Reprod Dom Anim 50 (Suppl. 2), 103–109 (2015); doi: 10.1111/rda.12558 ISSN 0936–6768
Review Article Efficient Boar Semen Production and Genetic Contribution: The Impact of Low-Dose Artificial Insemination on Fertility MLWJ Broekhuijse1, AH Gaustad2,3, A Bolarin Guillen4 and EF Knol1 1 Topigs Norsvin Research Center, Beuningen, The Netherlands; 2Topigs Norsvin, Hamar, Norway; 3University College of Hedmark, Hamar, erica, Madrid, Spain Norway; 4Topigs Norsvin AIM Ib
Contents Diluting semen from high fertile breeding boars, and by that inseminating many sows, is the core business for artificial insemination (AI) companies worldwide. Knowledge about fertility results is the reason by which an AI company can lower the concentration of a dose. Efficient use of AI boars with high genetic merit by decreasing the number of sperm cells per insemination dose is important to maximize dissemination of the genetic progress made in the breeding nucleus. However, a potential decrease in fertility performance in the field should be weighed against the added value of improved genetics and, in general, is not tolerated in commercial production. This overview provides some important aspects that influence the impact of low-dose AI on fertility: (i) the importance of monitoring field fertility, (ii) the need for accurate and precise semen assessment, (iii) the parameters that are taken into account, (iv) the application of information from genetic and genomic selection and (v) the optimization when using different AI techniques. Efficient semen production, processing and insemination in combination with increasing use of genetic and genomic applications result in maximum impact of genetic trend.
Monitoring Field Fertility Gives Insight into the Effect of the Artificial Insemination Dose Efficient artificial insemination (AI) is essential for future challenges in the pig industry. Knowledge on the exact relation between semen quality characteristics and fertility can have a major impact on both the genetic merit of future animals and the efficiency of AI. From a commercial perspective, it is important for an AI company to monitor its results in practice. Knowing the performance of semen in the field by keeping track of records enables efficient analysis of the causes of poor performance at individual farm level. Determining the proper number of motile cells in an AI dose, the maximum allowed morphological defects, the optimal value for computer-assisted semen analysis (CASA) motility parameters and the shelf life of a dose permits the AI company to work as efficiently as possible at lowest possible cost price and with maximum field fertility. An example is decreasing the number of cells in an AI dose. Lowering the number of cells in a dose is not possible without knowing the potential risk of
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decrease in field fertility. Risk mitigation of cut-off levels is guaranteed by continuing statistical analyses on these parameters. Over the years, the total number of sperm cells in an AI dose decreased in the Netherlands from 4.0 billion in 100-ml disposable bottles (1985) down to 1.5 billion in 80-ml disposable tubes (started in 2008) (Feitsma 2009). The relatively high number of sperm cells in a dose used in commercial AI practice likely masks reduced fertility (Foxcroft et al., 2008). Through the years, experiments were performed using low-dose inseminations. These data showed that in some cases, it was feasible to decrease the number of sperm cells in an AI dose without noticing a significant effect on fertility results. Recent analysis was performed on the effect of number of sperm cells on fertility based on data from 2007 until 2013, with 584 787 fertility records from 6241 boars. Insemination records were corrected for the significant sow-related parameters, such as parity, weaning to oestrous interval (WEI), herd, year, season (HYS), purebred/cross-bred, single/double AI and age of semen, and for the significant boar-related parameters, such as genetic line of the boar, age, AI station, year, season (AIYS), individual boar and ejaculate (within boar), and focused on concentration of the AI dose. Records were analysed from 1.2 until 2.5 billion motile cells per dose. It was concluded that there is no effect of concentration of an AI dose on farrowing rate (FR) (p = 0.2366) and no effect on total number of piglets born (TNB) (p = 0.1314), when the parameter is analysed as a separate entity. Some farms, however, could be more sensitive to the concentration of an AI dose; therefore, the interaction between farm and concentration was estimated as well. This interaction tested not significant for both FR (p = 0.4381) and TNB (p = 0.4517). Lowering the concentration of an AI dose results in using genetically high ranking boars more efficiently with an economic impact. Discriminating between 3.0, 2.5 and 1.5 billion cells in an AI dose has a clear effect on the number of boars needed for inseminating a herd. Topigs Norsvin Selection Index (TSI) is an index combining breeding values of individual traits with their respective economic relevance. The TSI gives an
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estimate of the genetic value of a pig; highest values are the most interesting for breeding. Using fewer boars, you can discard the lowest ranking boars and increase in TSI with additional economic consequences. This depends very much on the value of and the increase in TSI. On the other hand, economic losses due to boar reproductive failure can be substantial as well (Roberts 1986; Lunenfeld and Insler 1993). Efficiency of piglet production is dependent on many factors; one we can assess is the quality of semen. It is desirable to design a semen quality test that predicts the fertilizing capacity of the semen. To match the semen quality aspects measured with fertilizing ability of the semen, the most critical aspect is to have both semen quality assessments and fertility data that are specific, precise (repeatable) and accurate. These data are very difficult to obtain when problems occur such as (i) boars or sows that are not representative for the population and/or are too few in number, (ii) insufficient sows inseminated with semen from each sample, (iii) too few semen samples assessed per boar, (iv) inappropriate number of cells used for each insemination, and (v) fertility outcome is not reported properly (Broekhuijse et al. 2011a). Topigs Norsvin records standardly data from both boar (AI) and sow (production farm) level. To discriminate within boars between ejaculates (and due to legislation), it is important that no mixed semen is allowed; single sire doses are shipped from AI to the production farms. These large data sets are suitable to analyse effects of semen quality characteristics on field fertility. Variation in fertility in sows is large. The effect of service sire and more precisely the measured ejaculate parameters is relatively small and therefore impossible to find in smaller data sets. Large data sets allow for statistical corrections on parameters related to both sows and boars. Remaining sow fertility variation can then be tested for the influence of semen quality parameters, and differences in litter sizes as small as 0.1 piglets can be detected. Topigs Norsvin Research Center (Beuningen, the Netherlands) maintains a database currently containing approximately 26 million animals, almost 9 million litter records and semen quality information of over 1.4 million ejaculates. With this infrastructure, semen quality characteristics can be validated for their relation with field fertility, which is a very strong tool. Analysing records from 2006 until 2009 (45 532 ejaculates) revealed that boar- and semenrelated parameters explained 5.3% of the variation in FR and 5.9% of the variation in TNB (Broekhuijse et al. 2012b). This variation can be assigned to, among others, genetic line of the boar, individual boar and semen quality characteristics as morphological abnormal cells, semen motility and number of sperm cells in a dose. For AI centres, the implementation of a control policy for the processing and production of AI doses should be based on data analysis representing the relation between semen quality and field fertility.
Accurate and Precise Semen Assessment is Essential in Efficient Semen Production For the customers of AI companies, it is important to achieve the highest reproductive efficiency per purchased AI dose. Reproductive performance has a genetic basis; however, it is highly affected by environmental factors. For optimal genetic expression, the environmental effects should be reduced; one of the variables is the semen quality. Hence, reducing the variation in fertility caused by variation in semen quality will enhance genetic progress. Once the selection criteria for semen approval are validated, the semen dose production per ejaculate should be increased and become more efficient, resulting in a faster dissemination of desired genes. Rejecting or approving an ejaculate is based on semen quality assessments. From the initial stages of AI development until the present time, the assessment of the percentage moving (motile) semen is the most widely used test of semen quality (Salisbury et al. 1978; Broekhuijse et al. 2012a). To improve quantifying the semen motility, bright field microscopy, differential interference contrast microscopes, CASA, multiple stains and flow cytometry have been used. Currently, CASA is the most popular method used to assess semen concentration and quality (Verstegen et al. 2002; Gil et al. 2009). A large number of AI laboratories of Topigs Norsvin introduced CASA, with the primary goal to implement a more precise and thus reliable semen concentration measurement. Moreover, the use of CASA also allows obtaining more objective, detailed and repeatable determination of semen motility characteristics of sperm cells in boar ejaculates (Tejerina et al. 2008; Contri et al. 2010). Thus, it results in a lower degree of variation in motility characteristics of a given sample when compared to technician variation (performing semen motility assessments by eye) as well as lower variation between AI stations (Mortimer et al. 1986; Broekhuijse et al. 2011b). There are relatively few comparable studies on the value of CASA measurements. Significant correlations between motility and fertility have been described for several species (Budworth et al. 1988; Samper et al. 1991; Hirano et al. 2001; Lavara et al. 2005) and pigs (Holt et al. 1997; Vyt et al. 2008). Other studies have not demonstrated an association between sperm motility and fertility (Liu et al. 1991; Didion 2008). Our own research (Broekhuijse et al. 2012b) indicated a relation between motility, progressive motility and basic CASA motility parameters and field fertility. The main reservations for using CASA systems are the high investment costs, the extreme need for standardization and the validation of the system, which should be implemented before any practical use is possible, but on the other hand, CASA equipment is more precise and accurate and therefore more objective than microscopic semen motility assessment (Broekhuijse et al. 2011b). Production of AI doses using a CASA system with a standard operating procedure and with trained laboratory technicians © 2015 Blackwell Verlag GmbH
The Impact of Low Dose AI on Boar Fertility
improves efficiency and reliability in the production of AI doses. It has an additional value to conventional motility assessment in pig AI, which is beneficial for both farmer and AI companies. As mentioned before, it has been concluded that FR and TNB are independent of the total number of cells in an AI dose, as long as the number is above the threshold. This does not hold true automatically for all AI stations. Many other factors, such as hygiene, extender, transport conditions and health status of boars, might affect the threshold for the number of sperm cells per AI dose (Waberski et al. 1994; Althouse et al. 2000; Thibier and Guerin 2000; Vyt et al. 2004). When a pig breeder is using a dose of boar sperm for AI, he expects the highest quality of semen to ensure maximum litter sizes and FRs. However, the predictive value of the classical sperm assessments (such as concentration, motility and morphology) is quite low; semen quality is only explaining a small part of the total variation in fertility (Broekhuijse et al. 2012a,b). Other semen characteristics are important to explain remaining variation. A number of flow cytometry tests can be employed to assess the functional integrity of sperm, and a general assumption is that a higher degree of sperm deterioration is related to lower fertility competence of a given sperm sample. Yet, results obtained in different studies are often controversial (Sutkeviciene et al. 2005). Damage in the DNA is considered to affect the first cleavage divisions and therefore reduce embryo development (Larson-Cook et al., 2003; Fatehi et al., 2006). It has been shown that sperm cells with compromised chromatin organization show a reduced capability to bind to oviductal epithelium (Ardon et al., 2008), while spermatozoa that bind to oviductal epithelial cells have superior DNA integrity. It has been demonstrated that oocytes that are fertilized with sperm that carried damaged DNA do not develop (Larson-Cook et al., 2003; Fatehi et al., 2006). Sperm with damaged DNA can thus affect TNB and possibly even FR, which was prominent in our previous study (Broekhuijse et al. 2012c). Therefore, determining the DNA fragmentation index (DFI) can be a valuable test for the routine monitoring of fertility level for boars in AI practice. In general, a relatively low level of DFI has been reported in both fresh (Evenson et al. 1994; Rybar et al. 2004) and liquid-stored (Waberski et al. 2002, 2011; BoeHansen et al. 2005; De Ambrogi et al. 2006) boar semen. DNA fragmentation of the boars used in our study was below 10%, which is representing high fertility potential (Broekhuijse et al. 2012c). This is demonstrated in other studies (Evenson 1999; Waberski et al. 2011). The literature proposes cut-off levels for boar sperm cells between 2% and 18% for DFI (Rybar et al. 2004; Boe-Hansen et al. 2008; Didion et al. 2009; Waberski et al. 2011). In our study (Broekhuijse et al. 2012c), membrane integrity, acrosome intactness and responsiveness and mitochondrial potential did not show a relation with © 2015 Blackwell Verlag GmbH
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fertility. Probably, the AI doses as well as the sperm quality were so optimal that variation in the percentage of functionally intact sperm was not correlated with the fertility outcome. Damaged or instable DNA on the other hand may exist not only in the deteriorated but also in the functionally intact sperm population which thus can fertilize the oocytes and exert negative effects on embryonic or foetal development and the health of each offspring. When implementing a DNA fragmentation test in the routine pig AI practice, differences between genetic lines and individual boars always have to be taken into account, as well as other boar- and semen-related parameters, the costs of the test and the fact that the test is time-consuming. Currently, we are performing screening trials to determine the appropriate cut-off levels for boars located at AI centres of Topigs Norsvin.
Aspects to Take into Account When Producing and Distributing AI Low Doses One important technical aspect to observe when working in pig reproduction is to critically realize that there are parameters one has to take in for granted (just because there are, for example, different boar lines, and there is an effect of parity of the sow), and there are other parameters that can be optimized to obtain an optimal fertility result. Basic semen motility only explains a small part of the variation, which suggests that other factors involved in the fertilization process (Foote, 2003) are more relevant. The genetic line of the boars explained the largest part of the boar- and semen-related variation in FR and TNB (Broekhuijse et al. 2012a,b). This is in accordance with several studies (Kommisrud et al. 2002; Kondracki 2003; Gadea et al. 2004; Janett et al. 2005; Sondermann and Luebbe 2008). Most authors agree that no breed excels in all semen characteristics (Kennedy and Wilkins 1984; Oh et al. 2003). In general, the choice for a genetic line is not based on fertility results but on market perspective of the offspring. AI centres need to include breed differences into their decision-making processes to ensure adequate use of the (genetic line of the) boar. Of course, there are many more parameters affecting fertility, with main important parameter being the individual boar itself. The ability to identify and cull boars with reduced fertility is of considerable economic interest for AI centres. The effect of subfertile boars on field fertility is very large Therefore, it is important to identify them as soon as possible, preferably before semen is sold, but also retrospectively based on fertility records. In Denmark, a study was performed selecting boars based on statistically estimated litter size compared with the population average at the time of selection (case or control boars). The average estimated litter size for case boars was more than 2.5 piglets lower than the estimated litter size for control boars (Hansen et al., 2015, in press). Topigs Norsvin performs weekly similar ranking, based on the corrected FR and TNB,
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which is corrected for farm- and sow-related parameters (the so-called direct boar effect on FR or TNB). The direct boar effect represents the corrected difference from 0 (average) for the boar population of Topigs Norsvin with known records. For the worldwide population, the spread in results was between 5.98% and +4.05% for FR and between 1.01 and +0.97 for TNB (n = 1193 boars, 116 749 inseminations, Fig. 1a). In the Netherlands, this effect is even smaller between 5.31% and +3.60% for FR and between 0.68 and +0.83 for TNB (n = 616 boars, 43 141 inseminations, Fig. 1b).
Information from Genetic and Genomic Selection Contributes to AI Thousands of sows are inseminated with semen from a given boar. Economic impact of the boar fertility is obvious. The pig must adapt to the local environment, in terms of climate, housing system, health and availability of nutrients (Bouwman et al. 2010; Camerlink et al. 2014; Mathur et al. 2014). A limited number of purebred animals of sire and dam lines in nucleus herds are the basis for cross-breeding for the production level. Information from genetic and genomic selection gives us knowledge on how to decrease the distance between nucleus herds and production level. The available 1.5
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Fig. 1. Direct boar effect for farrowing rate (%) vs direct boar effect for total number of piglets born. (A) worldwide records, n = 1193 boars. (B) the Netherlands records, n = 616 boars
genetic variation is large, but with more efficient AI dose production, this variation will be reduced. Breeding goals have evolved from heritable traits, such as growth and feed efficiency, to sustainability related traits, such as litter size, mothering ability and piglet vitality. Breeding is becoming more and more technologically intensive. Sequencing the porcine genome and genomic selection is a good example. With these new tools, increasing AI research efforts would boost management of genetic diversity and evolution. Traditional boar breeding programs have based their selection mainly on production traits, neglecting the potential benefits of selection for improved boar semen traits on fertility. Commercially important traits, such as growth rate, feed efficiency and lean yield, should not be the unique criteria for selection, because this approach can result in reduced semen production due to the negative correlations between semen production and semen traits (Oh et al., 2003). As mentioned before, sperm motility is one of the most widely used parameters to evaluate boar semen quality. However, this trait can only be measured after puberty which prolongs the generation interval. Therefore, the use of genomic information is as an alternative to evaluate and improve selection for boar fertility traits earlier in life. Genomewide association studies (GWAS) can overcome these limitations using genotyping technologies to assay the single nucleotide polymorphisms (SNPs) and relate them to traits of interest (Pearson and Manolio, 2008). GWAS have been performed in pigs for many traits, such as boar taint (Duijvesteijn et al., 2010), sow reproductive traits (Onteru et al., 2012), feeding behaviour (Do et al., 2013) and body composition (Fan et al., 2011). Yet, GWAS applied to boar sperm motility have not been reported yet, but knowledge is very relevant for both pig breeding and reproduction. In our own study (Diniz et al. 2014), we identified six SNPs associated with sperm motility in a large white population. This is the first time that a QTL region for this trait was found between 117.26 and 119.56 Mb on SSC1. The most compelling candidate gene in this region is methionyl-tRNA formyltransferase (MTFMT). According to Tucker et al. (2011), the MTFMT gene is critical for efficient mitochondrial translation. Gur and Breitbart (2006) demonstrated that the inhibition of protein translation significantly reduced sperm motility, capacitation and in vitro fertilization rate. Thus, nuclear genes are expressed as proteins in sperm during their residence in the female reproductive tract until fertilization. Combining the information on the QTL and the candidate gene identified in our study provides useful information that could be used for marker-assisted selection or genomic selection applications in commercial pig populations.
Optimizing Semen Production Using Different AI Techniques Efficiency of AI is determined by the number of sperm cells per dose and the required number of inseminations © 2015 Blackwell Verlag GmbH
The Impact of Low Dose AI on Boar Fertility
per oestrous period (Watson and Behan 2002). Currently, AI is set as the norm in the pig industry. Techniques used in AI are as follows: cervical insemination (traditional AI), post-cervical insemination (PCAI) or intra-uterine insemination (IUI), deep intrauterine insemination and laparoscopic insemination (Vazquez et al. 2008). These techniques are accompanied by different problems and possibilities. General problems in AI are large backflow and phagocytosis of sperm cells by polymorphonuclear leucocytes, causing a loss of 90% of sperm cells (Rath 2002; Sumransap et al. 2007). In traditional AI, semen is deposited in the cervix of the sow, which is equivalent to semen deposition following natural mating, with the exception of the lack of the gel plug preventing backflow to a large extent. From the cervix, sperm cells are transported through the uterine body and uterine horns before temporary storage at the uterotubal junction (Rath 2002). The pig industry is developing new technologies such as sex sorting and freezing–thawing. The basic idea of these techniques is to deposit the semen closer to the site of fertilization using a lower number of sperm and volume than usual (Hern andez-Caravaca et al. 2012). Effective reduction in the sperms used per AI is necessary to keep the boar in good production. For these procedures, new AI techniques such as PCAI are demanded. In PCAI, the semen is inserted directly into the uterine body (Vazquez et al. 2008), thus deeper into the reproductive tract. This insemination technique requires lower number of sperm cells per AI dose, speculatively due to a lower occurrence of phagocytosis and backflow (Vazquez et al. 2008). This lower number of required sperm cells per dose through successful implementation of PCAI (Watson and Behan 2002; Roberts and Bilkei 2005) would give an opportunity for more rapid genetic improvement because more AI doses per boar ejaculate can be obtained. Ultimately, this leads to more efficient spread of genetically high ranking boars.
Conclusion: Know the Impact of Low-Dose AI on Fertility Artificial insemination has become a key factor for genetic development and pig production worldwide, aiming at maximum success rates and boar exploitation. Fertilization is a complex process involving a large number of events. Not only should fertility research be
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concerned about testing new assessment methods in vitro, but also knowing the impact of genetic improvement and validate the value for predicting field fertility in vivo. The pig industry requires increased efficiency in piglet production to remain competitive; however, there remains a wide variation in field fertility results. Worldwide, there are opportunities for improvement. Knowing the impact of low-dose AI on fertility can be profitable for AI companies (e.g. improved efficiency in AI dose production), for breeding companies (e.g. genetic merit in the breeding pyramid) and for sow farmers (e.g. improved field fertility). Efficient use of AI boars with high genetic merit by decreasing the number of sperm cells per AI dose is important to disseminate the genetic progress made in the breeding nucleus. However, decreasing field fertility performance cannot be tolerated in commercial production. Data recording in the field and at AI stations and merging and analysing this information enabled us to identify subfertile boars and/or ejaculates. Standardization of semen quality assessment and objective assessment methods gave a boost to further improving the quality of production. Studies on timing of insemination, effect of season and temperature on semen, effect of morphological abnormalities, reciprocal translocation, storage time of the semen and motility in relation to fertility lead to stricter criteria for the approval of ejaculates. In future, other efficiency improving steps are necessary. The knowledge on controlled semen production must be implemented worldwide. Genetic and genomic selection on semen quantity, quality and fertility parameters will help to rank boars and use the best ones most efficiently. Using selected boars with the best genetics will enable the pig industry to benefit from the genetic progress made by genetic companies faster and broader. The ongoing and ultimate goal was to be able to select for production traits in pig production without compromising sustainability in general, or fertility and fecundity of females and males in particular and to achieve high and healthy fertilization as efficiently as possible. Conflict of Interest All authors are employed at the breeding company Topigs Norsvin or affiliated companies.
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inseminations: results of two fertility trials. J Androl 18, 312–323. Janett F, Fuschini E, Keo S, Hassig M, Thun R, 2005: Seasonal changes of semen quality in the boar. Reprod Domest Anim 40, 356. Kennedy BW, Wilkins JN, 1984: Boar, breed and environmental factors influencing semen characteristics of boars used in artificial insemination. Can J Anim Sci 64, 833–843. Kommisrud E, Paulenz H, Sehested E, Grevle IS, 2002: Influence of boar and semen parameters on motility and acrosome integrity in liquid boar semen stored for five days. Acta Vet Scand 43, 49–55. Kondracki S, 2003: Breed differences in semen characteristics of boars used in artificial insemination in Poland. Pig News Info 24, 119–122. Larson-Cook KL, Brannian JD, Hansen KA, Kasperson KM, Aamold ET, Evenson DP, 2003: Relationship between the outcomes of assisted reproductive techniques and sperm DNA fragmentation as measured by the sperm chromatin structure assay. Fertility and Sterility 80, 895–902. Lavara R, Moce E, Lavara F, Viudes de Castro MP, Salvador Vicente J, 2005: Do parameters of seminal quality correlate with the results of on-farm inseminations in rabbits? Theriogenology 64, 1130–1141. Liu DY, Clarke CN, Gordon Baker HW, 1991: Relationship between sperm motility assessed with the Hamilton-Thorn motility analyzer and fertilization rates in vitro. J Androl 12, 231–239. Lunenfeld B, Insler V, 1993: Infertility: the dimension of the problem. In: Insler V, Lunenfeld B (eds), Infertility: Male and Female. Churchill Livingstone, Edinburgh, pp. 3–7. Mathur PK, Herrero-Medrano JM, Alexandri P, Knol EF, ten Napel J, Rashidi H, Mulder HA, 2014: Estimating challenge load due to disease outbreaks and other challenges using reproduction records of sows. J Anim Sci 92, 5374–5381. Mortimer D, Shu MA, Tan R, 1986: Standardization and quality control of sperm concentration and sperm motility counts in semen analysis. Human Reprod 1, 299– 303. Onteru SK, Fan B, Du Z-Q, Garrick DJ, Stalder KJ, Rothschild MF, 2012: A whole-genome association study for pig reproductive traits. Anim. Genet. 43(1), 18–26. Oh SH, See MT, Long TE, Galvin JM, 2003: Genetic correlations between boar semen traits. J Anim Sci 81, 317. Pearson TA, Manolio TA, 2008: How to interpret a genome-wide asso-ciation study. JAMA 299(11), 1335–1344. Rath D, 2002: Low dose insemination in the sow–a review. Reprod Domest Anim 37, 201–205. Roberts SJ, 1986: Infertility in male animals (andrology). In: Roberts SJ (ed.), Veterinary Obstetrics and Genital Diseases:
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The Impact of Low Dose AI on Boar Fertility Theriogenology, 3rd edn. Edwards Brothers, North Pomfre, VT, pp. 752–896. Roberts PK, Bilkei G, 2005: Field experiences on post-cervical artificial insemination in the sow. Reprod Domest Anim 40, 489–491. Rybar R, Faldikova L, Faldyna M, Machatkova M, Rubes J, 2004: Bull and boar sperm DNA integrity evaluated by sperm chromatin structure assay in the Czech Republic. Vet Med 49, 1–8. Salisbury GW, VanDemark NL, Lodge JR, 1978: Physiology of Reproduction and Artificial Insemination of Cattle, 2nd edn. W. H. Freeman Co., San Francisco, CA. Samper JC, Hellander JC, Crabo BG, 1991: Relationship between the fertility of fresh and frozen stallion semen and semen quality. J Reprod Fertil Suppl 44, 107– 114. Sondermann JP, Luebbe JJ, 2008: Semen production and fertility issues related to differences in genetic lines of boars. Theriogenology 70, 1380–1383. Sumransap P, Tummaruk P, Kunavongkrit A, 2007: Sperm distribution in the reproductive tract of sows after intrauterine insemination. Reprod Domest Anim 42, 113–117. Sutkeviciene N, Andersson MA, Zilinskas H, Andersson M, 2005: Assessment of boar semen quality in relation to fertility with special reference to methanol stress. Theriogenology 63, 739–747. Tejerina F, Buranaamnuay K, Saravia F, Wallgren M, Rogriguez-Martinez H,
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2008: Assessment of motility of ejaculated, liquid stored boar spermatozoa using computerized instruments. Theriogenology 69, 1129–1138. Thibier M, Guerin B, 2000: Hygienic aspects of storage and use of semen for artificial insemination. Anim Reprod Sci 62, 233– 251. Tucker EJ, Hershman SG, K€ ohrer C, Belcher-Timme CA, Patel J, Gold-berger OA, Christodoulou J, Silberstein JM, McKenzie M, Ryan MT, Compton AG, Jaffe JD, Carr SA, Calvo SE, RajBhandary UL, Thorburn DR, Mootha VK, 2011: Mutations in MTFMT underlie a human disorder of formylation causing impaired mitochondrial translation. Cell Metab 14, 428–434. Vazquez JM, Roca J, Gil MA, Cuello C, Parrilla I, Vazquez JL, Martınez EA, 2008: New developments in low-dose insemination technology. Theriogenology 70, 1216–1224. Verstegen J, Iguer-ouada M, Onclin K, 2002: Computer assisted semen analyzers in andrology and veterinary practice. Theriogenology 57, 149–179. Vyt P, Maes D, Dejonckheere E, Castryck F, Van Soom A, 2004: Comparative study on five different commercial extenders for boar semen. Reprod Domest Anim 39, 8–12. Vyt P, Maes D, Quinten C, Rijsselaere T, Deley W, Aerts M, de Kruif A, van Soom A, 2008: Detailed motility examination of porcine semen and its predictive value towards reproductive performance in
109 sows. Vlaams Diergeneeskd Tijdschr 77, 291–298. Waberski D, Meding S, Dirksen G, Weitze KF, Leiding C, Han R, 1994: Fertility of long-term-stored boar semen: influence of extender (Androhep and Kiev), storage time and plasma droplets in the semen. Anim Reprod Sci 36, 145–151. Waberski D, Helms D, Bayerbach M, Weitze KF, Bollwein H, Bluemig P, Wileke H, Acevedo N, 2002: Sperm chromatin structure in boars used in artificial insemination. Reprod Domest Anim 37, 257 (abstr). Waberski D, Schapmann E, Henning H, Riesenbeck A, Brandt H, 2011: Sperm chromatin structural integrity in normospermic boars is not related to semen storage and fertility after routine AI. Theriogenology 75, 337–345. Watson PF, Behan JR, 2002: Intrauterine insemination of sows with reduced sperm numbers: results of a commercially based field trial. Theriogenology 57, 1683–1693.
Submitted: 12 May 2015; Accepted: 16 May 2015 Author’s address (for correspondence): MLWJ Broekhuijse, Topigs Norsvin Research Center, P.O. Box 43, 6640 AA, Beuningen, The Netherlands. E-mail: marleen. [email protected]
Review Article Efficient Boar Semen Production and Genetic Contribution: The Impact of Low-Dose Artificial Insemination on Fertility MLWJ Broekhuijse1, AH Gaustad2,3, A Bolarin Guillen4 and EF Knol1 1 Topigs Norsvin Research Center, Beuningen, The Netherlands; 2Topigs Norsvin, Hamar, Norway; 3University College of Hedmark, Hamar, erica, Madrid, Spain Norway; 4Topigs Norsvin AIM Ib
Contents Diluting semen from high fertile breeding boars, and by that inseminating many sows, is the core business for artificial insemination (AI) companies worldwide. Knowledge about fertility results is the reason by which an AI company can lower the concentration of a dose. Efficient use of AI boars with high genetic merit by decreasing the number of sperm cells per insemination dose is important to maximize dissemination of the genetic progress made in the breeding nucleus. However, a potential decrease in fertility performance in the field should be weighed against the added value of improved genetics and, in general, is not tolerated in commercial production. This overview provides some important aspects that influence the impact of low-dose AI on fertility: (i) the importance of monitoring field fertility, (ii) the need for accurate and precise semen assessment, (iii) the parameters that are taken into account, (iv) the application of information from genetic and genomic selection and (v) the optimization when using different AI techniques. Efficient semen production, processing and insemination in combination with increasing use of genetic and genomic applications result in maximum impact of genetic trend.
Monitoring Field Fertility Gives Insight into the Effect of the Artificial Insemination Dose Efficient artificial insemination (AI) is essential for future challenges in the pig industry. Knowledge on the exact relation between semen quality characteristics and fertility can have a major impact on both the genetic merit of future animals and the efficiency of AI. From a commercial perspective, it is important for an AI company to monitor its results in practice. Knowing the performance of semen in the field by keeping track of records enables efficient analysis of the causes of poor performance at individual farm level. Determining the proper number of motile cells in an AI dose, the maximum allowed morphological defects, the optimal value for computer-assisted semen analysis (CASA) motility parameters and the shelf life of a dose permits the AI company to work as efficiently as possible at lowest possible cost price and with maximum field fertility. An example is decreasing the number of cells in an AI dose. Lowering the number of cells in a dose is not possible without knowing the potential risk of
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decrease in field fertility. Risk mitigation of cut-off levels is guaranteed by continuing statistical analyses on these parameters. Over the years, the total number of sperm cells in an AI dose decreased in the Netherlands from 4.0 billion in 100-ml disposable bottles (1985) down to 1.5 billion in 80-ml disposable tubes (started in 2008) (Feitsma 2009). The relatively high number of sperm cells in a dose used in commercial AI practice likely masks reduced fertility (Foxcroft et al., 2008). Through the years, experiments were performed using low-dose inseminations. These data showed that in some cases, it was feasible to decrease the number of sperm cells in an AI dose without noticing a significant effect on fertility results. Recent analysis was performed on the effect of number of sperm cells on fertility based on data from 2007 until 2013, with 584 787 fertility records from 6241 boars. Insemination records were corrected for the significant sow-related parameters, such as parity, weaning to oestrous interval (WEI), herd, year, season (HYS), purebred/cross-bred, single/double AI and age of semen, and for the significant boar-related parameters, such as genetic line of the boar, age, AI station, year, season (AIYS), individual boar and ejaculate (within boar), and focused on concentration of the AI dose. Records were analysed from 1.2 until 2.5 billion motile cells per dose. It was concluded that there is no effect of concentration of an AI dose on farrowing rate (FR) (p = 0.2366) and no effect on total number of piglets born (TNB) (p = 0.1314), when the parameter is analysed as a separate entity. Some farms, however, could be more sensitive to the concentration of an AI dose; therefore, the interaction between farm and concentration was estimated as well. This interaction tested not significant for both FR (p = 0.4381) and TNB (p = 0.4517). Lowering the concentration of an AI dose results in using genetically high ranking boars more efficiently with an economic impact. Discriminating between 3.0, 2.5 and 1.5 billion cells in an AI dose has a clear effect on the number of boars needed for inseminating a herd. Topigs Norsvin Selection Index (TSI) is an index combining breeding values of individual traits with their respective economic relevance. The TSI gives an
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estimate of the genetic value of a pig; highest values are the most interesting for breeding. Using fewer boars, you can discard the lowest ranking boars and increase in TSI with additional economic consequences. This depends very much on the value of and the increase in TSI. On the other hand, economic losses due to boar reproductive failure can be substantial as well (Roberts 1986; Lunenfeld and Insler 1993). Efficiency of piglet production is dependent on many factors; one we can assess is the quality of semen. It is desirable to design a semen quality test that predicts the fertilizing capacity of the semen. To match the semen quality aspects measured with fertilizing ability of the semen, the most critical aspect is to have both semen quality assessments and fertility data that are specific, precise (repeatable) and accurate. These data are very difficult to obtain when problems occur such as (i) boars or sows that are not representative for the population and/or are too few in number, (ii) insufficient sows inseminated with semen from each sample, (iii) too few semen samples assessed per boar, (iv) inappropriate number of cells used for each insemination, and (v) fertility outcome is not reported properly (Broekhuijse et al. 2011a). Topigs Norsvin records standardly data from both boar (AI) and sow (production farm) level. To discriminate within boars between ejaculates (and due to legislation), it is important that no mixed semen is allowed; single sire doses are shipped from AI to the production farms. These large data sets are suitable to analyse effects of semen quality characteristics on field fertility. Variation in fertility in sows is large. The effect of service sire and more precisely the measured ejaculate parameters is relatively small and therefore impossible to find in smaller data sets. Large data sets allow for statistical corrections on parameters related to both sows and boars. Remaining sow fertility variation can then be tested for the influence of semen quality parameters, and differences in litter sizes as small as 0.1 piglets can be detected. Topigs Norsvin Research Center (Beuningen, the Netherlands) maintains a database currently containing approximately 26 million animals, almost 9 million litter records and semen quality information of over 1.4 million ejaculates. With this infrastructure, semen quality characteristics can be validated for their relation with field fertility, which is a very strong tool. Analysing records from 2006 until 2009 (45 532 ejaculates) revealed that boar- and semenrelated parameters explained 5.3% of the variation in FR and 5.9% of the variation in TNB (Broekhuijse et al. 2012b). This variation can be assigned to, among others, genetic line of the boar, individual boar and semen quality characteristics as morphological abnormal cells, semen motility and number of sperm cells in a dose. For AI centres, the implementation of a control policy for the processing and production of AI doses should be based on data analysis representing the relation between semen quality and field fertility.
Accurate and Precise Semen Assessment is Essential in Efficient Semen Production For the customers of AI companies, it is important to achieve the highest reproductive efficiency per purchased AI dose. Reproductive performance has a genetic basis; however, it is highly affected by environmental factors. For optimal genetic expression, the environmental effects should be reduced; one of the variables is the semen quality. Hence, reducing the variation in fertility caused by variation in semen quality will enhance genetic progress. Once the selection criteria for semen approval are validated, the semen dose production per ejaculate should be increased and become more efficient, resulting in a faster dissemination of desired genes. Rejecting or approving an ejaculate is based on semen quality assessments. From the initial stages of AI development until the present time, the assessment of the percentage moving (motile) semen is the most widely used test of semen quality (Salisbury et al. 1978; Broekhuijse et al. 2012a). To improve quantifying the semen motility, bright field microscopy, differential interference contrast microscopes, CASA, multiple stains and flow cytometry have been used. Currently, CASA is the most popular method used to assess semen concentration and quality (Verstegen et al. 2002; Gil et al. 2009). A large number of AI laboratories of Topigs Norsvin introduced CASA, with the primary goal to implement a more precise and thus reliable semen concentration measurement. Moreover, the use of CASA also allows obtaining more objective, detailed and repeatable determination of semen motility characteristics of sperm cells in boar ejaculates (Tejerina et al. 2008; Contri et al. 2010). Thus, it results in a lower degree of variation in motility characteristics of a given sample when compared to technician variation (performing semen motility assessments by eye) as well as lower variation between AI stations (Mortimer et al. 1986; Broekhuijse et al. 2011b). There are relatively few comparable studies on the value of CASA measurements. Significant correlations between motility and fertility have been described for several species (Budworth et al. 1988; Samper et al. 1991; Hirano et al. 2001; Lavara et al. 2005) and pigs (Holt et al. 1997; Vyt et al. 2008). Other studies have not demonstrated an association between sperm motility and fertility (Liu et al. 1991; Didion 2008). Our own research (Broekhuijse et al. 2012b) indicated a relation between motility, progressive motility and basic CASA motility parameters and field fertility. The main reservations for using CASA systems are the high investment costs, the extreme need for standardization and the validation of the system, which should be implemented before any practical use is possible, but on the other hand, CASA equipment is more precise and accurate and therefore more objective than microscopic semen motility assessment (Broekhuijse et al. 2011b). Production of AI doses using a CASA system with a standard operating procedure and with trained laboratory technicians © 2015 Blackwell Verlag GmbH
The Impact of Low Dose AI on Boar Fertility
improves efficiency and reliability in the production of AI doses. It has an additional value to conventional motility assessment in pig AI, which is beneficial for both farmer and AI companies. As mentioned before, it has been concluded that FR and TNB are independent of the total number of cells in an AI dose, as long as the number is above the threshold. This does not hold true automatically for all AI stations. Many other factors, such as hygiene, extender, transport conditions and health status of boars, might affect the threshold for the number of sperm cells per AI dose (Waberski et al. 1994; Althouse et al. 2000; Thibier and Guerin 2000; Vyt et al. 2004). When a pig breeder is using a dose of boar sperm for AI, he expects the highest quality of semen to ensure maximum litter sizes and FRs. However, the predictive value of the classical sperm assessments (such as concentration, motility and morphology) is quite low; semen quality is only explaining a small part of the total variation in fertility (Broekhuijse et al. 2012a,b). Other semen characteristics are important to explain remaining variation. A number of flow cytometry tests can be employed to assess the functional integrity of sperm, and a general assumption is that a higher degree of sperm deterioration is related to lower fertility competence of a given sperm sample. Yet, results obtained in different studies are often controversial (Sutkeviciene et al. 2005). Damage in the DNA is considered to affect the first cleavage divisions and therefore reduce embryo development (Larson-Cook et al., 2003; Fatehi et al., 2006). It has been shown that sperm cells with compromised chromatin organization show a reduced capability to bind to oviductal epithelium (Ardon et al., 2008), while spermatozoa that bind to oviductal epithelial cells have superior DNA integrity. It has been demonstrated that oocytes that are fertilized with sperm that carried damaged DNA do not develop (Larson-Cook et al., 2003; Fatehi et al., 2006). Sperm with damaged DNA can thus affect TNB and possibly even FR, which was prominent in our previous study (Broekhuijse et al. 2012c). Therefore, determining the DNA fragmentation index (DFI) can be a valuable test for the routine monitoring of fertility level for boars in AI practice. In general, a relatively low level of DFI has been reported in both fresh (Evenson et al. 1994; Rybar et al. 2004) and liquid-stored (Waberski et al. 2002, 2011; BoeHansen et al. 2005; De Ambrogi et al. 2006) boar semen. DNA fragmentation of the boars used in our study was below 10%, which is representing high fertility potential (Broekhuijse et al. 2012c). This is demonstrated in other studies (Evenson 1999; Waberski et al. 2011). The literature proposes cut-off levels for boar sperm cells between 2% and 18% for DFI (Rybar et al. 2004; Boe-Hansen et al. 2008; Didion et al. 2009; Waberski et al. 2011). In our study (Broekhuijse et al. 2012c), membrane integrity, acrosome intactness and responsiveness and mitochondrial potential did not show a relation with © 2015 Blackwell Verlag GmbH
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fertility. Probably, the AI doses as well as the sperm quality were so optimal that variation in the percentage of functionally intact sperm was not correlated with the fertility outcome. Damaged or instable DNA on the other hand may exist not only in the deteriorated but also in the functionally intact sperm population which thus can fertilize the oocytes and exert negative effects on embryonic or foetal development and the health of each offspring. When implementing a DNA fragmentation test in the routine pig AI practice, differences between genetic lines and individual boars always have to be taken into account, as well as other boar- and semen-related parameters, the costs of the test and the fact that the test is time-consuming. Currently, we are performing screening trials to determine the appropriate cut-off levels for boars located at AI centres of Topigs Norsvin.
Aspects to Take into Account When Producing and Distributing AI Low Doses One important technical aspect to observe when working in pig reproduction is to critically realize that there are parameters one has to take in for granted (just because there are, for example, different boar lines, and there is an effect of parity of the sow), and there are other parameters that can be optimized to obtain an optimal fertility result. Basic semen motility only explains a small part of the variation, which suggests that other factors involved in the fertilization process (Foote, 2003) are more relevant. The genetic line of the boars explained the largest part of the boar- and semen-related variation in FR and TNB (Broekhuijse et al. 2012a,b). This is in accordance with several studies (Kommisrud et al. 2002; Kondracki 2003; Gadea et al. 2004; Janett et al. 2005; Sondermann and Luebbe 2008). Most authors agree that no breed excels in all semen characteristics (Kennedy and Wilkins 1984; Oh et al. 2003). In general, the choice for a genetic line is not based on fertility results but on market perspective of the offspring. AI centres need to include breed differences into their decision-making processes to ensure adequate use of the (genetic line of the) boar. Of course, there are many more parameters affecting fertility, with main important parameter being the individual boar itself. The ability to identify and cull boars with reduced fertility is of considerable economic interest for AI centres. The effect of subfertile boars on field fertility is very large Therefore, it is important to identify them as soon as possible, preferably before semen is sold, but also retrospectively based on fertility records. In Denmark, a study was performed selecting boars based on statistically estimated litter size compared with the population average at the time of selection (case or control boars). The average estimated litter size for case boars was more than 2.5 piglets lower than the estimated litter size for control boars (Hansen et al., 2015, in press). Topigs Norsvin performs weekly similar ranking, based on the corrected FR and TNB,
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which is corrected for farm- and sow-related parameters (the so-called direct boar effect on FR or TNB). The direct boar effect represents the corrected difference from 0 (average) for the boar population of Topigs Norsvin with known records. For the worldwide population, the spread in results was between 5.98% and +4.05% for FR and between 1.01 and +0.97 for TNB (n = 1193 boars, 116 749 inseminations, Fig. 1a). In the Netherlands, this effect is even smaller between 5.31% and +3.60% for FR and between 0.68 and +0.83 for TNB (n = 616 boars, 43 141 inseminations, Fig. 1b).
Information from Genetic and Genomic Selection Contributes to AI Thousands of sows are inseminated with semen from a given boar. Economic impact of the boar fertility is obvious. The pig must adapt to the local environment, in terms of climate, housing system, health and availability of nutrients (Bouwman et al. 2010; Camerlink et al. 2014; Mathur et al. 2014). A limited number of purebred animals of sire and dam lines in nucleus herds are the basis for cross-breeding for the production level. Information from genetic and genomic selection gives us knowledge on how to decrease the distance between nucleus herds and production level. The available 1.5
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genetic variation is large, but with more efficient AI dose production, this variation will be reduced. Breeding goals have evolved from heritable traits, such as growth and feed efficiency, to sustainability related traits, such as litter size, mothering ability and piglet vitality. Breeding is becoming more and more technologically intensive. Sequencing the porcine genome and genomic selection is a good example. With these new tools, increasing AI research efforts would boost management of genetic diversity and evolution. Traditional boar breeding programs have based their selection mainly on production traits, neglecting the potential benefits of selection for improved boar semen traits on fertility. Commercially important traits, such as growth rate, feed efficiency and lean yield, should not be the unique criteria for selection, because this approach can result in reduced semen production due to the negative correlations between semen production and semen traits (Oh et al., 2003). As mentioned before, sperm motility is one of the most widely used parameters to evaluate boar semen quality. However, this trait can only be measured after puberty which prolongs the generation interval. Therefore, the use of genomic information is as an alternative to evaluate and improve selection for boar fertility traits earlier in life. Genomewide association studies (GWAS) can overcome these limitations using genotyping technologies to assay the single nucleotide polymorphisms (SNPs) and relate them to traits of interest (Pearson and Manolio, 2008). GWAS have been performed in pigs for many traits, such as boar taint (Duijvesteijn et al., 2010), sow reproductive traits (Onteru et al., 2012), feeding behaviour (Do et al., 2013) and body composition (Fan et al., 2011). Yet, GWAS applied to boar sperm motility have not been reported yet, but knowledge is very relevant for both pig breeding and reproduction. In our own study (Diniz et al. 2014), we identified six SNPs associated with sperm motility in a large white population. This is the first time that a QTL region for this trait was found between 117.26 and 119.56 Mb on SSC1. The most compelling candidate gene in this region is methionyl-tRNA formyltransferase (MTFMT). According to Tucker et al. (2011), the MTFMT gene is critical for efficient mitochondrial translation. Gur and Breitbart (2006) demonstrated that the inhibition of protein translation significantly reduced sperm motility, capacitation and in vitro fertilization rate. Thus, nuclear genes are expressed as proteins in sperm during their residence in the female reproductive tract until fertilization. Combining the information on the QTL and the candidate gene identified in our study provides useful information that could be used for marker-assisted selection or genomic selection applications in commercial pig populations.
Optimizing Semen Production Using Different AI Techniques Efficiency of AI is determined by the number of sperm cells per dose and the required number of inseminations © 2015 Blackwell Verlag GmbH
The Impact of Low Dose AI on Boar Fertility
per oestrous period (Watson and Behan 2002). Currently, AI is set as the norm in the pig industry. Techniques used in AI are as follows: cervical insemination (traditional AI), post-cervical insemination (PCAI) or intra-uterine insemination (IUI), deep intrauterine insemination and laparoscopic insemination (Vazquez et al. 2008). These techniques are accompanied by different problems and possibilities. General problems in AI are large backflow and phagocytosis of sperm cells by polymorphonuclear leucocytes, causing a loss of 90% of sperm cells (Rath 2002; Sumransap et al. 2007). In traditional AI, semen is deposited in the cervix of the sow, which is equivalent to semen deposition following natural mating, with the exception of the lack of the gel plug preventing backflow to a large extent. From the cervix, sperm cells are transported through the uterine body and uterine horns before temporary storage at the uterotubal junction (Rath 2002). The pig industry is developing new technologies such as sex sorting and freezing–thawing. The basic idea of these techniques is to deposit the semen closer to the site of fertilization using a lower number of sperm and volume than usual (Hern andez-Caravaca et al. 2012). Effective reduction in the sperms used per AI is necessary to keep the boar in good production. For these procedures, new AI techniques such as PCAI are demanded. In PCAI, the semen is inserted directly into the uterine body (Vazquez et al. 2008), thus deeper into the reproductive tract. This insemination technique requires lower number of sperm cells per AI dose, speculatively due to a lower occurrence of phagocytosis and backflow (Vazquez et al. 2008). This lower number of required sperm cells per dose through successful implementation of PCAI (Watson and Behan 2002; Roberts and Bilkei 2005) would give an opportunity for more rapid genetic improvement because more AI doses per boar ejaculate can be obtained. Ultimately, this leads to more efficient spread of genetically high ranking boars.
Conclusion: Know the Impact of Low-Dose AI on Fertility Artificial insemination has become a key factor for genetic development and pig production worldwide, aiming at maximum success rates and boar exploitation. Fertilization is a complex process involving a large number of events. Not only should fertility research be
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concerned about testing new assessment methods in vitro, but also knowing the impact of genetic improvement and validate the value for predicting field fertility in vivo. The pig industry requires increased efficiency in piglet production to remain competitive; however, there remains a wide variation in field fertility results. Worldwide, there are opportunities for improvement. Knowing the impact of low-dose AI on fertility can be profitable for AI companies (e.g. improved efficiency in AI dose production), for breeding companies (e.g. genetic merit in the breeding pyramid) and for sow farmers (e.g. improved field fertility). Efficient use of AI boars with high genetic merit by decreasing the number of sperm cells per AI dose is important to disseminate the genetic progress made in the breeding nucleus. However, decreasing field fertility performance cannot be tolerated in commercial production. Data recording in the field and at AI stations and merging and analysing this information enabled us to identify subfertile boars and/or ejaculates. Standardization of semen quality assessment and objective assessment methods gave a boost to further improving the quality of production. Studies on timing of insemination, effect of season and temperature on semen, effect of morphological abnormalities, reciprocal translocation, storage time of the semen and motility in relation to fertility lead to stricter criteria for the approval of ejaculates. In future, other efficiency improving steps are necessary. The knowledge on controlled semen production must be implemented worldwide. Genetic and genomic selection on semen quantity, quality and fertility parameters will help to rank boars and use the best ones most efficiently. Using selected boars with the best genetics will enable the pig industry to benefit from the genetic progress made by genetic companies faster and broader. The ongoing and ultimate goal was to be able to select for production traits in pig production without compromising sustainability in general, or fertility and fecundity of females and males in particular and to achieve high and healthy fertilization as efficiently as possible. Conflict of Interest All authors are employed at the breeding company Topigs Norsvin or affiliated companies.
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Submitted: 12 May 2015; Accepted: 16 May 2015 Author’s address (for correspondence): MLWJ Broekhuijse, Topigs Norsvin Research Center, P.O. Box 43, 6640 AA, Beuningen, The Netherlands. E-mail: marleen. [email protected]
