Reprod Dom Anim 50 (Suppl. 2), 48–55 (2015); doi: 10.1111/rda.12530 ISSN 0936–6768

Review Article Boar Differences In Artificial Insemination Outcomes: Can They Be Minimized? J Roca1, MLWJ Broekhuijse2, I Parrilla1, H Rodriguez-Martinez3, EA Martinez1 and A Bolarin4 1 Veterinary Medicine, University of Murcia, Murcia, Spain; 2Topigs Norsvin Research Center BV, Beuningen, The Netherlands; 3Department of Clinical & Experimental Medicine (IKE), University of Link€ oping, Link€ oping, Sweden; 4AIM iberica, Topigs Norsvin, Las Rozas, Spain

Contents In Western countries, where pig breeding and production are intensive, there is a documented variability in fertility between farms with boar-related parameters only accounting to 6% of this total variation of in vivo fertility. Such low boar effect could be a result of the rigorous control of sires and ejaculates yielding AI-doses exerted by the highly specialized AI-centres that monopolize the market. However, some subfertile boars pass through these rigorous controls and consequently reach the AI-programmes. Here, we discuss why testing young boars for chromosomal defects, sperm nuclear chromatin integrity and in vitro fertilizing ability can be discriminative and economically sound for removing these less fertile boars. Alongside, we discuss why boars differ in the ability of their sperm to tolerate cryopreservation or sex sorting.

Introduction In countries with intensive pig production, artificial insemination (AI) is the most widely used breeding technique to spread genetic material to any level of the entire production pyramid. Accordingly, swine AI is in continuous progress as evidenced by the recent successful development of new insemination methods that allow a significant lowering in sperm number per AIdose without impairing fertility (Roca et al. 2011). This progress also involves significant changes in the production systems of semen AI-doses where classical small AI-centres housing a few boars and/or integrated within swine production farms are being replaced by independent and technically highly specialized AI-centres housing a large number of healthy and genetically superior boars of different breeds and genetic lines. In some European countries with intensive swine production, these specialized AI-centres, which either belong to genetic companies, large swine production enterprises, or are operated by third parties, produce approximately 90% of the semen AI-doses used. They produce, following strict regulations and guidelines to prevent disease spreading, large daily numbers of ready-to-use high-quality semen AI-doses available for rapid distribution. Swine farmers can therefore obtain healthy AI-doses from genetically superior boars, at affordable prices. Despite this rigorous control of boars and ejaculates, fertility drops attributable to semen AI-doses are still recorded in swine production farms. When the semen

doses and AI procedures are handled properly, such drops are indicative of differences in fertility among boars. When such differences are not detected, or they are not rapidly managed by changing boar donors, they can result in important losses of production efficiency at farm level, reducing the already meagre profit margin for the producers. It also conspires against the AI-centres credibility, often entailing economic claims. Consequently, the early identification and subsequent culling of less fertile boars is a priority for AI-centres. Bearing this challenge in mind, this review provides a current view of the magnitude that boar-related parameters impose on AI-fertility variation between farms, pointing possible causes and proposing measures to, at least, minimize the problem. As a complement, we illustrate differences between boars for their ability to sustain innovative sperm technologies, such as cryopreservation and sex sorting.

Boar Differences in Fertility The existence of variability in fertility between current highly selected breeding boars is a proven issue, however, considered more of individual nature that an event related to breed or to genetic lines (see reviews of Foxcroft et al. 2010 and Flowers 2013). Yet, highlighting this individual variation has not been an easy task because semen doses used in most commercial swine AIprogrammes for industry production are ‘pooled’ doses, for example they include aliquots of ejaculates from three or more boars (Knox et al. 2008), logically hiding individual boar variation in fertility (Ferreira et al. 2014). This scenario would explain the few studies dealing with this important matter found in the literature. How substantial are differences in boar fertility? There are large variations in fertility between swine farms. The question remains how much the boar impacts this variability. Currently, some European countries with intensive swine production are switching from pooled doses to single-boar AI-doses for sanitary and productivity reasons, including traceability. For instance, all AI-doses produced in the Netherlands are single-boar AI-doses, approaching 40% in Spain, while © 2015 Blackwell Verlag GmbH

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others (i.e. Sweden) remain at 10% level. Using only single-boar semen AI-doses offers an objective opportunity to assess differences between boars on field fertility and the quick detection of subfertile boars, having low farrowing rates and/or small/uneven litters. In this context, recent studies conducted in the Netherlands using a large data set of 165 000 sows inseminated twice per oestrus using single-sire, low sperm AI-doses from 7429 boars revealed that only 6% of the total variation in farm fertility was due to boar-related parameters (Broekhuijse et al. 2012a,b). This relatively low impact of boar in farm fertility means that the overall differences in fertility outcomes between the top and bottom boars housed in specialized AI-centres are small, as illustrated in Table 1. This large data set (a total of 116 749 sows inseminated with AI-doses from 1193 boars) shows differences of only 2–3% in farrowing and 0.5–0.6 piglets born per litter between the top 10% and the bottom 10% boars (unpublished data from Topigs Norsvin). These data also indicate that the number of subfertile boars in the specialized AI-centres is relatively low, as shown in Fig. 1, which would be clearly due to their careful production of contaminantfree semen AI-doses containing an acceptable number of viable, morphologically normal and progressively motile spermatozoa per dose. Moreover, specialized AI-centres usually objectively monitor sperm motility in semen AI-doses during storage as an additional quality filter to ensure that fertility is not compromised due to on-shelf storage. However, although numerical fertility differences among boars may seem minor, they can have an important negative economic impact for farmers. Looking at the data of Spanish AI-centres of Table 1, removal of 10% of less fertile boars would result in a total of 223 more piglets born per litter per 100 sows inseminated, with an economic equivalent of more than 10 000 euros/year for a farm of 1000 sows in production. Although infrequent, some boars with serious fertility problems, but with an apparent ‘normal’ spermiogram when using conventional semen exams of motility and sperm numbers, pass undetected, as shown in Fig. 1. Their use can provoke ‘gatecrashes’ in AI-centres, ultimately causing reproductive disasters in swine farms. An example is shown in Fig. 2, where boar

n° 1 shows a significantly lower fertility index than the average of the AI-centre. The magnitude of reproductive and economic losses caused by these boars can be more substantial when semen AI-doses from pooled ejaculates are used, because this allows subfertile boars to remain hidden until signs of a problem calls for alternative, individual examinations. Why are there boar differences in fertility and how can they be minimized? As indicated above, the relevance of the boar to explain field fertility differences is currently limited. Boars and/ or ejaculates are often selected via compensable seminal attributes, as sperm numbers, motility or morphology, because these variables correlate with field fertility of AI-doses (Flowers 2009; Broekhuijse et al. 2011). Moreover, they are also powerful enough to identify boars/ ejaculates with substantial fertility deficiencies (Foxcroft et al. 2010), but unfortunately, they are not always able to identify subfertile boars/ejaculates (Tardif et al. 1999). So it is reasonable to assume that we need to look for either other sperm parameters than the conventional ones or fertility-related but sperm-independent traits if we are to explain variability in fertility among selected AI-boars. The first ones would be linked to sperm attributes required for successfully oocyte fertilization and subsequent development of healthy embryos. Sperm must be able to penetrate oocytes, and not all progressively motile and morphologically normal sperm have this capability (Ruiz-Sanchez et al. 2006). In vitro fertilization (IVF) test, although failed to provide a close relationship with in vivo fertility outcomes (Rodriguez-Martinez 2007), can be able to identify boars with relatively lower in vivo fertility (Ruiz-Sanchez et al. 2006). Moreover, IVF tests using immature oocytes (homologous in vitro penetrability [hIVP] test) are proven able to discern between boars with relatively small differences in in vivo fertility (Gadea 2005). As illustrated in Fig. 3, boars of an AIcentre all showing good seminal parameters had pronounced differences in hIVP index, some showing indexes below the threshold 200, indicative of impaired in vivo fertility (Martinez et al. 1998; Roca et al. 2007).

Table 1. Data of direct boar effect* from the Netherlands, Spain and other 20 countries showing the variability in farrowing rate (FR) and total number piglets born per litter (TNB) among boars housed in artificial insemination centres. Data provided by Topigs Norsvin

Country The Netherlands (616 boars and 43 141 inseminations) Spain (98 boars and 13 825 inseminations) Other 20 countries (479 boars and 59 783 inseminations)

Fertility parameters FR, % TNB, n° FR, % TNB, n° FR, % TNB, n°

Variation (min ↔ max)

Difference top vs. bottom

Top 10% boars

↔ ↔ ↔ ↔ ↔ ↔

8.91 1.51 9.64 1.69 10.03 1.83

≥+1.06 ≥+0.32 ≥+1.06 ≥+0.25 ≥+0.91 ≥+0.35

5.31 0.68 5.68 1.01 5.98 0.86

+3.60 +0.83 +3.96 +0.68 +4.05 +0.97

Bottom 10% boars ≤ ≤ ≤ ≤ ≤ ≤

0.98 0.25 1.87 0.24 1.70 0.27

Difference top 10% vs bottom 10% 2.04 0.57 2.93 0.49 2.61 0.62

*At Topigs Norsvin Research Center B.V. (Beuningen, the Netherlands), a breeding database (Pigbase) is available containing fertility records from purebred and cross-bred swine farms which are recording fertility data. Sow fertility data were corrected for farm- and sow-related factors (e.g. parity, genetic line sow, farm, season, first or re-mating, purebred/crossbred, number of inseminations, age semen), and the remaining variation is the direct boar effect (DBE) on fertility.

© 2015 Blackwell Verlag GmbH

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J Roca, MLWJ Broekhuijse, I Parrilla, H Rodriguez-Martinez, EA Martinez and A Bolarin

(a)

(b)

Fig. 1. Variation in direct boar effect for (a) farrowing rate and (b) total number piglets born recorded in swine farms of 22 countries (1193 boars and 116 749 inseminations). Data provided by Topigs Norsvin Research Center B.V. (Beuningen, the Netherlands).

Fig. 2. Differences in the number of piglets born per 100 inseminated sows relative to the average number of piglets born (fertility index: 1200) of 56 breeding boars in a Spanish Artificial Insemination Center (over 100 sows inseminated per boar).

Another uncompensable sperm attribute to consider is the integrity of sperm nuclear DNA, which is not indispensable for fertilization, but it is essential for

further development of healthy embryos (Silva and Gadella 2006). The percentage of sperm with damaged nuclear DNA [DNA fragmentation index (DFI) © 2015 Blackwell Verlag GmbH

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Fig. 3. In vitro fertility index of 67 boars housed in a Spanish Artificial Insemination Center. hIVP index: number of sperm visualized within oocyte’s ooplasma per each 100 immature oocytes inseminated. An index below 200 is typical of in vivo subfertile boars (Roca et al. 2007).

through the sperm chromatin structure assay (SCSA)] is usually low in boar ejaculates (Fig. 4) and does not increase over liquid semen storage (De Ambrogi et al. 2006), and its relationship with field fertility results is controversial (Waberski et al. 2011). However, it seems clear that boars with high percentages of sperm with nuclear DNA damage are less fertile, as evidenced by the birth of small litters (Boe-Hansen et al. 2008; Didion et al. 2009; Broekhuijse et al. 2012c). Accordingly, we have recently observed significantly smaller litters (2–3 piglets less) in sows inseminated with semen AI-doses from boars with sperm having percentages above 20% of fragmented nuclear DNA. Consequently, early identification and removal of boars showing high levels of damaged nuclear DNA should be a priority of AI-centres to avoid drops of fertility of the delivered semen, thereby also reducing boar variability in field fertility. Moreover, interboar variability in fertility is not just a sperm issue. Qualitative and quantitative differences in the composition of seminal plasma (SP) could explain why some boars, showing good semen parameters, offer fertility outcomes lower than expected. The conscious-

Fig. 4. Sperm nuclear DNA fragmentation index (%DFI) obtained using the sperm chromatin structure assay (SCSA) in liquid stored semen samples of 207 breeding boars used in artificial insemination programmes (unpublished data provided by Topigs Norsvin). Differences in litter size can be observed with a threshold in the 2–6% range (Boe-Hansen et al. 2008; Didion et al. 2009).

© 2015 Blackwell Verlag GmbH

ness of the involvement of SP on the functional lifespan of sperm of semen AI-doses is increasing, even further to the extent that AI-centres are introducing semi-automatic methods for semen collection, forcing the collection of the entire ejaculate in one flask instead of only the usually selective sperm-rich fraction (Barrabes Aneas et al. 2008). It is well known that some SP components, particularly proteins, play an important role in sperm motility, capacitation and sperm–oocyte binding (Caballero et al. 2012; Juyena and Stelletta 2012). Besides, SP contains ovulation-inducing factors (Adams and Ratto 2013), which promotes synchronization between ovulation and sperm colonization of the uterotubal junction. Moreover, SP peptide components, such as cytokines and chemokines, interact with the uterine epithelium inducing an active immune tolerance essential for fertilization and further embryo development (Rodriguez-Martinez et al. 2011). Furthermore, some SP functional proteins with antioxidant properties, such as glutathione peroxidase 5 (GPX5) and paraoxonase-I, have proven to be related to boar fertility, allowing for discriminating boars per fertility levels, introducing them as potential fertility markers

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(Dyck et al. 2011; Barranco et al. 2015). These findings open for the quantification of certain components of SP and their use to identify the fertile potential of specific sires. Obviously, there is always a genomic component behind many cases of subfertility. Genetic disorders not affecting the seminal parameters but ultimately impairing fertility are known (McLachlan and O’Bryan 2010), among which we find reciprocal translocations (RT), where two broken-off chromosome pieces of non-homologous chromosomes are exchanged. Although the RT has an estimated

Boar Differences In Artificial Insemination Outcomes: Can They Be Minimized?

In Western countries, where pig breeding and production are intensive, there is a documented variability in fertility between farms with boar-related ...
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