ANDROLOGY
ISSN: 2047-2919
ORIGINAL ARTICLE
Correspondence: M.-G. Pang, Department of Animal Science and Technology, Chung-Ang University, Anseong, Gyeonggi-Do 456-756, Korea. E-mail: [email protected]
Keywords: boar spermatozoa, capacitation status, correlation, fertility, litter size Received: 10-Nov-2014 Revised: 14-Jan-2015 Accepted: 23-Jan-2015 doi: 10.1111/andr.12020
Improving litter size by boar spermatozoa: application of combined H33258/CTC staining in field trial with artificial insemination W.-S. Kwon M. S. Rahman J.-S. Lee Y.-A. You and *M.-G. Pang Department of Animal Science and Technology, Chung-Ang University, Anseong, Gyeonggi-Do, Korea
SUMMARY Conventional semen analysis offers basic information on infertility; however, its clinical value in predicting fertility status is unclear. To establish an accurate diagnosis of male fertility, semen analysis under capacitation condition is necessary because only capacitated spermatozoa are capable of fertilizing oocytes. The objective of this study was to verify male fertility based on conventional semen analysis before and after capacitation, including the assessment of motility (%), motion kinematics, and capacitation status of spermatozoa. A computer-assisted sperm analysis system and chlortetracycline staining were applied to evaluate the motility parameters and capacitation status, respectively. To enable efficacy of the two methods for predicting fertility, correlation analysis was performed with the historic litter size. Our results showed that sperm motility (%), motion kinematics, and their variations before and after capacitation represented a statistical non-significant correlation with litter size. Litter size showed significant correlation with acrosome reaction (AR) after capacitation (r = 0.375), as well as differences (D) in AR (r = 0.333) and capacitated (B) pattern (r = 0.447) before and after capacitation. The overall accuracy of the assay for predicting litter sizes using the AR and differences (D) in the AR and B pattern was 70%. On the basis of these results, we propose that capacitation status of spermatozoa is a more reliable indicator for evaluating male fertility status compared to motility parameters. Therefore, we suggest that analysis of capacitation status in company with conventional semen analysis may accept to evaluate more accurate diagnosis or prognosis of male fertility.
INTRODUCTION Artificial insemination (AI) has been extensively performed in countries with rigorous pig production industries. To establish a successful AI, prediction of male fertility is a matter of utmost importance (Park et al., 2003, 2012; Oh et al., 2010a). Currently, evaluation of semen quality significantly contributes to AI industries around the world. However, evaluation of semen quality still depends on conventional semen analysis (e.g., assessment of sperm morphology) (Bonde et al., 1998) and motility (Budworth et al., 1988). Semen analysis methods have been well accepted as the method for identifying infertile individuals; however, their efficacy in detecting sub-fertile individuals remains unclear (Collins et al., 2008). To fill the gap, swelling/eosin test (Milardi et al., 2013) and sperm penetration assay (Oh et al., 2010b) has been complemented with conventional analysis. Interestingly, these methods have also generated inconsistent results in predicting fertility (Braundmeier & Miller, 2001). It has been demonstrated that after ejaculation, mammalian spermatozoa inhabit the female genital tract for a considerable 552
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period to undergo necessary modifications, particularly capacitation (Zaneveld et al., 1991; Fraser & McDermott, 1992; Kirichok et al., 2006; Visconti, 2009; Kwon et al., 2013a,b). Therefore, the sperm function test contingent upon capacitation is often evaluated to predict male fertility. Additionally, comparative analysis of parameters between ejaculated and capacitated sperm function is also performed. We hereby describe a reliable approach for the selection of high fertility boar using several sperm function tests, including motility (%), motion kinematics, and assessment of capacitation status. To establish the strategy, detected sperm parameters from semen analysis were analyzed in relation to litter size. Finally, to validate the efficiency of this strategy, differences in the litter size were analyzed using significantly correlated parameters for male fertility evaluation.
MATERIALS AND METHODS All procedures were performed to ethical treatment of animals according to the guidelines and approval of the Institutional © 2015 American Society of Andrology and European Academy of Andrology
IMPROVING LITTER SIZE BY CAPACITATION STATUS
Animal Care and Use Committee of Chung-Ang University in Seoul, Republic of Korea (Approval no. 12-0020). Semen source and artificial insemination (AI) AI was performed in grand-grandparents farm of Sunjin Co. (Danyang, Korea). The animals were housed in a temperature (20 5 °C), ventilation, and light controlled shed. Therefore, no effect of season was observed during study periods. The entire ejaculate from AI stud boar was collected by gloved–hand technique twice per week (Almond et al., 1998) The low limit values for AI and semen analysis were as follows: volume > 150 mL, total concentration > 200 9 106, motility > 70%, and total abnormalities < 20% of the sample. The semen was diluted for AI to a density of 30 9 106 sperm cells/mL in 100 mL of a commercial extender (Beltsville thawing solution). Extended semen was cooled down to 17 °C and stored in a semen storage unit at 17 °C until insemination. Extended semen was inseminated within 3 days of ejaculation. Each male pig’s field fertility data were provided by Sunjin Co. (total live born/total breeding) and analyzed to select 27 Landrace boars (mean age: 29.96 1.35; range = 15–39 months; conception rate = 93.63 0.98; average litter size = 11.47 1.19) spanning a wide fertility range. To eliminate fertility variations according to parity, fertility data from the first parity were excluded (Oh et al., 2010b). A total of 983 multiparous (two to five pregnancies) sows were inseminated twice per estrus (after the sows showed signs of pro-estrus, the sows were examined for the onset of standing estrus every 6 h by using a back pressure test in the presence of a mature boar), and three billion spermatozoa were deposited in the cervix at insemination. The first insemination was performed within 24 h of estrus detection, and after 24 h of the first insemination, a second insemination completed the whole course of AI. Landrace sows were bred with semen collected from the 27 boars and inseminated twice per estrus by well-trained technicians. Historic fertility data of the 27 boars were collected from 2011 to 2013. To avoid individual variations, three extended semen samples were randomly selected from different batches of individual boar. The extended semen for analyses was used within 2 h of collection. The samples were washed with Dulbecco’s phosphate-buffered saline (DPBS) at 1500 g for 10 min and divided into two parts (before and after capacitation). A portion of the extended semen sample was used for the capacitation experiment; this sample was incubated with a modified tissue culture media (mTCM) 199 containing 10% fetal bovine serum (v/v), 0.91 mM sodium pyruvate, 3.05 mM D-glucose, 2.92 mM calcium lactate, and 10 lg/mL heparin for 30 min at 37 °C under 5% CO2 in air for capacitation (Oh et al., 2010a; Kwon et al., 2014). Computer-assisted sperm analysis (CASA) A CASA system (SAIS Plus version 10.1, Medical Supply, Seoul, Korea) was used for the analyses of sperm motility (%) and motion kinematics. Briefly, 10 lL of sample was placed in a 37 °C Makler chamber (Makler, Haifa, Israel). Using a 10 9 objective in-phase contrast mode, the image was relayed, digitized, and analyzed by CASA. The movement of at least 250 sperm cells was recorded for each sample from at least five random fields. The user-defined settings for the program were as follows: frames acquired, 20; frame rate, 30 Hz; minimum © 2015 American Society of Andrology and European Academy of Andrology
ANDROLOGY contrast, 7; minimum size, 5; low/high size gates, 0.4–1.5; low/ high intensity gates, 0.4–1.5; non-motile head size, 16; and nonmotile brightness, 14. With respect to the motility setting parameters, objects with a curvilinear velocity (VCL) more than 10 lm/ s were considered motile. Motility (%) and five motion kinematics parameter, i.e., VCL, straight-line velocity (VSL), average path velocity (VAP), linearity (LIN), and mean amplitude of head lateral displacement (ALH) were analyzed. Finally, average motility (%) and motion kinematic parameters of different batches of semen from individual boar were considered for quality assessment. Combined Hoechst 33258/chlortetracycline fluorescence assessment of capacitation status (H33258/CTC) Dual staining was performed according to the methods described by Kwon et al. (2013a,b, 2014), with some modifications. Briefly, 135 lL of non-capacitated and capacitated spermatozoa were added to 15 lL of H33258 solution (150 lM in Dulbecco’s phosphate-buffered saline, DPBS) and incubated for 10 min at room temperature. Excess dye was removed by layering the mixture on approximately 250 lL of 2% (w/v) polyvinylpyrrolidone in DPBS. After centrifugation at 400 g for 10 min, the supernatant was discarded and the pellet resuspended in 500 lL of DPBS; 500 lL of a freshly prepared chlortetracycline (CTC) fluorescence solution (750 mM CTC in 5 lL buffer: 20 mM Tris, 130 mM NaCl, and 5 mM cysteine, pH 7.4). Samples were observed with a Microphot-FXA microscope (Nikon) under epifluorescence illumination using ultraviolet BP 340– 380/LP 425 and BP 450–490/LP 515 excitation/emission filters for H33258 and CTC, respectively. The spermatozoa were classified as live non-capacitated (F-pattern, bright green fluorescence distributed uniformly over entire sperm head, with or without stronger fluorescent line at the equatorial segment), live capacitated (B pattern, green fluorescence over the acrosome region, and a dark post-acrosome), or live acrosome-reacted (AR pattern, spermatozoa showing mottled green fluorescence overhead, green fluorescence only in the post-acrosome region, or no fluorescence above the head) (Kwon et al., 2013a,b, 2014; Rahman et al., 2014b) Two slides per sample were evaluated using at least 400 spermatozoa per slide. Finally, average capacitation status of different batches of semen from individual boar was considered for quality assessment. Quality assessment of parameters Four key parameters were used in the screening tests: sensitivity, specificity, positive predictive value, and negative predictive value (Evans et al., 2002). Sensitivity is determined as the percentage of boars that will be correctly identified by the test as litter size. Specificity is determined as the percentage of boars that will test truly negative. The positive predictive value is determined as the percentage of boars that test positive but actually have litter size ≥ 12 or litter size < 12. The negative predictive value is determined as the percentage of boars that test negative but actually have a litter size of ≥ 12 or < 12. Statistical analysis The data were analyzed in SPSS (v. 18.0; Chicago, IL, USA). Pearson correlation coefficients were calculated to determine the association between motility, motion kinematics, capacitation status in capacitated/non-capacitated spermatozoa, Andrology, 2015, 3, 552–557
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Litter Size MOT (BC) VCL (BC) VSL (BC) VAP (BC) LIN (BC) ALH (BC) MOT (AC) VCL (AC) VSL (AC) VAP (AC) LIN (AC) ALH (AC) MOT (D) VCL (D) VSL (D) VAP (D) LIN (D)
MOT, motility (%); VCL, curvilinear velocity (lm/s); VSL, straight-line velocity (lm/s); VAP, average path velocity (lm/s); LIN, linearity; ALH, mean amplitude of head lateral displacement (lm). *p < 0.05. **p < 0.01.
0.272 0.495** 0.022 0.270 0.126 0.014 0.181 0.455** 0.348* 0.692** 0.454** 0.870** 0.025 0.020 0.160 0.729** 0.430* 0.091 0.161 0.514** 0.544** 0.660** 0.416* 0.684** 0.080 0.208 0.725** 0.711** 0.582** 0.459** 0.271 0.566** 0.882** 0.067 0.354* 0.349* 0.482** 0.409* 0.308 0.527** 0.143 0.108 0.892** 0.789** 0.791** 0.440* 0.226 0.368* 0.187 0.178 0.858** 0.159 0.301 0.417* 0.399* 0.335* 0.453** 0.337* 0.471** 0.065 0.533** 0.192 0.177 0.398* 0.014 0.097 0.027 0.139 0.015 0.483** 0.397* 0.208 0.334* 0.051 0.396* 0.036 0.203 0.069 0.237 0.246 0.062 0.170 0.536** 0.916** 0.624** 0.840** 0.008 0.274 0.296 0.289 0.245 0.263 0.480** 0.449** 0.327* 0.375* 0.776** 0.528** 0.174 0.008 0.197 0.077 0.059 0.262 0.068 0.296 0.573** 0.940** 0.243 0.130 0.215 0.034 0.025 0.343* 0.193 0.056 0.281 0.049 0.282 0.069 0.307 0.317 0.142 0.327* 0.612** 0.030 0.611** 0.023 0.424* 0.203 0.148 0.223 0.222 0.219 0.634** 0.790** 0.899** 0.590** 0.071 0.280 0.548** 0.016 0.310 0.314 0.237 0.519** 0.855** 0.321 0.532** 0.355*
ALH LIN VAP VSL VCL
0.182 0.037 0.129
LIN VAP VSL VCL MOT ALH LIN VAP VSL MOT MOT
VCL
After-capacitation (AC) Before-capacitation (BC)
Quality assessment of parameters To determine the cut-off value for litters of twelve, the AR pattern after capacitation (17.5%), as well as differences (D) in AR pattern (D = 18.97%) and B pattern (D = 0.68%) before and after capacitation were established as the lower limit (Tables 3, 4, and 5). In terms of AR pattern after capacitation, 6 boars had ≥ 17.5% AR pattern that produced ≥ 12 piglets, whereas 6 boars produced < 12 piglets. While 2 boars had < 17.5% AR pattern that produced ≥ 12 piglets, 13 other boars produced < 12 piglets. The sensitivity, specificity, negative predictive value, and positive predictive value were 75, 68.4, 86.7, and 50%, respectively. The overall accuracy of prediction of litters of twelve was 70% (Table 3). The average litter size of boars < 17.5% AR pattern after capacitation was 11.1 piglets, whereas the average litter size of boars with a ≥ 17.5% AR pattern after capacitation was 11.98 piglets (p < 0.05, Fig. 2A). Five boars had ≥ 18.97% differences (D) in AR pattern before and after capacitation that produced ≥ 12 piglets, whereas 6 other boars produced < 12 piglets. Three boars had < 18.97% differences (D) of AR pattern before and after capacitation and produced ≥ 12 piglets; 13 boars produced < 12 piglets. The sensitivity, specificity, negative predictive value, and positive predictive value were 62.5, 68.4, 81.3, and 45.5%, respectively. The overall accuracy of the prediction of litter size ≥ 12 was 70% (Table 4). The average litter size of boars with < 18.97% differences (D) in AR pattern before and after capacitation was 11.18 piglets, whereas the average litter size of boars with ≥ 18.97% differences (D) in AR pattern before and after capacitation was 11.88 piglets (p < 0.05, Fig. 2B).
Litter Size
Correlations between motility (%), motion kinematics, capacitation status before and after capacitation, their variation under capacitation condition, and litter size in boar Motility (%), motion kinematics before and after capacitation, and their variation under capacitation condition had no significant correlation with litter size (Table 1). In addition, capacitation status before capacitation showed no significant correlation with litter size (Table 2). However, AR after capacitation was positively correlated with litter size (r = 0.375; p < 0.05, Table 2, Fig. 1A). Differences (D) in AR and B patterns before and after capacitation also showed significantly correlations with litter size. Differences (D) in AR pattern before and after capacitation was positively correlated with litter size (r = 0.333; p < 0.05, Table 2, Fig. 1B). Differences (D) in the B pattern before and after capacitation was negatively correlated with litter size (r = 0.477; p < 0.05, Table 2, Fig. 1C).
Table 1 Correlations between motility, motion kinematics before and after capacitation, their differences (D) before after capacitation, and litter size in boar
RESULTS
Differences (D) between before- and after-capacitation
ALH
variation in capacitation status, motility, and motion kinematics following capacitation and litter size. Receiver-operating curves (ROCs) were used to assess the ability of individual analyzed parameters as a means of identifying litter size ≥ 12 or < 12 (based on average litter size). The cut-off value was calculated by ROCs. The cut-off value was determined in relation to the point that maximized specificity and sensitivity (Oh et al. (2010a,b); Park et al., 2012). The student’s two-tailed t test was used to compare predicted litter size by ROCs. p < 0.05 was considered significantly different. All data are expressed as mean SEM.
0.167 0.051 0.592** 0.519** 0.606** 0.550** 0.745** 0.171 0.339* 0.589** 0.627** 0.392* 0.531** 0.255 0.704** 0.751** 0.899** 0.138
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Table 2 Correlations between capacitation status before and after capacitation, their differences (D) between before- and after-capacitation, and litter size in boar Litter size
Litter size AR (BC) B (BC) F (BC) AR (AC) B (AC) F (AC) AR (D) B (D)
Before-capacitation (BC)
AR
B
0.298
0.230 0.072
After-capacitation (AC)
F
AR 0.311 0.398* 0.943**
0.375* 0.544** 0.144 0.076
B
Differences (D) between before- and aftercapacitation F
0.156 0.118 0.627** 0.538** 0.481**
AR 0.276 0.492** 0.398* 0.529** 0.685** 0.310
0.333* 0.310 0.151 0.036 0.966** 0.509** 0.625**
B
F 0.447** 0.221 0.409* 0.449** 0.433* 0.454** 0.093 0.423*
0.002 0.152 0.473** 0.384* 0.672** 0.179 0.580** 0.715** 0.331*
F, non-capacitated spermatozoa; B, capacitated spermatozoa; AR, acrosome-reacted spermatozoa. *p < 0.05. **p < 0.01.
Figure 1 Correlation between acrosome reaction (AR) after capacitation, differences (D) in AR and B pattern before and after capacitation, and litter size in boar. (A) Correlation between acrosome reaction (AR) after capacitation and litter size. (B) Correlation between differences (D) in AR pattern before and after capacitation, and litter size. (C) Correlation between differences (D) in B pattern before and after capacitation, and litter size.
(A)
(B)
(C)
Table 3 Correlation between the percentage of acrosome reaction (AR) pattern after capacitation and litter size in boar
AR (AC) ≥ 17.5% (n = 12) AR (AC) < 17.5% (n = 15) Sensitivity Specificity Negative predictive value Positive predictive value Overall accuracy
Litter size ≥ 12
Litter size < 12
6A 2C 75 68.4 86.7 50 70
6B 13D
Sensitivity = [A/(A + C)] 9 100; Specificity = [D/(B + D)] 9 100; Positive predictive value = [A/(A + B)] 9 100; Negative predictive value = [C/ (C + D)] 9 100; and overall accuracy = [(A + D)/(A + B + C + D)] 9 100.
Table 4 Correlation between the percentage of differences (D) of acrosome reaction (AR) pattern before and after capacitation and litter size in boar Litter size ≥ 12 AR (D) ≥ 18.97% (n = 11) AR (D) < 18.97% (n = 16) Sensitivity Specificity Negative predictive value Positive predictive value Overall accuracy
A
5 3C 62.5 68.4 81.3 45.5 70
Six boars had ≤ 0.68% differences (D) in B pattern before and after capacitation that produced more than 12 piglets, whereas 4 other boars produced < 12 piglets. While 2 boars had more than 0.68% differences (D) in B pattern before and after capacitation and produced ≥ 12 piglets, 15 boars produced < 12 piglets. The sensitivity, specificity, negative predictive value, and positive predictive value were 75, 79, 88.2, and 60%, respectively. The overall accuracy of the prediction of litter size ≥ 12 was 70% (Table 5). Average litter size of boars with more than 0.68% differences (D) of B pattern before and after capacitation was 11.19 piglets, whereas average litter size of boars with ≤ 0.68% differences (D) of B pattern before and after capacitation was 11.94 piglets (p < 0.05, Fig. 2C).
Table 5 Correlation between the percentage of differences (D) of B pattern before and after capacitation and litter size in boar
Litter size < 12 B
6 13D
Sensitivity = [A/(A + C)] 9 100; Specificity = [D/(B + D)] 9 100; Positive predictive value = [A/(A + B)] 9 100, negative predictive value = [C/(C + D)] 9 100, and overall accuracy = [(A + D)/(A + B + C + D)] 9 100.
© 2015 American Society of Andrology and European Academy of Andrology
Litter size ≥ 12 B (D) ≤ 0.68% (n = 11) B (D) > 0.68% (n = 16) Sensitivity Specificity Negative predictive value Positive predictive value Overall accuracy
A
6 2C 75 79 88.2 60 70
Litter size < 12 4B 15D
Sensitivity = [A/(A + C)] 9 100; Specificity = [D/(B + D)] 9 100; Positive predictive value = [A/(A + B)] 9 100; Negative predictive value = [C/(C + D)] 9 100; and overall accuracy = [(A + D)/(A + B + C + D)] 9 100.
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Figure 2 Prediction of average litter size by using acrosome reaction (AR), differences (D) in AR and B patterns. (A) Average litter size by percentage of ARpattern after capacitation. (B) Average litter size by differences (D) in AR pattern before and after capacitation. (C) Average litter size by differences (D) in B pattern before and after capacitation.
(A)
(B)
DISCUSSION Female fertility has a more influence on the reproductive performance in a pig industry than boar (Lin et al., 2006). However, substantial numbers of boar replaced in AI program for semen quality (Robinson & Buhr, 2005). Because semen quality contributes significantly to subsequent fertility, prediction or diagnosis of semen quality is of importance. In general, the diagnosis of male infertility has depended on microscopic assessment and biochemical assays of semen quality. These tests are vital in providing basic information regarding sub-fertility; however, their clinical value for predicting fertility has always been questionable (Braundmeier & Miller, 2001; Collins et al., 2008). To analyze the accuracy of diagnosis of male fertility, semen analysis under capacitation condition is required because only capacitated spermatozoa are capable of fertilizing oocytes both in vitro and in vivo (Fraser & McDermott, 1992; Visconti, 2009). Therefore, the present study was designed to establish a novel method for diagnosing male fertility based on conventional semen analysis before and after capacitation. In mammals, spermatozoa gain their motility following ejaculation. The patterns of sperm motility exhibited straightness, progressive trajectories, curvilinearity, and amplitude of lateral displacement that are suitable for the penetration of the cervical mucus of the female genital tract in vivo. A review of literature demonstrated that detection of sperm motility is one of the most easily observed characteristics of spermatozoa. Therefore, it has been widely used in both commercial and laboratory purpose for the assessment of sperm/semen quality. However, the relationship between the sperm motility and fertility has been contradictory (Selles et al., 2003; Oh et al., 2010a). In the present study, we applied the CASA system to evaluate the motility and other kinematics parameters; these approaches have been widely used to assess semen quality in domestic animal species (Petrunkina et al., 2007; Lee et al., 2013) and humans (Moruzzi et al., 1988). A correlation analysis between measured sperm motility parameters and the in vivo fertility of boars (i.e., litter size) were tested to evaluate their de novo relationship. The results showed that sperm motility, motion kinematics (VCL, VSL, VAP, LIN, and ALH), and their differences (D) before and after capacitation were correlated with each other (p < 0.05). However, those parameters of spermatozoa showed a statistically non-significant correlation with litter size (Table 1). Our results support the findings of a previous study on boar spermatozoa that was conducted by Oh et al. (2010a,b). Similarly, Park et al. (2012) and Lewis (2007) reported that although the conventional semen analysis (e.g., evaluation of sperm motility) is 556
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(C)
frequently used to evaluate male fertility, their clinical value has been the topic of intense debate. Based on the results of several published studies and our own, it is tempting to hypothesize that sperm motility and motion kinematics after capacitation provide a potential basis of sperm quality. However, their practical value in predicting in vivo fertility is questionable. On the contrary, it is believed that activation of sperm motility is a capacitationassociated change that confers fertilization (Yanagimachi, 1981; Suarez, 1996; Rahman et al., 2014a). Sharara et al. (1995) reported that the only motile spermatozoa are able to penetrate the cervical mucus, reaching the oocyte zona pellucida, and finally correlating with fertilization both in vitro and in vivo. Therefore, further studies are required to search relationships between sperm motility and fertility. A review of literature showed that there are no specific and direct methods that could evaluate sperm fertility (Larsson & Rodriguez-Martinez, 2000). In fact, fertilization is a set of complex events comprising several regulatory steps. However, it is quite difficult to test most of the sperm characteristics for fertilization. A number of authors have suggested very few approaches that are comparatively specific and handy to serve this purpose (Amann & Hammerstedt, 1993; Collins et al., 2008). In this regard, the evaluation of capacitation status of spermatozoa is considered an important indicator of semen quality. The capacitation status of spermatozoa can be directly measured by monitoring calcium-regulated changes using the fluorescent antibiotic chlortetracycline. The CTC-calcium complex specifically binds to hydrophobic regions such as the cell membrane, resulting in differential staining of F-, B-, and AR-pattern spermatozoa (Gillan et al., 2005). During fertilization, spermatozoa loss the membrane integrity of their head part, which is also known as the acrosome reaction, that facilitates sperm-zona binding following capacitation (Kwon et al., 2013a,b; Rahman et al., 2013; Shukla et al., 2013). In the present study, the capacitation status of spermatozoa was monitored to investigate their relationship with historic litter size. We observed that the AR pattern after capacitation and differences (D) in the AR pattern before and after capacitation were positively correlated with litter size (r = 0.375 and 0.333, respectively) (Table 2, Fig. 1A and 1B). The differences (D) in the B pattern before and after capacitation was negatively correlated with litter size (r = 0.447) (Table 2, Fig. 1C). The overall accuracy of the assay for the prediction of litter sizes using by the AR-, differences (D) in the AR(D) and B pattern (D) was 70% (Table 3, 4 and 5). The average litter size significantly increased from 11.1 to 11.98 by using boar semen that exhibited an AR pattern ≥ 17.5 after capacitation © 2015 American Society of Andrology and European Academy of Andrology
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(Fig. 2A). Similarly, a significant increase in average litter size because of differences (D) in AR pattern (D ≥ 18.97) and differences (D) in B pattern (D ≤ 0.68) before and after capacitation (Fig. 1B and C). These results show that the capacitation status of spermatozoa as measured by CTC staining is a useful predictor of male fertility. We did not consider the in vitro fertility of individual boar in the present study. However, previous reports have shown that the historic litter size is positively correlated with the results of the in vitro sperm penetration assay in boars. Almost similar conclusions regarding the effect of boar spermatozoa capacitation status on its in vitro fertility has been drawn in another study (Oh et al., 2010a). Considering all of these facts, we conclude that the capacitation status of spermatozoa should be considered as an integral part of initial semen analysis for the evaluation of male fertility of animal species and humans. Additionally, CTC staining to detect capacitation status of spermatozoa is comparatively simple, cost-effective, and accurate, as well as does not require intensive laboratory setting.
CONFLICT OF INTEREST All authors declare that no conflict of interest exists.
ACKNOWLEDGEMENTS This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. NRF-2014R1A2A2A01002706).
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ANDROLOGY Kwon WS, Rahman MS, Lee JS, Kim J, Yoon SJ, Park YJ, You YA, Hwang S & Pang MG. (2014) A comprehensive proteomic approach to identifying capacitation related proteins in boar spermatozoa. BMC Genom 15, 897. Larsson B & Rodriguez-Martinez H. (2000) Can we use in vitro fertilization tests to predict semen fertility? Anim Reprod Sci 60–61, 327–336. Lee MS, Rahman MS, Kwon WS, Chung HJ, Yang BS & Pang MG. (2013) Efficacy of four synchronization protocols on the estrus behavior and conception in native Korean cattle (Hanwoo). Theriogenology 80, 855– 861. Lewis SE. (2007) Is sperm evaluation useful in predicting human fertility? Reproduction 34, 31–40. Lin CL, Ponsuksili S, Tholen E, Jennen DG, Schellander K & Wimmers K. (2006) Candidate gene markers for sperm quality and fertility of boar. Anim Reprod Sci 92, 349–363. Milardi D, Grande G, Vincenzoni F, Castagnola M & Marana R. (2013) Proteomics of human seminal plasma: identification of biomarker candidates for fertility and infertility and the evolution of technology. Mol Reprod Dev 80, 350–357. Moruzzi JF, Wyrobek AJ, Mayall BH & Gledhill BL. (1988) Quantification and classification of human sperm morphology by computer-assisted image analysis. Fertil Steril 50, 142–152. Oh SA, Park YJ, You YA, Mohamed EA & Pang MG. (2010a) Capacitation status of stored boar spermatozoa is related to litter size of sows. Anim Reprod Sci 121, 131–138. Oh SA, You YA, Park YJ & Pang MG. (2010b) The sperm penetration assay predicts the litter size in pigs. Int J Andro 33, 604–612. Park YJ, Kwon WS, Oh SA & Pang MG. (2003) Fertility-related proteomic profiling bull spermatozoa separated by percoll. J Proteome Res 11, 4162–4168. Park YJ, Mohamed el-SA, Oh SA, Yoon SJ, Kwon WS, Kim HR, Lee MS, Lee K & Pang MG (2012) Sperm penetration assay as an indicator of bull fertility. J Reprod Dev 58, 461–466. € nzel-Apel AR & To € pfer-Petersen E. (2007) Petrunkina AM, Waberski D, Gu Determinants of sperm quality and fertility in domestic species. Reproduction 134, 3–17. Rahman MS, Lee JS, Kwon WS & Pang MG. (2013) Sperm proteomics: road to male fertility and contraception. Int J Endocrinol 2013, 360986. Rahman MS, Kwon WS & Pang MG (2014a) Calcium influx and male fertility in the context of the sperm proteome: an update. Biomed Res Int ????, 841615. Rahman MS, Kwon WS, Lee JS, Kim J, Yoon SJ, Park YJ, You YA, Hwang S & Pang MG. (2014b) Sodium nitroprusside suppresses male fertility in vitro. Andrology 2, 899–909. Robinson JA & Buhr MM. (2005) Impact of genetic selection on management of boar replacement. Theriogenology 63, 668–678. Selles E, Gadea J, Romar R, Matas C & Ruiz S. (2003) Analysis of in vitro fertilizing capacity to evaluate the freezing procedures of boar semen and to predict the subsequent fertility. Reprod Domest Anim 38, 66–72. Sharara FI & Illions EH, Coddington CC3rd & Scott RTJr (1995) Evaluation of the Tru-Trax cervical mucus penetration test in predicting fertilization and pregnancy rates in in-vitro fertilization. Hum Reprod 10, 1481–1485. Shukla KK, Kwon WS, Rahman MS, Park YJ, You YA & Pang MG. (2013) Nutlin-3a decreases male fertility via UQCRC2. PLoS ONE 8, e76959. Suarez SS. (1996) Hyperactivated motility in sperm. J Androl 17, 331–335. Visconti PE. (2009) Understanding the molecular basis of sperm capacitation through kinase design. Proc Natl Acad Sci USA 106, 667– 668. Yanagimachi R. (1981) Mechanisms of fertilization in mammals: fertilization and Embryonic Development in Vitro. In: Mastroianni L (ed. JD Biggers), pp. 81–182. Plenum Press, NY. Zaneveld LJ, De Jonge CJ, Anderson RA & Mack SR. (1991) Human sperm capacitation and the acrosome reaction. Hum Reprod 6, 1265–1274.
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ORIGINAL ARTICLE
Correspondence: M.-G. Pang, Department of Animal Science and Technology, Chung-Ang University, Anseong, Gyeonggi-Do 456-756, Korea. E-mail: [email protected]
Keywords: boar spermatozoa, capacitation status, correlation, fertility, litter size Received: 10-Nov-2014 Revised: 14-Jan-2015 Accepted: 23-Jan-2015 doi: 10.1111/andr.12020
Improving litter size by boar spermatozoa: application of combined H33258/CTC staining in field trial with artificial insemination W.-S. Kwon M. S. Rahman J.-S. Lee Y.-A. You and *M.-G. Pang Department of Animal Science and Technology, Chung-Ang University, Anseong, Gyeonggi-Do, Korea
SUMMARY Conventional semen analysis offers basic information on infertility; however, its clinical value in predicting fertility status is unclear. To establish an accurate diagnosis of male fertility, semen analysis under capacitation condition is necessary because only capacitated spermatozoa are capable of fertilizing oocytes. The objective of this study was to verify male fertility based on conventional semen analysis before and after capacitation, including the assessment of motility (%), motion kinematics, and capacitation status of spermatozoa. A computer-assisted sperm analysis system and chlortetracycline staining were applied to evaluate the motility parameters and capacitation status, respectively. To enable efficacy of the two methods for predicting fertility, correlation analysis was performed with the historic litter size. Our results showed that sperm motility (%), motion kinematics, and their variations before and after capacitation represented a statistical non-significant correlation with litter size. Litter size showed significant correlation with acrosome reaction (AR) after capacitation (r = 0.375), as well as differences (D) in AR (r = 0.333) and capacitated (B) pattern (r = 0.447) before and after capacitation. The overall accuracy of the assay for predicting litter sizes using the AR and differences (D) in the AR and B pattern was 70%. On the basis of these results, we propose that capacitation status of spermatozoa is a more reliable indicator for evaluating male fertility status compared to motility parameters. Therefore, we suggest that analysis of capacitation status in company with conventional semen analysis may accept to evaluate more accurate diagnosis or prognosis of male fertility.
INTRODUCTION Artificial insemination (AI) has been extensively performed in countries with rigorous pig production industries. To establish a successful AI, prediction of male fertility is a matter of utmost importance (Park et al., 2003, 2012; Oh et al., 2010a). Currently, evaluation of semen quality significantly contributes to AI industries around the world. However, evaluation of semen quality still depends on conventional semen analysis (e.g., assessment of sperm morphology) (Bonde et al., 1998) and motility (Budworth et al., 1988). Semen analysis methods have been well accepted as the method for identifying infertile individuals; however, their efficacy in detecting sub-fertile individuals remains unclear (Collins et al., 2008). To fill the gap, swelling/eosin test (Milardi et al., 2013) and sperm penetration assay (Oh et al., 2010b) has been complemented with conventional analysis. Interestingly, these methods have also generated inconsistent results in predicting fertility (Braundmeier & Miller, 2001). It has been demonstrated that after ejaculation, mammalian spermatozoa inhabit the female genital tract for a considerable 552
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period to undergo necessary modifications, particularly capacitation (Zaneveld et al., 1991; Fraser & McDermott, 1992; Kirichok et al., 2006; Visconti, 2009; Kwon et al., 2013a,b). Therefore, the sperm function test contingent upon capacitation is often evaluated to predict male fertility. Additionally, comparative analysis of parameters between ejaculated and capacitated sperm function is also performed. We hereby describe a reliable approach for the selection of high fertility boar using several sperm function tests, including motility (%), motion kinematics, and assessment of capacitation status. To establish the strategy, detected sperm parameters from semen analysis were analyzed in relation to litter size. Finally, to validate the efficiency of this strategy, differences in the litter size were analyzed using significantly correlated parameters for male fertility evaluation.
MATERIALS AND METHODS All procedures were performed to ethical treatment of animals according to the guidelines and approval of the Institutional © 2015 American Society of Andrology and European Academy of Andrology
IMPROVING LITTER SIZE BY CAPACITATION STATUS
Animal Care and Use Committee of Chung-Ang University in Seoul, Republic of Korea (Approval no. 12-0020). Semen source and artificial insemination (AI) AI was performed in grand-grandparents farm of Sunjin Co. (Danyang, Korea). The animals were housed in a temperature (20 5 °C), ventilation, and light controlled shed. Therefore, no effect of season was observed during study periods. The entire ejaculate from AI stud boar was collected by gloved–hand technique twice per week (Almond et al., 1998) The low limit values for AI and semen analysis were as follows: volume > 150 mL, total concentration > 200 9 106, motility > 70%, and total abnormalities < 20% of the sample. The semen was diluted for AI to a density of 30 9 106 sperm cells/mL in 100 mL of a commercial extender (Beltsville thawing solution). Extended semen was cooled down to 17 °C and stored in a semen storage unit at 17 °C until insemination. Extended semen was inseminated within 3 days of ejaculation. Each male pig’s field fertility data were provided by Sunjin Co. (total live born/total breeding) and analyzed to select 27 Landrace boars (mean age: 29.96 1.35; range = 15–39 months; conception rate = 93.63 0.98; average litter size = 11.47 1.19) spanning a wide fertility range. To eliminate fertility variations according to parity, fertility data from the first parity were excluded (Oh et al., 2010b). A total of 983 multiparous (two to five pregnancies) sows were inseminated twice per estrus (after the sows showed signs of pro-estrus, the sows were examined for the onset of standing estrus every 6 h by using a back pressure test in the presence of a mature boar), and three billion spermatozoa were deposited in the cervix at insemination. The first insemination was performed within 24 h of estrus detection, and after 24 h of the first insemination, a second insemination completed the whole course of AI. Landrace sows were bred with semen collected from the 27 boars and inseminated twice per estrus by well-trained technicians. Historic fertility data of the 27 boars were collected from 2011 to 2013. To avoid individual variations, three extended semen samples were randomly selected from different batches of individual boar. The extended semen for analyses was used within 2 h of collection. The samples were washed with Dulbecco’s phosphate-buffered saline (DPBS) at 1500 g for 10 min and divided into two parts (before and after capacitation). A portion of the extended semen sample was used for the capacitation experiment; this sample was incubated with a modified tissue culture media (mTCM) 199 containing 10% fetal bovine serum (v/v), 0.91 mM sodium pyruvate, 3.05 mM D-glucose, 2.92 mM calcium lactate, and 10 lg/mL heparin for 30 min at 37 °C under 5% CO2 in air for capacitation (Oh et al., 2010a; Kwon et al., 2014). Computer-assisted sperm analysis (CASA) A CASA system (SAIS Plus version 10.1, Medical Supply, Seoul, Korea) was used for the analyses of sperm motility (%) and motion kinematics. Briefly, 10 lL of sample was placed in a 37 °C Makler chamber (Makler, Haifa, Israel). Using a 10 9 objective in-phase contrast mode, the image was relayed, digitized, and analyzed by CASA. The movement of at least 250 sperm cells was recorded for each sample from at least five random fields. The user-defined settings for the program were as follows: frames acquired, 20; frame rate, 30 Hz; minimum © 2015 American Society of Andrology and European Academy of Andrology
ANDROLOGY contrast, 7; minimum size, 5; low/high size gates, 0.4–1.5; low/ high intensity gates, 0.4–1.5; non-motile head size, 16; and nonmotile brightness, 14. With respect to the motility setting parameters, objects with a curvilinear velocity (VCL) more than 10 lm/ s were considered motile. Motility (%) and five motion kinematics parameter, i.e., VCL, straight-line velocity (VSL), average path velocity (VAP), linearity (LIN), and mean amplitude of head lateral displacement (ALH) were analyzed. Finally, average motility (%) and motion kinematic parameters of different batches of semen from individual boar were considered for quality assessment. Combined Hoechst 33258/chlortetracycline fluorescence assessment of capacitation status (H33258/CTC) Dual staining was performed according to the methods described by Kwon et al. (2013a,b, 2014), with some modifications. Briefly, 135 lL of non-capacitated and capacitated spermatozoa were added to 15 lL of H33258 solution (150 lM in Dulbecco’s phosphate-buffered saline, DPBS) and incubated for 10 min at room temperature. Excess dye was removed by layering the mixture on approximately 250 lL of 2% (w/v) polyvinylpyrrolidone in DPBS. After centrifugation at 400 g for 10 min, the supernatant was discarded and the pellet resuspended in 500 lL of DPBS; 500 lL of a freshly prepared chlortetracycline (CTC) fluorescence solution (750 mM CTC in 5 lL buffer: 20 mM Tris, 130 mM NaCl, and 5 mM cysteine, pH 7.4). Samples were observed with a Microphot-FXA microscope (Nikon) under epifluorescence illumination using ultraviolet BP 340– 380/LP 425 and BP 450–490/LP 515 excitation/emission filters for H33258 and CTC, respectively. The spermatozoa were classified as live non-capacitated (F-pattern, bright green fluorescence distributed uniformly over entire sperm head, with or without stronger fluorescent line at the equatorial segment), live capacitated (B pattern, green fluorescence over the acrosome region, and a dark post-acrosome), or live acrosome-reacted (AR pattern, spermatozoa showing mottled green fluorescence overhead, green fluorescence only in the post-acrosome region, or no fluorescence above the head) (Kwon et al., 2013a,b, 2014; Rahman et al., 2014b) Two slides per sample were evaluated using at least 400 spermatozoa per slide. Finally, average capacitation status of different batches of semen from individual boar was considered for quality assessment. Quality assessment of parameters Four key parameters were used in the screening tests: sensitivity, specificity, positive predictive value, and negative predictive value (Evans et al., 2002). Sensitivity is determined as the percentage of boars that will be correctly identified by the test as litter size. Specificity is determined as the percentage of boars that will test truly negative. The positive predictive value is determined as the percentage of boars that test positive but actually have litter size ≥ 12 or litter size < 12. The negative predictive value is determined as the percentage of boars that test negative but actually have a litter size of ≥ 12 or < 12. Statistical analysis The data were analyzed in SPSS (v. 18.0; Chicago, IL, USA). Pearson correlation coefficients were calculated to determine the association between motility, motion kinematics, capacitation status in capacitated/non-capacitated spermatozoa, Andrology, 2015, 3, 552–557
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Litter Size MOT (BC) VCL (BC) VSL (BC) VAP (BC) LIN (BC) ALH (BC) MOT (AC) VCL (AC) VSL (AC) VAP (AC) LIN (AC) ALH (AC) MOT (D) VCL (D) VSL (D) VAP (D) LIN (D)
MOT, motility (%); VCL, curvilinear velocity (lm/s); VSL, straight-line velocity (lm/s); VAP, average path velocity (lm/s); LIN, linearity; ALH, mean amplitude of head lateral displacement (lm). *p < 0.05. **p < 0.01.
0.272 0.495** 0.022 0.270 0.126 0.014 0.181 0.455** 0.348* 0.692** 0.454** 0.870** 0.025 0.020 0.160 0.729** 0.430* 0.091 0.161 0.514** 0.544** 0.660** 0.416* 0.684** 0.080 0.208 0.725** 0.711** 0.582** 0.459** 0.271 0.566** 0.882** 0.067 0.354* 0.349* 0.482** 0.409* 0.308 0.527** 0.143 0.108 0.892** 0.789** 0.791** 0.440* 0.226 0.368* 0.187 0.178 0.858** 0.159 0.301 0.417* 0.399* 0.335* 0.453** 0.337* 0.471** 0.065 0.533** 0.192 0.177 0.398* 0.014 0.097 0.027 0.139 0.015 0.483** 0.397* 0.208 0.334* 0.051 0.396* 0.036 0.203 0.069 0.237 0.246 0.062 0.170 0.536** 0.916** 0.624** 0.840** 0.008 0.274 0.296 0.289 0.245 0.263 0.480** 0.449** 0.327* 0.375* 0.776** 0.528** 0.174 0.008 0.197 0.077 0.059 0.262 0.068 0.296 0.573** 0.940** 0.243 0.130 0.215 0.034 0.025 0.343* 0.193 0.056 0.281 0.049 0.282 0.069 0.307 0.317 0.142 0.327* 0.612** 0.030 0.611** 0.023 0.424* 0.203 0.148 0.223 0.222 0.219 0.634** 0.790** 0.899** 0.590** 0.071 0.280 0.548** 0.016 0.310 0.314 0.237 0.519** 0.855** 0.321 0.532** 0.355*
ALH LIN VAP VSL VCL
0.182 0.037 0.129
LIN VAP VSL VCL MOT ALH LIN VAP VSL MOT MOT
VCL
After-capacitation (AC) Before-capacitation (BC)
Quality assessment of parameters To determine the cut-off value for litters of twelve, the AR pattern after capacitation (17.5%), as well as differences (D) in AR pattern (D = 18.97%) and B pattern (D = 0.68%) before and after capacitation were established as the lower limit (Tables 3, 4, and 5). In terms of AR pattern after capacitation, 6 boars had ≥ 17.5% AR pattern that produced ≥ 12 piglets, whereas 6 boars produced < 12 piglets. While 2 boars had < 17.5% AR pattern that produced ≥ 12 piglets, 13 other boars produced < 12 piglets. The sensitivity, specificity, negative predictive value, and positive predictive value were 75, 68.4, 86.7, and 50%, respectively. The overall accuracy of prediction of litters of twelve was 70% (Table 3). The average litter size of boars < 17.5% AR pattern after capacitation was 11.1 piglets, whereas the average litter size of boars with a ≥ 17.5% AR pattern after capacitation was 11.98 piglets (p < 0.05, Fig. 2A). Five boars had ≥ 18.97% differences (D) in AR pattern before and after capacitation that produced ≥ 12 piglets, whereas 6 other boars produced < 12 piglets. Three boars had < 18.97% differences (D) of AR pattern before and after capacitation and produced ≥ 12 piglets; 13 boars produced < 12 piglets. The sensitivity, specificity, negative predictive value, and positive predictive value were 62.5, 68.4, 81.3, and 45.5%, respectively. The overall accuracy of the prediction of litter size ≥ 12 was 70% (Table 4). The average litter size of boars with < 18.97% differences (D) in AR pattern before and after capacitation was 11.18 piglets, whereas the average litter size of boars with ≥ 18.97% differences (D) in AR pattern before and after capacitation was 11.88 piglets (p < 0.05, Fig. 2B).
Litter Size
Correlations between motility (%), motion kinematics, capacitation status before and after capacitation, their variation under capacitation condition, and litter size in boar Motility (%), motion kinematics before and after capacitation, and their variation under capacitation condition had no significant correlation with litter size (Table 1). In addition, capacitation status before capacitation showed no significant correlation with litter size (Table 2). However, AR after capacitation was positively correlated with litter size (r = 0.375; p < 0.05, Table 2, Fig. 1A). Differences (D) in AR and B patterns before and after capacitation also showed significantly correlations with litter size. Differences (D) in AR pattern before and after capacitation was positively correlated with litter size (r = 0.333; p < 0.05, Table 2, Fig. 1B). Differences (D) in the B pattern before and after capacitation was negatively correlated with litter size (r = 0.477; p < 0.05, Table 2, Fig. 1C).
Table 1 Correlations between motility, motion kinematics before and after capacitation, their differences (D) before after capacitation, and litter size in boar
RESULTS
Differences (D) between before- and after-capacitation
ALH
variation in capacitation status, motility, and motion kinematics following capacitation and litter size. Receiver-operating curves (ROCs) were used to assess the ability of individual analyzed parameters as a means of identifying litter size ≥ 12 or < 12 (based on average litter size). The cut-off value was calculated by ROCs. The cut-off value was determined in relation to the point that maximized specificity and sensitivity (Oh et al. (2010a,b); Park et al., 2012). The student’s two-tailed t test was used to compare predicted litter size by ROCs. p < 0.05 was considered significantly different. All data are expressed as mean SEM.
0.167 0.051 0.592** 0.519** 0.606** 0.550** 0.745** 0.171 0.339* 0.589** 0.627** 0.392* 0.531** 0.255 0.704** 0.751** 0.899** 0.138
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Table 2 Correlations between capacitation status before and after capacitation, their differences (D) between before- and after-capacitation, and litter size in boar Litter size
Litter size AR (BC) B (BC) F (BC) AR (AC) B (AC) F (AC) AR (D) B (D)
Before-capacitation (BC)
AR
B
0.298
0.230 0.072
After-capacitation (AC)
F
AR 0.311 0.398* 0.943**
0.375* 0.544** 0.144 0.076
B
Differences (D) between before- and aftercapacitation F
0.156 0.118 0.627** 0.538** 0.481**
AR 0.276 0.492** 0.398* 0.529** 0.685** 0.310
0.333* 0.310 0.151 0.036 0.966** 0.509** 0.625**
B
F 0.447** 0.221 0.409* 0.449** 0.433* 0.454** 0.093 0.423*
0.002 0.152 0.473** 0.384* 0.672** 0.179 0.580** 0.715** 0.331*
F, non-capacitated spermatozoa; B, capacitated spermatozoa; AR, acrosome-reacted spermatozoa. *p < 0.05. **p < 0.01.
Figure 1 Correlation between acrosome reaction (AR) after capacitation, differences (D) in AR and B pattern before and after capacitation, and litter size in boar. (A) Correlation between acrosome reaction (AR) after capacitation and litter size. (B) Correlation between differences (D) in AR pattern before and after capacitation, and litter size. (C) Correlation between differences (D) in B pattern before and after capacitation, and litter size.
(A)
(B)
(C)
Table 3 Correlation between the percentage of acrosome reaction (AR) pattern after capacitation and litter size in boar
AR (AC) ≥ 17.5% (n = 12) AR (AC) < 17.5% (n = 15) Sensitivity Specificity Negative predictive value Positive predictive value Overall accuracy
Litter size ≥ 12
Litter size < 12
6A 2C 75 68.4 86.7 50 70
6B 13D
Sensitivity = [A/(A + C)] 9 100; Specificity = [D/(B + D)] 9 100; Positive predictive value = [A/(A + B)] 9 100; Negative predictive value = [C/ (C + D)] 9 100; and overall accuracy = [(A + D)/(A + B + C + D)] 9 100.
Table 4 Correlation between the percentage of differences (D) of acrosome reaction (AR) pattern before and after capacitation and litter size in boar Litter size ≥ 12 AR (D) ≥ 18.97% (n = 11) AR (D) < 18.97% (n = 16) Sensitivity Specificity Negative predictive value Positive predictive value Overall accuracy
A
5 3C 62.5 68.4 81.3 45.5 70
Six boars had ≤ 0.68% differences (D) in B pattern before and after capacitation that produced more than 12 piglets, whereas 4 other boars produced < 12 piglets. While 2 boars had more than 0.68% differences (D) in B pattern before and after capacitation and produced ≥ 12 piglets, 15 boars produced < 12 piglets. The sensitivity, specificity, negative predictive value, and positive predictive value were 75, 79, 88.2, and 60%, respectively. The overall accuracy of the prediction of litter size ≥ 12 was 70% (Table 5). Average litter size of boars with more than 0.68% differences (D) of B pattern before and after capacitation was 11.19 piglets, whereas average litter size of boars with ≤ 0.68% differences (D) of B pattern before and after capacitation was 11.94 piglets (p < 0.05, Fig. 2C).
Table 5 Correlation between the percentage of differences (D) of B pattern before and after capacitation and litter size in boar
Litter size < 12 B
6 13D
Sensitivity = [A/(A + C)] 9 100; Specificity = [D/(B + D)] 9 100; Positive predictive value = [A/(A + B)] 9 100, negative predictive value = [C/(C + D)] 9 100, and overall accuracy = [(A + D)/(A + B + C + D)] 9 100.
© 2015 American Society of Andrology and European Academy of Andrology
Litter size ≥ 12 B (D) ≤ 0.68% (n = 11) B (D) > 0.68% (n = 16) Sensitivity Specificity Negative predictive value Positive predictive value Overall accuracy
A
6 2C 75 79 88.2 60 70
Litter size < 12 4B 15D
Sensitivity = [A/(A + C)] 9 100; Specificity = [D/(B + D)] 9 100; Positive predictive value = [A/(A + B)] 9 100; Negative predictive value = [C/(C + D)] 9 100; and overall accuracy = [(A + D)/(A + B + C + D)] 9 100.
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Figure 2 Prediction of average litter size by using acrosome reaction (AR), differences (D) in AR and B patterns. (A) Average litter size by percentage of ARpattern after capacitation. (B) Average litter size by differences (D) in AR pattern before and after capacitation. (C) Average litter size by differences (D) in B pattern before and after capacitation.
(A)
(B)
DISCUSSION Female fertility has a more influence on the reproductive performance in a pig industry than boar (Lin et al., 2006). However, substantial numbers of boar replaced in AI program for semen quality (Robinson & Buhr, 2005). Because semen quality contributes significantly to subsequent fertility, prediction or diagnosis of semen quality is of importance. In general, the diagnosis of male infertility has depended on microscopic assessment and biochemical assays of semen quality. These tests are vital in providing basic information regarding sub-fertility; however, their clinical value for predicting fertility has always been questionable (Braundmeier & Miller, 2001; Collins et al., 2008). To analyze the accuracy of diagnosis of male fertility, semen analysis under capacitation condition is required because only capacitated spermatozoa are capable of fertilizing oocytes both in vitro and in vivo (Fraser & McDermott, 1992; Visconti, 2009). Therefore, the present study was designed to establish a novel method for diagnosing male fertility based on conventional semen analysis before and after capacitation. In mammals, spermatozoa gain their motility following ejaculation. The patterns of sperm motility exhibited straightness, progressive trajectories, curvilinearity, and amplitude of lateral displacement that are suitable for the penetration of the cervical mucus of the female genital tract in vivo. A review of literature demonstrated that detection of sperm motility is one of the most easily observed characteristics of spermatozoa. Therefore, it has been widely used in both commercial and laboratory purpose for the assessment of sperm/semen quality. However, the relationship between the sperm motility and fertility has been contradictory (Selles et al., 2003; Oh et al., 2010a). In the present study, we applied the CASA system to evaluate the motility and other kinematics parameters; these approaches have been widely used to assess semen quality in domestic animal species (Petrunkina et al., 2007; Lee et al., 2013) and humans (Moruzzi et al., 1988). A correlation analysis between measured sperm motility parameters and the in vivo fertility of boars (i.e., litter size) were tested to evaluate their de novo relationship. The results showed that sperm motility, motion kinematics (VCL, VSL, VAP, LIN, and ALH), and their differences (D) before and after capacitation were correlated with each other (p < 0.05). However, those parameters of spermatozoa showed a statistically non-significant correlation with litter size (Table 1). Our results support the findings of a previous study on boar spermatozoa that was conducted by Oh et al. (2010a,b). Similarly, Park et al. (2012) and Lewis (2007) reported that although the conventional semen analysis (e.g., evaluation of sperm motility) is 556
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(C)
frequently used to evaluate male fertility, their clinical value has been the topic of intense debate. Based on the results of several published studies and our own, it is tempting to hypothesize that sperm motility and motion kinematics after capacitation provide a potential basis of sperm quality. However, their practical value in predicting in vivo fertility is questionable. On the contrary, it is believed that activation of sperm motility is a capacitationassociated change that confers fertilization (Yanagimachi, 1981; Suarez, 1996; Rahman et al., 2014a). Sharara et al. (1995) reported that the only motile spermatozoa are able to penetrate the cervical mucus, reaching the oocyte zona pellucida, and finally correlating with fertilization both in vitro and in vivo. Therefore, further studies are required to search relationships between sperm motility and fertility. A review of literature showed that there are no specific and direct methods that could evaluate sperm fertility (Larsson & Rodriguez-Martinez, 2000). In fact, fertilization is a set of complex events comprising several regulatory steps. However, it is quite difficult to test most of the sperm characteristics for fertilization. A number of authors have suggested very few approaches that are comparatively specific and handy to serve this purpose (Amann & Hammerstedt, 1993; Collins et al., 2008). In this regard, the evaluation of capacitation status of spermatozoa is considered an important indicator of semen quality. The capacitation status of spermatozoa can be directly measured by monitoring calcium-regulated changes using the fluorescent antibiotic chlortetracycline. The CTC-calcium complex specifically binds to hydrophobic regions such as the cell membrane, resulting in differential staining of F-, B-, and AR-pattern spermatozoa (Gillan et al., 2005). During fertilization, spermatozoa loss the membrane integrity of their head part, which is also known as the acrosome reaction, that facilitates sperm-zona binding following capacitation (Kwon et al., 2013a,b; Rahman et al., 2013; Shukla et al., 2013). In the present study, the capacitation status of spermatozoa was monitored to investigate their relationship with historic litter size. We observed that the AR pattern after capacitation and differences (D) in the AR pattern before and after capacitation were positively correlated with litter size (r = 0.375 and 0.333, respectively) (Table 2, Fig. 1A and 1B). The differences (D) in the B pattern before and after capacitation was negatively correlated with litter size (r = 0.447) (Table 2, Fig. 1C). The overall accuracy of the assay for the prediction of litter sizes using by the AR-, differences (D) in the AR(D) and B pattern (D) was 70% (Table 3, 4 and 5). The average litter size significantly increased from 11.1 to 11.98 by using boar semen that exhibited an AR pattern ≥ 17.5 after capacitation © 2015 American Society of Andrology and European Academy of Andrology
IMPROVING LITTER SIZE BY CAPACITATION STATUS
(Fig. 2A). Similarly, a significant increase in average litter size because of differences (D) in AR pattern (D ≥ 18.97) and differences (D) in B pattern (D ≤ 0.68) before and after capacitation (Fig. 1B and C). These results show that the capacitation status of spermatozoa as measured by CTC staining is a useful predictor of male fertility. We did not consider the in vitro fertility of individual boar in the present study. However, previous reports have shown that the historic litter size is positively correlated with the results of the in vitro sperm penetration assay in boars. Almost similar conclusions regarding the effect of boar spermatozoa capacitation status on its in vitro fertility has been drawn in another study (Oh et al., 2010a). Considering all of these facts, we conclude that the capacitation status of spermatozoa should be considered as an integral part of initial semen analysis for the evaluation of male fertility of animal species and humans. Additionally, CTC staining to detect capacitation status of spermatozoa is comparatively simple, cost-effective, and accurate, as well as does not require intensive laboratory setting.
CONFLICT OF INTEREST All authors declare that no conflict of interest exists.
ACKNOWLEDGEMENTS This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. NRF-2014R1A2A2A01002706).
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