stability of each candidate house-keeping miRNA genes in the geNorm, NormFinder, and BestKeeper software. 2.6. Statistical analysis The statistical analysis of the expression levels of the reference miRNA genes was performed using Duncan multiple comparison with the SAS9.0 software. Each experiment was independently conducted three times. Differences of P < 0.05 were regarded as statistically significant.

2.4. Quantitative RT-PCR 3. Results Quantitative RT-PCR was performed on a StepOnePlus real-time PCR system (Applied BioSystems, USA). Reactions were performed in triplicate using SYBR PrimeScriptTM miRNA RT-PCR Kit (Takara Biotech, China) with some modifications. Briefly, each reaction comprised 5-mL SYBR Premix Ex Taq II, 1 mL of cDNA, 0.5 mL of Uni-miR qPCR Primer, and

3.1. Quality of extracted miRNA and verification of selected miRNAs The quality of the miRNA was determined using a NanoDrop ND1000 spectrophotometer. Only those RNA

Table 1 Selected candidate reference microRNA genes, primers, and polymerase chain reaction amplification efficiencies. R2

Gene name

GenBank/miRBase accession

Primer sequence (50 –30 )

Melting temperature ( C)

5S

AF329851

60

95.3

1.000

ssc-let-7c ssc-miR-16 ssc-miR-17-5p ssc-miR-20a ssc-miR-23a ssc-miR-24-3p ssc-miR-26a ssc-miR-27a ssc-miR-92a ssc-miR-103 ssc-miR-106a ssc-miR-107 ssc-miR-186 ssc-miR-221-3p

MIMAT0002151 MIMAT0007754 MIMAT0007755 MIMAT0002129 MIMAT0002133 MIMAT0002134 MIMAT0002135 MIMAT0002148 MIMAT0013908 MIMAT0002154 MIMAT0002118 MIMAT0002155 MIMAT0002162 MIMAT0007762

F: GCCCGATCTCGTCTGATCT R: AGCCTACAGCACCCGGTATT TGAGGTAGTAGGTTGTATGGTT TAGCAGCACGTAAATATTGGCG CAAAGTGCTTACAGTGCAGGTAG TAAAGTGCTTATAGTGCAGGTA ATCACATTGCCAGGGATTTCC TGGCTCAGTTCAGCAGGAACAG TTCAAGTAATCCAGGATAGGCT TTCACAGTGGCTAAGTTCCGC TATTGCACTTGTCCCGGCCTGT AGCAGCATTGTACAGGGCTATGA AAAAGTGCTTACAGTGCAGGTAGC AGCAGCATTGTACAGGGCTATCA CAAAGAATTCTCCTTTTGGGCTT AGCTACATTGTCTGCTGGGTTT

60 60 60 60 60 60 60 60 60 60 60 60 60 60

91.8 98.3 93.2 96.3 102.6 96.3 101.7 96.3 103.4 95.5 97.2 96.5 97.1 98.5

0.927 0.964 0.999 0.998 0.996 0.998 0.950 1.000 0.998 1.000 0.999 0.998 1.000 1.000

R2, correlation coefficient calculated from slope of the standard curve.

Amplification efficiency (%)

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Fig. 1. Mean ( standard deviation) quantitative reverse transcription polymerase chain reaction cycle threshold (Ct) values of candidate reference microRNA (miRNA) genes tested in boar fresh ejaculate, cryopreserved, and cauda epididymal spermatozoa. Means with different superscripts differ (P < 0.05) within gene.

samples with an optical density ratio of A260/A280 nm between 1.8 and 2.1 were used in the subsequent analyses. The 2% agarose gel electrophoresis and melting curve analysis demonstrated that all 15 reference miRNA gene primers amplified with a single peak and no primer dimer formation. The amplification efficiencies of the 15 candidate reference genes ranged from 91.8% to 103.4% (Table 1). Thus, the quality of the miRNA of boar spermatozoa was satisfactory for the following studies. 3.2. Expression levels of candidate reference miRNA genes Except for 5S, all other 14 miRNAs demonstrated the highest expression levels (Cq value) in the spermatozoa of cauda epididymis and the lowest expression in cryopreserved boar spermatozoa (Fig. 1). There were no significant differences in any miRNA expression levels from fresh ejaculates compared with FT boar spermatozoa, except let-7c-5p, ssc-miR-26a, and ssc-miR-186. No significant variations in the expression levels of 5S were observed in three different samples. 3.3. Expression stability of selected reference miRNA genes Using the geNorm algorithm, we ranked the 15 candidate miRNA genes on the basis of their stability values. This algorithm is rooted in a mathematical model of gene expression and provides a direct measure for the estimated expression variation [42]. geNorm generates a ranking of genes according to their M values, resulting in the identification of the genes with the most stable expression in the samples under analysis. Lower M values represent higher expression stabilities, whereas the least stable genes demonstrated the highest M values. In this study, the two

most stable genes were ssc-miR-103-3p and ssc-miR-186, which exhibited the lowest M values, followed by sscmiR-92a, ssc-miR-106a, and ssc-miR-16-5p. The least stable reference gene was 5S (Fig. 2). However, all the 15 candidate reference miRNA genes exhibited credible stability (M < 1.50). geNorm estimates the normalization factor (NF) for each tissue sample on the basis of the geometric mean of a number of reference genes. The algorithm then calculates a pairwise variation (V) on the basis of the normalization factor value (NFn) and an NF after the inclusion of the least stable reference gene (NFnþ1) and indicates if the extra reference gene adds to the stability of the NF. Our calculated V2/3 was equal to 0.058, which was below the threshold of 0.15 (Fig. 3). For this reason, it was unnecessary to include a third miRNA gene into the calculation of the NF. The geNorm calculations suggest that the two most stable genes, ssc-miR-103-3p and ssc-miR-186, were suitable reference genes for use in RT-qPCR of boar spermatozoa. We also used the NormFinder algorithm to examine the stability of our panel of potential miRNA reference genes. The results from the NormFinder analysis showed that the most stable reference gene was ssc-miR-27a-3p, followed by ssc-miR-186, ssc-miR-107-3p, and ssc-miR-23a (Fig. 4). The lease stable gene identified was 5S. Finally, we analyzed the gene stability using the BestKeeper algorithm. The most stable reference gene identified was ssc-miR-24-3p (standard deviation [SD], 0.83), whereas the least stable genes were ssc-miR-17-5p, sscmiR-16-5p, and ssc-miR-20a (Fig. 5). Generally, the genes with SD values greater than 1 are considered to have inconsistent gene expression and should be excluded [43,44]. On the basis of the results of the BestKeeper analysis, only ssc-miR-24-3p was stable (SD <1.0), and the

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Fig. 2. Average expression stability values (M) of candidate reference genes using geNorm analysis across all samples. The gene stability value M is based on the average pairwise variation between all tested reference genes. Expression stability of genes indicates the least stable (left) to the most stable (right) gene. miRNA, microRNA.

other 14 miRNA genes were considered to display unstable expressions (SD >1.0). 3.4. Comprehensive ranking order To obtain a comprehensive ranking order of the candidate reference miRNAs, we followed the methods described previously [45–47]. Each miRNA gene was ranked on the basis of the results obtained from the analysis with geNorm, NormFinder, and BestKeeper, with a rank of 1 indicating the most stable gene to a rank of 15 indicating the least stable gene. The arithmetic average of each gene rank was calculated. The rankings then integrated genetic stability, leading to a rank that was comprehensive for gene stability. Using this method, the three most stable miRNA genes were sscmiR-186, ssc-miR-23a, and ssc-miR-27a-3p, and the three

least stable miRNA genes were ssc-miR-16-5p, ssc-miR-175p, and 5S (Table 2). These results were partially consistent with those obtained from the NormFinder analysis, with the main difference being in the ranking orders of the most stable genes. 4. Discussion To obtain an exact comparison of miRNA expression, it is essential to select suitable endogenous reference genes because of the unstable expression of certain miRNA genes in different tissues or developmental time points [5,33,34]. The choice of unstable endogenous reference genes can frequently lead to a loss of accuracy and decreased statistical significance, because even small changes in miRNA expression levels may be biologically significant [29,30,32].

Fig. 3. Determination of the optimal number of reference genes based on the geNorm algorithm. Variable (V) value defines the pairwise variation between two sequential normalization factors containing an increasing number of reference genes.

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Fig. 4. Gene expression stability values analysis using NormFinder in all boar spermatozoa samples. The lower the stability value, the higher the expression stability. miRNA, microRNA.

In addition, the use of multiple endogenous reference genes for RT-qPCR can produce more accurate and reliable normalization results. Therefore, it is important to identify one or more suitable sperm-specific reference miRNA genes for miRNA expression analysis. In this study, 15 previously reported miRNA genes were selected to evaluate their expression stability in fresh, cryopreserved, and cauda epididymal boar spermatozoa. Compared with the miRNA expression of spermatozoa in the cauda epididymis, significantly higher miRNA expression levels (Cq values) were observed in the fresh and cryopreserved spermatozoa. Except for let-7c, ssc-miR-26a, and ssc-miR-186, there were no significant differences among all miRNA expression levels between fresh and cryopreserved boar spermatozoa. Numerous miRNAs have been identified in porcine spermatozoa that are associated with sperm motility, structural integrity, or metabolism

[4,5]. The differences between spermatozoa isolated from fresh ejaculates or FT cryopreserved fractions owe mainly to the damage in structure and function that occurs during cryopreservation. Cryopreservation has been shown to decrease sperm motility and viability and increase oxidative stress, DNA fragmentation, and apoptosis [48,49]. In distinct segments of human epididymis, 15 miRNAs were identified to be differentially expressed, with the pattern of the target gene expression in different regions correlating directly or indirectly with changes in sperm maturation and fertility [50]. During boar spermatozoa cryopreservation, the expression of three miRNAs, let-7c, ssc-miR-26a, and ssc-miR-186, was significantly altered in this study. Our results also demonstrated that freezing or cryopreservation results in expression changes of miRNAs in boar spermatozoa. However, how miRNAs are involved in the antifreeze or cryoinjury mechanism leading to changes in the potential fertility of the postthawed boar spermatozoa needs to be further clarified. MicroRNAs may be very important to regulate biological functions under changes of heating and cooling. In wood frogs, which are freezetolerant vertebrates, miRNAs act to establish rapid biological regulation of some metabolic activities for survival under freezing conditions [7]. As certain endogenous control genes are not stably expressed across different species or experimental conditions, identification of appropriate reference genes for an exact comparison of miRNA expression is urgently needed to identify changes in boar spermatozoa. A previous study has reported that miR-17/miR-103/miR-107, miR-17/miR107/miR-24, and miR-17/miR-23a/miR-103 were regarded as the optimal miRNA endogenous control genes for all 47 normal samples, fat-type tissues, and muscle-type tissues, respectively [29]. In addition, miR-16 was determined to be the most stable gene among three investigated reference genes (let-7a, miR-16, and miR-103) in gastric cancers [51]. miR-16 was also the most stable gene among the three

Fig. 5. Gene expression stability values of genes from the least stable (left) to most stable (right), by calculation of cycle threshold (Ct) data variation according to the BestKeeper. miRNA, microRNA.

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Table 2 Comprehensive ranking order of selected reference microRNA genes. Ranking order

geNorm

NormFinder

BestKeeper

Comprehensive ranking (mean rank value)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

miR-103 miR-186 miR-92a miR-106a miR-16 miR-20a miR-27a miR-26a miR-107 miR-23a let-7c miR-221 miR-17 miR-24 5S

miR-27a miR-186 miR-23a miR-107 miR-26a let-7c miR-103 miR-106a miR-221 miR-92a miR-24 miR-20a miR-17 miR-16 5S

miR-24 miR-221 miR-23a 5S miR-107 let-7c miR-26a miR-27a miR-92a miR-103 miR-186 miR-106a miR-20a miR-16 miR-17

miR-186 (5.00) miR-23a (5.33) miR-27a (5.33) miR-103 (6.00) miR-107 (6.00) miR-26a (6.67) miR-92a (7.33) let-7c (7.67) miR-221 (7.67) miR-106a (8.00) miR-24 (8.67) miR-20a (10.33) miR-16 (11.00) miR-17 (13.67) 5S (11.33)

reference genes (let-7a, miR-16, and miR-26a) examined in colorectal cancer samples [52]. As previously described, spermatozoa miRNA expression levels were normalized to endogenous miR-16 levels [5,33,34,53] because the expression levels of 5S were demonstrated to be irregular in porcine sperm [5]. However, our results suggest that let-7c and ssc-miR-26a were more stable than ssc-miR-16-5p in boar spermatozoa. Identification of endogenous miRNA gene expression stability to elucidate specific reference genes is necessary to evaluate changes in boar spermatozoa during cryopreservation. Using three different analysis algorithms, we identified the most stable miRNAs. Analysis with geNorm showed that ssc-miR-103-3p and ssc-miR-186 were the two most stable genes, followed by ssc-miR-92a and ssc-miR-106a (M <1.50), which is consistent with a previous study suggesting that miR-103 is a good reference gene for use in different pig tissues [29]. Moreover, we determined that the optimal number of reference genes was two in our study. The V2/3 value (V2/3 ¼ 0.058) was lower than the threshold V value of 0.15, which is the recommended cutoff value to determine the optimal number of reference genes [54]. In the NormFinder analysis results, ssc-miR-27a-3p, ssc-miR-186, and ssc-miR-23a were identified as the most stable genes. However, BestKeeper identified ssc-miR-243p as the reference gene with the least overall variation, which was different from the results of either geNorm or NormFinder. To identify the three most stable reference genes, we used a comprehensive ranking of each reference gene on the basis of the results from all three algorithms. Using this ranking system, we identified ssc-miR-186, sscmiR-23a, and ssc-miR-27a-3p. In addition, we determined that 5S was the least stable gene, which is consistent with other published reports [5,29]. 4.1. Conclusions Our study clearly indicates that accurate selection of reliable reference genes is an absolute prerequisite for the measurement of miRNA expression in cryopreserved spermatozoa using RT-qPCR. Fifteen potential reference miRNA genes in boar spermatozoa were evaluated, and sscmiR-186, ssc-miR-23a, and ssc-miR-27a were identified as

the most suitable reference genes using three different statistical algorithms (geNorm, NormFinder, and BestKeeper). The process of cryopreservation can cause miRNA expression changes in boar spermatozoa. Our results will benefit the studies of the miRNA expression by providing stable reference genes for use in RT-qPCR studies of boar spermatozoa and will facilitate exploration of the antifreeze or cryoinjury mechanisms during boar spermatozoa cryopreservation. Acknowledgments The authors give special thanks to Ying Ren and Jinyue Li for their kind help with the collection of the boar spermatozoa. This work was supported partly by a grant from the National Natural Science Foundation of China (No. 30901028) and a grant from Dual-supports Project of Sichuan Agricultural University (No. 01570105). Competing interests The authors declare no conflict of interest. References [1] Carvajal G, Cuello C, Ruiz M, Vázquez JM, Martínez EA, Roca J. Effects of centrifugation before freezing on boar sperm cryosurvival. J Androl 2004;25:389–96. [2] Khalifa T, Rekkas C, Samartzi F, Lymberopoulos A, Kousenidis K, Dovenski T. Highlights on artificial insemination (AI) technology in the pigs. Mac Vet Rev 2014;37:5–34. [3] Mishima T, Takizawa T, Luo S-S, Ishibashi O, Kawahigashi Y, Mizuguchi Y, et al. MicroRNA (miRNA) cloning analysis reveals sex differences in miRNA expression profiles between adult mouse testis and ovary. Reproduction 2008;136:811–22. [4] Curry E, Ellis S, Pratt S. Detection of porcine sperm microRNAs using a heterologous microRNA microarray and reverse transcriptase polymerase chain reaction. Mol Reprod Dev 2009;76:218–9. [5] Curry E, Safranski TJ, Pratt SL. Differential expression of porcine sperm microRNAs and their association with sperm morphology and motility. Theriogenology 2011;76:1532–9. [6] Wang C, Yang C, Chen X, Yao B, Yang C, Zhu C, et al. Altered profile of seminal plasma microRNAs in the molecular diagnosis of male infertility. Clin Chem 2011;57:1722–31. [7] Biggar KK, Dubuc A, Storey K. MicroRNA regulation below zero: differential expression of miRNA-21 and miRNA-16 during freezing in wood frogs. Cryobiology 2009;59:317–21.

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