Acta Veterinaria Hungarica DOI: 10.1556/AVet.2013.009

© 2013 Akadémiai Kiadó, Budapest

VALUE OF SEMEN PARAMETERS, WITH SPECIAL REFERENCE TO TNF-α, IN PREDICTING THE QUALITY

OF BOAR SEMEN AFTER SHORT-TERM STORAGE

Janko MRKUN, Marjan KOSEC and Petra ZRIMŠEK*

Clinic for Reproduction and Horses, Veterinary Faculty, University of Ljubljana, Gerbičeva 60, Ljubljana, Slovenia

(Received 6 January 2012; accepted 27 February 2012)

The aim of this study was to address the question whether changes in boar semen quality after short-term storage could be predicted on the basis of standard semen parameters and TNF-α level determined on the day of semen collection under commercial conditions. Progressive motility showed the highest positive correlation with morphology on day 0 of collection, and progressive motility on day 3 (P < 0.05) showed a negative correlation with acrosome abnormalities (P < 0.05). According to the area under receiver operating characteristics (ROC) curves (AUCs), progressive motility could also be used in predicting semen quality after 3 days of storage (AUC > 0.5; P < 0.05). TNF-α in seminal plasma is the only pa-rameter measured on day 0 to show a significant correlation with the percentage of viable spermatozoa after 3 days of semen storage (r = 0.495, P < 0.05). ROC analysis shows that TNF-α level is helpful in discriminating viability outcome af-ter semen storage (AUC = 0.94, P < 0.001). We can predict with 92.35% certainty that fresh semen samples with more than 150 pg/ml of TNF-α in the seminal plasma will retain more than 85% of viable spermatozoa after 3 days of storage. Thus, TNF-α can contribute to predicting the quality of short-term stored semen.

Key words: Semen, short-term storage, sperm characteristics, TNF-α, boar

The prediction of sperm fertilising ability is economically important when artificial insemination is used in breeding herds, as it enables the selection of boars with good reproductive performance (Gadea et al., 2004; Gadea, 2005). Semen used for artificial insemination (AI) of sows should be of high quality, but traditional estimates of semen quality are not sufficiently sensitive to discrimi-nate between samples that differ in terms of predicting quality following storage. An assay to study the oocyte–sperm interaction, as an in vitro fertilisation (IVF) system, has been developed, but it is expensive and time consuming, and cannot be applied under commercial conditions. Several tests relating to sperm function and fertility have been developed for animal species of economic importance, as *Corresponding author; E-mail: Petra.Zrimsek@vf.uni-lj.si; Phone: 00386 (14) 779-271; Fax: 00386 (12) 832-243

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well as for humans. However, most of these tests show relatively little correla-tion with sperm function and fertility (Gadea and Matas, 2000; Rodriguez-Martinez, 2003). There is, thus, an urgent need for new sperm function parame-ters that correlate better with in vivo fertility (Petrunkina et al., 2007). A simple, inexpensive and accurate assay would be especially valuable under commercial field conditions (Oh et al., 2010).

Recently, tumour necrosis factor (TNF-α) was identified in boar seminal plasma, showing positive correlations with several sperm quality factors (Turba et al., 2007). Under physiological conditions, spermatozoa and seminal plasma enter the uterus, bringing a certain set of cytokines. The induction of cytokines is considered to be one of the very first steps in the cascade of the events ultimately leading up to implantation (Robertson, 2005). However, the cytokine content of the porcine ejaculate has not yet been tested (Taylor et al., 2009). Cytokines meet uterine epithelial cells and resident leukocytes within the uterus. Porcine seminal plasma proteins stimulate macrophages and mast cells to release the pro-inflammatory cytokine TNF-α and the anti-inflammatory IL-4, respectively (Ass-reuy et al., 2003).

The aim of this study was to investigate whether changes in boar semen quality after 3 days of storage could be predicted using standard semen parame-ters obtained on the day of semen collection under commercial conditions, with special attention to TNF-α.

Material and methods

Semen samples and analysis

Nineteen semen samples from eleven (5 Swedish Landrace and 6 German Landrace), 12- to 24-month-old boars were collected and diluted with Beltswille Thawing Solution (BTS) at a ratio of 1:2. Semen was kept at room temperature and analysed within 1 h. Sperm concentration and motility characteristics were determined using assisted semen analysis (Hamilton Thorne IVOS 10.2, Hamil-ton Thorne Research, MA, USA) with a Makler counting chamber (Sefi Medical Instruments, Israel). Sperm viability was assessed using Hoechst staining (Pintado et al., 2000) and morphology on Giemsa-stained samples (Kovács and Foote, 1992). Semen was then stored at 17 °C for 3 days, with constant, gentle agitation. After storage, semen characteristics of liquid-stored semen samples were evalu-ated in the same way as for fresh samples, as stated above.

Seminal plasma preparation and TNF-α determination

Semen was centrifuged at 818 × g for 10 min at room temperature. The supernatant was removed and further centrifuged at 13,000 × g for 15 min at 4 °C to separate the seminal plasma, which was then aliquoted and frozen at –80 °C un-til analysis for TNF-α.

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TNF-α concentrations in the seminal plasma were assessed using a swine-specific commercial immunoassay kit (solid-phase sandwich ELISA) (swine TNF-α ELISA kit, Invitrogen). A monoclonal antibody specific for swine TNF-α was coated onto the wells of the microtitre strips provided. During the first incubation with samples, controls and standard solutions, the swine TNF-α antigen binds to the immobilised antibody of the site. After washing, a biotinylated monoclonal antibody specific for swine TNF-α was added, which binds to the immobilised swine TNF-α during incubation. After washing to remove the unbound material, streptavidin-peroxidase (enzyme) is added; this binds to the biotinylated anti-body to complete the four-member sandwich. After a third incubation and wash-ing to remove unbound enzyme, a colour reaction was developed by incubation with a substrate solution which was acted upon by the bound enzyme. The en-zymatic reaction was stopped by the addition of stop solution, and the absorb-ance was measured at 450 nm. Standard solutions of TNF-α from 0 to 750 pg/ml were tested to generate the standard curve. Concentrations of unknown samples and controls were read from the standard curve. All samples, controls and stan-dards were tested in duplicate.

Correlation analysis

Spearman rank correlation coefficient was used to determine the relation-ship between semen parameters on day 0 and day 3. Statistical comparison of the results obtained on day 0 and day 3 for each semen parameter was performed with the Mann-Whitney U-test.

Diagnostic evaluation

Diagnostic evaluation was performed to determine whether boar semen quality after 3 days of storage can be predicted from semen parameters obtained on the day of semen collection. Semen samples were divided into satisfactory (SAT) and unsatisfactory (UNSAT) groups according to semen parameters after 3 days of storage, using the following criteria for SAT semen samples: viability > 85%, motility: > 70%, progressive motility: > 25%, normal morphology: > 50%, acrosome abnormalities: < 20%.

According to the semen parameters determined on day 0, the test results were divided into a positive and a negative group. A positive test result (T+) was recorded when spermatozoa in a sample showed a value below the cut-off values for viability, motility, TNF-α and morphology. For acrosome abnormalities, a positive test result was recorded in samples with values above the breaking point. A negative test result (T–) was recorded when spermatozoa in a sample showed values above the cut-off for viability, motility, TNF-α and morphology and be-low the cut-off for acrosome abnormalities.

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Four categories of results were obtained: true positive (TP), true negative (TN), false positive (FP) and false negative (FN) (Table 1), and the following di-agnostic parameters were calculated: sensitivity, specificity, positive predictive value and negative predictive value (Petrie and Watson, 1999). Sensitivity and specificity indicate how effective the test is at identifying unsatisfactory (UNSAT) and satisfactory (SAT) semen samples. Sensitivity for each cut-off value was cal-culated as the proportion of positive test results (T+) for UNSAT samples and specificity as the proportion of negative test results (T–) for SAT samples. Pre-dictive values are also calculated: a positive predictive value (PPV) is the propor-tion of UNSAT samples in a group of positive test results (T+), whereas a nega-tive predictive value (NPV) is the proportion of SAT semen samples in a group of negative test results (T–) (Table 1).

Table 1

Measures of diagnostic parameters

Semen sample status Test result

UNSAT SAT

T+ TP FP TP + FP T– FN TN FN + TN TP + FN FP + TN

T+: positive test result; T–: negative test result; SAT: satisfactory semen sample after 3 days of storage; UNSAT: unsatisfactory se-men sample after 3 days of storage; TP: true positive result; TN: true negative result; FP: false positive result; FN: false negative re-sult. Calculation of diagnostic parameters: Sensitivity = TP / (TP + FN); Specificity = TN / (TN + FP); Positive predictive value = TP / (TP + FP); Negative predictive value = TN / (TN+ FN)

ROC curves plotted all sensitivity versus 1-specificity for the complete

range of cut-off points. Lowering the threshold reduces the sensitivity of the measures (the likelihood of correctly identifying UNSAT semen samples) but in-creases their specificity (the likelihood of correctly identifying SAT semen sam-ples). All possible combinations of sensitivity and specificity that can be achieved by changing the breaking point are summarised by a single parameter, i.e. the area under the ROC curve (AUC) (Greiner et al., 2000).

On the basis of the AUC for each semen parameter, we can predict whether or not the information is helpful in discriminating semen quality out-come after 3 days of storage. Helpful information about semen quality on day 3 is found when AUC close to 1 is observed together with statistical significance (P < 0.05). Cut-off values (breaking points) for fresh semen parameters that can differentiate satisfactory semen samples from unsatisfactory after 3 days of stor-age were identified.

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Results

Correlation between sperm characteristics on days 0 and 3

The values of semen parameters differ significantly between day 0 and day 3 according to all semen characteristics (P < 0.05) (Table 2).

Table 2

Summary of sperm parameters in AI dose

Day 0 Day 3

Mean ± SD Range (Min–Max) Mean ± SD Range

(Min–Max)

Sperm concentration (million/ml)

275.20 ± 66.25

154.83 – 382.17

275.20 ± 66.25

154.83 – 382.17

Motility (%) 82.95 ± 8.45 67.00 – 94.33 68.05 ± 14.89 25.67 – 89.33 Progressive motility (%) 39.46 ± 5.07 30.00 – 46.67 25.41 ± 8.09 13.00 – 43.00 Viability (Hoechst) (%) 93.64 ± 4.03 79.00 – 98.50 87.21 ± 4.75 76.00 – 93.50 Morphologically normal forms (%)

73.32 ± 9.15

53.25 – 84.75

54.99 ± 9.27

39.25 – 69.00

Acrosome abnormalities (%)

7.38 ± 6.02

1.25 – 21.75

25.61 ± 7.92

13.25 – 39.25

TNF-α (pg/ml) 245.16 ± 205.37 10.73 – 722.01 / /

Values are means ± SD (n = 19 samples)

Table 3 shows Spearman correlation coefficients between values obtained

on day 0 and day 3 for each semen parameter. Values of progressive motility, morphology and acrosome abnormalities on day 0 and day 3 correlate signifi-cantly (P < 0.05). Significant correlations were observed between progressive motility on day 0 and normal morphology (positive correlation) as well as acro-some abnormalities on day 3 (negative correlation) (P < 0.05). Additionally, morphology and acrosome abnormalities on day 0 correlate with progressive mo-tility on day 3. TNF-α in seminal plasma is the only parameter measured on day 0 to show a positive and significant correlation with the percentage of viable spermatozoa after 3 days of semen storage (r = 0.495, P < 0.05). Correlation be-low the level of significance (0.05 < P < 0.10) was observed between morphology (day 0) and viability (day 3), acrosome abnormalities (day 0) and motility after 3 days of storage as well as TNF-α in seminal plasma and morphology (day 3) and acrosome abnormalities (day 3). Motility and viability on day 0 did not show any significant correlation with semen characteristics on day 3.

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Table 3

Correlation (r value) between sperm parameters in boar semen on days 0 and 3 of liquid semen storage

Semen parameters – day 0 Semen parameters – day 3 Motility Progressive

motility Viability (Hoechst)

Morphologi-cally normal

forms

Acrosome abnormalities TNF-α

Motility 0.357 0.247 0.266 0.217 –0.420* –0.124 Progressive motility 0.032 0.522** –0.003 0.516** –0.457** 0.305 Viability (Hoechst) 0.134 0.382 0.106 0.419* –0.311 0.495** Morphologically normal forms

0.173

0.494**

0.075

0.812**

–0.635**

0.393*

Acrosome abnormalities

0.332

–0.563**

0.353

–0.379

0.604**

–0.421*

*0.05 < P < 0.10; **P < 0.05

Diagnostic evaluation

Receiver operating curves (ROC) were performed to determine the overall discriminating power of each semen variable (Table 4). ROC was also used to calculate the elective breaking point (cut-off value) for each semen variable in re-lation to semen quality after 3 days of liquid storage. The elective breaking point was chosen to maximise the sum of sensitivity and specificity. Cut-off values (breaking points) for fresh semen parameters that can differentiate satisfactory from unsatisfactory semen samples after 3 days of storage were identified. Sensi-tivity determines the percentage of all semen samples to be identified correctly by the fresh semen parameter as unsatisfactory after 3 days of liquid storage, and specificity shows what percentage of satisfactory samples after 3 days were test negative by fresh semen parameter. It is important to know what percentage of samples with a positive test result is actually unsatisfactory after 3 days of stor-age: this is the positive predictive value. The negative predictive value is the per-centage of samples with a negative test result that are actually satisfactory after storage.

Progressive motility of more than 40%, normal morphology of more than 72.5%, and acrosome abnormalities less than 5% provide helpful information in predicting SAT semen samples according to normal morphology outcome after storage (NPV between 88.9 and 100%). Progressive motility shows the highest predictive value for acrosome abnormalities (PPV 90%) after semen storage. UNSAT semen samples after storage, in terms of progressive motility, can be predicted with 80% or more from progressive motility, normal morphology and acrosome abnormalities of fresh semen samples. Only TNF-α provides relevant

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information about the viability of stored semen (AUC 0.94, P < 0.0001). The elective point of TNF-α for distinguishing stored semen samples according the viability of 85% was found at 150 pg/ml. Sensitivity indicates that the concentra-tion of TNF-α in fresh seminal plasma was less than 150 pg/ml in 80.0% of the samples, with viability after storage lower than 85%. In contrast, 85.7 % of the semen samples with more than 85% viability had at least 150 pg/ml TNF-α in the seminal plasma. The positive predictive value shows that 66.7% of all semen samples with TNF-α < 150 pg/ml have unsatisfactory viability (< 85%). High NPV was calculated; 92.3% of semen samples with concentrations of TNF-α in seminal plasma more than 150 pg/ml have more than 85% viable spermatozoa after 3 days of storage (Table 5).

Table 4

ROC analysis: parameters on day 0 for various criterion values

Criterion value on day 3 AUC (ROC) SE P

Progressive motility on day 0

Progressive motility, > 25% 0.82 0.102 0.0008 Morphologically normal forms, > 50% 0.69 0.141 0.0934 Acrosome abnormalities, < 20% 0.82 0.099 0.0006

Morphologically normal forms

Progressive motility, > 25% 0.78 0.117 0.0088 Morphologically normal forms, > 50% 0.95 0.048 < 0.0001

Acrosome abnormalities

Progressive motility, > 25% 0.79 0.115 0.0053 Morphologically normal forms, > 50% 0.79 0.112 0.0041 Acrosome abnormalities, < 20% 0.71 0.124 0.0439

TNF-α in the seminal plasma

Viability, > 85% 0.94 0.053 < 0.0001

AUC (ROC): area under the curve (receiver operating characteristics); SE: standard error; P: statis-tical significance

Discussion

In commercial farms, where short-term stored semen is used, boar semen is examined routinely with the object of predicting the male’s fertility. Since fer-tilisation is a complex process involving many events, not only must more pre-dictive laboratory tests be devised, but the results of different assays aiming at predicting male fertilising ability from spermatozoa should be properly com-bined to satisfy the numerous requirements for successful fertilisation (Quintero-

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Moreno et al., 2004). Therefore, tumour necrosis factor, TNF-α, which has re-cently been identified in boar seminal plasma (Turba et al., 2007), was included in our study of the predictive value of fresh seminal parameters on the quality of short-term stored boar semen.

Table 5

Cut-off values (breaking points) and diagnostic parameters of semen variables on day 0 related sig-nificantly to various semen parameters on day 3 calculated from ROC curves

Variable, day 3 AUC (P) Sens. Spec. PPV NPV

Progressive motility – day 0; breaking point: < 40%

Progressive motility 0.82 (0.0008) 80.0% 77.8% 80.0% 77.8 Morphologically normal forms 0.69 (0.0934) 83.3% 61.5% 50.0% 88.9 Acrosome abnormalities 0.82 (0.0006) 69.2% 83.3% 90.0% 55.6

Morphologically normal forms – day 0; breaking point: < 72.5%

Progressive motility 0.78 (0.0088) 70.0% 88.9% 87.5% 72.7 Morphologically normal forms 0.95 (< 0.0001) 100% 84.6% 75.0% 100.0

Acrosome abnormalities – day 0; breaking point: > 5%

Progressive motility 0.79 (0.0053) 80.0% 77.8% 80.0% 77.8 Morphologically normal forms 0.79 (0.0041) 100% 69.2% 60.0% 100.0 Acrosome abnormalities 0.71 (0.0439) 61.5% 66.7% 80.0% 44.4

TNF-α - day 0, breaking point: < 150 pg/ml

Viability 0.94 (< 0.0001) 80.0% 85.7% 66.7% 92.3

AUC: area under the ROC curve; P: statistical significance; Sens.: sensitivity; Spec.: specificity; PPV: positive predictive value; NPV: negative predictive value

Significant correlations were observed between progressive motility on

day 0 and normal morphology (positive correlation) as well as acrosome abnor-malities on day 3 (negative correlation) (P < 0.05). In a study concerning the predictive value of porcine seminal parameters for fertility outcome under com-mercial conditions, a poor correlation was found. Only sperm motility correlated significantly with farrowing rate and the total number of piglets born (Gadea et al., 2004). For example, sperm motility and methanol-stressed sperm motility pa-rameters correlated significantly with non-return rate, but not with litter size; however, a higher level of significance was observed for seven-day stored semen (Sutkeviciene et al., 2005). Reported correlations differ; characteristics such as having a normal sperm head or tail morphology (Gadea and Matas, 2000) and progressive motility (Flowers, 1997; Ivanova and Mollova, 1993) had a positive relationship with boar fertility, but not in other studies (Xu et al., 1996). These contradictory results could be due to experimental differences, including low

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numbers of ejaculates or of sows analysed. Moreover, a large number of sperm per dose could compensate for any fertility factor and could mask the relation-ship with sperm quality (Tardif et al., 1999; Johnson et al., 2000). Pre-selection of the ejaculates by motility under commercial conditions also tends to reduce the variability in semen parameters, showing higher correlations than where no pre-selection was performed (Gadea et al., 1998). Pre-selection could also reduce the correlation, as demonstrated for in vitro fertility (Gadea and Matas, 2000).

TNF-α in seminal plasma is the only parameter measured on day 0 to show a positive and significant correlation with the percentage of viable sper-matozoa after 3 days of semen storage (r = 0.495, P < 0.05). Our finding is in agreement with the results of positive correlations of TNF-α in boar seminal plasma with the motility and viability of fresh semen (Turba et al., 2007). A positive role of TNF-α was also suggested by Vera et al. (2003) in bull semen, in which TNF-α was negatively correlated with other pro-inflammatory cytokines, therefore indicating a possible protective role. The cytokine content of porcine ejaculate constituents has not yet been analysed (Taylor et al., 2009), unlike the many studies concerning cytokines in human seminal plasma. However, studies on the effect of cytokines in sperm function have provided controversial outcomes. TNF-α levels were significantly elevated in seminal plasma of men with de-creased motility (Ulcova-Gallova et al., 2009) and in an abnormal group (Quian et al., 2011), in direct contrast to the study of Eggert-Kruse et al. (2007), where TNF-α had no effect on sperm numbers or motility. Similarly, a study with si-multaneous expression of seven cytokines in the seminal plasma of men revealed different groups of male subfertility (Seshardi et al., 2009). The measurement of each cytokine separately in the seminal plasma of subfertile men, despite signifi-cant differences, does not have diagnostic value in male infertility. However, an assay of selective cytokines and leukocytes in the semen of men may prove to be a clinical value in the diagnosis of male accessory gland infection and facilitate treatment (Seshardi et al., 2009).

The significant correlations between semen parameters on day 0 and day 3 in our study led us to the diagnostic evaluation of parameters of fresh semen as predictors for semen quality outcome on the third day of semen storage. Accord-ing the AUCs, progressive motility could be used for predicting semen quality after 3 days of liquid storage in terms of morphology, progressive motility and acrosome abnormalities (AUC > 0.5; P < 0.05). In the present study we showed, on the basis of AUC, that only TNF-α in the seminal plasma provides useful in-formation in discriminating viability outcome after semen storage (AUC = 0.94, P < 0.001). Viability and membrane function are important factors in assessing the potential fertilisation capacity and are considered to be more accurate predic-tors of the fertilisation capacity of semen than motility (Frazer et al., 2002). Moreover, viability of seven-day stored semen stained with Hoechst dye was the only semen parameter that correlated significantly with litter size of multiparous

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farrowing (Sutkeviciene et al., 2005). The best cut-off to distinguish between SAT and UNSAT semen samples after storage, according to viability at 85%, was 150 pg/ml. We can predict with 92.35% certainty that fresh semen samples with more than 150 pg/ml of TNF-α in seminal plasma will retain more than 85% of viable spermatozoa after 3 days of storage. Therefore, TNF-α provides helpful information in discriminating the viability outcome following semen storage. Al-though he assessment of viability by fluorescence studies is essential, the meth-ods used are not suitable for routine assays at AI stations since they are time con-suming when using a microscope, and/or have a high price and running costs (when using flow cytometry) (Alm et al., 2001). Therefore, we propose that de-termining TNF-α in fresh seminal plasma by a commercial immunoassay kit could contribute to predicting the fertility outcome, but further studies are needed to prove our prediction. Our results have demonstrated that TNF-α, together with progressive motility and morphology, could contribute to the prediction of quality of short-term stored semen.

Acknowledgements

This work was supported by the Slovenian Research Agency, ‘Endocrine, im-mune, nervous and enzyme responses in healthy and sick animals’ (P4-0053). The au-thors thank Professor Roger Pain for the English revision of the manuscript.

References

Alm, K., Taponen, J., Dahlbom, M., Tuunainen, E., Koskinen, E. and Andersson, M. (2001): A novel automated fluorometric assay to evaluate sperm viability and fertility in dairy bulls. Theriogenology 56, 677–684.

Assreuy, A-M., Alencar, N. M., Cavada, B. S., Rocha-Filho, D. R., Feitosa, R. F., Cunha, F. Q., Calvete, J. J. and Ribeiro, R. A. (2003): Porcine spermadhesin PSP-I/PSP-II stimulates macrophages to release a neutrophil chemotactic substance: modulation by mast cells. Biol. Reprod. 68, 1836–1841.

Eggert-Kruse, W., Kiefer, I., Beck, C., Demiracka, T. and Strowitzki, T. (2007): Role for tumor necrosis factor alpha (TNF-alpha) and interleukin 1-beta (IL-1 beta) determination in seminal plasma during infertility investigation. Fertil. Steril. 87, 810–823.

Flowers, W. L. (1997): Management of boars for efficient semen production. J. Reprod. Fertil. Suppl 52, 67–78.

Frazer, L., Lecewicz, M. and Strzezek, J. (2002): Fluorimetric assessment of viability and mito-chondrial status of boar spermatozoa following liquid storage. Pol. J. Vet. Sci. 2, 85–92.

Gadea, J. (2005): Sperm factors related to in vitro and in vivo porcine fertility. Theriogenology 63, 431–444.

Gadea, J. and Matas, C. (2000): Sperm factors related to in vitro penetration of porcine oocytes. Theriogenology 54, 1343–1357.

Gadea, J., Matas, C. and Lucas, X. (1998): Prediction of porcine semen fertility by homologous in vitro penetration (hIVP) assay. Anim. Reprod. Sci. 54, 95–108.

TNF-α AS A PREDICTOR OF BOAR SEMEN QUALITY AFTER STORAGE 11

Acta Veterinaria Hungarica

Gadea, J., Selles, E. and Marco, M. A. (2004): The predictive value of porcine seminal parameters on fertility outcome under commercial conditions. Reprod. Dom. Anim. 39, 303–308.

Greiner, M., Pfeiffer, D. and Smith, R. D. (2000): Principles and practical application of the re-ceiver-operating characteristic analysis for diagnostic tests. Prev. Vet. Med. 54, 23–41.

Ivanova, M. and Mollova, M. (1993): Zona-penetration in vitro test for evaluating boar sperm fer-tility. Theriogenology 40, 397–410.

Johnson, L. A., Weitze, K. F., Fiser, P. and Maxwell, W. M. C. (2000): Storage of boar semen. Anim. Reprod. Sci. 62, 143–172.

Kovács, A. and Foote, R. H. (1992): Viability and acrosome staining of bull, boar and rabbit sper-matozoa. Biotech. Histochem. 67, 119–124.

Oh, S. A., Park, Y. J., You, Y. A., Mohamed, E. A. and Pang, M. G. (2010): Capacitation status of stored boar spermatozoa is related to litter size of sows. Anim. Reprod. Sci. 121, 131–138.

Petrie, A. and Watson, P. (1999): Diagnostic tests. In: Statistics for Veterinary and Animal Sci-ence. Blackwell Science, Oxford. pp. 168–181.

Petrunkina, A. M., Waberski, D., Günzel-Apel, A. R. and Töpfer-Petersen, E. (2007): Determinants of sperm quality and fertility in domestic species. Reproduction 134, 3–17.

Pintado, B., de la Fuente, J. and Roldan, E. R. S. (2000): Permeability of boar and bull spermato-zoa to the nucleic acid stains propidium iodide or Hoechst 33258, or to eosin: accuracy in the assessment of cell viability. J. Reprod. Fert. 118, 145–152.

Quian, L., Sun, G., Zhou, B., Wang, G., Song, J. and He, H. (2011): Study on the relationship be-tween different cytokines in the semen of infertility patients. Am. J. Reprod. Immunol. 66, 157–161.

Quintero-Moreno, A., Rigau, T. and Rodriguez-Gil, J. E. (2004): Regression analysis and motile sperm subpopulation structure study as improving tools in boar semen quality analysis. Theriogenology 61, 673–690.

Robertson, S. A. (2005): Seminal plasma and male factor signalling in the female reproductive tract. Cell Tissue Res. 322, 43–52.

Rodriguez-Martinez, H. (2003): Laboratory semen assessment and prediction of fertility: still uto-pia? Reprod. Domest. Anim. 38, 312–318.

Seshardi, S., Bates, M., Vince, G. and Jones, D. I. L. (2009): The role of cytokines expression in different subgroups of subfertile men. Am. J. Reprod. Immunol. 62, 275–282.

Sutkeviciene, N., Andersson, M. A., Zilinskas, H. and Andersson, M. (2005): Assessment of boar semen quality in relation to fertility with special reference to methanol stress. Theriogenol-ogy 63, 739–747.

Tardif, S., Laforest, J. P., Cormier, N. and Bailey, J. L. (1999): The importance of porcine sperm parameters on fertility in vivo. Theriogenology 52, 447–459.

Taylor, U., Zerbe, H., Seyfert, H. M., Rath, D., Baulain, U., Langner, K. F. and Schubert, H. J. (2009): Porcine spermatozoa inhibit post-breeding cytokine induction in uterine epithelial cells in vivo. Anim. Reprod. Sci. 115, 279–289.

Turba, M. E., Fantinati, P., Bernardini, C., Gentilini, F., Bacci, M. L. and Forni, M. (2007): Rela-tionship between innovative and traditional parameters to investigate semen quality in pigs. Anim. Reprod. Sci. 99, 72–81.

Ulcova-Gallova, Z., Gruberova, J., Vrzalova, J., Bibkova, B., Peknicova, J. and Micanova, Z. (2009): Sperm antibodies, intra-acrosomal sperm proteins, and cytokines in semen in men from infertile couples. Am. J. Reprod. Immunol. 61, 236–245.

Vera, O., Vasquez, L. A. and Muňoz, M. G. (2003): Semen quality and presence of cytokines in seminal fluid of bull ejaculates. Theriogenology 60, 553–558.

Xu, X., Seth, P. C., Harbison, D. S., Cheung, A. P. and Foxcroft, G. R. (1996): Semen dilution assessment of boar ejaculate quality in pig IVM and IVF system. Theriogenology 46, 1325–1337.